This is not a feature of Whisper, there are other systems that can do this, but they typically are good at spotting who is saying what and when, but not nearly as good as whisper at determining what was said. A popular method is to combine the two and use time stamps to sync up the accurate whisper word detection with the other systems ability to detect who sad it and when.
There are a number of python packages that use OS whisper with different techniques of vocal isolation in order to improve hallucinations and add in those timecodes. We have one hosted on replicate I can share if it helps.
Download Whisper For Mac
Download Zip 🔥 https://urlgoal.com/2y2NpU 🔥
If desired, set a reply hotkey to whisper back.
It is equally important here that you select the correct hotkey profile if there are several. Otherwise, hotkeys that have already been set will be overwritten.
Hold in mind that you need at least on client to be targeted (sitting in one of these channels) or else you will get an error that no target was found. Technically you are always whispering to clients and not to channels.
I temporarily switched from Rust to Python for machine learning, but quickly became fed up with Python's annoying versioning issues and runtime errors. I looked for a better path to machine learning and discovered burn, a deep learning framework for Rust. As my first burn project I decided to port OpenAI's Whisper transcription model. The project can be found at Gadersd/whisper-burn: A Rust implementation of OpenAI's Whisper model using the burn framework (github.com). I based it on the excellently concise tinygrad implementation that can be found here. The tinygrad version begrudgingly uses Torch's stft which I ported into a pure Rust short time Fourier transform along with the mel scale frequency conversion matrix function because I am curious and just a bit masochistic.
Whisper is a proprietary Android mobile app (also available on iOS) available without charge. It is a form of anonymous social media, allowing users to post and share photo and video messages anonymously,[4][5] although this claim has been challenged with privacy concerns over Whisper's handling of user data.[6] The postings, called "whispers", consist of text superimposed over an image, and the background imagery is either automatically retrieved from Whisper's search engine or uploaded by the user.[7][8][9] The app, launched in March 2012, is the main product of the media company WhisperText LLC, which was co-founded by CEO Michael Heyward, the son of the entertainment executive Andy Heyward,[10] and Brad Brooks, who is the CEO of mobile messaging service TigerText. Since 2015, the service has sought to become more of a brand advertising platform, with promotional partnerships with Netflix,[11] NBCUniversal,[12] Disney,[13] HBO,[14] and MTV.[15] According to TechCrunch, as of March 2017, Whisper has a total of 17 billion monthly pageviews on its mobile and desktop websites, social channels and publisher network, with 250 million monthly users across 187 countries.[16] It is owned by MediaLab.[17] In October 2022, Whisper was removed from the Apple App Store, and was added to the App Store again but has been removed in 2023 again.
I am using Whisper to transcribe an audio file. I have installed Python3.9, ffmpeg and the associated dependencies, and openai-whisper==20230308. I could import whisper, but when I try to run transcribe:
But the whisper recognition in german seems to be really bad, even with the larger models, I actually had the parameter set to german as well, if I did it right. Has anyone had similar experiences? I tested it on a raspberry pi 4. Does anyone have better models? Maybe finetuned for german language, which are best already converted?
Hello, I am a German teacher and I've never heard of something like whisper phones. I find this idea so great that I'll try and introduce it in my classroom as well ?
Thanks for the inspiration!
Using Chrome on two devices in the same room, with more than one Twitch tab open on each device (Settings, my own offline channel and its chat, a channel I'm watching, the Live Channels You Follow page), sometimes whispers begin to vanish with "Your whisper was not delivered." This goes away if I close some of the tabs. But having multiple tabs open is standard Twitch life.
Also, sometimes I am whispering to a mod something critical and time-sensitive, and detailed, and then the dreaded not delivered message comes. The worst part? The message has vanished and I have to type it again, while singing and playing music.
A Museum favorite since 1938, the acoustic Whispering Gallery still sounds as good as it looks. You and a friend stand with your backs to each other at either end of this long room. When you whisper into the curved dish in front of you, your friend across the room hears you as though you are just inches away. No wires, no power. Can you figure out how it works?
Just tested the new whisper add-on and it lags pretty badly on my RPi4 and the only sensible model option that actually runs, tiny-int8, has about 40% WER (word error rate) in my language (Polish) which is basically unusable for anything. I wanted to run whisper on an external beefier server, I made this docker-compose:
Not sure if it makes sense as the WER % drops off a cliff for the tiny & base models (suposedly from another reviewer) but yeah for a larger but dunno about running those on CPU as say running on GPU after some time screaming at my computer and trying to install cuda11.6 on ubuntu 22.04, use one of the Nvidia docker containers instead as I give up!
But install the right torch 1st
pip3 install torch torchvision torchaudio --extra-index-url
Then install Whisper
pip install git+
Using :Reagan_Space_Shuttle_Challenger_Speech.ogv 4m:48s
time whisper Reagan_Space_Shuttle_Challenger_Speech.ogv --best_of None --beam_size None --model medium.en --threads=8
Which is likely a much better fit for a Pi4 with 10M parameters being a quarter of the whisper Tiny model and very likely directly converts to inference speed.
I have always liked GitHub - TensorSpeech/TensorFlowASR: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords and maybe because of my preference over pytorch that you can do the same things with PyTorch but with TFLite I have a reasonable knowledge how easy it is to use a TFlite Coral Delegate, or Mali or whatever or partition a model so it runs across several simultaneously of cpu/gpu/npu which is why I have the RK3588.
Same with TTS with GitHub - TensorSpeech/TensorFlowTTS: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages) as conversion to Tflite and support for embedded and accelerators seem much better, or at least was and now its because I am dodging Pytorch I am lacking knowledge.
Here are some benchmarks and test similar as what @StuartIanNaylor posted with WhisperCPP cross compiled within the whole buildroot system. I might redo them later with libwhispercpp compiled with the OpenBLAS option. ff782bc1db