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Easily take an entire YouTube playlist and turn it into high quality transcripts using Whisper.
| Date | Stars |
|---|---|
| 2026-07-24 | 687 |
| 2026-07-25 | 687 |
| 2026-07-28 | 688 |
| 2026-07-30 | 686 |
| 2026-08-06 | 686 |
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# Bulk Transcribe Youtube Videos from Playlists ## Introduction This Python-based tool is designed for transcribing YouTube videos and playlists into text. It integrates various technologies like faster-whisper for transcription, SpaCy for natural language processing, and CUDA for GPU acceleration, aimed at processing video content efficiently. The script is capable of handling both individual videos and entire playlists, outputting accurate transcripts along with metadata. |  | |:--:| | *Bulk Transcripts Have Never Been This Easy!* | ## Functional Overview ### Core Components - **YouTube Downloading**: Uses `pytube` to download the audio from YouTube videos or playlists. - **Audio Transcription**: - Local Inference: Leverages `faster_whisper.WhisperModel` for converting audio to text. This model is a variant of OpenAI's Whisper designed for speed and accuracy. - OpenAI API: Optionally uses the OpenAI API for transcription, though this uses an older and less accurate version of the Whisper model. - **NLP Processing**: Optionally, integrates SpaCy for sophisticated sentence splitting, enhancing the readability and structure of the transcript. - **CUDA Acceleration**: Implements CUDA support for GPU utilization, enhancing processing speed for compatible hardware. - **Progress Tracking**: Utilizes `tqdm` for displaying progress bars during transcription. ### Detailed Workflow 1. **Initialization**: - The script starts by determining whether to process a single video or a playlist based on the `convert_single_video` flag. - It sets up necessary directories for storing downloaded audio, transcripts, and metadata. 2. **Environment Configuration**: - Adds CUDA Toolkit path to the system environment for GPU utilization. - Configures the number of workers for transcription based on the CPU core count. 3. **Video Processing**: - For each video in the playlist or the single video, the script downloads the audio. - It ensures unique naming for each audio file to avoid overwrites. 4. **Transcription**: - The audio files are passed to either the local WhisperModel or the OpenAI API for transcription. - The script handles GPU acceleration if available, defaulting to CPU otherwise for local inference. - Transcription results are split into sentences, either using SpaCy or a custom regex-based splitter. 5. **Metadata Generation**: - Along with the transcript, the script generates metadata including timestamps and confidence scores for each segment. 6. **Output**: - The transcripts are saved in plain text, CSV, and JSON formats, providing both the raw transcript and structured metadata. 7. **Display/Read**: - To make the transcripts easier to read, an html file is provided, `transcript_reader.html`, which does further clean up and offers a "Reader Mode" where you can choose the font, text size, text width, and toggle dark mode. Simply open this html file in your browser and paste in the transcript text from one of the generated files in the `generated_transcript_combined_texts` folder. |  | |:--:| | *Screenshot of it in Action* | |  |  | |:--:|:--:| | *Paste Transcript Text into the Transcript Reader HTML File* | *Reader using Dark Mode and Cambria Font* | ### Use Cases - **Content Analysis**: Useful for researchers an
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Jeff Emanuel
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a78b86a8bbe8ed37, topic:whisper, readme:transcription