readme: ---
  language:
  - be
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_10_0
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  - Riva
  license: cc-by-4.0
  model-index:
  - name: stt_be_conformer_ctc_large
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: MCV_10_be
        type: mcv_asr
        config: clean
        split: test
        args:
          language: be
      metrics:
      - name: Test WER
        type: wer
        value: 4.8
  ---
  
  # NVIDIA Conformer-CTC Large (be)
  
  <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-be--Belarusian-lightgrey#model-badge)](#datasets)
  | [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) |
  
  
  This model transcribes speech in lowercase Belarusian alphabet including spaces and apostrophes, and is trained on few hundreds of Belarusian 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.
  It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). 
  
  
  ## 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("nvidia/stt_be_conformer_ctc_large")
  ```
  
  ### Transcribing using Python
  
  Simply do:
  
  ```
  asr_model.transcribe(['sample.wav'])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_be_conformer_ctc_large" 
   audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
  ```
  
  ### Input
  
  This model accepts 16000 Hz 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 checkpoint of the language model used as the neural rescorer can be found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_be_conformer_ctc_large). You may find more info on how to train and use language models for ASR models here: [ASR Language Modeling](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html)
  
  ### Datasets
  
  All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several hundreds hours of Belarusian speech:
  
  - Mozilla Common Voice (v10.0)
  
  ## Performance
  
  Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. 
  
  | Version | Tokenizer            | Vocabulary Size | MCV 10 Test | Train Dataset |
  |---------|----------------------|-----------------|-------------|---------------|
  | 1.12.0  | Google Sentencepiece | 1024            | 4.8         | MCV 10        |
  
  ## Limitations
  
  Since all models are trained on just academic datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
  
  ## Deployment with NVIDIA Riva
  
  For the best real-time accuracy, latency, and throu