mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 00:30:15 +08:00
482 lines
21 KiB
Python
482 lines
21 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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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 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 T2SInitStep(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.fsdc = t2s.first_stage_decoder
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self.vits = vits
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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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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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[y, k, v, y_emb, x_example] = self.fsdc(self.encoder(all_phoneme_ids, bert), prompt, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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fake_logits = torch.zeros((1, 1025), dtype=torch.float32) # Dummy logits for ONNX export
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fake_samples = torch.zeros((1, 1), dtype=torch.int32) # Dummy samples for ONNX export
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return y, k, v, y_emb, x_example, fake_logits, fake_samples
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class T2SStageStep(nn.Module):
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def __init__(self, stage_decoder):
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super().__init__()
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self.stage_decoder = stage_decoder
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def forward(self, iy, ik, iv, iy_emb, ix_example, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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[y, k, v, y_emb, logits, samples] = self.stage_decoder(iy, ik, iv, iy_emb, ix_example, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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fake_x_example = torch.zeros((1, 512), dtype=torch.float32) # Dummy x_example for ONNX export
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return y, k, v, y_emb, fake_x_example, logits, samples
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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_step = T2SInitStep(self.t2s_model, self.vits_model)
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self.first_stage_decoder = self.t2s_model.first_stage_decoder
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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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# [1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N]
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y, k, v, y_emb, x_example, fake_logits, fake_samples = self.init_step(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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for idx in range(5): # This is a fake one! DO NOT take this as reference
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enco = self.stage_decoder(y, k, v, y_emb, x_example, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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y, k, v, y_emb, logits, samples = enco
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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_step,
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(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k, top_p, repetition_penalty, temperature),
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f"onnx/{project_name}/{project_name}_t2s_init_step.onnx",
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input_names=["ref_text_phones", "input_text_phones", "ref_text_bert", "input_text_bert", "hubert_ssl_content", "top_k", "top_p", "repetition_penalty", "temperature"],
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output_names=["y", "k", "v", "y_emb", "x_example", 'logits', 'samples'],
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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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)
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simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_init_step.onnx")
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y, k, v, y_emb, x_example, fake_logits, fake_samples = self.init_step(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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stage_step = T2SStageStep(self.stage_decoder)
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torch.onnx.export(
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stage_step,
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(y, k, v, y_emb, x_example, top_k, top_p, repetition_penalty, temperature),
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f"onnx/{project_name}/{project_name}_t2s_stage_step.onnx",
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input_names=["iy", "ik", "iv", "iy_emb", "ix_example", "top_k", "top_p", "repetition_penalty", "temperature"],
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output_names=["y", "k", "v", "y_emb","x_example", "logits", "samples"],
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dynamic_axes={
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"iy": {1: "iy_length"},
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"ik": {1: "ik_length"},
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"iv": {1: "iv_length"},
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"iy_emb": {1: "iy_emb_length"},
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"ix_example": {1: "ix_example_length"},
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"x_example": {1: "x_example_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_step.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(
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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):
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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)[0, 0]
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else:
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return self.vq_model(pred_semantic, text_seq, spectrum)[0, 0]
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class GptSoVits(nn.Module):
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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 forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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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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audio = self.vits(text_seq, pred_semantic, spectrum, sv_emb)
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return audio
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, 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),
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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"],
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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 combineInitStepAndStageStep(init_step_onnx_path, stage_step_onnx_path, combined_onnx_path):
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init_step_model = onnx.load(init_step_onnx_path)
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stage_step_model = onnx.load(stage_step_onnx_path)
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then_graph = init_step_model.graph
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then_graph.name = "init_step_graph"
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else_graph = stage_step_model.graph
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else_graph.name = "stage_step_graph"
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data_inputs_init = [input for input in init_step_model.graph.input]
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data_inputs_stage = [input for input in stage_step_model.graph.input]
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# Get all names from both lists
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names_list_init = {obj.name for obj in data_inputs_init}
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names_list_stage = {obj.name for obj in data_inputs_stage}
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# Find names that appear in both lists
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repeated_input_names = names_list_init.intersection(names_list_stage)
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# Filter out objects with repeated names
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data_inputs_stage = [obj for obj in data_inputs_stage if obj.name not in repeated_input_names]
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del then_graph.input[:]
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del else_graph.input[:]
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# The output names of the subgraphs must be the same.
