Venice Transcribe

Transcribe audio files to text using Venice AI's speech recognition (Whisper-based).

API Base URL: https://api.venice.ai/api/v1 Documentation: docs.venice.ai

Setup

1. Get your API key from venice.ai → Settings → API Keys
2. Set the environment variable:

export VENICE_API_KEY="your_api_key_here"

Transcribe Audio

Convert audio files to text.

uv run {baseDir}/scripts/transcribe.py --file recording.mp3

Options:

• --file (required): Audio file path
• --output: Save transcription to file (default: prints to stdout)
• --model: ASR model (default: openai/whisper-large-v3)
• --format: Output format: json or text (default: json)
• --timestamps: Include word/segment timestamps
• --language: Language hint (ISO 639-1 code, e.g., en, es, fr)

Supported audio formats:

• WAV, WAVE
• MP3
• FLAC
• M4A, AAC
• MP4 (audio track)

Examples

Basic transcription:

uv run {baseDir}/scripts/transcribe.py --file meeting.mp3

Get just the text (no JSON):

uv run {baseDir}/scripts/transcribe.py --file audio.wav --format text

With timestamps:

uv run {baseDir}/scripts/transcribe.py --file podcast.mp3 --timestamps

Spanish audio with language hint:

uv run {baseDir}/scripts/transcribe.py --file spanish.mp3 --language es

Save to file:

uv run {baseDir}/scripts/transcribe.py --file interview.mp3 --output transcript.json

Output Format

JSON format (default):

{
  "text": "Hello, this is a transcription test.",
  "duration": 3.5
}

JSON with timestamps:

{
  "text": "Hello world",
  "duration": 2.1,
  "timestamps": {
    "word": [
      {"word": "Hello", "start": 0.0, "end": 0.5},
      {"word": "world", "start": 0.6, "end": 1.0}
    ],
    "segment": [
      {"text": "Hello world", "start": 0.0, "end": 1.0}
    ]
  }
}

Text format:

Hello, this is a transcription test.

Runtime Note

This skill uses uv run which automatically installs Python dependencies (httpx) via PEP 723 inline script metadata. No manual Python package installation required - uv handles everything.

API Reference

Full API docs: docs.venice.ai