Popular GitHub tools for transcribing YouTube videos

Several powerful GitHub tools are available for transcribing YouTube videos, offering various features and capabilities.

Popular Transcription Tools

OpenAI Whisper A highly accurate AI-powered transcription tool that offers exceptional features including:

• Multi-language support with translation capabilities

• Capitalization and punctuation accuracy

• Speaker identification (diarization) in newer versions

• Ability to generate .vtt files for YouTube captions

Video2Text A streamlined tool that combines:

• Pytube for video downloading

• Whisper integration for accurate transcription

• Simple implementation process

YTWS (YouTube Faster-Whisper) A command-line interface tool featuring:

• One-command download and transcription

• Integration with yt-dlp for downloading

• GPU acceleration support

• Faster-whisper implementation for improved speed

Advanced Solutions

Bulk Transcribe Tool Designed for processing multiple videos with:

• Support for entire YouTube playlists

• CUDA acceleration for GPU processing

• Integration with faster-whisper

• Both local inference and OpenAI API options

TranscribeTube A Streamlit-based application offering:

• AI-powered detailed note generation

• Language selection options

• Adjustable summary length

• Download functionality for generated notes

Technical Implementation

To get started with these tools, most require basic dependencies:

pip install youtube-transcript-api
pip install pytube

The transcription process typically involves:

1. Audio extraction from YouTube videos

2. Processing through AI models

3. Generation of formatted transcripts

4. Optional translation and formatting features

---

What are the main steps to set up Whisper for YouTube transcription

The main steps to set up Whisper for YouTube transcription involve a straightforward process:

Installation Setup

pip install git+https://github.com/openai/whisper.git
pip install pytube
pip install pandas

Basic Implementation Steps

1. Audio Extraction

• Download the YouTube video's audio using Pytube

• Convert to a compatible format (MP3 or WAV)

2. Model Configuration

• Load the Whisper model

• Select appropriate model size (tiny, base, or larger versions)

3. Transcription Process

from pytube import YouTube
import whisper

# Download audio
video_url = "YOUR_YOUTUBE_URL"
audio_file = YouTube(video_url).streams.filter(only_audio=True).first().download()

# Load model and transcribe
model = whisper.load_model("base")
text = model.transcribe(audio_file)

Advanced Configuration

Model Options

• Tiny: Fastest but less accurate

• Base: Balanced performance

• Larger models: Higher accuracy but slower processing

Additional Settings

• Language selection for source audio

• Capitalization and punctuation options

• Speaker diarization when needed

The process typically takes a few minutes depending on video length and model size selected. The resulting transcription includes timestamps and can be exported in various formats including text and JSON.

---

How accurate is Whisper's transcription compared to human transcriptionists

Recent studies have revealed varying levels of accuracy for Whisper's transcription capabilities, with performance differing significantly based on model size and conditions:

Model Performance

Large Model Advantages

• Whisper's large model outperforms human transcribers in most conditions, except when dealing with pub noise where it performs on par with humans

• The large model achieves 99.8% accuracy in optimal conditions

Base Model Limitations

• The base version of Whisper performs worse than human transcriptionists

• Typical AI transcription tools achieve around 69% accuracy overall

Situational Factors

Environmental Challenges

• Performance decreases significantly with background noise and poor audio quality

• Accuracy drops notably when dealing with:

  • Pub noise and background chatter

  • Low signal-to-noise ratios

  • Face mask speech

Known Issues

• Problems found in 80% of public meeting transcriptions

• Tendency to generate hallucinations or fabricated content, especially during silence periods

• Particular challenges with medical transcriptions and patients with speech disorders

Language Considerations

The model shows varying performance across languages, with highest accuracy in:

• English

• Italian

• German

• Spanish

For comparison, professional human transcriptionists consistently achieve 95-99% accuracy rates, particularly excelling in complex scenarios requiring context understanding and technical terminology.

---

Github project support OpenAI whisper API to transcript youtube video, list in table format by title, description, github stars, url

Here are the notable GitHub projects that support YouTube video transcription using OpenAI's Whisper API:

Note: GitHub star counts are not available in the search results, so they have been omitted from the table. The projects are listed based on their feature completeness and documentation quality.

---

Github OSX app project that support YouTube video transcription using OpenAI's Whisper API

Here are the notable macOS applications available on GitHub for YouTube video transcription using Whisper:

These applications offer several key features:

• Local processing without additional dependencies

• User-friendly interfaces for managing transcriptions

• Support for multiple audio formats

• Built-in editing capabilities for transcribed text

• Options for exporting in various formats including SRT and TXT

The MacWhisper app stands out as particularly popular, offering a free standard version with a clean interface for transcription management.

---

Can I schedule automatic transcription of YouTube videos using Whisper on my Mac

Based on the current search results as of January 2025, there are several options for automating YouTube video transcription on Mac, though with some limitations:

Available Solutions

MacWhisper Pro

• Supports direct YouTube video transcription

• Offers batch transcription capabilities for multiple files

• Includes integration with OpenAI, Anthropic, and Groq APIs

QuickWhisper

• Provides one-click transcription for YouTube videos

• Supports batch processing capabilities

• Offers local processing for privacy

Current Limitations

The fully automated scheduling of YouTube transcriptions has some constraints:

• No built-in watch folder functionality for automatic processing

• Manual intervention is still needed to initiate the transcription process

• Limited automation options in the current Mac applications

Workaround Options

To achieve semi-automated transcription:

• Use folder monitoring tools like Hazel

• Create custom AppleScripts for automation

• Implement command-line solutions using pytube and whisper

For those needing full automation, developers have noted that watch folder functionality is a requested feature that may be implemented in future updates.