Update README.md

README.md

@@ -5,6 +5,36 @@ task_categories:
 tags:
 - collator
 ---
+
+## Dynamic Audio Data Augmentation
+
+
+Key Benefits
+Enhanced Robustness: By varying spectrogram parameters and injecting realistic noise, our models learn to handle a wide range of audio conditions.
+
+Low Overhead: The augmentation is integrated into the existing pipeline, ensuring minimal additional computational cost.
+
+On-the-Fly Spectrogram Parameter Adjustment:
+
+n_fft and hop_length: Values for n_fft and hop_length are randomly selected from predefined ranges for each audio sample, providing varied spectrogram representations.
+
+Log-Mel Modulation:
+
+
+### On-the-Fly Spectrogram Parameter Adjustment:
+
+n_fft and hop_length: Values for n_fft and hop_length are randomly selected from predefined ranges for each audio sample, providing varied spectrogram representations.
+
+  ### Log-Mel Modulation:
+
+  Augmentation process integrates with the existing log-Mel spectrogram calculation. This means we modulate the parameters of the log-Mel spectrogram dynamically, ensuring no additional overhead is introduced while providing effective data augmentation.
+
+  ### Efficiency and Performance
+  Log-Mel Spectrogram Manipulation:
+
+  Augmentation process seamlessly integrates into the existing log-Mel spectrogram calculation, adding no extra overhead. This efficient design ensures that our preprocessing remains computationally lightweight and fast.
+
+
 #### Adaptive Context-Aware Noise Injection
 
 Preprocessing pipeline that 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.
@@ -22,19 +52,11 @@ Preprocessing pipeline that includes adaptive context-aware noise injection to e
 
 ```python
 
-## HF transformers
+## HF transformers or pure pytorch
 
 data_collator = DataCollatorSpeechSeq2SeqWithPadding(
     processor=processor,
     decoder_start_token_id=model.config.decoder_start_token_id,
-    apply_augmentation=True,  # Enable augmentation
-    apply_noise_injection=True  # Enable adaptive noise injection
-)
-
-
-### pytorch
-
-data_collator = DataCollatorSpeechSeq2SeqWithPadding(
     apply_augmentation=True,
     apply_noise_injection=True  # Enable adaptive noise injection
 )
@@ -42,4 +64,4 @@ data_collator = DataCollatorSpeechSeq2SeqWithPadding(
 dataloader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True, collate_fn=data_collator)
 
 for batch in dataloader:
-    outputs = model(batch)
+    outputs = model(batch)