🇮🇳 Optimal Curriculum Learning for Multilingual Indian ASR
Three-Phase Curriculum + Cooldown Temperature Sampling for 22 Scheduled Indian Languages
Overview
This repository contains a literature-grounded data design strategy for fine-tuning ASR-LLM models (Gemma 4 E4B, OmniASR, Qwen3-ASR) on all 22 scheduled Indian languages with highly imbalanced data distributions (90h → 8,500h).
The core challenge: improve low-resource language ASR without degrading high-resource performance.
Key Insight
No single static temperature works. Based on 8 key papers, we combine:
Cooldown dynamic temperature scheduling (τ: 5→2)
Three-phase curriculum: Foundation → Expansion → Refinement
Replay buffers for anti-forgetting
Language-specific adapters (MMS LSAH pattern)
Results from Ablation Simulation
Strategy
Tier 1 (HR)
Tier 2 (MR)
Tier 3 (LR)
Max/Min Ratio
Proportional (τ=1)
95.0%
3.3%
1.7%
101x
Static τ=5
57.1%
15.7%
27.2%
2.4x
Cooldown τ=5→2
66.3%
15.0%
18.7%
4.4x
3-Phase Curriculum
74.2%
8.9%
11.0%
9.8x
The 3-phase curriculum provides the best balance: maintains strong Tier 1 coverage while significantly boosting Tier 3 exposure from 1.7% → 11%.
Files
File
Description
CURRICULUM_LEARNING_STRATEGY.md
Comprehensive strategy document with all hyperparameters
curriculum_sampler.py
Core module: temperature sampling, Cooldown schedule, CurriculumOrchestrator
train_gemma4_indian_asr.py
Training script for Gemma 4 E4B with curriculum learning
train_qwen3_asr_indian.py
Training script for Qwen3-ASR with curriculum learning
