mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
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862 lines
31 KiB
Python
862 lines
31 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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import argparse
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from typing import Optional
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from my_utils import load_audio
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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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from inference_webui import get_phones_and_bert
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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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config["model"]["dropout"] = float(config["model"]["dropout"])
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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(score < 0, score * repetition_penalty, score / repetition_penalty)
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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(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
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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(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
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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,
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previous_tokens=previous_tokens,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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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: Tensor, 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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@torch.jit.script
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class T2SMLP:
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def __init__(self, w1, b1, w2, b2):
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self.w1 = w1
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self.b1 = b1
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self.w2 = w2
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self.b2 = b2
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def forward(self, x):
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x = F.relu(F.linear(x, self.w1, self.b1))
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x = F.linear(x, self.w2, self.b2)
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return x
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@torch.jit.script
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class T2SBlock:
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def __init__(
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self,
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num_heads: int,
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hidden_dim: int,
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mlp: T2SMLP,
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qkv_w,
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qkv_b,
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out_w,
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out_b,
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norm_w1,
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norm_b1,
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norm_eps1: float,
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norm_w2,
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norm_b2,
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norm_eps2: float,
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):
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self.num_heads = num_heads
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self.mlp = mlp
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self.hidden_dim: int = hidden_dim
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self.qkv_w = qkv_w
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self.qkv_b = qkv_b
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self.out_w = out_w
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self.out_b = out_b
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self.norm_w1 = norm_w1
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self.norm_b1 = norm_b1
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self.norm_eps1 = norm_eps1
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self.norm_w2 = norm_w2
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self.norm_b2 = norm_b2
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self.norm_eps2 = norm_eps2
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self.false = torch.tensor(False, dtype=torch.bool)
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@torch.jit.ignore
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def to_mask(self, x: torch.Tensor, padding_mask: Optional[torch.Tensor]):
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if padding_mask is None:
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return x
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if padding_mask.dtype == torch.bool:
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return x.masked_fill(padding_mask, 0)
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else:
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return x * padding_mask
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def process_prompt(self, x: torch.Tensor, attn_mask: torch.Tensor, padding_mask: Optional[torch.Tensor] = None):
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q, k, v = F.linear(self.to_mask(x, padding_mask), self.qkv_w, self.qkv_b).chunk(3, dim=-1)
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batch_size = q.shape[0]
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q_len = q.shape[1]
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kv_len = k.shape[1]
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q = self.to_mask(q, padding_mask)
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k_cache = self.to_mask(k, padding_mask)
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v_cache = self.to_mask(v, padding_mask)
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q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
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k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
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attn = attn.permute(2, 0, 1, 3).reshape(batch_size * q_len, self.hidden_dim)
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attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
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attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)
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if padding_mask is not None:
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for i in range(batch_size):
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# mask = padding_mask[i,:,0]
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if self.false.device != padding_mask.device:
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self.false = self.false.to(padding_mask.device)
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idx = torch.where(padding_mask[i, :, 0] == self.false)[0]
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x_item = x[i, idx, :].unsqueeze(0)
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attn_item = attn[i, idx, :].unsqueeze(0)
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x_item = x_item + attn_item
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x_item = F.layer_norm(x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1)
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x_item = x_item + self.mlp.forward(x_item)
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x_item = F.layer_norm(
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x_item,
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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)
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x[i, idx, :] = x_item.squeeze(0)
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x = self.to_mask(x, padding_mask)
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else:
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x = x + attn
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x = F.layer_norm(x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1)
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x = x + self.mlp.forward(x)
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x = F.layer_norm(
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x,
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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)
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return x, k_cache, v_cache
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def decode_next_token(self, x: torch.Tensor, k_cache: torch.Tensor, v_cache: torch.Tensor):
