Release V2.1

This release represents the last release before the PyTorch Lightning Integration. This is important in case anyone would like to use the old code base before we pivot to Lightning.

AN4

Training command:

python train.py --rnn-type lstm --hidden-size 1024 --hidden-layers 5  --train-manifest data/an4_train_manifest.csv --val-manifest data/an4_val_manifest.csv --epochs 70 --num-workers 16 --cuda  --learning-anneal 1.01 --batch-size 32 --no-sortaGrad --visdom  --opt-level O1 --loss-scale 1 --id an4 --checkpoint --save-folder deepspeech.pytorch/an4/ --model-path deepspeech.pytorch/an4/deepspeech_final.pth

Test Command:

python test.py --model-path an4_pretrained_v2.pth --test-manifest data/an4_val_manifest.csv --cuda --half

Dataset	WER	CER
AN4 test	10.349	7.076

Download here.

Librispeech

Training command:

python train.py --rnn-type lstm --hidden-size 1024 --hidden-layers 5  --train-manifest data/libri_train_manifest.csv --val-manifest data/libri_val_manifest.csv --epochs 60 --num-workers 16 --cuda  --learning-anneal 1.01 --batch-size 64 --no-sortaGrad --visdom  --opt-level O1 --loss-scale 1 --id libri --checkpoint --save-folder deepspeech.pytorch/librispeech/ --model-path deepspeech.pytorch/librispeech/deepspeech_final.pth

Test Command:

python test.py --model-path librispeech_pretrained_v2.pth --test-manifest data/libri_test_clean.csv --cuda --half
python test.py --model-path librispeech_pretrained_v2.pth --test-manifest data/libri_test_other.csv --cuda --half

Dataset	WER	CER
Librispeech clean	9.919	3.307
Librispeech other	28.116	12.040

With 3-Gram ARPA LM with tuned alpha/beta values (alpha=1.97, beta=4.36, beam-width=1024)

Test Command:

python test.py --test-manifest libri_test_clean.csv --lm-path 3-gram.pruned.3e-7.arpa --decoder beam --alpha 1.97 --beta 4.36 --model-path librispeech_pretrained_v2.pth --lm-workers 8 --num-workers 16 --cuda --half --beam-width 1024
python test.py --test-manifest libri_test_other.csv --lm-path 3-gram.pruned.3e-7.arpa --decoder beam --alpha 1.97 --beta 4.36 --model-path librispeech_pretrained_v2.pth --lm-workers 8 --num-workers 16 --cuda --half --beam-width 1024

Dataset	WER	CER
Librispeech clean	6.654	2.705
Librispeech other	19.889	10.467

Download here.

TEDLIUM

Training command:

python train.py --rnn-type lstm --hidden-size 1024 --hidden-layers 5  --train-manifest data/ted_train_manifest.csv --val-manifest data/ted_val_manifest.csv --epochs 60 --num-workers 16 --cuda  --learning-anneal 1.01 --batch-size 64 --no-sortaGrad --visdom  --opt-level O1 --loss-scale 1 --id ted --checkpoint --save-folder deepspeech.pytorch/tedlium/ --model-path deepspeech.pytorch/tedlium/deepspeech_final.pth

Test Command:

python test.py --model-path ted_pretrained_v2.pth --test-manifest data/ted_test_manifest.csv --cuda --half

Dataset	WER	CER
Ted test	30.886	11.196

Download here.

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Release V2.1

AN4

Librispeech

With 3-Gram ARPA LM with tuned alpha/beta values (alpha=1.97, beta=4.36, beam-width=1024)

TEDLIUM

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