73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
# Copyright (c) 2017 Mycroft AI Inc.
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from typing import *
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import numpy as np
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from precise.params import pr
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from precise.util import load_audio
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inhibit_t = 0.4
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inhibit_dist_t = 1.0
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inhibit_hop_t = 0.1
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def vectorize_raw(audio: np.ndarray) -> np.ndarray:
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"""Turns audio into feature vectors, without clipping for length"""
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from speechpy.feature import mfcc
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return mfcc(audio, pr.sample_rate, pr.window_t, pr.hop_t, pr.n_mfcc, pr.n_filt, pr.n_fft)
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def vectorize(audio: np.ndarray) -> np.ndarray:
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"""
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Args:
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audio: Audio verified to be of `sample_rate`
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Returns:
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array<float>: Vector representation of audio
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"""
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if len(audio) > pr.max_samples:
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audio = audio[-pr.max_samples:]
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features = vectorize_raw(audio)
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if len(features) < pr.n_features:
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features = np.concatenate(
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[np.zeros((pr.n_features - len(features), len(features[0]))), features])
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if len(features) > pr.n_features:
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features = features[-pr.n_features:]
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return features
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def vectorize_inhibit(audio: np.ndarray) -> np.ndarray:
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"""
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Returns an array of inputs generated from the
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keyword audio that shouldn't cause an activation
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"""
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def samp(x):
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return int(pr.sample_rate * x)
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inputs = []
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for offset in range(samp(inhibit_t), samp(inhibit_dist_t), samp(inhibit_hop_t)):
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if len(audio) - offset < samp(pr.buffer_t / 2.):
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break
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inputs.append(vectorize(audio[:-offset]))
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return np.array(inputs) if inputs else np.empty((0, pr.n_features, pr.feature_size))
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def load_vector(name: str, vectorizer: Callable = vectorize) -> np.ndarray:
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"""Loads and caches a vector input from a wav or npy file"""
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import os
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save_name = name if name.endswith('.npy') else os.path.join('.cache', str(abs(hash(pr))),
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vectorizer.__name__ + '.' + name + '.npy')
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if os.path.isfile(save_name):
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return np.load(save_name)
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print('Loading ' + name + '...')
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os.makedirs(os.path.dirname(save_name), exist_ok=True)
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vec = vectorizer(load_audio(name))
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np.save(save_name, vec)
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return vec
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