whisper-ctranslate2 0.1.8

Whisper command line client that uses CTranslate2

Introduction

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

It uses CTranslate2 and 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

Just type:

pip install -U whisper-ctranslate2

Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:

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

Usage

Same command line that OpenAI whisper.

To transcribe:

whisper-ctranslate2 inaguracio2011.mp3 --model medium

To translate:

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

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 .

--compute_type {default,auto,int8,int8_float16,int16,float16,float32}

Type of quantization to use. On CPU int8 will give the best performance.

--model_directory MODEL_DIRECTORY

Directory where to find a CTranslate Whisper model, for example a fine-tunned Whisper model. The model should be in CTranslate2 format.

--device_index [DEVICE_INDEX ...]

Device IDs where to place this model on

--vad_filter VAD_FILTER

Enable the voice activity detection (VAD) to filter out parts of the audio without speech. This step is using the Silero VAD model https://github.com/snakers4/silero-vad.

--vad_min_silence_duration_ms VAD_MIN_SILENCE_DURATION_MS

When vad_filter is enabled, audio segments without speech for at least this number of milliseconds will be ignored.

Whisper-ctranslate2 specific options

On top of the OpenAI Whisper and CTranslate2, whisper-ctranslate2 provides some additional specific options:

--print-colors PRINT_COLORS

Adding the --print_colors True argument will print the transcribed text using an experimental color coding strategy based on whisper.cpp to highlight words with high or low confidence:

--live_transcribe

Adding the ----live_transcribe True will activate the live transcription mode from your microphone.

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

Need help?

Check our frequently asked questions for common questions.

Contact

Jordi Mas [e-mail]