Spaces:
Running on Zero
Running on Zero
| title: WaveGen | |
| emoji: 🔊 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.17.3 | |
| python_version: 3.12.12 | |
| app_file: app.py | |
| fullWidth: true | |
| license: mit | |
| models: | |
| - LeeSangHoon/WaveGen | |
| short_description: WaveGen V-Spec and Flash text-to-speech on ZeroGPU | |
| # WaveGen | |
| **WaveGen: Truly End-to-End Waveform Generation via Internal Spectral Trajectory Learning** | |
| Sang-Hoon Lee and Ha-Yeong Choi · Ajou University and KT · NeurIPS 2026 | |
| [Source Code](https://github.com/sh-lee-prml/WaveGen) · [Models](https://huggingface.co/LeeSangHoon/WaveGen) · [Demo Page](https://wavegen26.github.io/demo/) · [Benchmark Demo](https://sh-lee-prml.github.io/wavegen-tts-demo/) | |
| Generate English or Mandarin Chinese speech from a reference recording, its exact | |
| transcript, and the text to speak. The model emits 24 kHz waveform audio directly. | |
| | Model | Default sampling | | |
| |---|---| | |
| | **WaveGen V-Spec — Recommended, selected by default** | 1400k EMA, 50 Euler steps, Prompt Anchor, Scheduled K3, CFG 1.25 | | |
| | **WaveGen-Flash** | 48k EMA, four Euler steps, time grid `[0, 0.1, 0.25, 0.5, 1.0]`, CFG 0 | | |
| Both models default to initial Mel temperature 0 and skip the unused final Mel | |
| output head. Inference dropout follows each model's configuration. Model weights | |
| remain FP32; synthesis uses CUDA BF16 autocast. The reference plus predicted | |
| generated speech must fit within **30 seconds**. Split longer text into shorter | |
| passages; the app does not silently truncate it or automatically split it. | |
| ## Deploy this folder to Spaces | |
| 1. Create a **Gradio Space** under the intended account. Keep it **Private** while | |
| validating it, and select **ZeroGPU** hardware if the account is eligible. | |
| This README's SDK settings do not allocate hardware or change visibility. | |
| 2. Upload the contents of this folder to the Space repository root, including | |
| `src/`, the license files, `requirements.txt`, `constraints.txt`, and | |
| `packages.txt`. The root must contain `app.py` and this README. | |
| 3. If the model repository is private, add `HF_TOKEN` under the Space's **Settings | |
| → Secrets**, using a token with read access to `LeeSangHoon/WaveGen`. Keep the | |
| token out of source files, public variables, and commit messages. No token is | |
| needed once the selected model revision is publicly readable. | |
| 4. Let the Space build, then test the included example with **both models** and | |
| verify a downloaded WAV. Make the Space public only when ready for release. | |
| The Space downloads each model's Safetensors checkpoint, YAML, and vocabulary at | |
| startup. The model repository and revision can be set with Space **Variables**: | |
| | Variable | Default | | |
| |---|---| | |
| | `WAVEGEN_MODEL_REPO` | `LeeSangHoon/WaveGen` | | |
| | `WAVEGEN_MODEL_REVISION` | `da53c835a39272c05e01526fe3c10b2b9d4ce60b` | | |
| The default revision pins both model bundles to the same release. Use another | |
| full commit SHA to update them deliberately. Model files and authentication | |
| credentials are not included in this source bundle. | |
| ## ZeroGPU runtime | |
| The entry point imports `spaces` before PyTorch, loads the models during startup, | |
| and places their modules on CUDA before serving requests. The synthesis callback | |
| is wrapped with `@spaces.GPU`; model selection does not download weights or reload | |
| a model inside that callback. The app processes one generation at a time and | |
| returns a WAV file to preserve the model's output amplitude. | |
| ZeroGPU allocates a GPU for each request and releases it afterward. Queue time | |
| and daily usage quotas depend on the visitor's Hugging Face account. Running the | |
| app on a local CUDA device verifies model integration, but does not itself verify | |
| Hugging Face's GPU allocation and worker lifecycle. A successful Space build and | |
| on-Space request are still required before claiming ZeroGPU deployment is tested. | |
| See the official [ZeroGPU documentation](https://huggingface.co/docs/hub/spaces-zerogpu) | |
| and [Spaces configuration reference](https://huggingface.co/docs/hub/spaces-config-reference). | |
| ## Versions and local verification | |
| The existing development environment is not modified. The Space creates a | |
| separate Linux environment. Its inference and UI dependencies are pinned to the | |
| versions inspected in that environment, including: | |
| - Gradio **6.17.3** | |
| - Spaces SDK **0.51.3** in the current build configuration, managed by ZeroGPU (local SDK **0.50.4** unchanged) | |
| - PyTorch and TorchAudio **2.9.1+cu128** | |
| - Hugging Face Hub **0.36.2** | |
| - NumPy **2.2.6** | |
| The remote Python runtime is **3.12.12**, a documented ZeroGPU runtime. The local | |
| Python **3.12.14** installation remains unchanged. Linux CUDA dependencies are | |
| provided by the pinned PyTorch wheels; **hf-xet 1.8.0** is supplied for the Hub's | |
| Linux download transport. One existing transitive dependency cannot be copied | |
| verbatim into a fresh build: the local **Pillow 13.0.0** exceeds Gradio 6.17.3's | |
| declared `Pillow <13` requirement. The isolated Space therefore pins **Pillow | |
| 12.2.0**, while the local installation remains untouched. The platform's automatic | |
| Gradio MCP extra also requires **Pydantic 2.12.5** and **pydantic-core 2.41.5**; | |
| the local versions remain **2.14.2** and **2.50.2**. Exact deployment pins | |
| are inline in `requirements.txt`; `constraints.txt` is the reference manifest, | |
| including the observed platform-managed Spaces SDK. Spaces mounts only the requirements file during installation, so it | |
| cannot include a relative constraints file at that stage. The Torch requirement | |
| uses `2.9.1` for ZeroGPU's configuration parser, with the CUDA 12.8 wheel index. | |
| For local CUDA verification with already downloaded model folders, run this from | |
| the bundle folder using the existing Python interpreter: | |
| ```bash | |
| python app.py --local --model-dir /path/to/WaveGen/ckpts | |
| ``` | |
| The `--local` path uses the same models and sampling settings without requesting | |
| a Hugging Face GPU allocation. No package installation is necessary when using | |
| the already validated development environment. | |
| The `src/` files are unchanged copies of the inference APIs and the generator | |
| modules they import. Shared generator definitions include their original loss | |
| definitions, but training entry points, dataset loaders, evaluation tools, | |
| optimizer state, and checkpoints are excluded. `SOURCE_SHA256SUMS` records the | |
| copied files for comparison with the original source. | |
| ## Responsible use and license | |
| Use reference speech only with the speaker's consent and permission to use the | |
| recording. Do not use generated audio for deceptive impersonation, fraud, or | |
| harassment. Identify shared outputs as AI-generated and avoid uploading private | |
| or sensitive recordings to a public demo. | |
| Source code is distributed under the [MIT License](LICENSE), with the original | |
| [third-party notices](docs/THIRD_PARTY_NOTICES.md) preserved. Model weights are | |
| licensed separately under **CC BY-NC 4.0**, as stated in the | |
| [model card](https://huggingface.co/LeeSangHoon/WaveGen). | |