mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 17:10:02 +08:00
449 lines
19 KiB
Python
449 lines
19 KiB
Python
import torch
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import torch.nn.functional as F
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import torchaudio
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from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
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from feature_extractor import cnhubert
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from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
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from torch import nn
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from sv import SV
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import onnx
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from onnx import helper, TensorProto
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cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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from transformers import HubertModel, HubertConfig
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import os
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import json
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from text import cleaned_text_to_sequence
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import onnxsim
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from onnxconverter_common import float16
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def simplify_onnx_model(onnx_model_path: str):
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# Load the ONNX model
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model = onnx.load(onnx_model_path)
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# Simplify the model
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model_simplified, _ = onnxsim.simplify(model)
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# Save the simplified model
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onnx.save(model_simplified, onnx_model_path)
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def convert_onnx_to_half(onnx_model_path:str):
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try:
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model = onnx.load(onnx_model_path)
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model_fp16 = float16.convert_float_to_float16(model)
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onnx.save(model_fp16, onnx_model_path)
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except Exception as e:
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print(f"Error converting {onnx_model_path} to half precision: {e}")
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def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
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hann_window = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
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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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def resample_audio(audio: torch.Tensor, orig_sr: int, target_sr: int) -> torch.Tensor:
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"""
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Resample audio from orig_sr to target_sr using linear interpolation.
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audio: (batch, channels, samples) or (channels, samples) or (samples,)
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"""
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if audio.dim() == 1:
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audio = audio.unsqueeze(0).unsqueeze(0)
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elif audio.dim() == 2:
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audio = audio.unsqueeze(0)
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# audio shape: (batch, channels, samples)
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batch, channels, samples = audio.shape
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# Reshape to combine batch and channels for interpolation
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audio = audio.reshape(batch * channels, 1, samples)
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# Use scale_factor instead of a computed size for ONNX export compatibility
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resampled = F.interpolate(audio, scale_factor=target_sr / orig_sr, mode='linear', align_corners=False)
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new_samples = resampled.shape[-1]
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resampled = resampled.reshape(batch, channels, new_samples)
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resampled = resampled.squeeze(0).squeeze(0)
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return resampled
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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 T2SInitStage(nn.Module):
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def __init__(self, t2s, vits):
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super().__init__()
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self.encoder = t2s.onnx_encoder
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self.vits = vits
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self.num_layers = t2s.num_layers
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
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codes = self.vits.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 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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prompt = prompt_semantic.unsqueeze(0)
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x = self.encoder(all_phoneme_ids, bert)
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x_seq_len = torch.onnx.operators.shape_as_tensor(x)[1]
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y_seq_len = torch.onnx.operators.shape_as_tensor(prompt)[1]
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init_k = torch.zeros(((x_seq_len + y_seq_len), self.num_layers, 512), dtype=torch.float)
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init_v = torch.zeros(((x_seq_len + y_seq_len), self.num_layers, 512), dtype=torch.float)
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return x, prompt, init_k, init_v, x_seq_len, y_seq_len
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class T2SModel(nn.Module):
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def __init__(self, t2s_path, vits_model):
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super().__init__()
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dict_s1 = torch.load(t2s_path, map_location="cpu")
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self.config = dict_s1["config"]
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self.t2s_model = Text2SemanticLightningModule(self.config, "ojbk", is_train=False)
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self.t2s_model.load_state_dict(dict_s1["weight"])
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self.t2s_model.eval()
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self.vits_model = vits_model.vq_model
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self.hz = 50
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self.max_sec = self.config["data"]["max_sec"]
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self.t2s_model.model.top_k = torch.LongTensor([self.config["inference"]["top_k"]])
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self.t2s_model.model.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
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self.t2s_model = self.t2s_model.model
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self.t2s_model.init_onnx()
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self.init_stage = T2SInitStage(self.t2s_model, self.vits_model)
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self.stage_decoder = self.t2s_model.stage_decoder
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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x, prompt, init_k, init_v, x_seq_len, y_seq_len = self.init_stage(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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empty_tensor = torch.empty((1,0,512)).to(torch.float)
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# first step
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y, k, v, y_emb, logits, samples = self.stage_decoder(x, prompt, init_k, init_v,
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empty_tensor,
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top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature,
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first_infer=torch.LongTensor([1]), x_seq_len=x_seq_len, y_seq_len=y_seq_len)
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for idx in range(5): # This is a fake one! DO NOT take this as reference
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k = torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1))
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v = torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1))
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y_seq_len = y.shape[1]
