## Acoustic Loss on Codebook-0 ### Overview The graph shows the acoustic loss over training steps for different configurations of a model on the Codebook-0 dataset. The loss is measured in decibels (dB) and is plotted against the number of training steps. ### Components/Axes - **Y-axis**: Acoustic Loss in dB - **X-axis**: Training Step - **Legend**: Different configurations of the model with their respective settings (e.g., Pre-Norm, Gradient Clip, Run 1, etc.) ### Detailed Analysis or ### Content Details - **Pre-Norm**: The loss decreases sharply at the beginning of training and then stabilizes. - **Gradient Clip=10**: Similar to Pre-Norm, but with a slightly higher initial loss. - **Post-Norm**: Shows a more gradual decrease in loss. - **DeepNorm**: The loss decreases more steeply than Post-Norm. - **Attn. Relax**: The loss decreases the most, indicating the best performance among the configurations. ### Key Observations - **Pre-Norm and Gradient Clip=10**: Show the highest loss, indicating the worst performance. - **Post-Norm and DeepNorm**: Show moderate loss, indicating good performance. - **Attn. Relax**: Shows the lowest loss, indicating the best performance. ### Interpretation The data suggests that the model's performance is significantly affected by the normalization technique used. The "Attn. Relax" configuration, which likely includes attention mechanisms and relaxation techniques, results in the lowest loss, indicating the best performance. The "Pre-Norm" and "Gradient Clip=10" configurations show the highest loss, indicating the worst performance. The "Post-Norm" and "DeepNorm" configurations show moderate performance. The graph provides insights into the effectiveness of different normalization techniques in improving the model's performance on the Codebook-0 dataset.

## Line Chart: Acoustic MLM Loss on Codebook-0 vs Training Steps ### Overview The chart visualizes the convergence behavior of four different training configurations for an Acoustic MLM model. All lines show rapid initial loss reduction followed by stabilization, with notable differences in convergence speed and stability. ### Components/Axes - **X-axis**: Training Step (0K to 100K, logarithmic scale) - **Y-axis**: Acoustic MLM Loss on Codebook-0 (5 to 10, linear scale) - **Legend**: Located in top-right corner with four entries: 1. Blue circles: Pre-Norm | Gradient Clip=10 | Run 1 2. Orange triangles: Pre-Norm | Gradient Clip=1 | Run 2 3. Green triangles: Post-Norm | DeepNorm | Gradient Clip=1 4. Pink triangles: Pre-Norm | Attn. Relax | Gradient Clip=1 ### Detailed Analysis 1. **Blue Line (Gradient Clip=10)**: - Starts at ~9.5 loss at 0K - Drops sharply to ~5.8 by 10K - Stabilizes with minor fluctuations (~5.7-5.8) after 20K - Fastest convergence among all configurations 2. **Green Line (Post-Norm | DeepNorm)**: - Starts at ~9.5 loss at 0K - Sharp decline to ~5.9 by 15K - Abrupt spike to ~9.2 at 20K (anomaly) - Resumes decline to ~5.7 by 30K - Stabilizes with minor fluctuations (~5.6-5.7) after 40K 3. **Pink Line (Attn. Relax)**: - Starts at ~9.4 loss at 0K - Gradual decline to ~5.8 by 30K - Stabilizes with minor fluctuations (~5.7-5.8) after 40K 4. **Orange Line (Gradient Clip=1)**: - Starts at ~9.3 loss at 0K - Slowest decline, reaching ~5.9 by 50K - Stabilizes with minor fluctuations (~5.8-5.9) after 60K ### Key Observations - All configurations show similar initial loss values (~9.3-9.5) - Gradient Clip=10 (blue) achieves lowest final loss (~5.7) - Post-Norm (green) has fastest initial convergence but includes an anomalous spike - Gradient Clip=1 (orange) shows slowest convergence - Attn. Relax (pink) performs between Gradient Clip=10 and Gradient Clip=1 ### Interpretation The data suggests that higher gradient clipping (10) enables faster and more stable convergence compared to lower clipping (1). The Post-Norm configuration with DeepNorm demonstrates rapid initial improvement but includes an unexplained anomaly at 20K training steps, potentially indicating instability in that specific configuration. The Attn. Relax method shows intermediate performance, suggesting attention relaxation mechanisms provide moderate benefits without the instability seen in Post-Norm. The consistent stabilization patterns across all lines after ~30K-40K steps indicate that most configurations reach their asymptotic loss values within this range.