more import changes

GPT_SoVITS/AR/data/dataset.py

@@ -17,7 +17,7 @@ from transformers import AutoTokenizer
 
 version = os.environ.get('version',None)
 
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
 
 # from config import exp_dir
 

GPT_SoVITS/AR/models/t2s_model.py

@@ -884,6 +884,157 @@ class Text2SemanticDecoder(nn.Module):
             return y[:, :-1], 0
         return y[:, :-1], idx - 1
     
+    def infer_panel_generator(
+        self,
+        x: torch.LongTensor,
+        x_lens: torch.LongTensor,
+        prompts: torch.LongTensor,
+        bert_feature: torch.LongTensor,
+        cumulation_amount: int,
+        top_k: int = -100,
+        top_p: int = 100,
+        early_stop_num: int = -1,
+        temperature: float = 1.0,
+        repetition_penalty: float = 1.35,
+        **kwargs
+    ):
+        """
+        Generator method that yields generated tokens based on a specified cumulative amount.
+
+        Args:
+            x (torch.LongTensor): Input phoneme IDs.
+            x_lens (torch.LongTensor): Lengths of the input sequences.
+            prompts (torch.LongTensor): Initial prompt tokens.
+            bert_feature (torch.LongTensor): BERT features corresponding to the input.
+            cumulation_amount (int): Number of tokens to generate before yielding.
+            top_k (int): The number of highest probability vocabulary tokens to keep for top-k-filtering.
+            top_p (float): If set to < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+            early_stop_num (int): Early stopping after generating a certain number of tokens.
+            temperature (float): The value used to module the next token probabilities.
+            repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty.
+            **kwargs: Additional keyword arguments.
+
+        Yields:
+            torch.LongTensor: Generated tokens since the last yield.
+        """
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+
+        # AR Decoder
+        y = prompts
+
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device)
+        stop = False
+
+        # Initialize cumulative token counter
+        tokens_since_last_yield = 0
+
+        # Initialize last yield index
+        if y is not None:
+            prefix_len = y.shape[1]
+        else:
+            prefix_len = 0
+
+        last_yield_idx = prefix_len
+
+        k_cache = None
+        v_cache = None
+
+        ###################  first step ##########################
+        if y is not None:
+            y_emb = self.ar_audio_embedding(y)
+            y_len = y_emb.shape[1]
+            prefix_len = y.shape[1]
+            y_pos = self.ar_audio_position(y_emb)
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            ref_free = False
+        else:
+            y_emb = None
+            y_len = 0
+            prefix_len = 0
+            y_pos = None
+            xy_pos = x
+            y = torch.zeros(x.shape[0], 0, dtype=torch.int64, device=x.device)
+            ref_free = True
+
+        bsz = x.shape[0]
+        src_len = x_len + y_len
+        x_attn_mask_pad = F.pad(
+            x_attn_mask,
+            (0, y_len),  # Extend x_attn_mask to include y tokens
+            value=True,
+        )
+        y_attn_mask = F.pad(
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), diagonal=1),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)
+        xy_attn_mask = xy_attn_mask.unsqueeze(0).expand(bsz * self.num_head, -1, -1)
+        xy_attn_mask = xy_attn_mask.view(bsz, self.num_head, src_len, src_len)
+        xy_attn_mask = xy_attn_mask.to(device=x.device, dtype=torch.bool)
+
+        for idx in tqdm(range(1500)):
+            if xy_attn_mask is not None:
+                xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
+            else:
+                xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
+
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+
+            if idx == 0:
+                xy_attn_mask = None
+            if idx < 11:  # Ensure at least 10 tokens are generated before stopping
+                logits = logits[:, :-1]
+
+            samples = sample(
+                logits,
+                y,
+                top_k=top_k,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                temperature=temperature,
+            )[0]
+
+            y = torch.concat([y, samples], dim=1)
+            tokens_since_last_yield += 1
+
+            if tokens_since_last_yield >= cumulation_amount:
+                # Yield back the generated tokens since last yield
+                generated_tokens = y[:, last_yield_idx:]
+                # print(generated_tokens)
+                yield generated_tokens
+                last_yield_idx = y.shape[1]
+                tokens_since_last_yield = 0
+
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                print("Using early stop num:", early_stop_num)
+                stop = True
+
+            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+                stop = True
+
+            if stop:
+                if y.shape[1] == 0:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                    print("Bad zero prediction")
+                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
+                break
+
+            # Update for next step
+            y_emb = self.ar_audio_embedding(y[:, -1:])
+            y_len += 1
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[
+                :, y_len - 1
+            ].to(dtype=y_emb.dtype, device=y_emb.device)
+
+        # After loop ends, yield any remaining tokens
+        if last_yield_idx < y.shape[1]:
+            generated_tokens = y[:, last_yield_idx:]
+            yield generated_tokens
+    
     
     def infer_panel(
         self,

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -8,10 +8,10 @@ sys.path.append(now_dir)
 import re
 import torch
 import LangSegment
-from text import chinese
+from GPT_SoVITS.text import chinese
 from typing import Dict, List, Tuple
-from text.cleaner import clean_text
+from GPT_SoVITS.text.cleaner import clean_text
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
 