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y, k, v, y_emb, logits, samples = self.stage_decoder(empty_tensor, y, k, v,
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y_emb,
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top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature,
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first_infer=torch.LongTensor([0]), x_seq_len=x_seq_len, y_seq_len=y_seq_len)
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# if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
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# break
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return y[:, -5:].unsqueeze(0)
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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torch.onnx.export(
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self.init_stage,
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(ref_seq, text_seq, ref_bert, text_bert, ssl_content),
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f"onnx/{project_name}/{project_name}_t2s_init_stage.onnx",
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input_names=["ref_text_phones", "input_text_phones", "ref_text_bert", "input_text_bert", "hubert_ssl_content"],
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output_names=["x", "prompt", "init_k", "init_v", 'x_seq_len', 'y_seq_len'],
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dynamic_axes={
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"ref_text_phones": {1: "ref_length"},
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"input_text_phones": {1: "text_length"},
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"ref_text_bert": {0: "ref_length"},
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"input_text_bert": {0: "text_length"},
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"hubert_ssl_content": {2: "ssl_length"},
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},
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opset_version=16,
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do_constant_folding=False
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)
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simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_init_stage.onnx")
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x, prompt, init_k, init_v, x_seq_len, y_seq_len = self.init_stage(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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empty_tensor = torch.empty((1,0,512)).to(torch.float)
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x_seq_len = torch.Tensor([x_seq_len]).to(torch.int64)
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y_seq_len = torch.Tensor([y_seq_len]).to(torch.int64)
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y, k, v, y_emb, logits, samples = self.stage_decoder(x, prompt, init_k, init_v,
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empty_tensor,
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top_k, top_p, repetition_penalty, temperature,
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torch.LongTensor([1]), x_seq_len, y_seq_len)
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k = torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1))
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v = torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1))
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y_seq_len = torch.Tensor([y.shape[1]]).to(torch.int64)
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torch.onnx.export(
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self.stage_decoder,
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(x, y, k, v, y_emb, top_k, top_p, repetition_penalty, temperature, torch.LongTensor([0]), x_seq_len, y_seq_len),
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f"onnx/{project_name}/{project_name}_t2s_stage_decoder.onnx",
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input_names=["ix", "iy", "ik", "iv", "iy_emb", "top_k", "top_p", "repetition_penalty", "temperature", "if_init_step", "x_seq_len", "y_seq_len"],
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output_names=["y", "k", "v", "y_emb", "logits", "samples"],
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dynamic_axes={
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"ix": {1: "ix_length"},
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"iy": {1: "iy_length"},
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"ik": {0: "ik_length"},
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"iv": {0: "iv_length"},
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"iy_emb": {1: "iy_emb_length"},
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},
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verbose=False,
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opset_version=16,
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)
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simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_stage_decoder.onnx")
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class VitsModel(nn.Module):
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def __init__(self, vits_path, version:str = 'v2'):
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super().__init__()
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dict_s2 = torch.load(vits_path, map_location="cpu", weights_only=False)
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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"] = version
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self.is_v2p = version.lower() in ['v2pro', 'v2proplus']
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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 = 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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# print(f"filter_length:{self.hps.data.filter_length} sampling_rate:{self.hps.data.sampling_rate} hop_length:{self.hps.data.hop_length} win_length:{self.hps.data.win_length}")
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#v2 filter_length: 2048 sampling_rate: 32000 hop_length: 640 win_length: 2048
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def forward(self, text_seq, pred_semantic, spectrum, sv_emb, speed):
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if self.is_v2p:
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return self.vq_model(pred_semantic, text_seq, spectrum, sv_emb=sv_emb, speed=speed)[0, 0]
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else:
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return self.vq_model(pred_semantic, text_seq, spectrum, speed=speed)[0, 0]
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class GptSoVits():
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def __init__(self, vits, t2s):
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super().__init__()
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self.vits = vits
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self.t2s = t2s
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, speed, project_name, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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torch.onnx.export(
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self.vits,
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(text_seq, pred_semantic, spectrum, sv_emb, speed),
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f"onnx/{project_name}/{project_name}_vits.onnx",
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input_names=["input_text_phones", "pred_semantic", "spectrum", "sv_emb", "speed"],
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output_names=["audio"],
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dynamic_axes={
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"input_text_phones": {1: "text_length"},
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"pred_semantic": {2: "pred_length"},
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"spectrum": {2: "spectrum_length"},
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},
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opset_version=17,
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verbose=False,
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)
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simplify_onnx_model(f"onnx/{project_name}/{project_name}_vits.onnx")
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class AudioPreprocess(nn.Module):
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def __init__(self):
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super().__init__()
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# Load the model
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self.model = HubertModel.from_pretrained(cnhubert_base_path, local_files_only=True)
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self.model.eval()
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self.sv_model = SV("cpu", False)
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def forward(self, ref_audio_32k):
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spectrum = spectrogram_torch(
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ref_audio_32k,
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2048,
