Full Code of DemisEom/SpecAugment for AI

Repository: DemisEom/SpecAugment
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Commit: 7f1435963b37
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Directory structure:
gitextract_qwqfy5oq/

├── .gitignore
├── LICENSE
├── README.md
├── SpecAugment/
│   ├── __init__.py
│   ├── sparse_image_warp_np.py
│   ├── sparse_image_warp_pytorch.py
│   ├── spec_augment_pytorch.py
│   └── spec_augment_tensorflow.py
├── requirements.txt
├── setup.cfg
├── setup.py
└── tests/
    ├── spec_augment_test_TF.py
    └── spec_augment_test_pytorch.py

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FILE: .gitignore
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venv
.DS_Store
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FILE: LICENSE
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================================================
FILE: README.md
================================================
# SpecAugment [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
This is a implementation of SpecAugment that speech data augmentation method which directly process the spectrogram with Tensorflow & Pytorch, introduced by Google Brain[1]. This is currently under the Apache 2.0, Please feel free to use for your project. Enjoy!

## How to use

First, you need to have python 3 installed along with [Tensorflow](https://www.tensorflow.org/install/).

Next, you need to install some audio libraries work properly. To install the requirement packages. Run the following command:

```bash
pip3 install SpecAugment
```

And then, run the specAugment.py program. It modifies the spectrogram by warping it in the time direction, masking blocks of consecutive frequency channels, and masking blocks of utterances in time.

#### *Try your audio file SpecAugment*

```shell
$ python3
```

```python
>>> import librosa
>>> from specAugment import spec_augment_tensorflow
# If you are Pytorch, then import spec_augment_pytorch instead of spec_augment_tensorflow
>>> audio, sampling_rate = librosa.load(audio_path)
>>> mel_spectrogram = librosa.feature.melspectrogram(y=audio,
                                                     sr=sampling_rate,
                                                     n_mels=256,
                                                     hop_length=128,
                                                     fmax=8000)
>>> warped_masked_spectrogram = spec_augment_tensorflow.spec_augment(mel_spectrogram=mel_spectrogram)
>>> print(warped_masked_spectrogram)
'
[[1.54055389e-01 7.51822486e-01 7.29588015e-01 ... 1.03616300e-01
  1.04682689e-01 1.05411769e-01]
 [2.21608739e-01 1.38559084e-01 1.01564167e-01 ... 4.19907116e-02
  4.86430404e-02 5.27331798e-02]
 [3.62784019e-01 2.09934399e-01 1.79158230e-01 ... 2.42307431e-01
  3.18662338e-01 3.67405599e-01]
 ...
 [6.36117335e-07 8.06897948e-07 8.55346431e-07 ... 2.84445018e-07
  4.02975952e-07 5.57131738e-07]
 [6.27753429e-07 7.53681318e-07 8.13035033e-07 ... 1.35111146e-07
  2.74058225e-07 4.56901031e-07]
 [0.00000000e+00 7.48416680e-07 5.51771037e-07 ... 1.13901361e-07
  2.56365068e-07 4.43868592e-07]]
'
```
Learn more examples about how to do specific tasks in SpecAugment at the test code.

```bash
python spec_augment_test.py
```
In test code, we using one of the [LibriSpeech dataset](http://www.openslr.org/12/).

<p align="center">
  <img src="https://github.com/shelling203/SpecAugment/blob/master/images/Figure_1.png" alt="Example result of base spectrogram"/ width=600>
  <img src="https://github.com/shelling203/SpecAugment/blob/master/images/Figure_2.png" alt="Example result of base spectrogram"/ width=600>
</p>


# Reference

1. https://arxiv.org/pdf/1904.08779.pdf


================================================
FILE: SpecAugment/__init__.py
================================================


================================================
FILE: SpecAugment/sparse_image_warp_np.py
================================================
"""Image warping using sparse flow defined at control points."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import scipy as sp
import skimage
from scipy.interpolate import interp2d
from skimage.transform import warp

def _get_grid_locations(image_height, image_width):
  """Wrapper for np.meshgrid."""

