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Adjusted tests to remove dependencies on optional libraries (fixes #32)

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dscripka 2023-08-25 22:25:40 -04:00
parent 7056d28a3e
commit ee0a31891e
1 changed files with 30 additions and 23 deletions
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53
tests/test_models.py
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@ -30,6 +30,7 @@
import openwakeword
import os
import sys
import logging
import numpy as np
from pathlib import Path
import collections
@ -172,31 +173,37 @@ class TestModels:
assert 1 == 1
else:
# Load model with defaults
owwModel = openwakeword.Model(enable_speex_noise_suppression=True)
try:
owwModel = openwakeword.Model(enable_speex_noise_suppression=True)
# Get clips for each model (assumes that test clips will have the model name in the filename)
test_dict = {}
for mdl_name in owwModel.models.keys():
all_clips = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")]
test_dict[mdl_name] = [i for i in all_clips if mdl_name in i]
# Get clips for each model (assumes that test clips will have the model name in the filename)
test_dict = {}
for mdl_name in owwModel.models.keys():
all_clips = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")]
test_dict[mdl_name] = [i for i in all_clips if mdl_name in i]
# Predict
for model, clips in test_dict.items():
for clip in clips:
# Get predictions for reach frame in the clip
predictions = owwModel.predict_clip(clip)
owwModel.reset() # reset after each clip to ensure independent results
# Predict
for model, clips in test_dict.items():
for clip in clips:
# Get predictions for reach frame in the clip
predictions = owwModel.predict_clip(clip)
owwModel.reset() # reset after each clip to ensure independent results
# Make predictions dictionary flatter
predictions_flat = collections.defaultdict(list)
[predictions_flat[key].append(i[key]) for i in predictions for key in i.keys()]
# Make predictions dictionary flatter
predictions_flat = collections.defaultdict(list)
[predictions_flat[key].append(i[key]) for i in predictions for key in i.keys()]
# Check scores against default threshold (0.5)
for key in predictions_flat.keys():
if key in clip:
assert max(predictions_flat[key]) >= 0.5
else:
assert max(predictions_flat[key]) < 0.5
# Check scores against default threshold (0.5)
for key in predictions_flat.keys():
if key in clip:
assert max(predictions_flat[key]) >= 0.5
else:
assert max(predictions_flat[key]) < 0.5
except ImportError:
logging.warning("Attemped to test Speex noise cancelling functionality, but the 'speexdsp_ns' library was not installed!"
" If you want these tests to be run, install this library as shown in the openwakeword documentation."
)
assert 1 == 1
def test_models_with_vad(self):
# Load model with defaults
@ -264,8 +271,8 @@ class TestModels:
def test_get_positive_prediction_frames(self):
owwModel = openwakeword.Model(wakeword_models=[
os.path.join("openwakeword", "resources", "models", "alexa_v0.1.tflite")
], inference_framework="tflite")
os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
], inference_framework="onnx")
clip = os.path.join("tests", "data", "alexa_test.wav")
features = owwModel._get_positive_prediction_frames(clip)