Neural Sp Save
End-to-end ASR/LM implementation with PyTorch
Project README
NeuralSP: Neural network based Speech Processing
How to install
cd tools
make KALDI=/path/to/kaldi TOOL=/path/to/save/tools
Key features
Corpus
-
ASR
- AISHELL-1
- AISHELL-2
- AMI
- CSJ
- LaboroTVSpeech
- Librispeech
- Switchboard (+Fisher)
- TEDLIUM2/TEDLIUM3
- TIMIT
- WSJ
-
LM
- Penn Tree Bank
- WikiText2
Front-end
Encoder
- RNN encoder
- Transformer encoder [link]
- Conformer encoder [link]
- Time-depth separable (TDS) convolution encoder [link] [line]
- Gated CNN encoder (GLU) [link]
Connectionist Temporal Classification (CTC) decoder
- Beam search
- Shallow fusion
- Forced alignment
RNN-Transducer (RNN-T) decoder [link]
- Beam search
- Shallow fusion
Attention-based decoder
- RNN decoder
- Attention type
- location-based
- content-based
- dot-product
- GMM attention
- Streaming RNN decoder specific
- Transformer decoder [link]
- Streaming Transformer decoder specific
Language model (LM)
- RNNLM (recurrent neural network language model)
- Gated convolutional LM [link]
- Transformer LM
- Transformer-XL LM [link]
- Adaptive softmax [link]
Output units
- Phoneme
- Grapheme
- Wordpiece (BPE, sentencepiece)
- Word
- Word-char mix
Multi-task learning (MTL)
Multi-task learning (MTL) with different units are supported to alleviate data sparseness.
- Hybrid CTC/attention [link]
- Hierarchical Attention (e.g., word attention + character attention) [link]
- Hierarchical CTC (e.g., word CTC + character CTC) [link]
- Hierarchical CTC+Attention (e.g., word attention + character CTC) [link]
- Forward-backward attention [link]
- LM objective
ASR Performance
AISHELL-1 (CER)
| Model | dev | test |
|---|---|---|
| Conformer LAS | 4.1 | 4.5 |
| Transformer | 5.0 | 5.4 |
| Streaming MMA | 5.5 | 6.1 |
AISHELL-2 (CER)
| Model | test_android | test_ios | test_mic |
|---|---|---|---|
| Conformer LAS | 6.1 | 5.5 | 5.9 |
CSJ (WER)
| Model | eval1 | eval2 | eval3 |
|---|---|---|---|
| Conformer LAS | 5.7 | 4.4 | 4.9 |
| BLSTM LAS | 6.5 | 5.1 | 5.6 |
| LC-BLSTM MoChA | 7.4 | 5.6 | 6.4 |
Switchboard 300h (WER)
| Model | SWB | CH |
|---|---|---|
| BLSTM LAS | 9.1 | 18.8 |
Switchboard+Fisher 2000h (WER)
| Model | SWB | CH |
|---|---|---|
| BLSTM LAS | 7.8 | 13.8 |
LaboroTVSpeech (CER)
| Model | dev_4k | dev | tedx-jp-10k |
|---|---|---|---|
| Conformer LAS | 7.8 | 10.1 | 12.4 |
Librispeech (WER)
| Model | dev-clean | dev-other | test-clean | test-other |
|---|---|---|---|---|
| Conformer LAS | 1.9 | 4.6 | 2.1 | 4.9 |
| Transformer | 2.1 | 5.3 | 2.4 | 5.7 |
| BLSTM LAS | 2.5 | 7.2 | 2.6 | 7.5 |
| BLSTM RNN-T | 2.9 | 8.5 | 3.2 | 9.0 |
| UniLSTM RNN-T | 3.7 | 11.7 | 4.0 | 11.6 |
| UniLSTM MoChA | 4.1 | 11.0 | 4.2 | 11.2 |
| LC-BLSTM RNN-T | 3.3 | 9.8 | 3.5 | 10.2 |
| LC-BLSTM MoChA | 3.3 | 8.8 | 3.5 | 9.1 |
| Streaming MMA | 2.5 | 6.9 | 2.7 | 7.1 |
TEDLIUM2 (WER)
| Model | dev | test |
|---|---|---|
| Conformer LAS | 7.0 | 6.8 |
| BLSTM LAS | 8.1 | 7.5 |
| LC-BLSTM RNN-T | 8.0 | 7.7 |
| LC-BLSTM MoChA | 10.3 | 8.6 |
| UniLSTM RNN-T | 10.7 | 10.7 |
| UniLSTM MoChA | 13.5 | 11.6 |
WSJ (WER)
| Model | test_dev93 | test_eval92 |
|---|---|---|
| BLSTM LAS | 8.8 | 6.2 |
LM Performance
Penn Tree Bank (PPL)
| Model | valid | test |
|---|---|---|
| RNNLM | 87.99 | 86.06 |
| + cache=100 | 79.58 | 79.12 |
| + cache=500 | 77.36 | 76.94 |
WikiText2 (PPL)
| Model | valid | test |
|---|---|---|
| RNNLM | 104.53 | 98.73 |
| + cache=100 | 90.86 | 85.87 |
| + cache=2000 | 76.10 | 72.77 |
Reference
- https://github.com/kaldi-asr/kaldi
- https://github.com/espnet/espnet
- https://github.com/awni/speech
- https://github.com/HawkAaron/E2E-ASR
Dependency
Open Source Agenda is not affiliated with "Neural Sp" Project. README Source: hirofumi0810/neural_sp
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