readme: ---
  tags:
  - espnet
  - audio
  - automatic-speech-recognition
  language: et
  license: cc-by-4.0
  ---
  
  # Estonian Espnet2 ASR model
  
  ## Model description
  This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech.
  
  ## Intended uses & limitations
  
  This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.
  
  
  ## How to use
  ```python
  
  from espnet2.bin.asr_inference import Speech2Text
      
  model = Speech2Text.from_pretrained(
    "TalTechNLP/espnet2_estonian", 
    lm_weight=0.6, ctc_weight=0.4, beam_size=60
  )
  
  # read a sound file with 16k sample rate
  import soundfile
  speech, rate = soundfile.read("speech.wav")
  assert rate == 16000
  text, *_ = model(speech)
  print(text[0])
  ```
  
  #### Limitations and bias
  
  Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:
    * Speech containing technical and other domain-specific terms
    * Children's speech
    * Non-native speech
    * Speech recorded under very noisy conditions or with a microphone far from the speaker
    * Very spontaneous and overlapping speech
  
  ## Training data
  Acoustic training data:
  
  | Type                  | Amount (h) |
  |-----------------------|:------:|
  | Broadcast speech      |   591  |
  | Spontaneous speech    |   53   |
  | Elderly speech corpus |   53   |
  | Talks, lectures       |   49   |
  | Parliament speeches   |   31   |
  | *Total*                 |   *761*  |
  
  Language model training data:
   * Estonian National Corpus 2019
   * OpenSubtitles
   * Speech transcripts
  
  ## Training procedure
  
  Standard EspNet2 Conformer recipe.
  
  ## Evaluation results
  
  ### WER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/aktuaalne2021.testset|2864|56575|93.1|4.5|2.4|2.0|8.9|63.4|
  |decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/jutusaated.devset|273|4677|93.9|3.6|2.4|1.2|7.3|46.5|
  |decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/jutusaated.testset|818|11093|94.7|2.7|2.5|0.9|6.2|45.0|
  |decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/www-trans.devset|1207|13865|82.3|8.5|9.3|3.4|21.2|74.1|
  |decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/www-trans.testset|1648|22707|86.4|7.6|6.0|2.5|16.1|75.7|
  
  
  ### BibTeX entry and citation info
  
  
  
  #### 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}
  }
  ```