api_v2.py和inference_webui_fast.py的v3适配

.gitignore

@@ -17,4 +17,6 @@ SoVITS_weights_v3
 TEMP
 weight.json
 ffmpeg*
-ffprobe*
+ffprobe*
+tools/AP_BWE_main/24kto48k/*
+!tools/AP_BWE_main/24kto48k/readme.txt

GPT_SoVITS/BigVGAN/alias_free_activation/torch/act.py

@@ -2,7 +2,7 @@
 #   LICENSE is in incl_licenses directory.
 
 import torch.nn as nn
-from alias_free_activation.torch.resample import UpSample1d, DownSample1d
+from .resample import UpSample1d, DownSample1d
 
 
 class Activation1d(nn.Module):

GPT_SoVITS/BigVGAN/alias_free_activation/torch/resample.py

@@ -3,8 +3,8 @@
 
 import torch.nn as nn
 from torch.nn import functional as F
-from alias_free_activation.torch.filter import LowPassFilter1d
+from .filter import LowPassFilter1d
-from alias_free_activation.torch.filter import kaiser_sinc_filter1d
+from .filter import kaiser_sinc_filter1d
 
 
 class UpSample1d(nn.Module):

GPT_SoVITS/BigVGAN/bigvgan.py

@@ -14,10 +14,10 @@ import torch.nn as nn
 from torch.nn import Conv1d, ConvTranspose1d
 from torch.nn.utils import weight_norm, remove_weight_norm
 
-import activations
+from . import activations
-from utils0 import init_weights, get_padding
+from .utils0 import init_weights, get_padding
-from alias_free_activation.torch.act import Activation1d as TorchActivation1d
+from .alias_free_activation.torch.act import Activation1d as TorchActivation1d
-from env import AttrDict
+from .env import AttrDict
 
 from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
 
@@ -93,7 +93,7 @@ class AMPBlock1(torch.nn.Module):
 
         # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
         if self.h.get("use_cuda_kernel", False):
-            from alias_free_activation.cuda.activation1d import (
+            from .alias_free_activation.cuda.activation1d import (
                 Activation1d as CudaActivation1d,
             )
 
@@ -193,7 +193,7 @@ class AMPBlock2(torch.nn.Module):
 
         # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
         if self.h.get("use_cuda_kernel", False):
-            from alias_free_activation.cuda.activation1d import (
+            from .alias_free_activation.cuda.activation1d import (
                 Activation1d as CudaActivation1d,
             )
 
@@ -271,7 +271,7 @@ class BigVGAN(
 
         # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
         if self.h.get("use_cuda_kernel", False):
-            from alias_free_activation.cuda.activation1d import (
+            from .alias_free_activation.cuda.activation1d import (
                 Activation1d as CudaActivation1d,
             )
 

GPT_SoVITS/BigVGAN/meldataset.py

@@ -15,7 +15,7 @@ from librosa.filters import mel as librosa_mel_fn
 import pathlib
 from tqdm import tqdm
 from typing import List, Tuple, Optional
-from env import AttrDict
+from .env import AttrDict
 
 MAX_WAV_VALUE = 32767.0  # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
 

GPT_SoVITS/BigVGAN/utils0.py

@@ -9,7 +9,7 @@ from torch.nn.utils import weight_norm
 
 matplotlib.use("Agg")
 import matplotlib.pylab as plt
-from meldataset import MAX_WAV_VALUE
+from .meldataset import MAX_WAV_VALUE
 from scipy.io.wavfile import write
 
 

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -16,7 +16,7 @@ import torch
 import torch.nn.functional as F
 import yaml
 from transformers import AutoModelForMaskedLM, AutoTokenizer
-
+from tools.audio_sr import AP_BWE
 from AR.models.t2s_lightning_module import Text2SemanticLightningModule
 from feature_extractor.cnhubert import CNHubert
 from module.models import SynthesizerTrn, SynthesizerTrnV3
@@ -55,6 +55,65 @@ mel_fn=lambda x: mel_spectrogram_torch(x, **{
 })
 
