Initial upload of multilingual TTS dataset with organized structure

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+metadata/train.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,208 @@
+---
+language:
+- en
+- zh
+license: other
+license_name: mixed-cc-by-4.0-apache-2.0
+task_categories:
+- text-to-speech
+pretty_name: Multilingual TTS Dataset (LJSpeech Format)
+size_categories:
+- 10K<n<100K
+tags:
+- audio
+- speech
+- tts
+- text-to-speech
+- multilingual
+- english
+- chinese
+---
+
+# Multilingual TTS Dataset (LJSpeech Format)
+
+A high-quality multilingual Text-to-Speech dataset combining English and Chinese speech data, optimized for TTS training and suitable for commercial use.
+
+## 🎯 Quick Start
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("ayousanz/multi-dataset-v2")
+
+# Access data
+for item in dataset["train"]:
+    audio = item["audio"]          # 22050Hz mono audio
+    text = item["transcription"]   # Original text
+    speaker = item["speaker_id"]   # Speaker identifier  
+    language = item["language"]    # "en" or "zh"
+```
+
+## 📊 Dataset Statistics
+
+| **Metric** | **Value** |
+|------------|-----------|
+| **Total Duration** | 97.2 hours |
+| **Total Utterances** | 95,568 |
+| **Languages** | English, Chinese |
+| **Speakers** | 421 unique speakers |
+| **Audio Format** | 22050Hz, 16-bit, mono WAV |
+
+### Language Breakdown
+
+| **Language** | **Hours** | **Speakers** | **Utterances** |
+|--------------|-----------|--------------|----------------|
+| English | 48.6 | 247 | 32,310 |
+| Chinese | 48.6 | 174 | 63,258 |
+
+### Duration Distribution
+
+| **Range** | **Count** | **Percentage** |
+|-----------|-----------|----------------|
+| 0-2s | 28,555 | 29.9% |
+| 2-5s | 48,261 | 50.5% |
+| 5-10s | 14,167 | 14.8% |
+| 10-15s | 3,417 | 3.6% |
+| 15-20s | 1,168 | 1.2% |
+| 20s+ | 0 | 0.0% |
+
+## 📁 Repository Structure
+
+```
+├── audio/                          # Audio files (ZIP compressed)
+│   ├── train_english.zip          # English training audio
+│   ├── train_chinese.zip          # Chinese training audio  
+│   ├── validation_english.zip     # English validation audio
+│   ├── validation_chinese.zip     # Chinese validation audio
+│   ├── test_english.zip           # English test audio
+│   └── test_chinese.zip           # Chinese test audio
+├── metadata/                       # Metadata files
+│   ├── train.csv                  # Training metadata (all languages)
+│   ├── validation.csv             # Validation metadata (all languages)
+│   └── test.csv                   # Test metadata (all languages)
+├── dataset_info.json              # Dataset statistics and info
+├── multilingual_tts_ljspeech.py   # Dataset loader script
+└── README.md                      # This file
+```
+
+## 💾 Download Instructions
+
+### Option 1: Using Hugging Face CLI (Recommended)
+
+```bash
+# Install Hugging Face CLI
+pip install huggingface-hub
+
+# Download entire dataset
+huggingface-cli download ayousanz/multi-dataset-v2 --repo-type dataset --local-dir ./multilingual-tts
+
+# Download specific files only
+huggingface-cli download ayousanz/multi-dataset-v2 audio/train_english.zip metadata/train.csv --repo-type dataset --local-dir ./multilingual-tts
+```
+
+### Option 2: Using Python
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download entire dataset
+snapshot_download(
+    repo_id="ayousanz/multi-dataset-v2",
+    repo_type="dataset", 
+    local_dir="./multilingual-tts"
+)
+```
+
+### Extracting Audio Files
+
+After downloading, extract the ZIP files:
+
+```bash
+cd multilingual-tts
+for zip_file in audio/*.zip; do
+    unzip "$zip_file" -d audio_extracted/
+done
+```
+
+## 🚀 Usage Examples
+
+### Basic Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ayousanz/multi-dataset-v2")
+
+# Filter by language
+english_data = dataset["train"].filter(lambda x: x["language"] == "en")
+chinese_data = dataset["train"].filter(lambda x: x["language"] == "zh")
+
+# Filter by speaker
+speaker_data = dataset["train"].filter(lambda x: x["speaker_id"] == "en_1234")
+```
+
+### For TTS Training
+
+```python
+# Example with PyTorch DataLoader
+from torch.utils.data import DataLoader
+
+def collate_fn(batch):
+    audios = [item["audio"]["array"] for item in batch]
+    texts = [item["transcription"] for item in batch]
+    speakers = [item["speaker_id"] for item in batch]
+    return audios, texts, speakers
+
+dataloader = DataLoader(
+    dataset["train"], 
+    batch_size=32, 
+    collate_fn=collate_fn,
+    shuffle=True
+)
+```
+
+## 📋 Data Format
+
+Each sample contains:
+
+- **`audio_id`**: Unique identifier for the audio file
+- **`audio`**: Audio data (22050Hz, 16-bit, mono)
+- **`transcription`**: Original text transcription
+- **`normalized_text`**: Normalized text for TTS training
+- **`speaker_id`**: Speaker identifier with language prefix (`en_*` or `zh_*`)
+- **`language`**: Language code (`en` for English, `zh` for Chinese)
+
+## 📜 License
+
+This dataset combines data from multiple sources:
+
+- **English data (LibriTTS-R)**: CC BY 4.0 - requires attribution
+- **Chinese data (AISHELL-3)**: Apache 2.0
+
+### Attribution Requirements
+
+When using this dataset, please cite:
+
+```bibtex
+@dataset{{multilingual_tts_ljspeech,
+  title={{Multilingual TTS Dataset in LJSpeech Format}},
+  year={{2024}},
+  note={{English: LibriTTS-R (CC BY 4.0), Chinese: AISHELL-3 (Apache 2.0)}}
+}}
+```
+
+## 🔗 Source Datasets
+
+- **LibriTTS-R**: https://openslr.org/141/
+- **AISHELL-3**: https://openslr.org/93/
+
+## ⚡ Performance Notes
+
+- Audio files are stored in ZIP format for faster download
+- Use `datasets` library's built-in caching for optimal performance
+- Consider using `streaming=True` for large-scale training to save memory
+
+## 🤝 Contributing
+
+Found an issue? Please report it on the [repository issues page](https://huggingface.co/datasets/ayousanz/multi-dataset-v2/discussions).

