Update README.md

README.md

@@ -86,6 +86,12 @@ 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
@@ -201,3 +207,14 @@ The final dataset shows significant improvements:
 - 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.