mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 04:22:46 +08:00
优化 export_torch_script.py (#1739)
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zzz
GitHub
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@ -330,11 +330,12 @@ class T2STransformer:
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for i in range(self.num_blocks):
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x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
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return x, k_cache, v_cache
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class VitsModel(nn.Module):
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def __init__(self, vits_path):
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super().__init__()
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dict_s2 = torch.load(vits_path,map_location="cpu")
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# dict_s2 = torch.load(vits_path,map_location="cpu")
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dict_s2 = torch.load(vits_path)
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self.hps = dict_s2["config"]
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if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
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self.hps["model"]["version"] = "v1"
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@ -527,7 +528,7 @@ def build_phone_level_feature(res:Tensor, word2ph:IntTensor):
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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# [sum(word2ph), 1024]
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return phone_level_feature
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class MyBertModel(torch.nn.Module):
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def __init__(self, bert_model):
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super(MyBertModel, self).__init__()
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@ -535,7 +536,8 @@ class MyBertModel(torch.nn.Module):
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def forward(self, input_ids:torch.Tensor, attention_mask:torch.Tensor, token_type_ids:torch.Tensor, word2ph:IntTensor):
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outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
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res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
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# res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
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res = torch.cat(outputs[1][-3:-2], -1)[0][1:-1]
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return build_phone_level_feature(res, word2ph)
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class SSLModel(torch.nn.Module):
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@ -560,13 +562,20 @@ class ExportSSLModel(torch.nn.Module):
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audio = resamplex(ref_audio,src_sr,dst_sr).float()
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return audio
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def export_bert(ref_bert_inputs):
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def export_bert(output_path):
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt")
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ref_bert_inputs['word2ph'] = torch.Tensor([2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2]).int()
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text = "叹息声一声接着一声传出,木兰对着房门织布.听不见织布机织布的声音,只听见木兰在叹息.问木兰在想什么?问木兰在惦记什么?木兰答道,我也没有在想什么,也没有在惦记什么."
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ref_bert_inputs = tokenizer(text, return_tensors="pt")
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word2ph = []
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for c in text:
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if c in ['，','。','：','？',",",".","?"]:
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word2ph.append(1)
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else:
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word2ph.append(2)
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ref_bert_inputs['word2ph'] = torch.Tensor(word2ph).int()
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True,torchscript=True)
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my_bert_model = MyBertModel(bert_model)
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ref_bert_inputs = {
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@ -576,13 +585,17 @@ def export_bert(ref_bert_inputs):
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'word2ph': ref_bert_inputs['word2ph']
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}
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my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs)
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my_bert_model.save("onnx/bert_model.pt")
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print('#### exported bert ####')
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def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path):
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# export_bert(ref_bert_inputs)
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torch._dynamo.mark_dynamic(ref_bert_inputs['input_ids'], 1)
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torch._dynamo.mark_dynamic(ref_bert_inputs['attention_mask'], 1)
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torch._dynamo.mark_dynamic(ref_bert_inputs['token_type_ids'], 1)
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torch._dynamo.mark_dynamic(ref_bert_inputs['word2ph'], 0)
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my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs)
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output_path = os.path.join(output_path, "bert_model.pt")
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my_bert_model.save(output_path)
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print('#### exported bert ####')
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def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_bert_and_ssl=False, device='cpu'):
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if not os.path.exists(output_path):
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os.makedirs(output_path)
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print(f"目录已创建: {output_path}")
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@ -591,45 +604,57 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path):
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ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
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ssl = SSLModel()
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s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
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ssl_path = os.path.join(output_path, "ssl_model.pt")
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torch.jit.script(s).save(ssl_path)
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print('#### exported ssl ####')
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if export_bert_and_ssl:
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s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
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ssl_path = os.path.join(output_path, "ssl_model.pt")
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torch.jit.script(s).save(ssl_path)
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print('#### exported ssl ####')
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export_bert(output_path)
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else:
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s = ExportSSLModel(ssl)
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print(f"device: {device}")
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ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
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ref_seq = torch.LongTensor([ref_seq_id])
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ref_seq = torch.LongTensor([ref_seq_id]).to(device)
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ref_bert = ref_bert_T.T.to(ref_seq.device)
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text_seq_id,text_bert_T,norm_text = get_phones_and_bert("这是一条测试语音，说什么无所谓，只是给它一个例子","all_zh",'v2')
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text_seq = torch.LongTensor([text_seq_id])
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text_seq = torch.LongTensor([text_seq_id]).to(device)
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text_bert = text_bert_T.T.to(text_seq.device)
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ssl_content = ssl(ref_audio)
