Merge 57ef262ad0918267c3ce356aadbdbfb3414f02e0 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: |

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/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/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

@@ -315,7 +315,7 @@ 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 +331,22 @@ 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 +372,26 @@ 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 +400,7 @@ 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 +419,7 @@ 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 +428,15 @@ 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 +492,7 @@ 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/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 +1,134 @@
+# -*- coding: utf-8 -*-
+
+import sys
+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"]
+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 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
+
+# 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
+import shutil
+
+
+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 == 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 == 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 == 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/s1_train.py

@@ -1,29 +1,31 @@
 # 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
 
 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 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
+
+os.environ["MASTER_ADDR"] = "localhost"
+if platform.system() == "Windows":
+    os.environ["USE_LIBUV"] = "0"
 
 
 class my_model_ckpt(ModelCheckpoint):
@@ -49,35 +51,30 @@ class my_model_ckpt(ModelCheckpoint):
             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
+                    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))
                 self._save_topk_checkpoint(trainer, monitor_candidates)
-                if self.if_save_latest == True:
+                if self.if_save_latest is True:
                     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:
+                if self.if_save_every_weights is True:
                     to_save_od = 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)
 
@@ -91,6 +88,14 @@ 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 = 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(
         config=config,
@@ -106,8 +111,7 @@ 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",
@@ -117,9 +121,7 @@ def main(args):
         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,

GPT_SoVITS/s2_train.py

@@ -6,7 +6,6 @@ import os
 import utils
 
 hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
 import logging
 
 import torch

GPT_SoVITS/s2_train_v3.py

@@ -6,7 +6,6 @@ import os
 import utils
 
 hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
 import logging
 
 import torch

GPT_SoVITS/s2_train_v3_lora.py

@@ -6,7 +6,6 @@ import os
 import utils
 
 hps = utils.get_hparams(stage=2)
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
 import logging
 
 import torch
@@ -28,8 +27,8 @@ from module import commons
 from module.data_utils import (
     DistributedBucketSampler,
     TextAudioSpeakerCollateV3,
-    TextAudioSpeakerLoaderV3,
     TextAudioSpeakerCollateV4,
+    TextAudioSpeakerLoaderV3,
     TextAudioSpeakerLoaderV4,
 )
 from module.models import (

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":

README.md

@@ -248,13 +248,13 @@ if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-
 #### Others
 
 ```bash
-python webui.py <language(optional)>
+PYTHONPATH=. python webui.py <language(optional)>
 ```
 
 if you want to switch to V1,then
 
 ```bash
-python webui.py v1 <language(optional)>
+PYTHONPATH=. python webui.py v1 <language(optional)>
 ```
 
 Or maunally switch version in WebUI
@@ -285,7 +285,7 @@ python GPT_SoVITS/inference_webui.py <language(optional)>
 OR
 
 ```bash
-python webui.py
+PYTHONPATH=. python webui.py
 ```
 
 then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`

config.py

@@ -145,9 +145,20 @@ webui_port_subfix = 9871
 api_port = 9880
 
 
+def get_dtype(idx: int):
+    if not torch.cuda.is_available():
+        return torch.float32
+    capability = torch.cuda.get_device_capability(idx)
+    major, minor = capability
+    sm_version = major + minor / 10.0
+    if sm_version > 6.1:
+        return torch.float16
+    return torch.float32
+
+
 # Thanks to the contribution of @Karasukaigan and @XXXXRT666
 def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
-    cpu = torch.device("cpu")
+    cpu = torch.device("cpu:0")
     cuda = torch.device(f"cuda:{idx}")
     if not torch.cuda.is_available():
         return cpu, torch.float32, 0.0, 0.0
@@ -161,7 +172,7 @@ def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, flo
     is_16_series = bool(re.search(r"16\d{2}", name)) and sm_version == 7.5
     if mem_gb < 4 or sm_version < 5.3:
         return cpu, torch.float32, 0.0, 0.0
-    if sm_version == 6.1 or is_16_series == True:
+    if sm_version == 6.1 or is_16_series is True:
         return cuda, torch.float32, sm_version, mem_gb
     if sm_version > 6.1:
         return cuda, torch.float16, sm_version, mem_gb
@@ -190,8 +201,11 @@ if not GPU_INFOS:
     IS_GPU = False
     GPU_INFOS.append(CPU_INFO)
     GPU_INDEX.add(0)
+if torch.mps.is_available():
+    infer_device = torch.device("mps:0")
+else:
+    infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
 
-infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
 is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)
 
 
@@ -216,3 +230,22 @@ class Config:
         self.webui_port_subfix = webui_port_subfix
 
         self.api_port = api_port
+
+
+def get_implement(device: torch.device):
+    if torch.cuda.is_available():
+        idx = device.index
+        capability = torch.cuda.get_device_capability(idx)
+        major, minor = capability
+        sm_version = major + minor / 10.0
+        if sm_version >= 7.5:
+            return "flash_attn"
+        else:
+            if sys.platform == "linux":
+                return "sage_attn"
+            else:
+                return "naive"
+    elif torch.mps.is_available():
+        return "mlx"
+    else:
+        return "naive"

docs/cn/README.md

@@ -236,13 +236,13 @@ D:\GPT-SoVITS\xxx/xxx.wav|xxx|zh|我爱玩原神.
 #### 其他
 
 ```bash
-python webui.py <language(optional)>
+PYTHONPATH=. python webui.py <language(optional)>
 ```
 
 若想使用 V1,则
 
 ```bash
-python webui.py v1 <language(optional)>
+PYTHONPATH=. python webui.py v1 <language(optional)>
 ```
 
 或者在 webUI 内动态切换
@@ -273,7 +273,7 @@ python GPT_SoVITS/inference_webui.py <language(optional)>
 或者
 
 ```bash
-python webui.py
+PYTHONPATH=. python webui.py
 ```
 