  y_range = np.linspace(0, image_height - 1, image_height)
  x_range = np.linspace(0, image_width - 1, image_width)
  y_grid, x_grid = np.meshgrid(y_range, x_range, indexing='ij')
  return np.stack((y_grid, x_grid), -1)


def _expand_to_minibatch(np_array, batch_size):
  """Tile arbitrarily-sized np_array to include new batch dimension."""
  tiles = [batch_size] + [1] * np_array.ndim
  return np.tile(np.expand_dims(np_array, 0), tiles)


def _get_boundary_locations(image_height, image_width, num_points_per_edge):
  """Compute evenly-spaced indices along edge of image."""
  y_range = np.linspace(0, image_height - 1, num_points_per_edge + 2)
  x_range = np.linspace(0, image_width - 1, num_points_per_edge + 2)
  ys, xs = np.meshgrid(y_range, x_range, indexing='ij')
  is_boundary = np.logical_or(
      np.logical_or(xs == 0, xs == image_width - 1),
      np.logical_or(ys == 0, ys == image_height - 1))
  return np.stack([ys[is_boundary], xs[is_boundary]], axis=-1)


def _add_zero_flow_controls_at_boundary(control_point_locations,
                                        control_point_flows, image_height,
                                        image_width, boundary_points_per_edge):

  # batch_size = tensor_shape.dimension_value(control_point_locations.shape[0])
  batch_size = control_point_locations.shape[0]

  boundary_point_locations = _get_boundary_locations(image_height, image_width,
                                                     boundary_points_per_edge)

  boundary_point_flows = np.zeros([boundary_point_locations.shape[0], 2])

  type_to_use = control_point_locations.dtype
  # boundary_point_locations = constant_op.constant(
  #     _expand_to_minibatch(boundary_point_locations, batch_size),
  #     dtype=type_to_use)
  boundary_point_locations = _expand_to_minibatch(boundary_point_locations, batch_size)

  # boundary_point_flows = constant_op.constant(
  #     _expand_to_minibatch(boundary_point_flows, batch_size), dtype=type_to_use)
  boundary_point_flows = _expand_to_minibatch(boundary_point_flows, batch_size)

  # merged_control_point_locations = array_ops.concat(
  #     [control_point_locations, boundary_point_locations], 1)

  merged_control_point_locations = np.concatenate(
      [control_point_locations, boundary_point_locations], 1)

  # merged_control_point_flows = array_ops.concat(
  #     [control_point_flows, boundary_point_flows], 1)

  merged_control_point_flows = np.concatenate(
      [control_point_flows, boundary_point_flows], 1)

  return merged_control_point_locations, merged_control_point_flows


def sparse_image_warp_np(image,
                      source_control_point_locations,
                      dest_control_point_locations,
                      interpolation_order=2,
                      regularization_weight=0.0,
                      num_boundary_points=0):

  # image = ops.convert_to_tensor(image)
  # source_control_point_locations = ops.convert_to_tensor(
  #     source_control_point_locations)
  # dest_control_point_locations = ops.convert_to_tensor(
  #     dest_control_point_locations)

  control_point_flows = (
      dest_control_point_locations - source_control_point_locations)

  clamp_boundaries = num_boundary_points > 0
  boundary_points_per_edge = num_boundary_points - 1

  # batch_size, image_height, image_width, _ = image.get_shape().as_list()
  batch_size, image_height, image_width, _ = list(image.shape)

  # This generates the dense locations where the interpolant
  # will be evaluated.

  grid_locations = _get_grid_locations(image_height, image_width)

  flattened_grid_locations = np.reshape(grid_locations,
                                          [image_height * image_width, 2])

    # flattened_grid_locations = constant_op.constant(
    #     _expand_to_minibatch(flattened_grid_locations, batch_size), image.dtype)

  flattened_grid_locations = _expand_to_minibatch(flattened_grid_locations, batch_size)

  if clamp_boundaries:
    (dest_control_point_locations,
     control_point_flows) = _add_zero_flow_controls_at_boundary(
         dest_control_point_locations, control_point_flows, image_height,
         image_width, boundary_points_per_edge)

    # flattened_flows = interpolate_spline.interpolate_spline(
    #     dest_control_point_locations, control_point_flows,
    #     flattened_grid_locations, interpolation_order, regularization_weight)
  flattened_flows = sp.interpolate.spline(
        dest_control_point_locations, control_point_flows,
        flattened_grid_locations, interpolation_order, regularization_weight)

    # dense_flows = array_ops.reshape(flattened_flows,
    #                                 [batch_size, image_height, image_width, 2])
  dense_flows = np.reshape(flattened_flows,
                                    [batch_size, image_height, image_width, 2])

    # warped_image = dense_image_warp.dense_image_warp(image, dense_flows)
  warped_image = warp(image, dense_flows)

  return warped_image, dense_flows


def dense_image_warp(image, flow):
    # batch_size, height, width, channels = (array_ops.shape(image)[0],
    #                                        array_ops.shape(image)[1],
    #                                        array_ops.shape(image)[2],
    #                                        array_ops.shape(image)[3])
    batch_size, height, width, channels = (np.shape(image)[0],
                                           np.shape(image)[1],
                                           np.shape(image)[2],
                                           np.shape(image)[3])

