openai-cookbook
whisper.cpp
openai-cookbook | whisper.cpp | |
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6 days ago | 1 day ago | |
MDX | C | |
MIT License | MIT License |
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openai-cookbook
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Question-Answer System Architectures using LLMs
A pretrained LLM is a closed-book system: It can only access information that it was trained on. With domain fine-tuning, the system manifests additional material. An early prototype of this technique was shown in this OpenAi cookbook: For the target domain, text was embedded using an API, and then when using the LLM, embeddings were retrieved using semantic similarity search to formulate an answer. Although this approach evolved to retrieval-augmented generation, its still a technique to adapt a Gen2 (2020) or Gen3 (2022) LLM into a question-answering system.
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Ask HN: High quality Python scripts or small libraries to learn from
https://github.com/openai/openai-cookbook/blob/main/examples...
- Collection of notebooks showcasing some fun and effective ways of using Claude
- OpenAI Cookbook: Techniques to improve reliability
- OpenAI Cookbooks
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How to fine tune vit/convnet to focus on the layout of the input room image and ignore other things ?
It sounds like you are trying to tweak embeddings for similarity search. Rather than fine-tune the model's layers, you may want to try training a linear transformation the existing model's output embedding. Openai has a cookbook on how to do that. You will need some data though - but I think you can try it with ~20 pieces of synthetically generated data.
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Best base model 1B or 7B for full finetuning
tutorial from OpenAI https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
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Resources to learn ChatGPT and the OpenAI API
OpenAI Cookbook
- OpenAI Cookbook
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Another Major Outage Across ChatGPT and API
OpenAI community repo with lots of examples: https://github.com/openai/openai-cookbook
whisper.cpp
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Show HN: I created automatic subtitling app to boost short videos
whisper.cpp [1] has a karaoke example that uses ffmpeg's drawtext filter to display rudimentary karaoke-like captions. It also supports diarisation. Perhaps it could be a starting point to create a better script that does what you need.
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1: https://github.com/ggerganov/whisper.cpp/blob/master/README....
- LLaMA Now Goes Faster on CPUs
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LLMs on your local Computer (Part 1)
The ggml library is one of the first library for local LLM interference. Itβs a pure C library that converts models to run on several devices, including desktops, laptops, and even mobile device - and therefore, it can also be considered as a tinkering tool, trying new optimizations, that will then be incorporated into other downstream projects. This tool is at the heart of several other projects, powering LLM interference on desktop or even mobile phones. Subprojects for running specific LLMs or LLM families exists, such as whisper.cpp.
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Voxos.ai β An Open-Source Desktop Voice Assistant
I'm not sure if it is _fully_ openai compatible, but whispercpp has a server bundled that says it is "OAI-like": https://github.com/ggerganov/whisper.cpp/tree/master/example...
I don't have any direct experience with it... I've only played around with whisper locally, using scripts.
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Jarvis: A Voice Virtual Assistant in Python (OpenAI, ElevenLabs, Deepgram)
unless i'm misunderstanding `whisper.cpp` seems to support streaming & the repository includes a native example[0] and a WASM example[1] with a demo site[2].
[0]: https://github.com/ggerganov/whisper.cpp/tree/master/example...
- Wchess
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I've open sourced my Flutter plugin to run on-device LLMs on any platform. TestFlight builds available now.
Usage 1: Good to transcribe audio. An example use case could be to summarize YouTube videos or long courses. Usage 2: You talk with voice to your AI that responds with text (later with audio too). - https://github.com/ggerganov/whisper.cpp
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Scrybble is the ReMarkable highlights to Obsidian exporter I have been looking for
π£οΈποΈ whisper.cpp (offline speech-to-text transcription, models trained by OpenAI, CLI based, browser based)
- Whisper.wasm
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Whisper C++ not working for me. Anyone else?
Has anyone played around with Whisper C++ for swift? I'm hitting a snag even on the demo. I've downloaded the github repo and everything matches up with this video [ https://youtu.be/b10OHCDHDQ4 ] but when he hits the transcribe button, it actually prints out the captioning. When I do it, it skips that part and just says "Done...". But it, does everything else - plays the audio, says it's transcribing.. just doesn't show me the transcription: and it's not in the debug window either. But the demo isn't throwing any errors, and I haven't messed with the code really so this is their example. https://github.com/ggerganov/whisper.cpp
What are some alternatives?
langchain - β‘ Building applications with LLMs through composability β‘ [Moved to: https://github.com/langchain-ai/langchain]
faster-whisper - Faster Whisper transcription with CTranslate2
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
Whisper - High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
bark - π Text-Prompted Generative Audio Model
askai - Command Line Interface for OpenAi ChatGPT
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
whisperX - WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)
txtai - π‘ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
llama.cpp - LLM inference in C/C++