Transcribes an audio source to text using the OpenAI's Whisper API. The result is saved in the timestamps/ folder as a
Markdown file. The input can be URL to a video source, it will use yt-dlp to download the video and proceed with transcribing.
audios/folder contains extracted audio files from YouTube videos when the input is a YouTube URL. Large audio files are split into smaller chunks are also saved here.jsons/folder contains responses from Whisper API transcribing/translating the audio files. The data follows the schema intranscription.schema.json. Some non-speech audio segments may be falsely transcribed as speech by the Whisper model. These segments are filtered out and saved intojson/*-no_speech.jsonandjson/*-speech.jsonfiles.timestamps/folder contains the resulting Markdown files, that includes transcription and their timestamps. The timestamps link to the YouTube video at the matching times when the input when the input is a YouTube URL or the input file has the source YouTube URL embedded to the metadata underformat:tags:comment. The*-no_speech.jsonand*-speech.jsonfiles are also timestamped and saved here astimestamps/*-no_speech.mdandtimestamps/*-speech.md.
python main.py [-h] [-p PROMPT] [-l LANGUAGE] [-t] input
positional arguments:
input Path or URL to the audio source to transcribe, in one of these formats: mp3, mp4, mpeg, mpga,
m4a, wav, or webm.
options:
-h, --help show this help message and exit
-p PROMPT, --prompt PROMPT
A list of correct word spellings for problematic words.
-l LANGUAGE, --language LANGUAGE
The language of the input audio. Supplying the input language in ISO-639-1 format will improve
accuracy and latency.
-t, --translate Translate the audio file to English.
See OpenAI's documantation.