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改用修饰器

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XTer 2024-04-06 23:51:47 +08:00
parent 6591e86df3
commit 7f8892d004
1 changed files with 223 additions and 223 deletions
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GPT_SoVITS/TTS_infer_pack/TTS.py
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@ -568,8 +568,9 @@ class TTS:
Stop the inference process. Stop the inference process.
''' '''
self.stop_flag = True self.stop_flag = True
# 使用装饰器
@torch.no_grad()
def run(self, inputs:dict): def run(self, inputs:dict):
""" """
Text to speech inference. Text to speech inference.
@ -600,260 +601,259 @@ class TTS:
# 直接给全体套一个torch.no_grad()
with torch.no_grad(): ########## variables initialization ###########
########## variables initialization ########### self.stop_flag:bool = False
self.stop_flag:bool = False text:str = inputs.get("text", "")
text:str = inputs.get("text", "") text_lang:str = inputs.get("text_lang", "")
text_lang:str = inputs.get("text_lang", "") ref_audio_path:str = inputs.get("ref_audio_path", "")
ref_audio_path:str = inputs.get("ref_audio_path", "") prompt_text:str = inputs.get("prompt_text", "")
prompt_text:str = inputs.get("prompt_text", "") prompt_lang:str = inputs.get("prompt_lang", "")
prompt_lang:str = inputs.get("prompt_lang", "") top_k:int = inputs.get("top_k", 5)
top_k:int = inputs.get("top_k", 5) top_p:float = inputs.get("top_p", 1)
top_p:float = inputs.get("top_p", 1) temperature:float = inputs.get("temperature", 1)
temperature:float = inputs.get("temperature", 1) text_split_method:str = inputs.get("text_split_method", "cut0")
text_split_method:str = inputs.get("text_split_method", "cut0") batch_size = inputs.get("batch_size", 1)
batch_size = inputs.get("batch_size", 1) batch_threshold = inputs.get("batch_threshold", 0.75)
batch_threshold = inputs.get("batch_threshold", 0.75) speed_factor = inputs.get("speed_factor", 1.0)
speed_factor = inputs.get("speed_factor", 1.0) split_bucket = inputs.get("split_bucket", True)
split_bucket = inputs.get("split_bucket", True) return_fragment = inputs.get("return_fragment", False)
return_fragment = inputs.get("return_fragment", False) fragment_interval = inputs.get("fragment_interval", 0.3)
fragment_interval = inputs.get("fragment_interval", 0.3) seed = inputs.get("seed", -1)
seed = inputs.get("seed", -1) seed = -1 if seed in ["", None] else seed
seed = -1 if seed in ["", None] else seed actual_seed = set_seed(seed)
actual_seed = set_seed(seed)
if return_fragment:
# split_bucket = False
print(i18n("分段返回模式已开启"))
if split_bucket:
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 return_fragment:
# split_bucket = False
print(i18n("分段返回模式已开启"))
if split_bucket: if split_bucket:
print(i18n("分桶处理模式已开启")) 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 fragment_interval<0.01: if split_bucket:
fragment_interval = 0.01 print(i18n("分桶处理模式已开启"))
print(i18n("分段间隔过小，已自动设置为0.01"))
no_prompt_text = False if fragment_interval<0.01:
if prompt_text in [None, ""]: fragment_interval = 0.01
no_prompt_text = True print(i18n("分段间隔过小，已自动设置为0.01"))
assert text_lang in self.configs.languages no_prompt_text = False
if not no_prompt_text: if prompt_text in [None, ""]:
assert prompt_lang in self.configs.languages no_prompt_text = True
if ref_audio_path in [None, ""] and \ assert text_lang in self.configs.languages
((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)): if not no_prompt_text:
raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()") assert prompt_lang in self.configs.languages
###### setting reference audio and prompt text preprocessing ######## if ref_audio_path in [None, ""] and \
t0 = ttime() ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)):
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]): raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
self.set_ref_audio(ref_audio_path)
if not no_prompt_text: ###### setting reference audio and prompt text preprocessing ########
prompt_text = prompt_text.strip("\n") t0 = ttime()
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "." if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
print(i18n("实际输入的参考文本:"), prompt_text) self.set_ref_audio(ref_audio_path)
if self.prompt_cache["prompt_text"] != prompt_text:
self.prompt_cache["prompt_text"] = prompt_text
self.prompt_cache["prompt_lang"] = prompt_lang
phones, bert_features, norm_text = \
self.text_preprocessor.segment_and_extract_feature_for_text(
prompt_text,
prompt_lang)
self.prompt_cache["phones"] = phones
self.prompt_cache["bert_features"] = bert_features
self.prompt_cache["norm_text"] = norm_text
###### text preprocessing ######## if not no_prompt_text:
t1 = ttime() prompt_text = prompt_text.strip("\n")
data:list = None if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
