mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 00:30:15 +08:00
correctly setup onnx export, solved problem
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zpeng11
parent
94b31a250f
commit
aafa0561d8
@ -133,14 +133,11 @@ class T2SModel(nn.Module):
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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 = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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stop = False
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for idx in tqdm(range(1, 1500)):
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for idx in tqdm(range(1, 20)): # This is a fake one! do take this as reference
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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 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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@ -216,9 +213,7 @@ class VitsModel(nn.Module):
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else:
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self.hps["model"]["version"] = version
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self.sv_model = None
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if version == "v2ProPlus" or version == "v2Pro":
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self.sv_model = SV("cpu", False)
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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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@ -231,10 +226,10 @@ class VitsModel(nn.Module):
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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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#filter_length: 2048 sampling_rate: 32000 hop_length: 640 win_length: 2048
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def forward(self, text_seq, pred_semantic, ref_audio, spectrum):
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if self.sv_model is not None:
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sv_emb=self.sv_model.compute_embedding3_onnx(resample_audio(ref_audio, 32000, 16000))
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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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@ -244,19 +239,19 @@ class GptSoVits(nn.Module):
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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, spectrum, ssl_content):
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb):
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pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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audio = self.vits(text_seq, pred_semantic, ref_audio, spectrum)
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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, ref_audio, spectrum, ssl_content, project_name):
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, 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, spectrum),
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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=["text_seq", "pred_semantic", "ref_audio", "spectrum"],
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input_names=["text_seq", "pred_semantic", "spectrum", "sv_emb"],
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output_names=["audio"],
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dynamic_axes={
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"text_seq": {1: "text_length"},
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@ -269,7 +264,7 @@ class GptSoVits(nn.Module):
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)
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class HuBertSSLModel(nn.Module):
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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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self.config = HubertConfig.from_pretrained(cnhubert_base_path)
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@ -285,6 +280,8 @@ class HuBertSSLModel(nn.Module):
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)
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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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@ -294,22 +291,24 @@ class HuBertSSLModel(nn.Module):
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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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ref_audio_16k = resample_audio(ref_audio_32k, 32000, 16000).unsqueeze(0)
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zero_tensor = torch.zeros((1, 4800), 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
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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="v2"):
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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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ssl = HuBertSSLModel()
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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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@ -376,19 +375,19 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
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except:
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pass
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torch.onnx.export(ssl, (ref_audio32k,), f"onnx/{project_name}/{project_name}_hubertssl.onnx",
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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"],
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output_names=["hubert_ssl_output", "spectrum", "sv_emb"],
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dynamic_axes={
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"audio32k": {0: "batch_size", 1: "sequence_length"},
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"hubert_ssl_output": {0: "batch_size", 2: "hubert_length"},
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"spectrum": {0: "batch_size", 2: "spectrum_length"}
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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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[ssl_content, spectrum] = ssl(ref_audio32k)
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gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio32k, spectrum.float(), ssl_content.float())
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[ssl_content, spectrum, sv_emb] = preprocessor(ref_audio32k)
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gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float())
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# exit()
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gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio32k, spectrum.float(), ssl_content.float(), project_name)
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gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), project_name)
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if voice_model_version == "v1":
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symbols = symbols_v1
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@ -401,11 +400,11 @@ if __name__ == "__main__":
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except:
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pass
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# gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
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# exp_path = "v2_export"
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# version = "v2"
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# export(vits_path, gpt_path, exp_path, version)
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gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
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exp_path = "v2_export"
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version = "v2"
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export(vits_path, gpt_path, exp_path, version)
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gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
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@ -413,10 +412,10 @@ if __name__ == "__main__":
