From dbaeb42e7f3fd913b8131d74f2377b000e5625f8 Mon Sep 17 00:00:00 2001 From: csh <458761603@qq.com> Date: Mon, 23 Sep 2024 21:36:11 +0800 Subject: [PATCH] Add export_torch_script.py --- GPT_SoVITS/export_torch_script.py | 522 ++++++++++++++++++++++++++++++ 1 file changed, 522 insertions(+) create mode 100644 GPT_SoVITS/export_torch_script.py diff --git a/GPT_SoVITS/export_torch_script.py b/GPT_SoVITS/export_torch_script.py new file mode 100644 index 00000000..e40c8aac --- /dev/null +++ b/GPT_SoVITS/export_torch_script.py @@ -0,0 +1,522 @@ +# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py +# reference: https://github.com/lifeiteng/vall-e +from typing import Optional +from my_utils import load_audio +from onnx_export import VitsModel +from text import cleaned_text_to_sequence +import torch +import torchaudio + +from torch import IntTensor, LongTensor, nn +from torch.nn import functional as F + +from transformers import AutoModelForMaskedLM, AutoTokenizer +from feature_extractor import cnhubert + +from AR.models.t2s_lightning_module import Text2SemanticLightningModule + + +import os +import soundfile + +default_config = { + "embedding_dim": 512, + "hidden_dim": 512, + "num_head": 8, + "num_layers": 12, + "num_codebook": 8, + "p_dropout": 0.0, + "vocab_size": 1024 + 1, + "phoneme_vocab_size": 512, + "EOS": 1024, +} + +def logits_to_probs( + logits, + previous_tokens: Optional[torch.Tensor] = None, + temperature: float = 1.0, + top_k: Optional[int] = None, + top_p: Optional[int] = None, + repetition_penalty: float = 1.0, +): + # 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=1, index=previous_tokens) + score = torch.where( + score < 0, score * repetition_penalty, score / repetition_penalty + ) + 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 + indices_to_remove = sorted_indices_to_remove.scatter( + dim=1, index=sorted_indices, src=sorted_indices_to_remove + ) + logits = logits.masked_fill(indices_to_remove, -float("Inf")) + + logits = logits / max(temperature, 1e-5) + + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + pivot = v[: , -1].unsqueeze(-1) + logits = torch.where(logits < pivot, -float("Inf"), logits) + + probs = torch.nn.functional.softmax(logits, dim=-1) + return probs + +def get_raw_t2s_model(dict_s1) -> Text2SemanticLightningModule: + config = dict_s1["config"] + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) + t2s_model.load_state_dict(dict_s1["weight"]) + t2s_model = t2s_model.eval() + return t2s_model + +def multinomial_sample_one_no_sync( + probs_sort +): # Does multinomial sampling without a cuda synchronization + q = torch.randn_like(probs_sort) + return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) + + +def sample( + logits, + previous_tokens, + temperature: float = 1.0, + top_k: Optional[int] = None, + top_p: Optional[int] = None, + repetition_penalty: float = 1.0, +): + probs = logits_to_probs( + logits=logits, previous_tokens=previous_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty + ) + idx_next = multinomial_sample_one_no_sync(probs) + return idx_next, probs + + +class T2SModel(nn.Module): + def __init__(self, config,raw_t2s:Text2SemanticLightningModule, norm_first=False, top_k=3): + super(T2SModel, self).__init__() + self.model_dim = config["model"]["hidden_dim"] + self.embedding_dim = config["model"]["embedding_dim"] + self.num_head = config["model"]["head"] + self.num_layers = config["model"]["n_layer"] + self.vocab_size = config["model"]["vocab_size"] + self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"] + self.p_dropout = float(config["model"]["dropout"]) + self.EOS:int = config["model"]["EOS"] + self.norm_first = norm_first + assert self.EOS == self.vocab_size - 1 + self.hz = 50 + self.config = config + + # self.bert_proj = nn.Linear(1024, self.embedding_dim) + # self.ar_text_embedding = TokenEmbedding( + # self.embedding_dim, self.phoneme_vocab_size, self.p_dropout + # ) + # self.ar_text_position = SinePositionalEmbedding( + # self.embedding_dim, dropout=0.1, scale=False, alpha=True + # ) + # self.ar_audio_embedding = TokenEmbedding( + # self.embedding_dim, self.vocab_size, self.p_dropout + # ) + # self.ar_audio_position = SinePositionalEmbedding( + # self.embedding_dim, dropout=0.1, scale=False, alpha=True + # ) + + self.bert_proj = raw_t2s.model.bert_proj + self.ar_text_embedding = raw_t2s.model.ar_text_embedding + self.ar_text_position = raw_t2s.model.ar_text_position + self.ar_audio_embedding = raw_t2s.model.ar_audio_embedding + self.ar_audio_position = raw_t2s.model.ar_audio_position + + # self.t2s_transformer = T2STransformer(self.num_layers, blocks) + self.t2s_transformer = raw_t2s.model.t2s_transformer + + # self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False) + self.ar_predict_layer = raw_t2s.model.ar_predict_layer + # self.loss_fct = nn.CrossEntropyLoss(reduction="sum") + self.max_sec = self.config["data"]["max_sec"] + self.top_k = int(self.config["inference"]["top_k"]) + self.early_stop_num = torch.LongTensor([self.hz * self.max_sec]) + + # def forward(self, x:LongTensor, prompts:LongTensor): + def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor): + bert = torch.cat([ref_bert.T, text_bert.T], 1) + all_phoneme_ids = torch.cat([ref_seq, text_seq], 1) + bert = bert.unsqueeze(0) + + x = self.ar_text_embedding(all_phoneme_ids) + x = x + self.bert_proj(bert.transpose(1, 2)) + x:torch.Tensor = self.ar_text_position(x) + + early_stop_num = self.early_stop_num + + + #[1,N,512] [1,N] + # y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts) + y = prompts + # x_example = x[:,:,0] * 0.0 + + x_len = x.shape[1] + x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) + + y_emb = self.ar_audio_embedding(y) + y_len = y_emb.shape[1] + prefix_len = y.shape[1] + y_pos = self.ar_audio_position(y_emb) + xy_pos = torch.concat([x, y_pos], dim=1) + + bsz = x.shape[0] + src_len = x_len + y_len + x_attn_mask_pad = F.pad( + x_attn_mask, + (0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y) + value=True, + ) + y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y) + torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), + (x_len, 0), + value=False, + ) + xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\ + .unsqueeze(0)\ + .expand(bsz*self.num_head, -1, -1)\ + .view(bsz, self.num_head, src_len, src_len)\ + .to(device=x.device, dtype=torch.bool) + + idx = 0 + + xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None) + + logits = self.ar_predict_layer(xy_dec[:, -1]) + logits = logits[:, :-1] + samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0] + y = torch.concat([y, samples], dim=1) + 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) + + stop = False + # for idx in range(1, 50): + for idx in range(1, 1500): + #[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N] + # y, k, v, y_emb, logits, samples = self.stage_decoder(y, k, v, y_emb, x_example) + xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache) + logits = self.ar_predict_layer(xy_dec[:, -1]) + samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0] + + y = torch.concat([y, samples], dim=1) + + if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: + stop = True + if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: + stop = True + if stop: + break + + 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) + + # def first_stage_decoder(self, x, prompt): + # y = prompt + # x_example = x[:,:,0] * 0.0 + # #N, 1, 512 + # cache = { + # "all_stage": self.num_layers, + # "k": None, + # "v": None, + # "y_emb": None, + # "first_infer": 1, + # "stage": 0, + # } + + # y_emb = self.ar_audio_embedding(y) + + # cache["y_emb"] = y_emb + # y_pos = self.ar_audio_position(y_emb) + + # xy_pos = torch.concat([x, y_pos], dim=1) + + # y_example = y_pos[:,:,0] * 0.0 + # x_attn_mask = torch.matmul(x_example.transpose(0, 1) , x_example).bool() + # y_attn_mask = torch.ones_like(torch.matmul(y_example.transpose(0, 1), y_example), dtype=torch.int64) + # y_attn_mask = torch.cumsum(y_attn_mask, dim=1) - torch.cumsum( + # torch.ones_like(y_example.transpose(0, 1), dtype=torch.int64), dim=0 + # ) + # y_attn_mask = y_attn_mask > 0 + + # x_y_pad = torch.matmul(x_example.transpose(0, 1), y_example).bool() + # y_x_pad = torch.matmul(y_example.transpose(0, 1), x_example).bool() + # x_attn_mask_pad = torch.cat([x_attn_mask, torch.ones_like(x_y_pad)], dim=1) + # y_attn_mask = torch.cat([y_x_pad, y_attn_mask], dim=1) + # xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0) + # cache["k"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\ + # .unsqueeze(1).repeat(self.num_layers, 1, 1, 1) + # cache["v"] = torch.matmul(x_attn_mask_pad[0].float().unsqueeze(-1), torch.zeros((1, 512)))\ + # .unsqueeze(1).repeat(self.num_layers, 1, 1, 1) + + # xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache) + # logits = self.ar_predict_layer(xy_dec[:, -1]) + # samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0) + + # y = torch.concat([y, samples], dim=1) + + # return