460 lines
16 KiB
Python
460 lines
16 KiB
Python
from __future__ import annotations
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import os
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import logging
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import asyncio
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import json
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import time
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from dataclasses import dataclass, field
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import numpy as np
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from aiortc import RTCPeerConnection, RTCSessionDescription
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from dotenv import load_dotenv
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from fastapi import FastAPI, Request, WebSocket, WebSocketDisconnect
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from fastapi.responses import FileResponse, JSONResponse, StreamingResponse
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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from config import settings
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from core.pipeline import ChatPipeline
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from core.state_machine import Arbitrator
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from services.asr import ASRService
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from services.avatar import AvatarService
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from services.llm import LLMService
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from services.tts import TTSService
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from services.vad import VADService
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from webrtc.tracks import AudioBus, AvatarAudioTrack, AvatarVideoTrack
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load_dotenv()
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if settings.hf_token:
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os.environ["HF_TOKEN"] = settings.hf_token
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logging.basicConfig(level=logging.INFO)
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app = FastAPI(title="Visual Voice Chat")
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app.mount("/web", StaticFiles(directory="web"), name="web")
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app.add_middleware(
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CORSMiddleware,
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allow_origins=[o.strip() for o in settings.cors_origins.split(",")] if settings.cors_origins else ["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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arbitrator = Arbitrator()
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asr_service = ASRService()
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llm_service = LLMService()
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tts_service = TTSService()
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vad_service = VADService()
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avatar_service = AvatarService()
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pipeline = ChatPipeline(arbitrator, asr_service, llm_service, tts_service)
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audio_bus = AudioBus(sample_rate=48000)
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pcs: set[RTCPeerConnection] = set()
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subtitle_clients: set[WebSocket] = set()
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@dataclass
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class RuntimeState:
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last_user_text: str = ""
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last_reply_text: str = ""
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pipeline_runs: int = 0
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last_latency_ms: int = 0
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last_asr_latency_ms: int = 0
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last_llm_latency_ms: int = 0
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last_llm_source: str = "unknown"
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last_tts_latency_ms: int = 0
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last_tts_first_chunk_ms: int = 0
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last_barge_in_ms: int = 0
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barge_in_count: int = 0
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last_input_mode: str = "none"
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vad_start_count: int = 0
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vad_end_count: int = 0
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busy: bool = False
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buffer_16k: list[np.ndarray] = field(default_factory=list)
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runtime = RuntimeState()
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class TextChatRequest(BaseModel):
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text: str
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async def _broadcast_subtitle(
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role: str,
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text: str,
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source: str,
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*,
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partial: bool = False,
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final: bool = True,
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) -> None:
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if not text:
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return
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payload = {
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"role": role,
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"text": text,
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"source": source,
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"partial": partial,
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"final": final,
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"ts_ms": int(time.time() * 1000),
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}
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stale: list[WebSocket] = []
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for ws in list(subtitle_clients):
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try:
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await ws.send_text(json.dumps(payload, ensure_ascii=False))
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except Exception:
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stale.append(ws)
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for ws in stale:
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subtitle_clients.discard(ws)
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def _resample_mono(audio: np.ndarray, from_sr: int, to_sr: int) -> np.ndarray:
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if from_sr == to_sr:
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return audio.astype(np.float32, copy=False)
