Faster whisper

For reference, here's the time and memory usage that are required to transcribe 13 minutes of audio using different implementations:.

One feature of Whisper I think people underuse is the ability to prompt the model to influence the output tokens. Some examples from my terminal history:. Although I seem to have trouble to get the context to persist across hundreds of tokens. Tokens that are corrected may revert back to the model's underlying tokens if they weren't repeated enough. We need a better solution. It would be much better if there were an easy way to fine tune Whisper to learn new vocab. Why can Whisper not just reuse the prompt for every 30 second window?

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.

MobiusHorizons 3 months ago root parent next [—], faster whisper. I guess "streaming" would be a suitable expression in that case.

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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. Real-time speech recognition only needs to keep up with maybe — words per minute maybe a bit more if someone is a fast talker. We wanted to let the various GPUs stretch their legs a bit and show just how fast they can go. There are a few options for running Whisper, on Windows or otherwise. Getting WhisperDesktop running proved very easy, assuming you're willing to download and run someone's unsigned executable.

Faster whisper

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. The original code repository can be found here. Update: following the release of the paper, the Whisper authors announced a large-v2 model trained for 2.

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Skip to content. We've changed the URL now. It can do faster than real time. See the available VAD parameters and default values in the source code. MIT license. Large-v2 model on GPU. Dismiss alert. So what's in the secret sauce? Why do none of the benchmarks in the table match the headline? It's not fast. Here is a non exhaustive list of open-source projects using faster-whisper. More information is available from docker here and our announcement here. I guess "streaming" would be a suitable expression in that case. Whisper model that will be used for transcription. Custom properties.

Support distil-whisper model Robust knowledge distillation of the Whisper model via large-scale pseudo-labelling. Upgrade ctranslate2 version to 4.

Will this be any faster than running those by themselves? See the conversion API. Why do none of the benchmarks in the table match the headline? Is diarization only possible with stereo audio at the moment in whisper? Why can Whisper not just reuse the prompt for every 30 second window? We utilise the docker manifest for multi-platform awareness. We open pull requests on models[0] to get them running on Replicate so people can try out a demo of the model and run them with an API. Models can also be converted from the code. Founder of Replicate here. Branches Tags.