espnet
EmotiVoice
| espnet | EmotiVoice | |
|---|---|---|
| 15 | 5 | |
| 7,892 | 6,303 | |
| 1.3% | - | |
| 10.0 | 8.9 | |
| 1 day ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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espnet
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
You might check out this list from espnet. They list the different corpuses they use to train their models sorted by language and task (ASR, TTS etc):
https://github.com/espnet/espnet/blob/master/egs2/README.md
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[D] What's stopping you from working on speech and voice?
- https://github.com/espnet/espnet
- Íslensk talgervilsrödd sem hægt er að nota á Macca
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High quality, fast performing, local text to speech generation
This link has instructions for doing this for a Japanese model. It would have to be altered to work with ljspeech and the fine tune dataset.
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Text to speech generation
This work is made possible by the excellent advancements in text to speech modeling. ESPnet is a great project and should be checked out for more advanced and a wider range of use cases. This pipeline was also made possible by the great work from espnet_onnx in building a framework to export models to ONNX.
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[P] TorToiSe - a true zero-shot multi-voice TTS engine
CMU WavLab has ESPNet https://espnet.github.io/espnet/ which includes a number of high quality TTS models including VITS (which in my subjective experience is just as good as what is demonstrated here). Also the inference on various ESPNet pretrained TTS models is reasonable and sentences take on average 5 seconds per word to generate the waveform on my totally mid PC setup.
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How to get Job in NLP?
The reason I'm saying this is to point out that having and in-depth knowledge on speech processing/generation requires a lot of information about signal processing and human speech in general (eg. acoustics and phonetics). However, if you're not into learning everything there is to know about a subject, just take one state-of-the-art example and study that as best as you can. Pick one environment/toolkit, for example espnet and simply go with that.
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Help picking a good speech recognition library
https://github.com/espnet/espnet (kind of like a newer Kaldi, but also not beginner friendly)
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speechbrain VS espnet - a user suggested alternative
2 projects | 13 Oct 2021
both provide e2e ASR support but espnet does have more utilities where as speechbarain is clean
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Need help with training ASR model from scratch.
This is relatively small amount of speech to train the model from scratch, but you can train using another pre-trained model for initialization. There are numbers of end-to-end ASR toolkits which can be used for this: https://github.com/NVIDIA/NeMo and https://github.com/espnet/espnet
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
What are some alternatives?
speechbrain - A PyTorch-based Speech Toolkit
Cgml - GPU-targeted vendor-agnostic AI library for Windows, and Mistral model implementation.
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
k2 - FSA/FST algorithms, differentiable, with PyTorch compatibility.
draw-a-ui - Draw a mockup and generate html for it
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
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
kaldi-gstreamer-server - Real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framwork.
lhotse - Tools for handling speech data in machine learning projects.
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
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.