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README.md

@@ -147,18 +147,33 @@ from a wide variety of voices.
 The `datasets` library allows you to load and pre-process your dataset in pure
 Python, at scale.
 
+First, ensure you have the necessary dependencies installed to handle audio data:
+
+```bash
+pip install datasets[audio]
+```
+
 **Loading ASR Data**
 
 To load ASR data, point to the `data/ASR` directory.
 
 ```python
-from datasets import load_dataset
+from datasets import load_dataset, Audio
 
 # Load Shona (sna) ASR dataset
 asr_data = load_dataset("google/WaxalNLP", "sna", data_dir="data/ASR")
 
 # Access splits
 train = asr_data['train']
+val = asr_data['validation']
+test = asr_data['test']
+
+# Example: Accessing audio bytes and other fields
+example = train[0]
+print(f"Transcription: {example['transcription']}")
+print(f"Sampling Rate: {example['audio']['sampling_rate']}")
+# 'array' contains the decoded audio bytes as a numpy array
+print(f"Audio Array Shape: {example['audio']['array'].shape}")
 ```
 
 **Loading TTS Data**
@@ -229,8 +244,16 @@ train = tts_data['train']
 
 ### Data Splits
 
-Each language configuration contains four data splits: `train`, `validation`,
-`test`, and `unlabeled`.
+For the **ASR Dataset**, the data with transcriptions is split as follows:
+*   **train**: 80% of labeled data.
+*   **validation**: 10% of labeled data.
+*   **test**: 10% of labeled data.
+
+The **unlabeled** split contains all samples that do not have a corresponding
+transcription.
+
+The **TTS Dataset** follows a similar structure, with data split into `train`,
+`validation`, and `test` sets.
 
 ## Dataset Curation