Model reference · open weights

kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best

Available as managed deployment Audio espnet Speech→text 1 variants 1k dl/mo

kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best is an open-weight audio or speech model from espnet. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byespnet
TypeAudio & music
TaskSpeech→text
Runs withespnet
Released2022-03-02
Popularity1k downloads / month
LicenceOpen weights

About

What kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best is

kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best

♻️ Imported from This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.

Read the full model card

Demo: How to use in ESPnet2

# coming soon

Citing ESPnet

@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:

@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: 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

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How audio & music work

Audio or textinputAudio modelrecognise / synthesiseText or audiooutputSpeech-to-text turns audio into text; text-to-speech and music models turn text into audio.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys kamo-naoyuki-mini-an4-asr-train-raw-bpe-valid-acc-best for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kamo-naoyuki-mini-an4-asr-train-raw-bpe-valid-acc-best below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="kamo-naoyuki-mini-an4-asr-train-raw-bpe-valid-acc-best" -F file=@audio.mp3

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