{"id":50249,"date":"2025-11-09T08:00:25","date_gmt":"2025-11-09T08:00:25","guid":{"rendered":"https:\/\/youzum.net\/how-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence\/"},"modified":"2025-11-09T08:00:25","modified_gmt":"2025-11-09T08:00:25","slug":"how-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence","status":"publish","type":"post","link":"https:\/\/youzum.net\/th\/how-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence\/","title":{"rendered":"How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence"},"content":{"rendered":"

In this tutorial, we explore how to build an Agentic Voice AI Assistant capable of understanding, reasoning, and responding through natural speech in real time. We begin by setting up a self-contained voice intelligence pipeline that integrates speech recognition, intent detection, multi-step reasoning, and text-to-speech synthesis. Along the way, we design an agent that listens to commands, identifies goals, plans appropriate actions, and delivers spoken responses using models such as Whisper and SpeechT5. We approach the entire system from a practical standpoint, demonstrating how perception, reasoning, and execution interact seamlessly to create an autonomous conversational experience. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n

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import subprocess\nimport sys\nimport json\nimport re\nfrom datetime import datetime\nfrom typing import Dict, List, Tuple, Any\n\n\ndef install_packages():\n   packages = ['transformers', 'torch', 'torchaudio', 'datasets', 'soundfile',\n               'librosa', 'IPython', 'numpy']\n   for pkg in packages:\n       subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', pkg])\n\n\nprint(\"\"\ud83e\udd16\" Initializing Agentic Voice AI...\")\ninstall_packages()\n\n\nimport torch\nimport soundfile as sf\nimport numpy as np\nfrom transformers import (AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline,\n                        SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan)\nfrom IPython.display import Audio, display, HTML\nimport warnings\nwarnings.filterwarnings('ignore')<\/code><\/pre>\n<\/div>\n<\/div>\n
We begin by installing all the essential libraries, including Transformers, Torch, and SoundFile, to enable speech recognition and synthesis. We also configure the environment to suppress warnings and ensure smooth execution throughout the voice AI setup. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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class VoiceAgent:\n   def __init__(self):\n       self.memory = []\n       self.context = {}\n       self.tools = {}\n       self.goals = []\n      \n   def perceive(self, audio_input: str) -> Dict[str, Any]:\n       intent = self._extract_intent(audio_input)\n       entities = self._extract_entities(audio_input)\n       sentiment = self._analyze_sentiment(audio_input)\n       perception = {\n           'text': audio_input,\n           'intent': intent,\n           'entities': entities,\n           'sentiment': sentiment,\n           'timestamp': datetime.now().isoformat()\n       }\n       self.memory.append(perception)\n       return perception\n  \n   def _extract_intent(self, text: str) -> str:\n       text_lower = text.lower()\n       intent_patterns = {\n           'create': ['create', 'make', 'generate', 'write'],\n           'search': ['search', 'find', 'look for', 'show me'],\n           'analyze': ['analyze', 'explain', 'understand', 'what is'],\n           'calculate': ['calculate', 'compute', 'how much', 'sum'],\n           'schedule': ['schedule', 'plan', 'set reminder', 'meeting'],\n           'translate': ['translate', 'say in', 'convert to'],\n           'summarize': ['summarize', 'brief', 'tldr', 'overview']\n       }\n       for intent, keywords in intent_patterns.items():\n           if any(kw in text_lower for kw in keywords):\n               return intent\n       return 'conversation'\n  \n   def _extract_entities(self, text: str) -> Dict[str, List[str]]:\n       