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text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_gst+xvector_trasnformer` ♻️ Imported from https://zenodo.org/record/4409702/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_gst_xvector_trasnformer
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss` ♻️ Imported from https://zenodo.org/record/4418774/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_tts_train_gst_xvector_conformer_fastspeech2_trans-truncated-c3209b
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_tts_train_gst+xvector_trasnformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4409702/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_tts_train_gst_xvector_trasnformer_raw_phn_tacotro-truncated-250027
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss` ♻️ Imported from https://zenodo.org/record/4418754/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_tts_train_xvector_conformer_fastspeech2_transform-truncated-42b443
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_tts_train_xvector_trasnformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4409704/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_tts_train_xvector_trasnformer_raw_phn_tacotron_g2-truncated-e5fb13
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5521416/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no-truncated-09d645
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_xvector_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4418754/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_xvector_conformer_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/libritts_xvector_trasnformer` ♻️ Imported from https://zenodo.org/record/4409704/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_xvector_trasnformer
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/libritts_xvector_vits` ♻️ Imported from https://zenodo.org/record/5521416/ This model was trained by kan-bayashi using libritts/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["libritts"]}
espnet/kan-bayashi_libritts_xvector_vits
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:libritts", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4036268/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_conformer_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_fastspeech` ♻️ Imported from https://zenodo.org/record/3986231/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_fastspeech
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_fastspeech2` ♻️ Imported from https://zenodo.org/record/4036272/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_joint_finetune_conformer_fastspeech2_hifigan` ♻️ Imported from https://zenodo.org/record/5498896/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_joint_train_conformer_fastspeech2_hifigan` ♻️ Imported from https://zenodo.org/record/5498487/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_joint_train_conformer_fastspeech2_hifigan
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_tacotron2` ♻️ Imported from https://zenodo.org/record/3989498/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tacotron2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_transformer` ♻️ Imported from https://zenodo.org/record/4039194/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_transformer
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5498896/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_-truncated-737899
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036268/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_-truncated-ec9e34
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036272/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_tts_train_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best` ♻️ Imported from https://zenodo.org/record/3986231/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_tts_train_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5498487/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_joint_conformer_fastspeech2_hifigan_raw-truncated-af8fe0
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best` ♻️ Imported from https://zenodo.org/record/3989498/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4039194/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5443814/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/ljspeech_vits` ♻️ Imported from https://zenodo.org/record/5443814/ This model was trained by kan-bayashi using ljspeech/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"]}
espnet/kan-bayashi_ljspeech_vits
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "has_space", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/tsukuyomi_full_band_vits_prosody` ♻️ Imported from https://zenodo.org/record/5521446/ This model was trained by kan-bayashi using tsukuyomi/tts1 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} } ```
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["tsukuyomi"]}
espnet/kan-bayashi_tsukuyomi_full_band_vits_prosody
null
[ "espnet", "audio", "text-to-speech", "ja", "dataset:tsukuyomi", "arxiv:1804.00015", "license:cc-by-4.0", "has_space", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest` ♻️ Imported from https://zenodo.org/record/5521446/ This model was trained by kan-bayashi using tsukuyomi/tts1 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} } ```
{"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["tsukuyomi"]}
espnet/kan-bayashi_tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest
null
[ "espnet", "audio", "text-to-speech", "ja", "dataset:tsukuyomi", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/vctk_full_band_multi_spk_vits` ♻️ Imported from https://zenodo.org/record/5521431/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_full_band_multi_spk_vits
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4036264/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_conformer_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "has_space", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst_fastspeech` ♻️ Imported from https://zenodo.org/record/3986241/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_fastspeech
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst_fastspeech2` ♻️ Imported from https://zenodo.org/record/4036266/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst_tacotron2` ♻️ Imported from https://zenodo.org/record/3986237/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_tacotron2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst_transformer` ♻️ Imported from https://zenodo.org/record/4037456/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_transformer
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst+xvector_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4394608/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_xvector_conformer_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst+xvector_tacotron2` ♻️ Imported from https://zenodo.org/record/4394598/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_xvector_tacotron2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_gst+xvector_transformer` ♻️ Imported from https://zenodo.org/record/4393277/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_gst_xvector_transformer
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/vctk_multi_spk_vits` ♻️ Imported from https://zenodo.org/record/5500759/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_multi_spk_vits
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5521431/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g-truncated-50b003
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036264/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_-truncated-69081b
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4036266/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best` ♻️ Imported from https://zenodo.org/record/3986241/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best` ♻️ Imported from https://zenodo.org/record/3986237/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4037456/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394608/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_xvector_conformer_fastspeech2_transform-truncated-e051a9
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_gst+xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394598/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_gst_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## ESPnet2 TTS pretrained model ### `kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5500759/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394602/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_xvector_conformer_fastspeech2_transformer_t-truncated-69a657
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394600/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4393279/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_xvector_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4394602/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_xvector_conformer_fastspeech2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_xvector_tacotron2` ♻️ Imported from https://zenodo.org/record/4394600/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_xvector_tacotron2
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
## Example ESPnet2 TTS model ### `kan-bayashi/vctk_xvector_transformer` ♻️ Imported from https://zenodo.org/record/4393279/ This model was trained by kan-bayashi using vctk/tts1 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]}
espnet/kan-bayashi_vctk_xvector_transformer
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:vctk", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-to-speech
espnet
# ESPnet2 ASR pretrained model ## `kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave` ♻️ Imported from <https://zenodo.org/record/4017026#.YN70XJozZH4> This model was trained by kan-bayashi using ljspeech/tts1 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} } ``` ### Training config See full config in [`config.yaml`](./config.yaml) ```yaml config: conf/tuning/train_conformer_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"], "widget": [{"text": "Hello, how are you doing?"}]}
espnet/kan_bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave
null
[ "espnet", "audio", "text-to-speech", "en", "dataset:ljspeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR model ### `espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char` This model was trained by Pengcheng Guo using wenetspeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 5c21f63e45e0961a5d817017c282b0cafd68a3aa pip install -e . cd egs2/wenetspeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Wed Oct 6 15:11:20 CST 2021` - python version: `3.8.11 (default, Aug 3 2021, 15:09:35) [GCC 7.5.0]` - espnet version: `espnet 0.10.2a1` - pytorch version: `pytorch 1.9.0` - Git hash: `` - Commit date: `` ## asr_train_asr_conformer_raw_zh_char ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_asr_model_valid.acc.ave_10best/aishell_test|7176|7176|67.1|32.9|0.0|0.1|33.0|32.9| |decode_asr_rnn_asr_model_valid.acc.ave_10best/dev|13825|16684|32.1|54.1|13.8|0.1|68.0|64.2| |decode_asr_rnn_asr_model_valid.acc.ave_10best/test_meeting|8370|8599|13.4|84.6|2.0|0.1|86.7|86.8| |decode_asr_rnn_asr_model_valid.acc.ave_10best/test_net|24774|25995|46.2|50.4|3.4|1.1|54.9|52.5| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_asr_model_valid.acc.ave_10best/aishell_test|7176|104765|96.3|3.6|0.1|0.2|3.9|32.9| |decode_asr_rnn_asr_model_valid.acc.ave_10bestdev|13825|333357|90.7|3.4|5.9|0.4|9.7|64.2| |decode_asr_rnn_asr_model_valid.acc.ave_10best/test_meeting|8370|220614|84.6|5.0|10.4|0.5|15.9|86.8| |decode_asr_rnn_asr_model_valid.acc.ave_10best/test_net|24774|416968|91.8|5.3|2.9|0.6|8.8|52.5| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_raw_zh_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 44205 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 30 patience: null val_scheduler_criterion: - valid - acc early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 30000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_zh_char/train/speech_shape - exp/asr_stats_raw_zh_char/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_zh_char/valid/speech_shape - exp/asr_stats_raw_zh_char/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 51200 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_l/wav.scp - speech - sound - - dump/raw/train_l/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0015 scheduler: warmuplr scheduler_conf: warmup_steps: 30000 token_list: - <blank> - <unk> - 的 - 我 - 是 - 你 - 了 - 一 - 不 - 这 - 个 - 有 - 就 - 们 - 在 - 他 - 人 - 么 - 来 - 说 - 那 - 要 - 好 - 啊 - 大 - 到 - 上 - 也 - 没 - 都 - 去 - 能 - 子 - 会 - 为 - 得 - 时 - 还 - 可 - 以 - 什 - 家 - 后 - 看 - 呢 - 对 - 事 - 天 - 下 - 过 - 想 - 多 - 小 - 出 - 自 - 儿 - 生 - 给 - 里 - 现 - 着 - 然 - 吧 - 样 - 道 - 吗 - 心 - 跟 - 中 - 很 - 点 - 年 - 和 - 地 - 怎 - 知 - 十 - 老 - 当 - 把 - 话 - 别 - 所 - 之 - 情 - 实 - 开 - 面 - 回 - 行 - 国 - 做 - 己 - 经 - 如 - 真 - 起 - 候 - 些 - 让 - 发 - 她 - 觉 - 但 - 成 - 定 - 意 - 二 - 长 - 最 - 方 - 三 - 前 - 因 - 用 - 呀 - 种 - 只 - 走 - 其 - 问 - 再 - 果 - 而 - 分 - 两 - 打 - 学 - 间 - 您 - 本 - 于 - 明 - 手 - 公 - 听 - 比 - 作 - 女 - 太 - 今 - 从 - 关 - 妈 - 同 - 法 - 动 - 已 - 见 - 才 - 孩 - 感 - 吃 - 常 - 次 - 它 - 进 - 先 - 找 - 身 - 全 - 理 - 又 - 力 - 正 - 主 - 应 - 高 - 被 - 钱 - 快 - 等 - 头 - 重 - 车 - 谢 - 日 - 东 - 放 - 无 - 工 - 咱 - 哪 - 五 - 者 - 像 - 西 - 该 - 干 - 相 - 信 - 机 - 百 - 特 - 业 - 活 - 师 - 边 - 爱 - 友 - 新 - 外 - 位 - 更 - 直 - 几 - 第 - 非 - 四 - 题 - 接 - 少 - 哥 - 死 - 完 - 刚 - 电 - 气 - 安 - 爸 - 白 - 告 - 美 - 解 - 叫 - 月 - 带 - 欢 - 谁 - 体 - 喜 - 部 - 场 - 姐 - 军 - 万 - 结 - 合 - 难 - 八 - 每 - 目 - 亲 - 朋 - 认 - 总 - 加 - 通 - 办 - 马 - 件 - 受 - 任 - 请 - 住 - 王 - 思 - 门 - 名 - 平 - 系 - 文 - 帮 - 路 - 变 - 记 - 水 - 九 - 算 - 将 - 口 - 男 - 度 - 报 - 六 - 张 - 管 - 够 - 性 - 表 - 提 - 何 - 讲 - 期 - 拿 - 保 - 嘛 - 司 - 原 - 始 - 此 - 诉 - 处 - 清 - 内 - 产 - 金 - 晚 - 早 - 交 - 离 - 眼 - 队 - 七 - 入 - 山 - 代 - 市 - 海 - 物 - 零 - 望 - 世 - 婚 - 命 - 越 - 收 - 向 - 花 - 房 - 错 - 节 - 父 - 反 - 战 - 买 - 量 - 或 - 员 - 号 - 千 - 怕 - 底 - 且 - 品 - 民 - 化 - 爷 - 并 - 与 - 服 - 需 - 资 - 求 - 教 - 娘 - 医 - 数 - 院 - 书 - 利 - 往 - 确 - 各 - 单 - 风 - 送 - 必 - 条 - 包 - 准 - 光 - 整 - 病 - 弟 - 嗯 - 计 - 照 - 强 - 务 - 影 - 城 - 夫 - 俩 - 决 - 声 - 连 - 乐 - 息 - 远 - 北 - 至 - 饭 - 留 - 宝 - 神 - 近 - 考 - 备 - 案 - 界 - 容 - 况 - 母 - 较 - 持 - 证 - 选 - 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祟 - 辖 - 砂 - 韧 - 粪 - 诬 - 擒 - 黏 - 衔 - 溺 - 蜘 - 篷 - 贿 - 闫 - 焕 - 邢 - 兹 - 窖 - 旬 - 铸 - 咚 - 惭 - 佬 - 裴 - 裳 - 犀 - 弘 - 莓 - 钏 - 鄂 - 陋 - 伽 - 鞠 - 氪 - 垒 - 窜 - 橙 - 讳 - 甥 - 淫 - 拱 - 袱 - 坨 - 暧 - 渺 - 蕉 - 晗 - 茬 - 盔 - 妓 - 蚕 - 僻 - 朽 - 呛 - 挚 - 擎 - 绅 - 喇 - 鳄 - 巩 - 蜗 - 遛 - 俯 - 汹 - 猩 - 奠 - 钙 - 悍 - 躬 - 菱 - 翘 - 琉 - 虏 - 凄 - 稼 - 炕 - 皂 - 漱 - 斋 - 撂 - 敛 - 阮 - 芭 - 阀 - 缚 - 懦 - 亨 - 螃 - 侥 - 膨 - 筝 - 惟 - 黛 - 眯 - 茨 - 怠 - 辐 - 捎 - 殴 - 桓 - 瞄 - 冀 - 雍 - 霾 - 酵 - 檬 - 哺 - 裔 - 兢 - 麒 - 烹 - 绒 - 丐 - 娅 - 钞 - 垄 - 笛 - 赣 - 蕊 - 暮 - 噪 - 沮 - 肋 - 庇 - 橡 - 摁 - 痘 - 棘 - 拂 - 绷 - 刨 - 晾 - 蹬 - 鸥 - 璇 - 掠 - 瘟 - 俐 - 糙 - 骏 - 牡 - 撵 - 嘘 - 沥 - 庶 - 赁 - 喧 - 涡 - 瞳 - 迭 - 肘 - 颂 - 珑 - 觅 - 埔 - G - 跤 - 朔 - 詹 - 梭 - 暇 - 惺 - 甸 - 怯 - 聋 - 赦 - 屉 - 闸 - 坝 - 吟 - 凸 - 拴 - 堤 - 矣 - 斧 - 呸 - 啼 - 韬 - 钧 - 坞 - 纺 - 氢 - 嵩 - 镯 - 髓 - 檐 - 涕 - 剁 - 稽 - 烨 - 钮 - 闽 - 仕 - 驯 - 吭 - 漓 - 眸 - 鞅 - 枢 - 煞 - 昕 - 畔 - 疹 - 矶 - 呱 - 熄 - 吏 - 泻 - 拙 - 蛤 - 禽 - 甫 - 厮 - 乍 - 蝉 - 撬 - 嘀 - 衅 - 鲨 - 萱 - 霹 - 旷 - 辫 - 坷 - 眶 - 蟆 - 呜 - 猬 - 嬷 - 萎 - 靶 - 雳 - 煲 - 溯 - 蚀 - 狈 - 滤 - 恙 - 瑛 - 栓 - 嫣 - 碟 - 祷 - 驿 - 犊 - 灼 - 哆 - 宛 - 榨 - 寥 - 翟 - 栗 - 滔 - 馋 - 杖 - 茉 - 饲 - 庐 - 隋 - 旱 - 崎 - 颅 - 焉 - 墩 - 篱 - 晟 - 扳 - 咎 - 竿 - 僚 - 溶 - 俏 - 霆 - 堕 - 冕 - 叩 - 绰 - 洽 - 襄 - 蛊 - 缅 - 侨 - 伶 - 蕴 - 酥 - 坂 - 拇 - 庚 - 卒 - 诛 - 禧 - 瓢 - 锯 - 扉 - 饷 - 诅 - 烘 - 浏 - 痰 - 榆 - 窥 - 鲸 - 捋 - 戎 - 笋 - 璋 - 诫 - 珈 - 癫 - 囤 - 厥 - 癖 - 翩 - 芹 - 匣 - 噬 - 栖 - 蝎 - 锄 - 玺 - 疮 - 缕 - 猥 - 槿 - 蔑 - 汝 - 珂 - 撮 - 坪 - 蒲 - 倚 - 