rebyteai/rebyte-skills · Archived

speech-to-text

Transcribe audio to text using OpenAI Whisper.

First seen Apr 16, 2026

Installation

$ npx skills add rebyteai/rebyte-skills --skill speech-to-text

Summary

  • Transcribe audio to text using OpenAI Whisper.
  • Use when user wants to convert speech to text, transcribe audio files, generate subtitles, or extract text from recordings.
  • Triggers include "speech to text", "STT", "transcribe", "transcription", "subtitles", "captions", "audio to text", "convert audio to text".

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

License LICENSE
Default branch main
Open issues 0
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Version1

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,933 B
  • docs SUMMARY.md 332 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Speech to Text

Transcribe audio to text using OpenAI Whisper API.

Requires Rebyte API auth$AUTHTOKEN and $APIURL are set up per the agent's system prompt; use them as Bearer token and base URL.

When to Use

Use this skill when the user needs to:

  • Transcribe audio recordings to text
  • Generate subtitles or captions (SRT, VTT)
  • Extract spoken content from audio files
  • Convert voice memos or interviews to text

How It Works

Send audio directly via multipart/form-data — standard Whisper API format:

curl -s -X POST "$API_URL/api/data/stt/transcribe" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -F "[email protected]" \
  -F "model=whisper-1" \
  -F "language=en" \
  -F "response_format=json"

Response:

{
  "success": true,
  "data": {
    "text": "Hello, this is a transcription of the audio recording."
  }
}

Parameters

Parameter Type Required Default Description
file file Yes - Audio file (multipart/form-data)
language string No auto ISO-639-1 language code (e.g. "en", "es", "ja") — improves accuracy
prompt string No - Optional text to guide transcription style or continue a previous segment
model string No whisper-1 Model to use (currently only whisper-1)
response_format string No json Output format (see below)
temperature number No 0 Sampling temperature (0-1). Lower = more deterministic

Response Formats

Format Description Use Case
json Simple JSON with text field Default, quick text extraction
verbose_json JSON with timestamps, segments, duration When you need word-level timing
text Plain text only Simple text output
srt SubRip subtitle format Video subtitles
vtt WebVTT subtitle format Web video captions

Supported Audio Formats

Whisper accepts: mp3, mp4, mpeg, mpga, m4a, wav, webm, ogg, flac

Max file size: 25 MB

Example: Full Transcription Workflow

# Get auth
AUTH_TOKEN=$(/home/user/.local/bin/rebyte-auth)
API_URL=$(python3 -c "import json; print(json.load(open('/home/user/.rebyte.ai/auth.json'))['sandbox']['relay_url'])")

# Transcribe directly
RESULT=$(curl -s -X POST "$API_URL/api/data/stt/transcribe" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -F "[email protected]" \
  -F "language=en" \
  -F "response_format=json")

# Extract text
echo "$RESULT" | jq -r '.data.text' > transcript.txt
echo "Transcript saved to transcript.txt"

Example: Generate SRT Subtitles

RESULT=$(curl -s -X POST "$API_URL/api/data/stt/transcribe" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -F "[email protected]" \
  -F "response_format=srt")

# Save SRT file
echo "$RESULT" | jq -r '.data.text' > subtitles.srt

# Burn subtitles into video with ffmpeg
ffmpeg -i video.mp4 -vf subtitles=subtitles.srt output.mp4

Tips

  • Always specify language when you know it — improves accuracy and speed
  • Use verbose_json when you need timestamps for syncing with video
  • Use srt or vtt format to directly generate subtitle files
  • For long audio files, consider splitting with ffmpeg first: ffmpeg -i long.mp3 -f segment -segmenttime 300 -c copy chunk%03d.mp3
  • Set temperature to 0 (default) for most accurate results
  • The prompt parameter helps with domain-specific terms — include key vocabulary the model should recognize