 
+def speed_change(input_audio:np.ndarray, speed:float, sr:int):
+    # 将 NumPy 数组转换为原始 PCM 流
+    raw_audio = input_audio.astype(np.int16).tobytes()
+
+    # 设置 ffmpeg 输入流
+    input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
+
+    # 变速处理
+    output_stream = input_stream.filter('atempo', speed)
+
+    # 输出流到管道
+    out, _ = (
+        output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
+        .run(input=raw_audio, capture_stdout=True, capture_stderr=True)
+    )
+
+    # 将管道输出解码为 NumPy 数组
+    processed_audio = np.frombuffer(out, np.int16)
+
+    return processed_audio
+
+
+
+resample_transform_dict={}
+def resample(audio_tensor, sr0, device):
+    global resample_transform_dict
+    if sr0 not in resample_transform_dict:
+        resample_transform_dict[sr0] = torchaudio.transforms.Resample(
+            sr0, 24000
+        ).to(device)
+    return resample_transform_dict[sr0](audio_tensor)
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
 
 
 
@@ -303,6 +362,8 @@ class TTS:
         self.bert_model:AutoModelForMaskedLM = None
         self.cnhuhbert_model:CNHubert = None
         self.bigvgan_model:BigVGAN = None
+        self.sr_model:AP_BWE = None
+        self.sr_model_not_exist:bool = False
 
         self._init_models()
 
@@ -370,13 +431,13 @@ class TTS:
         hps = dict_s2["config"]
 
         hps["model"]["semantic_frame_rate"] = "25hz"
-        if 'enc_p.text_embedding.weight'not in dict_s2['weight']:
+        # if 'enc_p.text_embedding.weight'not in dict_s2['weight']:
-            hps["model"]["version"] = "v2"#v3model,v2sybomls
+        #     hps["model"]["version"] = "v2"#v3model,v2sybomls
-        elif dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+        # elif dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
-            hps["model"]["version"] = "v1"
+        #     hps["model"]["version"] = "v1"
-        else:
+        # else:
-            hps["model"]["version"] = "v2"
+        #     hps["model"]["version"] = "v2"
-        version = hps["model"]["version"]
+        # version = hps["model"]["version"]
 
         self.configs.filter_length = hps["data"]["filter_length"]
         self.configs.segment_size = hps["train"]["segment_size"]
@@ -386,8 +447,9 @@ class TTS:
         self.configs.n_speakers = hps["data"]["n_speakers"]
         self.configs.semantic_frame_rate = hps["model"]["semantic_frame_rate"]
         kwargs = hps["model"]
+        # print(f"self.configs.sampling_rate:{self.configs.sampling_rate}")
 
-        self.configs.update_version(version)
+        self.configs.update_version(model_version)
 
 
         if model_version!="v3":
@@ -397,7 +459,6 @@ class TTS:
                 n_speakers=self.configs.n_speakers,
                 **kwargs
             )
-            model_version=version
             if hasattr(vits_model, "enc_q"):
                 del vits_model.enc_q
             self.configs.is_v3_synthesizer = False
@@ -413,9 +474,9 @@ class TTS:
             
 
         if if_lora_v3==False:
-            print(f"Loading VITS weights from {weights_path}. {vits_model.load_state_dict(dict_s2["weight"], strict=False)}")
+            print(f"Loading VITS weights from {weights_path}. {vits_model.load_state_dict(dict_s2['weight'], strict=False)}")
         else:
-            print(f"Loading VITS pretrained weights from {weights_path}. {vits_model.load_state_dict(load_sovits_new(path_sovits_v3)["weight"], strict=False)}")
+            print(f"Loading VITS pretrained weights from {weights_path}. {vits_model.load_state_dict(load_sovits_new(path_sovits_v3)['weight'], strict=False)}")
             lora_rank=dict_s2["lora_rank"]
             lora_config = LoraConfig(
                 target_modules=["to_k", "to_q", "to_v", "to_out.0"],
@@ -424,7 +485,7 @@ class TTS:
                 init_lora_weights=True,
             )
             vits_model.cfm = get_peft_model(vits_model.cfm, lora_config)
-            print(f"Loading LoRA weights from {weights_path}. {vits_model.load_state_dict(dict_s2["weight"], strict=False)}")
+            print(f"Loading LoRA weights from {weights_path}. {vits_model.load_state_dict(dict_s2['weight'], strict=False)}")
             
             vits_model.cfm = vits_model.cfm.merge_and_unload()
 
