Openai Whisper

OpenAI Whisper — Speech-to-Text

Transcribe audio files using OpenAI's Whisper model. Two modes available depending on your needs:

| Mode | Latency | Cost | Privacy | Setup | |------|---------|------|---------|-------| | Local CLI | Slower (on-device GPU/CPU) | Free | Audio never leaves machine | Install whisper binary | | Cloud API | Fast | Per-minute pricing | Audio sent to OpenAI | OPENAI_API_KEY required |

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Mode 1: Local CLI

Run Whisper locally with no API key required. Models download to ~/.cache/whisper on first run.

Quick Start

whisper /path/audio.mp3 --model medium --output_format txt --output_dir .

Common Commands

# Transcribe to text file
whisper /path/audio.mp3 --model medium --output_format txt --output_dir .

# Transcribe with translation to English
whisper /path/audio.m4a --task translate --output_format srt

# Transcribe with specific language
whisper /path/audio.wav --model large --language en --output_format json

Model Selection

| Model | Speed | Accuracy | VRAM | |-------|-------|----------|------| | tiny | Fastest | Lowest | ~1 GB | | base | Fast | Low | ~1 GB | | small | Medium | Good | ~2 GB | | medium | Slow | Better | ~5 GB | | large | Slowest | Best | ~10 GB | | turbo | Fast | Good (default) | ~6 GB |

Output Formats

• txt — Plain text transcript
• srt — SubRip subtitle format with timestamps
• vtt — WebVTT subtitle format
• json — Detailed JSON with word-level timestamps
• tsv — Tab-separated values

Notes

• --model defaults to turbo on most installs
• Use smaller models for speed, larger for accuracy
• GPU acceleration used automatically when available

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Mode 2: Cloud API

Transcribe via OpenAI's /v1/audio/transcriptions endpoint. Faster for large batches, no local GPU needed.

Quick Start

{baseDir}/scripts/transcribe.sh /path/to/audio.m4a

Defaults:

• Model: whisper-1
• Output: .txt

Common Commands

# Basic transcription
{baseDir}/scripts/transcribe.sh /path/to/audio.m4a

# Specify model and output
{baseDir}/scripts/transcribe.sh /path/to/audio.ogg --model whisper-1 --out /tmp/transcript.txt

# With language hint
{baseDir}/scripts/transcribe.sh /path/to/audio.m4a --language en

# With speaker name hints (improves accuracy)
{baseDir}/scripts/transcribe.sh /path/to/audio.m4a --prompt "Speaker names: Peter, Daniel"

# JSON output with timestamps
{baseDir}/scripts/transcribe.sh /path/to/audio.m4a --json --out /tmp/transcript.json

Raw curl Example

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/audio.m4a" \
  -F model="whisper-1" \
  -F response_format="text"

API Key Setup

Set OPENAI_API_KEY environment variable, or configure in ~/.clawdbot/clawdbot.json:

{
  skills: {
    "openai-whisper-api": {
      apiKey: "OPENAI_KEY_HERE"
    }
  }
}

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Choosing Between Modes

| Consideration | Local CLI | Cloud API | |---------------|-----------|-----------| | Privacy-sensitive audio | Best | Audio sent to OpenAI | | Large batch processing | Slow without GPU | Fast and parallel | | Offline usage | Works offline | Requires internet | | Cost | Free (hardware cost) | Per-minute pricing | | Setup complexity | Install binary + models | API key only | | Audio format support | Most formats | Most formats |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: coco-research
• Source: coco-research/coco
• License: MIT

Install and usage instructions live in the source repository linked above.