Palash Debnathand4shil
1edd35cfd0
* Add per-segment audio effects DSP preset selector to dub pipeline * Add shape assertions to podcast, warm, and bright preset tests * Fix raw preset semantics, add preset validation, update docs, remove duplicate sys.path * Narrow OOM catch to model.generate only in dub_generate * Preserve original OOM exception context in dub_generate * Bind effect_preset to _gen via explicit parameter to avoid loop capture * Catch RuntimeError instead of torch.mps.MPSError for MPS OOM --------- Co-authored-by: 4shil <166588383+4shil@users.noreply.github.com>
140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
"""
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Tests for the audio effects chain DSP pipeline.
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Pure functions — no GPU or model loading required.
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Tests run in seconds on any machine.
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Note: sys.path for backend imports is handled by tests/conftest.py.
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"""
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import pytest
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import torch
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import math
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pedalboard = pytest.importorskip("pedalboard")
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from services.audio_dsp import (
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apply_effects_chain,
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get_effect_chain,
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list_effect_presets,
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EFFECT_PRESETS,
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)
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def _make_test_audio(duration_s=1.0, sample_rate=24000) -> torch.Tensor:
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"""Create a test audio tensor with a simple sine wave."""
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t = torch.linspace(0, duration_s, int(duration_s * sample_rate))
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return torch.sin(2 * math.pi * 440 * t).unsqueeze(0) # 440 Hz sine, mono
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class TestListEffectPresets:
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def test_returns_all_presets(self):
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presets = list_effect_presets()
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assert len(presets) == 6
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ids = [p["id"] for p in presets]
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assert "broadcast" in ids
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assert "cinematic" in ids
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assert "podcast" in ids
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assert "raw" in ids
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assert "warm" in ids
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assert "bright" in ids
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def test_preset_has_required_fields(self):
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for preset in list_effect_presets():
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assert "id" in preset
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assert "label" in preset
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assert "icon" in preset
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assert "description" in preset
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class TestGetEffectChain:
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def test_broadcast_returns_chain(self):
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chain = get_effect_chain("broadcast")
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assert len(chain) > 0
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types = [fx["type"] for fx in chain]
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assert "highpass" in types
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assert "compressor" in types
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assert "limiter" in types
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def test_cinematic_has_reverb(self):
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chain = get_effect_chain("cinematic")
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types = [fx["type"] for fx in chain]
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assert "reverb" in types
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def test_raw_returns_empty(self):
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chain = get_effect_chain("raw")
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assert chain == []
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def test_unknown_returns_empty(self):
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chain = get_effect_chain("nonexistent")
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assert chain == []
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class TestApplyEffectsChain:
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def test_raw_preset_returns_unmodified(self):
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audio = _make_test_audio()
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result = apply_effects_chain(audio, sample_rate=24000, chain=[])
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assert torch.equal(audio, result)
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def test_broadcast_preset_returns_tensor(self):
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audio = _make_test_audio()
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chain = get_effect_chain("broadcast")
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert isinstance(result, torch.Tensor)
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assert result.shape == audio.shape
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def test_cinematic_preset_returns_tensor(self):
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audio = _make_test_audio()
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chain = get_effect_chain("cinematic")
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert isinstance(result, torch.Tensor)
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assert result.shape == audio.shape
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def test_podcast_preset_returns_tensor(self):
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audio = _make_test_audio()
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chain = get_effect_chain("podcast")
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert isinstance(result, torch.Tensor)
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assert result.shape == audio.shape
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def test_warm_preset_returns_tensor(self):
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audio = _make_test_audio()
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chain = get_effect_chain("warm")
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert isinstance(result, torch.Tensor)
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assert result.shape == audio.shape
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def test_bright_preset_returns_tensor(self):
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audio = _make_test_audio()
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chain = get_effect_chain("bright")
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert isinstance(result, torch.Tensor)
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assert result.shape == audio.shape
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def test_all_presets_produce_output(self):
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"""Smoke test: every preset processes without error."""
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audio = _make_test_audio()
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for preset_id in EFFECT_PRESETS:
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chain = get_effect_chain(preset_id)
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert isinstance(result, torch.Tensor)
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assert result.shape[0] == 1 # mono
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def test_clipping_prevention(self):
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"""Output should not exceed [-1.0, 1.0] range after limiter presets."""
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audio = _make_test_audio()
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for preset_id in ("broadcast", "podcast", "bright"):
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chain = get_effect_chain(preset_id)
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result = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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assert result.abs().max() <= 1.0
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def test_different_presets_produce_different_output(self):
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"""Different presets should produce audibly different output."""
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audio = _make_test_audio(duration_s=2.0)
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results = {}
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for preset_id in ("broadcast", "cinematic", "raw"):
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chain = get_effect_chain(preset_id)
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results[preset_id] = apply_effects_chain(audio, sample_rate=24000, chain=chain)
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# broadcast and cinematic should differ from raw (unprocessed)
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assert not torch.equal(results["broadcast"], results["raw"])
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assert not torch.equal(results["cinematic"], results["raw"])
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