Datasets:

CowardDriver
/

ttsdata

license: other
license_name: see-source-datasets
tags:
  - audio
  - tts
  - latents
  - dac-vae
  - speech
size_categories:
  - 100K<n<1M
language:
  - en

EnvTTS Phase 1 — Pre-encoded DAC-VAE Latents

Pre-encoded audio latents for Phase 1 training of EnvAudioEdit (Small ~195 M CFM-DiT TTS model). Audio from three English speech datasets is encoded offline with DAC-VAE (48 kHz, hop=1920), saving GPU time during training by avoiding on-the-fly encoding.

latents.zip
└── latents/
    ├── cv/     # 180 000 files  (humanify/common_voice_english, 10%)
    ├── ps/     # 216 000 files  (humanify/ps, 10%)
    └── ht2/    # 313 000 files  (humanify/ht2_44khz, 10%)

Total: 709 000 .pt files, ~43 GB (zipped)

File Format

Each .pt file is a PyTorch tensor dict:

{
    "z":      Tensor[T, 128],   # DAC-VAE latent, float16
    "text":   str,              # transcript
    "length": int,              # = T (number of latent frames)
}

Field	Details
Audio codec	DAC-VAE (`matbee/sam-audio-small-onnx`)
Sample rate	48 000 Hz
Hop length	1 920 samples/frame
Latent dim	128
Max frames	500 (≈ 20 s) — longer clips truncated
dtype	float16

Time ↔ frame conversion: seconds = frames × 1920 / 48000

Source Datasets

Subdir	Source	Size used
`cv`	humanify/common_voice_english	10 % ≈ 180 K
`ps`	humanify/ps	10 % ≈ 216 K
`ht2`	humanify/ht2_44khz	10 % ≈ 313 K

Original audio is licensed under the respective source dataset licenses. This dataset distributes only derived latent representations.

Usage

Extract

unzip latents.zip -d data/
# → data/latents/cv/, data/latents/ps/, data/latents/ht2/

Load a single sample

import torch

sample = torch.load("data/latents/cv/000000049.pt", weights_only=False)
z      = sample["z"]       # Tensor[T, 128], float16
text   = sample["text"]    # str
length = sample["length"]  # int == z.shape[0]

Use in EnvAudioEdit training (local latents mode)

Set use_local_latents: true in your training config and point latent_dir at the extracted directories:

# configs/train_small_phase1.yaml
use_local_latents: true
latent_dir:
  - "data/latents/cv"
  - "data/latents/ps"
  - "data/latents/ht2"

Then launch training:

accelerate launch scripts/train_phase1.py --config configs/train_small_phase1.yaml

Encoding Environment

Package	Version
onnxruntime-gpu	1.23.2
nvidia-cudnn-cu12	9.5.1.17
torch	2.11.0+cu130

cuDNN 9.20 has a Conv1D bug; 8.x breaks PyTorch — pin to 9.5.1.17.

Model checkpoints: <your-username>/envtts-small-phase1
Architecture & training plan: see the model repo README.