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32000,
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640,
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2048,
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center=False,
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)
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ref_audio_16k = resample_audio(ref_audio_32k, 32000, 16000)
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sv_emb = self.sv_model.compute_embedding3_onnx(ref_audio_16k)
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zero_tensor = torch.zeros((1, 9600), dtype=torch.float32)
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ref_audio_16k = ref_audio_16k.unsqueeze(0)
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# concate zero_tensor with waveform
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ref_audio_16k = torch.cat([ref_audio_16k, zero_tensor], dim=1)
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ssl_content = self.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
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return ssl_content, spectrum, sv_emb
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def export(vits_path, gpt_path, project_name, voice_model_version, export_audio_preprocessor=True, half_precision=False):
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vits = VitsModel(vits_path, version=voice_model_version)
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gpt = T2SModel(gpt_path, vits)
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gpt_sovits = GptSoVits(vits, gpt)
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preprocessor = AudioPreprocess()
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ref_seq = torch.LongTensor(
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[
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cleaned_text_to_sequence(
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[
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"n",
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"i2",
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"h",
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"ao3",
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",",
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"w",
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"o3",
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"sh",
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"i4",
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"b",
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"ai2",
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"y",
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"e4",
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],
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version='v2',
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)
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]
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)
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text_seq = torch.LongTensor(
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[
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cleaned_text_to_sequence(
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[
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"w",
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"o3",
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"sh",
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"i4",
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"b",
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"ai2",
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"y",
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"e4",
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"w",
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"o3",
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"sh",
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"i4",
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"b",
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"ai2",
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"y",
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"e4",
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"w",
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"o3",
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"sh",
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"i4",
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"b",
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"ai2",
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"y",
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"e4",
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],
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version='v2',
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)
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]
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)
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ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
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text_bert = torch.randn((text_seq.shape[1], 1024)).float()
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ref_audio32k = torch.randn((1, 32000 * 5)).float() - 0.5 # 5 seconds of dummy audio
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top_k = torch.LongTensor([15])
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top_p = torch.FloatTensor([1.0])
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repetition_penalty = torch.FloatTensor([1.0])
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temperature = torch.FloatTensor([1.0])
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speed = torch.FloatTensor([1.0])
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os.makedirs(f"onnx/{project_name}", exist_ok=True)
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[ssl_content, spectrum, sv_emb] = preprocessor(ref_audio32k)
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gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), speed, project_name, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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if export_audio_preprocessor:
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torch.onnx.export(preprocessor, (ref_audio32k,), f"onnx/{project_name}/{project_name}_audio_preprocess.onnx",
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input_names=["audio32k"],
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output_names=["hubert_ssl_output", "spectrum", "sv_emb"],
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dynamic_axes={
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"audio32k": {1: "sequence_length"},
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"hubert_ssl_output": {2: "hubert_length"},
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"spectrum": {2: "spectrum_length"}
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})
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simplify_onnx_model(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx")
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if half_precision:
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if export_audio_preprocessor:
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convert_onnx_to_half(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx")
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convert_onnx_to_half(f"onnx/{project_name}/{project_name}_vits.onnx")
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convert_onnx_to_half(f"onnx/{project_name}/{project_name}_t2s_init_step.onnx")
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convert_onnx_to_half(f"onnx/{project_name}/{project_name}_t2s_stage_step.onnx")
|
||
|
||
configJson = {
|
||
"project_name": project_name,
|
||
"type": "GPTSoVITS",
|
||
"version" : voice_model_version,
|
||
"bert_base_path": 'GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large',
|
||
"cnhuhbert_base_path": 'GPT_SoVITS/pretrained_models/chinese-hubert-base',
|
||
"t2s_weights_path": gpt_path,
|
||
"vits_weights_path": vits_path,
|
||
"half_precision": half_precision
|
||
}
|
||
with open(f"onnx/{project_name}/config.json", "w", encoding="utf-8") as f:
|
||
json.dump(configJson, f, ensure_ascii=False, indent=4)
|
||
|
||
if __name__ == "__main__":
|
||
try:
|
||
os.mkdir("onnx")
|
||
except:
|
||
pass
|
||
|
||
# 因为io太频繁，可能导致模型导出出错(wsl非常明显)，请自行重试
|
||
|
||
gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
|
||
vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
|
||
exp_path = "v1_export"
|
||
version = "v1"
|
||
export(vits_path, gpt_path, exp_path, version)
|
||
|
||
gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
|
||
vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
|
||
exp_path = "v2_export"
|
||
version = "v2"
|
||
export(vits_path, gpt_path, exp_path, version)
|
||
|
||
|
||
gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
|
||
vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
|
||
exp_path = "v2pro_export"
|
||
version = "v2Pro"
|
||
export(vits_path, gpt_path, exp_path, version)
|
||
|
||
gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
|
||
vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
|
||
exp_path = "v2proplus_export"
|
||
version = "v2ProPlus"
|
||
export(vits_path, gpt_path, exp_path, version)
|
||
|
||
|