neptune-client
silero-models
neptune-client | silero-models | |
---|---|---|
24 | 32 | |
536 | 4,569 | |
5.6% | - | |
9.7 | 4.7 | |
9 days ago | 7 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
neptune-client
-
Show HN: A gallery of dev tool marketing examples
Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/.
Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase, Posthog, Auth0).
So past year and a half, I’ve been screenshoting examples of how companies that are good at dev marketing do things like pricing, landing page design, ads, videos, blog conversion ideas. And for each example I added a note as to why I thought it was good.
Now, it is ~140 examples organized by tags so you can browse all or get stuff for a particular topic.
Hope it is helpful to some dev tool founders and marketers in here.
wdyt?
Also, I am always looking for new companies/marketing ideas to add to this, so if you’d like to share good examples I’d really appreciate it.
-
How to structure/manage a machine learning experiment? (medical imaging)
There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do.
-
How to grow a developer blog to 3M annual visitors? with Jakub Czakon (Neptune.ai)
Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing endeavors, including the influential newsletter Developer Markepear and a thriving developer marketing Slack community.
-
[D] Is there any all in one deep learning platform or software
tbh I have done a pretty good search on this topic, I couldn't find any. I thought maybe community could help me find one, if people like you (who works at neptune.ai) have the same opinion then it is what it is :). anyway thank you for the suggestions that you gave, probably gonna use that.
-
New Data Scientist, want to get into MLOps, where to start?
To get started with MLOps, you will need to have some foundational skills in Python, SQL, mathematics, and machine learning algorithms and libraries. You will also need to learn about databases, model deployment, continuous integration, continuous delivery, continuous monitoring, and other best practices of MLOps. You can find some useful resources for each of these topics in the following blogs on neptune.ai (disclosure: I work for Neptune):
-
Does a fully sentient (Or at least as sentient as you and me) AI with free will have a soul?
arxiv.org2. apro-software.com3. en.wikipedia.org4. neptune.ai
-
[D] The hype around Mojo lang
Other companies followed the same route to promote their paid product, e.g. plotly -> dash, Pytorch Lightning -> Lightning AI, run.ai, neptune.ai . It's actually a fair strategy, but some people may fear the conflict of interest. Especially, when the tools require some time investment, and it seems like a serious vendor lock-in. Investing some time to learn a tool is not such a big deal, but once you adapt a workflow of an entire team it can be tough to go back.
-
[P] New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio
track and compare your model performance visually. In addition, Neptune integration can be used.
-
[D] New features and current problems with ml infrastructure?
I am working on a startup, I was wondering what people think are some gaps in current machine learning infrastructure solutions like WandB, or Neptune.ai.
- All your ML model metadata in a single place
silero-models
-
Weird A.I. Yankovic, a cursed deep dive into the world of voice cloning
I doubt it's currently actually "the best open source text to speech", but the answer I came up with when throwing a couple of hours at the problem some months ago was "Silero" [0, 1].
Following the "standalone" guide [2], it was pretty trivial to make the model render my sample text in about 100 English "voices" (many of which were similar to each other, and in varying quality). Sampling those, I got about 10 that were pretty "good". And maybe 6 that were the "best ones" (pretty natural, not annoying to listen to).
IIRC the license was free for noncommercial use only. I'm not sure exactly "how open source" they are, but it was simple to install the dependencies and write the basic Python to try it out; I had to write a for loop to try all the voices like I wanted. I ended using something else for the project for other reasons, but this could still be fairly good backup option for some use cases IMO.
[0] https://github.com/snakers4/silero-models#text-to-speech
- What's the best text-to-speech free non-cloud software?
- Hey can anyone else add the text to speech
-
Messing around with a TTS extension
Glados was the first experiment. I moved on to silero afterwards: https://github.com/snakers4/silero-models
-
Ask HN: Open-source video transcribing software?
Some months ago I tried the Silero Models: https://github.com/snakers4/silero-models
With the audio sources I had, in English, the transcription had many mistakes. The good side is that installing and running the software worked as described in their documentation, so maybe it’s worth giving it a try by yourself.
- Silero V3:20种语言的快速高质量文本到语音,有173种声音 (Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices)
-
Hacker News top posts: Jun 20, 2022
Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices\ (56 comments)
- Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
Caffe - Caffe: a fast open framework for deep learning.
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
piper - A fast, local neural text to speech system
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Porcupine - On-device wake word detection powered by deep learning