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in_len = audio.shape[0]
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out_len = max(1, int(in_len * to_sr / from_sr))
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x_old = np.linspace(0.0, 1.0, in_len, endpoint=False)
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x_new = np.linspace(0.0, 1.0, out_len, endpoint=False)
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return np.interp(x_new, x_old, audio).astype(np.float32)
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def _resample_to_16k_mono(pcm: np.ndarray, sample_rate: int, channels: int) -> np.ndarray:
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# aiortc audio ndarray is typically (channels, samples)
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if pcm.ndim == 2:
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mono = pcm.mean(axis=0)
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else:
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mono = pcm
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mono = mono.astype(np.float32) / 32768.0
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if sample_rate == 16000:
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return mono
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in_len = mono.shape[0]
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out_len = max(1, int(in_len * 16000 / sample_rate))
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x_old = np.linspace(0.0, 1.0, in_len, endpoint=False)
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x_new = np.linspace(0.0, 1.0, out_len, endpoint=False)
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return np.interp(x_new, x_old, mono).astype(np.float32)
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async def _run_pipeline_from_buffer() -> None:
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if runtime.busy:
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return
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if not runtime.buffer_16k:
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return
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runtime.busy = True
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start_t = time.perf_counter()
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try:
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runtime.last_input_mode = "voice"
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audio_16k = np.concatenate(runtime.buffer_16k, axis=0)
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runtime.buffer_16k.clear()
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async def on_audio_chunk(seg_audio: np.ndarray, seg_sr: int) -> None:
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audio_48k_seg = _resample_mono(seg_audio.astype(np.float32, copy=False), seg_sr, 48000)
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pcm_seg = np.clip(audio_48k_seg * 32767.0, -32768, 32767).astype(np.int16)
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await audio_bus.enqueue(pcm_seg)
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async def on_user_text_now(user_text: str) -> None:
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runtime.last_user_text = user_text
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await _broadcast_subtitle("user", user_text, "voice")
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async def on_reply_text_now(reply_text: str) -> None:
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runtime.last_reply_text = reply_text
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async def on_reply_segment_now(seg_text: str, idx: int, total: int) -> None:
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await _broadcast_subtitle("ai", seg_text, "voice", partial=True, final=(idx >= total - 1))
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result, audio, sr = await pipeline.process_turn(
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audio_16k,
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on_audio_chunk=on_audio_chunk,
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on_user_text=on_user_text_now,
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on_reply_text=on_reply_text_now,
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on_reply_segment=on_reply_segment_now,
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)
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asyncio.create_task(avatar_service.enqueue_musetalk_from_audio(audio, sr))
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runtime.last_user_text = result["user_text"]
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runtime.last_reply_text = result["reply_text"]
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runtime.pipeline_runs += 1
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runtime.last_latency_ms = int((time.perf_counter() - start_t) * 1000)
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runtime.last_asr_latency_ms = int(result.get("asr_latency_ms", 0))
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runtime.last_llm_latency_ms = int(result.get("llm_latency_ms", 0))
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runtime.last_llm_source = str(result.get("llm_source", "unknown"))
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runtime.last_tts_latency_ms = int(result.get("tts_latency_ms", 0))
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runtime.last_tts_first_chunk_ms = int(result.get("tts_first_chunk_ms", 0))
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# Fallback: if chunk callback path produced nothing, enqueue full audio.
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if result.get("audio_samples", 0) <= 1:
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audio_48k = _resample_mono(audio.astype(np.float32, copy=False), sr, 48000)
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pcm_int16 = np.clip(audio_48k * 32767.0, -32768, 32767).astype(np.int16)
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await audio_bus.enqueue(pcm_int16)
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finally:
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runtime.busy = False
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async def _consume_user_audio(track) -> None:
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vad_buffer = np.zeros(0, dtype=np.float32)
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while True:
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frame = await track.recv()
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pcm = frame.to_ndarray()
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sample_rate = getattr(frame, "sample_rate", 48000) or 48000
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layout = getattr(frame, "layout", None)
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channels = len(layout.channels) if layout and layout.channels else 1
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mono_16k = _resample_to_16k_mono(pcm, sample_rate, channels)
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runtime.buffer_16k.append(mono_16k)
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vad_buffer = np.concatenate([vad_buffer, mono_16k], axis=0)
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# Silero VAD requires exact 512 samples at 16k.
