Pre-trained models tracker

On each of the datasets provided, we must train a Deepspeech model. The overall architecture is encompassed in this command:

python train.py  --rnn_type gru --hidden_size 800 --hidden_layers 5 --checkpoint --visdom --train_manifest /path/to/train_manifest.csv --val_manifest /path/to/val_manifest.csv --epochs 100 --num_workers $(nproc) --cuda

In the above command you must replace the manifests paths with the correct paths to the dataset. A few notes:

• No noise injection for the pre-trained models, or augmentations
• Train till convergence (should get a nice smooth training curve hopefully!)
• For smaller datasets, you may need to reduce the learning rate annealing by adding the flag --learning anneal and setting it to a smaller value, like 1.01. For larger datasets, the default is fine (up to around 4.5k hours from internal testing on the deepspeech.torch version)

A release will be cut from the DeepSpeech package that will have the models, and a reference to the latest release added to the README to find latest models!

Progress tracker for datasets:

• AN4
• TEDLium
• LibriSpeech

Let me know if you plan on working on running any of these, and I’ll update the ticket with details!

[ryanleary]

AN4 model is complete. Librispeech is still in progress. Below are the current evaluations:

Corpus	Test Set	Network	WER	CER
an4	an4-test	5x800gru	10.521	4.772
libri1k	libri-val	5x800gru	20.758	7.787
libri1k	libri-test	5x800gru	22.088	8.194
libri1k	test-clean	5x800gru	11.546	3.538
libri1k	test-other	5x800gru	31.813	12.483

[ryanleary]

Kicked off a 1000 hr libri training. Will know later tonight if convergence looks promising. Will probably take at least a few days to converge since I only have 2x Titan Xs for it.