Merge a3f6b5f1e9431c512434d3a4af55ec54f06d6ebf into fdf794e31d1fd6f91c5cb4fbb0396094491a31ac

.github/build_windows_packages.ps1

@@ -115,12 +115,17 @@ Remove-Item $ffDir.FullName -Recurse -Force
 Write-Host "[INFO] Installing PyTorch..."
 & ".\runtime\python.exe" -m ensurepip
 & ".\runtime\python.exe" -m pip install --upgrade pip --no-warn-script-location
+
 switch ($cuda) {
     "cu124" {
-        & ".\runtime\python.exe" -m pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/cu124 --no-warn-script-location
+        & ".\runtime\python.exe" -m pip install psutil ninja packaging wheel "setuptools>=42" --no-warn-script-location
+        & ".\runtime\python.exe" -m pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu124 --no-warn-script-location
+        & ".\runtime\python.exe" -m pip install flash-attn -i https://xxxxrt666.github.io/PIP-Index/ --no-build-isolation
     }
     "cu128" {
+        & ".\runtime\python.exe" -m pip install psutil ninja packaging wheel "setuptools>=42" --no-warn-script-location
         & ".\runtime\python.exe" -m pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128 --no-warn-script-location
+        & ".\runtime\python.exe" -m pip install flash-attn -i https://xxxxrt666.github.io/PIP-Index/ --no-build-isolation
     }
     default {
         Write-Error "Unsupported CUDA version: $cuda"

.github/workflows/build_windows_packages.yaml

@@ -31,6 +31,15 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
+      - name: Install Windows CUDA 12.9
+        if: ${{ runner.os == 'Windows' && matrix.torch_cuda == '12.8' }}
+        uses: Jimver/cuda-toolkit
+        id: cuda-toolkit-win-129
+        with:
+          cuda: 12.9.1
+          method: "network"
+          sub-packages: '["nvcc", "cudart", "visual_studio_integration"]'
+
       - name: Run Build and Upload Script
         shell: pwsh
         run: |

.gitignore

@@ -16,8 +16,8 @@ ffprobe*
 cfg.json
 speakers.json
 ref_audios
-tools/AP_BWE_main/24kto48k/*
-!tools/AP_BWE_main/24kto48k/readme.txt
+tools/AP_BWE/24kto48k/*
+!tools/AP_BWE/24kto48k/readme.txt
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

Docker/miniconda_install.sh

@@ -23,8 +23,10 @@ fi
 
 if [ "$TARGETPLATFORM" = "linux/amd64" ]; then
     "${WGET_CMD[@]}" -O miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py311_25.3.1-1-Linux-x86_64.sh
+    SYSROOT_PKG="sysroot_linux-64>=2.28"
 elif [ "$TARGETPLATFORM" = "linux/arm64" ]; then
     "${WGET_CMD[@]}" -O miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py311_25.3.1-1-Linux-aarch64.sh
+    SYSROOT_PKG="sysroot_linux-aarch64>=2.28"
 else
     exit 1
 fi
@@ -45,20 +47,36 @@ rm miniconda.sh
 
 source "$HOME/miniconda3/etc/profile.d/conda.sh"
 
+"$HOME/miniconda3/bin/conda" init bash
+
+source "$HOME/.bashrc"
+
 "$HOME/miniconda3/bin/conda" config --add channels conda-forge
 
 "$HOME/miniconda3/bin/conda" update -q --all -y 1>/dev/null
 
 "$HOME/miniconda3/bin/conda" install python=3.11 -q -y
 
-"$HOME/miniconda3/bin/conda" install gcc=14 gxx ffmpeg cmake make unzip -q -y
+"$HOME/miniconda3/bin/conda" install gcc=11 gxx ffmpeg cmake make unzip $SYSROOT_PKG "libstdcxx-ng>=11" -q -y
 
 if [ "$CUDA_VERSION" = "12.8" ]; then
     "$HOME/miniconda3/bin/pip" install torch torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/cu128
+    "$HOME/miniconda3/bin/conda" install cuda-nvcc=12.8 -c nvidia
 elif [ "$CUDA_VERSION" = "12.6" ]; then
-    "$HOME/miniconda3/bin/pip" install torch==2.6 torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/cu126
+    "$HOME/miniconda3/bin/pip" install torch torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/cu126
+    "$HOME/miniconda3/bin/conda" install cuda-nvcc=12.6 -c nvidia
 fi
 
+CUDA_PATH=$(echo "$HOME/miniconda3/targets/"*-linux | awk '{print $1}')
+
+export CUDA_HOME=$CUDA_PATH
+export PATH="$HOME/miniconda3/bin:$PATH"
+export PATH="$CUDA_HOME/bin:$PATH"
+export PATH="$CUDA_HOME/nvvm/bin:$PATH"
+
+"$HOME/miniconda3/bin/pip" install psutil ninja packaging wheel "setuptools>=42"
+"$HOME/miniconda3/bin/pip" install flash-attn -i https://xxxxrt666.github.io/PIP-Index/ --no-build-isolation
+
 "$HOME/miniconda3/bin/pip" cache purge
 
 rm $LOG_PATH

GPT_SoVITS/AR/data/data_module.py

@@ -3,8 +3,8 @@
 from pytorch_lightning import LightningDataModule
 from torch.utils.data import DataLoader
 
-from AR.data.bucket_sampler import DistributedBucketSampler
-from AR.data.dataset import Text2SemanticDataset
+from GPT_SoVITS.AR.data.bucket_sampler import DistributedBucketSampler
+from GPT_SoVITS.AR.data.dataset import Text2SemanticDataset
 
 
 class Text2SemanticDataModule(LightningDataModule):

GPT_SoVITS/AR/data/dataset.py

@@ -220,7 +220,7 @@ class Text2SemanticDataset(Dataset):
 
         flag = 0
         path_bert = "%s/%s.pt" % (self.path3, item_name)
-        if os.path.exists(path_bert) == True:
+        if os.path.exists(path_bert) is True:
             bert_feature = torch.load(path_bert, map_location="cpu")
         else:
             flag = 1

GPT_SoVITS/AR/models/t2s_lightning_module.py

@@ -10,9 +10,9 @@ from typing import Dict
 import torch
 from pytorch_lightning import LightningModule
 
-from AR.models.t2s_model import Text2SemanticDecoder
-from AR.modules.lr_schedulers import WarmupCosineLRSchedule
-from AR.modules.optim import ScaledAdam
+from GPT_SoVITS.AR.models.t2s_model import Text2SemanticDecoder
+from GPT_SoVITS.AR.modules.lr_schedulers import WarmupCosineLRSchedule
+from GPT_SoVITS.AR.modules.optim import ScaledAdam
 
 
 class Text2SemanticLightningModule(LightningModule):
@@ -42,7 +42,7 @@ class Text2SemanticLightningModule(LightningModule):
     def training_step(self, batch: Dict, batch_idx: int):
         opt = self.optimizers()
         scheduler = self.lr_schedulers()
-        forward = self.model.forward if self.config["train"].get("if_dpo", False) == True else self.model.forward_old
+        forward = self.model.forward if self.config["train"].get("if_dpo", False) is True else self.model.forward_old
         loss, acc = forward(
             batch["phoneme_ids"],
             batch["phoneme_ids_len"],

GPT_SoVITS/AR/models/t2s_lightning_module_onnx.py

@@ -10,9 +10,9 @@ from typing import Dict
 import torch
 from pytorch_lightning import LightningModule
 
-from AR.models.t2s_model_onnx import Text2SemanticDecoder
-from AR.modules.lr_schedulers import WarmupCosineLRSchedule
-from AR.modules.optim import ScaledAdam
+from GPT_SoVITS.AR.models.t2s_model_onnx import Text2SemanticDecoder
+from GPT_SoVITS.AR.modules.lr_schedulers import WarmupCosineLRSchedule
+from GPT_SoVITS.AR.modules.optim import ScaledAdam
 
 
 class Text2SemanticLightningModule(LightningModule):

GPT_SoVITS/AR/models/t2s_model.py

@@ -9,7 +9,7 @@ from torch.nn import functional as F
 from torchmetrics.classification import MulticlassAccuracy
 from tqdm import tqdm
 
-from AR.models.utils import (
+from GPT_SoVITS.AR.models.utils import (
     dpo_loss,
     get_batch_logps,
     make_pad_mask,
@@ -18,8 +18,8 @@ from AR.models.utils import (
     sample,
     topk_sampling,
 )
-from AR.modules.embedding import SinePositionalEmbedding, TokenEmbedding
-from AR.modules.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
+from GPT_SoVITS.AR.modules.embedding import SinePositionalEmbedding, TokenEmbedding
+from GPT_SoVITS.AR.modules.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
 
 default_config = {
     "embedding_dim": 512,
@@ -420,7 +420,7 @@ class Text2SemanticDecoder(nn.Module):
             mask=xy_attn_mask,
         )
         x_len = x_lens.max()
-        logits = self.ar_predict_layer(xy_dec[:, x_len-1:])
+        logits = self.ar_predict_layer(xy_dec[:, x_len - 1 :])
 
         ###### DPO #############
         reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(
@@ -432,7 +432,7 @@ class Text2SemanticDecoder(nn.Module):
             mask=reject_xy_attn_mask,
         )
         x_len = x_lens.max()
-        reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len-1:])
+        reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len - 1 :])
 
         # loss
         # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
@@ -502,7 +502,7 @@ class Text2SemanticDecoder(nn.Module):
             (xy_pos, None),
             mask=xy_attn_mask,
         )
-        logits = self.ar_predict_layer(xy_dec[:, x_len-1:]).permute(0, 2, 1)
+        logits = self.ar_predict_layer(xy_dec[:, x_len - 1 :]).permute(0, 2, 1)
         # loss
         # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
         loss = F.cross_entropy(logits, targets, reduction="sum")
@@ -724,8 +724,8 @@ class Text2SemanticDecoder(nn.Module):
                 l1 = samples[:, 0] == self.EOS
                 l2 = tokens == self.EOS
                 l = l1.logical_or(l2)
-                removed_idx_of_batch_for_y = torch.where(l == True)[0].tolist()
-                reserved_idx_of_batch_for_y = torch.where(l == False)[0]
+                removed_idx_of_batch_for_y = torch.where(l is True)[0].tolist()
+                reserved_idx_of_batch_for_y = torch.where(l is False)[0]
                 # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
                 for i in removed_idx_of_batch_for_y:
                     batch_index = batch_idx_map[i]

GPT_SoVITS/AR/models/t2s_model_onnx.py

@@ -5,8 +5,8 @@ from torch import nn
 from torch.nn import functional as F
 from torchmetrics.classification import MulticlassAccuracy
 
-from AR.modules.embedding_onnx import SinePositionalEmbedding, TokenEmbedding
-from AR.modules.transformer_onnx import LayerNorm, TransformerEncoder, TransformerEncoderLayer
+from GPT_SoVITS.AR.modules.embedding_onnx import SinePositionalEmbedding, TokenEmbedding
+from GPT_SoVITS.AR.modules.transformer_onnx import LayerNorm, TransformerEncoder, TransformerEncoderLayer
 
 default_config = {
     "embedding_dim": 512,

GPT_SoVITS/AR/modules/activation.py

@@ -9,7 +9,7 @@ from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
 from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
 from torch.nn.parameter import Parameter
 
-from AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
+from GPT_SoVITS.AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
 
 F.multi_head_attention_forward = multi_head_attention_forward_patched
 

GPT_SoVITS/AR/modules/activation_onnx.py

@@ -8,7 +8,7 @@ from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
 from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
 from torch.nn.parameter import Parameter
 
-from AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched
+from GPT_SoVITS.AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched
 
 
 class MultiheadAttention(Module):

GPT_SoVITS/AR/modules/transformer.py

@@ -2,20 +2,15 @@
 import copy
 import numbers
 from functools import partial
-from typing import Any
-from typing import Callable
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import torch
-from AR.modules.activation import MultiheadAttention
-from AR.modules.scaling import BalancedDoubleSwish
-from torch import nn
-from torch import Tensor
+from torch import Tensor, nn
 from torch.nn import functional as F
 
+from GPT_SoVITS.AR.modules.activation import MultiheadAttention
+from GPT_SoVITS.AR.modules.scaling import BalancedDoubleSwish
+
 _shape_t = Union[int, List[int], torch.Size]
 
 

GPT_SoVITS/AR/modules/transformer_onnx.py

@@ -2,20 +2,15 @@
 import copy
 import numbers
 from functools import partial
-from typing import Any
-from typing import Callable
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import torch
-from AR.modules.activation_onnx import MultiheadAttention
-from AR.modules.scaling import BalancedDoubleSwish
-from torch import nn
-from torch import Tensor
+from torch import Tensor, nn
 from torch.nn import functional as F
 
+from GPT_SoVITS.AR.modules.activation_onnx import MultiheadAttention
+from GPT_SoVITS.AR.modules.scaling import BalancedDoubleSwish
+
 _shape_t = Union[int, List[int], torch.Size]
 
 

GPT_SoVITS/AR/text_processing/phonemizer.py

@@ -1,72 +0,0 @@
-# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/text_processing/phonemizer.py
-# reference: https://github.com/lifeiteng/vall-e
-import itertools
-import re
-from typing import Dict
-from typing import List
-
-import regex
-from gruut import sentences
-from gruut.const import Sentence
-from gruut.const import Word
-from AR.text_processing.symbols import SYMBOL_TO_ID
-
-
-class GruutPhonemizer:
-    def __init__(self, language: str):
-        self._phonemizer = sentences
-        self.lang = language
-        self.symbol_to_id = SYMBOL_TO_ID
-        self._special_cases_dict: Dict[str] = {
-            r"\.\.\.": "... ",
-            ";": "; ",
-            ":": ": ",
-            ",": ", ",
-            r"\.": ". ",
-            "!": "! ",
-            r"\?": "? ",
-            "—": "—",
-            "…": "… ",
-            "«": "«",
-            "»": "»",
-        }
-        self._punctuation_regexp: str = rf"([{''.join(self._special_cases_dict.keys())}])"
-
-    def _normalize_punctuation(self, text: str) -> str:
-        text = regex.sub(rf"\pZ+{self._punctuation_regexp}", r"\1", text)
-        text = regex.sub(rf"{self._punctuation_regexp}(\pL)", r"\1 \2", text)
-        text = regex.sub(r"\pZ+", r" ", text)
-        return text.strip()
-
-    def _convert_punctuation(self, word: Word) -> str:
-        if not word.phonemes:
-            return ""
-        if word.phonemes[0] in ["‖", "|"]:
-            return word.text.strip()
-
-        phonemes = "".join(word.phonemes)
-        # remove modifier characters ˈˌː with regex
-        phonemes = re.sub(r"[ˈˌː͡]", "", phonemes)
-        return phonemes.strip()
-
-    def phonemize(self, text: str, espeak: bool = False) -> str:
-        text_to_phonemize: str = self._normalize_punctuation(text)
-        sents: List[Sentence] = [sent for sent in self._phonemizer(text_to_phonemize, lang="en-us", espeak=espeak)]
-        words: List[str] = [self._convert_punctuation(word) for word in itertools.chain(*sents)]
-        return " ".join(words)
-
-    def transform(self, phonemes):
-        # convert phonemes to ids
-        # dictionary is in symbols.py
-        return [self.symbol_to_id[p] for p in phonemes if p in self.symbol_to_id.keys()]
-
-
-if __name__ == "__main__":
-    phonemizer = GruutPhonemizer("en-us")
-    # text -> IPA
-    phonemes = phonemizer.phonemize("Hello, wor-ld ?")
-    print("phonemes:", phonemes)
-    print("len(phonemes):", len(phonemes))
-    phoneme_ids = phonemizer.transform(phonemes)
-    print("phoneme_ids:", phoneme_ids)
-    print("len(phoneme_ids):", len(phoneme_ids))

GPT_SoVITS/AR/text_processing/symbols.py

@@ -1,12 +0,0 @@
-# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/text_processing/symbols.py
-# reference: https://github.com/lifeiteng/vall-e
-PAD = "_"
-PUNCTUATION = ';:,.!?¡¿—…"«»“” '
-LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
-IPA_LETTERS = (
-    "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-)
-SYMBOLS = [PAD] + list(PUNCTUATION) + list(LETTERS) + list(IPA_LETTERS)
-SPACE_ID = SYMBOLS.index(" ")
-SYMBOL_TO_ID = {s: i for i, s in enumerate(SYMBOLS)}
-ID_TO_SYMBOL = {i: s for i, s in enumerate(SYMBOLS)}

GPT_SoVITS/Accelerate/MLX/__init__.py

@@ -0,0 +1,11 @@
+import importlib.util
+
+if importlib.util.find_spec("mlx") is not None:
+    from .sample_funcs_mlx import sample_naive as sample_naive_mlx
+    from .t2s_engine_mlx import T2SEngine as T2SEngineMLX
+
+    backends = ["mlx_static", "mlx_quantized", "mlx_varlen"]
+else:
+    backends = []
+
+__all__ = ["T2SEngineMLX", "sample_naive_mlx", "backends"]

GPT_SoVITS/Accelerate/MLX/backends/mlx_quantized.py

@@ -0,0 +1,174 @@
+from __future__ import annotations
+
+from typing import cast
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from ..structs_mlx import KVCacheQ
+from ..t2s_model_abc import (
+    AttentionABC,
+    KVCache,
+    KVCacheHND,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Array = mx.array
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head: int, hidden_dim: int, max_seq_length: int):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+        self.kc_class = KVCacheHND
+
+    @staticmethod
+    def quantized_scaled_dot_product_attention(
+        queries: Array,
+        q_keys: tuple[Array, Array, Array],
+        q_values: tuple[Array, Array, Array],
+        scale: float,
+        mask: Array,
+        group_size: int = 32,
+        bits: int = 8,
+    ) -> Array:
+        queries *= scale
+
+        scores = mx.quantized_matmul(queries, *q_keys, transpose=True, group_size=group_size, bits=bits)
+        scores = mx.where(mask, scores, -mx.inf)
+        scores = mx.softmax(scores, axis=-1, precise=True)  # type: ignore
+        out = mx.quantized_matmul(scores, *q_values, transpose=False, group_size=group_size, bits=bits)
+
+        return out
+
+    def __call__(self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array):
+        bsz, seqlen, _ = cast(tuple[int, ...], x.shape)
+
+        q, k, v = self.in_proj(x).split(3, axis=-1)
+
+        q, k, v = map(lambda x: x.reshape(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
+
+        q, k, v = map(lambda x: x.swapaxes(1, 2), (q, k, v))
+
+        kv_cache = self.kc_class.update_cache(input_pos, k, v, kv_cache, cache_idx)
+        assert len(kv_cache) == 2
+
+        max_idx = int(input_pos.max())
+
+        q, k, v = map(lambda x: x[..., :max_idx, :], (q, *kv_cache))
+
+        mask = attn_mask[..., :max_idx]
+
+        attn = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=mask)
+
+        attn = attn.swapaxes(1, 2).reshape(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+    # def __call__(self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array):
+    #     bsz, seqlen, _ = cast(tuple[int, ...], x.shape)
+
+    #     q, k, v = self.in_proj(x).split(3, axis=-1)
+
+    #     q, k, v = map(lambda x: x.reshape(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
+
+    #     q, k, v = map(lambda x: x.swapaxes(1, 2), (q, k, v))
+
+    #     kv_cache = self.kc_class.update_cache(input_pos, k, v, kv_cache, cache_idx)
+
+    #     assert len(kv_cache) == 3
+    #     (k_q, k_s, k_b), (v_q, v_s, v_b), (group_size, bits) = kv_cache
+
+    #     k_q, k_s, k_b, v_q, v_s, v_b = map(lambda x: x[..., : int(input_pos.max()), :], (k_q, k_s, k_b, v_q, v_s, v_b))
+
+    #     mask = attn_mask[..., : int(input_pos.max())]
+
+    #     attn = Attention.quantized_scaled_dot_product_attention(
+    #         q,
+    #         (k_q, k_s, k_b),
+    #         (v_q, v_s, v_b),
+    #         self.scale,
+    #         mask,
+    #         group_size,
+    #         bits,
+    #     )
+
+    #     attn = attn.swapaxes(1, 2).reshape(bsz, seqlen, self.hidden_dim)
+
+    #     output = self.out_proj(attn)
+
+    #     return output
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int, *args, **kwds) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length, *args, **kwds)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length, *args, **kwds)
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim: int,
+        n_layer: int,
+        n_head: int,
+        ffn_dim: int,
+        vocab_size: int,
+        max_seq_length: int,
+        max_batch_size: int,
+        *args,
+        **kwds,
+    ) -> None:
+        super().__init__(
+            hidden_dim,
+            n_layer,
+            n_head,
+            ffn_dim,
+            vocab_size,
+            max_seq_length,
+            max_batch_size,
+            *args,
+            **kwds,
+        )
+
+        self.layers = [
+            TransformerBlock(
+                n_head,
+                ffn_dim,
+                hidden_dim,
+                max_seq_length,
+                *args,
+                **kwds,
+            )
+            for _ in range(n_layer)
+        ]
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config: dict,
+        max_seq_length: int = 1800,
+        max_batch_size: int = 10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.h = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHND
+        self.group_size = 32
+        self.bits = 8
+
+    # def init_cache(self, bsz: int = 0):
+    #     return super().init_cache(bsz, group_size=self.group_size, bits=self.bits)
+
+    def quantized(self):
+        for layer in self.h.layers:
+            # nn.quantize(layer.feed_forward, self.group_size, self.bits)
+            nn.quantize(layer.attention, self.group_size, self.bits)

GPT_SoVITS/Accelerate/MLX/backends/mlx_static.py

@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+from typing import cast
+
+import mlx.core as mx
+
+from ..structs_mlx import KVCache, KVCacheQ
+from ..t2s_model_abc import (
+    AttentionABC,
+    KVCacheHND,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Array = mx.array
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head: int, hidden_dim: int, max_seq_length: int):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+        self.kc_class = KVCacheHND
+
+    def __call__(self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array):
+        bsz, seqlen, _ = cast(tuple[int, ...], x.shape)
+
+        q, k, v = self.in_proj(x).split(3, axis=-1)
+
+        q, k, v = map(lambda x: x.reshape(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
+
+        q, k, v = map(lambda x: x.swapaxes(1, 2), (q, k, v))
+
+        kv_cache = self.kc_class.update_cache(input_pos, k, v, kv_cache, cache_idx)
+        assert len(kv_cache) == 2
+
+        k, v = kv_cache
+
+        attn = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=attn_mask)
+
+        attn = attn.swapaxes(1, 2).reshape(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim: int,
+        n_layer: int,
+        n_head: int,
+        ffn_dim: int,
+        vocab_size: int,
+        max_seq_length: int,
+        max_batch_size: int,
+    ) -> None:
+        super().__init__(
+            hidden_dim,
+            n_layer,
+            n_head,
+            ffn_dim,
+            vocab_size,
+            max_seq_length,
+            max_batch_size,
+        )
+
+        self.layers = [
+            TransformerBlock(
+                n_head,
+                ffn_dim,
+                hidden_dim,
+                max_seq_length,
+            )
+            for _ in range(n_layer)
+        ]
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config: dict,
+        max_seq_length: int = 1800,
+        max_batch_size: int = 10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.h = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHND

GPT_SoVITS/Accelerate/MLX/backends/mlx_varlen.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from typing import cast
+
+import mlx.core as mx
+
+from ..structs_mlx import KVCache, KVCacheQ
+from ..t2s_model_abc import (
+    AttentionABC,
+    KVCacheHND,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Array = mx.array
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head: int, hidden_dim: int, max_seq_length: int):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+        self.kc_class = KVCacheHND
+
+    def __call__(self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array):
+        bsz, seqlen, _ = cast(tuple[int, ...], x.shape)
+
+        q, k, v = self.in_proj(x).split(3, axis=-1)
+
+        q, k, v = map(lambda x: x.reshape(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
+
+        q, k, v = map(lambda x: x.swapaxes(1, 2), (q, k, v))
+
+        kv_cache = self.kc_class.update_cache(input_pos, k, v, kv_cache, cache_idx)
+        assert len(kv_cache) == 2
+
+        max_idx = int(input_pos.max())
+
+        q, k, v = map(lambda x: x[..., :max_idx, :], (q, *kv_cache))
+
+        mask = attn_mask[..., :max_idx]
+
+        attn = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=mask)
+
+        attn = attn.swapaxes(1, 2).reshape(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim: int,
+        n_layer: int,
+        n_head: int,
+        ffn_dim: int,
+        vocab_size: int,
+        max_seq_length: int,
+        max_batch_size: int,
+    ) -> None:
+        super().__init__(
+            hidden_dim,
+            n_layer,
+            n_head,
+            ffn_dim,
+            vocab_size,
+            max_seq_length,
+            max_batch_size,
+        )
+
+        self.layers = [
+            TransformerBlock(
+                n_head,
+                ffn_dim,
+                hidden_dim,
+                max_seq_length,
+            )
+            for _ in range(n_layer)
+        ]
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config: dict,
+        max_seq_length: int = 1800,
+        max_batch_size: int = 10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.h = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHND

GPT_SoVITS/Accelerate/MLX/sample_funcs_mlx.py

@@ -0,0 +1,64 @@
+from functools import partial
+from typing import Protocol, cast
+
+import mlx.core as mx
+
+Array = mx.array
+
+
+class SampleProtocolMLX(Protocol):
+    @staticmethod
+    def __call__(
+        logits: Array,
+        previous_tokens: Array,
+        temperature: float,
+        top_k: int,
+        top_p: float,
+        repetition_penalty: float,
+    ) -> Array: ...
+
+
+class sample_naive(SampleProtocolMLX):
+    # @partial(mx.compile)
+    @staticmethod
+    def __call__(
+        logits,
+        previous_tokens,
+        temperature,
+        top_k,
+        top_p,
+        repetition_penalty,
+    ):
+        if temperature <= 1e-5:
+            probs = mx.softmax(logits, axis=-1)
+            return mx.argmax(probs, axis=-1, keepdims=True).astype(mx.int32)
+
+        if repetition_penalty != 1.0:
+            batch_idx = mx.arange(cast(tuple[int, ...], previous_tokens.shape)[0])
+            previous_tokens = previous_tokens.astype(mx.int64)
+            selected_logists = logits[batch_idx, previous_tokens]
+            selected_logists = mx.where(
+                selected_logists < 0, selected_logists * repetition_penalty, selected_logists / repetition_penalty
+            )
+            logits[batch_idx, previous_tokens] = selected_logists
+
+        sorted_indices = mx.argsort(-logits, axis=-1)
+        sorted_logits = mx.take_along_axis(logits, sorted_indices, axis=-1)
+        cum_probs = mx.cumsum(mx.softmax(sorted_logits, axis=-1), axis=-1)
+        sorted_indices_to_remove = cum_probs > top_p
+        sorted_indices_to_remove[:, -1] = False
+        indices_to_remove = mx.zeros_like(logits).astype(mx.bool_)
+        batch_indices = mx.arange(cast(tuple[int, ...], logits.shape)[0])[:, None]
+        indices_to_remove[batch_indices, sorted_indices] = sorted_indices_to_remove
+        logits = mx.where(indices_to_remove, -mx.inf, logits)
+
+        logits = logits / temperature
+
+        v = mx.topk(logits, top_k)
+        pivot = mx.expand_dims(v[:, 0], -1)
+        logits = mx.where(logits < pivot, -mx.inf, logits)
+
+        gumbel_noise = mx.random.gumbel(shape=cast(tuple[int, ...], logits.shape), dtype=logits.dtype)
+        idx_next = mx.argmax(logits + gumbel_noise, axis=-1, keepdims=True).astype(mx.int32)
+
+        return idx_next

GPT_SoVITS/Accelerate/MLX/structs_mlx.py

@@ -0,0 +1,164 @@
+"""
+Modified From https://github.com/XXXXRT666/GPT-SoVITS
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import List, MutableSequence, Protocol, TypeAlias, cast
+
+import mlx.core as mx
+import torch
+
+from ..PyTorch.structs import T2SRequest, T2SResult
+from .sample_funcs_mlx import SampleProtocolMLX, sample_naive
+
+Tensor = torch.Tensor
+Array = mx.array
+
+
+@dataclass(slots=True)
+class T2SRequestMLX:
+    x: List[Array]
+    x_lens: Array
+    prompts: Array
+    bert_feature: List[Array]
+    valid_length: int
+    top_k: int = 5
+    top_p: float = 1
+    early_stop_num: int = -1
+    temperature: float = 1.0
+    repetition_penalty: float = 1.35
+
+    @classmethod
+    def from_torch(cls, request: T2SRequest) -> T2SRequestMLX:
+        x = list(map(lambda tensor: mx.array(tensor.cpu()), request.x))
+        x_lens = mx.array(request.x_lens.cpu())
+        prompts = mx.array(request.prompts.cpu())
+        bert_feature = list(map(lambda tensor: mx.array(tensor.cpu()), request.bert_feature))
+
+        return cls(
+            x,
+            x_lens,
+            prompts,
+            bert_feature,
+            request.valid_length,
+            request.top_k,
+            request.top_p,
+            request.early_stop_num,
+            request.temperature,
+            request.repetition_penalty,
+        )
+
+
+KVCache: TypeAlias = tuple[Array, Array]
+KVCacheQ: TypeAlias = tuple[tuple[Array, Array, Array], tuple[Array, Array, Array], tuple[int, int]]
+
+
+class KVCacheProtocol(Protocol):
+    @staticmethod
+    def empty(kv_cache: KVCache | KVCacheQ) -> None: ...
+
+    @staticmethod
+    def update_cache(
+        input_pos: Array, k_val: Array, v_val: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array
+    ) -> KVCache | KVCacheQ: ...
+
+    @staticmethod
+    def prefill_kv(k_val: Array, v_val: Array, kv_cache: KVCache | KVCacheQ) -> None: ...
+
+    @staticmethod
+    def init_cache(
+        batch_size: int, max_seq_length: int, n_heads: int, head_dim: int, dtype: mx.Dtype, *args, **kwds
+    ) -> KVCache | KVCacheQ: ...
+
+
+class T2SDecoderProtocol(Protocol):
+    max_seq_length: int
+    EOS: int
+    n_head: int
+
+    def embed(self, x: list[Array], y: Array, bert_features: list[Array]) -> Array: ...
+
+
+class T2SEngineProtocol(Protocol):
+    def _handle_request(self, request: T2SRequest) -> tuple[list[Array], float]: ...
+
+    def generate(self, request: T2SRequest) -> T2SResult: ...
+
+    @staticmethod
+    def load_decoder(
+        weights_path: os.PathLike, max_batch_size: int = 1, implement: str = "MLX"
+    ) -> T2SDecoderProtocol: ...
+
+
+class T2SSessionMLX:
+    def __init__(
+        self,
+        decoder: T2SDecoderProtocol,
+        request_torch: T2SRequest,
+        sample_func: type[SampleProtocolMLX] = sample_naive,
+        device: mx.Device = mx.Device(mx.cpu),
+        dtype: mx.Dtype = mx.float32,
+    ):
+        with mx.stream(device):
+            request = T2SRequestMLX.from_torch(request_torch)
+
+            self.decoder = decoder
+            self.request = request
+            self.device = device
+            self.dtype = dtype
+
+            bsz = len(request.x)
+            y_len: int = cast(tuple[int, ...], request.prompts.shape)[-1]
+            self.bsz = bsz
+            self.y_len = y_len
+
+            # Cache
+            self.kv_cache: MutableSequence[KVCache | KVCacheQ]
+            self.sample = sample_func()
+
+            # Forward args
+            self.x = [i.astype(mx.int32) for i in request.x]
+            self.x_lens = request.x_lens.astype(mx.int32)
+            self.y = mx.zeros((bsz, decoder.max_seq_length)).astype(mx.int32)
+            self.y[:, : cast(tuple[int, ...], request.prompts.shape)[-1]] = request.prompts.astype(mx.int32)
+            self.bert_feature = [i.astype(dtype) for i in request.bert_feature]
+
+            self.prefill_len = self.x_lens + cast(tuple[int, ...], request.prompts.shape)[1]
+
+            self.input_pos = mx.zeros_like(self.prefill_len)
+            self.input_pos += self.prefill_len
+
+            # EOS
+            self.completed = mx.array([False] * len(self.x)).astype(mx.bool_)
+            self.y_results: List[Array] = [None] * len(self.x)  # type: ignore
+
+            self.xy_pos = decoder.embed(self.x, request.prompts, self.bert_feature)
+
+            max_len = int(self.prefill_len.max(-1))
+            attn_mask = mx.zeros(shape=(bsz, max_len, max_len), dtype=mx.bool_)
+
+            for bs in range(bsz):
+                pos = int(self.x_lens[bs])
+                seq_len = pos + y_len
+
+                attn_mask[bs, :seq_len, :pos] = True
+
+                ar_mask = ~mx.triu(
+                    x=mx.ones(
+                        shape=(
+                            y_len,
+                            y_len,
+                        ),
+                        dtype=mx.bool_,
+                    ),
+                    k=1,
+                )
+                attn_mask[bs, pos:seq_len, pos:seq_len] = ar_mask
+
+            attn_mask = mx.repeat(mx.expand_dims(attn_mask, 1), decoder.n_head, 1)
+            self.attn_mask = attn_mask
+
+            mx.eval(self.attn_mask)

GPT_SoVITS/Accelerate/MLX/t2s_engine_mlx.py

@@ -0,0 +1,232 @@
+import gc
+import os
+import time
+import traceback
+from typing import cast
+
+import mlx.core as mx
+import torch
+from rich.progress import BarColumn, Progress, TextColumn, TimeRemainingColumn
+
+from ..logger import console, logger
+from ..PyTorch.structs import T2SEngineProtocol, T2SRequest
+from .backends import mlx_quantized, mlx_static, mlx_varlen
+from .structs_mlx import T2SResult, T2SSessionMLX
+from .t2s_model_abc import T2SDecoderABC
+
+Array = mx.array
+Tensor = torch.Tensor
+
+
+class T2SEngine(T2SEngineProtocol):
+    def __init__(
+        self,
+        decoder_model: T2SDecoderABC,
+        device: mx.Device | str = mx.Device(mx.cpu),
+        dtype: torch.dtype | mx.Dtype = torch.float32,
+    ) -> None:
+        if isinstance(device, str):
+            match device:
+                case "mx.cpu":
+                    device = mx.Device(mx.cpu)
+                case "mx.gpu":
+                    device = mx.Device(mx.gpu)
+
+        match dtype:
+            case torch.float32:
+                dtype = mx.float32
+            case torch.float16:
+                dtype = mx.float16
+            case torch.bfloat16:
+                dtype = mx.bfloat16
+
+        device = cast(mx.Device, device)
+        dtype = cast(mx.Dtype, dtype)
+
+        assert device.type.value in {0, 1}
+        assert dtype in {mx.float16, mx.bfloat16, mx.float32}
+
+        self.device = device
+        self.dtype = dtype
+
+        mx.set_default_device(device)
+        decoder_model.set_dtype(self.dtype)
+
+        self.decoder_model: T2SDecoderABC = decoder_model
+        # self.decoder_model.compile()
+
+    def _handle_request(self, request: T2SRequest):
+        decoder = self.decoder_model
+        session = T2SSessionMLX(decoder, request, device=self.device, dtype=self.dtype)
+        batch_idx = mx.arange(session.bsz)
+
+        t1 = 0.0
+        infer_speed = 0.0
+        infer_time = 0.0
+
+        with (
+            mx.stream(session.device),
+            Progress(
+                TextColumn("[cyan]{task.description}"),
+                BarColumn(),
+                TextColumn("{task.completed}/{task.total}"),
+                TimeRemainingColumn(),
+                console=console,
+                transient=True,
+            ) as progress,
+        ):
+            max_token = min(1800 - int(session.input_pos.max()), 1500)
+
+            task = progress.add_task("T2S Decoding", total=max_token)
+            for idx in range(1500):
+                progress.update(task, advance=1)
+                if idx == 0:
+                    session.kv_cache = decoder.init_cache(session.bsz)
+                    xy_dec = decoder.h.prefill(
+                        session.xy_pos,
+                        session.attn_mask,
+                        session.kv_cache,
+                    )  # bs, seq_len, embed_dim
+                    xy_dec = xy_dec[None, batch_idx, session.input_pos - 1]
+                else:
+                    args, kwds = decoder.pre_forward(session)
+                    xy_dec = decoder.h(
+                        session.input_pos,
+                        session.xy_pos,
+                        session.kv_cache,
+                        batch_idx,
+                        *args,
+                        **kwds,
+                    )
+
+                decoder.post_forward(idx, session)
+                logits = decoder.ar_predict_layer(xy_dec[:, -1])
+                session.input_pos += 1
+
+                if idx == 0:
+                    logits[:, -1] = -mx.inf
+
+                samples = session.sample(
+                    logits=logits,
+                    previous_tokens=session.y[:, : session.y_len + idx],
+                    top_k=request.top_k,
+                    top_p=request.top_p,
+                    repetition_penalty=request.repetition_penalty,
+                    temperature=request.temperature,
+                )
+
+                session.y[batch_idx, session.y_len + idx] = samples
+
+                argmax_token = mx.argmax(logits, axis=-1)
+                sample_token = samples.squeeze(1)
+                EOS_mask = (cast(Array, argmax_token == decoder.EOS)) | (sample_token == decoder.EOS)
+
+                newly_done_mask = EOS_mask & (~session.completed)
+                newly_done_indices = mx.where(newly_done_mask, batch_idx, -1)
+                pos = mx.where(newly_done_indices != -1, batch_idx, session.bsz)
+                pos_sorted = mx.sort(pos, axis=0)
+                valid_count = session.bsz - mx.sum(cast(Array, pos_sorted == session.bsz))
+                pos_final = pos_sorted[: int(valid_count)]
+                newly_done_indices = mx.expand_dims(newly_done_indices[pos_final], 0)
+
+                if newly_done_indices.size > 0:
+                    for i in newly_done_indices:
+                        session.y_results[int(i)] = session.y[i, session.y_len : session.y_len + idx]
+                        session.completed[newly_done_indices] = True
+
+                if mx.all(session.completed).item():
+                    if session.y[:, session.y_len :].sum() == 0:
+                        session.y_results = [mx.array([0]) for _ in range(session.bsz)]
+                        logger.error("Bad Zero Prediction")
+                    else:
+                        logger.info(
+                            f"T2S Decoding EOS {session.prefill_len.tolist().__str__().strip('[]')} -> {[cast(tuple[int, ...], i.shape)[-1] for i in session.y_results].__str__().strip('[]')}"
+                        )
+                        logger.info(f"Infer Speed: {(idx - 1) / (time.perf_counter() - t1):.2f} token/s")
+                        infer_time = time.perf_counter() - t1
+                        infer_speed = (idx - 1) / infer_time
+                    break
+
+                if (request.early_stop_num != -1 and idx >= request.early_stop_num) or idx == max_token - 1:
+                    for j in range(session.bsz):
+                        if not session.completed[j].item():
+                            session.y_results[j] = session.y[[j], session.y_len : session.y_len + 1499]
+                            session.completed[j] = True
+                    logger.error("Bad Full Prediction")
+                    logger.info(f"Infer Speed: {(idx - 1) / (time.perf_counter() - t1):.2f} token/s")
+                    infer_time = time.perf_counter() - t1
+                    infer_speed = (idx - 1) / infer_time
+                    break
+
+                y_emb = decoder.ar_audio_embedding(samples)
+                session.xy_pos = decoder.ar_audio_position(session.input_pos - session.x_lens, y_emb)
+                mx.eval(session.xy_pos, session.y)
+
+                if idx == 1:
+                    t1 = time.perf_counter()
+
+                if idx % 100 == 0:
+                    mx.clear_cache()
+
+        match session.device:
+            case mx.gpu:
+                mx.clear_cache()
+            case mx.cpu:
+                gc.collect()
+
+        result_mlx = session.y_results[: request.valid_length]
+        mx.eval(result_mlx)
+        result = [torch.tensor(k) for k in result_mlx]
+        return result, infer_speed, infer_time
+
+    def generate(self, request: T2SRequest):
+        try:
+            result, infer_speed, infer_time = self._handle_request(request)
+            t2s_result = T2SResult(result=result, infer_speed=(infer_speed, infer_time), status="Success")
+        except Exception as e:
+            t2s_result = T2SResult(status="Error", exception=e, traceback=traceback.format_exc())
+        return t2s_result
+
+    @staticmethod
+    def replace_key(state_dict: dict[str, Tensor]):
+        state_dict_mlx: list[tuple[str, Array]] = []
+        for key, value in state_dict.items():
+            key = (
+                key.replace("model.", "")
+                .replace("in_proj_", "in_proj.")
+                .replace("self_attn", "attention")
+                .replace("linear", "feed_forward.linear")
+                .replace("norm1", "attention_norm")
+                .replace("norm2", "ffn_norm")
+            )
+            value_mlx = mx.array(value)
+            state_dict_mlx.append((key, value_mlx))
+        return state_dict_mlx
+
+    @staticmethod
+    def load_decoder(weights_path: os.PathLike, max_batch_size: int = 1, backend: str = "MLX-Varlen"):
+        logger.info(f"Loading Text2Semantic Weights from {weights_path} with {backend} Backend")
+        dict_s1 = torch.load(weights_path, map_location="cpu", weights_only=False, mmap=True)
+        config = dict_s1["config"]
+        match backend:
+            case "MLX-Varlen":
+                decoder_cls: type[T2SDecoderABC] = mlx_varlen.T2SDecoder
+            case "MLX-Static":
+                decoder_cls = mlx_static.T2SDecoder
+            case "MLX-Quantized":
+                decoder_cls = mlx_quantized.T2SDecoder
+            case _:
+                raise RuntimeError(f"Backend {backend} Not Found")
+
+        decoder: T2SDecoderABC = decoder_cls(config, max_batch_size=max_batch_size)
+        state_dict = dict_s1["weight"]
+        state_dict_mlx = T2SEngine.replace_key(state_dict)
+        decoder.load_weights(state_dict_mlx)
+        decoder.eval()
+        mx.eval(decoder)
+
+        if "Quantized" in backend and isinstance(decoder, mlx_quantized.T2SDecoder):
+            decoder.quantized()
+            mx.eval(decoder)
+
+        return decoder

