vggish
copied
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Readme
Files and versions
79 lines
3.1 KiB
79 lines
3.1 KiB
# 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."""
|
|
|
|
# Modification: Return torch tensors rather than numpy arrays
|
|
import torch
|
|
import torchaudio
|
|
import numpy as np
|
|
|
|
import mel_features
|
|
import vggish_params
|
|
|
|
|
|
def waveform_to_examples(data, sample_rate, return_tensor=True):
|
|
"""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.
|
|
return_tensor: Return data as a Pytorch tensor ready for VGGish
|
|
|
|
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.
|
|
|
|
"""
|
|
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 = torch.from_numpy(data)
|
|
resampler = torchaudio.transforms.Resample(sample_rate, vggish_params.SAMPLE_RATE, dtype=data.dtype)
|
|
data = resampler(data).cpu().detach().numpy()
|
|
|
|
# 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)
|
|
|
|
if return_tensor:
|
|
log_mel_examples = torch.tensor(
|
|
log_mel_examples, requires_grad=True)[:, None, :, :].float()
|
|
|
|
return log_mel_examples
|