GPT_SoVITS/download.py

@@ -1,5 +1,5 @@
 import os, sys
 now_dir = os.getcwd()
 sys.path.insert(0, now_dir)
-from text.g2pw import G2PWPinyin
+from GPT_SoVITS.text.g2pw import G2PWPinyin
 g2pw = G2PWPinyin(model_dir="GPT_SoVITS/text/G2PWModel",model_source="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",v_to_u=False, neutral_tone_with_five=True)

GPT_SoVITS/export_torch_script.py

@@ -3,7 +3,7 @@
 import argparse
 from typing import Optional
 from my_utils import load_audio
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
 import torch
 import torchaudio
 
@@ -14,7 +14,7 @@ from transformers import AutoModelForMaskedLM, AutoTokenizer
 from feature_extractor import cnhubert
 
 from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from module.models_onnx import SynthesizerTrn
+from GPT_SoVITS.module.models_onnx import SynthesizerTrn
 
 from inference_webui import get_phones_and_bert
 

GPT_SoVITS/inference_webui.py

@@ -83,12 +83,12 @@ from feature_extractor import cnhubert
 
 cnhubert.cnhubert_base_path = cnhubert_base_path
 
-from module.models import SynthesizerTrn
+from GPT_SoVITS.module.models import SynthesizerTrn
 from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
-from text.cleaner import clean_text
+from GPT_SoVITS.text.cleaner import clean_text
 from time import time as ttime
-from module.mel_processing import spectrogram_torch
+from GPT_SoVITS.module.mel_processing import spectrogram_torch
 from tools.my_utils import load_audio
 from tools.i18n.i18n import I18nAuto, scan_language_list
 
@@ -303,7 +303,7 @@ def get_first(text):
     text = re.split(pattern, text)[0].strip()
     return text
 
-from text import chinese
+from GPT_SoVITS.text import chinese
 def get_phones_and_bert(text,language,version,final=False):
     if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
         language = language.replace("all_","")

GPT_SoVITS/module/models.py

@@ -9,18 +9,18 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-from module import commons
+from GPT_SoVITS.module import commons
-from module import modules
+from GPT_SoVITS.module import modules
-from module import attentions
+from GPT_SoVITS.module import attentions
 
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from module.commons import init_weights, get_padding
+from GPT_SoVITS.module.commons import init_weights, get_padding
-from module.mrte_model import MRTE
+from GPT_SoVITS.module.mrte_model import MRTE
-from module.quantize import ResidualVectorQuantizer
+from GPT_SoVITS.module.quantize import ResidualVectorQuantizer
-# from text import symbols
+# from GPT_SoVITS.text import symbols
-from text import symbols as symbols_v1
+from GPT_SoVITS.text import symbols as symbols_v1
-from text import symbols2 as symbols_v2
+from GPT_SoVITS.text import symbols2 as symbols_v2
 from torch.cuda.amp import autocast
 import contextlib
 

GPT_SoVITS/module/models_onnx.py

@@ -5,17 +5,17 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-from module import commons
+from GPT_SoVITS.module import commons
-from module import modules
+from GPT_SoVITS.module import modules
-from module import attentions_onnx as attentions
+from GPT_SoVITS.module import attentions_onnx as attentions
 
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from module.commons import init_weights, get_padding
+from GPT_SoVITS.module.commons import init_weights, get_padding
-from module.quantize import ResidualVectorQuantizer
+from GPT_SoVITS.module.quantize import ResidualVectorQuantizer
-# from text import symbols
+# from GPT_SoVITS.text import symbols
-from text import symbols as symbols_v1
+from GPT_SoVITS.text import symbols as symbols_v1
-from text import symbols2 as symbols_v2
+from GPT_SoVITS.text import symbols2 as symbols_v2
 from torch.cuda.amp import autocast
 
 

GPT_SoVITS/module/modules.py

@@ -7,9 +7,9 @@ from torch.nn import functional as F
 from torch.nn import Conv1d
 from torch.nn.utils import weight_norm, remove_weight_norm
 
-from module import commons
+from GPT_SoVITS.module import commons
-from module.commons import init_weights, get_padding
+from GPT_SoVITS.module.commons import init_weights, get_padding
-from module.transforms import piecewise_rational_quadratic_transform
+from GPT_SoVITS.module.transforms import piecewise_rational_quadratic_transform
 import torch.distributions as D
 
