Transcribe and translate audio across 99 languages using Whisper.
npx skills add https://github.com/handsomelong922/my-codex-skills --skill whisper-handsomelong922
Please help me install this Agent Skill. Skill: whisper Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/whisper Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill whisper-handsomelong922
Whisper enables automatic transcription and translation of long-form audio across 99 languages, turning speech into searchable, editable text for diverse workflows.
Install the Whisper package and run a transcription on an audio file to generate text.
High-intent search queries and answers about installing and using this skill.
Whisper transcribes podcast audio to text across 99 languages using scalable model sizes from tiny to large. It requires installing openai-whisper, transformers, and torch, with optional GPU acceleration to speed up processing long-form audio recordings.
Yes, Whisper translates spoken audio to English text from 99 languages. Beyond multilingual transcription, it performs on-demand translation to English and language identification to support cross-language content pipelines for meetings, podcasts, and video captioning.
GPU acceleration is optional for audio processing and speech-to-text transcription with Whisper, but significantly improves speed. CPU processing works, but running large model sizes for higher accuracy on long-form audio benefits greatly from GPU acceleration.
The best way to generate video captioning from audio files is using Whisper for multilingual speech-to-text transcription. It supports 99 languages and scalable model sizes, enabling automated generation of searchable, editable text from spoken content for video captioning workflows.
Yes, Whisper supports automated meeting notes transcription across 99 languages. It applies multilingual ASR to turn spoken meeting audio into searchable, editable text, with model sizes ranging from tiny to large to balance speed and accuracy for diverse workflows.
Use smaller Whisper model sizes when transcription speed is critical or resources are limited, and large models when maximum accuracy is needed. Scalable model sizes from tiny to large let you balance processing speed and transcription accuracy across 99 languages.
photonics-dhlTranscribe speech to text across 99 languages and translate to English.
hmzainjamilConvert audio and video recordings into transcripts, subtitles, and meeting notes.
pmcdowallTranscribe spoken audio into timestamped text across 99 languages.
steipeteTranscribe local audio files to text or subtitles using the Whisper CLI.
