import torch import torch.nn.functional as F import torchaudio from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule from feature_extractor import cnhubert from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2 from torch import nn from sv import SV cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base" from transformers import HubertModel, HubertConfig import json import os import soundfile from tqdm import tqdm from text import cleaned_text_to_sequence def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): hann_window = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) y = torch.nn.functional.pad( y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect", ) y = y.squeeze(1) spec = torch.stft( y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window, center=center, pad_mode="reflect", normalized=False, onesided=True, return_complex=False, ) spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) return spec def resample_audio(audio: torch.Tensor, orig_sr: int, target_sr: int) -> torch.Tensor: """ Resample audio from orig_sr to target_sr using linear interpolation. audio: (batch, channels, samples) or (channels, samples) or (samples,) """ if audio.dim() == 1: audio = audio.unsqueeze(0).unsqueeze(0) elif audio.dim() == 2: audio = audio.unsqueeze(0) # audio shape: (batch, channels, samples) batch, channels, samples = audio.shape # Reshape to combine batch and channels for interpolation audio = audio.reshape(batch * channels, 1, samples) # Use scale_factor instead of a computed size for ONNX export compatibility resampled = F.interpolate(audio, scale_factor=target_sr / orig_sr, mode='linear', align_corners=False) new_samples = resampled.shape[-1] resampled = resampled.reshape(batch, channels, new_samples) resampled = resampled.squeeze(0).squeeze(0) return resampled class DictToAttrRecursive(dict): def __init__(self, input_dict): super().__init__(input_dict) for key, value in input_dict.items(): if isinstance(value, dict): value = DictToAttrRecursive(value) self[key] = value setattr(self, key, value) def __getattr__(self, item): try: return self[item] except KeyError: raise AttributeError(f"Attribute {item} not found") def __setattr__(self, key, value): if isinstance(value, dict): value = DictToAttrRecursive(value) super(DictToAttrRecursive, self).__setitem__(key, value) super().__setattr__(key, value) def __delattr__(self, item): try: del self[item] except KeyError: raise AttributeError(f"Attribute {item} not found") class T2SEncoder(nn.Module): def __init__(self, t2s, vits): super().__init__() self.encoder = t2s.onnx_encoder self.fsdc = t2s.first_stage_decoder self.vits = vits def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content): codes = self.vits.extract_latent(ssl_content) prompt_semantic = codes[0, 0] bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 1) all_phoneme_ids = torch.cat([ref_seq, text_seq], 1) bert = bert.unsqueeze(0) prompt = prompt_semantic.unsqueeze(0) return self.fsdc(self.encoder(all_phoneme_ids, bert), prompt) class T2SModel(nn.Module): def __init__(self, t2s_path, vits_model): super().__init__() dict_s1 = torch.load(t2s_path, map_location="cpu") self.config = dict_s1["config"] self.t2s_model = Text2SemanticLightningModule(self.config, "ojbk", is_train=False) self.t2s_model.load_state_dict(dict_s1["weight"]) self.t2s_model.eval() self.vits_model = vits_model.vq_model self.hz = 50 self.max_sec = self.config["data"]["max_sec"] self.t2s_model.model.top_k = torch.LongTensor([self.config["inference"]["top_k"]]) self.t2s_model.model.early_stop_num = torch.LongTensor([self.hz * self.max_sec]) self.t2s_model = self.t2s_model.model self.t2s_model.init_onnx() self.onnx_encoder = T2SEncoder(self.t2s_model, self.vits_model) self.first_stage_decoder = self.t2s_model.first_stage_decoder self.stage_decoder = self.t2s_model.stage_decoder # self.t2s_model = torch.jit.script(self.t2s_model) def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content): early_stop_num = self.t2s_model.early_stop_num # [1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N] y, k, v, y_emb, x_example = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content) stop = False for idx in tqdm(range(1, 1500)): # [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N] enco = self.stage_decoder(y, k, v, y_emb, x_example) y, k, v, y_emb, logits, samples = enco if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS: stop = True if stop: break y[0, -1] = 0 return y[:, -idx:].unsqueeze(0) def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, dynamo=False): # self.onnx_encoder = torch.jit.script(self.onnx_encoder) if dynamo: export_options = torch.onnx.ExportOptions(dynamic_shapes=True) onnx_encoder_export_output = torch.onnx.dynamo_export( self.onnx_encoder, (ref_seq, text_seq, ref_bert, text_bert, ssl_content), export_options=export_options ) onnx_encoder_export_output.save(f"onnx/{project_name}/{project_name}_t2s_encoder.onnx") return torch.onnx.export( self.onnx_encoder, (ref_seq, text_seq, ref_bert, text_bert, ssl_content), f"onnx/{project_name}/{project_name}_t2s_encoder.onnx", input_names=["ref_seq", "text_seq", "ref_bert", "text_bert", "ssl_content"], output_names=["y", "k", "v", "y_emb", "x_example"], dynamic_axes={ "ref_seq": {1: "ref_length"}, "text_seq": {1: "text_length"}, "ref_bert": {0: "ref_length"}, "text_bert": {0: "text_length"}, "ssl_content": {2: "ssl_length"}, }, opset_version=16, ) y, k, v, y_emb, x_example = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content) # torch.onnx.export( # self.first_stage_decoder, # (x, prompts), # f"onnx/{project_name}/{project_name}_t2s_fsdec.onnx", # input_names=["x", "prompts"], # output_names=["y", "k", "v", "y_emb", "x_example"], # dynamic_axes={ # "x": {1: "x_length"}, # "prompts": {1: "prompts_length"}, # }, # verbose=False, # opset_version=16, # ) # y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts) torch.onnx.export( self.stage_decoder, (y, k, v, y_emb, x_example), f"onnx/{project_name}/{project_name}_t2s_sdec.onnx", input_names=["iy", "ik", "iv", "iy_emb", "ix_example"], output_names=["y", "k", "v", "y_emb", "logits", "samples"], dynamic_axes={ "iy": {1: "iy_length"}, "ik": {1: "ik_length"}, "iv": {1: "iv_length"}, "iy_emb": {1: "iy_emb_length"}, "ix_example": {1: "ix_example_length"}, }, verbose=False, opset_version=16, ) class VitsModel(nn.Module): def __init__(self, vits_path, version:str = 'v2'): super().__init__() dict_s2 = torch.load(vits_path, map_location="cpu") self.hps = dict_s2["config"] if dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322: self.hps["model"]["version"] = "v1" else: self.hps["model"]["version"] = version self.sv_model = None if version == "v2ProPlus" or version == "v2Pro": self.sv_model = SV("cpu", False) self.hps = DictToAttrRecursive(self.hps) self.hps.model.semantic_frame_rate = "25hz" self.vq_model = SynthesizerTrn( self.hps.data.filter_length // 2 + 1, self.hps.train.segment_size // self.hps.data.hop_length, n_speakers=self.hps.data.n_speakers, **self.hps.model, ) self.vq_model.eval() self.vq_model.load_state_dict(dict_s2["weight"], strict=False) #filter_length: 2048 sampling_rate: 32000 hop_length: 640 win_length: 2048 def forward(self, text_seq, pred_semantic, ref_audio, spectrum): if self.sv_model is not None: sv_emb=self.sv_model.compute_embedding3_onnx(resample_audio(ref_audio, 32000, 16000)) return self.vq_model(pred_semantic, text_seq, spectrum, sv_emb=sv_emb)[0, 0] return self.vq_model(pred_semantic, text_seq, spectrum)[0, 0] class GptSoVits(nn.Module): def __init__(self, vits, t2s): super().__init__() self.vits = vits self.t2s = t2s def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, spectrum, ssl_content): pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content) audio = self.vits(text_seq, pred_semantic, ref_audio, spectrum) return audio def export(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, spectrum, ssl_content, project_name): self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name) pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content) torch.onnx.export( self.vits, (text_seq, pred_semantic, ref_audio, spectrum), f"onnx/{project_name}/{project_name}_vits.onnx", input_names=["text_seq", "pred_semantic", "ref_audio", "spectrum"], output_names=["audio"], dynamic_axes={ "text_seq": {1: "text_length"}, "pred_semantic": {2: "pred_length"}, "ref_audio": {1: "audio_length"}, "spectrum": {2: "spectrum_length"}, }, opset_version=17, verbose=False, ) class HuBertSSLModel(nn.Module): def __init__(self): super().__init__() self.config = HubertConfig.from_pretrained(cnhubert_base_path) self.config._attn_implementation = "eager" # Use standard attention self.config.apply_spec_augment = False # Disable masking for inference self.config.layerdrop = 0.0 # Disable layer dropout # Load the model self.model = HubertModel.from_pretrained( cnhubert_base_path, config=self.config, local_files_only=True ) self.model.eval() def forward(self, ref_audio_32k): spectrum = spectrogram_torch( ref_audio_32k, 2048, 32000, 640, 2048, center=False, ) ref_audio_16k = resample_audio(ref_audio_32k, 32000, 16000).unsqueeze(0) zero_tensor = torch.zeros((1, 4800), dtype=torch.float32) # concate zero_tensor with waveform ref_audio_16k = torch.cat([ref_audio_16k, zero_tensor], dim=1) ssl_content = self.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2) return ssl_content, spectrum def export(vits_path, gpt_path, project_name, voice_model_version="v2"): vits = VitsModel(vits_path, version=voice_model_version) gpt = T2SModel(gpt_path, vits) gpt_sovits = GptSoVits(vits, gpt) ssl = HuBertSSLModel() ref_seq = torch.LongTensor( [ cleaned_text_to_sequence( [ "n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4", ], version='v2', ) ] ) text_seq = torch.LongTensor( [ cleaned_text_to_sequence( [ "w", "o3", "sh", "i4", "b", "ai2", "y", "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_audio = torch.randn((1, 48000 * 5)).float() # ref_audio = torch.tensor([load_audio("rec.wav", 48000)]).float() ref_audio32k = torchaudio.functional.resample(ref_audio, 48000, 32000).float() try: os.mkdir(f"onnx/{project_name}") except: pass torch.onnx.export(ssl, (ref_audio32k,), f"onnx/{project_name}/{project_name}_hubertssl.onnx", input_names=["audio32k"], output_names=["hubert_ssl_output", "spectrum"], dynamic_axes={ "audio32k": {0: "batch_size", 1: "sequence_length"}, "hubert_ssl_output": {0: "batch_size", 2: "hubert_length"}, "spectrum": {0: "batch_size", 2: "spectrum_length"} }) [ssl_content, spectrum] = ssl(ref_audio32k) gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio32k, spectrum.float(), ssl_content.float()) # exit() gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio32k, spectrum.float(), ssl_content.float(), project_name) if voice_model_version == "v1": symbols = symbols_v1 else: symbols = symbols_v2 if __name__ == "__main__": try: os.mkdir("onnx") except: pass # 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/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.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)