app.py

from transformers import pipeline
import gradio as gr
import os
import deepl
from pytube import YouTube

TARGET_LANG = "EN-GB"
deepl_key = os.environ.get('DEEPL_KEY')

translator = deepl.Translator(deepl_key)
pipe = pipeline("automatic-speech-recognition", model="FredBonux/whisper-small-it")

def transcribe(audio):
    ita = pipe(audio)["text"]
    eng = translator.translate_text(ita, target_lang=TARGET_LANG).text
    print(f"{ita} -> {eng}")
    return ita, eng

def transcribe_url(url):
    youtube = YouTube(str(url))
    print("Downloading video")
    audio = youtube.streams.filter(only_audio=True).first().download('yt_video')
    print("Downloaded")
    text_it = pipe(audio)["text"]
    print(f"{text_it}")
    text_en = translator.translate_text(text_it, target_lang=TARGET_LANG).text
    print(f"{text_en}")
    return text_it, text_en

url_demo = gr.Interface(
    fn=transcribe_url, 
    inputs="text", 
    outputs=[gr.Textbox(label="Transcribed text"),
             gr.Textbox(label="English translation")],
    title="Italian video to english text",
    description="Transcribing italian video to text and translating it to english!",
)

voice_demo = gr.Interface(
    fn=transcribe, 
    inputs=gr.Audio(sources=["microphone"], type="filepath"), 
    outputs=[gr.Textbox(label="Transcribed text"),
             gr.Textbox(label="English translation")],
    title="Italian recorded speech to english text",
    description="Transcribing italian speech to text and translating it to english!",
)

app = gr.TabbedInterface([url_demo, voice_demo], ["Video to English Text", "Audio to English Text"])

app.launch()