24f04b67db
New modules shipped (from Linnet integration): - acoustic.py: AST (MIT/ast-finetuned-audioset-10-10-0.4593) replaces YAMNet stub; 527 AudioSet classes mapped to queue/speaker/environ/scene labels; _LABEL_MAP includes hold_music, ringback, DTMF, background_shift, AMD signal chain - accent.py: facebook/mms-lid-126 language ID → regional accent labels (en_gb, en_us, en_au, fr, es, de, zh, …); lazy-loaded, gated by CF_VOICE_ACCENT - privacy.py: compound privacy risk scorer — public_env, background_voices, nature scene, accent signals; returns 0–3 score without storing any audio - prosody.py: openSMILE-backed prosody extractor (sarcasm_risk, flat_f0_score, speech_rate, pitch_range); mock mode returns neutral values - dimensional.py: audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim valence/arousal/dominance scorer; gated by CF_VOICE_DIMENSIONAL - trajectory.py: rolling buffer for arousal/valence deltas, trend detection (escalating/suppressed/stable), coherence scoring, suppression/reframe flags - telephony.py: TelephonyBackend Protocol + MockTelephonyBackend + SignalWireBackend + FreeSWITCHBackend; CallSession dataclass; make_telephony() factory - app.py: FastAPI service (port 8007) — /health + /classify; accepts base64 PCM chunks, returns full AudioEventOut including dimensional/prosody/accent fields - prefs.py: voice preference helpers (elcor_mode, confidence_threshold, whisper_model, elcor_prior_frames); cf-core and env-var fallback Tests: fix stale tests (YAMNetAcousticBackend → ASTAcousticBackend, scene field added to AcousticResult, speaker_at gap now resolves dominant speaker not UNKNOWN, make_io real path returns MicVoiceIO when sounddevice installed). 78 tests passing. Closes #2, #3.
131 lines
4.3 KiB
Python
131 lines
4.3 KiB
Python
# tests/test_diarize.py — SpeakerTracker and speaker_at() diarization logic
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#
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# All tests are pure Python — no GPU, no pyannote, no HF_TOKEN required.
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# The Diarizer class itself is only tested for its from_env() guard and the
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# speaker_at() method, both of which run without loading the model.
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from __future__ import annotations
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import os
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import pytest
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from cf_voice.diarize import (
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Diarizer,
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SpeakerSegment,
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SpeakerTracker,
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SPEAKER_MULTIPLE,
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SPEAKER_UNKNOWN,
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)
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# ── SpeakerTracker ────────────────────────────────────────────────────────────
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def test_tracker_first_speaker_is_a():
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t = SpeakerTracker()
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assert t.label("SPEAKER_00") == "Speaker A"
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def test_tracker_second_speaker_is_b():
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t = SpeakerTracker()
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t.label("SPEAKER_00")
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assert t.label("SPEAKER_01") == "Speaker B"
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def test_tracker_same_id_returns_same_label():
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t = SpeakerTracker()
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first = t.label("SPEAKER_00")
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second = t.label("SPEAKER_00")
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assert first == second == "Speaker A"
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def test_tracker_26_speakers():
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t = SpeakerTracker()
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labels = [t.label(f"SPEAKER_{i:02d}") for i in range(26)]
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assert labels[0] == "Speaker A"
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assert labels[25] == "Speaker Z"
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def test_tracker_27th_speaker_wraps():
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t = SpeakerTracker()
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for i in range(26):
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t.label(f"SPEAKER_{i:02d}")
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label_27 = t.label("SPEAKER_26")
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assert label_27 == "Speaker AA"
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def test_tracker_reset_clears_map():
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t = SpeakerTracker()
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t.label("SPEAKER_00")
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t.label("SPEAKER_01")
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t.reset()
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# After reset, SPEAKER_01 is seen as new and maps to "Speaker A" again
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assert t.label("SPEAKER_01") == "Speaker A"
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# ── Diarizer.speaker_at() ─────────────────────────────────────────────────────
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def _segs(*items: tuple[str, float, float]) -> list[SpeakerSegment]:
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return [SpeakerSegment(speaker_id=s, start_s=st, end_s=en) for s, st, en in items]
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def test_speaker_at_single_speaker():
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d = object.__new__(Diarizer) # bypass __init__ (no GPU needed)
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segs = _segs(("SPEAKER_00", 0.0, 2.0))
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t = SpeakerTracker()
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assert d.speaker_at(segs, 1.0, tracker=t) == "Speaker A"
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def test_speaker_at_no_coverage_returns_unknown():
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d = object.__new__(Diarizer)
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segs = _segs(("SPEAKER_00", 0.0, 1.0))
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assert d.speaker_at(segs, 1.5) == SPEAKER_UNKNOWN
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def test_speaker_at_empty_segments_returns_unknown():
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d = object.__new__(Diarizer)
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assert d.speaker_at([], 1.0) == SPEAKER_UNKNOWN
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def test_speaker_at_overlap_returns_multiple():
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d = object.__new__(Diarizer)
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segs = _segs(
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("SPEAKER_00", 0.0, 2.0),
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("SPEAKER_01", 0.5, 2.0), # overlaps SPEAKER_00 from 0.5s
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)
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assert d.speaker_at(segs, 1.0) == SPEAKER_MULTIPLE
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def test_speaker_at_boundary_inclusive():
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d = object.__new__(Diarizer)
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segs = _segs(("SPEAKER_00", 1.0, 2.0))
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t = SpeakerTracker()
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# Exact boundary timestamps are included
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assert d.speaker_at(segs, 1.0, tracker=t) == "Speaker A"
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assert d.speaker_at(segs, 2.0, tracker=t) == "Speaker A"
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def test_speaker_at_without_tracker_returns_raw_id():
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d = object.__new__(Diarizer)
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segs = _segs(("SPEAKER_00", 0.0, 2.0))
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assert d.speaker_at(segs, 1.0) == "SPEAKER_00"
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def test_speaker_at_two_speakers_no_overlap():
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d = object.__new__(Diarizer)
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t = SpeakerTracker()
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segs = _segs(
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("SPEAKER_00", 0.0, 1.0),
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("SPEAKER_01", 1.5, 2.5),
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)
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assert d.speaker_at(segs, 0.5, tracker=t) == "Speaker A"
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assert d.speaker_at(segs, 2.0, tracker=t) == "Speaker B"
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# Gap at 1.2s: window [0.7, 1.7] → SPEAKER_00 has 0.3s, SPEAKER_01 has 0.2s
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# Dominant speaker (SPEAKER_00 = "Speaker A") is returned, not SPEAKER_UNKNOWN.
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assert d.speaker_at(segs, 1.2, tracker=t) == "Speaker A"
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# ── Diarizer.from_env() guard ─────────────────────────────────────────────────
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def test_from_env_raises_without_hf_token(monkeypatch):
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monkeypatch.delenv("HF_TOKEN", raising=False)
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with pytest.raises(EnvironmentError, match="HF_TOKEN"):
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Diarizer.from_env()
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