mirror of
https://github.com/dscripka/openWakeWord.git
synced 2026-08-27 18:17:20 -04:00
96 lines
2.9 KiB
Python
96 lines
2.9 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 pyaudio
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import numpy as np
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from openwakeword.model import Model
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import argparse
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# Parse input arguments
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parser=argparse.ArgumentParser()
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parser.add_argument(
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"--chunk_size",
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help="How much audio (in number of samples) to predict on at once",
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type=int,
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default=1280,
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required=False
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)
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parser.add_argument(
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"--model_path",
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help="The path of a specific model to load",
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type=str,
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default="",
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required=False
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)
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parser.add_argument(
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"--inference_framework",
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help="The inference framework to use (either 'onnx' or 'tflite'",
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type=str,
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default='tflite',
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required=False
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)
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args=parser.parse_args()
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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 = args.chunk_size
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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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if args.model_path != "":
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owwModel = Model(wakeword_models=[args.model_path], inference_framework=args.inference_framework)
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else:
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owwModel = Model(inference_framework=args.inference_framework)
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n_models = len(owwModel.models.keys())
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# Run capture loop continuosly, checking for wakewords
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if __name__ == "__main__":
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# Generate output string header
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print("\n\n")
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print("#"*100)
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print("Listening for wakewords...")
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print("#"*100)
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print("\n"*(n_models*3))
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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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# Column titles
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n_spaces = 16
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output_string_header = """
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Model Name | Score | Wakeword Status
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--------------------------------------
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"""
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for mdl in owwModel.prediction_buffer.keys():
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# Add scores in formatted table
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scores = list(owwModel.prediction_buffer[mdl])
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curr_score = format(scores[-1], '.20f').replace("-", "")
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output_string_header += f"""{mdl}{" "*(n_spaces - len(mdl))} | {curr_score[0:5]} | {"--"+" "*20 if scores[-1] <= 0.5 else "Wakeword Detected!"}
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"""
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# Print results table
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print("\033[F"*(4*n_models+1))
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print(output_string_header, " ", end='\r')
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