Create README.md

README.md

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+---
+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.