GPT_SoVITS/Accelerate/MLX/t2s_model_abc.py

@@ -0,0 +1,530 @@
+from __future__ import annotations
+
+import math
+from abc import ABC, abstractmethod
+from typing import MutableSequence, cast
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .structs_mlx import KVCache, KVCacheProtocol, KVCacheQ, T2SDecoderProtocol, T2SSessionMLX
+
+Array = mx.array
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        vocab_size: int,
+    ):
+        super().__init__()
+
+        self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
+
+        self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim)
+
+    @property
+    def weight(self):
+        return self.word_embeddings.weight
+
+    def embedding(self, index: int):
+        return self.word_embeddings.weight[index : index + 1]
+
+    def __call__(self, x: Array):
+        x = self.word_embeddings(x)
+        return x
+
+
+class SinePositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        scale: bool = False,
+        max_batch_size: int = 10,
+        max_seq_len: int = 1800,
+    ):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
+        self.alpha = mx.ones(1)
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_len
+
+        self.reverse = False
+        self._pe = mx.zeros((max_batch_size, max_seq_len, embedding_dim))
+        self.compute_pe()
+
+    def compute_pe(self):
+        """Reset the positional encodings."""
+
+        if self.reverse:
+            position = mx.expand_dims(mx.arange(self.max_seq_len - 1, -1, -1.0), axis=1)
+        else:
+            position = mx.expand_dims(mx.arange(self.max_seq_len), axis=1)
+        div_term = mx.exp(
+            mx.arange(
+                0,
+                self.embedding_dim,
+                2,
+            )
+            * -(math.log(10000.0) / self.embedding_dim)
+        )
+        pe = self._pe
+        pe[:, :, 0::2] = mx.sin(position * div_term)
+        pe[:, :, 1::2] = mx.cos(position * div_term)
+
+    def __call__(self, input_pos: Array, x: Array):
+        """
+        Args:
+            input_pos (Array): [batch_size, ]
+            x (Array): [batch_size, 1, embed_dim]
+
+        Returns:
+            embedded_x (Array): [batch_size, 1, embed_dim]
+        """
+
+        batch_size = cast(tuple[int, ...], x.shape)[0]
+        pe_values = self._pe[mx.arange(batch_size), input_pos - 1]  # (batch_size, embed_dim)
+
+        return x * self.x_scale + self.alpha * mx.expand_dims(pe_values, 1)  # (batch_size, 1, embed_dim)
+
+    def prefill(self, x: Array):
+        """
+        Args:
+            x (Array): [batch_size, seq_len, embed_dim]
+
+        Returns:
+            embedded_x (Array): [batch_size, seq_len, embed_dim]
+        """
+        pe_values = self._pe[:, : cast(tuple[int, ...], x.shape)[-2]]
+        return x * self.x_scale + self.alpha * pe_values
+
+
+class KVCacheHND(KVCacheProtocol):
+    @staticmethod
+    def empty(kv_cache):
+        assert len(kv_cache) == 2
+        k_cache, v_cache = kv_cache
+
+        k_cache[:] = 0
+        v_cache[:] = 0
+
+    @staticmethod
+    def update_cache(input_pos, k_val, v_val, kv_cache, cache_idx):
+        # input_pos: [B, ], k_val: [B, H, 1, D]
+        assert len(kv_cache) == 2
+        k_out, v_out = kv_cache
+        ip0 = input_pos - 1
+
+        k_out[cache_idx, :, ip0, None] = k_val
+        v_out[cache_idx, :, ip0, None] = v_val
+
+        return k_out, v_out
+
+    @staticmethod
+    def prefill_kv(k_val, v_val, kv_cache):
+        # k_val: [B, S, H, D]
+        assert len(kv_cache) == 2
+        k_cache, v_cache = kv_cache
+
+        k_cache[..., : cast(tuple[int, ...], k_val.shape)[1], :] = k_val.swapaxes(1, 2)
+        v_cache[..., : cast(tuple[int, ...], v_val.shape)[1], :] = v_val.swapaxes(1, 2)
+
+    @staticmethod
+    def init_cache(batch_size: int, max_seq_length: int, n_heads: int, head_dim: int, dtype: mx.Dtype) -> KVCache:
+        cache_shape = (batch_size, n_heads, max_seq_length, head_dim)
+
+        return (mx.zeros(cache_shape, dtype=dtype), mx.zeros(cache_shape, dtype=dtype))
+
+
+class KVCacheHNDQuantized(KVCacheProtocol):
+    @staticmethod
+    def _el_per_int(bits: int) -> int:
+        return 32 // bits
+
+    @staticmethod
+    def _packed_dim(head_dim: int, bits: int = 8) -> int:
+        el_per_int = KVCacheHNDQuantized._el_per_int(bits)
+        if head_dim % el_per_int != 0:
+            raise ValueError(f"{head_dim=} is not divisible by {el_per_int=} ({bits=})")
+        return head_dim // el_per_int
+
+    @staticmethod
+    def _group_count(head_dim: int, group_size: int = 32) -> int:
+        assert group_size in {32, 64, 128}
+        if head_dim % group_size != 0:
+            raise ValueError(f"{head_dim} is not divisible by {group_size=}")
+        return head_dim // group_size
+
+    @staticmethod
+    def empty(kv_cache) -> None:
+        assert len(kv_cache) == 3
+        (k_q, k_s, k_b), (v_q, v_s, v_b), (_, __) = kv_cache
+
+        k_q[:] = 0
+        k_s[:] = 0
+        k_b[:] = 0
+        v_q[:] = 0
+        v_s[:] = 0
+        v_b[:] = 0
+
+    @staticmethod
+    def update_cache(
+        input_pos,
+        k_val,
+        v_val,
+        kv_cache,
+        cache_idx,
+    ):
+        # input_pos: [B, ], k_val: [B, H, 1, D]
+
+        assert len(kv_cache) == 3
+        (k_q_out, k_s_out, k_b_out), (v_q_out, v_s_out, v_b_out), (group_size, bits) = kv_cache
+
+        k_q, k_s, k_b = mx.quantize(k_val, group_size=group_size, bits=bits)
+        v_q, v_s, v_b = mx.quantize(v_val, group_size=group_size, bits=bits)
+
+        ip0 = input_pos - 1
+
+        k_q_out[cache_idx, :, ip0, None] = k_q
+        k_s_out[cache_idx, :, ip0, None] = k_s
+        k_b_out[cache_idx, :, ip0, None] = k_b
+
+        v_q_out[cache_idx, :, ip0, None] = v_q
+        v_s_out[cache_idx, :, ip0, None] = v_s
+        v_b_out[cache_idx, :, ip0, None] = v_b
+
+        return (k_q_out, k_s_out, k_b_out), (v_q_out, v_s_out, v_b_out), (group_size, bits)
+
+    @staticmethod
+    def prefill_kv(
+        k_val,
+        v_val,
+        kv_cache,
+    ) -> None:
+        assert len(kv_cache) == 3
+        (k_q_out, k_s_out, k_b_out), (v_q_out, v_s_out, v_b_out), (group_size, bits) = kv_cache
+
+        S = cast(tuple[int, ...], k_val.shape)[1]
+
+        k_sw = k_val.swapaxes(1, 2)
+        v_sw = v_val.swapaxes(1, 2)
+
+        k_q, k_s, k_b = mx.quantize(k_sw, group_size=group_size, bits=bits)
+        v_q, v_s, v_b = mx.quantize(v_sw, group_size=group_size, bits=bits)
+
+        k_q_out[..., :S, :] = k_q
+        k_s_out[..., :S, :] = k_s
+        k_b_out[..., :S, :] = k_b
+
+        v_q_out[..., :S, :] = v_q
+        v_s_out[..., :S, :] = v_s
+        v_b_out[..., :S, :] = v_b
+
+    @staticmethod
+    def init_cache(
+        batch_size: int,
+        max_seq_length: int,
+        n_heads: int,
+        head_dim: int,
+        dtype: mx.Dtype,
+        *,
+        group_size: int = 32,
+        bits: int = 8,
+    ) -> KVCacheQ:
+        packed_dim = KVCacheHNDQuantized._packed_dim(head_dim, bits=bits)
+        group_cnt = KVCacheHNDQuantized._group_count(head_dim, group_size=group_size)
+
+        packed_shape = (batch_size, n_heads, max_seq_length, packed_dim)
+        group_shape = (batch_size, n_heads, max_seq_length, group_cnt)
+
+        k_q = mx.zeros(packed_shape, dtype=mx.uint32)
+        k_s = mx.zeros(group_shape, dtype=dtype)
+        k_b = mx.zeros(group_shape, dtype=dtype)
+
+        v_q = mx.zeros(packed_shape, dtype=mx.uint32)
+        v_s = mx.zeros(group_shape, dtype=dtype)
+        v_b = mx.zeros(group_shape, dtype=dtype)
+
+        return (k_q, k_s, k_b), (v_q, v_s, v_b), (group_size, bits)
+
+
+class AttentionABC(ABC, nn.Module):
+    def __init__(self, n_head: int, hidden_dim: int, max_seq_length: int, *args, **kwds):
+        super().__init__()
+
+        self.n_head = n_head
+        self.hidden_dim = hidden_dim
+        assert hidden_dim % n_head == 0
+        self.head_dim = hidden_dim // n_head
+
+        self.max_seq_length = max_seq_length
+
+        # key, query, value projections for all heads, but in a batch
+        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 3, bias=True)
+        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+        self.scale = 1 / math.sqrt(self.head_dim)
+
+        self.kc_class: KVCacheProtocol
+
+    @abstractmethod
+    def __call__(
+        self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array
+    ) -> Array: ...
+
+    def prefill(self, x: Array, kv_cache: KVCache | KVCacheQ, attn_mask: Array):
+        bsz, seqlen, _ = cast(tuple[int, ...], x.shape)
+
+        q, k, v = self.in_proj(mx.expand_dims(x, 0)).split(3, axis=-1)
+
+        q, k, v = map(lambda x: x.reshape(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
+
+        self.kc_class.prefill_kv(k, v, kv_cache)
+
+        q, k, v = map(lambda x: x.swapaxes(1, 2), (q, k, v))
+
+        attn = mx.fast.scaled_dot_product_attention(q, k, v, mask=attn_mask, scale=self.scale)
+
+        attn = mx.nan_to_num(attn)
+
+        attn = attn.swapaxes(1, 2).reshape(1, -1, self.hidden_dim)
+
+        output = self.out_proj(attn)
+
+        return output
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int) -> None:
+        super().__init__()
+
+        self.linear1 = nn.Linear(dim, hidden_dim, bias=True)
+        self.linear2 = nn.Linear(hidden_dim, dim, bias=True)
+
+    def __call__(self, x: Array):
+        return self.linear2(nn.relu(self.linear1(x)))
+
+
+class TransformerBlockABC(nn.Module):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int, *args, **kwds) -> None:
+        super().__init__()
+
+        self.hidden_dim = hidden_dim
+        self.max_seq_length = max_seq_length
+
+        self.attention: AttentionABC
+
+        self.feed_forward = FeedForward(hidden_dim, ffn_dim)
+        self.attention_norm = nn.LayerNorm(self.hidden_dim)
+        self.ffn_norm = nn.LayerNorm(self.hidden_dim)
+
+    def __call__(self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array):
+        h = self.attention_norm(
+            x
+            + self.attention(
+                x,
+                input_pos,
+                kv_cache,
+                cache_idx,
+                attn_mask,
+            )
+        )
+        out = self.ffn_norm(h + self.feed_forward(h))
+        return out
+
+    def prefill(self, x: Array, attn_mask: Array, kv_cache: KVCache | KVCacheQ):
+        h = self.attention_norm(
+            x
+            + self.attention.prefill(
+                x,
+                kv_cache,
+                attn_mask,
+            )
+        )
+        out = self.ffn_norm(h + self.feed_forward(h))
+
+        return out
+
+
+class TransformerDecoderABC(nn.Module):
+    def __init__(
+        self,
+        hidden_dim: int,
+        n_layer: int,
+        n_head: int,
+        ffn_dim: int,
+        vocab_size: int,
+        max_seq_length: int,
+        max_batch_size: int,
+        *args,
+        **kwds,
+    ) -> None:
+        super().__init__()
+
+        self.hidden_dim = hidden_dim
+        self.n_head = n_head
+        assert hidden_dim % n_head == 0
+
+        self.head_dim = hidden_dim // n_head
+        self.vocab_size = vocab_size
+
+        self.n_layer = n_layer
+
+        self.layers: MutableSequence[TransformerBlockABC]
+
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+
+    def __call__(
+        self,
+        input_pos: Array,
+        x: Array,
+        kv_caches: MutableSequence[KVCache | KVCacheQ],
+        cache_idx: Array,
+        *args,
+        **kwds,
+    ):
+        for layer, kv_cache in zip(self.layers, kv_caches):
+            x = layer(
+                x,
+                input_pos,
+                kv_cache,
+                cache_idx,
+                *args,
+                **kwds,
+            )
+
+        return x
+
+    def prefill(self, x: Array, mask: Array, kv_caches: MutableSequence[KVCache | KVCacheQ]):
+        for layer, kv_cache in zip(self.layers, kv_caches):
+            x = layer.prefill(
+                x,
+                mask,
+                kv_cache,
+            )
+        return x
+
+
+class T2SDecoderABC(nn.Module, T2SDecoderProtocol):
+    def __init__(
+        self,
+        config: dict,
+        max_seq_length: int = 1800,
+        max_batch_size: int = 10,
+    ) -> None:
+        super().__init__()
+
+        hidden_dim: int = config["model"]["hidden_dim"]
+        embedding_dim: int = config["model"]["embedding_dim"]
+        n_head: int = config["model"]["head"]
+        n_layer: int = config["model"]["n_layer"]
+        vocab_size: int = config["model"]["vocab_size"]
+        phoneme_vocab_size: int = config["model"]["phoneme_vocab_size"]
+        EOS: int = config["model"]["EOS"]
+        ffn_dim: int = hidden_dim * 4
+
+        self.n_layer = int(n_layer)
+        self.hidden_dim = int(hidden_dim)
+        self.n_head = int(n_head)
+        assert hidden_dim % n_head == 0
+
+        self.head_dim = int(hidden_dim // n_head)
+        self.embedding_dim = int(embedding_dim)
+        self.ffn_dim = int(ffn_dim)
+        self.vocab_size = int(vocab_size)
+        self.phoneme_vocab_size = int(phoneme_vocab_size)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        self.EOS = EOS
+        assert self.EOS == self.vocab_size - 1
+
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_predict_layer = nn.Linear(self.hidden_dim, self.vocab_size, bias=False)
+        self.h: TransformerDecoderABC
+
+        self.ar_text_embedding = TokenEmbedding(self.embedding_dim, self.phoneme_vocab_size)
+        self.ar_text_position = SinePositionalEmbedding(
+            self.embedding_dim,
+            scale=False,
+            max_batch_size=max_batch_size,
+            max_seq_len=max_seq_length,
+        )
+        self.ar_audio_embedding = TokenEmbedding(self.embedding_dim, self.vocab_size)
+        self.ar_audio_position = SinePositionalEmbedding(
+            self.embedding_dim,
+            scale=False,
+            max_batch_size=max_batch_size,
+            max_seq_len=max_seq_length,
+        )
+
+        self.kv_class: KVCacheProtocol
+
+    def init_cache(self, bsz: int = 0, *args, **kwds) -> MutableSequence[KVCache | KVCacheQ]:
+        bsz = bsz or self.h.max_batch_size
+        assert bsz <= self.h.max_batch_size
+        seq_lens = self.h.max_seq_length
+        dtype = self.bert_proj.bias.dtype
+        cache: MutableSequence[KVCache | KVCacheQ] = [
+            self.kv_class.init_cache(bsz, seq_lens, self.n_head, self.head_dim, dtype, *args, **kwds)
+            for _ in range(self.n_layer)
+        ]
+        mx.eval(cache)
+        return cache
+
+    def embed(
+        self,
+        x: list[Array],
+        y: Array,
+        bert_features: list[Array],
+    ):
+        x_len: list[int] = [cast(tuple[int, ...], i.shape)[0] for i in x]
+        x_len_max = max(x_len)
+        xy_pos = mx.zeros((len(x), x_len_max + cast(tuple[int, ...], y.shape)[1], self.embedding_dim)).astype(
+            bert_features[0].dtype
+        )
+
+        bert_features = list(map(lambda x: x.swapaxes(0, 1), bert_features))
+
+        y_len = cast(tuple[int, ...], y.shape)[1]
+        y_emb = self.ar_audio_embedding(y)
+        y_pos = self.ar_audio_position.prefill(y_emb)
+
+        for bs, (x_, len_, bert_feature) in enumerate(zip(x, x_len, bert_features)):
+            x_emb = self.ar_text_embedding(x_)
+            bert = self.bert_proj(bert_feature)
+            x_emb = x_emb + bert
+            x_pos = self.ar_text_position.prefill(mx.expand_dims(x_emb, 0))
+            xy_pos[[bs], :len_] = x_pos
+            xy_pos[[bs], len_ : len_ + y_len] = y_pos
+
+        mx.eval(xy_pos)
+        return xy_pos
+
+    def compile(self):
+        setattr(self.h, "__call__", mx.compile(self.h.__call__))
+        # setattr(self.h, "prefill", mx.compile(self.h.prefill, shapeless=True))
+
+    def pre_forward(self, session: T2SSessionMLX):
+        attn_mask = session.attn_mask
+        return list(), dict(attn_mask=attn_mask)
+
+    def post_forward(self, idx: int, session: T2SSessionMLX) -> None:
+        if idx == 0:
+            prefill_len = session.prefill_len
+            bsz = session.bsz
+
+            range_tensor = mx.arange(self.max_seq_length).reshape(1, 1, 1, self.max_seq_length)
+            prefill_len_expanded = prefill_len.reshape(bsz, 1, 1, 1)
+            attn_mask = range_tensor < prefill_len_expanded
+            attn_mask = mx.repeat(attn_mask, self.n_head, 1)
+
+            session.attn_mask = attn_mask
+
+        attn_mask = session.attn_mask
+        input_pos = session.input_pos
+        attn_mask[mx.arange(session.bsz), :, :, input_pos] = True
+        mx.eval(attn_mask)

GPT_SoVITS/Accelerate/PyTorch/__init__.py

@@ -0,0 +1,28 @@
+import importlib.util
+
+import torch
+
+from .sample_funcs import sample_naive
+from .structs import T2SRequest, T2SResult
+from .t2s_engine import T2SEngine as T2SEngineTorch
+
+backends = ["torch_varlen"]
+if torch.cuda.is_available():
+    backends.append("torch_static_cuda_graph")
+    if importlib.util.find_spec("sageattention") is not None:
+        for i in range(torch.cuda.device_count()):
+            major, minor = torch.cuda.get_device_capability(i)
+            sm_version = major + minor / 10.0
+            if sm_version >= 7.0:
+                backends.append("sage_attn_varlen_cuda_graph")
+    if importlib.util.find_spec("flash_attn") is not None:
+        for i in range(torch.cuda.device_count()):
+            major, minor = torch.cuda.get_device_capability(i)
+            sm_version = major + minor / 10.0
+            if sm_version >= 7.5:
+                backends.append("flash_attn_varlen_cuda_graph")
+if torch.mps.is_available():
+    backends.append("mps_flash_attn_varlen")
+
+
+__all__ = ["T2SEngineTorch", "T2SRequest", "sample_naive", "T2SResult", "backends"]

GPT_SoVITS/Accelerate/PyTorch/backends/flash_attn_varlen_cuda_graph.py

@@ -0,0 +1,157 @@
+"""
+Modified From https://github.com/XXXXRT666/GPT-SoVITS
+"""
+
+from typing import Dict, List, Tuple
+
+import kernels
+import torch
+
+from .. import nn
+from ..structs import T2SSession
+from ..t2s_model_abc import (
+    AttentionABC,
+    CUDAGraphCacheABC,
+    FeedForward,
+    KVCacheNHD,
+    KVCacheProtocol,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+flash_attn_kernel = None
+try:
+    import flash_attn_interface as flash_attn  # type: ignore
+
+    flash_attn_kernel = flash_attn.flash_attn_with_kvcache
+except ModuleNotFoundError:
+    try:
+        import flash_attn  # type: ignore
+
+        flash_attn_kernel = flash_attn.flash_attn_with_kvcache
+
+    except ModuleNotFoundError:
+        pass
+
+if flash_attn_kernel is None:
+    flash_attn_kernel = kernels.get_kernel("kernels-community/flash-attn").flash_attn_with_kvcache
+
+
+Tensor = torch.Tensor
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head, hidden_dim, max_seq_length):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+
+        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 3, bias=True)
+        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def __call__(self, x: Tensor, input_pos: Tensor, kv_cache: KVCacheProtocol, *args, **kwds) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        q, k, v = self.in_proj(x).chunk(3, dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_head, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_head, self.head_dim)
+
+        attn: Tensor = flash_attn.flash_attn_with_kvcache(  # type: ignore
+            q, kv_cache.k_cache, kv_cache.v_cache, k, v, cache_seqlens=input_pos - 1
+        )
+
+        attn = attn.view(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head, ffn_dim, hidden_dim, max_seq_length) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+        self.feed_forward = FeedForward(hidden_dim, ffn_dim)
+        self.attention_norm = nn.LayerNorm([self.hidden_dim])
+        self.ffn_norm = nn.LayerNorm([self.hidden_dim])
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim,
+        n_layer,
+        n_head,
+        ffn_dim,
+        vocab_size,
+        max_seq_length,
+        max_batch_size,
+    ) -> None:
+        super().__init__(hidden_dim, n_layer, n_head, ffn_dim, vocab_size, max_seq_length, max_batch_size)
+
+        self.layers = nn.ModuleList(  # type: ignore
+            TransformerBlock(n_head, ffn_dim, hidden_dim, max_seq_length) for _ in range(n_layer)
+        )
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config,
+        max_seq_length=1800,
+        max_batch_size=10,
+    ) -> None:
+        assert torch.cuda.is_available()
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_predict_layer = nn.Linear(self.hidden_dim, self.vocab_size, bias=False)
+        self.h: TransformerDecoderABC = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheNHD
+
+    def post_forward(self, idx: int, session: T2SSession) -> None:
+        return super().post_forward(idx, session)
+
+    def pre_forward(self, session: T2SSession) -> Tuple[List, Dict]:
+        return super().pre_forward(session)
+
+
+class CUDAGraphCache(CUDAGraphCacheABC):
+    def __init__(
+        self,
+        decoder: T2SDecoder,
+    ) -> None:
+        super().__init__(decoder)
+
+    def release_graph(self, session: T2SSession):
+        if session.id != self.id:
+            self.assigned = False
+        else:
+            del session.graph, session.xy_pos_, session.xy_dec_, session.input_pos, session.kv_cache
+
+    def get_cache_graph(self, session: T2SSession):
+        assert self.graph
+        session.graph = self.graph
+        session.stream = self.stream
+
+        session.xy_pos_ = self.xy_pos
+        session.xy_dec_ = self.xy_dec
+        session.input_pos = self.input_pos.copy_(session.input_pos)
+
+        for cache, cache_ in zip(self.kv_cache, session.kv_cache):
+            cache.sync_cache(cache_)
+
+    def capture_new_graph(self, session: T2SSession):
+        session.xy_pos_ = self.xy_pos.clone()
+        session.xy_dec_ = self.xy_dec.clone()
+        session.input_pos = self.input_pos.clone().copy_(session.input_pos)
+
+        args, kwds = self.decoder.pre_forward(session)
+        graph = self.decoder.capture(self.input_pos, self.xy_pos, self.xy_dec, self.kv_cache, *args, **kwds)
+        session.graph = graph
+        session.stream = torch.cuda.Stream()  # type: ignore

GPT_SoVITS/Accelerate/PyTorch/backends/mps_flash_attn_varlen.py

@@ -0,0 +1,165 @@
+import torch
+from torch.nn import functional as F
+
+from .. import nn
+from ..structs import KVCacheProtocol, T2SSession
+from ..t2s_model_abc import (
+    AttentionABC,
+    CUDAGraphCacheABC,
+    FeedForward,
+    KVCacheHND,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Tensor = torch.Tensor
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head, hidden_dim, max_seq_length):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+
+        # key, query, value projections for all heads, but in a batch
+        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 3, bias=True)
+        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def __call__(self, x: Tensor, input_pos: Tensor, kv_cache: KVCacheProtocol, attn_mask: Tensor):
+        bsz, seqlen, _ = x.shape
+
+        q, k, v = self.in_proj(x).chunk(3, dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_head, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_head, self.head_dim)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        k, v = kv_cache.update(input_pos, k, v)
+
+        attn = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+
+        attn = attn.transpose(1, 2).contiguous().view(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+        self.feed_forward = FeedForward(hidden_dim, ffn_dim)
+        self.attention_norm = nn.LayerNorm([self.hidden_dim])
+        self.ffn_norm = nn.LayerNorm([self.hidden_dim])
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim,
+        n_layer,
+        n_head,
+        ffn_dim,
+        vocab_size,
+        max_seq_length,
+        max_batch_size,
+    ) -> None:
+        super().__init__(hidden_dim, n_layer, n_head, ffn_dim, vocab_size, max_seq_length, max_batch_size)
+
+        self.layers = nn.ModuleList(  # type: ignore
+            TransformerBlock(n_head, ffn_dim, hidden_dim, max_seq_length) for _ in range(n_layer)
+        )
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config,
+        max_seq_length=1800,
+        max_batch_size=10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_predict_layer = nn.Linear(self.hidden_dim, self.vocab_size, bias=False)
+        self.h: TransformerDecoderABC = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHND
+
+    def pre_forward(self, session: T2SSession):
+        attn_mask = session.attn_mask
+        return list(), dict(attn_mask=attn_mask)
+
+    def post_forward(self, idx: int, session: T2SSession) -> None:
+        if idx == 0:
+            prefill_len = session.prefill_len
+            bsz = session.bsz
+
+            range_tensor = torch.arange(self.max_seq_length).view(1, 1, 1, self.max_seq_length)
+            prefill_len_expanded = prefill_len.view(bsz, 1, 1, 1)
+            attn_mask = range_tensor < prefill_len_expanded
+            attn_mask = attn_mask.expand(-1, self.n_head, -1, -1)
+
+            session.attn_mask = attn_mask
+
+        attn_mask = session.attn_mask
+        input_pos = session.input_pos
+        attn_mask[torch.arange(session.bsz), :, :, input_pos] = True
+
+
+class CUDAGraphCache(CUDAGraphCacheABC):
+    def __init__(
+        self,
+        decoder,
+    ) -> None:
+        super().__init__(decoder)
+        if torch.cuda.is_available():
+            self.attn_mask = (
+                torch.randint(0, 2, (decoder.max_batch_size, decoder.n_head, 1, decoder.max_seq_length))
+                .bool()
+                .to(self.device, self.dtype)
+            )
+
+    def release_graph(self, session: T2SSession):
+        if session.id != self.id:
+            self.assigned = False
+        else:
+            del (
+                session.graph,
+                session.xy_pos_,
+                session.xy_dec_,
+                session.input_pos,
+                session.kv_cache,
+                session.attn_mask,
+            )
+
+    def get_cache_graph(self, session: T2SSession):
+        assert self.graph
+        session.graph = self.graph
+        session.stream = self.stream
+
+        session.xy_pos_ = self.xy_pos
+        session.xy_dec_ = self.xy_dec
+        session.input_pos = self.input_pos.copy_(session.input_pos)
+
+        session.attn_mask = self.attn_mask
+
+        for cache, cache_ in zip(self.kv_cache, session.kv_cache):
+            cache.sync_cache(cache_)
+
+    def capture_new_graph(self, session: T2SSession):
+        session.xy_pos_ = self.xy_pos.clone()
+        session.xy_dec_ = self.xy_dec.clone()
+        session.input_pos = self.input_pos.clone().copy_(session.input_pos)
+
+        session.attn_mask = self.attn_mask.clone().copy_(session.attn_mask)
+
+        args, kwds = self.decoder.pre_forward(session)
+        graph = self.decoder.capture(self.input_pos, self.xy_pos, self.xy_dec, self.kv_cache, *args, **kwds)
+        session.graph = graph
+        session.stream = torch.cuda.Stream()  # type: ignore

GPT_SoVITS/Accelerate/PyTorch/backends/sage_attn_varlen_cuda_graph.py

@@ -0,0 +1,176 @@
+from typing import MutableSequence
+
+import sageattention  # type: ignore
+import torch
+
+from .. import nn
+from ..structs import T2SSession
+from ..t2s_model_abc import (
+    AttentionABC,
+    CUDAGraphCacheABC,
+    FeedForward,
+    KVCacheHND,
+    KVCacheProtocol,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Tensor = torch.Tensor
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head, hidden_dim, max_seq_length):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+
+        # key, query, value projections for all heads, but in a batch
+        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 3, bias=True)
+        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def __call__(
+        self,
+        x: Tensor,
+        input_pos: Tensor,
+        kv_cache: KVCacheProtocol,
+        cu_seqlens_q: Tensor,
+        cu_seqlens_kv: Tensor,
+    ) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        q, k, v = self.in_proj(x).chunk(3, dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_head, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_head, self.head_dim)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        k, v = kv_cache.update(input_pos, k, v)
+
+        attn: Tensor = sageattention.sageattn_varlen(
+            q,
+            k,
+            v,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_kv=cu_seqlens_kv,
+            max_seqlen_q=1,
+            max_seqlen_k=self.max_seq_length,
+        )
+
+        attn = attn.transpose(1, 2).contiguous().view(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head, ffn_dim, hidden_dim, max_seq_length) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+        self.feed_forward = FeedForward(hidden_dim, ffn_dim)
+        self.attention_norm = nn.LayerNorm([self.hidden_dim])
+        self.ffn_norm = nn.LayerNorm([self.hidden_dim])
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim,
+        n_layer,
+        n_head,
+        ffn_dim,
+        vocab_size,
+        max_seq_length,
+        max_batch_size,
+    ) -> None:
+        super().__init__(hidden_dim, n_layer, n_head, ffn_dim, vocab_size, max_seq_length, max_batch_size)
+
+        self.layers = nn.ModuleList(  # type: ignore
+            TransformerBlock(n_head, ffn_dim, hidden_dim, max_seq_length) for _ in range(n_layer)
+        )
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config,
+        max_seq_length=1800,
+        max_batch_size=10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_predict_layer = nn.Linear(self.hidden_dim, self.vocab_size, bias=False)
+        self.h: TransformerDecoderABC = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHND
+
+    def pre_forward(self, session: T2SSession) -> tuple[list[Tensor], dict[str, Tensor]]:
+        return list(), dict(cu_seqlens_q=session.cu_seqlens_q, cu_seqlens_kv=session.cu_seqlens_kv)
+
+    def post_forward(self, idx: int, session: T2SSession):
+        if idx == 0:
+            session.cu_seqlens_q = torch.arange(0, session.bsz + 1, dtype=torch.int32)
+            session.cu_seqlens_kv = torch.cat([torch.tensor(0, dtype=torch.int32), session.input_pos])
+        else:
+            cu_seqlens_q = session.cu_seqlens_q
+            cu_seqlens_kv = session.cu_seqlens_kv
+            cu_seqlens_kv.add_(cu_seqlens_q)
+
+
+class CUDAGraphCache(CUDAGraphCacheABC):
+    def __init__(
+        self,
+        decoder: T2SDecoder,
+    ) -> None:
+        super().__init__(decoder)
+
+        if torch.cuda.is_available():
+            self.cu_seqlens_q = torch.arange(0, decoder.max_batch_size + 1, dtype=torch.int32).to(self.device)
+            self.cu_seqlens_kv = torch.cat([torch.tensor(0, dtype=torch.int32), self.input_pos]).to(self.device)
+
+    def release_graph(self, session: T2SSession):
+        if session.id != self.id:
+            self.assigned = False
+        else:
+            del (
+                session.graph,
+                session.xy_pos_,
+                session.xy_dec_,
+                session.input_pos,
+                session.kv_cache,
+                session.cu_seqlens_q,
+                session.cu_seqlens_kv,
+            )
+
+    def get_cache_graph(self, session: T2SSession):
+        assert self.graph
+        session.graph = self.graph
+        session.stream = self.stream
+
+        session.xy_pos_ = self.xy_pos
+        session.xy_dec_ = self.xy_dec
+        session.input_pos = self.input_pos.copy_(session.input_pos)
+
+        session.cu_seqlens_q = self.cu_seqlens_q
+        session.cu_seqlens_kv = self.cu_seqlens_kv
+
+        for cache, cache_ in zip(self.kv_cache, session.kv_cache):
+            cache.sync_cache(cache_)
+
+    def capture_new_graph(self, session: T2SSession):
+        session.xy_pos_ = self.xy_pos.clone()
+        session.xy_dec_ = self.xy_dec.clone()
+        session.input_pos = self.input_pos.clone().copy_(session.input_pos)
+
+        session.cu_seqlens_q = self.cu_seqlens_q.clone().copy_(session.cu_seqlens_q)
+        session.cu_seqlens_kv = self.cu_seqlens_kv.clone().copy_(session.cu_seqlens_kv)
+
+        args, kwds = self.decoder.pre_forward(session)
+        graph = self.decoder.capture(self.input_pos, self.xy_pos, self.xy_dec, self.kv_cache, *args, **kwds)
+        session.graph = graph
+        session.stream = torch.cuda.Stream()  # type: ignore

GPT_SoVITS/Accelerate/PyTorch/backends/torch_static_cuda_graph.py

@@ -0,0 +1,165 @@
+import torch
+from torch.nn import functional as F
+
+from .. import nn
+from ..structs import KVCacheProtocol, T2SSession
+from ..t2s_model_abc import (
+    AttentionABC,
+    CUDAGraphCacheABC,
+    FeedForward,
+    KVCacheHND,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Tensor = torch.Tensor
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head, hidden_dim, max_seq_length):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+
+        # key, query, value projections for all heads, but in a batch
+        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 3, bias=True)
+        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def __call__(self, x: Tensor, input_pos: Tensor, kv_cache: KVCacheProtocol, attn_mask: Tensor):
+        bsz, seqlen, _ = x.shape
+
+        q, k, v = self.in_proj(x).chunk(3, dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_head, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_head, self.head_dim)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        k, v = kv_cache.update(input_pos, k, v)
+
+        attn = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+
+        attn = attn.transpose(1, 2).contiguous().view(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+        self.feed_forward = FeedForward(hidden_dim, ffn_dim)
+        self.attention_norm = nn.LayerNorm([self.hidden_dim])
+        self.ffn_norm = nn.LayerNorm([self.hidden_dim])
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim,
+        n_layer,
+        n_head,
+        ffn_dim,
+        vocab_size,
+        max_seq_length,
+        max_batch_size,
+    ) -> None:
+        super().__init__(hidden_dim, n_layer, n_head, ffn_dim, vocab_size, max_seq_length, max_batch_size)
+
+        self.layers = nn.ModuleList(  # type: ignore
+            TransformerBlock(n_head, ffn_dim, hidden_dim, max_seq_length) for _ in range(n_layer)
+        )
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config,
+        max_seq_length=1800,
+        max_batch_size=10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_predict_layer = nn.Linear(self.hidden_dim, self.vocab_size, bias=False)
+        self.h: TransformerDecoderABC = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHND
+
+    def pre_forward(self, session: T2SSession):
+        attn_mask = session.attn_mask
+        return list(), dict(attn_mask=attn_mask)
+
+    def post_forward(self, idx: int, session: T2SSession) -> None:
+        if idx == 0:
+            prefill_len = session.prefill_len
+            bsz = session.bsz
+
+            range_tensor = torch.arange(self.max_seq_length).view(1, 1, 1, self.max_seq_length)
+            prefill_len_expanded = prefill_len.view(bsz, 1, 1, 1)
+            attn_mask = range_tensor < prefill_len_expanded
+            attn_mask = attn_mask.expand(-1, self.n_head, -1, -1)
+
+            session.attn_mask = attn_mask
+
+        attn_mask = session.attn_mask
+        input_pos = session.input_pos
+        attn_mask[torch.arange(session.bsz), :, :, input_pos] = True
+
+
+class CUDAGraphCache(CUDAGraphCacheABC):
+    def __init__(
+        self,
+        decoder,
+    ) -> None:
+        super().__init__(decoder)
+        if torch.cuda.is_available():
+            self.attn_mask = (
+                torch.randint(0, 2, (decoder.max_batch_size, decoder.n_head, 1, decoder.max_seq_length))
+                .bool()
+                .to(self.device, self.dtype)
+            )
+
+    def release_graph(self, session: T2SSession):
+        if session.id != self.id:
+            self.assigned = False
+        else:
+            del (
+                session.graph,
+                session.xy_pos_,
+                session.xy_dec_,
+                session.input_pos,
+                session.kv_cache,
+                session.attn_mask,
+            )
+
+    def get_cache_graph(self, session: T2SSession):
+        assert self.graph
+        session.graph = self.graph
+        session.stream = self.stream
+
+        session.xy_pos_ = self.xy_pos
+        session.xy_dec_ = self.xy_dec
+        session.input_pos = self.input_pos.copy_(session.input_pos)
+
+        session.attn_mask = self.attn_mask
+
+        for cache, cache_ in zip(self.kv_cache, session.kv_cache):
+            cache.sync_cache(cache_)
+
+    def capture_new_graph(self, session: T2SSession):
+        session.xy_pos_ = self.xy_pos.clone()
+        session.xy_dec_ = self.xy_dec.clone()
+        session.input_pos = self.input_pos.clone().copy_(session.input_pos)
+
+        session.attn_mask = self.attn_mask.clone().copy_(session.attn_mask)
+
+        args, kwds = self.decoder.pre_forward(session)
+        graph = self.decoder.capture(self.input_pos, self.xy_pos, self.xy_dec, self.kv_cache, *args, **kwds)
+        session.graph = graph
+        session.stream = torch.cuda.Stream()  # type: ignore

GPT_SoVITS/Accelerate/PyTorch/backends/torch_varlen.py

@@ -0,0 +1,144 @@
+from typing import NoReturn
+
+import torch
+from torch.nn import functional as F
+
+from .. import nn
+from ..structs import KVCacheProtocol, T2SSession
+from ..t2s_model_abc import (
+    AttentionABC,
+    CUDAGraphCacheABC,
+    FeedForward,
+    KVCacheHNDVarlen,
+    T2SDecoderABC,
+    TransformerBlockABC,
+    TransformerDecoderABC,
+)
+
+Tensor = torch.Tensor
+
+
+class Attention(AttentionABC):
+    def __init__(self, n_head, hidden_dim, max_seq_length):
+        super().__init__(n_head, hidden_dim, max_seq_length)
+
+        # key, query, value projections for all heads, but in a batch
+        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 3, bias=True)
+        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def __call__(self, x: Tensor, input_pos: Tensor, kv_cache: KVCacheProtocol, attn_mask: Tensor):
+        bsz, seqlen, _ = x.shape
+
+        q, k, v = self.in_proj(x).chunk(3, dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_head, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_head, self.head_dim)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        k, v = kv_cache.update(input_pos, k, v)
+
+        max_idx = input_pos.max()
+
+        q, k, v = map(lambda x: x[..., :max_idx, :], (q, k, v))
+
+        mask = attn_mask[..., :max_idx]
+
+        attn = F.scaled_dot_product_attention(q, k, v, mask)
+
+        attn = attn.transpose(1, 2).contiguous().view(bsz, seqlen, self.hidden_dim)
+
+        attn = self.out_proj(attn)
+
+        return attn
+
+
+class TransformerBlock(TransformerBlockABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
+        super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
+
+        self.attention = Attention(n_head, hidden_dim, max_seq_length)
+        self.feed_forward = FeedForward(hidden_dim, ffn_dim)
+        self.attention_norm = nn.LayerNorm([self.hidden_dim])
+        self.ffn_norm = nn.LayerNorm([self.hidden_dim])
+
+
+class TransformerDecoder(TransformerDecoderABC):
+    def __init__(
+        self,
+        hidden_dim,
+        n_layer,
+        n_head,
+        ffn_dim,
+        vocab_size,
+        max_seq_length,
+        max_batch_size,
+    ) -> None:
+        super().__init__(hidden_dim, n_layer, n_head, ffn_dim, vocab_size, max_seq_length, max_batch_size)
+
+        self.layers = nn.ModuleList(  # type: ignore
+            TransformerBlock(n_head, ffn_dim, hidden_dim, max_seq_length) for _ in range(n_layer)
+        )
+
+
+class T2SDecoder(T2SDecoderABC):
+    def __init__(
+        self,
+        config,
+        max_seq_length=1800,
+        max_batch_size=10,
+    ) -> None:
+        super().__init__(config, max_seq_length, max_batch_size)
+
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_predict_layer = nn.Linear(self.hidden_dim, self.vocab_size, bias=False)
+        self.h: TransformerDecoderABC = TransformerDecoder(
+            self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
+        )
+
+        self.kv_class = KVCacheHNDVarlen
+
+    def capture(
+        self,
+        *args,
+        **kwds,
+    ) -> NoReturn:
+        raise NotImplementedError("Cuda Graph Is Not Supported For Varlen Model")
+
+    def pre_forward(self, session: T2SSession):
+        attn_mask = session.attn_mask
+        return list(), dict(attn_mask=attn_mask)
+
+    def post_forward(self, idx: int, session: T2SSession) -> None:
+        if idx == 0:
+            prefill_len = session.prefill_len
+            bsz = session.bsz
+
+            range_tensor = torch.arange(self.max_seq_length).view(1, 1, 1, self.max_seq_length)
+            prefill_len_expanded = prefill_len.view(bsz, 1, 1, 1)
+            attn_mask = range_tensor < prefill_len_expanded
+            attn_mask = attn_mask.expand(-1, self.n_head, -1, -1)
+
+            session.attn_mask = attn_mask
+
+        attn_mask = session.attn_mask
+        input_pos = session.input_pos
+        attn_mask[torch.arange(session.bsz), :, :, input_pos] = True
+
+
+class CUDAGraphCache(CUDAGraphCacheABC):
+    def __init__(
+        self,
+        decoder,
+    ) -> None:
+        super().__init__(decoder, False)
+
+    def release_graph(self, session: T2SSession):
+        raise NotImplementedError("Cuda Graph Is Not Supported For Varlen Model")
+
+    def get_cache_graph(self, session: T2SSession):
+        raise NotImplementedError("Cuda Graph Is Not Supported For Varlen Model")
+
+    def capture_new_graph(self, session: T2SSession):
+        raise NotImplementedError("Cuda Graph Is Not Supported For Varlen Model")

GPT_SoVITS/Accelerate/PyTorch/nn.py

@@ -0,0 +1,69 @@
+"""
+Enhanced Type Hint nn.Module
+Modified From https://github.com/labmlai/labml/blob/master/helpers/labml_helpers/module.py
+"""
+
+from typing import Any
+
+import torch.nn
+from torch.nn import (
+    functional as functional,
+)
+from torch.nn import (
+    utils as utils,
+)
+from torch.nn.modules import *  # type: ignore # noqa: F403
+from torch.nn.parameter import (
+    Parameter as Parameter,
+)
+
+Tensor = torch.Tensor
+
+
+class Module(torch.nn.Module):
+    r"""
+    Wraps ``torch.nn.Module`` to overload ``__call__`` instead of
+    ``forward`` for better type checking.
+
+    `PyTorch Github issue for clarification <https://github.com/pytorch/pytorch/issues/44605>`_
+    """
+
+    def _forward_unimplemented(self, *input: Any) -> None:
+        # To stop PyTorch from giving abstract methods warning
+        pass
+
+    def __init_subclass__(cls, **kwargs):
+        if cls.__dict__.get("__call__", None) is None:
+            return
+
+        setattr(cls, "forward", cls.__dict__["__call__"])
+        delattr(cls, "__call__")
+
+    @property
+    def device(self) -> torch.device:
+        params = self.parameters()
+        try:
+            sample_param = next(params)
+            return sample_param.device
+        except StopIteration:
+            raise RuntimeError(f"Unable to determine device of {self.__class__.__name__}") from None
+
+
+class Linear(torch.nn.Linear):
+    def __call__(self, input: Tensor) -> Tensor:
+        return super().__call__(input)
+
+
+class Dropout(torch.nn.Dropout):
+    def __call__(self, input: Tensor) -> Tensor:
+        return super().__call__(input)
+
+
+class Embedding(torch.nn.Embedding):
+    def __call__(self, input: Tensor) -> Tensor:
+        return super().__call__(input)
+
+
+class LayerNorm(torch.nn.LayerNorm):
+    def __call__(self, input: Tensor) -> Tensor:
+        return super().__call__(input)

GPT_SoVITS/Accelerate/PyTorch/sample_funcs.py

@@ -0,0 +1,63 @@
+from typing import Protocol
+
+import torch
+import torch.nn.functional as F
+
+Tensor = torch.Tensor
+
+
+class SampleProtocol(Protocol):
+    @staticmethod
+    def __call__(
+        logits: Tensor,
+        previous_tokens: Tensor,
+        temperature: float,
+        top_k: int,
+        top_p: float,
+        repetition_penalty: float,
+    ) -> Tensor: ...
+
+
+class sample_naive(SampleProtocol):
+    @staticmethod
+    def __call__(
+        logits: Tensor,
+        previous_tokens: Tensor,
+        temperature: float,
+        top_k: int,
+        top_p: float,
+        repetition_penalty: float,
+    ):
+        if temperature <= 1e-5:
+            probs = F.softmax(logits, dim=-1)
+            return torch.argmax(probs, dim=-1, keepdim=True).to(dtype=torch.int32)
+
+        if repetition_penalty != 1.0:
+            previous_tokens = previous_tokens.long()
+            score = torch.gather(logits, dim=1, index=previous_tokens)
+            score = torch.where(
+                score < 0,
+                score * repetition_penalty,
+                score / repetition_penalty,
+            )
+            logits.scatter_(dim=1, index=previous_tokens, src=score)
+
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+        cum_probs[cum_probs > 1] = 1
+        sorted_indices_to_remove = cum_probs > top_p
+        sorted_indices_to_remove[:, 0] = False  # keep at least one option
+        indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
+        logits = logits.masked_fill(indices_to_remove, -float("Inf"))
+
+        logits /= temperature
+
+        v, _ = torch.topk(logits, top_k)
+        pivot = v[:, -1].unsqueeze(-1)
+        logits = torch.where(logits < pivot, -float("Inf"), logits)
+
+        probs = F.softmax(logits, dim=-1)
+        q = torch.empty_like(probs).exponential_(1.0)
+        idx_next = torch.argmax(probs / q, dim=-1, keepdim=True).to(dtype=torch.int32)
+
+        return idx_next

GPT_SoVITS/Accelerate/PyTorch/structs.py

@@ -0,0 +1,151 @@
+"""
+Modified From https://github.com/XXXXRT666/GPT-SoVITS
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal, MutableSequence, Optional, Protocol
+
+import torch
+
+from .sample_funcs import SampleProtocol, sample_naive
+
+Tensor = torch.Tensor
+
+
+@dataclass
+class T2SResult:
+    result: list[Tensor] | None = None
+    infer_speed: tuple[float, float] = (0.0, 0.0)
+    status: Literal["Success", "Error"] = "Success"
+    exception: Optional[Exception] = None
+    traceback: Optional[str] = None
+
+
+@dataclass
+class T2SRequest:
+    x: list[torch.Tensor]
+    x_lens: Tensor
+    prompts: torch.Tensor
+    bert_feature: list[Tensor]
+    valid_length: int
+    top_k: int = 5
+    top_p: float = 1
+    early_stop_num: int = -1
+    temperature: float = 1.0
+    repetition_penalty: float = 1.35
+    use_cuda_graph: bool = False
+    debug: bool = False
+
+
+class KVCacheProtocol(Protocol):
+    k_cache: Tensor
+    v_cache: Tensor
+
+    def __init__(self, batch_size: int, max_seq_length: int, n_heads: int, head_dim: int) -> None: ...
+
+    def empty(self) -> None: ...
+
+    def update(self, input_pos: Tensor, k_val: Tensor, v_val: Tensor, *args, **kwds) -> tuple[Tensor, Tensor]: ...
+
+    def prefill_kv(self, k_val: Tensor, v_val: Tensor) -> None: ...
+
+    def sync_cache(self, kv_cache: KVCacheProtocol) -> None: ...
+
+
+class T2SDecoderProtocol(Protocol):
+    max_seq_length: int
+    EOS: int
+    n_head: int
+
+    @property
+    def device(self) -> torch.device: ...
+
+    def embed(self, x: list[Tensor], y: Tensor, bert_features: list[Tensor]) -> Tensor: ...
+
+
+class T2SEngineProtocol(Protocol):
+    def _handle_request(self, request: T2SRequest) -> tuple[list[Tensor], float, float]: ...
+
+    def generate(self, request: T2SRequest) -> T2SResult: ...
+
+
+class T2SSession:
+    def __init__(
+        self,
+        decoder: T2SDecoderProtocol,
+        request: T2SRequest,
+        sapmle_func: type[SampleProtocol] = sample_naive,
+        device: torch.device = torch.device("cpu"),
+        dtype: torch.dtype = torch.float32,
+    ):
+        with device:
+            self.decoder = decoder
+            self.request = request
+            self.device = device
+            self.dtype = dtype
+
+            bsz = len(request.x)
+            y_len = request.prompts.size(-1)
+            self.bsz = bsz
+            self.y_len = y_len
+            request.prompts = request.prompts.to(device, torch.int32)
+
+            # Cache
+            self.kv_cache: MutableSequence[KVCacheProtocol]
+            self.sample = sapmle_func()
+
+            # Forward args
+            self.x = [i.to(device) for i in request.x]
+            self.x_lens = request.x_lens.to(torch.int32)
+            self.y = torch.zeros((bsz, decoder.max_seq_length)).to(torch.int32)
+            self.y[:, : request.prompts.shape[-1]] = request.prompts
+            self.bert_feature = [i.to(device, dtype) for i in request.bert_feature]
+
+            self.prefill_len = self.x_lens + request.prompts.size(1)
+
+            self.input_pos = torch.zeros_like(self.prefill_len)
+            self.input_pos.add_(self.prefill_len)
+
+            # CUDA Graph
+            self.stream: Optional[torch.cuda.Stream] = None
+            self.graph: Optional[torch.cuda.CUDAGraph] = None
+            self.xy_pos_: Tensor
+            self.xy_dec_: Tensor
+
+            # EOS
+            self.completed = torch.Tensor([False] * len(self.x)).bool().to(device)
+            self.y_results: list[Tensor] = [None] * len(self.x)  # type: ignore
+
+            self.xy_pos = decoder.embed(self.x, request.prompts, self.bert_feature)
+
+            max_len = int(self.prefill_len.max().item())
+            attn_mask = torch.zeros(size=(bsz, max_len, max_len), dtype=torch.bool)
+
+            for bs in range(bsz):
+                pos = int(self.x_lens[bs])
+                seq_len = pos + y_len
+
+                attn_mask[bs, :seq_len, :pos] = True
+
+                ar_mask = ~torch.triu(
+                    input=torch.ones(
+                        size=(
+                            y_len,
+                            y_len,
+                        ),
+                        dtype=torch.bool,
+                    ),
+                    diagonal=1,
+                )
+                attn_mask[bs, pos:seq_len, pos:seq_len] = ar_mask
+
+            self.attn_mask = attn_mask
+            self.attn_mask = attn_mask.unsqueeze(0).expand(-1, decoder.n_head, -1, -1)
+
+            self.id: int = -1
+
+            # Sage Attn & Transformer Engine Impl
+            self.cu_seqlens_q: Tensor
+            self.cu_seqlens_kv: Tensor

GPT_SoVITS/Accelerate/PyTorch/t2s_engine.py

@@ -0,0 +1,220 @@
+import contextlib
+import gc
+import os
+import sys
+import time
+import traceback
+from importlib import import_module
+
+import torch
+from rich.progress import BarColumn, Progress, TextColumn, TimeRemainingColumn
+
+from ..logger import console, logger
+from .structs import T2SEngineProtocol, T2SRequest, T2SResult, T2SSession
+from .t2s_model_abc import (
+    CUDAGraphCacheABC,
+    T2SDecoderABC,
+    TorchProfiler,
+)
+
+torch.set_grad_enabled(False)
+
+
+class T2SEngine(T2SEngineProtocol):
+    def __init__(
+        self,
+        decoder_model: T2SDecoderABC,
+        device: torch.device = torch.device("cpu"),
+        dtype: torch.dtype = torch.float32,
+    ) -> None:
+        assert device.type in {"cpu", "cuda", "mps", "xpu", "mtia"}
+        assert dtype in {torch.float16, torch.bfloat16, torch.float32}
+
+        self.device = device
+        self.dtype = dtype
+
+        self.decoder_model: T2SDecoderABC = decoder_model.to(self.device, self.dtype)
+
+        self.graphcache: CUDAGraphCacheABC = self.init_cache()
+
+    def _handle_request(self, request: T2SRequest):
+        with self.device:
+            decoder = self.decoder_model
+            session = T2SSession(decoder, request, device=self.device, dtype=self.dtype)
+            batch_idx = torch.arange(session.bsz)
+
+            t1 = 0.0
+            infer_speed = 0.0
+            infer_time = 0.0
+
+            torch_profiler = TorchProfiler(request.debug)
+            with (
+                torch_profiler.profiler(),
+                Progress(
+                    TextColumn("[cyan]{task.description}"),
+                    BarColumn(),
+                    TextColumn("{task.completed}/{task.total}"),
+                    TimeRemainingColumn(),
+                    console=console,
+                    transient=True,
+                ) as progress,
+            ):
+                max_token = min(1800 - session.input_pos.max(), 1500)
+                task = progress.add_task("T2S Decoding", total=max_token)
+
+                for idx in range(max_token):
+                    progress.update(task, advance=1)
+                    if idx == 0:
+                        session.kv_cache = decoder.init_cache(session.bsz)
+                        xy_dec = decoder.h.prefill(session.xy_pos, session.kv_cache, session.attn_mask)
+                        xy_dec = xy_dec[None, batch_idx, session.input_pos - 1]
+                    else:
+                        if request.use_cuda_graph and session.graph is None and torch.cuda.is_available():
+                            self.graphcache.assign_graph(session)
+
+                        with torch_profiler.record("AR"):
+                            if session.graph:
+                                assert session.stream
+                                session.stream.wait_stream(torch.cuda.default_stream())
+                                with torch.cuda.stream(session.stream):
+                                    session.xy_pos_.copy_(session.xy_pos)
+                                    session.graph.replay()
+                                    xy_dec = session.xy_dec_.clone()
+                            else:
+                                args, kwds = decoder.pre_forward(session)
+                                xy_dec = decoder.h(
+                                    session.input_pos,
+                                    session.xy_pos,
+                                    session.kv_cache,
+                                    *args,
+                                    **kwds,
+                                )
+
+                    with torch.cuda.stream(session.stream) if session.stream is not None else contextlib.nullcontext():
+                        decoder.post_forward(idx, session)
+                        logits = decoder.ar_predict_layer(xy_dec[:, -1])
+
+                        if idx == 0:
+                            logits[:, -1] = float("-inf")
+
+                        with torch_profiler.record("Sampling"):
+                            samples = session.sample(
+                                logits=logits,
+                                previous_tokens=session.y[:, : session.y_len + idx],
+                                top_k=request.top_k,
+                                top_p=request.top_p,
+                                repetition_penalty=request.repetition_penalty,
+                                temperature=request.temperature,
+                            )
+                            session.y[batch_idx, session.y_len + idx] = samples
+                            session.input_pos.add_(1)
+
+                        with torch_profiler.record("EOS"):
+                            argmax_token = torch.argmax(logits, dim=-1)
+                            sample_token = samples.squeeze(1)
+                            EOS_mask = (argmax_token == decoder.EOS) | (sample_token == decoder.EOS)
+
+                            newly_done_mask = EOS_mask & (~session.completed)
+                            newly_done_indices = newly_done_mask.nonzero()
+
+                            if newly_done_indices.numel() > 0:
+                                for i in newly_done_indices:
+                                    session.y_results[i] = session.y[i, session.y_len : session.y_len + idx]
+                                    session.completed[newly_done_indices] = True
+
+                            if torch.all(session.completed).item():
+                                if session.y[:, session.y_len :].sum() == 0:
+                                    session.y_results = [torch.tensor(0) for _ in range(session.bsz)]
+                                    logger.error("Bad Zero Prediction")
+                                else:
+                                    logger.info(
+                                        f"T2S Decoding EOS {session.prefill_len.tolist().__str__().strip('[]')} -> {[i.size(-1) for i in session.y_results].__str__().strip('[]')}"
+                                    )
+                                    logger.info(f"Infer Speed: {(idx - 1) / (time.perf_counter() - t1):.2f} token/s")
+                                    infer_time = time.perf_counter() - t1
+                                    infer_speed = (idx - 1) / infer_time
+                                break
+
+                            if (request.early_stop_num != -1 and idx >= request.early_stop_num) or idx == max_token - 1:
+                                for i in range(session.bsz):
+                                    if not session.completed[i].item():
+                                        session.y_results[i] = session.y[i, session.y_len : session.y_len + 1499]
+                                        session.completed[i] = True
+                                    logger.error("Bad Full Prediction")
+                                break
+
+                        with torch_profiler.record("NextPos"):
+                            y_emb = decoder.ar_audio_embedding(samples)
+                            session.xy_pos = decoder.ar_audio_position(session.input_pos - session.x_lens, y_emb)
+
+                        if idx == 1:
+                            torch_profiler.start()
+                            t1 = time.perf_counter()
+
+                        if idx == 51:
+                            torch_profiler.end()
+
+                        if idx % 100 == 0:
+                            match session.device.type:
+                                case "cuda":
+                                    torch.cuda.empty_cache()
+                                case "mps":
+                                    torch.mps.empty_cache()
+                                case "xpu":
+                                    torch.xpu.empty_cache()
+                                case "mtia":
+                                    torch.mtia.empty_cache()
+
+            match session.device.type:
+                case "cuda":
+                    if session.stream is not None:
+                        torch.cuda.current_stream().wait_stream(session.stream)
+                    torch.cuda.empty_cache()
+                case "mps":
+                    torch.mps.empty_cache()
+                case "xpu":
+                    torch.xpu.empty_cache()
+                case "mtia":
+                    torch.mtia.empty_cache()
+                case "cpu":
+                    gc.collect()
+
+            torch_profiler.end()
+            if request.use_cuda_graph and torch.cuda.is_available():
+                self.graphcache.release_graph(session)
+
+            return session.y_results[: request.valid_length], infer_speed, infer_time
+
+    def generate(self, request: T2SRequest):
+        try:
+            result, infer_speed, infer_time = self._handle_request(request)
+            t2s_result = T2SResult(result=result, infer_speed=(infer_speed, infer_time), status="Success")
+        except Exception as e:
+            t2s_result = T2SResult(status="Error", exception=e, traceback=traceback.format_exc())
+        return t2s_result
+
+    @staticmethod
+    def load_decoder(weights_path: os.PathLike, max_batch_size: int = 1, backend: str = "Flash Attn CUDAGraph"):
+        logger.info(f"Loading Text2Semantic Weights from {weights_path} with {backend} Backend")
+        module_path = f".backends.{backend.lower().replace('-', '_')}"
+        decoder_cls_name = "T2SDecoder"
+        decoder_mod = import_module(module_path, package=__package__)
+        decoder_cls: type[T2SDecoderABC] = getattr(decoder_mod, decoder_cls_name)
+        dict_s1 = torch.load(weights_path, map_location="cpu", weights_only=False, mmap=True)
+        config = dict_s1["config"]
+        decoder: T2SDecoderABC = decoder_cls(config, max_batch_size=max_batch_size)
+        state_dict = dict_s1["weight"]
+        decoder.load_state_dict(state_dict)
+
+        return decoder.eval()
+
+    def init_cache(self):
+        assert self.decoder_model
+
+        module_name = self.decoder_model.__class__.__module__
+        module = sys.modules.get(module_name)
+        assert module
+
+        target_class: type[CUDAGraphCacheABC] = getattr(module, "CUDAGraphCache")
+
+        return target_class(self.decoder_model)

GPT_SoVITS/Accelerate/PyTorch/t2s_model_abc.py

@@ -0,0 +1,670 @@
+"""
+Modified From https://github.com/XXXXRT666/GPT-SoVITS
+"""
+
+from __future__ import annotations
+
+import math
+import os
+import random
+from abc import ABC, abstractmethod
+from contextlib import nullcontext
+from typing import MutableSequence
+
+import torch
+import torch._inductor.config
+import torch.nn.functional as F
+from torch.cuda.graphs import CUDAGraph
+from torch.profiler import ProfilerAction, tensorboard_trace_handler
+
+from . import nn
+from .structs import KVCacheProtocol, T2SDecoderProtocol, T2SSession
+
+Tensor = torch.Tensor
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        vocab_size: int,
+    ):
+        super().__init__()
+
+        self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
+
+        self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim)
+
+    @property
+    def weight(self) -> Tensor:
+        return self.word_embeddings.weight
+
+    def embedding(self, index: int) -> Tensor:
+        return self.word_embeddings.weight[index : index + 1]
+
+    def __call__(self, x: Tensor):
+        x = self.word_embeddings(x)
+        return x
+
+
+class SinePositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        scale: bool = False,
+        alpha: bool = False,
+        max_batch_size: int = 10,
+        max_seq_len: int = 1800,
+    ):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
+        self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
+        self.max_batch_size = max_batch_size
+        self.max_seq_len = max_seq_len
+
+        self.reverse = False
+        self.register_buffer("pe", torch.zeros(max_batch_size, max_seq_len, embedding_dim), persistent=False)
+        self.pe: torch.Tensor
+        self.compute_pe()
+
+    def compute_pe(self):
+        """Reset the positional encodings."""
+        if self.reverse:
+            position = torch.arange(self.max_seq_len - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
+        else:
+            position = torch.arange(self.max_seq_len, dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) * -(math.log(10000.0) / self.embedding_dim)
+        )
+        pe = self.pe
+        pe[:, :, 0::2] = torch.sin(position * div_term)
+        pe[:, :, 1::2] = torch.cos(position * div_term)
+
+    def __call__(self, input_pos: Tensor, x: Tensor) -> Tensor:
+        """
+        Args:
+            input_pos (Tensor): [batch_size, ]
+            x (Tensor): [batch_size, 1, embed_dim]
+
+        Returns:
+            embedded_x (Tensor): [batch_size, 1, embed_dim]
+        """
+
+        batch_size = x.shape[0]
+        pe_values = self.pe[torch.arange(batch_size), input_pos - 1]  # (batch_size, embed_dim)
+
+        return x * self.x_scale + self.alpha * pe_values.unsqueeze(1)  # (batch_size, 1, embed_dim)
+
+    def prefill(self, x: Tensor) -> Tensor:
+        """
+        Args:
+            x (Tensor): [batch_size, seq_len, embed_dim]
+
+        Returns:
+            embedded_x (Tensor): [batch_size, seq_len, embed_dim]
+        """
+
+        pe_values = self.pe[:, : x.shape[-2]]
+        return x * self.x_scale + self.alpha.item() * pe_values
+
+
+class KVCacheABC(nn.Module, ABC, KVCacheProtocol):
+    def __init__(self, batch_size: int, max_seq_length: int, n_heads: int, head_dim: int) -> None:
+        super().__init__()
+
+        self.n_head = n_heads
+        self.head_dim = head_dim
+        self.batch_size = batch_size
+        self.max_seq_length = max_seq_length
+
+        self.k_cache: Tensor
+        self.v_cache: Tensor
+
+    def empty(self):
+        self.k_cache.zero_()
+        self.v_cache.zero_()
+
+    @abstractmethod
+    def update(self, input_pos: Tensor, k_val: Tensor, v_val: Tensor, *args, **kwds) -> tuple[Tensor, Tensor]: ...
+
+    @abstractmethod
+    def prefill_kv(self, k_val: Tensor, v_val: Tensor) -> None: ...
+
+    def sync_cache(self, kv_cache: KVCacheProtocol):
+        self.k_cache.copy_(kv_cache.k_cache)
+        self.v_cache.copy_(kv_cache.v_cache)
+
+
+class KVCacheNHD(KVCacheABC):
+    def __init__(self, batch_size, max_seq_length, n_heads, head_dim):
+        super().__init__(batch_size, max_seq_length, n_heads, head_dim)
+
+        assert batch_size > 0
+        cache_shape = (batch_size, max_seq_length, n_heads, head_dim)
+
+        self.register_buffer("k_cache", torch.zeros(size=cache_shape), persistent=False)
+        self.register_buffer("v_cache", torch.zeros(size=cache_shape), persistent=False)
+
+    def update(self, input_pos: Tensor, k_val: Tensor, v_val: Tensor):
+        # input_pos: [B, ], k_val: [B, 1, H, D]
+
+        index = (
+            (input_pos - 1)
+            .unsqueeze(-1)
+            .unsqueeze(-1)
+            .unsqueeze(-1)
+            .expand(
+                -1,
+                -1,
+                self.n_head,
+                self.head_dim,
+            )
+            .to(torch.int64)
+        )  # (bs, 1, num_head, head_dim)
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out.scatter_(1, index, k_val)
+        v_out.scatter_(1, index, v_val)
+
+        return k_out, v_out
+
+    def empty(self):
+        self.k_cache.zero_()
+        self.v_cache.zero_()
+
+    def prefill_kv(self, k_val: Tensor, v_val: Tensor):
+        # input_pos: int, k_val: [B, S, H, D]
+
+        self.k_cache[:, : k_val.shape[1]] = k_val
+        self.v_cache[:, : v_val.shape[1]] = v_val
+
+
+class KVCacheHND(KVCacheABC):
+    def __init__(self, batch_size, max_seq_length, n_heads, head_dim):
+        super().__init__(batch_size, max_seq_length, n_heads, head_dim)
+
+        cache_shape = (batch_size, n_heads, max_seq_length, head_dim)
+
+        self.register_buffer("k_cache", torch.zeros(size=cache_shape), persistent=False)
+        self.register_buffer("v_cache", torch.zeros(size=cache_shape), persistent=False)
+
+    def update(self, input_pos: Tensor, k_val: Tensor, v_val: Tensor):
+        # input_pos: [B, ], k_val: [B, H, 1, D]
+
+        index = (
+            (input_pos - 1)
+            .unsqueeze(-1)
+            .unsqueeze(-1)
+            .unsqueeze(-1)
+            .expand(
+                -1,
+                self.n_head,
+                -1,
+                self.head_dim,
+            )
+            .to(torch.int64)
+        )  # (bs, num_head, 1, head_dim)
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out.scatter_(2, index, k_val)
+        v_out.scatter_(2, index, v_val)
+
+        return k_out, v_out
+
+    def empty(self):
+        self.k_cache.zero_()
+        self.v_cache.zero_()
+
+    def prefill_kv(self, k_val: Tensor, v_val: Tensor):
+        # input_pos: int, k_val: [B, S, H, D]
+
+        self.k_cache[..., : k_val.shape[1], :] = k_val.transpose(1, 2)
+        self.v_cache[..., : v_val.shape[1], :] = v_val.transpose(1, 2)
+
+
+class KVCacheHNDVarlen(KVCacheABC):
+    def __init__(self, batch_size, max_seq_length, n_heads, head_dim):
+        super().__init__(batch_size, max_seq_length, n_heads, head_dim)
+
+        cache_shape = (batch_size, n_heads, max_seq_length, head_dim)
+        self.cache_idx: Tensor
+
+        self.register_buffer("cache_idx", torch.arange(batch_size), persistent=False)
+        self.register_buffer("k_cache", torch.zeros(size=cache_shape), persistent=False)
+        self.register_buffer("v_cache", torch.zeros(size=cache_shape), persistent=False)
+
+    def update(self, input_pos: Tensor, k_val: Tensor, v_val: Tensor):
+        # input_pos: [B, ], k_val: [B, H, 1, D]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+
+        ip0 = input_pos - 1
+
+        k_out[self.cache_idx, :, ip0, None] = k_val
+        v_out[self.cache_idx, :, ip0, None] = v_val
+
+        return k_out, v_out
+
+    def empty(self):
+        self.k_cache.zero_()
+        self.v_cache.zero_()
+
+    def prefill_kv(self, k_val: Tensor, v_val: Tensor):
+        # input_pos: int, k_val: [B, S, H, D]
+
+        self.k_cache[..., : k_val.shape[1], :] = k_val.transpose(1, 2)
+        self.v_cache[..., : v_val.shape[1], :] = v_val.transpose(1, 2)
+
+
+class AttentionABC(nn.Module, ABC):
+    def __init__(self, n_head: int, hidden_dim: int, max_seq_length: int):
+        super().__init__()
+
+        self.n_head = n_head
+        self.hidden_dim = hidden_dim
+        assert hidden_dim % n_head == 0
+        self.head_dim = hidden_dim // n_head
+
+        self.max_seq_length = max_seq_length
+
+        # key, query, value projections for all heads, but in a batch
+        self.in_proj: nn.Linear
+        self.out_proj: nn.Linear
+
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict: dict[str, Tensor], prefix, *args):
+        keys_to_modify = [key for key in state_dict if "in_proj_" in key]
+        for key in keys_to_modify:
+            new_key = key.replace("in_proj_", "in_proj.")  # in_proj_ -> in_proj.
+            state_dict[new_key] = state_dict.pop(key)
+
+    @abstractmethod
+    def __call__(self, x: Tensor, input_pos: Tensor, kv_cache: KVCacheProtocol, *args, **kwds) -> Tensor: ...
+
+    def prefill(self, x: Tensor, kv_cache: KVCacheProtocol, attn_mask: Tensor) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        q, k, v = self.in_proj(x.unsqueeze(0)).chunk(3, dim=-1)
+
+        q, k, v = map(lambda x: x.contiguous().view(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
+
+        kv_cache.prefill_kv(k, v)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        attn = F.scaled_dot_product_attention(q, k, v, attn_mask)
+
+        attn = attn.transpose(1, 2).contiguous().view(1, -1, self.hidden_dim)
+
+        output = self.out_proj(attn)
+
+        return output
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int) -> None:
+        super().__init__()
+
+        self.linear1 = nn.Linear(dim, hidden_dim, bias=True)
+        self.linear2 = nn.Linear(hidden_dim, dim, bias=True)
+
+    def __call__(self, x: Tensor):
+        return self.linear2(F.relu(self.linear1(x), inplace=True))
+
+
+class TransformerBlockABC(nn.Module, ABC):
+    def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
+        super().__init__()
+
+        self.hidden_dim = hidden_dim
+        self.max_seq_length = max_seq_length
+
+        self.attention: AttentionABC
+        self.feed_forward: FeedForward
+        self.attention_norm: nn.LayerNorm
+        self.ffn_norm: nn.LayerNorm
+
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict: dict[str, Tensor], prefix, *args):
+        for key in list(state_dict.keys()):
+            new_key = (
+                key.replace("self_attn", "attention")
+                .replace("linear", "feed_forward.linear")
+                .replace("norm1", "attention_norm")
+                .replace("norm2", "ffn_norm")
+            )
+            state_dict[new_key] = state_dict.pop(key)
+
+    def __call__(self, x: Tensor, input_pos: Tensor, kv_cache: KVCacheProtocol, *args, **kwds):
+        h = self.attention_norm(
+            x
+            + self.attention(
+                x,
+                input_pos,
+                kv_cache,
+                *args,
+                **kwds,
+            )
+        )
+        out = self.ffn_norm(h + self.feed_forward(h))
+        return out
+
+    def prefill(
+        self,
+        x: Tensor,
+        kv_cache: KVCacheProtocol,
+        attn_mask: Tensor,
+    ) -> Tensor:
+        h = self.attention_norm(
+            x
+            + self.attention.prefill(
+                x,
+                kv_cache,
+                attn_mask,
+            )
+        )
+        out = self.ffn_norm(h + self.feed_forward(h))
+        return out
+
+
+class TransformerDecoderABC(nn.Module, ABC):
+    def __init__(
+        self,
+        hidden_dim: int,
+        n_layer: int,
+        n_head: int,
+        ffn_dim: int,
+        vocab_size: int,
+        max_seq_length: int,
+        max_batch_size: int,
+    ) -> None:
+        super().__init__()
+
+        self.hidden_dim = hidden_dim
+        self.n_head = n_head
+        assert hidden_dim % n_head == 0
+
+        self.head_dim = hidden_dim // n_head
+        self.vocab_size = vocab_size
+
+        self.n_layer = n_layer
+
+        self.layers: MutableSequence[TransformerBlockABC]
+
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+
+    def __call__(self, input_pos: Tensor, x: Tensor, kv_caches: MutableSequence[KVCacheProtocol], *args, **kwds):
+        for layer, kv_cache in zip(self.layers, kv_caches):
+            x = layer(x, input_pos, kv_cache, *args, **kwds)
+        return x
+
+    def prefill(self, x: Tensor, kv_caches: MutableSequence[KVCacheProtocol], attn_mask: Tensor):
+        for layer, kv_cache in zip(self.layers, kv_caches):
+            x = layer.prefill(x, kv_cache, attn_mask)
+        return x
+
+
+class T2SDecoderABC(nn.Module, ABC, T2SDecoderProtocol):
+    def __init__(
+        self,
+        config: dict,
+        max_seq_length: int = 1800,
+        max_batch_size: int = 10,
+    ) -> None:
+        super().__init__()
+
+        hidden_dim: int = config["model"]["hidden_dim"]
+        embedding_dim: int = config["model"]["embedding_dim"]
+        n_head: int = config["model"]["head"]
+        n_layer: int = config["model"]["n_layer"]
+        vocab_size: int = config["model"]["vocab_size"]
+        phoneme_vocab_size: int = config["model"]["phoneme_vocab_size"]
+        EOS: int = config["model"]["EOS"]
+        ffn_dim: int = hidden_dim * 4
+
+        self.n_layer = int(n_layer)
+        self.hidden_dim = int(hidden_dim)
+        self.n_head = int(n_head)
+        assert hidden_dim % n_head == 0
+
+        self.head_dim = int(hidden_dim // n_head)
+        self.embedding_dim = int(embedding_dim)
+        self.ffn_dim = int(ffn_dim)
+        self.vocab_size = int(vocab_size)
+        self.phoneme_vocab_size = int(phoneme_vocab_size)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        self.EOS = EOS
+        assert self.EOS == self.vocab_size - 1
+
+        self.bert_proj: nn.Linear
+        self.ar_predict_layer: nn.Linear
+        self.h: TransformerDecoderABC
+
+        self.kv_class: type[KVCacheABC]
+
+        self.GraphCache: CUDAGraphCacheABC | None
+
+        self.ar_text_embedding = TokenEmbedding(self.embedding_dim, self.phoneme_vocab_size)
+        self.ar_text_position = SinePositionalEmbedding(
+            self.embedding_dim,
+            scale=False,
+            alpha=True,
+            max_batch_size=max_batch_size,
+            max_seq_len=max_seq_length,
+        )
+        self.ar_audio_embedding = TokenEmbedding(self.embedding_dim, self.vocab_size)
+        self.ar_audio_position = SinePositionalEmbedding(
+            self.embedding_dim,
+            scale=False,
+            alpha=True,
+            max_batch_size=max_batch_size,
+            max_seq_len=max_seq_length,
+        )
+
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict: dict[str, Tensor], prefix, *args):
+        model_keys = [key for key in state_dict if key.startswith("model.")]
+        for key in model_keys:
+            new_key = key[len("model.") :]
+            state_dict[new_key] = state_dict.pop(key)
+
+    def init_cache(self, bsz: int = 0) -> MutableSequence[KVCacheProtocol]:
+        bsz = bsz or self.h.max_batch_size
+        assert bsz <= self.h.max_batch_size
+        seq_lens = self.h.max_seq_length
+        dtype = self.bert_proj.bias.dtype
+        kvclass = self.kv_class
+
+        return nn.ModuleList(
+            [kvclass(bsz, seq_lens, self.n_head, self.head_dim) for _ in range(self.n_layer)],
+        ).to(self.device, dtype)  # type: ignore
+
+    def embed(
+        self,
+        x: list[torch.Tensor],
+        y: torch.Tensor,
+        bert_features: list[torch.Tensor],
+    ):
+        x_len: list[int] = [i.shape[0] for i in x]
+        x_len_max = max(x_len)
+        xy_pos = torch.zeros((len(x), x_len_max + y.shape[1], self.embedding_dim)).to(bert_features[0].dtype)
+
+        bert_features = list(map(lambda x: x.transpose(0, 1), bert_features))
+
+        y_len = y.shape[1]
+        y_emb = self.ar_audio_embedding(y)
+        y_pos = self.ar_audio_position.prefill(y_emb)
+
+        for bs, (x_, len_, bert_feature) in enumerate(zip(x, x_len, bert_features)):
+            x_emb = self.ar_text_embedding(x_)
+            bert = self.bert_proj(bert_feature)
+            x_emb = x_emb + bert
+            x_pos = self.ar_text_position.prefill(x_emb.unsqueeze(0))
+            xy_pos[[bs], :len_] = x_pos
+            xy_pos[[bs], len_ : len_ + y_len] = y_pos
+
+        return xy_pos
+
+    def compile(self, *args, **kwds):
+        # Experimental features to reduce compilation times, will be on by default in future
+        torch._inductor.config.triton.cudagraph_skip_dynamic_graphs = True
+        torch._inductor.config.coordinate_descent_tuning = True
+        torch._inductor.config.triton.unique_kernel_names = True
+        torch._inductor.config.fx_graph_cache = True
+        torch._inductor.config.triton.cudagraph_trees = True
+        torch._inductor.config.triton.cudagraph_support_input_mutation = True
+        self.h.compile(fullgraph=True, mode="reduce-overhead")
+
+    def capture(
+        self, input_pos: Tensor, x: Tensor, x_dec: Tensor, kv_caches: MutableSequence[KVCacheProtocol], *args, **kwds
+    ) -> CUDAGraph:
+        assert torch.cuda.is_available()
+        s = torch.cuda.Stream()
+        s.wait_stream(torch.cuda.current_stream())
+
+        graph = torch.cuda.CUDAGraph()
+
+        with torch.cuda.stream(s):  # type: ignore
+            for _ in range(5):
+                self.h(input_pos, x, kv_caches, *args, **kwds)
+        torch.cuda.current_stream().wait_stream(s)
+
+        with torch.cuda.graph(graph):
+            x_dec.copy_(self.h(input_pos, x, kv_caches, *args, **kwds))
+        torch.cuda.synchronize()
+
+        return graph
+
+    @abstractmethod
+    def pre_forward(self, session: T2SSession) -> tuple[list[Tensor], dict[str, Tensor]]:
+        return list(), dict()
+
+    @abstractmethod
+    def post_forward(self, idx: int, session: T2SSession) -> None:
+        return
+
+
+class CUDAGraphCacheABC(ABC):
+    def __init__(
+        self,
+        decoder: T2SDecoderABC,
+        enabled: bool = False,
+    ) -> None:
+        if torch.cuda.is_available() and enabled:
+            self.device: torch.device = decoder.device
+            self.dtype = decoder.bert_proj.bias.dtype
+
+            self.assigned: bool = False
+
+            self.decoder: T2SDecoderABC = decoder
+            self.kv_cache: MutableSequence[KVCacheProtocol] = decoder.init_cache(decoder.max_batch_size)
+            self.xy_pos = torch.rand(size=(decoder.max_batch_size, 1, decoder.embedding_dim), device=self.device).to(
+                self.dtype
+            )
+            self.xy_dec = self.xy_pos.clone()
+
+            self.input_pos = torch.tensor([10] * decoder.max_batch_size, device=self.device).int()
+            self.graph: torch.cuda.CUDAGraph | None = None
+            self.stream: torch.cuda.Stream | None
+
+            self.id: int = random.randint(1, 2**32 - 1)
+
+    def assign_graph(self, session: T2SSession):
+        if self.graph is None:
+            args, kwds = self.decoder.pre_forward(session)
+            graph = self.decoder.capture(self.input_pos, self.xy_pos, self.xy_dec, self.kv_cache, *args, **kwds)
+            self.graph = graph
+            self.stream = torch.cuda.Stream()  # type: ignore
+
+        if self.assigned is False:
+            self.get_cache_graph(session)
+            session.id = self.id
+            self.assigned = True
+        else:
+            self.capture_new_graph(session)
+
+    @abstractmethod
+    def release_graph(self, session: T2SSession): ...
+
+    @abstractmethod
+    def get_cache_graph(self, session: T2SSession):
+        pass
+
+    @abstractmethod
+    def capture_new_graph(self, session: T2SSession):
+        pass
+
+
+class TorchProfiler:
+    def __init__(self, debug: bool, log_dir: str = "./profiler") -> None:
+        self.debug = debug
+        self.log_dir = log_dir
+        self.__profiler: torch.profiler.profile
+
+        if self.debug and not os.path.exists(self.log_dir):
+            os.makedirs(self.log_dir)
+
+        self.tensorboard_handler = tensorboard_trace_handler(self.log_dir)
+
+    def profiler_callback(self, prof: torch.profiler.profile):
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=30))
+        print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=30))
+        self.tensorboard_handler(prof)
+
+    @staticmethod
+    def three_step_schedule(step: int) -> ProfilerAction:
+        if step == 0:
+            return ProfilerAction.NONE
+        elif step == 1:
+            return ProfilerAction.RECORD
+        elif step == 2:
+            return ProfilerAction.RECORD_AND_SAVE
+        else:
+            return ProfilerAction.NONE
+
+    def start(self):
+        if not self.debug:
+            return
+        assert self.__profiler is not None
+        self.__profiler.step()
+
+    def end(self):
+        if not self.debug:
+            return
+        assert self.__profiler is not None
+        self.__profiler.step()
+
+    def profiler(self):
+        if self.debug:
+            activities_list = [torch.profiler.ProfilerActivity.CPU]
+            if torch.cuda.is_available():
+                activities_list.append(torch.profiler.ProfilerActivity.CUDA)
+
+            self.__profiler = torch.profiler.profile(
+                activities=activities_list,
+                record_shapes=True,
+                with_stack=True,
+                with_modules=True,
+                profile_memory=True,
+                schedule=self.three_step_schedule,
+                on_trace_ready=self.profiler_callback,
+            )
+            return self.__profiler
+        else:
+            return nullcontext()
+
+    def record(self, func_name: str):
+        if self.debug:
+            return torch.profiler.record_function(func_name)
+        else:
+            return nullcontext()

GPT_SoVITS/Accelerate/__init__.py

@@ -0,0 +1,12 @@
+from . import MLX, PyTorch
+from .logger import logger, tb
+from .PyTorch import T2SEngineTorch, T2SRequest, T2SResult
+
+backends = PyTorch.backends + MLX.backends
+
+backends = [
+    b.replace("_", "-").title().replace("Mlx", "MLX").replace("Mps", "MPS").replace("Cuda", "CUDA") for b in backends
+]
+
+
+__all__ = ["T2SEngineTorch", "T2SRequest", "T2SResult", "backends", "MLX", "PyTorch", "logger", "tb"]

GPT_SoVITS/Accelerate/logger.py

@@ -0,0 +1,36 @@
+import sys
+
+from loguru import logger
+from rich.console import Console
+from rich.traceback import Traceback, install
+
+install()
+
+
+def rich_format(record):
+    level = record["level"].name
+    color = {
+        "DEBUG": "green",
+        "INFO": "cyan",
+        "WARNING": "yellow",
+        "ERROR": "red",
+        "CRITICAL": "magenta",
+    }.get(level, "black")
+    return f"[bold {color}][{level}][/bold {color}] {record['message']}"
+
+
+def tb(show_locals: bool = True):
+    exc_type, exc_value, exc_tb = sys.exc_info()
+    assert exc_type
+    assert exc_value
+    tb = Traceback.from_exception(exc_type, exc_value, exc_tb, show_locals=show_locals)
+
+    return tb
+
+
+console = Console()
+
+logger.remove()
+logger.add(console.print, format=rich_format)
+
+__all__ = ["logger", "console", "tb"]

GPT_SoVITS/BigVGAN/README.md

@@ -1,266 +0,0 @@
-## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
-
-#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
-
-[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
-
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/bigvgan-a-universal-neural-vocoder-with-large/speech-synthesis-on-libritts)](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
-
-<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
-
-## News
-- **Sep 2024 (v2.4):**
-  - We have updated the pretrained checkpoints trained for 5M steps. This is final release of the BigVGAN-v2 checkpoints.
-
-- **Jul 2024 (v2.3):**
-  - General refactor and code improvements for improved readability.
-  - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
-
-- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
-
-- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
-
-- **Jul 2024 (v2):** We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
-  - Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
-  - Improved discriminator and loss: BigVGAN-v2 is trained using a [multi-scale sub-band CQT discriminator](https://arxiv.org/abs/2311.14957) and a [multi-scale mel spectrogram loss](https://arxiv.org/abs/2306.06546).
-  - Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
-  - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
-
-## Installation
-
-The codebase has been tested on Python `3.10` and PyTorch `2.3.1` conda packages with either `pytorch-cuda=12.1` or `pytorch-cuda=11.8`. Below is an example command to create the conda environment:
-
-```shell
-conda create -n bigvgan python=3.10 pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
-conda activate bigvgan
-```
-
-Clone the repository and install dependencies:
-
-```shell
-git clone https://github.com/NVIDIA/BigVGAN
-cd BigVGAN
-pip install -r requirements.txt
-```
-
-## Inference Quickstart using 🤗 Hugging Face Hub
-
-Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.
-
-```python
-device = 'cuda'
-
-import torch
-import bigvgan
-import librosa
-from meldataset import get_mel_spectrogram
-
-# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
-model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_24khz_100band_256x', use_cuda_kernel=False)
-
-# remove weight norm in the model and set to eval mode
-model.remove_weight_norm()
-model = model.eval().to(device)
-
-# load wav file and compute mel spectrogram
-wav_path = '/path/to/your/audio.wav'
-wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
-wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
-
-# compute mel spectrogram from the ground truth audio
-mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
-
-# generate waveform from mel
-with torch.inference_mode():
-    wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
-wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
-
-# you can convert the generated waveform to 16 bit linear PCM
-wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype
-```
-
-## Local gradio demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>
-
-You can run a local gradio demo using below command:
-
-```python
-pip install -r demo/requirements.txt
-python demo/app.py
-```
-
-## Training
-
-Create symbolic link to the root of the dataset. The codebase uses filelist with the relative path from the dataset. Below are the example commands for LibriTTS dataset:
-
-```shell
-cd filelists/LibriTTS && \
-ln -s /path/to/your/LibriTTS/train-clean-100 train-clean-100 && \
-ln -s /path/to/your/LibriTTS/train-clean-360 train-clean-360 && \
-ln -s /path/to/your/LibriTTS/train-other-500 train-other-500 && \
-ln -s /path/to/your/LibriTTS/dev-clean dev-clean && \
-ln -s /path/to/your/LibriTTS/dev-other dev-other && \
-ln -s /path/to/your/LibriTTS/test-clean test-clean && \
-ln -s /path/to/your/LibriTTS/test-other test-other && \
-cd ../..
-```
-
-Train BigVGAN model. Below is an example command for training BigVGAN-v2 using LibriTTS dataset at 24kHz with a full 100-band mel spectrogram as input:
-
-```shell
-python train.py \
---config configs/bigvgan_v2_24khz_100band_256x.json \
---input_wavs_dir filelists/LibriTTS \
---input_training_file filelists/LibriTTS/train-full.txt \
---input_validation_file filelists/LibriTTS/val-full.txt \
---list_input_unseen_wavs_dir filelists/LibriTTS filelists/LibriTTS \
---list_input_unseen_validation_file filelists/LibriTTS/dev-clean.txt filelists/LibriTTS/dev-other.txt \
---checkpoint_path exp/bigvgan_v2_24khz_100band_256x
-```
-
-## Synthesis
-
-Synthesize from BigVGAN model. Below is an example command for generating audio from the model.
-It computes mel spectrograms using wav files from `--input_wavs_dir` and saves the generated audio to `--output_dir`.
-
-```shell
-python inference.py \
---checkpoint_file /path/to/your/bigvgan_v2_24khz_100band_256x/bigvgan_generator.pt \
---input_wavs_dir /path/to/your/input_wav \
---output_dir /path/to/your/output_wav
-```
-
-`inference_e2e.py` supports synthesis directly from the mel spectrogram saved in `.npy` format, with shapes `[1, channel, frame]` or `[channel, frame]`.
-It loads mel spectrograms from `--input_mels_dir` and saves the generated audio to `--output_dir`.
-
-Make sure that the STFT hyperparameters for mel spectrogram are the same as the model, which are defined in `config.json` of the corresponding model.
-
-```shell
-python inference_e2e.py \
---checkpoint_file /path/to/your/bigvgan_v2_24khz_100band_256x/bigvgan_generator.pt \
---input_mels_dir /path/to/your/input_mel \
---output_dir /path/to/your/output_wav
-```
-
-## Using Custom CUDA Kernel for Synthesis
-
-You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
-
-```python
-generator = BigVGAN(h, use_cuda_kernel=True)
-```
-
-You can also pass `--use_cuda_kernel` to `inference.py` and `inference_e2e.py` to enable this feature.
-
-When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_activation/cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
-
-Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
-
-We recommend running `test_cuda_vs_torch_model.py` first to build and check the correctness of the CUDA kernel. See below example command and its output, where it returns `[Success] test CUDA fused vs. plain torch BigVGAN inference`:
-
-```python
-python tests/test_cuda_vs_torch_model.py \
---checkpoint_file /path/to/your/bigvgan_generator.pt
-```
-
-```shell
-loading plain Pytorch BigVGAN
-...
-loading CUDA kernel BigVGAN with auto-build
-Detected CUDA files, patching ldflags
-Emitting ninja build file /path/to/your/BigVGAN/alias_free_activation/cuda/build/build.ninja..
-Building extension module anti_alias_activation_cuda...
-...
-Loading extension module anti_alias_activation_cuda...
-...
-Loading '/path/to/your/bigvgan_generator.pt'
-...
-[Success] test CUDA fused vs. plain torch BigVGAN inference
- > mean_difference=0.0007238413265440613
-...
-```
-
-If you see `[Fail] test CUDA fused vs. plain torch BigVGAN inference`, it means that the CUDA kernel inference is incorrect. Please check if `nvcc` installed in your system is compatible with your PyTorch version.
-
-## Pretrained Models
-
-We provide the [pretrained models on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
-One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
-
-| Model Name                                                                                               | Sampling Rate | Mel band | fmax  | Upsampling Ratio | Params | Dataset                    | Steps | Fine-Tuned |
-|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
-| [bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x)             | 44 kHz        | 128      | 22050 | 512              | 122M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x)             | 44 kHz        | 128      | 22050 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x)             | 24 kHz        | 100      | 12000 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x)               | 22 kHz        | 80       | 11025 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x) | 22 kHz        | 80       | 8000  | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
-| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band)                             | 24 kHz        | 100      | 12000 | 256              | 112M   | LibriTTS                   | 5M    | No         |
-| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band)                   | 24 kHz        | 100      | 12000 | 256              | 14M    | LibriTTS                   | 5M    | No         |
-| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band)                               | 22 kHz        | 80       | 8000  | 256              | 112M   | LibriTTS + VCTK + LJSpeech | 5M    | No         |
-| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band)                     | 22 kHz        | 80       | 8000  | 256              | 14M    | LibriTTS + VCTK + LJSpeech | 5M    | No         |
-
-The paper results are based on the original 24kHz BigVGAN models (`bigvgan_24khz_100band` and `bigvgan_base_24khz_100band`) trained on LibriTTS dataset.
-We also provide 22kHz BigVGAN models with band-limited setup (i.e., fmax=8000) for TTS applications.
-Note that the checkpoints use `snakebeta` activation with log scale parameterization, which have the best overall quality.
-
-You can fine-tune the models by:
-
-1. downloading the checkpoints (both the generator weight and its discriminator/optimizer states)
-2. resuming training using your audio dataset by specifying `--checkpoint_path` that includes the checkpoints when launching `train.py`
-
-## Training Details of BigVGAN-v2
-
-Comapred to the original BigVGAN, the pretrained checkpoints of BigVGAN-v2 used `batch_size=32` with a longer `segment_size=65536` and are trained using 8 A100 GPUs.
-
-Note that the BigVGAN-v2 `json` config files in `./configs` use `batch_size=4` as default to fit in a single A100 GPU for training. You can fine-tune the models adjusting `batch_size` depending on your GPUs.
-
-When training BigVGAN-v2 from scratch with small batch size, it can potentially encounter the early divergence problem mentioned in the paper. In such case, we recommend lowering the `clip_grad_norm` value (e.g. `100`) for the early training iterations (e.g. 20000 steps) and increase the value to the default `500`.
-
-## Evaluation Results of BigVGAN-v2
-
-Below are the objective results of the 24kHz model (`bigvgan_v2_24khz_100band_256x`) obtained from the LibriTTS `dev` sets. BigVGAN-v2 shows noticeable improvements of the metrics. The model also exhibits reduced perceptual artifacts, especially for non-speech audio.
-
-| Model      | Dataset                 | Steps | PESQ(↑)   | M-STFT(↓)  | MCD(↓)     | Periodicity(↓) | V/UV F1(↑) |
-|:----------:|:-----------------------:|:-----:|:---------:|:----------:|:----------:|:--------------:|:----------:|
-| BigVGAN    | LibriTTS                | 1M    | 4.027     | 0.7997     | 0.3745     | 0.1018         | 0.9598     |
-| BigVGAN    | LibriTTS                | 5M    | 4.256     | 0.7409     | 0.2988     | 0.0809         | 0.9698     |
-| BigVGAN-v2 | Large-scale Compilation | 3M    | 4.359     | 0.7134     | 0.3060     | 0.0621         | 0.9777     |
-| BigVGAN-v2 | Large-scale Compilation | 5M    | **4.362** | **0.7026** | **0.2903** | **0.0593**     | **0.9793** |
-
-## Speed Benchmark
-
-Below are the speed and VRAM usage benchmark results of BigVGAN from `tests/test_cuda_vs_torch_model.py`, using `bigvgan_v2_24khz_100band_256x` as a reference model.
-
-| GPU                        | num_mel_frame | use_cuda_kernel | Speed (kHz) | Real-time Factor | VRAM (GB) |
-|:--------------------------:|:-------------:|:---------------:|:-----------:|:----------------:|:---------:|
-| NVIDIA A100                | 256           | False           | 1672.1      | 69.7x            | 1.3       |
-|                            |               | True            | 3916.5      | 163.2x           | 1.3       |
-|                            | 2048          | False           | 1899.6      | 79.2x            | 1.7       |
-|                            |               | True            | 5330.1      | 222.1x           | 1.7       |
-|                            | 16384         | False           | 1973.8      | 82.2x            | 5.0       |
-|                            |               | True            | 5761.7      | 240.1x           | 4.4       |
-| NVIDIA GeForce RTX 3080    | 256           | False           | 841.1       | 35.0x            | 1.3       |
-|                            |               | True            | 1598.1      | 66.6x            | 1.3       |
-|                            | 2048          | False           | 929.9       | 38.7x            | 1.7       |
-|                            |               | True            | 1971.3      | 82.1x            | 1.6       |
-|                            | 16384         | False           | 943.4       | 39.3x            | 5.0       |
-|                            |               | True            | 2026.5      | 84.4x            | 3.9       |
-| NVIDIA GeForce RTX 2080 Ti | 256           | False           | 515.6       | 21.5x            | 1.3       |
-|                            |               | True            | 811.3       | 33.8x            | 1.3       |
-|                            | 2048          | False           | 576.5       | 24.0x            | 1.7       |
-|                            |               | True            | 1023.0      | 42.6x            | 1.5       |
-|                            | 16384         | False           | 589.4       | 24.6x            | 5.0       |
-|                            |               | True            | 1068.1      | 44.5x            | 3.2       |
-
-## Acknowledgements
-
-We thank Vijay Anand Korthikanti and Kevin J. Shih for their generous support in implementing the CUDA kernel for inference.
-
-## References
-
-- [HiFi-GAN](https://github.com/jik876/hifi-gan) (for generator and multi-period discriminator)
-- [Snake](https://github.com/EdwardDixon/snake) (for periodic activation)
-- [Alias-free-torch](https://github.com/junjun3518/alias-free-torch) (for anti-aliasing)
-- [Julius](https://github.com/adefossez/julius) (for low-pass filter)
-- [UnivNet](https://github.com/mindslab-ai/univnet) (for multi-resolution discriminator)
-- [descript-audio-codec](https://github.com/descriptinc/descript-audio-codec) and [vocos](https://github.com/gemelo-ai/vocos) (for multi-band multi-scale STFT discriminator and multi-scale mel spectrogram loss)
-- [Amphion](https://github.com/open-mmlab/Amphion) (for multi-scale sub-band CQT discriminator)

