|
|
--- |
|
|
license: cc-by-4.0 |
|
|
task_categories: |
|
|
- automatic-speech-recognition |
|
|
language: |
|
|
- en |
|
|
- he |
|
|
tags: |
|
|
- speech-to-text |
|
|
- stt |
|
|
- evaluation |
|
|
- technical-vocabulary |
|
|
size_categories: |
|
|
- n<1K |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: data/* |
|
|
--- |
|
|
|
|
|
# Small STT Eval Audio Dataset |
|
|
|
|
|
A small speech-to-text evaluation dataset containing 92 audio samples with ground truth transcriptions. Designed for evaluating STT systems on technical vocabulary, code-switching (English/Hebrew), and various speaking styles. |
|
|
|
|
|
## Dataset Description |
|
|
|
|
|
This dataset contains audio recordings with accompanying transcriptions across multiple categories: |
|
|
|
|
|
| Category | Count | Description | |
|
|
|----------|-------|-------------| |
|
|
| tech_github | 5 | GitHub-related technical vocabulary | |
|
|
| tech_huggingface | 4 | Hugging Face platform terminology | |
|
|
| tech_docker | 5 | Docker and containerization terms | |
|
|
| hebrew_daily | 10 | English with Hebrew words (daily life) | |
|
|
| hebrew_food | 3 | English with Hebrew food terms | |
|
|
| ai_ml | 9 | AI/ML technical vocabulary | |
|
|
| local_tools | 8 | Local development tools | |
|
|
| conversational | 10 | Casual conversational speech | |
|
|
| narrative | 6 | Narrative/storytelling style | |
|
|
| instructions | 7 | Instructional content | |
|
|
| tech_linux | 6 | Linux system administration | |
|
|
| tech_api | 4 | API and web services | |
|
|
| tech_python | 5 | Python programming | |
|
|
| mixed_workflow | 5 | Mixed technical workflows | |
|
|
| mixed_locale | 2 | Mixed locale content | |
|
|
| tech_web | 2 | Web development | |
|
|
| tech_data | 1 | Data processing | |
|
|
|
|
|
## Audio Specifications |
|
|
|
|
|
- **Format**: WAV (PCM signed 16-bit little-endian) |
|
|
- **Sample Rate**: 16kHz |
|
|
- **Channels**: Mono |
|
|
- **Average Duration**: ~5-10 seconds per sample |
|
|
|
|
|
## Dataset Structure |
|
|
|
|
|
``` |
|
|
data/ |
|
|
├── metadata.csv |
|
|
├── 001_tech_github.wav |
|
|
├── 002_tech_github.wav |
|
|
└── ... |
|
|
``` |
|
|
|
|
|
The `metadata.csv` contains: |
|
|
- `file_name`: Audio filename |
|
|
- `transcription`: Ground truth transcription |
|
|
- `category`: Content category |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
dataset = load_dataset("danielrosehill/Small-STT-Eval-Audio-Dataset") |
|
|
|
|
|
# Access a sample |
|
|
sample = dataset["train"][0] |
|
|
print(sample["transcription"]) |
|
|
# Play audio: sample["audio"] |
|
|
``` |
|
|
|
|
|
## Intended Use |
|
|
|
|
|
This dataset is intended for: |
|
|
- Evaluating STT model accuracy on technical vocabulary |
|
|
- Testing code-switching (English/Hebrew) recognition |
|
|
- Benchmarking STT systems on varied speaking styles |
|
|
- Development and testing of speech recognition pipelines |
|
|
|
|
|
## Recommended Evaluation Packages |
|
|
|
|
|
For WER (Word Error Rate) evaluation, we recommend using text normalization to handle variations in number formatting, punctuation, and casing: |
|
|
|
|
|
- **[whisper-normalizer](https://pypi.org/project/whisper-normalizer/)**: Text normalization for STT evaluation (handles "3000" vs "three thousand", punctuation, casing) |
|
|
- **[werpy](https://pypi.org/project/werpy/)**: WER calculation with detailed error analysis |
|
|
|
|
|
```python |
|
|
from whisper_normalizer.english import EnglishTextNormalizer |
|
|
from werpy import wer |
|
|
|
|
|
normalizer = EnglishTextNormalizer() |
|
|
|
|
|
# Normalize both reference and hypothesis before comparison |
|
|
reference = normalizer(ground_truth) |
|
|
hypothesis = normalizer(model_output) |
|
|
|
|
|
error_rate = wer(reference, hypothesis) |
|
|
``` |
|
|
|
|
|
## Limitations |
|
|
|
|
|
- Small dataset size (92 samples) |
|
|
- Single speaker |
|
|
- Controlled recording environment |
|
|
- Limited Hebrew vocabulary (loan words only, not full Hebrew speech) |
|
|
|
|
|
## License |
|
|
|
|
|
CC-BY-4.0 |
|
|
|
