whisper_skill

Overview

This skill transcribes audio files to text using OpenAI Whisper models. It supports common audio formats, automatic language detection for 90+ languages, optional timestamps, multiple model sizes, and text or JSON output. It is optimized for practical transcription workflows with speed/accuracy trade-offs across model sizes.

How this skill works

You provide an audio file and target output path; the skill runs a selected Whisper model to produce a transcript. Options let you choose model size (tiny to large), force or auto-detect language, include word- or segment-level timestamps, and export plain text or structured JSON with metadata. FFmpeg is used to preprocess and normalize audio so many file types are accepted.

When to use it

• Transcribing interviews, meetings, podcasts, or lectures into text.
• Generating captions or subtitles with optional timestamps for video content.
• Quickly transcribing short voice notes or long multi-hour recordings depending on model choice.
• Processing multilingual audio where automatic language detection is helpful.
• Converting audio archives into searchable text for indexing or compliance.

Best practices

• Choose model size based on trade-off: tiny/base for speed and low memory, medium/large for best accuracy on noisy or complex audio.
• Preprocess noisy recordings with noise reduction and ensure consistent sampling rate via FFmpeg for better accuracy.
• Specify language when known to improve transcription quality and reduce mis-detections.
• Use timestamps when you need alignment for subtitles or highlight extraction; omit them for faster, smaller outputs.
• Export JSON when downstream tooling needs structured segments, speaker labels, or timing metadata.

Example use cases

• Produce meeting minutes from a recorded team call with medium model and timestamps for quick reference.
• Create subtitles for a multilingual webinar using automatic language detection and JSON export for editing.
• Batch-transcribe a podcast series using the small model for cost/speed balance and plain text output for publishing.
• Index archived customer support calls by transcribing audio into searchable text for QA and training.
• Transcribe voice memos on mobile devices with tiny or base model for near-real-time feedback.