Release of deepspeech.pytorch, where we've moved to Pytorch Lightning!
Previous release checkpoints will not be compatible, as a lot was deprecated and cleaned up for the future. Please use V2.1 if you need compatibility.
- Rely on Pytorch Lightning for training
- Moved to native CTC function, removing warp-ctc
- Refactor model objects, clean up technical debt
- Move towards json structure for manifest files
Pre-Trained models
AN4
Training command:
python train.py +configs=an4Test Command:
python test.py model.model_path=an4_pretrained_v3.ckpt test_path=data/an4_test_manifest.json| Dataset | WER | CER |
|---|---|---|
| AN4 test | 9.573 | 5.515 |
Download here.
Librispeech
Training command:
python train.py +configs=librispeech
Test Command:
python test.py model.model_path=librispeech.ckpt test_path=libri_test_clean_manifest.json
python test.py model.model_path=librispeech.ckpt test_path=libri_test_other_manifest.json
| Dataset | WER | CER |
|---|---|---|
| Librispeech clean | 10.463 | 3.399 |
| Librispeech other | 28.285 | 12.036 |
With 3-Gram ARPA LM with tuned alpha/beta values (alpha=1.97, beta=4.36, beam-width=1024)
Test Command:
python test.py model.model_path=librispeech.ckpt test_path=data/libri_test_clean_manifest.json lm.decoder_type=beam lm.alpha=1.97 lm.beta=4.36 lm.beam_width=1024 lm.lm_path=3-gram.arpa lm.lm_workers=16
python test.py model.model_path=librispeech.ckpt test_path=data/libri_test_other_manifest.json lm.decoder_type=beam lm.alpha=1.97 lm.beta=4.36 lm.beam_width=1024 lm.lm_path=3-gram.arpa lm.lm_workers=16
| Dataset | WER | CER |
|---|---|---|
| Librispeech clean | 7.062 | 2.984 |
| Librispeech other | 19.984 | 11.178 |
Download here.
TEDLIUM
Training command:
python train.py +configs=tedlium
Test Command:
python test.py model.model_path=ted_pretrained_v3.ckpt test_path=ted_test_manifest.json
| Dataset | WER | CER |
|---|---|---|
| Ted test | 28.056 | 10.548 |
Download here.