mirror of
https://github.com/dscripka/openWakeWord.git
synced 2026-08-27 18:17:20 -04:00
50 lines
No EOL
2 KiB
Python
50 lines
No EOL
2 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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# Imports
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import openwakeword
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import numpy as np
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from pathlib import Path
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from collections import defaultdict
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# Define benchmark to assess inference speed of models at different audio chunk sizes
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# Smaller chunk sizes may increase model performance, at the cost of inference efficiency
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def run_benchmark():
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# Load models
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model_paths = [str(i) for i in Path("openwakeword/resources/models").glob("*.onnx") \
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if "embedding" not in str(i) and "melspectrogram" not in str(i)]
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M = openwakeword.Model(
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wakeword_model_paths=model_paths,
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input_sizes=[16]
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)
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# Create random data to use for benchmarking
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clip = np.random.random(16000*10).astype(np.float32)
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# Run the benchmark
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step_size = 1280
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preprocessing_times = []
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model_times = defaultdict(list)
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for i in range(0, clip.shape[0]-step_size, step_size):
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pred, timing_dict = M.predict(clip[i:i+step_size], timing=True)
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preprocessing_times.append(timing_dict["preprocessor"])
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for mdl_name in M.models.keys():
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model_times[mdl_name].append(timing_dict["models"][mdl_name])
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print(f"Average of {np.mean(preprocessing_times)} for audio preprocessing with a frame size of {step_size/16000} seconds")
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for mdl_name in M.models.keys():
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print(f"Average of {np.mean(model_times[mdl_name])} for model \"{mdl_name}\"", "\n\n")
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if __name__ == "__main__":
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run_benchmark() |