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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
推理功能增强和改进
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chasonjiang
parent
d60d8ea3fb
commit
252c9b7eb6
@ -707,19 +707,33 @@ class Text2SemanticDecoder(nn.Module):
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y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
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ref_free = True
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x_attn_mask_pad = F.pad(
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x_attn_mask,
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(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
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value=True,
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)
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y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
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##### create mask #####
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bsz = x.shape[0]
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src_len = x_len + y_len
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y_lens = torch.LongTensor([y_len]*bsz).to(x.device)
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y_mask = make_pad_mask(y_lens)
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x_mask = make_pad_mask(x_lens)
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# (bsz, x_len + y_len)
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xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
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x_mask = F.pad(
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x_attn_mask,
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(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
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value=True,
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)
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y_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
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torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
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(x_len, 0),
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value=False,
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)
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xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
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x.device
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)
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xy_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).expand(bsz*self.num_head, -1, -1).to(x.device)
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# xy_mask = torch.triu(torch.ones(src_len, src_len, dtype=torch.bool, device=x.device), diagonal=1)
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xy_padding_mask = xy_padding_mask.view(bsz, 1, src_len).expand(bsz, src_len, src_len).repeat(self.num_head, 1, 1)
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xy_attn_mask = xy_mask.logical_or(xy_padding_mask)
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new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
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xy_attn_mask = new_attn_mask.masked_fill(xy_attn_mask, float("-inf"))
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y_list = [None]*y.shape[0]
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batch_idx_map = list(range(y.shape[0]))
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@ -3,6 +3,8 @@ import math
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import os, sys
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import random
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import traceback
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from tqdm import tqdm
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import ffmpeg
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@ -552,14 +554,15 @@ class TTS:
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"prompt_text": "", # str. prompt text for the reference audio
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"prompt_lang": "", # str. language of the prompt text for the reference audio
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"top_k": 5, # int. top k sampling
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"top_p": 1, # float. top p sampling
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"temperature": 1, # float. temperature for sampling
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"top_p": 1, # float. top p sampling
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"temperature": 1, # float. temperature for sampling
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"text_split_method": "", # str. text split method, see text_segmentaion_method.py for details.
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"batch_size": 1, # int. batch size for inference
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"batch_threshold": 0.75, # float. threshold for batch splitting.
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"split_bucket: True, # bool. whether to split the batch into multiple buckets.
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"return_fragment": False, # bool. step by step return the audio fragment.
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"speed_factor":1.0, # float. control the speed of the synthesized audio.
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"fragment_interval":0.3, # float. to control the interval of the audio fragment.
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}
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returns:
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tulpe[int, np.ndarray]: sampling rate and audio data.
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@ -580,9 +583,10 @@ class TTS:
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speed_factor = inputs.get("speed_factor", 1.0)
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split_bucket = inputs.get("split_bucket", True)
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return_fragment = inputs.get("return_fragment", False)
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fragment_interval = inputs.get("fragment_interval", 0.3)
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if return_fragment:
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split_bucket = False
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# split_bucket = False
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print(i18n("分段返回模式已开启"))
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if split_bucket:
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split_bucket = False
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@ -590,7 +594,10 @@ class TTS:
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if split_bucket:
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print(i18n("分桶处理模式已开启"))
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if fragment_interval<0.01:
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fragment_interval = 0.01
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print(i18n("分段间隔过小，已自动设置为0.01"))
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no_prompt_text = False
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if prompt_text in [None, ""]:
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@ -627,19 +634,52 @@ class TTS:
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###### text preprocessing ########
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data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
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if len(data) == 0:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3),
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dtype=np.int16)
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return
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t1 = ttime()
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data, batch_index_list = self.to_batch(data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=split_bucket
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)
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data:list = None
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if not return_fragment:
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data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
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if len(data) == 0:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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return
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batch_index_list:list = None
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data, batch_index_list = self.to_batch(data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=split_bucket
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)
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else:
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print(i18n("############ 切分文本 ############"))
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texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
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data = []
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for i in range(len(texts)):
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if i%batch_size == 0:
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data.append([])
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data[-1].append(texts[i])
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def make_batch(batch_texts):
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batch_data = []
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print(i18n("############ 提取文本Bert特征 ############"))
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for text in tqdm(batch_texts):
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phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
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if phones is None:
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continue
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res={
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"phones": phones,
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"bert_features": bert_features,
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"norm_text": norm_text,
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}
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batch_data.append(res)
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batch, _ = self.to_batch(batch_data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=False
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)
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return batch[0]
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t2 = ttime()
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try:
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print("############ 推理 ############")
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@ -649,6 +689,9 @@ class TTS:
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audio = []
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for item in data:
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t3 = ttime()
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if return_fragment:
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item = make_batch(item)
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batch_phones = item["phones"]
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batch_phones_len = item["phones_len"]
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all_phoneme_ids = item["all_phones"]
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@ -734,14 +777,16 @@ class TTS:
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
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yield self.audio_postprocess([batch_audio_fragment],
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self.configs.sampling_rate,
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batch_index_list,
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None,
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speed_factor,
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split_bucket)
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False,
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fragment_interval
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)
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else:
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audio.append(batch_audio_fragment)
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if self.stop_flag:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3),
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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return
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@ -751,7 +796,9 @@ class TTS:
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self.configs.sampling_rate,
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batch_index_list,
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speed_factor,
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split_bucket)
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split_bucket,
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fragment_interval
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)
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except Exception as e:
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traceback.print_exc()
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# 必须返回一个空音频, 否则会导致显存不释放。
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@ -781,9 +828,11 @@ class TTS:
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sr:int,
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batch_index_list:list=None,
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speed_factor:float=1.0,
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split_bucket:bool=True)->tuple[int, np.ndarray]:
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split_bucket:bool=True,
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fragment_interval:float=0.3
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)->tuple[int, np.ndarray]:
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zero_wav = torch.zeros(
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int(self.configs.sampling_rate * 0.3),
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int(self.configs.sampling_rate * fragment_interval),
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dtype=self.precison,
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device=self.configs.device
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)
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@ -91,7 +91,7 @@ def inference(text, text_lang,
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top_p, temperature,
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text_split_method, batch_size,
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speed_factor, ref_text_free,
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split_bucket
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split_bucket,fragment_interval,
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):
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inputs={
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"text": text,
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@ -106,7 +106,8 @@ def inference(text, text_lang,
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"batch_size":int(batch_size),
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"speed_factor":float(speed_factor),
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"split_bucket":split_bucket,
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"return_fragment":False
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"return_fragment":False,
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"fragment_interval":fragment_interval,
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}
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for item in tts_pipline.run(inputs):
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@ -188,6 +189,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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with gr.Column():
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batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,interactive=True)
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fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label=i18n("分段间隔(秒)"),value=0.3,interactive=True)
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speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="speed_factor",value=1.0,interactive=True)
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top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
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top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
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@ -216,7 +218,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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top_k, top_p, temperature,
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how_to_cut, batch_size,
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speed_factor, ref_text_free,
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split_bucket
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split_bucket,fragment_interval,
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],
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[output],
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)
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