dschlueter
a986cbe42a
- stt_core.py: Whisper-Engine (einmal laden, CPU-Fallback), Audio-Dekodierung jedes Formats, Halluzinationsfilter, Äußerungserkennung per Silero-VAD - stt_service.py: /health, /v1/audio/transcriptions (OpenAI), /transcribe (Rohdaten), WS /ws/stream; Inferenz im Worker-Thread, begrenzte Warteschlange (503) - stt_wyoming.py: Home-Assistant-Anbindung (TCP 10300) - stt_cli.py: Datei/Mikrofon über den Dienst oder lokal - Tests (Unit + Integration), README, requirements.txt, deploy/stt-service.service Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""Echte Transkription mit Whisper (pytest -m integration).
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Nutzt espeak-ng als reproduzierbare Sprachquelle und standardmäßig das kleine,
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lokal gecachte Modell 'small' auf CPU (STT_TEST_MODEL / STT_TEST_DEVICE überschreibbar).
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"""
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import os
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import numpy as np
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import pytest
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import stt_core as core
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from conftest import espeak_wav, silence
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pytestmark = pytest.mark.integration
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@pytest.fixture(scope="module")
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def engine():
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cfg = core.EngineConfig(model=os.environ.get("STT_TEST_MODEL", "small"),
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device=os.environ.get("STT_TEST_DEVICE", "cpu"), language="de")
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eng = core.SttEngine(cfg)
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eng.load()
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return eng
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def _speech(text, voice="de"):
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wav = espeak_wav(text, voice)
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if wav is None:
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pytest.skip("espeak-ng fehlt")
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return core.decode_audio_bytes(wav)
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def _words(text):
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return set(text.lower().replace(".", " ").replace(",", " ").split())
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def test_german_command(engine):
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tr = engine.transcribe(_speech("Schalte das Licht im Wohnzimmer ein."))
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assert {"licht", "wohnzimmer"} <= _words(tr.text), tr.text
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def test_silence_gives_empty_text(engine):
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noise = (np.random.default_rng(1).standard_normal(3 * core.SAMPLE_RATE) * 0.003).astype(np.float32)
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assert engine.transcribe(noise).text == ""
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def test_language_detection(engine):
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tr = engine.transcribe(_speech("The weather is nice today.", "en"), language="auto")
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assert tr.language == "en", tr
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def test_segmenter_with_silero_on_real_speech(engine):
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speech = _speech("Schalte das Licht aus.")
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stream = np.concatenate([silence(0.5), speech, silence(1.5), speech, silence(1.5)])
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seg = core.UtteranceSegmenter()
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events = []
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for i in range(0, stream.size, 1600):
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events += seg.feed(stream[i:i + 1600])
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utterances = [e for e in events if e.kind == "utterance"]
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assert len(utterances) == 2, [(e.kind, e.time_s) for e in events]
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assert "licht" in _words(engine.transcribe(utterances[0].audio).text)
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