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while vad_buffer.shape[0] >= 512:
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chunk = vad_buffer[:512]
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vad_buffer = vad_buffer[512:]
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try:
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event = vad_service.push_chunk(chunk)
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except Exception as exc:
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logging.exception("VAD chunk processing failed: %s", exc)
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event = None
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if event and "start" in event:
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barge_t0 = time.perf_counter()
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was_avatar_speaking = str(arbitrator.state) == "avatar_speaking"
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await arbitrator.on_speech_start()
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await audio_bus.clear()
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if was_avatar_speaking:
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runtime.last_barge_in_ms = int((time.perf_counter() - barge_t0) * 1000)
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runtime.barge_in_count += 1
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runtime.vad_start_count += 1
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if event and "end" in event:
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await arbitrator.on_speech_end()
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runtime.vad_end_count += 1
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asyncio.create_task(_run_pipeline_from_buffer())
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@app.get("/health")
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async def health():
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return {
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"state": arbitrator.state,
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"peers": len(pcs),
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"pipeline_runs": runtime.pipeline_runs,
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"last_latency_ms": runtime.last_latency_ms,
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"last_asr_latency_ms": runtime.last_asr_latency_ms,
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"last_llm_latency_ms": runtime.last_llm_latency_ms,
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"last_llm_source": runtime.last_llm_source,
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"last_tts_latency_ms": runtime.last_tts_latency_ms,
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"last_tts_first_chunk_ms": runtime.last_tts_first_chunk_ms,
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"last_barge_in_ms": runtime.last_barge_in_ms,
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"barge_in_count": runtime.barge_in_count,
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"last_input_mode": runtime.last_input_mode,
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"vad_start_count": runtime.vad_start_count,
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"vad_end_count": runtime.vad_end_count,
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"last_user_text": runtime.last_user_text,
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"last_reply_text": runtime.last_reply_text,
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"pipeline_busy": runtime.busy,
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"llm": llm_service.health,
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"asr": asr_service.health,
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"tts": tts_service.health,
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"vad": vad_service.health,
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"avatar": avatar_service.health,
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}
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@app.get("/meta")
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async def meta():
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return {
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"stun_url": settings.stun_url,
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"https_enabled": bool(settings.ssl_certfile and settings.ssl_keyfile),
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"host": settings.webrtc_host,
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"port": settings.webrtc_port,
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}
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@app.get("/events")
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async def events():
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async def gen():
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while True:
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payload = {
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"state": str(arbitrator.state),
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"peers": len(pcs),
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"pipeline_runs": runtime.pipeline_runs,
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"last_latency_ms": runtime.last_latency_ms,
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"last_asr_latency_ms": runtime.last_asr_latency_ms,
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"last_llm_latency_ms": runtime.last_llm_latency_ms,
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"last_llm_source": runtime.last_llm_source,
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"last_tts_latency_ms": runtime.last_tts_latency_ms,
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"last_tts_first_chunk_ms": runtime.last_tts_first_chunk_ms,
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"last_barge_in_ms": runtime.last_barge_in_ms,
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"barge_in_count": runtime.barge_in_count,
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"last_input_mode": runtime.last_input_mode,
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"vad_start_count": runtime.vad_start_count,
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"vad_end_count": runtime.vad_end_count,
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"last_user_text": runtime.last_user_text,
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"last_reply_text": runtime.last_reply_text,
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"pipeline_busy": runtime.busy,
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"avatar": avatar_service.health,
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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await asyncio.sleep(1.0)
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return StreamingResponse(gen(), media_type="text/event-stream")
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@app.websocket("/ws/subtitles")
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async def ws_subtitles(websocket: WebSocket):
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await websocket.accept()
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subtitle_clients.add(websocket)
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try:
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while True:
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await websocket.send_text(json.dumps({"type": "ping"}, ensure_ascii=False))
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await asyncio.sleep(15)
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except WebSocketDisconnect:
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subtitle_clients.discard(websocket)
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except Exception:
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subtitle_clients.discard(websocket)
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@app.post("/demo/run-once")
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async def run_once():
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# 3 seconds of silence placeholder; replace with real microphone stream path.
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fake_audio = np.zeros(16000 * 3, dtype=np.float32)
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await arbitrator.on_speech_start()
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result = await pipeline.run_once(fake_audio)
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return result
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@app.post("/chat/text")
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async def chat_text(req: TextChatRequest):
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text = req.text.strip()
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if not text:
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return JSONResponse({"ok": False, "message": "text is empty"}, status_code=400)
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if runtime.busy:
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return JSONResponse({"ok": False, "message": "pipeline busy"}, status_code=429)
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runtime.busy = True
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start_t = time.perf_counter()
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try:
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async def on_audio_chunk(seg_audio: np.ndarray, seg_sr: int) -> None:
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audio_48k_seg = _resample_mono(seg_audio.astype(np.float32, copy=False), seg_sr, 48000)
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pcm_seg = np.clip(audio_48k_seg * 32767.0, -32768, 32767).astype(np.int16)
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await audio_bus.enqueue(pcm_seg)
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async def on_reply_text_now(reply_text: str) -> None:
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runtime.last_reply_text = reply_text
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async def on_reply_segment_now(seg_text: str, idx: int, total: int) -> None:
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await _broadcast_subtitle("ai", seg_text, "text", partial=True, final=(idx >= total - 1))
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await _broadcast_subtitle("user", text, "text")
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result, audio, sr = await pipeline.process_text_turn(
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text,
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on_audio_chunk=on_audio_chunk,
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on_reply_text=on_reply_text_now,
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on_reply_segment=on_reply_segment_now,
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)
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asyncio.create_task(avatar_service.enqueue_musetalk_from_audio(audio, sr))
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runtime.last_input_mode = "text"
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runtime.last_user_text = result["user_text"]
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runtime.last_reply_text = result["reply_text"]
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runtime.pipeline_runs += 1
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runtime.last_latency_ms = int((time.perf_counter() - start_t) * 1000)
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runtime.last_asr_latency_ms = 0
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runtime.last_llm_latency_ms = int(result.get("llm_latency_ms", 0))
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runtime.last_llm_source = str(result.get("llm_source", "unknown"))
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runtime.last_tts_latency_ms = int(result.get("tts_latency_ms", 0))
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runtime.last_tts_first_chunk_ms = int(result.get("tts_first_chunk_ms", 0))
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# Fallback: if no chunk was enqueued for some reason, enqueue full audio.