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# The 'If' node will have an output with this same name.
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subgraph_output_names = [output.name for output in then_graph.output]
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for i, output in enumerate(else_graph.output):
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assert subgraph_output_names[i] == output.name, "Subgraph output names must match"
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# Define the inputs for the main graph
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# 1. The boolean condition to select the branch
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cond_input = helper.make_tensor_value_info('if_init_step', TensorProto.BOOL, [])
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main_outputs = [output for output in init_step_model.graph.output]
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# Create the 'If' node
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if_node = helper.make_node(
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'If',
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inputs=['if_init_step'],
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outputs=subgraph_output_names, # This name MUST match the subgraph's output name
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then_branch=then_graph,
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else_branch=else_graph
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)
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# Combine the models (this is a simplified example; actual combination logic may vary)
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main_graph = helper.make_graph(
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nodes=[if_node],
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name="t2s_combined_graph",
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inputs=[cond_input] + data_inputs_init + data_inputs_stage,
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outputs=main_outputs
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)
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# Create the final combined model, specifying the opset and IR version
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opset_version = 16
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final_model = helper.make_model(main_graph,
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producer_name='GSV-ONNX-Exporter',
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ir_version=9, # For compatibility with older onnxruntime
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opset_imports=[helper.make_opsetid("", opset_version)])
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# Check the model for correctness
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onnx.checker.check_model(final_model)
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# Save the combined model
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onnx.save(final_model, combined_onnx_path)
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print(f"Combined model saved to {combined_onnx_path}")
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def export(vits_path, gpt_path, project_name, voice_model_version, t2s_model_combine=False, export_audio_preprocessor=True):
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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",
|
||
"w",
|
||
"o3",
|
||
"sh",
|
||
"i4",
|
||
"b",
|
||
"ai2",
|
||
"y",
|
||
"e4",
|
||
"w",
|
||
"o3",
|
||
"sh",
|
||
"i4",
|
||
"b",
|
||
"ai2",
|
||
"y",
|
||
"e4",
|
||
],
|
||
version='v2',
|
||
)
|
||
]
|
||
)
|
||
ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
|
||
text_bert = torch.randn((text_seq.shape[1], 1024)).float()
|
||
ref_audio32k = torch.randn((1, 32000 * 5)).float() - 0.5 # 5 seconds of dummy audio
|
||
top_k = torch.LongTensor([15])
|
||
top_p = torch.FloatTensor([1.0])
|
||
repetition_penalty = torch.FloatTensor([1.0])
|
||
temperature = torch.FloatTensor([1.0])
|
||
|
||
os.makedirs(f"onnx/{project_name}", exist_ok=True)
|
||
|
||
[ssl_content, spectrum, sv_emb] = preprocessor(ref_audio32k)
|
||
gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
|
||
# exit()
|
||
gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), project_name, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
|
||
|
||
if export_audio_preprocessor:
|
||
torch.onnx.export(preprocessor, (ref_audio32k,), f"onnx/{project_name}/{project_name}_audio_preprocess.onnx",
|
||
input_names=["audio32k"],
|
||
output_names=["hubert_ssl_output", "spectrum", "sv_emb"],
|
||
dynamic_axes={
|
||
"audio32k": {1: "sequence_length"},
|
||
"hubert_ssl_output": {2: "hubert_length"},
|
||
"spectrum": {2: "spectrum_length"}
|
||
})
|
||
simplify_onnx_model(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx")
|
||
|
||
if t2s_model_combine:
|
||
combineInitStepAndStageStep(f'onnx/{project_name}/{project_name}_t2s_init_step.onnx', f'onnx/{project_name}/{project_name}_t2s_stage_step.onnx', f'onnx/{project_name}/{project_name}_t2s_combined.onnx')
|
||
|
||
|
||
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, t2s_model_combine = True)
|
||
|
||
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, t2s_model_combine = True)
|
||
|
||
|
||
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, t2s_model_combine = True)
|
||
|
||
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, t2s_model_combine = True)
|
||
|
||
|