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Merge remote-tracking branch 'beta/fast_inference_'
This commit is contained in:
XTer
commit
6317c3a2f4
@ -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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@ -2,6 +2,9 @@ from copy import deepcopy
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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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@ -48,8 +51,18 @@ custom:
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"""
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# def set_seed(seed):
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# random.seed(seed)
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# os.environ['PYTHONHASHSEED'] = str(seed)
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# np.random.seed(seed)
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# torch.manual_seed(seed)
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# torch.cuda.manual_seed(seed)
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# torch.cuda.manual_seed_all(seed)
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# torch.backends.cudnn.deterministic = True
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# torch.backends.cudnn.benchmark = False
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# torch.backends.cudnn.enabled = True
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# set_seed(1234)
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class TTS_Config:
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default_configs={
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"device": "cpu",
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@ -541,14 +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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@ -569,9 +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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@ -579,7 +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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@ -616,147 +634,209 @@ 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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if len(batch_data) == 0:
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return None
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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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###### inference ######
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t_34 = 0.0
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t_45 = 0.0
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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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if item is None:
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continue
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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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all_phoneme_lens = item["all_phones_len"]
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all_bert_features = item["all_bert_features"]
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norm_text = item["norm_text"]
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# batch_phones = batch_phones.to(self.configs.device)
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batch_phones_len = batch_phones_len.to(self.configs.device)
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all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
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all_phoneme_lens = all_phoneme_lens.to(self.configs.device)
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all_bert_features = all_bert_features.to(self.configs.device)
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if self.configs.is_half:
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all_bert_features = all_bert_features.half()
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print("############ 推理 ############")
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###### inference ######
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t_34 = 0.0
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t_45 = 0.0
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audio = []
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for item in data:
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t3 = ttime()
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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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all_phoneme_lens = item["all_phones_len"]
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all_bert_features = item["all_bert_features"]
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norm_text = item["norm_text"]
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# batch_phones = batch_phones.to(self.configs.device)
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batch_phones_len = batch_phones_len.to(self.configs.device)
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all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
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all_phoneme_lens = all_phoneme_lens.to(self.configs.device)
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all_bert_features = all_bert_features.to(self.configs.device)
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if self.configs.is_half:
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all_bert_features = all_bert_features.half()
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print(i18n("前端处理后的文本(每句):"), norm_text)
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if no_prompt_text :
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prompt = None
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else:
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prompt = self.prompt_cache["prompt_semantic"].expand(all_phoneme_ids.shape[0], -1).to(self.configs.device)
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with torch.no_grad():
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pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_lens,
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prompt,
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all_bert_features,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=self.configs.hz * self.configs.max_sec,
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)
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t4 = ttime()
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t_34 += t4 - t3
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refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\
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.to(dtype=self.precison, device=self.configs.device)
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print(i18n("前端处理后的文本(每句):"), norm_text)
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if no_prompt_text :
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prompt = None
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else:
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prompt = self.prompt_cache["prompt_semantic"].expand(all_phoneme_ids.shape[0], -1).to(self.configs.device)
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batch_audio_fragment = []
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# ## vits并行推理 method 1
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# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
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# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
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# max_len = 0
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# for i in range(0, len(batch_phones)):
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# max_len = max(max_len, batch_phones[i].shape[-1])
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# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
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# batch_phones = batch_phones.to(self.configs.device)
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# batch_audio_fragment = (self.vits_model.batched_decode(
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc
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# ))
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# ## vits并行推理 method 2
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pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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upsample_rate = math.prod(self.vits_model.upsample_rates)
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audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
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audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
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all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
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_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
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_batch_audio_fragment = (self.vits_model.decode(
