Prhokbvf556/Audio-VAE-Phonk-Dataset

🚗 Phonk Audio Dataset for Generative ML

This dataset contains hundreds of hours of high-quality Phonk music (Drift Phonk, Hard Phonk, etc.), specifically scraped, pre-processed, and formatted for training deep learning audio models.

It is perfectly suited for training Audio VAEs, EnCodec, TiTok, or Audio Diffusion / Transformer Prior models from scratch.

📊 Dataset Specifications

The audio data has been heavily pre-processed to maximize training efficiency on TPUs/GPUs:

• Format: TFRecord
• Sample Rate: 32,000 Hz (Optimized for generative ML, capturing frequencies up to 16kHz)
• Channels: 1 (Mono)
• Chunk Size: 131,072 samples per chunk (Exactly ~4.09 seconds of audio)
• Data Type: Raw Waveform (float32 arrays)
• Quality Control:
  • Strict RMS-based silence removal.
  • MD5 hashing for chunk deduplication (no overlapping repeated segments).

🛠️ Preprocessing Pipeline

The dataset was constructed using a high-throughput multi-core pipeline:

1. Source: YouTube Phonk/Drift mixes and playlists.
2. Download & Extraction: yt-dlp (bestaudio) -> ffmpeg (conversion to 32kHz, Mono, s16).
3. Slicing: Audio is loaded into memory, sliced into exact 2^17 (131,072) sample chunks.
4. Filtering: Chunks with RMS energy below 0.01 are discarded.
5. Serialization: Saved as tf.train.Example directly into TFRecord shards.

💻 How to use (TensorFlow)

Since the data is stored in TFRecords, you can stream it directly into your training loop without downloading the entire dataset, which is ideal for Kaggle/Colab environments.

import tensorflow as tf

def parse_tfrecord_fn(example):
    feature_description = {
        "audio": tf.io.FixedLenFeature([131072], tf.float32),
    }
    example = tf.io.parse_single_example(example, feature_description)
    return example["audio"]

# Load dataset (can point directly to HF paths or local /dev/shm)
raw_dataset = tf.data.TFRecordDataset([
    "data/audio_vae_part_0001.tfrecord", 
    "data/audio_vae_part_0002.tfrecord"
])

parsed_dataset = raw_dataset.map(parse_tfrecord_fn, num_parallel_calls=tf.data.AUTOTUNE)
parsed_dataset = parsed_dataset.batch(32).prefetch(tf.data.AUTOTUNE)

for audio_batch in parsed_dataset.take(1):
    print(audio_batch.shape) 
    # Expected output: (32, 131072)

⚠️ Intended Use & Limitations This dataset is designed for research in music generation architectures. Due to the aggressive lossy compression of the source material (YouTube Opus/AAC) and the 32kHz downsampling, it is intended for Lo-Fi / Phonk style generation where extreme high-fidelity high-end frequencies (>16kHz) are not required.