Update README.md

README.md

@@ -8,30 +8,32 @@ tags:
 - Urdu
 language:
 - ur
-pretty_name: ' Munch Hashed Index '
+pretty_name: 'Munch Hashed Index'
 ---
 # Munch Hashed Index - Lightweight Audio Reference Dataset
 
 [![Original Dataset](https://img.shields.io/badge/🤗%20Original-Munch-blue)](https://huggingface.co/datasets/humair025/Munch)
 [![Hashed Index](https://img.shields.io/badge/🤗%20Index-hashed__data-green)](https://huggingface.co/datasets/humair025/hashed_data)
-[![Size](https://img.shields.io/badge/Size-~500MB-brightgreen)]()
-[![Original Size](https://img.shields.io/badge/Original-1+TB-orange)]()
-[![Space Saved](https://img.shields.io/badge/Space%20Saved-99.99%25-success)]()
+[![Size](https://img.shields.io/badge/Size-~1GB-brightgreen)]()
+[![Original Size](https://img.shields.io/badge/Original-1.27TB-orange)]()
+[![Space Saved](https://img.shields.io/badge/Space%20Saved-99.92%25-success)]()
 
 ## 📖 Overview
 
-**Munch Hashed Index** is a lightweight reference dataset that provides SHA-256 hashes for all audio files in the [Munch Urdu TTS Dataset](https://huggingface.co/datasets/humair025/Munch). Instead of storing 2.17 TB of raw audio, this index stores only metadata and cryptographic hashes, enabling:
+**Munch Hashed Index** is a lightweight reference dataset that provides SHA-256 hashes for all audio files in the [Munch Urdu TTS Dataset](https://huggingface.co/datasets/humair025/Munch). Instead of storing 1.27 TB of raw audio, this index stores only metadata and cryptographic hashes, enabling:
 
-- ✅ **Fast duplicate detection** across 2.5M+ audio samples
+- ✅ **Fast duplicate detection** across 4.17 million audio samples
 - ✅ **Efficient dataset exploration** without downloading terabytes
 - ✅ **Quick metadata queries** (voice distribution, text stats, etc.)
 - ✅ **Selective audio retrieval** - download only what you need
-- ✅ **Storage efficiency** - 99.99% space reduction (2.17 TB → ~150 MB)
+- ✅ **Storage efficiency** - 99.92% space reduction (1.27 TB → ~1 GB)
 
 ### 🔗 Related Datasets
 
-- **Original Dataset**: [humair025/Munch](https://huggingface.co/datasets/humair025/Munch) - Full audio dataset (1+ TB)
-- **This Index**: [humair025/hashed_data](https://huggingface.co/datasets/humair025/hashed_data) - Hashed reference (~500 MB)
+- **Original Dataset**: [humair025/Munch](https://huggingface.co/datasets/humair025/Munch) - Full audio dataset (1.27 TB)
+- **This Index**: [humair025/hashed_data](https://huggingface.co/datasets/humair025/hashed_data) - Hashed reference (~1 GB)
+- **Munch-1 (v2)**: [humair025/munch-1](https://huggingface.co/datasets/humair025/munch-1) - Newer version (3.28 TB, 3.86M samples)
+- **Munch-1 Index**: [humair025/hashed_data_munch_1](https://huggingface.co/datasets/humair025/hashed_data_munch_1) - Index for v2
 
 ---
 
@@ -39,9 +41,9 @@ pretty_name: ' Munch Hashed Index '
 
 ### The Challenge
 The original [Munch dataset](https://huggingface.co/datasets/humair025/Munch) contains:
-- 📊 **2.5M+ audio-text pairs**
-- 💾 **2.17 TB total size**
-- 📦 **5,000+ separate parquet files**
+- 📊 **4,167,500 audio-text pairs**
+- 💾 **1.27 TB total size**
+- 📦 **~8,300 separate parquet files**
 
 This makes it difficult to:
 - ❌ Quickly check if specific audio exists
@@ -54,7 +56,7 @@ This hashed index provides:
 - ✅ **All metadata** (text, voice, timestamps) without audio bytes
 - ✅ **SHA-256 hashes** for every audio file (unique fingerprint)
 - ✅ **File references** (which parquet contains each audio)
-- ✅ **Fast queries** - search 2.5M records in seconds
+- ✅ **Fast queries** - search 4.17M records in seconds
 - ✅ **Retrieve on demand** - download only specific audio when needed
 
 ---
@@ -73,7 +75,7 @@ pip install datasets pandas
 from datasets import load_dataset
 import pandas as pd
 
-# Load the entire hashed index (fast - only ~150 MB!)
+# Load the entire hashed index (fast - only ~1 GB!)
 ds = load_dataset("humair025/hashed_data", split="train")
 df = pd.DataFrame(ds)
 
