ai4bharat/indicconformer_stt_brx_hybrid_ctc_rnnt_large

IndicConformer

IndicConformer is a Hybrid CTC-RNNT conformer ASR(Automatic Speech Recognition) model.

Language

Bodo

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.

AI4Bharat NeMo:

To load, train, fine-tune or play with the model you will need to install 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

Download and load the model from Huggingface.

import torch
import nemo.collections.asr as nemo_asr

model = nemo_asr.models.ASRModel.from_pretrained("ai4bharat/indicconformer_stt_brx_hybrid_rnnt_large")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.freeze() # inference mode
model = model.to(device) # transfer model to device

Get an audio file ready by running the command shown below in your terminal. This will convert the audio to 16000 Hz and monochannel.

ffmpeg -i sample_audio.wav -ac 1 -ar 16000 sample_audio_infer_ready.wav

Inference using CTC decoder

model.cur_decoder = "ctc"
ctc_text = model.transcribe(['sample_audio_infer_ready.wav'], batch_size=1,logprobs=False, language_id='brx')[0]
print(ctc_text)

Inference using RNNT decoder

model.cur_decoder = "rnnt"
rnnt_text = model.transcribe(['sample_audio_infer_ready.wav'], batch_size=1, language_id='brx')[0]
print(rnnt_text)