Faster whisper

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

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.

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.

Please read the descriptions carefully and exercise caution when using unstable or development tags. Going further. Keep in mind umask is not chmod it subtracts from permissions based on it's value it does not add, faster whisper.

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

Faster whisper

Support distil-whisper model Robust knowledge distillation of the Whisper model via large-scale pseudo-labelling. Upgrade ctranslate2 version to 4. Upgrade PyAV version to Fix the broken tag v0. Some generation parameters that were available in the CTranslate2 API but not exposed in faster-whisper:. The WhisperModel constructor now accepts any repository ID as argument, for example:. When the model is loaded from its name like WhisperModel "large-v2" , a request is made to the Hugging Face Hub to check if some files should be downloaded. It can happen that this request raises an exception: the Hugging Face Hub is down, the internet is temporarily disconnected, etc. These types of exception are now catched and the library will try to directly load the model from the local cache if it exists.

Aed 15000

Faster Whisper transcription with CTranslate2. Is diarization only possible with stereo audio at the moment in whisper? Most of our images are static, versioned, and require an image update and container recreation to update the app inside. We utilise the docker manifest for multi-platform awareness. I also never said fine tuning would be easier than prompting. They're also packaged with Cog[1] so you can run them as a Docker image. We publish various Docker Mods to enable additional functionality within the containers. I've been playing around a lot with whisper. Basically the question is can the model be run in a streaming fashion, and is it still fast running that way. Branches Tags. Please read up here before asking for support. Tokens that are corrected may revert back to the model's underlying tokens if they weren't repeated enough. Folders and files Name Name Last commit message. We didn't submit it nor intend for it to be an ad. Whisper is really good at transcribing Greek but no diarization support, which makes it less than ideal for most use cases.

You read the title! Whisper just got faster with RunPod's new Faster-Whisper serverless endpoint.

Most of our images are static, versioned, and require an image update and container recreation to update the app inside. Install with pip Linux only. I use it for monitoring Reddit and all kinds of communities for mentions of my name and the titles of my books. Custom properties. Basically the question is can the model be run in a streaming fashion, and is it still fast running that way. I wonder if anyone is working on infusing the entire transcript with a "prompt" this way, it seems like a no brainer that would significantly improve accuracy. I'm sort of confused - is this just a CLI wrapper around faster-whisper, transformers and distil-whisper? It would just need to be easier than fine tuning currently is, not easier than prompting. Ensure any volume directories on the host are owned by the same user you specify and any permissions issues will vanish like magic. I also never said fine tuning would be easier than prompting. Founder of Replicate here. Insanely Fast Whisper github.