hifi-gan
speechbrain
| hifi-gan | speechbrain | |
|---|---|---|
| 5 | 26 | |
| 1,764 | 7,892 | |
| - | 2.5% | |
| 0.0 | 9.8 | |
| 9 months ago | 4 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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.)
speechbrain
- SpeechBrain 1.0: A free and open-source AI toolkit for all things speech
- FLaNK Stack Weekly 22 January 2024
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[D] Training ASR model using SpeechBrain
You likely have a very broken sample in one of your batches. It looks like your training actually went through a few batches before it horked the error at you. A quick google shows a similar issue in the github repo: https://github.com/speechbrain/speechbrain/issues/649 .
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Whisper.cpp
https://github.com/ggerganov/whisper.cpp https://speechbrain.github.io/
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[D] What is the best open source text to speech model?
I don't know if it's the best, but Speechbrain is supposed to be state of the art.
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[D] What's stopping you from working on speech and voice?
- https://github.com/speechbrain/speechbrain
- Specific Voice recognition
- How to get high-quality, low-cost Speech-to-Text transcription?
- [D] Speech Enhancement SOTA
- Speaker diarization
What are some alternatives?
WaveRNN - WaveRNN Vocoder + TTS
espnet - End-to-End Speech Processing Toolkit
wavegrad - A fast, high-quality neural vocoder.
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
Parallel-Tacotron2 - PyTorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Resemblyzer - A python package to analyze and compare voices with deep learning
diffwave - DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.
ukrainian-onnx-model - An ONNX model for speech recognition of the Ukrainian language
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
SincNet - SincNet is a neural architecture for efficiently processing raw audio samples.
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)
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)