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all_pred_semantic, _batch_phones,refer_audio_spepc
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).detach()[0, 0, :])
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audio_frag_end_idx.insert(0, 0)
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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))]
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with torch.no_grad():
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pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_lens,
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prompt,
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all_bert_features,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=self.configs.hz * self.configs.max_sec,
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)
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t4 = ttime()
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t_34 += t4 - t3
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refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\
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.to(dtype=self.precison, device=self.configs.device)
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batch_audio_fragment = []
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# ## vits串行推理
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# for i, idx in enumerate(idx_list):
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# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
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# _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
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# audio_fragment =(self.vits_model.decode(
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# _pred_semantic, phones, refer_audio_spepc
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# ).detach()[0, 0, :])
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# batch_audio_fragment.append(
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# audio_fragment
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# ) ###试试重建不带上prompt部分
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t5 = ttime()
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t_45 += t5 - t4
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if return_fragment:
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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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# ## vits并行推理 method 1
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# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
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# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
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# max_len = 0
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# for i in range(0, len(batch_phones)):
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# max_len = max(max_len, batch_phones[i].shape[-1])
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# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
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# batch_phones = batch_phones.to(self.configs.device)
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# batch_audio_fragment = (self.vits_model.batched_decode(
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc
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# ))
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# ## vits并行推理 method 2
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pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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upsample_rate = math.prod(self.vits_model.upsample_rates)
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audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
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audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
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all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
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_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
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_batch_audio_fragment = (self.vits_model.decode(
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all_pred_semantic, _batch_phones,refer_audio_spepc
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).detach()[0, 0, :])
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audio_frag_end_idx.insert(0, 0)
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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))]
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# ## vits串行推理
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# for i, idx in enumerate(idx_list):
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# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
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# _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
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# audio_fragment =(self.vits_model.decode(
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# _pred_semantic, phones, refer_audio_spepc
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# ).detach()[0, 0, :])
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# batch_audio_fragment.append(
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# audio_fragment
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# ) ###试试重建不带上prompt部分
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t5 = ttime()
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t_45 += t5 - t4
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if return_fragment:
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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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None,
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speed_factor,
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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),
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dtype=np.int16)
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return
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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,
|
||||
batch_index_list,
|
||||
speed_factor,
|
||||
split_bucket)
|
||||
else:
|
||||
audio.append(batch_audio_fragment)
|
||||
|
||||
if self.stop_flag:
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3),
|
||||
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,
|
||||
batch_index_list,
|
||||
speed_factor,
|
||||
split_bucket)
|
||||
|
||||
try:
|
||||
torch.cuda.empty_cache()
|
||||
split_bucket,
|
||||
fragment_interval
|
||||
)
|
||||
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)
|
||||
finally:
|
||||
self.empty_cache()
|
||||
|
||||
def empty_cache(self):
|
||||
try:
|
||||
if str(self.configs.device) == "cuda":
|
||||
torch.cuda.empty_cache()
|
||||
elif str(self.configs.device) == "mps":
|
||||
torch.mps.empty_cache()
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
|
||||
|
||||
def audio_postprocess(self,
|
||||
audio:List[torch.Tensor],
|
||||
sr:int,
|
||||
batch_index_list:list=None,
|
||||
speed_factor:float=1.0,
|
||||
split_bucket:bool=True)->tuple[int, np.ndarray]:
|
||||
split_bucket:bool=True,
|
||||
fragment_interval:float=0.3
|
||||
)->tuple[int, np.ndarray]:
|
||||
zero_wav = torch.zeros(
|
||||
int(self.configs.sampling_rate * 0.3),
|
||||
int(self.configs.sampling_rate * fragment_interval),
|
||||
dtype=self.precison,
|
||||
device=self.configs.device
|
||||
)
|
||||
|
||||
@ -91,7 +91,7 @@ def inference(text, text_lang,
|
||||
top_p, temperature,
|
||||
text_split_method, batch_size,
|
||||
speed_factor, ref_text_free,
|
||||
split_bucket
|
||||
split_bucket,fragment_interval,
|
||||
):
|
||||
inputs={
|
||||
"text": text,
|
||||
@ -106,7 +106,8 @@ def inference(text, text_lang,
|
||||
"batch_size":int(batch_size),
|
||||
"speed_factor":float(speed_factor),
|
||||
"split_bucket":split_bucket,
|
||||
"return_fragment":False
|
||||
"return_fragment":False,
|
||||
"fragment_interval":fragment_interval,
|
||||
}
|
||||
|
||||
for item in tts_pipline.run(inputs):
|
||||
@ -188,6 +189,7 @@ 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)
|
||||
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=0.25,maximum=4,step=0.05,label="speed_factor",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)
|
||||
@ -216,7 +218,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
top_k, top_p, temperature,
|
||||
how_to_cut, batch_size,
|
||||
speed_factor, ref_text_free,
|
||||
split_bucket
|
||||
split_bucket,fragment_interval,
|
||||
],
|
||||
[output],
|
||||
)
|
||||
|
||||
@ -894,6 +894,7 @@ class SynthesizerTrn(nn.Module):
|
||||
if freeze_quantizer:
|
||||
self.ssl_proj.requires_grad_(False)
|
||||
self.quantizer.requires_grad_(False)
|
||||
self.quantizer.eval()
|
||||
# self.enc_p.text_embedding.requires_grad_(False)
|
||||
# self.enc_p.encoder_text.requires_grad_(False)
|
||||
# self.enc_p.mrte.requires_grad_(False)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user