Update dataset README with comprehensive documentation

Rewrote the README to reflect the current state of the dataset and provide
comprehensive documentation for users and researchers.

Changes:
- Updated dataset status (190+ annotated voice notes)
- Added detailed feature descriptions and use cases
- Documented the Voice Note Dataset Manager interface
- Included comprehensive annotation schema overview
- Added auto-generated statistics file information
- Improved data organization documentation
- Added citation format and contact information
- Enhanced metadata descriptions (audio, text, technical specs)
- Clarified multilingual and real-world audio aspects
- Added dataset growth and management information

The README now accurately reflects the dataset's capabilities, management
tools, and research applications.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

README.md

@@ -3,7 +3,7 @@ task_categories:
 - automatic-speech-recognition
 language:
 - en
-pretty_name: "Voice Note Audio"
+pretty_name: "Voice Note Audio Dataset"
 size_categories:
 - "n<1K"
 tags:
@@ -11,122 +11,166 @@ tags:
 - noise-robustness
 - evaluation
 - whisper
+- real-world-audio
+- voice-notes
 license: mit
 ---
 
-# Voice Notes
-
-A dataset of voice notes collected by Daniel Rosehill in and around Jerusalem (mostly) in a variety of acoustic environments and in a variety of formats reflecting typical daily use with speech to text transcription apps.
-
-This dataset is a subsection of a voice note training dataset that I'm curating for STT fine-tuning and entity recognition.
-
-## Annotation
-
-The dataset includes rich annotations collected using Label Studio:
-
-- Corrected transcripts (manually corrected AI transcripts)
-- Audio quality ratings
-- Environmental information (recording location, microphone type, etc.)
-- Content classification
-- Audio challenges present
-- Language information
-- Entity recognition
-- Audio source identification
-
-## Label Studio Configuration Parameters
-
-### Audio Challenges Present
-Multiple selection options for identifying audio quality issues:
-- Traffic Noise: Road traffic sounds
-- Audible Conversations: Other people talking
-- Outdoor Noise (General): Street/urban sounds
-- Background Music: Music playing
-- **Crying Baby**: Baby crying sounds (newly added)
-
-### Incidental Audio Pickup Source
-Single selection for identifying the source of incidental audio:
-- **Speaker**: Audio from the primary speaker
-- **Others**: Audio from other sources
-
-### Background Conversation Language
-Single selection for identifying the language of background conversations:
-- **English**
-- **Hebrew** 
-- **Arabic**
-- **French**
-- **Russian**
-
-### Multilingual Transcript
-Single selection to indicate if the transcript contains multiple languages:
-- **True**: Transcript contains multiple languages
-- **False**: Transcript is in a single language
-
-### Entities Present in Note
-Multiple selection for identifying named entities mentioned in the voice note:
-- **Dates**: Specific dates or time references
-- **Persons**: Names of people
-- **Placenames**: Geographic locations or places
-- **Email Addresses**: Email addresses mentioned
-- **Blog Title**: Blog or article titles
-- **Acronym**: Acronyms or abbreviations
-- **Organisations**: Company or organization names
-
-### Bluetooth Codec
-Single selection for identifying the Bluetooth codec used during recording:
-- **SBC**: Standard Bluetooth codec
-- **AAC**: Advanced Audio Coding
-- **aptX**: Qualcomm aptX codec
-- **aptX HD**: High-definition aptX codec
-- **LDAC**: Sony LDAC high-quality codec
-- **LC3**: Low Complexity Communication Codec
-- **N/A**: Not applicable (wired/internal mic)
-- **Unknown**: Codec information unavailable
-
-## Microphones Used
-
-The voice notes in this dataset were recorded using various microphones:
-- **OnePlus Nord 3 Internal Microphone**: Built-in phone microphone
-- **Poly 5200**: Bluetooth-connected microphone
-- **ATR 4697**: Professional microphone
+# Voice Note Audio Dataset
+
+A curated dataset of real-world voice notes collected by Daniel Rosehill, primarily recorded in and around Jerusalem, Israel. This dataset captures authentic voice recordings in diverse acoustic environments and formats, reflecting typical daily usage patterns with speech-to-text transcription applications.
+
+**Current Status:** 190+ annotated voice notes with comprehensive metadata
+
+## Dataset Overview
+
+This dataset is part of a larger voice note training collection being curated for STT fine-tuning, entity recognition, and real-world speech recognition evaluation. Unlike studio-quality audio commonly used in speech recognition training, these recordings intentionally include the challenges present in everyday voice note usage:
+
+- Variable background noise (traffic, conversations, music)
+- Different recording environments (indoor, outdoor, vehicles)
+- Multiple microphone types and Bluetooth codecs
+- Natural speaking patterns and multilingual content
+- Real-world audio quality variations
+
+## Key Features
+
+### Comprehensive Annotations
+
+Each voice note includes rich metadata stored in JSON format:
+
+- **Audio Metadata**: Duration, bitrate, sample rate, file format, codec information
+- **Transcripts**: AI-generated (uncorrected) and manually corrected ground truth versions
+- **Text Metrics**: Word count, character count, lexical diversity, WPM (words per minute)
+- **Quality Ratings**: Audio quality assessments, noise type classification
+- **Environmental Context**: Recording location, time of day, background conditions
+- **Content Classification**: Note type (email draft, to-do, idea, meeting note, etc.)
+- **Language Information**: Primary language, multilingual indicators, mixed-language notes
+- **Technical Details**: Microphone type, Bluetooth codec, recording device
+
+### Dataset Statistics
+
+The repository includes an auto-generated `STATS.md` file with comprehensive metrics:
+
+- Total audio duration and word count across all recordings
+- Average duration and word count per note
+- Dataset completeness percentages (transcripts, corrections, annotations)
+- Character counts and text complexity metrics
+
+Statistics are automatically updated when new recordings are added to the dataset.
 