@@ -190,7 +192,7 @@ wav_io = pcm16_to_wav(audio_bytes)
     'voice': 'ash',
     'audio_bytes_hash': 'a3f7b2c8e9d1f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9',
     'audio_size_bytes': 52340,
-    'timestamp': '2024-12-03T13:03:14.123456',
+    'timestamp': '2025-12-03T13:03:14.123456',
     'error': None
 }
 ```
@@ -265,21 +267,24 @@ print(f"Similar audio candidates: {len(similar)}")
 
 | Metric | Original Dataset | Hashed Index | Reduction |
 |--------|------------------|--------------|-----------|
-| Total Size | 2.17 TB | ~500 MB | **99%** |
-| Download Time (100 Mbps) | ~X hours | ~12 seconds | **Thousand Time×** |
-| Load Time | Minutes | Seconds | **~100×** |
-| Memory Usage | Cannot fit in RAM | Fit | **Thousands  X×** |
+| Total Size | 1.27 TB | ~1 GB | **99.92%** |
+| Records | 4,167,500 | 4,167,500 | Same |
+| Files | ~8,300 parquet | Consolidated | **~8,300× fewer** |
+| Download Time (100 Mbps) | ~28 hours | ~90 seconds | **~1,100×** |
+| Load Time | Minutes-Hours | Seconds | **~100×** |
+| Memory Usage | Cannot fit in RAM | ~2-3 GB RAM | **Fits easily** |
 
 ### Content Statistics
 
 ```
 📊 Dataset Overview:
-   Total Records: ~2,500,000
-   Unique Audio: [Run analysis to determine]
+   Total Records: 4,167,500
+   Total Files: ~8,300 parquet files
    Voices: 13 (alloy, echo, fable, onyx, nova, shimmer, coral, verse, ballad, ash, sage, amuch, dan)
-   Languages: Urdu (primary), Mixed (some samples)
+   Language: Urdu (primary)
    Avg Audio Size: ~50-60 KB per sample
    Avg Duration: ~3-5 seconds per sample
+   Total Duration: ~3,500-5,800 hours of audio
 ```
 
 ---
@@ -403,7 +408,8 @@ import time
 
 # Load index
 start = time.time()
-df = pd.read_parquet('hashed_0_39.parquet')
+ds = load_dataset("humair025/hashed_data", split="train")
+df = pd.DataFrame(ds)
 print(f"Load time: {time.time() - start:.2f}s")
 
 # Query by hash
@@ -418,7 +424,7 @@ print(f"Voice filter: {(time.time() - start)*1000:.2f}ms")
 ```
 
 **Expected Performance**:
-- Load single file: < 1 second
+- Load full dataset: 10-30 seconds
 - Hash lookup: < 10 milliseconds  
 - Voice filter: < 50 milliseconds
 - Full dataset scan: < 5 seconds
@@ -431,7 +437,7 @@ print(f"Voice filter: {(time.time() - start)*1000:.2f}ms")
 
 ```python
 # 1. Query the index (fast)
-df = pd.read_parquet('hashed_index.parquet')
+df = pd.DataFrame(load_dataset("humair025/hashed_data", split="train"))
 target_rows = df[df['voice'] == 'ash'].head(100)
 
 # 2. Get unique parquet files
@@ -463,45 +469,41 @@ If you use this dataset in your research, please cite both the original dataset
 ### BibTeX
 
 ```bibtex
-@dataset{munch_hashed_index_2024,
+@dataset{munch_hashed_index_2025,
   title={Munch Hashed Index: Lightweight Reference Dataset for Urdu TTS},
-  author={humair025},
+  author={Munir, Humair},
   year={2025},
   publisher={Hugging Face},
-  howpublished={
-    \url{https://huggingface.co/datasets/humair025/hashed_data}
-  },
+  howpublished={\url{https://huggingface.co/datasets/humair025/hashed_data}},
   note={Index of humair025/Munch dataset with SHA-256 audio hashes}
 }
 
-@dataset{munch_urdu_tts_2024,
+@dataset{munch_urdu_tts_2025,
   title={Munch: Large-Scale Urdu Text-to-Speech Dataset},
-  author={humair025},
+  author={Munir, Humair},
   year={2025},
   publisher={Hugging Face},
-  howpublished={
-    \url{https://huggingface.co/datasets/humair025/Munch}
-  }
+  howpublished={\url{https://huggingface.co/datasets/humair025/Munch}}
 }
 ```
 
 ### APA Format
 
 ```
-humair025. (2024). Munch Hashed Index: Lightweight Reference Dataset for Urdu TTS 
+Munir, H. (2025). Munch Hashed Index: Lightweight Reference Dataset for Urdu TTS 
 [Dataset]. Hugging Face. https://huggingface.co/datasets/humair025/hashed_data
 
-humair025. (2024). Munch: Large-Scale Urdu Text-to-Speech Dataset [Dataset]. 
+Munir, H. (2025). Munch: Large-Scale Urdu Text-to-Speech Dataset [Dataset]. 
 Hugging Face. https://huggingface.co/datasets/humair025/Munch
 ```
 
 ### MLA Format
 
 ```
-humair025. "Munch Hashed Index: Lightweight Reference Dataset for Urdu TTS." 
-Hugging Face, 2024, https://huggingface.co/datasets/humair025/hashed_data.
+Munir, Humair. "Munch Hashed Index: Lightweight Reference Dataset for Urdu TTS." 
+Hugging Face, 2025, https://huggingface.co/datasets/humair025/hashed_data.
 
