initial import from https://zenodo.org/record/3957940

.gitattributes

@@ -6,3 +6,5 @@
 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- espnet
+- audio
+- asr
+language: en
+datasets:
+- ljspeech
+license: cc-by-4.0
+inference: false
+---
+
+## Example ESPnet2 ASR model 
+
+### `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
+
+♻️ Imported from https://zenodo.org/record/3957940#.X90XNelKjkM
+
+This model was trained by kamo-naoyuki using mini_an4 recipe in [espnet](https://github.com/espnet/espnet/).
+
+
+
+### Demo: How to use in ESPnet2
+
+```python
+# coming soon
+```
+
+### Citing ESPnet
+
+```BibTex
+@inproceedings{watanabe2018espnet,
+  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
+  title={{ESPnet}: End-to-End Speech Processing Toolkit},
+  year={2018},
+  booktitle={Proceedings of Interspeech},
+  pages={2207--2211},
+  doi={10.21437/Interspeech.2018-1456},
+  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
+}
+@inproceedings{hayashi2020espnet,
+  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
+  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
+  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={7654--7658},
+  year={2020},
+  organization={IEEE}
+}
+```
+
+or arXiv:
+
+```bibtex
+@misc{watanabe2018espnet,
+      title={ESPnet: End-to-End Speech Processing Toolkit}, 
+      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
+      year={2018},
+      eprint={1804.00015},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+

data/token_list/bpe_unigram30/bpe.model

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exp/asr_train_raw_bpe/1epoch.pth

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exp/asr_train_raw_bpe/RESULTS.md

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+<!-- Generated by scripts/utils/show_asr_result.sh -->
+# RESULTS
+## Environments
+- date: `Thu Jul 23 03:18:58 JST 2020`
+- python version: `3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]`
+- espnet version: `espnet 0.8.0`
+- pytorch version: `pytorch 1.0.1`
+- Git hash: `31794e2aec89e6159c1ac32643d4c09989200a30`
+  - Commit date: `Thu Jul 23 03:17:45 2020 +0900`
+
+## asr_train_raw_bpe
+### WER
+
+|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|
+|decode_test_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|2|10|0.0|20.0|80.0|0.0|100.0|100.0|
+|decode_test_seg_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|2|10|0.0|20.0|80.0|0.0|100.0|100.0|
+|decode_train_dev_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|1|1|0.0|100.0|0.0|0.0|100.0|100.0|
+
+### CER
+
+|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|
+|decode_test_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|2|67|53.7|44.8|1.5|206.0|252.2|100.0|
+|decode_test_seg_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|2|67|53.7|44.8|1.5|206.0|252.2|100.0|
+|decode_train_dev_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|1|3|33.3|66.7|0.0|1200.0|1266.7|100.0|
+
+### TER
+
+|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
+|---|---|---|---|---|---|---|---|---|
+|decode_test_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|2|45|15.6|82.2|2.2|208.9|293.3|100.0|
+|decode_test_seg_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|2|45|15.6|82.2|2.2|208.9|293.3|100.0|
+|decode_train_dev_decode_lm_train_bpe_valid.loss.best_asr_model_valid.acc.best|1|4|25.0|75.0|0.0|550.0|625.0|100.0|
+

exp/asr_train_raw_bpe/config.yaml

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+accum_grad: 1
+allow_variable_data_keys: false
+batch_bins: 1000000
+batch_size: 20
+batch_type: folded
+best_model_criterion:
+- - train
+  - loss
+  - min
+- - valid
+  - loss
+  - min
+- - train
+  - acc
+  - max
+- - valid
+  - acc
+  - max
+bpemodel: /Users/gibbon/Desktop/espnet_model_zoo/espnet_model_zoo/3962e0807e68f78b712263dc013036d3/data/token_list/bpe_unigram30/bpe.model
+chunk_length: 500
+chunk_shift_ratio: 0.5
+cleaner: null
+collect_stats: false
+config: null
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+  dropout_rate: 0.0
+  reduce: true
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+dist_rank: null
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+distributed: false
+dry_run: false
+early_stopping_criterion:
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+- loss
+- min
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+fold_length:
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+- 150
+frontend: default
+frontend_conf:
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+g2p: null
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+grad_noise: false
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+input_size: null
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+log_interval: null
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+  ignore_id: -1
+  length_normalized_loss: false
+  lsm_weight: 0.0
+  report_cer: true
+  report_wer: true
+  sym_blank: <blank>
+  sym_space: <space>
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+non_linguistic_symbols: null
+normalize: global_mvn
+normalize_conf:
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+patience: null
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+sort_batch: descending
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+specaug: null
+specaug_conf: {}
+token_list:
+- <blank>
+- <unk>
+- T
+- "\u2581"
+- I
+- H
+- G
+- O
+- AR
+- "\u2581T"
+- NE
+- E
+- EN
+- Y
+- "\u2581E"
+- "\u2581S"
+- EVEN
+- F
+- M
+- C
+- R
+- D
+- N
+- W
+- ENT
+- L
+- <sos/eos>
+token_type: bpe
+train_data_path_and_name_and_type:
+- - dump/raw/train_nodev/wav.scp
+  - speech
+  - sound
+- - dump/raw/train_nodev/text
+  - text
+  - text
+train_dtype: float32
+train_shape_file:
+- exp/asr_stats_raw/train/speech_shape
+- exp/asr_stats_raw/train/text_shape.bpe
+use_preprocessor: true
+val_scheduler_criterion:
+- valid
+- loss
+valid_batch_bins: null
+valid_batch_size: null
+valid_batch_type: null
+valid_data_path_and_name_and_type:
+- - dump/raw/train_dev/wav.scp
+  - speech
+  - sound
+- - dump/raw/train_dev/text
+  - text
+  - text
+valid_max_cache_size: null
+valid_shape_file:
+- exp/asr_stats_raw/valid/speech_shape
+- exp/asr_stats_raw/valid/text_shape.bpe
+write_collected_feats: false

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exp/lm_train_bpe/config.yaml

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+accum_grad: 1
+allow_variable_data_keys: false
+batch_bins: 1000000
+batch_size: 20
+batch_type: folded
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+- - train
+  - loss
+  - min
+- - valid
+  - loss
+  - min
+- - train
+  - acc
+  - max
+- - valid
+  - acc
+  - max
+bpemodel: /Users/gibbon/Desktop/espnet_model_zoo/espnet_model_zoo/3962e0807e68f78b712263dc013036d3/data/token_list/bpe_unigram30/bpe.model
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+- I
+- H
+- G
+- O
+- AR
+- "\u2581T"
+- NE
+- E
+- EN
+- Y
+- "\u2581E"
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+- EVEN
+- F
+- M
+- C
+- R
+- D
+- N
+- W
+- ENT
+- L
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+token_type: bpe
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+- - dump/raw/srctexts
+  - text
+  - text
+train_dtype: float32
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+use_preprocessor: true
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+- loss
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+- - dump/raw/train_dev/text
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+  - text
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meta.yaml

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+espnet: 0.8.0
+files:
+  asr_model_file: exp/asr_train_raw_bpe/1epoch.pth
+  lm_file: exp/lm_train_bpe/8epoch.pth
+python: "3.7.3 (default, Mar 27 2019, 22:11:17) \n[GCC 7.3.0]"
+timestamp: 1595523607.756054
+torch: 1.5.1
+yaml_files:
+  asr_train_config: exp/asr_train_raw_bpe/config.yaml
+  lm_train_config: exp/lm_train_bpe/config.yaml