@@ -466,6 +527,15 @@ class TTS:
         else:
             self.bigvgan_model = self.bigvgan_model.to(self.configs.device)
 
+    def init_sr_model(self):
+        if self.sr_model is not None:
+            return
+        try:
+            self.sr_model:AP_BWE=AP_BWE(self.configs.device,DictToAttrRecursive)
+            self.sr_model_not_exist = False
+        except FileNotFoundError:
+            print(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
+            self.sr_model_not_exist = True
 
 
     def enable_half_precision(self, enable: bool = True, save: bool = True):
@@ -523,6 +593,11 @@ class TTS:
             self.bert_model = self.bert_model.to(device)
         if self.cnhuhbert_model is not None:
             self.cnhuhbert_model = self.cnhuhbert_model.to(device)
+        if self.bigvgan_model is not None:
+            self.bigvgan_model = self.bigvgan_model.to(device)
+        if self.sr_model is not None:
+            self.sr_model = self.sr_model.to(device)
+        
 
     def set_ref_audio(self, ref_audio_path:str):
         '''
@@ -546,6 +621,11 @@ class TTS:
             self.prompt_cache["refer_spec"][0] = spec
 
     def _get_ref_spec(self, ref_audio_path):
+        raw_audio, raw_sr = torchaudio.load(ref_audio_path)
+        raw_audio=raw_audio.to(self.configs.device).float()
+        self.prompt_cache["raw_audio"] = raw_audio
+        self.prompt_cache["raw_sr"] = raw_sr
+
         audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
         audio = torch.FloatTensor(audio)
         maxx=audio.abs().max()
@@ -734,11 +814,11 @@ class TTS:
         Recovery the order of the audio according to the batch_index_list.
 
         Args:
-            data (List[list(np.ndarray)]): the out of order audio .
+            data (List[list(torch.Tensor)]): the out of order audio .
             batch_index_list (List[list[int]]): the batch index list.
 
         Returns:
-            list (List[np.ndarray]): the data in the original order.
+            list (List[torch.Tensor]): the data in the original order.
         '''
         length = len(sum(batch_index_list, []))
         _data = [None]*length
@@ -780,6 +860,8 @@ class TTS:
                     "seed": -1,                   # int. random seed for reproducibility.
                     "parallel_infer": True,       # bool. whether to use parallel inference.
                     "repetition_penalty": 1.35    # float. repetition penalty for T2S model.
+                    "sample_steps": 32,           # int. number of sampling steps for VITS model V3.
+                    "super_sampling": False,       # bool. whether to use super-sampling for audio when using VITS model V3.
                 }
         returns:
             Tuple[int, np.ndarray]: sampling rate and audio data.
@@ -807,7 +889,8 @@ class TTS:
         actual_seed = set_seed(seed)
         parallel_infer = inputs.get("parallel_infer", True)
         repetition_penalty = inputs.get("repetition_penalty", 1.35)
-        sample_steps = inputs.get("sample_steps", 16)
+        sample_steps = inputs.get("sample_steps", 32)
+        super_sampling = inputs.get("super_sampling", False)
 
         if parallel_infer:
             print(i18n("并行推理模式已开启"))
@@ -894,8 +977,7 @@ class TTS:
         if not return_fragment:
             data = self.text_preprocessor.preprocess(text, text_lang, text_split_method, self.configs.version)
             if len(data) == 0:
-                yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
+                yield 16000, np.zeros(int(16000), dtype=np.int16)
-                                                            dtype=np.int16)
                 return
 
             batch_index_list:list = None
@@ -949,6 +1031,7 @@ class TTS:
             t_34 = 0.0
             t_45 = 0.0
             audio = []
+            output_sr = self.configs.sampling_rate if not self.configs.is_v3_synthesizer else 24000
             for item in data:
                 t3 = ttime()
                 if return_fragment:
@@ -971,7 +1054,7 @@ class TTS:
                 else:
                     prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
 
