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README.md

@@ -1,23 +1,46 @@
-# Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English
+# Multilingual End-to-End Automatic Speech Recognition for Kazakh, Russian, and English
 
-## Dataset Card
+This repository contains the code and pretrained models for a study on multilingual end-to-end (E2E) automatic speech recognition (ASR) for Kazakh, Russian, and English, as described in [A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English](https://arxiv.org/abs/2108.01280).  The research explores the performance of multilingual E2E ASR models using Transformer networks, comparing different output grapheme set constructions (combined and independent), and evaluating the impact of language models (LMs) and data augmentation techniques.  The best monolingual and multilingual models achieved 20.9% and 20.5% average word error rates, respectively, on a combined test set.
 
-**Repository:** [https://github.com/issai-nu/MultilingualASR](https://github.com/issai-nu/MultilingualASR)
+**Repository:** [Insert Hugging Face Repository Link Here]
 
-**Paper:** [A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English](https://arxiv.org/abs/2108.01280)
 
-**Summary:** This repository contains the code and data for training a multilingual end-to-end (E2E) automatic speech recognition (ASR) model for Kazakh, Russian, and English.  The research explores two output grapheme set construction methods (combined and independent) and investigates the impact of language models (LMs) and data augmentation techniques on model performance.  The best monolingual and multilingual models achieved comparable performance with an average word error rate (WER) of 20.9% and 20.5%, respectively, on a combined test set.
+## Model Description
 
+This work trains a single E2E ASR model capable of recognizing Kazakh, Russian, and English speech.  Two variants of output grapheme set construction are explored: combined and independent.  The impact of LMs and data augmentation (Speed Perturbation and SpecAugment) on model performance is also investigated.  The models are based on Transformer networks.
 
-**Pre-trained Models:**
+## Evaluation Results
 
-| Model                     | Large Transformer                               | Large Transformer with Speed Perturbation (SP)                         | Large Transformer with SP and SpecAugment                         |
-|--------------------------|-------------------------------------------------|--------------------------------------------------------------------|--------------------------------------------------------------------|
-| Monolingual Kazakh        |  (link removed as per instructions)           | (link removed as per instructions)                                   | (link removed as per instructions)                                   |
-| Monolingual Russian       | (link removed as per instructions)           | (link removed as per instructions)                                   | (link removed as per instructions)                                   |
-| Monolingual English       | (link removed as per instructions)           | (link removed as per instructions)                                   | (link removed as per instructions)                                   |
-| Multilingual Combined     | (link removed as per instructions)           | (link removed as per instructions)                                   | (link removed as per instructions)                                   |
-| Multilingual Independent | (link removed as per instructions)           | (link removed as per instructions)                                   | (link removed as per instructions)                                   |
+The multilingual models achieve comparable performance to monolingual baselines with a similar number of parameters.
 
+| Model Type                     | Average Word Error Rate (%) |
+|---------------------------------|-----------------------------|
+| Best Monolingual               | 20.9                         |
+| Best Multilingual               | 20.5                         |
 
-**(Note: Links to pre-trained models have been removed as per the instructions.)**
+
+## Pre-trained Models
+
+The following pre-trained models are available:
+
+| Model                    | Large Transformer | Large Transformer w/ SP | Large Transformer w/ SP & SpecAugment |
+|---------------------------|--------------------|------------------------|---------------------------------------|
+| Monolingual Kazakh        |  [Model Link]     | [Model Link]          | [Model Link]                           |
+| Monolingual Russian       |  [Model Link]     | [Model Link]          | [Model Link]                           |
+| Monolingual English       |  [Model Link]     | [Model Link]          | [Model Link]                           |
+| Multilingual Combined     |  [Model Link]     | [Model Link]          | [Model Link]                           |
+| Multilingual Independent |  [Model Link]     | [Model Link]          | [Model Link]                           |
+
+**(Note: Replace "[Model Link]" with the actual Hugging Face model links.)**
+
+
+## Citation
+
+```bibtex
+@article{...,
+  title={A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English},
+  author={...},
+  journal={arXiv preprint arXiv:2108.01280},
+  year={2021}
+}
+```