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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 08:49:59 +08:00
feat:get ready for if node merge
This commit is contained in:
zpeng11
parent
403c5bf320
commit
16d30ce1e4
@ -6,7 +6,7 @@ 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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cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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from transformers import HubertModel, HubertConfig
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import json
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@ -103,9 +103,20 @@ class T2SInitStep(nn.Module):
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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.fsdc(self.encoder(all_phoneme_ids, bert), prompt)
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[y, k, v, y_emb, x_example] = self.fsdc(self.encoder(all_phoneme_ids, bert), prompt)
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fake_logits = torch.randn((1, 1025)) # Dummy logits for ONNX export
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fake_samples = torch.randn((1, 1)) # 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):
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[y, k, v, y_emb, logits, samples] = self.stage_decoder(iy, ik, iv, iy_emb, ix_example)
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fake_x_example = torch.randn((1, 512)) # 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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@ -131,12 +142,13 @@ class T2SModel(nn.Module):
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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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y, k, v, y_emb, x_example = self.init_step(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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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)
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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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print(logits.shape, samples.shape)
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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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y[0, -1] = 0
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@ -158,7 +170,7 @@ class T2SModel(nn.Module):
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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_init_step.onnx",
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input_names=["ref_text_phones", "input_text_phones", "ref_text_bert", "input_text_bert", "hubert_ssl_content"],
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output_names=["y", "k", "v", "y_emb", "x_example"],
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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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@ -168,35 +180,22 @@ class T2SModel(nn.Module):
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},
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opset_version=16,
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)
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y, k, v, y_emb, x_example = self.init_step(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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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: "x_length"},
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# "prompts": {1: "prompts_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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# y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
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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)
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stage_step = T2SStageStep(self.stage_decoder)
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torch.onnx.export(
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self.stage_decoder,
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stage_step,
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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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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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@ -303,6 +302,20 @@ class AudioPreprocess(nn.Module):
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return ssl_content, spectrum, sv_emb
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# def combineInitStepAndStageStep(init_step_onnx_path, stage_step_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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# # Combine the models (this is a simplified example; actual combination logic may vary)
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# combined_graph = helper.make_graph(
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# nodes=init_step_model.graph.node + stage_step_model.graph.node,
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# name="combined_graph",
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# inputs=init_step_model.graph.input,
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# outputs=stage_step_model.graph.output
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# )
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# combined_model = helper.make_model(combined_graph, producer_name='onnx-combiner')
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# onnx.save(combined_model, f"onnx/{project_name}/{project_name}_combined.onnx")
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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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@ -366,14 +379,14 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
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)
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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_audio32k = torchaudio.functional.resample(ref_audio, 48000, 32000).float()
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ref_audio32k = torch.randn((1, 32000 * 5)).float() - 0.5
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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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os.makedirs(f"onnx/{project_name}", exist_ok=True)
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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, ssl_content.float(), spectrum.float(), sv_emb.float(), project_name)
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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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@ -384,39 +397,31 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
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"spectrum": {2: "spectrum_length"}
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})
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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, 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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else:
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symbols = symbols_v2
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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 = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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exp_path = "v1_export"
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version = "v1"
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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/s1v3.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
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exp_path = "v2pro_export"
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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/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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# exp_path = "v1_export"
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# version = "v1"
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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/s1v3.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
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# exp_path = "v2pro_export"
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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/s1v3.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
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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/v1_export/v1"
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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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@ -56,7 +56,7 @@ def audio_preprocess(audio_path):
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def preprocess_text(text:str):
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preprocessor = TextPreprocessorOnnx("playground/bert")
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[phones, bert_features, norm_text] = preprocessor.segment_and_extract_feature_for_text(text, 'all_zh', 'v1')
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[phones, bert_features, norm_text] = preprocessor.segment_and_extract_feature_for_text(text, 'all_zh', 'v2')
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phones = np.expand_dims(np.array(phones, dtype=np.int64), axis=0)
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return phones, bert_features.T.astype(np.float32)
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@ -76,7 +76,7 @@ def preprocess_text(text:str):
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init_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_step.onnx")
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[y, k, v, y_emb, x_example] = init_step.run(None, {
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[y, k, v, y_emb, x_example, fake_logits, fake_samples] = init_step.run(None, {
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"input_text_phones": input_phones,
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"input_text_bert": input_bert,
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"ref_text_phones": ref_phones,
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@ -96,7 +96,7 @@ sdec = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
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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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[y, k, v, y_emb, fake_x_example, logits, samples] = sdec.run(None, {
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"iy": y,
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"ik": k,
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"iv": v,
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@ -123,7 +123,7 @@ vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
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"input_text_phones": input_phones,
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"pred_semantic": pred_semantic,
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"spectrum": spectrum.astype(np.float32),
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# "sv_emb": sv_emb.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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214
playground/onnx_if_node_test.py
Normal file
214
playground/onnx_if_node_test.py
Normal file
@ -0,0 +1,214 @@
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import torch
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import torch.nn as nn
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import onnx
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import onnxruntime as ort
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from onnx import helper, TensorProto
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import numpy as np
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import os
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# Define file paths
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PLAYGROUND_DIR = "playground"
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MODEL_A_PATH = os.path.join(PLAYGROUND_DIR, "a.onnx")
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MODEL_B_PATH = os.path.join(PLAYGROUND_DIR, "b.onnx")
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MODEL_C_PATH = os.path.join(PLAYGROUND_DIR, "c.onnx")
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# --- 1. Create two simple PyTorch modules ---
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class ModelA(nn.Module):
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"""This model adds 1 to the input."""
