Initial commit
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Zhongwei Li
262
extract_transcript_whisper.py
Executable file
262
extract_transcript_whisper.py
Executable file
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#!/usr/bin/env python3
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"""
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Fallback transcription using Whisper when no subtitles available
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Usage: extract_transcript_whisper.py [--mq|--hq] <YOUTUBE_URL> <OUTPUT_DIR>
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Options:
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--mq Use medium model (~5GB download)
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--hq Use large model for highest quality (slower, ~10GB download)
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default: small model (~2GB download)
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On macOS: Uses MLX Whisper if available (faster, Apple Silicon optimized). That uses only large model.
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Otherwise: Uses OpenAI Whisper.
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Output: SUCCESS: youtube_{VIDEO_ID}_transcript.vtt, Audio file: youtube_{VIDEO_ID}_audio.mp3 (ask user about deletion)
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"""
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import sys
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import os
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import subprocess
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import re
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def extract_video_id(url):
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"""Extract video ID from YouTube URL"""
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# Handle youtu.be format
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if 'youtu.be/' in url:
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video_id = url.split('youtu.be/')[-1].split('?')[0]
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return video_id
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# Handle youtube.com format with v= parameter
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match = re.search(r'[?&]v=([^&]+)', url)
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if match:
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return match.group(1)
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return None
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def check_yt_dlp():
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"""Check if yt-dlp is installed"""
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try:
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subprocess.run(['yt-dlp', '--version'], capture_output=True, check=True)
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except (subprocess.CalledProcessError, FileNotFoundError):
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print("ERROR: yt-dlp is not installed", file=sys.stderr)
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sys.exit(1)
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def check_uv():
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"""Check if uv is available"""
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try:
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subprocess.run(['uv', '-V'], capture_output=True, check=True)
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return True
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except (subprocess.CalledProcessError, FileNotFoundError):
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return False
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def check_mlx_whisper():
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"""Check if MLX Whisper is available, returns (variant, command_array)"""
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# Try uv run mlx_whisper first
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if check_uv():
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try:
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subprocess.run(['uv', 'run', 'mlx_whisper', '--help'], capture_output=True, check=True, timeout=10)
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return ('uv-mlx', ['uv', 'run', 'mlx_whisper'])
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except (subprocess.CalledProcessError, FileNotFoundError, subprocess.TimeoutExpired):
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pass
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# Try mlx-whisper command
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try:
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subprocess.run(['mlx-whisper', '--help'], capture_output=True, check=True)
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return ('mlx-whisper', ['mlx-whisper'])
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except (subprocess.CalledProcessError, FileNotFoundError):
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pass
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return (None, None)
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def check_whisper():
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"""Check if OpenAI Whisper is installed"""
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try:
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subprocess.run(['whisper', '--help'], capture_output=True, check=True)
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except (subprocess.CalledProcessError, FileNotFoundError):
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print("ERROR: Whisper is not installed", file=sys.stderr)
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print("Install options:", file=sys.stderr)
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print(" - macOS (slow): brew install openai-whisper", file=sys.stderr)
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print(" - macOS (faster): brew install ffmpeg uv; uv venv .venv; source .venv/bin/activate; uv pip install mlx-whisper", file=sys.stderr)
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print(" (Ask your AI what the above does. At the moment pipx, python 3.14, and mlx-whisper conflict, but uv works - 11/2025)", file=sys.stderr)
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print(" - All systems: pip3 install openai-whisper", file=sys.stderr)
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sys.exit(1)
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def check_audio_size(youtube_url):
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"""Get audio file size estimate"""
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print("Checking audio file size...")
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result = subprocess.run(
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['yt-dlp', '--print', '%(filesize,filesize_approx)s %(duration)s', youtube_url],
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capture_output=True,
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text=True
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)
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if result.returncode == 0:
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print(result.stdout.strip())
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def download_audio(youtube_url, audio_file):
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"""Download audio from YouTube video"""
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print("Downloading audio...")
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result = subprocess.run(
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['yt-dlp', '-x', '--audio-format', 'mp3', '--output', audio_file, youtube_url],
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capture_output=True,
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text=True
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)
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if result.returncode != 0:
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print("ERROR: Failed to download audio", file=sys.stderr)
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sys.exit(1)
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if not os.path.exists(audio_file):
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print("ERROR: Audio file not found", file=sys.stderr)
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sys.exit(1)
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def get_video_language(youtube_url):
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"""Get video language from YouTube"""
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result = subprocess.run(
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['yt-dlp', '--print', '%(language)s', youtube_url],
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capture_output=True,
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text=True
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)
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video_lang = result.stdout.strip() if result.returncode == 0 else "unknown"
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return video_lang
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def transcribe_with_whisper(audio_file, output_dir, mlx_command=None, quality='default'):
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"""Transcribe audio file with Whisper (MLX or OpenAI)"""
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if mlx_command:
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# MLX Whisper - default large-v3 as faster processing and better quality, supports other models
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print(f"Transcribing with MLX Whisper (Apple Silicon optimized, large-v3 model) - this may take a while...")
