ayousanz/multi-dataset-v2

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

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)

# 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

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:

cd multilingual-tts
for zip_file in audio/*.zip; do
    unzip "$zip_file" -d audio_extracted/
done

🚀 Usage Examples

Basic Usage

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

# 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:

@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.