if not return_fragment: print(i18n("实际输入的参考文本:"), prompt_text)
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) if self.prompt_cache["prompt_text"] != prompt_text:
if len(data) == 0: self.prompt_cache["prompt_text"] = prompt_text
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), self.prompt_cache["prompt_lang"] = prompt_lang
dtype=np.int16) phones, bert_features, norm_text = \
return self.text_preprocessor.segment_and_extract_feature_for_text(
prompt_text,
prompt_lang)
self.prompt_cache["phones"] = phones
self.prompt_cache["bert_features"] = bert_features
self.prompt_cache["norm_text"] = norm_text
batch_index_list:list = None ###### text preprocessing ########
data, batch_index_list = self.to_batch(data, t1 = ttime()
prompt_data=self.prompt_cache if not no_prompt_text else None, data:list = None
batch_size=batch_size, if not return_fragment:
threshold=batch_threshold, data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
split_bucket=split_bucket, if len(data) == 0:
device=self.configs.device, yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
precision=self.precision dtype=np.int16)
) return
else:
print(i18n("############ 切分文本 ############")) batch_index_list:list = None
texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method) data, batch_index_list = self.to_batch(data,
data = []
for i in range(len(texts)):
if i%batch_size == 0:
data.append([])
data[-1].append(texts[i])
def make_batch(batch_texts):
batch_data = []
print(i18n("############ 提取文本Bert特征 ############"))
for text in tqdm(batch_texts):
phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
if phones is None:
continue
res={
"phones": phones,
"bert_features": bert_features,
"norm_text": norm_text,
}
batch_data.append(res)
if len(batch_data) == 0:
return None
batch, _ = self.to_batch(batch_data,
prompt_data=self.prompt_cache if not no_prompt_text else None, prompt_data=self.prompt_cache if not no_prompt_text else None,
batch_size=batch_size, batch_size=batch_size,
threshold=batch_threshold, threshold=batch_threshold,
split_bucket=False, split_bucket=split_bucket,
device=self.configs.device, device=self.configs.device,
precision=self.precision precision=self.precision
) )
return batch[0] else:
print(i18n("############ 切分文本 ############"))
texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
data = []
for i in range(len(texts)):
if i%batch_size == 0:
data.append([])
data[-1].append(texts[i])
def make_batch(batch_texts):
batch_data = []
print(i18n("############ 提取文本Bert特征 ############"))
for text in tqdm(batch_texts):
phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
if phones is None:
continue
res={
"phones": phones,
"bert_features": bert_features,
"norm_text": norm_text,
}
batch_data.append(res)
if len(batch_data) == 0:
return None
batch, _ = self.to_batch(batch_data,
prompt_data=self.prompt_cache if not no_prompt_text else None,
batch_size=batch_size,
threshold=batch_threshold,
split_bucket=False,
device=self.configs.device,
precision=self.precision
)
return batch[0]
t2 = ttime() t2 = ttime()
try: try:
print("############ 推理 ############") print("############ 推理 ############")
###### inference ###### ###### inference ######
t_34 = 0.0 t_34 = 0.0
t_45 = 0.0 t_45 = 0.0
audio = [] audio = []
for item in data: for item in data:
t3 = ttime() t3 = ttime()
if return_fragment: if return_fragment:
item = make_batch(item) item = make_batch(item)
if item is None: if item is None:
continue continue
batch_phones:List[torch.LongTensor] = item["phones"] batch_phones:List[torch.LongTensor] = item["phones"]
batch_phones_len:torch.LongTensor = item["phones_len"] batch_phones_len:torch.LongTensor = item["phones_len"]
all_phoneme_ids:List[torch.LongTensor] = item["all_phones"] all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
all_phoneme_lens:torch.LongTensor = item["all_phones_len"] all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
all_bert_features:List[torch.LongTensor] = item["all_bert_features"] all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
norm_text:str = item["norm_text"] norm_text:str = item["norm_text"]
print(i18n("前端处理后的文本(每句):"), norm_text) print(i18n("前端处理后的文本(每句):"), norm_text)
if no_prompt_text : if no_prompt_text :
prompt = None prompt = None
else: else:
prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device) prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
all_phoneme_ids, all_phoneme_ids,
all_phoneme_lens, all_phoneme_lens,
prompt, prompt,
all_bert_features, all_bert_features,
# prompt_phone_len=ph_offset, # prompt_phone_len=ph_offset,
top_k=top_k, top_k=top_k,
top_p=top_p, top_p=top_p,
temperature=temperature, temperature=temperature,
early_stop_num=self.configs.hz * self.configs.max_sec, early_stop_num=self.configs.hz * self.configs.max_sec,
