mirror of
https://github.com/dscripka/openWakeWord.git
synced 2026-08-27 18:17:20 -04:00
191 lines
7.7 KiB
Python
191 lines
7.7 KiB
Python
# Copyright 2022 David Scripka. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# Copyright 2022 David Scripka. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
# Imports
|
|
import openwakeword
|
|
import os
|
|
import numpy as np
|
|
from pathlib import Path
|
|
import collections
|
|
import pytest
|
|
import platform
|
|
|
|
|
|
# Tests
|
|
class TestModels:
|
|
def test_load_models_by_path(self):
|
|
# Load model with defaults
|
|
owwModel = openwakeword.Model(wakeword_model_paths=[
|
|
os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
|
|
])
|
|
|
|
# Prediction on random data
|
|
owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
|
|
|
|
def test_custom_model_label_mapping_dict(self):
|
|
# Load model with model path
|
|
owwModel = openwakeword.Model(wakeword_model_paths=[
|
|
os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
|
|
],
|
|
class_mapping_dicts=[{"alexa_v0.1": {"0": "positive"}}]
|
|
)
|
|
|
|
# Prediction on random data
|
|
owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
|
|
|
|
def test_models(self):
|
|
# Load model with defaults
|
|
owwModel = openwakeword.Model()
|
|
|
|
# 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
|
|
|
|
# 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
|
|
|
|
def test_models_with_speex_noise_cancellation(self):
|
|
# Skip test on Windows for now
|
|
if platform.system() == "Windows":
|
|
assert 1 == 1
|
|
else:
|
|
# Load model with defaults
|
|
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]
|
|
|
|
# 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()]
|
|
|
|
# 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
|
|
|
|
def test_models_with_vad(self):
|
|
# Load model with defaults
|
|
owwModel = openwakeword.Model(vad_threshold=0.5)
|
|
|
|
# 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
|
|
|
|
# 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
|
|
|
|
def test_predict_clip_with_array(self):
|
|
# Load model with defaults
|
|
owwModel = openwakeword.Model()
|
|
|
|
# Make random array and predict
|
|
dat = np.random.random(16000)
|
|
predictions = owwModel.predict_clip(dat)
|
|
assert isinstance(predictions[0], dict)
|
|
|
|
def test_models_with_timing(self):
|
|
# Load model with defaults
|
|
owwModel = openwakeword.Model(vad_threshold=0.5)
|
|
|
|
owwModel.predict(np.zeros(1280).astype(np.int16), timing=True)
|
|
|
|
def test_prediction_with_patience(self):
|
|
owwModel = openwakeword.Model()
|
|
target_model_name = list(owwModel.models.keys())[0]
|
|
|
|
with pytest.raises(ValueError):
|
|
owwModel.predict(
|
|
np.zeros(1280),
|
|
patience={target_model_name: 5}
|
|
)
|
|
|
|
owwModel.predict(
|
|
np.zeros(1280),
|
|
patience={target_model_name: 5},
|
|
threshold={target_model_name: 0.5}
|
|
)
|
|
|
|
def test_get_parent_model_from_prediction_label(self):
|
|
owwModel = openwakeword.Model()
|
|
target_model_name = list(owwModel.models.keys())[0]
|
|
owwModel.get_parent_model_from_label(target_model_name)
|
|
|
|
def test_get_positive_prediction_frames(self):
|
|
owwModel = openwakeword.Model()
|
|
|
|
# Get a clip to use for the test
|
|
clip = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")][0]
|
|
features = owwModel._get_positive_prediction_frames(clip)
|
|
assert list(features.values())[0].shape[0] > 0
|