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transcribe-audio

Skill2 starsChanged 45 days agoMITView on GitHub

Transcribe audio files to text using a local Whisper ASR service. Use when: the user wants to transcribe an audio file to text, convert speech to text from an audio recording, extract text from voice recordings, or needs transcription of podcasts/interviews/voice notes. Only works for English language audio. The skill saves the transcription to a text file in the agent's local folder.

  • Sends data out

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SKILL.md

Transcribe Audio

Transcribe audio files to text using the local Whisper ASR service running at http://localhost:9000.

Prerequisites

  • The Whisper service must be running (check with curl http://localhost:9000/health)
  • Audio file must be in a supported format: OGG, MP3, M4A, WAV, or WebM
  • Audio must be in English (the local model is English-only)

Usage

Basic Transcription

To transcribe an audio file:

curl -X POST http://localhost:9000/asr \
  -F "audio_file=@/path/to/audio.wav" \
  -F "language=en"

Save to File

Save the transcription to the agent's local folder:

curl -X POST http://localhost:9000/asr \
  -F "audio_file=@/path/to/audio.wav" \
  -F "language=en" \
  -o transcription.txt

Or with a specific output path:

OUTPUT_FILE="${PWD}/$(basename /path/to/audio.wav .wav).txt"
curl -X POST http://localhost:9000/asr \
  -F "audio_file=@/path/to/audio.wav" \
  -F "language=en" \
  -o "$OUTPUT_FILE"

Quick Workflow

  1. Verify the service is running:
    curl -s http://localhost:9000/health
    Expected response: {"status": "ok"}
  2. Transcribe and save:
    curl -X POST http://localhost:9000/asr \
      -F "audio_file=@<audio-file-path>" \
      -F "language=en" \
      -o <output-file-path>
  3. Verify the output:
    cat <output-file-path>

Supported Audio Formats

FormatExtensionMIME Type
OGG Vorbis.oga, .oggaudio/ogg
MP3.mp3audio/mpeg
M4A.m4aaudio/mp4
WAV.wavaudio/wav
WebM.webmaudio/webm

Performance Notes

  • The local Whisper service uses the tiny.en model
  • Processing time is roughly 10-20% of audio duration (e.g., 1 minute of audio takes ~6-12 seconds)
  • The service only supports English transcription
  • For best results, ensure audio has clear speech with minimal background noise

Error Handling

Common issues and solutions:

ErrorCauseSolution
Connection refusedWhisper service not runningStart the local Whisper service using your own deployment method (for example Docker Compose in your chosen whisper-service directory)
Empty responseAudio file is silent or corruptedCheck the audio file can be played
Garbage textAudio is not in EnglishOnly English is supported
Slow transcriptionHigh CPU load or concurrent requestsWait for other transcriptions to complete

Example: Transcribe a Single File

# Set variables
AUDIO_FILE="/path/to/recording.wav"
OUTPUT_FILE="${PWD}/transcription.txt"

# Verify service
if ! curl -s http://localhost:9000/health | grep -q '"status": "ok"'; then
    echo "Error: Whisper service is not running"
    exit 1
fi

# Transcribe
curl -X POST http://localhost:9000/asr \
  -F "audio_file=@${AUDIO_FILE}" \
  -F "language=en" \
  -o "$OUTPUT_FILE"

echo "Transcription saved to: $OUTPUT_FILE"
cat "$OUTPUT_FILE"

Example: Batch Transcribe Multiple Files

for audio_file in *.wav; do
    output_file="${PWD}/$(basename "$audio_file" .wav).txt"
    echo "Transcribing: $audio_file -> $output_file"
    curl -X POST http://localhost:9000/asr \
      -F "audio_file=@${audio_file}" \
      -F "language=en" \
      -o "$output_file"
done

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