 然后在 `1-GPT-SoVITS-TTS/1C-推理` 中打开推理 webUI

docs/ja/README.md

@@ -222,13 +222,13 @@ V1 に切り替えたい場合は、`go-webui-v1.bat`をダブルクリックす
 #### その他
 
 ```bash
-python webui.py <言語(オプション)>
+PYTHONPATH=. python webui.py <言語(オプション)>
 ```
 
 V1 に切り替えたい場合は
 
 ```bash
-python webui.py v1 <言語(オプション)>
+PYTHONPATH=. python webui.py v1 <言語(オプション)>
 ```
 
 または WebUI で手動でバージョンを切り替えてください.
@@ -259,7 +259,7 @@ python GPT_SoVITS/inference_webui.py <言語(オプション)>
 または
 
 ```bash
-python webui.py
+PYTHONPATH=. python webui.py
 ```
 
 その後、`1-GPT-SoVITS-TTS/1C-inference`で推論 webui を開きます.

docs/ko/README.md

@@ -228,13 +228,13 @@ V1으로 전환하려면, `go-webui-v1.bat`을 더블 클릭하거나 `go-webui-
 #### 기타
 
 ```bash
-python webui.py <언어(옵션)>
+PYTHONPATH=. python webui.py <언어(옵션)>
 ```
 
 V1으로 전환하려면,
 
 ```bash
-python webui.py v1 <언어(옵션)>
+PYTHONPATH=. python webui.py v1 <언어(옵션)>
 ```
 
 또는 WebUI에서 수동으로 버전을 전환하십시오.
@@ -265,7 +265,7 @@ python GPT_SoVITS/inference_webui.py <언어(옵션)>
 또는
 
 ```bash
-python webui.py
+PYTHONPATH=. python webui.py
 ```
 
 그런 다음 `1-GPT-SoVITS-TTS/1C-inference`에서 추론 webui를 엽니다.

docs/tr/README.md

@@ -229,13 +229,13 @@ V1'e geçmek istiyorsanız, `go-webui-v1.bat` dosyasına çift tıklayın veya `
 #### Diğerleri
 
 ```bash
-python webui.py <dil(isteğe bağlı)>
+PYTHONPATH=. python webui.py <dil(isteğe bağlı)>
 ```
 
 V1'e geçmek istiyorsanız,
 
 ```bash
-python webui.py v1 <dil(isteğe bağlı)>
+PYTHONPATH=. python webui.py v1 <dil(isteğe bağlı)>
 ```
 
 veya WebUI'de manuel olarak sürüm değiştirin.
@@ -266,7 +266,7 @@ python GPT_SoVITS/inference_webui.py <dil(isteğe bağlı)>
 VEYA
 
 ```bash
-python webui.py
+PYTHONPATH=. python webui.py
 ```
 
 ardından çıkarım webui'sini `1-GPT-SoVITS-TTS/1C-inference` adresinde açın.

go-webui.bat

@@ -1,6 +1,7 @@
 set "SCRIPT_DIR=%~dp0"
 set "SCRIPT_DIR=%SCRIPT_DIR:~0,-1%"
 cd /d "%SCRIPT_DIR%"
-set "PATH=%SCRIPT_DIR%\runtime;%PATH%"
+set "PATH=%SCRIPT_DIR%\runtime"
+set "PYTHONPATH=%SCRIPT_DIR%"
 runtime\python.exe -I webui.py zh_CN
 pause

go-webui.ps1

@@ -2,6 +2,7 @@ $ErrorActionPreference = "SilentlyContinue"
 chcp 65001
 Set-Location $PSScriptRoot
 $runtimePath = Join-Path $PSScriptRoot "runtime"
-$env:PATH = "$runtimePath;$env:PATH"
+$env:PATH = "$runtimePath"
+$env:PYTHONPATH = "$runtimePath"
 & "$runtimePath\python.exe" -I "$PSScriptRoot\webui.py" zh_CN
 pause

