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app.py

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+import torch
+import torchaudio
+from pydub import AudioSegment
+import gradio as gr
+_original_load = torch.load
+def cpu_load(*args, **kwargs):
+    kwargs["map_location"] = torch.device('cpu')
+    return _original_load(*args, **kwargs)
+torch.load = cpu_load
+
+hubert = torch.hub.load("bshall/hubert:main", "hubert_soft", trust_repo=True).cpu()
+acoustic = torch.hub.load("bshall/acoustic-model:main", "hubert_soft", trust_repo=True).cpu()
+hifigan = torch.hub.load("bshall/hifigan:main", "hifigan_hubert_soft", trust_repo=True).cpu()
+
+def soft_vc(audio_path):
+  AudioSegment.from_file(audio_path).set_frame_rate(16000).set_channels(1).export(audio_path, format="wav")
+  source = torchaudio.load(audio_path)[0].unsqueeze(0).cpu()
+  with torch.inference_mode():
+      target = hifigan(acoustic.generate(hubert.units(source)).transpose(1, 2))
+  torchaudio.save("output.wav", target.squeeze(0).cpu(), 16000)
+  return "output.wav"
+
+gr.Interface(soft_vc,gr.Audio(label="Input Audio",type="filepath"),gr.Audio(label="Output Audio",type="filepath")).launch()

packages.txt

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+ffmpeg

requirements.txt

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+pydub
+torchaudio