Update README.md

README.md

@@ -1,3 +1,34 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- automatic-speech-recognition
+tags:
+- collator
+---
+#### Adaptive Context-Aware Noise Injection
+
+Our preprocessing pipeline includes adaptive context-aware noise injection to enhance model robustness. This method dynamically adjusts noise intensity based on the amplitude of the audio signal, ensuring realistic and effective augmentation.
+
+- **Types of Noise**: White, pink, and environmental noise.
+- **Dynamic Adjustment**: Noise intensity is scaled based on the amplitude of the audio signal.
+- **Integration**: The noise injection process is seamlessly integrated into our existing log-Mel spectrogram calculation pipeline, adding minimal overhead.
+
+##### Key Benefits
+
+- **Improved Generalization**: Models become more resilient to noise and diverse audio conditions.
+- **Low Overhead**: The augmentation process leverages the existing pipeline, ensuring efficient computation without significant additional cost.
+
+##### Example Usage
+
+```python
+data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+    processor=processor,
+    decoder_start_token_id=model.config.decoder_start_token_id,
+    apply_augmentation=True,
+    apply_noise_injection=True  # Enable adaptive noise injection
+)
+
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True, collate_fn=data_collator)
+
+for batch in dataloader:
+    outputs = model(batch)