GPT_SoVITS/BigVGAN/activations.py

@@ -1,122 +0,0 @@
-# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import torch
-from torch import nn, sin, pow
-from torch.nn import Parameter
-
-
-class Snake(nn.Module):
-    """
-    Implementation of a sine-based periodic activation function
-    Shape:
-        - Input: (B, C, T)
-        - Output: (B, C, T), same shape as the input
-    Parameters:
-        - alpha - trainable parameter
-    References:
-        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
-        https://arxiv.org/abs/2006.08195
-    Examples:
-        >>> a1 = snake(256)
-        >>> x = torch.randn(256)
-        >>> x = a1(x)
-    """
-
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
-        """
-        Initialization.
-        INPUT:
-            - in_features: shape of the input
-            - alpha: trainable parameter
-            alpha is initialized to 1 by default, higher values = higher-frequency.
-            alpha will be trained along with the rest of your model.
-        """
-        super(Snake, self).__init__()
-        self.in_features = in_features
-
-        # Initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale:  # Log scale alphas initialized to zeros
-            self.alpha = Parameter(torch.zeros(in_features) * alpha)
-        else:  # Linear scale alphas initialized to ones
-            self.alpha = Parameter(torch.ones(in_features) * alpha)
-
-        self.alpha.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        """
-        Forward pass of the function.
-        Applies the function to the input elementwise.
-        Snake ∶= x + 1/a * sin^2 (xa)
-        """
-        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # Line up with x to [B, C, T]
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
-
-        return x
-
-
-class SnakeBeta(nn.Module):
-    """
-    A modified Snake function which uses separate parameters for the magnitude of the periodic components
-    Shape:
-        - Input: (B, C, T)
-        - Output: (B, C, T), same shape as the input
-    Parameters:
-        - alpha - trainable parameter that controls frequency
-        - beta - trainable parameter that controls magnitude
-    References:
-        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
-        https://arxiv.org/abs/2006.08195
-    Examples:
-        >>> a1 = snakebeta(256)
-        >>> x = torch.randn(256)
-        >>> x = a1(x)
-    """
-
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
-        """
-        Initialization.
-        INPUT:
-            - in_features: shape of the input
-            - alpha - trainable parameter that controls frequency
-            - beta - trainable parameter that controls magnitude
-            alpha is initialized to 1 by default, higher values = higher-frequency.
-            beta is initialized to 1 by default, higher values = higher-magnitude.
-            alpha will be trained along with the rest of your model.
-        """
-        super(SnakeBeta, self).__init__()
-        self.in_features = in_features
-
-        # Initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale:  # Log scale alphas initialized to zeros
-            self.alpha = Parameter(torch.zeros(in_features) * alpha)
-            self.beta = Parameter(torch.zeros(in_features) * alpha)
-        else:  # Linear scale alphas initialized to ones
-            self.alpha = Parameter(torch.ones(in_features) * alpha)
-            self.beta = Parameter(torch.ones(in_features) * alpha)
-
-        self.alpha.requires_grad = alpha_trainable
-        self.beta.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        """
-        Forward pass of the function.
-        Applies the function to the input elementwise.
-        SnakeBeta ∶= x + 1/b * sin^2 (xa)
-        """
-        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # Line up with x to [B, C, T]
-        beta = self.beta.unsqueeze(0).unsqueeze(-1)
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-            beta = torch.exp(beta)
-        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
-
-        return x

GPT_SoVITS/BigVGAN/configs/bigvgan_22khz_80band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_24khz_100band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 100,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 24000,
-
-    "fmin": 0,
-    "fmax": 12000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_base_22khz_80band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [8,8,2,2],
-    "upsample_kernel_sizes": [16,16,4,4],
-    "upsample_initial_channel": 512,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_base_24khz_100band.json

@@ -1,45 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 32,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [8,8,2,2],
-    "upsample_kernel_sizes": [16,16,4,4],
-    "upsample_initial_channel": 512,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "segment_size": 8192,
-    "num_mels": 100,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 24000,
-
-    "fmin": 0,
-    "fmax": 12000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_22khz_80band_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-    
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-    
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_22khz_80band_fmax8k_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-    
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 80,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 22050,
-
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_44khz_128band_256x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [4,4,2,2,2,2],
-    "upsample_kernel_sizes": [8,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 128,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 256,
-    "win_size": 1024,
-
-    "sampling_rate": 44100,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-    
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/configs/bigvgan_v2_44khz_128band_512x.json

@@ -1,61 +0,0 @@
-{
-    "resblock": "1",
-    "num_gpus": 0,
-    "batch_size": 4,
-    "learning_rate": 0.0001,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.9999996,
-    "seed": 1234,
-
-    "upsample_rates": [8,4,2,2,2,2],
-    "upsample_kernel_sizes": [16,8,4,4,4,4],
-    "upsample_initial_channel": 1536,
-    "resblock_kernel_sizes": [3,7,11],
-    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
-
-    "use_tanh_at_final": false,
-    "use_bias_at_final": false,
-
-    "activation": "snakebeta",
-    "snake_logscale": true,
-
-    "use_cqtd_instead_of_mrd": true,
-    "cqtd_filters": 128,
-    "cqtd_max_filters": 1024,
-    "cqtd_filters_scale": 1,
-    "cqtd_dilations": [1, 2, 4],
-    "cqtd_hop_lengths": [512, 256, 256],
-    "cqtd_n_octaves": [9, 9, 9],
-    "cqtd_bins_per_octaves": [24, 36, 48],
-
-    "mpd_reshapes": [2, 3, 5, 7, 11],
-    "use_spectral_norm": false,
-    "discriminator_channel_mult": 1,
-
-    "use_multiscale_melloss": true,
-    "lambda_melloss": 15,
-
-    "clip_grad_norm": 500,
-
-    "segment_size": 65536,
-    "num_mels": 128,
-    "num_freq": 2049,
-    "n_fft": 2048,
-    "hop_size": 512,
-    "win_size": 2048,
-
-    "sampling_rate": 44100,
-
-    "fmin": 0,
-    "fmax": null,
-    "fmax_for_loss": null,
-
-    "num_workers": 4,
-
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1
-    }
-}

GPT_SoVITS/BigVGAN/discriminators.py

@@ -1,625 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv2d
-from torch.nn.utils import weight_norm, spectral_norm
-from torchaudio.transforms import Spectrogram, Resample
-
-from env import AttrDict
-from utils import get_padding
-import typing
-from typing import List, Tuple
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(
-        self,
-        h: AttrDict,
-        period: List[int],
-        kernel_size: int = 5,
-        stride: int = 3,
-        use_spectral_norm: bool = False,
-    ):
-        super().__init__()
-        self.period = period
-        self.d_mult = h.discriminator_channel_mult
-        norm_f = weight_norm if not use_spectral_norm else spectral_norm
-
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        int(32 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(32 * self.d_mult),
-                        int(128 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(128 * self.d_mult),
-                        int(512 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(512 * self.d_mult),
-                        int(1024 * self.d_mult),
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(5, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        int(1024 * self.d_mult),
-                        int(1024 * self.d_mult),
-                        (kernel_size, 1),
-                        1,
-                        padding=(2, 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, 0.1)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, h: AttrDict):
-        super().__init__()
-        self.mpd_reshapes = h.mpd_reshapes
-        print(f"mpd_reshapes: {self.mpd_reshapes}")
-        self.discriminators = nn.ModuleList(
-            [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
-        )
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorR(nn.Module):
-    def __init__(self, cfg: AttrDict, resolution: List[List[int]]):
-        super().__init__()
-
-        self.resolution = resolution
-        assert len(self.resolution) == 3, f"MRD layer requires list with len=3, got {self.resolution}"
-        self.lrelu_slope = 0.1
-
-        norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
-        if hasattr(cfg, "mrd_use_spectral_norm"):
-            print(f"[INFO] overriding MRD use_spectral_norm as {cfg.mrd_use_spectral_norm}")
-            norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
-        self.d_mult = cfg.discriminator_channel_mult
-        if hasattr(cfg, "mrd_channel_mult"):
-            print(f"[INFO] overriding mrd channel multiplier as {cfg.mrd_channel_mult}")
-            self.d_mult = cfg.mrd_channel_mult
-
-        self.convs = nn.ModuleList(
-            [
-                norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 9),
-                        stride=(1, 2),
-                        padding=(1, 4),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 9),
-                        stride=(1, 2),
-                        padding=(1, 4),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 9),
-                        stride=(1, 2),
-                        padding=(1, 4),
-                    )
-                ),
-                norm_f(
-                    nn.Conv2d(
-                        int(32 * self.d_mult),
-                        int(32 * self.d_mult),
-                        (3, 3),
-                        padding=(1, 1),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        fmap = []
-
-        x = self.spectrogram(x)
-        x = x.unsqueeze(1)
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, self.lrelu_slope)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-    def spectrogram(self, x: torch.Tensor) -> torch.Tensor:
-        n_fft, hop_length, win_length = self.resolution
-        x = F.pad(
-            x,
-            (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)),
-            mode="reflect",
-        )
-        x = x.squeeze(1)
-        x = torch.stft(
-            x,
-            n_fft=n_fft,
-            hop_length=hop_length,
-            win_length=win_length,
-            center=False,
-            return_complex=True,
-        )
-        x = torch.view_as_real(x)  # [B, F, TT, 2]
-        mag = torch.norm(x, p=2, dim=-1)  # [B, F, TT]
-
-        return mag
-
-
-class MultiResolutionDiscriminator(nn.Module):
-    def __init__(self, cfg, debug=False):
-        super().__init__()
-        self.resolutions = cfg.resolutions
-        assert len(self.resolutions) == 3, (
-            f"MRD requires list of list with len=3, each element having a list with len=3. Got {self.resolutions}"
-        )
-        self.discriminators = nn.ModuleList([DiscriminatorR(cfg, resolution) for resolution in self.resolutions])
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(x=y)
-            y_d_g, fmap_g = d(x=y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-# Method based on descript-audio-codec: https://github.com/descriptinc/descript-audio-codec
-# Modified code adapted from https://github.com/gemelo-ai/vocos under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class DiscriminatorB(nn.Module):
-    def __init__(
-        self,
-        window_length: int,
-        channels: int = 32,
-        hop_factor: float = 0.25,
-        bands: Tuple[Tuple[float, float], ...] = (
-            (0.0, 0.1),
-            (0.1, 0.25),
-            (0.25, 0.5),
-            (0.5, 0.75),
-            (0.75, 1.0),
-        ),
-    ):
-        super().__init__()
-        self.window_length = window_length
-        self.hop_factor = hop_factor
-        self.spec_fn = Spectrogram(
-            n_fft=window_length,
-            hop_length=int(window_length * hop_factor),
-            win_length=window_length,
-            power=None,
-        )
-        n_fft = window_length // 2 + 1
-        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
-        self.bands = bands
-        convs = lambda: nn.ModuleList(
-            [
-                weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
-                weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
-            ]
-        )
-        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
-
-        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
-
-    def spectrogram(self, x: torch.Tensor) -> List[torch.Tensor]:
-        # Remove DC offset
-        x = x - x.mean(dim=-1, keepdims=True)
-        # Peak normalize the volume of input audio
-        x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
-        x = self.spec_fn(x)
-        x = torch.view_as_real(x)
-        x = x.permute(0, 3, 2, 1)  # [B, F, T, C] -> [B, C, T, F]
-        # Split into bands
-        x_bands = [x[..., b[0] : b[1]] for b in self.bands]
-        return x_bands
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        x_bands = self.spectrogram(x.squeeze(1))
-        fmap = []
-        x = []
-
-        for band, stack in zip(x_bands, self.band_convs):
-            for i, layer in enumerate(stack):
-                band = layer(band)
-                band = torch.nn.functional.leaky_relu(band, 0.1)
-                if i > 0:
-                    fmap.append(band)
-            x.append(band)
-
-        x = torch.cat(x, dim=-1)
-        x = self.conv_post(x)
-        fmap.append(x)
-
-        return x, fmap
-
-
-# Method based on descript-audio-codec: https://github.com/descriptinc/descript-audio-codec
-# Modified code adapted from https://github.com/gemelo-ai/vocos under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class MultiBandDiscriminator(nn.Module):
-    def __init__(
-        self,
-        h,
-    ):
-        """
-        Multi-band multi-scale STFT discriminator, with the architecture based on https://github.com/descriptinc/descript-audio-codec.
-        and the modified code adapted from https://github.com/gemelo-ai/vocos.
-        """
-        super().__init__()
-        # fft_sizes (list[int]): Tuple of window lengths for FFT. Defaults to [2048, 1024, 512] if not set in h.
-        self.fft_sizes = h.get("mbd_fft_sizes", [2048, 1024, 512])
-        self.discriminators = nn.ModuleList([DiscriminatorB(window_length=w) for w in self.fft_sizes])
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for d in self.discriminators:
-            y_d_r, fmap_r = d(x=y)
-            y_d_g, fmap_g = d(x=y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-# Adapted from https://github.com/open-mmlab/Amphion/blob/main/models/vocoders/gan/discriminator/mssbcqtd.py under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class DiscriminatorCQT(nn.Module):
-    def __init__(self, cfg: AttrDict, hop_length: int, n_octaves: int, bins_per_octave: int):
-        super().__init__()
-        self.cfg = cfg
-
-        self.filters = cfg["cqtd_filters"]
-        self.max_filters = cfg["cqtd_max_filters"]
-        self.filters_scale = cfg["cqtd_filters_scale"]
-        self.kernel_size = (3, 9)
-        self.dilations = cfg["cqtd_dilations"]
-        self.stride = (1, 2)
-
-        self.in_channels = cfg["cqtd_in_channels"]
-        self.out_channels = cfg["cqtd_out_channels"]
-        self.fs = cfg["sampling_rate"]
-        self.hop_length = hop_length
-        self.n_octaves = n_octaves
-        self.bins_per_octave = bins_per_octave
-
-        # Lazy-load
-        from nnAudio import features
-
-        self.cqt_transform = features.cqt.CQT2010v2(
-            sr=self.fs * 2,
-            hop_length=self.hop_length,
-            n_bins=self.bins_per_octave * self.n_octaves,
-            bins_per_octave=self.bins_per_octave,
-            output_format="Complex",
-            pad_mode="constant",
-        )
-
-        self.conv_pres = nn.ModuleList()
-        for _ in range(self.n_octaves):
-            self.conv_pres.append(
-                nn.Conv2d(
-                    self.in_channels * 2,
-                    self.in_channels * 2,
-                    kernel_size=self.kernel_size,
-                    padding=self.get_2d_padding(self.kernel_size),
-                )
-            )
-
-        self.convs = nn.ModuleList()
-
-        self.convs.append(
-            nn.Conv2d(
-                self.in_channels * 2,
-                self.filters,
-                kernel_size=self.kernel_size,
-                padding=self.get_2d_padding(self.kernel_size),
-            )
-        )
-
-        in_chs = min(self.filters_scale * self.filters, self.max_filters)
-        for i, dilation in enumerate(self.dilations):
-            out_chs = min((self.filters_scale ** (i + 1)) * self.filters, self.max_filters)
-            self.convs.append(
-                weight_norm(
-                    nn.Conv2d(
-                        in_chs,
-                        out_chs,
-                        kernel_size=self.kernel_size,
-                        stride=self.stride,
-                        dilation=(dilation, 1),
-                        padding=self.get_2d_padding(self.kernel_size, (dilation, 1)),
-                    )
-                )
-            )
-            in_chs = out_chs
-        out_chs = min(
-            (self.filters_scale ** (len(self.dilations) + 1)) * self.filters,
-            self.max_filters,
-        )
-        self.convs.append(
-            weight_norm(
-                nn.Conv2d(
-                    in_chs,
-                    out_chs,
-                    kernel_size=(self.kernel_size[0], self.kernel_size[0]),
-                    padding=self.get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
-                )
-            )
-        )
-
-        self.conv_post = weight_norm(
-            nn.Conv2d(
-                out_chs,
-                self.out_channels,
-                kernel_size=(self.kernel_size[0], self.kernel_size[0]),
-                padding=self.get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
-            )
-        )
-
-        self.activation = torch.nn.LeakyReLU(negative_slope=0.1)
-        self.resample = Resample(orig_freq=self.fs, new_freq=self.fs * 2)
-
-        self.cqtd_normalize_volume = self.cfg.get("cqtd_normalize_volume", False)
-        if self.cqtd_normalize_volume:
-            print(
-                "[INFO] cqtd_normalize_volume set to True. Will apply DC offset removal & peak volume normalization in CQTD!"
-            )
-
-    def get_2d_padding(
-        self,
-        kernel_size: typing.Tuple[int, int],
-        dilation: typing.Tuple[int, int] = (1, 1),
-    ):
-        return (
-            ((kernel_size[0] - 1) * dilation[0]) // 2,
-            ((kernel_size[1] - 1) * dilation[1]) // 2,
-        )
-
-    def forward(self, x: torch.tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        fmap = []
-
-        if self.cqtd_normalize_volume:
-            # Remove DC offset
-            x = x - x.mean(dim=-1, keepdims=True)
-            # Peak normalize the volume of input audio
-            x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
-
-        x = self.resample(x)
-
-        z = self.cqt_transform(x)
-
-        z_amplitude = z[:, :, :, 0].unsqueeze(1)
-        z_phase = z[:, :, :, 1].unsqueeze(1)
-
-        z = torch.cat([z_amplitude, z_phase], dim=1)
-        z = torch.permute(z, (0, 1, 3, 2))  # [B, C, W, T] -> [B, C, T, W]
-
-        latent_z = []
-        for i in range(self.n_octaves):
-            latent_z.append(
-                self.conv_pres[i](
-                    z[
-                        :,
-                        :,
-                        :,
-                        i * self.bins_per_octave : (i + 1) * self.bins_per_octave,
-                    ]
-                )
-            )
-        latent_z = torch.cat(latent_z, dim=-1)
-
-        for i, l in enumerate(self.convs):
-            latent_z = l(latent_z)
-
-            latent_z = self.activation(latent_z)
-            fmap.append(latent_z)
-
-        latent_z = self.conv_post(latent_z)
-
-        return latent_z, fmap
-
-
-class MultiScaleSubbandCQTDiscriminator(nn.Module):
-    def __init__(self, cfg: AttrDict):
-        super().__init__()
-
-        self.cfg = cfg
-        # Using get with defaults
-        self.cfg["cqtd_filters"] = self.cfg.get("cqtd_filters", 32)
-        self.cfg["cqtd_max_filters"] = self.cfg.get("cqtd_max_filters", 1024)
-        self.cfg["cqtd_filters_scale"] = self.cfg.get("cqtd_filters_scale", 1)
-        self.cfg["cqtd_dilations"] = self.cfg.get("cqtd_dilations", [1, 2, 4])
-        self.cfg["cqtd_in_channels"] = self.cfg.get("cqtd_in_channels", 1)
-        self.cfg["cqtd_out_channels"] = self.cfg.get("cqtd_out_channels", 1)
-        # Multi-scale params to loop over
-        self.cfg["cqtd_hop_lengths"] = self.cfg.get("cqtd_hop_lengths", [512, 256, 256])
-        self.cfg["cqtd_n_octaves"] = self.cfg.get("cqtd_n_octaves", [9, 9, 9])
-        self.cfg["cqtd_bins_per_octaves"] = self.cfg.get("cqtd_bins_per_octaves", [24, 36, 48])
-
-        self.discriminators = nn.ModuleList(
-            [
-                DiscriminatorCQT(
-                    self.cfg,
-                    hop_length=self.cfg["cqtd_hop_lengths"][i],
-                    n_octaves=self.cfg["cqtd_n_octaves"][i],
-                    bins_per_octave=self.cfg["cqtd_bins_per_octaves"][i],
-                )
-                for i in range(len(self.cfg["cqtd_hop_lengths"]))
-            ]
-        )
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for disc in self.discriminators:
-            y_d_r, fmap_r = disc(y)
-            y_d_g, fmap_g = disc(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class CombinedDiscriminator(nn.Module):
-    """
-    Wrapper of chaining multiple discrimiantor architectures.
-    Example: combine mbd and cqtd as a single class
-    """
-
-    def __init__(self, list_discriminator: List[nn.Module]):
-        super().__init__()
-        self.discrimiantor = nn.ModuleList(list_discriminator)
-
-    def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor
-    ) -> Tuple[
-        List[torch.Tensor],
-        List[torch.Tensor],
-        List[List[torch.Tensor]],
-        List[List[torch.Tensor]],
-    ]:
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-
-        for disc in self.discrimiantor:
-            y_d_r, y_d_g, fmap_r, fmap_g = disc(y, y_hat)
-            y_d_rs.extend(y_d_r)
-            fmap_rs.extend(fmap_r)
-            y_d_gs.extend(y_d_g)
-            fmap_gs.extend(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs

GPT_SoVITS/BigVGAN/inference.py

@@ -1,85 +0,0 @@
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import os
-import argparse
-import json
-import torch
-import librosa
-from utils import load_checkpoint
-from meldataset import get_mel_spectrogram
-from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import MAX_WAV_VALUE
-from bigvgan import BigVGAN as Generator
-
-h = None
-device = None
-torch.backends.cudnn.benchmark = False
-
-
-def inference(a, h):
-    generator = Generator(h, use_cuda_kernel=a.use_cuda_kernel).to(device)
-
-    state_dict_g = load_checkpoint(a.checkpoint_file, device)
-    generator.load_state_dict(state_dict_g["generator"])
-
-    filelist = os.listdir(a.input_wavs_dir)
-
-    os.makedirs(a.output_dir, exist_ok=True)
-
-    generator.eval()
-    generator.remove_weight_norm()
-    with torch.no_grad():
-        for i, filname in enumerate(filelist):
-            # Load the ground truth audio and resample if necessary
-            wav, sr = librosa.load(os.path.join(a.input_wavs_dir, filname), sr=h.sampling_rate, mono=True)
-            wav = torch.FloatTensor(wav).to(device)
-            # Compute mel spectrogram from the ground truth audio
-            x = get_mel_spectrogram(wav.unsqueeze(0), generator.h)
-
-            y_g_hat = generator(x)
-
-            audio = y_g_hat.squeeze()
-            audio = audio * MAX_WAV_VALUE
-            audio = audio.cpu().numpy().astype("int16")
-
-            output_file = os.path.join(a.output_dir, os.path.splitext(filname)[0] + "_generated.wav")
-            write(output_file, h.sampling_rate, audio)
-            print(output_file)
-
-
-def main():
-    print("Initializing Inference Process..")
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--input_wavs_dir", default="test_files")
-    parser.add_argument("--output_dir", default="generated_files")
-    parser.add_argument("--checkpoint_file", required=True)
-    parser.add_argument("--use_cuda_kernel", action="store_true", default=False)
-
-    a = parser.parse_args()
-
-    config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    torch.manual_seed(h.seed)
-    global device
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-
-    inference(a, h)
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/BigVGAN/inference_e2e.py

@@ -1,100 +0,0 @@
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import glob
-import os
-import numpy as np
-import argparse
-import json
-import torch
-from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import MAX_WAV_VALUE
-from bigvgan import BigVGAN as Generator
-
-h = None
-device = None
-torch.backends.cudnn.benchmark = False
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print(f"Loading '{filepath}'")
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + "*")
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return ""
-    return sorted(cp_list)[-1]
-
-
-def inference(a, h):
-    generator = Generator(h, use_cuda_kernel=a.use_cuda_kernel).to(device)
-
-    state_dict_g = load_checkpoint(a.checkpoint_file, device)
-    generator.load_state_dict(state_dict_g["generator"])
-
-    filelist = os.listdir(a.input_mels_dir)
-
-    os.makedirs(a.output_dir, exist_ok=True)
-
-    generator.eval()
-    generator.remove_weight_norm()
-    with torch.no_grad():
-        for i, filname in enumerate(filelist):
-            # Load the mel spectrogram in .npy format
-            x = np.load(os.path.join(a.input_mels_dir, filname))
-            x = torch.FloatTensor(x).to(device)
-            if len(x.shape) == 2:
-                x = x.unsqueeze(0)
-
-            y_g_hat = generator(x)
-
-            audio = y_g_hat.squeeze()
-            audio = audio * MAX_WAV_VALUE
-            audio = audio.cpu().numpy().astype("int16")
-
-            output_file = os.path.join(a.output_dir, os.path.splitext(filname)[0] + "_generated_e2e.wav")
-            write(output_file, h.sampling_rate, audio)
-            print(output_file)
-
-
-def main():
-    print("Initializing Inference Process..")
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--input_mels_dir", default="test_mel_files")
-    parser.add_argument("--output_dir", default="generated_files_from_mel")
-    parser.add_argument("--checkpoint_file", required=True)
-    parser.add_argument("--use_cuda_kernel", action="store_true", default=False)
-
-    a = parser.parse_args()
-
-    config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    torch.manual_seed(h.seed)
-    global device
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-
-    inference(a, h)
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/BigVGAN/loss.py

@@ -1,238 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import torch
-import torch.nn as nn
-from librosa.filters import mel as librosa_mel_fn
-from scipy import signal
-
-import typing
-from typing import List, Tuple
-from collections import namedtuple
-import math
-import functools
-
-
-# Adapted from https://github.com/descriptinc/descript-audio-codec/blob/main/dac/nn/loss.py under the MIT license.
-#   LICENSE is in incl_licenses directory.
-class MultiScaleMelSpectrogramLoss(nn.Module):
-    """Compute distance between mel spectrograms. Can be used
-    in a multi-scale way.
-
-    Parameters
-    ----------
-    n_mels : List[int]
-        Number of mels per STFT, by default [5, 10, 20, 40, 80, 160, 320],
-    window_lengths : List[int], optional
-        Length of each window of each STFT, by default [32, 64, 128, 256, 512, 1024, 2048]
-    loss_fn : typing.Callable, optional
-        How to compare each loss, by default nn.L1Loss()
-    clamp_eps : float, optional
-        Clamp on the log magnitude, below, by default 1e-5
-    mag_weight : float, optional
-        Weight of raw magnitude portion of loss, by default 0.0 (no ampliciation on mag part)
-    log_weight : float, optional
-        Weight of log magnitude portion of loss, by default 1.0
-    pow : float, optional
-        Power to raise magnitude to before taking log, by default 1.0
-    weight : float, optional
-        Weight of this loss, by default 1.0
-    match_stride : bool, optional
-        Whether to match the stride of convolutional layers, by default False
-
-    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
-    Additional code copied and modified from https://github.com/descriptinc/audiotools/blob/master/audiotools/core/audio_signal.py
-    """
-
-    def __init__(
-        self,
-        sampling_rate: int,
-        n_mels: List[int] = [5, 10, 20, 40, 80, 160, 320],
-        window_lengths: List[int] = [32, 64, 128, 256, 512, 1024, 2048],
-        loss_fn: typing.Callable = nn.L1Loss(),
-        clamp_eps: float = 1e-5,
-        mag_weight: float = 0.0,
-        log_weight: float = 1.0,
-        pow: float = 1.0,
-        weight: float = 1.0,
-        match_stride: bool = False,
-        mel_fmin: List[float] = [0, 0, 0, 0, 0, 0, 0],
-        mel_fmax: List[float] = [None, None, None, None, None, None, None],
-        window_type: str = "hann",
-    ):
-        super().__init__()
-        self.sampling_rate = sampling_rate
-
-        STFTParams = namedtuple(
-            "STFTParams",
-            ["window_length", "hop_length", "window_type", "match_stride"],
-        )
-
-        self.stft_params = [
-            STFTParams(
-                window_length=w,
-                hop_length=w // 4,
-                match_stride=match_stride,
-                window_type=window_type,
-            )
-            for w in window_lengths
-        ]
-        self.n_mels = n_mels
-        self.loss_fn = loss_fn
-        self.clamp_eps = clamp_eps
-        self.log_weight = log_weight
-        self.mag_weight = mag_weight
-        self.weight = weight
-        self.mel_fmin = mel_fmin
-        self.mel_fmax = mel_fmax
-        self.pow = pow
-
-    @staticmethod
-    @functools.lru_cache(None)
-    def get_window(
-        window_type,
-        window_length,
-    ):
-        return signal.get_window(window_type, window_length)
-
-    @staticmethod
-    @functools.lru_cache(None)
-    def get_mel_filters(sr, n_fft, n_mels, fmin, fmax):
-        return librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-
-    def mel_spectrogram(
-        self,
-        wav,
-        n_mels,
-        fmin,
-        fmax,
-        window_length,
-        hop_length,
-        match_stride,
-        window_type,
-    ):
-        """
-        Mirrors AudioSignal.mel_spectrogram used by BigVGAN-v2 training from:
-        https://github.com/descriptinc/audiotools/blob/master/audiotools/core/audio_signal.py
-        """
-        B, C, T = wav.shape
-
-        if match_stride:
-            assert hop_length == window_length // 4, "For match_stride, hop must equal n_fft // 4"
-            right_pad = math.ceil(T / hop_length) * hop_length - T
-            pad = (window_length - hop_length) // 2
-        else:
-            right_pad = 0
-            pad = 0
-
-        wav = torch.nn.functional.pad(wav, (pad, pad + right_pad), mode="reflect")
-
-        window = self.get_window(window_type, window_length)
-        window = torch.from_numpy(window).to(wav.device).float()
-
-        stft = torch.stft(
-            wav.reshape(-1, T),
-            n_fft=window_length,
-            hop_length=hop_length,
-            window=window,
-            return_complex=True,
-            center=True,
-        )
-        _, nf, nt = stft.shape
-        stft = stft.reshape(B, C, nf, nt)
-        if match_stride:
-            """
-            Drop first two and last two frames, which are added, because of padding. Now num_frames * hop_length = num_samples.
-            """
-            stft = stft[..., 2:-2]
-        magnitude = torch.abs(stft)
-
-        nf = magnitude.shape[2]
-        mel_basis = self.get_mel_filters(self.sampling_rate, 2 * (nf - 1), n_mels, fmin, fmax)
-        mel_basis = torch.from_numpy(mel_basis).to(wav.device)
-        mel_spectrogram = magnitude.transpose(2, -1) @ mel_basis.T
-        mel_spectrogram = mel_spectrogram.transpose(-1, 2)
-
-        return mel_spectrogram
-
-    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-        """Computes mel loss between an estimate and a reference
-        signal.
-
-        Parameters
-        ----------
-        x : torch.Tensor
-            Estimate signal
-        y : torch.Tensor
-            Reference signal
-
-        Returns
-        -------
-        torch.Tensor
-            Mel loss.
-        """
-
-        loss = 0.0
-        for n_mels, fmin, fmax, s in zip(self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params):
-            kwargs = {
-                "n_mels": n_mels,
-                "fmin": fmin,
-                "fmax": fmax,
-                "window_length": s.window_length,
-                "hop_length": s.hop_length,
-                "match_stride": s.match_stride,
-                "window_type": s.window_type,
-            }
-
-            x_mels = self.mel_spectrogram(x, **kwargs)
-            y_mels = self.mel_spectrogram(y, **kwargs)
-            x_logmels = torch.log(x_mels.clamp(min=self.clamp_eps).pow(self.pow)) / torch.log(torch.tensor(10.0))
-            y_logmels = torch.log(y_mels.clamp(min=self.clamp_eps).pow(self.pow)) / torch.log(torch.tensor(10.0))
-
-            loss += self.log_weight * self.loss_fn(x_logmels, y_logmels)
-            loss += self.mag_weight * self.loss_fn(x_logmels, y_logmels)
-
-        return loss
-
-
-# Loss functions
-def feature_loss(fmap_r: List[List[torch.Tensor]], fmap_g: List[List[torch.Tensor]]) -> torch.Tensor:
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2  # This equates to lambda=2.0 for the feature matching loss
-
-
-def discriminator_loss(
-    disc_real_outputs: List[torch.Tensor], disc_generated_outputs: List[torch.Tensor]
-) -> Tuple[torch.Tensor, List[torch.Tensor], List[torch.Tensor]]:
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(
-    disc_outputs: List[torch.Tensor],
-) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses

GPT_SoVITS/BigVGAN/meldataset.py

@@ -1,370 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import math
-import os
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.filters import mel as librosa_mel_fn
-import pathlib
-from tqdm import tqdm
-from typing import List, Tuple, Optional
-from .env import AttrDict
-
-MAX_WAV_VALUE = 32767.0  # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
-
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
-    return dynamic_range_decompression_torch(magnitudes)
-
-
-mel_basis_cache = {}
-hann_window_cache = {}
-
-
-def mel_spectrogram(
-    y: torch.Tensor,
-    n_fft: int,
-    num_mels: int,
-    sampling_rate: int,
-    hop_size: int,
-    win_size: int,
-    fmin: int,
-    fmax: int = None,
-    center: bool = False,
-) -> torch.Tensor:
-    """
-    Calculate the mel spectrogram of an input signal.
-    This function uses slaney norm for the librosa mel filterbank (using librosa.filters.mel) and uses Hann window for STFT (using torch.stft).
-
-    Args:
-        y (torch.Tensor): Input signal.
-        n_fft (int): FFT size.
-        num_mels (int): Number of mel bins.
-        sampling_rate (int): Sampling rate of the input signal.
-        hop_size (int): Hop size for STFT.
-        win_size (int): Window size for STFT.
-        fmin (int): Minimum frequency for mel filterbank.
-        fmax (int): Maximum frequency for mel filterbank. If None, defaults to half the sampling rate (fmax = sr / 2.0) inside librosa_mel_fn
-        center (bool): Whether to pad the input to center the frames. Default is False.
-
-    Returns:
-        torch.Tensor: Mel spectrogram.
-    """
-    if torch.min(y) < -1.0:
-        print(f"[WARNING] Min value of input waveform signal is {torch.min(y)}")
-    if torch.max(y) > 1.0:
-        print(f"[WARNING] Max value of input waveform signal is {torch.max(y)}")
-
-    device = y.device
-    key = f"{n_fft}_{num_mels}_{sampling_rate}_{hop_size}_{win_size}_{fmin}_{fmax}_{device}"
-
-    if key not in mel_basis_cache:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)
-        hann_window_cache[key] = torch.hann_window(win_size).to(device)
-
-    mel_basis = mel_basis_cache[key]
-    hann_window = hann_window_cache[key]
-
-    padding = (n_fft - hop_size) // 2
-    y = torch.nn.functional.pad(y.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
-
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window,
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=True,
-    )
-    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
-
-    mel_spec = torch.matmul(mel_basis, spec)
-    mel_spec = spectral_normalize_torch(mel_spec)
-
-    return mel_spec
-
-
-def get_mel_spectrogram(wav, h):
-    """
-    Generate mel spectrogram from a waveform using given hyperparameters.
-
-    Args:
-        wav (torch.Tensor): Input waveform.
-        h: Hyperparameters object with attributes n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax.
-
-    Returns:
-        torch.Tensor: Mel spectrogram.
-    """
-    return mel_spectrogram(
-        wav,
-        h.n_fft,
-        h.num_mels,
-        h.sampling_rate,
-        h.hop_size,
-        h.win_size,
-        h.fmin,
-        h.fmax,
-    )
-
-
-def get_dataset_filelist(a):
-    training_files = []
-    validation_files = []
-    list_unseen_validation_files = []
-
-    with open(a.input_training_file, "r", encoding="utf-8") as fi:
-        training_files = [
-            os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
-        ]
-        print(f"first training file: {training_files[0]}")
-
-    with open(a.input_validation_file, "r", encoding="utf-8") as fi:
-        validation_files = [
-            os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
-        ]
-        print(f"first validation file: {validation_files[0]}")
-
-    for i in range(len(a.list_input_unseen_validation_file)):
-        with open(a.list_input_unseen_validation_file[i], "r", encoding="utf-8") as fi:
-            unseen_validation_files = [
-                os.path.join(a.list_input_unseen_wavs_dir[i], x.split("|")[0] + ".wav")
-                for x in fi.read().split("\n")
-                if len(x) > 0
-            ]
-            print(f"first unseen {i}th validation fileset: {unseen_validation_files[0]}")
-            list_unseen_validation_files.append(unseen_validation_files)
-
-    return training_files, validation_files, list_unseen_validation_files
-
-
-class MelDataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        training_files: List[str],
-        hparams: AttrDict,
-        segment_size: int,
-        n_fft: int,
-        num_mels: int,
-        hop_size: int,
-        win_size: int,
-        sampling_rate: int,
-        fmin: int,
-        fmax: Optional[int],
-        split: bool = True,
-        shuffle: bool = True,
-        device: str = None,
-        fmax_loss: Optional[int] = None,
-        fine_tuning: bool = False,
-        base_mels_path: str = None,
-        is_seen: bool = True,
-    ):
-        self.audio_files = training_files
-        random.seed(1234)
-        if shuffle:
-            random.shuffle(self.audio_files)
-        self.hparams = hparams
-        self.is_seen = is_seen
-        if self.is_seen:
-            self.name = pathlib.Path(self.audio_files[0]).parts[0]
-        else:
-            self.name = "-".join(pathlib.Path(self.audio_files[0]).parts[:2]).strip("/")
-
-        self.segment_size = segment_size
-        self.sampling_rate = sampling_rate
-        self.split = split
-        self.n_fft = n_fft
-        self.num_mels = num_mels
-        self.hop_size = hop_size
-        self.win_size = win_size
-        self.fmin = fmin
-        self.fmax = fmax
-        self.fmax_loss = fmax_loss
-        self.device = device
-        self.fine_tuning = fine_tuning
-        self.base_mels_path = base_mels_path
-
-        print("[INFO] checking dataset integrity...")
-        for i in tqdm(range(len(self.audio_files))):
-            assert os.path.exists(self.audio_files[i]), f"{self.audio_files[i]} not found"
-
-    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor, str, torch.Tensor]:
-        try:
-            filename = self.audio_files[index]
-
-            # Use librosa.load that ensures loading waveform into mono with [-1, 1] float values
-            # Audio is ndarray with shape [T_time]. Disable auto-resampling here to minimize overhead
-            # The on-the-fly resampling during training will be done only for the obtained random chunk
-            audio, source_sampling_rate = librosa.load(filename, sr=None, mono=True)
-
-            # Main logic that uses <mel, audio> pair for training BigVGAN
-            if not self.fine_tuning:
-                if self.split:  # Training step
-                    # Obtain randomized audio chunk
-                    if source_sampling_rate != self.sampling_rate:
-                        # Adjust segment size to crop if the source sr is different
-                        target_segment_size = math.ceil(self.segment_size * (source_sampling_rate / self.sampling_rate))
-                    else:
-                        target_segment_size = self.segment_size
-
-                    # Compute upper bound index for the random chunk
-                    random_chunk_upper_bound = max(0, audio.shape[0] - target_segment_size)
-
-                    # Crop or pad audio to obtain random chunk with target_segment_size
-                    if audio.shape[0] >= target_segment_size:
-                        audio_start = random.randint(0, random_chunk_upper_bound)
-                        audio = audio[audio_start : audio_start + target_segment_size]
-                    else:
-                        audio = np.pad(
-                            audio,
-                            (0, target_segment_size - audio.shape[0]),
-                            mode="constant",
-                        )
-
-                    # Resample audio chunk to self.sampling rate
-                    if source_sampling_rate != self.sampling_rate:
-                        audio = librosa.resample(
-                            audio,
-                            orig_sr=source_sampling_rate,
-                            target_sr=self.sampling_rate,
-                        )
-                        if audio.shape[0] > self.segment_size:
-                            # trim last elements to match self.segment_size (e.g., 16385 for 44khz downsampled to 24khz -> 16384)
-                            audio = audio[: self.segment_size]
-
-                else:  # Validation step
-                    # Resample full audio clip to target sampling rate
-                    if source_sampling_rate != self.sampling_rate:
-                        audio = librosa.resample(
-                            audio,
-                            orig_sr=source_sampling_rate,
-                            target_sr=self.sampling_rate,
-                        )
-                    # Trim last elements to match audio length to self.hop_size * n for evaluation
-                    if (audio.shape[0] % self.hop_size) != 0:
-                        audio = audio[: -(audio.shape[0] % self.hop_size)]
-
-                # BigVGAN is trained using volume-normalized waveform
-                audio = librosa.util.normalize(audio) * 0.95
-
-                # Cast ndarray to torch tensor
-                audio = torch.FloatTensor(audio)
-                audio = audio.unsqueeze(0)  # [B(1), self.segment_size]
-
-                # Compute mel spectrogram corresponding to audio
-                mel = mel_spectrogram(
-                    audio,
-                    self.n_fft,
-                    self.num_mels,
-                    self.sampling_rate,
-                    self.hop_size,
-                    self.win_size,
-                    self.fmin,
-                    self.fmax,
-                    center=False,
-                )  # [B(1), self.num_mels, self.segment_size // self.hop_size]
-
-            # Fine-tuning logic that uses pre-computed mel. Example: Using TTS model-generated mel as input
-            else:
-                # For fine-tuning, assert that the waveform is in the defined sampling_rate
-                # Fine-tuning won't support on-the-fly resampling to be fool-proof (the dataset should have been prepared properly)
-                assert source_sampling_rate == self.sampling_rate, (
-                    f"For fine_tuning, waveform must be in the spcified sampling rate {self.sampling_rate}, got {source_sampling_rate}"
-                )
-
-                # Cast ndarray to torch tensor
-                audio = torch.FloatTensor(audio)
-                audio = audio.unsqueeze(0)  # [B(1), T_time]
-
-                # Load pre-computed mel from disk
-                mel = np.load(
-                    os.path.join(
-                        self.base_mels_path,
-                        os.path.splitext(os.path.split(filename)[-1])[0] + ".npy",
-                    )
-                )
-                mel = torch.from_numpy(mel)
-
-                if len(mel.shape) < 3:
-                    mel = mel.unsqueeze(0)  # ensure [B, C, T]
-
-                if self.split:
-                    frames_per_seg = math.ceil(self.segment_size / self.hop_size)
-
-                    if audio.size(1) >= self.segment_size:
-                        mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
-                        mel = mel[:, :, mel_start : mel_start + frames_per_seg]
-                        audio = audio[
-                            :,
-                            mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size,
-                        ]
-
-                    # Pad pre-computed mel and audio to match length to ensuring fine-tuning without error.
-                    # NOTE: this may introduce a single-frame misalignment of the <pre-computed mel, audio>
-                    # To remove possible misalignment, it is recommended to prepare the <pre-computed mel, audio> pair where the audio length is the integer multiple of self.hop_size
-                    mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant")
-                    audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant")
-
-            # Compute mel_loss used by spectral regression objective. Uses self.fmax_loss instead (usually None)
-            mel_loss = mel_spectrogram(
-                audio,
-                self.n_fft,
-                self.num_mels,
-                self.sampling_rate,
-                self.hop_size,
-                self.win_size,
-                self.fmin,
-                self.fmax_loss,
-                center=False,
-            )  # [B(1), self.num_mels, self.segment_size // self.hop_size]
-
-            # Shape sanity checks
-            assert (
-                audio.shape[1] == mel.shape[2] * self.hop_size and audio.shape[1] == mel_loss.shape[2] * self.hop_size
-            ), (
-                f"Audio length must be mel frame length * hop_size. Got audio shape {audio.shape} mel shape {mel.shape} mel_loss shape {mel_loss.shape}"
-            )
-
-            return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
-
-        # If it encounters error during loading the data, skip this sample and load random other sample to the batch
-        except Exception as e:
-            if self.fine_tuning:
-                raise e  # Terminate training if it is fine-tuning. The dataset should have been prepared properly.
-            else:
-                print(f"[WARNING] Failed to load waveform, skipping! filename: {filename} Error: {e}")
-                return self[random.randrange(len(self))]
-
-    def __len__(self):
-        return len(self.audio_files)

