忽略ffmpeg .gitignore

	使t2s模型支持批量推理:   GPT_SoVITS/AR/models/t2s_model.py
	修复batch bug   GPT_SoVITS/AR/models/utils.py
    重构的tts infer   GPT_SoVITS/TTS_infer_pack/TTS.py
	文本预处理模块   GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
	new file   GPT_SoVITS/TTS_infer_pack/__init__.py
	文本拆分方法模块   GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
	tts infer配置文件   GPT_SoVITS/configs/tts_infer.yaml
	modified   GPT_SoVITS/feature_extractor/cnhubert.py
	modified   GPT_SoVITS/inference_gui.py
	重构的webui   GPT_SoVITS/inference_webui.py
	new file   GPT_SoVITS/inference_webui_old.py

.gitignore

@@ -10,5 +10,6 @@ reference
 GPT_weights
 SoVITS_weights
 TEMP
-
+ffmpeg.exe
+ffprobe.exe
 

GPT_SoVITS/AR/models/t2s_model.py

@@ -1,5 +1,4 @@
-# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
-# reference: https://github.com/lifeiteng/vall-e
+# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py
 import torch
 from tqdm import tqdm
 
@@ -386,7 +385,9 @@ def infer_panel(
  x.device
  )
 
-
+ y_list = [None]*y.shape[0]
+ batch_idx_map = list(range(y.shape[0]))
+ idx_list = [None]*y.shape[0]
  for idx in tqdm(range(1500)):
 
  xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
@@ -397,17 +398,45 @@ def infer_panel(
  if(idx==0):###第一次跑不能EOS否则没有了
  logits = logits[:, :-1] ###刨除1024终止符号的概率
  samples = sample(
- logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
- )[0].unsqueeze(0)
+ logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
+ )[0]
  # 本次生成的 semantic_ids 和之前的 y 构成新的 y
  # print(samples.shape)#[1,1]#第一个1是bs
  y = torch.concat([y, samples], dim=1) 
 
+ # 移除已经生成完毕的序列
+ reserved_idx_of_batch_for_y = None
+ if (self.EOS in torch.argmax(logits, dim=-1)) or \
+ (self.EOS in samples[:, 0]): ###如果生成到EOS，则停止
+ l = samples[:, 0]==self.EOS
+ removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
+ reserved_idx_of_batch_for_y = torch.where(l==False)[0]
+ # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
+ for i in removed_idx_of_batch_for_y:
+ batch_index = batch_idx_map[i]
+ idx_list[batch_index] = idx - 1
+ y_list[batch_index] = y[i, :-1]
+
+ batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
+
+ # 只保留未生成完毕的序列 
+ if reserved_idx_of_batch_for_y is not None:
+ # index = torch.LongTensor(batch_idx_map).to(y.device)
+ y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
+ if cache["y_emb"] is not None:
+ cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y)
+ if cache["k"] is not None:
+ for i in range(self.num_layers):
+ # 因为kv转置了，所以batch dim是1
+ cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y)
+ cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y)
+
+
  if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
  print("use early stop num:", early_stop_num)
  stop = True
-
- if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+ 
+ if not (None in idx_list):
  # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
  stop = True
  if stop:
@@ -443,6 +472,12 @@ def infer_panel(
  xy_attn_mask = torch.zeros(
  (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
  )
+
+ if (None in idx_list):
+ for i in range(x.shape[0]):
+ if idx_list[i] is None:
+ idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替
+
  if ref_free:
- return y[:, :-1], 0
- return y[:, :-1], idx-1
+ return y_list, [0]*x.shape[0]
+ return y_list, idx_list

GPT_SoVITS/AR/models/utils.py

@@ -115,27 +115,27 @@ def logits_to_probs(
  top_p: Optional[int] = None,
  repetition_penalty: float = 1.0,
 ):
- if previous_tokens is not None:
- previous_tokens = previous_tokens.squeeze()
+ # if previous_tokens is not None:
+ #  previous_tokens = previous_tokens.squeeze()
  # print(logits.shape,previous_tokens.shape)
  # pdb.set_trace()
  if previous_tokens is not None and repetition_penalty != 1.0:
  previous_tokens = previous_tokens.long()
- score = torch.gather(logits, dim=0, index=previous_tokens)
+ score = torch.gather(logits, dim=1, index=previous_tokens)
  score = torch.where(
  score < 0, score * repetition_penalty, score / repetition_penalty
  )
- logits.scatter_(dim=0, index=previous_tokens, src=score)
+ logits.scatter_(dim=1, index=previous_tokens, src=score)
 
  if top_p is not None and top_p < 1.0:
  sorted_logits, sorted_indices = torch.sort(logits, descending=True)
  cum_probs = torch.cumsum(
  torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
  )
  sorted_indices_to_remove = cum_probs > top_p
- sorted_indices_to_remove[0] = False # keep at least one option
+ sorted_indices_to_remove[:, 0] = False # keep at least one option
  indices_to_remove = sorted_indices_to_remove.scatter(
- dim=0, index=sorted_indices, src=sorted_indices_to_remove
+ dim=1, index=sorted_indices, src=sorted_indices_to_remove
  )
  logits = logits.masked_fill(indices_to_remove, -float("Inf"))