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.
For reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:
| Implementation | Precision | Beam size | Time | Max. GPU memory | Max. CPU memory |
|---|---|---|---|---|---|
| openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB |
| faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB |
| faster-whisper | int8 | 5 | 59s | 3091MB | 3117MB |
Executed with CUDA 11.7.1 on a NVIDIA Tesla V100S.
| Implementation | Precision | Beam size | Time | Max. memory |
|---|---|---|---|---|
| openai/whisper | fp32 | 5 | 10m31s | 3101MB |
| whisper.cpp | fp32 | 5 | 17m42s | 1581MB |
| whisper.cpp | fp16 | 5 | 12m39s | 873MB |
| faster-whisper | fp32 | 5 | 2m44s | 1675MB |
| faster-whisper | int8 | 5 | 2m04s | 995MB |
Executed with 8 threads on a Intel(R) Xeon(R) Gold 6226R.
| Implementation | Precision | Beam size | Time | Gigaspeech WER |
|---|---|---|---|---|
| distil-whisper/distil-large-v2 | fp16 | 4 | - | 10.36 |
| faster-distil-large-v2 | fp16 | 5 | - | 10.28 |
| distil-whisper/distil-medium.en | fp16 | 4 | - | 11.21 |
| faster-distil-medium.en | fp16 | 5 | - | 11.21 |
Executed with CUDA 11.4 on a NVIDIA 3090.
testing details (click to expand)
For distil-whisper/distil-large-v2, the WER is tested with code sample from link. for faster-distil-whisper, the WER is tested with setting:
from faster_whisper import WhisperModel
model_size = "distil-large-v2"
# model_size = "distil-medium.en"
# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.mp3", beam_size=5, language="en")- Python 3.8 or greater
GPU execution requires the following NVIDIA libraries to be installed:
Note: Latest versions of ctranslate2 support CUDA 12 only. For CUDA 11, the current workaround is downgrading to the 3.24.0 version of ctranslate2 (This can be done with pip install --force-reinstall ctranslate2==3.24.0 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.
The libraries (cuBLAS, cuDNN) are installed in these official NVIDIA CUDA Docker images: nvidia/cuda:12.0.0-runtime-ubuntu20.04 or nvidia/cuda:12.0.0-runtime-ubuntu22.04.
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
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__))'`Note: Version 9+ of nvidia-cudnn-cu12 appears to cause issues due its reliance on cuDNN 9 (Faster-Whisper does not currently support cuDNN 9). Ensure your version of the Python package is for cuDNN 8.
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.
The module can be installed from PyPI:
pip install faster-whisperOther installation methods (click to expand)
pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz"pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"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.To directly use the model for improved language detection, the following code snippet can be used:
from faster_whisper import WhisperModel
model = WhisperModel("medium", device="cuda", compute_type="float16")
language_info = model.detect_language_multi_segment("audio.mp3")The batched version of faster-whisper is inspired by whisper-x licensed under the BSD-4 Clause license and integrates its VAD model to this library. This product includes software developed by Max Bain. We modify this implementation and also added kaldi-based feature extraction. It improves the speed upto 10-12x compared to openAI implementation and 3-4x compared to the sequential faster_whisper version. It works by transcribing semantically meaningful audio chunks as batches leading to faster inference.
The following code snippet illustrates how to run inference with batched version on an example audio file. Please also refer to the test scripts of batched faster whisper.
from faster_whisper import WhisperModel, BatchedInferencePipeline
model = WhisperModel("medium", 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))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.
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))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),
)The library logging level can be configured like this:
import logging
logging.basicConfig()
logging.getLogger("faster_whisper").setLevel(logging.DEBUG)See more model and transcription options in the WhisperModel class implementation.
Here is a non exhaustive list of open-source projects using faster-whisper. Feel free to add your project to the list!
- faster-whisper-server 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
.lrcfiles 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.
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- The option
--modelaccepts a model name on the Hub or a path to a model directory. - If the option
--copy_files tokenizer.jsonis not used, the tokenizer configuration is automatically downloaded when the model is loaded later.
Models can also be converted from the code. See the conversion API.
- Directly load the model from a local directory:
model = faster_whisper.WhisperModel("whisper-large-v3-ct2")- Upload your model to the Hugging Face Hub and load it from its name:
model = faster_whisper.WhisperModel("username/whisper-large-v3-ct2")If you are comparing the performance against other Whisper implementations, you should make sure to run the comparison with similar settings. In particular:
- Verify that the same transcription options are used, especially the same beam size. For example in openai/whisper,
model.transcribeuses a default beam size of 1 but here we use a default beam size of 5. - When running on CPU, make sure to set the same number of threads. Many frameworks will read the environment variable
OMP_NUM_THREADS, which can be set when running your script:
OMP_NUM_THREADS=4 python3 my_script.py