328 lines
10 KiB
Python
328 lines
10 KiB
Python
from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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SUPPORTED_LLM_PROVIDERS = frozenset({"openai", "fastgpt"})
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_LLM_PROVIDER_ALIASES = {"llm": "openai", "openai": "openai", "fastgpt": "fastgpt"}
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@dataclass(frozen=True)
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class ServerConfig:
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host: str = "0.0.0.0"
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port: int = 8000
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cors_origins: list[str] = field(default_factory=list)
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serve_webpage: bool = True
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webpage_mount: str = "/voice-demo"
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@dataclass(frozen=True)
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class AudioConfig:
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sample_rate_hz: int = 16000
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channels: int = 1
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frame_ms: int = 20
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@property
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def frame_bytes(self) -> int:
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return int(self.sample_rate_hz * self.frame_ms / 1000) * self.channels * 2
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@dataclass(frozen=True)
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class AudioFilterConfig:
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"""Optional input audio filter applied by the Pipecat transport before VAD/STT."""
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enabled: bool = False
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provider: str = "none"
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lib_path: str | None = None
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model_path: str | None = None
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model_sample_rate_hz: int = 48000
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atten_lim_db: float = 100.0
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post_filter_beta: float = 0.0
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log_level: str | None = None
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@dataclass(frozen=True)
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class SessionConfig:
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inactivity_timeout_sec: int = 60
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@dataclass(frozen=True)
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class VADConfig:
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"""Voice Activity Detection thresholds for the Silero analyzer.
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These map directly to ``pipecat.audio.vad.vad_analyzer.VADParams``.
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Defaults are tuned a touch more conservative than upstream pipecat so
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short pauses in continuous speech don't end the user turn prematurely.
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"""
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confidence: float = 0.7
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start_secs: float = 0.2
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stop_secs: float = 0.6
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min_volume: float = 0.6
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@dataclass(frozen=True)
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class TurnConfig:
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"""User-turn segmentation policy.
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``user_speech_timeout_sec`` is the grace window (in seconds) after VAD
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has confirmed silence during which the user is allowed to resume
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speaking before the aggregator finalizes the turn. Used by
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``SpeechTimeoutUserTurnStopStrategy``. Higher = more tolerant of
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natural mid-sentence pauses; lower = snappier turn-taking.
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The combined "user pause before turn ends" budget is roughly
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``vad.stop_secs + user_speech_timeout_sec``.
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"""
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vad: VADConfig = field(default_factory=VADConfig)
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user_speech_timeout_sec: float = 1.0
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idle_prompt_timeout_sec: float = 0.0
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idle_prompt_max_count: int = 1
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idle_prompt_text: str = (
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"我先停在这里。你可以继续说你的想法，"
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"或者让我根据刚才的内容帮你整理下一步。"
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)
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interruption_min_chars: int = 3
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interruption_use_interim: bool = True
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interruption_short_replies: list[str] = field(
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default_factory=lambda: [
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"是",
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"是的",
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"对",
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"对的",
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"嗯",
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"好",
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"好的",
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"行",
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"可以",
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"没问题",
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"不是",
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"不",
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"不行",
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"不用",
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"不要",
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"没有",
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"否",
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"no",
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"yes",
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"ok",
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"okay",
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]
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)
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@dataclass(frozen=True)
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class ResponseStateConfig:
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enabled: bool = False
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tag: str = "state"
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event_type: str = "response.state"
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max_prefix_chars: int = 256
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@dataclass(frozen=True)
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class AgentConfig:
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system_prompt: str = "You are a helpful, friendly voice assistant."
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greeting: str | None = None
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greeting_mode: str = "generated"
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response_state: ResponseStateConfig = field(default_factory=ResponseStateConfig)
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@dataclass(frozen=True)
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class LLMConfig:
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"""LLM backend selection via ``provider``.
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Set ``provider`` to ``"openai"`` (alias ``"llm"``) for OpenAI-compatible chat
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completions, or ``"fastgpt"`` for FastGPT server-side memory via ``chat_id``.
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"""
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provider: str = "openai"
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api_key: str = ""
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base_url: str | None = None
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model: str = "gpt-4o-mini"
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app_id: str | None = None
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temperature: float | None = 0.7
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chat_id: str | None = None
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variables: dict[str, str] = field(default_factory=dict)
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detail: bool = False
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timeout_sec: float = 60.0
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send_system_prompt: bool = False
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@property
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def is_fastgpt(self) -> bool:
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return self.provider == "fastgpt"
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@property
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def is_openai(self) -> bool:
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return self.provider == "openai"
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@property
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def uses_local_context_history(self) -> bool:
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"""Whether the pipeline should seed and maintain local LLM context history."""
