Add generate_from_tokens method, example (#53)

examples/phoneme_example.py

@@ -0,0 +1,62 @@
+from kokoro import KPipeline, KModel
+import torch
+from scipy.io import wavfile
+
+def save_audio(audio: torch.Tensor, filename: str):
+    """Helper function to save audio tensor as WAV file"""
+    if audio is not None:
+        # Ensure audio is on CPU and in the right format
+        audio_cpu = audio.cpu().numpy()
+        
+        # Save using scipy.io.wavfile
+        wavfile.write(
+            filename,
+            24000,  # Kokoro uses 24kHz sample rate
+            audio_cpu
+        )
+        print(f"Audio saved as '{filename}'")
+    else:
+        print("No audio was generated")
+
+def main():
+    # Initialize pipeline with American English
+    pipeline = KPipeline(lang_code='a')
+    
+    # The phoneme string for:
+    # "How are you today? I am doing reasonably well, thank you for asking"
+    phonemes = "hˌW ɑɹ ju tədˈA? ˌI ɐm dˈuɪŋ ɹˈizənəbli wˈɛl, θˈæŋk ju fɔɹ ˈæskɪŋ"
+    
+    try:
+        print("\nExample 1: Using generate_from_tokens with raw phonemes")
+        results = list(pipeline.generate_from_tokens(
+            tokens=phonemes,
+            voice="af_bella",
+            speed=1.0
+        ))
+        if results:
+            save_audio(results[0].audio, 'phoneme_output_new.wav')
+        
+        # Example 2: Using generate_from_tokens with pre-processed tokens
+        print("\nExample 2: Using generate_from_tokens with pre-processed tokens")
+        #  get the tokens through G2P or any other method
+        text = "How are you today? I am doing reasonably well, thank you for asking"
+        _, tokens = pipeline.g2p(text)
+        
+        # Then generate from tokens
+        for result in pipeline.generate_from_tokens(
+            tokens=tokens,
+            voice="af_bella",
+            speed=1.0
+        ):
+            # Each result may contain timestamps if available
+            if result.tokens:
+                for token in result.tokens:
+                    if hasattr(token, 'start_ts') and hasattr(token, 'end_ts'):
+                        print(f"Token: {token.text} ({token.start_ts:.2f}s - {token.end_ts:.2f}s)")
+            save_audio(result.audio, f'token_output_{hash(result.phonemes)}.wav')
+            
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    main()