GPT_SoVITS/BigVGAN/nv-modelcard++/.gitkeep

@@ -1 +0,0 @@
-

GPT_SoVITS/BigVGAN/nv-modelcard++/bias.md

@@ -1,4 +0,0 @@
-| Field                                                                                                      | Response                                             |
-| :--------------------------------------------------------------------------------------------------------- | :--------------------------------------------------- |
-| Participation considerations from adversely impacted groups protected classes in model design and testing: | None                                                 |
-| Measures taken to mitigate against unwanted bias:                                                          | No measures taken to mitigate against unwanted bias. |

GPT_SoVITS/BigVGAN/nv-modelcard++/explainability.md

@@ -1,13 +0,0 @@
-| Field                                                                                                 | Response                                                                                                                                                                                                               |
-| :---------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Intended Application & Domain:                                                                        | Generating waveform from mel spectrogram.                                                                                                                                                                              |
-| Model Type:                                                                                           | Convolutional Neural Network (CNN)                                                                                                                                                                                     |
-| Intended Users:                                                                                       | This model is intended for developers to synthesize and generate waveforms from the AI-generated mel spectrograms.                                                                                                     |
-| Output:                                                                                               | Audio Waveform                                                                                                                                                                                                         |
-| Describe how the model works:                                                                         | Model generates audio waveform corresponding to the input mel spectrogram.                                                                                                                                             |
-| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable                                                                                                                                                                                                         |
-| Technical Limitations:                                                                                | This may not perform well on synthetically-generated mel spectrograms that deviate significantly from the profile of mel spectrograms on which this was trained.                                                       |
-| Verified to have met prescribed NVIDIA quality standards:                                             | Yes                                                                                                                                                                                                                    |
-| Performance Metrics:                                                                                  | Perceptual Evaluation of Speech Quality (PESQ), Virtual Speech Quality Objective Listener (VISQOL), Multi-resolution STFT (MRSTFT), Mel cepstral distortion (MCD), Periodicity RMSE, Voice/Unvoiced F1 Score (V/UV F1) |
-| Potential Known Risks:                                                                                | This model may generate low-quality or distorted soundwaves.                                                                                                                                                           |
-| Licensing:                                                                                            | https://github.com/NVIDIA/BigVGAN/blob/main/LICENSE                                                                                                                                                                    |

GPT_SoVITS/BigVGAN/nv-modelcard++/overview.md

@@ -1,126 +0,0 @@
-# Model Overview
-
-## Description:
-
-BigVGAN is a generative AI model specialized in synthesizing audio waveforms using Mel spectrogram as inputs.
-
-<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
-
-BigVGAN is a fully convolutional architecture with several upsampling blocks using transposed convolution followed by multiple residual dilated convolution layers.
-
-BigVGAN consists of a novel module, called anti-aliased multi-periodicity composition (AMP), which is specifically designed for generating waveforms. AMP is specialized in synthesizing high-frequency and periodic soundwaves drawing inspiration from audio signal processing principles.
-
-It applies a periodic activation function, called Snake, which provides an inductive bias to the architecture in generating periodic soundwaves. It also applies anti-aliasing filters to reduce undesired artifacts in the generated waveforms. <br>
-
-This model is ready for commercial use.<br>
-
-## References(s):
-
-- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://arxiv.org/abs/2206.04658) <br>
-- [Project Page](https://research.nvidia.com/labs/adlr/projects/bigvgan/) <br>
-- [Audio Demo](https://bigvgan-demo.github.io/) <br>
-
-## Model Architecture:
-
-**Architecture Type:** Convolution Neural Network (CNN) <br>
-**Network Architecture:** You can see the details of this model on this link: https://github.com/NVIDIA/BigVGAN and the related paper can be found here: https://arxiv.org/abs/2206.04658<br>
-**Model Version:** 2.0 <br>
-
-## Input:
-
-**Input Type:** Audio <br>
-**Input Format:** Mel Spectrogram <br>
-**Input Parameters:** None <br>
-**Other Properties Related to Input:** The input mel spectrogram has shape `[batch, channels, frames]`, where `channels` refers to the number of mel bands defined by the model and `frames` refers to the temporal length. The model supports arbitrary long `frames` that fits into the GPU memory.
-
-## Output:
-
-**Input Type:** Audio <br>
-**Output Format:** Audio Waveform <br>
-**Output Parameters:** None <br>
-**Other Properties Related to Output:** The output audio waveform has shape `[batch, 1, time]`, where `1` refers to the mono audio channels and `time` refers to the temporal length. `time` is defined as a fixed integer multiple of input `frames`, which is an upsampling ratio of the model (`time = upsampling ratio * frames`). The output audio waveform consitutes float values with a range of `[-1, 1]`.
-
-## Software Integration:
-
-**Runtime Engine(s):** PyTorch
-
-**Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Turing, NVIDIA Volta <br>
-
-## Preferred/Supported Operating System(s):
-
-Linux
-
-## Model Version(s):
-
-v2.0
-
-## Training, Testing, and Evaluation Datasets:
-
-### Training Dataset:
-
-The dataset contains diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
-
-**Links:**
-
-- [AAM: Artificial Audio Multitracks Dataset](https://zenodo.org/records/5794629)
-- [AudioCaps](https://audiocaps.github.io/)
-- [AudioSet](https://research.google.com/audioset/index.html)
-- [common-accent](https://huggingface.co/datasets/DTU54DL/common-accent)
-- [Crowd Sourced Emotional Multimodal Actors Dataset (CREMA-D)](https://ieeexplore.ieee.org/document/6849440)
-- [DCASE2017 Challenge, Task 4: Large-scale weakly supervised sound event detection for smart cars](https://dcase.community/challenge2017/task-large-scale-sound-event-detection)
-- [FSDnoisy18k](https://zenodo.org/records/2529934)
-- [Free Universal Sound Separation Dataset](https://zenodo.org/records/3694384)
-- [Greatest Hits dataset](https://andrewowens.com/vis/)
-- [GTZAN](https://ieeexplore.ieee.org/document/1021072)
-- [JL corpus](https://www.kaggle.com/datasets/tli725/jl-corpus)
-- [Medley-solos-DB: a cross-collection dataset for musical instrument recognition](https://zenodo.org/records/3464194)
-- [MUSAN: A Music, Speech, and Noise Corpus](https://www.openslr.org/17/)
-- [MusicBench](https://huggingface.co/datasets/amaai-lab/MusicBench)
-- [MusicCaps](https://www.kaggle.com/datasets/googleai/musiccaps)
-- [MusicNet](https://www.kaggle.com/datasets/imsparsh/musicnet-dataset)
-- [NSynth](https://magenta.tensorflow.org/datasets/nsynth)
-- [OnAir-Music-Dataset](https://github.com/sevagh/OnAir-Music-Dataset)
-- [Audio Piano Triads Dataset](https://zenodo.org/records/4740877)
-- [Pitch Audio Dataset (Surge synthesizer)](https://zenodo.org/records/4677097)
-- [SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network](https://zenodo.org/records/3966543)
-- [VocalSound: A Dataset for Improving Human Vocal Sounds Recognition](https://arxiv.org/abs/2205.03433)
-- [WavText5K](https://github.com/microsoft/WavText5K)
-- [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://github.com/Kyubyong/css10)
-- [Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS)](https://www.openslr.org/109/)
-- [IIIT-H Indic Speech Databases](http://festvox.org/databases/iiit_voices/)
-- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://arxiv.org/abs/1912.07875)
-- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://www.openslr.org/60)
-- [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://www.openslr.org/141/)
-- [The SIWIS French Speech Synthesis Database](https://datashare.ed.ac.uk/handle/10283/2353)
-- [Crowdsourced high-quality Colombian Spanish speech data set](https://openslr.org/72/)
-- [TTS-Portuguese Corpus](https://github.com/Edresson/TTS-Portuguese-Corpus)
-- [CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit](https://datashare.ed.ac.uk/handle/10283/3443)
-
-\*\* Data Collection Method by dataset <br>
-
-- Human <br>
-
-\*\* Labeling Method by dataset (for those with labels) <br>
-
-- Hybrid: Automated, Human, Unknown <br>
-
-### Evaluating Dataset:
-
-Properties: The audio generation quality of BigVGAN is evaluated using `dev` splits of the [LibriTTS dataset](https://www.openslr.org/60/) and [Hi-Fi TTS dataset](https://www.openslr.org/109/). The datasets include speech in English language with equal balance of genders.
-
-\*\* Data Collection Method by dataset <br>
-
-- Human <br>
-
-\*\* Labeling Method by dataset <br>
-
-- Automated <br>
-
-## Inference:
-
-**Engine:** PyTorch <br>
-**Test Hardware:** NVIDIA A100 GPU <br>
-
-## Ethical Considerations:
-
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

GPT_SoVITS/BigVGAN/nv-modelcard++/privacy.md

@@ -1,14 +0,0 @@
-| Field                                                                                                                                  | Response                                       |
-| :------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------- |
-| Generatable or reverse engineerable personal information?                                                                              | None                                           |
-| Protected class data used to create this model?                                                                                        | None                                           |
-| Was consent obtained for any personal data used?                                                                                       | Not Applicable (No Personal Data)              |
-| How often is dataset reviewed?                                                                                                         | Before Release                                 |
-| Is a mechanism in place to honor data subject right of access or deletion of personal data?                                            | Not Applicable                                 |
-| If personal collected for the development of the model, was it collected directly by NVIDIA?                                           | Not Applicable                                 |
-| If personal collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable                                 |
-| If personal collected for the development of this AI model, was it minimized to only what was required?                                | Not Applicable                                 |
-| Is data in dataset traceable?                                                                                                          | Yes                                            |
-| Is there provenance for all datasets used in training?                                                                                 | Yes                                            |
-| Does data labeling (annotation, metadata) comply with privacy laws?                                                                    | Yes                                            |
-| Is data compliant with data subject requests for data correction or removal, if such a request was made?                               | No, not possible with externally-sourced data. |

GPT_SoVITS/BigVGAN/nv-modelcard++/safety.md

@@ -1,6 +0,0 @@
-| Field                                           | Response                                                                                                                                                                                                          |
-| :---------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Model Application(s):                           | Synethic Audio Generation                                                                                                                                                                                         |
-| Describe the life critical impact (if present). | Not Applicable                                                                                                                                                                                                    |
-| Use Case Restrictions:                          | None                                                                                                                                                                                                              |
-| Model and dataset restrictions:                 | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |

GPT_SoVITS/BigVGAN/requirements.txt

@@ -1,13 +0,0 @@
-torch
-numpy
-librosa>=0.8.1
-scipy
-tensorboard
-soundfile
-matplotlib
-pesq
-auraloss
-tqdm
-nnAudio
-ninja
-huggingface_hub>=0.23.4

GPT_SoVITS/BigVGAN/tests/test_activation.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import sys
-
-# to import modules from parent_dir
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(parent_dir)
-
-import torch
-from alias_free_activation.cuda import activation1d
-from activations import Snake
-
-
-def test_load_fused_kernels():
-    try:
-        print("[Success] load_fused_kernels")
-    except ImportError as e:
-        print("[Fail] load_fused_kernels")
-        raise e
-
-
-def test_anti_alias_activation():
-    data = torch.rand((10, 10, 200), device="cuda")
-
-    # Check activations.Snake cuda vs. torch
-    fused_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=True).cuda()
-    fused_activation_output = fused_anti_alias_activation(data)
-
-    torch_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=False).cuda()
-    torch_activation_output = torch_anti_alias_activation(data)
-
-    test_result = (fused_activation_output - torch_activation_output).abs()
-
-    while test_result.dim() != 1:
-        test_result = test_result.mean(dim=-1)
-
-    diff = test_result.mean(dim=-1)
-
-    if diff <= 1e-3:
-        print(
-            f"\n[Success] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}"
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-    else:
-        print(
-            f"\n[Fail] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}, "
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}, "
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-
-
-if __name__ == "__main__":
-    from alias_free_activation.cuda import load
-
-    load.load()
-    test_load_fused_kernels()
-    test_anti_alias_activation()

GPT_SoVITS/BigVGAN/tests/test_activation_snake_beta.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import sys
-
-# to import modules from parent_dir
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(parent_dir)
-
-import torch
-from alias_free_activation.cuda import activation1d
-from activations import SnakeBeta
-
-
-def test_load_fused_kernels():
-    try:
-        print("[Success] load_fused_kernels")
-    except ImportError as e:
-        print("[Fail] load_fused_kernels")
-        raise e
-
-
-def test_anti_alias_activation():
-    data = torch.rand((10, 10, 200), device="cuda")
-
-    # Check activations, Snake CUDA vs. Torch
-    fused_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=True).cuda()
-    fused_activation_output = fused_anti_alias_activation(data)
-
-    torch_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=False).cuda()
-    torch_activation_output = torch_anti_alias_activation(data)
-
-    test_result = (fused_activation_output - torch_activation_output).abs()
-
-    while test_result.dim() != 1:
-        test_result = test_result.mean(dim=-1)
-
-    diff = test_result.mean(dim=-1)
-
-    if diff <= 1e-3:
-        print(
-            f"\n[Success] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}"
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-    else:
-        print(
-            f"\n[Fail] test_fused_anti_alias_activation"
-            f"\n > mean_difference={diff}, "
-            f"\n > fused_values={fused_activation_output[-1][-1][:].tolist()}, "
-            f"\n > torch_values={torch_activation_output[-1][-1][:].tolist()}"
-        )
-
-
-if __name__ == "__main__":
-    from alias_free_activation.cuda import load
-
-    load.load()
-    test_load_fused_kernels()
-    test_anti_alias_activation()

GPT_SoVITS/BigVGAN/tests/test_cuda_vs_torch_model.py

@@ -1,215 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-import os
-import sys
-
-# to import modules from parent_dir
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(parent_dir)
-
-import torch
-import json
-from env import AttrDict
-from bigvgan import BigVGAN
-from time import time
-from tqdm import tqdm
-from meldataset import mel_spectrogram, MAX_WAV_VALUE
-from scipy.io.wavfile import write
-import numpy as np
-
-import argparse
-
-torch.backends.cudnn.benchmark = True
-
-# For easier debugging
-torch.set_printoptions(linewidth=200, threshold=10_000)
-
-
-def generate_soundwave(duration=5.0, sr=24000):
-    t = np.linspace(0, duration, int(sr * duration), False, dtype=np.float32)
-
-    modulation = np.sin(2 * np.pi * t / duration)
-
-    min_freq = 220
-    max_freq = 1760
-    frequencies = min_freq + (max_freq - min_freq) * (modulation + 1) / 2
-    soundwave = np.sin(2 * np.pi * frequencies * t)
-
-    soundwave = soundwave / np.max(np.abs(soundwave)) * 0.95
-
-    return soundwave, sr
-
-
-def get_mel(x, h):
-    return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print(f"Loading '{filepath}'")
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Test script to check CUDA kernel correctness.")
-    parser.add_argument(
-        "--checkpoint_file",
-        type=str,
-        required=True,
-        help="Path to the checkpoint file. Assumes config.json exists in the directory.",
-    )
-
-    args = parser.parse_args()
-
-    config_file = os.path.join(os.path.split(args.checkpoint_file)[0], "config.json")
-    with open(config_file) as f:
-        config = f.read()
-    json_config = json.loads(config)
-    h = AttrDict({**json_config})
-
-    print("loading plain Pytorch BigVGAN")
-    generator_original = BigVGAN(h).to("cuda")
-    print("loading CUDA kernel BigVGAN with auto-build")
-    generator_cuda_kernel = BigVGAN(h, use_cuda_kernel=True).to("cuda")
-
-    state_dict_g = load_checkpoint(args.checkpoint_file, "cuda")
-    generator_original.load_state_dict(state_dict_g["generator"])
-    generator_cuda_kernel.load_state_dict(state_dict_g["generator"])
-
-    generator_original.remove_weight_norm()
-    generator_original.eval()
-    generator_cuda_kernel.remove_weight_norm()
-    generator_cuda_kernel.eval()
-
-    # define number of samples and length of mel frame to benchmark
-    num_sample = 10
-    num_mel_frame = 16384
-
-    # CUDA kernel correctness check
-    diff = 0.0
-    for i in tqdm(range(num_sample)):
-        # Random mel
-        data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
-
-        with torch.inference_mode():
-            audio_original = generator_original(data)
-
-        with torch.inference_mode():
-            audio_cuda_kernel = generator_cuda_kernel(data)
-
-        # Both outputs should be (almost) the same
-        test_result = (audio_original - audio_cuda_kernel).abs()
-        diff += test_result.mean(dim=-1).item()
-
-    diff /= num_sample
-    if diff <= 2e-3:  # We can expect a small difference (~1e-3) which does not affect perceptual quality
-        print(
-            f"\n[Success] test CUDA fused vs. plain torch BigVGAN inference"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={audio_cuda_kernel[-1][-1][-30:].tolist()}"
-            f"\n > torch_values={audio_original[-1][-1][-30:].tolist()}"
-        )
-    else:
-        print(
-            f"\n[Fail] test CUDA fused vs. plain torch BigVGAN inference"
-            f"\n > mean_difference={diff}"
-            f"\n > fused_values={audio_cuda_kernel[-1][-1][-30:].tolist()}, "
-            f"\n > torch_values={audio_original[-1][-1][-30:].tolist()}"
-        )
-
-    del data, audio_original, audio_cuda_kernel
-
-    # Variables for tracking total time and VRAM usage
-    toc_total_original = 0
-    toc_total_cuda_kernel = 0
-    vram_used_original_total = 0
-    vram_used_cuda_kernel_total = 0
-    audio_length_total = 0
-
-    # Measure Original inference in isolation
-    for i in tqdm(range(num_sample)):
-        torch.cuda.reset_peak_memory_stats(device="cuda")
-        data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
-        torch.cuda.synchronize()
-        tic = time()
-        with torch.inference_mode():
-            audio_original = generator_original(data)
-        torch.cuda.synchronize()
-        toc = time() - tic
-        toc_total_original += toc
-
-        vram_used_original_total += torch.cuda.max_memory_allocated(device="cuda")
-
-        del data, audio_original
-        torch.cuda.empty_cache()
-
-    # Measure CUDA kernel inference in isolation
-    for i in tqdm(range(num_sample)):
-        torch.cuda.reset_peak_memory_stats(device="cuda")
-        data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
-        torch.cuda.synchronize()
-        tic = time()
-        with torch.inference_mode():
-            audio_cuda_kernel = generator_cuda_kernel(data)
-        torch.cuda.synchronize()
-        toc = time() - tic
-        toc_total_cuda_kernel += toc
-
-        audio_length_total += audio_cuda_kernel.shape[-1]
-
-        vram_used_cuda_kernel_total += torch.cuda.max_memory_allocated(device="cuda")
-
-        del data, audio_cuda_kernel
-        torch.cuda.empty_cache()
-
-    # Calculate metrics
-    audio_second = audio_length_total / h.sampling_rate
-    khz_original = audio_length_total / toc_total_original / 1000
-    khz_cuda_kernel = audio_length_total / toc_total_cuda_kernel / 1000
-    vram_used_original_gb = vram_used_original_total / num_sample / (1024**3)
-    vram_used_cuda_kernel_gb = vram_used_cuda_kernel_total / num_sample / (1024**3)
-
-    # Print results
-    print(
-        f"Original BigVGAN: took {toc_total_original:.2f} seconds to generate {audio_second:.2f} seconds of audio, {khz_original:.1f}kHz, {audio_second / toc_total_original:.1f} faster than realtime, VRAM used {vram_used_original_gb:.1f} GB"
-    )
-    print(
-        f"CUDA kernel BigVGAN: took {toc_total_cuda_kernel:.2f} seconds to generate {audio_second:.2f} seconds of audio, {khz_cuda_kernel:.1f}kHz, {audio_second / toc_total_cuda_kernel:.1f} faster than realtime, VRAM used {vram_used_cuda_kernel_gb:.1f} GB"
-    )
-    print(f"speedup of CUDA kernel: {khz_cuda_kernel / khz_original}")
-    print(f"VRAM saving of CUDA kernel: {vram_used_original_gb / vram_used_cuda_kernel_gb}")
-
-    # Use artificial sine waves for inference test
-    audio_real, sr = generate_soundwave(duration=5.0, sr=h.sampling_rate)
-    audio_real = torch.tensor(audio_real).to("cuda")
-    # Compute mel spectrogram from the ground truth audio
-    x = get_mel(audio_real.unsqueeze(0), h)
-
-    with torch.inference_mode():
-        y_g_hat_original = generator_original(x)
-        y_g_hat_cuda_kernel = generator_cuda_kernel(x)
-
-    audio_real = audio_real.squeeze()
-    audio_real = audio_real * MAX_WAV_VALUE
-    audio_real = audio_real.cpu().numpy().astype("int16")
-
-    audio_original = y_g_hat_original.squeeze()
-    audio_original = audio_original * MAX_WAV_VALUE
-    audio_original = audio_original.cpu().numpy().astype("int16")
-
-    audio_cuda_kernel = y_g_hat_cuda_kernel.squeeze()
-    audio_cuda_kernel = audio_cuda_kernel * MAX_WAV_VALUE
-    audio_cuda_kernel = audio_cuda_kernel.cpu().numpy().astype("int16")
-
-    os.makedirs("tmp", exist_ok=True)
-    output_file_real = os.path.join("tmp", "audio_real.wav")
-    output_file_original = os.path.join("tmp", "audio_generated_original.wav")
-    output_file_cuda_kernel = os.path.join("tmp", "audio_generated_cuda_kernel.wav")
-    write(output_file_real, h.sampling_rate, audio_real)
-    write(output_file_original, h.sampling_rate, audio_original)
-    write(output_file_cuda_kernel, h.sampling_rate, audio_cuda_kernel)
-    print("Example generated audios of original vs. fused CUDA kernel written to tmp!")
-    print("Done")

GPT_SoVITS/BigVGAN/train.py

@@ -1,716 +0,0 @@
-# Copyright (c) 2024 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-import itertools
-import os
-import time
-import argparse
-import json
-import torch
-import torch.nn.functional as F
-from torch.utils.tensorboard import SummaryWriter
-from torch.utils.data import DistributedSampler, DataLoader
-import torch.multiprocessing as mp
-from torch.distributed import init_process_group
-from torch.nn.parallel import DistributedDataParallel
-from env import AttrDict, build_env
-from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist, MAX_WAV_VALUE
-
-from bigvgan import BigVGAN
-from discriminators import (
-    MultiPeriodDiscriminator,
-    MultiResolutionDiscriminator,
-    MultiBandDiscriminator,
-    MultiScaleSubbandCQTDiscriminator,
-)
-from loss import (
-    feature_loss,
-    generator_loss,
-    discriminator_loss,
-    MultiScaleMelSpectrogramLoss,
-)
-
-from utils import (
-    plot_spectrogram,
-    plot_spectrogram_clipped,
-    scan_checkpoint,
-    load_checkpoint,
-    save_checkpoint,
-    save_audio,
-)
-import torchaudio as ta
-from pesq import pesq
-from tqdm import tqdm
-import auraloss
-
-torch.backends.cudnn.benchmark = False
-
-
-def train(rank, a, h):
-    if h.num_gpus > 1:
-        # initialize distributed
-        init_process_group(
-            backend=h.dist_config["dist_backend"],
-            init_method=h.dist_config["dist_url"],
-            world_size=h.dist_config["world_size"] * h.num_gpus,
-            rank=rank,
-        )
-
-    # Set seed and device
-    torch.cuda.manual_seed(h.seed)
-    torch.cuda.set_device(rank)
-    device = torch.device(f"cuda:{rank:d}")
-
-    # Define BigVGAN generator
-    generator = BigVGAN(h).to(device)
-
-    # Define discriminators. MPD is used by default
-    mpd = MultiPeriodDiscriminator(h).to(device)
-
-    # Define additional discriminators. BigVGAN-v1 uses UnivNet's MRD as default
-    # New in BigVGAN-v2: option to switch to new discriminators: MultiBandDiscriminator / MultiScaleSubbandCQTDiscriminator
-    if h.get("use_mbd_instead_of_mrd", False):  # Switch to MBD
-        print("[INFO] using MultiBandDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator")
-        # Variable name is kept as "mrd" for backward compatibility & minimal code change
-        mrd = MultiBandDiscriminator(h).to(device)
-    elif h.get("use_cqtd_instead_of_mrd", False):  # Switch to CQTD
-        print("[INFO] using MultiScaleSubbandCQTDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator")
-        mrd = MultiScaleSubbandCQTDiscriminator(h).to(device)
-    else:  # Fallback to original MRD in BigVGAN-v1
-        mrd = MultiResolutionDiscriminator(h).to(device)
-
-    # New in BigVGAN-v2: option to switch to multi-scale L1 mel loss
-    if h.get("use_multiscale_melloss", False):
-        print("[INFO] using multi-scale Mel l1 loss of BigVGAN-v2 instead of the original single-scale loss")
-        fn_mel_loss_multiscale = MultiScaleMelSpectrogramLoss(
-            sampling_rate=h.sampling_rate
-        )  # NOTE: accepts waveform as input
-    else:
-        fn_mel_loss_singlescale = F.l1_loss
-
-    # Print the model & number of parameters, and create or scan the latest checkpoint from checkpoints directory
-    if rank == 0:
-        print(generator)
-        print(mpd)
-        print(mrd)
-        print(f"Generator params: {sum(p.numel() for p in generator.parameters())}")
-        print(f"Discriminator mpd params: {sum(p.numel() for p in mpd.parameters())}")
-        print(f"Discriminator mrd params: {sum(p.numel() for p in mrd.parameters())}")
-        os.makedirs(a.checkpoint_path, exist_ok=True)
-        print(f"Checkpoints directory: {a.checkpoint_path}")
-
-    if os.path.isdir(a.checkpoint_path):
-        # New in v2.1: If the step prefix pattern-based checkpoints are not found, also check for renamed files in Hugging Face Hub to resume training
-        cp_g = scan_checkpoint(a.checkpoint_path, prefix="g_", renamed_file="bigvgan_generator.pt")
-        cp_do = scan_checkpoint(
-            a.checkpoint_path,
-            prefix="do_",
-            renamed_file="bigvgan_discriminator_optimizer.pt",
-        )
-
-    # Load the latest checkpoint if exists
-    steps = 0
-    if cp_g is None or cp_do is None:
-        state_dict_do = None
-        last_epoch = -1
-    else:
-        state_dict_g = load_checkpoint(cp_g, device)
-        state_dict_do = load_checkpoint(cp_do, device)
-        generator.load_state_dict(state_dict_g["generator"])
-        mpd.load_state_dict(state_dict_do["mpd"])
-        mrd.load_state_dict(state_dict_do["mrd"])
-        steps = state_dict_do["steps"] + 1
-        last_epoch = state_dict_do["epoch"]
-
-    # Initialize DDP, optimizers, and schedulers
-    if h.num_gpus > 1:
-        generator = DistributedDataParallel(generator, device_ids=[rank]).to(device)
-        mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
-        mrd = DistributedDataParallel(mrd, device_ids=[rank]).to(device)
-
-    optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
-    optim_d = torch.optim.AdamW(
-        itertools.chain(mrd.parameters(), mpd.parameters()),
-        h.learning_rate,
-        betas=[h.adam_b1, h.adam_b2],
-    )
-
-    if state_dict_do is not None:
-        optim_g.load_state_dict(state_dict_do["optim_g"])
-        optim_d.load_state_dict(state_dict_do["optim_d"])
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)
-
-    # Define training and validation datasets
-
-    """
-    unseen_validation_filelist will contain sample filepaths outside the seen training & validation dataset
-    Example: trained on LibriTTS, validate on VCTK
-    """
-    training_filelist, validation_filelist, list_unseen_validation_filelist = get_dataset_filelist(a)
-
-    trainset = MelDataset(
-        training_filelist,
-        h,
-        h.segment_size,
-        h.n_fft,
-        h.num_mels,
-        h.hop_size,
-        h.win_size,
-        h.sampling_rate,
-        h.fmin,
-        h.fmax,
-        shuffle=False if h.num_gpus > 1 else True,
-        fmax_loss=h.fmax_for_loss,
-        device=device,
-        fine_tuning=a.fine_tuning,
-        base_mels_path=a.input_mels_dir,
-        is_seen=True,
-    )
-
-    train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None
-
-    train_loader = DataLoader(
-        trainset,
-        num_workers=h.num_workers,
-        shuffle=False,
-        sampler=train_sampler,
-        batch_size=h.batch_size,
-        pin_memory=True,
-        drop_last=True,
-    )
-
-    if rank == 0:
-        validset = MelDataset(
-            validation_filelist,
-            h,
-            h.segment_size,
-            h.n_fft,
-            h.num_mels,
-            h.hop_size,
-            h.win_size,
-            h.sampling_rate,
-            h.fmin,
-            h.fmax,
-            False,
-            False,
-            fmax_loss=h.fmax_for_loss,
-            device=device,
-            fine_tuning=a.fine_tuning,
-            base_mels_path=a.input_mels_dir,
-            is_seen=True,
-        )
-        validation_loader = DataLoader(
-            validset,
-            num_workers=1,
-            shuffle=False,
-            sampler=None,
-            batch_size=1,
-            pin_memory=True,
-            drop_last=True,
-        )
-
-        list_unseen_validset = []
-        list_unseen_validation_loader = []
-        for i in range(len(list_unseen_validation_filelist)):
-            unseen_validset = MelDataset(
-                list_unseen_validation_filelist[i],
-                h,
-                h.segment_size,
-                h.n_fft,
-                h.num_mels,
-                h.hop_size,
-                h.win_size,
-                h.sampling_rate,
-                h.fmin,
-                h.fmax,
-                False,
-                False,
-                fmax_loss=h.fmax_for_loss,
-                device=device,
-                fine_tuning=a.fine_tuning,
-                base_mels_path=a.input_mels_dir,
-                is_seen=False,
-            )
-            unseen_validation_loader = DataLoader(
-                unseen_validset,
-                num_workers=1,
-                shuffle=False,
-                sampler=None,
-                batch_size=1,
-                pin_memory=True,
-                drop_last=True,
-            )
-            list_unseen_validset.append(unseen_validset)
-            list_unseen_validation_loader.append(unseen_validation_loader)
-
-        # Tensorboard logger
-        sw = SummaryWriter(os.path.join(a.checkpoint_path, "logs"))
-        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-            os.makedirs(os.path.join(a.checkpoint_path, "samples"), exist_ok=True)
-
-    """
-    Validation loop, "mode" parameter is automatically defined as (seen or unseen)_(name of the dataset).
-    If the name of the dataset contains "nonspeech", it skips PESQ calculation to prevent errors 
-    """
-
-    def validate(rank, a, h, loader, mode="seen"):
-        assert rank == 0, "validate should only run on rank=0"
-        generator.eval()
-        torch.cuda.empty_cache()
-
-        val_err_tot = 0
-        val_pesq_tot = 0
-        val_mrstft_tot = 0
-
-        # Modules for evaluation metrics
-        pesq_resampler = ta.transforms.Resample(h.sampling_rate, 16000).cuda()
-        loss_mrstft = auraloss.freq.MultiResolutionSTFTLoss(device="cuda")
-
-        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-            os.makedirs(
-                os.path.join(a.checkpoint_path, "samples", f"gt_{mode}"),
-                exist_ok=True,
-            )
-            os.makedirs(
-                os.path.join(a.checkpoint_path, "samples", f"{mode}_{steps:08d}"),
-                exist_ok=True,
-            )
-
-        with torch.no_grad():
-            print(f"step {steps} {mode} speaker validation...")
-
-            # Loop over validation set and compute metrics
-            for j, batch in enumerate(tqdm(loader)):
-                x, y, _, y_mel = batch
-                y = y.to(device)
-                if hasattr(generator, "module"):
-                    y_g_hat = generator.module(x.to(device))
-                else:
-                    y_g_hat = generator(x.to(device))
-                y_mel = y_mel.to(device, non_blocking=True)
-                y_g_hat_mel = mel_spectrogram(
-                    y_g_hat.squeeze(1),
-                    h.n_fft,
-                    h.num_mels,
-                    h.sampling_rate,
-                    h.hop_size,
-                    h.win_size,
-                    h.fmin,
-                    h.fmax_for_loss,
-                )
-                min_t = min(y_mel.size(-1), y_g_hat_mel.size(-1))
-                val_err_tot += F.l1_loss(y_mel[..., :min_t], y_g_hat_mel[..., :min_t]).item()
-
-                # PESQ calculation. only evaluate PESQ if it's speech signal (nonspeech PESQ will error out)
-                if "nonspeech" not in mode:  # Skips if the name of dataset (in mode string) contains "nonspeech"
-                    # Resample to 16000 for pesq
-                    y_16k = pesq_resampler(y)
-                    y_g_hat_16k = pesq_resampler(y_g_hat.squeeze(1))
-                    y_int_16k = (y_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
-                    y_g_hat_int_16k = (y_g_hat_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
-                    val_pesq_tot += pesq(16000, y_int_16k, y_g_hat_int_16k, "wb")
-
-                # MRSTFT calculation
-                min_t = min(y.size(-1), y_g_hat.size(-1))
-                val_mrstft_tot += loss_mrstft(y_g_hat[..., :min_t], y[..., :min_t]).item()
-
-                # Log audio and figures to Tensorboard
-                if j % a.eval_subsample == 0:  # Subsample every nth from validation set
-                    if steps >= 0:
-                        sw.add_audio(f"gt_{mode}/y_{j}", y[0], steps, h.sampling_rate)
-                        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-                            save_audio(
-                                y[0],
-                                os.path.join(
-                                    a.checkpoint_path,
-                                    "samples",
-                                    f"gt_{mode}",
-                                    f"{j:04d}.wav",
-                                ),
-                                h.sampling_rate,
-                            )
-                        sw.add_figure(
-                            f"gt_{mode}/y_spec_{j}",
-                            plot_spectrogram(x[0]),
-                            steps,
-                        )
-
-                    sw.add_audio(
-                        f"generated_{mode}/y_hat_{j}",
-                        y_g_hat[0],
-                        steps,
-                        h.sampling_rate,
-                    )
-                    if a.save_audio:  # Also save audio to disk if --save_audio is set to True
-                        save_audio(
-                            y_g_hat[0, 0],
-                            os.path.join(
-                                a.checkpoint_path,
-                                "samples",
-                                f"{mode}_{steps:08d}",
-                                f"{j:04d}.wav",
-                            ),
-                            h.sampling_rate,
-                        )
-                    # Spectrogram of synthesized audio
-                    y_hat_spec = mel_spectrogram(
-                        y_g_hat.squeeze(1),
-                        h.n_fft,
-                        h.num_mels,
-                        h.sampling_rate,
-                        h.hop_size,
-                        h.win_size,
-                        h.fmin,
-                        h.fmax,
-                    )
-                    sw.add_figure(
-                        f"generated_{mode}/y_hat_spec_{j}",
-                        plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()),
-                        steps,
-                    )
-
-                    """
-                    Visualization of spectrogram difference between GT and synthesized audio, difference higher than 1 is clipped for better visualization.
-                    """
-                    spec_delta = torch.clamp(
-                        torch.abs(x[0] - y_hat_spec.squeeze(0).cpu()),
-                        min=1e-6,
-                        max=1.0,
-                    )
-                    sw.add_figure(
-                        f"delta_dclip1_{mode}/spec_{j}",
-                        plot_spectrogram_clipped(spec_delta.numpy(), clip_max=1.0),
-                        steps,
-                    )
-
-            val_err = val_err_tot / (j + 1)
-            val_pesq = val_pesq_tot / (j + 1)
-            val_mrstft = val_mrstft_tot / (j + 1)
-            # Log evaluation metrics to Tensorboard
-            sw.add_scalar(f"validation_{mode}/mel_spec_error", val_err, steps)
-            sw.add_scalar(f"validation_{mode}/pesq", val_pesq, steps)
-            sw.add_scalar(f"validation_{mode}/mrstft", val_mrstft, steps)
-
-        generator.train()
-
-    # If the checkpoint is loaded, start with validation loop
-    if steps != 0 and rank == 0 and not a.debug:
-        if not a.skip_seen:
-            validate(
-                rank,
-                a,
-                h,
-                validation_loader,
-                mode=f"seen_{train_loader.dataset.name}",
-            )
-        for i in range(len(list_unseen_validation_loader)):
-            validate(
-                rank,
-                a,
-                h,
-                list_unseen_validation_loader[i],
-                mode=f"unseen_{list_unseen_validation_loader[i].dataset.name}",
-            )
-    # Exit the script if --evaluate is set to True
-    if a.evaluate:
-        exit()
-
-    # Main training loop
-    generator.train()
-    mpd.train()
-    mrd.train()
-    for epoch in range(max(0, last_epoch), a.training_epochs):
-        if rank == 0:
-            start = time.time()
-            print(f"Epoch: {epoch + 1}")
-
-        if h.num_gpus > 1:
-            train_sampler.set_epoch(epoch)
-
-        for i, batch in enumerate(train_loader):
-            if rank == 0:
-                start_b = time.time()
-            x, y, _, y_mel = batch
-
-            x = x.to(device, non_blocking=True)
-            y = y.to(device, non_blocking=True)
-            y_mel = y_mel.to(device, non_blocking=True)
-            y = y.unsqueeze(1)
-
-            y_g_hat = generator(x)
-            y_g_hat_mel = mel_spectrogram(
-                y_g_hat.squeeze(1),
-                h.n_fft,
-                h.num_mels,
-                h.sampling_rate,
-                h.hop_size,
-                h.win_size,
-                h.fmin,
-                h.fmax_for_loss,
-            )
-
-            optim_d.zero_grad()
-
-            # MPD
-            y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
-            loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
-
-            # MRD
-            y_ds_hat_r, y_ds_hat_g, _, _ = mrd(y, y_g_hat.detach())
-            loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
-
-            loss_disc_all = loss_disc_s + loss_disc_f
-
-            # Set clip_grad_norm value
-            clip_grad_norm = h.get("clip_grad_norm", 1000.0)  # Default to 1000
-
-            # Whether to freeze D for initial training steps
-            if steps >= a.freeze_step:
-                loss_disc_all.backward()
-                grad_norm_mpd = torch.nn.utils.clip_grad_norm_(mpd.parameters(), clip_grad_norm)
-                grad_norm_mrd = torch.nn.utils.clip_grad_norm_(mrd.parameters(), clip_grad_norm)
-                optim_d.step()
-            else:
-                print(f"[WARNING] skipping D training for the first {a.freeze_step} steps")
-                grad_norm_mpd = 0.0
-                grad_norm_mrd = 0.0
-
-            # Generator
-            optim_g.zero_grad()
-
-            # L1 Mel-Spectrogram Loss
-            lambda_melloss = h.get("lambda_melloss", 45.0)  # Defaults to 45 in BigVGAN-v1 if not set
-            if h.get("use_multiscale_melloss", False):  # uses wav <y, y_g_hat> for loss
-                loss_mel = fn_mel_loss_multiscale(y, y_g_hat) * lambda_melloss
-            else:  # Uses mel <y_mel, y_g_hat_mel> for loss
-                loss_mel = fn_mel_loss_singlescale(y_mel, y_g_hat_mel) * lambda_melloss
-
-            # MPD loss
-            y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
-            loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
-            loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
-
-            # MRD loss
-            y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = mrd(y, y_g_hat)
-            loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
-            loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
-
-            if steps >= a.freeze_step:
-                loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
-            else:
-                print(f"[WARNING] using regression loss only for G for the first {a.freeze_step} steps")
-                loss_gen_all = loss_mel
-
-            loss_gen_all.backward()
-            grad_norm_g = torch.nn.utils.clip_grad_norm_(generator.parameters(), clip_grad_norm)
-            optim_g.step()
-
-            if rank == 0:
-                # STDOUT logging
-                if steps % a.stdout_interval == 0:
-                    mel_error = loss_mel.item() / lambda_melloss  # Log training mel regression loss to stdout
-                    print(
-                        f"Steps: {steps:d}, "
-                        f"Gen Loss Total: {loss_gen_all:4.3f}, "
-                        f"Mel Error: {mel_error:4.3f}, "
-                        f"s/b: {time.time() - start_b:4.3f} "
-                        f"lr: {optim_g.param_groups[0]['lr']:4.7f} "
-                        f"grad_norm_g: {grad_norm_g:4.3f}"
-                    )
-
-                # Checkpointing
-                if steps % a.checkpoint_interval == 0 and steps != 0:
-                    checkpoint_path = f"{a.checkpoint_path}/g_{steps:08d}"
-                    save_checkpoint(
-                        checkpoint_path,
-                        {"generator": (generator.module if h.num_gpus > 1 else generator).state_dict()},
-                    )
-                    checkpoint_path = f"{a.checkpoint_path}/do_{steps:08d}"
-                    save_checkpoint(
-                        checkpoint_path,
-                        {
-                            "mpd": (mpd.module if h.num_gpus > 1 else mpd).state_dict(),
-                            "mrd": (mrd.module if h.num_gpus > 1 else mrd).state_dict(),
-                            "optim_g": optim_g.state_dict(),
-                            "optim_d": optim_d.state_dict(),
-                            "steps": steps,
-                            "epoch": epoch,
-                        },
-                    )
-
-                # Tensorboard summary logging
-                if steps % a.summary_interval == 0:
-                    mel_error = loss_mel.item() / lambda_melloss  # Log training mel regression loss to tensorboard
-                    sw.add_scalar("training/gen_loss_total", loss_gen_all.item(), steps)
-                    sw.add_scalar("training/mel_spec_error", mel_error, steps)
-                    sw.add_scalar("training/fm_loss_mpd", loss_fm_f.item(), steps)
-                    sw.add_scalar("training/gen_loss_mpd", loss_gen_f.item(), steps)
-                    sw.add_scalar("training/disc_loss_mpd", loss_disc_f.item(), steps)
-                    sw.add_scalar("training/grad_norm_mpd", grad_norm_mpd, steps)
-                    sw.add_scalar("training/fm_loss_mrd", loss_fm_s.item(), steps)
-                    sw.add_scalar("training/gen_loss_mrd", loss_gen_s.item(), steps)
-                    sw.add_scalar("training/disc_loss_mrd", loss_disc_s.item(), steps)
-                    sw.add_scalar("training/grad_norm_mrd", grad_norm_mrd, steps)
-                    sw.add_scalar("training/grad_norm_g", grad_norm_g, steps)
-                    sw.add_scalar("training/learning_rate_d", scheduler_d.get_last_lr()[0], steps)
-                    sw.add_scalar("training/learning_rate_g", scheduler_g.get_last_lr()[0], steps)
-                    sw.add_scalar("training/epoch", epoch + 1, steps)
-
-                # Validation
-                if steps % a.validation_interval == 0:
-                    # Plot training input x so far used
-                    for i_x in range(x.shape[0]):
-                        sw.add_figure(
-                            f"training_input/x_{i_x}",
-                            plot_spectrogram(x[i_x].cpu()),
-                            steps,
-                        )
-                        sw.add_audio(
-                            f"training_input/y_{i_x}",
-                            y[i_x][0],
-                            steps,
-                            h.sampling_rate,
-                        )
-
-                    # Seen and unseen speakers validation loops
-                    if not a.debug and steps != 0:
-                        validate(
-                            rank,
-                            a,
-                            h,
-                            validation_loader,
-                            mode=f"seen_{train_loader.dataset.name}",
-                        )
-                        for i in range(len(list_unseen_validation_loader)):
-                            validate(
-                                rank,
-                                a,
-                                h,
-                                list_unseen_validation_loader[i],
-                                mode=f"unseen_{list_unseen_validation_loader[i].dataset.name}",
-                            )
-            steps += 1
-
-            # BigVGAN-v2 learning rate scheduler is changed from epoch-level to step-level
-            scheduler_g.step()
-            scheduler_d.step()
-
-        if rank == 0:
-            print(f"Time taken for epoch {epoch + 1} is {int(time.time() - start)} sec\n")
-
-
-def main():
-    print("Initializing Training Process..")
-
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--group_name", default=None)
-
-    parser.add_argument("--input_wavs_dir", default="LibriTTS")
-    parser.add_argument("--input_mels_dir", default="ft_dataset")
-    parser.add_argument("--input_training_file", default="tests/LibriTTS/train-full.txt")
-    parser.add_argument("--input_validation_file", default="tests/LibriTTS/val-full.txt")
-
-    parser.add_argument(
-        "--list_input_unseen_wavs_dir",
-        nargs="+",
-        default=["tests/LibriTTS", "tests/LibriTTS"],
-    )
-    parser.add_argument(
-        "--list_input_unseen_validation_file",
-        nargs="+",
-        default=["tests/LibriTTS/dev-clean.txt", "tests/LibriTTS/dev-other.txt"],
-    )
-
-    parser.add_argument("--checkpoint_path", default="exp/bigvgan")
-    parser.add_argument("--config", default="")
-
-    parser.add_argument("--training_epochs", default=100000, type=int)
-    parser.add_argument("--stdout_interval", default=5, type=int)
-    parser.add_argument("--checkpoint_interval", default=50000, type=int)
-    parser.add_argument("--summary_interval", default=100, type=int)
-    parser.add_argument("--validation_interval", default=50000, type=int)
-
-    parser.add_argument(
-        "--freeze_step",
-        default=0,
-        type=int,
-        help="freeze D for the first specified steps. G only uses regression loss for these steps.",
-    )
-
-    parser.add_argument("--fine_tuning", default=False, type=bool)
-
-    parser.add_argument(
-        "--debug",
-        default=False,
-        type=bool,
-        help="debug mode. skips validation loop throughout training",
-    )
-    parser.add_argument(
-        "--evaluate",
-        default=False,
-        type=bool,
-        help="only run evaluation from checkpoint and exit",
-    )
-    parser.add_argument(
-        "--eval_subsample",
-        default=5,
-        type=int,
-        help="subsampling during evaluation loop",
-    )
-    parser.add_argument(
-        "--skip_seen",
-        default=False,
-        type=bool,
-        help="skip seen dataset. useful for test set inference",
-    )
-    parser.add_argument(
-        "--save_audio",
-        default=False,
-        type=bool,
-        help="save audio of test set inference to disk",
-    )
-
-    a = parser.parse_args()
-
-    with open(a.config) as f:
-        data = f.read()
-
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    build_env(a.config, "config.json", a.checkpoint_path)
-
-    torch.manual_seed(h.seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        h.num_gpus = torch.cuda.device_count()
-        h.batch_size = int(h.batch_size / h.num_gpus)
-        print(f"Batch size per GPU: {h.batch_size}")
-    else:
-        pass
-
-    if h.num_gpus > 1:
-        mp.spawn(
-            train,
-            nprocs=h.num_gpus,
-            args=(
-                a,
-                h,
-            ),
-        )
-    else:
-        train(0, a, h)
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -21,20 +21,20 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 import yaml
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
 from BigVGAN.bigvgan import BigVGAN
 from feature_extractor.cnhubert import CNHubert
-from module.mel_processing import mel_spectrogram_torch, spectrogram_torch
-from module.models import SynthesizerTrn, SynthesizerTrnV3, Generator
 from peft import LoraConfig, get_peft_model
-from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
+from sv import SV
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 
+from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spectrogram_torch
+from GPT_SoVITS.module.models import Generator, SynthesizerTrn, SynthesizerTrnV3
+from GPT_SoVITS.process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
 from tools.audio_sr import AP_BWE
 from tools.i18n.i18n import I18nAuto, scan_language_list
 from TTS_infer_pack.text_segmentation_method import splits
 from TTS_infer_pack.TextPreprocessor import TextPreprocessor
-from sv import SV
 