gpt-sovits_kaggle.ipynb

@@ -1,243 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9fd922fb",
-   "metadata": {},
-   "source": [
-    "# Deprecated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "45857cb2",
-   "metadata": {
-    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
-    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
-    "execution": {
-     "iopub.execute_input": "2024-02-18T14:43:46.735480Z",
-     "iopub.status.busy": "2024-02-18T14:43:46.735183Z",
-     "iopub.status.idle": "2024-02-18T14:48:10.724175Z",
-     "shell.execute_reply": "2024-02-18T14:48:10.723059Z"
-    },
-    "papermill": {
-     "duration": 263.994935,
-     "end_time": "2024-02-18T14:48:10.726613",
-     "exception": false,
-     "start_time": "2024-02-18T14:43:46.731678",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!git clone https://github.com/RVC-Boss/GPT-SoVITS.git\n",
-    "%cd GPT-SoVITS\n",
-    "!apt-get update && apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && git lfs install\n",
-    "!pip install -r requirements.txt\n",
-    "!pip install -r extra-req.txt --no-deps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b9d346b4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-18T14:48:10.815802Z",
-     "iopub.status.busy": "2024-02-18T14:48:10.814899Z",
-     "iopub.status.idle": "2024-02-18T14:50:31.253276Z",
-     "shell.execute_reply": "2024-02-18T14:50:31.252024Z"
-    },
-    "papermill": {
-     "duration": 140.484893,
-     "end_time": "2024-02-18T14:50:31.255720",
-     "exception": false,
-     "start_time": "2024-02-18T14:48:10.770827",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# @title Download pretrained models 下载预训练模型\n",
-    "!mkdir -p /kaggle/working/GPT-SoVITS/GPT_SoVITS/pretrained_models\n",
-    "!mkdir -p /kaggle/working/GPT-SoVITS/tools/asr/models\n",
-    "!mkdir -p /kaggle/working/GPT-SoVITS/tools/uvr5\n",
-    "%cd /kaggle/working/GPT-SoVITS/GPT_SoVITS/pretrained_models\n",
-    "!git clone https://huggingface.co/lj1995/GPT-SoVITS\n",
-    "%cd /kaggle/working/GPT-SoVITS/tools/asr/models\n",
-    "!git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git\n",
-    "!git clone https://www.modelscope.cn/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch.git\n",
-    "!git clone https://www.modelscope.cn/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git\n",
-    "# # @title UVR5 pretrains 安装uvr5模型\n",
-    "%cd /kaggle/working/GPT-SoVITS/tools/uvr5\n",
-    "!git clone https://huggingface.co/Delik/uvr5_weights\n",
-    "!git config core.sparseCheckout true\n",
-    "!mv /kaggle/working/GPT-SoVITS/GPT_SoVITS/pretrained_models/GPT-SoVITS/* /kaggle/working/GPT-SoVITS/GPT_SoVITS/pretrained_models/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ea94d245",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-18T14:29:01.071549Z",
-     "iopub.status.busy": "2024-02-18T14:29:01.070592Z",
-     "iopub.status.idle": "2024-02-18T14:40:45.318368Z",
-     "shell.execute_reply": "2024-02-18T14:40:45.317130Z",
-     "shell.execute_reply.started": "2024-02-18T14:29:01.071512Z"
-    },
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": false,
-     "start_time": "2024-02-18T14:50:31.309013",
-     "status": "running"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# @title launch WebUI 启动WebUI\n",
-    "%cd /kaggle/working/GPT-SoVITS/\n",
-    "!npm install -g localtunnel\n",
-    "import subprocess\n",
-    "import threading\n",
-    "import time\n",
-    "import socket\n",
-    "import urllib.request\n",
-    "\n",
-    "\n",
-    "def iframe_thread(port):\n",
-    "    while True:\n",
-    "        time.sleep(0.5)\n",
-    "        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
-    "        result = sock.connect_ex((\"127.0.0.1\", port))\n",
-    "        if result == 0:\n",
-    "            break\n",
-    "        sock.close()\n",
-    "\n",
-    "        from colorama import Fore, Style\n",
-    "    print(\n",
-    "        Fore.GREEN + \"\\nIP: \",\n",
-    "        Fore.RED,\n",
-    "        urllib.request.urlopen(\"https://ipv4.icanhazip.com\").read().decode(\"utf8\").strip(\"\\n\"),\n",
-    "        \"\\n\",\n",
-    "        Style.RESET_ALL,\n",
-    "    )\n",
-    "    p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n",
-    "    for line in p.stdout:\n",
-    "        print(line.decode(), end=\"\")\n",
-    "\n",
-    "\n",
-    "threading.Thread(target=iframe_thread, daemon=True, args=(9874,)).start()\n",
-    "\n",
-    "!python  webui.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dda88a6d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-18T14:40:56.880608Z",
-     "iopub.status.busy": "2024-02-18T14:40:56.879879Z"
-    },
-    "papermill": {
-     "duration": null,
-     "end_time": null,
-     "exception": null,
-     "start_time": null,
-     "status": "pending"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# 开启推理页面\n",
-    "%cd /kaggle/working/GPT-SoVITS/\n",
-    "!npm install -g localtunnel\n",
-    "import threading\n",
-    "\n",
-    "\n",
-    "def iframe_thread(port):\n",
-    "    while True:\n",
-    "        time.sleep(0.5)\n",
-    "        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
-    "        result = sock.connect_ex((\"127.0.0.1\", port))\n",
-    "        if result == 0:\n",
-    "            break\n",
-    "        sock.close()\n",
-    "\n",
-    "        from colorama import Fore, Style\n",
-    "    print(\n",
-    "        Fore.GREEN + \"\\nIP: \",\n",
-    "        Fore.RED,\n",
-    "        urllib.request.urlopen(\"https://ipv4.icanhazip.com\").read().decode(\"utf8\").strip(\"\\n\"),\n",
-    "        \"\\n\",\n",
-    "        Style.RESET_ALL,\n",
-    "    )\n",
-    "    p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n",
-    "    for line in p.stdout:\n",
-    "        print(line.decode(), end=\"\")\n",
-    "\n",
-    "\n",
-    "threading.Thread(target=iframe_thread, daemon=True, args=(9872,)).start()\n",
-    "\n",
-    "!python  ./GPT_SoVITS/inference_webui.py"
-   ]
-  }
- ],
- "metadata": {
-  "kaggle": {
-   "accelerator": "nvidiaTeslaT4",
-   "dataSources": [
-    {
-     "datasetId": 4459328,
-     "sourceId": 7649639,
-     "sourceType": "datasetVersion"
-    }
-   ],
-   "dockerImageVersionId": 30646,
-   "isGpuEnabled": true,
-   "isInternetEnabled": true,
-   "language": "python",
-   "sourceType": "notebook"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  },
-  "papermill": {
-   "default_parameters": {},
-   "duration": null,
-   "end_time": null,
-   "environment_variables": {},
-   "exception": null,
-   "input_path": "__notebook__.ipynb",
-   "output_path": "__notebook__.ipynb",
-   "parameters": {},
-   "start_time": "2024-02-18T14:43:44.011910",
-   "version": "2.5.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

install.ps1

@@ -40,6 +40,10 @@ function Write-Info($msg) {
     Write-Host "[INFO]:" -ForegroundColor Green -NoNewline
     Write-Host " $msg"
 }
+function Write-Warning($msg) {
+    Write-Host "[Warning]:" -ForegroundColor Yellow -NoNewline
+    Write-Host " $msg"
+}
 function Write-Success($msg) {
     Write-Host "[SUCCESS]:" -ForegroundColor Blue -NoNewline
     Write-Host " $msg"
@@ -137,7 +141,7 @@ chcp 65001
 Set-Location $PSScriptRoot
 
 Write-Info "Installing FFmpeg & CMake..."
-Invoke-Conda  ffmpeg cmake
+Invoke-Conda  ffmpeg cmake vc14_runtime
 Write-Success "FFmpeg & CMake Installed"
 
