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为sovits_v3 适配并行推理

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ChasonJiang 2025-03-30 19:55:26 +08:00
parent ee4a466f79
commit 43ce16a530
1 changed files with 183 additions and 24 deletions
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189
GPT_SoVITS/TTS_infer_pack/TTS.py
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@ -3,7 +3,7 @@ import math
import os, sys, gc
import random
import traceback
import time
import torchaudio
from tqdm import tqdm
now_dir = os.getcwd()
@ -908,11 +908,14 @@ class TTS:
split_bucket = False
print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
if split_bucket and speed_factor==1.0:
if split_bucket and speed_factor==1.0 and not (self.configs.is_v3_synthesizer and parallel_infer):
print(i18n("分桶处理模式已开启"))
elif speed_factor!=1.0:
print(i18n("语速调节不支持分桶处理，已自动关闭分桶处理"))
split_bucket = False
elif self.configs.is_v3_synthesizer and parallel_infer:
print(i18n("当开启并行推理模式时，SoVits V3模型不支持分桶处理，已自动关闭分桶处理"))
split_bucket = False
else:
print(i18n("分桶处理模式已关闭"))
@ -936,7 +939,7 @@ class TTS:
raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
###### setting reference audio and prompt text preprocessing ########
t0 = ttime()
t0 = time.perf_counter()
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
if not os.path.exists(ref_audio_path):
raise ValueError(f"{ref_audio_path} not exists")
@ -975,7 +978,7 @@ class TTS:
###### text preprocessing ########
t1 = ttime()
t1 = time.perf_counter()
data:list = None
if not return_fragment:
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method, self.configs.version)
@ -1027,7 +1030,7 @@ class TTS:
return batch[0]
t2 = ttime()
t2 = time.perf_counter()
try:
print("############ 推理 ############")
###### inference ######
@ -1036,7 +1039,7 @@ class TTS:
audio = []
output_sr = self.configs.sampling_rate if not self.configs.is_v3_synthesizer else 24000
for item in data:
t3 = ttime()
t3 = time.perf_counter()
if return_fragment:
item = make_batch(item)
if item is None:
@ -1071,7 +1074,7 @@ class TTS:
max_len=max_len,
repetition_penalty=repetition_penalty,
)
t4 = ttime()
t4 = time.perf_counter()
t_34 += t4 - t3
refer_audio_spec:torch.Tensor = [item.to(dtype=self.precision, device=self.configs.device) for item in self.prompt_cache["refer_spec"]]
@ -1094,6 +1097,7 @@ class TTS:
print(f"############ {i18n('合成音频')} ############")
if not self.configs.is_v3_synthesizer:
if speed_factor == 1.0:
print(f"{i18n('并行合成中')}...")
# ## vits并行推理 method 2
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
upsample_rate = math.prod(self.vits_model.upsample_rates)
@ -1117,6 +1121,20 @@ class TTS:
batch_audio_fragment.append(
audio_fragment
) ###试试重建不带上prompt部分
else:
if parallel_infer:
print(f"{i18n('并行合成中')}...")
# 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_fragments = self.v3_synthesis_batched_infer(
idx_list,
pred_semantic_list,
batch_phones,
speed=speed_factor,
sample_steps=sample_steps
)
batch_audio_fragment.extend(audio_fragments)
else:
for i, idx in enumerate(tqdm(idx_list)):
phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
@ -1128,7 +1146,7 @@ class TTS:
audio_fragment
)
t5 = ttime()
t5 = time.perf_counter()
t_45 += t5 - t4
if return_fragment:
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
@ -1219,13 +1237,13 @@ class TTS:
if super_sampling:
print(f"############ {i18n('音频超采样')} ############")
t1 = ttime()
t1 = time.perf_counter()
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()
t2 = time.perf_counter()
print(f"超采样用时：{t2-t1:.3f}s")
else:
audio = audio.cpu().numpy()
@ -1260,7 +1278,7 @@ class TTS:
ref_audio = ref_audio.mean(0).unsqueeze(0)
if ref_sr!=24000:
ref_audio=resample(ref_audio, ref_sr, self.configs.device)
# print("ref_audio",ref_audio.abs().mean())
# print("ref_audio",ref_audio.abs().mean())W
mel2 = mel_fn(ref_audio)
mel2 = norm_spec(mel2)
T_min = min(mel2.shape[2], fea_ref.shape[2])
@ -1285,15 +1303,156 @@ class TTS:
cfm_res = self.vits_model.cfm.inference(fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0)
cfm_res = cfm_res[:, :, mel2.shape[2]:]
