/
vggish_input.py
42 lines (36 loc) · 1.54 KB
/
vggish_input.py
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# MFCC Spectrogram conversion code from VGGish, Google Inc.
# https://github.com/tensorflow/models/tree/master/research/audioset
import numpy as np
from scipy.io import wavfile
import mel_features
import vggish_params
def waveform_to_examples(data, sample_rate):
# Convert to mono.
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# 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):
sr, wav_data = wavfile.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)