README.md

---
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.