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q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
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k_cache = torch.cat([k_cache, k], dim=1)
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v_cache = torch.cat([v_cache, v], dim=1)
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batch_size = q.shape[0]
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q_len = q.shape[1]
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kv_len = k_cache.shape[1]
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q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
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k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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attn = F.scaled_dot_product_attention(q, k, v)
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attn = attn.permute(2, 0, 1, 3).reshape(batch_size * q_len, self.hidden_dim)
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attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
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attn = F.linear(attn, self.out_w, self.out_b)
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x = x + attn
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x = F.layer_norm(x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1)
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x = x + self.mlp.forward(x)
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x = F.layer_norm(
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x,
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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)
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return x, k_cache, v_cache
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@torch.jit.script
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class T2STransformer:
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def __init__(self, num_blocks: int, blocks: list[T2SBlock]):
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self.num_blocks: int = num_blocks
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self.blocks = blocks
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def process_prompt(self, x: torch.Tensor, attn_mask: torch.Tensor, padding_mask: Optional[torch.Tensor] = None):
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k_cache: list[torch.Tensor] = []
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v_cache: list[torch.Tensor] = []
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for i in range(self.num_blocks):
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x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask)
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k_cache.append(k_cache_)
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v_cache.append(v_cache_)
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return x, k_cache, v_cache
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def decode_next_token(self, x: torch.Tensor, k_cache: list[torch.Tensor], v_cache: list[torch.Tensor]):
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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)
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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, speed=1.0):
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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, speed)[0, 0]
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class T2SModel(nn.Module):
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def __init__(self, raw_t2s: Text2SemanticLightningModule):
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super(T2SModel, self).__init__()
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self.model_dim = raw_t2s.model.model_dim
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self.embedding_dim = raw_t2s.model.embedding_dim
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self.num_head = raw_t2s.model.num_head
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self.num_layers = raw_t2s.model.num_layers
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self.vocab_size = raw_t2s.model.vocab_size
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self.phoneme_vocab_size = raw_t2s.model.phoneme_vocab_size
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# self.p_dropout = float(raw_t2s.model.p_dropout)
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self.EOS: int = int(raw_t2s.model.EOS)
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self.norm_first = raw_t2s.model.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.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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blocks = []
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h = raw_t2s.model.h
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for i in range(self.num_layers):
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layer = h.layers[i]
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t2smlp = T2SMLP(layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias)
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block = T2SBlock(
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self.num_head,
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self.model_dim,
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t2smlp,
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layer.self_attn.in_proj_weight,
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layer.self_attn.in_proj_bias,
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layer.self_attn.out_proj.weight,
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layer.self_attn.out_proj.bias,
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layer.norm1.weight,
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layer.norm1.bias,
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layer.norm1.eps,
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layer.norm2.weight,
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layer.norm2.bias,
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layer.norm2.eps,
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)
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blocks.append(block)
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self.t2s_transformer = T2STransformer(self.num_layers, blocks)
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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 = raw_t2s.config["data"]["max_sec"]
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self.top_k = int(raw_t2s.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(
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self,
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prompts: LongTensor,
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ref_seq: LongTensor,
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text_seq: LongTensor,
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ref_bert: torch.Tensor,
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text_bert: torch.Tensor,
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top_k: LongTensor,
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):
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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]
|
||
# 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
|
||
top_k = int(top_k)
|
||
|
||
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=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])
|
||
|
||
if idx < 11: ###至少预测出10个token不然不给停止（0.4s）
|
||
logits = logits[:, :-1]
|
||
|
||
samples = sample(logits, y, top_k=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:
|
||
if y.shape[1] == 0:
|
||
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
||
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)
|
||
|
||
|
||
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
|
||
|
||
|
||
@torch.jit.script
|
||
def build_phone_level_feature(res: Tensor, word2ph: IntTensor):
|
||
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, word2ph: IntTensor
|
||
):
|
||
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]
|
||
res = torch.cat(outputs[1][-3:-2], -1)[0][1:-1]
|
||
return build_phone_level_feature(res, word2ph)
|
||
|
||
|
||
class SSLModel(torch.nn.Module):
|
||
def __init__(self):
|
||
super().__init__()
|
||
self.ssl = cnhubert.get_model().model
|
||
|
||
def forward(self, ref_audio_16k) -> torch.Tensor:
|
||
ssl_content = self.ssl(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
|
||
return ssl_content
|
||
|
||
|
||
class ExportSSLModel(torch.nn.Module):
|
||
def __init__(self, ssl: SSLModel):
|
||
super().__init__()
|
||
self.ssl = ssl
|
||
|
||
def forward(self, ref_audio: torch.Tensor):
|
||
return self.ssl(ref_audio)
|
||
|
||
@torch.jit.export
|
||
def resample(self, ref_audio: torch.Tensor, src_sr: int, dst_sr: int) -> torch.Tensor:
|
||
audio = resamplex(ref_audio, src_sr, dst_sr).float()
|
||
return audio
|
||
|
||
|
||
def export_bert(output_path):
|
||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||
|
||
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."