 

GPT_SoVITS/module/mrte_model.py

@@ -3,7 +3,7 @@
 import torch
 from torch import nn
 from torch.nn.utils import remove_weight_norm, weight_norm
-from module.attentions import MultiHeadAttention
+from GPT_SoVITS.module.attentions import MultiHeadAttention
 
 
 class MRTE(nn.Module):

GPT_SoVITS/module/quantize.py

@@ -13,7 +13,7 @@ import typing as tp
 import torch
 from torch import nn
 
-from module.core_vq import ResidualVectorQuantization
+from GPT_SoVITS.module.core_vq import ResidualVectorQuantization
 
 
 @dataclass

GPT_SoVITS/onnx_export.py

@@ -1,4 +1,4 @@
-from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
+from GPT_SoVITS.module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
 from GPT_SoVITS.AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
 import torch
 import torchaudio
@@ -8,7 +8,7 @@ from feature_extractor import cnhubert
 cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
 cnhubert.cnhubert_base_path = cnhubert_base_path
 ssl_model = cnhubert.get_model()
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
 import soundfile
 from tools.my_utils import load_audio
 import os

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -18,7 +18,7 @@ import sys, numpy as np, traceback, pdb
 import os.path
 from glob import glob
 from tqdm import tqdm
-from text.cleaner import clean_text
+from GPT_SoVITS.text.cleaner import clean_text
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 import numpy as np
 from tools.my_utils import clean_path

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -23,7 +23,7 @@ import torch.multiprocessing as mp
 from glob import glob
 from tqdm import tqdm
 import logging, librosa, utils
-from module.models import SynthesizerTrn
+from GPT_SoVITS.module.models import SynthesizerTrn
 from tools.my_utils import clean_path
 logging.getLogger("numba").setLevel(logging.WARNING)
 # from config import pretrained_s2G

GPT_SoVITS/s2_train.py

@@ -18,19 +18,19 @@ logging.getLogger("matplotlib").setLevel(logging.INFO)
 logging.getLogger("h5py").setLevel(logging.INFO)
 logging.getLogger("numba").setLevel(logging.INFO)
 from random import randint
-from module import commons
+from GPT_SoVITS.module import commons
 
-from module.data_utils import (
+from GPT_SoVITS.module.data_utils import (
     TextAudioSpeakerLoader,
     TextAudioSpeakerCollate,
     DistributedBucketSampler,
 )
-from module.models import (
+from GPT_SoVITS.module.models import (
     SynthesizerTrn,
     MultiPeriodDiscriminator,
 )
-from module.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
+from GPT_SoVITS.module.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
-from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
 from process_ckpt import savee
 
 torch.backends.cudnn.benchmark = False

GPT_SoVITS/text/__init__.py

@@ -1,11 +1,11 @@
 import os
 # if os.environ.get("version","v1")=="v1":
-#   from text.symbols import symbols
+#   from GPT_SoVITS.text.symbols import symbols
 # else:
-#   from text.symbols2 import symbols
+#   from GPT_SoVITS.text.symbols2 import symbols
 
-from text import symbols as symbols_v1
+from GPT_SoVITS.text import symbols as symbols_v1
-from text import symbols2 as symbols_v2
+from GPT_SoVITS.text import symbols2 as symbols_v2
 
 _symbol_to_id_v1 = {s: i for i, s in enumerate(symbols_v1.symbols)}
 _symbol_to_id_v2 = {s: i for i, s in enumerate(symbols_v2.symbols)}

GPT_SoVITS/text/cantonese.py

@@ -5,8 +5,8 @@ import re
 import cn2an
 
 from pyjyutping import jyutping
-from text.symbols import punctuation
+from GPT_SoVITS.text.symbols import punctuation
-from text.zh_normalization.text_normlization import TextNormalizer
+from GPT_SoVITS.text.zh_normalization.text_normlization import TextNormalizer
 
 normalizer = lambda x: cn2an.transform(x, "an2cn")
 
@@ -182,7 +182,7 @@ def get_jyutping(text):
 
 
 def get_bert_feature(text, word2ph):
-    from text import chinese_bert
+    from GPT_SoVITS.text import chinese_bert
 
     return chinese_bert.get_bert_feature(text, word2ph)
 

GPT_SoVITS/text/chinese.py

@@ -5,9 +5,9 @@ import re
 import cn2an
 from pypinyin import lazy_pinyin, Style
 
-from text.symbols import punctuation
+from GPT_SoVITS.text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
+from GPT_SoVITS.text.tone_sandhi import ToneSandhi
-from text.zh_normalization.text_normlization import TextNormalizer
+from GPT_SoVITS.text.zh_normalization.text_normlization import TextNormalizer
 
 normalizer = lambda x: cn2an.transform(x, "an2cn")
 

