titu_stt_bn_conformer_large

Summary

titu_stt_bn_conformer_large is a conformer based model trained on ~4.4K Hours Non-Telephony open datasets.

Using method

This model can be used for transcribing Bangla audio and also can be used as pre-trained model to fine-tuning on custom datasets using NeMo framework.

Installation

To install NeMo check NeMo documentation.

pip install -q 'nemo_toolkit[asr]'

Inferencing

Download test_bn_conformer.wav

# pip install -q 'nemo_toolkit[asr]'

import nemo.collections.asr as nemo_asr

asr_model = nemo_asr.models.ASRModel.from_pretrained("hishab/titu_stt_bn_conformer_large")

auido_file = "test_bn_conformer.wav"
transcriptions = asr_model.transcribe([auido_file])

print(transcriptions)
# ['আজ সরকারি ছুটির দিন দেশের সব শিক্ষা প্রতিষ্ঠান সহ সরকারি আধাসরকারি
স্বায়ত্তশাসিত প্রতিষ্ঠান ও ভবনে জাতীয় পতাকা অর্ধনমিত ও কালো পতাকা উত্তোলন করা হয়েছে']

Training Datasets

Training Details

For training the model, we selected ~4.4K hours of data from aforementioned datasets. We took nvidia/stt_en_conformer_ctc_large as base model and fine-tuned on 4xH100 GPU. Below are some more training parameters.

epoch = 100
batch_size = 64

Evaluation

For evaluating the model, we used test set with transcripts available from aforementioned datasets. Below are the details. For each dataset, we calculate WER/CER in percentage. We evaluated our ASR model on 9 different datasets and we got an average 7.9% WER