Upload usage_example.py with huggingface_hub

usage_example.py

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+# Saudi TTS Usage Example
+import torch
+import torchaudio
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+
+def load_model():
+    """Load the Saudi TTS model"""
+    config = XttsConfig()
+    config.load_json("config.json")
+    
+    model = Xtts.init_from_config(config)
+    model.load_checkpoint(config, checkpoint_dir="./", vocab_path="vocab.json")
+    
+    if torch.cuda.is_available():
+        model.cuda()
+    
+    gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
+        audio_path=["speaker.wav"]
+    )
+    
+    return model, gpt_cond_latent, speaker_embedding
+
+def generate_speech(model, gpt_cond_latent, speaker_embedding, text, language="ar", temperature=0.75):
+    """Generate speech from text"""
+    out = model.inference(text, language, gpt_cond_latent, speaker_embedding, temperature=temperature)
+    return out["wav"]
+
+# Example usage
+if __name__ == "__main__":
+    model, gpt_cond_latent, speaker_embedding = load_model()
+    
+    # Arabic example
+    arabic_text = "مرحباً بكم"
+    audio = generate_speech(model, gpt_cond_latent, speaker_embedding, arabic_text, "ar")
+    torchaudio.save("arabic_output.wav", torch.tensor(audio).unsqueeze(0), 24000)
+    
+    # English example
+    english_text = "Hello world"
+    audio = generate_speech(model, gpt_cond_latent, speaker_embedding, english_text, "en")
+    torchaudio.save("english_output.wav", torch.tensor(audio).unsqueeze(0), 24000)
+    
+    print("Audio files generated!")