Upload embodiment_pipeline.py

models/embodiment_pipeline.py

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+import asyncio
+from dataclasses import dataclass
+from typing import Dict, Any, Optional
+
+try:
+    from TTS.api import TTS
+except ImportError:
+    TTS = None
+
+try:
+    import whisper
+except ImportError:
+    whisper = None
+
+
+@dataclass
+class AvatarState:
+    facial_expression: str = "neutral"
+    gaze: str = "forward"
+    gesture: str = "idle"
+    posture: str = "balanced"
+
+
+class EmbodimentSynchronizer:
+    def __init__(self, tts_model: str = "tts_models/en/ljspeech/tacotron2-DDC_ph"):
+        self.avatar_state = AvatarState()
+        self.tts = TTS(tts_model) if TTS is not None else None
+
+    def map_prosody(self, prosody: Dict[str, float]) -> AvatarState:
+        energy = prosody.get("energy", 0.5)
+        pitch = prosody.get("pitch", 0.5)
+        focus = prosody.get("focus", 0.5)
+
+        if energy > 0.8:
+            self.avatar_state.facial_expression = "excited"
+            self.avatar_state.gesture = "open_hands"
+        elif energy < 0.3:
+            self.avatar_state.facial_expression = "calm"
+            self.avatar_state.gesture = "hands_down"
+        else:
+            self.avatar_state.facial_expression = "attentive"
+            self.avatar_state.gesture = "subtle"
+
+        if pitch > 0.7:
+            self.avatar_state.gaze = "upward"
+        elif pitch < 0.3:
+            self.avatar_state.gaze = "downward"
+        else:
+            self.avatar_state.gaze = "forward"
+
+        if focus > 0.8:
+            self.avatar_state.posture = "lean_forward"
+        else:
+            self.avatar_state.posture = "balanced"
+
+        return self.avatar_state
+
+    def synthesize_audio(self, text: str, emotion_weight: float = 0.5) -> Optional[bytes]:
+        if self.tts is None:
+            raise RuntimeError("Coqui TTS is not installed. Install it with `pip install TTS`." )
+        wav = self.tts.tts(text=text, speaker="alloy", sample_rate=24000)
+        return wav
+
+    def synchronize(self, text: str, prosody: Dict[str, float], qualia_strength: float) -> Dict[str, Any]:
+        avatar = self.map_prosody(prosody)
+        audio = self.synthesize_audio(text=text, emotion_weight=qualia_strength)
+        return {
+            "avatar_state": avatar,
+            "audio": audio,
+            "text": text,
+        }
+
+
+class StreamingVoicePipeline:
+    def __init__(self, stt_model_name: str = "base", tts_model: str = "tts_models/en/ljspeech/tacotron2-DDC_ph"):
+        self.whisper = whisper.load_model(stt_model_name) if whisper is not None else None
+        self.embodiment = EmbodimentSynchronizer(tts_model=tts_model)
+        self.vad_active = True
+
+    def transcribe_audio(self, audio_path: str) -> str:
+        if self.whisper is None:
+            raise RuntimeError("Whisper is not installed. Install it with `pip install openai-whisper`.")
+        result = self.whisper.transcribe(audio_path)
+        return result.get("text", "")
+
+    async def process_turn(self, audio_path: str, prosody: Dict[str, float], qualia_strength: float) -> Dict[str, Any]:
+        transcript = self.transcribe_audio(audio_path)
+        output_text = f"Processed: {transcript}"
+        return self.embodiment.synchronize(output_text, prosody, qualia_strength)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Embodiment and voice pipeline for Syntelligence.")
+    parser.add_argument("--audio", help="Path to input audio file.")
+    args = parser.parse_args()
+
+    if args.audio is None:
+        print("Provide --audio to process a voice turn.")
+    else:
+        pipeline = StreamingVoicePipeline()
+        result = asyncio.run(pipeline.process_turn(args.audio, {"energy": 0.7, "pitch": 0.5, "focus": 0.8}, qualia_strength=0.7))
+        print(result)