---
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 approximately 329 million text segments derived from 485,090 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

### 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: 220 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}
}
```