嗷 - 撰 - 荧 - 芙 - 豚 - 筱 - 敖 - 孵 - 猝 - D - 弈 - 徊 - 辗 - 赘 - 徘 - 烙 - 娲 - 嚎 - 迢 - 绥 - 羁 - 屌 - 铅 - 澎 - S - 嬛 - 晦 - 煽 - 逾 - 饵 - 虞 - 筐 - 哧 - 抒 - 醇 - 祀 - 瑕 - 岐 - 潼 - 惚 - C - 苑 - 靡 - 菠 - 赡 - 惰 - 梓 - 铛 - 澈 - 莞 - 呕 - 驭 - 邝 - 砰 - 轼 - 窒 - 慷 - 绞 - 絮 - 虔 - 惮 - 柬 - 嗡 - 拣 - 羲 - 蹋 - 隘 - 帜 - 卤 - 雌 - 唾 - 邹 - 俑 - 碾 - 婪 - 咏 - 粟 - 崭 - 钝 - 彝 - 陡 - 谛 - 秤 - 磅 - 淌 - 炊 - 鲤 - 羹 - 殉 - 曰 - 萤 - 阐 - 鬟 - 拭 - T - 沁 - 滇 - 梧 - 烁 - 瞻 - 淤 - 凹 - 撸 - 棕 - 腌 - 缪 - 祺 - 痊 - 忑 - 柠 - 矜 - 忐 - 讹 - 瀚 - 尧 - 昼 - 芊 - 憨 - 鳞 - 匮 - 鸳 - 鸯 - 湃 - 屿 - 馍 - 沽 - 栾 - 蝠 - 窘 - 绛 - 巍 - 悯 - 焊 - 谴 - 浊 - 娴 - 畴 - 湛 - 螂 - 韭 - 哮 - 拷 - 攥 - 凛 - 颓 - 恺 - 蝙 - 襟 - 粑 - 洼 - 笃 - 渝 - 骁 - 殃 - 酌 - 乒 - 臊 - 疵 - 诧 - 谬 - 锈 - 袄 - 膛 - 瘸 - 嫖 - 梢 - 沼 - 棱 - 嚓 - 耸 - 喳 - 舵 - 橱 - 涮 - 檀 - 瞩 - 腑 - 岑 - 痪 - 墟 - 蔚 - 捍 - 徙 - 棣 - 猖 - 掷 - 恬 - 嫦 - 噔 - 饪 - 掂 - 恤 - 叱 - 芷 - 弩 - 楷 - 镶 - 茧 - 诠 - 咙 - 匡 - 擂 - 亵 - 杞 - 乓 - 渤 - 藉 - 憔 - 渭 - 禹 - 睐 - 趾 - 抉 - 悴 - 忒 - 茸 - 纬 - 懊 - 浚 - 溅 - 遏 - 琛 - 靴 - 戮 - 翎 - 谕 - 濒 - 锵 - 嬉 - 籽 - 殆 - 叼 - 苔 - 灏 - 嗖 - 俪 - 亢 - 冶 - 嗜 - 磋 - 汀 - 讪 - 萃 - 菁 - 镑 - 紊 - 脯 - 缆 - 哉 - 赂 - 婊 - B - 蕃 - 迄 - 蜓 - 舜 - 嚏 - 昱 - 黔 - 犟 - 汐 - 昵 - 嗣 - 唆 - 蛾 - 黯 - 绯 - 瀑 - 憬 - 狩 - 掖 - 崴 - 褪 - 髦 - 酝 - 弧 - 咄 - 吝 - 馄 - 娩 - 窿 - 蜻 - 袒 - 玮 - 阙 - 篡 - 邯 - 朦 - 邑 - 喃 - 粽 - 捶 - 嫔 - 钗 - 穗 - 骼 - 胭 - 寐 - 噎 - M - 碱 - 荤 - 笙 - 矢 - 芥 - 廓 - 扼 - 厄 - 毋 - 糯 - 惋 - 纶 - 碜 - 胧 - 懿 - 偃 - 沏 - 痹 - 慑 - 鹦 - 娠 - 铐 - 绢 - 傀 - 孜 - 饨 - 儡 - 孰 - 焱 - 峭 - 伎 - 幌 - 椰 - 譬 - 藕 - 坍 - 铝 - 鞍 - 蘸 - 貂 - 猿 - 炙 - 琊 - 峙 - 硝 - 幂 - 钰 - 眩 - 亥 - 簇 - 鹉 - 睫 - 斟 - 簧 - 颐 - 薰 - 癞 - 祛 - 燎 - 缎 - 簸 - 咣 - 绚 - 簿 - 邋 - 嵌 - 肮 - 稷 - 辍 - 闵 - 枸 - 撅 - 曙 - 苇 - K - 悼 - 汶 - 匕 - 皖 - 腮 - 琶 - 汲 - 鼹 - 礁 - 颊 - 怔 - 汕 - 喀 - 砌 - 釜 - 畸 - 鹃 - 峨 - 奄 - 骡 - 斐 - 芈 - 莘 - 蟑 - 荔 - 缇 - 犒 - 宓 - 汾 - 沌 - 宦 - 憧 - 咤 - 吆 - 攘 - 漩 - 梵 - 阂 - 吒 - 芜 - 缔 - 秧 - 翊 - 晌 - 剐 - 蜕 - 芋 - 彷 - 牟 - 诲 - 臀 - 徨 - Q - 杵 - 荫 - 榄 - 蹿 - 豌 - 迂 - 琵 - 拗 - 帷 - 楞 - 嘶 - 橄 - 胺 - 圭 - 砚 - 藻 - 凋 - 啄 - 褒 - 嗝 - 殡 - 嫡 - 恃 - 濡 - 缜 - 孺 - 泸 - 妊 - 衩 - 驹 - 榻 - 腆 - 鹂 - 箍 - 璧 - 熔 - 悚 - 遢 - 弛 - 诋 - 羚 - 鹭 - 嘚 - 骸 - 瘪 - 铠 - 瞿 - 屹 - 邸 - 痨 - 辘 - 浒 - 忏 - 钊 - 潦 - 怅 - 肴 - 蚯 - 胚 - 茵 - 蚓 - 戬 - 瘀 - 翡 - 恪 - 卉 - 蝌 - 雏 - 祯 - 谏 - 蚪 - 钵 - 馊 - 嗒 - 犁 - 寅 - V - 锥 - 娼 - 晖 - 啬 - 纣 - 淆 - 丙 - 夯 - 竣 - 褚 - 褥 - 轧 - 氨 - 褂 - 钳 - 轲 - 竺 - 疡 - 淞 - 胤 - 摹 - 鳅 - 珀 - 偕 - 匾 - 觑 - 扈 - 傣 - 绫 - 枷 - 阑 - 柚 - 烊 - 怦 - 腼 - 珺 - 缀 - 裘 - 碉 - 峪 - 俸 - 羯 - 姊 - 疟 - 砺 - 盎 - 嘣 - 釉 - 溥 - 熠 - 垢 - 摞 - 哽 - 槟 - 囧 - 胰 - 遁 - 痞 - 熹 - 忡 - 稠 - 顷 - 瑚 - 卯 - 渎 - 炅 - 褶 - 烽 - 瞑 - 嘈 - 硫 - 壹 - 悖 - 酪 - 跺 - 阜 - 帛 - 漪 - 蝗 - 迦 - 蟒 - 咀 - 谤 - 睬 - 辕 - 绮 - 搀 - 裆 - 鳖 - 囡 - 羔 - 痣 - 滕 - 佘 - 樟 - 韶 - 霓 - 劾 - 赈 - 唏 - 闰 - 脐 - 沓 - 瓮 - 篓 - 笠 - 暄 - 涅 - 诽 - 洱 - 栅 - 蚱 - 囔 - 攸 - 酣 - 阪 - 榕 - 骇 - 婧 - 陨 - 憎 - 沂 - 磷 - 壕 - 醺 - 惬 - 璀 - 璨 - 喋 - P - 炽 - 瘁 - 羿 - 褐 - 簪 - 冽 - 驮 - 芮 - 辄 - 咆 - 渍 - 觐 - 炷 - 蛰 - 驷 - 帚 - 蜷 - O - X - 邂 - 逅 - 缭 - 秽 - 琰 - 龌 - 龊 - 俨 - 涟 - 噼 - 掇 - 哔 - 炬 - 佯 - 粱 - 霁 - 鱿 - 夭 - 擀 - 陇 - 瞥 - 壑 - 盹 - 馁 - 蚌 - 焖 - 蛟 - 囱 - 蚝 - 抿 - 脓 - 蒿 - 飓 - 渲 - 宸 - 酗 - 荻 - 缥 - 弑 - 偎 - 宕 - 耘 - 瞌 - 瘴 - 溉 - 涝 - 咿 - 垛 - 垦 - 缈 - 苞 - 惆 - 汛 - 鹑 - 町 - 抡 - 慵 - 浣 - 耙 - 砥 - 噱 - 孬 - 札 - 弼 - 酋 - 镳 - 萦 - 泾 - 挞 - 钾 - 讷 - 圃 - 舶 - 穹 - 戾 - 汴 - 锂 - 昀 - 镀 - 眺 - 捺 - 猕 - 阚 - 骋 - 悸 - 蜚 - 咩 - 讥 - 篆 - 鸠 - 哐 - 锚 - 幢 - 翱 - 螳 - 徇 - 踞 - 蔗 - 蔼 - 漉 - 衲 - N - 漳 - 枭 - 漾 - 歆 - 烬 - 曳 - 岌 - 孚 - 戛 - 呲 - 箫 - 娓 - 桨 - 涓 - 獭 - 芃 - 摒 - 戍 - 踝 - 轱 - 沱 - 锢 - 堰 - 抨 - 昙 - 鹌 - 蔻 - 迸 - 泯 - 龈 - 痔 - 骛 - 淄 - 泵 - 烯 - 蔫 - F - 胥 - 忱 - 纫 - 搪 - 茎 - 暨 - 泞 - 踵 - 璞 - 佗 - 荃 - 鬓 - 蚣 - 罔 - 臆 - 贻 - 橇 - 麓 - 槌 - 琥 - I - 纥 - 薅 - 樵 - 苓 - 熨 - 钨 - 骞 - 诣 - 涤 - 踊 - 醛 - 碴 - 蹴 - 缤 - 赊 - 岖 - 戊 - 禺 - 坯 - 戟 - 楂 - 隅 - 酶 - 邃 - 蛀 - 皎 - 炯 - 垣 - 锹 - 镰 - 夙 - 甬 - 叵 - 茁 - 珞 - 妲 - 涸 - 兀 - 嘤 - 谙 - 噗 - 榔 - 稣 - 剽 - 奚 - 啕 - 袅 - 讧 - 钠 - 怄 - 晤 - 肛 - 氰 - 迥 - 唰 - 诩 - 籁 - 砒 - 谩 - 诟 - 斓 - 泷 - 幡 - 爻 - 痫 - 眈 - 漕 - 惘 - 挎 - 噶 - 喱 - 氯 - U - 跆 - 嗤 - 锏 - 睽 - 缮 - 蟋 - 蠕 - 扪 - 狞 - 飒 - 吮 - 弋 - 奘 - 蟠 - 梆 - 拈 - 帧 - 蟀 - 胯 - 掳 - 蝈 - 帼 - 瞰 - 嵇 - 阉 - 篝 - 笆 - 亘 - L - 喔 - 愕 - 谚 - 轶 - 岱 - 丕 - 婕 - 羌 - 毡 - 呻 - 鼾 - 蜥 - 偌 - 庵 - 敝 - 蛐 - 麝 - 鞘 - 拮 - 涣 - 葆 - 雹 - 踌 - 蜈 - 馥 - 跻 - 狰 - 桀 - 毗 - 皿 - 缨 - 磐 - 啾 - 牒 - 缰 - 躇 - 踮 - 糠 - 嗲 - 刽 - 咫 - 殇 - 瀛 - 胱 - 炀 - 虱 - 砾 - 獒 - 涎 - 袤 - 鄱 - 瓯 - 锭 - 塾 - 蹉 - 珏 - 豺 - 锌 - 蜿 - 牦 - 瓒 - 莆 - 蜴 - 氮 - 跎 - 咛 - 骜 - 郸 - 搐 - 堑 - 涞 - 寰 - 跛 - 鸵 - 毂 - 妩 - 铤 - 薏 - 烩 - 遐 - 煦 - 仃 - 髅 - 酮 - 榷 - 腋 - 珩 - 臃 - 愫 - 蜒 - 荼 - 侬 - 淬 - 婵 - 偻 - 焯 - 骊 - 恻 - 濮 - 泱 - 庖 - 惴 - 鲫 - 硌 - 肓 - 芪 - 礴 - 磺 - 腱 - 冢 - 谪 - 骷 - 哏 - 腩 - 蓦 - 焙 - 桢 - 阖 - 睾 - 疱 - 郴 - 铿 - 铡 - 祉 - 跄 - 桦 - 椭 - 拄 - 皙 - 膈 - 裱 - 髋 - 伢 - 罹 - 鳍 - 赝 - 嬴 - 痤 - 藿 - 镐 - 铎 - 瘠 - 簌 - 杳 - 铢 - 阡 - 忤 - 舀 - 悻 - 媲 - 茗 - 湍 - 舫 - 瘙 - 瞟 - 擞 - 荀 - 刍 - J - 潍 - 莴 - 斛 - 郦 - 栩 - 绾 - 蕙 - 黜 - 湄 - 藓 - 躏 - 锱 - 捻 - 佼 - 砝 - E - 罡 - 忻 - 鹜 - 滟 - 傥 - 蛳 - W - 铀 - 魇 - 觎 - 蹂 - 佞 - 诃 - 灞 - 镣 - 痱 - 侏 - 峦 - 榛 - 饽 - 龋 - 嗔 - 芍 - 椿 - 璎 - 渥 - 蟾 - 骰 - 吠 - 挛 - 倜 - 鳝 - 糜 - 噢 - 黝 - 藐 - 绡 - 掣 - 鳗 - 璜 - 犷 - 痉 - 膺 - 罄 - 阄 - 纨 - 纭 - 彗 - 嵘 - 埠 - 潢 - 桔 - 耷 - 逵 - 诓 - 怵 - 蚤 - 苯 - 邈 - 谑 - 颌 - 珐 - 踱 - 髻 - 倏 - 啷 - 篑 - 冗 - 蹶 - 荥 - 涧 - 镂 - 踉 - 呷 - 衢 - 荟 - 箴 - 桧 - 恿 - 坳 - 瑙 - 珅 - 莅 - 膘 - 宥 - 氟 - 秆 - 诙 - 蹑 - 茴 - 翳 - 渚 - H - 唁 - 诿 - 窈 - 窕 - 膻 - 荨 - 蛔 - 筵 - 钛 - 獾 - 琏 - 箩 - 栀 - 隼 - 煸 - 罂 - 蛎 - 咂 - 谗 - 颦 - 佝 - 苣 - 搡 - 仄 - 垠 - 濂 - 泗 - 亟 - 蔺 - 蛆 - 霏 - 榈 - 裟 - 瑁 - 酚 - 蝼 - 怆 - 犄 - 沣 - 揖 - 斡 - 刎 - 鲟 - 峒 - 瞭 - 晁 - 袈 - 蓟 - 镁 - 骥 - 掸 - 玳 - 娑 - 馀 - 跚 - 槃 - 缄 - 猢 - 粕 - 隍 - 佃 - 獗 - 唢 - 菏 - 酰 - 腚 - 笈 - 哙 - 孢 - 飕 - 嘹 - 茱 - 蹒 - 殓 - 柩 - 谀 - 姣 - 戌 - 柑 - 粼 - 淅 - 啧 - 盅 - 鼬 - 啜 - 绉 - 咻 - 锲 - 铆 - Y - 螨 - 茯 - 憩 - 臼 - 谄 - 讴 - 濠 - 雎 - 噻 - 淦 - 懋 - 尕 - 氦 - 褛 - 颉 - 喆 - 铬 - 褴 - 燮 - 銮 - 侗 - 蹙 - 煜 - 邺 - 锃 - 麋 - 矗 - 娆 - 匐 - 噌 - 潸 - 碘 - 浔 - 檄 - 皈 - 铂 - 遨 - 炜 - 曜 - 饴 - 舷 - 胫 - 叟 - 祎 - 沅 - 潺 - 楣 - 埂 - 瞠 - 幔 - 稞 - 抻 - 匝 - 幄 - 殒 - 瑭 - 袂 - 囫 - 瓴 - 攫 - 鲈 - 箔 - 哝 - 馗 - 蜍 - 痧 - 脘 - 姘 - 苒 - 缢 - 觞 - 蛹 - 饬 - 胄 - 筏 - 鸾 - 儆 - 痿 - 矬 - 酊 - 纾 - 铖 - 荏 - 掬 - 膑 - 贮 - 觊 - 囵 - 泓 - 搔 - 汞 - 蚩 - 婀 - 谧 - 恣 - 霎 - 饕 - 赅 - 鲶 - 梏 - 獠 - 俶 - 龛 - 桅 - 鹄 - 旌 - 鲲 - 姒 - 蠡 - 繇 - 祜 - 诨 - 汩 - 觥 - 孀 - R - 谥 - 蕨 - 祐 - 榭 - 皑 - 纂 - 獐 - 覃 - 痂 - 孑 - 砧 - 圩 - 桎 - 啵 - 葚 - 嗫 - 浃 - 荠 - 阈 - 遴 - 枇 - 狒 - 秸 - 筠 - 硒 - 卞 - 玷 - 杈 - 狲 - 忿 - 俎 - 拚 - 颍 - 睢 - 颧 - 滦 - 霭 - 雉 - 毽 - 蓑 - 歙 - 鳃 - 鹬 - 墉 - 楔 - 舐 - 绔 - 弭 - 馏 - 挝 - 奂 - 嘭 - 忪 - 箕 - 诌 - 谒 - 颚 - 滂 - 醍 - 洵 - 鹫 - 虢 - 苋 - 玥 - 臾 - 蹩 - Z - 杷 - 痍 - 酉 - 疸 - 鄢 - 垩 - 烷 - 湮 - 钎 - 樽 - 旮 - 葭 - 邬 - 缱 - 糍 - 亳 - 咦 - 苷 - 伉 - 隽 - 伫 - 聒 - 匍 - 飚 - 桠 - 睑 - 脍 - 焘 - 谶 - 赳 - 萸 - 讣 - 疽 - 臧 - 巽 - 毓 - 鸢 - 纰 - 啐 - 噙 - 舛 - 敕 - 醐 - 痢 - 嚯 - 婺 - 勖 - 岷 - 溧 - 骅 - 犸 - 麾 - 嗟 - 诘 - 懑 - 貔 - 貅 - 啉 - 崂 - 鸩 - 镭 - 绻 - 逑 - 煨 - 褓 - 姝 - 藜 - 溟 - 儋 - 谡 - 欸 - 郢 - 荚 - 疝 - 遽 - 陂 - 饯 - 孪 - 巳 - 荞 - 泔 - 岿 - 谆 - 镍 - 洙 - 佻 - 盂 - 睨 - 铄 - 餮 - 酯 - 癣 - 浜 - 酩 - 焗 - 挲 - 鬃 - 鲠 - 仞 - 诰 - 谔 - 胛 - 萼 - 涿 - 莠 - 珲 - 旯 - 蜢 - 黍 - 肽 - 涪 - 髡 - 氙 - 陉 - 鬶 - 侩 - 糅 - 氤 - 芾 - 砷 - 鳕 - 钣 - 锒 - 闱 - 铵 - 镊 - 玑 - 砀 - 癜 - 颔 - 楹 - 螈 - 醚 - 琮 - 铩 - 笄 - 瓤 - 裨 - 潋 - 悌 - 聿 - 祢 - 郜 - 汨 - 棂 - 氲 - 嶙 - 聩 - 菅 - 腧 - 妯 - 龇 - 谲 - 耄 - 耋 - 囿 - 黢 - 揄 - 鲇 - 仝 - 個 - 忖 - 峋 - 揶 - 迩 - 诳 - 踽 - 骐 - 趸 - 颞 - 撺 - 辇 - 猷 - 铉 - 羸 - 徜 - 徉 - 襁 - 镌 - 孱 - 钒 - 铣 - 呤 - 遑 - 俾 - 皋 - 笕 - 笺 - 趔 - 趄 - 辋 - 鄞 - 殚 - 岫 - 跬 - 嘌 - 苻 - 绶 - 郅 - 瑄 - 萋 - 蘼 - 湎 - 砣 - 钜 - 捭 - 喹 - 恹 - 娌 - 螯 - 锰 - 祚 - 阆 - 矾 - 厩 - 龅 - 炝 - 黠 - 妁 - 濑 - 鞑 - 柒 - 滁 - 淖 - 鸬 - 鬣 - 晔 - 恸 - 赓 - 侉 - 溏 - 還 - 珮 - 鸨 - 嚅 - 笤 - 靥 - 啮 - 滓 - 俚 - 唳 - 苜 - 蓿 - 鹚 - 耦 - 莜 - 麸 - 粳 - 綦 - 盱 - 噤 - 遒 - 玟 - 魍 - 魉 - 旖 - 栉 - 锷 - 醴 - 泮 - 恁 - 甾 - 琬 - 丶 - 擤 - 桉 - 踟 - 誊 - 谟 - 澧 - 玖 - 畿 - 顼 - 兖 - 贰 - 茏 - 愎 - 豇 - 旎 - 蹰 - 蜃 - 屐 - 芡 - 鎏 - 癸 - 卅 - 枥 - 陟 - 琨 - 粝 - 掮 - 妪 - 姹 - 鏖 - 捯 - 钴 - 竽 - 恽 - 佰 - 胗 - 崧 - 磴 - 绺 - 鳏 - 槁 - 啖 - 矍 - 徕 - 忾 - 烃 - 喏 - 囹 - 圄 - 砭 - 邕 - 犍 - 鸮 - 剜 - 琚 - 瘢 - 魑 - 眦 - 锉 - 柘 - 痦 - 苕 - 牯 - 湟 - 厝 - 濛 - 赭 - 馐 - 蜇 - 嶂 - 贲 - 靼 - 臬 - 陲 - 潞 - 芩 - 腓 - 锨 - 寮 - 於 - 洇 - 愠 - 疖 - 鹧 - 鸪 - 茕 - 戕 - 壬 - 庾 - 莒 - 鹈 - 鹕 - 蠹 - 勐 - 疥 - 辎 - 耒 - 嗬 - 沔 - 睥 - 邙 - 篾 - 揩 - 肱 - 胍 - 磬 - 菟 - 豢 - 垓 - 唑 - 剌 - 阗 - 汜 - 佤 - 璟 - 麽 - 鬻 - 怏 - 蕤 - 茭 - 睚 - 淙 - 牍 - 榫 - 濯 - 稹 - 媾 - 悱 - 骶 - 蛭 - 鞣 - 椁 - 槊 - 擢 - 滢 - 佚 - 菡 - 沭 - 扦 - 镆 - 闾 - 缛 - 窠 - 疣 - 骠 - 俅 - 喙 - 蹼 - 硼 - 黩 - 腴 - 醮 - 邛 - 漯 - 豉 - 昶 - 刿 - 凇 - 鲅 - 舸 - 邳 - 俟 - 铰 - 翌 - 鳟 - 葳 - 寤 - 碣 - 秭 - 揠 - 熵 - 燧 - 靛 - 嵊 - 窨 - 鹗 - 芎 - 颢 - 佶 - 骢 - 圜 - 岘 - 燊 - 壅 - 畲 - 萘 - 煊 - 粲 - 倌 - 嗳 - 橹 - 椽 - 夔 - 鲑 - 赧 - 殄 - 沆 - 瀣 - 廪 - 舢 - 狍 - 挈 - 鹳 - 蚜 - 彧 - 羟 - 盥 - 镛 - 痈 - 蜊 - 皲 - 篦 - 喑 - 鲢 - 邡 - 蕲 - 僳 - 秣 - 蛉 - 讫 - 祗 - 鹩 - 撷 - 狎 - 郓 - 镕 - 榉 - 鲷 - 娣 - 淝 - 桷 - 镉 - 郫 - 髌 - 醪 - 僭 - 伧 - 嵬 - 苁 - 鹘 - 徭 - 歃 - 阕 - 鸱 - 貉 - 闳 - 坻 - 缙 - 媪 - 莨 - 菪 - 绦 - 恫 - 崆 - 喟 - 葺 - 逶 - 迤 - 骈 - 馔 - 苎 - 溘 - 垭 - 樯 - 诤 - 魃 - 搽 - 绀 - 蚴 - 澶 - 蒺 - 罘 - 眙 - 怍 - 來 - 荪 - 贶 - 亓 - 唻 - 畈 - 谌 - 芨 - 鲀 - 窸 - 窣 - 荜 - 楫 - 衮 - 趵 - 勰 - 髯 - 椴 - 缶 - 荸 - 秫 - 菖 - 甙 - 翦 - 椟 - 峤 - 掼 - 謇 - 洄 - 鄯 - 妗 - 浐 - 颀 - 箸 - 畦 - 痼 - 橛 - 鲛 - 蝾 - 愍 - 蒹 - 嘁 - 韪 - 劭 - 垅 - 暹 - 僮 - 稗 - 筚 - 煅 - 嬅 - 蜉 - 骝 - 碚 - 冼 - 吶 - 洹 - 郧 - 炴 - 绌 - 泠 - 呓 - 簋 - 溴 - 篁 - 仟 - 锟 - 羧 - 鹞 - 嘬 - 渌 - 笸 - 霰 - 稔 - 钡 - 齁 - 胪 - 衾 - 尻 - 洮 - 蘅 - 鲳 - 殂 - 腭 - 涔 - 蝣 - 孳 - 澍 - 钼 - 蒡 - 枳 - 渑 - 茼 - 馕 - 埙 - 珣 - 菘 - 邰 - 樾 - 铱 - 鳐 - 唔 - 篙 - 箜 - 篌 - 耆 - 啫 - 枞 - 杼 - 嵋 - 舂 - 娉 - 铨 - 崃 - 笳 - 邗 - 逡 - 僖 - 泫 - 疴 - 捱 - 醅 - 堇 - 肄 - 荇 - 虬 - 谯 - 酞 - 桡 - 艮 - 膦 - 艹 - 啻 - 滏 - 茆 - 圪 - 磡 - 麼 - 闼 - 郯 - 仡 - 氐 - 贽 - 俦 - 蓖 - 跹 - 帏 - 氅 - 趿 - 暝 - 缟 - 棹 - 滹 - 毖 - 蝰 - 虻 - 缫 - 诮 - 闩 - ○ - 潴 - 樨 - 瘘 - 襦 - 妤 - 郾 - 衿 - 鸷 - 旰 - 镢 - 傈 - 倨 - 笏 - 蒽 - 醌 - 驽 - 浠 - 涠 - 蓁 - 柞 - 钺 - 蜮 - 诂 - 徵 - 锆 - 椋 - 叻 - 廿 - 藁 - 乜 - 摈 - 這 - 茌 - 辊 - 岬 - 郇 - 杓 - 轳 - 酎 - 蟥 - 時 - 镒 - 蚬 - 澹 - 赟 - 後 - 怿 - 箐 - 囍 - 揆 - 蹁 - 鬄 - 苫 - 蕖 - 卺 - 辔 - 偈 - 俳 - 吲 - 哚 - 瘆 - 蕞 - 笞 - 氩 - 嫘 - 墁 - 帔 - 褡 - 裢 - 乩 - 褊 - 颏 - 喒 - 錾 - 皌 - 戗 - 唪 - 啭 - 伥 - 茔 - 斫 - 齉 - 仵 - 赉 - 吡 - 啶 - 蹇 - 螅 - 汊 - 湓 - 凫 - 珙 - 腈 - 洌 - Ω - 憷 - 跶 - 抔 - 濞 - 崤 - 殍 - 浥 - 铳 - 酽 - 馑 - 髂 - 隗 - 韫 - 晷 - 诒 - 埭 - 鹪 - 蕻 - 昃 - 瓠 - 萁 - 癔 - 怩 - 疳 - 跖 - 疔 - 簟 - 汆 - 疠 - 卟 - 墒 - 穰 - 铍 - 珥 - 钤 - 隻 - 樓 - 墎 - 鳜 - 沒 - 岀 - 杪 - 単 - 鲧 - 呋 - 彀 - 祇 - 豸 - 胴 - 唷 - 丨 - 燚 - 麴 - 觇 - 缑 - 橐 - 蚡 - 朊 - 俣 - 垡 - <sos/eos> init: null input_size: null ctc_conf: ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true use_preprocessor_valid: false token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_utt_prefix: null rir_apply_prob: 1.0 noise_scp: null noise_utt_prefix: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_zh_char/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish macaron_style: true use_cnn_module: true cnn_module_kernel: 15 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 required: - output_dir - token_list version: 0.10.2a1 distributed: true ``` </details> ## LM config <details><summary>expand</summary> ``` NONE ``` </details>
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["wenetspeech"]}
espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char
null
[ "espnet", "audio", "automatic-speech-recognition", "zh", "dataset:wenetspeech", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
espnet
## ESPnet2 ASR model ### `espnet/roshansh_how2_asr_raw_ft_sum_valid.acc` This model was trained by roshansh-cmu using how2 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout e6f42a9783a5d9eba0687c19417f933e890722d7 pip install -e . cd egs2/how2/sum1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/roshansh_how2_asr_raw_ft_sum_valid.acc ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Mon Feb 7 15:24:21 EST 2022` - python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1` - Git hash: `04561cdf3b6c3bc1d51edb04c93b953759ef551d` - Commit date: `Mon Feb 7 09:06:12 2022 -0500` ## asr_raw_ft_sum |dataset|Snt|Wrd|ROUGE-1|ROUGE-2|ROUGE-L|METEOR|BERTScore| |---|---|---|---|---|---|---|---| |decode_sum_asr_model_valid.acc.best/dev5_test_sum|2127|69795|60.72|44.7|56.1|29.36|91.53| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer_vid_lf.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_raw_ft_sum ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45875 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 10 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 10 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 5000 use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - exp/asr_raw_utt_conformer/valid.acc.ave_10best.pth:::ctc ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 60000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_vid_sum/train/speech_shape - exp/asr_stats_raw_vid_sum/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_vid_sum/valid/speech_shape - exp/asr_stats_raw_vid_sum/valid/text_shape.bpe batch_type: length valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr_2000h_sum_trim/wav.scp - speech - sound - - dump/raw/tr_2000h_sum_trim/text - text - text valid_data_path_and_name_and_type: - - dump/raw/cv05_sum_trim/wav.scp - speech - sound - - dump/raw/cv05_sum_trim/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 1 token_list: - <blank> - <unk> - '[hes]' - S - ▁THE - ▁TO - '''' - ▁AND - ▁YOU - ▁A - ▁IT - T - ▁THAT - ▁OF - ▁I - ▁IS - RE - ▁IN - ING - ▁WE - M - ▁GOING - ▁SO - ▁THIS - ▁YOUR - ▁ON - E - D - ▁BE - ▁CAN - N - Y - O - ER - ▁HAVE - ▁JUST - ▁FOR - ▁WITH - ▁DO - ED - ▁ARE - ▁WANT - ▁UP - R - LL - P - ▁ - L - B - ▁IF - C - ▁ONE - ▁S - ▁OR - A - ▁GO - ▁LIKE - ▁NOW - ▁HERE - VE - LE - U - ▁GET - ▁WHAT - ▁OUT - IN - W - ▁C - ▁LITTLE - ▁THERE - LY - ▁AS - ▁MAKE - I - ▁THEY - ▁MY - K - ▁THEN - ▁BUT - AL - G - ▁ALL - OR - ▁BACK - ▁NOT - ▁ABOUT - ▁RIGHT - ▁OUR - EN - ▁SOME - ▁DOWN - F - ▁WHEN - CH - ▁F - ▁HOW - AR - ▁WILL - ▁RE - CK - ▁G - ES - CE - ▁TAKE - ▁AT - ▁FROM - ▁WAY - TER - ▁SEE - RA - ▁USE - ▁REALLY - RI - TH - ▁TWO - ▁ME - ▁VERY - ▁E - ▁B - AT - ▁THEM - ▁DON - ▁AN - ▁BECAUSE - ▁MORE - RO - H - 'ON' - LI - ▁PUT - ▁ST - IL - ▁BIT - ▁START - ▁NEED - ▁INTO - UR - ▁TIME - ▁OVER - ▁W - ▁DE - ▁LOOK - ▁THESE - ▁LET - ▁GOOD - ▁ALSO - AN - ▁OFF - ▁HE - ▁KIND - ▁SIDE - ▁CO - ▁SURE - ▁AGAIN - ▁MA - ▁KNOW - IT - ▁WOULD - IC - ▁OTHER - LA - ▁P - ▁WHICH - '-' - IR - ▁LA - ▁HAND - EL - ▁LOT - ▁WHERE - ▁THREE - ▁PA - ION - LO - ▁KEEP - ▁SHOW - ▁THING - ▁FIRST - TE - ENT - ATE - ▁COME - AD - ▁GOT - NG - ▁NICE - ▁T - ET - ▁MO - ▁ANY - ▁ACTUALLY - ▁DIFFERENT - ▁SE - GE - ▁WORK - ▁THROUGH - ▁O - KE - V - ▁AROUND - ▁BA - PE - ▁HI - ▁BY - SH - ATION - ▁SU - ▁CA - ▁D - ▁LO - ▁HAS - ▁LI - ▁PLAY - Z - ▁ADD - ▁RO - ▁TA - AS - ▁FOUR - ▁CON - ▁THOSE - MP - NE - ▁SP - UT - ▁GIVE - ▁WELL - ▁BALL - TING - RY - X - ▁HO - INE - IVE - ▁NEXT - ▁PO - ▁STEP - ▁EVEN - TION - ▁MI - MENT - ▁CUT - ▁BO - ▁LINE - ▁MUCH - ▁THINGS - ▁TALK - UN - ▁PART - ▁WAS - ▁FA - ▁SOMETHING - PP - ANCE - ND - DI - ▁RA - AGE - ▁SAME - ▁EXPERT - ▁DOING - ▁LEFT - IST - ▁DI - ▁NO - RU - ME - TA - UL - TI - ▁VILLAGE - DE - ERS - ▁PEOPLE - ▁TURN - VER - ▁FL - ▁LEG - ▁ONCE - ▁COLOR - ▁PULL - ▁USING - VI - ▁WATER - ▁SHE - ▁TOP - ▁OKAY - ▁ANOTHER - ▁THEIR - ▁SAY - URE - ▁HA - ▁IMPORTANT - ▁PIECE - ▁FOOT - ▁TRA - ▁SC - ▁BODY - ▁SET - ▁POINT - ▁HELP - ▁TODAY - ▁BRING - ▁V - ▁END - MA - ▁CH - ▁MOST - ▁K - ▁AHEAD - ▁HER - OL - ▁SA - AM - IES - ▁THINK - ▁NAME - ▁TRY - ▁MOVE - ONE - ▁LE - ▁TOO - TO - UM - ▁PLACE - ▁COULD - ▁FIND - ▁FIVE - ▁ALWAYS - ID - TY - NT - ▁FEEL - ▁HEAD - ▁THAN - NA - ▁EX - ▁EYE - ITY - CI - OP - ▁SHOULD - ▁MIGHT - ▁HOLD - ▁CAR - AND - ▁GREAT - ▁RI - ▁BU - ▁HIGH - ▁OPEN - ▁BEFORE - US - ▁FRONT - ▁LONG - ▁TOGETHER - NI - ▁HAIR - ▁LIGHT - ▁TEN - ▁HIT - EST - OUS - ▁PRETTY - ▁TYPE - IP - CO - ▁FINGER - ▁JO - ▁UN - ▁PRO - ▁STRAIGHT - ▁BEHALF - ▁TI - ▁SIX - ▁CLEAN - ▁DIS - ▁DA - ▁POSITION - IGHT - ACT - ▁CHA - ▁PE - GG - AP - ▁MEAN - ▁COMP - FI - ▁KNEE - ▁CALLED - ▁HANDS - ▁PRE - ▁FORWARD - ▁AREA - ANT - ▁TE - ▁WA - ▁AFTER - ▁SMALL - ▁THROW - ▁EVERY - ▁SHOULDER - NC - PER - ▁MAYBE - ▁ABLE - ▁BASICALLY - ▁AM - ▁READY - ▁BOTTOM - IE - ▁HALF - FF - ▁BIG - ▁EACH - ▁PUSH - ▁EIGHT - ▁NEW - ▁DONE - ▁MAY - ▁GETTING - HO - ▁HIS - ▁HARD - ▁CLOSE - ALLY - ▁SECOND - ▁FEET - ICAL - ▁JA - ▁PAINT - ▁LEARN - ▁SOUND - HE - ▁ROLL - ▁ONLY - ▁DOESN - WA - ▁DRAW - ▁VI - ▁DID - ▁SHA - ▁CENTER - CU - ▁CLIP - ▁PI - ▁CARD - ▁INSIDE - ▁PERSON - ▁STILL - ▁MAKING - 'NO' - ▁EVERYTHING - . - ▁FUN - ARD - ▁REMEMBER - ▁AWAY - ATED - COM - ▁SEVEN - ▁BEEN - ▁MANY - ABLE - ▁DAY - ▁SIT - IZE - ▁REAL - ▁HIP - ▁BASIC - ▁KICK - ▁TU - ATING - ▁STICK - ▁FLAT - ▁WHO - END - HA - ▁EXP - ▁PICK - ▁MIX - ▁TRI - ▁BI - ▁WHOLE - ▁STRETCH - ▁BOTH - ▁PROBABLY - CA - ▁HIM - ▁STRING - ▁EDGE - ▁BASE - ▁COMING - UGH - ▁LIFT - ▁STA - ▁WORKING - ▁MU - ▁QUICK - ▁SOMETIMES - ▁HAPPEN - ▁YOURSELF - ▁TALKING - ▁DR - ▁TELL - ▁ANYTHING - ▁BRA - ▁LOOKING - ▁SLOW - ▁NE - ▁STAND - NER - ▁COMES - ▁GOES - ISE - BE - ▁USED - ▁UNDER - ▁BETWEEN - ▁HU - ▁CREATE - ▁NA - ▁USUALLY - ▁ARM - ▁DRY - ▁RUN - LING - ▁BRUSH - ▁COVER - ▁HEAR - ▁DOES - ▁STAY - ▁EN - ▁FOLD - ▁CHANGE - ▁LAST - ▁EASY - ▁US - ▁PER - ▁FACE - ▁EAR - ▁TIGHT - ▁FE - ▁PIN - ▁MAN - ▁BETTER - ▁CALL - ▁PRI - ▁BEST - ▁KI - ▁COUPLE - ▁WHILE - ▁SHAPE - ▁GAME - IV - ▁SHOT - ▁PAPER - ▁OWN - ▁ALRIGHT - ▁HAD - TIC - ▁BREATH - ▁TOOL - '2' - ▁ENOUGH - ▁COURSE - ▁SKIN - ▁SPIN - ▁VA - ▁ARMS - ▁TEA - ▁BREAK - ▁DOG - ▁1 - QUE - ▁DROP - ▁NUMBER - IG - ▁RED - ▁NOTE - ▁WEIGHT - WARD - ▁PLAYING - ▁FINISH - ▁MINUTE - ▁R - ▁PRESS - ▁EITHER - ▁CHE - ▁PU - BER - ▁FEW - ▁SIZE - ▁MADE - ▁LEAVE - ▁GA - ▁ALREADY - ▁GUY - ▁FAR - ▁HOME - ▁BAR - UP - ▁GRAB - ▁MARK - ▁WHITE - ▁PROPER - ▁CAUSE - ▁OK - ▁ART - HI - ▁SORT - ▁EXERCISE - ▁LOWER - PORT - ▁PLANT - ▁BOARD - ▁CASE - ▁YEAR - CENT - ▁DU - ▁CHECK - ▁WHATEVER - ▁OIL - ▁IDEA - ▁SIMPLE - ▁PRACTICE - ▁FAST - '0' - ▁CONTROL - ▁J - ▁KEY - ▁MIDDLE - ▁FULL - ▁GLASS - ▁OUTSIDE - ▁LOW - ▁REST - ▁STUFF - ▁ACT - ▁UNTIL - ▁BLACK - ▁POP - ▁CLICK - ▁HOLE - ▁Z - ▁COUNT - ▁POT - ▁ALLOW - ▁HAVING - ▁TRYING - ▁MUSCLE - ▁GU - ▁BOX - ▁NOTICE - ▁EXAMPLE - UND - ▁ALONG - FUL - ISH - ▁STORE - ▁LU - ▁FLOOR - ▁MOVING - ▁LARGE - ▁STOP - ▁PH - ▁WALK - '5' - ▁QU - ▁TECHNIQUE - ▁SOFT - ▁GROUND - ▁JUMP - ▁JU - ▁FILL - ▁WHY - ▁BUY - ▁GREEN - ▁WALL - ▁HEEL - NESS - ▁LEVEL - ▁UNDERNEATH - ▁PATTERN - ▁BEHIND - ▁OLD - ▁TIP - ▁COMPLETE - ▁WON - ▁TEACH - ▁FIT - ▁NECK - ▁REMOVE - ▁TRICK - ▁MOVEMENT - ▁TOWARDS - ▁PARTICULAR - ▁CHI - ▁EFFECT - J - ▁FREE - ▁ACROSS - ▁BEND - ▁SAFE - ▁SLIDE - ▁PROBLEM - ▁BLOCK - ▁PAN - ▁NATURAL - ▁TOUCH - ▁CHILD - LINE - ▁CROSS - ▁REASON - '4' - ▁POWER - ▁APPLY - ▁FOLLOW - ▁DESIGN - ▁SPACE - ▁ORDER - ▁WOOD - ▁RID - '3' - ▁COOK - ▁BEGIN - ▁WATCH - ▁STYLE - QUA - ▁PRODUCT - ▁TAKING - ▁PUTTING - ▁EXHALE - ▁THOUGH - ▁DEEP - IAN - ▁REACH - ▁FOOD - ▁ALMOST - ▁COOL - ▁SECTION - ▁SAID - ▁ANGLE - ▁MUSIC - ▁RELAX - ▁CORNER - ▁DARK - ▁CHORD - ▁ESPECIALLY - ▁SCALE - ▁WARM - ▁WITHOUT - ▁WHEEL - ▁SEGMENT - ▁TABLE - ▁BOOK - ▁PASS - ▁ELBOW - ▁ROUND - ▁INHALE - ▁SMOOTH - ▁ROOM - / - ▁NINE - ▁SHORT - ▁MEASURE - ▁LESS - ▁TWIST - ▁BALANCE - ▁PROCESS - ▁SWITCH - ▁GENERAL - ▁CLAY - ▁CERTAIN - ▁NEVER - ▁BLUE - ▁CUP - ▁HOUSE - ▁EXTRA - ▁MOTION - ▁PRESSURE - ▁FIRE - ▁SIMPLY - ▁DOUBLE - ▁TWENTY - ▁CATCH - ▁BECOME - ▁BUILD - ▁SPEED - ▁TRANS - ▁DRUM - ▁CHEST - ▁PICTURE - ▁LENGTH - ▁CONTINUE - ▁COMFORTABLE - ▁FISH - ▁PHOTO - ▁LOOSE - ▁SKI - ▁LIFE - ▁DEGREE - ▁OPTION - ▁WORD - ▁SHARP - ▁SHOOT - ▁FOUND - ▁STRONG - ▁QUITE - ▁THIRD - ▁GLUE - ▁MIND - ▁DEFINITELY - ▁EASIER - GRAPH - ▁HOOK - ▁CLEAR - ▁POSE - ▁BUTTON - ▁CHOOSE - ▁THICK - ▁SYSTEM - ▁PERFECT - ▁BEAUTIFUL - ▁SPOT - ▁GROW - ▁SIGN - ▁ELSE - ▁CONNECT - ▁SELECT - ▁PUNCH - ▁DIRECTION - ▁WRAP - ▁RELEASE - QUI - SIDE - ▁CAREFUL - ▁VIDEO - ▁INSTEAD - ▁CIRCLE - ▁WIRE - ▁NOSE - ▁AMOUNT - ▁FOCUS - ▁NORMAL - ▁MAJOR - ▁WHETHER - ▁SURFACE - ▁THUMB - ▁DRIVE - ▁SCREW - ▁POSSIBLE - ▁OBVIOUSLY - ▁COMMON - ▁REGULAR - ▁ADJUST - ▁WIDE - ▁BLADE - ▁FRET - ▁RECOMMEND - ▁BOWL - BOARD - ▁IMAGE - ▁DEPENDING - ▁PROTECT - ▁CLOTH - ▁HEALTH - ▁WRIST - ▁CLUB - ▁DRINK - ▁SINCE - ▁FRIEND - '00' - ▁RUNNING - ▁ITSELF - ▁RECORD - ▁SWING - ▁DIRECT - ▁MATERIAL - ▁YO - ▁LEAST - ▁EXACTLY - ▁BEGINNING - ▁SLIGHTLY - ▁TREAT - ▁CAMERA - ▁QUARTER - ▁WINDOW - '8' - ▁SOMEBODY - ▁BURN - ▁DEMONSTRATE - ▁DIFFERENCE - ▁COMPUTER - IBLE - ▁SHOE - ▁PERFORM - ▁SQUARE - ▁CONSIDER - ▁DRILL - ▁TEXT - ▁FILE - ▁RUB - ▁FABRIC - ▁HUNDRED - ▁GRIP - ▁CHARACTER - ▁SPECIFIC - ▁KNOT - ▁CURL - ▁STITCH - ▁BLEND - ▁FRAME - ▁THIRTY - '1' - ▁HORSE - ▁ATTACH - ▁GROUP - ▁STROKE - ▁GUITAR - ▁APART - ▁MACHINE - ▁CLASS - ▁COMB - ▁ROOT - ▁HELLO - ▁ENERGY - ▁ATTACK - ▁CORRECT - ▁EXTEND - ▁MINOR - ▁PROFESSIONAL - ▁MONEY - ▁STRIP - ▁FLAVOR - ▁EVERYBODY - ▁RULE - ▁DIFFICULT - ▁PROJECT - ▁DISCUSS - ▁FIGURE - ▁HOWEVER - ▁FINAL - ▁STRENGTH - ▁ENTIRE - ▁FIELD - ▁CONTACT - ▁SUPPORT - ▁PALM - ▁SERIES - ▁ENJOY - '6' - ▁WORLD - ▁DECIDE - ▁SPEAK - ▁SEVERAL - ▁WRITE - ▁PROGRAM - ABILITY - ▁KNIFE - ▁PLASTIC - ▁ORGAN - '7' - ▁UNDERSTAND - ▁FIFTEEN - ▁FLEX - ▁INFORMATION - ▁TWELVE - ▁DETAIL - ▁STRIKE - ▁ACTUAL - ▁SPRAY - ▁LOCAL - ▁MOUTH - ▁NIGHT - ▁VEHICLE - ▁OPPOSITE - ▁SCHOOL - '9' - ▁QUESTION - ▁SPECIAL - ▁BIGGER - ▁DEVELOP - ▁PEPPER - ▁PREFER - Q - '%' - ']' - '[' - '&' - ',' - _ - '#' - '=' - '@' - + - '*' - $ - '~' - <sos/eos> init: null input_size: null ctc_conf: ignore_nan_grad: true model_conf: ctc_weight: 0.0 lsm_weight: 0.15 length_normalized_loss: false use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram1000/bpe.model non_linguistic_symbols: data/nlsyms cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 hop_length: 256 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_vid_sum/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: abs_pos selfattention_layer_type: lf_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 attention_windows: - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 attention_dilation: - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 attention_mode: tvm decoder: transformer decoder_conf: attention_heads: 4 linear_units: 512 num_blocks: 6 dropout_rate: 0.15 positional_dropout_rate: 0.15 self_attention_dropout_rate: 0.15 src_attention_dropout_rate: 0.15 required: - output_dir - token_list version: 0.10.0 distributed: true ``` </details> Please cite the following paper if you use this recipe: ```BibTex @misc{sharma2022speech, title={Speech Summarization using Restricted Self-Attention}, author={Roshan Sharma and Shruti Palaskar and Alan W Black and Florian Metze}, year={2022}, eprint={2110.06263}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### 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 Yalta 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} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title##3={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 Yalta 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} ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-summarization"], "datasets": ["how2"]}
espnet/roshansh_how2_asr_raw_ft_sum_valid.acc
null
[ "espnet", "audio", "automatic-speech-summarization", "en", "dataset:how2", "arxiv:2110.06263", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
# ESPnet2 ASR pretrained model ## `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best, fs=16k, lang=en` ♻️ Imported from <https://zenodo.org/record/3966501#.YOAOUZozZH5> This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Python API ```text See https://github.com/espnet/espnet_model_zoo ``` ### Evaluate in the recipe ```python # coming soon ``` ### Results ```bash # RESULTS ## Environments - date: `Tue Jul 21 07:58:39 EDT 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 1.4.0` - Git hash: `75db853dd26a40d3d4dd979b2ff2457fbbb0cd69` - Commit date: `Mon Jul 20 10:49:12 2020 -0400` ## asr_train_asr_transformer_e18_raw_bpe_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|54402|97.9|1.8|0.2|0.2|2.3|28.2| |decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|54402|97.9|1.9|0.2|0.3|2.4|29.5| |decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|50948|94.6|4.7|0.7|0.7|6.0|46.6| |decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|50948|94.4|5.0|0.5|0.8|6.3|47.5| |decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|52576|97.7|2.0|0.3|0.3|2.6|30.4| |decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|52576|97.7|2.0|0.2|0.3|2.6|30.1| |decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|52343|94.5|4.8|0.7|0.7|6.2|49.7| |decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|52343|94.3|5.1|0.6|0.8|6.5|50.3| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|288456|99.3|0.3|0.3|0.2|0.9|28.2| |decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|288456|99.3|0.4|0.3|0.2|0.9|29.5| |decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|265951|97.7|1.2|1.1|0.6|2.9|46.6| |decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|265951|97.7|1.3|1.0|0.8|3.0|47.5| |decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|281530|99.3|0.3|0.4|0.3|1.0|30.4| |decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|281530|99.4|0.3|0.3|0.3|0.9|30.1| |decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|272758|97.8|1.1|1.1|0.7|2.9|49.7| |decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|272758|97.9|1.2|0.9|0.8|2.9|50.3| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|69307|97.2|1.8|1.0|0.4|3.2|28.2| |decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|69307|97.2|1.9|1.0|0.5|3.3|29.5| |decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|64239|93.3|4.4|2.2|1.2|7.9|46.6| |decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|64239|93.2|4.9|1.9|1.5|8.3|47.5| |decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|66712|97.0|1.9|1.1|0.4|3.3|30.4| |decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|66712|97.1|1.9|1.0|0.5|3.3|30.1| |decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|66329|93.1|4.5|2.4|1.0|7.9|49.7| |decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|66329|93.1|4.8|2.1|1.4|8.3|50.3| ``` ### Training config See full config in [`config.yaml`](./exp/asr_train_asr_transformer_e18_raw_bpe_sp/config.yaml) ```yaml config: conf/tuning/train_asr_transformer_e18.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_e18_raw_bpe_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 3 local_rank: 3 dist_master_addr: localhost dist_master_port: 33643 dist_launcher: null multiprocessing_distributed: true cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"], "inference": false}
espnet/shinji-watanabe-librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 SLU pretrained model ### `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best` ♻️ Imported from https://zenodo.org/record/5590204 This model was trained by siddhana using fsc/asr1 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} } ``` 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc"]}
espnet/siddhana_fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:fsc", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR pretrained model ### `siddhana/fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best` ♻️ Imported from https://zenodo.org/record/5656007 This model was trained by siddhana using fsc_challenge/asr1 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} } ``` 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc_challenge"]}
espnet/siddhana_fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_r-truncated-36174d
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:fsc_challenge", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR pretrained model ### `siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best` ♻️ Imported from https://zenodo.org/record/5655832 This model was trained by siddhana using fsc_unseen/asr1 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} } ``` 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc_unseen"]}
espnet/siddhana_fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_fine-truncated-ef9dab
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:fsc_unseen", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR model ### `espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best` This model was trained by Siddhant using slue-voxceleb recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 17758ad804fd7c4b6f88ef5601f475a241dc4605 pip install -e . cd egs2/slue-voxceleb/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Tue Dec 28 12:28:28 EST 2021` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a2` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `6bf3c2a4f138d35331634d2e879bbc5c32a5266e` - Commit date: `Mon Dec 22 15:41:32 EST 2021` ## Using Conformer based encoder and Transformer based decoder with spectral augmentation and predicting transcript along with intent - ASR config: [conf/train_asr.yaml](conf/tuning/train_asr_conformer.yaml) - token_type: word |dataset|Snt|Intent Classification Accuracy (%)|Intent Classification Macro F1 (%)| |---|---|---|---| |inference_asr_model_valid.acc.ave_10best/devel|955|80.2|29.7| ### Detailed Classification Report |dataset|Label|Snt|Prec|Recall|F1| |---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_10best/devel|Neutral|784|85|93|89| |inference_asr_model_valid.acc.ave_10best/devel|Positive|167|40|24|30| |inference_asr_model_valid.acc.ave_10best/devel|Negative|3|0|0|0| |inference_asr_model_valid.acc.ave_10best/devel|Mixed|1|0|0|0| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_raw_en_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_word/train/speech_shape - exp/asr_stats_raw_en_word/train/text_shape.word valid_shape_file: - exp/asr_stats_raw_en_word/valid/speech_shape - exp/asr_stats_raw_en_word/valid/text_shape.word batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - sound - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/devel/wav.scp - speech - sound - - dump/raw/devel/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0002 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁i - s - ▁and - '''' - ▁the - ▁a - ▁to - ▁it - Neutral - ▁you - ▁that - ▁of - t - ing - ▁in - ▁was - ed - ▁uh - ▁know - e - m - ▁he - y - er - ▁so - ▁we - re - a - o - d - ▁um - i - ▁s - c - ▁like - n - ▁is - ▁be - ▁f - ▁but - ▁c - Positive - en - l - ve - ▁just - ▁m - st - ▁they - le - an - ▁on - ▁p - u - ▁my - ar - p - ▁this - ▁for - ▁b - ▁think - in - ▁with - g - or - ▁h - r - ly - w - ▁me - ▁d - ▁e - ▁have - ▁she - it - ▁t - ▁what - b - ▁st - al - es - ▁there - ▁really - ic - ▁g - ▁as - ▁w - ▁l - ▁do - ll - v - ▁all - at - 'on' - as - ▁about - h - ▁not - ▁re - ▁o - ▁at - k - ▁don - ▁had - ▁when - ou - ent - is - ra - ▁who - ri - ▁go - se - f - ▁out - ▁get - ▁an - ▁people - nd - ▁kind - ▁very - ce - ▁because - ▁are - ion - ▁some - et - ▁can - ge - ▁or - me - ▁up - ▁n - ▁if - ▁no - ▁one - ▁were - ct - ▁mean - ad - ▁time - ▁ch - ▁then - ro - ▁ex - ▁mo - ▁her - ▁every - ▁would - ▁co - ▁work - ir - ▁sh - ay - ▁se - ol - ver - ▁su - ▁got - ▁k - th - ▁love - ▁from - ld - ation - ▁him - ▁said - ▁how - ▁well - ▁lot - ▁show - ch - ard - ie - ▁pro - ▁de - ▁gonna - ▁bo - ▁say - ▁see - ▁li - one - ▁his - ther - ▁been - ur - ▁any - ▁great - ▁ - ▁yeah - pe - ▁which - ▁come - ▁them - ot - ▁play - ab - ite - ▁way - ally - id - gh - ▁r - ▁sc - our - x - mp - ers - ong - ate - ▁your - ss - ast - ▁did - ▁sort - ▁am - am - and - ▁make - ant - ▁thing - ▁ha - ▁te - ▁has - ess - ▁v - ▁something - ▁back - ▁where - ▁things - red - ▁al - ut - el - ight - ment - un - ive - ▁th - ▁le - il - ▁j - op - ▁more - ▁ro - ill - ▁fi - ies - ▁much - ck - ▁ne - ▁wh - ▁always - ▁act - ine - pp - z - ▁now - ▁con - thing - ▁us - body - ▁want - ▁other - ort - ice - ▁doing - ▁sa - ▁feel - ow - ▁int - ne - ▁these - ▁could - ▁good - ▁cause - Negative - ▁actually - ▁wr - ▁little - ain - ▁being - ▁look - ▁into - ere - ul - ▁our - ▁guy - ▁first - ud - ▁by - ▁fun - ▁qu - ▁didn - us - ity - ▁jo - od - ▁u - ▁part - ▁off - ▁pre - ▁right - ▁film - ▁start - ok - ▁two - ving - ▁never - pt - um - te - ▁movie - ▁going - ff - nder - ke - ▁ag - ▁en - ▁try - ful - im - ays - ▁life - ▁different - ach - are - ▁di - ist - ▁oh - au - ▁po - nt - ▁com - all - ▁lo - om - ▁real - ▁y - ame - ▁went - ry - ber - ▁even - ci - ▁ho - ▁years - ▁their - ▁happen - ure - self - per - ▁pl - ▁those - ble - 'no' - ▁day - ▁take - ▁does - ien - ▁br - be - wn - ▁thought - ▁fe - ght - ▁tr - ▁story - ty - ▁down - ous - ish - ▁wom - ▁wanna - ▁put - ▁through - ide - ▁ab - ▁new - ▁also - ▁big - ▁call - ▁around - ▁character - ▁read - iz - ▁came - act - ily - ath - ag - ree - ▁per - ▁will - ▁mu - ▁talk - ▁over - ▁friend - atch - ▁bl - ade - ▁world - ▁many - ▁sp - sic - ▁cl - ▁bit - ▁man - ace - ▁person - ft - ip - ▁than - ▁wanted - ▁may - ven - ick - ious - ▁mar - ▁before - ▁rel - j - ting - ▁set - sh - ep - ▁un - ue - ▁aw - ▁find - ▁kid - tain - ▁such - ter - ▁end - ▁tw - ind - aking - ▁after - ▁fam - ars - ig - ore - ▁bec - ak - art - reat - ust - rou - ack - ▁ye - ould - ime - itt - ▁gu - qu - ose - fe - ▁wor - lf - alk - ▁charact - ▁mov - out - ich - ▁happ - ▁thou - ith - <mixed> - rom - ake - ▁diff - ▁char - na - round - ory - ink - ually - ▁gon - ▁pe - right - ody - ah - rie - riend - now - so - ause - ▁fil - ▁pers - fore - very - ▁differe - rough - q - ▁fir - anna - ways - ':' - '&' - fter - <sos/eos> transcript_token_list: null init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 postdecoder: null postdecoder_conf: {} required: - output_dir - token_list version: 0.10.3a2 distributed: false ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slue-voxceleb"]}
espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:slue-voxceleb", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 SLU (Entity Classification) pretrained model ### `siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best` ♻️ Imported from https://zenodo.org/record/5590204 This model was trained by siddhana using fsc/asr1 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} } ``` 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc"]}
espnet/siddhana_slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:fsc", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 SLU pretrained model ### `siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best` ♻️ Imported from https://zenodo.org/record/5590384 This model was trained by siddhana using slurp/asr1 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} } ``` 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slurp"]}
espnet/siddhana_slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:slurp", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR model ### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout b0ff60946ada6753af79423a2e6063984bec2926 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp ``` ## ASR config <details><summary>expand</summary> ``` ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR model ### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout b0ff60946ada6753af79423a2e6063984bec2926 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp ``` ## ASR config <details><summary>expand</summary> ``` ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR model ### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout b0ff60946ada6753af79423a2e6063984bec2926 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Tue Jan 4 20:52:48 EST 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.5a1` - pytorch version: `pytorch 1.8.1` - Git hash: `` - Commit date: `` ## asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|98.4|1.4|0.1|0.2|1.7|23.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|96.7|3.0|0.3|0.3|3.6|35.5| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|98.4|1.5|0.1|0.2|1.8|23.7| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|96.7|3.0|0.3|0.4|3.7|37.9| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.7|0.2|0.2|0.2|0.5|23.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|98.9|0.6|0.4|0.4|1.5|35.5| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.6|0.2|0.2|0.2|0.6|23.7| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|99.1|0.5|0.4|0.4|1.3|37.9| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|98.2|1.4|0.4|0.3|2.1|23.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|96.0|3.1|0.9|0.9|4.9|35.5| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|98.1|1.4|0.5|0.4|2.3|23.7| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|96.1|2.9|1.0|0.8|4.7|37.9| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer7_wavlm_large.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 num_targets: 1 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45342 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 35 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 3 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 40000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_960_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_960_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0025 scheduler: warmuplr scheduler_conf: warmup_steps: 40000 token_list: - <blank> - <unk> - ▁THE - S - ▁AND - ▁OF - ▁TO - ▁A - ▁IN - ▁I - ▁HE - ▁THAT - ▁WAS - ED - ▁IT - '''' - ▁HIS - ING - ▁YOU - ▁WITH - ▁FOR - ▁HAD - T - ▁AS - ▁HER - ▁IS - ▁BE - ▁BUT - ▁NOT - ▁SHE - D - ▁AT - ▁ON - LY - ▁HIM - ▁THEY - ▁ALL - ▁HAVE - ▁BY - ▁SO - ▁THIS - ▁MY - ▁WHICH - ▁ME - ▁SAID - ▁FROM - ▁ONE - Y - E - ▁WERE - ▁WE - ▁NO - N - ▁THERE - ▁OR - ER - ▁AN - ▁WHEN - ▁ARE - ▁THEIR - ▁WOULD - ▁IF - ▁WHAT - ▁THEM - ▁WHO - ▁OUT - M - ▁DO - ▁WILL - ▁UP - ▁BEEN - P - R - ▁MAN - ▁THEN - ▁COULD - ▁MORE - C - ▁INTO - ▁NOW - ▁VERY - ▁YOUR - ▁SOME - ▁LITTLE - ES - ▁TIME - RE - ▁CAN - ▁LIKE - LL - ▁ABOUT - ▁HAS - ▁THAN - ▁DID - ▁UPON - ▁OVER - IN - ▁ANY - ▁WELL - ▁ONLY - B - ▁SEE - ▁GOOD - ▁OTHER - ▁TWO - L - ▁KNOW - ▁GO - ▁DOWN - ▁BEFORE - A - AL - ▁OUR - ▁OLD - ▁SHOULD - ▁MADE - ▁AFTER - ▁GREAT - ▁DAY - ▁MUST - ▁COME - ▁HOW - ▁SUCH - ▁CAME - LE - ▁WHERE - ▁US - ▁NEVER - ▁THESE - ▁MUCH - ▁DE - ▁MISTER - ▁WAY - G - ▁S - ▁MAY - ATION - ▁LONG - OR - ▁AM - ▁FIRST - ▁BACK - ▁OWN - ▁RE - ▁AGAIN - ▁SAY - ▁MEN - ▁WENT - ▁HIMSELF - ▁HERE - NESS - ▁THINK - V - IC - ▁EVEN - ▁THOUGHT - ▁HAND - ▁JUST - ▁O - ▁UN - VE - ION - ▁ITS - 'ON' - ▁MAKE - ▁MIGHT - ▁TOO - K - ▁AWAY - ▁LIFE - TH - ▁WITHOUT - ST - ▁THROUGH - ▁MOST - ▁TAKE - ▁DON - ▁EVERY - F - O - ▁SHALL - ▁THOSE - ▁EYES - AR - ▁STILL - ▁LAST - ▁HOUSE - ▁HEAD - ABLE - ▁NOTHING - ▁NIGHT - ITY - ▁LET - ▁MANY - ▁OFF - ▁BEING - ▁FOUND - ▁WHILE - EN - ▁SAW - ▁GET - ▁PEOPLE - ▁FACE - ▁YOUNG - CH - ▁UNDER - ▁ONCE - ▁TELL - AN - ▁THREE - ▁PLACE - ▁ROOM - ▁YET - ▁SAME - IL - US - U - ▁FATHER - ▁RIGHT - EL - ▁THOUGH - ▁ANOTHER - LI - 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▁APPROVE - ▁CEREMONY - ▁CRIMINAL - ▁SCIENTIFIC - ▁DUCHESS - ▁VIVID - ▁SHIFT - ▁AVAIL - ▁DAMP - ▁JOHNSON - ▁SLENDER - ▁CONTRAST - ▁AMUSEMENT - ▁PLOT - ▁LYN - ▁ASSOCIATION - ▁SNATCH - ▁UNCERTAIN - ▁PRESSURE - ▁PERCH - ▁APPLY - ▁PLANET - ▁NOTWITHSTANDING - ▁SWUNG - ▁STIRRED - ▁ATTENDANT - ▁ENJOYMENT - ▁WORRY - ▁ALBERT - ▁NAKED - ▁TALENT - ▁MARIAN - ▁REFORM - ▁DELIBERATE - ▁INTELLIGENT - ▁SENSITIVE - ▁YONDER - ▁PUPIL - ▁FRIGHTFUL - ▁DOUBTFUL - ▁STANDARD - ▁MAGISTRATE - ▁SHEPHERD - ▁STOMACH - ▁DEPOSIT - ▁RENEW - ▁HEDGE - ▁FRANCS - ▁POSSIBILITY - ▁RESEMBLE - ▁FATIGUE - ▁PORTRAIT - ▁FAVORITE - ▁CREAM - ▁BURG - ▁SECRETARY - ▁DIVERS - ▁ACTIVITY - ▁SPECULAT - ▁HUMOUR - ▁FITTED - ▁EXTERNAL - ▁CETERA - ▁WRAPPED - ▁WHIT - ▁FRED - ▁EXAMINATION - ▁LODGING - ▁OWING - ▁JAW - ▁CROW - ▁BALANCE - ▁PUFF - ▁TENDERNESS - ▁PORTHOS - ▁ANCHOR - ▁INTERRUPT - ▁NECESSARILY - ▁PERPETUAL - ▁AGONY - ▁POPE - ▁SCHOLAR - ▁SCOTLAND - ▁SUPPRESS - ▁WRATH - ▁WRECK - ▁EXCEED - ▁PERFECTION - ▁INDIA - ▁TRADITION - ▁SECTION - ▁EASTERN - ▁DOORWAY - ▁WIVES - ▁CONVENTION - ▁ANNOUNC - ▁EGYPT - ▁CONTRADICT - ▁SCRATCH - ▁CENTRAL - ▁GLOVE - ▁WAX - ▁PREPARE - ▁ACCOMPANY - ▁INCREASING - ▁LIBERAL - ▁RAISING - ▁ORANGE - ▁SHOE - ▁ATTRIBUTE - ▁LITERATURE - ▁PUZZLED - ▁WITHDRAW - ▁WHITHER - ▁HAWK - ▁MOONLIGHT - ▁EXAMINE - ▁HAPPILY - ▁PRECEDE - ▁DETECTIVE - ▁INCHES - ▁SOLITARY - ▁DUTCH - ▁NAPOLEON - ▁UNEASY - ▁CARDINAL - ▁BLEW - ▁FOWL - ▁DECORAT - ▁CHILDHOOD - ▁TORMENT - ▁LOSING - ▁PERMISSION - ▁BLANK - ▁UPSTAIRS - ▁CAPACITY - ▁TRIFLE - ▁FOLLY - ▁RECOGNIZE - ▁REMOVE - ▁VENGEANCE - ▁ENTERPRISE - ▁BEDROOM - ▁ANYHOW - ▁INQUIRY - ▁ASHES - ▁DRAG - ▁HUSH - ▁AWKWARD - ▁SATURDAY - ▁GENUINE - ▁SURVIV - ▁SKIRT - ▁AFFECTIONATE - ▁TANG - ▁MUTUAL - ▁DISPUTE - ▁EAGLE - ▁INCOME - ▁BIND - ▁FAME - ▁IMPROVEMENT - ROVING - ▁DIFFER - ▁AWOKE - ▁SLEEVE - ▁SOLITUDE - ▁FAVOURITE - JI - ▁DETECT - ▁COMPREHEND - ▁PREPARING - ▁SERPENT - ▁SUMMIT - ▁KNOT - ▁KNIT - ▁COPY - ▁STOPPING - ▁FADED - ▁HIDEOUS - ▁JULIE - STEAD - ▁SHINE - ▁CONFLICT - ▁PROPOSITION - ▁REFUGE - ▁GALLERY - ▁BUNDLE - ▁AXE - ▁SLAVERY - ▁MASK - ▁ALYOSHA - ▁LADDER - ▁DEPARTMENT - ▁DISCHARGE - ▁DEPRESS - ▁GALLOP - ▁SCARLET - ▁KITTY - ▁RECEIVING - ▁SURRENDER - ▁SUSTAIN - ▁TWILIGHT - ▁CONGRESS - ▁IRELAND - ▁FUNNY - ▁LEND - ▁CONSTITUTE - ▁FUNERAL - ▁CRYSTAL - ▁SPAIN - ▁EXCEEDINGLY - ▁DAMN - ▁COMMUN - ▁CIVILIZATION - ▁PREJUDICE - ▁PORCH - ▁ASSISTANT - ▁INDUSTRY - ▁TUMBLE - ▁DEFENCE - ▁HITHER - ▁SMOT - ▁COLONI - ▁AMAZEMENT - ▁MARGUERITE - ▁MIRACLE - ▁INHERIT - ▁BEGGAR - ▁ENVELOPE - ▁INDIGNATION - ▁NATASHA - ▁PROPOSAL - ▁FRAGMENT - ▁ROUSED - ▁ROAST - ENCIES - ▁COMMENCED - ▁RESOURCE - ▁POPULATION - ▁QUOTH - ▁PURSUE - ▁EDUCAT - ▁AFFLICT - ▁CONTACT - ▁CRIMSON - ▁DIVISION - ▁DISORDER - ▁COPPER - ▁SOLICIT - ▁MODERATE - ▁DRUM - ▁SWIM - ▁SALUTE - ▁ASSUME - ▁MUSCLE - ▁OVERWHELM - ▁SHAKESPEARE - ▁STRUGGLING - ▁TRANQUIL - ▁CHICKEN - ▁TREAD - ▁CLAW - ▁BIBLE - ▁RIDGE - ▁THREAT - ▁VELVET - ▁EXPOSED - ▁IDIOT - ▁BARREL - ▁PENNY - ▁TEMPTATION - ▁DANGLARS - ▁CENTURIES - ▁DISTRIBUT - ▁REJECT - ▁RETORTED - ▁CONCENTRAT - ▁CORDIAL - ▁MOTOR - ▁CANNON - KEEP - ▁WRETCH - ▁ASSURANCE - ▁THIEF - ▁SURVEY - ▁VITAL - ▁RAILWAY - ▁JACKSON - ▁CRASH - ▁GROWL - ▁COMBAT - ▁RECOLLECTION - ▁SECURITY - ▁JACOB - ▁CLUTCH - ▁BLANKET - ▁NANCY - ▁CELLAR - ▁CONVENIENT - ▁INDIGNANT - ▁COARSE - ▁WORM - ▁SCREEN - ▁TRANSPORT - ▁BULLET - ▁APPRECIATE - ▁DEVOTION - ▁INVISIBLE - ▁DRIED - ▁MIXTURE - ▁CANDID - ▁PERFORMANCE - ▁RIPE - ▁EXQUISITE - ▁BARGAIN - ▁TOBACCO - ▁LOYAL - ▁MOULD - ▁ATTENTIVE - ▁DOROTHY - ▁BRUTE - ▁ESTABLISHMENT - ▁ABILITY - ▁INHABIT - ▁OBSCURE - ▁BORROW - ▁ESSENCE - ▁DISMAY - ▁FLEE - ▁BLADE - ▁PLUCK - ▁COFFIN - ▁SUNSET - ▁STEPHEN - ▁ECONOMIC - ▁HOLIDAY - ▁MECHANICAL - ▁COTTON - ▁AWAKENED - ▁SEIZE - ▁RIDICULOUS - ▁SANCHO - ▁HESITATION - ▁CORPSE - ▁SAVING - HOLD - FOOT - ▁ELDEST - ▁DESPITE - ▁EDITH - ▁CHERISH - ▁RESISTANCE - ▁WILSON - ▁ARGUE - ▁INQUIRE - ▁APPREHENSION - ▁AVENUE - ▁DRAKE - ▁PROPOSE - HURST - ▁INFERIOR - ▁STAIRCASE - ▁WHEREFORE - ▁CARLYLE - ▁COUCH - ▁ROUTE - ▁POLITICS - ▁TOMORROW - ▁THRONG - ▁NAUGHT - ▁SUNLIGHT - ▁INDIFFERENCE - ▁OBEDIENCE - ▁RECEPTION - ▁VEGETABLE - ▁IMPERFECT - ▁RESIDENCE - ▁TURKEY - ▁VIOLET - ▁SARAH - ▁ALTAR - ▁GRIEVE - ▁JERK - ▁ENSU - ▁MAGICIAN - ▁BLOSSOM - ▁LANTERN - ▁RESOLUTE - ▁THOUGHTFULLY - ▁FORTNIGHT - ▁TRUMPET - ▁VALJEAN - ▁UNWILLING - ▁LECTURE - ▁WHEREUPON - ▁HOLLAND - ▁CHANGING - ▁CREEK - ▁SLICE - ▁NORMAL - ▁ANNIE - ▁ACCENT - ▁FREDERICK - ▁DISAGREEABLE - ▁RUBBED - ▁DUMB - ▁ESTABLISH - ▁IMPORT - ▁AFFIRM - ▁MATTHEW - ▁BRISK - ▁CONVERT - ▁BENDING - ▁IVAN - ▁MADEMOISELLE - ▁MICHAEL - ▁EASIER - ▁JONES - ▁FACING - ▁EXCELLENCY - ▁LITERARY - ▁GOSSIP - ▁DEVOUR - ▁STAGGER - ▁PENCIL - ▁AVERAGE - ▁HAMMER - ▁TRIUMPHANT - ▁PREFERRED - ▁APPLICATION - ▁OCCUPY - ▁AUTHORITIES - BURN - ▁ASCERTAIN - ▁CORRIDOR - ▁DELICIOUS - ▁PRACTISE - ▁UNIVERSE - ▁SHILLING - ▁CONTEST - ▁ASHORE - ▁COMMIT - ▁ADMINISTRATION - ▁STUDIED - ▁RIGID - ▁ADORN - ▁ELSEWHERE - ▁INNOCENCE - ▁JOURNAL - ▁LANDSCAPE - ▁TELEGRAPH - ▁ANGRILY - ▁CAMPAIGN - ▁UNJUST - ▁CHALLENGE - ▁TORRENT - ▁RELATE - ▁ASSEMBLED - ▁IMPRESSED - ▁CANOE - ▁CONCLUD - ▁QUIXOTE - ▁SATISFACTORY - ▁NIECE - ▁DEAF - ▁RAFT - ▁JIMMY - ▁GLID - ▁REGULAT - ▁CHATTER - ▁GLACIER - ▁ENVY - ▁STATUE - ▁BOSTON - ▁RICHMOND - ▁DENIED - ▁FANNY - ▁SOLOMON - ▁VULGAR - ▁STALK - ▁REPLACE - ▁SPOON - ▁BASIN - ▁FEATURE - ▁CONVICT - ▁ARCHITECT - ▁ADMIRAL - ▁RIBBON - ▁PERMANENT - ▁APRIL - ▁JOLLY - ▁NEIGHBORHOOD - ▁IMPART - BOROUGH - CAMP - ▁HORRID - ▁IMMORTAL - ▁PRUDENCE - ▁SPANIARD - ▁SUPPOSING - ▁TELEPHONE - ▁TEMPERATURE - ▁PENETRATE - ▁OYSTER - ▁APPOINTMENT - ▁EGYPTIAN - ▁DWELT - ▁NEPHEW - ▁RAILROAD - ▁SEPTEMBER - ▁DEVICE - ▁WHEAT - ▁GILBERT - ▁ELEGANT - ▁ADVERTISE - ▁RATIONAL - ▁TURTLE - ▁BROOD - ▁ASSEMBLY - ▁CULTIVATE - ▁EDITOR - ▁SPECIMEN - ▁UNDOUBTEDLY - ▁WHALE - ▁DROPPING - ▁BALLOON - ▁MEDICAL - COMB - ▁COMPOSITION - ▁FOOTSTEPS - ▁LAUNCELOT - ▁DISCOURSE - ▁ERRAND - ▁CONVERSE - ▁ADVANCING - ▁DOWNSTAIRS - ▁TUMULT - ▁CORRUPT - ▁SUFFICE - ▁ANGUISH - ▁SHAGGY - ▁RETIRE - ▁TIMBER - ▁BLAZE - ▁ABSTRACT - ▁EMBROIDER - ▁PHOTOGRAPH - ▁PROSPERITY - ▁TERRIBLY - ▁TERRITORY - ▁THRESHOLD - ▁PAVEMENT - ▁INJURED - ▁LIMP - ▁AGITATION - ▁RASCAL - ▁PRESUME - ▁OBSERVING - ▁OBSTACLE - ▁SIMPLICITY - ▁SLUMBER - ▁SUPPLIED - ▁COMBINATION - ▁DRAIN - ▁WILDERNESS - ▁BELIEVING - ▁VILLAIN - ▁RECKLESS - ▁INJURY - ▁CLAPP - ▁FRIDAY - ▁HERCULES - ▁KENNEDY - ▁SYMPTOM - ▁SLEDGE - ▁CEILING - ▁LEMON - ▁PLAGUE - ▁MONDAY - ▁CANVAS - ▁IMPATIENCE - ▁UNCOMFORTABLE - ▁ACCESS - ▁FROZEN - ▁SENATOR - ▁FRANZ - ▁SWIMMING - ▁BARRIER - ▁ADJUST - ▁COMPARISON - ▁PROCLAIM - ▁WRINKL - ▁OVERLOOK - ▁MITYA - ▁GUILT - ▁PERCEPTION - ▁PRECAUTION - ▁SPECTATOR - ▁SURPRISING - ▁DISTRACT - ▁DISDAIN - ▁BONNET - ▁MAGNET - ▁PROFESS - ▁CONFOUND - ▁NARRATIVE - ▁STRUCTURE - ▁SKETCH - ▁ULTIMATE - ▁GLOBE - ▁INSECT - FICIENCY - ▁ORCHARD - ▁AMIABLE - ▁DESCENT - ▁INDEPENDENCE - ▁MANUFACTURE - ▁SPRINKLE - ▁NIGHTINGALE - ▁CUSHION - ▁EMINENT - ▁SCOTT - ▁ARRAY - ▁COSETTE - ▁WAVING - ▁EXTRACT - ▁IRREGULAR - ▁PERSECUT - ▁DERIVED - ▁WITHDREW - ▁CAUTION - ▁SUSPICIOUS - ▁MEMORIES - ▁NOWHERE - ▁SUBTLE - ▁THOROUGH - Q - ▁APPROPRIATE - ▁SLAUGHTER - ▁YOURSELVES - ▁THUMB - ▁TWAS - ▁ABODE - ▁BIDDING - ▁CONSPICUOUS - ▁REBECCA - ▁SERGEANT - ▁APRON - ▁ANTICIPATE - ▁DISCIPLINE - ▁GLANCING - ▁PILGRIM - ▁SULLEN - ▁CONTRIBUTE - ▁PRAIRIE - ▁CARVED - ▁COMMERCE - ▁EXCLAMATION - ▁MUSCULAR - ▁NOVEMBER - ▁PHENOMENA - ▁SYMBOL - ▁UMBRELLA - ▁DIMINISH - ▁PARLOUR - ▁THREATENING - ▁STUMP - ▁EXTENSIVE - ▁PLEASING - ▁REMEMBRANCE - ▁COMBINED - ▁SHERIFF - ▁SHAFT - ▁LAURA - ▁INTERCOURSE - ▁STRICKEN - ▁SUPPLIES - ▁LANDLORD - ▁SHRINK - ▁PRICK - ▁CAESAR - ▁DRUG - ▁BEWILDERED - ▁NAUTILUS - ▁BRUTAL - ▁COMMERCIAL - ▁MAGGIE - ▁SPHERE - ▁VIRGIN - ▁BRETHREN - ▁DESTINY - ▁POLICY - ▁TERRIFIED - ▁HOUSEKEEPER - ▁CRAZY - ▁ARDENT - ▁DISCERN - ▁WRAP - ▁MARQUIS - ▁RUSSIA - MOUTH - ▁BRITAIN - ▁HARBOUR - ▁CONCERT - ▁DONKEY - ▁DAMAGE - ▁SLIM - ABOUT - ▁LUXURY - ▁MONSTROUS - ▁TENDENCY - ▁PARADISE - ▁CULTURE - ▁JULIUS - ▁RAOUL - ▁REMEDY - ▁DECAY - ▁SCOLD - ▁SPLIT - ▁ASSAULT - ▁DECEMBER - ▁MOSCOW - ▁EXPLORE - ▁TROUSERS - ▁WRIST - PIECE - ▁MUSKET - ▁VALENTINE - ▁TYRANT - ▁ABRAHAM - ▁MEDIUM - ▁ARTIFICIAL - ▁FACULTY - ▁OBLIGATION - ▁RESEMBLANCE - ▁INQUIRIES - ▁DETAIN - ▁SWARM - ▁PLEDGE - ▁ADMIRABLE - ▁DEFECT - ▁SUPERINTEND - ▁PATRIOT - ▁CLUNG - ▁DISMAL - ▁RECIT - ▁IGNOR - ▁AMELIA - ▁JUSTIFY - ▁ELEPHANT - ▁ESTIMATE - ▁KNELT - ▁SERVING - ▁WHIM - ▁SHRILL - ▁STUDIO - ▁TEXT - ▁ALEXANDER - ▁WROUGHT - ▁ABUNDANT - ▁SITUATED - ▁REGAIN - ▁FIERY - ▁SNEER - ▁SWEAT - ▁GLARE - ▁NIGH - ▁ESCORT - ▁INEVITABLE - ▁PSMITH - ▁RELUCTANT - ▁PRECEDING - ▁RESORT - ▁OUTRAGE - ▁AMBASSADOR - ▁CONSOLATION - ▁RECOGNITION - ▁REMORSE - ▁BEHALF - ▁FORMIDABLE - ▁GRAVITY - ▁DIVIDE - ▁CONFRONT - ▁GIGANTIC - ▁OCTOBER - ▁FLANK - ▁SLEW - ▁CLARA - ▁FILM - ▁BULK - ▁POMP - ▁ELEANOR - ▁EMPHASIS - ▁JAPANESE - ▁CAVALRY - ▁EXCLUSIVE - ▁PERFUME - ▁BRONZE - ▁FEDERAL - ▁LIQUID - ▁RUBBING - ▁OVEN - DOLPH - ▁CONVULS - ▁DEPRIVED - ▁RESPONSIBILITY - ▁SIGNIFICANT - ▁WAISTCOAT - ▁CLUSTER - ▁MARTHA - ▁REVERSE - ▁ATTORNEY - ▁DROOP - ▁SKILFUL - ▁HABITUAL - ▁PUMP - ▁INTERVEN - ▁OWL - ▁CONJECTURE - ▁FANTASTIC - ▁RESPONSIBLE - ▁DESTINED - ▁DOCUMENT - ▁THEREUPON - ▁GODDESS - ▁PACIFIC - ▁WARRANT - ▁COSTUME - ▁BRIDLE - ▁CALIFORNIA - ▁DEMOCRATIC - ▁EUSTACE - ▁SQUIRREL - ▁UNCOMMON - ▁MARVELLOUS - ▁PLOUGH - ▁TRAGEDY - ▁VAULT - ▁HESITATE - ▁REFRAIN - ▁ADMIRING - ▁CORPORAL - ▁ENTITLED - ▁SHREWD - ▁SQUEEZ - ▁ACCURATE - ▁TEMPEST - ▁MONUMENT - ▁SIEGE - ▁CHINESE - ▁RAVEN - ▁LOUNG - ▁ASSASSIN - ▁INFLICT - ▁AGITATED - ▁DESIRABLE - ▁EARLIEST - ▁LAUNCH - ▁PILOT - ▁PULSE - ▁MUTE - LEIGH - ▁LIQUOR - ▁SCARECROW - ▁SKULL - ▁DESOLATE - ▁SUBLIME - ▁SERENE - ▁RECESS - ▁WAKING - ▁CHARLOTTE - ▁CIRCULAR - ▁INJUSTICE - ▁PINOCCHIO - ▁PRISCILLA - ▁THYSELF - ▁OCCURRENCE - ▁CASUAL - ▁FRANTIC - ▁LEGEND - ▁FERTIL - ▁BACKGROUND - ▁DELICACY - ▁ESTRALLA - ▁MANUSCRIPT - ▁RESPONSE - ▁UNIVERSITY - ▁WOLVES - ▁SCANDAL - ▁STUMBLE - ▁HOARSE - ▁BODILY - ▁CONVENT - ▁EXAMINING - ▁INCAPABLE - ▁PERCEIVING - ▁PHILADELPHIA - ▁SUBSEQUENT - ▁THIEVES - ▁ACCUMULAT - ▁DAMSEL - ▁SCOTCH - ▁UNDERNEATH - ▁NOBILITY - ▁SMASH - ▁REVOLT - ▁ENGAGE - ▁CATHEDRAL - ▁CHAMPION - ▁DESPATCH - ▁ETERNITY - ▁JANUARY - ▁PLEADED - ▁PROBABILITY - ▁JIMMIE - ▁PARALLEL - ▁FISHERMAN - ▁JERRY - ▁SWORE - ▁DRAUGHT - ▁OPPONENT - ▁PRIMITIVE - ▁SIGNIFICANCE - ▁SUBSTANTIAL - ▁AMAZED - ▁DUNBAR - ▁COMMEND - ▁CONTEMPLATE - ▁TESTIMONY - ▁IMPERIAL - ▁ADAPT - ▁JUICE - ▁CALAMIT - CULAR - ▁CHATEAU - ▁PHOENIX - ▁PRUDENT - ▁SOLUTION - ▁VILLEFORT - ▁REACTION - ▁RELAX - ▁YU - ▁PROHIBIT - ▁DISTRUST - ▁PLUNDER - ▁WELFARE - ▁NAVIGAT - ▁PARLOR - ▁LAZY - ▁DETACH - OMETER - ▁PRIV - ▁DISCOURAGE - ▁OBSTINATE - ▁REJOICING - ▁SERMON - ▁VEHICLE - ▁FANCIES - ▁ENLIGHTEN - ▁ACUTE - ▁ILLUSION - ▁ANTHEA - ▁MARTIAN - ▁EXCITE - ▁GENEROSITY - OLOGIST - ▁AMAZING - ▁UNWORTHY - ▁INTERNAL - ▁INCENSE - ▁VIBRAT - ▁ADHERE - ROACH - ▁FEBRUARY - ▁MEXICAN - ▁POTATOES - ▁INCESSANT - ▁INTERPOSED - ▁PARCEL - ▁VEXED - ▁PROMOTE - MIDST - ▁ARISTOCRAT - ▁CYRIL - ▁EMBARK - ▁ABUNDANCE - ▁LITERALLY - ▁SURGEON - ▁TERRACE - ▁ATLANTIC - ▁MARTYR - ▁SPECK - ▁SENATE - ▁LOAF - ▁ADMINISTER - ▁APPREHEND - ▁SUBDUED - ▁TEMPORARY - ▁DOMINION - ▁ELABORATE - ▁DIGNIFIED - ▁ELIZA - ▁SPLASH - ▁CONSEIL - ▁DEXTER - ▁UNSEEN - ▁TRAGIC - VOCATION - ▁GRATIFY - ▁BACHELOR - ▁DEFENSE - ▁EXCURSION - ▁FACULTIES - ▁PROPRIETOR - ▁SYMPATHETIC - ▁UNNECESSARY - ▁RADIANT - ▁VACANT - ▁OUNCE - ▁SCREW - ▁PHENOMENON - ▁PROMINENT - ▁WORRIED - ▁STUDIES - ▁CLIMATE - ▁KEITH - ▁ARAMIS - ▁BLISS - ▁CONTINUAL - ▁SURPASS - ▁HEBREW - ▁IDENTITY - ▁PROVOKE - ▁TEMPERAMENT - ▁CHARIOT - ▁HARBOR - ▁NINTH - ▁PRIOR - ▁DESIROUS - ▁JERUSALEM - ▁UNDERTAKING - ▁EDISON - ▁MIRTH - ▁SCOUT - ▁APPARATUS - ▁ILLUSTRATION - ▁INTELLIGIBLE - ▁INVARIABLY - ▁PIERCED - ▁REVIEW - ▁FLICKER - ▁HAZARD - ▁REVELATION - ▁DIXON - ▁EXCITING - ▁GOSPEL - ▁CONSTANCE - ▁OVERTAKE - ▁GUINEA - ▁ALADDIN - ▁CHICAGO - ▁TULLIVER - ▁HAMILTON - ▁GARRISON - ▁DISCIPLE - ▁INTENSITY - ▁TRAITOR - ▁CHANCELLOR - ▁PROVERB - ▁DAGGER - ▁FORESEE - ▁CONFIDE - ▁GLIMMER - ▁CHAUVELIN - ▁ILLUSTRATE - ▁VOLUNTEER - ▁JUNGLE - ▁STREAK - ▁SUNRISE - ▁DISSOLV - ▁QUEST - ▁AWHILE - ▁FELICITY - ▁LEGISLATURE - ▁LEONORA - ▁MAGAZINE - ▁PITIFUL - ▁COLONY - ▁SHAWL - ▁ARRIVING - ▁FUNDAMENTAL - ▁CARPENTER - ▁OVERFLOW - ▁EXPAND - ▁HARVEST - ▁FEMININE - ▁INNUMERABLE - ▁SCRAMBLE - ▁TWENTIETH - ▁TRIFLING - ▁GHASTL - ▁CONQUEST - ▁DANIEL - ▁FACILIT - ▁FORSAKE - ▁BEHAVIOUR - ▁GORGEOUS - ▁PRODUCING - ▁HAPPIER - ▁PROMISING - ▁RAINBOW - ▁INSTINCTIVELY - ▁DECREE - ▁EYEBROWS - ▁IRRESISTIBLE - ▁PHARAOH - ▁SCROOGE - ▁UNNATURAL - ▁CRUMBS - ▁REFINED - ▁DREARY - ▁TRENCH - ▁CONVINCE - ▁FRINGE - ▁EXTREMITY - ▁INTIMACY - ▁SCOUNDREL - ▁SUFFRAGE - ▁UNEASINESS - ▁BARRICADE - ▁CIRCULAT - ▁SAMUEL - ▁BRUCE - ▁DARCY - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram5000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.5a1 distributed: true ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR pretrained model ### `su_openslr36` ♻️ Imported from https://zenodo.org/record/5090135/ This model was trained by su_openslr36 using su_openslr36/asr1 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} } ``` 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} } ```
{"language": "su", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["su_openslr36"]}
espnet/su_openslr36
null
[ "espnet", "audio", "automatic-speech-recognition", "su", "dataset:su_openslr36", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR model ### `espnet/sujay_catslu_map` This model was trained by Sujay S Kumar using catslu recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout e31965d55993766461f0964216a0bb9aea3cfb7a pip install -e . cd egs2/catslu/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/sujay_catslu_map ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sun Oct 3 12:53:16 EDT 2021` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a3` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `b41391336042a4876e30d9fe5c66afb4e4be404c` - Commit date: `Wed Sep 22 10:02:03 2021 -0400` ## asr_train_asr_smaller_aishell_xlsr_raw_zh_word ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/test|1577|11441|46.1|30.1|23.7|2.5|56.4|81.3| |inference_asr_model_valid.acc.ave_5best/valid|921|6438|49.4|29.2|21.4|2.7|53.4|79.2| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/test|1577|45924|74.4|13.0|12.5|3.2|28.8|81.3| |inference_asr_model_valid.acc.ave_5best/valid|921|26110|77.0|11.9|11.1|2.7|25.7|79.2| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_smaller_aishell_xlsr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp_train_asr_smaller_aishell_xlsr/asr_train_asr_smaller_aishell_xlsr_raw_zh_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: 5 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/train/speech_shape - exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/train/text_shape.word valid_shape_file: - exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/valid/speech_shape - exp_train_asr_smaller_aishell_xlsr/asr_stats_raw_zh_word/valid/text_shape.word batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - sound - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/valid/wav.scp - speech - sound - - dump/raw/valid/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0001 scheduler: warmuplr scheduler_conf: warmup_steps: 2500 token_list: - <blank> - <unk> - 航 - 导 - inform_操作_none - inform_终点名称_none - 去 - none_none_none - 我 - 到 - inform_poi名称_none - unknown - 要 - 市 - side - 一 - 个 - 路 - 区 - 第 - 大 - 县 - 你 - inform_序列号_none - 小 - 城 - 站 - 家 - 南 - 中 - 山 - 州 - 好 - 镇 - 场 - 的 - 院 - 西 - 店 - 东 - 车 - 阳 - 学 - 北 - 园 - dialect - 安 - 新 - 海 - 回 - 公 - 医 - 二 - 不 - 三 - 广 - 天 - 村 - 有 - 闭 - 开 - 酒 - 下 - 江 - 消 - 人 - 帮 - 金 - 是 - 取 - 花 - 近 - 政 - 民 - 口 - 十 - 里 - 河 - 府 - 请 - 关 - 国 - 了 - 华 - 那 - 高 - robot - 出 - 平 - 湖 - 在 - 省 - 定 - 号 - 门 - 想 - 街 - 四 - 道 - 水 - 龙 - 京 - 啊 - 地 - 行 - 么 - 五 - 都 - 桥 - 上 - 给 - 明 - 业 - 哪 - 附 - 八 - 宁 - 心 - 长 - 馆 - 百 - 这 - 汽 - 机 - 工 - 庄 - 方 - 商 - 司 - 石 - 确 - 兴 - 火 - 走 - 乡 - 万 - 通 - 加 - 银 - 青 - 发 - 校 - 速 - 交 - 退 - 德 - 际 - 电 - 楼 - 宾 - 找 - 苑 - 和 - 嗯 - 油 - 林 - 乐 - 景 - 打 - 达 - 来 - 七 - 川 - inform_请求类型_none - 最 - noise - 兰 - 湾 - 台 - 所 - 保 - 什 - 福 - 建 - 说 - 就 - 沙 - 页 - 宝 - 子 - 厂 - 科 - 尔 - 光 - inform_页码_none - 六 - 费 - 环 - 成 - 昌 - 吗 - 汉 - 白 - 黄 - 限 - 局 - 泉 - 怎 - 云 - 武 - 源 - 吃 - 前 - 点 - 收 - 物 - 滨 - 溪 - 马 - 贵 - 务 - 世 - 岛 - 没 - 生 - 常 - 理 - 会 - 们 - 重 - 浦 - 名 - 合 - 运 - 顺 - 美 - 儿 - 头 - 乌 - 设 - 厦 - 化 - 郑 - 时 - inform_poi目标_none - 现 - 农 - 港 - 泰 - 停 - 宜 - 昆 - 九 - 对 - 管 - 看 - 界 - 张 - 庆 - 文 - 博 - 嘉 - 零 - 苏 - 能 - 面 - 客 - 红 - 搜 - 远 - 古 - 津 - 始 - 王 - 呃 - 用 - 瑞 - 后 - 雅 - 带 - 流 - 木 - 之 - 汇 - 夏 - 他 - 还 - 清 - 临 - 服 - 渡 - 日 - 幺 - 济 - 田 - 锦 - 吉 - 呀 - 利 - 神 - 饭 - 香 - 太 - 双 - 永 - 图 - 洲 - 集 - 特 - 吧 - request_位置_none - 技 - 把 - 寺 - 爱 - 丰 - 春 - 盛 - 罗 - 队 - 也 - 亚 - 线 - 玉 - 哦 - 贸 - 果 - 连 - 正 - 结 - 与 - 米 - 鲁 - 警 - 信 - 捷 - 样 - 温 - 岭 - 丽 - 育 - 凤 - 位 - 听 - 动 - 可 - 原 - 年 - 经 - 纪 - 齐 - 索 - inform_对象_none - 义 - 多 - 叫 - 况 - 气 - 老 - 派 - 池 - 曲 - 营 - 返 - 置 - 品 - 程 - 同 - 辉 - 批 - 音 - 康 - 威 - 幼 - 斯 - 库 - 拉 - 星 - 团 - 风 - 岗 - 话 - 放 - 泽 - 晋 - 部 - 知 - 外 - 塔 - 沈 - 奇 - 卫 - 月 - 庭 - 眼 - 总 - 梅 - 房 - 千 - 哈 - 自 - 字 - 呢 - 豪 - 直 - 盘 - 屯 - 超 - 祥 - 佳 - 恒 - 过 - 以 - 两 - 蓝 - 修 - 入 - 松 - 铁 - 职 - 珠 - 凯 - 快 - 丹 - 体 - 书 - 游 - 转 - 莱 - 寨 - 克 - 当 - 李 - 钱 - s - 货 - 惠 - 格 - 岳 - 淮 - 束 - 社 - 莞 - 森 - 堵 - 内 - 蒙 - 分 - 柏 - 富 - 碧 - 凰 - 陵 - 桐 - 边 - 坡 - 胶 - 得 - 力 - 滚 - 喀 - 旗 - 料 - 歌 - 块 - 滩 - 查 - 虹 - 续 - 为 - 驾 - 许 - 峰 - 问 - 真 - 视 - 选 - 接 - 语 - 洪 - 众 - 全 - 徽 - 鄂 - 实 - 未 - 杭 - 尚 - 胜 - 塘 - 产 - 鱼 - 叉 - 岸 - 洛 - 随 - 哎 - 配 - 丁 - 继 - 迪 - 牛 - 坪 - 无 - 深 - 圳 - 韩 - 法 - 灵 - 迁 - 间 - 逼 - 步 - 咸 - 期 - 菜 - 紫 - 邢 - 赣 - 横 - 播 - 鼎 - 进 - 止 - 铜 - 便 - 鸡 - 巴 - 仁 - 财 - 佛 - 桂 - 官 - 英 - 绵 - 奥 - 矿 - 波 - 治 - 元 - 首 - 钟 - 计 - 飞 - 坊 - 阿 - 代 - 周 - 朝 - 固 - 错 - 向 - 潭 - 隆 - 装 - 纳 - 伊 - 将 - 军 - 师 - 途 - 影 - 怀 - 择 - 药 - 术 - 手 - 于 - 离 - 族 - 莲 - 布 - 呼 - 峡 - 迈 - 委 - 叮 - 咚 - 阴 - 宏 - 郡 - 健 - 本 - 洋 - 再 - 支 - 划 - 郊 - 绿 - 妈 - 旅 - 堰 - 肥 - 玛 - 左 - 网 - inform_途经点名称_none - 拜 - 材 - inform_终点修饰_none - 辽 - 煤 - 谢 - 则 - 土 - 草 - 埠 - 伦 - 堂 - 卡 - 肉 - 底 - 灯 - 树 - 寻 - 掉 - 展 - 庙 - 赵 - 余 - 见 - 望 - 故 - 事 - 相 - 杨 - inform_终点目标_none - 馨 - 税 - 属 - 资 - 井 - 艺 - 越 - 微 - 包 - 阜 - 记 - 窗 - 维 - 甲 - 鑫 - 休 - 啥 - 锡 - 渝 - 岩 - 彩 - 少 - 处 - 往 - 从 - 封 - 联 - 觉 - 验 - 容 - 萨 - 普 - 弄 - 干 - 强 - 鲜 - 柳 - 衡 - 规 - request_路况_none - 靖 - 沃 - 板 - 防 - 约 - 球 - 居 - 至 - 坝 - 翠 - 持 - 具 - 烟 - 榆 - 枫 - 照 - 意 - 目 - t - 凌 - 邦 - 报 - 码 - 轻 - 欣 - 复 - 买 - 玻 - 璃 - 住 - 恩 - 女 - 嘴 - 级 - 振 - 邵 - 浴 - 茂 - 黔 - 您 - 比 - 显 - 渭 - 钢 - 妇 - 易 - 党 - 版 - 介 - 姐 - 才 - 览 - k - 崇 - 桃 - 厅 - 虎 - 皮 - 仪 - 赤 - 寓 - 洞 - 绍 - 饰 - 很 - 病 - 度 - 胡 - 像 - 邮 - 又 - 充 - 贤 - 御 - 然 - 潍 - 基 - 启 - 聊 - 驶 - inform_路线偏好_none - 澄 - 几 - 等 - 塑 - 监 - 办 - 沧 - 亭 - 观 - 螺 - 领 - 秀 - 咋 - 坨 - 奎 - 优 - 半 - 贡 - 唐 - 写 - 今 - 慢 - 傻 - 反 - 次 - 甘 - 肃 - 它 - 泗 - 贺 - 拍 - 咱 - 留 - ktv - 察 - 顶 - 啦 - 别 - 润 - 谷 - 仙 - 慧 - 朱 - 靠 - 座 - 锅 - 麦 - 雁 - 羊 - 共 - 邓 - 荣 - 食 - 陕 - 邑 - 右 - 铺 - 梁 - 宣 - 幸 - 哥 - 士 - 员 - 招 - 番 - 徐 - 检 - 巷 - 私 - 堡 - 跟 - 器 - 峪 - 立 - 氏 - 教 - 圣 - 购 - 印 - 黑 - 完 - 条 - 唉 - 燕 - 屿 - 闸 - 茶 - 任 - 种 - 蛋 - 荆 - 岔 - inform_value_none - 黎 - 奉 - 准 - 熟 - 薛 - 朔 - 范 - 械 - 菲 - 雪 - 腾 - 备 - 琼 - 尹 - 垣 - 吴 - 示 - 嫖 - 宫 - 冲 - 毛 - 绘 - 菏 - 嘞 - 浙 - 遵 - 各 - 饶 - 嗷 - 简 - 施 - 俱 - 岚 - 豆 - 栋 - 险 - 岘 - 滇 - 叶 - 卓 - 荔 - 刘 - 滕 - 系 - 统 - e - 做 - 巡 - 坐 - 研 - 究 - 盐 - 冀 - 象 - 斗 - 娄 - 先 - 陆 - deny_操作_none - 户 - 额 - 价 - 更 - 拆 - 溧 - 量 - 帝 - 断 - 态 - 智 - 蜀 - 庐 - 舟 - 摄 - 泡 - 洗 - 历 - 咖 - 啡 - 湘 - 甸 - 泾 - 卖 - 朗 - 芜 - 棠 - 凉 - 嵩 - 焦 - 让 - 夫 - 吐 - 童 - 薇 - 旺 - 浩 - 息 - 裕 - 禄 - 睡 - 狮 - 质 - 樱 - 递 - 鸣 - 句 - 韶 - 色 - 典 - 厉 - 测 - 应 - 尉 - 汤 - 己 - 宸 - 漳 - 证 - 沟 - 巩 - 扬 - 笨 - 旁 - 湟 - 主 - 浪 - 殡 - request_前方路况_none - 竹 - 列 - 季 - 唱 - 冠 - 泥 - 懂 - 秋 - 君 - 祁 - 声 - 拥 - 曹 - 嘛 - 静 - 嗨 - 起 - 刚 - 墨 - 宿 - 络 - 襄 - 葫 - 芦 - 漫 - 峨 - 需 - 眉 - 瓦 - 如 - 根 - 域 - 式 - 何 - 鞍 - 饺 - 票 - 冶 - 喷 - 映 - 组 - 昭 - 延 - 萌 - 角 - 解 - 玲 - 蟹 - 晃 - 瀑 - 纽 - 逸 - 些 - 猪 - 蹄 - 亲 - 野 - 蒋 - 喂 - 荷 - 窝 - 锁 - 试 - 桑 - 沥 - 非 - 制 - 督 - 贝 - 址 - 识 - 侬 - 烧 - 翡 - 堤 - 伟 - 驼 - 昊 - 牌 - 陶 - 室 - 轩 - 鹰 - 钉 - 空 - 着 - 蛳 - 已 - 砖 - 姓 - 顿 - 麓 - 亿 - 售 - 功 - 淄 - 澳 - 斜 - 击 - 活 - 缴 - 输 - 雍 - 鄄 - 降 - 革 - 恢 - 卸 - 承 - 箬 - 澧 - 栈 - 疗 - 传 - 媒 - 血 - 战 - 舞 - 姨 - 婆 - 辆 - 蚌 - 鹅 - 剧 - 湛 - 亳 - b - 敦 - 煌 - 迎 - 味 - 数 - 妞 - 嫂 - 厚 - hi - 邹 - 摁 - 榄 - 梨 - 亮 - 纺 - 婚 - 培 - 训 - inform_起点名称_none - 护 - 霍 - 升 - 考 - m - 呗 - 摩 - 送 - 段 - 悦 - 餐 - 早 - 议 - 互 - 助 - 抚 - 慈 - 按 - 调 - 杰 - 份 - 兵 - 粥 - 邻 - 墅 - 鬃 - 泳 - 朋 - 良 - 缘 - 鼓 - 赛 - 枝 - 藏 - 鸿 - 冷 - 匀 - 征 - 欢 - 闯 - 汝 - 讲 - 肤 - 响 - 浮 - 录 - 冰 - 圆 - 算 - 思 - 储 - 蓄 - 苗 - 聚 - 湿 - 肇 - 阆 - 拿 - 沣 - 渔 - 铝 - 植 - 托 - 盟 - 宇 - 但 - 渠 - 告 - 丘 - 拓 - 陇 - 鹤 - 操 - 珙 - deny_poi名称_none - 询 - 攀 - 寿 - 副 - 或 - 假 - 焰 - 夜 - 妓 - 而 - 漆 - 濮 - 胥 - 密 - 志 - 苹 - 彭 - 陪 - 添 - 满 - 章 - 骨 - 栖 - 呦 - 善 - 乖 - 姑 - 爷 - 鸟 - 璧 - 专 - 洧 - 依 - 仔 - 晨 - 沂 - 券 - 晓 - 压 - 涨 - 闻 - 男 - 诊 - 融 - 怡 - 蓬 - 廊 - 殖 - 益 - 必 - 靓 - 蒲 - beyond - i - love - you - 旋 - 尖 - 驿 - 貂 - 蝉 - 足 - 迹 - 翰 - 杏 - 牡 - 帅 - 雨 - 呈 - 迷 - 哟 - 召 - 娼 - 辛 - 顾 - 殷 - 闵 - 潮 - 脑 - 彗 - 枣 - 杆 - 洁 - 画 - 片 - 认 - 灰 - 鞋 - 宠 - 劫 - 潘 - 烤 - 破 - 隶 - 搞 - 忠 - 仕 - 郴 - 梧 - 酌 - 涵 - 醍 - 候 - 俩 - 馈 - 磨 - 骤 - 翔 - 莘 - 希 - 娅 - 剑 - 权 - 壹 - 冕 - 蛟 - 拨 - 诶 - 盖 - 楠 - 只 - 编 - 虾 - 尽 - 尧 - 晚 - 珍 - 因 - 捆 - 绑 - 端 - 盱 - 眙 - 贩 - 卷 - 养 - 陂 - 晟 - 巧 - 椿 - 毕 - 沭 - 供 - 秒 - 眠 - 状 - 璟 - 受 - 伤 - 萍 - 奔 - 效 - 禽 - 玫 - 瑰 - request_剩余距离_none - 序 - 鹃 - 齿 - 厕 - 厨 - 忻 - 埔 - 茅 - 芳 - 雕 - 刻 - 蜜 - 筝 - g - 橄 - 畜 - 牧 - 仑 - 臣 - 溆 - 纱 - 卉 - 群 - 痛 - 疼 - 仟 - 赶 - 紧 - 闫 - 嘶 - 潼 - 烽 - 勾 - 驰 - 麻 - 烦 - 遍 - 樟 - 浜 - 极 - 酷 - 晶 - 穿 - 芽 - 害 - 钓 - 棍 - 核 - 橙 - 琴 - 滋 - 柯 - 箐 - 株 - 陌 - 坤 - 炳 - 槐 - 协 - 湄 - 滏 - 旦 - 策 - 虞 - 陈 - 情 - 潞 - 藁 - 豹 - 若 - 垃 - 圾 - 舰 - 造 - 珥 - 董 - 泼 - 乾 - 瑶 - 龚 - 撤 - 钛 - 责 - 吶 - 喜 - 隔 - 碗 - 倒 - 椰 - 冬 - 伯 - 乳 - 隐 - 尼 - 境 - 圩 - 卧 - 抱 - 使 - 玩 - 饮 - 峤 - 炉 - 终 - 霸 - 晴 - 糕 - 疫 - 弥 - 萧 - 围 - 邬 - 贞 - 逊 - 祠 - 泛 - 逯 - 侯 - 距 - 织 - 谋 - 嵋 - 楚 - 瑜 - 妹 - 误 - 念 - 镜 - 粮 - 涮 - 值 - 鹿 - 捞 - 沅 - 移 - 涉 - 模 - 饿 - 佩 - 汀 - 朐 - 魔 - 细 - 者 - 暖 - 汕 - 谛 - 棣 - 敖 - 此 - 背 - 鲅 - 圈 - 逻 - 绕 - 锋 - 班 - 珲 - 汾 - 著 - 参 - 且 - 摇 - 宕 - 缅 - 柔 - 脂 - 肪 - 变 - 谱 - 积 - 礼 - 凡 - 落 - 羽 - 歇 - 仰 - 聋 - 雷 - 磊 - 繁 - 吭 - 皇 - 晖 - 粤 - 腊 - 习 - 题 - 绅 - 畔 - 啤 - 弋 - 匹 - 订 - 单 - ok - 灶 - 描 - 婺 - 沿 - 莉 - 弘 - 茵 - 换 - 屏 - 瞎 - 较 - 岁 - 湫 - 塞 - 疏 - 勒 - 涟 - 巫 - 违 - 戈 - 吾 - 脏 - 葛 - 轮 - 胎 - 霞 - 鹭 - 废 - 稍 - 谨 - 慎 - 淡 - 注 - 每 - 既 - 删 - 喝 - 付 - 诸 - 暨 - 戴 - 綦 - 伍 - 诚 - 坦 - 兜 - 残 - 韵 - 喽 - 廖 - 麒 - 麟 - n - 感 - 籍 - 难 - 死 - 笑 - 哭 - 孩 - 频 - 舍 - 溶 - 垸 - 淀 - 奸 - 改 - 藤 - 狭 - 隧 - 翁 - 陀 - 扎 - 肯 - 揭 - 壁 - 件 - 刷 - 牙 - 节 - 恋 - 淹 - 桦 - 幢 - 棉 - 俺 - 屎 - 彬 - 牟 - 亩 - 傣 - 裴 - 翼 - 辰 - 剪 - 挡 - 凹 - 投 - 碣 - 妆 - 荡 - 驻 - 颍 - 狐 - 享 - 恐 - 汶 - 寅 - 仍 - 睿 - 搁 - 尊 - 泊 - 仲 - 午 - 枞 - 仓 - 卞 - 瀚 - 佰 - 暮 - 拐 - 崔 - 榭 - 棵 - 孕 - 潜 - 俏 - 葡 - 萄 - 采 - 摘 - 癜 - 屑 - 芙 - 蓉 - 咏 - 忙 - 漂 - 父 - 母 - 差 - 彻 - 魏 - 绥 - 闲 - 遥 - 棕 - 榈 - 壶 - 疆 - 苍 - 磁 - 辅 - 泸 - 淅 - a - 呐 - 燃 - 沱 - 禺 - 宛 - 友 - 俊 - 筑 - 贾 - 宋 - 梯 - 吨 - inform_poi修饰_none - 础 - 碑 - request_剩余路程_none - 创 - 孙 - 枢 - 翟 - 浑 - 糖 - 舜 - 橱 - 柜 - 浠 - 莒 - 乔 - 幕 - 磅 - 嘿 - 曼 - 昔 - 衣 - 铭 - 浏 - 喆 - 垦 - 墓 - 戍 - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: wav2vec2_xlsr download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 15 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 4 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 required: - output_dir - token_list version: 0.10.3a3 distributed: false ``` </details> ## LM config <details><summary>expand</summary> ``` NONE ``` </details>
{"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["catslu"]}
espnet/sujay_catslu_map
null
[ "espnet", "audio", "automatic-speech-recognition", "zh", "dataset:catslu", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
## ESPnet2 ASR pretrained model ### `https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1` ♻️ Imported from https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1 This model was trained by vectominist using seame/asr1 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} } ``` 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} } ```
{"language": ["en", "zh", "multilingual"], "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["seame"]}
espnet/vectominist_seame_asr_conformer_bpe5626
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "zh", "multilingual", "dataset:seame", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
# ESPnet2 ASR pretrained model ## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en` This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Python API ```text See https://github.com/espnet/espnet_model_zoo ``` ### Evaluate in the recipe ```python # coming soon ``` ### Results ```bash # RESULTS ## Environments - date: `Fri Aug 6 11:44:39 JST 2021` - python version: `3.7.9 (default, Apr 23 2021, 13:48:31) [GCC 5.5.0 20171010]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.7.0` - Git hash: `0f7558a716ab830d0c29da8785840124f358d47b` - Commit date: `Tue Jun 8 15:33:49 2021 -0400` ## asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.5|1.3|0.2|0.2|1.7|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|96.8|2.8|0.4|0.3|3.4|33.7| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.4|1.4|0.2|0.2|1.8|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|96.8|2.8|0.4|0.4|3.6|36.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.8|0.6|0.6|0.3|1.5|33.7| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.6|0.2|0.2|0.2|0.6|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.9|0.5|0.5|0.4|1.4|36.0| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|98.2|1.3|0.5|0.4|2.2|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|96.0|2.8|1.2|0.6|4.6|33.7| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|98.1|1.3|0.6|0.4|2.3|22.1| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|96.0|2.7|1.3|0.6|4.6|36.0| ``` ### Training config See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml) ```yaml config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp ngpu: 3 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 3 local_rank: 3 dist_master_addr: localhost dist_master_port: 33643 dist_launcher: null multiprocessing_distributed: true cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"], "inference": false}
espnet/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
