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https://github.com/RVC-Boss/GPT-SoVITS.git
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541 lines
22 KiB
Python
541 lines
22 KiB
Python
# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
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# reference: https://github.com/lifeiteng/vall-e
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from typing import Optional
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from my_utils import load_audio
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from text import cleaned_text_to_sequence
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import torch
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import torchaudio
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from torch import IntTensor, LongTensor, Tensor, nn
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from torch.nn import functional as F
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from feature_extractor import cnhubert
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from module.models_onnx import SynthesizerTrn
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import os
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import soundfile
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default_config = {
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"embedding_dim": 512,
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"hidden_dim": 512,
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"num_head": 8,
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"num_layers": 12,
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"num_codebook": 8,
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"p_dropout": 0.0,
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"vocab_size": 1024 + 1,
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"phoneme_vocab_size": 512,
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"EOS": 1024,
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}
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def get_raw_t2s_model(dict_s1) -> Text2SemanticLightningModule:
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config = dict_s1["config"]
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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t2s_model = t2s_model.eval()
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return t2s_model
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@torch.jit.script
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def logits_to_probs(
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logits,
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previous_tokens: Optional[torch.Tensor] = None,
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temperature: float = 1.0,
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top_k: Optional[int] = None,
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top_p: Optional[int] = None,
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repetition_penalty: float = 1.0,
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):
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# if previous_tokens is not None:
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# previous_tokens = previous_tokens.squeeze()
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# print(logits.shape,previous_tokens.shape)
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# pdb.set_trace()
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if previous_tokens is not None and repetition_penalty != 1.0:
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previous_tokens = previous_tokens.long()
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score = torch.gather(logits, dim=1, index=previous_tokens)
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score = torch.where(
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score < 0, score * repetition_penalty, score / repetition_penalty
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)
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logits.scatter_(dim=1, index=previous_tokens, src=score)
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if top_p is not None and top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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cum_probs = torch.cumsum(
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torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
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)
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sorted_indices_to_remove = cum_probs > top_p
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sorted_indices_to_remove[:, 0] = False # keep at least one option
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indices_to_remove = sorted_indices_to_remove.scatter(
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dim=1, index=sorted_indices, src=sorted_indices_to_remove
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)
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logits = logits.masked_fill(indices_to_remove, -float("Inf"))
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logits = logits / max(temperature, 1e-5)
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if top_k is not None:
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v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
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pivot = v[: , -1].unsqueeze(-1)
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logits = torch.where(logits < pivot, -float("Inf"), logits)
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probs = torch.nn.functional.softmax(logits, dim=-1)
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return probs
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@torch.jit.script
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def multinomial_sample_one_no_sync(probs_sort):
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# Does multinomial sampling without a cuda synchronization
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q = torch.randn_like(probs_sort)
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return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
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@torch.jit.script
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def sample(
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logits,
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previous_tokens,
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temperature: float = 1.0,
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top_k: Optional[int] = None,
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top_p: Optional[int] = None,
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repetition_penalty: float = 1.0,
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):
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probs = logits_to_probs(
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logits=logits, previous_tokens=previous_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty
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)
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idx_next = multinomial_sample_one_no_sync(probs)
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return idx_next, probs
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@torch.jit.script
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def spectrogram_torch(y, n_fft:int, sampling_rate:int, hop_size:int, win_size:int, center:bool=False):
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hann_window = torch.hann_window(win_size,device=y.device,dtype=y.dtype)
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
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mode="reflect",
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)
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y = y.squeeze(1)
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spec = torch.stft(
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y,
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n_fft,
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hop_length=hop_size,
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win_length=win_size,
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window=hann_window,
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center=center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=False,
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)
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
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return spec
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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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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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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else:
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self.hps["model"]["version"] = "v2"
