Update README.md

README.md

@@ -41,31 +41,24 @@ size_categories:
 
 | Property | Value |
 |----------|-------|
-| Format | WAV/MP3/MP4/M4A |
-| Sample Rate | 16 kHz |
+| Sampling Rate | 16 kHz |
 | Channels | Mono |
 
 ---
 
-## Dataset Structure
+## Dataset Format
 
-```
-KenSpeech/
-├── README.md
-├── metadata.csv          # Main dataset with audio paths and transcripts
-├── transcripts_only.csv  # Additional transcripts without audio
-├── lexicon.csv           # Pronunciation dictionary (31K+ words)
-└── audio/                # Audio files
-    └── *.wav, *.mp3, *.mp4, *.m4a
-```
+The dataset is distributed as **Parquet files** with embedded audio for optimal compatibility:
 
-## Metadata Schema
+- **Format**: Apache Parquet (with embedded audio bytes)
+- **Encoding**: UTF-8 for text fields
+- **Compatibility**: Works with `datasets` 4.0.0+ without custom loading scripts
 
-The `metadata.csv` file contains:
+## Data Fields
 
 | Column | Type | Description |
 |--------|------|-------------|
-| file_name | string | Path to audio file (e.g., `audio/female_speaker_1_sample_261.mp4`) |
+| audio | Audio | Audio waveform (decoded array + sampling_rate) |
 | source_folder | string | Origin folder (`stt_dictionary` or `stt_transcripts`) |
 | gender | string | Speaker gender (`male` or `female`) |
 | speaker | string | Speaker identifier (`speaker_1`, `speaker_2`, etc.) |
@@ -75,7 +68,7 @@ The `metadata.csv` file contains:
 
 ```python
 {
-    'file_name': 'audio/female_speaker_1_sample_261.mp4',
+    'audio': {'path': '...', 'array': array([0.001, -0.003, ...]), 'sampling_rate': 16000},
     'source_folder': 'stt_dictionary',
     'gender': 'female',
     'speaker': 'speaker_1',
@@ -92,18 +85,16 @@ The `metadata.csv` file contains:
 ```python
 from datasets import load_dataset
 
-# Load the dataset (auto-detected as AudioFolder via metadata.csv)
+# Load the dataset
 dataset = load_dataset("Kencorpus/KenSpeech")
 
-# Access samples
+# Access a sample
 sample = dataset['train'][0]
-print(sample)
-# {'file_name': 'audio/female_speaker_1_sample_261.mp4', 
-#  'audio': {'path': '...', 'array': array([...]), 'sampling_rate': 16000},
-#  'source_folder': 'stt_dictionary',
-#  'gender': 'female', 
-#  'speaker': 'speaker_1', 
-#  'transcript': 'masaa mawili kabla basi kuwasili...'}
+print(sample['transcript'])
+print(sample['gender'])
+print(sample['speaker'])
+print(sample['audio']['sampling_rate'])  # 16000
+print(sample['audio']['array'].shape)    # audio waveform
 ```
 
 ### Filtering by Gender
@@ -122,36 +113,45 @@ male_data = dataset['train'].filter(lambda x: x['gender'] == 'male')
 print(f"Male samples: {len(male_data)}")
 ```
 
-### Iterating Over Samples
+### Training an ASR Model
 
 ```python
 from datasets import load_dataset
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 
+# Load dataset
 dataset = load_dataset("Kencorpus/KenSpeech")
 
-for sample in dataset['train']:
-    audio_array = sample['audio']['array']
-    sampling_rate = sample['audio']['sampling_rate']
-    transcript = sample['transcript']
-    gender = sample['gender']
-    print(f"[{gender}] {transcript[:50]}...")
+# Load a multilingual model
+model_name = "facebook/wav2vec2-large-xlsr-53"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+# Process a sample
+sample = dataset['train'][0]
+inputs = processor(sample['audio']['array'], sampling_rate=16000, return_tensors="pt")
 ```
 
 ---
 
-## Pronunciation Lexicon
+## Additional Resources
 
-The `lexicon.csv` file contains over 31,000 Swahili words with their phonetic transcriptions.
+### Pronunciation Lexicon (`lexicon.csv`)
+
+A Swahili lexicon-phone dictionary with over 31,000 words and their phonetic transcriptions.
 
 **Format:** `word,phoneme_sequence`
 
-**Example entries:**
 ```
 wanapaswa,W AH N AH P AH S W AH
 wanasema,W AH N AH S EH M AH
 wanataka,W AH N AH T AH K AH
 ```
 
+### Transcript-only Data (`transcripts_only.csv`)
+
+Additional transcripts from the stt_transcripts collection without corresponding audio.
+
 ---
 
 ## Speech Types