-
+                print(f"############ {i18n('预测语义Token')} ############")
                 pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
                     all_phoneme_ids,
                     all_phoneme_lens,
@@ -1005,7 +1088,7 @@ class TTS:
                 # batch_audio_fragment = (self.vits_model.batched_decode(
                 #         pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
                 #     ))
-
+                print(f"############ {i18n('合成音频')} ############")
                 if not self.configs.is_v3_synthesizer:
                     if speed_factor == 1.0:
                         # ## vits并行推理 method 2
@@ -1022,7 +1105,7 @@ class TTS:
                         batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
                     else:
                     # ## vits串行推理
-                        for i, idx in enumerate(idx_list):
+                        for i, idx in enumerate(tqdm(idx_list)):
                             phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
                             _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0))   # .unsqueeze(0)#mq要多unsqueeze一次
                             audio_fragment =(self.vits_model.decode(
@@ -1032,7 +1115,7 @@ class TTS:
                                 audio_fragment
                             )  ###试试重建不带上prompt部分
                 else:
-                    for i, idx in enumerate(idx_list):
+                    for i, idx in enumerate(tqdm(idx_list)):
                         phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
                         _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0))   # .unsqueeze(0)#mq要多unsqueeze一次
                         audio_fragment = self.v3_synthesis(
@@ -1047,39 +1130,38 @@ class TTS:
                 if return_fragment:
                     print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
                     yield self.audio_postprocess([batch_audio_fragment],
-                                                    self.configs.sampling_rate,
+                                                    output_sr,
                                                     None,
                                                     speed_factor,
                                                     False,
-                                                    fragment_interval
+                                                    fragment_interval,
+                                                    super_sampling if self.configs.is_v3_synthesizer else False
                                                     )
                 else:
                     audio.append(batch_audio_fragment)
 
                 if self.stop_flag:
-                    yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
+                    yield 16000, np.zeros(int(16000), dtype=np.int16)
-                                                            dtype=np.int16)
                     return
 
             if not return_fragment:
                 print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
                 if len(audio) == 0:
-                    yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
+                    yield 16000, np.zeros(int(16000), dtype=np.int16)
-                                                                dtype=np.int16)
                     return
                 yield self.audio_postprocess(audio,
-                                                self.configs.sampling_rate,
+                                                output_sr,
                                                 batch_index_list,
                                                 speed_factor,
                                                 split_bucket,
-                                                fragment_interval
+                                                fragment_interval,
+                                                super_sampling if self.configs.is_v3_synthesizer else False
                                                 )
 
         except Exception as e:
             traceback.print_exc()
             # 必须返回一个空音频, 否则会导致显存不释放。
-            yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
+            yield 16000, np.zeros(int(16000), dtype=np.int16)
-                                                            dtype=np.int16)
             # 重置模型, 否则会导致显存释放不完全。
             del self.t2s_model
             del self.vits_model
@@ -1107,7 +1189,8 @@ class TTS:
                           batch_index_list:list=None,
                           speed_factor:float=1.0,
                           split_bucket:bool=True,
-                          fragment_interval:float=0.3
+                          fragment_interval:float=0.3,
+                          super_sampling:bool=False,
                           )->Tuple[int, np.ndarray]:
         zero_wav = torch.zeros(
                         int(self.configs.sampling_rate * fragment_interval),
@@ -1120,7 +1203,7 @@ class TTS:
                 max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
                 if max_audio>1: audio_fragment/=max_audio
                 audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
-                audio[i][j] = audio_fragment.cpu().numpy()
+                audio[i][j] = audio_fragment
 
 
         if split_bucket:
@@ -1129,8 +1212,21 @@ class TTS:
             # audio = [item for batch in audio for item in batch]
             audio = sum(audio, [])
 
+        audio = torch.cat(audio, dim=0)
+
+        if super_sampling:
+            print(f"############ {i18n('音频超采样')} ############")
+            t1 = ttime()
+            self.init_sr_model()
+            if not self.sr_model_not_exist:
+                audio,sr=self.sr_model(audio.unsqueeze(0),sr)
+                max_audio=np.abs(audio).max()
+                if max_audio > 1: audio /= max_audio
+            t2 = ttime()
+            print(f"超采样用时：{t2-t1:.3f}s")
+        else:
+            audio = audio.cpu().numpy()
 
-        audio = np.concatenate(audio, 0)
         audio = (audio * 32768).astype(np.int16)
 
         # try:
@@ -1146,10 +1242,10 @@ class TTS:
                      semantic_tokens:torch.Tensor, 
                      phones:torch.Tensor, 
                      speed:float=1.0,
-                     sample_steps:int=16
+                     sample_steps:int=32
                      ):
             
