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--- |
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license: cc-by-nc-4.0 |
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language: |
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- bn |
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library_name: nemo |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- ASR |
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- Automatic Speech Recognition |
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- Bangla ASR |
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- Bengali ASR |
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- bn asr |
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- Bangla fastconformer |
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- https://arxiv.org/abs/2311.03196 |
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--- |
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## Summary |
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__titu_stt_bn_fastconformer__ is a [fastconformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer) based model trained on ~18K Hours [MegaBNSpeech]() corpus. |
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Details on paper: [https://aclanthology.org/2023.banglalp-1.16/](https://aclanthology.org/2023.banglalp-1.16/) |
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## Using method |
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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](https://github.com/NVIDIA/NeMo) framework. |
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### Installation |
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To install [NeMo](https://github.com/NVIDIA/NeMo) check NeMo documentation. |
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``` |
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pip install -q 'nemo_toolkit[asr]' |
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``` |
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### Inferencing |
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[Download test_bn_fastconformer.wav](https://huggingface.co/hishab/hishab_bn_fastconformer/blob/main/test_bn_fastconformer.wav) |
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```py |
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# pip install -q 'nemo_toolkit[asr]' |
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import nemo.collections.asr as nemo_asr |
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asr_model = nemo_asr.models.ASRModel.from_pretrained("hishab/titu_stt_bn_fastconformer") |
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auido_file = "test_bn_fastconformer.wav" |
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transcriptions = asr_model.transcribe([auido_file]) |
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print(transcriptions) |
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# ['আজ সরকারি ছুটির দিন দেশের সব শিক্ষা প্রতিষ্ঠান সহ সরকারি আধা সরকারি স্বায়ত্তশাসিত প্রতিষ্ঠান ও ভবনে জাতীয় পতাকা অর্ধনমিত ও কালো পতাকা উত্তোলন করা হয়েছে'] |
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``` |
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Colab Notebook for Infer: [Bangla FastConformer Infer.ipynb](https://colab.research.google.com/drive/1J3bxXlLBgSf1zOKVKbRYu1VrbEJFLlUc?usp=sharing) |
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## Training Datasets |
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| Channels Category | Hours | |
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| ----------------- | ----------- | |
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| News | 17,640.00 | |
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| Talkshow | 688.82 | |
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| Vlog | 0.02 | |
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| Crime Show | 4.08 | |
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| Total | 18,332.92 | |
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## Training Details |
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For training the model, the dataset we selected comprises 17.64k hours of news chan- nel content, 688.82 hours of talk shows, 0.02 hours of vlogs, and 4.08 hours of crime shows. |
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## Evaluation |
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## Citation |
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``` |
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@inproceedings{nandi-etal-2023-pseudo, |
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title = "Pseudo-Labeling for Domain-Agnostic {B}angla Automatic Speech Recognition", |
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author = "Nandi, Rabindra Nath and |
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Menon, Mehadi and |
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Muntasir, Tareq and |
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Sarker, Sagor and |
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Muhtaseem, Quazi Sarwar and |
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Islam, Md. Tariqul and |
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Chowdhury, Shammur and |
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Alam, Firoj", |
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editor = "Alam, Firoj and |
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Kar, Sudipta and |
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Chowdhury, Shammur Absar and |
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Sadeque, Farig and |
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Amin, Ruhul", |
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booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.banglalp-1.16", |
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doi = "10.18653/v1/2023.banglalp-1.16", |
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pages = "152--162", |
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abstract = "One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR", |
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} |
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``` |
