Transcribe and translate audio across 99 languages with openai-whisper.
npx skills add https://github.com/gqf2008/hermez-ai --skill whisper-gqf2008
Please help me install this Agent Skill. Skill: whisper Source: https://github.com/gqf2008/hermez-ai/tree/main/skills/mlops/models/whisper Command: npx skills add https://github.com/gqf2008/hermez-ai --skill whisper-gqf2008
Whisper automates the conversion of spoken language into written text across 99 languages, enabling transcription, translation to English, and language identification in a single model.
Install the Whisper package and run a sample transcription on your audio file.
High-intent search queries and answers about installing and using this skill.
Multilingual transcription is handled by a single model covering 99 languages, converting spoken audio into written text. It also performs language identification to detect the input audio's language automatically.
Yes, translation to English is supported directly. The model transcribes spoken audio from any of the 99 supported languages and outputs the resulting text translated into English for broader accessibility.
Yes, this speech-to-text processing requires openai-whisper, transformers, and torch. These dependencies provide the underlying deep learning framework and model architecture needed to run the audio transcription workflows.
Balance speed and accuracy by choosing from six model sizes, tiny to large. Smaller models process faster for quick podcast transcription, while larger models deliver higher accuracy for complex audio workflows.
Yes, meeting notes automation is a supported use case. The model transcribes spoken audio into written text, making it applicable for automatically generating text records from recorded meetings and multilingual audio processing.
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