"""Echte Transkription mit Whisper (pytest -m integration).

Nutzt espeak-ng als reproduzierbare Sprachquelle und standardmäßig das kleine,
lokal gecachte Modell 'small' auf CPU (STT_TEST_MODEL / STT_TEST_DEVICE überschreibbar).
"""

import os

import numpy as np
import pytest

import stt_core as core
from conftest import espeak_wav, silence

pytestmark = pytest.mark.integration


@pytest.fixture(scope="module")
def engine():
    cfg = core.EngineConfig(model=os.environ.get("STT_TEST_MODEL", "small"),
                            device=os.environ.get("STT_TEST_DEVICE", "cpu"), language="de")
    eng = core.SttEngine(cfg)
    eng.load()
    return eng


def _speech(text, voice="de"):
    wav = espeak_wav(text, voice)
    if wav is None:
        pytest.skip("espeak-ng fehlt")
    return core.decode_audio_bytes(wav)


def _words(text):
    return set(text.lower().replace(".", " ").replace(",", " ").split())


def test_german_command(engine):
    tr = engine.transcribe(_speech("Schalte das Licht im Wohnzimmer ein."))
    assert {"licht", "wohnzimmer"} <= _words(tr.text), tr.text


def test_silence_gives_empty_text(engine):
    noise = (np.random.default_rng(1).standard_normal(3 * core.SAMPLE_RATE) * 0.003).astype(np.float32)
    assert engine.transcribe(noise).text == ""


def test_language_detection(engine):
    tr = engine.transcribe(_speech("The weather is nice today.", "en"), language="auto")
    assert tr.language == "en", tr


def test_segmenter_with_silero_on_real_speech(engine):
    speech = _speech("Schalte das Licht aus.")
    stream = np.concatenate([silence(0.5), speech, silence(1.5), speech, silence(1.5)])
    seg = core.UtteranceSegmenter()
    events = []
    for i in range(0, stream.size, 1600):
        events += seg.feed(stream[i:i + 1600])
    utterances = [e for e in events if e.kind == "utterance"]
    assert len(utterances) == 2, [(e.kind, e.time_s) for e in events]
    assert "licht" in _words(engine.transcribe(utterances[0].audio).text)