WhisperLive
faster-whisper
WhisperLive | faster-whisper | |
---|---|---|
4 | 23 | |
1,253 | 9,014 | |
17.0% | 10.3% | |
9.4 | 8.1 | |
7 days ago | 6 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
WhisperLive
-
Show HN: WhisperFusion – Ultra-low latency conversations with an AI chatbot
Everything runs locally, we use:
- WhisperLive for the transcription - https://github.com/collabora/WhisperLive
-
WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
Check out WhisperLive: https://github.com/collabora/WhisperLive
If you're grappling with the slow march from cool tech demos to real-world language model apps, you might wanna check out WhisperLive. It's this rad open-source project that’s all about leveraging Whisper models for slick live transcription. Think real-time, on-the-fly translated captions for those global meetups. It's a neat example of practical, user-focused tech in action. Dive into the details on their GitHub page
-
Whisper: Nvidia RTX 4090 vs. M1 Pro with MLX
https://github.com/collabora/WhisperLive
The is another one that uses huggingface's implementation, but I haven't tried it since my spec doesn't support flash-att2
-
Triple Threat: The Power of Transcription, Summary, and Translation
Curious to see how this works? Check out our demo page - https://col.la/transcription to generate your own transcription, summary, and translation, or use our browser extension - https://github.com/collabora/WhisperLive to get live transcriptions.
faster-whisper
-
Creando Subtítulos Automáticos para Vídeos con Python, Faster-Whisper, FFmpeg, Streamlit, Pillow
Faster-whisper (https://github.com/SYSTRAN/faster-whisper)
-
Using Groq to Build a Real-Time Language Translation App
For our real-time STT needs, we'll employ a fantastic library called faster-whisper.
-
Apple Explores Home Robotics as Potential 'Next Big Thing'
Thermostats: https://www.sinopetech.com/en/products/thermostat/
I haven't tried running a local text-to-speech engine backed by an LLM to control Home Assistant. Maybe someone is working on this already?
TTS: https://github.com/SYSTRAN/faster-whisper
LLM: https://github.com/Mozilla-Ocho/llamafile/releases
LLM: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-D...
It would take some tweaking to get the voice commands working correctly.
-
Whisper: Nvidia RTX 4090 vs. M1 Pro with MLX
Could someone elaborate how is this accomplished and is there any quality disparity compared to original whisper?
Repos like https://github.com/SYSTRAN/faster-whisper makes immediate sense about why it's faster than the original, but this one, not so much, especially considering it's even much faster.
-
Now I Can Just Print That Video
Cool! I had the same project idea recently. You may be interested in this for the step of speech2text: https://github.com/SYSTRAN/faster-whisper
-
Distil-Whisper: distilled version of Whisper that is 6 times faster, 49% smaller
That's the implication. If the distil models are same format as original openai models then the Distil models can be converted for faster-whisper use as per the conversion instructions on https://github.com/guillaumekln/faster-whisper/
So then we'll see whether we get the 6x model speedup on top of the stated 4x faster-whisper code speedup.
-
AMD May Get Across the CUDA Moat
> While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant".
It easily is. See the benchmarks[0] from faster-whisper which uses Ctranslate2. That's 5x faster than OpenAI reference code on a Tesla V100. Needless to say something like a 4080 easily multiplies that.
> https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant.
With all due respect to the author of the article this is "my first entry into ML" territory. They talk about a 5-10 second delay, my project can do sub 1 second times[1] even with ancient GPUs thanks to Ctranslate2. I don't have an RTX 4080 but if you look at the performance stats for the closest thing (RTX 4090) the performance numbers are positively bonkers - completely untouchable for anything ROCm based. Same goes for the other projects I linked, lmdeploy does over 100 tokens/s in a single session with LLama2 13b on my RTX 4090 and almost 600 tokens/s across eight simultaneous sessions.
> EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... )
I don't understand what you're saying here. It (along with the other projects I linked) are fantastic examples of just how far behind the ROCm ecosystem is. ROCm isn't even on the radar for most of them as your linked issue highlights.
Things always get implemented in CUDA first (ten years in this space and I've never seen ROCm first) and ROCm users either wait months (minimum) for sub-par performance or never get it at all.
[0] - https://github.com/guillaumekln/faster-whisper#benchmark
[1] - https://heywillow.io/components/willow-inference-server/#ben...
-
Open Source Libraries
guillaumekln/faster-whisper
-
Whisper Turbo: transcribe 20x faster than realtime using Rust and WebGPU
Neat to see a new implementation, although I'll note that for those looking for a drop-in replacement for the whisper library, I believe that both faster-whisper https://github.com/guillaumekln/faster-whisper and https://github.com/m-bain/whisperX are easier (PyTorch-based, doesn't require a web browser), and a lot faster (WhisperX is up to 70X realtime).
-
Whisper.api: An open source, self-hosted speech-to-text with fast transcription
One caveat here is that whisper.cpp does not offer any CUDA support at all, acceleration is only available for Apple Silicon.
If you have Nvidia hardware the ctranslate2 based faster-whisper is very very fast: https://github.com/guillaumekln/faster-whisper
What are some alternatives?
cog-whisper-diarization - Cog implementation of transcribing + diarization pipeline with Whisper & Pyannote
whisper.cpp - Port of OpenAI's Whisper model in C/C++
whisper-writer - 💬📝 A small dictation app using OpenAI's Whisper speech recognition model.
whisperX - WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)
obs-zoom-and-follow - Dynamic zoom and mouse tracking script for OBS Studio
stable-ts - Transcription, forced alignment, and audio indexing with OpenAI's Whisper
gpt_chatbot - This chatbot lets you use your microphone to communicate with GPT-4. It uses the OpenAI text to speech to respond with a voice. It uses Pinecone to store long term information and retrieves it to create context. API keys for OpenAI and Pinecone required. Tested on Windows
whisper-diarization - Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
whisper_streaming - Whisper realtime streaming for long speech-to-text transcription and translation
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
gpt-voice-conversation-chatbot - Allows you to have an engaging and safely emotive spoken / CLI conversation with the AI ChatGPT / GPT-4 while giving you the option to let it remember things discussed.
whisper-realtime - Whisper runs in realtime on a laptop GPU (8GB)