faster-whisper README

The project's README from github.com/SYSTRAN/faster-whisper. As one markdown file: README.md.

CI PyPI version

Faster Whisper transcription with CTranslate2

faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models.

This implementation is up to 4 times faster than openai/whisper for the same accuracy while using less memory. The efficiency can be further improved with 8-bit quantization on both CPU and GPU.

Benchmark

Whisper

For reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:

Large-v2 model on GPU

Implementation Precision Beam size Time VRAM Usage
openai/whisper fp16 5 2m23s 4708MB
whisper.cpp (Flash Attention) fp16 5 1m05s 4127MB
transformers (SDPA)[^1] fp16 5 1m52s 4960MB
faster-whisper fp16 5 1m03s 4525MB
faster-whisper (batch_size=8) fp16 5 17s 6090MB
faster-whisper int8 5 59s 2926MB
faster-whisper (batch_size=8) int8 5 16s 4500MB

distil-whisper-large-v3 model on GPU

Implementation Precision Beam size Time YT Commons WER
transformers (SDPA) (batch_size=16) fp16 5 46m12s 14.801
faster-whisper (batch_size=16) fp16 5 25m50s 13.527

GPU Benchmarks are Executed with CUDA 12.4 on a NVIDIA RTX 3070 Ti 8GB. [^1]: transformers OOM for any batch size > 1

Small model on CPU

Implementation Precision Beam size Time RAM Usage
openai/whisper fp32 5 6m58s 2335MB
whisper.cpp fp32 5 2m05s 1049MB
whisper.cpp (OpenVINO) fp32 5 1m45s 1642MB
faster-whisper fp32 5 2m37s 2257MB
faster-whisper (batch_size=8) fp32 5 1m06s 4230MB
faster-whisper int8 5 1m42s 1477MB
faster-whisper (batch_size=8) int8 5 51s 3608MB

Executed with 8 threads on an Intel Core i7-12700K.

Requirements

Unlike openai-whisper, FFmpeg does not need to be installed on the system. The audio is decoded with the Python library PyAV which bundles the FFmpeg libraries in its package.

GPU

GPU execution requires the following NVIDIA libraries to be installed:

Note: The latest versions of ctranslate2 only support CUDA 12 and cuDNN 9. For CUDA 11 and cuDNN 8, the current workaround is downgrading to the 3.24.0 version of ctranslate2, for CUDA 12 and cuDNN 8, downgrade to the 4.4.0 version of ctranslate2, (This can be done with pip install --force-reinstall ctranslate2==4.4.0 or specifying the version in a requirements.txt).

There are multiple ways to install the NVIDIA libraries mentioned above. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.

Other installation methods (click to expand)

Note: For all these methods below, keep in mind the above note regarding CUDA versions. Depending on your setup, you may need to install the CUDA 11 versions of libraries that correspond to the CUDA 12 libraries listed in the instructions below.

Use Docker

The libraries (cuBLAS, cuDNN) are installed in this official NVIDIA CUDA Docker images: nvidia/cuda:12.3.2-cudnn9-runtime-ubuntu22.04.

Install with pip (Linux only)

On Linux these libraries can be installed with pip. Note that LD_LIBRARY_PATH must be set before launching Python.

pip install nvidia-cublas-cu12 nvidia-cudnn-cu12==9.*

export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`

Download the libraries from Purfview's repository (Windows & Linux)

Purfview's whisper-standalone-win provides the required NVIDIA libraries for Windows & Linux in a single archive. Decompress the archive and place the libraries in a directory included in the PATH.

Installation

The module can be installed from PyPI:

pip install faster-whisper
Other installation methods (click to expand)

Install the master branch

pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz"

Install a specific commit

pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"

Usage

Faster-whisper

from faster_whisper import WhisperModel

model_size = "large-v3"

# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")

# or run on GPU with INT8
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio.mp3", beam_size=5)

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

Warning: segments is a generator so the transcription only starts when you iterate over it. The transcription can be run to completion by gathering the segments in a list or a for loop:

segments, _ = model.transcribe("audio.mp3")
segments = list(segments)  # The transcription will actually run here.

Batched Transcription

The following code snippet illustrates how to run batched transcription on an example audio file. BatchedInferencePipeline.transcribe is a drop-in replacement for WhisperModel.transcribe

from faster_whisper import WhisperModel, BatchedInferencePipeline

model = WhisperModel("turbo", device="cuda", compute_type="float16")
batched_model = BatchedInferencePipeline(model=model)
segments, info = batched_model.transcribe("audio.mp3", batch_size=16)

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

Faster Distil-Whisper

The Distil-Whisper checkpoints are compatible with the Faster-Whisper package. In particular, the latest distil-large-v3 checkpoint is intrinsically designed to work with the Faster-Whisper transcription algorithm. The following code snippet demonstrates how to run inference with distil-large-v3 on a specified audio file:

from faster_whisper import WhisperModel

model_size = "distil-large-v3"

model = WhisperModel(model_size, device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.mp3", beam_size=5, language="en", condition_on_previous_text=False)

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

For more information about the distil-large-v3 model, refer to the original model card.