-        prompt_semantic_tokens = self.prompt_cache["prompt_semantic"].unsqueeze(0).to(self.configs.device)
+        prompt_semantic_tokens = self.prompt_cache["prompt_semantic"].unsqueeze(0).unsqueeze(0).to(self.configs.device)
         prompt_phones = torch.LongTensor(self.prompt_cache["phones"]).unsqueeze(0).to(self.configs.device)
         refer_audio_spec = self.prompt_cache["refer_spec"][0].to(dtype=self.precision, device=self.configs.device)
 
@@ -1160,7 +1256,7 @@ class TTS:
         if (ref_audio.shape[0] == 2):
             ref_audio = ref_audio.mean(0).unsqueeze(0)
         if ref_sr!=24000:
-            ref_audio=resample(ref_audio, ref_sr)
+            ref_audio=resample(ref_audio, ref_sr, self.configs.device)
         # print("ref_audio",ref_audio.abs().mean())
         mel2 = mel_fn(ref_audio)
         mel2 = norm_spec(mel2)
@@ -1198,36 +1294,3 @@ class TTS:
             audio=wav_gen[0][0]#.cpu().detach().numpy()
     
         return audio
-
-
-def speed_change(input_audio:np.ndarray, speed:float, sr:int):
-    # 将 NumPy 数组转换为原始 PCM 流
-    raw_audio = input_audio.astype(np.int16).tobytes()
-
-    # 设置 ffmpeg 输入流
-    input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
-
-    # 变速处理
-    output_stream = input_stream.filter('atempo', speed)
-
-    # 输出流到管道
-    out, _ = (
-        output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
-        .run(input=raw_audio, capture_stdout=True, capture_stderr=True)
-    )
-
-    # 将管道输出解码为 NumPy 数组
-    processed_audio = np.frombuffer(out, np.int16)
-
-    return processed_audio
-
-
-
-resample_transform_dict={}
-def resample(audio_tensor, sr0, device):
-    global resample_transform_dict
-    if sr0 not in resample_transform_dict:
-        resample_transform_dict[sr0] = torchaudio.transforms.Resample(
-            sr0, 24000
-        ).to(device)
-    return resample_transform_dict[sr0](audio_tensor)

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -199,6 +199,7 @@ class TextPreprocessor:
         return phone_level_feature.T
 
     def clean_text_inf(self, text:str, language:str, version:str="v2"):
+        language = language.replace("all_","")
         phones, word2ph, norm_text = clean_text(text, language, version)
         phones = cleaned_text_to_sequence(phones, version)
         return phones, word2ph, norm_text

GPT_SoVITS/configs/tts_infer.yaml

@@ -3,10 +3,10 @@ custom:
   cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
   device: cuda
   is_half: true
-  t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
+  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
-  version: v2
+  version: v3
-  vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
+  vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
-default:
+v1:
   bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
   cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
   device: cpu
@@ -14,7 +14,7 @@ default:
   t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
   version: v1
   vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
-default_v2:
+v2:
   bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
   cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
   device: cpu
@@ -22,3 +22,11 @@ default_v2:
   t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
   version: v2
   vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
+v3:
+  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
+  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
+  device: cpu
+  is_half: false
+  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
+  version: v3
+  vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth

GPT_SoVITS/inference_webui_fast.py

@@ -7,7 +7,7 @@
 全部按日文识别
 '''
 import random
-import os, re, logging
+import os, re, logging, json
 import sys
 now_dir = os.getcwd()
 sys.path.append(now_dir)
@@ -62,6 +62,9 @@ if torch.cuda.is_available():
 else:
     device = "cpu"
 
+# is_half = False
+# device = "cpu"
+
 dict_language_v1 = {
     i18n("中文"): "all_zh",#全部按中文识别
     i18n("英文"): "en",#全部按英文识别#######不变
@@ -123,7 +126,7 @@ def inference(text, text_lang,
               speed_factor, ref_text_free,
               split_bucket,fragment_interval,
               seed, keep_random, parallel_infer,
-              repetition_penalty
+              repetition_penalty, sample_steps, super_sampling,
               ):
 
     seed = -1 if keep_random else seed
@@ -147,6 +150,8 @@ def inference(text, text_lang,
         "seed":actual_seed,
         "parallel_infer": parallel_infer,
         "repetition_penalty": repetition_penalty,
+        "sample_steps": int(sample_steps),
+        "super_sampling": super_sampling,
     }
     for item in tts_pipeline.run(inputs):
         yield item, actual_seed
@@ -163,19 +168,38 @@ def change_choices():
     SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
     return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
 