automatic-speech-recognition
espnet
# ESPnet2 ASR pretrained model ## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en` This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/). ### Python API ```text See https://github.com/espnet/espnet_model_zoo ``` ### Evaluate in the recipe ```python # coming soon ``` ### Results ```bash # RESULTS ## Environments - date: `Sat Jul 3 23:10:19 JST 2021` - python version: `3.7.9 (default, Apr 23 2021, 13:48:31) [GCC 5.5.0 20171010]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.7.0` - Git hash: `0f7558a716ab830d0c29da8785840124f358d47b` - Commit date: `Tue Jun 8 15:33:49 2021 -0400` ## asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.3|1.6|0.2|0.2|1.9|24.9| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|95.1|4.3|0.6|0.4|5.4|42.8| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.2|26.8| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|95.3|4.1|0.6|0.5|5.2|45.8| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.5|0.2|0.2|0.2|0.6|24.9| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.1|1.0|0.9|0.5|2.4|42.8| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.5|0.2|0.3|0.2|0.7|26.8| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.3|0.8|0.9|0.5|2.3|45.8| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|97.8|1.6|0.6|0.4|2.6|24.9| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|94.1|4.3|1.6|1.1|7.0|42.8| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|97.6|1.6|0.8|0.4|2.8|26.8| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|94.3|4.0|1.8|1.0|6.7|45.8| ``` ### Training config See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml) ```yaml config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp ngpu: 3 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 3 local_rank: 3 dist_master_addr: localhost dist_master_port: 33643 dist_launcher: null multiprocessing_distributed: true cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"], "inference": false}
espnet/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_larg-truncated-5b94d9
null
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:librispeech", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
audio-to-audio
espnet
# ESPnet2 ENH pretrained model ## `neillu23/dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best, fs=16k, lang=en` ♻️ Imported from <https://zenodo.org/record/4923697#.YOAOIpozZH4>. This model was trained by neillu23 using dns_ins20 recipe in [espnet](https://github.com/espnet/espnet/). ### Python API ```text See https://github.com/espnet/espnet_model_zoo ``` ### Evaluate in the recipe ```python # coming soon ``` ### Results ```bash # RESULTS ## Environments - date: `Wed Jun 9 09:49:34 CST 2021` - python version: `3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.4.0` - Git hash: `c1dfefb98bf59f654e0907b9681668eaca8ddfcc` - Commit date: `Tue Jun 8 17:23:26 2021 +0800` ## enh_train_enh_blstm_tf_raw config: ./conf/tuning/train_enh_blstm_tf.yaml |dataset|STOI|SAR|SDR|SIR| |---|---|---|---|---| |enhanced_cv_synthetic|0.98|23.87|23.87|0.00| |enhanced_tt_synthetic_no_reverb|0.96|15.94|15.94|0.00| |enhanced_tt_synthetic_with_reverb|0.84|11.86|11.86|0.00| ``` ### Training config See full config in [`config.yaml`](./exp/enh_train_enh_blstm_tf_raw/config.yaml) ```yaml config: ./conf/tuning/train_enh_blstm_tf.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/enh_train_enh_blstm_tf_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45398 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true ```
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["dns_ins20"], "inference": false}
espnet/yen-ju-lu-dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best
null
[ "espnet", "audio", "audio-source-separation", "audio-to-audio", "en", "dataset:dns_ins20", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
estanislao/estarlin
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
null
# Bot Edan
{"tags": ["conversational"]}
estehpanas/pascalbot
null
[ "conversational", "region:us" ]
null
2022-03-02T23:29:05+00:00
question-answering
transformers
# camembert-base-squadFR-fquad-piaf ## Description Question-answering French model, using base [CamemBERT](https://camembert-model.fr/) fine-tuned on a combo of three French Q&A datasets: 1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/) 2. [FQuADv1.0](https://fquad.illuin.tech/) 3. [SQuAD-FR (SQuAD automatically translated to French)](https://github.com/Alikabbadj/French-SQuAD) ## Training hyperparameters ```shell python run_squad.py \ --model_type camembert \ --model_name_or_path camembert-base \ --do_train --do_eval \ --train_file data/SQuAD+fquad+piaf.json \ --predict_file data/fquad_valid.json \ --per_gpu_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 4 \ --max_seq_length 384 \ --doc_stride 128 \ --save_steps 10000 ``` ## Evaluation results ### FQuAD v1.0 Evaluation ```shell {"f1": 79.81, "exact_match": 55.14} ``` ### SQuAD-FR Evaluation ```shell {"f1": 80.61, "exact_match": 59.54} ``` ## Usage ```python from transformers import pipeline nlp = pipeline('question-answering', model='etalab-ia/camembert-base-squadFR-fquad-piaf', tokenizer='etalab-ia/camembert-base-squadFR-fquad-piaf') nlp({ 'question': "Qui est Claude Monet?", 'context': "Claude Monet, né le 14 novembre 1840 à Paris et mort le 5 décembre 1926 à Giverny, est un peintre français et l’un des fondateurs de l'impressionnisme." }) ``` ## Acknowledgments This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224). ## Citations ### PIAF ``` @inproceedings{KeraronLBAMSSS20, author = {Rachel Keraron and Guillaume Lancrenon and Mathilde Bras and Fr{\'{e}}d{\'{e}}ric Allary and Gilles Moyse and Thomas Scialom and Edmundo{-}Pavel Soriano{-}Morales and Jacopo Staiano}, title = {Project {PIAF:} Building a Native French Question-Answering Dataset}, booktitle = {{LREC}}, pages = {5481--5490}, publisher = {European Language Resources Association}, year = {2020} } ``` ### FQuAD ``` @article{dHoffschmidt2020FQuADFQ, title={FQuAD: French Question Answering Dataset}, author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich}, journal={ArXiv}, year={2020}, volume={abs/2002.06071} } ``` ### SQuAD-FR ``` @MISC{kabbadj2018, author = "Kabbadj, Ali", title = "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ", editor = "linkedin.com", month = "November", year = "2018", url = "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}", note = "[Online; posted 11-November-2018]", } ``` ### CamemBERT HF model card : [https://huggingface.co/camembert-base](https://huggingface.co/camembert-base) ``` @inproceedings{martin2020camembert, title={CamemBERT: a Tasty French Language Model}, author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020} } ```
{"language": "fr", "datasets": ["piaf", "FQuAD", "SQuAD-FR"], "widget": [{"text": "Comment s'appelle le portail open data du gouvernement ?", "context": "Etalab est une administration publique fran\u00e7aise qui fait notamment office de Chief Data Officer de l'\u00c9tat et coordonne la conception et la mise en \u0153uvre de sa strat\u00e9gie dans le domaine de la donn\u00e9e (ouverture et partage des donn\u00e9es publiques ou open data, exploitation des donn\u00e9es et intelligence artificielle...). Ainsi, Etalab d\u00e9veloppe et maintient le portail des donn\u00e9es ouvertes du gouvernement fran\u00e7ais data.gouv.fr. Etalab promeut \u00e9galement une plus grande ouverture l'administration sur la soci\u00e9t\u00e9 (gouvernement ouvert) : transparence de l'action publique, innovation ouverte, participation citoyenne... elle promeut l\u2019innovation, l\u2019exp\u00e9rimentation, les m\u00e9thodes de travail ouvertes, agiles et it\u00e9ratives, ainsi que les synergies avec la soci\u00e9t\u00e9 civile pour d\u00e9cloisonner l\u2019administration et favoriser l\u2019adoption des meilleures pratiques professionnelles dans le domaine du num\u00e9rique. \u00c0 ce titre elle \u00e9tudie notamment l\u2019opportunit\u00e9 de recourir \u00e0 des technologies en voie de maturation issues du monde de la recherche. Cette entit\u00e9 charg\u00e9e de l'innovation au sein de l'administration doit contribuer \u00e0 l'am\u00e9lioration du service public gr\u00e2ce au num\u00e9rique. Elle est rattach\u00e9e \u00e0 la Direction interminist\u00e9rielle du num\u00e9rique, dont les missions et l\u2019organisation ont \u00e9t\u00e9 fix\u00e9es par le d\u00e9cret du 30 octobre 2019.\u2009 Dirig\u00e9 par Laure Lucchesi depuis 2016, elle rassemble une \u00e9quipe pluridisciplinaire d'une trentaine de personnes."}]}
AgentPublic/camembert-base-squadFR-fquad-piaf
null
[ "transformers", "pytorch", "tf", "safetensors", "camembert", "question-answering", "fr", "dataset:piaf", "dataset:FQuAD", "dataset:SQuAD-FR", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
transformers
# dpr-ctx_encoder-fr_qa-camembert ## Description French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A ## Data ### French Q&A We use a combination of three French Q&A datasets: 1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/) 2. [FQuADv1.0](https://fquad.illuin.tech/) 3. [SQuAD-FR (SQuAD automatically translated to French)](https://github.com/Alikabbadj/French-SQuAD) ### Training We are using 90 562 random questions for `train` and 22 391 for `dev`. No question in `train` exists in `dev`. For each question, we have a single `positive_context` (the paragraph where the answer to this question is found) and around 30 `hard_negtive_contexts`. Hard negative contexts are found by querying an ES instance (via bm25 retrieval) and getting the top-k candidates **that do not contain the answer**. The files are over [here](https://drive.google.com/file/d/1W5Jm3sqqWlsWsx2sFpA39Ewn33PaLQ7U/view?usp=sharing). ### Evaluation We use FQuADv1.0 and French-SQuAD evaluation sets. ## Training Script We use the official [Facebook DPR implentation](https://github.com/facebookresearch/DPR) with a slight modification: by default, the code can work with Roberta models, still we changed a single line to make it easier to work with Camembert. This modification can be found [over here](https://github.com/psorianom/DPR). ### Hyperparameters ```shell python -m torch.distributed.launch --nproc_per_node=8 train_dense_encoder.py \ --max_grad_norm 2.0 \ --encoder_model_type fairseq_roberta \ --pretrained_file data/camembert-base \ --seed 12345 \ --sequence_length 256 \ --warmup_steps 1237 \ --batch_size 16 \ --do_lower_case \ --train_file ./data/DPR_FR_train.json \ --dev_file ./data/DPR_FR_dev.json \ --output_dir ./output/ \ --learning_rate 2e-05 \ --num_train_epochs 35 \ --dev_batch_size 16 \ --val_av_rank_start_epoch 30 \ --pretrained_model_cfg ./data/camembert-base/ ``` ### ## Evaluation results We obtain the following evaluation by using FQuAD and SQuAD-FR evaluation (or validation) sets. To obtain these results, we use [haystack's evaluation script](https://github.com/deepset-ai/haystack/blob/db4151bbc026f27c6d709fefef1088cd3f1e18b9/tutorials/Tutorial5_Evaluation.py) (**we report Retrieval results only**). ### DPR #### FQuAD v1.0 Evaluation ```shell For 2764 out of 3184 questions (86.81%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.87 Retriever Mean Avg Precision: 0.57 ``` #### SQuAD-FR Evaluation ```shell For 8945 out of 10018 questions (89.29%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.89 Retriever Mean Avg Precision: 0.63 ``` ### BM25 For reference, BM25 gets the results shown below. As in the original paper, regarding SQuAD-like datasets, the results of DPR are consistently superseeded by BM25. #### FQuAD v1.0 Evaluation ```shell For 2966 out of 3184 questions (93.15%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.93 Retriever Mean Avg Precision: 0.74 ``` #### SQuAD-FR Evaluation ```shell For 9353 out of 10018 questions (93.36%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.93 Retriever Mean Avg Precision: 0.77 ``` ## Usage The results reported here are obtained with the `haystack` library. To get to similar embeddings using exclusively HF `transformers` library, you can do the following: ```python from transformers import AutoTokenizer, AutoModel query = "Salut, mon chien est-il mignon ?" tokenizer = AutoTokenizer.from_pretrained("etalab-ia/dpr-ctx_encoder-fr_qa-camembert", do_lower_case=True) input_ids = tokenizer(query, return_tensors='pt')["input_ids"] model = AutoModel.from_pretrained("etalab-ia/dpr-ctx_encoder-fr_qa-camembert", return_dict=True) embeddings = model.forward(input_ids).pooler_output print(embeddings) ``` And with `haystack`, we use it as a retriever: ``` retriever = DensePassageRetriever( document_store=document_store, query_embedding_model="etalab-ia/dpr-question_encoder-fr_qa-camembert", passage_embedding_model="etalab-ia/dpr-ctx_encoder-fr_qa-camembert", model_version=dpr_model_tag, infer_tokenizer_classes=True, ) ``` ## Acknowledgments This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224). ## Citations ### Datasets #### PIAF ``` @inproceedings{KeraronLBAMSSS20, author = {Rachel Keraron and Guillaume Lancrenon and Mathilde Bras and Fr{\'{e}}d{\'{e}}ric Allary and Gilles Moyse and Thomas Scialom and Edmundo{-}Pavel Soriano{-}Morales and Jacopo Staiano}, title = {Project {PIAF:} Building a Native French Question-Answering Dataset}, booktitle = {{LREC}}, pages = {5481--5490}, publisher = {European Language Resources Association}, year = {2020} } ``` #### FQuAD ``` @article{dHoffschmidt2020FQuADFQ, title={FQuAD: French Question Answering Dataset}, author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich}, journal={ArXiv}, year={2020}, volume={abs/2002.06071} } ``` #### SQuAD-FR ``` @MISC{kabbadj2018, author = "Kabbadj, Ali", title = "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ", editor = "linkedin.com", month = "November", year = "2018", url = "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}", note = "[Online; posted 11-November-2018]", } ``` ### Models #### CamemBERT HF model card : [https://huggingface.co/camembert-base](https://huggingface.co/camembert-base) ``` @inproceedings{martin2020camembert, title={CamemBERT: a Tasty French Language Model}, author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020} } ``` #### DPR ``` @misc{karpukhin2020dense, title={Dense Passage Retrieval for Open-Domain Question Answering}, author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih}, year={2020}, eprint={2004.04906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
{"language": "fr", "datasets": ["piaf", "FQuAD", "SQuAD-FR"]}
AgentPublic/dpr-ctx_encoder-fr_qa-camembert
null
[ "transformers", "pytorch", "camembert", "fr", "dataset:piaf", "dataset:FQuAD", "dataset:SQuAD-FR", "arxiv:2004.04906", "arxiv:1911.03894", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
feature-extraction
