Update README.md

README.md

@@ -27,27 +27,21 @@ size_categories:
 ## Dataset Structure
 **The dataset is provided in sharded Parquet format for optimized loading. The original folder structure ("Speakers/" and "Text/") and naming conventions are preserved as metadata features ("speaker_id", "section", "session", etc.) within the dataset.**
 
-The dataset is organized into two main directories:
-* **Speakers/**: Contains the `.wav` audio files, organized by Speaker ID, Corpus Section, and Session.
-* **Text/**: Contains the `.txt` transcripts, organized by Corpus Section and Session.
-
-## Naming Convention
-The files follow a strict naming convention that encodes the metadata:
-
-### Text Files
-Format: `Greek-<corpus>-<session>-<sentence>.txt`
-* `<corpus>`: The section of the corpus (e.g., `Harvard`, `B2`, `C1`, `C2`).
-* `<session>`: The session number (e.g., `01`, `29`).
-* `<sentence>`: The unique sentence ID within that session.
-
-### Audio Files
-Format: `Greek-<speaker>-<corpus>-<session>-<sentence>.wav`
-* `<speaker>`: The speaker identifier.
+## Data Fields
+
+When you load the dataset, the metadata originally encoded in the raw file names is automatically parsed into the following distinct columns for easier filtering and analysis:
+
+* **`speaker_id`** *(string)*: The speaker identifier.
     * `M`: Main Male Speaker
     * `F`: Main Female Speaker
     * `M1`...`MX`: Other Male Speakers
     * `F1`...`FX`: Other Female Speakers
-
+* **`section`** *(string)*: The section of the corpus the text originated from (e.g., `Harvard`, `B2`, `C1`, `C2`).
+* **`session`** *(int64)*: The recording session number.
+* **`sentence_id`** *(int64)*: The unique sentence identifier within that specific session.
+* **`text`** *(string)*: The Greek transcript of the audio.
+* **`audio`** *(audio)*: The loaded audio feature, which includes the raw audio array and the sampling rate.
+* **`file_name`** *(string)*: The original filename of the source `.wav` audio file (e.g., `Greek-F1-B2-05-20.wav`).
 ## Usage
 You can load this dataset directly in Python using the Hugging Face `datasets` library. 
 
@@ -66,6 +60,9 @@ print("Text:", sample["text"])
 # Print metadata
 print("Speaker:", sample["speaker_id"])
 print("Corpus Section:", sample["section"])
+print("Session:", sample["session"])
+print("Sentence ID:", sample["sentence_id"])
+print("Original File Name:", sample["file_name"])
 
 # The 'audio' column is automatically decoded into a dictionary with 'array' and 'sampling_rate'
 audio_data = sample["audio"]["array"]