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command = mlx_command
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model = 'mlx-community/whisper-large-v3-mlx'
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# MLX uses hyphens in arguments
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output_format_arg = '--output-format'
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output_dir_arg = '--output-dir'
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else:
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# OpenAI Whisper - configurable quality
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# All models are multilingual and support ~99 languages
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# Model sizes: tiny (~1GB, fast), base (~1GB, balanced), small (~2GB), medium (~5GB), large (~10GB, best)
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command = ['whisper']
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if quality == 'hq':
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model = 'large'
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size_info = '~10GB'
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elif quality == 'mq':
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model = 'medium'
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size_info = '~5GB'
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else:
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model = 'small'
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size_info = '~2GB'
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print(f"Transcribing with OpenAI Whisper model '{model}' ({size_info}) - this may take a while...")
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# OpenAI uses underscores in arguments
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output_format_arg = '--output_format'
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output_dir_arg = '--output_dir'
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result = subprocess.run(
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command + [audio_file, '--model', model, output_format_arg, 'vtt', output_dir_arg, output_dir],
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capture_output=True,
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text=True
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)
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if result.returncode != 0:
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print(f"ERROR: {' '.join(command)} transcription failed", file=sys.stderr)
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if os.path.exists(audio_file):
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os.remove(audio_file)
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sys.exit(1)
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if not mlx_command and quality == 'default':
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print("NOTE: If transcription quality is poor, try --mq (medium, ~5GB) or --hq (large, ~10GB)")
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def rename_vtt_file(audio_file, base_name, output_dir):
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"""Rename VTT file to standard name"""
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# Whisper creates VTT file with same name as audio file
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audio_basename = os.path.splitext(os.path.basename(audio_file))[0]
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vtt_file = os.path.join(output_dir, f"{audio_basename}.vtt")
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final_vtt = os.path.join(output_dir, f"{base_name}_transcript.vtt")
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if os.path.exists(vtt_file):
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try:
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os.rename(vtt_file, final_vtt)
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except Exception as e:
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print(f"ERROR: Failed to rename VTT file: {e}", file=sys.stderr)
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if os.path.exists(audio_file):
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os.remove(audio_file)
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sys.exit(1)
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print(f"SUCCESS: {final_vtt}")
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print(f"Audio file: {audio_file} (delete with: rm {audio_file})")
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return final_vtt
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else:
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print("ERROR: VTT file not created", file=sys.stderr)
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if os.path.exists(audio_file):
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os.remove(audio_file)
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sys.exit(1)
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def main():
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# Parse options
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quality = 'default'
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args = []
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for arg in sys.argv[1:]:
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if arg == '--hq':
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quality = 'hq'
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elif arg == '--mq':
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quality = 'mq'
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else:
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args.append(arg)
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# Parse arguments
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if len(args) != 2:
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print("Usage: extract_transcript_whisper.py [--mq|--hq] <YOUTUBE_URL> <OUTPUT_DIR>", file=sys.stderr)
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sys.exit(1)
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youtube_url = args[0]
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output_dir = args[1]
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# Validate arguments
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if not youtube_url:
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print("ERROR: No YouTube URL provided", file=sys.stderr)
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sys.exit(1)
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# Check required commands
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check_yt_dlp()
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# Check which Whisper variant is available
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mlx_variant, mlx_command = check_mlx_whisper()
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if mlx_variant:
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print(f"Using MLX Whisper via {mlx_variant} (Apple Silicon optimized)")
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if quality != 'default':
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print("NOTE: Quality flags (--mq, --hq) are ignored with MLX Whisper (always uses large-v3)")
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else:
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check_whisper()
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print("Using OpenAI Whisper")
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mlx_command = None
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# Extract video ID from URL
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video_id = extract_video_id(youtube_url)
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if not video_id:
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print("ERROR: Could not extract video ID from URL", file=sys.stderr)
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sys.exit(1)
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base_name = f"youtube_{video_id}"
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# Create output directory if it doesn't exist
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os.makedirs(output_dir, exist_ok=True)
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# Check audio size
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check_audio_size(youtube_url)
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# Get video language
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video_lang = get_video_language(youtube_url)
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print(f"Video language: {video_lang}")
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# Download audio
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audio_file = os.path.join(output_dir, f"{base_name}_audio.mp3")
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download_audio(youtube_url, audio_file)
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# Transcribe with Whisper (MLX or OpenAI variant)
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transcribe_with_whisper(audio_file, output_dir, mlx_command=mlx_command, quality=quality)
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# Rename VTT file
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rename_vtt_file(audio_file, base_name, output_dir)
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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print(f"ERROR: {str(e)}", file=sys.stderr)
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sys.exit(1)
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