transformers
# dpr-question_encoder-fr_qa-camembert ## Description French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A ## Data ### French Q&A We use a combination of three French Q&A datasets: 1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/) 2. [FQuADv1.0](https://fquad.illuin.tech/) 3. [SQuAD-FR (SQuAD automatically translated to French)](https://github.com/Alikabbadj/French-SQuAD) ### Training We are using 90 562 random questions for `train` and 22 391 for `dev`. No question in `train` exists in `dev`. For each question, we have a single `positive_context` (the paragraph where the answer to this question is found) and around 30 `hard_negtive_contexts`. Hard negative contexts are found by querying an ES instance (via bm25 retrieval) and getting the top-k candidates **that do not contain the answer**. The files are over [here](https://drive.google.com/file/d/1W5Jm3sqqWlsWsx2sFpA39Ewn33PaLQ7U/view?usp=sharing). ### Evaluation We use FQuADv1.0 and French-SQuAD evaluation sets. ## Training Script We use the official [Facebook DPR implentation](https://github.com/facebookresearch/DPR) with a slight modification: by default, the code can work with Roberta models, still we changed a single line to make it easier to work with Camembert. This modification can be found [over here](https://github.com/psorianom/DPR). ### Hyperparameters ```shell python -m torch.distributed.launch --nproc_per_node=8 train_dense_encoder.py \ --max_grad_norm 2.0 --encoder_model_type hf_bert --pretrained_file data/bert-base-multilingual-uncased \ --seed 12345 --sequence_length 256 --warmup_steps 1237 --batch_size 16 --do_lower_case \ --train_file DPR_FR_train.json \ --dev_file ./data/100_hard_neg_ctxs/DPR_FR_dev.json \ --output_dir ./output/bert --learning_rate 2e-05 --num_train_epochs 35 \ --dev_batch_size 16 --val_av_rank_start_epoch 25 \ --pretrained_model_cfg ./data/bert-base-multilingual-uncased ``` ### ## Evaluation results We obtain the following evaluation by using FQuAD and SQuAD-FR evaluation (or validation) sets. To obtain these results, we use [haystack's evaluation script](https://github.com/deepset-ai/haystack/blob/db4151bbc026f27c6d709fefef1088cd3f1e18b9/tutorials/Tutorial5_Evaluation.py) (**we report Retrieval results only**). ### DPR #### FQuAD v1.0 Evaluation ```shell For 2764 out of 3184 questions (86.81%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.87 Retriever Mean Avg Precision: 0.57 ``` #### SQuAD-FR Evaluation ```shell For 8945 out of 10018 questions (89.29%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.89 Retriever Mean Avg Precision: 0.63 ``` ### BM25 For reference, BM25 gets the results shown below. As in the original paper, regarding SQuAD-like datasets, the results of DPR are consistently superseeded by BM25. #### FQuAD v1.0 Evaluation ```shell For 2966 out of 3184 questions (93.15%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.93 Retriever Mean Avg Precision: 0.74 ``` #### SQuAD-FR Evaluation ```shell For 9353 out of 10018 questions (93.36%), the answer was in the top-20 candidate passages selected by the retriever. Retriever Recall: 0.93 Retriever Mean Avg Precision: 0.77 ``` ## Usage The results reported here are obtained with the `haystack` library. To get to similar embeddings using exclusively HF `transformers` library, you can do the following: ```python from transformers import AutoTokenizer, AutoModel query = "Salut, mon chien est-il mignon ?" tokenizer = AutoTokenizer.from_pretrained("etalab-ia/dpr-question_encoder-fr_qa-camembert", do_lower_case=True) input_ids = tokenizer(query, return_tensors='pt')["input_ids"] model = AutoModel.from_pretrained("etalab-ia/dpr-question_encoder-fr_qa-camembert", return_dict=True) embeddings = model.forward(input_ids).pooler_output print(embeddings) ``` And with `haystack`, we use it as a retriever: ``` retriever = DensePassageRetriever( document_store=document_store, query_embedding_model="etalab-ia/dpr-question_encoder-fr_qa-camembert", passage_embedding_model="etalab-ia/dpr-ctx_encoder-fr_qa-camembert", model_version=dpr_model_tag, infer_tokenizer_classes=True, ) ``` ## Acknowledgments This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224). ## Citations ### Datasets #### PIAF ``` @inproceedings{KeraronLBAMSSS20, author = {Rachel Keraron and Guillaume Lancrenon and Mathilde Bras and Fr{\'{e}}d{\'{e}}ric Allary and Gilles Moyse and Thomas Scialom and Edmundo{-}Pavel Soriano{-}Morales and Jacopo Staiano}, title = {Project {PIAF:} Building a Native French Question-Answering Dataset}, booktitle = {{LREC}}, pages = {5481--5490}, publisher = {European Language Resources Association}, year = {2020} } ``` #### FQuAD ``` @article{dHoffschmidt2020FQuADFQ, title={FQuAD: French Question Answering Dataset}, author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich}, journal={ArXiv}, year={2020}, volume={abs/2002.06071} } ``` #### SQuAD-FR ``` @MISC{kabbadj2018, author = "Kabbadj, Ali", title = "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ", editor = "linkedin.com", month = "November", year = "2018", url = "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}", note = "[Online; posted 11-November-2018]", } ``` ### Models #### CamemBERT HF model card : [https://huggingface.co/camembert-base](https://huggingface.co/camembert-base) ``` @inproceedings{martin2020camembert, title={CamemBERT: a Tasty French Language Model}, author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020} } ``` #### DPR ``` @misc{karpukhin2020dense, title={Dense Passage Retrieval for Open-Domain Question Answering}, author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih}, year={2020}, eprint={2004.04906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
{"language": "fr", "datasets": ["piaf", "FQuAD", "SQuAD-FR"]}
AgentPublic/dpr-question_encoder-fr_qa-camembert
null
[ "transformers", "pytorch", "camembert", "feature-extraction", "fr", "dataset:piaf", "dataset:FQuAD", "dataset:SQuAD-FR", "arxiv:2004.04906", "arxiv:1911.03894", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
AgentPublic/ranker_question-text-fr_base_camembert
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
ethan21814/CLMOpenAIGPT
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
ethanbmehta/test_repo
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
text-classification
transformers
# Guwen CLS A Classical Chinese Text Classifier. See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/cclue/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=cclue&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/guwenbert/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwenbert&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a>
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "text classificatio"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "text-classification", "widget": [{"text": "\u5b50\u66f0\uff1a\u201c\u5f1f\u5b50\u5165\u5219\u5b5d\uff0c\u51fa\u5219\u608c\uff0c\u8c28\u800c\u4fe1\uff0c\u6cdb\u7231\u4f17\uff0c\u800c\u4eb2\u4ec1\u3002\u884c\u6709\u9980\u529b\uff0c\u5219\u4ee5\u5b66\u6587\u3002\u201d"}]}
ethanyt/guwen-cls
null
[ "transformers", "pytorch", "roberta", "text-classification", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "text classificatio", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
token-classification
transformers
# Guwen NER A Classical Chinese Named Entity Recognizer. Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to [Guwen Models](https://github.com/ethan-yt/guwen-models). See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/cclue/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=cclue&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/guwenbert/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwenbert&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a>
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classification", "widget": [{"text": "\u53ca\u79e6\u59cb\u7687\uff0c\u706d\u5148\u4ee3\u5178\u7c4d\uff0c\u711a\u4e66\u5751\u5112\uff0c\u5929\u4e0b\u5b66\u58eb\u9003\u96be\u89e3\u6563\uff0c\u6211\u5148\u4eba\u7528\u85cf\u5176\u5bb6\u4e66\u4e8e\u5c4b\u58c1\u3002\u6c49\u5ba4\u9f99\u5174\uff0c\u5f00\u8bbe\u5b66\u6821\uff0c\u65c1\u6c42\u5112\u96c5\uff0c\u4ee5\u9610\u5927\u7337\u3002\u6d4e\u5357\u4f0f\u751f\uff0c\u5e74\u8fc7\u4e5d\u5341\uff0c\u5931\u5176\u672c\u7ecf\uff0c\u53e3\u4ee5\u4f20\u6388\uff0c\u88c1\u4e8c\u5341\u9980\u7bc7\uff0c\u4ee5\u5176\u4e0a\u53e4\u4e4b\u4e66\uff0c\u8c13\u4e4b\u5c1a\u4e66\u3002\u767e\u7bc7\u4e4b\u4e49\uff0c\u4e16\u83ab\u5f97\u95fb\u3002"}]}
ethanyt/guwen-ner
null
[ "transformers", "pytorch", "jax", "roberta", "token-classification", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
token-classification
transformers
# Guwen Punc A Classical Chinese Punctuation Marker. See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/cclue/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=cclue&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/guwenbert/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwenbert&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a>
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "punctuation marker"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classification", "widget": [{"text": "\u53ca\u79e6\u59cb\u7687\u706d\u5148\u4ee3\u5178\u7c4d\u711a\u4e66\u5751\u5112\u5929\u4e0b\u5b66\u58eb\u9003\u96be\u89e3\u6563\u6211\u5148\u4eba\u7528\u85cf\u5176\u5bb6\u4e66\u4e8e\u5c4b\u58c1\u6c49\u5ba4\u9f99\u5174\u5f00\u8bbe\u5b66\u6821\u65c1\u6c42\u5112\u96c5\u4ee5\u9610\u5927\u7337\u6d4e\u5357\u4f0f\u751f\u5e74\u8fc7\u4e5d\u5341\u5931\u5176\u672c\u7ecf\u53e3\u4ee5\u4f20\u6388\u88c1\u4e8c\u5341\u9980\u7bc7\u4ee5\u5176\u4e0a\u53e4\u4e4b\u4e66\u8c13\u4e4b\u5c1a\u4e66\u767e\u7bc7\u4e4b\u4e49\u4e16\u83ab\u5f97\u95fb"}]}
ethanyt/guwen-punc
null
[ "transformers", "pytorch", "roberta", "token-classification", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "punctuation marker", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
token-classification
transformers
# Guwen Quote A Classical Chinese Quotation Detector. Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to [Guwen Models](https://github.com/ethan-yt/guwen-models). See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/cclue/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=cclue&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/guwenbert/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwenbert&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a>
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "quotation detection"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classification", "widget": [{"text": "\u5b50\u66f0\u5b66\u800c\u65f6\u4e60\u4e4b\u4e0d\u4ea6\u8bf4\u4e4e\u6709\u670b\u81ea\u8fdc\u65b9\u6765\u4e0d\u4ea6\u4e50\u4e4e\u4eba\u4e0d\u77e5\u800c\u4e0d\u6120\u4e0d\u4ea6\u541b\u5b50\u4e4e\u6709\u5b50\u66f0\u5176\u4e3a\u4eba\u4e5f\u5b5d\u5f1f\u800c\u597d\u72af\u4e0a\u8005\u9c9c\u77e3\u4e0d\u597d\u72af\u4e0a\u800c\u597d\u4f5c\u4e71\u8005\u672a\u4e4b\u6709\u4e5f\u541b\u5b50\u52a1\u672c\u672c\u7acb\u800c\u9053\u751f\u5b5d\u5f1f\u4e5f\u8005\u5176\u4e3a\u4ec1\u4e4b\u672c\u4e0e\u5b50\u66f0\u5de7\u8a00\u4ee4\u8272\u9c9c\u77e3\u4ec1\u66fe\u5b50\u66f0\u543e\u65e5\u4e09\u7701\u543e\u8eab\u4e3a\u4eba\u8c0b\u800c\u4e0d\u5fe0\u4e4e\u4e0e\u670b\u53cb\u4ea4\u800c\u4e0d\u4fe1\u4e4e\u4f20\u4e0d\u4e60\u4e4e\u5b50\u66f0\u9053\u5343\u4e58\u4e4b\u56fd\u656c\u4e8b\u800c\u4fe1\u8282\u7528\u800c\u7231\u4eba\u4f7f\u6c11\u4ee5\u65f6"}]}
ethanyt/guwen-quote
null
[ "transformers", "pytorch", "roberta", "token-classification", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "quotation detection", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
token-classification
transformers
# Guwen Seg A Classical Chinese Sentence Segmenter. See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/cclue/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=cclue&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/guwenbert/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwenbert&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a>
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "sentence segmentation"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classification", "widget": [{"text": "\u53ca\u79e6\u59cb\u7687\u706d\u5148\u4ee3\u5178\u7c4d\u711a\u4e66\u5751\u5112\u5929\u4e0b\u5b66\u58eb\u9003\u96be\u89e3\u6563\u6211\u5148\u4eba\u7528\u85cf\u5176\u5bb6\u4e66\u4e8e\u5c4b\u58c1\u6c49\u5ba4\u9f99\u5174\u5f00\u8bbe\u5b66\u6821\u65c1\u6c42\u5112\u96c5\u4ee5\u9610\u5927\u7337\u6d4e\u5357\u4f0f\u751f\u5e74\u8fc7\u4e5d\u5341\u5931\u5176\u672c\u7ecf\u53e3\u4ee5\u4f20\u6388\u88c1\u4e8c\u5341\u9980\u7bc7\u4ee5\u5176\u4e0a\u53e4\u4e4b\u4e66\u8c13\u4e4b\u5c1a\u4e66\u767e\u7bc7\u4e4b\u4e49\u4e16\u83ab\u5f97\u95fb"}]}
ethanyt/guwen-seg
null
[ "transformers", "pytorch", "roberta", "token-classification", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "sentence segmentation", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-classification
transformers
# Guwen Sent A Classical Chinese Poem Sentiment Classifier. See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/cclue/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=cclue&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a> <a href="https://github.com/ethan-yt/guwenbert/"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwenbert&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=true" /> </a>
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "sentiment classificatio"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "text-classification", "widget": [{"text": "\u6eda\u6eda\u957f\u6c5f\u4e1c\u901d\u6c34\uff0c\u6d6a\u82b1\u6dd8\u5c3d\u82f1\u96c4"}, {"text": "\u5bfb\u5bfb\u89c5\u89c5\uff0c\u51b7\u51b7\u6e05\u6e05\uff0c\u51c4\u51c4\u60e8\u60e8\u621a\u621a"}, {"text": "\u6267\u624b\u76f8\u770b\u6cea\u773c\uff0c\u7adf\u65e0\u8bed\u51dd\u564e\uff0c\u5ff5\u53bb\u53bb\uff0c\u5343\u91cc\u70df\u6ce2\uff0c\u66ae\u972d\u6c89\u6c89\u695a\u5929\u9614\u3002"}, {"text": "\u5ffd\u5982\u4e00\u591c\u6625\u98ce\u6765\uff0c\u5e72\u6811\u4e07\u6811\u68a8\u82b1\u5f00"}]}
ethanyt/guwen-sent
null
[ "transformers", "pytorch", "roberta", "text-classification", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "sentiment classificatio", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
fill-mask
transformers
