Add 2 voice notes via SQLite backend export

README.md

@@ -1,217 +1,74 @@
----
-task_categories:
-- automatic-speech-recognition
-language:
-- en
-pretty_name: "Voice Note Audio Dataset"
-size_categories:
-- "n<1K"
-tags:
-- speech-to-text
-- noise-robustness
-- evaluation
-- whisper
-- real-world-audio
-- voice-notes
-license: mit
----
-
-# 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
+# Voice Notes Dataset
 
-### Dataset Statistics
+## Dataset Description
 
-The repository includes an auto-generated `STATS.md` file with comprehensive metrics:
+This dataset contains real-world voice recordings with transcripts and comprehensive annotations.
 
-- 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
+### Dataset Statistics
 
-Statistics are automatically updated when new recordings are added to the dataset.
+- **Total Entries**: 2
+- **Audio Files**: 2
+- **Uncorrected Transcripts**: 2
+- **Ground Truth Transcripts**: 0
+- **Annotation Files**: 2
+- **Export Date**: 2025-10-27
 
-## Data Organization
+### Dataset Structure
 
 ```
-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
-├── schema/                         # Annotation schema (versioned)
-│   ├── annotation_schema_v1.json  # Schema definition v1.0.0
-│   ├── README.md                  # Schema documentation
-│   └── CHANGELOG.md               # Version history
-├── STATS.md                       # Auto-generated dataset statistics
-└── README.md                      # This file
+audio/                      # Audio recordings (MP3, etc.)
+├── 1.mp3
+├── 2.mp3
+└── ...
+
+transcripts/
+├── uncorrected/           # Original STT transcripts
+│   ├── 1.txt
+│   ├── 2.txt
+│   └── ...
+└── ground_truths/         # Corrected transcripts (when available)
+    ├── 1.txt
+    ├── 2.txt
+    └── ...
+
+annotations/               # Metadata and annotations
+├── 1.json
+├── 2.json
+└── ...
 ```
 
-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, versioned annotation schema to ensure consistency and enable schema evolution over time.
-
-**Current Schema Version: 1.0.0** (Released: 2025-10-26)
-
-### Schema Versioning
-
-The annotation schema follows [Semantic Versioning](https://semver.org/) (MAJOR.MINOR.PATCH):
-- **MAJOR**: Incompatible schema changes
-- **MINOR**: Backward-compatible additions
-- **PATCH**: Backward-compatible bug fixes
+### Annotation Schema
 
-Each annotation automatically includes a `schema_version` field, enabling:
-- Tracking which schema version was used for each annotation
-- Backward compatibility as the schema evolves
-- Migration paths when schema updates occur
-- Historical analysis of annotation practices
+Each annotation file contains:
+- Audio metadata (duration, bitrate, sample rate, etc.)
+- Text metrics (word count, WPM, lexical diversity)
+- Temporal information (recording date, time of day)
+- Custom annotations (audio quality, classification, etc.)
 
-Schema files and documentation are maintained in the `schema/` directory:
-- `annotation_schema_v1.json` - Current schema definition
-- `README.md` - Schema usage and documentation
-- `CHANGELOG.md` - Version history and changes
+### Use Cases
 
-### Schema Coverage
+1. **Real-World STT Evaluation**: Test speech-to-text models on non-ideal conditions
+2. **Voice Note Classification**: Train models to categorize voice notes
+3. **Audio Quality Assessment**: Analyze impact of background noise on transcription
 
-#### Classification (31 categories)
-Comprehensive note type classification including:
-- **Communication**: Email drafts, replies
-- **Task Management**: To-do lists, reminders, shopping lists
-- **Content Creation**: Blog posts, articles, social media, scripts, presentations
-- **Development**: Prompts (general, development, creative), documentation, code comments, bug reports, feature requests
-- **Personal & Professional**: Journal entries, memos, ideas, meeting notes, research notes, project planning
-- **General**: Questions, other
+### Citation
 
-#### Audio Defects (10 categories)
-Real-world audio challenges for STT evaluation:
-- Background noise, music, conversations
-- Crying baby, traffic sounds
-- Poor quality (distortion, clipping)
-- Multiple speakers, wind noise, echo
-- Phone ringing/notifications
+If you use this dataset, please cite:
 
