EmotiVoice
clearml
EmotiVoice | clearml | |
---|---|---|
5 | 20 | |
6,369 | 5,279 | |
- | 2.1% | |
8.9 | 7.7 | |
3 months ago | 3 days ago | |
Python | Python | |
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.
EmotiVoice
- FLaNK Stack Weekly 12 February 2024
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
Interested to see how it performs for Mandarin Chinese speech synthesis, especially with prosody and emotion. The highest quality open source model I've seen so far is EmotiVoice[0], which I've made a CLI wrapper around to generate audio for flashcards.[1] For EmotiVoice, you can apparently also clone your own voice with a GPU, but I have not tested this.[2]
[0] https://github.com/netease-youdao/EmotiVoice
[1] https://github.com/siraben/emotivoice-cli
[2] https://github.com/netease-youdao/EmotiVoice/wiki/Voice-Clon...
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Microsoft releases Windows AI studio to run and fine tune models locally
Interesting. I'll have to check to be sure, but I think maybe something is happening automagically if you have reasonably up to date nvidia drivers on the host OS, because I was able to run the EmotiVoice TTS docker (which requires nvidia gpu) from WSL2.
https://github.com/netease-youdao/EmotiVoice
- FLaNK Stack Weekly for 13 November 2023
- EmotiVoice: A Multi-Voice and Prompt-Controlled TTS Engine
clearml
- FLaNK Stack Weekly 12 February 2024
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
- Is there any workflow orchestrator that is Hydra friendly ?
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Show HN: Open-source infra for data scientists
It looks like Magniv is targeting Python in general. This is similar to ClearML. What are the differentiating points to Magniv compared to similar products?
It seems like the product also integrates with SCM systems. Are you using gitea and then containers to push code and data to execution like CodeOcean?
https://github.com/allegroai/clearml
https://codeocean.com/
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[D] Drop your best open source Deep learning related Project
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code.
- Everything you need to log, share, and version experiments, orchestrate pipelines, and scale within one open-source MLOps solution.
- Start with powerful experiment management and scale into full MLOps with only 2 lines of code
What are some alternatives?
Cgml - GPU-targeted vendor-agnostic AI library for Windows, and Mistral model implementation.
MLflow - Open source platform for the machine learning lifecycle
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
draw-a-ui - Draw a mockup and generate html for it
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
MockingBird - 🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time
kedro-great - The easiest way to integrate Kedro and Great Expectations
lhotse - Tools for handling speech data in machine learning projects.
streamlit - Streamlit — A faster way to build and share data apps.
voice100 - Voice100 includes neural TTS/ASR models. Inference of Voice100 is low cost as its models are tiny and only depend on CNN without autoregression.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️