    # The flow is defined on the image grid. Turn the flow into a list of query
    # points in the grid space.
    # grid_x, grid_y = array_ops.meshgrid(
    #     math_ops.range(width), math_ops.range(height))
    # stacked_grid = math_ops.cast(
    #     array_ops.stack([grid_y, grid_x], axis=2), flow.dtype)
    # batched_grid = array_ops.expand_dims(stacked_grid, axis=0)
    # query_points_on_grid = batched_grid - flow
    # query_points_flattened = array_ops.reshape(query_points_on_grid,
    #                                            [batch_size, height * width, 2])
    grid_x, grid_y = np.meshgrid(
        np.range(width), np.range(height))
    stacked_grid = np.cast(
        np.stack([grid_y, grid_x], axis=2), flow.dtype)
    batched_grid = np.expand_dims(stacked_grid, axis=0)
    query_points_on_grid = batched_grid - flow
    query_points_flattened = np.reshape(query_points_on_grid,
                                        [batch_size, height * width, 2])
    # Compute values at the query points, then reshape the result back to the
    # image grid.
    interpolated = interp2d(image, query_points_flattened)
    interpolated = np.reshape(interpolated,
                              [batch_size, height, width, channels])
    return interpolated



================================================
FILE: SpecAugment/sparse_image_warp_pytorch.py
================================================
# Copyright 2019 RnD at Spoon Radio
#
# 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.
# ==============================================================================
# import torch
# import numpy as np
# from torch.autograd import Variable
# import librosa
import random
import numpy as np
# import scipy.signal
import torch
# import torchaudio
# from torchaudio import transforms
# import math
# from torch.utils.data import DataLoader
# from torch.utils.data import Dataset


def time_warp(spec, W=5):
    spec = spec.view(1, spec.shape[0], spec.shape[1])
    num_rows = spec.shape[1]
    spec_len = spec.shape[2]

    y = num_rows // 2
    horizontal_line_at_ctr = spec[0][y]
    assert len(horizontal_line_at_ctr) == spec_len

    point_to_warp = horizontal_line_at_ctr[random.randrange(W, spec_len - W)]
    assert isinstance(point_to_warp, torch.Tensor)

    # Uniform distribution from (0,W) with chance to be up to W negative
    dist_to_warp = random.randrange(-W, W)
    src_pts, dest_pts = torch.tensor([[[y, point_to_warp]]]), torch.tensor([[[y, point_to_warp + dist_to_warp]]])
    warped_spectro, dense_flows = SparseImageWarp.sparse_image_warp(spec, src_pts, dest_pts)
    return warped_spectro.squeeze(3)


def freq_mask(spec, F=15, num_masks=1, replace_with_zero=False):
    cloned = spec.clone()
    num_mel_channels = cloned.shape[1]

    for i in range(0, num_masks):
        f = random.randrange(0, F)
        f_zero = random.randrange(0, num_mel_channels - f)

        # avoids randrange error if values are equal and range is empty
        if (f_zero == f_zero + f): return cloned

        mask_end = random.randrange(f_zero, f_zero + f)
        if (replace_with_zero):
            cloned[0][f_zero:mask_end] = 0
        else:
            cloned[0][f_zero:mask_end] = cloned.mean()

    return cloned


def time_mask(spec, T=15, num_masks=1, replace_with_zero=False):
    cloned = spec.clone()
    len_spectro = cloned.shape[2]

    for i in range(0, num_masks):
        t = random.randrange(0, T)
        t_zero = random.randrange(0, len_spectro - t)

        # avoids randrange error if values are equal and range is empty
        if (t_zero == t_zero + t): return cloned

        mask_end = random.randrange(t_zero, t_zero + t)
        if (replace_with_zero):
            cloned[0][:, t_zero:mask_end] = 0
        else:
            cloned[0][:, t_zero:mask_end] = cloned.mean()
    return cloned


def sparse_image_warp(img_tensor,
                      source_control_point_locations,
                      dest_control_point_locations,
                      interpolation_order=2,
                      regularization_weight=0.0,
                      num_boundaries_points=0):
    control_point_flows = (dest_control_point_locations - source_control_point_locations)

    batch_size, image_height, image_width = img_tensor.shape
    grid_locations = get_grid_locations(image_height, image_width)
    flattened_grid_locations = torch.tensor(flatten_grid_locations(grid_locations, image_height, image_width))

    flattened_flows = interpolate_spline(
        dest_control_point_locations,
        control_point_flows,
        flattened_grid_locations,
        interpolation_order,
        regularization_weight)

    dense_flows = create_dense_flows(flattened_flows, batch_size, image_height, image_width)

    warped_image = dense_image_warp(img_tensor, dense_flows)

    return warped_image, dense_flows


def get_grid_locations(image_height, image_width):
    """Wrapper for np.meshgrid."""