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ssl_content = ssl(ref_audio).to(device)
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# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
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vits = VitsModel(vits_path)
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vits = VitsModel(vits_path).to(device)
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vits.eval()
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# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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raw_t2s = get_raw_t2s_model(dict_s1)
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# dict_s1 = torch.load(gpt_path, map_location=device)
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dict_s1 = torch.load(gpt_path)
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raw_t2s = get_raw_t2s_model(dict_s1).to(device)
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print('#### get_raw_t2s_model ####')
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print(raw_t2s.config)
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t2s_m = T2SModel(raw_t2s)
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t2s_m.eval()
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t2s = torch.jit.script(t2s_m)
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t2s = torch.jit.script(t2s_m).to(device)
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print('#### script t2s_m ####')
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print("vits.hps.data.sampling_rate:",vits.hps.data.sampling_rate)
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gpt_sovits = GPT_SoVITS(t2s,vits)
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gpt_sovits = GPT_SoVITS(t2s,vits).to(device)
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gpt_sovits.eval()
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ref_audio_sr = s.resample(ref_audio,16000,32000)
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ref_audio_sr = s.resample(ref_audio,16000,32000)
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print('ref_audio_sr:',ref_audio_sr.shape)
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ref_audio_sr = s.resample(ref_audio,16000,32000)
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print('ref_audio_sr:',ref_audio_sr.shape)
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gpt_sovits_export = torch.jit.trace(
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ref_audio_sr = s.resample(ref_audio,16000,32000).to(device)
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torch._dynamo.mark_dynamic(ssl_content, 2)
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torch._dynamo.mark_dynamic(ref_audio_sr, 1)
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torch._dynamo.mark_dynamic(ref_seq, 1)
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torch._dynamo.mark_dynamic(text_seq, 1)
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torch._dynamo.mark_dynamic(ref_bert, 0)
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torch._dynamo.mark_dynamic(text_bert, 0)
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with torch.no_grad():
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gpt_sovits_export = torch.jit.trace(
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gpt_sovits,
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example_inputs=(
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ssl_content,
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@ -639,9 +664,9 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path):
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ref_bert,
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text_bert))
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gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt")
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gpt_sovits_export.save(gpt_sovits_path)
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print('#### exported gpt_sovits ####')
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gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt")
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gpt_sovits_export.save(gpt_sovits_path)
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print('#### exported gpt_sovits ####')
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@torch.jit.script
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def parse_audio(ref_audio):
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@ -674,6 +699,8 @@ def test():
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parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
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parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
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parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
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parser.add_argument('--output_path', required=True, help="Path to the output directory")
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args = parser.parse_args()
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gpt_path = args.gpt_model
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@ -682,42 +709,63 @@ def test():
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ref_text = args.ref_text
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
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bert = MyBertModel(bert_model)
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# bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')
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# bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True,torchscript=True)
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# bert = MyBertModel(bert_model)
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my_bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')
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# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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raw_t2s = get_raw_t2s_model(dict_s1)
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t2s = T2SModel(raw_t2s)
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t2s.eval()
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# dict_s1 = torch.load(gpt_path, map_location="cuda")
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# raw_t2s = get_raw_t2s_model(dict_s1)
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# t2s = T2SModel(raw_t2s)
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# t2s.eval()
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# t2s = torch.jit.load("onnx/xw/t2s_model.pt",map_location='cuda')
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# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
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vits = VitsModel(vits_path)
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vits.eval()
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# vits = VitsModel(vits_path)
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# vits.eval()
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ssl = ExportSSLModel(SSLModel())
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ssl.eval()
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# ssl = ExportSSLModel(SSLModel()).to('cuda')
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# ssl.eval()
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ssl = torch.jit.load("onnx/by/ssl_model.pt",map_location='cuda')
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gpt_sovits = GPT_SoVITS(t2s,vits)
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# vits = torch.jit.load("onnx/xw/vits_model.pt",map_location='cuda')
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# ssl = torch.jit.load("onnx/xw/ssl_model.pt",map_location='cuda')
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# gpt_sovits = GPT_SoVITS(t2s,vits)
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gpt_sovits = torch.jit.load("onnx/by/gpt_sovits_model.pt",map_location='cuda')
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ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
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ref_seq = torch.LongTensor([ref_seq_id])
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ref_bert = ref_bert_T.T.to(ref_seq.device)
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text_seq_id,text_bert_T,norm_text = get_phones_and_bert("问木兰在想什么？问木兰在惦记什么？木兰答道,我也没有在想什么，也没有在惦记什么。","all_zh",'v2')
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# text_seq_id,text_bert_T,norm_text = get_phones_and_bert("昨天晚上看见征兵文书,知道君主在大规模征兵,那么多卷征兵文册,每一卷上都有父亲的名字.","all_zh",'v2')
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text = "昨天晚上看见征兵文书,知道君主在大规模征兵,那么多卷征兵文册,每一卷上都有父亲的名字."