|
||
ref_bert_inputs = tokenizer(text, return_tensors="pt")
|
||
word2ph = []
|
||
for c in text:
|
||
if c in ["，", "。", "：", "？", ",", ".", "?"]:
|
||
word2ph.append(1)
|
||
else:
|
||
word2ph.append(2)
|
||
ref_bert_inputs["word2ph"] = torch.Tensor(word2ph).int()
|
||
|
||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path, output_hidden_states=True, torchscript=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"],
|
||
}
|
||
|
||
torch._dynamo.mark_dynamic(ref_bert_inputs["input_ids"], 1)
|
||
torch._dynamo.mark_dynamic(ref_bert_inputs["attention_mask"], 1)
|
||
torch._dynamo.mark_dynamic(ref_bert_inputs["token_type_ids"], 1)
|
||
torch._dynamo.mark_dynamic(ref_bert_inputs["word2ph"], 0)
|
||
|
||
my_bert_model = torch.jit.trace(my_bert_model, example_kwarg_inputs=ref_bert_inputs)
|
||
output_path = os.path.join(output_path, "bert_model.pt")
|
||
my_bert_model.save(output_path)
|
||
print("#### exported bert ####")
|
||
|
||
|
||
def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_bert_and_ssl=False, device="cpu"):
|
||
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(ref_audio_path, 16000)]).float()
|
||
ssl = SSLModel()
|
||
if export_bert_and_ssl:
|
||
s = ExportSSLModel(torch.jit.trace(ssl, example_inputs=(ref_audio)))
|
||
ssl_path = os.path.join(output_path, "ssl_model.pt")
|
||
torch.jit.script(s).save(ssl_path)
|
||
print("#### exported ssl ####")
|
||
export_bert(output_path)
|
||
else:
|
||
s = ExportSSLModel(ssl)
|
||
|
||
print(f"device: {device}")
|
||
|
||
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]).to(device)
|
||
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]).to(device)
|
||
text_bert = text_bert_T.T.to(text_seq.device)
|
||
|
||
ssl_content = ssl(ref_audio).to(device)
|
||
|
||
# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
|
||
vits = VitsModel(vits_path).to(device)
|
||
vits.eval()
|
||
|
||
# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
|
||
# dict_s1 = torch.load(gpt_path, map_location=device)
|
||
dict_s1 = torch.load(gpt_path)
|
||
raw_t2s = get_raw_t2s_model(dict_s1).to(device)
|
||
print("#### get_raw_t2s_model ####")
|
||
print(raw_t2s.config)
|
||
t2s_m = T2SModel(raw_t2s)
|
||
t2s_m.eval()
|
||
t2s = torch.jit.script(t2s_m).to(device)
|
||
print("#### script t2s_m ####")
|
||
|
||
print("vits.hps.data.sampling_rate:", vits.hps.data.sampling_rate)
|
||
gpt_sovits = GPT_SoVITS(t2s, vits).to(device)
|
||
gpt_sovits.eval()
|
||
|
||
ref_audio_sr = s.resample(ref_audio, 16000, 32000).to(device)
|
||
|
||
torch._dynamo.mark_dynamic(ssl_content, 2)
|
||
torch._dynamo.mark_dynamic(ref_audio_sr, 1)
|
||
torch._dynamo.mark_dynamic(ref_seq, 1)
|
||
torch._dynamo.mark_dynamic(text_seq, 1)
|
||
torch._dynamo.mark_dynamic(ref_bert, 0)
|
||
torch._dynamo.mark_dynamic(text_bert, 0)
|
||
|
||
top_k = torch.LongTensor([5]).to(device)
|
||
|
||
with torch.no_grad():
|
||
gpt_sovits_export = torch.jit.trace(
|
||
gpt_sovits, example_inputs=(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert, top_k)
|
||
)
|
||
|
||
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
|
||
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,
|
||
top_k: LongTensor,
|
||
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, top_k)
|
||
audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed)
|
||
return audio
|
||
|
||
|
||
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")
|
||
parser.add_argument("--output_path", required=True, help="Path to the output directory")
|
||
|
||
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,torchscript=True)
|
||
# bert = MyBertModel(bert_model)
|
||
my_bert = torch.jit.load("onnx/bert_model.pt", map_location="cuda")
|
||
|
||
# dict_s1 = torch.load(gpt_path, map_location="cuda")
|
||
# raw_t2s = get_raw_t2s_model(dict_s1)
|
||
# t2s = T2SModel(raw_t2s)
|
||
# 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()).to('cuda')
|
||
# ssl.eval()
|
||
ssl = torch.jit.load("onnx/by/ssl_model.pt", map_location="cuda")
|
||
|
||
# gpt_sovits = GPT_SoVITS(t2s,vits)
|
||
gpt_sovits = torch.jit.load("onnx/by/gpt_sovits_model.pt", map_location="cuda")
|
||
|
||
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("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')
|
||
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."