    y_range = np.linspace(0, image_height - 1, image_height)
    x_range = np.linspace(0, image_width - 1, image_width)
    y_grid, x_grid = np.meshgrid(y_range, x_range, indexing='ij')
    return np.stack((y_grid, x_grid), -1)


def flatten_grid_locations(grid_locations, image_height, image_width):
    return np.reshape(grid_locations, [image_height * image_width, 2])


def create_dense_flows(flattened_flows, batch_size, image_height, image_width):
    # possibly .view
    return torch.reshape(flattened_flows, [batch_size, image_height, image_width, 2])


def interpolate_spline(train_points, train_values, query_points, order, regularization_weight=0.0, ):
    # First, fit the spline to the observed data.
    w, v = solve_interpolation(train_points, train_values, order, regularization_weight)
    # Then, evaluate the spline at the query locations.
    query_values = apply_interpolation(query_points, train_points, w, v, order)

    return query_values


def solve_interpolation(train_points, train_values, order, regularization_weight):
    b, n, d = train_points.shape
    k = train_values.shape[-1]

    # First, rename variables so that the notation (c, f, w, v, A, B, etc.)
    # follows https://en.wikipedia.org/wiki/Polyharmonic_spline.
    # To account for python style guidelines we use
    # matrix_a for A and matrix_b for B.

    c = train_points
    f = train_values.float()

    matrix_a = phi(cross_squared_distance_matrix(c, c), order).unsqueeze(0)  # [b, n, n]
    #     if regularization_weight > 0:
    #         batch_identity_matrix = array_ops.expand_dims(
    #           linalg_ops.eye(n, dtype=c.dtype), 0)
    #         matrix_a += regularization_weight * batch_identity_matrix

    # Append ones to the feature values for the bias term in the linear model.
    ones = torch.ones(1, dtype=train_points.dtype).view([-1, 1, 1])
    matrix_b = torch.cat((c, ones), 2).float()  # [b, n, d + 1]

    # [b, n + d + 1, n]
    left_block = torch.cat((matrix_a, torch.transpose(matrix_b, 2, 1)), 1)

    num_b_cols = matrix_b.shape[2]  # d + 1

    # In Tensorflow, zeros are used here. Pytorch gesv fails with zeros for some reason we don't understand.
    # So instead we use very tiny randn values (variance of one, zero mean) on one side of our multiplication.
    lhs_zeros = torch.randn((b, num_b_cols, num_b_cols)) / 1e10
    right_block = torch.cat((matrix_b, lhs_zeros),
                            1)  # [b, n + d + 1, d + 1]
    lhs = torch.cat((left_block, right_block),
                    2)  # [b, n + d + 1, n + d + 1]

    rhs_zeros = torch.zeros((b, d + 1, k), dtype=train_points.dtype).float()
    rhs = torch.cat((f, rhs_zeros), 1)  # [b, n + d + 1, k]

    # Then, solve the linear system and unpack the results.
    X, LU = torch.solve(rhs, lhs)
    w = X[:, :n, :]
    v = X[:, n:, :]

    return w, v


def cross_squared_distance_matrix(x, y):
    """Pairwise squared distance between two (batch) matrices' rows (2nd dim).
        Computes the pairwise distances between rows of x and rows of y
        Args:
        x: [batch_size, n, d] float `Tensor`
        y: [batch_size, m, d] float `Tensor`
        Returns:
        squared_dists: [batch_size, n, m] float `Tensor`, where
        squared_dists[b,i,j] = ||x[b,i,:] - y[b,j,:]||^2
    """
    x_norm_squared = torch.sum(torch.mul(x, x))
    y_norm_squared = torch.sum(torch.mul(y, y))

    x_y_transpose = torch.matmul(x.squeeze(0), y.squeeze(0).transpose(0, 1))