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dataset_info.json

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+  "dataset_name": "multilingual-tts-ljspeech",
+  "dataset_version": "1.0.0",
+  "languages": [
+    "en",
+    "zh"
+  ],
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+    "text-to-speech"
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metadata/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d4a922999e078abc6a8779d6ca5656035ea6c39880f2dcadffdb7ded9bd9120
+size 17158142

multilingual_tts_ljspeech.py

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+"""Multilingual TTS Dataset in LJSpeech Format"""
+
+import csv
+import json
+import os
+import zipfile
+from pathlib import Path
+
+import datasets
+from datasets import Features, Value, Audio
+
+
+_CITATION = """@dataset{multilingual_tts_ljspeech,
+  title={Multilingual TTS Dataset in LJSpeech Format},
+  year={2024},
+  note={English: LibriTTS-R (CC BY 4.0), Chinese: AISHELL-3 (Apache 2.0)}
+}
+"""
+
+_DESCRIPTION = """A high-quality multilingual Text-to-Speech dataset in LJSpeech format, 
+containing English and Chinese speech data suitable for commercial use.
+
+This dataset combines:
+- English: LibriTTS-R (~49 hours, 247 speakers, 32K utterances)
+- Chinese: AISHELL-3 (~49 hours, 174 speakers, 63K utterances)
+
+Total: ~97 hours, 421 speakers, 95K utterances
+All audio normalized to 22050Hz, 16-bit, mono WAV format.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/ayousanz/multi-dataset-v2"
+_LICENSE = "Mixed: CC BY 4.0 (English), Apache 2.0 (Chinese)"
+
+
+class MultilingualTTSLJSpeech(datasets.GeneratorBasedBuilder):
+    """Multilingual TTS Dataset in LJSpeech Format"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = Features({
+            "audio_id": Value("string"),
+            "audio": Audio(sampling_rate=22050),
+            "transcription": Value("string"),
+            "normalized_text": Value("string"),
+            "speaker_id": Value("string"),
+            "language": Value("string"),
+        })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Download files with clean structure
+        files_to_download = {
+            # Metadata files
+            "train_csv": "metadata/train.csv",
+            "validation_csv": "metadata/validation.csv", 
+            "test_csv": "metadata/test.csv",
+            
+            # Audio ZIP files
+            "train_en_zip": "audio/train_english.zip",
+            "train_zh_zip": "audio/train_chinese.zip",
+            "validation_en_zip": "audio/validation_english.zip",
+            "validation_zh_zip": "audio/validation_chinese.zip",
+            "test_en_zip": "audio/test_english.zip",
+            "test_zh_zip": "audio/test_chinese.zip",
+        }
+        
+        downloaded_files = dl_manager.download(files_to_download)
+        
+        # Extract audio ZIP files
+        extracted_dirs = {}
+        for split in ["train", "validation", "test"]:
+            for lang in ["en", "zh"]:
+                lang_name = "english" if lang == "en" else "chinese"
+                key = f"{split}_{lang}_zip"
+                if key in downloaded_files:
+                    extracted_dirs[f"{split}_{lang}"] = dl_manager.extract(downloaded_files[key])
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "metadata_path": downloaded_files["train_csv"],
+                    "extracted_dirs": extracted_dirs,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "metadata_path": downloaded_files["validation_csv"],
+                    "extracted_dirs": extracted_dirs,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "metadata_path": downloaded_files["test_csv"],
+                    "extracted_dirs": extracted_dirs,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, metadata_path, extracted_dirs, split):
+        """Yields examples."""
+        with open(metadata_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="|")
+            
+            for idx, row in enumerate(reader):
+                audio_id = row["audio_id"]
+                language = row["language"]
+                
+                # Find the audio file in extracted directories
+                audio_dir_key = f"{split}_{language}"
+                if audio_dir_key in extracted_dirs:
+                    audio_path = Path(extracted_dirs[audio_dir_key]) / split / language / "wavs" / f"{audio_id}.wav"
+                    
+                    if audio_path.exists():
+                        yield idx, {
+                            "audio_id": audio_id,
+                            "audio": str(audio_path),
+                            "transcription": row["transcription"],
+                            "normalized_text": row["normalized_text"],
+                            "speaker_id": row["speaker_id"],
+                            "language": language,
+                        }