|
||
|
||
text_seq_id, text_bert_T, norm_text = get_phones_and_bert(text, "all_zh", "v2")
|
||
|
||
test_bert = tokenizer(text, return_tensors="pt")
|
||
word2ph = []
|
||
for c in text:
|
||
if c in ["，", "。", "：", "？", "?", ",", "."]:
|
||
word2ph.append(1)
|
||
else:
|
||
word2ph.append(2)
|
||
test_bert["word2ph"] = torch.Tensor(word2ph).int()
|
||
|
||
test_bert = my_bert(
|
||
test_bert["input_ids"].to("cuda"),
|
||
test_bert["attention_mask"].to("cuda"),
|
||
test_bert["token_type_ids"].to("cuda"),
|
||
test_bert["word2ph"].to("cuda"),
|
||
)
|
||
|
||
text_seq = torch.LongTensor([text_seq_id])
|
||
text_bert = text_bert_T.T.to(text_seq.device)
|
||
|
||
print("text_bert:", text_bert.shape, text_bert)
|
||
print("test_bert:", test_bert.shape, test_bert)
|
||
print(torch.allclose(text_bert.to("cuda"), test_bert))
|
||
|
||
print("text_seq:", text_seq.shape)
|
||
print("text_bert:", text_bert.shape, text_bert.type())
|
||
|
||
# [1,N]
|
||
ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float().to("cuda")
|
||
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:")
|
||
print("ssl_content:", ssl_content.shape)
|
||
print("ref_audio_sr:", ref_audio_sr.shape)
|
||
print("ref_seq:", ref_seq.shape)
|
||
ref_seq = ref_seq.to("cuda")
|
||
print("text_seq:", text_seq.shape)
|
||
text_seq = text_seq.to("cuda")
|
||
print("ref_bert:", ref_bert.shape)
|
||
ref_bert = ref_bert.to("cuda")
|
||
print("text_bert:", text_bert.shape)
|
||
text_bert = text_bert.to("cuda")
|
||
|
||
top_k = torch.LongTensor([5]).to("cuda")
|
||
|
||
with torch.no_grad():
|
||
audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, test_bert, top_k)
|
||
print("start write wav")
|
||
soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
|
||
|
||
|
||
import text
|
||
import json
|
||
|
||
|
||
def export_symbel(version="v2"):
|
||
if version == "v1":
|
||
symbols = text._symbol_to_id_v1
|
||
with open("onnx/symbols_v1.json", "w") as file:
|
||
json.dump(symbols, file, indent=4)
|
||
else:
|
||
symbols = text._symbol_to_id_v2
|
||
with open("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")
|
||
parser.add_argument("--export_common_model", action="store_true", help="Export Bert and SSL model")
|
||
parser.add_argument("--device", help="Device to use")
|
||
|
||
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,
|
||
device=args.device,
|
||
export_bert_and_ssl=args.export_common_model,
|
||
)
|
||
|
||
|
||
import inference_webui
|
||
|
||
if __name__ == "__main__":
|
||
inference_webui.is_half = False
|
||
inference_webui.dtype = torch.float32
|
||
main()
|
||
# test()
|