Datasetpublic

TTS-AGI/podcast-dramabox-dacvae-pairs

podcast-dramabox-dacvae-pairs Paired audio-codec latents for training a DramaBox → DACVAE latent translator. Both codecs share an identical grid: 25 Hz, 128-dim, frame-aligned (same length). Derived from TTS-AGI/podcast-tokenized-bg3.5-enj5. How it was built (per sample) DACVAE latent (from source dataset, = target) → DACVAE.decode → 48 kHz mono wav → duplicate to stereo → DramaBox/LTX-2.3 audio VAE encode → patchify → DramaBox latent (= input). Both latents… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/podcast-dramabox-dacvae-pairs.

sourceHugging Faceotherupdated 3mo agoView on Hugging Face

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Dataset Card

podcast-dramabox-dacvae-pairs

Paired audio-codec latents for training a DramaBox → DACVAE latent translator. Both codecs share an identical grid: 25 Hz, 128-dim, frame-aligned (same length).

Derived from `TTS-AGI/podcast-tokenized-bg3.5-enj5`.

How it was built (per sample)

DACVAE latent (from source dataset, = target) → DACVAE.decode → 48 kHz mono wav → duplicate to stereo → DramaBox/LTX-2.3 audio VAE encode → patchify → DramaBox latent (= input). Both latents trimmed to the min length (they differ by ≤1 frame).

—DACVAE: facebook/dacvae-watermarked (encoderrates [2,8,10,12], latentdim 1024, codebook_dim 128, 48 kHz).
—DramaBox: LTX-2.3 audio VAE from ResembleAI/Dramabox (dramabox-audio-components.safetensors), 16 kHz, 25 Hz latents.

Format

Each shard XX_XX_XXXX.npz (numpy.savez, load with `allow_pickle=True`):

key	type	description
`db`	object array of (T,128) float16	DramaBox/LTX latent — translator input
`dac`	object array of (T,128) float16	DACVAE latent — translator target
`keys`	(N,) str	sample keys (match the source dataset)
`meta`	(N,) str	JSON: transcript + emotion scores

python

import numpy as np
z = np.load("00_00_0000.npz", allow_pickle=True)
db, dac = z["db"], z["dac"]          # paired latents
x, y = db[0], dac[0]                 # (T,128), (T,128)