Update README.md

README.md

@@ -46,3 +46,193 @@ configs:
   - split: validated
     path: data/validated-*
 ---
+
+# Improving CommonVoice 17 Turkish Dataset
+
+I recently worked on enhancing the Mozilla CommonVoice 17 Turkish dataset to create a higher quality training set for speech recognition models.  
+Here's an overview of my process and findings.
+
+## Initial Analysis and Split Organization
+
+My first step was analyzing the dataset organization to understand its structure.  
+Through analysis of filename stems as unique keys, I revealed and documented an important aspect of CommonVoice's design that might not be immediately clear to all users:
+
+- The validated set (113,699 total files) completely contained all samples from:
+  - Train split (35,035 files)
+  - Test split (11,290 files)
+  - Validation split (11,247 files)
+- Additionally, the validated set had ~56K unique samples not present in any other split
+
+This design follows CommonVoice's documentation, where dev/test/train are carefully reviewed subsets of the validated data.  
+However, this structure needs to be clearly understood to avoid potential data leakage when working with the dataset.  
+For example, using the validated set for training while evaluating on the test split would be problematic since the test data is already included in the validated set.
+
+To create a clean dataset without overlaps, I:
+
+1. Identified all overlapping samples using filename stems as unique keys
+2. Removed samples that were already in train/test/validation splits from the validated set
+3. Created a clean, non-overlapping validated split with unique samples only
+
+This approach ensures that researchers can either:
+- Use the original train/test/dev splits as curated by CommonVoice, OR
+- Use my cleaned validated set with their own custom splits
+
+Both approaches are valid, but mixing them could lead to evaluation issues.
+
+## Audio Processing and Quality Improvements
+
+### Silence Trimming
+I processed all audio files to remove unnecessary silence and noise:
+- Used Silero VAD with a threshold of 0.6 to detect speech segments
+- Trimmed leading and trailing silences
+- Removed microphone noise and clicks at clip boundaries
+
+### Duration Filtering and Analysis
+
+I analyzed each split separately after trimming silences. Here are the detailed findings per split:
+
+| Split | Files Before | Files After | Short Files | Duration Before (hrs) | Duration After (hrs) | Duration Reduction % | Short Files Duration (hrs) | Files Reduction % |
+|---|--:|--:|--:|--:|--:|--:|--:|--:|
+| Train | 11,290 | 9,651 | 1,626 | 13.01 | 7.34 | 43.6% | 0.37 | 14.5% |
+| Validation | 11,247 | 8,640 | 2,609 | 11.17 | 6.27 | 43.9% | 0.60 | 23.2% |
+| Test | 35,035 | 26,501 | 8,633 | 35.49 | 19.84 | 44.1% | 2.00 | 24.4% |
+| Validated | 56,127 | 46,348 | 9,991 | 56.71 | 32.69 | 42.4% | 2.29 | 17.4% |
+| **Total** | **113,699** | **91,140** | **22,859** | **116.38** | **66.14** | **43.2%** | **5.26** | **19.8%** |
+
+Note: Files with duration shorter than 1.0 seconds were removed from the dataset.
+
+#### Validation Split Analysis (formerly Eval)
+- Original files: 11,247
+- Found 2,609 files shorter than 1.0s
+- Statistics for short files:
+  - Total duration: 26.26 minutes
+  - Average duration: 0.83 seconds
+  - Shortest file: 0.65 seconds
+  - Longest file: 0.97 seconds
+
+#### Train Split Analysis
+- Original files: 35,035
+- Found 8,633 files shorter than 1.0s
+- Statistics for short files:
+  - Total duration: 2.29 hours
+  - Average duration: 0.82 seconds
+  - Shortest file: 0.08 seconds
+  - Longest file: 0.97 seconds
+
+#### Test Split Analysis
+- Original files: 11,290
+- Found 1,626 files shorter than 1.0s
+- Statistics for short files:
+  - Total duration: 56.26 minutes
+  - Average duration: 0.85 seconds
+  - Shortest file: 0.65 seconds
+  - Longest file: 0.97 seconds
+
+#### Validated Split Analysis
+- Original files: 56,127
+- Found 9,991 files shorter than 1.0s
+- Statistics for short files:
+  - Total duration: 36.26 minutes
+  - Average duration: 0.83 seconds
+  - Shortest file: 0.65 seconds
+  - Longest file: 0.97 seconds
+
+All short clips were removed from the dataset to ensure consistent quality. The final dataset maintains only clips longer than 1.0 seconds, with average durations between 2.54-2.69 seconds across splits.
