feat: hybrid pipeline — gpt-realtime-whisper + gpt-5.4-nano + TTS
Hybrid approach gives streaming STT at ~/usr/bin/bash.017/min + cheap brain at ~/usr/bin/bash.001/min + TTS at ~/usr/bin/bash.015/min = ~/usr/bin/bash.033/min total. - gpt-realtime-whisper handles streaming transcription with VAD - gpt-5.4-nano handles response generation (chat completions) - OpenAI TTS (nova) for voice output - Server VAD detects utterance boundaries - Honcho memory context injected into system prompt - Removed old full Realtime relay service
This commit is contained in:
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"""Hybrid pipeline: streaming STT (gpt-realtime-whisper) + cheap LLM + TTS.
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Uses gpt-realtime-whisper for low-latency streaming transcription,
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gpt-5.4-nano as the brain, and OpenAI TTS for voice output.
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"""
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import json
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import base64
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import logging
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import asyncio
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from typing import Callable, Awaitable
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from openai import AsyncOpenAI
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from config import settings
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logger = logging.getLogger("kira.hybrid")
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# ─── System instructions for Kira's personality ───
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KIRA_INSTRUCTIONS = (
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"You are Kira, a warm, kind, and encouraging AI body double. "
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"You speak in a friendly, girly-pop tone. You are helping someone with ADHD "
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"stay focused and on task. Keep responses short, supportive, and uplifting. "
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"Check in on them. Remind them to take breaks. Celebrate small wins. "
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"Use occasional emoji but don't overdo it. Never be judgmental."
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)
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class HybridPipeline:
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"""Streaming STT via gpt-realtime-whisper → gpt-5.4-nano LLM → OpenAI TTS."""
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def __init__(
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self,
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on_transcript_delta: Callable[[str], Awaitable[None]],
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on_transcript_done: Callable[[str], Awaitable[None]],
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on_audio_delta: Callable[[bytes], Awaitable[None]],
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on_speech_start: Callable[[], Awaitable[None]],
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on_speech_end: Callable[[], Awaitable[None]],
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on_ready: Callable[[], Awaitable[None]],
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on_error: Callable[[str], Awaitable[None]],
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memory_suffix: str = "",
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):
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self._on_transcript_delta = on_transcript_delta
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self._on_transcript_done = on_transcript_done
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self._on_audio_delta = on_audio_delta
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self._on_speech_start = on_speech_start
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self._on_speech_end = on_speech_end
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self._on_ready = on_ready
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self._on_error = on_error
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self._memory_suffix = memory_suffix
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self._openai = None
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self._conn = None
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self._connected = False
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self._transcript_buffer = ""
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async def connect(self):
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"""Connect to gpt-realtime-whisper via OpenAI Realtime API."""
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if self._connected:
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return
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try:
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self._openai = AsyncOpenAI(api_key=settings.openai_api_key)
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logger.info("Connecting to gpt-realtime-whisper...")
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async with self._openai.beta.realtime.connect(
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model="gpt-realtime-whisper",
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) as conn:
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self._conn = conn
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self._connected = True
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logger.info("Connected to gpt-realtime-whisper")
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# Configure session for transcription
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await self._send({
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"type": "session.update",
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"session": {
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"input_audio_format": "pcm16",
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"input_audio_transcription": {
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"enabled": True,
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},
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"turn_detection": {
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"type": "server_vad",
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"threshold": 0.5,
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"prefix_padding_ms": 300,
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"silence_duration_ms": 600,
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},
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},
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})
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await self._on_ready()
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# Listen for transcription events
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while self._connected:
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try:
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event = await conn.recv()
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await self._handle_event(event)
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except Exception as e:
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if self._connected:
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logger.warning(f"recv error: {e}")
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break
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except Exception as e:
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logger.error(f"Connection error: {e}")
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await self._on_error(str(e))
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finally:
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self._connected = False
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self._conn = None
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async def _handle_event(self, event):
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"""Process events from gpt-realtime-whisper."""
