CircuitForge voice annotation pipeline — VoiceFrame API, tone classifiers, speaker diarization
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pyr0ball 335d51f02f feat: lock ToneEvent SSE wire format (cf-core#40)
- AudioEvent: add speaker_id field (was on VoiceFrame only; needed on all events)
- ToneEvent: add session_id field for session correlation across embedded consumers
- README: full wire format documentation — JSON shape, field reference table,
  SSE envelope, Elcor mode subtext table, module license map
- ToneEvent docstring references cf-core#40 as the wire format spec

Closes cf-core#40
2026-04-06 17:51:09 -07:00
cf_voice feat: lock ToneEvent SSE wire format (cf-core#40) 2026-04-06 17:51:09 -07:00
tests feat: initial cf-voice stub — VoiceFrame API, mock IO, context classifier 2026-04-06 16:03:07 -07:00
.env.example feat: real inference pipeline — STT, tone classifier, diarization, mic capture 2026-04-06 17:33:51 -07:00
.gitignore feat: initial cf-voice stub — VoiceFrame API, mock IO, context classifier 2026-04-06 16:03:07 -07:00
pyproject.toml feat: real inference pipeline — STT, tone classifier, diarization, mic capture 2026-04-06 17:33:51 -07:00
README.md feat: lock ToneEvent SSE wire format (cf-core#40) 2026-04-06 17:51:09 -07:00

cf-voice

CircuitForge voice annotation pipeline. Produces VoiceFrame objects from a live audio stream — tone label, confidence, speaker identity, and shift magnitude — and exposes ToneEvent as the stable SSE wire type for downstream consumers (Linnet, Osprey, Falcon).

Status: Notation v0.1.x — real inference pipeline live (faster-whisper STT, wav2vec2 SER, librosa prosody, pyannote diarization). Mock mode available for dev/CI without GPU or mic.

Install

# Mock mode only (no GPU required)
pip install -e ../cf-voice

# Real inference (STT + tone classifier + diarization)
pip install -e "../cf-voice[inference]"

Copy .env.example to .env and fill in HF_TOKEN for diarization.

Quick start

from cf_voice.context import ContextClassifier

# Mock mode (no hardware needed)
classifier = ContextClassifier.mock()
async for frame in classifier.stream():
    print(frame.label, frame.confidence)

# Real mic capture (requires [inference] extras + CF_VOICE_MOCK unset)
classifier = ContextClassifier.from_env()
async for frame in classifier.stream():
    print(frame.label, frame.confidence)

CLI smoke-test:

CF_VOICE_MOCK=1 cf-voice-demo

VoiceFrame

Produced by cf_voice.io (audio capture layer). MIT licensed.

@dataclass
class VoiceFrame:
    label: str            # tone descriptor, e.g. "Warmly impatient"
    confidence: float     # 0.01.0
    speaker_id: str       # ephemeral local label, e.g. "speaker_a"
    shift_magnitude: float  # delta from previous frame, 0.01.0
    timestamp: float      # session-relative seconds

    def is_reliable(self, threshold=0.6) -> bool: ...
    def is_shift(self, threshold=0.3) -> bool: ...

ToneEvent — SSE wire format

ToneEvent is the stable SSE wire type emitted by Linnet's annotation stream and consumed by <LinnetWidget /> embeds in Osprey, Falcon, and other products.

Field names are locked as of cf-voice v0.1.0 (cf-core#40).

JSON shape

{
  "event_type": "tone",
  "timestamp": 4.82,
  "label": "Warmly impatient",
  "confidence": 0.79,
  "speaker_id": "speaker_a",
  "subtext": "Tone: Frustrated",
  "affect": "frustrated",
  "shift_magnitude": 0.74,
  "shift_direction": "more_urgent",
  "prosody_flags": ["fast_rate", "rising"],
  "session_id": "ses_abc123"
}

Field reference

Field Type Stable Description
event_type "tone" yes Always "tone" for ToneEvent
timestamp float yes Seconds since session start
label str yes Human-readable tone descriptor ("Warmly impatient")
confidence float yes 0.01.0. Below ~0.55 = speculative
speaker_id str yes Ephemeral diarization label ("speaker_a"). Resets per session
subtext str | null yes Annotation text. Generic: "Tone: Frustrated". Elcor: "With barely concealed frustration:"
affect str yes AFFECT_LABELS key ("frustrated"). See cf_voice.events.AFFECT_LABELS
shift_magnitude float yes 0.01.0. High = meaningful register change from previous frame
shift_direction str yes "warmer" | "colder" | "more_urgent" | "stable"
prosody_flags str[] no Raw prosody signals ("fast_rate", "rising", "flat_pitch", "low_energy"). Subject to change
session_id str yes Caller-assigned. Correlates events to a conversation session

SSE envelope

Linnet emits events in standard SSE format:

event: tone-event
data: {"event_type":"tone","timestamp":4.82,...}

Host apps subscribing via <LinnetWidget /> receive MessageEvent with type === "tone-event".

Elcor mode

subtext switches format when the session is in Elcor mode (easter egg, unlocked by cumulative session time). Generic is always available; Elcor is opt-in via the session flag:

Affect Generic Elcor
frustrated Tone: Frustrated With barely concealed frustration:
warm Tone: Warm Warmly:
scripted Tone: Scripted Reading from a script:
dismissive Tone: Dismissive With polite dismissiveness:
tired Tone: Tired With audible fatigue:

Mock mode

Set CF_VOICE_MOCK=1 or pass mock=True to make_io(). Emits synthetic VoiceFrame objects on a timer. No GPU, microphone, or HF_TOKEN required. All API surface is identical to real mode.


Module structure

Module License Purpose
cf_voice.models MIT VoiceFrame dataclass
cf_voice.events MIT AudioEvent, ToneEvent, wire format types
cf_voice.io MIT VoiceIO base, MockVoiceIO, make_io() factory
cf_voice.capture BSL 1.1 MicVoiceIO — real mic capture, 2s windowing
cf_voice.stt BSL 1.1 WhisperSTT — faster-whisper async wrapper
cf_voice.classify BSL 1.1 ToneClassifier — wav2vec2 SER + librosa prosody
cf_voice.diarize BSL 1.1 Diarizer — pyannote.audio async wrapper
cf_voice.context BSL 1.1 ContextClassifier — high-level consumer API

BSL applies to inference modules. IO + types + wire format = MIT.


Consumed by

  • Circuit-Forge/linnet — real-time tone annotation PWA (primary consumer)
  • Circuit-Forge/osprey — telephony bridge voice context (Navigation v0.2.x)
  • Circuit-Forge/falcon (planned) — phone form-filling, IVR navigation