    # squared_dists[b,i,j] = ||x_bi - y_bj||^2 = x_bi'x_bi- 2x_bi'x_bj + x_bj'x_bj
    squared_dists = x_norm_squared - 2 * x_y_transpose + y_norm_squared

    return squared_dists.float()


def phi(r, order):
    """Coordinate-wise nonlinearity used to define the order of the interpolation.
    See https://en.wikipedia.org/wiki/Polyharmonic_spline for the definition.
    Args:
    r: input op
    order: interpolation order
    Returns:
    phi_k evaluated coordinate-wise on r, for k = r
    """
    EPSILON = torch.tensor(1e-10)
    # using EPSILON prevents log(0), sqrt0), etc.
    # sqrt(0) is well-defined, but its gradient is not
    if order == 1:
        r = torch.max(r, EPSILON)
        r = torch.sqrt(r)
        return r
    elif order == 2:
        return 0.5 * r * torch.log(torch.max(r, EPSILON))
    elif order == 4:
        return 0.5 * torch.square(r) * torch.log(torch.max(r, EPSILON))
    elif order % 2 == 0:
        r = torch.max(r, EPSILON)
        return 0.5 * torch.pow(r, 0.5 * order) * torch.log(r)
    else:
        r = torch.max(r, EPSILON)
        return torch.pow(r, 0.5 * order)


def apply_interpolation(query_points, train_points, w, v, order):
    """Apply polyharmonic interpolation model to data.
    Given coefficients w and v for the interpolation model, we evaluate
    interpolated function values at query_points.
    Args:
    query_points: `[b, m, d]` x values to evaluate the interpolation at
    train_points: `[b, n, d]` x values that act as the interpolation centers
                    ( the c variables in the wikipedia article)
    w: `[b, n, k]` weights on each interpolation center
    v: `[b, d, k]` weights on each input dimension
    order: order of the interpolation
    Returns:
    Polyharmonic interpolation evaluated at points defined in query_points.
    """
    query_points = query_points.unsqueeze(0)
    # First, compute the contribution from the rbf term.
    pairwise_dists = cross_squared_distance_matrix(query_points.float(), train_points.float())
    phi_pairwise_dists = phi(pairwise_dists, order)

    rbf_term = torch.matmul(phi_pairwise_dists, w)

    # Then, compute the contribution from the linear term.
    # Pad query_points with ones, for the bias term in the linear model.
    ones = torch.ones_like(query_points[..., :1])
    query_points_pad = torch.cat((
        query_points,
        ones
    ), 2).float()
    linear_term = torch.matmul(query_points_pad, v)

    return rbf_term + linear_term


def dense_image_warp(image, flow):
    """Image warping using per-pixel flow vectors.
    Apply a non-linear warp to the image, where the warp is specified by a dense
    flow field of offset vectors that define the correspondences of pixel values
    in the output image back to locations in the  source image. Specifically, the
    pixel value at output[b, j, i, c] is
    images[b, j - flow[b, j, i, 0], i - flow[b, j, i, 1], c].
    The locations specified by this formula do not necessarily map to an int
    index. Therefore, the pixel value is obtained by bilinear
    interpolation of the 4 nearest pixels around
    (b, j - flow[b, j, i, 0], i - flow[b, j, i, 1]). For locations outside
    of the image, we use the nearest pixel values at the image boundary.
    Args:
    image: 4-D float `Tensor` with shape `[batch, height, width, channels]`.
    flow: A 4-D float `Tensor` with shape `[batch, height, width, 2]`.
    name: A name for the operation (optional).
    Note that image and flow can be of type tf.half, tf.float32, or tf.float64,
    and do not necessarily have to be the same type.
    Returns:
    A 4-D float `Tensor` with shape`[batch, height, width, channels]`
    and same type as input image.
    Raises:
    ValueError: if height < 2 or width < 2 or the inputs have the wrong number
    of dimensions.
    """
    image = image.unsqueeze(3)  # add a single channel dimension to image tensor
    batch_size, height, width, channels = image.shape

    # The flow is defined on the image grid. Turn the flow into a list of query
    # points in the grid space.
    grid_x, grid_y = torch.meshgrid(
        torch.arange(width), torch.arange(height))

    stacked_grid = torch.stack((grid_y, grid_x), dim=2).float()

    batched_grid = stacked_grid.unsqueeze(-1).permute(3, 1, 0, 2)

    query_points_on_grid = batched_grid - flow
    query_points_flattened = torch.reshape(query_points_on_grid,
                                           [batch_size, height * width, 2])
    # Compute values at the query points, then reshape the result back to the
    # image grid.
    interpolated = interpolate_bilinear(image, query_points_flattened)
    interpolated = torch.reshape(interpolated,
                                 [batch_size, height, width, channels])
    return interpolated


