# Getting Started with WavePulse Radio Transcripts Dataset

This tutorial will help you get started with using the WavePulse Radio Transcripts dataset from Hugging Face.

## Prerequisites

Before starting, make sure you have the required packages installed:

```bash
pip install datasets
pip install huggingface-hub
```

## Basic Setup

First, let's set up our environment with some helpful configurations:

```python
from datasets import load_dataset
import huggingface_hub

# Increase timeout for large downloads
huggingface_hub.constants.HF_HUB_DOWNLOAD_TIMEOUT = 60

# Set up cache directory (optional)
cache_dir = "wavepulse_dataset"
```

## Loading Strategies

### 1. Loading a Specific State (Recommended for Beginners)
Instead of loading the entire dataset, start with one state:

```python
# Load data for just New York
ny_dataset = load_dataset("nyu-dice-lab/wavepulse-radio-raw-transcripts", 
                         "NY",
                         cache_dir=cache_dir)
```

### 2. Streaming Mode (Memory Efficient)
If you're working with limited RAM:

```python
# Stream the dataset
stream_dataset = load_dataset("nyu-dice-lab/wavepulse-radio-raw-transcripts",
                            streaming=True,
                            cache_dir=cache_dir)

# Access data in a streaming fashion
for example in stream_dataset["train"].take(5):
    print(example["text"])
```

## Common Tasks

### 1. Filtering by Date Range

```python
# Filter for August 2024
filtered_ds = dataset.filter(
    lambda x: "2024-08-01" <= x['datetime'] <= "2024-08-31"
)
```

### 2. Finding Specific Stations

```python
# Get unique stations
stations = set(dataset["train"]["station"])

# Filter for a specific station
station_ds = dataset.filter(lambda x: x['station'] == 'KENI')
```

### 3. Analyzing Transcripts

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

# Sort segments by their index to maintain order
sorted_segments = sorted(transcript_ds, key=lambda x: x['segment_index'])
```

## Best Practices

1. **Memory Management**:
   - Start with a single state or small sample
   - Use streaming mode for large-scale processing
   - Clear cache when needed: `from datasets import clear_cache; clear_cache()`

2. **Disk Space**:
   - Ensure at least 75-80 GB free space for full dataset
   - Use state-specific loading to reduce space requirements
   - Regular cache cleanup

3. **Error Handling**:
   - Always include timeout configurations
   - Implement retry logic for large downloads
   - Handle connection errors gracefully

## Example Use Cases

### 1. Basic Content Analysis

```python
# Count segments per station
from collections import Counter

station_counts = Counter(dataset["train"]["station"])
print("Most common stations:", station_counts.most_common(5))
```

### 2. Time-based Analysis

```python
# Get distribution of segments across hours
import datetime

hour_distribution = Counter(
    datetime.datetime.fromisoformat(dt).hour 
    for dt in dataset["train"]["datetime"]
)
```

### 3. Speaker Analysis

```python
# Analyze speaker patterns in a transcript
def analyze_speakers(transcript_id):
    segments = dataset.filter(
        lambda x: x['transcript_id'] == transcript_id
    )
    speakers = [seg['speaker'] for seg in segments]
    return Counter(speakers)
```

## Common Issues and Solutions

1. **Timeout Errors**:
   ```python
   # Increase timeout duration
   huggingface_hub.constants.HF_HUB_DOWNLOAD_TIMEOUT = 120
   ```

2. **Memory Errors**:
   ```python
   # Use streaming to process in chunks
   for batch in dataset.iter(batch_size=1000):
       process_batch(batch)
   ```

3. **Disk Space Issues**:
   ```python
   # Check available space before downloading
   import shutil
   total, used, free = shutil.disk_usage("/")
   print(f"Free disk space: {free // (2**30)} GB")
   ```

## Need Help?

- Dataset documentation: https://huggingface.co/datasets/nyu-dice-lab/wavepulse-radio-raw-transcripts
- Project website: https://wave-pulse.io
- Report issues: https://github.com/nyu-dice-lab/wavepulse/issues

Remember to cite the dataset in your work:

```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}
}
```