 resample_transform_dict = {}
 
@@ -64,33 +64,32 @@ def denorm_spec(x):
     return (x + 1) / 2 * (spec_max - spec_min) + spec_min
 
 
-mel_fn = lambda x: mel_spectrogram_torch(
-    x,
-    **{
-        "n_fft": 1024,
-        "win_size": 1024,
-        "hop_size": 256,
-        "num_mels": 100,
-        "sampling_rate": 24000,
-        "fmin": 0,
-        "fmax": None,
-        "center": False,
-    },
-)
+def mel_fn(x):
+    return mel_spectrogram_torch(
+        y=x,
+        n_fft=1024,
+        num_mels=100,
+        sampling_rate=24000,
+        hop_size=256,
+        win_size=1024,
+        fmin=0,
+        fmax=None,
+        center=False,
+    )
 
-mel_fn_v4 = lambda x: mel_spectrogram_torch(
-    x,
-    **{
-        "n_fft": 1280,
-        "win_size": 1280,
-        "hop_size": 320,
-        "num_mels": 100,
-        "sampling_rate": 32000,
-        "fmin": 0,
-        "fmax": None,
-        "center": False,
-    },
-)
+
+def mel_fn_v4(x):
+    return mel_spectrogram_torch(
+        y=x,
+        n_fft=1280,
+        num_mels=100,
+        sampling_rate=32000,
+        hop_size=320,
+        win_size=1280,
+        fmin=0,
+        fmax=None,
+        center=False,
+    )
 
 
 def speed_change(input_audio: np.ndarray, speed: float, sr: int):
@@ -488,7 +487,7 @@ class TTS:
             self.init_sv_model()
         path_sovits = self.configs.default_configs[model_version]["vits_weights_path"]
 
-        if if_lora_v3 == True and os.path.exists(path_sovits) == False:
+        if if_lora_v3 is True and os.path.exists(path_sovits) is False:
             info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version)
             raise FileExistsError(info)
 
@@ -549,7 +548,7 @@ class TTS:
 
         self.is_v2pro = model_version in {"v2Pro", "v2ProPlus"}
 
-        if if_lora_v3 == False:
+        if if_lora_v3 is False:
             print(
                 f"Loading VITS weights from {weights_path}. {vits_model.load_state_dict(dict_s2['weight'], strict=False)}"
             )
@@ -580,8 +579,6 @@ class TTS:
 
         self.configs.save_configs()
 
-
-
     def init_t2s_weights(self, weights_path: str):
         print(f"Loading Text2Semantic weights from {weights_path}")
         self.configs.t2s_weights_path = weights_path
@@ -654,7 +651,7 @@ class TTS:
             self.vocoder_configs["overlapped_len"] = 12
 
         self.vocoder = self.vocoder.eval()
-        if self.configs.is_half == True:
+        if self.configs.is_half is True:
             self.vocoder = self.vocoder.half().to(self.configs.device)
         else:
             self.vocoder = self.vocoder.to(self.configs.device)
@@ -784,7 +781,7 @@ class TTS:
         )
         if self.configs.is_half:
             spec = spec.half()
-        if self.is_v2pro == True:
+        if self.is_v2pro is True:
             audio = resample(audio, self.configs.sampling_rate, 16000, self.configs.device)
             if self.configs.is_half:
                 audio = audio.half()

GPT_SoVITS/eres2net/ERes2Net_huge.py

@@ -9,11 +9,12 @@ ERes2Net-huge is an upgraded version of ERes2Net that uses a larger number of pa
 recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
 """
 
-import torch
 import math
+
+import pooling_layers as pooling_layers
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import pooling_layers as pooling_layers
 from fusion import AFF
 
 
@@ -252,7 +253,7 @@ class ERes2Net(nn.Module):
         out4 = self.layer4(out3)
         fuse_out123_downsample = self.layer3_downsample(fuse_out123)
         fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1, end_dim=2)  # bs,20480,T
-        if if_mean == False:
+        if if_mean is False:
             mean = fuse_out1234[0].transpose(1, 0)  # (T,20480),bs=T
         else:
             mean = fuse_out1234.mean(2)  # bs,20480

GPT_SoVITS/eres2net/kaldi.py

@@ -663,7 +663,7 @@ def fbank(
         # avoid log of zero (which should be prevented anyway by dithering)
         mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log()
 
-    # if use_energy then add it as the last column for htk_compat == true else first column
+    # if use_energy then add it as the last column for htk_compat is True else first column
     if use_energy:
         signal_log_energy = signal_log_energy.unsqueeze(1)  # size (m, 1)
         # returns size (m, num_mel_bins + 1)
@@ -826,7 +826,7 @@ def mfcc(
         lifter_coeffs = _get_lifter_coeffs(num_ceps, cepstral_lifter).unsqueeze(0)
         feature *= lifter_coeffs.to(device=device, dtype=dtype)
 
-    # if use_energy then replace the last column for htk_compat == true else first column
+    # if use_energy then replace the last column for htk_compat is True else first column
     if use_energy:
         feature[:, 0] = signal_log_energy
 

GPT_SoVITS/export_torch_script.py

@@ -1,28 +1,24 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
 # reference: https://github.com/lifeiteng/vall-e
 import argparse
+import os
 from io import BytesIO
 from typing import Optional
-from my_utils import load_audio
+
+import kaldi as Kaldi
+import soundfile
 import torch
 import torchaudio
-
+from feature_extractor import cnhubert
+from inference_webui import get_phones_and_bert
+from my_utils import load_audio
+from sv import SV
 from torch import IntTensor, LongTensor, Tensor, nn
 from torch.nn import functional as F
-
 from transformers import AutoModelForMaskedLM, AutoTokenizer
-from feature_extractor import cnhubert
 
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from module.models_onnx import SynthesizerTrn
-
-from inference_webui import get_phones_and_bert
-
-from sv import SV
-import kaldi as Kaldi
-
-import os
-import soundfile
+from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from GPT_SoVITS.module.models_onnx import SynthesizerTrn
 
 default_config = {
     "embedding_dim": 512,
@@ -477,7 +473,7 @@ class T2SModel(nn.Module):
 
         # avoid dtype inconsistency when exporting
         bert = bert.to(dtype=self.bert_proj.weight.dtype)
-        
+
         x = x + self.bert_proj(bert.transpose(1, 2))
         x: torch.Tensor = self.ar_text_position(x)
 
@@ -737,7 +733,7 @@ def export_prov2(
     device="cpu",
     is_half=True,
 ):
-    if sv_cn_model == None:
+    if sv_cn_model is None:
         init_sv_cn(device, is_half)
 
     if not os.path.exists(output_path):
@@ -1041,9 +1037,10 @@ def test():
     soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
 
 
-import text
 import json
 
+import text
+
 
 def export_symbel(version="v2"):
     if version == "v1":

GPT_SoVITS/export_torch_script_v3v4.py

@@ -1,4 +1,13 @@
+import logging
 import os
+
+import librosa
+import numpy as np
+import soundfile
+import torch
+import torch._dynamo.config
+import torchaudio
+import uvicorn
 from export_torch_script import (
     T2SModel,
     get_raw_t2s_model,
@@ -6,22 +15,12 @@ from export_torch_script import (
     spectrogram_torch,
 )
 from f5_tts.model.backbones.dit import DiT
-from inference_webui import get_phones_and_bert
-import librosa
-from module import commons
-from module.mel_processing import mel_spectrogram_torch
-from module.models_onnx import CFM, Generator, SynthesizerTrnV3
-import numpy as np
-import torch._dynamo.config
-import torchaudio
-import logging
-import uvicorn
-import torch
-import soundfile
+from inference_webui import get_phones_and_bert, get_spepc, norm_spec, resample, ssl_model
 from librosa.filters import mel as librosa_mel_fn
 
-
-from inference_webui import get_spepc, norm_spec, resample, ssl_model
+from GPT_SoVITS.module import commons
+from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch
+from GPT_SoVITS.module.models_onnx import CFM, Generator, SynthesizerTrnV3
 
 logging.config.dictConfig(uvicorn.config.LOGGING_CONFIG)
 logger = logging.getLogger("uvicorn")
@@ -176,32 +175,33 @@ class ExportCFM(torch.nn.Module):
         return cfm_res, fea_ref, mel2
 
 
-mel_fn = lambda x: mel_spectrogram_torch(
-    x,
-    **{
-        "n_fft": 1024,
-        "win_size": 1024,
-        "hop_size": 256,
-        "num_mels": 100,
-        "sampling_rate": 24000,
-        "fmin": 0,
-        "fmax": None,
-        "center": False,
-    },
-)
-mel_fn_v4 = lambda x: mel_spectrogram_torch(
-    x,
-    **{
-        "n_fft": 1280,
-        "win_size": 1280,
-        "hop_size": 320,
-        "num_mels": 100,
-        "sampling_rate": 32000,
-        "fmin": 0,
-        "fmax": None,
-        "center": False,
-    },
-)
+def mel_fn(x):
+    return mel_spectrogram_torch(
+        y=x,
+        n_fft=1024,
+        num_mels=100,
+        sampling_rate=24000,
+        hop_size=256,
+        win_size=1024,
+        fmin=0,
+        fmax=None,
+        center=False,
+    )
+
+
+def mel_fn_v4(x):
+    return mel_spectrogram_torch(
+        y=x,
+        n_fft=1280,
+        num_mels=100,
+        sampling_rate=32000,
+        hop_size=320,
+        win_size=1280,
+        fmin=0,
+        fmax=None,
+        center=False,
+    )
+
 
 spec_min = -12
 spec_max = 2
@@ -511,7 +511,7 @@ def init_bigvgan():
     # remove weight norm in the model and set to eval mode
     bigvgan_model.remove_weight_norm()
     bigvgan_model = bigvgan_model.eval()
-    if is_half == True:
+    if is_half is True:
         bigvgan_model = bigvgan_model.half().to(device)
     else:
         bigvgan_model = bigvgan_model.to(device)
@@ -536,7 +536,7 @@ def init_hifigan():
         "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu"
     )
     print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
-    if is_half == True:
+    if is_half is True:
         hifigan_model = hifigan_model.half().to(device)
     else:
         hifigan_model = hifigan_model.to(device)
@@ -578,7 +578,7 @@ class DictToAttrRecursive(dict):
             raise AttributeError(f"Attribute {item} not found")
 
 
-from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
+from GPT_SoVITS.process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
 
 v3v4set = {"v3", "v4"}
 
@@ -588,7 +588,7 @@ def get_sovits_weights(sovits_path):
     is_exist_s2gv3 = os.path.exists(path_sovits_v3)
 
     version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
-    if if_lora_v3 == True and is_exist_s2gv3 == False:
+    if if_lora_v3 is True and is_exist_s2gv3 is False:
         logger.info("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")
 
     dict_s2 = load_sovits_new(sovits_path)
@@ -617,7 +617,7 @@ def get_sovits_weights(sovits_path):
     model_version = hps.model.version
     logger.info(f"模型版本: {model_version}")
 
-    if is_half == True:
+    if is_half is True:
         vq_model = vq_model.half().to(device)
     else:
         vq_model = vq_model.to(device)
@@ -729,11 +729,11 @@ def export_1(ref_wav_path, ref_wav_text, version="v3"):
     # ref_wav_path = "onnx/ad/ref.wav"
     speed = 1.0
     sample_steps = 8
-    dtype = torch.float16 if is_half == True else torch.float32
+    dtype = torch.float16 if is_half is True else torch.float32
     refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)
     zero_wav = np.zeros(
         int(hps.data.sampling_rate * 0.3),
-        dtype=np.float16 if is_half == True else np.float32,
+        dtype=np.float16 if is_half is True else np.float32,
     )
 
     with torch.no_grad():
@@ -741,7 +741,7 @@ def export_1(ref_wav_path, ref_wav_text, version="v3"):
         wav16k = torch.from_numpy(wav16k)
         zero_wav_torch = torch.from_numpy(zero_wav)
 
-        if is_half == True:
+        if is_half is True:
             wav16k = wav16k.half().to(device)
             zero_wav_torch = zero_wav_torch.half().to(device)
         else:
@@ -940,11 +940,11 @@ def test_export(
     speed = 1.0
     sample_steps = 8
 
-    dtype = torch.float16 if is_half == True else torch.float32
+    dtype = torch.float16 if is_half is True else torch.float32
 
     zero_wav = np.zeros(
         int(16000 * 0.3),
-        dtype=np.float16 if is_half == True else np.float32,
+        dtype=np.float16 if is_half is True else np.float32,
     )
 
     with torch.no_grad():
@@ -952,7 +952,7 @@ def test_export(
         wav16k = torch.from_numpy(wav16k)
         zero_wav_torch = torch.from_numpy(zero_wav)
 
-        if is_half == True:
+        if is_half is True:
             wav16k = wav16k.half().to(device)
             zero_wav_torch = zero_wav_torch.half().to(device)
         else:
@@ -1058,11 +1058,11 @@ def test_export(
     speed = 1.0
     sample_steps = torch.LongTensor([16])
 
-    dtype = torch.float16 if is_half == True else torch.float32
+    dtype = torch.float16 if is_half is True else torch.float32
 
     zero_wav = np.zeros(
         int(out_sr * 0.3),
-        dtype=np.float16 if is_half == True else np.float32,
+        dtype=np.float16 if is_half is True else np.float32,
     )
 
     with torch.no_grad():
@@ -1070,7 +1070,7 @@ def test_export(
         wav16k = torch.from_numpy(wav16k)
         zero_wav_torch = torch.from_numpy(zero_wav)
 
-        if is_half == True:
+        if is_half is True:
             wav16k = wav16k.half().to(device)
             zero_wav_torch = zero_wav_torch.half().to(device)
         else:

GPT_SoVITS/f5_tts/model/backbones/dit.py

@@ -12,20 +12,18 @@ from __future__ import annotations
 import torch
 from torch import nn
 from torch.utils.checkpoint import checkpoint
-
 from x_transformers.x_transformers import RotaryEmbedding
 
 from GPT_SoVITS.f5_tts.model.modules import (
-    TimestepEmbedding,
+    AdaLayerNormZero_Final,
     ConvNeXtV2Block,
     ConvPositionEmbedding,
     DiTBlock,
-    AdaLayerNormZero_Final,
-    precompute_freqs_cis,
+    TimestepEmbedding,
     get_pos_embed_indices,
+    precompute_freqs_cis,
 )
-
-from module.commons import sequence_mask
+from GPT_SoVITS.module.commons import sequence_mask
 
 
 class TextEmbedding(nn.Module):

GPT_SoVITS/feature_extractor/cnhubert.py

@@ -1,5 +1,6 @@
-import torch
 import os
+
+import torch
 from transformers import logging as tf_logging
 
 tf_logging.set_verbosity_error()
@@ -8,13 +9,13 @@ import logging
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 
+import torch.nn as nn
 from transformers import (
-    Wav2Vec2FeatureExtractor,
     HubertModel,
+    Wav2Vec2FeatureExtractor,
 )
 
-import utils
-import torch.nn as nn
+import GPT_SoVITS.utils as utils
 
 cnhubert_base_path = None
 

GPT_SoVITS/inference_cli.py

@@ -1,86 +0,0 @@
-import argparse
-import os
-import soundfile as sf
-
-from tools.i18n.i18n import I18nAuto
-from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav
-
-i18n = I18nAuto()
-
-
-def synthesize(
-    GPT_model_path,
-    SoVITS_model_path,
-    ref_audio_path,
-    ref_text_path,
-    ref_language,
-    target_text_path,
-    target_language,
-    output_path,
-):
-    # Read reference text
-    with open(ref_text_path, "r", encoding="utf-8") as file:
-        ref_text = file.read()
-
-    # Read target text
-    with open(target_text_path, "r", encoding="utf-8") as file:
-        target_text = file.read()
-
-    # Change model weights
-    change_gpt_weights(gpt_path=GPT_model_path)
-    change_sovits_weights(sovits_path=SoVITS_model_path)
-
-    # Synthesize audio
-    synthesis_result = get_tts_wav(
-        ref_wav_path=ref_audio_path,
-        prompt_text=ref_text,
-        prompt_language=i18n(ref_language),
-        text=target_text,
-        text_language=i18n(target_language),
-        top_p=1,
-        temperature=1,
-    )
-
-    result_list = list(synthesis_result)
-
-    if result_list:
-        last_sampling_rate, last_audio_data = result_list[-1]
-        output_wav_path = os.path.join(output_path, "output.wav")
-        sf.write(output_wav_path, last_audio_data, last_sampling_rate)
-        print(f"Audio saved to {output_wav_path}")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
-    parser.add_argument("--gpt_model", required=True, help="Path to the GPT model file")
-    parser.add_argument("--sovits_model", required=True, help="Path to the SoVITS model file")
-    parser.add_argument("--ref_audio", required=True, help="Path to the reference audio file")
-    parser.add_argument("--ref_text", required=True, help="Path to the reference text file")
-    parser.add_argument(
-        "--ref_language", required=True, choices=["中文", "英文", "日文"], help="Language of the reference audio"
-    )
-    parser.add_argument("--target_text", required=True, help="Path to the target text file")
-    parser.add_argument(
-        "--target_language",
-        required=True,
-        choices=["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"],
-        help="Language of the target text",
-    )
-    parser.add_argument("--output_path", required=True, help="Path to the output directory")
-
-    args = parser.parse_args()
-
-    synthesize(
-        args.gpt_model,
-        args.sovits_model,
-        args.ref_audio,
-        args.ref_text,
-        args.ref_language,
-        args.target_text,
-        args.target_language,
-        args.output_path,
-    )
-
-
-if __name__ == "__main__":
-    main()

GPT_SoVITS/inference_gui.py

@@ -1,316 +0,0 @@
-import os
-import sys
-from PyQt5.QtCore import QEvent
-from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton, QTextEdit
-from PyQt5.QtWidgets import QGridLayout, QVBoxLayout, QWidget, QFileDialog, QStatusBar, QComboBox
-import soundfile as sf
-
-from tools.i18n.i18n import I18nAuto
-
-i18n = I18nAuto()
-
-from inference_webui import gpt_path, sovits_path, change_gpt_weights, change_sovits_weights, get_tts_wav
-
-
-class GPTSoVITSGUI(QMainWindow):
-    GPT_Path = gpt_path
-    SoVITS_Path = sovits_path
-
-    def __init__(self):
-        super().__init__()
-
-        self.setWindowTitle("GPT-SoVITS GUI")
-        self.setGeometry(800, 450, 950, 850)
-
-        self.setStyleSheet("""
-            QWidget {
-                background-color: #a3d3b1; 
-            }
-
-            QTabWidget::pane {
-                background-color: #a3d3b1;  
-            }
-
-            QTabWidget::tab-bar {
-                alignment: left;
-            }
-
-            QTabBar::tab {
-                background: #8da4bf; 
-                color: #ffffff;  
-                padding: 8px;
-            }
-
-            QTabBar::tab:selected {
-                background: #2a3f54; 
-            }
-
-            QLabel {
-                color: #000000;  
-            }
-
-            QPushButton {
-                background-color: #4CAF50; 
-                color: white;  
-                padding: 8px;
-                border: 1px solid #4CAF50;
-                border-radius: 4px;
-            }
-
-            QPushButton:hover {
-                background-color: #45a049;  
-                border: 1px solid #45a049;
-                box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.1);
-            }
-        """)
-
-        license_text = (
-            "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. "
-            "如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE."
-        )
-        license_label = QLabel(license_text)
-        license_label.setWordWrap(True)
-
-        self.GPT_model_label = QLabel("选择GPT模型:")
-        self.GPT_model_input = QLineEdit()
-        self.GPT_model_input.setPlaceholderText("拖拽或选择文件")
-        self.GPT_model_input.setText(self.GPT_Path)
-        self.GPT_model_input.setReadOnly(True)
-        self.GPT_model_button = QPushButton("选择GPT模型文件")
-        self.GPT_model_button.clicked.connect(self.select_GPT_model)
-
-        self.SoVITS_model_label = QLabel("选择SoVITS模型:")
-        self.SoVITS_model_input = QLineEdit()
-        self.SoVITS_model_input.setPlaceholderText("拖拽或选择文件")
-        self.SoVITS_model_input.setText(self.SoVITS_Path)
-        self.SoVITS_model_input.setReadOnly(True)
-        self.SoVITS_model_button = QPushButton("选择SoVITS模型文件")
-        self.SoVITS_model_button.clicked.connect(self.select_SoVITS_model)
-
-        self.ref_audio_label = QLabel("上传参考音频:")
-        self.ref_audio_input = QLineEdit()
-        self.ref_audio_input.setPlaceholderText("拖拽或选择文件")
-        self.ref_audio_input.setReadOnly(True)
-        self.ref_audio_button = QPushButton("选择音频文件")
-        self.ref_audio_button.clicked.connect(self.select_ref_audio)
-
-        self.ref_text_label = QLabel("参考音频文本:")
-        self.ref_text_input = QLineEdit()
-        self.ref_text_input.setPlaceholderText("直接输入文字或上传文本")
-        self.ref_text_button = QPushButton("上传文本")
-        self.ref_text_button.clicked.connect(self.upload_ref_text)
-
-        self.ref_language_label = QLabel("参考音频语言:")
-        self.ref_language_combobox = QComboBox()
-        self.ref_language_combobox.addItems(["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"])
-        self.ref_language_combobox.setCurrentText("多语种混合")
-
-        self.target_text_label = QLabel("合成目标文本:")
-        self.target_text_input = QLineEdit()
-        self.target_text_input.setPlaceholderText("直接输入文字或上传文本")
-        self.target_text_button = QPushButton("上传文本")
-        self.target_text_button.clicked.connect(self.upload_target_text)
-
-        self.target_language_label = QLabel("合成音频语言:")
-        self.target_language_combobox = QComboBox()
-        self.target_language_combobox.addItems(["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"])
-        self.target_language_combobox.setCurrentText("多语种混合")
-
-        self.output_label = QLabel("输出音频路径:")
-        self.output_input = QLineEdit()
-        self.output_input.setPlaceholderText("拖拽或选择文件")
-        self.output_input.setReadOnly(True)
-        self.output_button = QPushButton("选择文件夹")
-        self.output_button.clicked.connect(self.select_output_path)
-
-        self.output_text = QTextEdit()
-        self.output_text.setReadOnly(True)
-
-        self.add_drag_drop_events(
-            [
-                self.GPT_model_input,
-                self.SoVITS_model_input,
-                self.ref_audio_input,
-                self.ref_text_input,
-                self.target_text_input,
-                self.output_input,
-            ]
-        )
-
-        self.synthesize_button = QPushButton("合成")
-        self.synthesize_button.clicked.connect(self.synthesize)
-
-        self.clear_output_button = QPushButton("清空输出")
-        self.clear_output_button.clicked.connect(self.clear_output)
-
-        self.status_bar = QStatusBar()
-
-        main_layout = QVBoxLayout()
-
-        input_layout = QGridLayout(self)
-        input_layout.setSpacing(10)
-
-        input_layout.addWidget(license_label, 0, 0, 1, 3)
-
-        input_layout.addWidget(self.GPT_model_label, 1, 0)
-        input_layout.addWidget(self.GPT_model_input, 2, 0, 1, 2)
-        input_layout.addWidget(self.GPT_model_button, 2, 2)
-
-        input_layout.addWidget(self.SoVITS_model_label, 3, 0)
-        input_layout.addWidget(self.SoVITS_model_input, 4, 0, 1, 2)
-        input_layout.addWidget(self.SoVITS_model_button, 4, 2)
-
-        input_layout.addWidget(self.ref_audio_label, 5, 0)
-        input_layout.addWidget(self.ref_audio_input, 6, 0, 1, 2)
-        input_layout.addWidget(self.ref_audio_button, 6, 2)
-
-        input_layout.addWidget(self.ref_language_label, 7, 0)
-        input_layout.addWidget(self.ref_language_combobox, 8, 0, 1, 1)
-        input_layout.addWidget(self.ref_text_label, 9, 0)
-        input_layout.addWidget(self.ref_text_input, 10, 0, 1, 2)
-        input_layout.addWidget(self.ref_text_button, 10, 2)
-
-        input_layout.addWidget(self.target_language_label, 11, 0)
-        input_layout.addWidget(self.target_language_combobox, 12, 0, 1, 1)
-        input_layout.addWidget(self.target_text_label, 13, 0)
-        input_layout.addWidget(self.target_text_input, 14, 0, 1, 2)
-        input_layout.addWidget(self.target_text_button, 14, 2)
-
-        input_layout.addWidget(self.output_label, 15, 0)
-        input_layout.addWidget(self.output_input, 16, 0, 1, 2)
-        input_layout.addWidget(self.output_button, 16, 2)
-
-        main_layout.addLayout(input_layout)
-
-        output_layout = QVBoxLayout()
-        output_layout.addWidget(self.output_text)
-        main_layout.addLayout(output_layout)
-
-        main_layout.addWidget(self.synthesize_button)
-
-        main_layout.addWidget(self.clear_output_button)
-
-        main_layout.addWidget(self.status_bar)
-
-        self.central_widget = QWidget()
-        self.central_widget.setLayout(main_layout)
-        self.setCentralWidget(self.central_widget)
-
-    def dragEnterEvent(self, event):
-        if event.mimeData().hasUrls():
-            event.acceptProposedAction()
-
-    def dropEvent(self, event):
-        if event.mimeData().hasUrls():
-            file_paths = [url.toLocalFile() for url in event.mimeData().urls()]
-            if len(file_paths) == 1:
-                self.update_ref_audio(file_paths[0])
-            else:
-                self.update_ref_audio(", ".join(file_paths))
-
-    def add_drag_drop_events(self, widgets):
-        for widget in widgets:
-            widget.setAcceptDrops(True)
-            widget.installEventFilter(self)
-
-    def eventFilter(self, obj, event):
-        if event.type() in (QEvent.DragEnter, QEvent.Drop):
-            mime_data = event.mimeData()
-            if mime_data.hasUrls():
-                event.acceptProposedAction()
-
-        return super().eventFilter(obj, event)
-
-    def select_GPT_model(self):
-        file_path, _ = QFileDialog.getOpenFileName(self, "选择GPT模型文件", "", "GPT Files (*.ckpt)")
-        if file_path:
-            self.GPT_model_input.setText(file_path)
-
-    def select_SoVITS_model(self):
-        file_path, _ = QFileDialog.getOpenFileName(self, "选择SoVITS模型文件", "", "SoVITS Files (*.pth)")
-        if file_path:
-            self.SoVITS_model_input.setText(file_path)
-
-    def select_ref_audio(self):
-        file_path, _ = QFileDialog.getOpenFileName(self, "选择参考音频文件", "", "Audio Files (*.wav *.mp3)")
-        if file_path:
-            self.update_ref_audio(file_path)
-
-    def upload_ref_text(self):
-        file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)")
-        if file_path:
-            with open(file_path, "r", encoding="utf-8") as file:
-                content = file.read()
-                self.ref_text_input.setText(content)
-
-    def upload_target_text(self):
-        file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)")
-        if file_path:
-            with open(file_path, "r", encoding="utf-8") as file:
-                content = file.read()
-                self.target_text_input.setText(content)
-
-    def select_output_path(self):
-        options = QFileDialog.Options()
-        options |= QFileDialog.DontUseNativeDialog
-        options |= QFileDialog.ShowDirsOnly
-
-        folder_dialog = QFileDialog()
-        folder_dialog.setOptions(options)
-        folder_dialog.setFileMode(QFileDialog.Directory)
-
-        if folder_dialog.exec_():
-            folder_path = folder_dialog.selectedFiles()[0]
-            self.output_input.setText(folder_path)
-
-    def update_ref_audio(self, file_path):
-        self.ref_audio_input.setText(file_path)
-
-    def clear_output(self):
-        self.output_text.clear()
-
-    def synthesize(self):
-        GPT_model_path = self.GPT_model_input.text()
-        SoVITS_model_path = self.SoVITS_model_input.text()
-        ref_audio_path = self.ref_audio_input.text()
-        language_combobox = self.ref_language_combobox.currentText()
-        language_combobox = i18n(language_combobox)
-        ref_text = self.ref_text_input.text()
-        target_language_combobox = self.target_language_combobox.currentText()
-        target_language_combobox = i18n(target_language_combobox)
-        target_text = self.target_text_input.text()
-        output_path = self.output_input.text()
-
-        if GPT_model_path != self.GPT_Path:
-            change_gpt_weights(gpt_path=GPT_model_path)
-            self.GPT_Path = GPT_model_path
-        if SoVITS_model_path != self.SoVITS_Path:
-            change_sovits_weights(sovits_path=SoVITS_model_path)
-            self.SoVITS_Path = SoVITS_model_path
-
-        synthesis_result = get_tts_wav(
-            ref_wav_path=ref_audio_path,
-            prompt_text=ref_text,
-            prompt_language=language_combobox,
-            text=target_text,
-            text_language=target_language_combobox,
-        )
-
-        result_list = list(synthesis_result)
-
-        if result_list:
-            last_sampling_rate, last_audio_data = result_list[-1]
-            output_wav_path = os.path.join(output_path, "output.wav")
-            sf.write(output_wav_path, last_audio_data, last_sampling_rate)
-
-            result = "Audio saved to " + output_wav_path
-
-        self.status_bar.showMessage("合成完成！输出路径：" + output_wav_path, 5000)
-        self.output_text.append("处理结果：\n" + result)
-
-
-if __name__ == "__main__":
-    app = QApplication(sys.argv)
-    mainWin = GPTSoVITSGUI()
-    mainWin.show()
-    sys.exit(app.exec_())

GPT_SoVITS/inference_webui_fast.py

@@ -6,19 +6,24 @@
 全部按英文识别
 全部按日文识别
 """
-import psutil
+
 import os
 
+import psutil
+
+
 def set_high_priority():
     """把当前 Python 进程设为 HIGH_PRIORITY_CLASS"""
     if os.name != "nt":
-        return # 仅 Windows 有效
+        return  # 仅 Windows 有效
     p = psutil.Process(os.getpid())
     try:
         p.nice(psutil.HIGH_PRIORITY_CLASS)
         print("已将进程优先级设为 High")
     except psutil.AccessDenied:
         print("权限不足，无法修改优先级（请用管理员运行）")
+
+
 set_high_priority()
 import json
 import logging
@@ -225,7 +230,7 @@ with open("./weight.json", "r", encoding="utf-8") as file:
     if isinstance(sovits_path, list):
         sovits_path = sovits_path[0]
 
-from process_ckpt import get_sovits_version_from_path_fast
+from GPT_SoVITS.process_ckpt import get_sovits_version_from_path_fast
 
 v3v4set = {"v3", "v4"}
 
@@ -238,7 +243,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
     # print(sovits_path,version, model_version, if_lora_v3)
     is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
     path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
-    if if_lora_v3 == True and is_exist == False:
+    if if_lora_v3 is True and is_exist is False:
         info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重")
         gr.Warning(info)
         raise FileExistsError(info)
@@ -315,7 +320,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
     with gr.Column():
         # with gr.Group():
         gr.Markdown(value=i18n("模型切换"))
-        with gr.Row():
+        with gr.Row(equal_height=True):
             GPT_dropdown = gr.Dropdown(
                 label=i18n("GPT模型列表"),
                 choices=sorted(GPT_names, key=custom_sort_key),
@@ -331,18 +336,22 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
             refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
             refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
 
-    with gr.Row():
+    with gr.Row(equal_height=True):
         with gr.Column():
             gr.Markdown(value=i18n("*请上传并填写参考信息"))
-            with gr.Row():
-                inp_ref = gr.Audio(label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"), type="filepath")
+            with gr.Row(equal_height=True):
+                inp_ref = gr.Audio(
+                    label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"),
+                    type="filepath",
+                    waveform_options={"show_recording_waveform": False},
+                )
                 inp_refs = gr.File(
                     label=i18n("辅参考音频(可选多个，或不选)"),
                     file_count="multiple",
                     visible=True if model_version != "v3" else False,
                 )
             prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2)
-            with gr.Row():
+            with gr.Row(equal_height=True):
                 prompt_language = gr.Dropdown(
                     label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文")
                 )
@@ -368,26 +377,26 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
 
     with gr.Group():
         gr.Markdown(value=i18n("推理设置"))
-        with gr.Row():
+        with gr.Row(equal_height=True):
             with gr.Column():
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     batch_size = gr.Slider(
                         minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True
                     )
                     sample_steps = gr.Radio(
                         label=i18n("采样步数(仅对V3/4生效)"), value=32, choices=[4, 8, 16, 32, 64, 128], visible=True
                     )
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     fragment_interval = gr.Slider(
                         minimum=0.01, maximum=1, step=0.01, label=i18n("分段间隔(秒)"), value=0.3, interactive=True
                     )
                     speed_factor = gr.Slider(
                         minimum=0.6, maximum=1.65, step=0.05, label="语速", value=1.0, interactive=True
                     )
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=5, interactive=True)
                     top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True)
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     temperature = gr.Slider(
                         minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True
                     )
@@ -396,7 +405,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
                     )
 
             with gr.Column():
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     how_to_cut = gr.Dropdown(
                         label=i18n("怎么切"),
                         choices=[
@@ -415,7 +424,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
                         label=i18n("音频超采样(仅对V3生效))"), value=False, interactive=True, show_label=True
                     )
 
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     parallel_infer = gr.Checkbox(label=i18n("并行推理"), value=True, interactive=True, show_label=True)
                     split_bucket = gr.Checkbox(
                         label=i18n("数据分桶(并行推理时会降低一点计算量)"),
@@ -424,12 +433,15 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
                         show_label=True,
                     )
 
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     seed = gr.Number(label=i18n("随机种子"), value=-1)
                     keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True)
 
-                output = gr.Audio(label=i18n("输出的语音"))
-                with gr.Row():
+                output = gr.Audio(
+                    label=i18n("输出的语音"),
+                    waveform_options={"show_recording_waveform": False},
+                )
+                with gr.Row(equal_height=True):
                     inference_button = gr.Button(i18n("合成语音"), variant="primary")
                     stop_infer = gr.Button(i18n("终止合成"), variant="primary")
 
@@ -485,7 +497,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
                 "文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"
             )
         )
-        with gr.Row():
+        with gr.Row(equal_height=True):
             text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4)
             with gr.Column():
                 _how_to_cut = gr.Radio(

GPT_SoVITS/module/attentions.py

@@ -1,10 +1,11 @@
 import math
+
 import torch
 from torch import nn
 from torch.nn import functional as F
 
-from module import commons
-from module.modules import LayerNorm
+from GPT_SoVITS.module import commons
+from GPT_SoVITS.module.modules import LayerNorm
 
 
 class Encoder(nn.Module):

GPT_SoVITS/module/attentions_onnx.py

@@ -1,11 +1,11 @@
 import math
+from typing import Optional
+
 import torch
 from torch import nn
 from torch.nn import functional as F
 
-from module import commons
-
-from typing import Optional
+from GPT_SoVITS.module import commons
 
 
 class LayerNorm(nn.Module):

GPT_SoVITS/module/data_utils.py

@@ -1,13 +1,14 @@
 import os
 import random
 import traceback
+
 import torch
+import torch.nn.functional as F
 import torch.utils.data
+from text import cleaned_text_to_sequence
 from tqdm import tqdm
 
-from module.mel_processing import spectrogram_torch, spec_to_mel_torch
-from text import cleaned_text_to_sequence
-import torch.nn.functional as F
+from GPT_SoVITS.module.mel_processing import spec_to_mel_torch, spectrogram_torch
 from tools.my_utils import load_audio
 
 version = os.environ.get("version", None)

GPT_SoVITS/module/losses.py

@@ -4,7 +4,7 @@ import torch
 
 
 def feature_loss(fmap_r, fmap_g):
-    loss = 0
+    loss = torch.tensor(0)
     for dr, dg in zip(fmap_r, fmap_g):
         for rl, gl in zip(dr, dg):
             rl = rl.float().detach()
@@ -15,7 +15,7 @@ def feature_loss(fmap_r, fmap_g):
 
 
 def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
+    loss = torch.tensor(0)
     r_losses = []
     g_losses = []
     for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
@@ -31,7 +31,7 @@ def discriminator_loss(disc_real_outputs, disc_generated_outputs):
 
 
 def generator_loss(disc_outputs):
-    loss = 0
+    loss = torch.tensor(0)
     gen_losses = []
     for dg in disc_outputs:
         dg = dg.float()

GPT_SoVITS/module/models.py

@@ -1,28 +1,27 @@
-import warnings
-
-warnings.filterwarnings("ignore")
+import contextlib
 import math
+import random
+import warnings
 
 import torch
 from torch import nn
-from torch.nn import functional as F
-
-from module import commons
-from module import modules
-from module import attentions
-from f5_tts.model import DiT
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from module.commons import init_weights, get_padding
-from module.mrte_model import MRTE
-from module.quantize import ResidualVectorQuantizer
-
-# from text import symbols
-from text import symbols as symbols_v1
-from text import symbols2 as symbols_v2
 from torch.cuda.amp import autocast
-import contextlib
-import random
+from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from GPT_SoVITS.f5_tts.model import DiT
+from GPT_SoVITS.module import attentions, commons, modules
+from GPT_SoVITS.module.commons import get_padding, init_weights
+from GPT_SoVITS.module.mrte_model import MRTE
+from GPT_SoVITS.module.quantize import ResidualVectorQuantizer
+from GPT_SoVITS.text import symbols as symbols_v1
+from GPT_SoVITS.text import symbols2 as symbols_v2
+from GPT_SoVITS.utils import HParams
+
+warnings.filterwarnings("ignore")
+
+torch.serialization.add_safe_globals([(HParams, "utils.HParams")])
 
 
 class StochasticDurationPredictor(nn.Module):
@@ -230,25 +229,6 @@ class TextEncoder(nn.Module):
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return y, m, logs, y_mask
 
-    def extract_latent(self, x):
-        x = self.ssl_proj(x)
-        quantized, codes, commit_loss, quantized_list = self.quantizer(x)
-        return codes.transpose(0, 1)
-
-    def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
-        quantized = self.quantizer.decode(codes)
-
-        y = self.vq_proj(quantized) * y_mask
-        y = self.encoder_ssl(y * y_mask, y_mask)
-
-        y = self.mrte(y, y_mask, refer, refer_mask, ge)
-
-        y = self.encoder2(y * y_mask, y_mask)
-
-        stats = self.proj(y) * y_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return y, m, logs, y_mask, quantized
-
 
 class ResidualCouplingBlock(nn.Module):
     def __init__(
@@ -483,7 +463,7 @@ class DiscriminatorP(torch.nn.Module):
         super(DiscriminatorP, self).__init__()
         self.period = period
         self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(
@@ -560,7 +540,7 @@ class DiscriminatorP(torch.nn.Module):
 class DiscriminatorS(torch.nn.Module):
     def __init__(self, use_spectral_norm=False):
         super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv1d(1, 16, 15, 1, padding=7)),
@@ -1206,7 +1186,7 @@ class SynthesizerTrnV3(nn.Module):
             100,
             DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),
         )  # text_dim is condition feature dim
-        if self.freeze_quantizer == True:
+        if self.freeze_quantizer is True:
             set_no_grad(self.ssl_proj)
             set_no_grad(self.quantizer)
             set_no_grad(self.enc_p)
@@ -1245,7 +1225,7 @@ class SynthesizerTrnV3(nn.Module):
     def decode_encp(self, codes, text, refer, ge=None, speed=1):
         # print(2333333,refer.shape)
         # ge=None
-        if ge == None:
+        if ge is None:
             refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
             refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
             ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
@@ -1409,7 +1389,7 @@ class SynthesizerTrnV3b(nn.Module):
     def decode_encp(self, codes, text, refer, ge=None):
         # print(2333333,refer.shape)
         # ge=None
-        if ge == None:
+        if ge is None:
             refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
             refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
             ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)

GPT_SoVITS/module/models_onnx.py

@@ -1,23 +1,21 @@
 import math
 from typing import Optional
+
 import torch
-from torch import nn
-from torch.nn import functional as F
-
-from module import commons
-from module import modules
-from module import attentions_onnx as attentions
-
 from f5_tts.model import DiT
 
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from module.commons import init_weights, get_padding
-from module.quantize import ResidualVectorQuantizer
-
 # from text import symbols
 from text import symbols as symbols_v1
 from text import symbols2 as symbols_v2
+from torch import nn
+from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from GPT_SoVITS.module import attentions_onnx as attentions
+from GPT_SoVITS.module import commons, modules
+from GPT_SoVITS.module.commons import get_padding, init_weights
+from GPT_SoVITS.module.quantize import ResidualVectorQuantizer
 
 
 class StochasticDurationPredictor(nn.Module):
@@ -459,7 +457,7 @@ class DiscriminatorP(torch.nn.Module):
         super(DiscriminatorP, self).__init__()
         self.period = period
         self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(
@@ -536,7 +534,7 @@ class DiscriminatorP(torch.nn.Module):
 class DiscriminatorS(torch.nn.Module):
     def __init__(self, use_spectral_norm=False):
         super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv1d(1, 16, 15, 1, padding=7)),
@@ -1057,7 +1055,7 @@ class SynthesizerTrnV3(nn.Module):
             100,
             DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),
         )  # text_dim is condition feature dim
-        if freeze_quantizer == True:
+        if freeze_quantizer is True:
             set_no_grad(self.ssl_proj)
             set_no_grad(self.quantizer)
             set_no_grad(self.enc_p)

GPT_SoVITS/module/modules.py

@@ -2,17 +2,15 @@ import math
 
 import numpy as np
 import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from module import commons
-from module.commons import init_weights, get_padding
-from module.transforms import piecewise_rational_quadratic_transform
 import torch.distributions as D
+from torch import nn
+from torch.nn import Conv1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
 
+from GPT_SoVITS.module import commons
+from GPT_SoVITS.module.commons import get_padding, init_weights
+from GPT_SoVITS.module.transforms import piecewise_rational_quadratic_transform
 
 LRELU_SLOPE = 0.1
 

GPT_SoVITS/module/mrte_model.py

@@ -3,7 +3,8 @@
 import torch
 from torch import nn
 from torch.nn.utils import remove_weight_norm, weight_norm
-from module.attentions import MultiHeadAttention
+
+from GPT_SoVITS.module.attentions import MultiHeadAttention
 
 
 class MRTE(nn.Module):
@@ -23,7 +24,7 @@ class MRTE(nn.Module):
         self.c_post = nn.Conv1d(hidden_size, out_channels, 1)
 
     def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None):
-        if ge == None:
+        if ge is None:
             ge = 0
         attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1)
 

GPT_SoVITS/module/quantize.py

@@ -6,13 +6,13 @@
 
 """Residual vector quantizer implementation."""
 
