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Update app.py
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app.py
CHANGED
@@ -1,4 +1,3 @@
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import spaces # Para ambientes específicos como Hugging Face Spaces
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USING_SPACES = True
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@@ -11,48 +10,33 @@ import torchaudio
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from functools import partial
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from resemble_enhance.enhancer.inference import denoise, enhance
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def gpu_decorator(func):
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kwargs['device'] = device
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return func(*args, **kwargs)
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return wrapper
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@gpu_decorator
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def _fn(path, solver, nfe, tau, denoising, unlimited
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if path is None:
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return "Please upload an audio file.", None
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info = torchaudio.info(path)
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if not unlimited and (info.num_frames / info.sample_rate > 60):
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return "Only audio files shorter than 60 seconds are supported.", None
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solver = solver.lower()
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nfe = int(nfe)
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lambd = 0.9 if denoising else 0.1
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dwav, sr = torchaudio.load(path)
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dwav = dwav.mean(dim=0)
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wav2, new_sr = enhance(dwav, sr, device, nfe=nfe, solver=solver, lambd=lambd, tau=tau)
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wav1 = wav1.cpu().numpy()
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wav2 = wav2.cpu().numpy()
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return (new_sr, wav1), (new_sr, wav2)
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Cria a interface do Gradio.
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"""
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inputs = [
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gr.Audio(type="filepath", label="Input Audio"),
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gr.Dropdown(
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@@ -83,12 +67,10 @@ def create_app():
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label="Allow Unlimited Audio Length (supports files longer than 60 seconds)",
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),
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]
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outputs = [
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gr.Audio(label="Output Denoised Audio"),
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gr.Audio(label="Output Enhanced Audio"),
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]
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# Interface Gradio
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interface = gr.Interface(
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fn=_fn,
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@@ -97,23 +79,17 @@ def create_app():
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inputs=inputs,
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outputs=outputs,
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)
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return interface.queue() # Ativa filas para processamento
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def main(
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"""
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Inicializa a aplicação dependendo do ambiente.
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"""
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global app
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print("Starting app...")
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app
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app.launch(server_name=host, server_port=port, share=share, show_api=api)
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if __name__ == "__main__":
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if not USING_SPACES:
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main()
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else:
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app
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app.launch()
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try:
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import spaces # Para ambientes específicos como Hugging Face Spaces
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USING_SPACES = True
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from functools import partial
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from resemble_enhance.enhancer.inference import denoise, enhance
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def gpu_decorator(func):
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if USING_SPACES:
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return spaces.GPU(func)
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else:
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return func
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@gpu_decorator
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def _fn(path, solver, nfe, tau, denoising, unlimited):
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if path is None:
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return "Please upload an audio file.", None
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info = torchaudio.info(path)
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if not unlimited and (info.num_frames / info.sample_rate > 60):
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return "Only audio files shorter than 60 seconds are supported.", None
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solver = solver.lower()
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nfe = int(nfe)
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lambd = 0.9 if denoising else 0.1
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dwav, sr = torchaudio.load(path)
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dwav = dwav.mean(dim=0)
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wav1, new_sr = denoise(dwav, sr) # Remove o argumento device
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wav2, new_sr = enhance(dwav, sr, nfe=nfe, solver=solver, lambd=lambd, tau=tau) # Remove o argumento device
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wav1 = wav1.cpu().numpy()
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wav2 = wav2.cpu().numpy()
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return (new_sr, wav1), (new_sr, wav2)
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with gr.Blocks() as app:
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inputs = [
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gr.Audio(type="filepath", label="Input Audio"),
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gr.Dropdown(
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label="Allow Unlimited Audio Length (supports files longer than 60 seconds)",
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),
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]
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outputs = [
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gr.Audio(label="Output Denoised Audio"),
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gr.Audio(label="Output Enhanced Audio"),
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]
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# Interface Gradio
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interface = gr.Interface(
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fn=_fn,
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inputs=inputs,
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outputs=outputs,
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)
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app = interface.queue()
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def main():
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global app
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print("Starting app...")
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app.launch(share=True)
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if __name__ == "__main__":
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if not USING_SPACES:
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main()
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else:
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app.launch(share=True)
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