+
+### Final Split Statistics
+The cleaned dataset was organized into:
+- Train: 26,501 files (19.84 hours, avg duration: 2.69s, min: 1.04s, max: 9.58s)
+- Test: 9,650 files (7.33 hours, avg duration: 2.74s, min: 1.08s, max: 9.29s)
+- Validation: 8,639 files (6.27 hours, avg duration: 2.61s, min: 1.04s, max: 9.18s)
+- Validated: 46,345 files (32.69 hours, avg duration: 2.54s, min: 1.04s, max: 9.07s)
+
+### Final Dataset Split Metrics
+
+| Split       | Files  | Duration (hours) | Avg Duration (s) | Min Duration (s) | Max Duration (s) |
+|-------------|--------|------------------|------------------|------------------|------------------|
+| TRAIN       | 26501  | 19.84           | 2.69             | 1.04             | 9.58             |
+| TEST        | 9650   | 7.33            | 2.74             | 1.08             | 9.29             |
+| VALIDATION  | 8639   | 6.27            | 2.61             | 1.04             | 9.18             |
+| VALIDATED   | 46345  | 32.69           | 2.54             | 1.04             | 9.07             |
+
+Total files processed: 91,135
+Valid entries created: 91,135
+Files skipped: 0
+Total dataset duration: 66.13 hours
+Average duration across all splits: 2.61 seconds
+
+The dataset was processed in the following order:
+1. Train split (26,501 files)
+2. Test split (9,650 files)
+3. Validation split (8,639 files) - Note: Also known as "eval" split in some CommonVoice versions
+4. Validated split (46,348 files)
+
+Note: The validation split (sometimes referred to as "eval" split in CommonVoice documentation) serves the same purpose - it's a held-out set for model validation during training.  
+We've standardized the naming to "validation" throughout this documentation for consistency with common machine learning terminology.
+
+One text file in the validated split was flagged for being too short (2 characters), but was still included in the final dataset.
+
+The processed dataset was saved as 'commonvoice_17_tr_fixed' with corresponding split metrics in JSON format.
+
+### Detailed Split Metrics (JSON)
+
+```json
+{
+  "train": {
+    "file_count": 26501,
+    "total_duration_hours": 19.84,
+    "min_duration_seconds": 1.04,
+    "max_duration_seconds": 9.58,
+    "avg_duration_seconds": 2.69
+  },
+  "test": {
+    "file_count": 9650,
+    "total_duration_hours": 7.33,
+    "min_duration_seconds": 1.08,
+    "max_duration_seconds": 9.29,
+    "avg_duration_seconds": 2.74
+  },
+  "validation": {
+    "file_count": 8639,
+    "total_duration_hours": 6.27,
+    "min_duration_seconds": 1.04,
+    "max_duration_seconds": 9.18,
+    "avg_duration_seconds": 2.61
+  },
+  "validated": {
+    "file_count": 46345,
+    "total_duration_hours": 32.69,
+    "min_duration_seconds": 1.04,
+    "max_duration_seconds": 9.07,
+    "avg_duration_seconds": 2.54
+  }
+}
+```
+
+This JSON format makes it easy to use these metrics programmatically in other tools and analyses.
+
+## Text Processing and Standardization
+
+### Character Set Optimization
+- Created a comprehensive charset from all text labels
+- Simplified the character set by:
+  - Standardizing quotation marks
+  - Removing infrequently used special characters
+  - Normalizing Turkish-specific characters
+
+### Text Quality Improvements
+- Generated word frequency metrics to identify potential issues
+- Corrected common Turkish typos and grammar errors
+- Standardized punctuation and spacing
+- Fixed inconsistent letter casing
+
+## Results
+
+The final dataset shows significant improvements:
+- Clean, non-overlapping splits preventing data leakage
+- Removed unnecessary silence and noise from audio
+- Consistent audio durations above 1.0 seconds
+- Standardized text with corrected Turkish grammar and typography
+- Maintained original metadata (age, upvotes, etc.)
+
+These improvements make the dataset more suitable for training speech recognition models while maintaining the diversity and richness of the original CommonVoice collection.