app.py

import gradio as gr

from main_pipeline import main_pipeline
from scipy.io.wavfile import write

title = "audio_denoise and speakser diarization. Faster inference [tg_bot]()"
description = '''Faster inference  tg_bot - https://t.me/diarizarion_bot '''

example_list = [
    ["dialog.mp3"]
]


def app_pipeline(audio):
    audio_path = 'test.wav'
    write(audio_path, audio[0], audio[1])
    denoised_audio_path, result_diarization = main_pipeline(audio_path)

    return result_diarization + [None] * (10 - len(result_diarization))



gr.Interface(
    app_pipeline,
    gr.Audio(type="numpy", label="Input"),
    [gr.Audio(visible=True) for i in range(10)],
    title=title,
    examples=example_list,
    cache_examples=False,
    description=description

).launch(enable_queue=True)