129 lines
5.3 KiB
Markdown
129 lines
5.3 KiB
Markdown
# Faster Whisper transcription with CTranslate2
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This repository demonstrates how to implement the Whisper transcription using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), which is a fast inference engine for Transformer models.
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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.
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## Benchmark
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For reference, here's the time and memory usage that are required to transcribe **13 minutes** of audio using different implementations:
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* [openai/whisper](https://github.com/openai/whisper)@[6dea21fd](https://github.com/openai/whisper/commit/6dea21fd7f7253bfe450f1e2512a0fe47ee2d258)
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* [whisper.cpp](https://github.com/ggerganov/whisper.cpp)@[3b010f9](https://github.com/ggerganov/whisper.cpp/commit/3b010f9bed9a6068609e9faf52383aea792b0362)
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* [faster-whisper](https://github.com/guillaumekln/faster-whisper)@[cce6b53e](https://github.com/guillaumekln/faster-whisper/commit/cce6b53e4554f71172dad188c45f10fb100f6e3e)
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### Large-v2 model on GPU
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| Implementation | Precision | Beam size | Time | Max. GPU memory | Max. CPU memory |
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| --- | --- | --- | --- | --- | --- |
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| openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB |
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| faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB |
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| faster-whisper | int8 | 5 | 59s | 3091MB | 3117MB |
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*Executed with CUDA 11.7.1 on a NVIDIA Tesla V100S.*
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### Small model on CPU
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| Implementation | Precision | Beam size | Time | Max. memory |
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| --- | --- | --- | --- | --- |
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| openai/whisper | fp32 | 5 | 10m31s | 3101MB |
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| whisper.cpp | fp32 | 5 | 17m42s | 1581MB |
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| whisper.cpp | fp16 | 5 | 12m39s | 873MB |
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| faster-whisper | fp32 | 5 | 2m44s | 1675MB |
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| faster-whisper | int8 | 5 | 2m04s | 995MB |
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*Executed with 8 threads on a Intel(R) Xeon(R) Gold 6226R.*
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## Installation
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```bash
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pip install faster-whisper
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```
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The model conversion script requires the modules `transformers` and `torch` which can be installed with the `[conversion]` extra requirement:
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```bash
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pip install faster-whisper[conversion]
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```
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**Other installation methods:**
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```bash
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# Install the master branch:
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pip install --force-reinstall "faster-whisper @ https://github.com/guillaumekln/faster-whisper/archive/refs/heads/master.tar.gz"
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# Install a specific commit:
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pip install --force-reinstall "faster-whisper @ https://github.com/guillaumekln/faster-whisper/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"
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# Install for development:
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git clone https://github.com/guillaumekln/faster-whisper.git
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pip install -e faster-whisper/
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```
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### GPU support
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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).
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## Usage
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### Model conversion
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A Whisper model should be first converted into the CTranslate2 format. We provide a script to download and convert models from the [Hugging Face model repository](https://huggingface.co/models?sort=downloads&search=whisper).
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For example the command below converts the "large-v2" Whisper model and saves the weights in FP16:
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```bash
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ct2-transformers-converter --model openai/whisper-large-v2 --output_dir whisper-large-v2-ct2 \
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--copy_files tokenizer.json --quantization float16
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```
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If the option `--copy_files tokenizer.json` is not used, the tokenizer configuration is automatically downloaded when the model is loaded later.
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Models can also be converted from the code. See the [conversion API](https://opennmt.net/CTranslate2/python/ctranslate2.converters.TransformersConverter.html).
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### Transcription
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```python
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from faster_whisper import WhisperModel
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model_path = "whisper-large-v2-ct2/"
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# Run on GPU with FP16
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model = WhisperModel(model_path, device="cuda", compute_type="float16")
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# or run on GPU with INT8
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# model = WhisperModel(model_path, device="cuda", compute_type="int8_float16")
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# or run on CPU with INT8
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# model = WhisperModel(model_path, device="cpu", compute_type="int8")
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segments, info = model.transcribe("audio.mp3", beam_size=5)
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print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
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for segment in segments:
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print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
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```
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#### Word-level timestamps
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```python
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segments, _ = model.transcribe("audio.mp3", word_timestamps=True)
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for segment in segments:
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for word in segment.words:
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print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))
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```
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See more model and transcription options in the [`WhisperModel`](https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/transcribe.py) class implementation.
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## Comparing performance against other implementations
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If you are comparing the performance against other Whisper implementations, you should make sure to run the comparison with similar settings. In particular:
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* 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.
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* 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:
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```bash
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OMP_NUM_THREADS=4 python3 my_script.py
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```
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