app.py

import os
from transformers import pipeline
import gradio as gr
import torch
import pytube as pt

checkpoint = "GGmorello/whisper-small-it"
device = 0 if torch.cuda.is_available() else "cpu"
print(device)

pipe = pipeline(task = "automatic-speech-recognition", model = checkpoint,chunk_length_s=30,device = device)

def transcribe(audio):
    text = pipe(audio)["text"]
    return text

def transcribe_url(yt_url):
  yt = pt.YouTube(yt_url)
  stream = yt.streams.filter(only_audio=True)[0]
  stream.download(filename = "audio.mp3")
  text = pipe("audio.mp3")["text"]
  return text


demo = gr.Blocks()

microphone_interface = gr.Interface(
    fn=transcribe,
    inputs = gr.Audio(sources="microphone", type="filepath"),
    outputs="text",
    title="Whisper Small Italian Finetuned raw microphone audio",
    description="Realtime demo for Italian speech recognition using a fine-tuned Whisper small model."

)


file_interface = gr.Interface(
    fn=transcribe,
    inputs = gr.Audio(sources="upload", type="filepath"),
    outputs="text",
    title="Whisper Small Italian Finetuned for audio file.",
    description="Realtime demo for Italian speech recognition using a fine-tuned Whisper small model."

)


url_interface = gr.Interface(
    fn = transcribe_url,
    inputs = gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
    outputs = "text",
    title = "Whisper Small Italian Finetuned for URL transcription",
    description = "Realtime demo for Italian speech recognition using a fine-tuned Whisper small model."


)

with demo:
    gr.TabbedInterface([microphone_interface,file_interface, url_interface], ["Transcribe Audio", "Transcribe File" , "Transcribe YouTube"])

demo.launch(share=True)