mel2 = cfm_res[:, :, -T_min:]
mel2 = cfm_res[:, :, -T_min:]
fea_ref = fea_todo_chunk[:, :, -T_min:]
cfm_resss.append(cfm_res)
cmf_res = torch.cat(cfm_resss, 2)
cmf_res = denorm_spec(cmf_res)
cfm_res = torch.cat(cfm_resss, 2)
cfm_res = denorm_spec(cfm_res)
with torch.inference_mode():
wav_gen = self.bigvgan_model(cmf_res)
wav_gen = self.bigvgan_model(cfm_res)
audio=wav_gen[0][0]#.cpu().detach().numpy()
return audio
def v3_synthesis_batched_infer(self,
idx_list:List[int],
semantic_tokens_list:List[torch.Tensor],
batch_phones:List[torch.Tensor],
speed:float=1.0,
sample_steps:int=32
)->List[torch.Tensor]:
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)
fea_ref,ge = self.vits_model.decode_encp(prompt_semantic_tokens, prompt_phones, refer_audio_spec)
ref_audio:torch.Tensor = self.prompt_cache["raw_audio"]
ref_sr = self.prompt_cache["raw_sr"]
ref_audio=ref_audio.to(self.configs.device).float()
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, self.configs.device)
mel2 = mel_fn(ref_audio)
mel2 = norm_spec(mel2)
T_min = min(mel2.shape[2], fea_ref.shape[2])
mel2 = mel2[:, :, :T_min]
fea_ref = fea_ref[:, :, :T_min]
if (T_min > 468):
mel2 = mel2[:, :, -468:]
fea_ref = fea_ref[:, :, -468:]
T_min = 468
chunk_len = 934 - T_min
mel2=mel2.to(self.precision)
# #### batched inference
overlapped_len = 12
feat_chunks = []
feat_lens = []
feat_list = []
for i, idx in enumerate(idx_list):
phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
semantic_tokens = semantic_tokens_list[i][-idx:].unsqueeze(0).unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
feat, _ = self.vits_model.decode_encp(semantic_tokens, phones, refer_audio_spec, ge, speed)
feat_list.append(feat)
feat_lens.append(feat.shape[2])
feats = torch.cat(feat_list, 2)
feats_padded = F.pad(feats, (overlapped_len,0), "constant", 0)
pos = 0
padding_len = 0
while True:
if pos ==0:
chunk = feats_padded[:, :, pos:pos + chunk_len]
else:
pos = pos - overlapped_len
chunk = feats_padded[:, :, pos:pos + chunk_len]
pos += chunk_len
if (chunk.shape[-1] == 0): break
# padding for the last chunk
padding_len = chunk_len - chunk.shape[2]
if padding_len != 0:
chunk = F.pad(chunk, (0,padding_len), "constant", 0)
feat_chunks.append(chunk)
feat_chunks = torch.cat(feat_chunks, 0)
bs = feat_chunks.shape[0]
fea_ref = fea_ref.repeat(bs,1,1)
fea = torch.cat([fea_ref, feat_chunks], 2).transpose(2, 1)
pred_spec = self.vits_model.cfm.inference(fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0)
pred_spec = pred_spec[:, :, -chunk_len:]
dd = pred_spec.shape[1]
pred_spec = pred_spec.permute(1, 0, 2).contiguous().view(dd, -1).unsqueeze(0)
# pred_spec = pred_spec[..., :-padding_len]
pred_spec = denorm_spec(pred_spec)
with torch.no_grad():
wav_gen = self.bigvgan_model(pred_spec)
audio = wav_gen[0][0]#.cpu().detach().numpy()
audio_fragments = []
upsample_rate = 256
pos = 0
while pos < audio.shape[-1]:
audio_fragment = audio[pos:pos+chunk_len*upsample_rate]
audio_fragments.append(audio_fragment)
pos += chunk_len*upsample_rate
audio = self.sola_algorithm(audio_fragments, overlapped_len*upsample_rate)
audio = audio[overlapped_len*upsample_rate:-padding_len*upsample_rate]
audio_fragments = []
for feat_len in feat_lens:
audio_fragment = audio[:feat_len*upsample_rate]
audio_fragments.append(audio_fragment)
audio = audio[feat_len*upsample_rate:]
return audio_fragments
def sola_algorithm(self,
audio_fragments:List[torch.Tensor],
overlap_len:int,
):
for i in range(len(audio_fragments)-1):
f1 = audio_fragments[i]
f2 = audio_fragments[i+1]
w1 = f1[-overlap_len:]
w2 = f2[:overlap_len]
assert w1.shape == w2.shape
corr = F.conv1d(w1.view(1,1,-1), w2.view(1,1,-1),padding=w2.shape[-1]//2).view(-1)[:-1]
idx = corr.argmax()
f1_ = f1[:-(overlap_len-idx)]
audio_fragments[i] = f1_
f2_ = f2[idx:]
window = torch.hann_window((overlap_len-idx)*2, device=f1.device, dtype=f1.dtype)
f2_[:(overlap_len-idx)] = window[:(overlap_len-idx)]*f2_[:(overlap_len-idx)] + window[(overlap_len-idx):]*f1[-(overlap_len-idx):]
audio_fragments[i+1] = f2_
return torch.cat(audio_fragments, 0)