[REPACK] Download Whisper

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.

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.

Download Whisper

DOWNLOAD 🔥 https://tlniurl.com/2y2Nzz 🔥

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.

In normal speech, the vocal cords alternate between states of voice and voicelessness. In whispering, only the voicing segments change, so that the vocal cords alternate between whisper and voicelessness (though the acoustic difference between the two states is minimal).[2] Because of this, implementing speech recognition for whispered speech is more difficult, as the characteristic spectral range needed to detect syllables and words is not given through the total absence of tone.[3] More advanced techniques such as neural networks may be used, however, as is done by Amazon Alexa.[4]

There is no symbol in the IPA for whispered phonation, since it is not used phonemically in any language. However, a sub-dot under phonemically voiced segments is sometimes seen in the literature, as [] for whispered should.

Whispering is generally used quietly, to limit the hearing of speech to those closest to the speaker; for example, to convey secret information without being overheard or to avoid disturbing others in a quiet place such as a library or place of worship. Loud whispering, known as a stage whisper, is generally used only for dramatic or emphatic purposes. Whispering can strain the vocal cords more than regular speech in some people, for whom speaking softly is recommended instead.[5]

In 2010, it was discovered that whispering is one of the many triggers of ASMR,[6] a tingling sensation caused by listening to soft, relaxing sounds. This phenomenon made news headlines after videos on YouTube of people speaking up close to the camera in a soft whisper, giving the viewer tingles.[7] People often listen to these videos to help them sleep and to relax.[8]

The prevalence and function of low-amplitude signaling by non-humans are poorly characterized.[9] As such, it is difficult to ascertain the existence of whispering in non-humans. This is made more difficult by the specific physiology of human whispering. By sufficiently relaxing the definition of whispering, it can be argued any number of non-human species demonstrate whisper-like behaviors. Often these behaviors function to increase fitness.[9]

If whispering is restricted to include only acoustic signals which are significantly different than those produced at high amplitude, whispering is still observed across biological taxa.[9] An unlikely example is the croaking gourami. Croaking gouramis produce a high-amplitude "croak" during agonistic disputes by beating specialized pectoral fins.[10] Female gouramis additionally use these fins to produce an acoustically distinct, low-amplitude "purr" during copulation.[11]

If whispering is restricted to include only creatures possessing vocal folds (i.e., mammals and some reptiles),[12] whispering has been observed in species including cotton-top tamarins and a variety of bats.[9] In captive cotton-top tamarins, whisper-like behavior is speculated to enable troop communication while not alerting predators.[a][13] Numerous species of bats (e.g., spotted bats,[14] northern long-eared bats,[15] and western barbastelles)[16] alter their echolocation calls[b] to avoid detection by prey.[c]

Such a relaxed definition of whispering (i.e., production of short-range, low-amplitude acoustic signals which are significantly different than those produced at high amplitude) cannot be applied to humans without including vocalizations distinct from human whispering (e.g., creaky voice, and falsetto). Further research is needed to ascertain the existence of whispering in non-humans as established in the larger article.

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?

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.

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!

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