whisper
text-generation-webui
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whisper | text-generation-webui | |
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343 | 876 | |
59,916 | 35,862 | |
5.3% | - | |
6.8 | 9.9 | |
12 days ago | 4 days ago | |
Python | Python | |
MIT License | GNU Affero General Public License v3.0 |
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.
whisper
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Why I Care Deeply About Web Accessibility And You Should Too
Let’s not talk about local models as the hardware requirements are way beyond most of these people’s reach. I have a MacBook Air with an M2 chip and 8GB of RAM and can hardly run Whisper locally, so I use this HuggingFace space.
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How I built NotesGPT – a full-stack AI voice note app
Last week, I launched notesGPT, a free and open source voice note app that has 35,000 visitors, 7,000 users, and over 1,000 GitHub stars so far in the last week. It allows you to record a voice note, transcribes it uses Whisper, and uses Mixtral via Together to extract action items and display them in an action items view. It’s also fully open source and comes equipped with authentication, storage, vector search, action items, and is fully responsive on mobile for ease of use.
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Ask HN: Can AI break a speech audio into individual words?
I found a pretty good discussion in the topic here:
https://github.com/openai/whisper/discussions/1243
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
There is a plot of language performance on their repo: https://github.com/openai/whisper
I am not aware of a multi-lingual leaderboard for speech recognition models.
- Ask HN: AI that allows you to make phone calls in a language you don't speak?
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Ask HN: Favorite Podcast Episodes of 2023?
I don't know how OP does it, but here's how I'd do it:
* Generate a transcript by runing Whisper against the podcast audio file: https://github.com/openai/whisper
* Upload transcript to ChatGPT and ask it to summarize.
* Automate all the above.
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Need advice
Ahh, that makes sense. I've been building something like that, but only from other languages into English using Whisper
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Subtitle is now open-source
Whisper already generates subtitles[0], supporting VTT and SRT so this is just a thin wrapper around that.
[0]: https://github.com/openai/whisper/blob/e58f28804528831904c3b...
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StyleTTS2 – open-source Eleven Labs quality Text To Speech
> although it does require you to wear headphones so the bot doesn't hear itself and get interrupted.
Maybe you can rely on some sort of speaker identification to sort this out?
https://github.com/openai/whisper/discussions/264
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Federated Finetuning of OpenAI's Whisper on Raspberry Pi 5
v3 only comes in one flavor: large.
I don’t think you’re going to have a good time running the large model on a Pi of any kind.
The large models are 32x slower than the tiny models, roughly.[0]
I’m seeing people report that the Pi 4 can transcribe 30 seconds of audio in somewhere between 30 seconds and 60 seconds with the tiny model.
You can do the math… 32x = 16 minutes to 32 minutes to transcribe 30 seconds of audio with the large model. Not a good time for most people.
The Pi 5 could be 2x to 3x faster.
I should benchmark my Pi 4 sometime (or Pi 5, if it ever shows up).
[0]: https://github.com/openai/whisper/blob/main/README.md#availa...
text-generation-webui
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Ask HN: What is the current (Apr. 2024) gold standard of running an LLM locally?
Some of the tools offer a path to doing tool use (fetching URLs and doing things with them) or RAG (searching your documents). I think Oobabooga https://github.com/oobabooga/text-generation-webui offers the latter through plugins.
Our tool, https://github.com/transformerlab/transformerlab-app also supports the latter (document search) using local llms.
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Ask HN: How to get started with local language models?
You can use webui https://github.com/oobabooga/text-generation-webui
Once you get a version up and running I make a copy before I update it as several times updates have broken my working version and caused headaches.
a decent explanation of parameters outside of reading archive papers: https://github.com/oobabooga/text-generation-webui/wiki/03-%...
a news ai website:
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text-generation-webui VS LibreChat - a user suggested alternative
2 projects | 29 Feb 2024
- Show HN: I made an app to use local AI as daily driver
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Ask HN: People who switched from GPT to their own models. How was it?
The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.
If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui
All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.
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AI Girlfriend Is a Data-Harvesting Horror Show
The example waifu in text-generation-webui is good enough for me.
https://github.com/oobabooga/text-generation-webui/blob/main...
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Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
> Downloading text-generation-webui takes a minute, let's you use any model and get going.
What you're missing here is you're already in this area deep enough to know what ooogoababagababa text-generation-webui is. Let's back out to the "average Windows desktop user" level. Assuming they even know how to find it:
1) Go to https://github.com/oobabooga/text-generation-webui?tab=readm...
2) See a bunch of instructions opening a terminal window and running random batch/powershell scripts. Powershell, etc will likely prompt you with a scary warning. Then you start wondering who ooobabagagagaba is...
3) Assuming you get this far (many users won't even get to step 1) you're greeted with a web interface[0] FILLED to the brim with technical jargon and extremely overwhelming options just to get a model loaded, which is another mind warp because you get to try to select between a bunch of random models with no clear meaning and non-sensical/joke sounding names from someone called "TheBloke". Ok...
Let's say you somehow braved this gauntlet and get this far now you get to chat with it. Ok, what about my local documents? text-generation-webui itself has nothing for that. Repeat this process over the 10 random open source projects from a bunch of names you've never heard of in an attempt to accomplish that.
This is "I saw this thing from Nvidia explode all over media, twitter, youtube, etc. I downloaded it from Nvidia, double-clicked, pointed it at a folder with documents, and it works".
That's the difference and it's very significant.
[0] - https://raw.githubusercontent.com/oobabooga/screenshots/main...
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Ask HN: What are your top 3 coolest software engineering tools?
Maybe a copout answer, but setting up a local LLM on my development machine has been invaluable. I use Deep Seek Coder 6.7 [0] and Oobabooga's UI [1]. It helps me solve simple problems and find bugs, while still leaving the larger architecture decisions to me.
[0] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instr...
[1] https://github.com/oobabooga/text-generation-webui
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Meta AI releases Code Llama 70B
You can download it and run it with [this](https://github.com/oobabooga/text-generation-webui). There's an API mode that you could leverage from your VS Code extension.
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Ollama Python and JavaScript Libraries
Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).
For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]
[1] https://github.com/oobabooga/text-generation-webui/issues/53...
[2] https://github.com/langroid/langroid/blob/main/langroid/lang...
Related question - I assume ollama auto detects and applies the right chat formatting template for a model?
What are some alternatives?
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
KoboldAI
silero-vad - Silero VAD: pre-trained enterprise-grade Voice Activity Detector
llama.cpp - LLM inference in C/C++
buzz - Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper.
gpt4all - gpt4all: run open-source LLMs anywhere
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
whisper.cpp - Port of OpenAI's Whisper model in C/C++
KoboldAI-Client
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.