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Fast local speech-to-text on Apple Silicon using MLX Whisper. 10x faster than OpenAI Whisper.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | mlx-whisper |
| description | Fast local speech-to-text on Apple Silicon using MLX Whisper. 10x faster than OpenAI Whisper. |
| metadata | {"xiaodazi":{"dependency_level":"external","os":["darwin"],"backend_type":"local","user_facing":true}} |
| capabilities | ["stt","speech_recognition"] |
利用 Apple Silicon 的 MLX 框架本地转录语音，速度是 OpenAI Whisper 的 10 倍。完全离线，隐私安全。
pip install mlx-whisper
需要 Apple Silicon Mac（M1/M2/M3/M4）。
import mlx_whisper
result = mlx_whisper.transcribe(
"audio.mp3",
path_or_hf_repo="mlx-community/whisper-large-v3-turbo",
)
print(result["text"])
result = mlx_whisper.transcribe(
"audio.mp3",
path_or_hf_repo="mlx-community/whisper-large-v3-turbo",
word_timestamps=True,
)
for segment in result["segments"]:
print(f"[{segment['start']:.1f}s - {segment['end']:.1f}s] {segment['text']}")
| 模型 | 大小 | 速度 | 准确度 |
|---|---|---|---|
whisper-tiny | 39M | 最快 | 一般 |
whisper-base | 74M | 快 | 较好 |
whisper-small | 244M | 中 | 好 |
whisper-large-v3-turbo | 809M | 较慢 | 最佳 |
默认使用 large-v3-turbo，短音频（<1分钟）可用 small 加速。
result = mlx_whisper.transcribe("audio.mp3", language="zh")
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