129 lines
5.1 KiB
Python
129 lines
5.1 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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# Silero VAD License
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#######################
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# MIT License
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# Copyright (c) 2020-present Silero Team
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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########################################
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# This file contains the implementation of a class for voice activity detection (VAD),
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# based on the pre-trained model from Silero (https://github.com/snakers4/silero-vad).
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# It can be used as with the openWakeWord library, or independently.
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# Imports
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import onnxruntime as ort
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import numpy as np
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import os
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from collections import deque
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class VAD():
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"""
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A model class for a voice activity detection (VAD) based on Silero's model:
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https://github.com/snakers4/silero-vad
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"""
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def __init__(self,
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model_path: str = os.path.join(
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os.path.dirname(os.path.abspath(__file__)),
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"resources",
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"models",
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"silero_vad.onnx"
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)
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):
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"""Initialize the VAD model object.
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Args:
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model_path (str): The path to the Silero VAD ONNX model.
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"""
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# Initialize the ONNX model
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sessionOptions = ort.SessionOptions()
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sessionOptions.inter_op_num_threads = 1
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sessionOptions.intra_op_num_threads = 1
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self.model = ort.InferenceSession(model_path, sess_options=sessionOptions,
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providers=["CPUExecutionProvider"])
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# Create buffer
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self.prediction_buffer: deque = deque(maxlen=125) # buffer lenght of 10 seconds
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# Set model parameters
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self.sample_rate = np.array(16000).astype(np.int64)
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# Reset model to start
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self.reset_states()
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def reset_states(self, batch_size=1):
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self._h = np.zeros((2, batch_size, 64)).astype('float32')
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self._c = np.zeros((2, batch_size, 64)).astype('float32')
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self._last_sr = 0
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self._last_batch_size = 0
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def predict(self, x, frame_size=480):
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"""
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Get the VAD predictions for the input audio frame.
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Args:
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x (np.ndarray): The input audio, must be 16 khz and 16-bit PCM format.
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If longer than the input frame, will be split into
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chunks of length `frame_size` and the predictions for
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each chunk returned. Must be a length that is integer
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multiples of the `frame_size` argument.
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frame_size (int): The frame size in samples. The reccomended
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default is 480 samples (30 ms @ 16khz),
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but smaller and larger values
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can be used (though performance may decrease).
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Returns
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float: The average predicted score for the audio frame
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"""
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chunks = [(x[i:i+frame_size]/32767).astype(np.float32)
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for i in range(0, x.shape[0], frame_size)]
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frame_predictions = []
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for chunk in chunks:
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ort_inputs = {'input': chunk[None, ],
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'h': self._h, 'c': self._c, 'sr': self.sample_rate}
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ort_outs = self.model.run(None, ort_inputs)
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out, self._h, self._c = ort_outs
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frame_predictions.append(out[0][0])
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return np.mean(frame_predictions)
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def __call__(self, x, frame_size=160*4):
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self.prediction_buffer.append(self.predict(x, frame_size))
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