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README.md

@@ -1,22 +1,93 @@
----
-dataset_info:
-  features:
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-        decode: false
-  - name: transcript
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 159562872.0
-    num_examples: 197
-  download_size: 118431359
-  dataset_size: 159562872.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: mit
+task_categories:
+- automatic-speech-recognition
+language:
+- en
+tags:
+- audio
+- speech
+- transcription
+- asr
+- voice-recording
+size_categories:
+- n<1K
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+    sample_rate: 16000
+  - name: transcript
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 197
+---
+
+# Audio Transcription Dataset
+
+This dataset contains 197 audio recordings with their corresponding transcriptions for automatic speech recognition (ASR) tasks.
+
+## Dataset Description
+
+This dataset includes:
+- **Audio files**: High-quality voice recordings (.wav format)
+- **Transcriptions**: Accurate text transcriptions of the spoken content
+- **Proper Audio feature type**: Ready for model training (not just file paths!)
+
+## Dataset Statistics
+
+- **Total samples**: 197
+- **Audio format**: WAV files at 16kHz sampling rate
+- **Average transcript length**: 56.6 characters
+- **Language**: English
+
+## Sample Data
+
+| Audio File | Transcript |
+|------------|------------|
+| R1.wav | Hello, my name is Rocky. |
+| R10.wav | I am speaking English for a voice recording. |
+| R11.wav | This is a test sentence for training the model. |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Aashish17405/audio-dataset")
+
+# Access audio data (proper Audio type, not string!)
+audio_sample = dataset['train'][0]['audio']
+print(f"Sampling rate: {audio_sample['sampling_rate']}")
+print(f"Audio array shape: {audio_sample['array'].shape}")
+print(f"Transcript: {dataset['train'][0]['transcript']}")
+
+# Ready for model training with transformers
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
+
+# Process audio
+inputs = processor(audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt")
+```
+
+## Features
+
+✅ **Proper Audio Type**: Audio column shows as "Audio" feature, not "string"  
+✅ **High Quality**: Clear voice recordings  
+✅ **Diverse Content**: Various sentences and topics  
+✅ **Training Ready**: Formatted for immediate use with speech models  
+
+## Use Cases
+
+- Fine-tuning speech recognition models (Whisper, Wav2Vec2, etc.)
+- Voice training and accent recognition
+- Speech-to-text model development
+- Audio processing research
+
+## License
+
+MIT License - Free to use for research and commercial purposes.