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 hundreds of tokens, faster whisper. Tokens that are corrected may revert back to the model's underlying tokens faster whisper they weren't repeated enough.

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. 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.

Someone mentioned Alexa-style home assistants, which would have faster whisper enough audio snippets that initial prompt would actually be useful. It can do faster than real time.

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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. However, it is difficult to use these LLMs because they require significant computational resources to train and run effectively. More computational resources require complex scaling infrastructure and often results in higher cloud costs. To help solve this massive problem of using LLMs at scale, Q Blocks has introduced a decentralized GPU computing approach coupled with optimized model deployment which not only reduces the cost of execution by multi-folds but also increases the throughput resulting in more sample serving per second.

Faster whisper

The Whisper models from OpenAI are best-in-class in the field of automatic speech recognition in terms of quality. However, transcribing audio with these models still takes time. Is there a way to reduce the required transcription time? Of course, it is always possible to upgrade hardware. However, it is wise to start with the software. This brings us to the project faster-whisper. CTranslate2 is a library for efficient inference with transformer models. This is made possible by applying various methods to increase efficiency, such as weight quantization, layer fusion, batch reordering, etc.

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MIT license. We've changed the URL now. It's ideal for streaming meeting transcripts. WhisperModel "whisper-large-v3-ct2". On Linux these libraries can be installed with pip. Install a specific commit. Whisper model that will be used for transcription. 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. It's there software to monitor when you're mentioned on HN or did you just happen to browse it? Load a converted model. Decompress the archive and place the libraries in a directory included in the PATH. MobiusHorizons 3 months ago root parent next [—]. Tokens that are corrected may revert back to the model's underlying tokens if they weren't repeated enough. Whisper is really good at transcribing Greek but no diarization support, which makes it less than ideal for most use cases.

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Small model on CPU. I'm curious, How did you know about this thread here? Here is a non exhaustive list of open-source projects using faster-whisper. This image provides various versions that are available via tags. Notifications Fork Star 7. For usage of faster-distil-whisper , please refer to: It's not fast. Nvidia's Nemo has some support for speaker recognition and speaker diarization too. Feel free to add your project to the list! MaximilianEmel 3 months ago root parent prev next [—]. So what's in the secret sauce? 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. The default behavior is conservative and only removes silence longer than 2 seconds. There are multiple ways to install these libraries. Why do none of the benchmarks in the table match the headline?