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… compatibility issue with newer NumPy versions
…ent accumulation, and optimized DataLoader
…on-blocking transfers, and inference mode
…ding, and vectorized operations
…rkers, and mixed precision enabled
…n monitoring and comparison
…with different modes
…ter compatibility
… (max_seg_per_spk, seed)
- Add defaults for max_frames, max_seg_per_spk, seed, nPerSpeaker, distributed - Fix syntax error in sampler creation - Benchmark comparison now works: 2.46x speedup achieved
- Import from SpeakerNet_performance_updated and DatasetLoader_performance_updated - Fix find_option_type to handle store_true/store_false actions properly - Training now starts successfully with all optimizations enabled
- OPTIMIZATION_COMPLETE.txt: Summary of all optimizations - OPTIMIZATION_GUIDE.md: Detailed optimization guide - analyze_performance.py: Performance analysis tool - debug_repo.py: Repository debugging tool - quick_optimize.py: Quick optimization script
- Detailed performance benchmarks (2.46x speedup) - Complete file structure overview - Quick start guide with multiple options - Dataset information and validation statistics - Configuration documentation - Commit history highlights - Acknowledgments and contact information
- Convert EER and MinDCF to float to avoid numpy array formatting issues - Handle threshold value which can be array, tuple, or scalar - Fixes TypeError: unsupported format string passed to numpy.ndarray.__format__
- Updated ResNetSE34L.py, ResNetSE34V2.py, RawNet3.py, VGGVox.py
- Changed torch.cuda.amp.autocast() to torch.amp.autocast('cuda')
- Fixes FutureWarning for PyTorch 2.x compatibility
- Also improved test_validation_phase.py model loading
- New parameter: --max_test_pairs (0 = use all pairs) - Can be set in config file or command line - Default: 10,000 pairs (~75 seconds validation) - Full dataset: 553,550 pairs (~70 minutes validation) - Added quick_test_validation.py for fast GPU/pipeline testing - Added test_max_pairs_param.py to verify parameter works Examples: --max_test_pairs 1000 (~10 seconds - quick test) --max_test_pairs 10000 (~75 seconds - default) --max_test_pairs 0 (~70 minutes - full validation)
- Add eval_batch_size parameter (default: 64, quick test: 128) - Batch process embeddings instead of one-by-one (32x speedup potential) - Improved GPU memory utilization (0.03 GB -> 0.16 GB cached) - Speed: ~145 pairs/second (was ~130 pairs/s) - Estimated 40K pairs: ~4.5 minutes (was ~5 minutes) - Full 553K pairs: ~64 minutes (was ~70 minutes) Performance improvements: - Larger batches = better GPU utilization - Configurable via --eval_batch_size parameter - Safe defaults: 32 (training), 64 (config), 128 (quick test)
- Use threshold_val (float) instead of current_threshold (numpy array) - Fixes TypeError: unsupported format string passed to numpy.ndarray - Occurs when saving best model after achieving new best EER
- Create mini-VoxCeleb2: 140 speakers, 30,179 files (~7.1 GB) * Script: create_mini_voxceleb2.py * Config: configs/mini_voxceleb2_config.yaml * Training list: mini_voxceleb2_train_list.txt (30,179 entries) * Documentation: MINI_VOXCELEB2_README.md * Uses symbolic links to save disk space * ~4x faster training than full dataset - Create mini-VoxCeleb1: 50 speakers, 6,286 files (~1.6 GB) * Script: create_mini_voxceleb1.py with BALANCED test pairs * Config: configs/mini_voxceleb1_config.yaml * Training list: mini_voxceleb1_train_list.txt (6,286 entries) * Test list: mini_test_list.txt (930 BALANCED pairs, 50/50 split) * Documentation: MINI_VOXCELEB1_README.md * Fixed imbalance issue (was 96.3% positive, now 50/50) - Add comprehensive TRAINING_GUIDE.md * Quick start commands (foreground, tmux, script) * Monitoring and troubleshooting * Configuration explanations * Performance tips and expected training times * Common issues and solutions - Update experiment_01_performance_updated.yaml * Adjusted test_interval and max_test_pairs settings Benefits: - Fast experimentation and development - Reduced training time for testing - Balanced evaluation metrics - Complete documentation for new users
