Description
#Video to Text #AI Summary #Subtitle Extraction #FasterWhisper #FastAPI #yt-dlp #FFmpeg #YouTube #TikTok #Bilibili #ApplePodcasts #SoundCloud #Docker #Open Source Tool
AI-Video-Transcriber is an open-source tool that can automatically organize videos, podcasts, and local audio and video files into transcripts and summaries. It currently has about 3K Stars on GitHub. It is especially suitable for handling content like two-hour podcasts, long videos, and course recordings: just drop a link in, and the tool will automatically complete subtitle retrieval, speech transcription, text optimization, and summary generation, eliminating the need to repeatedly listen at increased speed and manually take notes.
Its processing logic is also quite smart. When encountering videos that already have subtitles provided by the platform, it will prioritize extracting the native subtitles; if there are no subtitles, it will use Faster-Whisper for audio speech recognition. After obtaining the raw transcription text, it will further clean and optimize it using a large model to address common typos, sentence breaks, and expression issues found in machine transcriptions.
In addition, it can generate multilingual summaries based on the content. If the original content language differs from the target summary language, it can also conveniently complete the translation, effectively handling "listening to content, organizing text, extracting key points, and cross-language reading" all at once.
Direct Link to Text: Simply submit a video or podcast link, and it will automatically fetch the content and generate an organized transcript, reducing manual download and processing steps.
Prioritize Using Platform Native Subtitles: If the video already provides subtitles, it will prioritize direct extraction, which is usually faster than redoing speech recognition and can reduce additional computational consumption.
No Subtitles Automatic Speech Recognition: When there are no available subtitles on the platform, it will call Faster-Whisper to transcribe the audio, converting video or podcast content into text.
AI Optimized Transcription Text: It goes beyond just "speech to text" and can further use a large model to organize the transcription results, optimizing typos, sentence breaks, and expressions to make the final transcript more suitable for direct reading.
Automatically Generate Multilingual Summaries: It can automatically extract key points from long videos, podcasts, etc., outputting a more concise summary for quick understanding of core information.
Supports Automatic Translation: When the original transcript and target summary use different languages, it can simultaneously complete the translation without needing to copy to other translation tools.
Supports Multiple Content Platforms: It can handle sources like YouTube, TikTok, Bilibili, Apple Podcasts, SoundCloud, and also cover other websites supported by yt-dlp.
Supports Local Files: In addition to online links, you can also directly upload local audio and video files for processing, with a default single file size limit of 200 MB.
Complete Local Processing Tech Stack: The backend is based on FastAPI, combined with yt-dlp, FFmpeg, and Faster-Whisper to complete media downloading, audio processing, and speech recognition.
Supports Docker Deployment: It can be directly installed to run locally or deployed via Docker, making it suitable for personal long-term use or setting up your own audio and video summarization service.
For those who often need to digest courses, interviews, podcasts, and long videos, it is more like an automated content digestion pipeline: converting several hours of audio-visual content directly into searchable, readable, storable, and reviewable text materials.
AI-Video-Transcriber is an open-source tool that can automatically organize videos, podcasts, and local audio and video files into transcripts and summaries. It currently has about 3K Stars on GitHub. It is especially suitable for handling content like two-hour podcasts, long videos, and course recordings: just drop a link in, and the tool will automatically complete subtitle retrieval, speech transcription, text optimization, and summary generation, eliminating the need to repeatedly listen at increased speed and manually take notes.
Its processing logic is also quite smart. When encountering videos that already have subtitles provided by the platform, it will prioritize extracting the native subtitles; if there are no subtitles, it will use Faster-Whisper for audio speech recognition. After obtaining the raw transcription text, it will further clean and optimize it using a large model to address common typos, sentence breaks, and expression issues found in machine transcriptions.
In addition, it can generate multilingual summaries based on the content. If the original content language differs from the target summary language, it can also conveniently complete the translation, effectively handling "listening to content, organizing text, extracting key points, and cross-language reading" all at once.
Software Features
Direct Link to Text: Simply submit a video or podcast link, and it will automatically fetch the content and generate an organized transcript, reducing manual download and processing steps.
Prioritize Using Platform Native Subtitles: If the video already provides subtitles, it will prioritize direct extraction, which is usually faster than redoing speech recognition and can reduce additional computational consumption.
No Subtitles Automatic Speech Recognition: When there are no available subtitles on the platform, it will call Faster-Whisper to transcribe the audio, converting video or podcast content into text.
AI Optimized Transcription Text: It goes beyond just "speech to text" and can further use a large model to organize the transcription results, optimizing typos, sentence breaks, and expressions to make the final transcript more suitable for direct reading.
Automatically Generate Multilingual Summaries: It can automatically extract key points from long videos, podcasts, etc., outputting a more concise summary for quick understanding of core information.
Supports Automatic Translation: When the original transcript and target summary use different languages, it can simultaneously complete the translation without needing to copy to other translation tools.
Supports Multiple Content Platforms: It can handle sources like YouTube, TikTok, Bilibili, Apple Podcasts, SoundCloud, and also cover other websites supported by yt-dlp.
Supports Local Files: In addition to online links, you can also directly upload local audio and video files for processing, with a default single file size limit of 200 MB.
Complete Local Processing Tech Stack: The backend is based on FastAPI, combined with yt-dlp, FFmpeg, and Faster-Whisper to complete media downloading, audio processing, and speech recognition.
Supports Docker Deployment: It can be directly installed to run locally or deployed via Docker, making it suitable for personal long-term use or setting up your own audio and video summarization service.
For those who often need to digest courses, interviews, podcasts, and long videos, it is more like an automated content digestion pipeline: converting several hours of audio-visual content directly into searchable, readable, storable, and reviewable text materials.
