Weaviate
lyra
Our great sponsors
| Weaviate | lyra | |
|---|---|---|
| 76 | 18 | |
| 9,436 | 3,720 | |
| 4.8% | 0.9% | |
| 10.0 | 0.0 | |
| 7 days ago | over 1 year ago | |
| Go | C++ | |
| BSD 3-clause "New" or "Revised" License | 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.
Weaviate
-
pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
- FLaNK Stack 29 Jan 2024
- Qdrant, the Vector Search Database, raised $28M in a Series A round
-
How to use Weaviate to store and query vector embeddings
In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.
-
Choosing vector database: a side-by-side comparison
This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424
-
Who's hiring developer advocates? (October 2023)
Link to GitHub -->
-
Do we think about vector dbs wrong?
Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:
1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.
2. Parameter tuning. lowering `efConstruction` can speed up imports.
3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.
In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.
- Weaviate vector database
- Weaviate 1.21: Support for ImageBind and GPT4all and more
- Weaviate Vector Database
lyra
-
TSAC: Low Bitrate Audio Compression
Since Ballard's codec is "AI" based, can you add google's lyrav2 ( https://github.com/google/lyra ) and Facebook's/meta EnCodec ( https://github.com/facebookresearch/encodec ).
Also I don't seem to be able to access your page, so there might be error.
Finally, when doing opus comparison it's good now to denote if it is using Lace or NoLace decoder post processing filters that became available in opus 1.5 (note, this feature need to be enabled at compile time, and defying decode a new API call needs to be made to force higher complexity decoder) . See https://opus-codec.org/demo/opus-1.5/
-
Opus Databending Drumkit
I've thought about doing something similar for google's voice compression lyra https://github.com/google/lyra
-
Is it safe to say AV1 for video and OPUS for audio are best codecs respectively?
edit: It seems Lyra is opensource https://github.com/google/lyra
-
New Release of Audio Codec "Lyra" 1.3 (43% smaller and 20% faster)
1) https://github.com/google/lyra/releases/tag/v1.3.0
- Release Lyra 1.3.0 · google/lyra - performing arithmetic operations in 8-bit integers instead of 32-bit floats, the new model is 43% smaller (TFLite model size) and 20% faster
- Using AI to compress audio files for quick and easy sharing
-
Lyra V2 – a better, faster, and more versatile speech codec
Very impressive.
It'd be interesting to see what the lift would be to get encoding & decoding running in webassembly/wasm. Further, it'd be really neat to try to take something like the tflife_model_wrapper[1] and to get it backed by something like tsjs-tflite[2] perhaps even atop for example tfjs-backend-webgpu[3].
Longer run, the web-nn[4] spec should hopefully simplify/bake-in some of these libraries to the web platform, make running inference much easier. But there's still an interesting challenge & question, that I'm not sure how to tackle; how to take native code, compile it to wasm, but to have some of the implementation provided else-where.
[1] https://github.com/google/lyra/pull/89/files#diff-ed2f131a63...
[2] https://www.npmjs.com/package/@tensorflow/tfjs-tflite
[3] https://www.npmjs.com/package/@tensorflow/tfjs-backend-webgp...
[4] https://www.w3.org/TR/webnn/
-
Lyra 1.2.0 released with 5x speed improvement, higher quality speech, selectable bitrate (3.2, 6.0 and 9.2 kb/s), lower latency and Mac and Windows support
You can find an Android, Linux and macOS app here: https://github.com/google/lyra/actions/runs/3156735950
-
(Noob): Can Signal implement Lyra-Codec (developed by Google) for better audio quality?
Here's the repository: https://github.com/google/lyra and it's licensed under Apache.
- Lyra 0.0.2 ·The main improvement is the open-source release of the sparse_matmul library code, which was co-developed by Google and DeepMind. no more pre-compiled .so dynamic library binaries and no more restrictions on which toolchain to use, which opens up the door to port onto different platforms
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
codec2 - Open source speech codec designed for communications quality speech between 700 and 3200 bit/s. The main application is low bandwidth HF/VHF digital radio.
faiss - A library for efficient similarity search and clustering of dense vectors.
ESP32_Codec2 - Codec2 library for ESP32 (Arduino)
pgvector - Open-source vector similarity search for Postgres
minisearch - Tiny and powerful JavaScript full-text search engine for browser and Node
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Bazel - a fast, scalable, multi-language and extensible build system
jina - ☁️ Build multimodal AI applications with cloud-native stack
elasticsearch-py - Official Python client for Elasticsearch
vald - Vald. A Highly Scalable Distributed Vector Search Engine
regex-benchmark - It's just a simple regex benchmark of different programming languages.