whisper-ctranslate2 0.5.7
Whisper command line client that uses CTranslate2 and faster-whisper
License: MIT

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# Introduction

Whisper command line client compatible with original [OpenAI client](https://github.com/openai/whisper) based on CTranslate2.

It uses [CTranslate2](https://github.com/OpenNMT/CTranslate2/) and [Faster-whisper](https://github.com/SYSTRAN/faster-whisper) Whisper implementation that is up to 4 times faster than openai/whisper for the same accuracy while using less memory.

Key features:

- ✅ 4x faster than OpenAI's Whisper at the same accuracy (up to 16X faster with batched inference)
- ✅ OpenAI Whisper CLI compatibility (easier migration)
- ✅ Docker image with pre-loaded models
- ✅ Speaker Diarization (Speaker Identification)
- ✅ Voice Activity Detection (VAD) Filter to improve quality
- ✅ Load your own fine-tuned Whisper models
- ✅ Live transcription from microphone
- ✅ Color-coded confidence visualization

# Installation

## Python package

To install the latest stable version, just type:

    pip install whisper-ctranslate2

## Using prebuild Docker image

You can use build docker image. First pull the image:

    docker pull ghcr.io/softcatala/whisper-ctranslate2:latest

The Docker image includes the small, medium" and large-v2.

To run it:

    docker run --gpus "device=0" \
        -v "$(pwd)":/srv/files/ \
        -it ghcr.io/softcatala/whisper-ctranslate2:latest \
        /srv/files/e2e-tests/gossos.mp3 \
        --output_dir /srv/files/
    
Notes:
* _--gpus "device=0"_ gives access to the GPU. If you do not have a GPU, remove this.
* _"$(pwd)":/srv/files/_ maps your current directory to /srv/files/ inside the container

If you always need to use a model that is not in the image, you can create a derived Docker image with the model preloaded or use Docker volumes to persist and share the model files.

# CPU and GPU support

GPU and CPU support is provided by [CTranslate2](https://github.com/OpenNMT/CTranslate2/).

It has compatibility with x86-64 and AArch64/ARM64 CPU and integrates multiple backends that are optimized for these platforms: Intel MKL, oneDNN, OpenBLAS, Ruy, and Apple Accelerate.

GPU execution requires the NVIDIA libraries cuBLAS 11.x to be installed on the system. Please refer to the [CTranslate2 documentation](https://opennmt.net/CTranslate2/installation.html)

By default the best hardware available is selected for inference. You can use the options `--device` and `--device_index` to control manually the selection.
    
# Usage

Same command line as OpenAI Whisper.

To transcribe:

    whisper-ctranslate2 inaguracio2011.mp3 --model medium
    
<img alt="image" src="https://user-images.githubusercontent.com/309265/226923541-8326c575-7f43-4bba-8235-2a4a8bdfb161.png">

To translate:

    whisper-ctranslate2 inaguracio2011.mp3 --model medium --task translate

<img alt="image" src="https://user-images.githubusercontent.com/309265/226923535-b6583536-2486-4127-b17b-c58d85cdb90f.png">

Whisper translate task translates the transcription from the source language to English (the only target language supported).

Additionally using:

    whisper-ctranslate2 --help

All the supported options with their help are shown.

# CTranslate2 specific options

On top of the OpenAI Whisper command line options, there are some specific options provided by CTranslate2 or whiper-ctranslate2.

## Batched inference

Batched inference transcribes each segment in-dependently which can provide an additional 2x-4x speed increase:

    whisper-ctranslate2 inaguracio2011.mp3 --batched True
    
You can additionally use the --batch_size to specify the maximum number of parallel requests to model for decoding.

Batched inference uses Voice Activity Detection (VAD) filter and ignores the following parameters: compression_ratio_threshold, logprob_threshold,
no_speech_threshold, condition_on_previous_text, prompt_reset_on_temperature, prefix, hallucination_silence_threshold.

## Quantization

`--compute_type` option which accepts _default,auto,int8,int8_float16,int16,float16,float32_ values indicates the type of [quantization](https://opennmt.net/CTranslate2/quantization.html) to use. On CPU _int8_ will give the best performance:

    whisper-ctranslate2 myfile.mp3 --compute_type int8

## Loading the model from a directory

`--model_directory` option allows to specify the directory from which you want to load a CTranslate2 Whisper model. For example, if you want to load your own quantified [Whisper model](https://opennmt.net/CTranslate2/conversion.html) version or using your own [Whisper fine-tuned](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event) version. The model must be in CTranslate2 format.

## Using Voice Activity Detection (VAD) filter

`--vad_filter` option enables the voice activity detection (VAD) to filter out parts of the audio without speech. This step uses the [Silero VAD model](https://github.com/snakers4/silero-vad):

    whisper-ctranslate2 myfile.mp3 --vad_filter True

The VAD filter accepts multiple additional options to determine the filter behavior:

    --vad_onset VALUE (float)

Probabilities above this value are considered as speech.

    --vad_min_speech_duration_ms (int)

Final speech chunks shorter min_speech_duration_ms are thrown out.

    --vad_max_speech_duration_s VALUE (int)

Maximum duration of speech chunks in seconds. Longer will be split at the timestamp of the last silence.


## Print colors

`--print_colors True` options prints the transcribed text using an experimental color coding strategy based on [whisper.cpp](https://github.com/ggerganov/whisper.cpp) to highlight words with high or low confidence:

    whisper-ctranslate2 myfile.mp3 --print_colors True

<img alt="image" src="https://user-images.githubusercontent.com/309265/228054378-48ac6af4-ce4b-44da-b4ec-70ce9f2f2a6c.png">

## Live transcribe from your microphone

`--live_transcribe True` option activates the live transcription mode from your microphone:

    whisper-ctranslate2 --live_transcribe True --language en

https://user-images.githubusercontent.com/309265/231533784-e58c4b92-e9fb-4256-b4cd-12f1864131d9.mov

## Diarization (speaker identification)

There is experimental diarization support using [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) to identify speakers. At the moment, the support is at segment level.

To enable diarization you need to follow these steps:

1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) with `pip install "pyannote.audio==4.0"`
2. Accept [`pyannote/speaker-diarization-community-1`](https://huggingface.co/pyannote/speaker-diarization-community-1) user conditions
3. Create an access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens).

And then execute passing the HuggingFace API token as parameter to enable diarization:

    whisper-ctranslate2 --hf_token YOUR_HF_TOKEN

and then the name of the speaker is added in the output files (e.g. JSON, VTT and STR files):

_[SPEAKER_00]: There is a lot of people in this room_

The option `--speaker_name SPEAKER_NAME` allows to use your own string to identify the speaker.

# Need help?

Check our [frequently asked questions](FAQ.md) for common questions.

# Contact

Jordi Mas <jmas@softcatala.org>