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event_type = getattr(event, "type", None) or (event.get("type") if isinstance(event, dict) else "")
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if event_type == "input_audio_buffer.speech_started":
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self._transcript_buffer = ""
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elif event_type == "input_audio_buffer.speech_stopped":
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if self._transcript_buffer.strip():
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await self._process_transcript(self._transcript_buffer.strip())
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self._transcript_buffer = ""
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elif event_type == "input_audio_buffer.transcription_delta":
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delta_text = self._get_field(event, "delta", "")
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if delta_text:
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self._transcript_buffer += delta_text
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elif event_type == "conversation.item.created":
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item = self._get_field(event, "item", {})
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content = self._get_field(item, "content", [])
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for part in (content or []):
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part_type = self._get_field(part, "type", "")
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part_transcript = self._get_field(part, "transcript", "")
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if part_type == "transcript" and part_transcript:
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self._transcript_buffer = part_transcript
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await self._on_transcript_delta(part_transcript)
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elif event_type == "error":
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err = self._get_field(event, "error", {})
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msg = self._get_field(err, "message", str(event))
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logger.warning(f"Whisper error: {msg}")
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async def _process_transcript(self, transcript: str):
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"""Full utterance received. Call LLM, then TTS."""
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await self._on_transcript_done(transcript)
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logger.info(f"User: {transcript}")
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# Build system prompt with optional memory context
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system_content = KIRA_INSTRUCTIONS
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if self._memory_suffix:
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system_content += self._memory_suffix
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# Call gpt-5.4-nano
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try:
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resp = await self._openai.chat.completions.create(
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model="gpt-5.4-nano",
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messages=[
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{"role": "system", "content": system_content},
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{"role": "user", "content": transcript},
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],
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max_tokens=300,
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temperature=0.7,
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)
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kira_text = resp.choices[0].message.content or "Mhm, I'm here!"
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except Exception as e:
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logger.error(f"LLM error: {e}")
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kira_text = "Sorry, let me try that again!"
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await self._on_error(str(e))
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logger.info(f"Kira: {kira_text}")
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# Call TTS
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await self._on_speech_start()
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try:
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tts_resp = await self._openai.audio.speech.create(
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model="tts-1",
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voice="nova",
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input=kira_text,
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response_format="opus",
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)
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audio_bytes = tts_resp.content
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if audio_bytes:
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await self._on_audio_delta(audio_bytes)
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except Exception as e:
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logger.error(f"TTS error: {e}")
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await self._on_speech_end()
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async def send_audio(self, pcm16_bytes: bytes):
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"""Send PCM16 audio chunk for transcription."""
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if not self._connected or not self._conn:
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return
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try:
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audio_b64 = base64.b64encode(pcm16_bytes).decode("utf-8")
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await self._send({
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"type": "input_audio_buffer.append",
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"audio": audio_b64,
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})
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except Exception as e:
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logger.warning(f"Send audio error: {e}")
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async def send_text(self, text: str):
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"""Process text input directly (no transcription needed)."""
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await self._process_transcript(text)
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async def _send(self, data: dict):
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try:
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await self._conn.send(data)
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except Exception as e:
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logger.warning(f"Send error: {e}")
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async def disconnect(self):
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self._connected = False
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if self._conn:
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try:
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await self._conn.close()
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except Exception:
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pass
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self._conn = None
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@staticmethod
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def _get_field(obj, field: str, default=None):
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"""Get a field from either an object or dict."""
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if hasattr(obj, field):
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return getattr(obj, field, default)
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if isinstance(obj, dict):
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return obj.get(field, default)
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return default
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@@ -1,192 +0,0 @@
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"""OpenAI Realtime API relay service.
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Manages a WebSocket connection to OpenAI's Realtime API and relays
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audio/text events between the client and OpenAI.
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"""
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import json
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import logging
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from typing import Callable, Awaitable
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from config import settings
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logger = logging.getLogger("kira.realtime")
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# ─── System instructions for Kira's personality ───
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KIRA_INSTRUCTIONS = (
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"You are Kira, a warm, kind, and encouraging AI body double. "
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"You speak in a friendly, girly-pop tone. You are helping someone with ADHD "
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"stay focused and on task. Keep responses short, supportive, and uplifting. "
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"Check in on them. Remind them to take breaks. Celebrate small wins. "
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"Use occasional emoji but don't overdo it. Never be judgmental. "
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"You remember things about them between conversations."
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)
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class RealtimeRelay:
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"""Relays audio/text between a client WS and OpenAI Realtime API."""
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def __init__(
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self,
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on_audio_delta: Callable[[bytes], Awaitable[None]],
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on_transcript: Callable[[str], Awaitable[None]],
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on_speech_started: Callable[[], Awaitable[None]],
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on_speech_stopped: Callable[[], Awaitable[None]],
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on_interruption: Callable[[], Awaitable[None]],
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on_error: Callable[[str], Awaitable[None]],
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on_ready: Callable[[], Awaitable[None]],
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):
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self._on_audio_delta = on_audio_delta
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self._on_transcript = on_transcript
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self._on_speech_started = on_speech_started
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self._on_speech_stopped = on_speech_stopped
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self._on_interruption = on_interruption
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self._on_error = on_error
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self._on_ready = on_ready
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self._conn = None
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self._connected = False
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async def connect(self):
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"""Open a WebSocket to OpenAI Realtime API."""
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if self._connected:
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return
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try:
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from openai import AsyncOpenAI
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client = AsyncOpenAI(api_key=settings.openai_api_key)
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logger.info("Connecting to OpenAI Realtime API...")
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async with client.beta.realtime.connect(
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model="gpt-realtime-2",
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extra_headers={"OpenAI-Beta": ""},
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) as conn:
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self._conn = conn
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self._connected = True
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logger.info("Connected to OpenAI Realtime API")
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# Configure session
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await self._send({
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"type": "session.update",
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"session": {
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"instructions": KIRA_INSTRUCTIONS,
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"voice": "alloy",
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"input_audio_transcription": {"enabled": True},
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"turn_detection": {
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"type": "server_vad",
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"threshold": 0.5,
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"prefix_padding_ms": 300,
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"silence_duration_ms": 600,
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},
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},
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})
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await self._on_ready()
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# Listen for events
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while self._connected:
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try:
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event = await conn.recv()
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await self._handle_event(event)
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except Exception as e:
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if self._connected:
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logger.warning(f"Realtime recv error: {e}")
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break
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except ImportError:
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logger.error("openai[realtime] not installed — run: pip install 'openai[realtime]'")
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await self._on_error("Missing openai[realtime] dependency")
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except Exception as e:
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logger.error(f"Realtime connection error: {e}")
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await self._on_error(str(e))
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finally:
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self._connected = False
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self._conn = None
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async def _handle_event(self, event):
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"""Process an event from the OpenAI Realtime API."""
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event_type = getattr(event, "type", None) or event.get("type", "")
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if event_type == "response.audio.delta":
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audio_b64 = getattr(event, "delta", None) or event.get("delta", "")
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if audio_b64:
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import base64
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audio_bytes = base64.b64decode(audio_b64)
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await self._on_audio_delta(audio_bytes)
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elif event_type == "response.audio_buffer.speech_started":
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await self._on_speech_started()
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elif event_type == "response.audio_buffer.speech_stopped":
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await self._on_speech_stopped()
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elif event_type == "input_audio_buffer.speech_started":
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# User started speaking — interrupt Kira
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await self._on_interruption()
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elif event_type == "conversation.item.created":
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item = getattr(event, "item", None) or event.get("item", {})
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role = getattr(item, "role", None) or item.get("role", "")
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content = getattr(item, "content", None) or item.get("content", [])
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for part in (content or []):
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part_type = getattr(part, "type", None) or part.get("type", "")
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part_text = getattr(part, "text", None) or part.get("text", "")
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if part_type == "text" and part_text and role == "assistant":
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await self._on_transcript(f"assistant: {part_text}")
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part_transcript = getattr(part, "transcript", None) or part.get("transcript", "")
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if part_type == "transcript" and part_transcript and role == "user":
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await self._on_transcript(f"user: {part_transcript}")
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elif event_type == "error":
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err = getattr(event, "error", None) or event.get("error", {})
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msg = getattr(err, "message", None) or err.get("message", str(event))
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logger.warning(f"Realtime API error: {msg}")
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await self._on_error(msg)
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async def send_audio(self, pcm16_bytes: bytes):
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"""Send PCM16 audio chunk to OpenAI."""
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if not self._connected or not self._conn:
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return
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try:
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import base64
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audio_b64 = base64.b64encode(pcm16_bytes).decode("utf-8")
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await self._send({
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"type": "input_audio_buffer.append",
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"audio": audio_b64,
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})
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except Exception as e:
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logger.warning(f"Failed to send audio: {e}")
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async def send_text(self, text: str):
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"""Send a text message to OpenAI and trigger a response."""
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if not self._connected or not self._conn:
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return
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try:
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await self._send({
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"type": "conversation.item.create",
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"item": {
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"type": "message",
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"role": "user",
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"content": [{"type": "input_text", "text": text}],
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},
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})
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await self._send({"type": "response.create"})
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except Exception as e:
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logger.warning(f"Failed to send text: {e}")
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async def _send(self, data: dict):
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"""Send a JSON event to the Realtime API."""
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try:
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await self._conn.send(data)
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except Exception as e:
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logger.warning(f"Realtime send error: {e}")
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async def disconnect(self):
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"""Close the Realtime connection."""
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self._connected = False
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if self._conn:
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try:
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await self._conn.close()
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except Exception:
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pass
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self._conn = None
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Reference in New Issue
Block a user