parakeet-api 0.2.0

OpenAI Whisper-compatible API endpoint for Parakeet STT models

Parakeet API

OpenAI Whisper-compatible API endpoint for Parakeet STT models. (MLX for Apple Silicon, Sherpa-ONNX for others)

Performance on a 3.85s wav file:

Machine (Engine)	Latency (ms)	Speedup
Intel 255H (Sherpa-ONNX, CPU)	174.46	22.1x
M2 Air (MLX, GPU)	194.29	19.8x

Installation & Setup

The easiest way to install and run parakeet-api is using uv.

1. Install the CLI

For Linux, Windows, or Intel Mac (Sherpa-ONNX / CPU):

uv tool install parakeet-api

For Apple Silicon (MLX):

uv tool install "parakeet-api[mlx]"

2. Install System Dependencies

ffmpeg must be installed on your system for non-WAV audio support.

• macOS: brew install ffmpeg
• Ubuntu/Debian: sudo apt-get install ffmpeg

3. Download Models

Models are saved to your platform's standard data directory (e.g., ~/.local/share/parakeet-api/models).

Default Models

Download the default English/European model for your engine:

Sherpa-ONNX:

parakeet-api download sherpa

MLX:

parakeet-api download mlx

Custom Models

You can use different Parakeet models by specifying a URL or Repo ID.

Sherpa-ONNX:

• Sherpa-ONNX Pretrained Models
• Sherpa-ONNX GitHub Releases

1. Download using the script with --url:

   parakeet-api download sherpa --url https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-parakeet-tdt_ctc-0.6b-ja-35000-int8.tar.bz2
   

   For hotwords support on Transducer models (e.g. Parakeet TDT), also generate bpe.vocab:

   parakeet-api download sherpa --generate-bpe-vocab
   

2. Update STT__SHERPA__MODEL_ID in your .env (or set as environment variable):

   STT__SHERPA__MODEL_ID=sherpa-onnx-nemo-parakeet-tdt_ctc-0.6b-ja-35000-int8
   

[!NOTE] The default model is a NeMo Parakeet TDT (Transducer). Other architectures like Zipformer (e.g. sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01) are also supported but must be downloaded manually via --url.

MLX:

• Parakeet - a mlx-community Collection

1. Download using the script with --id:

   parakeet-api download mlx --id mlx-community/parakeet-tdt_ctc-0.6b-ja
   

2. Update STT__MLX__MODEL_ID in your .env (or set as environment variable):

   STT__MLX__MODEL_ID=mlx-community/parakeet-tdt_ctc-0.6b-ja
   

4. Run the Server

parakeet-api serve

The API will be available at http://localhost:8816.

5. (Optional) Run as a Background Service

You can install parakeet-api as a background service (launchd on macOS, systemd on Linux).

parakeet-api install-daemon

This will create a service file and set up a configuration file (e.g. ~/.local/share/parakeet-api/.env).
To uninstall: parakeet-api uninstall-daemon

Running with Docker (Sherpa-ONNX)

For Linux or CPU environments, you can use Docker and Docker Compose.

# Download .env.example
curl -o .env.example https://github.com/likeablob/parakeet-api/raw/refs/heads/main/.env.example

# Edit .env to set your SERVER__API_KEY and other settings
cp .env.example .env
editor .env

# Create compose.yaml
cat << 'EOF' > compose.yaml
services:
  api:
    image: ghcr.io/likeablob/parakeet-api:latest
    ports:
      - "8816:8816"
    env_file:
      - .env
    volumes:
      - type: bind
        source: ./models
        target: /app/models
    environment:
      - SERVER__HOST=0.0.0.0
      - SERVER__PORT=8816
      - STT__MODELS_DIR=/app/models
    restart: unless-stopped
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "3"
EOF

# Download model
mkdir models
docker compose run --rm api download sherpa --out /app/models

# Start the server
docker compose up -d

Usage

API Endpoints

POST /v1/audio/transcriptions

Transcribe audio to text using the OpenAI Whisper-compatible API format.

Example with curl:

curl -X POST "http://localhost:8816/v1/audio/transcriptions" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@/path/to/audio.wav" \
  -F "response_format=json"

POST /v1/audio/transcriptions/raw

Same as above but accepts raw audio bytes in the request body.

curl -X POST "http://localhost:8816/v1/audio/transcriptions/raw" \
  -H "Content-Type: audio/wav" \
  --data-binary @/path/to/audio.wav

Supported Parameters

Parameter	Type	Default	Description
file	file	-	The audio file to transcribe.
response_format	string	json	json, text, verbose_json, srt, vtt.
timestamp_granularities[]	array	["segment"]	word, segment (used with verbose_json).
hotwords	string	-	Comma-separated hotwords for contextual biasing (e.g. OpenAI:2.5,GPT-4).

[!NOTE] Limitations of Response Formats: The current implementation provides simplified timestamp information. Consequently:

• srt / vtt: Return a single segment covering the entire audio duration (0.0 to end).
• verbose_json: Timestamps for words and segments are placeholders/estimations.

[!NOTE] Ignored Parameters: The following parameters are accepted for compatibility with the OpenAI API but are currently ignored: model, language, prompt, temperature.

Hotwords (Extension): The hotwords parameter is a parakeet-api extension for contextual biasing. Supported on Sherpa-ONNX Transducer models only (NeMo TDT, Zipformer, Conformer). CTC models do not support hotwords. Requires bpe.vocab for NeMo TDT models (generate via parakeet-api download sherpa --generate-bpe-vocab).

Examples

Check the examples/ directory for client implementations:

• examples/client_requests.py: Basic transcription using requests.
• examples/client_openai_sdk.py: Using the official OpenAI Python SDK.

For full API compatibility details, refer to the OpenAI Audio API Reference and their OpenAPI specification.

Development

Setup from Source

1. Clone the repository:

   git clone https://github.com/likeablob/parakeet-api.git
   cd parakeet-api
   

2. Install dependencies:

   # Includes dev tools (ruff, ty, pytest) and optional mlx support
   uv sync --all-extras --dev
   

3. Run:

   uv run parakeet-api serve
   

Code Quality & Tests

# Linting & Formatting
uv run ruff check .
uv run ruff format .

# Type Checking
uv run ty check src/ tests/

# Run Tests
uv run pytest tests/mock
uv run pytest tests/inference # Requires models

Related Projects

• push-to-whisper: Push key to record audio & STT.

License

MIT