GPT_SoVITS/text/chinese2.py

@@ -6,9 +6,9 @@ import cn2an
 from pypinyin import lazy_pinyin, Style
 from pypinyin.contrib.tone_convert import to_normal, to_finals_tone3, to_initials, to_finals
 
-from text.symbols import punctuation
+from GPT_SoVITS.text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
+from GPT_SoVITS.text.tone_sandhi import ToneSandhi
-from text.zh_normalization.text_normlization import TextNormalizer
+from GPT_SoVITS.text.zh_normalization.text_normlization import TextNormalizer
 
 normalizer = lambda x: cn2an.transform(x, "an2cn")
 
@@ -25,7 +25,7 @@ import jieba_fast.posseg as psg
 is_g2pw = True#True if is_g2pw_str.lower() == 'true' else False
 if is_g2pw:
     print("当前使用g2pw进行拼音推理")
-    from text.g2pw import G2PWPinyin, correct_pronunciation
+    from GPT_SoVITS.text.g2pw import G2PWPinyin, correct_pronunciation
     parent_directory = os.path.dirname(current_file_path)
     g2pw = G2PWPinyin(model_dir="GPT_SoVITS/text/G2PWModel",model_source=os.environ.get("bert_path","GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"),v_to_u=False, neutral_tone_with_five=True)
 

GPT_SoVITS/text/cleaner.py

@@ -1,14 +1,14 @@
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
 import os
 # if os.environ.get("version","v1")=="v1":
-#     from text import chinese
+#     from GPT_SoVITS.text import chinese
-#     from text.symbols import symbols
+#     from GPT_SoVITS.text.symbols import symbols
 # else:
-#     from text import chinese2 as chinese
+#     from GPT_SoVITS.text import chinese2 as chinese
-#     from text.symbols2 import symbols
+#     from GPT_SoVITS.text.symbols2 import symbols
 
-from text import symbols as symbols_v1
+from GPT_SoVITS.text import symbols as symbols_v1
-from text import symbols2 as symbols_v2
+from GPT_SoVITS.text import symbols2 as symbols_v2
 
 special = [
     # ("%", "zh", "SP"),

GPT_SoVITS/text/english.py

@@ -4,9 +4,9 @@ import re
 import wordsegment
 from g2p_en import G2p
 
-from text.symbols import punctuation
+from GPT_SoVITS.text.symbols import punctuation
 
-from text.symbols2 import symbols
+from GPT_SoVITS.text.symbols2 import symbols
 
 import unicodedata
 from builtins import str as unicode

GPT_SoVITS/text/g2pw/__init__.py

@@ -1 +1 @@
-from text.g2pw.g2pw import *
+from GPT_SoVITS.text.g2pw.g2pw import *

GPT_SoVITS/text/japanese.py

@@ -31,7 +31,7 @@ except Exception as e:
     pass
 
 
-from text.symbols import punctuation
+from GPT_SoVITS.text.symbols import punctuation
 # Regular expression matching Japanese without punctuation marks:
 _japanese_characters = re.compile(
     r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"

GPT_SoVITS/text/korean.py

@@ -5,7 +5,7 @@ from jamo import h2j, j2hcj
 import ko_pron
 from g2pk2 import G2p
 
-from text.symbols2 import symbols 
+from GPT_SoVITS.text.symbols2 import symbols 
 
 # This is a list of Korean classifiers preceded by pure Korean numerals.
 _korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'

GPT_SoVITS/text/zh_normalization/__init__.py

@@ -11,4 +11,4 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from text.zh_normalization.text_normlization import *
+from GPT_SoVITS.text.zh_normalization.text_normlization import *

api.py

@@ -162,11 +162,11 @@ from transformers import AutoModelForMaskedLM, AutoTokenizer
 import numpy as np
 from feature_extractor import cnhubert
 from io import BytesIO
-from module.models import SynthesizerTrn
+from GPT_SoVITS.module.models import SynthesizerTrn
 from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
-from text.cleaner import clean_text
+from GPT_SoVITS.text.cleaner import clean_text
-from module.mel_processing import spectrogram_torch
+from GPT_SoVITS.module.mel_processing import spectrogram_torch
 from tools.my_utils import load_audio
 import config as global_config
 import logging
@@ -312,7 +312,7 @@ def get_bert_inf(phones, word2ph, norm_text, language):
 
     return bert
 
-from text import chinese
+from GPT_SoVITS.text import chinese
 def get_phones_and_bert(text,language,version,final=False):
     if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
         language = language.replace("all_","")