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from transformers import pipeline
from datasets import load_dataset
import gradio as gr
import numpy as np
import os
whisper = pipeline(model='jlvdoorn/whisper-large-v3-atco2-asr-atcosim')
def transcribe(audio):
if audio is not None:
return whisper(audio)['text']
else:
return 'There was no audio to transcribe...'
file_iface = gr.Interface(
fn = transcribe,
inputs = gr.Audio(source='upload', interactive=True),
outputs = gr.Textbox(label='Transcription'),
title = 'Whisper ATC - Large v3',
description = 'Transcribe ATC speech',
)
mic_iface = gr.Interface(
fn = transcribe,
inputs = gr.Audio(source='microphone', type='filepath'),
outputs = gr.Textbox(label='Transcription'),
title = 'Whisper ATC - Large v3',
description = 'Transcribe ATC speech',
)
demo = gr.TabbedInterface([file_iface, mic_iface], ["File", "Microphone"])
demo.launch(server_name='0.0.0.0')
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