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def forward(self, x):
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return x + 1.0
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class ModelB(nn.Module):
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"""This model multiplies the input by 2."""
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def forward(self, x):
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return x * 2.0
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def create_and_export_models():
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"""Creates two nn.Modules and exports them to ONNX."""
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print("Step 1: Creating and exporting PyTorch models A and B...")
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os.makedirs(PLAYGROUND_DIR, exist_ok=True)
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# Define a dummy input with a dynamic axis
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batch_size = 1
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sequence_length = 10 # This dimension will be dynamic
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features = 4
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dummy_input = torch.randn(batch_size, sequence_length, features)
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# Export Model A
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print(f"Exporting Model A to {MODEL_A_PATH}")
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torch.onnx.export(
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ModelA(),
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dummy_input,
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MODEL_A_PATH,
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input_names=['inputA'],
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output_names=['output'],
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dynamic_axes={'inputA': {1: 'sequenceA'}, 'output': {1: 'sequence'}},
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opset_version=11 # If node requires opset >= 11
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)
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# Export Model B
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print(f"Exporting Model B to {MODEL_B_PATH}")
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torch.onnx.export(
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ModelB(),
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dummy_input,
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MODEL_B_PATH,
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input_names=['inputB'],
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output_names=['output'],
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dynamic_axes={'inputB': {1: 'sequenceB'}, 'output': {1: 'sequence'}},
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opset_version=11
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)
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print("Models A and B exported successfully.")
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def combine_models_with_if():
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"""
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Reads two ONNX models and combines them into a third model
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using an 'If' operator.
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"""
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print("\nStep 2: Combining models A and B into C with an 'If' node...")
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# Load the two exported ONNX models
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model_a = onnx.load(MODEL_A_PATH)
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model_b = onnx.load(MODEL_B_PATH)
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# The graphs for the 'then' and 'else' branches of the 'If' operator
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then_graph = model_a.graph
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then_graph.name = "then_branch_graph"
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else_graph = model_b.graph
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else_graph.name = "else_branch_graph"
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# The data input for the main graph is defined here.
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# We take it from one of the original models.
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data_inputA = model_a.graph.input[0]
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data_inputB = model_b.graph.input[0]
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# For some onnxruntime versions, subgraphs should not have their own
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# explicit 'input' list if the inputs are captured from the parent graph.
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# We clear the input lists of the subgraphs to force implicit capture.
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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_name = model_a.graph.output[0].name
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assert subgraph_output_name == model_b.graph.output[0].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_use_a', TensorProto.BOOL, [])
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# The main graph's output is the output from the 'If' node.
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# We can use the ValueInfoProto from one of the subgraphs directly.
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main_output = model_a.graph.output[0]
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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_use_a'],
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outputs=[subgraph_output_name], # 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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# Create the main graph containing the 'If' node. Its inputs are the condition
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# AND the data that the subgraphs will capture.
