Update README.md

README.md

@@ -1,47 +1,35 @@
----
-license: cc-by-4.0
-task_categories:
-- text-generation
-- text-to-speech
-- automatic-speech-recognition
-tags:
-- Urdu
-language:
-- ur
-pretty_name: ' Munch-1 Hashed Index '
----
-# Munch Hashed Index - Lightweight Audio Reference Dataset
+# Munch-1 Hashed Index - Lightweight Audio Reference Dataset
 
-[![Original Dataset](https://img.shields.io/badge/🤗%20Original-Munch-blue)](https://huggingface.co/datasets/humair025/munch-1)
+[![Original Dataset](https://img.shields.io/badge/🤗%20Original-Munch--1-blue)](https://huggingface.co/datasets/humair025/munch-1)
 [![Hashed Index](https://img.shields.io/badge/🤗%20Index-hashed__data-green)](https://huggingface.co/datasets/humair025/hashed_data_munch_1)
-[![Size](https://img.shields.io/badge/Size-~1000MB-brightgreen)]()
-[![Original Size](https://img.shields.io/badge/Original-3.28 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-3.28TB-orange)]()
+[![Space Saved](https://img.shields.io/badge/Space%20Saved-99.97%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-1). Instead of storing 3+ TB of raw audio, this index stores only metadata and cryptographic hashes, enabling:
+**Munch-1 Hashed Index** is a lightweight reference dataset that provides SHA-256 hashes for all audio files in the [Munch-1 Urdu TTS Dataset](https://huggingface.co/datasets/humair025/munch-1). Instead of storing 3.28 TB of raw audio, this index stores only metadata and cryptographic hashes, enabling:
 
 - ✅ **Fast duplicate detection** across 3.86M+ 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 (3.28  TB → ~1000 MB)
+- ✅ **Storage efficiency** - 99.97% space reduction (3.28 TB → ~1 GB)
 
 ### 🔗 Related Datasets
 
-- **Original Dataset**: [humair025/Munch](https://huggingface.co/datasets/humair025/munch-1) - Full audio dataset (3.86 TB)
-- **This Index**: [humair025/hashed_data](https://huggingface.co/datasets/humair025/hashed_data_munch_1) - Hashed reference (~1000 MB)
+- **Original Dataset**: [humair025/munch-1](https://huggingface.co/datasets/humair025/munch-1) - Full audio dataset (3.28 TB)
+- **This Index**: [humair025/hashed_data_munch_1](https://huggingface.co/datasets/humair025/hashed_data_munch_1) - Hashed reference (~1 GB)
 
 ---
 
 ## 🎯 What Problem Does This Solve?
 
 ### The Challenge
-The original [Munch dataset](https://huggingface.co/datasets/humair025/munch-1) contains:
-- 📊 **3.86M+ audio-text pairs**
-- 💾 **3.7+ TB total size**
-- 📦 **7,000+ separate parquet files**
+The original [Munch-1 dataset](https://huggingface.co/datasets/humair025/munch-1) contains:
+- 📊 **3,856,500 audio-text pairs**
+- 💾 **3.28 TB total size**
+- 📦 **~7,714 separate parquet files** (~400 MB each)
 
 This makes it difficult to:
 - ❌ Quickly check if specific audio exists
@@ -73,8 +61,8 @@ pip install datasets pandas
 from datasets import load_dataset
 import pandas as pd
 
-# Load the entire hashed index (fast - only ~150 MB!)
-ds = load_dataset("humair025/hashed_data", split="train")
+# Load the entire hashed index (fast - only ~1 GB!)
+ds = load_dataset("humair025/hashed_data_munch_1", split="train")
 df = pd.DataFrame(ds)
 
 print(f"Total records: {len(df)}")
@@ -133,7 +121,7 @@ def get_audio_by_hash(audio_hash, index_df):
     
