mirror of
https://github.com/dscripka/openWakeWord.git
synced 2026-08-27 18:17:20 -04:00
112 lines
No EOL
3.6 KiB
Python
112 lines
No EOL
3.6 KiB
Python
# Copyright 2023 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 in a simple web server receiving audio
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# from a web page using websockets.
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#######################################################################################
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# Imports
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import aiohttp
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from aiohttp import web
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import numpy as np
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from openwakeword import Model
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import resampy
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import argparse
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import json
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# Define websocket handler
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async def websocket_handler(request):
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ws = web.WebSocketResponse()
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await ws.prepare(request)
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# Send loaded models
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await ws.send_str(json.dumps({"loaded_models": list(owwModel.models.keys())}))
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# Start listening for websocket messages
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async for msg in ws:
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# Get the sample rate of the microphone from the browser
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if msg.type == aiohttp.WSMsgType.TEXT:
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sample_rate = int(msg.data)
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elif msg.type == aiohttp.WSMsgType.ERROR:
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print(f"WebSocket error: {ws.exception()}")
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else:
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# Get audio data from websocket
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audio_bytes = msg.data
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# Add extra bytes of silence if needed
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if len(msg.data) % 2 == 1:
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audio_bytes += (b'\x00')
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# Convert audio to correct format and sample rate
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data = np.frombuffer(audio_bytes, dtype=np.int16)
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if sample_rate != 16000:
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data = resampy.resample(data, sample_rate, 16000)
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# Get openWakeWord predictions and set to browser client
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predictions = owwModel.predict(data)
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activations = []
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for key in predictions:
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if predictions[key] >= 0.5:
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activations.append(key)
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if activations != []:
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await ws.send_str(json.dumps({"activations": activations}))
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return ws
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# Define static file handler
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async def static_file_handler(request):
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return web.FileResponse('./streaming_client.html')
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app = web.Application()
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app.add_routes([web.get('/ws', websocket_handler), web.get('/', static_file_handler)])
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if __name__ == '__main__':
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# Parse CLI 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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# Load 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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# Start webapp
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web.run_app(app, host='localhost', port=9000) |