def interpolate_bilinear(grid,
                         query_points,
                         name='interpolate_bilinear',
                         indexing='ij'):
    """Similar to Matlab's interp2 function.
    Finds values for query points on a grid using bilinear interpolation.
    Args:
    grid: a 4-D float `Tensor` of shape `[batch, height, width, channels]`.
    query_points: a 3-D float `Tensor` of N points with shape `[batch, N, 2]`.
    name: a name for the operation (optional).
    indexing: whether the query points are specified as row and column (ij),
      or Cartesian coordinates (xy).
    Returns:
    values: a 3-D `Tensor` with shape `[batch, N, channels]`
    Raises:
    ValueError: if the indexing mode is invalid, or if the shape of the inputs
      invalid.
    """
    if indexing != 'ij' and indexing != 'xy':
        raise ValueError('Indexing mode must be \'ij\' or \'xy\'')

    shape = grid.shape
    if len(shape) != 4:
        msg = 'Grid must be 4 dimensional. Received size: '
        raise ValueError(msg + str(grid.shape))

    batch_size, height, width, channels = grid.shape

    shape = [batch_size, height, width, channels]
    query_type = query_points.dtype
    grid_type = grid.dtype

    num_queries = query_points.shape[1]

    alphas = []
    floors = []
    ceils = []
    index_order = [0, 1] if indexing == 'ij' else [1, 0]
    unstacked_query_points = query_points.unbind(2)

    for dim in index_order:
        queries = unstacked_query_points[dim]

        size_in_indexing_dimension = shape[dim + 1]

        # max_floor is size_in_indexing_dimension - 2 so that max_floor + 1
        # is still a valid index into the grid.
        max_floor = torch.tensor(size_in_indexing_dimension - 2, dtype=query_type)
        min_floor = torch.tensor(0.0, dtype=query_type)
        maxx = torch.max(min_floor, torch.floor(queries))
        floor = torch.min(maxx, max_floor)
        int_floor = floor.long()
        floors.append(int_floor)
        ceil = int_floor + 1
        ceils.append(ceil)

        # alpha has the same type as the grid, as we will directly use alpha
        # when taking linear combinations of pixel values from the image.
        alpha = torch.tensor(queries - floor, dtype=grid_type)
        min_alpha = torch.tensor(0.0, dtype=grid_type)
        max_alpha = torch.tensor(1.0, dtype=grid_type)
        alpha = torch.min(torch.max(min_alpha, alpha), max_alpha)

        # Expand alpha to [b, n, 1] so we can use broadcasting
        # (since the alpha values don't depend on the channel).
        alpha = torch.unsqueeze(alpha, 2)
        alphas.append(alpha)

    flattened_grid = torch.reshape(
        grid, [batch_size * height * width, channels])
    batch_offsets = torch.reshape(
        torch.arange(batch_size) * height * width, [batch_size, 1])

    # This wraps array_ops.gather. We reshape the image data such that the
    # batch, y, and x coordinates are pulled into the first dimension.
    # Then we gather. Finally, we reshape the output back. It's possible this
    # code would be made simpler by using array_ops.gather_nd.
    def gather(y_coords, x_coords, name):
        linear_coordinates = batch_offsets + y_coords * width + x_coords
        gathered_values = torch.gather(flattened_grid.t(), 1, linear_coordinates)
        return torch.reshape(gathered_values,
                             [batch_size, num_queries, channels])

    # grab the pixel values in the 4 corners around each query point
    top_left = gather(floors[0], floors[1], 'top_left')
    top_right = gather(floors[0], ceils[1], 'top_right')
    bottom_left = gather(ceils[0], floors[1], 'bottom_left')
    bottom_right = gather(ceils[0], ceils[1], 'bottom_right')

    interp_top = alphas[1] * (top_right - top_left) + top_left
    interp_bottom = alphas[1] * (bottom_right - bottom_left) + bottom_left
    interp = alphas[0] * (interp_bottom - interp_top) + interp_top

    return interp

================================================
FILE: SpecAugment/spec_augment_pytorch.py
================================================
# Copyright 2019 RnD at Spoon Radio
#
# 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.
# ==============================================================================
"""SpecAugment Implementation for Tensorflow.
Related paper : https://arxiv.org/pdf/1904.08779.pdf
In this paper, show summarized parameters by each open datasets in Tabel 1.
-----------------------------------------
Policy | W  | F  | m_F |  T  |  p  | m_T
-----------------------------------------
None   |  0 |  0 |  -  |  0  |  -  |  -
-----------------------------------------
LB     | 80 | 27 |  1  | 100 | 1.0 | 1
-----------------------------------------
LD     | 80 | 27 |  2  | 100 | 1.0 | 2
-----------------------------------------
SM     | 40 | 15 |  2  |  70 | 0.2 | 2
-----------------------------------------
SS     | 40 | 27 |  2  |  70 | 0.2 | 2
-----------------------------------------
LB : LibriSpeech basic
LD : LibriSpeech double
SM : Switchboard mild
SS : Switchboard strong
"""