-humair025. "Munch: Large-Scale Urdu Text-to-Speech Dataset." Hugging Face, 2024, 
+Munir, Humair. "Munch: Large-Scale Urdu Text-to-Speech Dataset." Hugging Face, 2025, 
 https://huggingface.co/datasets/humair025/Munch.
 ```
 
@@ -543,8 +545,10 @@ Under the terms:
 
 ## 🔗 Important Links
 
-- 🎧 [**Original Audio Dataset**](https://huggingface.co/datasets/humair025/Munch) - Full 1.+ TB audio
+- 🎧 [**Original Audio Dataset**](https://huggingface.co/datasets/humair025/Munch) - Full 1.27 TB audio
 - 📊 [**This Hashed Index**](https://huggingface.co/datasets/humair025/hashed_data) - Lightweight reference
+- 🔄 [**Munch-1 (v2)**](https://huggingface.co/datasets/humair025/munch-1) - Newer version (3.28 TB)
+- 📇 [**Munch-1 Index**](https://huggingface.co/datasets/humair025/hashed_data_munch_1) - Index for v2
 - 💬 [**Discussions**](https://huggingface.co/datasets/humair025/hashed_data/discussions) - Ask questions
 - 🐛 [**Report Issues**](https://huggingface.co/datasets/humair025/hashed_data/discussions) - Bug reports
 
@@ -553,7 +557,7 @@ Under the terms:
 ## ❓ FAQ
 
 ### Q: Why use hashes instead of audio?
-**A:** Hashes provide unique fingerprints for audio files while taking only 64 bytes vs ~50kb-12MB per audio. This enables duplicate detection and fast queries without storing massive audio files.
+**A:** Hashes provide unique fingerprints for audio files while taking only 64 bytes vs ~50KB per audio. This enables duplicate detection and fast queries without storing massive audio files.
 
 ### Q: Can I reconstruct audio from hashes?
 **A:** No. SHA-256 is a one-way cryptographic hash. You must download the original audio from the [Munch dataset](https://huggingface.co/datasets/humair025/Munch) using the file reference provided.
@@ -565,7 +569,10 @@ Under the terms:
 **A:** Use the `parquet_file_name` and `id` fields to locate and download the specific audio from the [original dataset](https://huggingface.co/datasets/humair025/Munch). See examples above.
 
 ### Q: Is this dataset complete?
-**A:** This index is continuously updated as new batches are processed. Check the file list to see coverage.
+**A:** Yes, this index covers all 4,167,500 rows across all ~8,300 parquet files from the original Munch dataset.
+
+### Q: What's the difference between this and Munch-1 Index?
+**A:** This indexes the original Munch dataset (1.27 TB, 4.17M samples). The [Munch-1 Index](https://huggingface.co/datasets/humair025/hashed_data_munch_1) indexes the newer Munch-1 dataset (3.28 TB, 3.86M samples).
 
 ### Q: Can I contribute?
 **A:** Yes! Help verify hashes, report inconsistencies, or suggest improvements via discussions.
@@ -576,7 +583,7 @@ Under the terms:
 
 - **Original Dataset**: [humair025/Munch](https://huggingface.co/datasets/humair025/Munch)
 - **TTS Generation**: OpenAI-compatible models
-- **Voices**: 13 high-quality voices
+- **Voices**: 13 high-quality voices (alloy, echo, fable, onyx, nova, shimmer, coral, verse, ballad, ash, sage, amuch, dan)
 - **Infrastructure**: HuggingFace Datasets platform
 - **Hashing**: SHA-256 cryptographic hash function
 
@@ -584,16 +591,17 @@ Under the terms:
 
 ## 📝 Version History
 
-- **v1.0.0** (December 2025): Initial release with hash index
-  - Processed [X] out of N parquet files
-  - [Y] unique audio hashes identified
-  - [Z]% deduplication achieved
+- **v1.0.0** (December 2025): Initial release
+  - Processed all ~8,300 parquet files
+  - 4,167,500 audio samples indexed
+  - SHA-256 hashes computed for all audio
+  - ~99.92% space reduction achieved
 
 ---
 
 **Last Updated**: December 2025
 
-**Status**: 🔄 Actively Processing (check file count for latest progress)
+**Status**: ✅ Complete
 
 ---