regipa_bangla

Team_Khita_Kortesi_Model: Bengali Text to IPA Transcription based on fine-tuned ByT5-small

Solution Summary:

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.

Approach:

Data Preprocessing:

We preprocess the Bengali text data to handle linguistic variations, tokenization, and normalization.

Model Architecture:

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.

Training:

The model is trained on the linguist-validated dataset, optimizing for accuracy, robustness, and generalization across various dialects and linguistic contexts.

Validation:

We validate the model's performance using rigorous evaluation metrics, ensuring its effectiveness in accurately transcribing Bengali text into IPA.

Deployment:

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.

Key Features:

• Phonetic Accuracy: Our model prioritizes phonetic accuracy, ensuring faithful transcription of Bengali text into IPA symbols.
• 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.
• 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.
• 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.

Impact:

• 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.
• 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.
• 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.

Example Inference:

from transformers import T5ForConditionalGeneration
import torch

model = T5ForConditionalGeneration.from_pretrained('abdullaharean/regipa_bangla')

input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens

loss = model(input_ids, labels=labels).loss # forward pass