Garchen Rinpoche Training Data Requirements Estimation

[Ganga_Gyatso]

Tibetan ASR Training Data Estimation: Beyond Linear Projections

Current Experimental Results

Our current experiments with fine-tuning the MMS Wav2Vec2 model on Tibetan teachings have shown promising results:

Model Stage	Training Data (hours)	CER (%)
Base Model	0	27.67
Fine-tuned	5.6	22.93

Understanding Model Improvement Patterns

Initial Linear Projection

Based on our limited data points, a simple linear projection suggests:

CER = -0.8482 × Hours + 27.67

This would indicate:

• Improvement rate: ~0.85% CER reduction per hour of training data

• To reach 5% CER: Approximately 26.7 hours of high-quality training data needed

• Additional data required: ~21.1 hours beyond our current dataset

Important Note: While this linear projection provides a rough estimation based on our current data points, speech recognition model improvement typically follows a non-linear pattern.

Realistic Model Improvement Trajectory

In practice, ASR model performance improvement typically follows a logarithmic or exponential decay curve rather than a linear relationship.For Example Here’s why:

1. Initial Rapid Gains: The first batch of domain-specific training data often yields the most significant improvements as the model adapts to the target language characteristics, domain vocabulary, and acoustic properties.

2. Diminishing Returns: As more training data is added, the rate of improvement tends to decrease. Each additional hour of training data generally yields smaller CER reductions.

3. Performance Floor: Models often approach a performance floor where additional data alone doesn’t significantly improve results without architectural changes or improved data quality.

A more realistic projection might follow this pattern:

Training Data (hours)	Projected CER (%)	CER Decrease (%)	Notes
0 (Base)	27.67	-	Starting point
5.6	22.93	4.74	Current progress
10	17-19	~5	Significant gains from doubling data
15	14-16	~3	Continued improvement at slower rate
20	12-14	~2	Diminishing returns begin to appear
30	10-12	~2	Approaching initial performance floor
50+	6-8	~4	Additional techniques may be needed

Iterative Training Approach

Rather than relying solely on data quantity, we recommend an iterative approach:

Phase 1: Current Status (Complete)

• Training Data: 5.6 hours

• Current CER: 22.93%

Phase 2: Expanded Dataset (Next Milestone)

• Training Data: ~10 hours

• Expected CER: 17-19%

• Evaluation: Analyze error patterns to guide next iteration

Phase 3: Targeted Data Collection

• Training Data: ~15-20 hours

• Expected CER: 12-16%

• Focus: Address specific error patterns identified in Phase 2

• Techniques: Begin implementing data augmentation strategies

Phase 4: Advanced Optimization

• Training Data: ~30-50 hours

• Target CER: < 6-12%

• Techniques:

• Hyperparameter optimization

• Model architecture adjustments

• Advanced data augmentation

Factors Affecting CER Improvement

The relationship between training data volume and CER is influenced by:

1. Data Quality: Well-transcribed, clearly spoken audio improves results more than quantity alone

2. Domain Relevance: Training data closely matching the target domain yields better results

3. Model Architecture: Different model architectures respond differently to data volume

4. Augmentation Techniques: Effective data augmentation can simulate larger datasets

Visualizing CER Projections for Garchen Rinpoche Audio Fine-tuning

Initial Linear Projection

Based on our two current data points (base model and 5.6 hours fine-tuned), we can create an initial linear projection:

Figure 1: Linear projection based on initial data points, suggesting approximately 27 hours needed for 5% CER.

Equation: CER = -0.8482 × Hours + 27.67

Expected Non-Linear Performance Curve

Figure 2: Expected non-linear performance curve showing diminishing returns as training data increases.

Recommended Approach

1. Milestone-Based Evaluation: Re-evaluate and plot the actual CER curve at 10, 15, 20, and 25 hour milestones

2. Error Analysis: At each milestone, analyze error patterns to guide targeted data collection

3. Technique Expansion: Introduce additional training techniques beyond raw data volume increases

4. Regular Stakeholder Updates: Update projections based on actual measurements rather than theoretical projections

Updating Projections with Iterative Data Points

Figure 3: As more data points are collected through iterative training, the projection curve will be refined to more accurately predict training requirements.

To create and update this visualization:

1. Start with the initial two data points and linear projection

2. After each milestone (10 hours, 15 hours, etc.), add the new data point to the graph

3. Refit the curve based on all available data points

4. Update the projection for reaching 5% CER based on the new curve

By following this iterative, data-driven approach, we can more accurately track our progress toward the 5% CER target and make informed decisions about resource allocation for fine-tuning the model specifically for Garchen Rinpoche’s teachings.

[Ganga_Gyatso]

High-Quality Data Scenario: Breaking the Expected Pattern

While the standard projection follows an exponential decay with diminishing returns, there are scenarios where introducing exceptionally high-quality data can break this pattern and lead to greater-than-expected CER decreases.

Why High-Quality Data Can Accelerate CER Reduction

There are several reasons why high-quality training data might lead to unexpectedly large improvements:

1. Balanced Phonetic Coverage: Training data with comprehensive coverage of all phonetic units in Tibetan, especially rare ones, can dramatically reduce errors for previously challenging phonemes.

2. Cleaner Audio Characteristics: Audio with higher recording quality, consistent volume levels, minimal background noise, and clear articulation allows the model to learn more accurate acoustic representations.

3. Consistent Speaking Style: If new data closely matches Garchen Rinpoche’s specific speaking style, rhythm, and intonation patterns, it can lead to outsized improvements for this specific speaker.

4. Better Transcription Accuracy: Higher-quality transcriptions with standardized spelling, proper punctuation, and fewer human errors provide clearer learning signals to the model.

5. Representative Content Diversity: Data that covers the specific vocabulary, phrases, and domain-specific terminology common in Garchen Rinpoche’s teachings can fill critical gaps in the model’s understanding.

Visualizing the High-Quality Data Impact

Figure 4: Comparison between standard data collection approach and introducing high-quality data after the 20-hour mark. Notice the steeper CER decrease and earlier achievement of the 5% target.

As shown in the visualization, introducing carefully curated high-quality data at the 20-hour mark could lead to:

• A ~5% CER decrease between 20-30 hours (compared to only ~2% with standard data)

• Another ~5% decrease between 30-50 hours

• Reaching the 5% CER target with significantly fewer total training hours

Implementation Strategy for High-Quality Data Collection

To maximize the potential of high-quality data:

1. Detailed Error Analysis: Perform comprehensive error analysis on validation sets to identify specific patterns of errors (e.g., certain phonemes, speaking contexts, or vocabulary).

2. Targeted Recording Sessions: Design recording sessions specifically to address identified weaknesses, ensuring optimal audio quality and clear articulation.

3. Expert Transcription Review: Engage linguistic experts familiar with Tibetan to verify transcription quality and ensure standardized representations.

4. Signal Processing Optimization: Apply specialized pre-processing to clean existing audio and enhance acoustic clarity.

5. Contextual Sampling: Prioritize collecting samples from contexts similar to where the model will be deployed, focusing on typical speech patterns in Garchen Rinpoche’s teachings.