mirror of
https://github.com/dscripka/openWakeWord.git
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282 lines
13 KiB
Python
282 lines
13 KiB
Python
# Copyright 2022 David Scripka. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Copyright 2022 David Scripka. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Imports
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import openwakeword
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import os
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import sys
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import logging
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import numpy as np
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from pathlib import Path
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import collections
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import pytest
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import platform
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import pickle
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import tempfile
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import mock
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# Download models needed for tests
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openwakeword.utils.download_models()
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# Tests
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class TestModels:
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def test_load_models_by_path(self):
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# Load model with defaults
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owwModel = openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
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], inference_framework="onnx")
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owwModel = openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.tflite")
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], inference_framework="tflite")
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# Prediction on random data
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owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
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def test_predict_with_different_frame_sizes(self):
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# Test with binary model
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owwModel1 = openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
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], inference_framework="onnx")
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owwModel2 = openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
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], inference_framework="onnx")
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# Prediction on random data with integer multiples of standard chunk size (1280 samples)
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predictions1 = owwModel1.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1280)
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predictions2 = owwModel2.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1280*2)
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np.testing.assert_approx_equal(max([i['alexa_v0.1'] for i in predictions1]), max([i['alexa_v0.1'] for i in predictions2]), 5)
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# Prediction on data with a chunk size not an integer multiple of 1280
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predictions1 = owwModel1.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1024)
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predictions2 = owwModel2.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1024*2)
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np.testing.assert_approx_equal(max([i['alexa_v0.1'] for i in predictions1]), max([i['alexa_v0.1'] for i in predictions2]), 5)
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# Test with multiclass model
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owwModel1 = openwakeword.Model(wakeword_models=["timer"], inference_framework="onnx")
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owwModel2 = openwakeword.Model(wakeword_models=["timer"], inference_framework="onnx")
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# Prediction on random data with integer multiples of standard chunk size (1280 samples)
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predictions1 = owwModel1.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1280)
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predictions2 = owwModel2.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1280*2)
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assert abs(max([i['1_minute_timer'] for i in predictions1]) - max([i['1_minute_timer'] for i in predictions2])) < 0.00001
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# Prediction on data with a chunk size not an integer multiple of 1280
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predictions1 = owwModel1.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1024)
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predictions2 = owwModel2.predict_clip(os.path.join("tests", "data", "alexa_test.wav"), chunk_size=1024*2)
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assert abs(max([i['1_minute_timer'] for i in predictions1]) - max([i['1_minute_timer'] for i in predictions2])) < 0.00001
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def test_exception_handling_for_inference_framework(self):
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with mock.patch.dict(sys.modules, {'onnxruntime': None}):
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with pytest.raises(ValueError):
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openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
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], inference_framework="onnx")
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with mock.patch.dict(sys.modules, {'tflite_runtime': None}):
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openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.tflite")
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], inference_framework="tflite")
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def test_predict_with_custom_verifier_model(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Train custom verifier model with random data
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verifier_model = openwakeword.custom_verifier_model.train_verifier_model(np.random.random((2, 1536)), np.array([0, 1]))
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pickle.dump(verifier_model, open(os.path.join(tmp_dir, "test_verifier.pkl"), "wb"))
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# Load model with verifier
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owwModel = openwakeword.Model(
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wakeword_models=[os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")],
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inference_framework="onnx",
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custom_verifier_models={"alexa_v0.1": os.path.join(tmp_dir, "test_verifier.pkl")},
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custom_verifier_threshold=0.0
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)
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owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
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def test_load_pretrained_model_by_name(self):
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# Load model with defaults
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owwModel = openwakeword.Model(wakeword_models=["alexa", "hey mycroft"], inference_framework="onnx")
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owwModel = openwakeword.Model(wakeword_models=["alexa", "hey mycroft"], inference_framework="tflite")
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# Prediction on random data
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owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
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def test_custom_model_label_mapping_dict(self):
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# Load model with model path
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owwModel = openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
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],
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class_mapping_dicts=[{"alexa_v0.1": {"0": "positive"}}],
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inference_framework="onnx"
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)
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# Prediction on random data
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owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
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def test_models(self):
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# Load model with defaults
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owwModel = openwakeword.Model()
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# Get clips for each model (assumes that test clips will have the model name in the filename)
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test_dict = {}
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for mdl_name in owwModel.models.keys():
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all_clips = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")]
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test_dict[mdl_name] = [i for i in all_clips if mdl_name in i]
