mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
修复了OutOfMemoryError时，显存无法释放的问题
This commit is contained in:
chasonjiang
parent
f2cbc826c7
commit
d60d8ea3fb
@ -2,6 +2,7 @@ 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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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 +49,18 @@ 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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@ -630,125 +641,141 @@ class TTS:
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split_bucket=split_bucket
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)
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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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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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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, 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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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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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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dtype=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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try:
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torch.cuda.empty_cache()
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split_bucket)
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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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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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# 重置模型, 否则会导致显存释放不完全。
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del self.t2s_model
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del self.vits_model
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self.t2s_model = None
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self.vits_model = None
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self.init_t2s_weights(self.configs.t2s_weights_path)
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self.init_vits_weights(self.configs.vits_weights_path)
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finally:
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self.empty_cache()
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def empty_cache(self):
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try:
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if str(self.configs.device) == "cuda":
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torch.cuda.empty_cache()
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elif str(self.configs.device) == "mps":
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torch.mps.empty_cache()
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except:
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pass
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def audio_postprocess(self,
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audio:List[torch.Tensor],
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sr:int,
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