Word-level timestamps

segments, _ = model.transcribe("audio.mp3", word_timestamps=True)

for segment in segments:
    for word in segment.words:
        print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))

VAD filter

The library integrates the Silero VAD model to filter out parts of the audio without speech:

segments, _ = model.transcribe("audio.mp3", vad_filter=True)

The default behavior is conservative and only removes silence longer than 2 seconds. See the available VAD parameters and default values in the source code. They can be customized with the dictionary argument vad_parameters:

segments, _ = model.transcribe(
    "audio.mp3",
    vad_filter=True,
    vad_parameters=dict(min_silence_duration_ms=500),
)

Vad filter is enabled by default for batched transcription.

Logging

The library logging level can be configured like this:

import logging

logging.basicConfig()
logging.getLogger("faster_whisper").setLevel(logging.DEBUG)

Going further

See more model and transcription options in the WhisperModel class implementation.

Community integrations

Here is a non exhaustive list of open-source projects using faster-whisper. Feel free to add your project to the list!

Model conversion

When loading a model from its size such as WhisperModel("large-v3"), the corresponding CTranslate2 model is automatically downloaded from the Hugging Face Hub.

We also provide a script to convert any Whisper models compatible with the Transformers library. They could be the original OpenAI models or user fine-tuned models.

For example the command below converts the original "large-v3" Whisper model and saves the weights in FP16:

pip install transformers[torch]>=4.23

ct2-transformers-converter --model openai/whisper-large-v3 --output_dir whisper-large-v3-ct2
--copy_files tokenizer.json preprocessor_config.json --quantization float16

Models can also be converted from the code. See the conversion API.

Load a converted model

  1. Directly load the model from a local directory:
model = faster_whisper.WhisperModel("whisper-large-v3-ct2")
  1. Upload your model to the Hugging Face Hub and load it from its name:
model = faster_whisper.WhisperModel("username/whisper-large-v3-ct2")

Comparing performance against other implementations

If you are comparing the performance against other Whisper implementations, you should make sure to run the comparison with similar settings. In particular:

OMP_NUM_THREADS=4 python3 my_script.py

faster-whisper (SYSTRAN/faster-whisper)

Searching Database…

25.9K ·GitHub
Faster-whisper
这是什么?它是 OpenAI 的 Whisper 模型在 CTranslate2(一个快速的 Transformer 推理引擎)上的 Python 重新实现——在精度相同的情况下比原版最快快 4 倍,内存占用更少,并且可选在 CPU 和 GPU 上做 8 位量化。
它是一个我在代码里调用的库,还是一个现成的应用?它是一个 Python 库,需要在你自己的代码里调用(WhisperModel 类)。README 还列出了基于它构建的社区工具:命令行工具(whisper-ctranslate2)、图形界面应用(aTrain)和兼容 OpenAI 接口的服务器(speaches)。
我该怎么安装并开始转写?先运行 pip install faster-whisper,然后按尺寸加载模型,例如 WhisperModel('large-v3')——转换好的权重会在首次使用时自动从 Hugging Face Hub 下载。
除了 Python 之外还需要别的吗,比如 FFmpeg?需要 Python 3.9 或更高版本。不需要系统里安装 FFmpeg:音频由 PyAV 解码,它自带的安装包里就打包了 FFmpeg 的库,所以纯 CPU 环境没有任何额外的系统依赖。
API 或 CUDA 配置上有什么坑?segments 是一个生成器,只有迭代它才会真正开始转写——要把它包成 list 或放进循环里才会实际运行。另外,当前的 ctranslate2 只支持 CUDA 12 和 cuDNN 9;老环境必须降级 ctranslate2。
它到底比 OpenAI 原版的 Whisper 快多少?以 README 的基准测试为例(13 分钟音频,large-v2,RTX 3070 Ti):openai/whisper 的 fp16 用时 2 分 23 秒,占用 4708MB 显存;faster-whisper 的 int8 用时 59 秒,占用 2926MB,批大小设为 8 时更是只需 16 秒。在 CPU 上(小模型,i7-12700K),int8 把时间从 6 分 58 秒缩短到 1 分 42 秒。
除了普通的转写,它还能做别的吗?可以:支持词级时间戳,还内置了 Silero VAD 过滤器,能剔除非语音音频(默认保守地只移除超过 2 秒的静音)。批处理模式下 VAD 默认开启,而且 BatchedInferencePipeline.transcribe 可以直接替换普通的转写调用。
我能运行 Distil-Whisper 或者自己微调的模型吗?可以。distil-large-v3 开箱即用地适配 faster-whisper;任何与 Transformers 兼容的 Whisper 模型——原版或微调过的——都可以用自带的脚本转换成 CTranslate2 格式,然后从本地目录或 Hub 加载。
有哪些项目是基于 faster-whisper 构建的?有几个知名项目:WhisperX(说话人分离 + 经 wav2vec2 对齐的词级时间戳)、speaches(兼容 OpenAI 接口的服务器,可用 Docker 部署)、whisper-ctranslate2(与原版 whisper 客户端兼容的命令行工具)、aTrain(Windows 和 Linux 上的图形界面应用)以及 WhisperLive(接近实时的转写)。
What are similar projects?whisperX, whisper, WhisperLiveKit, insanely-fast-whisper, speechbrain, kaldi, Qwen3-ASR, qxotic
Which topics is it in?speech, asr, transcription, whisper, audio-processing, speech-to-text, transformers, speech-recognition, quantization, transformer, audio, inference
Creator
SYSTRAN
Size
39.7 MB
Stars
25.9K
Updated
CommitOct 16Releasev1.2.1Oct 31
Age
3y
Commits · 90 Days