Indicconformer Stt Kn Hybrid Ctc Rnnt Large

🚀 IndicConformer

IndicConformer is a Hybrid CTC - RNNT conformer ASR (Automatic Speech Recognition) model, which can transcribe Kannada speech.

🚀 Quick Start

Prerequisites

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

Download and Load the Model

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_kn_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

Prepare the Audio File

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

Inference using CTC decoder

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

✨ Features

Language

Kannada

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.

📚 Documentation

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.

📄 License

This project is licensed under the MIT license.