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Yifan Yang
GitHub
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efbb577b88
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README.md
151
README.md
@ -28,14 +28,15 @@ We provide the following recipes:
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- [yesno][yesno]
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- [LibriSpeech][librispeech]
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- [GigaSpeech][gigaspeech]
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- [Aishell][aishell]
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- [Aishell2][aishell2]
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- [Aishell4][aishell4]
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- [TIMIT][timit]
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- [TED-LIUM3][tedlium3]
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- [GigaSpeech][gigaspeech]
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- [Aidatatang_200zh][aidatatang_200zh]
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- [WenetSpeech][wenetspeech]
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- [Alimeeting][alimeeting]
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- [Aishell4][aishell4]
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- [TAL_CSASR][tal_csasr]
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### yesno
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@ -46,9 +47,7 @@ Training takes less than 30 seconds and gives you the following WER:
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```
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[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
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```
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We do provide a Colab notebook for this recipe.
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[](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
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We provide a Colab notebook for this recipe: [](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
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### LibriSpeech
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@ -82,7 +81,7 @@ The WER for this model is:
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|-----|------------|------------|
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| WER | 6.59 | 17.69 |
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing)
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing)
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#### Transducer: Conformer encoder + LSTM decoder
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@ -118,19 +117,54 @@ We provide a Colab notebook to run a pre-trained transducer conformer + stateles
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 2.57 | 5.95 |
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| WER | 2.15 | 5.20 |
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Note: No auxiliary losses are used in the training and no LMs are used
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in the decoding.
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#### k2 pruned RNN-T + GigaSpeech
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 2.00 | 4.63 |
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| WER | 1.78 | 4.08 |
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Note: No auxiliary losses are used in the training and no LMs are used
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in the decoding.
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#### k2 pruned RNN-T + GigaSpeech + CommonVoice
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 1.90 | 3.98 |
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Note: No auxiliary losses are used in the training and no LMs are used
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in the decoding.
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### GigaSpeech
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We provide two models for this recipe: [Conformer CTC model][GigaSpeech_conformer_ctc]
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and [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][GigaSpeech_pruned_transducer_stateless2].
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#### Conformer CTC
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| | Dev | Test |
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|-----|-------|-------|
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| WER | 10.47 | 10.58 |
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#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
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| | Dev | Test |
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|----------------------|-------|-------|
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| greedy search | 10.51 | 10.73 |
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| fast beam search | 10.50 | 10.69 |
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| modified beam search | 10.40 | 10.51 |
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### Aishell
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We provide two models for this recipe: [conformer CTC model][Aishell_conformer_ctc]
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and [TDNN LSTM CTC model][Aishell_tdnn_lstm_ctc].
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We provide three models for this recipe: [conformer CTC model][Aishell_conformer_ctc],
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[TDNN LSTM CTC model][Aishell_tdnn_lstm_ctc], and [Transducer Stateless Model][Aishell_pruned_transducer_stateless7],
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#### Conformer CTC Model
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@ -140,20 +174,6 @@ The best CER we currently have is:
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|-----|------|
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| CER | 4.26 |
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We provide a Colab notebook to run a pre-trained conformer CTC model: [
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#### Transducer Stateless Model
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The best CER we currently have is:
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| | test |
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|-----|------|
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| CER | 4.68 |
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We provide a Colab notebook to run a pre-trained TransducerStateless model: [](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
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#### TDNN LSTM CTC Model
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The CER for this model is:
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@ -164,6 +184,46 @@ The CER for this model is:
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1jbyzYq3ytm6j2nlEt-diQm-6QVWyDDEa?usp=sharing)
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#### Transducer Stateless Model
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The best CER we currently have is:
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| | test |
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|-----|------|
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| CER | 4.38 |
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We provide a Colab notebook to run a pre-trained TransducerStateless model: [](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
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### Aishell2
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We provide one model for this recipe: [Transducer Stateless Model][Aishell2_pruned_transducer_stateless5].
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#### Transducer Stateless Model
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The best WER we currently have is:
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| | dev-ios | test-ios |
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|-----|------------|------------|
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| WER | 5.32 | 5.56 |
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### Aishell4
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We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aishell4_pruned_transducer_stateless5].
