license: mit
language:
- or
pipeline_tag: automatic-speech-recognition
library_name: nemo
IndicConformer
IndicConformer is a Hybrid CTC-RNNT conformer ASR(Automatic Speech Recognition) model.
Language
Odia
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_or_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='or')[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='or')[0]
print(rnnt_text)