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icefall/README.md
Shreyas0410 5cebecf2dc
updated broken link in read.me file (#1342)
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<div align="center">
<img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168>
</div>
## Introduction
icefall contains ASR recipes for various datasets
using <https://github.com/k2-fsa/k2>.
You can use <https://github.com/k2-fsa/sherpa> to deploy models
trained with icefall.
You can try pre-trained models from within your browser without the need
to download or install anything by visiting <https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>
See <https://k2-fsa.github.io/icefall/huggingface/spaces.html> for more details.
## Installation
Please refer to <https://icefall.readthedocs.io/en/latest/installation/index.html>
for installation.
## Recipes
Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
for more information.
We provide the following recipes:
- [yesno][yesno]
- [LibriSpeech][librispeech]
- [GigaSpeech][gigaspeech]
- [AMI][ami]
- [Aishell][aishell]
- [Aishell2][aishell2]
- [Aishell4][aishell4]
- [TIMIT][timit]
- [TED-LIUM3][tedlium3]
- [Aidatatang_200zh][aidatatang_200zh]
- [WenetSpeech][wenetspeech]
- [Alimeeting][alimeeting]
- [Switchboard][swbd]
- [TAL_CSASR][tal_csasr]
### yesno
This is the simplest ASR recipe in `icefall` and can be run on CPU.
Training takes less than 30 seconds and gives you the following WER:
```
[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
```
We provide a Colab notebook for this recipe: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
### LibriSpeech
Please see <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>
for the **latest** results.
We provide 5 models for this recipe:
- [conformer CTC model][LibriSpeech_conformer_ctc]
- [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc]
- [Transducer: Conformer encoder + LSTM decoder][LibriSpeech_transducer]
- [Transducer: Conformer encoder + Embedding decoder][LibriSpeech_transducer_stateless]
- [Transducer: Zipformer encoder + Embedding decoder][LibriSpeech_zipformer]
#### Conformer CTC Model
The best WER we currently have is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.42 | 5.73 |
We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
#### TDNN LSTM CTC Model
The WER for this model is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 6.59 | 17.69 |
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing)
#### Transducer: Conformer encoder + LSTM decoder
Using Conformer as encoder and LSTM as decoder.
The best WER with greedy search is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 3.07 | 7.51 |
We provide a Colab notebook to run a pre-trained RNN-T conformer model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1_u6yK9jDkPwG_NLrZMN2XK7Aeq4suMO2?usp=sharing)
#### Transducer: Conformer encoder + Embedding decoder
Using Conformer as encoder. The decoder consists of 1 embedding layer
and 1 convolutional layer.
The best WER using modified beam search with beam size 4 is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.56 | 6.27 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.
We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CO1bXJ-2khDckZIW8zjOPHGSKLHpTDlp?usp=sharing)
#### k2 pruned RNN-T
| Encoder | Params | test-clean | test-other | epochs | devices |
|-----------------|--------|------------|------------|---------|------------|
| zipformer | 65.5M | 2.21 | 4.79 | 50 | 4 32G-V100 |
| zipformer-small | 23.2M | 2.42 | 5.73 | 50 | 2 32G-V100 |
| zipformer-large | 148.4M | 2.06 | 4.63 | 50 | 4 32G-V100 |
| zipformer-large | 148.4M | 2.00 | 4.38 | 174 | 8 80G-A100 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.
#### k2 pruned RNN-T + GigaSpeech
| | test-clean | test-other |
|-----|------------|------------|
| WER | 1.78 | 4.08 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.
#### k2 pruned RNN-T + GigaSpeech + CommonVoice
| | test-clean | test-other |
|-----|------------|------------|
| WER | 1.90 | 3.98 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.
### GigaSpeech
We provide three models for this recipe:
- [Conformer CTC model][GigaSpeech_conformer_ctc]
- [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][GigaSpeech_pruned_transducer_stateless2].