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self.hps = DictToAttrRecursive(self.hps)
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self.hps.model.semantic_frame_rate = "25hz"
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self.vq_model = SynthesizerTrn(
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model
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)
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self.vq_model.eval()
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self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
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def forward(self, text_seq, pred_semantic, ref_audio):
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refer = spectrogram_torch(
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ref_audio,
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self.hps.data.filter_length,
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self.hps.data.sampling_rate,
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self.hps.data.hop_length,
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self.hps.data.win_length,
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center=False
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)
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return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
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class T2SModel(nn.Module):
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def __init__(self, config,raw_t2s:Text2SemanticLightningModule, norm_first=False, top_k=3):
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super(T2SModel, self).__init__()
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self.model_dim = config["model"]["hidden_dim"]
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self.embedding_dim = config["model"]["embedding_dim"]
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self.num_head = config["model"]["head"]
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self.num_layers = config["model"]["n_layer"]
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self.vocab_size = config["model"]["vocab_size"]
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self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"]
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self.p_dropout = float(config["model"]["dropout"])
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self.EOS:int = config["model"]["EOS"]
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self.norm_first = norm_first
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assert self.EOS == self.vocab_size - 1
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self.hz = 50
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self.config = config
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# self.bert_proj = nn.Linear(1024, self.embedding_dim)
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# self.ar_text_embedding = TokenEmbedding(
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# self.embedding_dim, self.phoneme_vocab_size, self.p_dropout
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# )
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# self.ar_text_position = SinePositionalEmbedding(
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# self.embedding_dim, dropout=0.1, scale=False, alpha=True
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# )
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# self.ar_audio_embedding = TokenEmbedding(
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# self.embedding_dim, self.vocab_size, self.p_dropout
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# )
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# self.ar_audio_position = SinePositionalEmbedding(
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# self.embedding_dim, dropout=0.1, scale=False, alpha=True
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# )
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self.bert_proj = raw_t2s.model.bert_proj
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self.ar_text_embedding = raw_t2s.model.ar_text_embedding
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self.ar_text_position = raw_t2s.model.ar_text_position
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self.ar_audio_embedding = raw_t2s.model.ar_audio_embedding
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self.ar_audio_position = raw_t2s.model.ar_audio_position
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# self.t2s_transformer = T2STransformer(self.num_layers, blocks)
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self.t2s_transformer = raw_t2s.model.t2s_transformer
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# self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
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self.ar_predict_layer = raw_t2s.model.ar_predict_layer
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# self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
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self.max_sec = self.config["data"]["max_sec"]
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self.top_k = int(self.config["inference"]["top_k"])
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self.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
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def forward(self,prompts:LongTensor, ref_seq:LongTensor, text_seq:LongTensor, ref_bert:torch.Tensor, text_bert:torch.Tensor):
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bert = torch.cat([ref_bert.T, text_bert.T], 1)
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all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
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bert = bert.unsqueeze(0)
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x = self.ar_text_embedding(all_phoneme_ids)
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x = x + self.bert_proj(bert.transpose(1, 2))
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x:torch.Tensor = self.ar_text_position(x)
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early_stop_num = self.early_stop_num
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#[1,N,512] [1,N]
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# y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
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y = prompts
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# x_example = x[:,:,0] * 0.0
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x_len = x.shape[1]
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x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
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y_emb = self.ar_audio_embedding(y)
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y_len = y_emb.shape[1]
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prefix_len = y.shape[1]
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y_pos = self.ar_audio_position(y_emb)
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xy_pos = torch.concat([x, y_pos], dim=1)
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bsz = x.shape[0]
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src_len = x_len + y_len
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x_attn_mask_pad = F.pad(
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x_attn_mask,
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(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
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value=True,
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)
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y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
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torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
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(x_len, 0),
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value=False,
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)
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xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\
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.unsqueeze(0)\
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.expand(bsz*self.num_head, -1, -1)\
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.view(bsz, self.num_head, src_len, src_len)\
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.to(device=x.device, dtype=torch.bool)
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idx = 0
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xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
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logits = self.ar_predict_layer(xy_dec[:, -1])
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logits = logits[:, :-1]
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samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
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y = torch.concat([y, samples], dim=1)
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y_emb = self.ar_audio_embedding(y[:, -1:])
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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)
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stop = False
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# for idx in range(1, 50):
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for idx in range(1, 1500):
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#[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
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# y, k, v, y_emb, logits, samples = self.stage_decoder(y, k, v, y_emb, x_example)
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xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
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logits = self.ar_predict_layer(xy_dec[:, -1])
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samples = sample(logits, y, top_k=self.top_k, top_p=1, repetition_penalty=1.35, temperature=1.0)[0]
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y = torch.concat([y, samples], dim=1)
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if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
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stop = True
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if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
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stop = True
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if stop:
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break
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y_emb = self.ar_audio_embedding(y[:, -1:])
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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)
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y[0, -1] = 0
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return y[:, -idx:].unsqueeze(0)
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bert_path = os.environ.get(
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"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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)
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cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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cnhubert.cnhubert_base_path = cnhubert_base_path
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@torch.jit.script
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def build_phone_level_feature(res:Tensor, word2ph:IntTensor):
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phone_level_feature = []
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for i in range(word2ph.shape[0]):
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repeat_feature = res[i].repeat(word2ph[i].item(), 1)
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phone_level_feature.append(repeat_feature)
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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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self.bert = bert_model
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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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return build_phone_level_feature(res, word2ph)
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class SSLModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.ssl = cnhubert.get_model().model
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def forward(self, ref_audio_16k)-> torch.Tensor:
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ssl_content = self.ssl(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
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return ssl_content
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class ExportSSLModel(torch.nn.Module):
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def __init__(self,ssl:SSLModel):
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super().__init__()
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self.ssl = ssl
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def forward(self, ref_audio:torch.Tensor):
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return self.ssl(ref_audio)
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@torch.jit.export
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def resample(self,ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
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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(tokenizer,ref_text,word2ph):
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ref_bert_inputs = tokenizer(ref_text, return_tensors="pt")
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ref_bert_inputs = {
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'input_ids': torch.jit.annotate(torch.Tensor,ref_bert_inputs['input_ids']),
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'attention_mask': torch.jit.annotate(torch.Tensor,ref_bert_inputs['attention_mask']),
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'token_type_ids': torch.jit.annotate(torch.Tensor,ref_bert_inputs['token_type_ids']),
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'word2ph': torch.jit.annotate(torch.Tensor,word2ph)
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}
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
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my_bert_model = MyBertModel(bert_model)
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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):
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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_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')])
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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_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt")
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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')])
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text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int()
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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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# export_bert(tokenizer,"声音,是有温度的.夜晚的声音,会发光",ref_bert_inputs['word2ph'])
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|
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ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float()
|
||
ssl = SSLModel()
|
||
s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(torch.jit.annotate(torch.Tensor,ref_audio))))
|
||
torch.jit.script(s).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)
|
||
ssl_content = ssl(ref_audio)
|
||
|
||
# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
|
||
vits = VitsModel(vits_path)
|
||
vits.eval()
|
||
|
||
# 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()
|
||
t2s = torch.jit.script(t2s_m)
|
||
print('exported t2s_m')
|
||
|
||
gpt_sovits = GPT_SoVITS(t2s,vits)
|
||
ref_audio_sr = ssl.resample(ref_audio,16000,32000)
|
||
|
||
# audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert)
|
||
|
||
torch.jit.trace(gpt_sovits,example_inputs=(
|
||
torch.jit.annotate(torch.Tensor,ssl_content),
|
||
torch.jit.annotate(torch.Tensor,ref_audio_sr),
|
||
torch.jit.annotate(torch.Tensor,ref_seq),
|
||
torch.jit.annotate(torch.Tensor,text_seq),
|
||
torch.jit.annotate(torch.Tensor,ref_bert),
|
||
torch.jit.annotate(torch.Tensor,text_bert))).save("onnx/xw/gpt_sovits_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
|
||
|
||
@torch.jit.script
|
||
def resamplex(ref_audio:torch.Tensor,src_sr:int,dst_sr:int)->torch.Tensor:
|
||
return torchaudio.functional.resample(ref_audio,src_sr,dst_sr).float()
|
||
|
||
class GPT_SoVITS(nn.Module):
|
||
def __init__(self, t2s:T2SModel,vits:VitsModel):
|
||
super().__init__()
|
||
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())
|
||
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)
|
||
return audio
|
||
|
||
def test():
|
||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
|
||
bert = MyBertModel(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)
|
||
vits.eval()
|
||
|
||
ssl = ExportSSLModel(SSLModel())
|
||
ssl.eval()
|
||
|
||
gpt_sovits = GPT_SoVITS(t2s,vits)
|
||
|
||
# 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'])
|
||
|
||
#[1,N]
|
||
ref_audio = torch.tensor(load_audio("output/denoise_opt/xw.mp3_0000000000_0000156480.wav", 16000)).float().unsqueeze(0)
|
||
print('ref_audio:',ref_audio.shape)
|
||
|
||
ref_audio_sr = ssl.resample(ref_audio,16000,32000)
|
||
print('start ssl')
|
||
ssl_content = ssl(ref_audio)
|
||
|
||
print('start gpt_sovits:')
|
||
with torch.no_grad():
|
||
audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert)
|
||
print('start write wav')
|
||
soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
|
||
|
||
# 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() |