readme: ---
  language:
  - rw
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_9_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_rw_conformer_ctc_large
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Mozilla Common Voice 9.0
        type: mozilla-foundation/common_voice_9_0
        config: rw
        split: test
        args:
          language: rw
      metrics:
      - name: Test WER
        type: wer
        value: 18.22
  ---
  
  # NVIDIA Conformer-CTC Large (Kinyarwanda)
  
  <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-rw-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 into lowercase Latin alphabet including spaces, and apostroph, and is trained on around 2000 hours of Kinyarwanda 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_rw_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_rw_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 vocabulary we use contains 28 characters:
  ```python
  [' ', "'", 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
  ```
  Rare symbols with diacritics were replaced during preprocessing.
  
  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).
  For vocabulary of size 128 we restrict maximum subtoken length to 2 symbols to avoid populating vocabulary with specific frequent words from the dataset. This does not affect the model performance and potentially helps to adapt to other domain without retraining tokenizer.
  
  Full config can be found inside the .nemo files.
  
  ### Datasets
  
  All the models in this collection are trained on MCV-9.0 Kinyarwanda dataset, which contains around 2000 hours training, 32 hours of development and 32 hours of testing speech audios.
  
  ## Performance
  
  The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
  
  | Version | Tokenizer             | Vocabulary Size | Dev WER| Test WER| Train Dataset   |
  |---------|-----------------------|-----------------|-----|------|-----------------|
  | 1.11.0  | SentencePiece BPE, maxlen=2 | 128             |15.16 | 18.22 | MCV-9.0