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faster-whisper-0.5.1
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Faster Whisper transcription with CTranslate2
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | faster-whisper-0.5.1 |
| نام | faster-whisper |
| نسخه کتابخانه | 0.5.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Guillaume Klein |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/guillaumekln/faster-whisper |
| آدرس اینترنتی | https://pypi.org/project/faster-whisper/ |
| مجوز | MIT |
[](https://github.com/guillaumekln/faster-whisper/actions?query=workflow%3ACI) [](https://badge.fury.io/py/faster-whisper)
# Faster Whisper transcription with CTranslate2
**faster-whisper** is a reimplementation of OpenAI's Whisper model using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), which is a fast inference engine for Transformer models.
This implementation is up to 4 times faster than [openai/whisper](https://github.com/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
For reference, here's the time and memory usage that are required to transcribe [**13 minutes**](https://www.youtube.com/watch?v=0u7tTptBo9I) of audio using different implementations:
* [openai/whisper](https://github.com/openai/whisper)@[6dea21fd](https://github.com/openai/whisper/commit/6dea21fd7f7253bfe450f1e2512a0fe47ee2d258)
* [whisper.cpp](https://github.com/ggerganov/whisper.cpp)@[3b010f9](https://github.com/ggerganov/whisper.cpp/commit/3b010f9bed9a6068609e9faf52383aea792b0362)
* [faster-whisper](https://github.com/guillaumekln/faster-whisper)@[cce6b53e](https://github.com/guillaumekln/faster-whisper/commit/cce6b53e4554f71172dad188c45f10fb100f6e3e)
### Large-v2 model on GPU
| 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.*
### Small model on CPU
| 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.*
## Installation
The module can be installed from [PyPI](https://pypi.org/project/faster-whisper/):
```bash
pip install faster-whisper
```
**Other installation methods:**
```bash
# Install the master branch:
pip install --force-reinstall "faster-whisper @ https://github.com/guillaumekln/faster-whisper/archive/refs/heads/master.tar.gz"
# Install a specific commit:
pip install --force-reinstall "faster-whisper @ https://github.com/guillaumekln/faster-whisper/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"
# Install for development:
git clone https://github.com/guillaumekln/faster-whisper.git
pip install -e faster-whisper/
```
### GPU support
GPU execution requires the NVIDIA libraries cuBLAS 11.x and cuDNN 8.x to be installed on the system. Please refer to the [CTranslate2 documentation](https://opennmt.net/CTranslate2/installation.html).
## Usage
```python
from faster_whisper import WhisperModel
model_size = "large-v2"
# 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:
```python
segments, _ = model.transcribe("audio.mp3")
segments = list(segments) # The transcription will actually run here.
```
### Word-level timestamps
```python
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](https://github.com/snakers4/silero-vad) model to filter out parts of the audio without speech:
```python
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 function [`get_speech_timestamps`](https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/vad.py). They can be customized with the dictionary argument `vad_parameters`:
```python
segments, _ = model.transcribe("audio.mp3", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500))
```
### Logging
The library logging level can be configured like this:
```python
import logging
logging.basicConfig()
logging.getLogger("faster_whisper").setLevel(logging.DEBUG)
```
### Going further
See more model and transcription options in the [`WhisperModel`](https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/transcribe.py) 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!
* [whisper-ctranslate2](https://github.com/jordimas/whisper-ctranslate2) is a command line client based on `faster-whisper` and compatible with the original client from openai/whisper.
## Model conversion
When loading a model from its size such as `WhisperModel("large-v2")`, the correspondig CTranslate2 model is automatically downloaded from the [Hugging Face Hub](https://huggingface.co/guillaumekln).
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-v2" Whisper model](https://huggingface.co/openai/whisper-large-v2) and saves the weights in FP16:
```bash
pip install transformers[torch]>=4.23
ct2-transformers-converter --model openai/whisper-large-v2 --output_dir whisper-large-v2-ct2 \
--copy_files tokenizer.json --quantization float16
```
* The option `--model` accepts a model name on the Hub or a path to a model directory.
* If the option `--copy_files tokenizer.json` is 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](https://opennmt.net/CTranslate2/python/ctranslate2.converters.TransformersConverter.html).
## 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:
* Verify that the same transcription options are used, especially the same beam size. For example in openai/whisper, `model.transcribe` uses 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:
```bash
OMP_NUM_THREADS=4 python3 my_script.py
```
نیازمندی
| مقدار | نام |
|---|---|
| ==10.* | av |
| <4,>=3.10 | ctranslate2 |
| >=0.13 | huggingface-hub |
| ==0.13.* | tokenizers |
| ==1.14.* | onnxruntime |
| >=4.23 | transformers[torch] |
| ==23.* | black |
| ==6.* | flake8 |
| ==5.* | isort |
| ==7.* | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl faster-whisper-0.5.1:
pip install faster-whisper-0.5.1.whl
نصب پکیج tar.gz faster-whisper-0.5.1:
pip install faster-whisper-0.5.1.tar.gz