Update README.md

README.md

@@ -1,3 +1,75 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- automatic-speech-recognition
+language:
+- uz
+---
+# Speech-to-Text Evaluation Dataset
+
+## Dataset Overview
+
+This dataset is designed for evaluating Uzbek speech-to-text (STT) models on real-world conversational speech data. The audio samples were collected from various open Telegram groups, capturing natural voice messages in diverse acoustic conditions and speaking styles.
+
+### Key Statistics
+
+- **Total Samples**: 745 audio files
+- **Total Duration**: 1 hour 40 minutes (~100 minutes)
+- **Average Duration**: ~8 seconds per sample
+- **Source**: Voice messages from various open Telegram groups
+- **Transcriptions**: Manually annotated
+
+## Dataset Structure
+
+The dataset is saved as a `datasets.Dataset` object in Arrow format, containing the following fields:
+
+- `name`: Name of audio file
+- `audio`: Audio file data (dict with `array`, and `sampling_rate`)
+- `transcription`: Ground truth text transcription (manually annotated)
+
+## Loading the Dataset
+
+### Installation
+
+To use this dataset, you need to install the Hugging Face `datasets` library:
+
+```bash
+pip install datasets
+```
+
+### Basic Loading
+
+```python
+from datasets import load_dataset
+
+# Load the dataset from the Arrow files
+dataset = load_dataset("OvozifyLabs/asr_evaluate_set")
+
+# View dataset information
+print(dataset)
+print(f"Number of samples: {len(dataset)}")
+```
+
+## Data Characteristics
+
+### Audio Properties
+
+- **Source Domain**: Conversational voice messages from Telegram
+- **Variability**: Multiple speakers, diverse acoustic environments
+- **Recording Conditions**: Real-world
+- **Language**: Uzbek
+
+### Transcription Details
+
+- **Annotation Method**: Manual transcription
+- **Quality**: Human-verified ground truth labels
+- **Convention**: punctuation removed, lowercased
+
+## Use Cases
+
+This dataset is suitable for:
+
+- Evaluating speech-to-text model performance on conversational speech
+- Benchmarking ASR systems on real-world voice messages
+- Testing model robustness to varied acoustic conditions
+- Comparing different STT models