# GuwenBERT ## Model description ![GuwenBERT](https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png) This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recognition, and so on. For more information about RoBERTa, take a look at the RoBERTa's offical repo. ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-base") model = AutoModel.from_pretrained("ethanyt/guwenbert-base") ``` ## Training data The training data is daizhige dataset (殆知阁古代文献) which is contains of 15,694 books in Classical Chinese, covering Buddhism, Confucianism, Medicine, History, Zi, Yi, Yizang, Shizang, Taoism, and Jizang. 76% of them are punctuated. The total number of characters is 1.7B (1,743,337,673). All traditional Characters are converted to simplified characters. The vocabulary is constructed from this data set and the size is 23,292. ## Training procedure The models are initialized with `hfl/chinese-roberta-wwm-ext` and then pre-trained with a 2-step strategy. In the first step, the model learns MLM with only word embeddings updated during training, until convergence. In the second step, all parameters are updated during training. The models are trained on 4 V100 GPUs for 120K steps (20K for step#1, 100K for step#2) with a batch size of 2,048 and a sequence length of 512. The optimizer used is Adam with a learning rate of 2e-4, adam-betas of (0.9,0.98), adam-eps of 1e-6, a weight decay of 0.01, learning rate warmup for 5K steps, and linear decay of learning rate after. ## Eval results ### "Gulian Cup" Ancient Books Named Entity Recognition Evaluation Second place in the competition. Detailed test results: | NE Type | Precision | Recall | F1 | |:----------:|:-----------:|:------:|:-----:| | Book Name | 77.50 | 73.73 | 75.57 | | Other Name | 85.85 | 89.32 | 87.55 | | Micro Avg. | 83.88 | 85.39 | 84.63 | ## About Us We are from [Datahammer](https://datahammer.net), Beijing Institute of Technology. For more cooperation, please contact email: ethanyt [at] qq.com > Created with ❤️ by Tan Yan [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/Ethan-yt) and Zewen Chi [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/CZWin32768)
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "[MASK]\u592a\u5143\u4e2d\uff0c\u6b66\u9675\u4eba\u6355\u9c7c\u4e3a\u4e1a\u3002"}, {"text": "\u95ee\u5f81\u592b\u4ee5\u524d\u8def\uff0c\u6068\u6668\u5149\u4e4b[MASK]\u5fae\u3002"}, {"text": "\u6d54\u9633\u6c5f\u5934\u591c\u9001\u5ba2\uff0c\u67ab\u53f6[MASK]\u82b1\u79cb\u745f\u745f\u3002"}]}
ethanyt/guwenbert-base
null
[ "transformers", "pytorch", "jax", "roberta", "fill-mask", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
fill-mask
transformers
# GuwenBERT ## Model description ![GuwenBERT](https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png) This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recognition, and so on. For more information about RoBERTa, take a look at the RoBERTa's offical repo. ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-large") model = AutoModel.from_pretrained("ethanyt/guwenbert-large") ``` ## Training data The training data is daizhige dataset (殆知阁古代文献) which is contains of 15,694 books in Classical Chinese, covering Buddhism, Confucianism, Medicine, History, Zi, Yi, Yizang, Shizang, Taoism, and Jizang. 76% of them are punctuated. The total number of characters is 1.7B (1,743,337,673). All traditional Characters are converted to simplified characters. The vocabulary is constructed from this data set and the size is 23,292. ## Training procedure The models are initialized with `hfl/chinese-roberta-wwm-ext-large` and then pre-trained with a 2-step strategy. In the first step, the model learns MLM with only word embeddings updated during training, until convergence. In the second step, all parameters are updated during training. The models are trained on 4 V100 GPUs for 120K steps (20K for step#1, 100K for step#2) with a batch size of 2,048 and a sequence length of 512. The optimizer used is Adam with a learning rate of 1e-4, adam-betas of (0.9,0.98), adam-eps of 1e-6, a weight decay of 0.01, learning rate warmup for 5K steps, and linear decay of learning rate after. ## Eval results ### "Gulian Cup" Ancient Books Named Entity Recognition Evaluation Second place in the competition. Detailed test results: | NE Type | Precision | Recall | F1 | |:----------:|:-----------:|:------:|:-----:| | Book Name | 77.50 | 73.73 | 75.57 | | Other Name | 85.85 | 89.32 | 87.55 | | Micro Avg. | 83.88 | 85.39 | 84.63 | ## About Us We are from [Datahammer](https://datahammer.net), Beijing Institute of Technology. For more cooperation, please contact email: ethanyt [at] qq.com > Created with ❤️ by Tan Yan [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/Ethan-yt) and Zewen Chi [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/CZWin32768)
{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "[MASK]\u592a\u5143\u4e2d\uff0c\u6b66\u9675\u4eba\u6355\u9c7c\u4e3a\u4e1a\u3002"}, {"text": "\u95ee\u5f81\u592b\u4ee5\u524d\u8def\uff0c\u6068\u6668\u5149\u4e4b[MASK]\u5fae\u3002"}, {"text": "\u6d54\u9633\u6c5f\u5934\u591c\u9001\u5ba2\uff0c\u67ab\u53f6[MASK]\u82b1\u79cb\u745f\u745f\u3002"}]}
ethanyt/guwenbert-large
null
[ "transformers", "pytorch", "jax", "roberta", "fill-mask", "chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
ethman/test
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
# ai-msgbot GPT2-L + daily dialogues _NOTE: this model card is a WIP_ GPT2-L (774M parameters) fine-tuned on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using `aitextgen`. This model was then subsequently further fine-tuned on the [Daily Dialogues](http://yanran.li/dailydialog) dataset for an additional 40k steps, this time with **35** of 36 layers frozen. Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ended chatbot (of course, if other use cases arise, have at it). ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. This is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` to the **end** of the prompt text. The model is forced to respond to the entered chat prompt instead of adding to the entered prompt and then responding to that (which may cut off the response text due to the Inference API limits). ### Example prompt: ``` do you like to eat beans? person beta: ``` ### Resulting output ``` do you like to eat beans? person beta: no, i don't like ``` ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
{}
ethzanalytics/ai-msgbot-gpt2-L-dialogue
null
[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
# ai-msgbot GPT2-L _NOTE: model card is WIP_ GPT2-L (774M parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with 34/36 layers frozen using `aitextgen`. Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ended chatbot (of course, if other use cases arise have at it). ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. this is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` to the **end** of the prompt text. The model is forced to respond to the entered chat prompt instead of adding to the entered prompt and then responding to that (which may cut off the response text due to the Inference API limits). ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
{}
ethzanalytics/ai-msgbot-gpt2-L
null
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
# ai-msgbot GPT-2 M Conversational A GPT-2 M 355M parameter model for usage with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create a chatbot-like tool. This model was fine-tuned on a parsed version of [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 10,000 steps. 20/24 layers were frozen for the fine-tuning process. ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. this is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## usage ### in ai-msgbot ``` python ai_single_response.py --model GPT2_conversational_355M_WoW10k --prompt "hi! what are your hobbies?" ... generating... finished! 'i like to read.' ``` ### examples with Inference API The model training (and the ai-msgbot scripts) "force" GPT-2 to generate text in a chat-like structure. If you want non-garbage outputs, these need to be specified manually: ``` person alpha: hi! what are your hobbies? ``` then model will respond, ideally with person beta: "response text" --- - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` to the **end** of the prompt text. The model is forced to respond to the entered chat prompt instead of adding to the entered prompt and then responding to that (which may cut off the response text due to the Inference API limits). ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
{}
ethzanalytics/ai-msgbot-gpt2-M
null
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
# ai-msgbot: GPT2-XL-dialogue GPT2-XL (~1.5 B parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with **33**/36 layers frozen using `aitextgen`. The resulting model was then **further fine-tuned** on the [Daily Dialogues](http://yanran.li/dailydialog) for 40k steps, with **34**/36 layers frozen. Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ended chatbot (of course, if other use cases arise, have at it). ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. This is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` into the prompt text the model is forced to respond to instead of adding onto the entered prompt. ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
{"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt"], "datasets": ["natural_questions"], "widget": [{"text": "Do you like my new haircut?\nperson beta:\n\n", "example_title": "haircut"}, {"text": "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n", "example_title": "teaching"}, {"text": "What's your favorite animal? Mine is the dog? \nperson beta:\n\n", "example_title": "favorite"}, {"text": "how much does it cost?\nperson beta:\n\n", "example_title": "money"}], "inference": {"parameters": {"min_length": 2, "max_length": 64, "length_penalty": 0.6, "no_repeat_ngram_size": 3, "do_sample": true, "top_p": 0.85, "top_k": 10, "repetition_penalty": 2.1}}}
ethzanalytics/ai-msgbot-gpt2-XL-dialogue
null
[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "gpt", "en", "dataset:natural_questions", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
# ai-msgbot GPT2-XL _NOTE: model card is WIP_ GPT2-XL (~1.5 B parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with **33**/36 layers frozen using `aitextgen`. Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ended chatbot (of course, if other use cases arise, have at it). ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. This is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` into the prompt text the model is forced to respond to instead of adding onto the entered prompt. ### Example prompt: ``` do you like to eat beans? person beta: ``` ### Resulting output ``` do you like to eat beans?person beta: yes, i like fried beans. person alpha: i wonder when the first beans were cultivated and how they were processed. person beta: nitrogenic bacteria (in ``` _Note: the Inference API cuts off generation due to length, if run elsewhere you would see what comes after "(in"_ ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
{"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt"], "datasets": ["natural questions"], "widget": [{"text": "Do you like my new haircut?\nperson beta:\n\n", "example_title": "haircut"}, {"text": "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n", "example_title": "teaching"}, {"text": "What's your favorite animal? Mine is the dog? \nperson beta:\n\n", "example_title": "favorite"}, {"text": "how much does it cost?\nperson beta:\n\n", "example_title": "money"}], "inference": {"parameters": {"min_length": 2, "max_length": 64, "length_penalty": 0.6, "no_repeat_ngram_size": 3, "do_sample": true, "top_p": 0.85, "top_k": 10, "repetition_penalty": 2.1}}}
ethzanalytics/ai-msgbot-gpt2-XL
null
[ "transformers", "pytorch", "gpt2", "text-generation", "gpt", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
# distilgpt2-tiny-conversational This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a parsed version of Wizard of Wikipedia. Persona alpha/beta framework designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot). It achieves the following results on the evaluation set: - Loss: 2.2461 ## Model description - a basic dialogue model for conversation. It can be used as a chatbot. - check out a [simple demo here](https://huggingface.co/spaces/ethzanalytics/dialogue-demo) ## Intended uses & limitations - usage is designed for integrating with this repo: [ai-msgbot](https://github.com/pszemraj/ai-msgbot) - the main specific information to know is that the model generates whole conversations between two entities, `person alpha` and `person beta`. These entity names are used functionally as custom `<bos>` tokens to extract when one response ends and another begins. ## Training and evaluation data - [wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia/) parsed, from parlAI ## Training procedure - deepspeed + huggingface trainer, an example notebook is in [ai-msgbot](https://github.com/pszemraj/ai-msgbot) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 418 | 2.7793 | | 2.9952 | 2.0 | 836 | 2.6914 | | 2.7684 | 3.0 | 1254 | 2.6348 | | 2.685 | 4.0 | 1672 | 2.5938 | | 2.6243 | 5.0 | 2090 | 2.5625 | | 2.5816 | 6.0 | 2508 | 2.5332 | | 2.5816 | 7.0 | 2926 | 2.5098 | | 2.545 | 8.0 | 3344 | 2.4902 | | 2.5083 | 9.0 | 3762 | 2.4707 | | 2.4793 | 10.0 | 4180 | 2.4551 | | 2.4531 | 11.0 | 4598 | 2.4395 | | 2.4269 | 12.0 | 5016 | 2.4238 | | 2.4269 | 13.0 | 5434 | 2.4102 | | 2.4051 | 14.0 | 5852 | 2.3945 | | 2.3777 | 15.0 | 6270 | 2.3848 | | 2.3603 | 16.0 | 6688 | 2.3711 | | 2.3394 | 17.0 | 7106 | 2.3613 | | 2.3206 | 18.0 | 7524 | 2.3516 | | 2.3206 | 19.0 | 7942 | 2.3398 | | 2.3026 | 20.0 | 8360 | 2.3301 | | 2.2823 | 21.0 | 8778 | 2.3203 | | 2.2669 | 22.0 | 9196 | 2.3105 | | 2.2493 | 23.0 | 9614 | 2.3027 | | 2.2334 | 24.0 | 10032 | 2.2930 | | 2.2334 | 25.0 | 10450 | 2.2852 | | 2.2194 | 26.0 | 10868 | 2.2754 | | 2.2014 | 27.0 | 11286 | 2.2695 | | 2.1868 | 28.0 | 11704 | 2.2598 | | 2.171 | 29.0 | 12122 | 2.2539 | | 2.1597 | 30.0 | 12540 | 2.2461 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.0
{"license": "apache-2.0", "tags": ["text-generation", "chatbot", "dialogue", "distilgpt2", "gpt2", "ai-msgbot"], "widget": [{"text": "I know you're tired, but can we go for another walk this evening?\nperson beta:\n\n", "example_title": "walk"}, {"text": "Have you done anything exciting lately?\nperson beta:\n\n", "example_title": "activities"}, {"text": "hey - do you have a favorite grocery store around here?\nperson beta:\n\n", "example_title": "grocery"}, {"text": "Can you take me for dinner somewhere nice this time?\nperson beta:\n\n", "example_title": "dinner"}, {"text": "What's your favorite form of social media?\nperson beta:\n\n", "example_title": "social media"}, {"text": "Hi, how are you?\nperson beta:\n\n", "example_title": "greeting"}, {"text": "I am the best; my sister is the worst. What am I?\nperson beta:\n\n", "example_title": "sister"}, {"text": "What do you call an alligator who's just had surgery to remove his left arm?\nperson beta:\n\n", "example_title": "alligator"}, {"text": "A man walks into a bar and asks for a drink. The bartender asks for $10, and he pays him $1. What did he pay him with?\nperson beta:\n\n", "example_title": "dollar"}, {"text": "What did I say was in the mailbox when it was actually in the cabinet?\nperson beta:\n\n", "example_title": "mailbox"}, {"text": "My friend says that she knows every language, but she doesn't speak any of them.. what's wrong with her?\nperson beta:\n\n", "example_title": "language"}], "inference": {"parameters": {"min_length": 2, "max_length": 64, "length_penalty": 0.7, "no_repeat_ngram_size": 2, "do_sample": true, "top_p": 0.95, "top_k": 20, "temperature": 0.3, "repetition_penalty": 3.5}}}
ethzanalytics/distilgpt2-tiny-conversational
null
[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "chatbot", "dialogue", "distilgpt2", "ai-msgbot", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
ethzhou/joobboob
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
ethzhou/joobjoob
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
null
{"tags": ["conversational"]}
ethzhou/jooby
null
[ "conversational", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
null
{"tags": ["conversational"]}
ethzhou/joobyChat
null
[ "conversational", "region:us" ]
null
2022-03-02T23:29:05+00:00
text-generation
transformers
#blabla
{"tags": ["conversational"]}
ethzhou/newJooby
null
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2022-03-02T23:29:05+00:00
null
null
{}
eubinecto/wisdomifier-ver-4
null
[ "region:us" ]
null
2022-03-02T23:29:05+00:00