diff --git a/GPT_SoVITS/export_torch_script.py b/GPT_SoVITS/export_torch_script.py index c7f1306..ce8821b 100644 --- a/GPT_SoVITS/export_torch_script.py +++ b/GPT_SoVITS/export_torch_script.py @@ -1,5 +1,6 @@ # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py # reference: https://github.com/lifeiteng/vall-e +import argparse from typing import Optional from my_utils import load_audio from text import cleaned_text_to_sequence @@ -15,7 +16,7 @@ from feature_extractor import cnhubert from AR.models.t2s_lightning_module import Text2SemanticLightningModule from module.models_onnx import SynthesizerTrn - +from inference_webui import get_phones_and_bert import os import soundfile @@ -351,7 +352,7 @@ class VitsModel(nn.Module): self.vq_model.eval() self.vq_model.load_state_dict(dict_s2["weight"], strict=False) - def forward(self, text_seq, pred_semantic, ref_audio): + def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0): refer = spectrogram_torch( ref_audio, self.hps.data.filter_length, @@ -360,7 +361,7 @@ class VitsModel(nn.Module): self.hps.data.win_length, center=False ) - return self.vq_model(pred_semantic, text_seq, refer)[0, 0] + return self.vq_model(pred_semantic, text_seq, refer, speed)[0, 0] class T2SModel(nn.Module): def __init__(self,raw_t2s:Text2SemanticLightningModule): @@ -507,6 +508,8 @@ class T2SModel(nn.Module): y_emb = self.ar_audio_embedding(y[:, -1:]) xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device) + y[0,-1] = 0 + return y[:, -idx:].unsqueeze(0) bert_path = os.environ.get( @@ -558,44 +561,48 @@ class ExportSSLModel(torch.nn.Module): return audio def export_bert(ref_bert_inputs): + tokenizer = AutoTokenizer.from_pretrained(bert_path) + + ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt") + 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() + + bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True) + my_bert_model = MyBertModel(bert_model) + ref_bert_inputs = { 'input_ids': ref_bert_inputs['input_ids'], 'attention_mask': ref_bert_inputs['attention_mask'], 'token_type_ids': ref_bert_inputs['token_type_ids'], 'word2ph': ref_bert_inputs['word2ph'] } - bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True) - my_bert_model = MyBertModel(bert_model) my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs) my_bert_model.save("onnx/bert_model.pt") print('#### exported bert ####') -def export(gpt_path, vits_path): - tokenizer = AutoTokenizer.from_pretrained(bert_path) - - ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt") - ref_seq = torch.LongTensor([cleaned_text_to_sequence(['sh','eng1','y','in1',',','sh','i4','y','ou3','w','en1','d','u4','d','e','.','y','e4','w','an3','d','e','sh','eng1','y','in1',',','h','ui4','f','a1','g','uang1'],version='v2')]) - 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() - - text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt") - text_seq = torch.LongTensor([cleaned_text_to_sequence(["d", "a4", "j", "ia1", "h", "ao3",",","w","o3","y", "ou3","y","i2","g","e4","q","i2","g","uai4","w","en4","t","i2","."],version='v2')]) - text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int() - - bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True) - - bert = MyBertModel(bert_model) - +def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path): # export_bert(ref_bert_inputs) + + if not os.path.exists(output_path): + os.makedirs(output_path) + print(f"目录已创建: {output_path}") + else: + print(f"目录已存在: {output_path}") - ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float() + ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float() ssl = SSLModel() s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio))) - torch.jit.script(s).save("onnx/xw/ssl_model.pt") + ssl_path = os.path.join(output_path, "ssl_model.pt") + torch.jit.script(s).save(ssl_path) print('#### exported ssl ####') - ref_bert = bert(**ref_bert_inputs) - text_bert = bert(**text_berf_inputs) + ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2') + ref_seq = torch.LongTensor([ref_seq_id]) + ref_bert = ref_bert_T.T.to(ref_seq.device) + 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') + text_seq = torch.LongTensor([text_seq_id]) + text_bert = text_bert_T.T.to(text_seq.device) + ssl_content = ssl(ref_audio) # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth" @@ -605,6 +612,8 @@ def export(gpt_path, vits_path): # gpt_path = "GPT_weights_v2/xw-e15.ckpt" dict_s1 = torch.load(gpt_path, map_location="cpu") raw_t2s = get_raw_t2s_model(dict_s1) + print('#### get_raw_t2s_model ####') + print(raw_t2s.config) t2s_m = T2SModel(raw_t2s) t2s_m.eval() t2s = torch.jit.script(t2s_m) @@ -614,6 +623,10 @@ def export(gpt_path, vits_path): gpt_sovits = GPT_SoVITS(t2s,vits) gpt_sovits.eval() ref_audio_sr = s.resample(ref_audio,16000,32000) + ref_audio_sr = s.resample(ref_audio,16000,32000) + print('ref_audio_sr:',ref_audio_sr.shape) + + ref_audio_sr = s.resample(ref_audio,16000,32000) print('ref_audio_sr:',ref_audio_sr.shape) gpt_sovits_export = torch.jit.trace( @@ -624,10 +637,10 @@ def export(gpt_path, vits_path): ref_seq, text_seq, ref_bert, - text_bert), - check_trace=False) # 默认是True 但是 check 的时候可能是随机生成的一个奇怪维度的值，导致报错 + text_bert)) - gpt_sovits_export.save("onnx/xw/gpt_sovits_model.pt") + gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt") + gpt_sovits_export.save(gpt_sovits_path) print('#### exported gpt_sovits ####') @torch.jit.script @@ -646,16 +659,28 @@ class GPT_SoVITS(nn.Module): self.t2s = t2s self.vits = vits - def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor): - codes = self.vits.vq_model.extract_latent(ssl_content.float()) + def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor, speed=1.0): + codes = self.vits.vq_model.extract_latent(ssl_content) prompt_semantic = codes[0, 0] prompts = prompt_semantic.unsqueeze(0) pred_semantic = self.t2s(prompts, ref_seq, text_seq, ref_bert, text_bert) - audio = self.vits(text_seq, pred_semantic, ref_audio_sr) + audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed) return audio -def test(gpt_path, vits_path): +def test(): + parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool") + parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file") + parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file") + parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file") + parser.add_argument('--ref_text', required=True, help="Path to the reference text file") + + args = parser.parse_args() + gpt_path = args.gpt_model + vits_path = args.sovits_model + ref_audio_path = args.ref_audio + ref_text = args.ref_text + tokenizer = AutoTokenizer.from_pretrained(bert_path) bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True) bert = MyBertModel(bert_model) @@ -680,29 +705,19 @@ def test(gpt_path, vits_path): # vits = torch.jit.load("onnx/xw/vits_model.pt",map_location='cuda') # ssl = torch.jit.load("onnx/xw/ssl_model.pt",map_location='cuda') - - ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt") - ref_seq = torch.LongTensor([cleaned_text_to_sequence(['sh','eng1','y','in1',',','sh','i4','y','ou3','w','en1','d','u4','d','e','.','y','e4','w','an3','d','e','sh','eng1','y','in1',',','h','ui4','f','a1','g','uang1'],version='v2')]) - 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() - - text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt") - text_seq = torch.LongTensor([cleaned_text_to_sequence(["d", "a4", "j", "ia1", "h", "ao3",",","w","o3","y", "ou3","y","i2","g","e4","q","i2","g","uai4","w","en4","t","i2","."],version='v2')]) - text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int() - - ref_bert = bert( - ref_bert_inputs['input_ids'], - ref_bert_inputs['attention_mask'], - ref_bert_inputs['token_type_ids'], - ref_bert_inputs['word2ph'] - ) - - text_bert = bert(text_berf_inputs['input_ids'], - text_berf_inputs['attention_mask'], - text_berf_inputs['token_type_ids'], - text_berf_inputs['word2ph']) + ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2') + ref_seq = torch.LongTensor([ref_seq_id]) + ref_bert = ref_bert_T.T.to(ref_seq.device) + 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') + text_seq = torch.LongTensor([text_seq_id]) + print('text_seq:',text_seq_id) + text_bert = text_bert_T.T.to(text_seq.device) + # text_bert = torch.zeros(text_bert.shape, dtype=text_bert.dtype).to(text_bert.device) + print('text_seq:',text_seq.shape) + print('text_bert:',text_bert.shape) #[1,N] - ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float() + ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float() print('ref_audio:',ref_audio.shape) ref_audio_sr = ssl.resample(ref_audio,16000,32000) @@ -731,7 +746,19 @@ def export_symbel(version='v2'): with open(f"onnx/symbols_v2.json", "w") as file: json.dump(symbols, file, indent=4) +def main(): + parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool") + parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file") + parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file") + parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file") + parser.add_argument('--ref_text', required=True, help="Path to the reference text file") + parser.add_argument('--output_path', required=True, help="Path to the output directory") + + args = parser.parse_args() + 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) + +import inference_webui if __name__ == "__main__": - export(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth") - # test(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth") - # export_symbel() \ No newline at end of file + inference_webui.is_half=False + inference_webui.dtype=torch.float32 + main() \ No newline at end of file diff --git a/GPT_SoVITS/module/models_onnx.py b/GPT_SoVITS/module/models_onnx.py index c5d96d0..abe2a3c 100644 --- a/GPT_SoVITS/module/models_onnx.py +++ b/GPT_SoVITS/module/models_onnx.py @@ -231,7 +231,7 @@ class TextEncoder(nn.Module): self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - def forward(self, y, text, ge): + def forward(self, y, text, ge, speed=1): y_mask = torch.ones_like(y[:1,:1,:]) y = self.ssl_proj(y * y_mask) * y_mask @@ -244,6 +244,9 @@ class TextEncoder(nn.Module): y = self.mrte(y, y_mask, text, text_mask, ge) y = self.encoder2(y * y_mask, y_mask) + if(speed!=1): + y = F.interpolate(y, size=int(y.shape[-1] / speed)+1, mode="linear") + y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest") stats = self.proj(y) * y_mask m, logs = torch.split(stats, self.out_channels, dim=1) @@ -887,7 +890,7 @@ class SynthesizerTrn(nn.Module): # self.enc_p.encoder_text.requires_grad_(False) # self.enc_p.mrte.requires_grad_(False) - def forward(self, codes, text, refer): + def forward(self, codes, text, refer,noise_scale=0.5, speed=1): refer_mask = torch.ones_like(refer[:1,:1,:]) if (self.version == "v1"): ge = self.ref_enc(refer * refer_mask, refer_mask) @@ -900,10 +903,10 @@ class SynthesizerTrn(nn.Module): quantized = dquantized.contiguous().view(1, self.ssl_dim, -1) x, m_p, logs_p, y_mask = self.enc_p( - quantized, text, ge + quantized, text, ge, speed ) - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) + z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale z = self.flow(z_p, y_mask, g=ge, reverse=True)