Voidces

Audio dataset with transcriptions for voice training.

Latest Upload: task_1770352558371

• Samples: 2450
• Parquet files: 5
• ZIP file: task_1770352558371/dataset_audio.zip
• Metadata: task_1770352558371/metadata.json

Dataset Structure

Files are organized by task ID:

task_1770352558371/
├── train-00000-of-00005.parquet
├── train-00001-of-00005.parquet
├── ...
├── dataset_audio.zip
└── metadata.json

Each parquet file contains:

• audio: Binary audio data (WAV format)
• transcription: Text transcription
• file_name: Reference (format: audio/name_00001.wav)

The metadata.json file contains:

• Processing parameters
• Detailed segment information
• Summary statistics
• Timestamps and file sizes

Usage

from datasets import load_dataset
import json

# Load dataset
ds = load_dataset("Translsis/Voidces", data_files="task_1770352558371/*.parquet")

# Load metadata
import requests
metadata_url = "https://huggingface.co/datasets/Translsis/Voidces/resolve/main/task_1770352558371/metadata.json"
metadata = requests.get(metadata_url).json()

# Or download ZIP
# https://huggingface.co/datasets/Translsis/Voidces/resolve/main/task_1770352558371/dataset_audio.zip

# Access audio
import io
import soundfile as sf

sample = ds['train'][0]
audio_bytes = sample['audio']
audio_array, sr = sf.read(io.BytesIO(audio_bytes))

Stats

• Total samples: 2450
• Parquet files: 5
• Format: WAV (binary bytes)

Created with automated pipeline: Whisper → YAMNet → BS-RoFormer