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main_graph = helper.make_graph(
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nodes=[if_node],
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name='if_main_graph',
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inputs=[cond_input, data_inputA, data_inputB],
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outputs=[main_output]
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)
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# Create the final combined model, specifying the opset and IR version
|
||||
opset_version = 16
|
||||
final_model = helper.make_model(main_graph,
|
||||
producer_name='onnx-if-combiner',
|
||||
ir_version=9, # For compatibility with older onnxruntime
|
||||
opset_imports=[helper.make_opsetid("", opset_version)])
|
||||
|
||||
# Check the model for correctness
|
||||
onnx.checker.check_model(final_model)
|
||||
|
||||
# Save the combined model
|
||||
onnx.save(final_model, MODEL_C_PATH)
|
||||
print(f"Combined model C saved to {MODEL_C_PATH}")
|
||||
|
||||
def verify_combined_model():
|
||||
"""
|
||||
Loads the combined ONNX model and runs inference to verify
|
||||
that the 'If' branching and dynamic shapes work correctly.
|
||||
"""
|
||||
print("\nStep 3: Verifying the combined model C...")
|
||||
sess = ort.InferenceSession(MODEL_C_PATH)
|
||||
|
||||
# --- Test Case 1: Select Model A (if_use_a = True) ---
|
||||
print("\n--- Verifying 'then' branch (Model A) ---")
|
||||
use_a = np.array(True)
|
||||
# Use a different sequence length to test dynamic axis
|
||||
test_seq_len_a = 15
|
||||
test_seq_len_b = 10
|
||||
input_data_a = np.random.randn(1, test_seq_len_a, 4).astype(np.float32)
|
||||
input_data_b = np.random.randn(1, test_seq_len_a, 4).astype(np.float32)
|
||||
|
||||
# Run inference
|
||||
outputs = sess.run(
|
||||
None,
|
||||
{'if_use_a': use_a, 'inputA': input_data_a, 'inputB': input_data_b}
|
||||
)
|
||||
result_a = outputs[0]
|
||||
|
||||
# Calculate expected output from Model A
|
||||
expected_a = input_data_a + 1.0
|
||||
|
||||
# Verify the output and shape
|
||||
np.testing.assert_allclose(result_a, expected_a, rtol=1e-5, atol=1e-5)
|
||||
assert result_a.shape[1] == test_seq_len_a, "Dynamic shape failed for branch A"
|
||||
print("✅ Branch A (if_use_a=True) works correctly.")
|
||||
print(f"✅ Dynamic shape test passed (input seq_len={test_seq_len_a}, output seq_len={result_a.shape[1]})")
|
||||
|
||||
# --- Test Case 2: Select Model B (if_use_a = False) ---
|
||||
print("\n--- Verifying 'else' branch (Model B) ---")
|
||||
use_b = np.array(False)
|
||||
# Use another sequence length
|
||||
test_seq_len_a = 8
|
||||
test_seq_len_b = 5
|
||||
input_data_a = np.random.randn(1, test_seq_len_a, 4).astype(np.float32)
|
||||
input_data_b = np.random.randn(1, test_seq_len_b, 4).astype(np.float32)
|
||||
|
||||
# Run inference
|
||||
outputs = sess.run(
|
||||
None,
|
||||
{'if_use_a': use_b, 'inputA': input_data_a, 'inputB': input_data_b}
|
||||
)
|
||||
result_b = outputs[0]
|
||||
|
||||
# Calculate expected output from Model B
|
||||
expected_b = input_data_b * 2.0
|
||||
|
||||
# Verify the output and shape
|
||||
np.testing.assert_allclose(result_b, expected_b, rtol=1e-5, atol=1e-5)
|
||||
assert result_b.shape[1] == test_seq_len_b, "Dynamic shape failed for branch B"
|
||||
print("✅ Branch B (if_use_a=False) works correctly.")
|
||||
print(f"✅ Dynamic shape test passed (input seq_len={test_seq_len_b}, output seq_len={result_b.shape[1]})")
|
||||
|
||||
def cleanup():
|
||||
"""Removes the intermediate ONNX files."""
|
||||
print("\nCleaning up intermediate files...")
|
||||
for path in [MODEL_A_PATH, MODEL_B_PATH]:
|
||||
if os.path.exists(path):
|
||||
os.remove(path)
|
||||
print(f"Removed {path}")
|
||||
|
||||
def main():
|
||||
"""Main function to run the entire process."""
|
||||
try:
|
||||
create_and_export_models()
|
||||
combine_models_with_if()
|
||||
verify_combined_model()
|
||||
finally:
|
||||
cleanup()
|
||||
print("\nAll steps completed successfully!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user