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| # Copyright 2018 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Utility class for extracting features from the text and audio input.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import codecs | |
| import numpy as np | |
| def compute_spectrogram_feature(samples, sample_rate, stride_ms=10.0, | |
| window_ms=20.0, max_freq=None, eps=1e-14): | |
| """Compute the spectrograms for the input samples(waveforms). | |
| More about spectrogram computation, please refer to: | |
| https://en.wikipedia.org/wiki/Short-time_Fourier_transform. | |
| """ | |
| if max_freq is None: | |
| max_freq = sample_rate / 2 | |
| if max_freq > sample_rate / 2: | |
| raise ValueError("max_freq must not be greater than half of sample rate.") | |
| if stride_ms > window_ms: | |
| raise ValueError("Stride size must not be greater than window size.") | |
| stride_size = int(0.001 * sample_rate * stride_ms) | |
| window_size = int(0.001 * sample_rate * window_ms) | |
| # Extract strided windows | |
| truncate_size = (len(samples) - window_size) % stride_size | |
| samples = samples[:len(samples) - truncate_size] | |
| nshape = (window_size, (len(samples) - window_size) // stride_size + 1) | |
| nstrides = (samples.strides[0], samples.strides[0] * stride_size) | |
| windows = np.lib.stride_tricks.as_strided( | |
| samples, shape=nshape, strides=nstrides) | |
| assert np.all( | |
| windows[:, 1] == samples[stride_size:(stride_size + window_size)]) | |
| # Window weighting, squared Fast Fourier Transform (fft), scaling | |
| weighting = np.hanning(window_size)[:, None] | |
| fft = np.fft.rfft(windows * weighting, axis=0) | |
| fft = np.absolute(fft) | |
| fft = fft**2 | |
| scale = np.sum(weighting**2) * sample_rate | |
| fft[1:-1, :] *= (2.0 / scale) | |
| fft[(0, -1), :] /= scale | |
| # Prepare fft frequency list | |
| freqs = float(sample_rate) / window_size * np.arange(fft.shape[0]) | |
| # Compute spectrogram feature | |
| ind = np.where(freqs <= max_freq)[0][-1] + 1 | |
| specgram = np.log(fft[:ind, :] + eps) | |
| return np.transpose(specgram, (1, 0)) | |
| class AudioFeaturizer(object): | |
| """Class to extract spectrogram features from the audio input.""" | |
| def __init__(self, | |
| sample_rate=16000, | |
| window_ms=20.0, | |
| stride_ms=10.0): | |
| """Initialize the audio featurizer class according to the configs. | |
| Args: | |
| sample_rate: an integer specifying the sample rate of the input waveform. | |
| window_ms: an integer for the length of a spectrogram frame, in ms. | |
| stride_ms: an integer for the frame stride, in ms. | |
| """ | |
| self.sample_rate = sample_rate | |
| self.window_ms = window_ms | |
| self.stride_ms = stride_ms | |
| def compute_label_feature(text, token_to_idx): | |
| """Convert string to a list of integers.""" | |
| tokens = list(text.strip().lower()) | |
| feats = [token_to_idx[token] for token in tokens] | |
| return feats | |
| class TextFeaturizer(object): | |
| """Extract text feature based on char-level granularity. | |
| By looking up the vocabulary table, each input string (one line of transcript) | |
| will be converted to a sequence of integer indexes. | |
| """ | |
| def __init__(self, vocab_file): | |
| lines = [] | |
| with codecs.open(vocab_file, "r", "utf-8") as fin: | |
| lines.extend(fin.readlines()) | |
| self.token_to_index = {} | |
| self.index_to_token = {} | |
| self.speech_labels = "" | |
| index = 0 | |
| for line in lines: | |
| line = line[:-1] # Strip the '\n' char. | |
| if line.startswith("#"): | |
| # Skip from reading comment line. | |
| continue | |
| self.token_to_index[line] = index | |
| self.index_to_token[index] = line | |
| self.speech_labels += line | |
| index += 1 | |