README.md

---
license: mit
language:
- hi
pipeline_tag: automatic-speech-recognition
library_name: nemo
---
## IndicConformer

  IndicConformer is a Hybrid RNNT conformer model built for Hindi.

  ## 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 indicconformer_stt_hi_hybrid_rnnt_large.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 conformer-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.