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| # Copyright 2017 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. | |
| # ============================================================================== | |
| """Compute input examples for VGGish from audio waveform.""" | |
| import numpy as np | |
| import resampy | |
| import mel_features | |
| import vggish_params | |
| try: | |
| import soundfile as sf | |
| def wav_read(wav_file): | |
| wav_data, sr = sf.read(wav_file, dtype='int16') | |
| return wav_data, sr | |
| except ImportError: | |
| def wav_read(wav_file): | |
| raise NotImplementedError('WAV file reading requires soundfile package.') | |
| def waveform_to_examples(data, sample_rate): | |
| """Converts audio waveform into an array of examples for VGGish. | |
| Args: | |
| data: np.array of either one dimension (mono) or two dimensions | |
| (multi-channel, with the outer dimension representing channels). | |
| Each sample is generally expected to lie in the range [-1.0, +1.0], | |
| although this is not required. | |
| sample_rate: Sample rate of data. | |
| Returns: | |
| 3-D np.array of shape [num_examples, num_frames, num_bands] which represents | |
| a sequence of examples, each of which contains a patch of log mel | |
| spectrogram, covering num_frames frames of audio and num_bands mel frequency | |
| bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS. | |
| """ | |
| # Convert to mono. | |
| if len(data.shape) > 1: | |
| data = np.mean(data, axis=1) | |
| # Resample to the rate assumed by VGGish. | |
| if sample_rate != vggish_params.SAMPLE_RATE: | |
| data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE) | |
| # Compute log mel spectrogram features. | |
| log_mel = mel_features.log_mel_spectrogram( | |
| data, | |
| audio_sample_rate=vggish_params.SAMPLE_RATE, | |
| log_offset=vggish_params.LOG_OFFSET, | |
| window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS, | |
| hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS, | |
| num_mel_bins=vggish_params.NUM_MEL_BINS, | |
| lower_edge_hertz=vggish_params.MEL_MIN_HZ, | |
| upper_edge_hertz=vggish_params.MEL_MAX_HZ) | |
| # Frame features into examples. | |
| features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS | |
| example_window_length = int(round( | |
| vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate)) | |
| example_hop_length = int(round( | |
| vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate)) | |
| log_mel_examples = mel_features.frame( | |
| log_mel, | |
| window_length=example_window_length, | |
| hop_length=example_hop_length) | |
| return log_mel_examples | |
| def wavfile_to_examples(wav_file): | |
| """Convenience wrapper around waveform_to_examples() for a common WAV format. | |
| Args: | |
| wav_file: String path to a file, or a file-like object. The file | |
| is assumed to contain WAV audio data with signed 16-bit PCM samples. | |
| Returns: | |
| See waveform_to_examples. | |
| """ | |
| wav_data, sr = wav_read(wav_file) | |
| assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype | |
| samples = wav_data / 32768.0 # Convert to [-1.0, +1.0] | |
| return waveform_to_examples(samples, sr) | |