-#### Content Issues (5 categories)
-Recording-level problems:
-- Side conversations, partial content
-- False starts, thinking aloud
-- Self-correction during recording
-
-#### Languages (7 supported)
-Multi-language support for:
-- English, Hebrew, Arabic
-- Russian, French, Spanish, German
-
-#### Transcription Quality (5 levels)
-STT output assessment:
-- Excellent, Good, Fair, Poor, Unusable
-
-#### Additional Metadata
-- **Audio Quality Indicators**: Quality ratings, noise types, environmental factors
-- **Technical Specifications**: Microphone types, Bluetooth codecs, audio formats
-- **Text Analysis**: Word/character counts, lexical diversity, speaking rate (WPM)
-- **Context**: Recording location, time of day, multi-language indicators
-
-See `schema/README.md` and `schema/CHANGELOG.md` for complete schema documentation and version history.
-
-## 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
-
-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.
-
-## Citation
-
-If you use this dataset in your research, please cite:
-
-```bibtex
-@dataset{rosehill_voicenote_2024,
-  author = {Rosehill, Daniel},
-  title = {Voice Note Audio Dataset},
-  year = {2024},
+```
+@dataset{voice_notes_dataset,
+  author = {Daniel Rosehill},
+  title = {Voice Notes Dataset},
+  year = {2025},
   publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/datasets/danielrosehill/Voice-Note-Audio}}
+  howpublished = {\url{https://huggingface.co/datasets/...}}
 }
 ```
 
-## License
-
-This dataset is released under the MIT License, allowing for both commercial and non-commercial use with attribution.
-
-## Contact
-
-**Daniel Rosehill**
-- Website: [danielrosehill.com](https://danielrosehill.com)
-- Email: public@danielrosehill.com
-- Hugging Face: [@danielrosehill](https://huggingface.co/danielrosehill)
+### License
 
-## Acknowledgments
+[Specify your license here]
 
-AI transcripts provided by [Voicenotes.com](https://voicenotes.com), serving as baseline uncorrected transcripts for comparison with ground truth corrections.
+### Contact
 
-Dataset management interface built using Gradio and Hugging Face Spaces.
+For questions or feedback, please contact: public@danielrosehill.com

annotations/1.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:641dbbf16f7ab795cb972766bdcb923c67ec205b81390fa2811b468e62921a75
+size 650

annotations/2.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:730408fb19fe96aa95017428c16a1f8d8012c7cf4c19d2d50d5a3b38dde592ae
+size 654

audio/1.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7ed9e2ba97d3231457b3e699f67130488af59df2827599cecbaa4f054e1ccf1
+size 1524716

audio/2.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff2b40d06add3f07ca26e609ca0fef0270b9f4e72bbfe33a31bf193bcee7e96b
+size 4384556

transcripts/uncorrected/1.txt

@@ -0,0 +1 @@
+I'd like to consider a wee factor and then just give me your thoughts about this so currently it's a file based backend what I was wondering is would it make more sense to have a lightweight database backend SQLite let's say and and the important part of the utility which is the Hugging Face dataset push is what I'm using for the classification model would actually be a job whereby locally it will create the dataset from the local backend.<br><br>In other words, rather than having this sit in place as files, it's going to be constructed periodically. Basically when I say okay I've uploaded another batch, let's push, would that be easier and more logical to integrate with the front end?

transcripts/uncorrected/2.txt

@@ -0,0 +1 @@
+Okay, so I'd like to add to the VoiceNote dataset manager. So I have really annotations, there's two main objectives for this project as I currently conceive of it. And I think on the front end it would be useful to, when I'm uploading stuff and annotating, to have two separate sections for it, a little bit more clearly delineated. and so on.<br><br>So, if we have delineated, for example, where we have upload new voice note, that can firstly just be called maybe upload, next section transcripts, next section, and by next section I'm defining the headers, next section classification, next section annotations.<br><br>So in classification, I'll just add a few more recurrent ones that we should have. Prompt General, Development Prompt, Read Me Dictation, Social Media Post, and then in Annotations.<br><br>So content issues call that Audio defects and let add one for a significant background noise In audio quality issues, what I'd like to have actually maybe is, and again, we're going to, I mean, in the process of defining the annotations and might have to sort of work backwards initially, but most of them haven't been annotated yet. I'm not going to start annotating until the schema is defined so it would actually be a lagging annotation process.<br><br>The ones that are missing currently are background music. You have background noise but I think background music is actually very important because from a copyright standpoint that could be an issue. and for multi-language don't actually even have English Hebrew I'd have to keep it open-ended as to what other languages are present and I'd like to have one for background conversations actually and tagging by language so English Hebrew Arabic Russian French I'm hard these would be the ones that encounter my local environments a lot