README.md

---
language:
- en
license: mit
task_categories:
- automatic-speech-recognition
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: text
    dtype: string
  - name: chunk_id
    dtype: string
  - name: original_file
    dtype: string
  - name: start_time
    dtype: float64
  - name: end_time
    dtype: float64
  - name: speaker_id
    dtype: string
  - name: domain
    dtype: string
  - name: noise_conditions
    dtype: string
  - name: language
    dtype: string
  splits:
  - name: train_0
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_1
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_2
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_3
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_4
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_5
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_6
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_7
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_8
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_9
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_10
    num_bytes: 14241773830.618
    num_examples: 36239
  - name: train_11
    num_bytes: 14241773830.618
    num_examples: 36239
  download_size: 161139820068
  dataset_size: 170901285967.41602
configs:
- config_name: default
  data_files:
  - split: train_0
    path: data/train_0-*
  - split: train_1
    path: data/train_1-*
  - split: train_2
    path: data/train_2-*
  - split: train_3
    path: data/train_3-*
  - split: train_4
    path: data/train_4-*
  - split: train_5
    path: data/train_5-*
  - split: train_6
    path: data/train_6-*
  - split: train_7
    path: data/train_7-*
  - split: train_8
    path: data/train_8-*
  - split: train_9
    path: data/train_9-*
  - split: train_10
    path: data/train_10-*
  - split: train_11
    path: data/train_11-*
---

# Streaming ASR Dataset

This dataset is designed for training real-time (streaming) ASR models, with a focus on handling chunk-based audio processing. It contains standardized audio segments from LibriSpeech dev-clean, processed for streaming ASR applications.

## Dataset Description

### Dataset Summary
- Source: LibriSpeech dev-clean
- Total chunks: 2,703
- Total duration: ~20 hours (1,212.26 seconds)
- Unique speakers: 40
- Audio format: 16 kHz mono WAV
- Language: English
- Domain: Audiobooks (clean speech)

### Dataset Structure
```
openwhisper/
├── chunks/          # Audio files (16kHz mono WAV)
├── transcripts/     # Text transcriptions
└── metadata/        # JSON files with detailed information
```

### Data Fields
Each sample consists of:
1. Audio file (WAV)
   - 16 kHz sampling rate
   - Mono channel
   - 16-bit PCM format

2. Transcript file (TXT)
   - Clean text transcription
   - Includes punctuation and casing
   - Aligned with audio chunks

3. Metadata file (JSON)
   - speaker_id: Unique speaker identifier
   - chunk_id: Unique chunk identifier
   - start_time: Start time in original audio
   - end_time: End time in original audio
   - duration: Chunk duration in seconds
   - language: Language code (en)
   - noise_conditions: Audio quality label (clean)
   - original_file: Source file reference

### Data Splits
This dataset contains only the dev-clean portion of LibriSpeech, processed into overlapping chunks suitable for streaming ASR training.

## Dataset Creation

### Preprocessing
1. Audio standardization
   - Resampling to 16 kHz
   - Conversion to mono channel
   - Format conversion to WAV

2. Chunking strategy
   - Fixed chunk duration with overlap
   - Natural pause boundary detection
   - Consistent chunk size for training stability

3. Transcript processing
   - Alignment with audio chunks
   - Preservation of punctuation and casing
   - Clean text normalization

## Usage

### Loading the Dataset
```python
from datasets import load_dataset

dataset = load_dataset("orgho98/openwhisper")
```

### Training Example
```python
# Example code for loading audio and transcript pairs
for sample in dataset:
    audio = sample['audio']
    transcript = sample['text']
    metadata = sample['metadata']
    
    # Process for streaming ASR training
    # ...
```

## License
This dataset is released under the MIT License, following LibriSpeech's licensing terms.

## Citation
If you use this dataset, please cite:
```bibtex
@misc{openwhisper2024,
  title={Streaming ASR Dataset},
  author={Automagically AI},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/orgho98/openwhisper}}
}
```

## Limitations
- Limited to clean speech from audiobooks
- Single language (English)
- May not represent real-world streaming conditions perfectly

## Additional Information
- **Curated by:** Automagically AI
- **License:** MIT
- **Version:** 1.0.0