mirror of
https://github.com/dscripka/openWakeWord.git
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121 lines
No EOL
4 KiB
Python
121 lines
No EOL
4 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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##################################
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# This example scripts runs openWakeWord continuously on a microphone stream,
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# and saves 5 seconds of audio immediately before the activation as WAV clips
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# in the specified output location.
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##################################
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# Imports
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import os
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import platform
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import collections
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import time
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if platform.system() == "Windows":
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import pyaudiowpatch as pyaudio
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else:
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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 scipy.io.wavfile
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import datetime
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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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"--output_dir",
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help="Where to save the audio that resulted in an activation",
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type=str,
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default="./",
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required=True
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)
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parser.add_argument(
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"--threshold",
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help="The score threshold for an activation",
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type=float,
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default=0.5,
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required=False
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)
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parser.add_argument(
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"--vad_threshold",
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help="""The threshold to use for voice activity detection (VAD) in the openWakeWord instance.
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The default (0.0), disables VAD.""",
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type=float,
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default=0.0,
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required=False
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)
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parser.add_argument(
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"--noise_suppression",
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help="Whether to enable speex noise suppression in the openWakeWord instance.",
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type=bool,
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default=False,
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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 = 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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enable_speex_noise_suppression=args.noise_suppression,
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vad_threshold = args.vad_threshold
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)
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# Set waiting period after activation before saving clip (to get some audio context after the activation)
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save_delay = 1 # seconds
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# Set cooldown period before another clip can be saved
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cooldown = 4 # seconds
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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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last_save = time.time()
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activation_times = collections.defaultdict(list)
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print("\n\nListening for wakewords...\n")
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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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# Check for model activations (score above threshold), and save clips
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for mdl in prediction.keys():
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if prediction[mdl] >= args.threshold:
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activation_times[mdl].append(time.time())
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if activation_times.get(mdl) and (time.time() - last_save) >= cooldown \
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and (time.time() - activation_times.get(mdl)[0]) >= save_delay:
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last_save = time.time()
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activation_times[mdl] = []
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detect_time = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
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print(f'Detected activation from \"{mdl}\" model at time {detect_time}!')
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# Capture total of 5 seconds, with the audio associated with the
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# activation around the ~4 second point
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audio_context = np.array(list(owwModel.preprocessor.raw_data_buffer)[-16000*5:]).astype(np.int16)
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fname = detect_time + f"_{mdl}.wav"
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scipy.io.wavfile.write(os.path.join(os.path.abspath(args.output_dir), fname), 16000, audio_context) |