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# Predict
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for model, clips in test_dict.items():
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for clip in clips:
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# Get predictions for reach frame in the clip
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predictions = owwModel.predict_clip(clip)
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owwModel.reset() # reset after each clip to ensure independent results
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# Make predictions dictionary flatter
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predictions_flat = collections.defaultdict(list)
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[predictions_flat[key].append(i[key]) for i in predictions for key in i.keys()]
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# Check scores against default threshold (0.5)
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for key in predictions_flat.keys():
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if key in clip:
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assert max(predictions_flat[key]) >= 0.5
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else:
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assert max(predictions_flat[key]) < 0.5
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def test_models_with_speex_noise_cancellation(self):
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# Skip test on Windows for now
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if platform.system() == "Windows":
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assert 1 == 1
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else:
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# Load model with defaults
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try:
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owwModel = openwakeword.Model(enable_speex_noise_suppression=True)
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# Get clips for each model (assumes that test clips will have the model name in the filename)
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test_dict = {}
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for mdl_name in owwModel.models.keys():
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all_clips = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")]
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test_dict[mdl_name] = [i for i in all_clips if mdl_name in i]
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# Predict
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for model, clips in test_dict.items():
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for clip in clips:
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# Get predictions for reach frame in the clip
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predictions = owwModel.predict_clip(clip)
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owwModel.reset() # reset after each clip to ensure independent results
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# Make predictions dictionary flatter
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predictions_flat = collections.defaultdict(list)
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[predictions_flat[key].append(i[key]) for i in predictions for key in i.keys()]
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# Check scores against default threshold (0.5)
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for key in predictions_flat.keys():
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if key in clip:
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assert max(predictions_flat[key]) >= 0.5
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else:
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assert max(predictions_flat[key]) < 0.5
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except ImportError:
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logging.warning("Attemped to test Speex noise cancelling functionality, but the 'speexdsp_ns' library was not installed!"
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" If you want these tests to be run, install this library as shown in the openwakeword documentation."
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)
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assert 1 == 1
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def test_models_with_vad(self):
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# Load model with defaults
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owwModel = openwakeword.Model(vad_threshold=0.5)
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# Get clips for each model (assumes that test clips will have the model name in the filename)
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test_dict = {}
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for mdl_name in owwModel.models.keys():
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all_clips = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")]
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test_dict[mdl_name] = [i for i in all_clips if mdl_name in i]
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# Predict
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for model, clips in test_dict.items():
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for clip in clips:
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# Get predictions for reach frame in the clip
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predictions = owwModel.predict_clip(clip)
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owwModel.reset() # reset after each clip to ensure independent results
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# Make predictions dictionary flatter
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predictions_flat = collections.defaultdict(list)
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[predictions_flat[key].append(i[key]) for i in predictions for key in i.keys()]
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# Check scores against default threshold (0.5)
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for key in predictions_flat.keys():
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if key in clip:
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assert max(predictions_flat[key]) >= 0.5
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else:
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assert max(predictions_flat[key]) < 0.5
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def test_predict_clip_with_array(self):
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# Load model with defaults
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owwModel = openwakeword.Model()
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# Make random array and predict
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dat = np.random.random(16000)
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predictions = owwModel.predict_clip(dat)
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assert isinstance(predictions[0], dict)
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def test_models_with_timing(self):
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# Load model with defaults
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owwModel = openwakeword.Model(vad_threshold=0.5)
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owwModel.predict(np.zeros(1280).astype(np.int16), timing=True)
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def test_prediction_with_patience(self):
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owwModel = openwakeword.Model()
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target_model_name = list(owwModel.models.keys())[0]
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with pytest.raises(ValueError):
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owwModel.predict(
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np.zeros(1280),
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patience={target_model_name: 5}
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)
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owwModel.predict(
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np.zeros(1280),
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patience={target_model_name: 5},
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threshold={target_model_name: 0.5}
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)
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def test_get_parent_model_from_prediction_label(self):
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owwModel = openwakeword.Model()
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target_model_name = list(owwModel.models.keys())[0]
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owwModel.get_parent_model_from_label(target_model_name)
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def test_get_positive_prediction_frames(self):
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owwModel = openwakeword.Model(wakeword_models=[
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os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
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], inference_framework="onnx")
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clip = os.path.join("tests", "data", "alexa_test.wav")
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features = owwModel._get_positive_prediction_frames(clip)
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assert list(features.values())[0].shape[0] > 0
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