 ## Data Organization
 
-- `audio/` - Processed audio files (MP3/WAV)
-- `transcripts/` - Transcript files
-  - `uncorrected/` - AI-generated transcripts
-  - `ground_truths/` - Manually corrected transcripts (ground truth)
-- `annotations/` - Annotation task files and completed annotations
-- `candidate-parameters.md` - Additional parameters for future implementation
-- `preprocessing/` - Workflow for adding new data (see preprocessing/README.md) 
+```
+Voice-Note-Audio/
+├── audio/                          # Audio files (MP3, WAV, M4A, OGG)
+├── transcripts/
+│   ├── uncorrected/               # AI-generated transcripts from STT
+│   └── ground_truths/             # Manually corrected transcripts
+├── annotations/                    # JSON metadata for each recording
+├── STATS.md                       # Auto-generated dataset statistics
+├── ANNOTATION_SCHEMA.md           # Detailed schema documentation
+└── annotation_schema_v1.json      # JSON schema specification
+```
+
+Files are numbered sequentially (e.g., `1.mp3`, `1.txt`, `1.json`) for easy cross-referencing.
+
+## Dataset Management
+
+This dataset is actively managed using a custom Hugging Face Space application: **Voice Note Dataset Manager**
+
+The management interface provides:
+- Quick upload functionality with batch processing
+- Automated metadata extraction and calculation
+- Real-time statistics tracking and visualization
+- Browse, edit, and delete capabilities
+- Comprehensive annotation support
+- Automatic stats file generation
+
+## Use Cases
+
+### 1. STT Model Fine-Tuning
+Train and evaluate speech recognition models on real-world voice notes with natural noise and speaking patterns, improving accuracy for everyday recording conditions.
+
+### 2. Noise Robustness Evaluation
+Benchmark STT systems against various background noise types and acoustic challenges commonly encountered in voice note applications.
+
+### 3. Entity Recognition Development
+Develop specialized NER (Named Entity Recognition) models for voice notes to identify dates, names, locations, organizations, and other entities in spoken content.
+
+### 4. Voice Note Classification
+Train models to automatically categorize voice notes by type (to-do items, meeting notes, ideas, etc.) based on audio characteristics and content.
+
+### 5. Multilingual Speech Research
+Study code-switching and multilingual speech patterns in authentic voice recordings containing mixed English, Hebrew, and other languages.
+
+## Annotation Schema
+
+The dataset uses a comprehensive annotation schema covering:
+
+### Audio Quality Indicators
+- Quality ratings: Clear, Good, Fair, Poor, Very Poor
+- Noise types: Traffic, conversations, music, wind, office noise, crying baby
+- Audio challenges and environmental factors
+
+### Content & Context
+- Note type classification (8+ categories)
+- Recording location and environment
+- Multi-language indicators
+- Time-of-day metadata
+
+### Technical Specifications
+- Microphone types (OnePlus Nord 3 Internal, Poly 5200, ATR 4697, etc.)
+- Bluetooth codecs (SBC, AAC, aptX, aptX HD, LDAC, LC3)
+- Audio format and encoding details
+
+### Text Analysis
+- Word count and character count metrics
+- Lexical diversity measurements
+- Speaking rate (WPM) calculations
+- Sentence structure analysis
+
+See `ANNOTATION_SCHEMA.md` for complete schema documentation.
+
+## Recording Equipment
+
+Voice notes were captured using:
+- **OnePlus Nord 3**: Internal microphone (primary device)
+- **Poly 5200**: Bluetooth headset microphone
+- **ATR 4697**: Professional wired microphone
+
+Various Bluetooth codecs documented in metadata when applicable.
+
+## Dataset Growth
 
