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Tutorial.md

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+# 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"])
+```
+
+### 3. Loading a Small Sample
+For testing or exploration:
+
+```python
+# Load just 1000 examples
+sample_dataset = load_dataset("nyu-dice-lab/wavepulse-radio-raw-transcripts",
+                            split="train[:1000]",
+                            cache_dir=cache_dir)
+```
+
+## 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}
+}
+```