+path_sovits_v3="GPT_SoVITS/pretrained_models/s2Gv3.pth"
+pretrained_sovits_name=["GPT_SoVITS/pretrained_models/s2G488k.pth", "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",path_sovits_v3]
+pretrained_gpt_name=["GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt","GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1v3.ckpt"]
 
-pretrained_sovits_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", "GPT_SoVITS/pretrained_models/s2G488k.pth"]
-pretrained_gpt_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"]
 _ =[[],[]]
-for i in range(2):
+for i in range(3):
-    if os.path.exists(pretrained_gpt_name[i]):
+    if os.path.exists(pretrained_gpt_name[i]):_[0].append(pretrained_gpt_name[i])
-        _[0].append(pretrained_gpt_name[i])
+    if os.path.exists(pretrained_sovits_name[i]):_[-1].append(pretrained_sovits_name[i])
-    if os.path.exists(pretrained_sovits_name[i]):
-        _[-1].append(pretrained_sovits_name[i])
 pretrained_gpt_name,pretrained_sovits_name = _
 
-SoVITS_weight_root=["SoVITS_weights_v2","SoVITS_weights"]
+
-GPT_weight_root=["GPT_weights_v2","GPT_weights"]
+if os.path.exists(f"./weight.json"):
+    pass
+else:
+    with open(f"./weight.json", 'w', encoding="utf-8") as file:json.dump({'GPT':{},'SoVITS':{}},file)
+
+with open(f"./weight.json", 'r', encoding="utf-8") as file:
+    weight_data = file.read()
+    weight_data=json.loads(weight_data)
+    gpt_path = os.environ.get(
+        "gpt_path", weight_data.get('GPT',{}).get(version,pretrained_gpt_name))
+    sovits_path = os.environ.get(
+        "sovits_path", weight_data.get('SoVITS',{}).get(version,pretrained_sovits_name))
+    if isinstance(gpt_path,list):
+        gpt_path = gpt_path[0]
+    if isinstance(sovits_path,list):
+        sovits_path = sovits_path[0]
+
+
+
+SoVITS_weight_root=["SoVITS_weights","SoVITS_weights_v2","SoVITS_weights_v3"]
+GPT_weight_root=["GPT_weights","GPT_weights_v2","GPT_weights_v3"]
 for path in SoVITS_weight_root+GPT_weight_root:
     os.makedirs(path,exist_ok=True)
 
@@ -194,12 +218,10 @@ def get_weights_names(GPT_weight_root, SoVITS_weight_root):
 SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
 
 
-from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
+from process_ckpt import get_sovits_version_from_path_fast
 def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
     global version, dict_language
     version, model_version, if_lora_v3=get_sovits_version_from_path_fast(sovits_path)
-    # print(sovits_path,version, model_version, if_lora_v3)
-    path_sovits_v3="GPT_SoVITS/pretrained_models/s2Gv3.pth"
 
     if if_lora_v3 and not os.path.exists(path_sovits_v3):
         info= path_sovits_v3 + i18n("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")
@@ -228,7 +250,11 @@ def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
             visible_inp_refs=True
         yield  {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update,{"__type__": "update", "visible": visible_sample_steps},{"__type__": "update", "visible": visible_inp_refs},{"__type__": "update", "value": False,"interactive":True if model_version!="v3"else False},{"__type__": "update", "visible":True if model_version=="v3"else False}
 
