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Fast local speech-to-text on Apple Silicon using MLX Whisper. 10x faster than OpenAI Whisper.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
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Baseado na classificação ocupacional SOC
| 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")