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#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with all subsets)
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The best CER we currently have is:
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| | test |
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|-----|------------|
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| CER | 29.08 |
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
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### TIMIT
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We provide two models for this recipe: [TDNN LSTM CTC model][TIMIT_tdnn_lstm_ctc]
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@ -187,7 +247,8 @@ The PER for this model is:
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|--|--|
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|PER| 17.66% |
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We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [](https://colab.research.google.com/drive/11IT-k4HQIgQngXz1uvWsEYktjqQt7Tmb?usp=sharing)
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We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
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### TED-LIUM3
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@ -215,24 +276,6 @@ The best WER using modified beam search with beam size 4 is:
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)
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### GigaSpeech
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We provide two models for this recipe: [Conformer CTC model][GigaSpeech_conformer_ctc]
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and [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][GigaSpeech_pruned_transducer_stateless2].
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#### Conformer CTC
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| | Dev | Test |
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|-----|-------|-------|
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| WER | 10.47 | 10.58 |
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#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
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| | Dev | Test |
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|----------------------|-------|-------|
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| greedy search | 10.51 | 10.73 |
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| fast beam search | 10.50 | 10.69 |
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| modified beam search | 10.40 | 10.51 |
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### Aidatatang_200zh
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@ -248,6 +291,7 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1wNSnSj3T5oOctbh5IGCa393gKOoQw2GH?usp=sharing)
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### WenetSpeech
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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].
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@ -284,20 +328,6 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing)
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### Aishell4
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We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aishell4_pruned_transducer_stateless5].
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#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with all subsets)
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The best CER(%) results:
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| | test |
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|----------------------|--------|
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| greedy search | 29.89 |
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| fast beam search | 28.91 |
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| modified beam search | 29.08 |
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
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### TAL_CSASR
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@ -333,6 +363,9 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
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[LibriSpeech_transducer_stateless]: egs/librispeech/ASR/transducer_stateless
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[Aishell_tdnn_lstm_ctc]: egs/aishell/ASR/tdnn_lstm_ctc
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[Aishell_conformer_ctc]: egs/aishell/ASR/conformer_ctc
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[Aishell_pruned_transducer_stateless7]: egs/aishell/ASR/pruned_transducer_stateless7_bbpe
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[Aishell2_pruned_transducer_stateless5]: egs/aishell2/ASR/pruned_transducer_stateless5
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[Aishell4_pruned_transducer_stateless5]: egs/aishell4/ASR/pruned_transducer_stateless5
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[TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc
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[TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
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[TED-LIUM3_transducer_stateless]: egs/tedlium3/ASR/transducer_stateless
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@ -343,17 +376,17 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
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[WenetSpeech_pruned_transducer_stateless2]: egs/wenetspeech/ASR/pruned_transducer_stateless2
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[WenetSpeech_pruned_transducer_stateless5]: egs/wenetspeech/ASR/pruned_transducer_stateless5
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[Alimeeting_pruned_transducer_stateless2]: egs/alimeeting/ASR/pruned_transducer_stateless2
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[Aishell4_pruned_transducer_stateless5]: egs/aishell4/ASR/pruned_transducer_stateless5
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[TAL_CSASR_pruned_transducer_stateless5]: egs/tal_csasr/ASR/pruned_transducer_stateless5
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[yesno]: egs/yesno/ASR
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[librispeech]: egs/librispeech/ASR
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[aishell]: egs/aishell/ASR
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[aishell2]: egs/aishell2/ASR
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[aishell4]: egs/aishell4/ASR
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[timit]: egs/timit/ASR
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[tedlium3]: egs/tedlium3/ASR
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[gigaspeech]: egs/gigaspeech/ASR
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[aidatatang_200zh]: egs/aidatatang_200zh/ASR
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[wenetspeech]: egs/wenetspeech/ASR
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[alimeeting]: egs/alimeeting/ASR
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[aishell4]: egs/aishell4/ASR
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[tal_csasr]: egs/tal_csasr/ASR
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[k2]: https://github.com/k2-fsa/k2
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