| --- |
| 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. |
|
|
| --- |
|
|
| ## Contents |
|
|
| ``` |
| 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: |
|
|
| ```python |
| { |
| "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](https://huggingface.co/datasets/humanify/common_voice_english) | 10 % ≈ 180 K | |
| | `ps` | [humanify/ps](https://huggingface.co/datasets/humanify/ps) | 10 % ≈ 216 K | |
| | `ht2` | [humanify/ht2_44khz](https://huggingface.co/datasets/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 |
|
|
| ```bash |
| unzip latents.zip -d data/ |
| # → data/latents/cv/, data/latents/ps/, data/latents/ht2/ |
| ``` |
|
|
| ### Load a single sample |
|
|
| ```python |
| 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: |
|
|
| ```yaml |
| # configs/train_small_phase1.yaml |
| use_local_latents: true |
| latent_dir: |
| - "data/latents/cv" |
| - "data/latents/ps" |
| - "data/latents/ht2" |
| ``` |
|
|
| Then launch training: |
|
|
| ```bash |
| 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. |
|
|
| --- |
|
|
| ## Related |
|
|
| - Model checkpoints: [`<your-username>/envtts-small-phase1`](https://huggingface.co/<your-username>/envtts-small-phase1) |
| - Architecture & training plan: see the model repo README. |
|
|
