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README.md

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+---
+language: [ar]
+license: apache-2.0
+base_model: openbmb/VoxCPM2
+pipeline_tag: text-to-speech
+tags: [tunisian, derja, arabic-dialect, voxcpm, tts]
+---
+
+# Code-Quasar/voxcpm-tn
+
+VoxCPM2 fine-tuned for **Tunisian Derja**.
+
+- base: `openbmb/VoxCPM2` (2B, tokenizer-free, 48 kHz)
+- method: full fine-tuning, 147 steps
+- data: ~? h Tunisian read speech, 16 kHz mono
+
+## Important: the dialect tag
+
+Every training transcript was prefixed with `(Tunisian Dialect)`, so **untagged text is
+out of distribution**. Always prefix it:
+
+```python
+from voxcpm import VoxCPM
+
+model = VoxCPM.from_pretrained("Code-Quasar/voxcpm-tn", load_denoiser=False)
+wav = model.generate(text="(Tunisian Dialect) عسلامة، شنوة أحوالك اليوم؟")
+
+import soundfile as sf
+sf.write("out.wav", wav, 48000)
+```
+
+On a GPU with under ~8 GB, disable compilation:
+
+```python
+import os
+os.environ["TORCHDYNAMO_DISABLE"] = "1"
+```
+
+## Limitations
+
+Trained on a small corpus of read speech, so expect limited prosodic range and
+weaker long-form phrasing. Derived from source corpora with their own licence
+terms; the voices belong to real speakers.