- Fix TypeError: unsupported format string for numpy.ndarray in threshold saving * Changed print statement to use threshold_val (float) instead of current_threshold (array) * Ensures consistent float formatting across all threshold operations - Fix ROC curve plotting errors with type conversions * Explicitly convert fprs/fnrs lists to numpy arrays with float64 dtype * Use float literal (1.0) instead of int (1) for array arithmetic * Calculate EER point index once and reuse for both FPR and TPR * Resolves 'unsupported operand type(s) for -: int and list' error - Update mini_voxceleb1_config.yaml for variable-length evaluation * Set eval_frames=0 to use full audio length (no truncation) * Set eval_batch_size=1 for variable-length processing * Update to 140 speakers for mini VoxCeleb2 training dataset * Increase n_mels from 64 to 80 for better feature extraction * Reduce nOut from 512 to 256 for faster experimentation
- Add MINDCF_IMPROVEMENT_GUIDE.md: Complete guide for improving MinDCF * 6 improvement strategies with expected gains * Model architecture recommendations (ResNetSE34L/V2, RawNet3) * Loss function analysis and comparisons * Phase-by-phase implementation roadmap * Expected: 40-60% total MinDCF reduction - Add ZEROSHOT_VS_FEWSHOT_ANALYSIS.md: Learning paradigm analysis * Confirmed current setup is zero-shot (disjoint train/test speakers) * Complete zero-shot vs few-shot comparison * Impact analysis for switching approaches * When to use each learning paradigm * Performance expectations for both approaches - Add optimized configs: * mini_voxceleb1_optimized_phase1.yaml: Quick wins config (15-30% improvement) * mini_voxceleb1_fewshot_ge2e.yaml: GE2E few-shot config * mini_voxceleb1_fewshot_proto.yaml: Prototypical few-shot config - Update research log 2025-10-30.md with detailed analysis notes * MinDCF improvement strategies summary * Zero-shot vs few-shot findings * Key insights and recommendations * Documentation files overview
- Implement 4-level nested learning architecture for speaker verification - Features: multi-path aggregation where each level receives ALL previous levels - Components: DepthwiseSeparableConv, SE blocks, learnable weights, GroupNorm - 1.62M parameters, supports SAP/ASP encoders - Includes stability fixes: adaptive pooling, dropout, gradient-friendly design
- nested_4level.yaml: Main config with stability-optimized hyperparameters - nested_4level_asp.yaml: Variant with ASP encoder instead of SAP - nested_5level_asp.yaml: Extended 5-level architecture config - Tuned for stability: lr=0.001, decay=0.98, weight_decay=5e-5, batch_size=48
- Tests 5 different nested configurations (3/4/5 levels, SAP/ASP encoders) - Validates forward pass, output shapes, and parameter counts - Compares inference speed with baseline ResNetSE34L - All tests passing: ensures architecture implementation correctness
- visualize_nested_architecture.py: Generates architecture diagrams - Shows 4 levels with nested connections (red dashed arrows) - Includes spatial dimensions and parameter counts - Comparison with ResNetSE34L baseline architecture - Output: PNG (300 DPI) and PDF (vector) formats for publication
- Comprehensive root cause analysis of NaN loss issues - Documents 7 different stabilization strategies attempted - Includes validation checklist and rollback procedures - Technical reference for gradient explosion in nested architectures - Useful for research documentation and future debugging
- Documents complete implementation and evaluation of nested learning - Three training attempts with progressive stability improvements - Best result: 18.71% EER (before NaN collapse at epoch 12) - Comprehensive scientific justification and domain analysis: * Mathematical gradient flow analysis (25-80× larger than vision) * Feature space topology differences (negative correlation in audio) * Information theory perspective (47% entropy increase) * Optimization landscape analysis (condition number 160,000) * Theoretical framework for domain compatibility (audio: 0/5 criteria) - Conclusions: Nested learning unsuitable for speaker verification - Recommendation: Abandon approach, pursue LSTM + Autoencoder instead - Publication-ready with empirical results and theoretical justification
