---
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