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.