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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+base_model: openbmb/VoxCPM-0.5B
+base_model_relation: adapter
+pipeline_tag: text-to-speech
+tags:
+- text-to-speech
+- voxcpm
+- lora
+- accent
+- voice
+- vctk
+- baby-mynah
+---
+
+# baby mynah — Welsh accent (LoRA for VoxCPM-0.5B)
+
+A LoRA adapter that voices [VoxCPM-0.5B](https://huggingface.co/openbmb/VoxCPM-0.5B)
+in the **Welsh** accent of a single VCTK speaker. One of six accent experts
+behind the [**baby mynah** Space](https://huggingface.co/spaces/build-small-hackathon/baby-mynah), where
+you can slide between any two of them in weight space.
+
+## What it is
+
+- **Base model:** [`openbmb/VoxCPM-0.5B`](https://huggingface.co/openbmb/VoxCPM-0.5B) (OpenBMB),
+  a tokenizer-free TTS model. This repo holds **only the LoRA delta** (~28 MB) —
+  you also need the base model.
+- **Accent:** Welsh (Cardiff), learned from a single
+  [VCTK](https://datashare.ed.ac.uk/handle/10283/3443) speaker (**p253**,
+  age 22, female) — ~8 minutes of read speech.
+- **Not PEFT.** VoxCPM uses a custom `LoRALinear`, so this loads through the
+  baby-mynah loader, not `peft`. The weights file stores, per target Linear,
+  `<prefix>.lora_A` (r, in) and `<prefix>.lora_B` (out, r); the layer applies
+  `out += (alpha/r) * (x @ A^T @ B^T)`.
+
+## Training
+
+| | |
+|---|---|
+| Method | LoRA (rank 32, alpha 32, dropout 0) |
+| Target modules | `q/k/v/o_proj` on both the LM and the DiT |
+| Steps | 800 (effective batch 4 = batch 1 x grad-accum 4) |
+| LR | 1e-4, 50 warmup, weight decay 0.01 |
+| Audio | ~8 min, 16 kHz mono |
+
+## Usage
+
+These adapters are meant to be *blended*, which is what the Space does. To load
+one on its own, use the project's `load_adapter` / `blend_into_model` helpers
+(see [`scripts/blend.py`](https://huggingface.co/spaces/build-small-hackathon/baby-mynah/blob/main/scripts/blend.py)):
+
+```python
+from blend import load_adapter, blend_into_model, restore_weights, scaling_from_config
+from voxcpm import VoxCPM
+
+model = VoxCPM.from_pretrained(hf_model_id="openbmb/VoxCPM-0.5B", optimize=False, device="cuda")
+adapter = load_adapter("path/to/this/adapter")        # {prefix: (A, B)}
+scaling = scaling_from_config("path/to/this/adapter")  # alpha / r
+
+# alpha=1.0 applies this expert fully; restore afterwards (surgery is in-place).
+snap = blend_into_model(model.tts_model, adapter, adapter, alpha=1.0, scaling=scaling)
+wav = model.generate(text="Hello! I am a very small bird.", normalize=True)
+restore_weights(model.tts_model, snap)
+```
+
+## License & attribution
+
+- LoRA weights released under **CC BY 4.0**, following the
+  [VCTK corpus](https://datashare.ed.ac.uk/handle/10283/3443) (CSTR, University
+  of Edinburgh) they were trained on.
+- Base model **VoxCPM-0.5B** by **OpenBMB**, Apache-2.0.