---
{}
---

    ## IndicConformer

    IndicConformer is an Hybrid RNNT conformer model built for Manipuri.

    ## AI4Bharat NeMo:

    To load, train, fine-tune or play with the model you will need to install [AI4Bharat NeMo](https://github.com/AI4Bharat/NeMo). We recommend you install it using the command shown below
    ```
    git clone https://github.com/AI4Bharat/NeMo.git && cd NeMo && git checkout nemo-v2 && bash reinstall.sh
    ```

    ## Usage

    ```bash
    $ python inference.py --help
    usage: inference.py [-h] -c CHECKPOINT -f AUDIO_FILEPATH -d (cpu,cuda) -l LANGUAGE_CODE

    options:
    -h, --help            show this help message and exit
    -c CHECKPOINT, --checkpoint CHECKPOINT
                            Path to .nemo file
    -f AUDIO_FILEPATH, --audio_filepath AUDIO_FILEPATH
                            Audio filepath
    -d (cpu,cuda), --device (cpu,cuda)
                            Device (cpu/gpu)
    -l LANGUAGE_CODE, --language_code LANGUAGE_CODE
                            Language Code (eg. hi)
    ```

    ## Example command
    ```
    python inference.py -c ai4b_indicConformer_hi.nemo -f hindi-16khz.wav -d cuda -l hi
    ```
    Expected output - 

    ```
    Loading model..
    ...
    Transcibing..
    ----------
    Transcript: 
    Took ** seconds.
    ----------
    ```

    ### Input

    This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

    ### Output

    This model provides transcribed speech as a string for a given audio sample.

    ## Model Architecture

    This model is a onformer-Large model, consisting of 120M parameters, as the encoder, with a hybrid CTC-RNNT  decoder. The model has 17 conformer blocks with
    512 as the model dimension.

    ## Training

    <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>

    ### Datasets

    <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>

    ## Performance

    <LIST THE SCORES OF THE MODEL -
        OR
    USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>

    ## Limitations

    <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>

    Eg:
    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.


    ## References

    <ADD ANY REFERENCES HERE AS NEEDED>

    [1] [AI4Bharat NeMo Toolkit](https://github.com/AI4Bharat/NeMo)