Kamo Naoyuki Mini An4 Asr Train Raw Bpe Valid.acc.best

🚀 ESPnet2自動語音識別預訓練模型

本模型是ESPnet2的自動語音識別（ASR）預訓練模型，可用於音頻的自動語音識別任務，基於espnet框架訓練，具有較高的實用性和可擴展性。

🚀 快速開始

演示：如何在ESPnet2中使用

# coming soon

✨ 主要特性

• 預訓練模型：該模型kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best是預訓練好的，可直接用於相關任務。
• 來源清晰：♻️ 從 https://zenodo.org/record/3957940#.YN7zwJozZH4 導入，由kan - bayashi使用jsut/tts1配方在 espnet 中訓練得到。

📚 詳細文檔

引用ESPnet

如果你在研究中使用了ESPnet，可以使用以下BibTex格式進行引用：

@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}
}

或者使用arXiv格式：

@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}
}

訓練配置

完整的配置文件請參考 config.yaml

config: null
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_raw_bpe
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

📄 許可證

本模型採用CC - BY - 4.0許可證。