|
--- |
|
dataset_info: |
|
features: |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: transcription |
|
dtype: string |
|
- name: duration |
|
dtype: float32 |
|
- name: up_votes |
|
dtype: int32 |
|
- name: down_votes |
|
dtype: int32 |
|
- name: age |
|
dtype: string |
|
- name: gender |
|
dtype: string |
|
- name: accent |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 249774324 |
|
num_examples: 26501 |
|
- name: test |
|
num_bytes: 90296575 |
|
num_examples: 9650 |
|
- name: validation |
|
num_bytes: 78834938 |
|
num_examples: 8639 |
|
- name: validated |
|
num_bytes: 412113612 |
|
num_examples: 46345 |
|
download_size: 818561949 |
|
dataset_size: 831019449 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: data/train-* |
|
- split: test |
|
path: data/test-* |
|
- split: validation |
|
path: data/validation-* |
|
- split: validated |
|
path: data/validated-* |
|
license: cc0-1.0 |
|
task_categories: |
|
- automatic-speech-recognition |
|
language: |
|
- tr |
|
--- |
|
|
|
# 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 |
|
|
|
### Audio Resampling |
|
All audio files were resampled to 16 kHz to: |
|
- Make the dataset directly compatible with Whisper and similar models |
|
- Eliminate the need for runtime resampling during training |
|
- Ensure consistent audio quality across the dataset |
|
|
|
### 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'. |
|
|
|
## 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 |
|
|
|
### Text Quality Improvements |
|
- Generated word frequency metrics to identify potential issues |
|
- Corrected common Turkish typos and grammar errors |
|
- Standardized punctuation and spacing |
|
|
|
## Results |
|
|
|
The final dataset shows significant improvements: |
|
- 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. |
|
|
|
## Tools Used |
|
|
|
This dataset processing work was completed using [ASRTK (Automatic Speech Recognition Toolkit)](https://github.com/ysdede/asrtk), an open-source Python toolkit designed to streamline the development and enhancement of ASR systems. ASRTK provides utilities for: |
|
|
|
- Audio processing with advanced splitting and resampling capabilities |
|
- Text normalization and cleaning |
|
- Forced alignment using Silero VAD models |
|
- Efficient batch processing with multi-threading support |
|
|
|
The toolkit is available under the MIT license and welcomes contributions from the community. |
|
|
