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Add Onnx Export
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Ναρουσέ·μ·γιουμεμί·Χινακάννα
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314
GPT_SoVITS/onnx_export.py
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314
GPT_SoVITS/onnx_export.py
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from module.models_onnx import SynthesizerTrn, symbols
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from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
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import torch
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import torchaudio
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from torch import nn
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from feature_extractor import cnhubert
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cnhubert_base_path = "pretrained_models/chinese-hubert-base"
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cnhubert.cnhubert_base_path=cnhubert_base_path
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ssl_model = cnhubert.get_model()
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from text import cleaned_text_to_sequence
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import soundfile
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from my_utils import load_audio
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import os
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import json
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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(
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dtype=y.dtype, device=y.device
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)
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
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mode="reflect",
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)
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y = y.squeeze(1)
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spec = torch.stft(
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y,
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n_fft,
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hop_length=hop_size,
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win_length=win_size,
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window=hann_window,
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center=center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=False,
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)
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
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return spec
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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class T2SEncoder(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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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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return self.encoder(all_phoneme_ids, bert), prompt
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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.onnx_encoder = T2SEncoder(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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#self.t2s_model = torch.jit.script(self.t2s_model)
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
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early_stop_num = self.t2s_model.early_stop_num
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#[1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N]
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x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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prefix_len = prompts.shape[1]
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#[1,N,512] [1,N]
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y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
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stop = False
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for idx in range(1, 1500):
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#[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
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enco = self.stage_decoder(y, k, v, y_emb, x_example)
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y, k, v, y_emb, logits, samples = enco
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if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
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stop = True
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if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
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stop = True
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if stop:
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break
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y[0, -1] = 0
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return y[:, -idx:].unsqueeze(0)
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, dynamo=False):
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#self.onnx_encoder = torch.jit.script(self.onnx_encoder)
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if dynamo:
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export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
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onnx_encoder_export_output = torch.onnx.dynamo_export(
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self.onnx_encoder,
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(ref_seq, text_seq, ref_bert, text_bert, ssl_content),
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export_options=export_options
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)
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onnx_encoder_export_output.save(f"onnx/{project_name}/{project_name}_t2s_encoder.onnx")
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return
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torch.onnx.export(
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self.onnx_encoder,
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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_encoder.onnx",
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input_names=["ref_seq", "text_seq", "ref_bert", "text_bert", "ssl_content"],
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output_names=["x", "prompts"],
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dynamic_axes={
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"ref_seq": [1],
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"text_seq": [1],
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"ref_bert": [0],
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"text_bert": [0],
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"ssl_content": [2],
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},
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opset_version=16
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)
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x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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torch.exp
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torch.onnx.export(
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self.first_stage_decoder,
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(x, prompts),
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f"onnx/{project_name}/{project_name}_t2s_fsdec.onnx",
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input_names=["x", "prompts"],
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output_names=["y", "k", "v", "y_emb", "x_example"],
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dynamic_axes={
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"x": [1],
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"prompts": [1],
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},
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verbose=True,
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opset_version=16
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)
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y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
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torch.onnx.export(
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self.stage_decoder,
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(y, k, v, y_emb, x_example),
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f"onnx/{project_name}/{project_name}_t2s_sdec.onnx",
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input_names=["iy", "ik", "iv", "iy_emb", "ix_example"],
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output_names=["y", "k", "v", "y_emb", "logits", "samples"],
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dynamic_axes={
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"iy": [1],
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"ik": [1],
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"iv": [1],
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"iy_emb": [1],
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"ix_example": [1],
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},
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verbose=True,
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opset_version=16
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)
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class VitsModel(nn.Module):
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def __init__(self, vits_path):
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super().__init__()
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dict_s2 = torch.load(vits_path,map_location="cpu")
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self.hps = dict_s2["config"]
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self.hps = DictToAttrRecursive(self.hps)
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self.hps.model.semantic_frame_rate = "25hz"
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self.vq_model = SynthesizerTrn(
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model
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)
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self.vq_model.eval()
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self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
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def forward(self, text_seq, pred_semantic, ref_audio):
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refer = spectrogram_torch(
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ref_audio,
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self.hps.data.filter_length,
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self.hps.data.sampling_rate,
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self.hps.data.hop_length,
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self.hps.data.win_length,
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center=False
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)
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return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
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class 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, ref_audio, ssl_content):
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pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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return self.vits(text_seq, pred_semantic, ref_audio)
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, project_name):
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self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name)
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pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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torch.onnx.export(
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self.vits,
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(text_seq, pred_semantic, ref_audio),
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f"onnx/{project_name}/{project_name}_vits.onnx",
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input_names=["text_seq", "pred_semantic", "ref_audio"],
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output_names=["audio"],
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dynamic_axes={
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"text_seq": [1],
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"pred_semantic": [2],
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"ref_audio": [1],
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},
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opset_version=17
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)
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class SSLModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.ssl = ssl_model
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def forward(self, ref_audio_16k):
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return self.ssl.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
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def export(vits_path, gpt_path, project_name):
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vits = VitsModel(vits_path)
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gpt = T2SModel(gpt_path, vits)
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gpt_sovits = GptSoVits(vits, gpt)
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ssl = SSLModel()
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ref_seq = torch.LongTensor([cleaned_text_to_sequence(["n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
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text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
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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_audio = torch.randn((1, 48000 * 5)).float()
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# ref_audio = torch.tensor([load_audio("rec.wav", 48000)]).float()
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ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()
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ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,vits.hps.data.sampling_rate).float()
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try:
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os.mkdir(f"onnx/{project_name}")
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except:
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pass
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ssl_content = ssl(ref_audio_16k).float()
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a = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content).detach().cpu().numpy()
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# soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
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gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)
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MoeVSConf = {
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"Folder" : f"{project_name}",
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"Name" : f"{project_name}",
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"Type" : "GPT-SoVits",
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"Rate" : vits.hps.data.sampling_rate,
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"NumLayers": gpt.t2s_model.num_layers,
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"EmbeddingDim": gpt.t2s_model.embedding_dim,
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"Dict": "BasicDict",
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"BertPath": "chinese-roberta-wwm-ext-large",
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"Symbol": symbols,
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"AddBlank": False
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}
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MoeVSConfJson = json.dumps(MoeVSConf)
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with open(f"onnx/{project_name}.json", 'w') as MoeVsConfFile:
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json.dump(MoeVSConf, MoeVsConfFile, indent = 4)
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if __name__ == "__main__":
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try:
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os.mkdir("onnx")
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except:
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pass
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gpt_path = "pt_model/koharu-e20.ckpt"
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vits_path = "pt_model/koharu_e20_s4960.pth"
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exp_path = "koharu"
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export(vits_path, gpt_path, exp_path)
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# soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
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