import librosa
import librosa.display
import numpy as np
import random
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from SpecAugment.sparse_image_warp_pytorch import sparse_image_warp
import torch


def time_warp(spec, W=5):
    num_rows = spec.shape[1]
    spec_len = spec.shape[2]

    y = num_rows // 2
    horizontal_line_at_ctr = spec[0][y]
    # assert len(horizontal_line_at_ctr) == spec_len

    point_to_warp = horizontal_line_at_ctr[random.randrange(W, spec_len-W)]
    # assert isinstance(point_to_warp, torch.Tensor)

    # Uniform distribution from (0,W) with chance to be up to W negative
    dist_to_warp = random.randrange(-W, W)
    src_pts = torch.tensor([[[y, point_to_warp]]])
    dest_pts = torch.tensor([[[y, point_to_warp + dist_to_warp]]])
    warped_spectro, dense_flows = sparse_image_warp(spec, src_pts, dest_pts)
    return warped_spectro.squeeze(3)


def spec_augment(mel_spectrogram, time_warping_para=80, frequency_masking_para=27,
                 time_masking_para=100, frequency_mask_num=1, time_mask_num=1):
    """Spec augmentation Calculation Function.
    'SpecAugment' have 3 steps for audio data augmentation.
    first step is time warping using Tensorflow's image_sparse_warp function.
    Second step is frequency masking, last step is time masking.
    # Arguments:
      mel_spectrogram(numpy array): audio file path of you want to warping and masking.
      time_warping_para(float): Augmentation parameter, "time warp parameter W".
        If none, default = 80 for LibriSpeech.
      frequency_masking_para(float): Augmentation parameter, "frequency mask parameter F"
        If none, default = 100 for LibriSpeech.
      time_masking_para(float): Augmentation parameter, "time mask parameter T"
        If none, default = 27 for LibriSpeech.
      frequency_mask_num(float): number of frequency masking lines, "m_F".
        If none, default = 1 for LibriSpeech.
      time_mask_num(float): number of time masking lines, "m_T".
        If none, default = 1 for LibriSpeech.
    # Returns
      mel_spectrogram(numpy array): warped and masked mel spectrogram.
    """
    v = mel_spectrogram.shape[1]
    tau = mel_spectrogram.shape[2]

    # Step 1 : Time warping
    warped_mel_spectrogram = time_warp(mel_spectrogram, W=time_warping_para)

    # Step 2 : Frequency masking
    for i in range(frequency_mask_num):
        f = np.random.uniform(low=0.0, high=frequency_masking_para)
        f = int(f)
        f0 = random.randint(0, v-f)
        warped_mel_spectrogram[:, f0:f0+f, :] = 0

    # Step 3 : Time masking
    for i in range(time_mask_num):
        t = np.random.uniform(low=0.0, high=time_masking_para)
        t = int(t)
        t0 = random.randint(0, tau-t)
        warped_mel_spectrogram[:, :, t0:t0+t] = 0

    return warped_mel_spectrogram


def visualization_spectrogram(mel_spectrogram, title):
    """visualizing result of SpecAugment
    # Arguments:
      mel_spectrogram(ndarray): mel_spectrogram to visualize.
      title(String): plot figure's title
    """
    # Show mel-spectrogram using librosa's specshow.
    plt.figure(figsize=(10, 4))
    librosa.display.specshow(librosa.power_to_db(mel_spectrogram[0, :, :], ref=np.max), y_axis='mel', fmax=8000, x_axis='time')
    # plt.colorbar(format='%+2.0f dB')
    plt.title(title)
    plt.tight_layout()
    plt.show()


================================================
FILE: SpecAugment/spec_augment_tensorflow.py
================================================
# Copyright 2019 RnD at Spoon Radio
#
# 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.
# ==============================================================================
"""SpecAugment Implementation for Tensorflow.
Related paper : https://arxiv.org/pdf/1904.08779.pdf

In this paper, show summarized parameters by each open datasets in Tabel 1.
-----------------------------------------
Policy | W  | F  | m_F |  T  |  p  | m_T
-----------------------------------------
None   |  0 |  0 |  -  |  0  |  -  |  -
-----------------------------------------
LB     | 80 | 27 |  1  | 100 | 1.0 | 1
-----------------------------------------
LD     | 80 | 27 |  2  | 100 | 1.0 | 2
-----------------------------------------
SM     | 40 | 15 |  2  |  70 | 0.2 | 2
-----------------------------------------
SS     | 40 | 27 |  2  |  70 | 0.2 | 2
-----------------------------------------
LB : LibriSpeech basic
LD : LibriSpeech double
SM : Switchboard mild
SS : Switchboard strong
"""

import librosa
import librosa.display
import tensorflow as tf
from tensorflow_addons.image import sparse_image_warp
import numpy as np
import matplotlib.pyplot as plt


def sparse_warp(mel_spectrogram, time_warping_para=80):
    """Spec augmentation Calculation Function.

