feat: add device examples and pipeline updates (#27)

examples/device_examples.py

@@ -0,0 +1,48 @@
+"""
+Quick example to show how device selection can be controlled, and was checked
+"""
+# import warnings, time
+import time
+# import warnings
+from kokoro import KPipeline
+# warnings.filterwarnings('ignore')
+# import torch; torch.set_warn_always(False)
+
+def generate_audio(pipeline, text):
+    for _, _, audio in pipeline(text, voice='af_bella'):
+        samples = audio.shape[0] if audio is not None else 0
+        assert samples > 0, "No audio generated"
+        return samples
+
+def time_synthesis(device=None):
+    try:
+        start = time.perf_counter()
+        pipeline = KPipeline(lang_code='a', device=device)
+        samples = generate_audio(pipeline, "The quick brown fox jumps over the lazy dog.")
+        ms = (time.perf_counter() - start) * 1000
+        print(f"✓ {device or 'auto':<6} | {ms:>5.1f}ms total | {samples:>6,d} samples")
+    except RuntimeError as e:
+        print(f"✗ {'cuda' if 'CUDA' in str(e) else device or 'auto':<6} | {'not available' if 'CUDA' in str(e) else str(e)}")
+
+def compare_shared_model():
+    try:
+        start = time.perf_counter()
+        en_us = KPipeline(lang_code='a')
+        en_uk = KPipeline(lang_code='a', model=en_us.model)
+        
+        for pipeline in [en_us, en_uk]:
+            generate_audio(pipeline, "Testing model reuse.")
+                
+        ms = (time.perf_counter() - start) * 1000
+        print(f"✓ reuse  | {ms:>5.1f}ms for both models")
+    except Exception as e:
+        print(f"✗ reuse  | {str(e)}")
+
+if __name__ == '__main__':
+    print("\nDevice Selection & Performance:")
+    print("----------------------------------------")
+    time_synthesis()
+    time_synthesis('cuda')
+    time_synthesis('cpu') 
+    print("----------------------------------------")
+    compare_shared_model()

kokoro/pipeline.py

@@ -47,15 +47,40 @@ class KPipeline:
 
     A "loud" KPipeline _with_ a model yields (graphemes, phonemes, audio).
     '''
-    def __init__(self, lang_code: str, model: Union[KModel, bool] = True, trf: bool = False):
+    def __init__(
+        self,
+        lang_code: str,
+        model: Union[KModel, bool] = True,
+        trf: bool = False,
+        device: Optional[str] = None
+    ):
+        """Initialize a KPipeline.
+        
+        Args:
+            lang_code: Language code for G2P processing
+            model: KModel instance, True to create new model, False for no model
+            trf: Whether to use transformer-based G2P
+            device: Override default device selection ('cuda' or 'cpu', or None for auto)
+                   If None, will auto-select cuda if available
+                   If 'cuda' and not available, will explicitly raise an error
+        """
         assert lang_code in LANG_CODES, (lang_code, LANG_CODES)
         self.lang_code = lang_code
         self.model = None
         if isinstance(model, KModel):
             self.model = model
         elif model:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-            self.model = KModel().to(device).eval()
+            if device == 'cuda' and not torch.cuda.is_available():
+                raise RuntimeError("CUDA requested but not available")
+            if device is None:
+                device = 'cuda' if torch.cuda.is_available() else 'cpu'
+            try:
+                self.model = KModel().to(device).eval()
+            except RuntimeError as e:
+                if device == 'cuda':
+                    raise RuntimeError(f"""Failed to initialize model on CUDA: {e}. 
+                                       Try setting device='cpu' or check CUDA installation.""")
+                raise
         self.voices = {}
         if lang_code in 'ab':
             try: