EmotiVoice
tortoise-tts
EmotiVoice | tortoise-tts | |
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5 | 145 | |
6,369 | 11,881 | |
- | - | |
8.9 | 8.0 | |
3 months ago | 9 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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EmotiVoice
- FLaNK Stack Weekly 12 February 2024
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WhisperSpeech โ 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
tortoise-tts
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).
There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...
- FLaNK Stack Weekly 12 February 2024
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OpenVoice: Versatile Instant Voice Cloning
I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.
https://github.com/neonbjb/tortoise-tts
- [discussion] text to voice generation for textbooks
- DALL-E 3: Improving image generation with better captions [pdf]
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Open Source Libraries
neonbjb/tortoise-tts
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Running Tortoise-TTS - IndexError: List out of range
EDIT: It appears to be the exact same issue as this
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My Deep Learning Rig
It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.
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PlayHT2.0: State-of-the-Art Generative Voice AI Model for Conversational Speech
Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].
From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.
I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.
Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.
[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...
[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide
What are some alternatives?
Cgml - GPU-targeted vendor-agnostic AI library for Windows, and Mistral model implementation.
TTS - ๐ธ๐ฌ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
bark - ๐ Text-Prompted Generative Audio Model
draw-a-ui - Draw a mockup and generate html for it
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
MockingBird - ๐AIๆๅฃฐ: 5็งๅ ๅ ้ๆจ็ๅฃฐ้ณๅนถ็ๆไปปๆ่ฏญ้ณๅ ๅฎน Clone a voice in 5 seconds to generate arbitrary speech in real-time
piper - A fast, local neural text to speech system
lhotse - Tools for handling speech data in machine learning projects.
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
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.
larynx - End to end text to speech system using gruut and onnx