- ASP (Attentive Statistics Pooling) instead of SAP - Expected 8-10% improvement: 14.2% EER target vs 15.48% baseline - ASP captures both mean AND variance (first & second-order statistics) - Benefits: More discriminative embeddings, better variance modeling - Hyperparameters tuned for ASP: batch_size=48, lr_decay=0.97, patience=20 - Reference: Okabe et al., Interspeech 2018
Architecture: - Denoising autoencoder: n_mels (80) → 128 latent dimensions * Learns robust spectral representations * Can be pre-trained unsupervised for noise removal - Bidirectional LSTM: 2 layers, 256 hidden units per direction * Captures temporal dependencies and speaking patterns * Models prosody and rhythm information - Attentive statistics pooling (ASP) * Aggregates LSTM outputs over time * Computes mean and standard deviation Key Features: - 3.87M parameters (2.6× larger than ResNetSE34L) - Temporal modeling for better speaker discrimination - Noise robustness through autoencoder denoising - Expected 20-35% improvement over baseline Training Configuration: - Batch size: 32 (with gradient accumulation = effective 64) - Learning rate: 0.0005 (lower for LSTM stability) - LR decay: 0.98 (gentler) - Patience: 25 epochs - Expected target: 10-12% EER (vs 13.98% ASP baseline) Based on deep learning approaches for temporal sequence modeling in speaker verification tasks.
Adapted from: 'A Speaker Verification System Based on a Modified MLP-Mixer Student Model for Transformer Compression' Key Features: - MLP-Mixer architecture adapted for mel-spectrogram input - Knowledge distillation from LSTM+Autoencoder teacher (9.68% EER) - Paper's innovations: ID Conv, MFM activation, grouped projections - 2.66M parameters (31% fewer than LSTM+AE's 3.87M) - 2.04× faster inference than LSTM+AE (parallel processing) Implementation: - models/MLPMixerSpeaker.py: Main model (373 lines) * MLPMixerBlock with ID Convolution + MFM * TokenMixingMLP, ChannelMixingMLP (grouped projections) * AttentiveStatsPooling (ASP aggregation) - DistillationWrapper.py: Knowledge distillation framework (267 lines) * DistillationSpeakerNet: Combined student+teacher training * TeacherModelWrapper: Frozen teacher with checkpoint loading * DistillationLoss: (1-α)×classification + α×MSE distillation - configs/mlp_mixer_distillation_config.yaml: Training configuration * hidden_dim=192, num_blocks=6, expansion_factor=3 * Teacher: exps/lstm_autoencoder/model/model000000057.model * Distillation: alpha=0.5, temperature=4.0 - test_mlp_mixer.py: Validation suite * Tests: instantiation, forward pass, speed benchmark * Confirmed: 2.04× speedup vs LSTM+AE - research_logs/2025-12-30-mlp-mixer-implementation.md: Documentation * Architecture details, hyperparameters, training instructions * Comparison with paper, performance targets, next steps Performance Targets: - EER: 10-11% (distillation gap from 9.68% teacher) - Speed: 2-3× faster (confirmed 2.04× on CPU) - Size: 2.66M params (31% reduction) - Training: 40-50 epochs expected Architecture Highlights: 1. ID Convolution: Captures local temporal dependencies 2. Max-Feature-Map: Speaker-discriminative feature selection 3. Grouped Projections: 4× parameter efficiency 4. ASP Pooling: Mean + std statistics Compatibility: Zero impact on existing models - Modular design (separate .py file) - Config-driven selection (model: MLPMixerSpeaker) - Can still run ResNetSE34L, LSTM+AE, NestedSpeakerNet Next Steps: - Modify trainSpeakerNet_performance_updated.py for distillation - Train with distillation (batch_size=64, lr=0.001) - Ablation: alpha variations (0.3, 0.5, 0.7) - Evaluate on full VoxCeleb dataset
…ting models)
Created standalone distillation scripts to enable knowledge distillation
WITHOUT modifying existing training pipeline. All existing models remain
100% functional with original scripts.
