README.md

---
language: en
license: apache-2.0
datasets:
- nyu-dice-lab/wavepulse-radio-raw-transcripts
tags:
- radio
- news
- politics
- media
- transcription
- united-states
- time-series
- temporal
- real-time
- streaming
- current-events
- political-discourse
- media-analysis
task_categories:
- text-generation
- text-classification
task_ids:
- news-articles-summarization
- topic-classification
- sentiment-analysis
- text-scoring
size_categories:
- 100M<n<1B
pretty_name: WavePulse Radio Raw Transcripts
---

# WavePulse Radio Raw Transcripts

## Dataset Summary

WavePulse Radio Raw Transcripts is a large-scale dataset containing segment-level transcripts from 396 radio stations across the United States, collected between June 26, 2024, and Dec 29th, 2024. The dataset comprises >250 million text segments derived from 750,000+ hours of radio broadcasts, primarily covering news, talk shows, and political discussions.

The summarized version of these transcripts is available [here](https://huggingface.co/datasets/nyu-dice-lab/wavepulse-radio-summarized-transcripts). For more info, visit https://wave-pulse.io

## Dataset Details

### Dataset Sources

- **Source**: Live radio streams from 396 stations across all 50 US states and DC
- **Time Period**: June 26, 2024 - December 29th, 2024
- **Collection Method**: Automated recording and processing using the WavePulse system
- **Audio Processing**: WhisperX for transcription and speaker diarization
- **Format**: Parquet files organized by state and month, with segment-level granularity

Find recordings samples [here](https://huggingface.co/datasets/nyu-dice-lab/wavepulse-radio-raw-transcripts/tree/main/recordings).

### Data Collection Process

1. **Recording**: Continuous recording of radio livestreams
2. **Transcription**: Audio processed using WhisperX for accurate transcription
3. **Diarization**: Speaker separation and identification
4. **Quality Control**: Automated checks for content quality and completeness
5. **Removal of personal information** only for cleaning purpose. Radio is fair use.

### Dataset Statistics

- Number of Stations: 396
- Number of States: 50 + DC
- Total individual 30-minute transcripts - 1,555,032
- Average Segments per 30-min: ~150
- Total Segments: > 250 million
- Total Words: >5 billion
  
## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("nyu-dice-lab/wavepulse-radio-raw-transcripts")

# Load specific state
dataset = load_dataset("nyu-dice-lab/wavepulse-radio-raw-transcripts", "NY")

# Filter by date range
filtered_ds = dataset.filter(
    lambda x: "2024-08-01" <= x['datetime'] <= "2024-08-31"
)

# Filter by station
station_ds = dataset.filter(lambda x: x['station'] == 'WXYZ')

# Get all segments from a specific transcript
transcript_ds = dataset.filter(lambda x: x['transcript_id'] == 'AK_KAGV_2024_08_25_13_00')
```

### Data Schema

```python
{
    'transcript_id': str,  # e.g., 'AK_KAGV_2024_08_25_13_00'
    'segment_index': int,  # Position in original transcript
    'start_time': float,   # Start time in seconds
    'end_time': float,     # End time in seconds
    'text': str,          # Segment text
    'speaker': str,       # Speaker ID (unique *within* transcript)
    'station': str,       # Radio station callsign
    'datetime': datetime, # Timestamp in ET
    'state': str         # Two-letter state code
}
```

### Example Entry

```python
{
    'transcript_id': 'AK_KAGV_2024_08_25_13_00',
    'segment_index': 0,
    'start_time': 0.169,
    'end_time': 2.351,
    'text': 'FM 91.9, the Nana.',
    'speaker': 'SPEAKER_01',
    'station': 'KAGV',
    'datetime': '2024-08-25 13:00:00',
    'state': 'AK'
}
```

### Important Notes

- Speaker IDs (e.g., SPEAKER_01) are only unique within a single transcript. The same ID in different transcripts may refer to different speakers.
- Segments maintain their original order through the segment_index field.
- All timestamps are relative to the start of their 30-minute transcript.

### Data Quality

- Word Error Rate (WER) for transcription: 8.4% ± 4.6%
- Complete coverage of broadcast hours from 5:00 AM to 3:00 AM ET (i.e. 12 AM PT)
- Consistent metadata across all entries
- Preserved temporal relationships between segments

## Intended Uses

This dataset is designed to support research in:

- Media analysis and content tracking
- Information dissemination patterns
- Regional news coverage differences
- Political narrative analysis
- Public discourse studies
- Temporal news analysis
- Speaker diarization analysis
- Conversational analysis
- Turn-taking patterns in radio shows

## Limitations

- Limited to stations with internet streams
- English-language content only
- Coverage varies by region and time zone
- Potential transcription errors in noisy segments
- Some stations have gaps due to technical issues
- Speaker IDs don't persist across transcripts
- Background music or effects may affect transcription quality

## Ethical Considerations

- Contains only publicly broadcast content
- Commercial use may require additional licensing
- Attribution should be given to original broadcasters
- Content should be used responsibly and in context

## Citation

```bibtex
@article{mittal2024wavepulse,
  title={WavePulse: Real-time Content Analytics of Radio Livestreams},
  author={Mittal, Govind and Gupta, Sarthak and Wagle, Shruti and Chopra, Chirag and DeMattee, Anthony J and Memon, Nasir and Ahamad, Mustaque and Hegde, Chinmay},
  journal={arXiv preprint arXiv:2412.17998},
  year={2024}
}
```