docs/guides/TRAINING_IMPROVEMENTS.md

Whisper Training Pipeline - Improvements Summary

Overview

This document summarizes the comprehensive improvements made to the Whisper fine-tuning pipeline to fix training issues and enable proper evaluation.

Critical Fixes

1. Trainer API Issues (Breaking Bugs)

Problem: Training was using incorrect/deprecated API parameters Fixes:

• ✅ Changed eval_strategy="epoch" → evaluation_strategy="epoch"
  • Impact: Evaluation was never running during training
• ✅ Changed processing_class=processor → tokenizer=processor
  • Impact: Tokenizer wasn't properly saved with checkpoints
• ✅ Added predict_with_generate=True
  • Impact: Enables proper sequence generation for WER evaluation

2. Language/Task Conditioning (Critical for Non-English)

Problem: Model wasn't conditioned for German transcription Fix:

model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(
    language="german", 
    task="transcribe"
)
model.config.suppress_tokens = []

Impact:

• Model now knows it's transcribing German
• Decoder generates German text consistently
• Training targets are properly aligned

3. Hyperparameter Issues

Learning Rate (Too Conservative)

Before: 5e-6 for all dataset sizes After:

• Large datasets (>400): 2e-5
• Medium datasets (100-400): 1.5e-5
• Small datasets (<100): 1e-5

Impact: 2-4x higher learning rate enables actual learning with limited data

Warmup Strategy

Before: warmup_steps=min(100, len(train)//10) (could be 50%+ of training) After: warmup_ratio=0.03-0.05 (3-5% of total steps)

Impact: More stable warmup that scales with dataset size

Precision/Dtype Conflict

Before: Model loaded with torch_dtype=torch.float16, Trainer uses bf16=True After: Let Trainer control precision entirely

# Model loading - no dtype specified
model = WhisperForConditionalGeneration.from_pretrained(
    "openai/whisper-small",
    config=config,
    device_map="auto"
)

# Trainer handles precision
bf16=torch.cuda.is_bf16_supported()

Impact: Eliminates dtype mismatches and training instability

4. Data Quality Filtering

Added Filters:

• ✅ Duration: 0.5s ≤ audio ≤ 30s
• ✅ Transcript: Not empty, 2+ chars, <500 chars
• ✅ Audio validation: Valid array and sampling rate
• ✅ Text normalization: Lowercase, remove punctuation, strip whitespace

Impact: Removes noisy samples that can dominate small datasets

5. Evaluation & Metrics

Added:

• ✅ WER (Word Error Rate) computation with jiwer
• ✅ Text normalization for consistent metrics
• ✅ Best model selection by WER (not just loss)
• ✅ load_best_model_at_end=True
• ✅ metric_for_best_model="wer"

Impact: Can now track actual transcription quality improvements

6. TensorBoard Logging

Added:

report_to=["tensorboard"]
logging_dir="./logs"
logging_steps=10
logging_first_step=True

Metrics Logged:

• Training/Evaluation Loss
• WER (Word Error Rate)
• Learning Rate schedule
• Gradient norms
• Training speed

Usage:

tensorboard --logdir=./logs
# Open http://localhost:6006

7. Additional Optimizations

• ✅ group_by_length=True - Reduces padding overhead
• ✅ generation_max_length=448 - Full Whisper context (was 128)
• ✅ Data filtering before preprocessing
• ✅ Better epoch/batch size scaling by dataset size

Expected Improvements

Before (v1.0)

• ❌ No evaluation running (API bug)
• ❌ No language conditioning
• ❌ LR too low (5e-6)
• ❌ No WER tracking
• ❌ No data filtering
• ❌ Dtype conflicts
• ❌ Model selection by loss only

Result: Training appeared to run but model didn't improve

After (v2.0)

• ✅ Evaluation runs every epoch
• ✅ German language/task conditioning
• ✅ Proper LR (1e-5 to 2e-5)
• ✅ WER metric tracking
• ✅ Quality data filtering
• ✅ Consistent precision
• ✅ Best model by WER

Expected Result: Visible WER improvements, better transcription quality

Hugging Face Compatibility

Current Status: ✅ Fully Compatible

Using:

• transformers.WhisperForConditionalGeneration
• transformers.WhisperProcessor
• transformers.Seq2SeqTrainer
• datasets.load_dataset / load_from_disk
• Standard HF checkpoint format

To Push to Hub:

# In TrainingArguments
push_to_hub=True
hub_model_id="your-username/whisper-small-german"
hub_token="your_hf_token"

# Or manually after training
model.push_to_hub("your-username/whisper-small-german")
processor.push_to_hub("your-username/whisper-small-german")

GitHub Readiness

Added Files

• ✅ requirements.txt - All dependencies with versions
• ✅ Updated README_WHISPER_PROJECT.md - Installation, usage, TensorBoard
• ✅ TRAINING_IMPROVEMENTS.md - This document

Reproducibility

• ✅ Pinned dependency versions
• ✅ Seed set to 42
• ✅ Clear installation instructions
• ✅ Dataset download script
• ✅ Training/inference scripts

Missing (Optional)

• .gitignore for checkpoints/logs
• LICENSE file
• GitHub Actions for CI/CD
• Model card template

Data Processing vs Whisper Paper

Whisper Paper Approach

• 30-second audio chunks
• 80-channel log-mel spectrogram
• 16kHz sampling rate
• Padding/truncation to 30s

Our Implementation: ✅ Matches Paper

# WhisperProcessor handles this automatically
input_features = processor(
    audio_array,           # Raw audio
    sampling_rate=16000,   # 16kHz ✅
    return_tensors="pt"
).input_features          # Returns 80x3000 mel spectrogram ✅

What happens:

1. Audio resampled to 16kHz ✅
2. Converted to 80-channel log-mel spectrogram ✅
3. Padded/truncated to 3000 frames (30s at 16kHz) ✅
4. Normalized ✅

For longer audio: Would need sliding window with stride (not needed for MINDS14)

Next Steps

Immediate

1. Install dependencies: pip install -r requirements.txt
2. Retrain model: python project1_whisper_train.py
3. Monitor with TensorBoard: tensorboard --logdir=./logs
4. Check WER improvements: Should see decreasing WER each epoch

Recommended

1. Use medium or large dataset (300-600 samples)
2. Monitor TensorBoard for convergence
3. Compare WER across epochs
4. Test on real-world German audio

Advanced

1. Try Whisper-medium for better quality
2. Add data augmentation (SpecAugment)
3. Push best model to Hugging Face Hub
4. Create demo/API endpoint

Summary

Root Causes of "No Learning":

1. Evaluation never ran (API typo)
2. No language conditioning for German
3. Learning rate too conservative
4. No quality metrics (WER)
5. Dtype conflicts

All Fixed: Training should now show measurable WER improvements and produce usable German ASR models.