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text_seq_id,text_bert_T,norm_text = get_phones_and_bert(text,"all_zh",'v2')
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test_bert = tokenizer(text, return_tensors="pt")
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word2ph = []
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for c in text:
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if c in ['，','。','：','？',"?",",","."]:
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word2ph.append(1)
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else:
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word2ph.append(2)
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test_bert['word2ph'] = torch.Tensor(word2ph).int()
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test_bert = my_bert(
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test_bert['input_ids'].to('cuda'),
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test_bert['attention_mask'].to('cuda'),
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test_bert['token_type_ids'].to('cuda'),
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test_bert['word2ph'].to('cuda')
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)
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text_seq = torch.LongTensor([text_seq_id])
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print('text_seq:',text_seq_id)
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text_bert = text_bert_T.T.to(text_seq.device)
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# text_bert = torch.zeros(text_bert.shape, dtype=text_bert.dtype).to(text_bert.device)
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print('text_bert:',text_bert.shape,text_bert)
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print('test_bert:',test_bert.shape,test_bert)
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print(torch.allclose(text_bert.to('cuda'),test_bert))
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print('text_seq:',text_seq.shape)
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print('text_bert:',text_bert.shape)
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print('text_bert:',text_bert.shape,text_bert.type())
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#[1,N]
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ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
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ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float().to('cuda')
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print('ref_audio:',ref_audio.shape)
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ref_audio_sr = ssl.resample(ref_audio,16000,32000)
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@ -725,13 +773,22 @@ def test():
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ssl_content = ssl(ref_audio)
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print('start gpt_sovits:')
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print('ssl_content:',ssl_content.shape)
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print('ref_audio_sr:',ref_audio_sr.shape)
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print('ref_seq:',ref_seq.shape)
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ref_seq=ref_seq.to('cuda')
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print('text_seq:',text_seq.shape)
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text_seq=text_seq.to('cuda')
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print('ref_bert:',ref_bert.shape)
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ref_bert=ref_bert.to('cuda')
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print('text_bert:',text_bert.shape)
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text_bert=text_bert.to('cuda')
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with torch.no_grad():
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audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert)
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audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, test_bert)
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print('start write wav')
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soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
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# audio = vits(text_seq, pred_semantic1, ref_audio)
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# soundfile.write("out.wav", audio, 32000)
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import text
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import json
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@ -753,12 +810,23 @@ def main():
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parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
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parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
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parser.add_argument('--output_path', required=True, help="Path to the output directory")
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parser.add_argument('--export_common_model', action='store_true', help="Export Bert and SSL model")
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parser.add_argument('--device', help="Device to use")
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args = parser.parse_args()
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export(gpt_path=args.gpt_model, vits_path=args.sovits_model, ref_audio_path=args.ref_audio, ref_text=args.ref_text, output_path=args.output_path)
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export(
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gpt_path=args.gpt_model,
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vits_path=args.sovits_model,
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ref_audio_path=args.ref_audio,
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ref_text=args.ref_text,
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output_path=args.output_path,
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device=args.device,
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export_bert_and_ssl=args.export_common_model,
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)
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import inference_webui
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if __name__ == "__main__":
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inference_webui.is_half=False
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inference_webui.dtype=torch.float32
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main()
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main()
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# test()
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