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version = "v2Pro"
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export(vits_path, gpt_path, exp_path, version)
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# gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
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# exp_path = "v2proplus_export"
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# version = "v2ProPlus"
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# export(vits_path, gpt_path, exp_path, version)
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gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
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exp_path = "v2proplus_export"
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version = "v2ProPlus"
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export(vits_path, gpt_path, exp_path, version)
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@ -7,7 +7,7 @@ import torch
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from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx
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MODEL_PATH = "onnx/v2pro_export/v2pro"
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MODEL_PATH = "onnx/v2proplus_export/v2proplus"
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def audio_postprocess(
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audios,
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@ -31,42 +31,7 @@ def audio_postprocess(
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return audio
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# def load_and_preprocess_audio(audio_path, max_length=160000):
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# """Load and preprocess audio file to 16k"""
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# waveform, sample_rate = torchaudio.load(audio_path)
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# # Resample to 16kHz if needed
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# if sample_rate != 16000:
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# resampler = torchaudio.transforms.Resample(sample_rate, 16000)
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# waveform = resampler(waveform)
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# # Take first channel
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# if waveform.shape[0] > 1:
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# waveform = waveform[0:1]
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# # Limit length for testing (10 seconds at 16kHz)
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# if waveform.shape[1] > max_length:
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# waveform = waveform[:, :max_length]
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# # make a zero tensor that has length 3200*0.3
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# zero_tensor = torch.zeros((1, 4800), dtype=torch.float32)
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# # concate zero_tensor with waveform
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# waveform = torch.cat([waveform, zero_tensor], dim=1)
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# return waveform
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# def get_audio_hubert(audio_path):
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# """Get HuBERT features for the audio file"""
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# waveform = load_and_preprocess_audio(audio_path)
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# ort_session = ort.InferenceSession("playground/hubert/chinese-hubert-base.onnx")
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# ort_inputs = {ort_session.get_inputs()[0].name: waveform.numpy()}
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# hubert_feature = ort_session.run(None, ort_inputs)[0].astype(np.float32)
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# # transpose axis 1 and 2 with numpy
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# hubert_feature = hubert_feature.transpose(0, 2, 1)
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# return hubert_feature
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def load_and_preprocess_audio(audio_path):
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def load_audio(audio_path):
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"""Load and preprocess audio file to 32k"""
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waveform, sample_rate = torchaudio.load(audio_path)
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@ -81,15 +46,13 @@ def load_and_preprocess_audio(audio_path):
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return waveform
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def get_audio_hubert(audio_path):
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def audio_preprocess(audio_path):
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"""Get HuBERT features for the audio file"""
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waveform = load_and_preprocess_audio(audio_path)
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ort_session = ort.InferenceSession(MODEL_PATH + "_export_hubertssl.onnx")
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waveform = load_audio(audio_path)
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ort_session = ort.InferenceSession(MODEL_PATH + "_export_audio_preprocess.onnx")
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ort_inputs = {ort_session.get_inputs()[0].name: waveform.numpy()}
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[hubert_feature, spectrum] = ort_session.run(None, ort_inputs)
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# transpose axis 1 and 2 with numpy
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# hubert_feature = hubert_feature.transpose(0, 2, 1)
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return hubert_feature, spectrum
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[hubert_feature, spectrum, sv_emb] = ort_session.run(None, ort_inputs)
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return hubert_feature, spectrum, sv_emb
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def preprocess_text(text:str):
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preprocessor = TextPreprocessorOnnx("playground/bert")
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@ -108,7 +71,7 @@ def preprocess_text(text:str):
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[ref_phones, ref_bert] = preprocess_text("今日江苏苏州荷花市集开张热闹与浪漫交织")
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[audio_prompt_hubert, spectrum] = get_audio_hubert("playground/ref/audio.wav")
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[audio_prompt_hubert, spectrum, sv_emb] = audio_preprocess("playground/ref/audio.wav")
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encoder = ort.InferenceSession(MODEL_PATH+"_export_t2s_encoder.onnx")
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@ -131,7 +94,6 @@ sdec = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
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# })
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stop = False
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for idx in tqdm(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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[y, k, v, y_emb, logits, samples] = sdec.run(None, {
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@ -160,8 +122,8 @@ vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
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[audio] = vtis.run(None, {
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"text_seq": input_phones,
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"pred_semantic": pred_semantic,
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"ref_audio": ref_audio,
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"spectrum": spectrum.astype(np.float32)
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"spectrum": spectrum.astype(np.float32),
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"sv_emb": sv_emb.astype(np.float32)
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})
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audio_postprocess([audio])
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