NEW FILES (Distillation-Only):
- trainSpeakerNet_distillation.py: Copy of training script with distillation
- SpeakerNet_distillation.py: Auto-detects teacher_model in config
- train_mlp_mixer.sh: Convenience script for MLP-Mixer training
UNCHANGED FILES (Backward Compatibility):
- trainSpeakerNet_performance_updated.py: Still works for all models
- SpeakerNet_performance_updated.py: Untouched, existing models safe
- All existing configs: Work unchanged
- All existing models: ResNetSE34L, LSTM+AE, NestedSpeakerNet
Auto-Detection Logic in SpeakerNet_distillation.py:
- IF teacher_model + teacher_checkpoint in config:
→ Use DistillationSpeakerNet (student learns from teacher)
→ Print: '🎓 DISTILLATION MODE ENABLED'
→ Returns: (loss, accuracy, distillation_loss)
- ELSE:
→ Use standard SpeakerNet (backward compatible)
→ Print: '📚 STANDARD CLASSIFICATION MODE'
→ Returns: (loss, accuracy)
Usage:
# Old models (unchanged)
python3 trainSpeakerNet_performance_updated.py \
--config configs/lstm_autoencoder_config.yaml
# New distillation
python3 trainSpeakerNet_distillation.py \
--config configs/mlp_mixer_distillation_config.yaml
Safety Guarantee:
All existing training commands work unchanged
All existing configs work unchanged
Can train any model anytime with original scripts
Distillation is opt-in via separate script
…ght) Experiment: V2_Large_LowAlpha - MLP-Mixer with reduced alpha for large student Summary: -------- Implements P2 variant testing hypothesis that large student models (capacity > teacher) require lower distillation weight (alpha) to achieve optimal performance. New Files: ---------- 1. configs/mlp_mixer_distillation_v2_large_lowAlpha.yaml - P2 variant configuration - Architecture: 8 blocks, 256 hidden, 4 expansion (7.84M params) - Distillation alpha: 0.4 (reduced from 0.7) - Rationale: Student (7.84M) > Teacher (3.87M) needs more hard labels - Dataset: Mini VoxCeleb2 (30K samples, 140 speakers) 2. check_corrupted_audio.py - Utility script to scan and identify corrupted audio files - Prevents LibsndfileError crashes during training - Supports .wav, .flac, .m4a, .aac formats - Generates corrupted_audio_files.txt exclusion list 3. research_logs/2025-12-30-31-experimental-results-analysis.md - Comprehensive 27-page experimental analysis - Documents V1, V2, V2_Large, V2_Large_LowAlpha experiments - Detailed ablation studies and performance comparisons - Theoretical insights and learned principles Key Findings: ------------- V2_Large_LowAlpha: 10.11% EER (alpha=0.4) - HYPOTHESIS VALIDATED V2_Large: 14.84% EER (alpha=0.7) - Capacity mismatch issue V2: 10.32% EER (alpha=0.7, 2.66M params) - Best efficiency V1: 16.13% EER (MSE loss) - Distillation broken Conclusions: ----------- 1. Alpha must be tuned based on student/teacher capacity ratio 2. Small student (<teacher): High alpha (0.7) optimal 3. Large student (>teacher): Low alpha (0.4) optimal 4. V2 remains best model: same EER as V2_LA with 2.7x fewer params Results: -------- V2_Large_LowAlpha (7.84M params, alpha=0.4): - Best VEER: 10.11% (Epoch 90) - Final VEER: 10.32% (Epoch 100) - vs V2_Large: -4.73% improvement (validated hypothesis) - vs V2: Same performance but 195% more parameters - Inference: 220 samples/sec (1.5x faster than teacher) Training Configuration: ----------------------- - Teacher: LSTM+Autoencoder (9.68% EER, 3.87M params) - Distillation: Cosine similarity loss (proven effective) - Alpha: 0.4 (60% classification, 40% distillation) - Optimizer: Adam (lr=0.001, decay=0.95) - Epochs: 100 - Dataset: Mini VoxCeleb2 (30,179 samples) Impact: ------- - Establishes alpha-tuning principle for knowledge distillation - Proves capacity scaling requires hyperparameter adjustment - Validates V2 as production model (best efficiency) - Opens path for P3 (multi-stage distillation) experiments See: research_logs/2025-12-30-31-experimental-results-analysis.md for complete experimental details, ablation studies, and future work. Signed-off-by: Anuraj <anuraj@example.com>
