whisper.cpp首页、文档和下载

2023-03-13 10:27| 来源: 网络整理| 查看: 265

whisper.cpp 是 OpenAI 的 Whisper 自动语音识别 (ASR) 模型的 C/C++ 移植。

特性没有依赖项的普通 C/C++ 实现 Apple silicon 一等公民 - 通过 Arm Neon 和 Accelerate 框架优化 AVX 内在函数支持 x86 架构 VSX 内在函数支持 POWER 架构混合 F16 / F32 精度内存使用率低（Flash Attention）运行时零内存分配在 CPU 上运行 C 风格的 API

支持的平台：

Mac OS (Intel and Arm) iOS Android Linux / FreeBSD WebAssembly Windows (MSVC and MinGW] Raspberry Pi

模型的整个实现包含在2个源文件中：

张量运算： ggml.h / ggml.c Transformer 推断： whisper.h / whisper.cpp

这种轻量级的模型实现允许容易地将 OpenAI 的 Whisper 模型集成到不同的平台和应用程序中。

实现细节核心张量运算在 C 中实现 (ggml.h / ggml.c) 转换器模型和高级 C 风格的 API 是用 C++ 实现的 (whisper.h / whisper.cpp) main.cpp 中演示了示例用法 stream.cpp 中演示了麦克风的实时音频转录示例 examples 文件夹中提供了各种其他示例

张量运算符针对 Apple 芯片的 CPU 进行了大量优化。根据计算大小，使用 Arm Neon SIMD instrisics 或 CBLAS Accelerate 框架例程。后者对于更大的尺寸特别有效，因为 Accelerate 框架利用现代 Apple 产品中提供的专用 AMX 协处理器。

Quick start 快速开始

首先，下载一个转换为 ggml 格式的 Whisper 模型。例如：

bash ./models/download-ggml-model.sh base.en

构建主要示例并转录一个音频文件，如下所示：

# build the main example make # transcribe an audio file ./main -f samples/jfk.wav

要快速演示，只需运行 make base.en ：

$ make base.en cc -I. -O3 -std=c11 -pthread -DGGML_USE_ACCELERATE -c ggml.c -o ggml.o c++ -I. -I./examples -O3 -std=c++11 -pthread -c whisper.cpp -o whisper.o c++ -I. -I./examples -O3 -std=c++11 -pthread examples/main/main.cpp whisper.o ggml.o -o main -framework Accelerate ./main -h usage: ./main [options] file0.wav file1.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 -bo N, --best-of N [5 ] number of best candidates to keep -bs N, --beam-size N [-1 ] beam size for beam search -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 -su, --speed-up [false ] speed up audio by x2 (reduced accuracy) -tr, --translate [false ] translate from source language to english -di, --diarize [false ] stereo audio diarization -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 -owts, --output-words [false ] output script for generating karaoke video -ocsv, --output-csv [false ] output result in a CSV file -of FNAME, --output-file FNAME [ ] output file path (without file extension) -ps, --print-special [false ] print special tokens -pc, --print-colors [false ] print colors -pp, --print-progress [false ] print progress -nt, --no-timestamps [true ] do not print timestamps -l LANG, --language LANG [en ] spoken language ('auto' for auto-detect) --prompt PROMPT [ ] initial prompt -m FNAME, --model FNAME [models/ggml-base.en.bin] model path -f FNAME, --file FNAME [ ] input WAV file path bash ./models/download-ggml-model.sh base.en Downloading ggml model base.en ... ggml-base.en.bin 100%[========================>] 141.11M 6.34MB/s in 24s Done! Model 'base.en' saved in 'models/ggml-base.en.bin' You can now use it like this: $ ./main -m models/ggml-base.en.bin -f samples/jfk.wav =============================================== Running base.en on all samples in ./samples ... =============================================== ---------------------------------------------- [+] Running base.en on samples/jfk.wav ... (run 'ffplay samples/jfk.wav' to listen) ---------------------------------------------- whisper_init_from_file: loading model from 'models/ggml-base.en.bin' whisper_model_load: loading model whisper_model_load: n_vocab = 51864 whisper_model_load: n_audio_ctx = 1500 whisper_model_load: n_audio_state = 512 whisper_model_load: n_audio_head = 8 whisper_model_load: n_audio_layer = 6 whisper_model_load: n_text_ctx = 448 whisper_model_load: n_text_state = 512 whisper_model_load: n_text_head = 8 whisper_model_load: n_text_layer = 6 whisper_model_load: n_mels = 80 whisper_model_load: f16 = 1 whisper_model_load: type = 2 whisper_model_load: mem required = 215.00 MB (+ 6.00 MB per decoder) whisper_model_load: kv self size = 5.25 MB whisper_model_load: kv cross size = 17.58 MB whisper_model_load: adding 1607 extra tokens whisper_model_load: model ctx = 140.60 MB whisper_model_load: model size = 140.54 MB system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ... [00:00:00.000 --> 00:00:11.000] And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country. whisper_print_timings: fallbacks = 0 p / 0 h whisper_print_timings: load time = 113.81 ms whisper_print_timings: mel time = 15.40 ms whisper_print_timings: sample time = 11.58 ms / 27 runs ( 0.43 ms per run) whisper_print_timings: encode time = 266.60 ms / 1 runs ( 266.60 ms per run) whisper_print_timings: decode time = 66.11 ms / 27 runs ( 2.45 ms per run) whisper_print_timings: total time = 476.31 ms

该命令下载转换为自定义 ggml 格式的 base.en 模型，并对文件夹 samples 中的所有 .wav 样本运行推理。

有关详细的使用说明，请运行： ./main -h

请注意，主要示例当前仅使用 16 位 WAV 文件运行，因此请确保在运行该工具之前转换您的输入。例如，您可以像这样使用 ffmpeg ：

ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav 内存使用状况 Model Disk Mem SHA tiny 75 MB ~125 MB bd577a113a864445d4c299885e0cb97d4ba92b5f base 142 MB ~210 MB 465707469ff3a37a2b9b8d8f89f2f99de7299dac small 466 MB ~600 MB 55356645c2b361a969dfd0ef2c5a50d530afd8d5 medium 1.5 GB ~1.7 GB fd9727b6e1217c2f614f9b698455c4ffd82463b4 large 2.9 GB ~3.3 GB 0f4c8e34f21cf1a914c59d8b3ce882345ad349d6

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