Faster whisper

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 faster whisper of 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 ,.

I'd be interested in running this over a home camera system, but it would need to handle not talking well. Containers are configured using parameters passed at runtime such as those 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. 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

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.

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Whisper is really good at transcribing Greek but no diarization support, which makes it less than ideal for most use cases. Could be useful in a home automation context - give it a bit of clues about the environment and tech. Tokens that are corrected may revert back to the model's underlying tokens if they weren't repeated enough. It would be much better if there were an easy way to fine tune Whisper to learn new vocab. We recommend Diun for update notifications. 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. Containers are configured using parameters passed at runtime such as those above. Basically the question is can the model be run in a streaming fashion, and is it still fast running that way. MaximilianEmel 3 months ago root parent prev next [—] But it will influence the initial text generated, which influences the subsequent text as well. Why do none of the benchmarks in the table match the headline? Decompress the archive and place the libraries in a directory included in the PATH. Faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models. MobiusHorizons 3 months ago root parent next [—].

Large language models LLMs are AI models that use deep learning algorithms, such as transformers, to process vast amounts of text data, enabling them to learn patterns of human language and thus generate high-quality text outputs. They are used in applications like speech to text, chatbots, virtual assistants, language translation, and sentiment analysis.

About Faster Whisper transcription with CTranslate2 Topics deep-learning inference transformer speech-recognition openai speech-to-text quantization whisper. There are multiple ways to install these libraries. We didn't submit it nor intend for it to be an ad. It's ideal for streaming meeting transcripts. It would be much better if there were an easy way to fine tune Whisper to learn new vocab. Prompting is infinitely easier than fine-tuning in every aspect. Also movies and music might cause issues, too. There doesn't seem to be much code here, so this is why I'm wondering if this is actually something to get excited about if I already am aware of those projects. Here is a non exhaustive list of open-source projects using faster-whisper. Last commit date. Although I seem to have trouble to get the context to persist across hundreds of tokens.