faster-whisper2 2.1.1

Faster Whisper transcription with CTranslate2

Faster Whisper 2 - transcription with CTranslate2

faster-whisper2 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.

Requirements

• Python 3.9 or greater

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:

• cuBLAS for CUDA 12
• cuDNN 9 for CUDA 12

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__))'`

Installation

The module can be installed from PyPI:

pip install faster-whisper2

Other installation methods (click to expand)

Install the master branch

pip install --force-reinstall "faster-whisper2 @ https://github.com/BBC-Esq/faster-whisper2/archive/refs/heads/master.tar.gz"

Install a specific commit

pip install --force-reinstall "faster-whisper2 @ https://github.com/BBC-Esq/faster-whisper2/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"

Community integrations (click to expand)

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

• speaches is an OpenAI compatible server using faster-whisper. It's easily deployable with Docker, works with OpenAI SDKs/CLI, supports streaming, and live transcription.
• WhisperX is an award-winning Python library that offers speaker diarization and accurate word-level timestamps using wav2vec2 alignment
• whisper-ctranslate2 is a command line client based on faster-whisper and compatible with the original client from openai/whisper.
• whisper-diarize is a speaker diarization tool that is based on faster-whisper and NVIDIA NeMo.
• whisper-standalone-win Standalone CLI executables of faster-whisper for Windows, Linux & macOS.
• asr-sd-pipeline provides a scalable, modular, end to end multi-speaker speech to text solution implemented using AzureML pipelines.
• Open-Lyrics is a Python library that transcribes voice files using faster-whisper, and translates/polishes the resulting text into .lrc files in the desired language using OpenAI-GPT.
• wscribe is a flexible transcript generation tool supporting faster-whisper, it can export word level transcript and the exported transcript then can be edited with wscribe-editor
• aTrain is a graphical user interface implementation of faster-whisper developed at the BANDAS-Center at the University of Graz for transcription and diarization in Windows (Windows Store App) and Linux.
• Whisper-Streaming implements real-time mode for offline Whisper-like speech-to-text models with faster-whisper as the most recommended back-end. It implements a streaming policy with self-adaptive latency based on the actual source complexity, and demonstrates the state of the art.
• WhisperLive is a nearly-live implementation of OpenAI's Whisper which uses faster-whisper as the backend to transcribe audio in real-time.
• Faster-Whisper-Transcriber is a simple but reliable voice transcriber that provides a user-friendly interface.
• Open-dubbing is open dubbing is an AI dubbing system which uses machine learning models to automatically translate and synchronize audio dialogue into different languages.
• Whisper-FastAPI whisper-fastapi is a very simple script that provides an API backend compatible with OpenAI, HomeAssistant, and Konele (Android voice typing) formats.

Compute type compatibility reference (click to expand)

When you specify a compute_type, faster-whisper2 validates that it is compatible with your device before downloading anything. If an incompatible combination is detected, a clear error is raised. For quantized types (e.g. int8), the library automatically downloads the best source precision for your hardware and CTranslate2 handles the runtime conversion.

Supported compute types by device

Compute Type	CPU	CUDA >= 6.1	CUDA >= 7.0	CUDA >= 8.0
float32	Yes	Yes	Yes	Yes
float16	No	No	Yes	Yes
bfloat16	No	No	No	Yes
int8	Yes	Yes	Yes	Yes
int8_float16	No	No	Yes	Yes
int8_float32	Yes	Yes	Yes	Yes
int8_bfloat16	No	No	No	Yes
int16	Yes (Intel MKL only)	No	No	No
auto	Yes	Yes	Yes	Yes
default	Yes	Yes	Yes	Yes

Which model precision is downloaded

Device	Requested Compute Type	Downloaded Precision
CPU	Any	float32
CUDA (any)	float32	float32
CUDA (any)	float16	float16
CUDA (any)	bfloat16	bfloat16
CUDA >= 8.0 (Ampere+)	int8, int8_float16, int8_float32, int8_bfloat16, auto, default	bfloat16
CUDA < 8.0 (pre-Ampere)	int8, int8_float16, int8_float32, int8_bfloat16, auto, default	float16

CUDA compute capability by GPU generation

Generation	Compute Capability	Example GPUs
Maxwell	5.x	GTX 950, GTX 970, GTX 980
Pascal	6.x	GTX 1060, GTX 1070, GTX 1080
Turing	7.x	RTX 2060, RTX 2070, RTX 2080
Ampere	8.x	RTX 3060, RTX 3070, RTX 3080, A100
Ada Lovelace	8.9	RTX 4060, RTX 4070, RTX 4080, RTX 4090
Hopper	9.0	H100
Blackwell	10.0	RTX 5070, RTX 5080, RTX 5090, B200