 $PretrainedURL  = ""
@@ -208,12 +212,30 @@ if ($DownloadUVR5) {
 
 switch ($Device) {
     "CU128" {
+        $cudaLine = nvidia-smi | Select-String "CUDA Version"
+        $version = ($cudaLine -split "CUDA Version:")[1].Trim()
+        Write-Info "Maximum CUDA Version Supported By Current Driver: $version"
+        if ([version](nvidia-smi | Select-String "CUDA Version" | ForEach-Object { ($_ -split "CUDA Version:")[1].Trim() }) -ge [version]"12.8") {
+            Write-Warning "CUDA 12.8 Is Not Supported By Current Driver"
+        }
         Write-Info "Installing PyTorch For CUDA 12.8..."
         Invoke-Pip torch torchaudio --index-url "https://download.pytorch.org/whl/cu128"
+        Invoke-Conda cuda-nvcc=12.8
+        Invoke-Pip psutil ninja packaging wheel "setuptools>=42"
+        Invoke-Pip flash-attn -i https://xxxxrt666.github.io/PIP-Index/ --no-build-isolation
     }
     "CU126" {
+        $cudaLine = nvidia-smi | Select-String "CUDA Version"
+        $version = ($cudaLine -split "CUDA Version:")[1].Trim()
+        Write-Info "Maximum CUDA Version Supported By Current Driver: $version"
+        if ([version](nvidia-smi | Select-String "CUDA Version" | ForEach-Object { ($_ -split "CUDA Version:")[1].Trim() }) -ge [version]"12.8") {
+            Write-Warning "CUDA 12.6 Is Not Supported By Current Driver"
+        }
         Write-Info "Installing PyTorch For CUDA 12.6..."
         Invoke-Pip torch torchaudio --index-url "https://download.pytorch.org/whl/cu126"
+        Invoke-Conda cuda-nvcc=12.6
+        Invoke-Pip psutil ninja packaging wheel "setuptools>=42"
+        Invoke-Pip flash-attn -i https://xxxxrt666.github.io/PIP-Index/ --no-build-isolation
     }
     "CPU" {
         Write-Info "Installing PyTorch For CPU..."

install.sh

@@ -127,7 +127,7 @@ while [[ $# -gt 0 ]]; do
             USE_ROCM=true
             ;;
         MPS)
-            USE_CPU=true
+            USE_MPS=true
             ;;
         CPU)
             USE_CPU=true
@@ -157,7 +157,7 @@ while [[ $# -gt 0 ]]; do
     esac
 done
 
-if ! $USE_CUDA && ! $USE_ROCM && ! $USE_CPU; then
+if ! $USE_CUDA && ! $USE_ROCM && ! $USE_MPS && ! $USE_CPU; then
     echo -e "${ERROR}Error: Device is REQUIRED"
     echo ""
     print_help
@@ -322,13 +322,29 @@ if [ "$USE_ROCM" = true ] && [ "$WORKFLOW" = false ]; then
 fi
 
 if [ "$USE_CUDA" = true ] && [ "$WORKFLOW" = false ]; then
+    CUDAVERSION=$(nvidia-smi | grep "CUDA Version" | sed -E 's/.*CUDA Version: ([0-9]+\.[0-9]+).*/\1/')
+    echo -e "${INFO}Maximum CUDA Version Supported By Current Driver: $CUDAVERSION"
     if [ "$CUDA" = 128 ]; then
+        if awk "BEGIN {exit !($CUDAVERSION < 12.8)}"; then
+            echo -r "${WARNING}CUDA 12.8 Is Not Supported By Current Driver"
+        fi
         echo -e "${INFO}Installing PyTorch For CUDA 12.8..."
         run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cu128"
+        run_conda_quiet cuda-nvcc=12.8
     elif [ "$CUDA" = 126 ]; then
+        if awk "BEGIN {exit !($CUDAVERSION < 12.6)}"; then
+            echo -r "${WARNING}CUDA 12.6 Is Not Supported By Current Driver"
+        fi
         echo -e "${INFO}Installing PyTorch For CUDA 12.6..."
         run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cu126"
+        run_conda_quiet cuda-nvcc=12.6
     fi
+    run_pip_quiet psutil ninja packaging wheel "setuptools>=42"
+    run_pip_quiet flash-attn -i https://xxxxrt666.github.io/PIP-Index/ --no-build-isolation
+elif [ "$USE_MPS" = true ] && [ "$WORKFLOW" = false ]; then
+    echo -e "${INFO}Installing PyTorch For MPS..."
+    run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cpu"
+    run_pip_quiet mlx
 elif [ "$USE_ROCM" = true ] && [ "$WORKFLOW" = false ]; then
     echo -e "${INFO}Installing PyTorch For ROCm 6.2..."
     run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/rocm6.2"

requirements.txt

@@ -1,45 +1,36 @@
 --no-binary=opencc
-numpy<2.0
-scipy
-tensorboard
-librosa==0.10.2
-numba
-pytorch-lightning>=2.4
-gradio<5
+
+cn2an
 ffmpeg-python
+g2pk2
+g2p_en
+jieba_fast
+kernels
+ko_pron
+modelscope
+opencc
+peft
+pypinyin
+split-lang
+transformers
+tensorboard
+ToJyutping 
+wordsegment
+x_transformers
+
 onnxruntime; platform_machine == "aarch64" or platform_machine == "arm64"
 onnxruntime-gpu; platform_machine == "x86_64" or platform_machine == "AMD64"
-tqdm
-funasr==1.0.27
-cn2an
-pypinyin
-pyopenjtalk>=0.4.1
-g2p_en
-torchaudio
-modelscope==1.10.0
-sentencepiece
-transformers>=4.43,<=4.50
-peft
-chardet
-PyYAML
-psutil
-jieba_fast
-jieba
-split-lang
-fast_langdetect>=0.3.1
-wordsegment
-rotary_embedding_torch
-ToJyutping 
-g2pk2
-ko_pron
-opencc
 python_mecab_ko; sys_platform != 'win32'
-fastapi[standard]>=0.115.2
-x_transformers
-torchmetrics<=1.5
-pydantic<=2.10.6
-ctranslate2>=4.0,<5
-huggingface_hub>=0.13
-tokenizers>=0.13,<1
+
 av>=11
-tqdm
+ctranslate2>=4.0,<5
+fastapi[standard]>=0.115.2
+fast_langdetect>=0.3.1
+funasr==1.0.27
+gradio==5.25.0
+librosa==0.10.2
+numpy<2.0
+pydantic<=2.10.6
+pyopenjtalk>=0.4.1
+pytorch-lightning>=2.4
+torchmetrics<=1.5