- [Transducer: Zipformer encoder + Embedding decoder][GigaSpeech_zipformer]
#### Conformer CTC
| | Dev | Test |
|-----|-------|-------|
| WER | 10.47 | 10.58 |
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 10.51 | 10.73 |
| fast beam search | 10.50 | 10.69 |
| modified beam search | 10.40 | 10.51 |
#### Transducer: Zipformer encoder + Embedding decoder
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 10.31 | 10.50 |
| fast beam search | 10.26 | 10.48 |
| modified beam search | 10.25 | 10.38 |
### Aishell
We provide three models for this recipe: [conformer CTC model][Aishell_conformer_ctc],
[TDNN LSTM CTC model][Aishell_tdnn_lstm_ctc], and [Transducer Stateless Model][Aishell_pruned_transducer_stateless7],
#### Conformer CTC Model
The best CER we currently have is:
| | test |
|-----|------|
| CER | 4.26 |
#### TDNN LSTM CTC Model
The CER for this model is:
| | test |
|-----|-------|
| CER | 10.16 |
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jbyzYq3ytm6j2nlEt-diQm-6QVWyDDEa?usp=sharing)
#### Transducer Stateless Model
The best CER we currently have is:
| | test |
|-----|------|
| CER | 4.38 |
We provide a Colab notebook to run a pre-trained TransducerStateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
### Aishell2
We provide one model for this recipe: [Transducer Stateless Model][Aishell2_pruned_transducer_stateless5].
#### Transducer Stateless Model
The best WER we currently have is:
| | dev-ios | test-ios |
|-----|------------|------------|
| WER | 5.32 | 5.56 |
### Aishell4
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aishell4_pruned_transducer_stateless5].
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with all subsets)
The best CER we currently have is:
| | test |
|-----|------------|
| CER | 29.08 |
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
### TIMIT
We provide two models for this recipe: [TDNN LSTM CTC model][TIMIT_tdnn_lstm_ctc]
and [TDNN LiGRU CTC model][TIMIT_tdnn_ligru_ctc].
#### TDNN LSTM CTC Model
The best PER we currently have is:
||TEST|
|--|--|
|PER| 19.71% |
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Hs9DA4V96uapw_30uNp32OMJgkuR5VVd?usp=sharing)
#### TDNN LiGRU CTC Model
The PER for this model is:
||TEST|
|--|--|
|PER| 17.66% |
We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
### TED-LIUM3
We provide two models for this recipe: [Transducer Stateless: Conformer encoder + Embedding decoder][TED-LIUM3_transducer_stateless] and [Pruned Transducer Stateless: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TED-LIUM3_pruned_transducer_stateless].
#### Transducer Stateless: Conformer encoder + Embedding decoder
The best WER using modified beam search with beam size 4 is:
| | dev | test |
|-----|-------|--------|
| WER | 6.91 | 6.33 |
Note: No auxiliary losses are used in the training and no LMs are used in the decoding.
We provide a Colab notebook to run a pre-trained Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MmY5bBxwvKLNT4A2DJnwiqRXhdchUqPN?usp=sharing)
#### Pruned Transducer Stateless: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
The best WER using modified beam search with beam size 4 is:
| | dev | test |
|-----|-------|--------|
| WER | 6.77 | 6.14 |
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)
### Aidatatang_200zh
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aidatatang_200zh_pruned_transducer_stateless2].
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
| | Dev | Test |
|----------------------|-------|-------|
| greedy search | 5.53 | 6.59 |
| fast beam search | 5.30 | 6.34 |
| modified beam search | 5.27 | 6.33 |
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wNSnSj3T5oOctbh5IGCa393gKOoQw2GH?usp=sharing)
### WenetSpeech
We provide some models for this recipe: [Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless2] and [Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless5].
#### Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset, offline ASR)
| | Dev | Test-Net | Test-Meeting |
|----------------------|-------|----------|--------------|
| greedy search | 7.80 | 8.75 | 13.49 |
| modified beam search| 7.76 | 8.71 | 13.41 |
| fast beam search | 7.94 | 8.74 | 13.80 |
#### Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset)
**Streaming**:
| | Dev | Test-Net | Test-Meeting |
|----------------------|-------|----------|--------------|
| greedy_search | 8.78 | 10.12 | 16.16 |
| modified_beam_search | 8.53| 9.95 | 15.81 |
| fast_beam_search| 9.01 | 10.47 | 16.28 |
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless2 model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
### Alimeeting
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Alimeeting_pruned_transducer_stateless2].