entities = {\n           'numbers': re.findall(r'd+', text),\n           'dates': re.findall(r'bd{1,2}\/d{1,2}\/d{2,4}b', text),\n           'times': re.findall(r'bd{1,2}:d{2}s*(?:am|pm)?b', text.lower()),\n           'emails': re.findall(r'b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Z|a-z]{2,}b', text)\n       }\n       return {k: v for k, v in entities.items() if v}\n  \n   def _analyze_sentiment(self, text: str) -> str:\n       positive = ['good', 'great', 'excellent', 'happy', 'love', 'thank']\n       negative = ['bad', 'terrible', 'sad', 'hate', 'angry', 'problem']\n       text_lower = text.lower()\n       pos_count = sum(1 for word in positive if word in text_lower)\n       neg_count = sum(1 for word in negative if word in text_lower)\n       if pos_count > neg_count:\n           return 'positive'\n       elif neg_count > pos_count:\n           return 'negative'\n       return 'neutral'<\/code><\/pre>\n<\/div>\n<\/div>\n
Here, we implement the perception layer of our agent. We design methods to extract intents, entities, and sentiment from spoken text, enabling the system to understand user input within its context. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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def reason(self, perception: Dict) -> Dict[str, Any]:\n       intent = perception['intent']\n       reasoning = {\n           'goal': self._identify_goal(intent),\n           'prerequisites': self._check_prerequisites(intent),\n           'plan': self._create_plan(intent, perception['entities']),\n           'confidence': self._calculate_confidence(perception)\n       }\n       return reasoning\n  \n   def act(self, reasoning: Dict) -> str:\n       plan = reasoning['plan']\n       results = []\n       for step in plan['steps']:\n           result = self._execute_step(step)\n           results.append(result)\n       response = self._generate_response(results, reasoning)\n       return response\n  \n   def _identify_goal(self, intent: str) -> str:\n       goal_mapping = {\n           'create': 'Generate new content',\n           'search': 'Retrieve information',\n           'analyze': 'Understand and explain',\n           'calculate': 'Perform computation',\n           'schedule': 'Organize time-based tasks',\n           'translate': 'Convert between languages',\n           'summarize': 'Condense information'\n       }\n       return goal_mapping.get(intent, 'Assist user')\n  \n   def _check_prerequisites(self, intent: str) -> List[str]:\n       prereqs = {\n           'search': ['internet access', 'search tool'],\n           'calculate': ['math processor'],\n           'translate': ['translation model'],\n           'schedule': ['calendar access']\n       }\n       return prereqs.get(intent, ['language understanding'])\n  \n   def _create_plan(self, intent: str, entities: Dict) -> Dict:\n       plans = {\n           'create': {'steps': ['understand_requirements', 'generate_content', 'validate_output'], 'estimated_time': '10s'},\n           'analyze': {'steps': ['parse_input', 'analyze_components', 'synthesize_explanation'], 'estimated_time': '5s'},\n           'calculate': {'steps': ['extract_numbers', 'determine_operation', 'compute_result'], 'estimated_time': '2s'}\n       }\n       default_plan = {'steps': ['understand_query', 'process_information', 'formulate_response'], 'estimated_time': '3s'}\n       return plans.get(intent, default_plan)<\/code><\/pre>\n<\/div>\n<\/div>\n
We now focus on reasoning and planning. We teach the agent how to identify goals, check prerequisites, and generate structured multi-step plans to execute user commands logically. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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 def _calculate_confidence(self, perception: Dict) -> float:\n       base_confidence = 0.7\n       if perception['entities']:\n           base_confidence += 0.15\n       if perception['sentiment'] != 'neutral':\n           base_confidence += 0.1\n       if len(perception['text'].split()) > 5:\n           base_confidence += 0.05\n       return min(base_confidence, 1.0)\n  \n   def _execute_step(self, step: str) -> Dict:\n       return {'step': step, 'status': 'completed', 'output': f'Executed {step}'}\n  \n   def _generate_response(self, results: List, reasoning: Dict) -> str:\n       intent = reasoning['goal']\n       confidence = reasoning['confidence']\n       prefix = \"I understand you want to\" if confidence > 0.8 else \"I think you're asking me to\"\n       response = f\"{prefix} {intent.lower()}. \"\n       if len(self.memory) > 1:\n           response += \"Based on our conversation, \"\n       response += f\"I've analyzed your request and completed {len(results)} steps. \"\n       return response<\/code><\/pre>\n<\/div>\n<\/div>\n
In this section, we implement helper functions that calculate confidence levels, execute each planned step, and generate meaningful natural language responses for the user. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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class VoiceIO:\n   def __init__(self):\n       print(\"Loading voice models...\")\n       device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n       self.stt_pipe = pipeline(\"automatic-speech-recognition\", model=\"openai\/whisper-base\", device=device)\n       self.tts_processor = SpeechT5Processor.from_pretrained(\"microsoft\/speecht5_tts\")\n       self.tts_model = SpeechT5ForTextToSpeech.from_pretrained(\"microsoft\/speecht5_tts\")\n       self.vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft\/speecht5_hifigan\")\n       self.speaker_embeddings = torch.randn(1, 512) * 0.1\n       print(\"\u2713 Voice I\/O ready\")\n  \n   def listen(self, audio_path: str) -> str:\n       result = self.stt_pipe(audio_path)\n       return result['text']\n  \n   def speak(self, text: str, output_path: str = \"response.wav\") -> Tuple[str, np.ndarray]:\n       inputs = self.tts_processor(text=text, return_tensors=\"pt\")\n       speech = self.tts_model.generate_speech(inputs[\"input_ids\"], self.speaker_embeddings, vocoder=self.vocoder)\n       sf.write(output_path, speech.numpy(), samplerate=16000)\n       return output_path, speech.numpy()\n\n\n\n\nclass AgenticVoiceAssistant:\n   def __init__(self):\n       self.agent = VoiceAgent()\n       self.voice_io = VoiceIO()\n       self.interaction_count = 0\n      \n   def process_voice_input(self, audio_path: str) -> Dict:\n       text_input = self.voice_io.listen(audio_path)\n       perception = self.agent.perceive(text_input)\n       reasoning = self.agent.reason(perception)\n       response_text = self.agent.act(reasoning)\n       audio_path, audio_array = self.voice_io.speak(response_text)\n       self.interaction_count += 1\n       return {\n           'input_text': text_input,\n           'perception': perception,\n           'reasoning': reasoning,\n           'response_text': response_text,\n           'audio_path': audio_path,\n           'audio_array': audio_array\n       }<\/code><\/pre>\n<\/div>\n<\/div>\n
We set up the core voice input and output pipeline using Whisper for transcription and SpeechT5 for speech synthesis. We then integrate these with the agent\u2019s reasoning engine to form a complete interactive assistant. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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  def display_reasoning(self, result: Dict):\n       html = f\"\"\"\n       <div style='background: #1e1e1e; color: #fff; padding: 20px; border-radius: 10px; font-family: monospace;'>\n           <h2 style='color: #4CAF50;'>\"\ud83e\udd16\" Agent Reasoning Process<\/h2>\n           <div><strong style='color: #2196F3;'>\"\ud83d\udce5\" INPUT:<\/strong> {result['input_text']}<\/div>\n           <div><strong style='color: #FF9800;'>\"\ud83e\udde0\" PERCEPTION:<\/strong>\n               <ul>\n                   <li>Intent: {result['perception']['intent']}<\/li>\n                   <li>Entities: {result['perception']['entities']}<\/li>\n                   <li>Sentiment: {result['perception']['sentiment']}<\/li>\n               <\/ul>\n           <\/div>\n           <div><strong style='color: #9C27B0;'>\"\ud83d\udcad\" REASONING:<\/strong>\n               <ul>\n                   <li>Goal: {result['reasoning']['goal']}<\/li>\n                   <li>Plan: {len(result['reasoning']['plan']['steps'])} steps<\/li>\n                   <li>Confidence: {result['reasoning']['confidence']:.2%}<\/li>\n               <\/ul>\n           <\/div>\n           <div><strong style='color: #4CAF50;'>\"\ud83d\udcac\" RESPONSE:<\/strong> {result['response_text']}<\/div>\n       <\/div>\n       \"\"\"\n       display(HTML(html))\n\n\n\n\ndef run_agentic_demo():\n   print(\"n\" + \"=\"*70)\n   print(\"\"\ud83e\udd16\" AGENTIC VOICE AI ASSISTANT\")\n   print(\"=\"*70 + \"n\")\n   assistant = AgenticVoiceAssistant()\n   scenarios = [\n       \"Create a summary of machine learning concepts\",\n       \"Calculate the sum of twenty five and thirty seven\",\n       \"Analyze the benefits of renewable energy\"\n   ]\n   for i, scenario_text in enumerate(scenarios, 1):\n       print(f\"n--- Scenario {i} ---\")\n       print(f\"Simulated Input: '{scenario_text}'\")\n       audio_path, _ = assistant.voice_io.speak(scenario_text, f\"input_{i}.wav\")\n       result = assistant.process_voice_input(audio_path)\n       assistant.display_reasoning(result)\n       print(\"n\"\ud83d\udd0a\" Playing agent's voice response...\")\n       display(Audio(result['audio_array'], rate=16000))\n       print(\"n\" + \"-\"*70)\n   print(f\"n\"\u2705\" Completed {assistant.interaction_count} agentic interactions\")\n   print(\"n\"\ud83c\udfaf\" Key Agentic Capabilities Demonstrated:\")\n   print(\"  \u2022 Autonomous perception and understanding\")\n   print(\"  \u2022 Intent recognition and entity extraction\")\n   print(\"  \u2022 Multi-step reasoning and planning\")\n   print(\"  \u2022 Goal-driven action execution\")\n   print(\"  \u2022 Natural language response generation\")\n   print(\"  \u2022 Memory and context management\")\n\n\nif __name__ == \"__main__\":\n   run_agentic_demo()<\/code><\/pre>\n<\/div>\n<\/div>\n
Finally, we run a demo to visualize the agent\u2019s full reasoning process and hear it respond. We test multiple scenarios to showcase perception, reasoning, and voice response working in perfect harmony.<\/p>\n
In conclusion, we constructed an intelligent voice assistant that understands what we say and also reasons, plans, and speaks like a true agent. We experienced how perception, reasoning, and action work in harmony to create a natural and adaptive voice interface. Through this implementation, we aim to bridge the gap between passive voice commands and autonomous decision-making, demonstrating how agentic intelligence can enhance human\u2013AI voice interactions.<\/p>\n
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Check out the\u00a0FULL CODES here<\/a><\/strong>.\u00a0Feel free to check out our\u00a0GitHub Page for Tutorials, Codes and Notebooks<\/a><\/mark><\/strong>.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0100k+ ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0our Newsletter<\/a><\/strong>. Wait! are you on telegram?\u00a0now you can join us on telegram as well.<\/a><\/strong><\/p>\n
The post How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence<\/a> appeared first on MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"
In this tutorial, we explore how to build an Agentic Voice AI Assistant capable of understanding, reasoning, and responding through natural speech in real time. We begin by setting up a self-contained voice intelligence pipeline that integrates speech recognition, intent detection, multi-step reasoning, and text-to-speech synthesis. Along the way, we design an agent that listens to commands, identifies goals, plans appropriate actions, and delivers spoken responses using models such as Whisper and SpeechT5. We approach the entire system from a practical standpoint, demonstrating how perception, reasoning, and execution interact seamlessly to create an autonomous conversational experience. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser import subprocess import sys import json import re from datetime import datetime from typing import Dict, List, Tuple, Any def install_packages(): packages = [‘transformers’, ‘torch’, ‘torchaudio’, ‘datasets’, ‘soundfile’, ‘librosa’, ‘IPython’, ‘numpy’] for pkg in packages: subprocess.check_call([sys.executable, ‘-m’, ‘pip’, ‘install’, ‘-q’, pkg]) print(” Initializing Agentic Voice AI…”) install_packages() import torch import soundfile as sf import numpy as np from transformers import (AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan) from IPython.display import Audio, display, HTML import warnings warnings.filterwarnings(‘ignore’) We begin by installing all the essential libraries, including Transformers, Torch, and SoundFile, to enable speech recognition and synthesis. We also configure the environment to suppress warnings and ensure smooth execution throughout the voice AI setup. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser class VoiceAgent: def __init__(self): self.memory = [] self.context = {} self.tools = {} self.goals = [] def perceive(self, audio_input: str) -> Dict[str, Any]: intent = self._extract_intent(audio_input) entities = self._extract_entities(audio_input) sentiment = self._analyze_sentiment(audio_input) perception = { ‘text’: audio_input, ‘intent’: intent, ‘entities’: entities, ‘sentiment’: sentiment, ‘timestamp’: datetime.now().isoformat() } self.memory.append(perception) return perception def _extract_intent(self, text: str) -> str: text_lower = text.lower() intent_patterns = { ‘create’: [‘create’, ‘make’, ‘generate’, ‘write’], ‘search’: [‘search’, ‘find’, ‘look for’, ‘show me’], ‘analyze’: [‘analyze’, ‘explain’, ‘understand’, ‘what is’], ‘calculate’: [‘calculate’, ‘compute’, ‘how much’, ‘sum’], ‘schedule’: [‘schedule’, ‘plan’, ‘set reminder’, ‘meeting’], ‘translate’: [‘translate’, ‘say in’, ‘convert to’], ‘summarize’: [‘summarize’, ‘brief’, ‘tldr’, ‘overview’] } for intent, keywords in intent_patterns.items(): if any(kw in text_lower for kw in keywords): return intent return ‘conversation’ def _extract_entities(self, text: str) -> Dict[str, List[str]]: entities = { ‘numbers’: re.findall(r’d+’, text), ‘dates’: re.findall(r’bd{1,2}\/d{1,2}\/d{2,4}b’, text), ‘times’: re.findall(r’bd{1,2}:d{2}s*(?:am|pm)?b’, text.lower()), ’emails’: re.findall(r’b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Z|a-z]{2,}b’, text) } return {k: v for k, v in entities.items() if v} def _analyze_sentiment(self, text: str) -> str: positive = [‘good’, ‘great’, ‘excellent’, ‘happy’, ‘love’, ‘thank’] negative = [‘bad’, ‘terrible’, ‘sad’, ‘hate’, ‘angry’, ‘problem’] text_lower = text.lower() pos_count = sum(1 for word in positive if word in text_lower) neg_count = sum(1 for word in negative if word in text_lower) if pos_count > neg_count: return ‘positive’ elif neg_count > pos_count: return ‘negative’ return ‘neutral’ Here, we implement the perception layer of our agent. We design methods to extract intents, entities, and sentiment from spoken text, enabling the system to understand user input within its context. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser def reason(self, perception: Dict) -> Dict[str, Any]: intent = perception[‘intent’] reasoning = { ‘goal’: self._identify_goal(intent), ‘prerequisites’: self._check_prerequisites(intent), ‘plan’: self._create_plan(intent, perception[‘entities’]), ‘confidence’: self._calculate_confidence(perception) } return reasoning def act(self, reasoning: Dict) -> str: plan = reasoning[‘plan’] results = [] for step in plan[‘steps’]: result = self._execute_step(step) results.append(result) response = self._generate_response(results, reasoning) return response def _identify_goal(self, intent: str) -> str: goal_mapping = { ‘create’: ‘Generate new content’, ‘search’: ‘Retrieve information’, ‘analyze’: ‘Understand and explain’, ‘calculate’: ‘Perform computation’, ‘schedule’: ‘Organize time-based tasks’, ‘translate’: ‘Convert between languages’, ‘summarize’: ‘Condense information’ } return goal_mapping.get(intent, ‘Assist