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--- |
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license: mit |
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language: |
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- bn |
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--- |
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# Team_Khita_Kortesi_Model: Bengali Text to IPA Transcription based on fine-tuned ByT5-small |
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## Solution Summary: |
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Our team's solution focuses on developing a robust model for transcribing Bengali text into International Phonetic Alphabet (IPA), contributing to computational linguistics and NLP research in Bengali. Leveraging a linguist-validated dataset encompassing diverse domains of Bengali text, our model aims to accurately capture the phonetic nuances and regional dialects present in Bengali language. |
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## Approach: |
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### Data Preprocessing: |
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We preprocess the Bengali text data to handle linguistic variations, tokenization, and normalization. |
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### Model Architecture: |
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Our model architecture employs state-of-the-art deep learning techniques, such as recurrent neural networks (RNNs) or transformer-based models, to capture the sequential and contextual information inherent in language. |
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### Training: |
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The model is trained on the linguist-validated dataset, optimizing for accuracy, robustness, and generalization across various dialects and linguistic contexts. |
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### Validation: |
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We validate the model's performance using rigorous evaluation metrics, ensuring its effectiveness in accurately transcribing Bengali text into IPA. |
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### Deployment: |
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Upon successful validation, the model is deployed as an open-source tool, extending the capabilities of generalized Bengali Text-to-Speech systems and facilitating further research in Bengali computational linguistics. |
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## Key Features: |
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- **Phonetic Accuracy:** Our model prioritizes phonetic accuracy, ensuring faithful transcription of Bengali text into IPA symbols. |
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- **Regional Dialects:** The model is designed to accommodate the diverse regional dialects and linguistic variations present in Bengali language, capturing the nuances specific to each region. |
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- **Scalability:** With a scalable architecture, our solution can handle large volumes of text data efficiently, making it suitable for real-world applications and research purposes. |
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- **Accessibility:** By open-sourcing our model, we aim to make IPA transcription accessible to a wider audience, fostering collaboration and innovation in Bengali computational linguistics. |
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## Impact: |
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- **Advancing Research:** Our solution contributes to advancing research in Bengali computational linguistics and NLP, providing researchers with a valuable tool for studying language dynamics and linguistic diversity. |
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- **Community Engagement:** By open-sourcing our model and making it accessible to all, we empower the Bengali language community to engage in linguistic research and exploration. |
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- **Technological Innovation:** Our model extends the capabilities of existing Bengali Text-to-Speech systems, paving the way for innovative applications in speech synthesis, language learning, and accessibility. |
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## Example Inference: |
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```python |
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from transformers import T5ForConditionalGeneration |
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import torch |
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model = T5ForConditionalGeneration.from_pretrained('abdullaharean/regipa_bangla') |
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input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens |
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labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens |
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loss = model(input_ids, labels=labels).loss # forward pass |
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``` |
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