# -*- coding: utf-8 -*- """Real human player and low-latency microphone/ASR/TTS voice session.""" from __future__ import annotations import json import os import re import subprocess import tempfile import time import wave from pathlib import Path from typing import List, Optional from .agent import PlayerAgent from .roles import Role class LiveVoiceSession: """Cascaded real-time voice transport with VAD and optional barge-in. AI speech is synthesized with the configured OpenAI TTS endpoint and played immediately. Human speech is captured from the microphone until end-of-speech silence and sent to the configured ASR endpoint. During public AI speech the microphone can terminate playback and turn the interruption into a public utterance. Headphones are recommended so speaker echo is not mistaken for barge-in. """ def __init__(self, out_dir: str, *, allow_interruptions: bool = True): from openai import OpenAI if not os.getenv("OPENAI_API_KEY"): raise RuntimeError("真人实时语音需要可用的 OPENAI_API_KEY（ASR/TTS 不走 OpenRouter）") self.client = OpenAI(api_key=os.environ["OPENAI_API_KEY"], timeout=60, max_retries=1) self.out_dir = Path(out_dir) self.out_dir.mkdir(parents=True, exist_ok=True) self.allow_interruptions = allow_interruptions self.sample_rate = int(os.getenv("VOICE_SAMPLE_RATE", "16000")) self.threshold = float(os.getenv("VOICE_RMS_THRESHOLD", "0.025")) self.silence_seconds = float(os.getenv("VOICE_SILENCE_SECONDS", "0.8")) self.max_utterance = float(os.getenv("VOICE_MAX_UTTERANCE_SECONDS", "25")) self.player = os.getenv("AUDIO_PLAYER", "afplay") self.events = [] self._sequence = 0 def _event(self, type_: str, **data): self._sequence += 1 self.events.append({ "sequence": self._sequence, "monotonic": time.monotonic(), "wall_time": time.strftime("%Y-%m-%dT%H:%M:%S%z"), "type": type_, **data, }) (self.out_dir / "voice_trace.json").write_text( json.dumps(self.events, ensure_ascii=False, indent=2), encoding="utf-8" ) def _tts(self, speaker: str, text: str, round_no: int) -> Path: self._sequence += 1 path = self.out_dir / f"r{round_no}_{speaker}_{self._sequence}.mp3" started = time.monotonic() response = self.client.audio.speech.create( model=os.getenv("OPENAI_TTS_MODEL", "tts-1"), voice=os.getenv("OPENAI_TTS_VOICE", "coral"), input=text, ) response.stream_to_file(path) self._event("tts_ready", speaker=speaker, latency_seconds=round(time.monotonic() - started, 3), file=str(path)) return path def _transcribe(self, wav_path: Path, *, kind: str) -> str: started = time.monotonic() with wav_path.open("rb") as audio: response = self.client.audio.transcriptions.create( model=os.getenv("OPENAI_ASR_MODEL", "whisper-1"), file=audio, language=os.getenv("VOICE_LANGUAGE", "zh"), ) text = response.text.strip() self._event(kind, latency_seconds=round(time.monotonic() - started, 3), transcript=text) return text def _write_wav(self, frames, path: Path): import numpy as np pcm = (np.concatenate(frames).clip(-1, 1) * 32767).astype(" str: import numpy as np import sounddevice as sd if prompt: self.say("judge", prompt, 0, allow_barge_in=False) print(" [真人麦克风] 请发言；句末静音后自动识别……") block = 1024 frames, heard, silent = [], False, 0 silence_blocks = max(1, int(self.silence_seconds * self.sample_rate / block)) deadline = time.monotonic() + self.max_utterance with sd.InputStream(samplerate=self.sample_rate, channels=1, dtype="float32", blocksize=block) as stream: while time.monotonic() < deadline: data, _ = stream.read(block) mono = data[:, 0].copy() frames.append(mono) rms = float(np.sqrt(np.mean(np.square(mono)))) if rms >= self.threshold: heard, silent = True, 0 elif heard: silent += 1 if silent >= silence_blocks: break if not heard: raise TimeoutError("规定时间内没有检测到真人语音") path = Path(tempfile.mkstemp(suffix=".wav")[1]) try: self._write_wav(frames, path) text = self._transcribe(path, kind="human_asr") print(f" [ASR] 