Senad Uka
714aa1782b
- AudioSocket server (asyncio TCP, Asterisk wire protocol) bridging calls into the same M2 agent used by chat - Call session engine: greeting → energy-VAD utterance collection → STT → agent turn → TTS playback with barge-in (120ms of caller speech stops playback); inactivity + max-duration guards; unintelligible audio reaches the agent as '[nerazumljivo]' so the two-attempt rule stays in the prompt - STTProvider (faster-whisper, lang hint 'sr', GPU/CPU via env) and TTSProvider (Azure Neural raw-8k PCM / ElevenLabs Flash) + test fakes - Recording (mixed caller+agent WAV), transcripts, per-turn latency trace, metering via record_agent_call (§12) - Internal dialplan API: /internal/calls/register (ring targets computed from working hours + ring settings §5.2), /answered (human outcome, no minutes), /hangup (abandoned → missed-call SMS §5.5); shared-token auth - Asterisk config generator (pjsip.conf + extensions.conf from DB): worker endpoints, per-tenant test-caller endpoint, register→Dial→AudioSocket dialplan with h-extension reporting — validated against a real Asterisk 20 container (modules, dialplan, endpoints all load) - docker-compose 'voice' profile (voice server + Asterisk), Dockerfile.voice, docs/VOICE_TESTING.md softphone runbook - 16 new tests incl. full fake-call e2e and a real-TCP AudioSocket wire test Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
"""Utterance detection: adaptive energy VAD with hangover.
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Deliberately simple and dependency-free; swap for silero/webrtcvad later if the
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Phase-0 PoC shows it is needed. Operates on 20 ms SLIN frames.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from gogo.voice.audio import FRAME_MS, rms
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@dataclass
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class VadConfig:
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# energy threshold = max(min_threshold, noise_floor * ratio)
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min_threshold: int = 500
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noise_ratio: float = 3.0
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start_ms: int = 120 # this much voiced audio starts an utterance
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end_ms: int = 700 # this much silence ends it
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max_utterance_ms: int = 15000 # hard cap
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pre_roll_ms: int = 200 # audio kept from before the trigger
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@dataclass
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class UtteranceCollector:
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"""Feed 20 ms frames; returns the full utterance PCM when one completes."""
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config: VadConfig = field(default_factory=VadConfig)
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_noise_floor: float = 200.0
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_voiced_ms: int = 0
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_silence_ms: int = 0
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_in_utterance: bool = False
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_buffer: bytearray = field(default_factory=bytearray)
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_pre_roll: bytearray = field(default_factory=bytearray)
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def is_speech(self, frame: bytes) -> bool:
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energy = rms(frame)
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threshold = max(self.config.min_threshold, self._noise_floor * self.config.noise_ratio)
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speech = energy > threshold
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if not speech:
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# slowly track the noise floor on non-speech frames
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self._noise_floor = 0.95 * self._noise_floor + 0.05 * energy
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return speech
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def feed(self, frame: bytes) -> bytes | None:
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"""Returns utterance PCM when a complete utterance is detected, else None."""
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speech = self.is_speech(frame)
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if not self._in_utterance:
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self._pre_roll.extend(frame)
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max_pre = self.config.pre_roll_ms * len(frame) // FRAME_MS
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if len(self._pre_roll) > max_pre:
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del self._pre_roll[: len(self._pre_roll) - max_pre]
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if speech:
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self._voiced_ms += FRAME_MS
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if self._voiced_ms >= self.config.start_ms:
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self._in_utterance = True
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self._buffer = bytearray(self._pre_roll)
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self._silence_ms = 0
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else:
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self._voiced_ms = 0
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return None
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self._buffer.extend(frame)
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if speech:
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self._silence_ms = 0
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else:
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self._silence_ms += FRAME_MS
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utterance_ms = len(self._buffer) * FRAME_MS // len(frame)
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if self._silence_ms >= self.config.end_ms or utterance_ms >= self.config.max_utterance_ms:
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utterance = bytes(self._buffer)
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self.reset()
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return utterance
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return None
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def reset(self) -> None:
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self._in_utterance = False
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self._voiced_ms = 0
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self._silence_ms = 0
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self._buffer = bytearray()
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self._pre_roll = bytearray()
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