-
+    with open("./weight.json")as f:
+        data=f.read()
+        data=json.loads(data)
+        data["SoVITS"][version]=sovits_path
+    with open("./weight.json","w")as f:f.write(json.dumps(data))
 
 with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(
@@ -274,12 +300,14 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
 
             with gr.Column():
                 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生效)"),value=32,choices=[4,8,16,32],visible=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="speed_factor",value=1.0,interactive=True)
+                speed_factor = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label="语速",value=1.0,interactive=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)
                 temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
                 repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label=i18n("重复惩罚"),value=1.35,interactive=True)
+                
             with gr.Column():
                 with gr.Row():
                     how_to_cut = gr.Dropdown(
@@ -288,10 +316,14 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                             value=i18n("凑四句一切"),
                             interactive=True, scale=1
                         )
+                    super_sampling = gr.Checkbox(label=i18n("音频超采样(仅对V3生效))"), value=False, interactive=True, show_label=True)
+
+                with gr.Row():
                     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)"), value=True, interactive=True, show_label=True)
 
                 with gr.Row():
+                    
                     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)
 
@@ -311,7 +343,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                 speed_factor, ref_text_free,
                 split_bucket,fragment_interval,
                 seed, keep_random, parallel_infer,
-                repetition_penalty
+                repetition_penalty, sample_steps, super_sampling,
              ],
             [output, seed],
         )

api_v2.py

@@ -39,6 +39,8 @@ POST:
     "seed": -1,                   # int. random seed for reproducibility.
     "parallel_infer": True,       # bool. whether to use parallel inference.
     "repetition_penalty": 1.35    # float. repetition penalty for T2S model.
+    "sample_steps": 32,           # int. number of sampling steps for VITS model V3.
+    "super_sampling": False,       # bool. whether to use super-sampling for audio when using VITS model V3.
 }
 ```
 
@@ -164,6 +166,8 @@ class TTS_Request(BaseModel):
     streaming_mode:bool = False
     parallel_infer:bool = True
     repetition_penalty:float = 1.35
+    sample_steps:int = 32
+    super_sampling:bool = False
 
 ### modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files
 def pack_ogg(io_buffer:BytesIO, data:np.ndarray, rate:int):
@@ -294,7 +298,9 @@ async def tts_handle(req:dict):
                 "media_type": "wav",          # str. media type of the output audio, support "wav", "raw", "ogg", "aac".
                 "streaming_mode": False,      # bool. whether to return a streaming response.
                 "parallel_infer": True,       # bool.(optional) whether to use parallel inference.
-                "repetition_penalty": 1.35    # float.(optional) repetition penalty for T2S model.          
+                "repetition_penalty": 1.35    # float.(optional) repetition penalty for T2S model.   
+                "sample_steps": 32,           # int. number of sampling steps for VITS model V3.
+                "super_sampling": False,       # bool. whether to use super-sampling for audio when using VITS model V3.       
             }
     returns:
         StreamingResponse: audio stream response.
@@ -316,10 +322,12 @@ async def tts_handle(req:dict):
         
         if streaming_mode:
             def streaming_generator(tts_generator:Generator, media_type:str):
-                if media_type == "wav":
+                if_frist_chunk = True
-                    yield wave_header_chunk()
-                    media_type = "raw"
                 for sr, chunk in tts_generator:
+                    if if_frist_chunk and media_type == "wav":
+                        yield wave_header_chunk(sample_rate=sr)
+                        media_type = "raw"
+                        if_frist_chunk = False
                     yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue()
             # _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}"
             return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}")
@@ -365,7 +373,9 @@ async def tts_get_endpoint(
                         media_type:str = "wav",
                         streaming_mode:bool = False,
                         parallel_infer:bool = True,
-                        repetition_penalty:float = 1.35
+                        repetition_penalty:float = 1.35,
+                        sample_steps:int =32,
+                        super_sampling:bool = False
                         ):
     req = {
         "text": text,
@@ -387,7 +397,9 @@ async def tts_get_endpoint(
         "media_type":media_type,
         "streaming_mode":streaming_mode,
         "parallel_infer":parallel_infer,
-        "repetition_penalty":float(repetition_penalty)
+        "repetition_penalty":float(repetition_penalty),
+        "sample_steps":int(sample_steps),
+        "super_sampling":super_sampling
     }
     return await tts_handle(req)
                 

tools/audio_sr.py

@@ -39,6 +39,11 @@ class AP_BWE():
         self.model=model
         self.h=h
 
+    def to(self, *arg, **kwargs):
+        self.model.to(*arg, **kwargs)
+        self.device = self.model.conv_pre_mag.weight.device
+        return self
+
     def __call__(self, audio,orig_sampling_rate):
         with torch.no_grad():
             # audio, orig_sampling_rate = torchaudio.load(inp_path)