tools/i18n/locale/en_US.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb): Best choice for dual-channel reverberation, cannot remove single-channel reverberation;",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho: Removes delay effects. Aggressive mode removes more thoroughly than Normal mode. DeReverb additionally removes reverberation, can remove mono reverberation, but does not clean heavily high-frequency plate reverberation.",
-    "*实验/模型名": "*Experiment/model name",
-    "*文本标注文件": "*Text labelling file",
-    "*训练集音频文件目录": "*Audio dataset folder",
-    "*请上传并填写参考信息": "*Please upload and fill reference information",
-    "*请填写需要合成的目标文本和语种模式": "*Please fill in the target text and language mode for synthesis",
+    "实验/模型名": "Experiment/model name",
+    "文本标注文件": "Text labelling file",
+    "训练集音频文件目录": "Audio dataset folder",
+    "请上传并填写参考信息": "Please upload and fill reference information",
+    "请填写需要合成的目标文本和语种模式": "Please fill in the target text and language mode for synthesis",
     ".限制范围越小判别效果越好。": "Less Multilingual is better",
     "1-GPT-SoVITS-TTS": "1-GPT-SOVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. The DeEcho-DeReverb model's processing time is nearly twice that of the other two DeEcho models.",
@@ -222,5 +222,6 @@
     "预训练SoVITS-D模型路径": "Pretrained SoVITS-D Model Path",
     "预训练SoVITS-G模型路径": "Pretrained SoVITS-G Model Path",
     "预训练中文BERT模型路径": "Pretrained Chinese BERT Model Path",
-    "预训练模型路径": "Pretrained Model Path"
+    "预训练模型路径": "Pretrained Model Path",
+    "推理后端": "Inference Backend"
 }

tools/i18n/locale/es_ES.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net (onnx_dereverb): reverberación estéreo, la mejor opción; no puede eliminar reverberación mono",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho: Eliminar el efecto de retardo. Aggressive elimina más que Normal, DeReverb elimina reverberación adicional, puede eliminar reverberación mono, pero no limpia bien la reverberación de placa de alta frecuencia",
-    "*实验/模型名": "*Nombre del experimento/modelo",
-    "*文本标注文件": "*Archivo de etiquetado de texto",
-    "*训练集音频文件目录": "*Directorio de archivos de audio de entrenamiento",
-    "*请上传并填写参考信息": "*Por favor, suba y complete la información de referencia",
-    "*请填写需要合成的目标文本和语种模式": "*Por favor, complete el texto objetivo a sintetizar y el modo de idioma",
+    "实验/模型名": "Nombre del experimento/modelo",
+    "文本标注文件": "Archivo de etiquetado de texto",
+    "训练集音频文件目录": "Directorio de archivos de audio de entrenamiento",
+    "请上传并填写参考信息": "Por favor, suba y complete la información de referencia",
+    "请填写需要合成的目标文本和语种模式": "Por favor, complete el texto objetivo a sintetizar y el modo de idioma",
     ".限制范围越小判别效果越好。": ".Cuanto más pequeño sea el rango, mejor será el efecto de discriminación.",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. El modelo DeEcho-DeReverb tarda casi el doble que los otros dos modelos DeEcho",

tools/i18n/locale/fr_FR.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1) MDX-Net (onnx_dereverb) : C'est le meilleur choix pour la réverbération à deux canaux, mais il ne peut pas éliminer la réverbération à un seul canal;",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho : Supprime les effets de délai. Aggressive est plus exhaustif que Normal dans la suppression, DeReverb élimine également la réverbération, peut supprimer la réverbération monocanal, mais n'élimine pas complètement la réverbération de plaque à haute fréquence.",
-    "*实验/模型名": "*Nom de l'expérience/modèle",
-    "*文本标注文件": "*Fichier d'annotation de texte",
-    "*训练集音频文件目录": "*Répertoire des fichiers audio d'entraînement",
-    "*请上传并填写参考信息": "*Veuillez télécharger et remplir les informations de référence",
-    "*请填写需要合成的目标文本和语种模式": "*Veuillez saisir le texte cible à synthétiser et le mode de langue.",
+    "实验/模型名": "Nom de l'expérience/modèle",
+    "文本标注文件": "Fichier d'annotation de texte",
+    "训练集音频文件目录": "Répertoire des fichiers audio d'entraînement",
+    "请上传并填写参考信息": "Veuillez télécharger et remplir les informations de référence",
+    "请填写需要合成的目标文本和语种模式": "Veuillez saisir le texte cible à synthétiser et le mode de langue.",
     ".限制范围越小判别效果越好。": "Moins il y a de langues, mieux c'est",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. Le temps de traitement du modèle DeEcho-DeReverb est presque le double de celui des deux autres modèles DeEcho;",