Implementation Details: - Added DistillationWrapper with teacher-student knowledge transfer - Integrated cosine similarity loss for embedding distillation - Updated training pipeline to support distillation workflow - Added comprehensive evaluation with distillation mode support Key Components: 1. DistillationWrapper.py (20 changes): - Cosine similarity loss for normalized embeddings (replaces MSE) - Loss magnitude: 0.2-0.4 (vs MSE: 0.0002) - Temperature scaling for soft targets (T=4.0) - Combined loss: α*L_distill + (1-α)*L_hard 2. SpeakerNet_distillation.py (19 changes): - Auto-detection of teacher model architecture - Distillation mode evaluation support - Fixed __L__ attribute access for wrapped models 3. trainSpeakerNet_distillation.py (42 changes): - Added distillation-specific argument parsing - Teacher checkpoint loading and freezing - Distillation hyperparameters (alpha, temperature) Critical Bug Fixes: - Fixed MSE loss magnitude issue (1000× too small) - Cosine loss provides proper gradient scale - Added try-except for distillation mode evaluation - Normalized embedding comparison Research Documentation: - Updated research_logs/2025-12-30-mlp-mixer-implementation.md - Added V1 training results (MSE failure, 16.13% EER) - Documented bug fixes and convergence analysis - Added performance comparison table Experimental Results (documented in commit 80a63e7): - V1 (MSE loss): 16.13% EER ❌ (distillation broken) - V2 (Cosine loss): 10.32% EER ✅ (5.81% improvement) - V2_Large_lowAlpha: 10.11% EER ✅ (validates α-tuning) Impact: - Cosine loss critical for embedding distillation (36% improvement) - Enables effective knowledge transfer from teacher to student - Foundation for all subsequent distillation experiments Files Modified: DistillationWrapper.py: 20 changes SpeakerNet_distillation.py: 19 changes trainSpeakerNet_distillation.py: 42 changes research_logs/2025-12-30-mlp-mixer-implementation.md: 358 additions Related Commits: - 80a63e7: P2 variant results and comprehensive analysis - See research_logs/2025-12-30-31-experimental-results-analysis.md
Implementation Details:
- Raw waveform input instead of mel-spectrogram preprocessing
- SincNet learnable bandpass filters (80 filters, replaces fixed mel-filterbanks)
- Additional CNN feature extraction layers
- Same MLP-Mixer encoder as V2 (6 blocks, hidden_dim=192)
- Zero impact on existing code (all new files)
Architecture Comparison:
V2 (Mel-based): Raw Audio → Mel-Spec (fixed) → CNN → MLP-Mixer → Embedding
P3 (Raw wave): Raw Audio → SincNet (learn) → CNN → MLP-Mixer → Embedding
Model Statistics:
- Parameters: 3.48M (+30.9% vs V2: 2.66M)
- Additional params from SincNet frontend + CNN layers
- Learnable filters: 80 bandpass filters with mel-scale initialization
- Filter specs: 251 samples kernel (~15ms), 160 samples stride (10ms)
Research Hypothesis:
Raw waveform input with learnable filters may capture speaker-discriminative
features automatically, potentially matching or outperforming fixed mel-spectrogram
preprocessing (V2: 10.32% EER baseline to beat).
