readme: ---
  language:
  - ru
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_10_0
  - SberDevices/Golos
  - Russian-LibriSpeech
  - SOVA-Dataset
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  - Riva
  license: cc-by-4.0
  model-index:
  - name: stt_ru_conformer_ctc_large
    results:
    - task:
        type: Automatic Speech Recognition
        name: speech-recognition
      dataset:
        name: Mozilla Common Voice 10.0
        type: mozilla-foundation/common_voice_10_0
        config: ru
        split: test
        args:
          language: ru
      metrics:
      - name: Test WER
        type: wer
        value: 4.28
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Mozilla Common Voice 10.0
        type: mozilla-foundation/common_voice_10_0
        config: ru
        split: dev
        args:
          language: ru
      metrics:
      - name: Dev WER
        type: wer
        value: 3.94
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Sberdevices Golos (crowd)
        type: SberDevices/Golos
        config: crowd
        split: test
        args:
          language: ru
      metrics:
      - name: Test WER
        type: wer
        value: 2.77
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Sberdevices Golos (farfield)
        type: SberDevices/Golos
        config: farfield
        split: test
        args:
          language: ru
      metrics:
      - name: Test WER
        type: wer
        value: 7.15
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Russian LibriSpeech
        type: RuLS
        config: ru
        split: test
        args:
          language: ru
      metrics:
      - name: Test WER
        type: wer
        value: 13.60
  ---
  # NVIDIA Conformer-CTC Large (Russian)
  
  <style>
  img {
   display: inline;
  }
  </style>
  
  | [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--CTC-lightgrey#model-badge)](#model-architecture)
  | [![Model size](https://img.shields.io/badge/Params-120M-lightgrey#model-badge)](#model-architecture)
  | [![Language](https://img.shields.io/badge/Language-ru-lightgrey#model-badge)](#datasets)
  
  This model transcribes speech into lowercase Cyrillic alphabet including space, and is trained on around 1636 hours of Russian speech data.
  It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters.
  See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details.
  
  ## Usage
  
  The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
  
  To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
  
  ```
  pip install nemo_toolkit['all']
  ```
  
  ### Automatically instantiate the model
  
  ```python
  import nemo.collections.asr as nemo_asr
  asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_ru_conformer_ctc_large")
  ```
  
  ### Transcribing using Python
  Simply do:
  ```
  asr_model.transcribe(['<your_audio>.wav'])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_ru_conformer_ctc_large" 
   audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
  ```
  
  ### Input
  
  This model accepts 16 kHz mono-channel Audio (wav files) as input.
  
  ### Output
  
  This model provides transcribed speech as a string for a given audio sample.
  
  ## Model Architecture
  
  Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc). 
  
  ## Training
  
  The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml).
  
  The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
  
  
  The vocabulary we use contains 33 characters:
  ```python
  [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я']
  ```
  
  Rare symbo