azalea/VITS-fast-fine-tuning
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Plachta
2023-02-15 15:33:23 +08:00
parent 3f8e95a859
commit b6481a0937
1 changed files with 2 additions and 10 deletions
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VC_inference.py
+2 -10
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@@ -12,7 +12,7 @@ import webbrowser
device = "cuda:0" if torch.cuda.is_available() else "cpu"
def create_vc_fn(model, hps, speaker_ids):
def vc_fn(original_speaker, target_speaker, record_audio, upload_audio, denoise):
def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
input_audio = record_audio if record_audio is not None else upload_audio
if input_audio is None:
return "You need to record or upload an audio", None
@@ -28,13 +28,6 @@ def create_vc_fn(model, hps, speaker_ids):
with no_grad():
y = torch.FloatTensor(audio)
y = y / max(-y.min(), y.max()) / 0.99
if denoise:
torchaudio.save("infer.wav", y.cpu(), 22050, channels_first=True)
os.system(f"demucs --two-stems=vocals infer.wav")
y, sr = torchaudio.load(f"./separated/htdemucs/infer/vocals.wav", frame_offset=0, num_frames=-1, normalize=True, channels_first=True)
y = y.mean(dim=0).unsqueeze(0)
if sr != 22050:
y = torchaudio.transforms.Resample(orig_freq=sr, new_freq=22050)(y)
y = y.to(device)
spec = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
@@ -80,12 +73,11 @@ if __name__ == "__main__":
upload_audio = gr.Audio(label="or upload audio here", source="upload")
source_speaker = gr.Dropdown(choices=speakers, value="User", label="source speaker")
target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
denoise_checkbox = gr.Checkbox(label="denoise using demucs", value=False)
with gr.Column():
message_box = gr.Textbox(label="Message")
converted_audio = gr.Audio(label='converted audio')
btn = gr.Button("Convert!")
btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio, denoise_checkbox],
btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
outputs=[message_box, converted_audio])
webbrowser.open("http://127.0.0.1:7860")
app.launch(share=args.share)