TTS-AGI/commonvoice22-sidon-dacvae

{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

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

url = "https://huggingface.co/datasets/TTS-AGI/commonvoice22-sidon-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']}")

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