mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-10-03 17:15:19 -07:00
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
"""Test Kokoro short prompt audio trimming and engine config."""
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from backend.backends import engine_needs_trim, get_model_config
|
|
from backend.utils.audio import trim_tts_output
|
|
|
|
|
|
def test_kokoro_engine_needs_trim_enabled():
|
|
"""Verify Kokoro engine is registered with needs_trim=True in model config."""
|
|
assert engine_needs_trim("kokoro") is True
|
|
config = get_model_config("kokoro")
|
|
assert config is not None
|
|
assert config.needs_trim is True
|
|
|
|
|
|
def test_kokoro_trim_tts_output_removes_trailing_dead_space():
|
|
"""Verify trim_tts_output removes trailing silence past speech."""
|
|
sr = 24000
|
|
speech = np.full(int(sr * 1.5), 0.2, dtype=np.float32) # 1.5s speech
|
|
trailing_silence = np.zeros(int(sr * 1.0), dtype=np.float32) # 1.0s trailing dead space
|
|
raw_audio = np.concatenate([speech, trailing_silence])
|
|
|
|
trimmed = trim_tts_output(raw_audio, sample_rate=sr)
|
|
|
|
# Trimming cuts trailing silence from 2.5s down to speech duration boundary (1.5s)
|
|
expected_dur_samples = int(sr * 1.5)
|
|
assert len(trimmed) == expected_dur_samples
|
|
assert len(trimmed) < len(raw_audio)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_kokoro_backend_generate_applies_trimming(monkeypatch):
|
|
"""Verify KokoroTTSBackend.generate applies trimming on synthesized output."""
|
|
from backend.backends.kokoro_backend import KokoroTTSBackend, KOKORO_SAMPLE_RATE
|
|
|
|
backend = KokoroTTSBackend()
|
|
|
|
# Mock _load_model_sync to avoid requiring real model load in pure unit test
|
|
monkeypatch.setattr(backend, "_load_model_sync", lambda: None)
|
|
monkeypatch.setattr(backend, "_model", object())
|
|
|
|
# Mock KPipeline output to yield audio with 1s trailing silence
|
|
sr = KOKORO_SAMPLE_RATE
|
|
speech = np.full(int(sr * 1.0), 0.2, dtype=np.float32)
|
|
silence = np.zeros(int(sr * 1.0), dtype=np.float32)
|
|
fake_audio = np.concatenate([speech, silence])
|
|
|
|
class FakeResult:
|
|
def __init__(self, audio):
|
|
self.audio = audio
|
|
|
|
class FakePipeline:
|
|
def __call__(self, text, voice, speed=1.0):
|
|
yield FakeResult(fake_audio)
|
|
|
|
monkeypatch.setattr(backend, "_get_pipeline", lambda lang: FakePipeline())
|
|
|
|
audio, sample_rate = await backend.generate("Read it back to me.", voice_prompt={})
|
|
|
|
assert sample_rate == sr
|
|
# Original fake audio was 2.0s (1s speech + 1s silence).
|
|
# Trimmed cuts trailing silence down to 1.0s speech boundary.
|
|
assert len(audio) / sr == pytest.approx(1.0, abs=0.05)
|
|
assert len(audio) < len(fake_audio)
|