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with far subset)
| | Eval | Test-Net |
|----------------------|--------|----------|
| greedy search | 31.77 | 34.66 |
| fast beam search | 31.39 | 33.02 |
| modified beam search | 30.38 | 34.25 |
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing)
### TAL_CSASR
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TAL_CSASR_pruned_transducer_stateless5].
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
The best results for Chinese CER(%) and English WER(%) respectively (zh: Chinese, en: English):
|decoding-method | dev | dev_zh | dev_en | test | test_zh | test_en |
|--|--|--|--|--|--|--|
|greedy_search| 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|
|modified_beam_search| 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
|fast_beam_search| 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77|
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DmIx-NloI1CMU5GdZrlse7TRu4y3Dpf8?usp=sharing)
## Deployment with C++
Once you have trained a model in icefall, you may want to deploy it with C++,
without Python dependencies.
Please refer to the documentation
<https://icefall.readthedocs.io/en/latest/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html#deployment-with-c>
for how to do this.
We also provide a Colab notebook, showing you how to run a torch scripted model in [k2][k2] with C++.
Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1BIGLWzS36isskMXHKcqC9ysN6pspYXs_?usp=sharing)
[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc
[LibriSpeech_conformer_ctc]: egs/librispeech/ASR/conformer_ctc
[LibriSpeech_transducer]: egs/librispeech/ASR/transducer
[LibriSpeech_transducer_stateless]: egs/librispeech/ASR/transducer_stateless
[LibriSpeech_zipformer]: egs/librispeech/ASR/zipformer
[Aishell_tdnn_lstm_ctc]: egs/aishell/ASR/tdnn_lstm_ctc
[Aishell_conformer_ctc]: egs/aishell/ASR/conformer_ctc
[Aishell_pruned_transducer_stateless7]: egs/aishell/ASR/pruned_transducer_stateless7_bbpe
[Aishell2_pruned_transducer_stateless5]: egs/aishell2/ASR/pruned_transducer_stateless5
[Aishell4_pruned_transducer_stateless5]: egs/aishell4/ASR/pruned_transducer_stateless5
[TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc
[TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
[TED-LIUM3_transducer_stateless]: egs/tedlium3/ASR/transducer_stateless
[TED-LIUM3_pruned_transducer_stateless]: egs/tedlium3/ASR/pruned_transducer_stateless
[GigaSpeech_conformer_ctc]: egs/gigaspeech/ASR/conformer_ctc
[GigaSpeech_pruned_transducer_stateless2]: egs/gigaspeech/ASR/pruned_transducer_stateless2
[GigaSpeech_zipformer]: egs/gigaspeech/ASR/zipformer
[Aidatatang_200zh_pruned_transducer_stateless2]: egs/aidatatang_200zh/ASR/pruned_transducer_stateless2
[WenetSpeech_pruned_transducer_stateless2]: egs/wenetspeech/ASR/pruned_transducer_stateless2
[WenetSpeech_pruned_transducer_stateless5]: egs/wenetspeech/ASR/pruned_transducer_stateless5
[Alimeeting_pruned_transducer_stateless2]: egs/alimeeting/ASR/pruned_transducer_stateless2
[TAL_CSASR_pruned_transducer_stateless5]: egs/tal_csasr/ASR/pruned_transducer_stateless5
[yesno]: egs/yesno/ASR
[librispeech]: egs/librispeech/ASR
[aishell]: egs/aishell/ASR
[aishell2]: egs/aishell2/ASR
[aishell4]: egs/aishell4/ASR
[timit]: egs/timit/ASR
[tedlium3]: egs/tedlium3/ASR
[gigaspeech]: egs/gigaspeech/ASR
[aidatatang_200zh]: egs/aidatatang_200zh/ASR
[wenetspeech]: egs/wenetspeech/ASR
[alimeeting]: egs/alimeeting/ASR
[tal_csasr]: egs/tal_csasr/ASR
[ami]: egs/ami
[swbd]: egs/swbd/ASR
[k2]: https://github.com/k2-fsa/k2
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