# parakeet-mlx: Nvidia's Parakeet ASR Models on Apple Silicon with MLX

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parakeet-mlx is an open-source project that implements Nvidia's advanced Automatic Speech Recognition (ASR) Parakeet models for Apple Silicon, leveraging the MLX framework for optimized performance. This Python library offers both a command-line interface and a flexible Python API, enabling efficient transcription of audio files, including real-time streaming capabilities. It provides a powerful solution for developers and researchers working with speech processing on Apple hardware.

GitHub: https://github.com/senstella/parakeet-mlx
OSRepos URL: https://osrepos.com/repo/senstella-parakeet-mlx

## Summary

parakeet-mlx is an open-source project that implements Nvidia's advanced Automatic Speech Recognition (ASR) Parakeet models for Apple Silicon, leveraging the MLX framework for optimized performance. This Python library offers both a command-line interface and a flexible Python API, enabling efficient transcription of audio files, including real-time streaming capabilities. It provides a powerful solution for developers and researchers working with speech processing on Apple hardware.

## Topics

- Python
- ASR
- Speech Recognition
- MLX
- Apple Silicon
- Deep Learning
- Audio Processing
- Transcription

## Repository Information

Last analyzed by OSRepos: Thu Jan 29 2026 20:01:24 GMT+0000 (Western European Standard Time)
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## Content

## Introduction

`parakeet-mlx` is an implementation of Nvidia's Parakeet models, which are Automatic Speech Recognition (ASR) models, optimized for Apple Silicon using the MLX framework. This open-source project allows users to efficiently transcribe audio files, leveraging Apple hardware for superior performance.

With `parakeet-mlx`, you can easily convert speech to text using a straightforward command-line interface (CLI) or integrate advanced ASR capabilities into your Python applications. It supports various output options, including subtitles with word-level timestamps, and offers features like beam decoding, audio chunking for long files, and real-time streaming transcription.

## Installation

Before installing, make sure you have `ffmpeg` installed on your system, as it is required for the CLI to work properly.

**Using `uv` (recommended):**

To add as a project dependency:

bash
uv add parakeet-mlx -U


Or, for the CLI globally:

bash
uv tool install parakeet-mlx -U


**Using `pip`:**

bash
pip install parakeet-mlx -U


## Examples

<p><strong>CLI Quick Start</strong></p>

Transcribe a single audio file:

bash
parakeet-mlx audio.mp3


Transcribe multiple files and generate VTT subtitles with word-level timestamps:

bash
parakeet-mlx *.mp3 --output-format vtt --highlight-words


Generate all available output formats:

bash
parakeet-mlx audio.mp3 --output-format all


<p><strong>Python API Quick Start</strong></p>

**Transcribe a file:**

python
from parakeet_mlx import from_pretrained

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

result = model.transcribe("audio_file.wav")

print(result.text)


**Check timestamps:**

python
from parakeet_mlx import from_pretrained

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

result = model.transcribe("audio_file.wav")

print(result.sentences)
# [AlignedSentence(text="Hello World.", start=1.01, end=2.04, duration=1.03, tokens=[...])]


**Do chunking:**

python
from parakeet_mlx import from_pretrained

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

result = model.transcribe("audio_file.wav", chunk_duration=60 * 2.0, overlap_duration=15.0)

print(result.sentences)


**Streaming Transcription:**

For real-time transcription, use the `transcribe_stream` method:

python
from parakeet_mlx import from_pretrained
from parakeet_mlx.audio import load_audio
import numpy as np

model = from_pretrained("mlx-community/parakeet-tdt-0.6b-v3")

# Create a streaming context
with model.transcribe_stream(
    context_size=(256, 256),  # (left_context, right_context) frames
) as transcriber:
    # Simulate real-time audio chunks
    audio_data = load_audio("audio_file.wav", model.preprocessor_config.sample_rate)
    chunk_size = model.preprocessor_config.sample_rate  # 1 second chunks

    for i in range(0, len(audio_data), chunk_size):
        chunk = audio_data[i:i+chunk_size]
        transcriber.add_audio(chunk)

        # Access current transcription
        result = transcriber.result
        print(f"Current text: {result.text}")


## Why Use `parakeet-mlx`?

`parakeet-mlx` stands out as an essential tool for anyone needing high-performance ASR capabilities on Apple Silicon devices.

*   **Optimized for Apple Silicon:** By leveraging the MLX framework, `parakeet-mlx` delivers native and efficient performance, making it ideal for Mac users.
*   **High-Quality ASR:** It implements Nvidia's Parakeet models, known for their accuracy and robustness in speech recognition.
*   **Versatility:** Whether you prefer a command-line tool for quick tasks or a flexible Python API for integration into larger projects, `parakeet-mlx` has you covered.
*   **Advanced Features:** From detailed word and sentence-level timestamps to advanced decoding options and real-time streaming transcription, the project offers a rich set of functionalities for diverse needs.
*   **Ease of Use:** With clear installation instructions and comprehensive examples, it is accessible to both beginners and experienced developers.

## Links

For more details, documentation, and to contribute to the project, visit the official GitHub repository:

*   [parakeet-mlx GitHub Repository](https://github.com/senstella/parakeet-mlx){target="_blank"}