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