幽冥大陆(五十七)ASR whisper-cli命令行使用 C语言

whisper-cli实际通常指whisper.cpp提供的命令行工具（main可执行文件），以下是 Windows 环境下的核心使用说明，适配轻量级、CPU 优先的本地化语音识别需求：

一、基础命令格式

bash

运行

./main -m models/ggml-base.en.bin -f audio.wav

核心参数说明：

-m <model-path>：指定模型文件路径（必须项，需提前下载对应量化模型，如ggml-base.en.bin）
-f <audio-path>：指定待识别的音频文件路径（支持 WAV/MP3 等，推荐 16kHz 单声道 WAV）

二、关键优化参数（适配 CPU 优先 + 实时需求）

量化与低资源配置
bash
运行
```
./main -m models/ggml-tiny.en.bin -f audio.wav -c 0 --threads 4
```
- -c <ctx-size>：设置上下文窗口大小，-c 0自动适配，降低内存占用
- --threads <num>：指定 CPU 线程数（建议设为 CPU 核心数的一半，平衡速度与资源消耗）
- 模型选择：优先选tiny/base级别的英文模型（ggml-tiny.en.bin），体积小、识别快，适合垂直领域指令
实时语音识别（麦克风输入）
bash
运行
```
./main -m models/ggml-base.en.bin --mic 1 --step 500 --length 5000
```
- --mic <device-id>：指定麦克风设备 ID（Windows 下可通过./main --list-mics查看设备列表）
- --step <ms>：实时识别的步长（单位毫秒，越小越实时，建议 500）
- --length <ms>：每次识别的音频长度（建议 5000，即 5 秒）
垂直领域优化（商业服务指令）
bash
运行
```
./main -m models/ggml-base.en.bin -f audio.wav -k 10 --prompt "收款 配镜 验光 取镜"
```
- --prompt <text>：添加领域关键词提示，引导模型优先识别商业服务相关指令
- -k <max-words>：限制输出最大词数，适配短语音指令场景

三、输出与格式控制

-otxt：将识别结果保存为 TXT 文件
-ojson：输出 JSON 格式结果（便于程序调用）
bash
运行
```
./main -m models/ggml-base.en.bin -f audio.wav -ojson
```

命令行全说明

usage: D:\ai\asr\whisper64\whisper-cli.exe [options] file0 file1 ... supported audio formats: flac, mp3, ogg, wav options: -h, --help [default] show this help message and exit -t N, --threads N [4 ] number of threads to use during computation -p N, --processors N [1 ] number of processors to use during computation -ot N, --offset-t N [0 ] time offset in milliseconds -on N, --offset-n N [0 ] segment index offset -d N, --duration N [0 ] duration of audio to process in milliseconds -mc N, --max-context N [-1 ] maximum number of text context tokens to store -ml N, --max-len N [0 ] maximum segment length in characters -sow, --split-on-word [false ] split on word rather than on token -bo N, --best-of N [5 ] number of best candidates to keep -bs N, --beam-size N [5 ] beam size for beam search -ac N, --audio-ctx N [0 ] audio context size (0 - all) -wt N, --word-thold N [0.01 ] word timestamp probability threshold -et N, --entropy-thold N [2.40 ] entropy threshold for decoder fail -lpt N, --logprob-thold N [-1.00 ] log probability threshold for decoder fail -nth N, --no-speech-thold N [0.60 ] no speech threshold -tp, --temperature N [0.00 ] The sampling temperature, between 0 and 1 -tpi, --temperature-inc N [0.20 ] The increment of temperature, between 0 and 1 -debug, --debug-mode [false ] enable debug mode (eg. dump log_mel) -tr, --translate [false ] translate from source language to english -di, --diarize [false ] stereo audio diarization -tdrz, --tinydiarize [false ] enable tinydiarize (requires a tdrz model) -nf, --no-fallback [false ] do not use temperature fallback while decoding -otxt, --output-txt [false ] output result in a text file -ovtt, --output-vtt [false ] output result in a vtt file -osrt, --output-srt [false ] output result in a srt file -olrc, --output-lrc [false ] output result in a lrc file -owts, --output-words [false ] output script for generating karaoke video -fp, --font-path [/System/Library/Fonts/Supplemental/Courier New Bold.ttf] path to a monospace font for karaoke video -ocsv, --output-csv [false ] output result in a CSV file -oj, --output-json [false ] output result in a JSON file -ojf, --output-json-full [false ] include more information in the JSON file -of FNAME, --output-file FNAME [ ] output file path (without file extension) -np, --no-prints [false ] do not print anything other than the results -ps, --print-special [false ] print special tokens -pc, --print-colors [false ] print colors --print-confidence [false ] print confidence -pp, --print-progress [false ] print progress -nt, --no-timestamps [false ] do not print timestamps -l LANG, --language LANG [en ] spoken language ('auto' for auto-detect) -dl, --detect-language [false ] exit after automatically detecting language --prompt PROMPT [ ] initial prompt (max n_text_ctx/2 tokens) --carry-initial-prompt [false ] always prepend initial prompt -m FNAME, --model FNAME [models/ggml-base.en.bin] model path -f FNAME, --file FNAME [ ] input audio file path -oved D, --ov-e-device DNAME [CPU ] the OpenVINO device used for encode inference -dtw MODEL --dtw MODEL [ ] compute token-level timestamps -ls, --log-score [false ] log best decoder scores of tokens -ng, --no-gpu [false ] disable GPU -fa, --flash-attn [true ] enable flash attention -nfa, --no-flash-attn [false ] disable flash attention -sns, --suppress-nst [false ] suppress non-speech tokens --suppress-regex REGEX [ ] regular expression matching tokens to suppress --grammar GRAMMAR [ ] GBNF grammar to guide decoding --grammar-rule RULE [ ] top-level GBNF grammar rule name --grammar-penalty N [100.0 ] scales down logits of nongrammar tokens Voice Activity Detection (VAD) options: --vad [false ] enable Voice Activity Detection (VAD) -vm FNAME, --vad-model FNAME [ ] VAD model path -vt N, --vad-threshold N [0.50 ] VAD threshold for speech recognition -vspd N, --vad-min-speech-duration-ms N [250 ] VAD min speech duration (0.0-1.0) -vsd N, --vad-min-silence-duration-ms N [100 ] VAD min silence duration (to split segments) -vmsd N, --vad-max-speech-duration-s N [FLT_MAX] VAD max speech duration (auto-split longer) -vp N, --vad-speech-pad-ms N [30 ] VAD speech padding (extend segments) -vo N, --vad-samples-overlap N [0.10 ] VAD samples overlap (seconds between segments)

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