Vol Building AGI
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Past topics: speech synthesis, transformers, LSTM, recurrence
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Prosody annotations for Switchboard: https://groups.inf.ed.ac.uk/switchboard/index.html
Convolutional Pitch Tracker (ICASSP 2018)

https://marl.github.io/crepe/

PyTorch port with lots of usage details: https://github.com/maxrmorrison/torchcrepe
Transformer-based sprocket successor, uses TTS pretraining. Available as egs/arctic/vc1 in ESPnet. Sounds much worse than sprocket.


http://www.kecl.ntt.co.jp/people/kameoka.hirokazu/Demos/vtn/index.html
Ephraim1985_Speech_enhancement_using_a_minimum_mean_square_error.pdf
311.1 KB
Dealing with residual vocoder noise:

LogMMSE Speech Enhancement and Noise Reduction

https://github.com/rajivpoddar/logmmse



y_enh = logmmse(y, sr, output_file=None, initial_noise=1, window_size=160, noise_threshold=0.15)
Vol Building AGI
StarGANv2-VC authors mentioned this method as one achieving highest MOS on VCC-2020 🤯 https://github.com/yl4579/StarGANv2-VC I need to take a closer look at VTN
VTN is T23,

T10 is ASR and prosody encoder fed into speaker-dependent TTS fed into WaveNet with single Gaussian outputs. The alternative system of T10 was an autoregressive LSTM that converted PPG into melspc and was used for two male-male parallel speakers.
On AMP and HiFi-GAN: may need to remove the bias from convolution
ICLR 2022

HiFi-GAN + chunked autoregression trains faster and keeps track of pitch better

https://github.com/descriptinc/cargan
👍1
https://serrjoa.github.io/projects/universe/

Score-based diffusion for universal speech enhancement (55 distortion types)

Base model: 49M parameters, 5 days, 2xV100, AMP
The paper goes on to describe improvements to the model
Scaled up model: 189M parameters, 14 days 8xV100