y, cache["k"], cache["v"], cache["y_emb"], x_example + + # def stage_decoder(self, y, k, v, y_emb, x_example): + # cache = { + # "all_stage": self.num_layers, + # "k": torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1)), + # "v": torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1)), + # "y_emb": y_emb, + # "first_infer": 0, + # "stage": 0, + # } + + # y_emb = torch.cat( + # [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1 + # ) + # cache["y_emb"] = y_emb + # y_pos = self.ar_audio_position(y_emb) + + # xy_pos = y_pos[:, -1:] + + # y_example = y_pos[:,:,0] * 0.0 + + # xy_attn_mask = torch.cat([x_example, y_example], dim=1) + # xy_attn_mask = torch.zeros_like(xy_attn_mask, dtype=torch.bool) + + # xy_dec = self.h(xy_pos, mask=xy_attn_mask, cache=cache) + # logits = self.ar_predict_layer(xy_dec[:, -1]) + # samples = sample(logits[0], y, top_k=self.top_k, top_p=1.0, repetition_penalty=1.35)[0].unsqueeze(0) + + # y = torch.concat([y, samples], dim=1) + + # return y, cache["k"], cache["v"], cache["y_emb"], logits, samples + + + +bert_path = os.environ.get( + "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" +) +cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base" +cnhubert.cnhubert_base_path = cnhubert_base_path + +class BertModel(torch.nn.Module): + def __init__(self, bert_model): + super(BertModel, self).__init__() + self.bert = bert_model + + def forward(self, input_ids, attention_mask, token_type_ids, word2ph:IntTensor): + res = self.bert(input_ids, attention_mask, token_type_ids) + phone_level_feature = [] + for i in range(word2ph.shape[0]): + repeat_feature = res[i].repeat(word2ph[i].item(), 1) + phone_level_feature.append(repeat_feature) + phone_level_feature = torch.cat(phone_level_feature, dim=0) + # [sum(word2ph), 1024] + return phone_level_feature + +class MyBertModel(torch.nn.Module): + def __init__(self, bert_model): + super(MyBertModel, self).__init__() + self.bert = bert_model + + def forward(self, input_ids:torch.Tensor, attention_mask:torch.Tensor, token_type_ids:torch.Tensor): + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) + res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1] + return res + +class SSLModel(torch.nn.Module): + def __init__(self,vits:VitsModel): + super().__init__() + self.ssl = cnhubert.get_model().model + self.ssl_proj = vits.vq_model.ssl_proj + self.quantizer = vits.vq_model.quantizer + + def forward(self, ref_audio)->LongTensor: + ref_audio_16k,ref_audio_sr = parse_audio(ref_audio) + ssl_content = self.ssl(ref_audio_16k)["last_hidden_state"].transpose(1, 2) + codes = self.extract_latent(ssl_content.float()) + prompt_semantic = codes[0, 0] + prompts = prompt_semantic.unsqueeze(0) + return prompts,ref_audio_sr + + def extract_latent(self, x): + ssl = self.ssl_proj(x) + quantized, codes, commit_loss, quantized_list = self.quantizer(ssl) + return codes.transpose(0, 1) + +def export_bert(tokenizer): + ref_bert_inputs = tokenizer("在参加挼死特春晚的时候有人问了这样一个问题", return_tensors="pt") + ref_bert_inputs = { + 'input_ids': torch.jit.annotate(torch.Tensor,ref_bert_inputs['input_ids']), + 'attention_mask': torch.jit.annotate(torch.Tensor,ref_bert_inputs['attention_mask']), + 'token_type_ids': torch.jit.annotate(torch.Tensor,ref_bert_inputs['token_type_ids']), + } + 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) + print('trace my_bert_model') + bert = BertModel(my_bert_model) + torch.jit.script(bert).save("onnx/bert_model.pt") + print('exported bert') + +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) + t2s_m = T2SModel(dict_s1['config'],raw_t2s,top_k=3) + t2s_m.eval() + torch.jit.script(t2s_m).save("onnx/xw/t2s_model.pt") + print('exported t2s_m') + + 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() + + text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt") + 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) + + my_bert_model = MyBertModel(bert_model) + + bert = BertModel(my_bert_model) + + # export_bert(tokenizer) + + + # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth" + vits = VitsModel(vits_path) + vits.eval() + + ref_audio = torch.tensor([load_audio("output/denoise_opt/xw.mp3_0000000000_0000156480.wav", 48000)]).float() + ssl = SSLModel(vits) + torch.jit.trace(ssl,example_inputs=(torch.jit.annotate(torch.Tensor,ref_audio))).save("onnx/xw/ssl_model.pt") + print('exported ssl') + + # ref_seq = torch.LongTensor([cleaned_text_to_sequence(["zh", "ai4", "ch", "an1","j" ,"ia1","r","ua4","s","i3","t","e3","ch","un1","w","an3","d","e1", "sh", "i2", "h", "ou4", "y", "ou3", "r", "en2","w","en4","l","e1","zh","e4","y","ang4","y","i2","g","e4","w","en4","t","i2"],version='v2')]) + 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')]) + 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')]) + ref_bert = bert(**ref_bert_inputs) + text_bert = bert(**text_berf_inputs) + prompts,ref_audio_sr = ssl(ref_audio) + pred_semantic = t2s_m(prompts, ref_seq, text_seq, ref_bert, text_bert) + + torch.jit.trace(vits,example_inputs=( + torch.jit.annotate(torch.Tensor,text_seq), + torch.jit.annotate(torch.Tensor,pred_semantic), + torch.jit.annotate(torch.Tensor,ref_audio_sr))).save("onnx/xw/vits_model.pt") + print('exported vits') + +@torch.jit.script +def parse_audio(ref_audio): + ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()#.to(ref_audio.device) + ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,32000).float()#.to(ref_audio.device) + return ref_audio_16k,ref_audio_sr + +def test(): + + tokenizer = AutoTokenizer.from_pretrained(bert_path) + # bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True) + # bert_model.bert.embeddings = MyBertEmbeddings(bert_model.bert.config) + # bert_model.bert.encoder = MyBertEncoder(bert_model.bert.config) + # my_bert_model = MyBertModel(bert_model) + # bert = BertModel(my_bert_model) + bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda') + + # 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) + # t2s = T2SModel(dict_s1['config'],raw_t2s,top_k=3) + # t2s.eval() + t2s = torch.jit.load("onnx/xw/t2s_model.pt",map_location='cuda') + + # vits_path = "SoVITS_weights_v2/xw_e8_s216.pth" + # vits = VitsModel(vits_path).to('cuda') + # vits.eval() + + # ssl = SSLModel(vits).to('cuda') + # ssl.eval() + + 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_seq = torch.LongTensor([cleaned_text_to_sequence(["zh", "ai4", "ch", "an1","j" ,"ia1","r","ua4","s","i3","t","e3","ch","un1","w","an3","d","e1", "sh", "i2", "h", "ou4", "y", "ou3", "r", "en2","w","en4","l","e1","zh","e4","y","ang4","y","i2","g","e4","w","en4","t","i2"],version='v2')]) + ref_seq=ref_seq.to('cuda') + 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","d","e1","w","en4","t","i2","."],version='v2')]) + text_seq=text_seq.to('cuda') + + ref_bert_inputs = tokenizer("在参加挼死特春晚的时候有人问了这样一个问题", return_tensors="pt") + ref_bert_inputs['word2ph'] = torch.Tensor([2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2]).int().to('cuda') + ref_bert = bert( + ref_bert_inputs['input_ids'].to('cuda'), + ref_bert_inputs['attention_mask'].to('cuda'), + ref_bert_inputs['token_type_ids'].to('cuda'), + ref_bert_inputs['word2ph'].to('cuda')) + + print('ref_bert:',ref_bert.device) + + text_berf_inputs = tokenizer("大家好,我有一个奇怪的问题.", return_tensors="pt") + text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,2,1]).int().to('cuda') + text_bert = bert(text_berf_inputs['input_ids'].to('cuda'), + text_berf_inputs['attention_mask'].to('cuda'), + text_berf_inputs['token_type_ids'].to('cuda'), + text_berf_inputs['word2ph']) + + ref_audio = torch.tensor([load_audio("output/denoise_opt/xw.mp3_0000000000_0000156480.wav", 48000)]).float().to('cuda') + + print('start ssl') + prompts,ref_audio_sr = ssl(ref_audio) + + pred_semantic = t2s(prompts, ref_seq, text_seq, ref_bert, text_bert) + + print('start vits:',pred_semantic.shape) + print('ref_audio_sr:',ref_audio_sr.device) + audio = vits(text_seq, pred_semantic, ref_audio_sr) + print('start write wav') + soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000) + torch.load("onnx/symbols_v2.json") + + # audio = vits(text_seq, pred_semantic1, ref_audio) + # soundfile.write("out.wav", audio, 32000) + +import text +import json + +def export_symbel(version='v2'): + if version=='v1': + symbols = text._symbol_to_id_v1 + with open(f"onnx/symbols_v1.json", "w") as file: + json.dump(symbols, file, indent=4) + else: + symbols = text._symbol_to_id_v2 + with open(f"onnx/symbols_v2.json", "w") as file: + json.dump(symbols, file, indent=4) + +if __name__ == "__main__": + export(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth") + # test() + # export_symbel() + # tokenizer = AutoTokenizer.from_pretrained(bert_path) + # text_berf_inputs = tokenizer("大家好,我有一个奇怪的问题.", return_tensors="pt") + # print(text_berf_inputs) + # ref_audio = load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 48000) + # print(ref_audio.shape) + # soundfile.write("chen1_ref.wav", ref_audio, 48000) \ No newline at end of file