tools/i18n/locale/it_IT.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net (onnx_dereverb): È la scelta migliore per la riverberazione a due canali, ma non può rimuovere la riverberazione a canale singolo;",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho: Rimuove gli effetti di ritardo. Aggressive è più completo di Normal nella rimozione, DeReverb rimuove ulteriormente la riverberazione, può rimuovere la riverberazione a canale singolo, ma non rimuove completamente la riverberazione a piastra ad alta frequenza.",
-    "*实验/模型名": "*Nome dell'esperimento/modello",
-    "*文本标注文件": "*File di annotazione del testo",
-    "*训练集音频文件目录": "*Directory dei file audio del set di addestramento",
-    "*请上传并填写参考信息": "*Carica e compila le informazioni di riferimento",
-    "*请填写需要合成的目标文本和语种模式": "*Si prega di inserire il testo di destinazione da sintetizzare e la modalità lingua",
+    "实验/模型名": "Nome dell'esperimento/modello",
+    "文本标注文件": "File di annotazione del testo",
+    "训练集音频文件目录": "Directory dei file audio del set di addestramento",
+    "请上传并填写参考信息": "Carica e compila le informazioni di riferimento",
+    "请填写需要合成的目标文本和语种模式": "Si prega di inserire il testo di destinazione da sintetizzare e la modalità lingua",
     ".限制范围越小判别效果越好。": "Meno multilingue è meglio",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. Il tempo di elaborazione del modello DeEcho-DeReverb è quasi il doppio di quello degli altri due modelli DeEcho;",

tools/i18n/locale/ja_JP.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb):二重チャンネルのリバーブに最適な選択ですが、単一チャンネルのリバーブは除去できません；",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho:遅延効果を除去します。AggressiveはNormalよりも徹底的に除去し、DeReverbは追加でリバーブを除去し、モノラルリバーブを除去できますが、高周波数のプレートリバーブは完全には除去できません。",
-    "*实验/模型名": "*実験/モデル名",
-    "*文本标注文件": "*テキスト注釈ファイル",
-    "*训练集音频文件目录": "*トレーニングデータのオーディオファイルディレクトリ",
-    "*请上传并填写参考信息": "*参照情報をアップロードして記入してください",
-    "*请填写需要合成的目标文本和语种模式": "*合成対象テキストと言語モードを入力してください",
+    "实验/模型名": "実験/モデル名",
+    "文本标注文件": "テキスト注釈ファイル",
+    "训练集音频文件目录": "トレーニングデータのオーディオファイルディレクトリ",
+    "请上传并填写参考信息": "参照情報をアップロードして記入してください",
+    "请填写需要合成的目标文本和语种模式": "合成対象テキストと言語モードを入力してください",
     ".限制范围越小判别效果越好。": "多言語対応を減らした方が良い",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1、DeEcho-DeReverbモデルの処理時間は、他の2つのDeEchoモデルのほぼ2倍です；",

tools/i18n/locale/ko_KR.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net (onnx_dereverb): 듀얼 채널 리버브에는 가장 적합하지만, 싱글 채널 리버브는 제거할 수 없습니다",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho:지연 효과를 제거합니다. Aggressive는 Normal보다 더 철저하게 제거하며, DeReverb는 추가로 리버브를 제거하여 단일 채널 리버브를 제거할 수 있지만 고주파 리버브는 완전히 제거하지 못합니다.",
-    "*实验/模型名": "*실험/모델 이름",
-    "*文本标注文件": "*텍스트 주석 파일",
-    "*训练集音频文件目录": "*훈련 세트 오디오 파일 디렉터리",
-    "*请上传并填写参考信息": "*참고 정보를 업로드하고 입력하십시오",
-    "*请填写需要合成的目标文本和语种模式": "*합성할 목표 텍스트와 언어 모드를 입력하세요",
+    "实验/模型名": "실험/모델 이름",
+    "文本标注文件": "텍스트 주석 파일",
+    "训练集音频文件目录": "훈련 세트 오디오 파일 디렉터리",
+    "请上传并填写参考信息": "참고 정보를 업로드하고 입력하십시오",
+    "请填写需要合成的目标文本和语种模式": "합성할 목표 텍스트와 언어 모드를 입력하세요",
     ".限制范围越小判别效果越好。": "다언어 지원을 줄이는 것이 더 좋습니다",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. DeEcho-DeReverb 모델의 처리 시간은 다른 두 DeEcho 모델의 거의 두 배입니다;",

tools/i18n/locale/pt_BR.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net (onnx_dereverb): É a melhor opção para reverberação de dois canais, mas não pode remover a reverberação de um único canal;",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho:Remove os efeitos de atraso. Aggressive é mais completo que Normal na remoção, DeReverb remove adicionalmente a reverberação, pode remover a reverberação de um canal único, mas não remove completamente a reverberação de placa de alta frequência.",
-    "*实验/模型名": "*Nome do experimento/modelo",
-    "*文本标注文件": "*Arquivo de marcação de texto",
-    "*训练集音频文件目录": "*Diretório de arquivos de áudio do conjunto de treinamento",
-    "*请上传并填写参考信息": "Por favor, faça o upload e preencha as informações de referência",
-    "*请填写需要合成的目标文本和语种模式": "*Por favor, insira o texto alvo a ser sintetizado e o modo de idioma.",
+    "实验/模型名": "Nome do experimento/modelo",
+    "文本标注文件": "Arquivo de marcação de texto",
+    "训练集音频文件目录": "Diretório de arquivos de áudio do conjunto de treinamento",
+    "请上传并填写参考信息": "Por favor, faça o upload e preencha as informações de referência",
+    "请填写需要合成的目标文本和语种模式": "*Por favor, insira o texto alvo a ser sintetizado e o modo de idioma.",
     ".限制范围越小判别效果越好。": "Menos multilinguismo é melhor",
     "1-GPT-SoVITS-TTS": "1-GPT-SOVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. O tempo de processamento do modelo DeEcho-DeReverb é quase o dobro dos outros dois modelos DeEcho;",

