mirror of
https://github.com/dscripka/openWakeWord.git
synced 2026-08-27 18:17:20 -04:00
56 lines
1.8 KiB
Python
56 lines
1.8 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 plotext as plt
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import pyaudio
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import numpy as np
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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, rate=RATE, input=True, frames_per_buffer=CHUNK)
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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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# Predict continuously on audio stream
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while True:
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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 = owwModel.predict(audio)
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# Get predictions from prediction buffers and plot
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plt.cld()
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plt.clt()
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for mdl in owwModel.prediction_buffer.keys():
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# Plot scores in graph
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scores = list(owwModel.prediction_buffer[mdl])
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plt.plot(scores)
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# Plot text showing name of model with scores >= 0.5 (default threshold)
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if max(scores) >= 0.5:
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plt.text(mdl, 15, 0.9, alignment="center", color = "blue", style="bold")
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plt.ylim(0,1)
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plt.show()
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plt.sleep(0.005)
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