ubisoft-laforge-daft-exprt
hifi-gan
| ubisoft-laforge-daft-exprt | hifi-gan | |
|---|---|---|
| 3 | 5 | |
| 114 | 1,768 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| about 1 year ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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ubisoft-laforge-daft-exprt
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Using deepfake voice programms for devolopement - Possible/practical?
Ubisoft has their Daft-Exprt stuff on github that does a tolerable job of prosody/tone transfer, which is pretty much necessary to naturalize shit if you're going to be doing a cloning pipeline that isn't using a service's packaged voices. Without this I wouldn't even consider an ai speech pipeline due to how hardly constrained the range of tone is even with something like replicant studios actor shit.
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Using A.I voices or Sound Fonts (i.e. Undertale or Animal Crossing)
Ubisoft has some stuff that works to naturalize pretty well via prosody transfer https://github.com/ubisoft/ubisoft-laforge-daft-exprt
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Anyone have experience with AI voices?
Prosody transfer (I use https://github.com/ubisoft/ubisoft-laforge-daft-exprt), use an example speech segment to change the timing, intonation, and other properties of a different segment of speech. Such as taking evenly paced ML generated speech, and turning it into Captain Kirk iambic pentameter.
hifi-gan
- [D] What is the best open source text to speech model?
- I made Lisa-nee TTS (Imai Lisa)
- HiFi-GAN: Generative Adversarial Networks for Efficient and Hi-Fi Speech Synth
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[2108.13320] Neural HMMs are all you need (for high-quality attention-free TTS)
It will be interesting to see if the artefacts you noticed persist once we've trained the model for longer and switch to a better vocoder such as HiFi-GAN. (The paper and audio examples use WaveGlow since that's the default of the repository we compared ourselves to.) That said, "choppiness" sounds to me like it might be related to the temporal evolution, in which case it's something that a non-causal, convolutional post-net might be able to smooth over.
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The dangers of AI
Hey, as far as I know this paper is the current SoTA on public data that is open source. Github is here. If you are interested in really getting into speech synthesis, this page has everything (modern stuff on the bottom.)
What are some alternatives?
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
WaveRNN - WaveRNN Vocoder + TTS
STYLER - Official repository of STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech, INTERSPEECH 2021
wavegrad - A fast, high-quality neural vocoder.
vits - VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
Parallel-Tacotron2 - PyTorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
diffwave - DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.
EmotiVoice - EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
TensorFlowTTS - :stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
mlp-singer - Official implementation of MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis (IEEE MLSP 2021)