metadata

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. 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

Recording: Continuous recording of radio livestreams
Transcription: Audio processed using WhisperX for accurate transcription
Diarization: Speaker separation and identification
Quality Control: Automated checks for content quality and completeness
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

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

{
    '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

{
    '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

@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}
}