     # Download only the specific parquet file containing this audio
     ds = load_original(
-        "humair025/Munch",
+        "humair025/munch-1",
         data_files=[row['parquet_file_name']],
         split="train"
     )
@@ -170,7 +158,7 @@ wav_io = pcm16_to_wav(audio_bytes)
 | Field | Type | Description |
 |-------|------|-------------|
 | `id` | int | Original paragraph ID from source dataset |
-| `parquet_file_name` | string | Source file in [Munch](https://huggingface.co/datasets/humair025/munch-1) dataset |
+| `parquet_file_name` | string | Source file in [munch-1](https://huggingface.co/datasets/humair025/munch-1) dataset |
 | `text` | string | Original Urdu text |
 | `transcript` | string | TTS transcript (may differ from input) |
 | `voice` | string | Voice used (alloy, echo, fable, onyx, nova, shimmer, coral, verse, ballad, ash, sage, amuch, dan) |
@@ -228,11 +216,11 @@ long_text = df[df['text'].str.len() > 200]
 ```python
 # Download only specific voices
 ash_files = df[df['voice'] == 'ash']['parquet_file_name'].unique()
-ds = load_dataset("humair025/Munch", data_files=list(ash_files))
+ds = load_dataset("humair025/munch-1", data_files=list(ash_files))
 
 # Download only short audio samples
 small_files = df[df['audio_size_bytes'] < 40000]['parquet_file_name'].unique()
-ds = load_dataset("humair025/Munch", data_files=list(small_files[:10]))
+ds = load_dataset("humair025/munch-1", data_files=list(small_files[:10]))
 ```
 
 ### 4. **Deduplication Pipeline**
@@ -265,21 +253,24 @@ 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 | 3.28 TB | ~1 GB | **99.97%** |
+| Records | 3,856,500 | 3,856,500 | Same |
+| Files | 7,714 parquet | Consolidated | **~7,700× fewer** |
+| Download Time (100 Mbps) | ~73 hours | ~90 seconds | **~3,000×** |
+| 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: 3,856,500
+   Total Files: 7,714 parquet files (~400 MB each)
    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: ~3,200-4,800 hours of audio
 ```
 
 ---
@@ -292,7 +283,7 @@ print(f"Similar audio candidates: {len(similar)}")
 # Analyze all hash files
 from datasets import load_dataset
 
-ds = load_dataset("humair025/hashed_data", split="train")
+ds = load_dataset("humair025/hashed_data_munch_1", split="train")
 df = pd.DataFrame(ds)
 
 # Group by voice
@@ -319,7 +310,7 @@ def verify_hash_exists(audio_hash, parquet_file):
     import hashlib
     
     ds = load_dataset(
-        "humair025/Munch",
+        "humair025/munch-1",
         data_files=[parquet_file],
         split="train"
     )
@@ -403,7 +394,8 @@ import time
 
 # Load index
 start = time.time()
-df = pd.read_parquet('hashed_0_39.parquet')
+ds = load_dataset("humair025/hashed_data_munch_1", split="train")
+df = pd.DataFrame(ds)
 print(f"Load time: {time.time() - start:.2f}s")
 
 # Query by hash
@@ -418,7 +410,7 @@ 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 +423,7 @@ 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_munch_1", split="train"))
 target_rows = df[df['voice'] == 'ash'].head(100)
 
 # 2. Get unique parquet files
@@ -440,7 +432,7 @@ files_needed = target_rows['parquet_file_name'].unique()
 # 3. Download only needed files (selective)
 from datasets import load_dataset
 ds = load_dataset(
-    "humair025/Munch",
+    "humair025/munch-1",
     data_files=list(files_needed),
     split="train"
 )
@@ -463,46 +455,42 @@ If you use this dataset in your research, please cite both the original dataset
 ### BibTeX
 
 ```bibtex
-@dataset{munch_hashed_index_V2_2025,
-  title={Munch Hashed Index: Lightweight Reference Dataset for Urdu TTS},
+@dataset{munch_hashed_index_2025,
+  title={Munch-1 Hashed Index: Lightweight Reference Dataset for Urdu TTS},
   author={humair025},
   year={2025},
   publisher={Hugging Face},
-  howpublished={
-    \url{https://huggingface.co/datasets/humair025/hashed_data_munch_1}
-  },
-  note={Index of humair025/Munch dataset with SHA-256 audio hashes}
+  howpublished={\url{https://huggingface.co/datasets/humair025/hashed_data_munch_1}},
+  note={Index of humair025/munch-1 dataset with SHA-256 audio hashes}
 }
 
-@dataset{munch_urdu_tts_V2_2025,
-  title={Munch V2: Large-Scale Urdu Text-to-Speech Dataset},
+@dataset{munch_urdu_tts_2025,
+  title={Munch-1: Large-Scale Urdu Text-to-Speech Dataset},
   author={humair025},
   year={2025},
   publisher={Hugging Face},
-  howpublished={
-    \url{https://huggingface.co/datasets/humair025/munch-1}
-  }
+  howpublished={\url{https://huggingface.co/datasets/humair025/munch-1}}
 }
 ```
 
 ### APA Format
 