Experimental Setup:
1. Phase 1 - Baseline (no distillation):
- Config: configs/mlp_mixer_rawwaveform_baseline.yaml
- Training: 50 epochs, mini dataset
- Expected EER: 12-14% (validates raw waveform processing)
- Script: train_mlp_mixer_rawwaveform_baseline.sh
2. Phase 2 - Distillation:
- Config: configs/mlp_mixer_rawwaveform_distillation.yaml
- Teacher: LSTM+Autoencoder (9.68% EER)
- Training: 100 epochs, mini dataset - Distillation:
- Expected EER: 10.5-11.5% (compare with V2: 10.32%)
- Script: train_mlp_mixer_rawwaveform_distillation.sh
Success Criteria:
- Baseline EER < 14%: Validates raw waveform approach
- Distillation EER ≤ 10.5%: Matches/beats mel-based V2 (replace mel preprocessing)
- Distillation EER 10.5-11.5%: Competitive (use case dependent)
- Distillation EER > 11.5%: Mel preprocessing superior (archive experiment)
Technical Implementation:
1. SincNet Frontend (models/MLPMixerSpeaker_RawWaveform.py):
- Learnable low cutoff frequencies (initialized 30 Hz - 7.6 kHz)
- Learnable bandwidths (initialized 23 Hz - 261 Hz)
- Mel-scale spacing initialization
- Hamming window for filter smoothing
2. Feature Extraction:
- Conv1d(80, 80, k=5) → LeakyReLU → MaxPool(3)
- Conv1d(80, 80, k=5) → LeakyReLU → MaxPool(3)
- Instance normalization of learned features
3. Testing (test_mlp_mixer_rawwaveform.py):
- All tests passed ✓
- Forward pass validated (multiple input lengths)
- Filter initialization verified
- Parameter count confirmed: 3.48M
Files Created:
models/MLPMixerSpeaker_RawWaveform.py (389 lines)
- SincConv_fast: Learnable bandpass filters
- MLPMixerSpeakerNet_RawWaveform: Main model
configs/mlp_mixer_rawwaveform_baseline.yaml (95 lines)
- Phase 1: Baseline training configuration
configs/mlp_mixer_rawwaveform_distillation.yaml (97 lines)
- Phase 2: Distillation training configuration
test_mlp_mixer_rawwaveform.py (106 lines)
- Validation suite (all tests passed)
train_mlp_mixer_rawwaveform_baseline.sh
- Phase 1 training script
train_mlp_mixer_rawwaveform_distillation.sh
- Phase 2 training script
README_RAW_WAVEFORM_EXPERIMENT.md (400+ lines)
- Comprehensive experiment documentation
- Architecture details
- Expected results and success metrics
- How-to guide
Zero Impact Guarantee:
- No modifications to existing files
- Separate model class (MLPMixerSpeaker_RawWaveform)
- Separate config files
- Uses existing training infrastructure
- Compatible with current distillation framework
References:
- SincNet: Ravanelli & Bengio, "Speaker Recognition from Raw Waveform
with SincNet", IEEE SLT 2018
- MLP-Mixer paper modifications (ID Conv, MFM, grouped projections)
- Previous experiments: V1 (16.13%), V2 (10.32%), V2_Large_lowAlpha (10.11%)
Next Steps:
1. Run baseline training: bash train_mlp_mixer_rawwaveform_baseline.sh
2. If successful (EER < 14%), run distillation training
3. Compare results with mel-based V2 (10.32% EER)
4. Document findings in research log
Status: ✓ Implementation complete, ready for training
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