修复了t2s模型无prompt输入时的bug GPT_SoVITS/AR/models/t2s_model.py
增加一些新特性，并修复了一些bug GPT_SoVITS/TTS_infer_pack/TTS.py 优化网页布局 GPT_SoVITS/inference_webui.py
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chasonjiang
@@ -1,8 +1,7 @@
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import os, sys
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import ffmpeg
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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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import os
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from typing import Generator, List, Union
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import numpy as np
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@@ -164,6 +163,9 @@ class TTS:
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"bert_features":None,
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"norm_text":None,
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}
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self.stop_flag:bool = False
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def _init_models(self,):
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self.init_t2s_weights(self.configs.t2s_weights_path)
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@@ -310,7 +312,7 @@ class TTS:
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batch = torch.stack(padded_sequences)
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return batch
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def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75):
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def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75, split_bucket:bool=True):
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_data:list = []
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index_and_len_list = []
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@@ -318,30 +320,35 @@ class TTS:
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norm_text_len = len(item["norm_text"])
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index_and_len_list.append([idx, norm_text_len])
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index_and_len_list.sort(key=lambda x: x[1])
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# index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)]
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index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
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# for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list):
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batch_index_list = []
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batch_index_list_len = 0
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pos = 0
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while pos <index_and_len_list.shape[0]:
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# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
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pos_end = min(pos+batch_size,index_and_len_list.shape[0])
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while pos < pos_end:
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batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
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score=batch[(pos_end-pos)//2]/batch.mean()
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if (score>=threshold) or (pos_end-pos==1):
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batch_index=index_and_len_list[pos:pos_end, 0].tolist()
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batch_index_list_len += len(batch_index)
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batch_index_list.append(batch_index)
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pos = pos_end
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break
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pos_end=pos_end-1
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assert batch_index_list_len == len(data)
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if split_bucket:
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index_and_len_list.sort(key=lambda x: x[1])
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index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
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batch_index_list_len = 0
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pos = 0
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while pos <index_and_len_list.shape[0]:
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# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
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pos_end = min(pos+batch_size,index_and_len_list.shape[0])
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while pos < pos_end:
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batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
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score=batch[(pos_end-pos)//2]/batch.mean()
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if (score>=threshold) or (pos_end-pos==1):
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batch_index=index_and_len_list[pos:pos_end, 0].tolist()
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batch_index_list_len += len(batch_index)
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batch_index_list.append(batch_index)
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pos = pos_end
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break
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pos_end=pos_end-1
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assert batch_index_list_len == len(data)
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else:
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for i in range(len(data)):
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if i%batch_size == 0:
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batch_index_list.append([])
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batch_index_list[-1].append(i)
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for batch_idx, index_list in enumerate(batch_index_list):
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item_list = [data[idx] for idx in index_list]
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@@ -399,7 +406,8 @@ class TTS:
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_data[index] = data[i][j]
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return _data
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def stop(self,):
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self.stop_flag = True
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def run(self, inputs:dict):
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@@ -409,22 +417,26 @@ class TTS:
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Args:
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inputs (dict):
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{
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"text": "",
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"text_lang: "",
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"ref_audio_path": "",
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"prompt_text": "",
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"prompt_lang": "",
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"top_k": 5,
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"top_p": 0.9,
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"temperature": 0.6,
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"text_split_method": "",
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"batch_size": 1,
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"batch_threshold": 0.75,
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"speed_factor":1.0,
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"text": "", # str. text to be synthesized
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"text_lang: "", # str. language of the text to be synthesized
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"ref_audio_path": "", # str. reference audio path
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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": 0.9, # float. top p sampling
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"temperature": 0.6, # 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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}
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returns:
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tulpe[int, np.ndarray]: sampling rate and audio data.
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"""
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self.stop_flag:bool = False
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text:str = inputs.get("text", "")
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text_lang:str = inputs.get("text_lang", "")
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ref_audio_path:str = inputs.get("ref_audio_path", "")
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@@ -437,7 +449,20 @@ class TTS:
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batch_size = inputs.get("batch_size", 1)
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batch_threshold = inputs.get("batch_threshold", 0.75)
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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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if return_fragment:
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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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print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
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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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no_prompt_text = False
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if prompt_text in [None, ""]:
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no_prompt_text = True
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@@ -481,7 +506,9 @@ class TTS:
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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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threshold=batch_threshold,
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split_bucket=split_bucket
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)
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t2 = ttime()
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zero_wav = torch.zeros(
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int(self.configs.sampling_rate * 0.3),
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@@ -557,27 +584,57 @@ class TTS:
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audio_fragment.cpu().numpy()
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) ###试试重建不带上prompt部分
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audio.append(batch_audio_fragment)
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# audio.append(zero_wav)
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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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batch_index_list,
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speed_factor,
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split_bucket)
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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, (zero_wav.cpu().numpy()).astype(np.int16)
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return
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if not return_fragment:
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
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yield self.audio_postprocess(audio,
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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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def audio_postprocess(self,
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audio:np.ndarray,
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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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if split_bucket:
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audio = self.recovery_order(audio, batch_index_list)
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else:
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audio = [item for batch in audio for item in batch]
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audio = self.recovery_order(audio, batch_index_list)
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
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audio = np.concatenate(audio, 0)
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audio = (audio * 32768).astype(np.int16)
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try:
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if speed_factor != 1.0:
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audio = speed_change(audio, speed=speed_factor, sr=int(self.configs.sampling_rate))
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audio = speed_change(audio, speed=speed_factor, sr=int(sr))
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except Exception as e:
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print(f"Failed to change speed of audio: \n{e}")
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yield self.configs.sampling_rate, audio
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return sr, audio
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def speed_change(input_audio:np.ndarray, speed:float, sr:int):
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# 将 NumPy 数组转换为原始 PCM 流
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