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if result.get("audio_samples", 0) <= 1:
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audio_48k = _resample_mono(audio.astype(np.float32, copy=False), sr, 48000)
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pcm_int16 = np.clip(audio_48k * 32767.0, -32768, 32767).astype(np.int16)
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await audio_bus.enqueue(pcm_int16)
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return {"ok": True, **result}
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finally:
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runtime.busy = False
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@app.post("/chat/reset")
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async def chat_reset():
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llm_service.clear_history()
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runtime.last_user_text = ""
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runtime.last_reply_text = ""
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runtime.pipeline_runs = 0
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runtime.last_latency_ms = 0
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runtime.last_asr_latency_ms = 0
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runtime.last_llm_latency_ms = 0
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runtime.last_llm_source = "unknown"
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runtime.last_tts_latency_ms = 0
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runtime.last_tts_first_chunk_ms = 0
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runtime.last_barge_in_ms = 0
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runtime.barge_in_count = 0
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runtime.last_input_mode = "none"
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runtime.vad_start_count = 0
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runtime.vad_end_count = 0
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return {"ok": True}
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@app.get("/demo/frame-idle-shape")
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async def frame_idle_shape():
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frame = await avatar_service.idle_frame()
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return {"shape": list(frame.shape)}
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@app.get("/")
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async def root():
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return FileResponse("web/index.html")
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@app.post("/webrtc/offer")
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async def webrtc_offer(request: Request):
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params = await request.json()
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offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"])
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# Single-connection mode: kick existing peers before accepting new one.
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replaced_previous = len(pcs) > 0
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if replaced_previous:
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old_peers = list(pcs)
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await asyncio.gather(*(peer.close() for peer in old_peers), return_exceptions=True)
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for peer in old_peers:
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pcs.discard(peer)
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pc = RTCPeerConnection()
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pcs.add(pc)
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@pc.on("connectionstatechange")
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async def on_connectionstatechange():
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if pc.connectionState in {"failed", "closed", "disconnected"}:
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await pc.close()
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pcs.discard(pc)
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@pc.on("track")
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def on_track(track):
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if track.kind == "audio":
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asyncio.create_task(_consume_user_audio(track))
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pc.addTrack(AvatarVideoTrack(avatar_service, arbitrator, audio_bus, settings.avatar_fps))
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pc.addTrack(AvatarAudioTrack(audio_bus=audio_bus))
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await pc.setRemoteDescription(offer)
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answer = await pc.createAnswer()
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await pc.setLocalDescription(answer)
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return JSONResponse(
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{
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"sdp": pc.localDescription.sdp,
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"type": pc.localDescription.type,
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"replaced_previous": replaced_previous,
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}
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)
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@app.on_event("shutdown")
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async def on_shutdown():
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await asyncio.gather(*(pc.close() for pc in list(pcs)), return_exceptions=True)
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pcs.clear()
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if __name__ == "__main__":
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import uvicorn
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uvicorn_kwargs = {
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"app": app,
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"host": settings.webrtc_host,
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"port": settings.webrtc_port,
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}
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if settings.ssl_certfile and settings.ssl_keyfile:
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uvicorn_kwargs["ssl_certfile"] = settings.ssl_certfile
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uvicorn_kwargs["ssl_keyfile"] = settings.ssl_keyfile
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|
uvicorn.run(**uvicorn_kwargs)
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