kaldi nnet3 suprised me with some pretty detailed training diagnostics and sophisticated tricks early on, like weight averaging, LR scheduling, per-layer gradient statistics, etc. Icefall (recipes for k2) now has a similar diagnostics toolkit ported over using PyTorch hooks, see

https://github.com/k2-fsa/icefall/blob/c0101185d7be5e353db01dad326d530faa4ea718/icefall/diagnostics.py

The talk above describes what you can achieve if you get your diagnostics together

There is a blog post now https://medium.com/@nadirapovey/next-gen-kaldi-reworked-conformer-model-8a3828f364af

G2P for 8000 languages: https://github.com/xinjli/transphone

Happy Interspeech 2022 Tutorial Day!

First up, Learning from Weak Labels by Bhiksha Raj, Anurag Kumar, Ankit Shah

Slides and Code (MATLAB) in https://github.com/cmu-mlsp/Interspeech-Tutorial-2022-Learning_from_weak_labels

An afternoon tutorial is a by Xu Tan and Hung-yi Lee on Neural Speech Synthesis

Xu Tan has a massive survey paper on a Speech Synthesis, check out tts-tutorial on GitHub for more.

Hung-yi Lee has a very broad Machine Learning lectures channel in Mandarin with slides in English. A lot of lectures are about speech. https://www.youtube.com/c/HungyiLeeNTU

As an upgrade from this year’s ICASSP, it features a whole part devoted to Voice Conversion.

https://github.com/tts-tutorial/interspeech2022

Self-supervised Representation Learning for Speech Processing by Hung-yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff

This tutorial looks like an upgrade from a preceding one at NAACL 2022. There is an abstract paper inside (13 pages) and a link to s3prl on GitHub.

https://aclanthology.org/2022.naacl-tutorials.2/

SSL tutorial slides have been published:

https://docs.google.com/presentation/d/12W-L8EPQ3SfCPmgNcSm4B-_Psksqmvw_O6YD8CULelY/edit?resourcekey=0-ZBTF2RG_dHWNqq9q_w-eeg

source: https://twitter.com/HungyiLee2/status/1571511025475948549

PyTorch profiling tutorial k2 edition

https://github.com/k2-fsa/icefall/issues/610

That model warmup trick is also known as ReZero when you gradually change alpha from 0 to 1

https://proceedings.mlr.press/v161/bachlechner21a/bachlechner21a.pdf

https://twitter.com/92hschoi/status/1593467656379990016

Disentangled self-supervised representations for timbre, language and pitch using contrastive and reconstruction objectives.

DEMAND noise is used for linguistic contrast, CQT cropping is used for pitch. No extra augmentation seems to be used for timbre. Global speaker encoder tokens are turned into time-dependent using cross-attention in the synthesizer.

10k hours of pretraining data on 10x 3090. Beats supervised YourTTS on some tasks.

Rotate the phase of each frequency bin to simulate one-to-many mapping. Useful augmentation for GAN vocoder training. Differentiable.

https://mindslab-ai.github.io/phaseaug/

On augmentations for voice conversion:

> On the other hand, the use of more carefully designed augmentation techniques (such as timbre transformation with VoTrans and modifying prosody with NoisyF0) may be helpful in achieving more realistic target speaker identity and better audio quality

https://arxiv.org/abs/2212.13581

New scaling laws paper for combining different modalities (e.g. speech and text) using Chinchilla style notation.

https://twitter.com/ArmenAgha/status/1613192899646324736

I've been thinking about Transformers a lot — I've trained 5 GPT-2 models of different sizes, my first decoder-only Transformers over the past few weeks and trained my first encoder-decoder transformer on Math tasks from DeepMind.

It's magical how much faster these models converge if you care to spend engineering effort to scale them up. Recent advancements with Flash Attention and Triton make them go faster, 8-bit Adam saves memory, FP8 training is around the block.

I found a few training tricks people use in various combinations: gradient clipping, LR warmup + cooldown, model warmup (stochastic depth)

Going to share this important paper that moves Layer Norm to the top of the block (norm_first=True in PyTorch)
https://arxiv.org/abs/2002.04745

Sometimes, you can only train once. No time to tune learning rates.

Zero-shot Hyperparameter transfer: https://www.microsoft.com/en-us/research/blog/µtransfer-a-technique-for-hyperparameter-tuning-of-enormous-neural-networks/