Whisper ASR — Speech-to-Text

提供本地语音转文字能力：默认使用 FunASR SenseVoice 引擎（中文效果佳、速度快），可切换 OpenAI Whisper 引擎支持 90 多种语言与翻译模式。输入音频文件，可指定语言、Whisper 模型大小和转录或翻译任务，结果输出到标准输出或写入文件。首次运行需联网下载模型权重，依赖 ffmpeg。

vincentlau2046-sudo

热度 43.19

最近更新 2026-05-23 17:04:47

分类：效率依赖：代码执行 / 读写文件应用场景：文件转换 / 翻译

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add asr-funasr

Metadata

name: asr-funasr
version: 1.2.0
description: Automatic Speech Recognition using OpenAI Whisper (local GPU). Supports Chinese, English, and 90+ languages. Auto-detects language.
metadata: {"openclaw":{"emoji":"🎤","requires":{"bins":["python3"]}}}
tags: ["asr","speech-to-text","whisper","funasr","sensevoice","transcription","audio"]

ASR — Speech-to-Text (FunASR + Whisper)

Two engines for different scenarios:

Engine	Best For	Chinese Quality	Speed
FunASR SenseVoice (default)	Chinese, Japanese, Korean	⭐⭐⭐ 简体	Fast (0.03 RTF)
OpenAI Whisper	Multilingual, translation	⭐⭐ (繁体)	Slower

Quick Start

# Default: FunASR SenseVoice (best Chinese)
{baseDir}/scripts/asr.py --input audio.mp3

# Whisper for multilingual / translation
{baseDir}/scripts/asr.py --input audio.mp3 --engine whisper

Options

Option	Default	Description
`--input`	(required)	Input audio file (mp3, wav, m4a, etc.)
`--engine`	funasr	ASR engine: `funasr` (SenseVoice) or `whisper`
`--language`	auto	Language code: zh, en, ja, ko, etc. (auto-detect if omitted)
`--model`	base	Whisper model size: tiny/base/small/medium/large (whisper only)
`--task`	transcribe	transcribe or translate (whisper only)
`--output`		Write transcript to file (default: stdout)

Engine Details

FunASR SenseVoice-Small (Default)

Model: iic/SenseVoiceSmall (893MB, auto-downloaded from ModelScope)
Strengths: 简体中文最佳、情感识别、语音事件检测、速度极快
Output: 简体中文，自动去除特殊标记
Languages: zh, en, ja, ko, yue (Cantonese)

OpenAI Whisper

Model: base (139MB, auto-downloaded)
Strengths: 90+ languages、翻译模式、多语言场景
Output: 中文输出繁体字（已知问题，换 small 模型可改善）
Whisper model sizes:

Model	VRAM	Speed	Accuracy
tiny	~1GB	Fastest	Low
base	~1GB	Fast	OK
small	~2GB	Medium	Good
medium	~5GB	Slow	Better
large	~10GB	Slowest	Best

Examples

# Chinese audio → FunASR (default, best quality)
{baseDir}/scripts/asr.py --input meeting.mp3

# Force Chinese language
{baseDir}/scripts/asr.py --input podcast.wav --language zh

# Multilingual audio → Whisper
{baseDir}/scripts/asr.py --input mixed.wav --engine whisper

# Whisper with better model
{baseDir}/scripts/asr.py --input lecture.mp3 --engine whisper --model small

# Translate Chinese speech to English text
{baseDir}/scripts/asr.py --input speech.mp3 --engine whisper --language zh --task translate

# Save transcript to file
{baseDir}/scripts/asr.py --input audio.wav --output transcript.txt

Dependencies

funasr + modelscope (FunASR engine)
openai-whisper (Whisper engine)
imageio-ffmpeg (bundled ffmpeg binary)
First run downloads model weights (auto-cached in ~/.cache/)