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import argparse
import torch
from scipy.io.wavfile import write
from main_pipeline import CleaningPipeline
import gradio as gr
title = "Audio denoising and speaker diarization "
example_list = [
["dialog.mp3"]
]
def app_pipeline(audio):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
cleaning_pipeline = CleaningPipeline(device)
audio_path = 'test.wav'
write(audio_path, audio[0], audio[1])
result = cleaning_pipeline(audio_path)
if result != []:
return result
app = gr.Interface(
app_pipeline,
gr.Audio(type="numpy", label="Input_audio"),
[gr.Audio(visible=True, label='denoised_audio' if i == 0 else f'speaker{i}') for i in range(20)],
title=title,
examples=example_list,
cache_examples=False,
)
app.launch(debug=True, enable_queue=True,
)
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