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verified audio, text, synthesizer all working, todo:dig into t2s for error

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zpeng11 2025-08-22 01:36:58 -04:00
parent e8fdf472c0
commit 63cbb6efa7
2 changed files with 22 additions and 32 deletions
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35
GPT_SoVITS/onnx_export.py
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@ -264,17 +264,9 @@ class GptSoVits(nn.Module):
class AudioPreprocess(nn.Module): class AudioPreprocess(nn.Module):
def __init__(self): def __init__(self):
super().__init__() 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 # Load the model
self.model = HubertModel.from_pretrained( self.model = HubertModel.from_pretrained(cnhubert_base_path, local_files_only=True)
cnhubert_base_path,
config=self.config,
local_files_only=True
)
self.model.eval() self.model.eval()
self.sv_model = SV("cpu", False) self.sv_model = SV("cpu", False)
@ -292,7 +284,7 @@ class AudioPreprocess(nn.Module):
sv_emb = self.sv_model.compute_embedding3_onnx(ref_audio_16k) sv_emb = self.sv_model.compute_embedding3_onnx(ref_audio_16k)
zero_tensor = torch.zeros((1, 4800), dtype=torch.float32) zero_tensor = torch.zeros((1, 9600), dtype=torch.float32)
ref_audio_16k = ref_audio_16k.unsqueeze(0) ref_audio_16k = ref_audio_16k.unsqueeze(0)
# concate zero_tensor with waveform # concate zero_tensor with waveform
ref_audio_16k = torch.cat([ref_audio_16k, zero_tensor], dim=1) ref_audio_16k = torch.cat([ref_audio_16k, zero_tensor], dim=1)
@ -452,11 +444,12 @@ if __name__ == "__main__":
# version = "v1" # version = "v1"
# export(vits_path, gpt_path, exp_path, version) # 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" 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" vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
# exp_path = "v2_export" exp_path = "v2_export"
# version = "v2" version = "v2"
# export(vits_path, gpt_path, exp_path, version) export(vits_path, gpt_path, exp_path, version)
combineInitStepAndStageStep('onnx/v2_export/v2_export_t2s_init_step.onnx', 'onnx/v2_export/v2_export_t2s_sdec.onnx', 'onnx/v2_export/v2_export_t2s_combined.onnx')
# gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt" # gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth" # vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
@ -464,11 +457,11 @@ if __name__ == "__main__":
# version = "v2Pro" # version = "v2Pro"
# export(vits_path, gpt_path, exp_path, version) # export(vits_path, gpt_path, exp_path, version)
gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt" # 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" # vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
exp_path = "v2proplus_export" # exp_path = "v2proplus_export"
version = "v2ProPlus" # version = "v2ProPlus"
export(vits_path, gpt_path, exp_path, version) # export(vits_path, gpt_path, exp_path, version)
combineInitStepAndStageStep('onnx/v2proplus_export/v2proplus_export_t2s_init_step.onnx', 'onnx/v2proplus_export/v2proplus_export_t2s_sdec.onnx', 'onnx/v2proplus_export/v2proplus_export_t2s_combined.onnx') # combineInitStepAndStageStep('onnx/v2proplus_export/v2proplus_export_t2s_init_step.onnx', 'onnx/v2proplus_export/v2proplus_export_t2s_sdec.onnx', 'onnx/v2proplus_export/v2proplus_export_t2s_combined.onnx')

19
playground/freerun.py
View File

@ -7,7 +7,7 @@ import torch
from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx
MODEL_PATH = "onnx/v2proplus_export/v2proplus" MODEL_PATH = "onnx/v2_export/v2"
def audio_postprocess( def audio_postprocess(
audios, audios,
@ -63,7 +63,7 @@ def preprocess_text(text:str):
# input_phones_saved = np.load("playground/ref/input_phones.npy") # input_phones_saved = np.load("playground/ref/input_phones.npy")
# input_bert_saved = np.load("playground/ref/input_bert.npy").T.astype(np.float32) # input_bert_saved = np.load("playground/ref/input_bert.npy").T.astype(np.float32)
[input_phones, input_bert] = preprocess_text("地上的人儿吵吵闹闹在地上追") [input_phones, input_bert] = preprocess_text("天上的风筝在天上飞，地上的人儿在地上追。")
# ref_phones = np.load("playground/ref/ref_phones.npy") # ref_phones = np.load("playground/ref/ref_phones.npy")
@ -73,8 +73,12 @@ def preprocess_text(text:str):
[audio_prompt_hubert, spectrum, sv_emb] = audio_preprocess("playground/ref/audio.wav") [audio_prompt_hubert, spectrum, sv_emb] = audio_preprocess("playground/ref/audio.wav")
np.save("playground/ref/audio_prompt_hubert.npy", audio_prompt_hubert.astype(np.float16))
# audio_prompt_hubert_saved = np.load("playground/ref/audio_prompt_hubert.npy").astype(np.float32)
t2s_combined = ort.InferenceSession(MODEL_PATH+"_export_t2s_combined.onnx") t2s_combined = ort.InferenceSession(MODEL_PATH+"_export_t2s_combined.onnx")
# t2s_init_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_step.onnx")
[y, k, v, y_emb, x_example, fake_logits, fake_samples] = t2s_combined.run(None, { [y, k, v, y_emb, x_example, fake_logits, fake_samples] = t2s_combined.run(None, {
"if_init_step": np.array(True, dtype=bool), "if_init_step": np.array(True, dtype=bool),
@ -90,7 +94,7 @@ t2s_combined = ort.InferenceSession(MODEL_PATH+"_export_t2s_combined.onnx")
"ix_example":np.empty((1, 0), dtype=np.float32) "ix_example":np.empty((1, 0), dtype=np.float32)
}) })
# t2s_stage_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
for idx in tqdm(range(1, 1500)): 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] # [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
@ -113,13 +117,6 @@ y[0, -1] = 0
pred_semantic = np.expand_dims(y[:, -idx:], axis=0) pred_semantic = np.expand_dims(y[:, -idx:], axis=0)
# Read and resample reference audio
waveform, sample_rate = torchaudio.load("playground/ref/audio.wav")
if sample_rate != 32000:
resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=32000)
waveform = resampler(waveform)
print(f"Waveform shape: {waveform.shape}")
ref_audio = waveform.numpy().astype(np.float32)
vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx") vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
@ -127,7 +124,7 @@ vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
"input_text_phones": input_phones, "input_text_phones": input_phones,
"pred_semantic": pred_semantic, "pred_semantic": pred_semantic,
"spectrum": spectrum.astype(np.float32), "spectrum": spectrum.astype(np.float32),
"sv_emb": sv_emb.astype(np.float32) # "sv_emb": sv_emb.astype(np.float32)
}) })
audio_postprocess([audio]) audio_postprocess([audio])