---
language:
- en
task_categories:
- automatic-speech-recognition
task_ids:
- speech-recognition
tags:
- speech
- audio
- asr
- noise-augmentation
- custom
size_categories:
- n<1K
license: cc-by-4.0
---

# Singaporean district with noise

## Dataset Description

Singaporean district speech dataset with controlled noise augmentation for ASR training

### Dataset Summary

- **Language**: EN
- **Task**: Automatic Speech Recognition  
- **Total Samples**: 252
- **Audio Sample Rate**: 16kHz
- **Base Dataset**: Custom dataset
- **Processing**: Noise-augmented

## Dataset Structure

### Data Fields

- `audio`: Audio file (16kHz WAV format)
- `text`: Transcription text
- `noise_type`: Type of background noise added
- `actual_snr_db`: Measured Signal-to-Noise Ratio in dB
- `target_snr_db`: Target SNR used for augmentation
- `sample_index`: Original sample index from base dataset

### Data Splits

The dataset contains 252 samples in the training split.


### Noise Augmentation Details
- **Noise Types and Distribution**:
  - station: 134
  - bus: 118
- **SNR Statistics**:
  - Min SNR: 5.0 dB
  - Max SNR: 10.0 dB  
  - Average SNR: 7.4 dB
  - SNR Standard Deviation: 1.4 dB

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("thucdangvan020999/singaporean_district_noise_snr_5_10")

# Access the data
for sample in dataset['train']:
    audio_array = sample['audio']['array']
    sampling_rate = sample['audio']['sampling_rate'] 
    text = sample['text']
    
    # Your processing code here
    print(f"Audio shape: {audio_array.shape}")
    print(f"Text: {text}")
```

### Example Usage with Transformers

```python
from datasets import load_dataset
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# Load dataset and model
dataset = load_dataset("thucdangvan020999/singaporean_district_noise_snr_5_10")
processor = WhisperProcessor.from_pretrained("openai/whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")

# Process a sample
sample = dataset['train'][0]
inputs = processor(
    sample['audio']['array'], 
    sampling_rate=sample['audio']['sampling_rate'], 
    return_tensors="pt"
)

# Generate transcription
with torch.no_grad():
    predicted_ids = model.generate(inputs["input_features"])
    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
```

## Dataset Creation

This dataset was created using the Universal ASR Processor with the following configuration:

- **Base Dataset**: Custom source
- **Processing Method**: Noise augmentation with SNR control
- **Sample Rate**: 16kHz
- **Quality Control**: SNR-based noise mixing

### Processing Details

The dataset was processed to ensure:
- Consistent audio quality and format
- Proper noise mixing with controlled SNR levels
- Metadata preservation for reproducibility
- Quality validation and statistics

## Licensing and Usage

This dataset is released under the cc-by-4.0 license. 

### Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{thucdangvan020999_singaporean_district_noise_snr_5_10_dataset,
  title={Singaporean district with noise},
  author={Dataset Creator},
  url={https://huggingface.co/datasets/thucdangvan020999/singaporean_district_noise_snr_5_10},
  year={2024}
}
```



## Contact

For questions or issues regarding this dataset, please open an issue in the dataset repository.

---

*This dataset card was automatically generated using the Universal ASR Processor.*