-from dataclasses import dataclass, field
 import typing as tp
+from dataclasses import dataclass, field
 
 import torch
 from torch import nn
 
-from module.core_vq import ResidualVectorQuantization
+from GPT_SoVITS.module.core_vq import ResidualVectorQuantization
 
 
 @dataclass

GPT_SoVITS/onnx_export.py

@@ -1,10 +1,11 @@
 import torch
 import torchaudio
-from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
 from feature_extractor import cnhubert
-from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
 from torch import nn
 
+from GPT_SoVITS.AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
+from GPT_SoVITS.module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
+
 cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
 cnhubert.cnhubert_base_path = cnhubert_base_path
 ssl_model = cnhubert.get_model()

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -1,113 +1,24 @@
-# -*- coding: utf-8 -*-
-
+import argparse
 import os
-
-inp_text = os.environ.get("inp_text")
-inp_wav_dir = os.environ.get("inp_wav_dir")
-exp_name = os.environ.get("exp_name")
-i_part = os.environ.get("i_part")
-all_parts = os.environ.get("all_parts")
-if "_CUDA_VISIBLE_DEVICES" in os.environ:
-    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
-opt_dir = os.environ.get("opt_dir")
-bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
-import torch
-
-is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
-version = os.environ.get("version", None)
-import traceback
 import os.path
-from text.cleaner import clean_text
+import traceback
+from multiprocessing import Process, Queue, set_start_method
+
+import torch
+from rich.progress import track
 from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from GPT_SoVITS.Accelerate import logger, tb
+from GPT_SoVITS.text.cleaner import clean_text
 from tools.my_utils import clean_path
 
-# inp_text=sys.argv[1]
-# inp_wav_dir=sys.argv[2]
-# exp_name=sys.argv[3]
-# i_part=sys.argv[4]
-# all_parts=sys.argv[5]
-# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]#i_gpu
-# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
-# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
+torch.set_grad_enabled(False)
 
-from time import time as ttime
-import shutil
+set_start_method("spawn", force=True)
 
 
-def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
-    dir = os.path.dirname(path)
-    name = os.path.basename(path)
-    # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
-    tmp_path = "%s%s.pth" % (ttime(), i_part)
-    torch.save(fea, tmp_path)
-    shutil.move(tmp_path, "%s/%s" % (dir, name))
-
-
-txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
-if os.path.exists(txt_path) == False:
-    bert_dir = "%s/3-bert" % (opt_dir)
-    os.makedirs(opt_dir, exist_ok=True)
-    os.makedirs(bert_dir, exist_ok=True)
-    if torch.cuda.is_available():
-        device = "cuda:0"
-    # elif torch.backends.mps.is_available():
-    #     device = "mps"
-    else:
-        device = "cpu"
-    if os.path.exists(bert_pretrained_dir):
-        ...
-    else:
-        raise FileNotFoundError(bert_pretrained_dir)
-    tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
-    bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
-    if is_half == True:
-        bert_model = bert_model.half().to(device)
-    else:
-        bert_model = bert_model.to(device)
-
-    def get_bert_feature(text, word2ph):
-        with torch.no_grad():
-            inputs = tokenizer(text, return_tensors="pt")
-            for i in inputs:
-                inputs[i] = inputs[i].to(device)
-            res = bert_model(**inputs, output_hidden_states=True)
-            res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
-
-        assert len(word2ph) == len(text)
-        phone_level_feature = []
-        for i in range(len(word2ph)):
-            repeat_feature = res[i].repeat(word2ph[i], 1)
-            phone_level_feature.append(repeat_feature)
-
-        phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-        return phone_level_feature.T
-
-    def process(data, res):
-        for name, text, lan in data:
-            try:
-                name = clean_path(name)
-                name = os.path.basename(name)
-                print(name)
-                phones, word2ph, norm_text = clean_text(text.replace("%", "-").replace("￥", ","), lan, version)
-                path_bert = "%s/%s.pt" % (bert_dir, name)
-                if os.path.exists(path_bert) == False and lan == "zh":
-                    bert_feature = get_bert_feature(norm_text, word2ph)
-                    assert bert_feature.shape[-1] == len(phones)
-                    # torch.save(bert_feature, path_bert)
-                    my_save(bert_feature, path_bert)
-                phones = " ".join(phones)
-                # res.append([name,phones])
-                res.append([name, phones, word2ph, norm_text])
-            except:
-                print(name, text, traceback.format_exc())
-
-    todo = []
-    res = []
-    with open(inp_text, "r", encoding="utf8") as f:
-        lines = f.read().strip("\n").split("\n")
-
-    language_v1_to_language_v2 = {
+def lang_map(lang: str) -> str:
+    m = {
         "ZH": "zh",
         "zh": "zh",
         "JP": "ja",
@@ -124,20 +35,179 @@ if os.path.exists(txt_path) == False:
         "YUE": "yue",
         "Yue": "yue",
     }
-    for line in lines[int(i_part) :: int(all_parts)]:
-        try:
-            wav_name, spk_name, language, text = line.split("|")
-            # todo.append([name,text,"zh"])
-            if language in language_v1_to_language_v2.keys():
-                todo.append([wav_name, text, language_v1_to_language_v2.get(language, language)])
-            else:
-                print(f"\033[33m[Waring] The {language = } of {wav_name} is not supported for training.\033[0m")
-        except:
-            print(line, traceback.format_exc())
+    return m.get(lang, "")
 
-    process(todo, res)
-    opt = []
-    for name, phones, word2ph, norm_text in res:
-        opt.append("%s\t%s\t%s\t%s" % (name, phones, word2ph, norm_text))
-    with open(txt_path, "w", encoding="utf8") as f:
-        f.write("\n".join(opt) + "\n")
+
+def parse_inp_text_line(line: str) -> tuple[str, str, str]:
+    wav_name, _, language, text = line.split("|", 3)
+    return wav_name, language, text
+
+
+def build_device_strings(device_type: str, device_ids: list[int], procs_per_device: int) -> list[str]:
+    devices = []
+    for device_id in device_ids:
+        dstr = f"{device_type}:{device_id}"
+        devices.extend([dstr] * procs_per_device)
+    return devices
+
+
+def worker_run(
+    wid: int,
+    device_str: str,
+    tasks_q: Queue[tuple[int, str, str, str]],
+    results_q: Queue[tuple[int, tuple[str, str, list[int] | None, str]]],
+    bert_pretrained_dir: str,
+    opt_dir: str,
+    fp16: bool,
+    version: str,
+):
+    device = torch.device(device_str)
+
+    if device.type == "cuda":
+        assert torch.cuda.is_available()
+        torch.cuda.set_device(device.index)
+    elif device.type == "mps":
+        assert torch.backends.mps.is_available()
+
+    bert_dir = os.path.join(opt_dir, "3-bert")
+    os.makedirs(bert_dir, exist_ok=True)
+
+    if not os.path.exists(bert_pretrained_dir):
+        raise FileNotFoundError(bert_pretrained_dir)
+
+    tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
+    bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
+
+    if fp16:
+        bert_model = bert_model.half().to(device)
+    else:
+        bert_model = bert_model.to(device)
+
+    def get_bert_feature(text: str, word2ph: list[int]) -> torch.Tensor:
+        inputs = tokenizer(text, return_tensors="pt")
+        for k in inputs:
+            inputs[k] = inputs[k].to(device)
+        out = bert_model(**inputs, output_hidden_states=True)
+        layer = out.hidden_states[-3][0].cpu()[1:-1]  # [seq-2, hid]
+        assert len(word2ph) == len(text)
+        phone_level_feature = []
+        for i in range(len(word2ph)):
+            phone_level_feature.append(layer[i].repeat(word2ph[i], 1))
+        feats = torch.cat(phone_level_feature, dim=0)  # [phones, hid]
+        return feats.T  # [hid, phones]
+
+    while True:
+        item = tasks_q.get()
+        if item is None:
+            break
+
+        idx, wav_name, language, text = item
+        try:
+            name = clean_path(os.path.basename(wav_name))
+            mapped_lang = lang_map(language)
+            if not mapped_lang:
+                logger.warning(f"[W{wid}] Unsupported language: {language} of {wav_name}")
+                results_q.put((idx, ("", "", [], "")))
+                continue
+
+            phones, word2ph, norm_text = clean_text(
+                text.replace("%", "-").replace("￥", ","),
+                mapped_lang,
+                version,
+            )
+
+            if mapped_lang == "zh":
+                path_bert = os.path.join(bert_dir, f"{name}.pt")
+                if not os.path.exists(path_bert):
+                    assert word2ph
+                    bert_feature = get_bert_feature(norm_text, word2ph)
+                    assert bert_feature.shape[-1] == len(phones)
+                    torch.save(bert_feature, path_bert)
+
+            phones_str = " ".join(phones)
+            results_q.put((idx, (name, phones_str, word2ph, norm_text)))
+        except Exception:
+            logger.error(f"[W{wid}] Failed: {wav_name} | {text}\n{tb()}")
+            results_q.put((idx, ("", "", [], "")))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--inp", type=str, required=True, help="list File：wav|spk|lang|text")
+    parser.add_argument("--opt", type=str, required=True)
+    parser.add_argument("--bert", type=str, required=True)
+    parser.add_argument("--version", type=str, default=None)
+    parser.add_argument("--device", type=str, default="cpu", choices=["cpu", "cuda", "mps"])
+    parser.add_argument("--device-id", type=str, default="0", help="CUDA_VISIBLE_DEVICE")
+    parser.add_argument("--nproc", type=int, default=1)
+    parser.add_argument("--fp16", action="store_true")
+    args = parser.parse_args()
+
+    device_ids = [int(x) for x in args.devices.split(",") if x.strip() != ""]
+    if args.device in {"cpu", "mps"} and device_ids != [0]:
+        raise ValueError(f"Invalid Device ID {device_ids}")
+    if args.nproc < 1:
+        raise ValueError(f"Invalid Num Process {args.nproc}")
+
+    os.makedirs(args.opt, exist_ok=True)
+    merged_path = os.path.join(args.opt, "2-name2text.txt")
+
+    with open(args.inp, "r", encoding="utf8") as f:
+        lines = [ln for ln in f.read().splitlines() if ln.strip()]
+
+    tasks_all: list[tuple[int, str, str, str]] = []
+    for idx, line in enumerate(lines):
+        try:
+            wav_name, language, text = parse_inp_text_line(line)
+            tasks_all.append((idx, wav_name, language, text))
+        except Exception:
+            logger.error(f"Skip line {idx}: {line}\n{traceback.format_exc()}")
+
+    n_tasks = len(tasks_all)
+    if n_tasks == 0:
+        logger.warning("Empty list")
+        with open(merged_path, "w", encoding="utf8") as fout:
+            pass
+        return
+
+    device_strs = build_device_strings(args.device, device_ids, args.nproc)
+    total_workers = len(device_strs)
+
+    tasks_q: Queue[tuple[int, str, str, str] | None] = Queue(maxsize=total_workers * 2)
+    results_q: Queue = Queue()
+
+    for task in tasks_all:
+        tasks_q.put(task)
+    for _ in range(total_workers):
+        tasks_q.put(None)
+
+    procs: list[Process] = []
+    for wid, dstr in enumerate(device_strs):
+        p = Process(
+            target=worker_run,
+            args=(wid, dstr, tasks_q, results_q, args.bert, args.opt, bool(args.fp16), args.version),
+            daemon=False,
+        )
+        p.start()
+        procs.append(p)
+
+    ordered: list[tuple[str, str, list[int], str]] = [("", "", [], "")] * n_tasks
+    for _ in track(range(n_tasks)):
+        idx, tup = results_q.get()  # (idx, (name, phones_str, word2ph, norm_text))
+        ordered[idx] = tup
+
+    for p in procs:
+        p.join()
+
+    with open(merged_path, "w", encoding="utf8") as fout:
+        for name, phones_str, word2ph, norm_text in ordered:
+            if name == "":
+                pass
+            else:
+                fout.write(f"{name}\t{phones_str}\t{word2ph}\t{norm_text}\n")
+
+    logger.info(f"Done: {merged_path}")
+
+
+if __name__ == "__main__":
+    main()

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k copy.py

@@ -0,0 +1,135 @@
+# -*- coding: utf-8 -*-
+
+import os
+import sys
+
+inp_text = os.environ.get("inp_text")
+inp_wav_dir = os.environ.get("inp_wav_dir")
+exp_name = os.environ.get("exp_name")
+i_part = os.environ.get("i_part")
+all_parts = os.environ.get("all_parts")
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+from feature_extractor import cnhubert
+
+opt_dir = os.environ.get("opt_dir")
+cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
+import torch
+
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+
+import traceback
+
+import librosa
+import numpy as np
+from scipy.io import wavfile
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import shutil
+
+# from config import cnhubert_base_path
+# cnhubert.cnhubert_base_path=cnhubert_base_path
+# inp_text=sys.argv[1]
+# inp_wav_dir=sys.argv[2]
+# exp_name=sys.argv[3]
+# i_part=sys.argv[4]
+# all_parts=sys.argv[5]
+# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]
+# cnhubert.cnhubert_base_path=sys.argv[7]
+# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
+from time import time as ttime
+
+from tools.my_utils import clean_path, load_audio
+
+
+def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
+    dir = os.path.dirname(path)
+    name = os.path.basename(path)
+    # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
+    tmp_path = "%s%s.pth" % (ttime(), i_part)
+    torch.save(fea, tmp_path)
+    shutil.move(tmp_path, "%s/%s" % (dir, name))
+
+
+hubert_dir = "%s/4-cnhubert" % (opt_dir)
+wav32dir = "%s/5-wav32k" % (opt_dir)
+os.makedirs(opt_dir, exist_ok=True)
+os.makedirs(hubert_dir, exist_ok=True)
+os.makedirs(wav32dir, exist_ok=True)
+
+maxx = 0.95
+alpha = 0.5
+if torch.cuda.is_available():
+    device = "cuda:0"
+# elif torch.backends.mps.is_available():
+#     device = "mps"
+else:
+    device = "cpu"
+model = cnhubert.get_model()
+# is_half=False
+if is_half is True:
+    model = model.half().to(device)
+else:
+    model = model.to(device)
+
+nan_fails = []
+
+
+def name2go(wav_name, wav_path):
+    hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
+    if os.path.exists(hubert_path):
+        return
+    tmp_audio = load_audio(wav_path, 32000)
+    tmp_max = np.abs(tmp_audio).max()
+    if tmp_max > 2.2:
+        print("%s-filtered,%s" % (wav_name, tmp_max))
+        return
+    tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
+    tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
+    tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000)  # 不是重采样问题
+    tensor_wav16 = torch.from_numpy(tmp_audio)
+    if is_half is True:
+        tensor_wav16 = tensor_wav16.half().to(device)
+    else:
+        tensor_wav16 = tensor_wav16.to(device)
+    ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu()  # torch.Size([1, 768, 215])
+    if np.isnan(ssl.detach().numpy()).sum() != 0:
+        nan_fails.append((wav_name, wav_path))
+        print("nan filtered:%s" % wav_name)
+        return
+    wavfile.write(
+        "%s/%s" % (wav32dir, wav_name),
+        32000,
+        tmp_audio32.astype("int16"),
+    )
+    my_save(ssl, hubert_path)
+
+
+with open(inp_text, "r", encoding="utf8") as f:
+    lines = f.read().strip("\n").split("\n")
+
+for line in lines[int(i_part) :: int(all_parts)]:
+    try:
+        # wav_name,text=line.split("\t")
+        wav_name, spk_name, language, text = line.split("|")
+        wav_name = clean_path(wav_name)
+        if inp_wav_dir != "" and inp_wav_dir != None:
+            wav_name = os.path.basename(wav_name)
+            wav_path = "%s/%s" % (inp_wav_dir, wav_name)
+
+        else:
+            wav_path = wav_name
+            wav_name = os.path.basename(wav_name)
+        name2go(wav_name, wav_path)
+    except:
+        print(line, traceback.format_exc())
+
+if len(nan_fails) > 0 and is_half is True:
+    is_half = False
+    model = model.float()
+    for wav in nan_fails:
+        try:
+            name2go(wav[0], wav[1])
+        except:
+            print(wav_name, traceback.format_exc())

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
-import sys
 import os
+import sys
 
 inp_text = os.environ.get("inp_text")
 inp_wav_dir = os.environ.get("inp_wav_dir")
@@ -19,13 +19,14 @@ import torch
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 
 import traceback
+
+import librosa
 import numpy as np
 from scipy.io import wavfile
-import librosa
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-from tools.my_utils import load_audio, clean_path
+import shutil
 
 # from config import cnhubert_base_path
 # cnhubert.cnhubert_base_path=cnhubert_base_path
@@ -37,9 +38,9 @@ from tools.my_utils import load_audio, clean_path
 # os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]
 # cnhubert.cnhubert_base_path=sys.argv[7]
 # opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
-
 from time import time as ttime
-import shutil
+
+from tools.my_utils import clean_path, load_audio
 
 
 def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
@@ -67,7 +68,7 @@ else:
     device = "cpu"
 model = cnhubert.get_model()
 # is_half=False
-if is_half == True:
+if is_half is True:
     model = model.half().to(device)
 else:
     model = model.to(device)
@@ -88,7 +89,7 @@ def name2go(wav_name, wav_path):
     tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
     tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000)  # 不是重采样问题
     tensor_wav16 = torch.from_numpy(tmp_audio)
-    if is_half == True:
+    if is_half is True:
         tensor_wav16 = tensor_wav16.half().to(device)
     else:
         tensor_wav16 = tensor_wav16.to(device)
@@ -124,7 +125,7 @@ for line in lines[int(i_part) :: int(all_parts)]:
     except:
         print(line, traceback.format_exc())
 
-if len(nan_fails) > 0 and is_half == True:
+if len(nan_fails) > 0 and is_half is True:
     is_half = False
     model = model.float()
     for wav in nan_fails:

GPT_SoVITS/prepare_datasets/2-get-sv.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
-import sys
 import os
+import sys
 
 inp_text = os.environ.get("inp_text")
 inp_wav_dir = os.environ.get("inp_wav_dir")
@@ -18,16 +18,19 @@ import torch
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 
 import traceback
+
 import torchaudio
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 sys.path.append(f"{now_dir}/GPT_SoVITS/eres2net")
-from tools.my_utils import clean_path
-from time import time as ttime
 import shutil
-from ERes2NetV2 import ERes2NetV2
+from time import time as ttime
+
 import kaldi as Kaldi
+from ERes2NetV2 import ERes2NetV2
+
+from tools.my_utils import clean_path
 
 
 def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
@@ -63,7 +66,7 @@ class SV:
         embedding_model.eval()
         self.embedding_model = embedding_model
         self.res = torchaudio.transforms.Resample(32000, 16000).to(device)
-        if is_half == False:
+        if is_half is False:
             self.embedding_model = self.embedding_model.to(device)
         else:
             self.embedding_model = self.embedding_model.half().to(device)
@@ -72,7 +75,7 @@ class SV:
     def compute_embedding3(self, wav):  # (1,x)#-1~1
         with torch.no_grad():
             wav = self.res(wav)
-            if self.is_half == True:
+            if self.is_half is True:
                 wav = wav.half()
             feat = torch.stack(
                 [Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -29,18 +29,19 @@ else:
 import torch
 
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
-import traceback
 import sys
+import traceback
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 import logging
-import utils
+
+import GPT_SoVITS.utils as utils
 
 if version != "v3":
-    from module.models import SynthesizerTrn
+    from GPT_SoVITS.module.models import SynthesizerTrn
 else:
-    from module.models import SynthesizerTrnV3 as SynthesizerTrn
+    from GPT_SoVITS.module.models import SynthesizerTrnV3 as SynthesizerTrn
 from tools.my_utils import clean_path
 
 logging.getLogger("numba").setLevel(logging.WARNING)
@@ -56,7 +57,7 @@ logging.getLogger("numba").setLevel(logging.WARNING)
 
 hubert_dir = "%s/4-cnhubert" % (opt_dir)
 semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
-if os.path.exists(semantic_path) == False:
+if os.path.exists(semantic_path) is False:
     os.makedirs(opt_dir, exist_ok=True)
 
     if torch.cuda.is_available():
@@ -73,7 +74,7 @@ if os.path.exists(semantic_path) == False:
         version=version,
         **hps.model,
     )
-    if is_half == True:
+    if is_half is True:
         vq_model = vq_model.half().to(device)
     else:
         vq_model = vq_model.to(device)
@@ -88,10 +89,10 @@ if os.path.exists(semantic_path) == False:
 
     def name2go(wav_name, lines):
         hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
-        if os.path.exists(hubert_path) == False:
+        if os.path.exists(hubert_path) is False:
             return
         ssl_content = torch.load(hubert_path, map_location="cpu")
-        if is_half == True:
+        if is_half is True:
             ssl_content = ssl_content.half().to(device)
         else:
             ssl_content = ssl_content.to(device)

GPT_SoVITS/process_ckpt.py

@@ -1,15 +1,17 @@
+import os
+import shutil
 import traceback
 from collections import OrderedDict
 from time import time as ttime
-import shutil
-import os
+
 import torch
+
 from tools.i18n.i18n import I18nAuto
 
 i18n = I18nAuto()
 
 
-def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
+def my_save(fea, path):  # fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
     tmp_path = "%s.pth" % (ttime())
@@ -17,27 +19,6 @@ def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
     shutil.move(tmp_path, "%s/%s" % (dir, name))
 
 
-from io import BytesIO
-
-model_version2byte = {
-    "v3": b"03",
-    "v4": b"04",
-    "v2Pro": b"05",
-    "v2ProPlus": b"06",
-}
-
-
-def my_save2(fea, path, model_version):
-    bio = BytesIO()
-    torch.save(fea, bio)
-    bio.seek(0)
-    data = bio.getvalue()
-    byte = model_version2byte[model_version]
-    data = byte + data[2:]
-    with open(path, "wb") as f:
-        f.write(data)
-
-
 def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
     try:
         opt = OrderedDict()
@@ -50,89 +31,33 @@ def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
         opt["info"] = "%sepoch_%siteration" % (epoch, steps)
         if lora_rank:
             opt["lora_rank"] = lora_rank
-            my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
-        elif model_version != None and "Pro" in model_version:
-            my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
-        else:
-            my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
+        my_save(opt, f"{hps.save_weight_dir}/{name}.pth")
         return "Success."
-    except:
+    except Exception:
         return traceback.format_exc()
 
 
-"""
-00:v1
-01:v2
-02:v3
-03:v3lora
-04:v4lora
-05:v2Pro
-06:v2ProPlus
-"""
-head2version = {
-    b"00": ["v1", "v1", False],
-    b"01": ["v2", "v2", False],
-    b"02": ["v2", "v3", False],
-    b"03": ["v2", "v3", True],
-    b"04": ["v2", "v4", True],
-    b"05": ["v2", "v2Pro", False],
-    b"06": ["v2", "v2ProPlus", False],
-}
-hash_pretrained_dict = {
-    "dc3c97e17592963677a4a1681f30c653": ["v2", "v2", False],  # s2G488k.pth#sovits_v1_pretrained
-    "43797be674a37c1c83ee81081941ed0f": ["v2", "v3", False],  # s2Gv3.pth#sovits_v3_pretrained
-    "6642b37f3dbb1f76882b69937c95a5f3": ["v2", "v2", False],  # s2G2333K.pth#sovits_v2_pretrained
-    "4f26b9476d0c5033e04162c486074374": ["v2", "v4", False],  # s2Gv4.pth#sovits_v4_pretrained
-    "c7e9fce2223f3db685cdfa1e6368728a": ["v2", "v2Pro", False],  # s2Gv2Pro.pth#sovits_v2Pro_pretrained
-    "66b313e39455b57ab1b0bc0b239c9d0a": ["v2", "v2ProPlus", False],  # s2Gv2ProPlus.pth#sovits_v2ProPlus_pretrained
-}
-import hashlib
+def inspect_version(f: str):
+    dict_s2 = torch.load(f, map_location="cpu", mmap=True)
+    hps = dict_s2["config"]
+    version = None
+    if "version" in hps:
+        version = hps.version
+    is_lora = "lora_rank" in dict_s2.keys()
 
-
-def get_hash_from_file(sovits_path):
-    with open(sovits_path, "rb") as f:
-        data = f.read(8192)
-    hash_md5 = hashlib.md5()
-    hash_md5.update(data)
-    return hash_md5.hexdigest()
-
-
-def get_sovits_version_from_path_fast(sovits_path):
-    ###1-if it is pretrained sovits models, by hash
-    hash = get_hash_from_file(sovits_path)
-    if hash in hash_pretrained_dict:
-        return hash_pretrained_dict[hash]
-    ###2-new weights, by head
-    with open(sovits_path, "rb") as f:
-        version = f.read(2)
-    if version != b"PK":
-        return head2version[version]
-    ###3-old weights, by file size
-    if_lora_v3 = False
-    size = os.path.getsize(sovits_path)
-    """
-            v1weights:about 82942KB
-                half thr:82978KB
-            v2weights:about 83014KB
-            v3weights:about 750MB
-    """
-    if size < 82978 * 1024:
-        model_version = version = "v1"
-    elif size < 700 * 1024 * 1024:
-        model_version = version = "v2"
+    if version is not None:
+        lang_version = "v2"
+        model_version = version
     else:
-        version = "v2"
-        model_version = "v3"
-    return version, model_version, if_lora_v3
+        if "dec.conv_pre.weight" in dict_s2["weight"].keys():
+            if dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322:
+                lang_version = model_version = "v1"
+            else:
+                lang_version = model_version = "v2"
+        else:
+            lang_version = "v2"
+            model_version = "v3"
+            if dict_s2["info"] == "pretrained_s2G_v4":
+                model_version = "v4"
 
-
-def load_sovits_new(sovits_path):
-    f = open(sovits_path, "rb")
-    meta = f.read(2)
-    if meta != b"PK":
-        data = b"PK" + f.read()
-        bio = BytesIO()
-        bio.write(data)
-        bio.seek(0)
-        return torch.load(bio, map_location="cpu", weights_only=False)
-    return torch.load(sovits_path, map_location="cpu", weights_only=False)
+    return model_version, lang_version, is_lora, hps, dict_s2

GPT_SoVITS/s1_train.py

@@ -1,32 +1,36 @@
 # modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/train_t2s.py
-import os
-
-if "_CUDA_VISIBLE_DEVICES" in os.environ:
-    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
 import argparse
 import logging
+import os
 import platform
+from collections import OrderedDict
 from pathlib import Path
+from typing import Any
 
 import torch
-from AR.data.data_module import Text2SemanticDataModule
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from AR.utils.io import load_yaml_config
 from pytorch_lightning import Trainer, seed_everything
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger  # WandbLogger
-from pytorch_lightning.strategies import DDPStrategy
+from pytorch_lightning.strategies import DDPStrategy, SingleDeviceStrategy
+from pytorch_lightning.strategies.strategy import Strategy
+
+from GPT_SoVITS.AR.data.data_module import Text2SemanticDataModule
+from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from GPT_SoVITS.AR.utils import get_newest_ckpt
+from GPT_SoVITS.AR.utils.io import load_yaml_config
+from GPT_SoVITS.process_ckpt import my_save
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 logging.getLogger("matplotlib").setLevel(logging.WARNING)
 torch.set_float32_matmul_precision("high")
-from collections import OrderedDict
-
-from AR.utils import get_newest_ckpt
-from process_ckpt import my_save
 
 
-class my_model_ckpt(ModelCheckpoint):
+os.environ["MASTER_ADDR"] = "localhost"
+if platform.system() == "Windows":
+    os.environ["USE_LIBUV"] = "0"
+
+
+class ARModelCheckpoint(ModelCheckpoint):
     def __init__(
         self,
         config,
@@ -44,40 +48,31 @@ class my_model_ckpt(ModelCheckpoint):
         self.config = config
 
     def on_train_epoch_end(self, trainer, pl_module):
-        # if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer):
         if self._should_save_on_train_epoch_end(trainer):
             monitor_candidates = self._monitor_candidates(trainer)
             if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0:
-                if (
-                    self.if_save_latest == True
-                ):  ####如果设置只保存最后一个ckpt，在保存下一个ckpt后要清理掉之前的所有ckpt
-                    to_clean = list(os.listdir(self.dirpath))
                 self._save_topk_checkpoint(trainer, monitor_candidates)
-                if self.if_save_latest == True:
+                if self.if_save_latest is True:  # 如果设置只保存最后一个ckpt，在保存下一个ckpt后要清理掉之前的所有ckpt
+                    to_clean = list(os.listdir(self.dirpath))
                     for name in to_clean:
                         try:
-                            os.remove("%s/%s" % (self.dirpath, name))
-                        except:
+                            os.remove(f"{self.dirpath}/{name}")
+                        except Exception as _:
                             pass
-                if self.if_save_every_weights == True:
-                    to_save_od = OrderedDict()
+                if self.if_save_every_weights is True:
+                    to_save_od: OrderedDict[str, Any] = OrderedDict()
                     to_save_od["weight"] = OrderedDict()
                     dictt = trainer.strategy._lightning_module.state_dict()
                     for key in dictt:
                         to_save_od["weight"][key] = dictt[key].half()
                     to_save_od["config"] = self.config
-                    to_save_od["info"] = "GPT-e%s" % (trainer.current_epoch + 1)
+                    to_save_od["info"] = f"GPT-e{trainer.current_epoch + 1}"
                     # torch.save(
                     # print(os.environ)
                     if os.environ.get("LOCAL_RANK", "0") == "0":
                         my_save(
                             to_save_od,
-                            "%s/%s-e%s.ckpt"
-                            % (
-                                self.half_weights_save_dir,
-                                self.exp_name,
-                                trainer.current_epoch + 1,
-                            ),
+                            f"{self.half_weights_save_dir}/{self.exp_name}-e{trainer.current_epoch + 1}.ckpt",
                         )
             self._save_last_checkpoint(trainer, monitor_candidates)
 
@@ -91,8 +86,17 @@ def main(args):
     ckpt_dir = output_dir / "ckpt"
     ckpt_dir.mkdir(parents=True, exist_ok=True)
 
+    if torch.cuda.is_available():
+        if torch.cuda.device_count() > 1:
+            strategy: Strategy = DDPStrategy(process_group_backend="nccl" if platform.system() != "Windows" else "gloo")
+        else:
+            strategy = SingleDeviceStrategy("cuda")
+    else:
+        strategy = SingleDeviceStrategy("cpu")
+
     seed_everything(config["train"]["seed"], workers=True)
-    ckpt_callback: ModelCheckpoint = my_model_ckpt(
+
+    ckpt_callback: ModelCheckpoint = ARModelCheckpoint(
         config=config,
         if_save_latest=config["train"]["if_save_latest"],
         if_save_every_weights=config["train"]["if_save_every_weights"],
@@ -106,20 +110,15 @@ def main(args):
         dirpath=ckpt_dir,
     )
     logger = TensorBoardLogger(name=output_dir.stem, save_dir=output_dir)
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["USE_LIBUV"] = "0"
+
     trainer: Trainer = Trainer(
         max_epochs=config["train"]["epochs"],
         accelerator="gpu" if torch.cuda.is_available() else "cpu",
-        # val_check_interval=9999999999999999999999,###不要验证
-        # check_val_every_n_epoch=None,
         limit_val_batches=0,
         devices=-1 if torch.cuda.is_available() else 1,
         benchmark=False,
         fast_dev_run=False,
-        strategy=DDPStrategy(process_group_backend="nccl" if platform.system() != "Windows" else "gloo")
-        if torch.cuda.is_available()
-        else "auto",
+        strategy=strategy,
         precision=config["train"]["precision"],
         logger=logger,
         num_sanity_val_steps=0,
@@ -133,8 +132,6 @@ def main(args):
         config,
         train_semantic_path=config["train_semantic_path"],
         train_phoneme_path=config["train_phoneme_path"],
-        # dev_semantic_path=args.dev_semantic_path,
-        # dev_phoneme_path=args.dev_phoneme_path
     )
 
     try:

GPT_SoVITS/s2_train.py

@@ -1,53 +1,56 @@
-import warnings
-
-warnings.filterwarnings("ignore")
-import os
-
-import utils
-
-hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
 import logging
+import os
+import platform
+import warnings
+from random import randint
 
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
-from torch.cuda.amp import GradScaler, autocast
+import torch.multiprocessing.spawn as mp
+from torch.amp.autocast_mode import autocast
+from torch.amp.grad_scaler import GradScaler
 from torch.nn import functional as F
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
-logging.getLogger("matplotlib").setLevel(logging.INFO)
-logging.getLogger("h5py").setLevel(logging.INFO)
-logging.getLogger("numba").setLevel(logging.INFO)
-from random import randint
-
-from module import commons
-from module.data_utils import (
+import GPT_SoVITS.utils as utils
+from GPT_SoVITS.module import commons
+from GPT_SoVITS.module.data_utils import (
     DistributedBucketSampler,
     TextAudioSpeakerCollate,
     TextAudioSpeakerLoader,
 )
-from module.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
-from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from module.models import (
+from GPT_SoVITS.module.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
+from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from GPT_SoVITS.module.models import (
     MultiPeriodDiscriminator,
     SynthesizerTrn,
 )
-from process_ckpt import savee
+from GPT_SoVITS.process_ckpt import savee
 
+logging.getLogger("matplotlib").setLevel(logging.INFO)
+logging.getLogger("h5py").setLevel(logging.INFO)
+logging.getLogger("numba").setLevel(logging.INFO)
+
+hps = utils.get_hparams(stage=2)
+
+warnings.filterwarnings("ignore")
 torch.backends.cudnn.benchmark = False
 torch.backends.cudnn.deterministic = False
-###反正A100fp32更快，那试试tf32吧
-torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cuda.matmul.allow_tf32 = True  ###反正A100fp32更快，那试试tf32吧
 torch.backends.cudnn.allow_tf32 = True
 torch.set_float32_matmul_precision("medium")  # 最低精度但最快（也就快一丁点），对于结果造成不了影响
-# from config import pretrained_s2G,pretrained_s2D
+
 global_step = 0
 
-device = "cpu"  # cuda以外的设备，等mps优化后加入
+if torch.cuda.is_available():
+    device_str = "cuda"
+else:
+    device_str = "cpu"
+
+multigpu = torch.cuda.device_count() > 1 if torch.cuda.is_available() else False
 
 
 def main():
@@ -70,19 +73,23 @@ def main():
 
 def run(rank, n_gpus, hps):
     global global_step
+    device = torch.device("{device_str}:{rank}")
     if rank == 0:
         logger = utils.get_logger(hps.data.exp_dir)
         logger.info(hps)
-        # utils.check_git_hash(hps.s2_ckpt_dir)
         writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
         writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
+    else:
+        logger = writer = writer_eval = None
+
+    if multigpu:
+        dist.init_process_group(
+            backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
+            init_method="env://?use_libuv=False" if platform.system() == "Windows" else "env://",
+            world_size=n_gpus,
+            rank=rank,
+        )
 
-    dist.init_process_group(
-        backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
-        init_method="env://?use_libuv=False",
-        world_size=n_gpus,
-        rank=rank,
-    )
     torch.manual_seed(hps.train.seed)
     if torch.cuda.is_available():
         torch.cuda.set_device(rank)
@@ -126,33 +133,19 @@ def run(rank, n_gpus, hps):
         persistent_workers=True,
         prefetch_factor=4,
     )
-    # if rank == 0:
-    #     eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True)
-    #     eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
-    #                              batch_size=1, pin_memory=True,
-    #                              drop_last=False, collate_fn=collate_fn)
 
-    net_g = (
-        SynthesizerTrn(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            **hps.model,
-        ).cuda(rank)
-        if torch.cuda.is_available()
-        else SynthesizerTrn(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            **hps.model,
-        ).to(device)
-    )
+    net_g = SynthesizerTrn(
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    ).to(device)
+
+    net_d = MultiPeriodDiscriminator(
+        hps.model.use_spectral_norm,
+        version=hps.model.version,
+    ).to(device)
 
-    net_d = (
-        MultiPeriodDiscriminator(hps.model.use_spectral_norm, version=hps.model.version).cuda(rank)
-        if torch.cuda.is_available()
-        else MultiPeriodDiscriminator(hps.model.use_spectral_norm, version=hps.model.version).to(device)
-    )
     for name, param in net_g.named_parameters():
         if not param.requires_grad:
             print(name, "not requires_grad")
@@ -165,10 +158,6 @@ def run(rank, n_gpus, hps):
         net_g.parameters(),
     )
 
-    # te_p=net_g.enc_p.text_embedding.parameters()
-    # et_p=net_g.enc_p.encoder_text.parameters()
-    # mrte_p=net_g.enc_p.mrte.parameters()
-
     optim_g = torch.optim.AdamW(
         # filter(lambda p: p.requires_grad, net_g.parameters()),###默认所有层lr一致
         [
@@ -196,7 +185,7 @@ def run(rank, n_gpus, hps):
         betas=hps.train.betas,
         eps=hps.train.eps,
     )
-    if torch.cuda.is_available():
+    if multigpu:
         net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
         net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
     else:
@@ -204,49 +193,49 @@ def run(rank, n_gpus, hps):
         net_d = net_d.to(device)
 
     try:  # 如果能加载自动resume
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_*.pth"),
+        epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path(f"{(hps.data.exp_dir,)}/logs_s2_{hps.model.version}", "D_*.pth"),
             net_d,
             optim_d,
-        )  # D多半加载没事
+        )[-1]  # D多半加载没事
         if rank == 0:
             logger.info("loaded D")
         # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"),
+        epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path(f"{hps.data.exp_dir}/logs_s2_{hps.model.version}", "G_*.pth"),
             net_g,
             optim_g,
-        )
+        )[-1]
         epoch_str += 1
         global_step = (epoch_str - 1) * len(train_loader)
         # epoch_str = 1
         # global_step = 0
-    except:  # 如果首次不能加载，加载pretrain
+    except Exception:  # 如果首次不能加载，加载pretrain
         # traceback.print_exc()
         epoch_str = 1
         global_step = 0
         if (
             hps.train.pretrained_s2G != ""
-            and hps.train.pretrained_s2G != None
+            and hps.train.pretrained_s2G is not None
             and os.path.exists(hps.train.pretrained_s2G)
         ):
             if rank == 0:
-                logger.info("loaded pretrained %s" % hps.train.pretrained_s2G)
+                logger.info(f"loaded pretrained {hps.train.pretrained_s2G}")
             print(
                 "loaded pretrained %s" % hps.train.pretrained_s2G,
                 net_g.module.load_state_dict(
-                    torch.load(hps.train.pretrained_s2G, map_location="cpu", weights_only=False)["weight"],
+                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
                     strict=False,
                 )
-                if torch.cuda.is_available()
+                if multigpu
                 else net_g.load_state_dict(
-                    torch.load(hps.train.pretrained_s2G, map_location="cpu", weights_only=False)["weight"],
+                    torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
                     strict=False,
                 ),
             )  ##测试不加载优化器
         if (
             hps.train.pretrained_s2D != ""
-            and hps.train.pretrained_s2D != None
+            and hps.train.pretrained_s2D is not None
             and os.path.exists(hps.train.pretrained_s2D)
         ):
             if rank == 0:
@@ -254,11 +243,11 @@ def run(rank, n_gpus, hps):
             print(
                 "loaded pretrained %s" % hps.train.pretrained_s2D,
                 net_d.module.load_state_dict(
-                    torch.load(hps.train.pretrained_s2D, map_location="cpu", weights_only=False)["weight"], strict=False
+                    torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"], strict=False
                 )
-                if torch.cuda.is_available()
+                if multigpu
                 else net_d.load_state_dict(
-                    torch.load(hps.train.pretrained_s2D, map_location="cpu", weights_only=False)["weight"],
+                    torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"],
                 ),
             )
 
