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README.md

@@ -20,10 +20,28 @@ size_categories:
 
 This dataset contains audio segments with transcriptions from Peaky Blinders for Text-to-Speech training.
 
+## Dataset Generation
+
+This dataset was generated using the [TTS-Dataset-Maker](https://github.com/ahk-d/TTS-Dataset-Maker) pipeline, which provides:
+
+- **Silero VAD-based silence removal** - Removes long silences while preserving natural speech gaps
+- **DeepFilterNet denoising** - CPU-optimized audio denoising with gentle attenuation (15dB)
+- **AssemblyAI transcription** - High-quality speech-to-text with speaker diarization
+- **Existing transcript support** - Uses pre-existing transcripts to skip transcription
+- **Precise segmentation** - Exact audio segment extraction matching transcript timings
+- **Unique file naming** - Source-aware segment names to prevent collisions
+
+### Processing Pipeline
+1. Audio preprocessing with DeepFilterNet denoising (atten_lim_db=15.0 for gentle noise reduction)
+2. Voice Activity Detection using Silero VAD to remove long silences
+3. Speaker diarization and transcription (or loading existing transcripts)
+4. Precise audio segmentation based on transcript timestamps
+5. Quality validation and filtering of segments
+
 ## Dataset Structure
-- `audio_segments/`: Audio files (WAV format, 24kHz)
-- `metadata/`: Contains TTS metadata JSON files
-- `transcription_cache/`: Transcription cache files
+- `audio/`: Audio files (WAV format, 44.1kHz)
+- `metadata.json`: Complete segment metadata for Label Studio
+- `transcripts/`: Per-file transcript JSON files
 
 ## Usage
 ```python
@@ -31,26 +49,27 @@ import json
 import os
 
 # Load metadata
-metadata_dir = "metadata"
-for file in os.listdir(metadata_dir):
-    if file.startswith("tts_metadata_") and file.endswith(".json"):
-        with open(os.path.join(metadata_dir, file), 'r') as f:
-            data = json.load(f)
-        break
+with open("metadata.json", 'r') as f:
+    data = json.load(f)
 
 # Access segments
 for segment in data:
     print(f"Text: {segment['text']}")
     print(f"Speaker: {segment['speaker_id']}")
     print(f"Audio: {segment['audio_file']}")
+    print(f"Duration: {segment['duration']}s")
+    print(f"Quality Score: {segment['quality_score']}")
 ```
 
 ## Dataset Details
 - **Language**: English
-- **Format**: WAV audio files (24kHz)
+- **Format**: WAV audio files (44.1kHz, 16-bit PCM)
 - **Speakers**: Multiple speakers (A, B, C, D, E)
-- **Duration**: Variable segment lengths
-- **Quality**: High-quality audio with quality scores
+- **Duration**: Variable segment lengths (filtered for >1s duration)
+- **Quality**: High-quality audio with quality scores and confidence metrics
+- **Total Segments**: 356 segments
+- **Total Duration**: ~89 minutes
+- **Processing**: Denoised with DeepFilterNet, silence-removed with Silero VAD
 
 ## Metadata Format
 Each segment contains:
@@ -60,6 +79,9 @@ Each segment contains:
 - `duration`: Duration in seconds
 - `quality_score`: Audio quality score
 - `confidence_score`: Transcription confidence
+- `start_time`: Start time in milliseconds
+- `end_time`: End time in milliseconds
+- `source_file`: Original source file path
 
 ## License
 MIT License
@@ -68,7 +90,10 @@ MIT License
 If you use this dataset, please cite:
 @dataset{peaky_blinders_tts,
 title={TTS Dataset - Peaky Blinders},
-author={Your Name},
+author={ahk-d},
 year={2024},
 url={https://huggingface.co/datasets/ahk-d/peaky-blinders-learning-purpose-only}
-}
+}
+
+## Generation Tool
+Generated using [TTS-Dataset-Maker](https://github.com/ahk-d/TTS-Dataset-Maker) - A comprehensive pipeline for creating high-quality TTS datasets from audio files with automatic transcription, denoising, and segmentation.