tools/i18n/locale/ru_RU.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb):Это лучший выбор для реверберации с двумя каналами, но он не может устранить реверберацию с одним каналом;",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho:Устраняет эффект задержки. Aggressive устраняет более тщательно, чем Normal, DeReverb дополнительно устраняет реверберацию, может устранить реверберацию с одного канала, но не полностью устраняет высокочастотную реверберацию.",
-    "*实验/模型名": "*Название эксперимента/модели",
-    "*文本标注文件": "*Файл текстовой аннотации",
-    "*训练集音频文件目录": "*Директория аудиофайлов обучающего набора",
-    "*请上传并填写参考信息": "*Пожалуйста, загрузите и заполните референтные данные",
-    "*请填写需要合成的目标文本和语种模式": "*Пожалуйста, введите целевой текст для синтеза и режим языка",
+    "实验/模型名": "Название эксперимента/модели",
+    "文本标注文件": "Файл текстовой аннотации",
+    "训练集音频文件目录": "Директория аудиофайлов обучающего набора",
+    "请上传并填写参考信息": "Пожалуйста, загрузите и заполните референтные данные",
+    "请填写需要合成的目标文本和语种模式": "Пожалуйста, введите целевой текст для синтеза и режим языка",
     ".限制范围越小判别效果越好。": "Чем меньше языков, тем лучше",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. Время обработки модели DeEcho-DeReverb почти вдвое больше, чем у двух других моделей DeEcho;",

tools/i18n/locale/tr_TR.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb):İki kanallı yankılar için en iyi seçimdir, ancak tek kanallı yankıları ortadan kaldıramaz;",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho:Gecikme etkilerini giderir. Aggressive, Normal'dan daha kapsamlı bir şekilde giderir, DeReverb ek olarak yankıyı giderir, tek kanallı yankıyı giderebilir, ancak yüksek frekanslı plaka yankısını tamamen gideremez.",
-    "*实验/模型名": "*Deney/model adı",
-    "*文本标注文件": "*Metin etiketleme dosyası",
-    "*训练集音频文件目录": "*Eğitim seti ses dosyası dizini",
-    "*请上传并填写参考信息": "*Lütfen referans bilgilerini yükleyin ve doldurun",
-    "*请填写需要合成的目标文本和语种模式": "*Lütfen sentezlenecek hedef metni ve dil modunu giriniz.",
+    "实验/模型名": "Deney/model adı",
+    "文本标注文件": "Metin etiketleme dosyası",
+    "训练集音频文件目录": "Eğitim seti ses dosyası dizini",
+    "请上传并填写参考信息": "Lütfen referans bilgilerini yükleyin ve doldurun",
+    "请填写需要合成的目标文本和语种模式": "Lütfen sentezlenecek hedef metni ve dil modunu giriniz.",
     ".限制范围越小判别效果越好。": "Daha az çok dilli olmak daha iyidir",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1. DeEcho-DeReverb modelinin işleme süresi, diğer iki DeEcho modelinin neredeyse iki katıdır;",

tools/i18n/locale/zh_CN.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho:去除延迟效果。Aggressive 比 Normal 去除得更彻底，DeReverb 额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。",
-    "*实验/模型名": "*实验/模型名",
-    "*文本标注文件": "*文本标注文件",
-    "*训练集音频文件目录": "*训练集音频文件目录",
-    "*请上传并填写参考信息": "*请上传并填写参考信息",
-    "*请填写需要合成的目标文本和语种模式": "*请填写需要合成的目标文本和语种模式",
+    "实验/模型名": "实验/模型名",
+    "文本标注文件": "文本标注文件",
+    "训练集音频文件目录": "训练集音频文件目录",
+    "请上传并填写参考信息": "请上传并填写参考信息",
+    "请填写需要合成的目标文本和语种模式": "请填写需要合成的目标文本和语种模式",
     ".限制范围越小判别效果越好。": ".限制范围越小判别效果越好。",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；",
@@ -222,5 +222,6 @@
     "预训练SoVITS-D模型路径": "预训练SoVITS-D模型路径",
     "预训练SoVITS-G模型路径": "预训练SoVITS-G模型路径",
     "预训练中文BERT模型路径": "预训练中文BERT模型路径",
-    "预训练模型路径": "预训练模型路径"
+    "预训练模型路径": "预训练模型路径",
+    "推理后端": "推理后端"
 }

tools/i18n/locale/zh_HK.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb):對於雙通道混響是最佳選擇，但不能去除單通道混響；",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho: 去除延遲效果。Aggressive 比 Normal 去除得更徹底，DeReverb 額外去除混響，可去除單聲道混響，但對高頻重的板式混響去不乾淨。",
-    "*实验/模型名": "*實驗/模型名",
-    "*文本标注文件": "*文本標注文件",
-    "*训练集音频文件目录": "*訓練集音頻文件目錄",
-    "*请上传并填写参考信息": "*請上傳並填寫參考信息",
-    "*请填写需要合成的目标文本和语种模式": "請填寫需要合成的目標文本和語言模式",
+    "实验/模型名": "實驗/模型名",
+    "文本标注文件": "文本標注文件",
+    "训练集音频文件目录": "訓練集音頻文件目錄",
+    "请上传并填写参考信息": "請上傳並填寫參考信息",
+    "请填写需要合成的目标文本和语种模式": "請填寫需要合成的目標文本和語言模式",
     ".限制范围越小判别效果越好。": ".限制范围越小判别效果越好。",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1、DeEcho-DeReverb 模型的處理時間是另外兩個 DeEcho 模型的接近兩倍；",

tools/i18n/locale/zh_SG.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb)：對於雙通道混響是最好的選擇，不能去除單通道混響；",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho：去除延遲效果。Aggressive 比 Normal 去除得更徹底，DeReverb 額外去除混響，可去除單聲道混響，但是對高頻重的板式混響去不乾淨。",
-    "*实验/模型名": "*實驗/模型名",
-    "*文本标注文件": "*文本標註文件",
-    "*训练集音频文件目录": "*訓練集音頻文件目錄",
-    "*请上传并填写参考信息": "*請上傳並填寫參考信息",
-    "*请填写需要合成的目标文本和语种模式": "請填寫需要合成的目標文本和語言模式",
+    "实验/模型名": "實驗/模型名",
+    "文本标注文件": "文本標註文件",
+    "训练集音频文件目录": "訓練集音頻文件目錄",
+    "请上传并填写参考信息": "請上傳並填寫參考信息",
+    "请填写需要合成的目标文本和语种模式": "請填寫需要合成的目標文本和語言模式",
     ".限制范围越小判别效果越好。": ".限制范围越小判别效果越好。",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1、DeEcho-DeReverb 模型的耗時是另外兩個 DeEcho 模型的接近兩倍；",

