app.py

import yt_dlp
import whisper
from pydub import AudioSegment
import os
import time

# Function to download audio from YouTube video
def download_audio_from_youtube(url):
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': 'video.%(ext)s',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([url])
    return 'video.mp3'

# Function to split audio into chunks
def split_audio(file_path, chunk_length_ms=60000):
    audio = AudioSegment.from_file(file_path)
    chunks = [audio[i:i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
    chunk_files = []
    for i, chunk in enumerate(chunks):
        chunk_file = f"chunk_{i}.mp3"
        chunk.export(chunk_file, format="mp3")
        chunk_files.append(chunk_file)
    return chunk_files

# Function to convert audio to text using Whisper model
def transcribe_audio(file_path):
    model = whisper.load_model("small")

    result = model.transcribe(file_path)
    return result["text"]

# Example usage
# video_url = "https://youtu.be/x6KuoHihktM?si=VoqHMP8emTnXKJHa"
video_url=input()

# Step 1: Download audio from YouTube
print(f"Downloading audio from URL: {video_url}")
audio_path = download_audio_from_youtube(video_url)

# Step 2: Split the audio into chunks
print(f"Splitting audio into chunks from path: {audio_path}")
chunk_files = split_audio(audio_path)

# Step 3: Transcribe each chunk and combine results
print("Transcribing audio chunks...")
transcriptions = []
for chunk_file in chunk_files:
    print(f"Transcribing chunk: {chunk_file}")
    transcriptions.append(transcribe_audio(chunk_file))
    os.remove(chunk_file)  # Clean up chunk file after transcription
    time.sleep(1)  # Introduce a delay to avoid rate limiting

transcribed_text = " ".join(transcriptions)
print(f"Transcribed text: {transcribed_text}")