) )
t4 = ttime() t4 = ttime()
t_34 += t4 - t3 t_34 += t4 - t3
refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\ refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
.to(dtype=self.precision, device=self.configs.device) .to(dtype=self.precision, device=self.configs.device)
batch_audio_fragment = [] batch_audio_fragment = []
# 这里要记得加 torch.no_grad() 不然速度慢一大截 # 这里要记得加 torch.no_grad() 不然速度慢一大截
# with torch.no_grad(): # with torch.no_grad():
# ## vits并行推理 method 1 # ## vits并行推理 method 1
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device) # pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0) # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
# max_len = 0 # max_len = 0
# for i in range(0, len(batch_phones)): # for i in range(0, len(batch_phones)):
# max_len = max(max_len, batch_phones[i].shape[-1]) # max_len = max(max_len, batch_phones[i].shape[-1])
# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len) # batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
# batch_phones = batch_phones.to(self.configs.device) # batch_phones = batch_phones.to(self.configs.device)
# batch_audio_fragment = (self.vits_model.batched_decode( # batch_audio_fragment = (self.vits_model.batched_decode(
# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec # pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
# )) # ))
# ## vits并行推理 method 2 # ## vits并行推理 method 2
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
upsample_rate = math.prod(self.vits_model.upsample_rates) upsample_rate = math.prod(self.vits_model.upsample_rates)
audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))] audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))] audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device) all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device) _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
_batch_audio_fragment = (self.vits_model.decode( _batch_audio_fragment = (self.vits_model.decode(
all_pred_semantic, _batch_phones, refer_audio_spec all_pred_semantic, _batch_phones, refer_audio_spec
).detach()[0, 0, :]) ).detach()[0, 0, :])
audio_frag_end_idx.insert(0, 0) audio_frag_end_idx.insert(0, 0)
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))] 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))]
# ## vits串行推理 # ## vits串行推理
# for i, idx in enumerate(idx_list): # for i, idx in enumerate(idx_list):
# phones = batch_phones[i].unsqueeze(0).to(self.configs.device) # 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 # _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( # audio_fragment =(self.vits_model.decode(
# _pred_semantic, phones, refer_audio_spec # _pred_semantic, phones, refer_audio_spec
# ).detach()[0, 0, :]) # ).detach()[0, 0, :])
# batch_audio_fragment.append( # batch_audio_fragment.append(
# audio_fragment # audio_fragment
# ) ###试试重建不带上prompt部分 # ) ###试试重建不带上prompt部分
t5 = ttime() t5 = ttime()
t_45 += t5 - t4 t_45 += t5 - t4
if return_fragment: if return_fragment:
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4)) print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
yield self.audio_postprocess([batch_audio_fragment], yield self.audio_postprocess([batch_audio_fragment],
self.configs.sampling_rate,
None,
speed_factor,
False,
fragment_interval
)
else:
audio.append(batch_audio_fragment)
if self.stop_flag:
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16)
return
if not return_fragment:
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
yield self.audio_postprocess(audio,
self.configs.sampling_rate, self.configs.sampling_rate,
batch_index_list, None,
speed_factor, speed_factor,
split_bucket, False,
fragment_interval fragment_interval
) )
else:
audio.append(batch_audio_fragment)
except Exception as e: if self.stop_flag:
traceback.print_exc() yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
# 必须返回一个空音频, 否则会导致显存不释放。 dtype=np.int16)
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), return
dtype=np.int16)
# 重置模型, 否则会导致显存释放不完全。 if not return_fragment:
del self.t2s_model print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
del self.vits_model yield self.audio_postprocess(audio,
self.t2s_model = None self.configs.sampling_rate,
self.vits_model = None batch_index_list,
self.init_t2s_weights(self.configs.t2s_weights_path) speed_factor,
self.init_vits_weights(self.configs.vits_weights_path) split_bucket,
raise e fragment_interval
finally: )
self.empty_cache()
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),
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
self.t2s_model = None
self.vits_model = None
self.init_t2s_weights(self.configs.t2s_weights_path)
self.init_vits_weights(self.configs.vits_weights_path)
raise e
finally:
self.empty_cache()
def empty_cache(self): def empty_cache(self):
try: try: