bootcamp
vosk-api
bootcamp | vosk-api | |
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24 | 61 | |
1,634 | 7,085 | |
3.2% | 2.2% | |
9.1 | 6.6 | |
1 day ago | 4 days ago | |
HTML | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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.
bootcamp
- FLaNK AI - 01 April 2024
- FLaNK Stack Weekly 22 January 2024
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Milvus Adventures Jan 5, 2023
Metadata Filtering with Zilliz Cloud Pipelines This tutorial discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on Getting started with RAG in just 5 minutes. You can find its code in this notebook and scroll down to Cell #27.
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Build a search engine, not a vector DB
Partially agree.
Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.
As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.
The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.
No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.
With this idea I built a Retrieval-as-a-service solution, and here is its demo:
https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...
Curious to learn your thoughts.
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Vector Database in a Jupyter Notebook
Although it's common to use vector databases in conjunction with LLMs, I like to talk about vector databases in the context of unstructured data, i.e. any data that you can vectorize with (or without) an ML model. Yes, this includes text, but it also includes things like visual data, molecular structures, and geospatial data.
For folks who want to learn a bit more, there are examples of vector database use cases beyond semantic text search in our bootcamp: https://github.com/milvus-io/bootcamp
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Beginner-ish resources for choosing a vector database?
Easy to get started: Here are some tutorials for Milvus in a Jupyter Notebook that I wrote - reverse image search, semantic text search
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Semantic Similarity Search
I think you can just store your vector embeddings in the vector store somewhere and then query with your second document. I created a short tutorial on this that shows how to get the top 2 vector embeddings from a text query
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[D] Looking for open source projects to contribute
For more beginner tasks associated with the Milvus vector database, you can contribute to the Bootcamp project( https://github.com/milvus-io/bootcamp), where we build a lot of data-driven solutions using ML and Milvus vector database, including reverse image search, recommender systems, etc.
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I built an image similarity search system... Suggestions needed: what are some fun image datasets or scenarios I can use with this? :)
Source code here: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search
- Faiss: Facebook's open source vector search library
vosk-api
- Infini-Gram: Scaling unbounded n-gram language models to a trillion tokens
- VOSK Offline Speech Recognition API
- Apollo dev posts backend code to Git to disprove Reddit’s claims of scrapping and inefficiency
- Working Vosk model?
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Creating a live transcript bot using Vosk Ai
So I don't know if my issue comes from my lack of knowledge of discord.js/voice or VOSK. so I guess the most important thing I need to see is if I am creating a proper stream for the Vosk API to capture the audio. if I can figure out how to capture an audio stream I can probably import that in to vosk and figure out how to use vosk myself. but right now I can't even get close! Thank you in advance...Sorry if this isn't the right place for this
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What are the aplications of rust in machine learning ?
I remember a while ago checking out the issues with Vosk speech recognition (written in C). A handful of it's issues are related to segfaults and null pointers.
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Show HN: Willow – Open-Source Privacy-Focused Voice Assistant Hardware
first, good initiative! thanks for sharing. i think you gotta be more diligent and careful with the problem statement.
checking the weather in Sofia, Bulgaria requires cloud, current information. it's not "random speech". ESP SR capability issues don't mean that you cannot process it locally.
the comment was on "voice processing" i.e. sending speech to the cloud, not sending a call request to get the weather information.
besides, local intent detection, beyond 400 commands, there are great local STT options, working better than most cloud STTs for "random speech"
https://github.com/alphacep/vosk-api
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ChatGPT API is now officially available, priced at $0.002 per 1k tokens
I did a one-off text to speech tool for someone last year and had pretty good results with VOSK. One upside is that it works offline, although I imagine if you use TTS a lot you'll notice issues I didn't.
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Looking to mod a Vector with GPT-3, what are my options?
You can use vosk-api (https://github.com/alphacep/vosk-api) to listen to your audio, transform it to text, and then post the text to GPT-3, then using the vector sdk, have your responses said by vector.
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A new voice assistant that looks promising
The set up script wants to download https://github.com/alphacep/vosk-api/releases/download/v0.3.45/vosk-model-en-v0.3.45.zip, but this resource is not found. AFAICT all releases never contained a model file. Remedy: hardcode one model from https://alphacephei.com/vosk/models. I guessed and picked the one with the closest name, vosk-model-en-us-0.22.zip, just so I could continue.
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
google-research - Google Research
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
docarray - Represent, send, store and search multimodal data
vosk-server - WebSocket, gRPC and WebRTC speech recognition server based on Vosk and Kaldi libraries
es-clip-image-search - Sample implementation of natural language image search with OpenAI's CLIP and Elasticsearch or Opensearch.
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
AutoSub - A CLI script to generate subtitle files (SRT/VTT/TXT) for any video using either DeepSpeech or Coqui
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
DeepSpeech - Install Mozilla DeepSpeech on a Raspberry Pi 4