docs: add substantive model card with description, training data provenance, and inference snippet

README.md

@@ -8,4 +8,55 @@ base_model:
 pipeline_tag: text-to-speech
 tags:
 - vits
----
+- phoonnx
+- onnx
+- tts
+---
+
+# phoonnx\_ast\_miro\_unicode
+
+A text-to-speech voice model for **Asturian (ast)** using the phoonnx VITS
+architecture, exported to ONNX for efficient CPU inference.  The voice
+persona is **Miro** — a male speaker adapted via voice conversion from a
+Galician base model (`phoonnx_gl-ES_miro_unicode`).
+
+## Training data
+
+Trained on
+[TigreGotico/tts\_vc\_mcv-scripted-v23.0\_ast\_miro](https://huggingface.co/datasets/TigreGotico/tts_vc_mcv-scripted-v23.0_ast_miro),
+a synthetic voice-conversion dataset derived from Mozilla Common Voice
+scripted prompts (release 23.0).  Audio was synthesised with a donor TTS
+voice and then converted to the Miro speaker identity using voice-conversion
+techniques, producing a multi-hour single-speaker corpus for Asturian.
+
+## Usage
+
+```python
+import wave
+from phoonnx.config import SynthesisConfig
+from phoonnx.voice import TTSVoice
+
+voice = TTSVoice.from_hf("OpenVoiceOS/phoonnx_ast_miro_unicode")
+
+synthesis_config = SynthesisConfig(noise_scale=0.667, length_scale=1.0, noise_w_scale=0.8)
+
+with wave.open("output.wav", "wb") as wav_file:
+    voice.synthesize_wav("Bones díes, ¿cómo tas?", wav_file, synthesis_config)
+```
+
+Or via the OVOS TTS plugin:
+
+```json
+{
+  "tts": {
+    "module": "ovos-tts-plugin-phoonnx",
+    "ovos-tts-plugin-phoonnx": {
+      "voice": "OpenVoiceOS/phoonnx_ast_miro_unicode"
+    }
+  }
+}
+```
+
+## License
+
+Apache 2.0