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return not self.is_fastgpt or self.send_system_prompt
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@dataclass(frozen=True)
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class STTConfig:
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provider: str = "openai"
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app_id: str = ""
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api_key: str = ""
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api_secret: str = ""
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base_url: str | None = None
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model: str = "gpt-4o-mini-transcribe"
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language: str | None = "en"
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domain: str = "iat"
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accent: str = "mandarin"
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encoding: str = "raw"
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frame_size: int = 1280
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timeout_sec: float = 10.0
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dynamic_correction: bool = False
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@dataclass(frozen=True)
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class TTSConfig:
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provider: str = "openai"
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app_id: str = ""
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api_key: str = ""
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api_secret: str = ""
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base_url: str | None = None
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model: str = "gpt-4o-mini-tts"
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voice: str = "alloy"
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aue: str = "raw"
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tte: str = "UTF8"
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speed: int = 50
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volume: int = 50
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pitch: int = 50
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timeout_sec: float = 30.0
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source_sample_rate_hz: int | None = None
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oral_level: str = "mid"
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text_aggregation_mode: str | None = None
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@dataclass(frozen=True)
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class ServicesConfig:
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llm: LLMConfig = field(default_factory=LLMConfig)
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stt: STTConfig = field(default_factory=STTConfig)
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tts: TTSConfig = field(default_factory=TTSConfig)
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@dataclass(frozen=True)
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class EngineConfig:
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server: ServerConfig = field(default_factory=ServerConfig)
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audio: AudioConfig = field(default_factory=AudioConfig)
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audio_filter: AudioFilterConfig = field(default_factory=AudioFilterConfig)
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session: SessionConfig = field(default_factory=SessionConfig)
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turn: TurnConfig = field(default_factory=TurnConfig)
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agent: AgentConfig = field(default_factory=AgentConfig)
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services: ServicesConfig = field(default_factory=ServicesConfig)
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def load_config(path: str | Path = "config.json") -> EngineConfig:
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config_path = Path(path)
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if not config_path.exists() and str(path) == "config.json":
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config_path = Path(__file__).resolve().parent.parent / "config.json"
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data = json.loads(config_path.read_text(encoding="utf-8"))
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if not isinstance(data, dict):
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raise ValueError(f"Config file must contain a JSON object: {config_path}")
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return config_from_dict(data)
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def config_from_dict(data: dict) -> EngineConfig:
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services = _dict(data.get("services"))
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agent = _dict(data.get("agent"))
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if agent.get("greeting") == "":
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agent["greeting"] = None
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if agent.get("greeting_mode") not in (None, "generated", "fixed", "off"):
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raise ValueError("agent.greeting_mode must be one of: generated, fixed, off")
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response_state = ResponseStateConfig(**_dict(agent.pop("response_state")))
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if response_state.max_prefix_chars < 1:
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raise ValueError("agent.response_state.max_prefix_chars must be greater than 0")
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if not response_state.tag:
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raise ValueError("agent.response_state.tag must not be empty")
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if not response_state.event_type:
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raise ValueError("agent.response_state.event_type must not be empty")
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stt = _dict(services.get("stt") or services.get("asr"))
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if stt.get("language") == "":
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stt["language"] = None
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llm = _dict(services.get("llm"))
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llm["provider"] = _normalize_llm_provider(llm.get("provider", LLMConfig().provider))
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if llm.get("chat_id") == "":
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llm["chat_id"] = None
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if llm.get("app_id") == "":
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llm["app_id"] = None
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if not isinstance(llm.get("variables"), dict):
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llm["variables"] = {}
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turn = _dict(data.get("turn"))
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vad = _dict(turn.get("vad"))
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return EngineConfig(
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server=ServerConfig(**_dict(data.get("server"))),
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audio=AudioConfig(**_dict(data.get("audio"))),
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audio_filter=AudioFilterConfig(**_normalize_audio_filter(_dict(data.get("audio_filter")))),
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session=SessionConfig(**_dict(data.get("session"))),
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turn=TurnConfig(
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vad=VADConfig(**vad),
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user_speech_timeout_sec=float(
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turn.get("user_speech_timeout_sec", TurnConfig().user_speech_timeout_sec)
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),
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idle_prompt_timeout_sec=float(
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turn.get("idle_prompt_timeout_sec", TurnConfig().idle_prompt_timeout_sec)
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),
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idle_prompt_max_count=int(
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turn.get("idle_prompt_max_count", TurnConfig().idle_prompt_max_count)
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),
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idle_prompt_text=str(
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turn.get("idle_prompt_text", TurnConfig().idle_prompt_text)
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),
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interruption_min_chars=int(
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turn.get("interruption_min_chars", TurnConfig().interruption_min_chars)
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),
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interruption_use_interim=bool(
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turn.get("interruption_use_interim", TurnConfig().interruption_use_interim)
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),
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interruption_short_replies=list(
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turn.get(
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"interruption_short_replies",
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TurnConfig().interruption_short_replies,
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)
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),
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),
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agent=AgentConfig(**agent, response_state=response_state),
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services=ServicesConfig(
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llm=LLMConfig(**llm),
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stt=STTConfig(**stt),
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tts=TTSConfig(**_dict(services.get("tts"))),
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),
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)
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def _dict(value: object) -> dict:
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return dict(value) if isinstance(value, dict) else {}
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def _normalize_audio_filter(value: dict) -> dict:
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if value.get("lib_path") == "":
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value["lib_path"] = None
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if value.get("model_path") == "":
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value["model_path"] = None
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if value.get("log_level") == "":
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value["log_level"] = None
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if "provider" in value:
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value["provider"] = str(value["provider"]).strip().lower()
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return value
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def _normalize_llm_provider(value: object) -> str:
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provider = str(value or LLMConfig().provider).strip().lower()
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normalized = _LLM_PROVIDER_ALIASES.get(provider)
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if normalized is None:
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supported = ", ".join(sorted(SUPPORTED_LLM_PROVIDERS | {"llm"}))
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raise ValueError(
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f"services.llm.provider must be one of: {supported}; got {value!r}"
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)
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return normalized
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