Faster whisper

One faster whisper of Whisper I think people underuse is the ability to prompt the model to influence the output tokens.

For reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:. Unlike openai-whisper, FFmpeg does not need to be installed on the system. There are multiple ways to install these libraries. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below. On Linux these libraries can be installed with pip. Decompress the archive and place the libraries in a directory included in the PATH. The module can be installed from PyPI :.

Faster whisper

Faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models. This container provides a Wyoming protocol server for faster-whisper. We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here. Simply pulling lscr. This image provides various versions that are available via tags. Please read the descriptions carefully and exercise caution when using unstable or development tags. When using the gpu tag with Nvidia GPUs, make sure you set the container to use the nvidia runtime and that you have the Nvidia Container Toolkit installed on the host and that you run the container with the correct GPU s exposed. See the Nvidia Container Toolkit docs for more details. For more information see the faster-whisper docs ,. To help you get started creating a container from this image you can either use docker-compose or the docker cli. Containers are configured using parameters passed at runtime such as those above. For example, -p would expose port 80 from inside the container to be accessible from the host's IP on port outside the container. Keep in mind umask is not chmod it subtracts from permissions based on it's value it does not add.

I think it's more context-dependent than it is "hard". We already do it with languages, faster whisper, why not with concepts? Please read the descriptions carefully and exercise caution when using unstable or development tags.

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The best graphics cards aren't just for gaming, especially not when AI-based algorithms are all the rage. The last one is our subject today, and it can provide substantially faster than real-time transcription of audio via your GPU, with the entire process running locally for free. You can also run it on your CPU, though the speed drops precipitously. Note also that Whisper can be used in real-time to do speech recognition, similar to what you can get through Windows or Dragon NaturallySpeaking. We did not attempt to use it in that fashion, as we were more interesting in checking performance.

Faster whisper

Released: Mar 1, View statistics for this project via Libraries. Tags openai, whisper, speech, ctranslate2, inference, quantization, transformer.

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RockRobotRock 3 months ago parent [—]. It's also much easier to just rattle off a list of potential words that you know are going to be in the transcription that are difficult or spelled differently. One feature of Whisper I think people underuse is the ability to prompt the model to influence the output tokens. Please read up here before asking for support. For usage of faster-distil-whisper , please refer to: Reload to refresh your session. You signed out in another tab or window. Notifications Fork Star 7. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below. Basically the question is can the model be run in a streaming fashion, and is it still fast running that way. Real time transcription is not necessarily short snippets. I think there is more to it than just batch speed. I think it's more context-dependent than it is "hard". Give it a try. I hope the submission gets replaced with the upstream repo so the author gets full credit.

Whisper is a pre-trained model for automatic speech recognition ASR and speech translation. Trained on k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.

Tip We recommend Diun for update notifications. I'm not sure why you're so dismissive when real-time transcription is an important use-case that falls under that bucket of "quick snippets". Basically the question is can the model be run in a streaming fashion, and is it still fast running that way. I'm sort of confused - is this just a CLI wrapper around faster-whisper, transformers and distil-whisper? Insanely Fast Whisper github. Load a converted model. On Linux these libraries can be installed with pip. WhisperModel "whisper-large-v3-ct2". View all files. Large-v2 model on GPU. For reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:.