mirror of
https://github.com/dscripka/openWakeWord.git
synced 2026-08-27 18:17:20 -04:00
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
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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 pyaudio
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import numpy as np
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import os
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import argparse
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parser=argparse.ArgumentParser()
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from openwakeword.model import Model
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# Get microphone stream
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FORMAT = pyaudio.paInt16
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CHANNELS = 1
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RATE = 16000
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CHUNK = 1280
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audio = pyaudio.PyAudio()
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mic_stream = audio.open(format=FORMAT, channels=CHANNELS,
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rate=RATE, input=True, frames_per_buffer=CHUNK)
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# Get desired number of CPU cores per calculation
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parser.add_argument(
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"--ncores",
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help="How many CPU cores to use for the efficiency estimation",
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type=int,
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default=1
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)
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args=parser.parse_args()
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# Load pre-trained openwakeword models
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owwModel = Model()
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# Run capture loop, checking for hotwords
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if __name__ == "__main__":
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# Continuously predict and estimate CPU usage
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print("\n############################\n\n")
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for i in range(1000000):
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# Get audio
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audio = np.frombuffer(mic_stream.read(CHUNK), dtype=np.int16)
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# Feed to openWakeWord model
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prediction, timing_dict = owwModel.predict(audio, timing=True)
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# Estimate CPU usage
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total_time = sum([i for i in timing_dict["models"].values()])
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avg_model_time = np.mean([timing_dict["models"][i] for i in timing_dict["models"].keys() if i != "preprocessor"])
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n_possible_models = int((0.08 - total_time)/avg_model_time) + int(0.08/avg_model_time)*(args.ncores-1)
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if i % 10 == 0:
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print(f"Using {round((total_time)/.08*100, 3)}% of {args.ncores} CPU core(s). "
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f"Could run up to {n_possible_models} additional models.", end=' \r')
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