真人：{text}") return text finally: path.unlink(missing_ok=True) def say(self, speaker: str, text: str, round_no: int, *, allow_barge_in: bool = False) -> Optional[str]: path = self._tts(speaker, text, round_no) if not (allow_barge_in and self.allow_interruptions): subprocess.run([self.player, str(path)], check=False) return None # Real barge-in: monitor microphone during playback, cancel output on speech, # then hand the captured utterance to ASR as an interruption turn. import numpy as np import sounddevice as sd block = 1024 proc = subprocess.Popen([self.player, str(path)], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) frames = [] consecutive = 0 with sd.InputStream(samplerate=self.sample_rate, channels=1, dtype="float32", blocksize=block) as stream: while proc.poll() is None: data, _ = stream.read(block) mono = data[:, 0].copy() rms = float(np.sqrt(np.mean(np.square(mono)))) consecutive = consecutive + 1 if rms >= self.threshold else 0 if consecutive >= 2: frames.extend([mono]) proc.terminate() self._event("barge_in", interrupted_speaker=speaker) break if not frames: return None silent = 0 silence_blocks = max(1, int(self.silence_seconds * self.sample_rate / block)) deadline = time.monotonic() + self.max_utterance while time.monotonic() < deadline: data, _ = stream.read(block) mono = data[:, 0].copy() frames.append(mono) rms = float(np.sqrt(np.mean(np.square(mono)))) silent = silent + 1 if rms < self.threshold else 0 if silent >= silence_blocks: break wav_path = Path(tempfile.mkstemp(suffix=".wav")[1]) try: self._write_wav(frames, wav_path) return self._transcribe(wav_path, kind="interruption_asr") finally: wav_path.unlink(missing_ok=True) # Judge expects a TTS-like object with synth(). def synth(self, speaker: str, text: str, round_no: int): return self.say(speaker, text, round_no, allow_barge_in=True) class HumanPlayerAgent(PlayerAgent): """A real human seat that obeys the same private-memory boundary as AI seats.""" def __init__(self, name: str, role: Role, voice: LiveVoiceSession): super().__init__(name, role, offline=True) self.voice = voice self.is_human = True self.is_user = True @staticmethod def _explicit_none(text: str) -> bool: folded = text.casefold() return bool( any(word in folded for word in ("放弃", "不用", "弃票")) or re.search(r"\b(?:none|abstain|skip)\b", folded) ) @staticmethod def _spoken_target(text: str, candidates: List[str], allow_none: bool) -> Optional[str]: if allow_none and HumanPlayerAgent._explicit_none(text): return None match = re.search(r"(?:P|player\s*|玩家\s*|[投查验救毒刀]\s*)(\d+)", text, re.I) if match and f"P{int(match.group(1))}" in candidates: return f"P{int(match.group(1))}" chinese = {"一": 1, "二": 2, "三": 3, "四": 4, "五": 5, "六": 6, "七": 7, "八": 8} match = re.search(r"([一二三四五六七八])号", text) if match and f"P{chinese[match.group(1)]}" in candidates: return f"P{chinese[match.group(1)]}" english = { "one": 1, "two": 2, "three": 3, "four": 4, "five": 5, "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, } match = re.search( r"(?:player|seat)\s+(one|two|three|four|five|six|seven|eight|nine|ten)\b", text, re.I, ) if match and f"P{english[match.group(1).casefold()]}" in candidates: return f"P{english[match.group(1).casefold()]}" return PlayerAgent._parse_target(text, candidates, allow_none) def speak(self, players: List[str]) -> str: return self.voice.listen("现在轮到您公开发言。请结合已知信息说出您的分析。") def choose_target(self, prompt: str, candidates: List[str], players: List[str], allow_none: bool = False): answer = self.voice.listen(f"{prompt} 候选：{'、'.join(candidates)}。请说出玩家编号。") return self._spoken_target(answer, candidates, allow_none) def vote(self, candidates: List[str], players: List[str]): answer = self.voice.listen(f"现在投票放逐。候选：{'、'.join(candidates)}。请说出玩家编号或弃票。") return self._spoken_target(answer, candidates, True)