-## Purpose
+This is an actively growing dataset. New voice notes are continuously added with full annotations and metadata. Check `STATS.md` in the repository for current dataset size and metrics.
 
-This collection, consisting of voice notes recorded by Daniel Rosehill using Voicenotes.com, is specifically gathered to evaluate and improve the robustness of Speech-to-Text (STT) systems under non-ideal, real-world conditions. Unlike studio-quality audio used for training, these notes often contain various types of background noise, overlapping conversations, and environmental distortions typical of everyday recording scenarios.
+## Citation
 
-This dataset serves three primary objectives:
+If you use this dataset in your research, please cite:
 
-### 1. Personal STT Fine-Tuning
-Improve speech recognition accuracy for personal voice notes by creating a refined transcription model tailored to individual speech patterns and common recording environments.
+```bibtex
+@dataset{rosehill_voicenote_2024,
+  author = {Rosehill, Daniel},
+  title = {Voice Note Audio Dataset},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/danielrosehill/Voice-Note-Audio}}
+}
+```
 
-### 2. Voice Note Entity Recognition
-Develop a specialized model for the "Voice Router" application to classify and identify entities within voice note recordings, enabling intelligent routing and categorization of voice-based content.
+## License
 
-### 3. Public Research Dataset
-Generate a comprehensive, open-source dataset with rich annotations for various audio recording conditions, enabling STT model evaluation across different acoustic environments and contributing to the broader speech recognition research community.
+This dataset is released under the MIT License, allowing for both commercial and non-commercial use with attribution.
 
-The dataset contains approximately 700 voice notes totaling 13 hours of audio. Each audio file comes with an AI-generated transcript provided by Voicenotes.com's STT service, serving as a baseline for comparison. A subset of these transcripts will be manually corrected to create a high-quality ground truth dataset for fine-tuning STT models and developing a comprehensive, nuanced speech recognition research and development framework focused on real-world voice note transcription challenges.
+## Contact
 
-## Contents
+**Daniel Rosehill**
+- Website: [danielrosehill.com](https://danielrosehill.com)
+- Email: public@danielrosehill.com
+- Hugging Face: [@danielrosehill](https://huggingface.co/danielrosehill)
 
-- `audio/`: Folder containing the original MP3 audio files of the voice notes.
-- `transcripts/`: Folder containing transcript files
-  - `uncorrected/`: Raw, AI-generated transcripts corresponding to the audio files
-  - `ground_truths/`: Manually corrected transcripts for training and evaluation
-- `dataset_metadata.json`: Metadata associated with the dataset entries.
-- `label_studio_config.xml`: Configuration file for Label Studio, an annotation tool.
-- `setup_annotation.py`: Script to help set up the annotation process.
-- `parameters.md`: A detailed list of parameters to be annotated for each voice note.
+## Acknowledgments
 
-## Annotation
+AI transcripts provided by [Voicenotes.com](https://voicenotes.com), serving as baseline uncorrected transcripts for comparison with ground truth corrections.
 
-The `parameters.md` file specifies the key aspects to be annotated for each voice note, including audio quality, speaker characteristics, transcription accuracy, and contextual information. This structured annotation will provide valuable metadata for analyzing STT performance and guiding model improvements.
+Dataset management interface built using Gradio and Hugging Face Spaces.