readme: ---
  language:
  - en
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_10_0
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  license: cc-by-4.0
  model-index:
  - name: stt_be_conformer_transducer_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: 3.8
  ---
  
  # NVIDIA Conformer-Transducer Large (be-Bel)
  
  <style>
  img {
   display: inline;
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  | [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--Transducer-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)
  
  
  This model transcribes speech in lower case Belarusian alphabet along with spaces and apostrophes.
  It is an "large" versions of Conformer-Transducer (around 120M parameters) model.  
  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-transducer) for complete architecture details.
  
  ## NVIDIA NeMo: Training
  
  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']
  '''
  '''
  (if it causes an error): 
  pip install nemo_toolkit[all]
  ``` 
  
  ## How to Use this Model
  
  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.
  
  ### Automatically instantiate the model
  
  ```python
  import nemo.collections.asr as nemo_asr
  asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_be_conformer_transducer_large")
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_be_conformer_transducer_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-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html). 
  
  ## 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_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_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).
  
  ### 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            | 3.8         | MCV 10        |
  
  ## Limitations
  Since this model was trained on publicly available speech 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.
  
  ## NVIDIA Riva: Deployment
  
  [NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. 
  Additionally, Riva provides: 
  
  * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours 
  * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization 
  * Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support 
  
  Although this model isn’t suppor