@@ -279,13 +268,13 @@ def run(rank, n_gpus, hps):
         scheduler_g.step()
         scheduler_d.step()
 
-    scaler = GradScaler(enabled=hps.train.fp16_run)
+    scaler = GradScaler(device=device.type, enabled=hps.train.fp16_run)
 
     print("start training from epoch %s" % epoch_str)
     for epoch in range(epoch_str, hps.train.epochs + 1):
         if rank == 0:
             train_and_evaluate(
-                rank,
+                device,
                 epoch,
                 hps,
                 [net_g, net_d],
@@ -299,7 +288,7 @@ def run(rank, n_gpus, hps):
             )
         else:
             train_and_evaluate(
-                rank,
+                device,
                 epoch,
                 hps,
                 [net_g, net_d],
@@ -315,7 +304,7 @@ def run(rank, n_gpus, hps):
     print("training done")
 
 
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
+def train_and_evaluate(device: torch.device, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
     net_g, net_d = nets
     optim_g, optim_d = optims
     # scheduler_g, scheduler_d = schedulers
@@ -331,54 +320,19 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
     for batch_idx, data in enumerate(tqdm(train_loader)):
         if hps.model.version in {"v2Pro", "v2ProPlus"}:
             ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb = data
+            ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb = map(
+                lambda x: x.to(device, non_blocking=True),
+                (ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb),
+            )
         else:
             ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths = data
-        if torch.cuda.is_available():
-            spec, spec_lengths = (
-                spec.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-                spec_lengths.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
+            ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths = map(
+                lambda x: x.to(device, non_blocking=True),
+                (ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths),
             )
-            y, y_lengths = (
-                y.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-                y_lengths.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-            )
-            ssl = ssl.cuda(rank, non_blocking=True)
-            ssl.requires_grad = False
-            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = (
-                text.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-                text_lengths.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-            )
-            if hps.model.version in {"v2Pro", "v2ProPlus"}:
-                sv_emb = sv_emb.cuda(rank, non_blocking=True)
-        else:
-            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
-            y, y_lengths = y.to(device), y_lengths.to(device)
-            ssl = ssl.to(device)
-            ssl.requires_grad = False
-            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = text.to(device), text_lengths.to(device)
-            if hps.model.version in {"v2Pro", "v2ProPlus"}:
-                sv_emb = sv_emb.to(device)
-        with autocast(enabled=hps.train.fp16_run):
+        ssl.requires_grad = False
+
+        with autocast(device_type=device.type, dtype=torch.float16, enabled=hps.train.fp16_run):
             if hps.model.version in {"v2Pro", "v2ProPlus"}:
                 (y_hat, kl_ssl, ids_slice, x_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q), stats_ssl) = net_g(
                     ssl, spec, spec_lengths, text, text_lengths, sv_emb
@@ -418,22 +372,23 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
 
             # Discriminator
             y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-            with autocast(enabled=False):
+            with autocast(device_type=device.type, enabled=False):
                 loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
                     y_d_hat_r,
                     y_d_hat_g,
                 )
                 loss_disc_all = loss_disc
+
         optim_d.zero_grad()
         scaler.scale(loss_disc_all).backward()
         scaler.unscale_(optim_d)
         grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
         scaler.step(optim_d)
 
-        with autocast(enabled=hps.train.fp16_run):
+        with autocast(device_type=device.type, dtype=torch.float16, enabled=hps.train.fp16_run):
             # Generator
             y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            with autocast(enabled=False):
+            with autocast(device_type=device.type, enabled=False):
                 loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
                 loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
 
@@ -448,7 +403,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
         scaler.step(optim_g)
         scaler.update()
 
-        if rank == 0:
+        if device.index == 0:
             if global_step % hps.train.log_interval == 0:
                 lr = optim_g.param_groups[0]["lr"]
                 losses = [loss_disc, loss_gen, loss_fm, loss_mel, kl_ssl, loss_kl]
@@ -480,7 +435,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
                 # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
                 image_dict = None
-                try:  ###Some people installed the wrong version of matplotlib.
+                try:  # Some people installed the wrong version of matplotlib.
                     image_dict = {
                         "slice/mel_org": utils.plot_spectrogram_to_numpy(
                             y_mel[0].data.cpu().numpy(),
@@ -495,7 +450,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                             stats_ssl[0].data.cpu().numpy(),
                         ),
                     }
-                except:
+                except Exception as _:
                     pass
                 if image_dict:
                     utils.summarize(
@@ -511,7 +466,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                         scalars=scalar_dict,
                     )
         global_step += 1
-    if epoch % hps.train.save_every_epoch == 0 and rank == 0:
+    if epoch % hps.train.save_every_epoch == 0 and device.index == 0:
         if hps.train.if_save_latest == 0:
             utils.save_checkpoint(
                 net_g,
@@ -519,8 +474,8 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 hps.train.learning_rate,
                 epoch,
                 os.path.join(
-                    "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version),
-                    "G_{}.pth".format(global_step),
+                    f"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
+                    f"G_{global_step}.pth",
                 ),
             )
             utils.save_checkpoint(
@@ -529,8 +484,8 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 hps.train.learning_rate,
                 epoch,
                 os.path.join(
-                    "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version),
-                    "D_{}.pth".format(global_step),
+                    "{hps.data.exp_dir}/logs_s2_{hps.model.version}",
+                    "D_{global_step}.pth",
                 ),
             )
         else:
@@ -540,8 +495,8 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 hps.train.learning_rate,
                 epoch,
                 os.path.join(
-                    "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version),
-                    "G_{}.pth".format(233333333333),
+                    f"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
+                    "G_233333333333.pth",
                 ),
             )
             utils.save_checkpoint(
@@ -550,11 +505,11 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 hps.train.learning_rate,
                 epoch,
                 os.path.join(
-                    "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version),
-                    "D_{}.pth".format(233333333333),
+                    "{hps.data.exp_dir}/logs_s2_{hps.model.version}",
+                    "D_233333333333.pth",
                 ),
             )
-        if rank == 0 and hps.train.if_save_every_weights == True:
+        if device.index == 0 and hps.train.if_save_every_weights is True:
             if hasattr(net_g, "module"):
                 ckpt = net_g.module.state_dict()
             else:
@@ -566,7 +521,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                     epoch,
                     savee(
                         ckpt,
-                        hps.name + "_e%s_s%s" % (epoch, global_step),
+                        hps.name + f"_e{epoch}_s{global_step}",
                         epoch,
                         global_step,
                         hps,
@@ -575,11 +530,11 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 )
             )
 
-    if rank == 0:
-        logger.info("====> Epoch: {}".format(epoch))
+    if device.index == 0:
+        logger.info(f"====> Epoch: {epoch}")
 
 
-def evaluate(hps, generator, eval_loader, writer_eval):
+def evaluate(hps, generator, eval_loader, writer_eval, device):
     generator.eval()
     image_dict = {}
     audio_dict = {}
@@ -595,17 +550,10 @@ def evaluate(hps, generator, eval_loader, writer_eval):
             text,
             text_lengths,
         ) in enumerate(eval_loader):
-            print(111)
-            if torch.cuda.is_available():
-                spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
-                y, y_lengths = y.cuda(), y_lengths.cuda()
-                ssl = ssl.cuda()
-                text, text_lengths = text.cuda(), text_lengths.cuda()
-            else:
-                spec, spec_lengths = spec.to(device), spec_lengths.to(device)
-                y, y_lengths = y.to(device), y_lengths.to(device)
-                ssl = ssl.to(device)
-                text, text_lengths = text.to(device), text_lengths.to(device)
+            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
+            y, y_lengths = y.to(device), y_lengths.to(device)
+            ssl = ssl.to(device)
+            text, text_lengths = text.to(device), text_lengths.to(device)
             for test in [0, 1]:
                 y_hat, mask, *_ = (
                     generator.module.infer(
@@ -665,11 +613,6 @@ def evaluate(hps, generator, eval_loader, writer_eval):
                 )
                 audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
 
-        # y_hat, mask, *_ = generator.module.infer(ssl, spec_lengths, speakers, y=None)
-        # audio_dict.update({
-        #     f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
-        # })
-
     utils.summarize(
         writer=writer_eval,
         global_step=global_step,

GPT_SoVITS/s2_train_v3.py

@@ -1,53 +1,51 @@
-import warnings
-
-warnings.filterwarnings("ignore")
-import os
-
-import utils
-
-hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
 import logging
+import os
+import platform
+import warnings
+from random import randint
 
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
-from torch.cuda.amp import GradScaler, autocast
+import torch.multiprocessing.spawn as mp
+from torch.amp.autocast_mode import autocast
+from torch.amp.grad_scaler import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
+import GPT_SoVITS.utils as utils
+from GPT_SoVITS.module import commons
+from GPT_SoVITS.module.data_utils import (
+    DistributedBucketSampler,
+    TextAudioSpeakerCollateV3,
+    TextAudioSpeakerLoaderV3,
+)
+from GPT_SoVITS.module.models import SynthesizerTrnV3
+from GPT_SoVITS.process_ckpt import savee
+
+hps = utils.get_hparams(stage=2)
+
+warnings.filterwarnings("ignore")
 logging.getLogger("matplotlib").setLevel(logging.INFO)
 logging.getLogger("h5py").setLevel(logging.INFO)
 logging.getLogger("numba").setLevel(logging.INFO)
-from random import randint
-
-from module import commons
-from module.data_utils import (
-    DistributedBucketSampler,
-)
-from module.data_utils import (
-    TextAudioSpeakerCollateV3 as TextAudioSpeakerCollate,
-)
-from module.data_utils import (
-    TextAudioSpeakerLoaderV3 as TextAudioSpeakerLoader,
-)
-from module.models import (
-    SynthesizerTrnV3 as SynthesizerTrn,
-)
-from process_ckpt import savee
 
 torch.backends.cudnn.benchmark = False
 torch.backends.cudnn.deterministic = False
-###反正A100fp32更快，那试试tf32吧
-torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cuda.matmul.allow_tf32 = True  ###反正A100fp32更快，那试试tf32吧
 torch.backends.cudnn.allow_tf32 = True
 torch.set_float32_matmul_precision("medium")  # 最低精度但最快（也就快一丁点），对于结果造成不了影响
-# from config import pretrained_s2G,pretrained_s2D
+
 global_step = 0
 
-device = "cpu"  # cuda以外的设备，等mps优化后加入
+if torch.cuda.is_available():
+    device_str = "cuda"
+else:
+    device_str = "cpu"
+
+
+multigpu = torch.cuda.device_count() > 1 if torch.cuda.is_available() else False
 
 
 def main():
@@ -70,24 +68,29 @@ def main():
 
 def run(rank, n_gpus, hps):
     global global_step
+    device = torch.device("{device_str}:{rank}")
+
     if rank == 0:
         logger = utils.get_logger(hps.data.exp_dir)
         logger.info(hps)
-        # utils.check_git_hash(hps.s2_ckpt_dir)
         writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
         writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
+    else:
+        logger = writer = writer_eval = None
+
+    if multigpu:
+        dist.init_process_group(
+            backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
+            init_method="env://?use_libuv=False" if platform.system() == "Windows" else "env://",
+            world_size=n_gpus,
+            rank=rank,
+        )
 
-    dist.init_process_group(
-        backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
-        init_method="env://?use_libuv=False",
-        world_size=n_gpus,
-        rank=rank,
-    )
     torch.manual_seed(hps.train.seed)
     if torch.cuda.is_available():
         torch.cuda.set_device(rank)
 
-    train_dataset = TextAudioSpeakerLoader(hps.data)  ########
+    train_dataset = TextAudioSpeakerLoaderV3(hps.data)
     train_sampler = DistributedBucketSampler(
         train_dataset,
         hps.train.batch_size,
@@ -101,21 +104,12 @@ def run(rank, n_gpus, hps):
             800,
             900,
             1000,
-            # 1100,
-            # 1200,
-            # 1300,
-            # 1400,
-            # 1500,
-            # 1600,
-            # 1700,
-            # 1800,
-            # 1900,
         ],
         num_replicas=n_gpus,
         rank=rank,
         shuffle=True,
     )
-    collate_fn = TextAudioSpeakerCollate()
+    collate_fn = TextAudioSpeakerCollateV3()
     train_loader = DataLoader(
         train_dataset,
         num_workers=6,
@@ -127,31 +121,17 @@ def run(rank, n_gpus, hps):
         prefetch_factor=4,
     )
     # if rank == 0:
-    #     eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True)
+    #     eval_dataset = TextAudioSpeakerLoaderV3(hps.data.validation_files, hps.data, val=True)
     #     eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
     #                              batch_size=1, pin_memory=True,
     #                              drop_last=False, collate_fn=collate_fn)
 
-    net_g = (
-        SynthesizerTrn(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            **hps.model,
-        ).cuda(rank)
-        if torch.cuda.is_available()
-        else SynthesizerTrn(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            **hps.model,
-        ).to(device)
-    )
-
-    # net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to(device)
-    # for name, param in net_g.named_parameters():
-    #     if not param.requires_grad:
-    #         print(name, "not requires_grad")
+    net_g = SynthesizerTrnV3(
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    ).to(device)
 
     optim_g = torch.optim.AdamW(
         filter(lambda p: p.requires_grad, net_g.parameters()),  ###默认所有层lr一致
@@ -159,44 +139,26 @@ def run(rank, n_gpus, hps):
         betas=hps.train.betas,
         eps=hps.train.eps,
     )
-    # optim_d = torch.optim.AdamW(
-    #     net_d.parameters(),
-    #     hps.train.learning_rate,
-    #     betas=hps.train.betas,
-    #     eps=hps.train.eps,
-    # )
-    if torch.cuda.is_available():
+
+    if multigpu:
         net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-        # net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
     else:
         net_g = net_g.to(device)
-        # net_d = net_d.to(device)
 
     try:  # 如果能加载自动resume
-        # _, _, _, epoch_str = utils.load_checkpoint(
-        #     utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_*.pth"),
-        #     net_d,
-        #     optim_d,
-        # )  # D多半加载没事
-        # if rank == 0:
-        #     logger.info("loaded D")
-        # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
         _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"),
+            utils.latest_checkpoint_path(f"{hps.data.exp_dir}/logs_s2_{hps.model.version}", "G_*.pth"),
             net_g,
             optim_g,
         )
         epoch_str += 1
         global_step = (epoch_str - 1) * len(train_loader)
-        # epoch_str = 1
-        # global_step = 0
-    except:  # 如果首次不能加载，加载pretrain
-        # traceback.print_exc()
+    except Exception:  # 如果首次不能加载，加载pretrain
         epoch_str = 1
         global_step = 0
         if (
             hps.train.pretrained_s2G != ""
-            and hps.train.pretrained_s2G != None
+            and hps.train.pretrained_s2G is not None
             and os.path.exists(hps.train.pretrained_s2G)
         ):
             if rank == 0:
@@ -207,42 +169,26 @@ def run(rank, n_gpus, hps):
                     torch.load(hps.train.pretrained_s2G, map_location="cpu", weights_only=False)["weight"],
                     strict=False,
                 )
-                if torch.cuda.is_available()
+                if multigpu
                 else net_g.load_state_dict(
                     torch.load(hps.train.pretrained_s2G, map_location="cpu", weights_only=False)["weight"],
                     strict=False,
                 ),
-            )  ##测试不加载优化器
-        # if hps.train.pretrained_s2D != ""and hps.train.pretrained_s2D != None and os.path.exists(hps.train.pretrained_s2D):
-        #     if rank == 0:
-        #         logger.info("loaded pretrained %s" % hps.train.pretrained_s2D)
-        #     print(
-        #         net_d.module.load_state_dict(
-        #             torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
-        #         ) if torch.cuda.is_available() else net_d.load_state_dict(
-        #             torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"]
-        #         )
-        #     )
-
-    # scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    # scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
+            )
 
     scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=-1)
-    # scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
-    #     optim_d, gamma=hps.train.lr_decay, last_epoch=-1
-    # )
+
     for _ in range(epoch_str):
         scheduler_g.step()
-        # scheduler_d.step()
 
-    scaler = GradScaler(enabled=hps.train.fp16_run)
+    scaler = GradScaler(device=device.type, enabled=hps.train.fp16_run)
 
     net_d = optim_d = scheduler_d = None
-    print("start training from epoch %s" % epoch_str)
+    print(f"start training from epoch {epoch_str}")
     for epoch in range(epoch_str, hps.train.epochs + 1):
         if rank == 0:
             train_and_evaluate(
-                rank,
+                device,
                 epoch,
                 hps,
                 [net_g, net_d],
@@ -256,7 +202,7 @@ def run(rank, n_gpus, hps):
             )
         else:
             train_and_evaluate(
-                rank,
+                device,
                 epoch,
                 hps,
                 [net_g, net_d],
@@ -273,7 +219,7 @@ def run(rank, n_gpus, hps):
 
 
 def train_and_evaluate(
-    rank,
+    device: torch.device,
     epoch,
     hps,
     nets,
@@ -309,40 +255,14 @@ def train_and_evaluate(
     for batch_idx, (ssl, spec, mel, ssl_lengths, spec_lengths, text, text_lengths, mel_lengths) in enumerate(
         tqdm(train_loader)
     ):
-        if torch.cuda.is_available():
-            spec, spec_lengths = (
-                spec.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-                spec_lengths.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-            )
-            mel, mel_lengths = mel.cuda(rank, non_blocking=True), mel_lengths.cuda(rank, non_blocking=True)
-            ssl = ssl.cuda(rank, non_blocking=True)
-            ssl.requires_grad = False
-            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = (
-                text.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-                text_lengths.cuda(
-                    rank,
-                    non_blocking=True,
-                ),
-            )
-        else:
-            spec, spec_lengths = spec.to(device), spec_lengths.to(device)
-            mel, mel_lengths = mel.to(device), mel_lengths.to(device)
-            ssl = ssl.to(device)
-            ssl.requires_grad = False
-            # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = text.to(device), text_lengths.to(device)
+        spec, spec_lengths = spec.to(device, non_blocking=True), spec_lengths.to(device, non_blocking=True)
+        mel, mel_lengths = mel.to(device, non_blocking=True), mel_lengths.to(device, non_blocking=True)
+        ssl = ssl.to(device, non_blocking=True)
+        ssl.requires_grad = False
+        # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
+        text, text_lengths = text.to(device, non_blocking=True), text_lengths.to(device, non_blocking=True)
 
-        with autocast(enabled=hps.train.fp16_run):
+        with autocast(device_type=device.type, dtype=torch.float16, enabled=hps.train.fp16_run):
             cfm_loss = net_g(
                 ssl,
                 spec,
@@ -362,7 +282,7 @@ def train_and_evaluate(
         scaler.step(optim_g)
         scaler.update()
 
-        if rank == 0:
+        if device.index == 0:
             if global_step % hps.train.log_interval == 0:
                 lr = optim_g.param_groups[0]["lr"]
                 # losses = [commit_loss,cfm_loss,mel_loss,loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
@@ -376,12 +296,6 @@ def train_and_evaluate(
                 logger.info([x.item() for x in losses] + [global_step, lr])
 
                 scalar_dict = {"loss/g/total": loss_gen_all, "learning_rate": lr, "grad_norm_g": grad_norm_g}
-                # image_dict = {
-                #     "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                #     "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                #     "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                #     "all/stats_ssl": utils.plot_spectrogram_to_numpy(stats_ssl[0].data.cpu().numpy()),
-                # }
                 utils.summarize(
                     writer=writer,
                     global_step=global_step,
@@ -389,16 +303,8 @@ def train_and_evaluate(
                     scalars=scalar_dict,
                 )
 
-            # if global_step % hps.train.eval_interval == 0:
-            #     # evaluate(hps, net_g, eval_loader, writer_eval)
-            #     utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,os.path.join(hps.s2_ckpt_dir, "G_{}.pth".format(global_step)),scaler)
-            #     # utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,os.path.join(hps.s2_ckpt_dir, "D_{}.pth".format(global_step)),scaler)
-            #     # keep_ckpts = getattr(hps.train, 'keep_ckpts', 3)
-            #     # if keep_ckpts > 0:
-            #     #     utils.clean_checkpoints(path_to_models=hps.s2_ckpt_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
         global_step += 1
-    if epoch % hps.train.save_every_epoch == 0 and rank == 0:
+    if epoch % hps.train.save_every_epoch == 0 and device.index == 0:
         if hps.train.if_save_latest == 0:
             utils.save_checkpoint(
                 net_g,
@@ -406,19 +312,11 @@ def train_and_evaluate(
                 hps.train.learning_rate,
                 epoch,
                 os.path.join(
-                    "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version),
-                    "G_{}.pth".format(global_step),
+                    f"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
+                    f"G_{global_step}.pth",
                 ),
             )
-            # utils.save_checkpoint(
-            #     net_d,
-            #     optim_d,
-            #     hps.train.learning_rate,
-            #     epoch,
-            #     os.path.join(
-            #         "%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_{}.pth".format(global_step)
-            #     ),
-            # )
+
         else:
             utils.save_checkpoint(
                 net_g,
@@ -426,41 +324,27 @@ def train_and_evaluate(
                 hps.train.learning_rate,
                 epoch,
                 os.path.join(
-                    "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version),
-                    "G_{}.pth".format(233333333333),
+                    f"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
+                    "G_233333333333.pth",
                 ),
             )
-            # utils.save_checkpoint(
-            #     net_d,
-            #     optim_d,
-            #     hps.train.learning_rate,
-            #     epoch,
-            #     os.path.join(
-            #         "%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_{}.pth".format(233333333333)
-            #     ),
-            # )
-        if rank == 0 and hps.train.if_save_every_weights == True:
+
+        if device.index == 0 and hps.train.if_save_every_weights is True:
             if hasattr(net_g, "module"):
                 ckpt = net_g.module.state_dict()
             else:
                 ckpt = net_g.state_dict()
-            logger.info(
-                "saving ckpt %s_e%s:%s"
-                % (
-                    hps.name,
-                    epoch,
-                    savee(
-                        ckpt,
-                        hps.name + "_e%s_s%s" % (epoch, global_step),
-                        epoch,
-                        global_step,
-                        hps,
-                    ),
-                )
+            save_info = savee(
+                ckpt,
+                hps.name + f"_e{epoch}_s{global_step}",
+                epoch,
+                global_step,
+                hps,
             )
+            logger.info(f"saving ckpt {hps.name}_e{epoch}:{save_info}")
 
-    if rank == 0:
-        logger.info("====> Epoch: {}".format(epoch))
+    if device.index == 0:
+        logger.info(f"====> Epoch: {epoch}")
 
 
 if __name__ == "__main__":

GPT_SoVITS/s2_train_v3_lora.py

@@ -3,10 +3,9 @@ import warnings
 warnings.filterwarnings("ignore")
 import os
 
-import utils
+import GPT_SoVITS.utils as utils
 
 hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
 import logging
 
 import torch
@@ -24,19 +23,20 @@ logging.getLogger("numba").setLevel(logging.INFO)
 from collections import OrderedDict as od
 from random import randint
 
-from module import commons
-from module.data_utils import (
+from peft import LoraConfig, get_peft_model
+
+from GPT_SoVITS.module import commons
+from GPT_SoVITS.module.data_utils import (
     DistributedBucketSampler,
     TextAudioSpeakerCollateV3,
-    TextAudioSpeakerLoaderV3,
     TextAudioSpeakerCollateV4,
+    TextAudioSpeakerLoaderV3,
     TextAudioSpeakerLoaderV4,
 )
-from module.models import (
+from GPT_SoVITS.module.models import (
     SynthesizerTrnV3 as SynthesizerTrn,
 )
-from peft import LoraConfig, get_peft_model
-from process_ckpt import savee
+from GPT_SoVITS.process_ckpt import savee
 
 torch.backends.cudnn.benchmark = False
 torch.backends.cudnn.deterministic = False
@@ -343,7 +343,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 epoch,
                 os.path.join(save_root, "G_{}.pth".format(233333333333)),
             )
-        if rank == 0 and hps.train.if_save_every_weights == True:
+        if rank == 0 and hps.train.if_save_every_weights is True:
             if hasattr(net_g, "module"):
                 ckpt = net_g.module.state_dict()
             else:

GPT_SoVITS/sv.py

@@ -1,21 +1,22 @@
-import sys
 import os
-import torch
+import sys
+
+import torch
+import torchaudio
+
+from GPT_SoVITS.eres2net.ERes2NetV2 import ERes2NetV2
 
-sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
 sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
-from ERes2NetV2 import ERes2NetV2
-import kaldi as Kaldi
 
 
 class SV:
     def __init__(self, device, is_half):
-        pretrained_state = torch.load(sv_path, map_location="cpu", weights_only=False)
+        pretrained_state = torch.load(sv_path, map_location="cpu")
         embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
         embedding_model.load_state_dict(pretrained_state)
         embedding_model.eval()
         self.embedding_model = embedding_model
-        if is_half == False:
+        if is_half is False:
             self.embedding_model = self.embedding_model.to(device)
         else:
             self.embedding_model = self.embedding_model.half().to(device)
@@ -23,10 +24,15 @@ class SV:
 
     def compute_embedding3(self, wav):
         with torch.no_grad():
-            if self.is_half == True:
+            if self.is_half is True:
                 wav = wav.half()
             feat = torch.stack(
-                [Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
+                [
+                    torchaudio.compliance.kaldi.fbank(
+                        wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0
+                    )
+                    for wav0 in wav
+                ]
             )
             sv_emb = self.embedding_model.forward3(feat)
         return sv_emb

GPT_SoVITS/text/LangSegmenter/langsegmenter.py

@@ -1,40 +1,41 @@
 import logging
 import re
+from pathlib import Path
 
-# jieba静音
+import fast_langdetect
 import jieba
+from split_lang import LangSplitter
+
 jieba.setLogLevel(logging.CRITICAL)
 
-# 更改fast_langdetect大模型位置
-from pathlib import Path
-import fast_langdetect
-fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(fast_langdetect.infer.LangDetectConfig(cache_dir=Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect"))
-
-
-from split_lang import LangSplitter
+fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(
+    fast_langdetect.infer.LangDetectConfig(
+        cache_dir=str(Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect")
+    )
+)
 
 
 def full_en(text):
-    pattern = r'^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$'
+    pattern = r"^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$"
     return bool(re.match(pattern, text))
 
 
 def full_cjk(text):
     # 来自wiki
     cjk_ranges = [
-        (0x4E00, 0x9FFF),        # CJK Unified Ideographs
-        (0x3400, 0x4DB5),        # CJK Extension A
-        (0x20000, 0x2A6DD),      # CJK Extension B
-        (0x2A700, 0x2B73F),      # CJK Extension C
-        (0x2B740, 0x2B81F),      # CJK Extension D
-        (0x2B820, 0x2CEAF),      # CJK Extension E
-        (0x2CEB0, 0x2EBEF),      # CJK Extension F
-        (0x30000, 0x3134A),      # CJK Extension G
-        (0x31350, 0x323AF),      # CJK Extension H
-        (0x2EBF0, 0x2EE5D),      # CJK Extension H
+        (0x4E00, 0x9FFF),  # CJK Unified Ideographs
+        (0x3400, 0x4DB5),  # CJK Extension A
+        (0x20000, 0x2A6DD),  # CJK Extension B
+        (0x2A700, 0x2B73F),  # CJK Extension C
+        (0x2B740, 0x2B81F),  # CJK Extension D
+        (0x2B820, 0x2CEAF),  # CJK Extension E
+        (0x2CEB0, 0x2EBEF),  # CJK Extension F
+        (0x30000, 0x3134A),  # CJK Extension G
+        (0x31350, 0x323AF),  # CJK Extension H
+        (0x2EBF0, 0x2EE5D),  # CJK Extension H
     ]
 
-    pattern = r'[0-9、-〜。！？.!?… /]+$'
+    pattern = r"[0-9、-〜。！？.!?… /]+$"
 
     cjk_text = ""
     for char in text:
@@ -45,7 +46,7 @@ def full_cjk(text):
     return cjk_text
 
 
-def split_jako(tag_lang,item):
+def split_jako(tag_lang, item):
     if tag_lang == "ja":
         pattern = r"([\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]+(?:[0-9、-〜。！？.!?… ]+[\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]*)*)"
     else:
@@ -53,41 +54,42 @@ def split_jako(tag_lang,item):
 
     lang_list: list[dict] = []
     tag = 0
-    for match in re.finditer(pattern, item['text']):
+    for match in re.finditer(pattern, item["text"]):
         if match.start() > tag:
-            lang_list.append({'lang':item['lang'],'text':item['text'][tag:match.start()]})
+            lang_list.append({"lang": item["lang"], "text": item["text"][tag : match.start()]})
 
         tag = match.end()
-        lang_list.append({'lang':tag_lang,'text':item['text'][match.start():match.end()]})
+        lang_list.append({"lang": tag_lang, "text": item["text"][match.start() : match.end()]})
 
-    if tag < len(item['text']):
-        lang_list.append({'lang':item['lang'],'text':item['text'][tag:len(item['text'])]})
+    if tag < len(item["text"]):
+        lang_list.append({"lang": item["lang"], "text": item["text"][tag : len(item["text"])]})
 
     return lang_list
 
 
 def merge_lang(lang_list, item):
-    if lang_list and item['lang'] == lang_list[-1]['lang']:
-        lang_list[-1]['text'] += item['text']
+    if lang_list and item["lang"] == lang_list[-1]["lang"]:
+        lang_list[-1]["text"] += item["text"]
     else:
         lang_list.append(item)
     return lang_list
 
 
-class LangSegmenter():
+class LangSegmenter:
     # 默认过滤器, 基于gsv目前四种语言
     DEFAULT_LANG_MAP = {
         "zh": "zh",
         "yue": "zh",  # 粤语
         "wuu": "zh",  # 吴语
         "zh-cn": "zh",
-        "zh-tw": "x", # 繁体设置为x
+        "zh-tw": "x",  # 繁体设置为x
         "ko": "ko",
         "ja": "ja",
         "en": "en",
     }
 
-    def getTexts(text,default_lang = ""):
+    @staticmethod
+    def getTexts(text, default_lang=""):
         lang_splitter = LangSplitter(lang_map=LangSegmenter.DEFAULT_LANG_MAP)
         lang_splitter.merge_across_digit = False
         substr = lang_splitter.split_by_lang(text=text)
@@ -97,31 +99,31 @@ class LangSegmenter():
         have_num = False
 
         for _, item in enumerate(substr):
-            dict_item = {'lang':item.lang,'text':item.text}
+            dict_item = {"lang": item.lang, "text": item.text}
 
-            if dict_item['lang'] == 'digit':
+            if dict_item["lang"] == "digit":
                 if default_lang != "":
-                    dict_item['lang'] = default_lang
+                    dict_item["lang"] = default_lang
                 else:
                     have_num = True
-                lang_list = merge_lang(lang_list,dict_item)
+                lang_list = merge_lang(lang_list, dict_item)
                 continue
 
             # 处理短英文被识别为其他语言的问题
-            if full_en(dict_item['text']):  
-                dict_item['lang'] = 'en'
-                lang_list = merge_lang(lang_list,dict_item)
+            if full_en(dict_item["text"]):
+                dict_item["lang"] = "en"
+                lang_list = merge_lang(lang_list, dict_item)
                 continue
 
             if default_lang != "":
-                dict_item['lang'] = default_lang
-                lang_list = merge_lang(lang_list,dict_item)
+                dict_item["lang"] = default_lang
+                lang_list = merge_lang(lang_list, dict_item)
                 continue
             else:
                 # 处理非日语夹日文的问题(不包含CJK)
                 ja_list: list[dict] = []
-                if dict_item['lang'] != 'ja':
-                    ja_list = split_jako('ja',dict_item)
+                if dict_item["lang"] != "ja":
+                    ja_list = split_jako("ja", dict_item)
 
                 if not ja_list:
                     ja_list.append(dict_item)
@@ -130,8 +132,8 @@ class LangSegmenter():
                 ko_list: list[dict] = []
                 temp_list: list[dict] = []
                 for _, ko_item in enumerate(ja_list):
-                    if ko_item["lang"] != 'ko':
-                        ko_list = split_jako('ko',ko_item)
+                    if ko_item["lang"] != "ko":
+                        ko_list = split_jako("ko", ko_item)
 
                     if ko_list:
                         temp_list.extend(ko_list)
@@ -141,77 +143,76 @@ class LangSegmenter():
                 # 未存在非日韩文夹日韩文
                 if len(temp_list) == 1:
                     # 未知语言检查是否为CJK
-                    if dict_item['lang'] == 'x':
-                        cjk_text = full_cjk(dict_item['text'])
+                    if dict_item["lang"] == "x":
+                        cjk_text = full_cjk(dict_item["text"])
                         if cjk_text:
-                            dict_item = {'lang':'zh','text':cjk_text}
-                            lang_list = merge_lang(lang_list,dict_item)
+                            dict_item = {"lang": "zh", "text": cjk_text}
+                            lang_list = merge_lang(lang_list, dict_item)
                         else:
-                            lang_list = merge_lang(lang_list,dict_item)
+                            lang_list = merge_lang(lang_list, dict_item)
                         continue
                     else:
-                        lang_list = merge_lang(lang_list,dict_item)
+                        lang_list = merge_lang(lang_list, dict_item)
                         continue
 
                 # 存在非日韩文夹日韩文
                 for _, temp_item in enumerate(temp_list):
                     # 未知语言检查是否为CJK
-                    if temp_item['lang'] == 'x':
-                        cjk_text = full_cjk(temp_item['text'])
+                    if temp_item["lang"] == "x":
+                        cjk_text = full_cjk(temp_item["text"])
                         if cjk_text:
-                            lang_list = merge_lang(lang_list,{'lang':'zh','text':cjk_text})
+                            lang_list = merge_lang(lang_list, {"lang": "zh", "text": cjk_text})
                         else:
-                            lang_list = merge_lang(lang_list,temp_item)
+                            lang_list = merge_lang(lang_list, temp_item)
                     else:
-                        lang_list = merge_lang(lang_list,temp_item)
+                        lang_list = merge_lang(lang_list, temp_item)
 
         # 有数字
         if have_num:
             temp_list = lang_list
             lang_list = []
             for i, temp_item in enumerate(temp_list):
-                if temp_item['lang'] == 'digit':
+                if temp_item["lang"] == "digit":
                     if default_lang:
-                        temp_item['lang'] = default_lang
+                        temp_item["lang"] = default_lang
                     elif lang_list and i == len(temp_list) - 1:
-                        temp_item['lang'] = lang_list[-1]['lang']
+                        temp_item["lang"] = lang_list[-1]["lang"]
                     elif not lang_list and i < len(temp_list) - 1:
-                        temp_item['lang'] = temp_list[1]['lang']
+                        temp_item["lang"] = temp_list[1]["lang"]
                     elif lang_list and i < len(temp_list) - 1:
-                        if lang_list[-1]['lang'] == temp_list[i + 1]['lang']:
-                            temp_item['lang'] = lang_list[-1]['lang']
-                        elif lang_list[-1]['text'][-1] in [",",".","!","?","，","。","！","？"]:
-                            temp_item['lang'] = temp_list[i + 1]['lang']
-                        elif temp_list[i + 1]['text'][0] in [",",".","!","?","，","。","！","？"]:
-                            temp_item['lang'] = lang_list[-1]['lang']
-                        elif temp_item['text'][-1] in ["。","."]:
-                            temp_item['lang'] = lang_list[-1]['lang']
-                        elif len(lang_list[-1]['text']) >= len(temp_list[i + 1]['text']):
-                            temp_item['lang'] = lang_list[-1]['lang']
+                        if lang_list[-1]["lang"] == temp_list[i + 1]["lang"]:
+                            temp_item["lang"] = lang_list[-1]["lang"]
+                        elif lang_list[-1]["text"][-1] in [",", ".", "!", "?", "，", "。", "！", "？"]:
+                            temp_item["lang"] = temp_list[i + 1]["lang"]
+                        elif temp_list[i + 1]["text"][0] in [",", ".", "!", "?", "，", "。", "！", "？"]:
+                            temp_item["lang"] = lang_list[-1]["lang"]
+                        elif temp_item["text"][-1] in ["。", "."]:
+                            temp_item["lang"] = lang_list[-1]["lang"]
+                        elif len(lang_list[-1]["text"]) >= len(temp_list[i + 1]["text"]):
+                            temp_item["lang"] = lang_list[-1]["lang"]
                         else:
-                            temp_item['lang'] = temp_list[i + 1]['lang']
+                            temp_item["lang"] = temp_list[i + 1]["lang"]
                     else:
-                        temp_item['lang'] = 'zh'
-
-                lang_list = merge_lang(lang_list,temp_item)
+                        temp_item["lang"] = "zh"
 
+                lang_list = merge_lang(lang_list, temp_item)
 
         # 筛X
         temp_list = lang_list
         lang_list = []
         for _, temp_item in enumerate(temp_list):
-            if temp_item['lang'] == 'x':
+            if temp_item["lang"] == "x":
                 if lang_list:
-                    temp_item['lang'] = lang_list[-1]['lang']
+                    temp_item["lang"] = lang_list[-1]["lang"]
                 elif len(temp_list) > 1:
-                    temp_item['lang'] = temp_list[1]['lang']
+                    temp_item["lang"] = temp_list[1]["lang"]
                 else:
-                    temp_item['lang'] = 'zh'
+                    temp_item["lang"] = "zh"
 
-            lang_list = merge_lang(lang_list,temp_item)
+            lang_list = merge_lang(lang_list, temp_item)
 
         return lang_list
-    
+
 
 if __name__ == "__main__":
     text = "MyGO?,你也喜欢まいご吗？"
@@ -221,5 +222,5 @@ if __name__ == "__main__":
     print(LangSegmenter.getTexts(text))
 
     text = "当时ThinkPad T60刚刚发布，一同推出的还有一款名为Advanced Dock的扩展坞配件。这款扩展坞通过连接T60底部的插槽，扩展出包括PCIe在内的一大堆接口，并且自带电源，让T60可以安装桌面显卡来提升性能。"
-    print(LangSegmenter.getTexts(text,"zh"))
-    print(LangSegmenter.getTexts(text))
+    print(LangSegmenter.getTexts(text, "zh"))
+    print(LangSegmenter.getTexts(text))

GPT_SoVITS/text/cleaner.py

@@ -1,12 +1,13 @@
-from text import cleaned_text_to_sequence
 import os
+
+from text import cleaned_text_to_sequence
+
 # if os.environ.get("version","v1")=="v1":
 #     from text import chinese
 #     from text.symbols import symbols
 # else:
 #     from text import chinese2 as chinese
 #     from text.symbols2 import symbols
-
 from text import symbols as symbols_v1
 from text import symbols2 as symbols_v2
 
@@ -18,7 +19,7 @@ special = [
 ]
 
 
-def clean_text(text, language, version=None):
+def clean_text(text, language, version=None) -> tuple[list[str], list[int] | None, str]:
     if version is None:
         version = os.environ.get("version", "v2")
     if version == "v1":

GPT_SoVITS/text/en_normalization/expend.py

@@ -3,9 +3,10 @@
 from __future__ import print_function
 
 import re
-import inflect
 import unicodedata
 
+import inflect
+
 # 后缀计量单位替换表
 measurement_map = {
     "m": ["meter", "meters"],
@@ -109,7 +110,7 @@ def _expand_measurement(m):
     num = int(m.group(1).replace(sign, "").replace(".", ""))
     decimal_part = m.group(2)
     # 上面判断的漏洞，比如 0.1 的情况，在这里排除了
-    if decimal_part == None and num == 1:
+    if decimal_part is None and num == 1:
         ptr = 0
     return m.group(1).replace(sign, " " + measurement_map[sign][ptr])
 

GPT_SoVITS/utils.py

@@ -3,9 +3,11 @@ import glob
 import json
 import logging
 import os
+import shutil
 import subprocess
 import sys
 import traceback
+from time import time as ttime
 
 import librosa
 import numpy as np
@@ -42,9 +44,9 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
                 saved_state_dict[k].shape,
                 v.shape,
             )
-        except:
+        except AssertionError:
             traceback.print_exc()
-            print("error, %s is not in the checkpoint" % k)  # shape不对也会，比如text_embedding当cleaner修改时
+            print(f"error, {k} is not in the checkpoint")  # shape不对也会，比如text_embedding当cleaner修改时
             new_state_dict[k] = v
     if hasattr(model, "module"):
         model.module.load_state_dict(new_state_dict)
@@ -60,14 +62,10 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
     return model, optimizer, learning_rate, iteration
 
 
-import shutil
-from time import time as ttime
-
-
 def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
-    tmp_path = "%s.pth" % (ttime())
+    tmp_path = f"{ttime()}.pth"
     torch.save(fea, tmp_path)
     shutil.move(tmp_path, "%s/%s" % (dir, name))
 
@@ -136,8 +134,7 @@ def plot_spectrogram_to_numpy(spectrogram):
     plt.tight_layout()
 
     fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    data = np.asarray(fig.canvas.renderer.buffer_rgba(), dtype=np.uint8)[:, :, :3]
     plt.close()
     return data
 
@@ -169,8 +166,7 @@ def plot_alignment_to_numpy(alignment, info=None):
     plt.tight_layout()
 
     fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    data = np.asarray(fig.canvas.renderer.buffer_rgba(), dtype=np.uint8)[:, :, :3]
     plt.close()
     return data
 
@@ -245,18 +241,31 @@ def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_tim
     import re
 
     ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-    name_key = lambda _f: int(re.compile("._(\d+)\.pth").match(_f).group(1))
-    time_key = lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))
+
+    def name_key(_f):
+        return int(re.compile("._(\d+)\.pth").match(_f).group(1))
+
+    def time_key(_f):
+        return os.path.getmtime(os.path.join(path_to_models, _f))
+
     sort_key = time_key if sort_by_time else name_key
-    x_sorted = lambda _x: sorted(
-        [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
-        key=sort_key,
-    )
+
+    def x_sorted(_x):
+        return sorted(
+            [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
+            key=sort_key,
+        )
+
     to_del = [
         os.path.join(path_to_models, fn) for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
     ]
-    del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-    del_routine = lambda x: [os.remove(x), del_info(x)]
+
+    def del_info(fn):
+        return logger.info(f".. Free up space by deleting ckpt {fn}")
+
+    def del_routine(x):
+        return [os.remove(x), del_info(x)]
+
     rs = [del_routine(fn) for fn in to_del]
 
 
@@ -324,7 +333,7 @@ def get_logger(model_dir, filename="train.log"):
 class HParams:
     def __init__(self, **kwargs):
         for k, v in kwargs.items():
-            if type(v) == dict:
+            if isinstance(v, dict):
                 v = HParams(**v)
             self[k] = v
 
@@ -352,10 +361,13 @@ class HParams:
     def __repr__(self):
         return self.__dict__.__repr__()
 
+    def __getstate__(self):
+        def to_dict(obj):
+            if isinstance(obj, HParams):
+                return {k: to_dict(v) for k, v in obj.items()}
+            elif isinstance(obj, dict):
+                return {k: to_dict(v) for k, v in obj.items()}
+            else:
+                return obj
 
-if __name__ == "__main__":
-    print(
-        load_wav_to_torch(
-            "/home/fish/wenetspeech/dataset_vq/Y0000022499_wHFSeHEx9CM/S00261.flac",
-        )
-    )
+        return to_dict(self)