tools/i18n/locale/zh_TW.json

@@ -1,11 +1,11 @@
 {
     "(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；": "(1)MDX-Net(onnx_dereverb)：對於雙通道混響是最好的選擇，不能去除單通道混響；",
     "(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。": "(234)DeEcho：去除延遲效果。Aggressive 比 Normal 去除得更徹底，DeReverb 額外去除混響，可去除單聲道混響，但是對高頻重的板式混響去不乾淨。",
-    "*实验/模型名": "*實驗/模型名",
-    "*文本标注文件": "*文本標注文件",
-    "*训练集音频文件目录": "*訓練集音頻文件目錄",
-    "*请上传并填写参考信息": "*請上傳並填寫參考資訊",
-    "*请填写需要合成的目标文本和语种模式": "請填寫需要合成的目標文本和語言模式",
+    "实验/模型名": "實驗/模型名",
+    "文本标注文件": "文本標注文件",
+    "训练集音频文件目录": "訓練集音頻文件目錄",
+    "请上传并填写参考信息": "請上傳並填寫參考資訊",
+    "请填写需要合成的目标文本和语种模式": "請填寫需要合成的目標文本和語言模式",
     ".限制范围越小判别效果越好。": ".限制范围越小判别效果越好。",
     "1-GPT-SoVITS-TTS": "1-GPT-SoVITS-TTS",
     "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；": "1、DeEcho-DeReverb 模型的耗時是另外兩個 DeEcho 模型的接近兩倍；",

tools/my_utils.py

@@ -37,13 +37,11 @@ def load_audio(file, sr):
     return np.frombuffer(out, np.float32).flatten()
 
 
-def clean_path(path_str: str):
+def clean_path(path_str: str) -> str:
     if path_str.endswith(("\\", "/")):
         return clean_path(path_str[0:-1])
     path_str = path_str.replace("/", os.sep).replace("\\", os.sep)
-    return path_str.strip(
-        " '\n\"\u202a"
-    )  # path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
+    return path_str.strip(" '\n\"\u202a")
 
 
 def check_for_existance(file_list: list = None, is_train=False, is_dataset_processing=False):

tools/subfix_webui.py

@@ -1,4 +1,5 @@
 import sys
+
 from tools.i18n.i18n import I18nAuto, scan_language_list
 
 language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto"
@@ -314,7 +315,7 @@ if __name__ == "__main__":
                 "Submit Text: 将当前页所有文本框内容手工保存到内存和文件(翻页前后或者退出标注页面前如果没点这个按钮，你再翻回来就回滚了，白忙活。)"
             )
         )
-        with gr.Row():
+        with gr.Row(equal_height=True):
             btn_change_index = gr.Button("Change Index")
             btn_submit_change = gr.Button("Submit Text")
             btn_merge_audio = gr.Button("Merge Audio")
@@ -322,7 +323,7 @@ if __name__ == "__main__":
             btn_previous_index = gr.Button("Previous Index")
             btn_next_index = gr.Button("Next Index")
 
-        with gr.Row():
+        with gr.Row(equal_height=True):
             index_slider = gr.Slider(minimum=0, maximum=g_max_json_index, value=g_index, step=1, label="Index", scale=3)
             splitpoint_slider = gr.Slider(
                 minimum=0, maximum=120.0, value=0, step=0.1, label="Audio Split Point(s)", scale=3
@@ -331,18 +332,23 @@ if __name__ == "__main__":
             btn_save_json = gr.Button("Save File", visible=True, scale=1)
             btn_invert_selection = gr.Button("Invert Selection", scale=1)
 
-        with gr.Row():
+        with gr.Row(equal_height=True):
             with gr.Column():
                 for _ in range(0, g_batch):
-                    with gr.Row():
+                    with gr.Row(equal_height=True):
                         text = gr.Textbox(label="Text", visible=True, scale=5)
-                        audio_output = gr.Audio(label="Output Audio", visible=True, scale=5)
+                        audio_output = gr.Audio(
+                            label="Output Audio",
+                            visible=True,
+                            scale=5,
+                            waveform_options={"show_recording_waveform": False},
+                        )
                         audio_check = gr.Checkbox(label="Yes", show_label=True, info="Choose Audio", scale=1)
                         g_text_list.append(text)
                         g_audio_list.append(audio_output)
                         g_checkbox_list.append(audio_check)
 
-        with gr.Row():
+        with gr.Row(equal_height=True):
             batchsize_slider = gr.Slider(
                 minimum=1, maximum=g_batch, value=g_batch, step=1, label="Batch Size", scale=3, interactive=False
             )

tools/uvr5/webui.py

@@ -168,7 +168,7 @@ with gr.Blocks(title="UVR5 WebUI", analytics_enabled=False) as app:
                     "h4",
                 )
             )
-            with gr.Row():
+            with gr.Row(equal_height=True):
                 with gr.Column():
                     model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
                     dir_wav_input = gr.Textbox(
@@ -197,9 +197,9 @@ with gr.Blocks(title="UVR5 WebUI", analytics_enabled=False) as app:
                         interactive=True,
                     )
                     with gr.Column():
-                        with gr.Row():
+                        with gr.Row(equal_height=True):
                             but2 = gr.Button(i18n("转换"), variant="primary")
-                        with gr.Row():
+                        with gr.Row(equal_height=True):
                             vc_output4 = gr.Textbox(label=i18n("输出信息"), lines=3)
                 but2.click(
                     uvr,