user’) def _check_prerequisites(self, intent: str) -> List[str]: prereqs = { ‘search’: [‘internet access’, ‘search tool’], ‘calculate’: [‘math processor’], ‘translate’: [‘translation model’], ‘schedule’: [‘calendar access’] } return prereqs.get(intent, [‘language understanding’]) def _create_plan(self, intent: str, entities: Dict) -> Dict: plans = { ‘create’: {‘steps’: [‘understand_requirements’, ‘generate_content’, ‘validate_output’], ‘estimated_time’: ’10s’}, ‘analyze’: {‘steps’: [‘parse_input’, ‘analyze_components’, ‘synthesize_explanation’], ‘estimated_time’: ‘5s’}, ‘calculate’: {‘steps’: [‘extract_numbers’, ‘determine_operation’, ‘compute_result’], ‘estimated_time’: ‘2s’} } default_plan = {‘steps’: [‘understand_query’, ‘process_information’, ‘formulate_response’], ‘estimated_time’: ‘3s’} return plans.get(intent, default_plan) We now focus on reasoning and planning. We teach the agent how to identify goals, check prerequisites, and generate structured multi-step plans to execute user commands logically. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser def _calculate_confidence(self, perception: Dict) -> float: base_confidence = 0.7 if perception[‘entities’]: base_confidence += 0.15 if perception[‘sentiment’] != ‘neutral’: base_confidence += 0.1 if len(perception[‘text’].split()) > 5: base_confidence += 0.05 return min(base_confidence, 1.0) def _execute_step(self, step: str) -> Dict: return {‘step’: step, ‘status’: ‘completed’, ‘output’: f’Executed {step}’} def _generate_response(self, results: List, reasoning: Dict) -> str: intent = reasoning[‘goal’] confidence = reasoning[‘confidence’] prefix = “I understand you want to” if confidence > 0.8 else “I think you’re asking me to” response = f”{prefix} {intent.lower()}. ” if len(self.memory) > 1: response += “Based on our conversation, ” response += f”I’ve analyzed your request and completed {len(results)} steps. ” return response In this section, we implement helper functions that calculate confidence levels, execute each planned step, and generate meaningful natural language responses for the user. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser class VoiceIO: def __init__(self): print(“Loading voice models…”) device = “cuda:0” if torch.cuda.is_available() else “cpu” self.stt_pipe = pipeline(“automatic-speech-recognition”, model=”openai\/whisper-base”, device=device) self.tts_processor = SpeechT5Processor.from_pretrained(“microsoft\/speecht5_tts”) self.tts_model = SpeechT5ForTextToSpeech.from_pretrained(“microsoft\/speecht5_tts”) self.vocoder = SpeechT5HifiGan.from_pretrained(“microsoft\/speecht5_hifigan”) self.speaker_embeddings = torch.randn(1, 512) * 0.1 print(“\u2713 Voice I\/O ready”) def listen(self, audio_path: str) -> str: result = self.stt_pipe(audio_path) return result[‘text’] def speak(self, text: str, output_path: str = “response.wav”) -> Tuple[str, np.ndarray]: inputs = self.tts_processor(text=text, return_tensors=”pt”) speech = self.tts_model.generate_speech(inputs[“input_ids”], self.speaker_embeddings, vocoder=self.vocoder) sf.write(output_path, speech.numpy(), samplerate=16000) return output_path, speech.numpy() class AgenticVoiceAssistant: def __init__(self): self.agent = VoiceAgent() self.voice_io = VoiceIO() self.interaction_count = 0 def process_voice_input(self, audio_path: str) -> Dict: text_input = self.voice_io.listen(audio_path) perception = self.agent.perceive(text_input) reasoning = self.agent.reason(perception) response_text = self.agent.act(reasoning) audio_path, audio_array = self.voice_io.speak(response_text) self.interaction_count += 1 return { ‘input_text’: text_input, ‘perception’: perception, ‘reasoning’: reasoning, ‘response_text’: response_text, ‘audio_path’: audio_path, ‘audio_array’: audio_array } We set up the core voice input and output pipeline using Whisper for transcription and SpeechT5 for speech synthesis. We then integrate these with the agent\u2019s reasoning engine to form a complete interactive assistant. Check out the\u00a0FULL CODES here. 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