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https://github.com/Softcatala/whisper-ctranslate2

Whisper command line client compatible with original OpenAI client based on CTranslate2.
https://github.com/Softcatala/whisper-ctranslate2

openai- openai-whisper speech-recognition speech-to-text whisper

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Whisper command line client compatible with original OpenAI client based on CTranslate2.

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README 

        
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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.



Goals of the project:

* Provide an easy way to use the CTranslate2 Whisper implementation

* Ease the migration for people using OpenAI Whisper CLI



# Installation



To install the latest stable version, just type:



    pip install -U whisper-ctranslate2



Alternatively, if you are interested in the latest development (non-stable) version from this repository, just type:



    pip install git+https://github.com/Softcatala/whisper-ctranslate2



# CPU and GPU support



GPU and CPU support are 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 and cuDNN 8.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

    

image



To translate:



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



image



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.



## 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-tunned](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_threshold 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



image



## 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 a 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`

2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions

3. Accept [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) user conditions

4. Create 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