 ```
-humair025. (2025). Munch Hashed Index V2: Lightweight Reference Dataset for Urdu TTS 
+humair025. (2025). Munch-1 Hashed Index: Lightweight Reference Dataset for Urdu TTS 
 [Dataset]. Hugging Face. https://huggingface.co/datasets/humair025/hashed_data_munch_1
 
-humair025. (2025). Munch: Large-Scale Urdu Text-to-Speech Dataset [Dataset]. 
+humair025. (2025). Munch-1: Large-Scale Urdu Text-to-Speech Dataset [Dataset]. 
 Hugging Face. https://huggingface.co/datasets/humair025/munch-1
 ```
 
 ### MLA Format
 
 ```
-humair025. "Munch Hashed Index V2: Lightweight Reference Dataset for Urdu TTS." 
-Hugging Face, 2024, https://huggingface.co/datasets/humair025/hashed_data.
+humair025. "Munch-1 Hashed Index: Lightweight Reference Dataset for Urdu TTS." 
+Hugging Face, 2025, https://huggingface.co/datasets/humair025/hashed_data_munch_1.
 
-humair025. "Munch: Large-Scale Urdu Text-to-Speech Dataset." Hugging Face, 2024, 
-https://huggingface.co/datasets/humair025/Munch.
+humair025. "Munch-1: Large-Scale Urdu Text-to-Speech Dataset." Hugging Face, 2025, 
+https://huggingface.co/datasets/humair025/munch-1.
 ```
 
 ---
@@ -527,7 +515,7 @@ We welcome suggestions for:
 
 ## 📄 License
 
-This index dataset inherits the license from the original [Munch dataset](https://huggingface.co/datasets/humair025/Munch):
+This index dataset inherits the license from the original [Munch-1 dataset](https://huggingface.co/datasets/humair025/munch-1):
 
 **Creative Commons Attribution 4.0 International (CC-BY-4.0)**
 
@@ -543,20 +531,20 @@ Under the terms:
 
 ## 🔗 Important Links
 
-- 🎧 [**Original Audio Dataset**](https://huggingface.co/datasets/humair025/munch) - Full 3.28  TB audio
+- 🎧 [**Original Audio Dataset**](https://huggingface.co/datasets/humair025/munch-1) - Full 3.28 TB audio
 - 📊 [**This Hashed Index**](https://huggingface.co/datasets/humair025/hashed_data_munch_1) - Lightweight reference
-- 💬 [**Discussions**](https://huggingface.co/datasets/humair025/hashed_data/discussions) - Ask questions
-- 🐛 [**Report Issues**](https://huggingface.co/datasets/humair025/hashed_data/discussions) - Bug reports
+- 💬 [**Discussions**](https://huggingface.co/datasets/humair025/hashed_data_munch_1/discussions) - Ask questions
+- 🐛 [**Report Issues**](https://huggingface.co/datasets/humair025/hashed_data_munch_1/discussions) - Bug reports
 
 ---
 
 ## ❓ 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-1) using the file reference provided.
+**A:** No. SHA-256 is a one-way cryptographic hash. You must download the original audio from the [Munch-1 dataset](https://huggingface.co/datasets/humair025/munch-1) using the file reference provided.
 
 ### Q: How accurate are the hashes?
 **A:** SHA-256 has virtually zero collision probability. If two hashes match, the audio is identical (byte-for-byte).
@@ -565,7 +553,7 @@ 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-1). 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 3,856,500 rows across all 7,714 parquet files from the original Munch-1 dataset.
 
 ### Q: Can I contribute?
 **A:** Yes! Help verify hashes, report inconsistencies, or suggest improvements via discussions.
@@ -574,9 +562,9 @@ Under the terms:
 
 ## 🙏 Acknowledgments
 
-- **Original Dataset**: [humair025/Munch](https://huggingface.co/datasets/humair025/munch-1)
+- **Original Dataset**: [humair025/munch-1](https://huggingface.co/datasets/humair025/munch-1)
 - **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 +572,17 @@ 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 7,714 parquet files
+  - 3,856,500 audio samples indexed
+  - SHA-256 hashes computed for all audio
+  - ~99.97% space reduction achieved
 
 ---
 
 **Last Updated**: December 2025
 
-**Status**: 🔄 Actively Processing (check file count for latest progress)
+**Status**: ✅ Complete
 
 ---