TTS-AGI/mls-enhanced-dacvae

Multilingual LibriSpeech converted to DAC VAE latents

Source

facebook/multilingual_librispeech

Format

Each tar shard (~2GB) contains samples with three files per sample:

{sample_key}.audio.flac       # Original audio (FLAC, original sample rate)
{sample_key}.dacvae.npy       # DAC VAE latent [T_latent, 128] numpy float32
{sample_key}.metadata.json    # All metadata + duration_seconds + chars_per_second

DAC VAE Latent Format

• Model: mrfakename/dacvae-watermarked (Facebook DACVAE)
• Input sample rate: 48,000 Hz (audio resampled before encoding)
• Latent shape: [T_latent, 128] where T_latent = ceil(audio_samples / 1920)
• Latent rate: 25 frames/second
• Storage: numpy float32

Shard Naming

{LANG}-{split}-{index:05d}.tar (e.g., EN-train-00000.tar, DE-train-00001.tar)

Loading

With WebDataset

import webdataset as wds
import numpy as np
import json
import soundfile as sf
import io

url = "https://huggingface.co/datasets/TTS-AGI/mls-enhanced-dacvae/resolve/main/EN-train-00000.tar"
dataset = wds.WebDataset(url).decode()

for sample in dataset:
    audio_bytes = sample["audio.flac"]
    latent = np.load(io.BytesIO(sample["dacvae.npy"]))  # [T, 128]
    meta = json.loads(sample["metadata.json"])
    print(f"Text: {meta['text']}, Duration: {meta['duration_seconds']}s, CPS: {meta['chars_per_second']}")

Decoding Latents Back to Audio

from dacvae import DACVAE
from huggingface_hub import hf_hub_download
import torch, numpy as np

model = DACVAE.load(hf_hub_download("mrfakename/dacvae-watermarked", "weights.pth")).cuda().eval()
latent = np.load("sample.dacvae.npy")  # [T_latent, 128]
z = torch.from_numpy(latent.T).unsqueeze(0).cuda()  # [1, 128, T_latent]
audio_48k = model.decode(z).squeeze(0).cpu()  # [1, T_audio] at 48kHz

Current Status

Shards uploaded: 23

Progress by Language

Language	Samples
DE_train	15,216
ES_train	18,264
FR_train	23,176
IT_train	8,132
NL_train	25,104
PL_train	8,800
PT_train	8,320

Metadata Fields

Each metadata.json contains:

• dataset: Source dataset name
• language: Language code
• split: Data split (train/dev/test)
• sample_id: Original sample identifier
• text: Transcript
• duration_seconds: Audio duration in seconds
• chars_per_second: Text characters per second of audio
• original_sample_rate: Original audio sample rate
• dacvae_sample_rate: 48000 (DAC VAE input rate)
• latent_frames: Number of latent time frames
• Plus all original dataset-specific fields

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