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Ναρουσέ·μ·γιουμεμί·Χινακάννα
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@ -140,6 +140,7 @@ class T2SModel(nn.Module):
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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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@ -147,16 +148,16 @@ class T2SModel(nn.Module):
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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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"ref_seq": {1 : "ref_length"},
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"text_seq": {1 : "text_length"},
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"ref_bert": {0 : "ref_length"},
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"text_bert": {0 : "text_length"},
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"ssl_content": {2 : "ssl_length"},
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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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@ -164,10 +165,10 @@ class T2SModel(nn.Module):
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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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"x": {1 : "x_length"},
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"prompts": {1 : "prompts_length"},
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},
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verbose=True,
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verbose=False,
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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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@ -179,13 +180,13 @@ class T2SModel(nn.Module):
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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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"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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},
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verbose=True,
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verbose=False,
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opset_version=16
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)
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@ -224,9 +225,19 @@ 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, ssl_content):
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, debug=False):
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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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audio = self.vits(text_seq, pred_semantic, ref_audio)
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if debug:
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import onnxruntime
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sess = onnxruntime.InferenceSession("onnx/koharu/koharu_vits.onnx", providers=["CPU"])
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audio1 = sess.run(None, {
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"text_seq" : text_seq.detach().cpu().numpy(),
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"pred_semantic" : pred_semantic.detach().cpu().numpy(),
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"ref_audio" : ref_audio.detach().cpu().numpy()
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})
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return audio, audio1
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return 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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@ -238,11 +249,12 @@ class GptSoVits(nn.Module):
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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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"text_seq": {1 : "text_length"},
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"pred_semantic": {2 : "pred_length"},
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"ref_audio": {1 : "audio_length"},
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},
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opset_version=17
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opset_version=17,
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verbose=False
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)
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@ -261,7 +273,7 @@ def export(vits_path, gpt_path, project_name):
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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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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"])])
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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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@ -275,10 +287,18 @@ def export(vits_path, gpt_path, project_name):
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pass
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ssl_content = ssl(ref_audio_16k).float()
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debug = False
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if debug:
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a, b = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, debug=debug)
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soundfile.write("out1.wav", a.cpu().detach().numpy(), vits.hps.data.sampling_rate)
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soundfile.write("out2.wav", b[0], vits.hps.data.sampling_rate)
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return
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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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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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@ -306,9 +326,9 @@ if __name__ == "__main__":
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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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gpt_path = "GPT_weights/nahida-e25.ckpt"
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vits_path = "SoVITS_weights/nahida_e30_s3930.pth"
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exp_path = "nahida"
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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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