    'SpecAugment' have 3 steps for audio data augmentation.
    first step is time warping using Tensorflow's image_sparse_warp function.
    Second step is frequency masking, last step is time masking.

    # Arguments:
      mel_spectrogram(numpy array): audio file path of you want to warping and masking.
      time_warping_para(float): Augmentation parameter, "time warp parameter W".
        If none, default = 80 for LibriSpeech.

    # Returns
      mel_spectrogram(numpy array): warped and masked mel spectrogram.
    """

    fbank_size = tf.shape(mel_spectrogram)
    n, v = fbank_size[1], fbank_size[2]

    # Step 1 : Time warping
    # Image warping control point setting.
    # Source
    pt = tf.random.uniform([], time_warping_para, n-time_warping_para, tf.int32) # radnom point along the time axis
    src_ctr_pt_freq = tf.range(v // 2)  # control points on freq-axis
    src_ctr_pt_time = tf.ones_like(src_ctr_pt_freq) * pt  # control points on time-axis
    src_ctr_pts = tf.stack((src_ctr_pt_time, src_ctr_pt_freq), -1)
    src_ctr_pts = tf.cast(src_ctr_pts, dtype=tf.float32)

    # Destination
    w = tf.random.uniform([], -time_warping_para, time_warping_para, tf.int32)  # distance
    dest_ctr_pt_freq = src_ctr_pt_freq
    dest_ctr_pt_time = src_ctr_pt_time + w
    dest_ctr_pts = tf.stack((dest_ctr_pt_time, dest_ctr_pt_freq), -1)
    dest_ctr_pts = tf.cast(dest_ctr_pts, dtype=tf.float32)

    # warp
    source_control_point_locations = tf.expand_dims(src_ctr_pts, 0)  # (1, v//2, 2)
    dest_control_point_locations = tf.expand_dims(dest_ctr_pts, 0)  # (1, v//2, 2)

    warped_image, _ = sparse_image_warp(mel_spectrogram,
                                        source_control_point_locations,
                                        dest_control_point_locations)
    return warped_image


def frequency_masking(mel_spectrogram, v, frequency_masking_para=27, frequency_mask_num=2):
    """Spec augmentation Calculation Function.

    'SpecAugment' have 3 steps for audio data augmentation.
    first step is time warping using Tensorflow's image_sparse_warp function.
    Second step is frequency masking, last step is time masking.

    # Arguments:
      mel_spectrogram(numpy array): audio file path of you want to warping and masking.
      frequency_masking_para(float): Augmentation parameter, "frequency mask parameter F"
        If none, default = 100 for LibriSpeech.
      frequency_mask_num(float): number of frequency masking lines, "m_F".
        If none, default = 1 for LibriSpeech.

    # Returns
      mel_spectrogram(numpy array): warped and masked mel spectrogram.
    """
    # Step 2 : Frequency masking
    fbank_size = tf.shape(mel_spectrogram)
    n, v = fbank_size[1], fbank_size[2]

    for i in range(frequency_mask_num):
        f = tf.random.uniform([], minval=0, maxval=frequency_masking_para, dtype=tf.int32)
        v = tf.cast(v, dtype=tf.int32)
        f0 = tf.random.uniform([], minval=0, maxval=v-f, dtype=tf.int32)

        # warped_mel_spectrogram[f0:f0 + f, :] = 0
        mask = tf.concat((tf.ones(shape=(1, n, v - f0 - f, 1)),
                          tf.zeros(shape=(1, n, f, 1)),
                          tf.ones(shape=(1, n, f0, 1)),
                          ), 2)
        mel_spectrogram = mel_spectrogram * mask
    return tf.cast(mel_spectrogram, dtype=tf.float32)


def time_masking(mel_spectrogram, tau, time_masking_para=100, time_mask_num=2):
    """Spec augmentation Calculation Function.

    'SpecAugment' have 3 steps for audio data augmentation.
    first step is time warping using Tensorflow's image_sparse_warp function.
    Second step is frequency masking, last step is time masking.

    # Arguments:
      mel_spectrogram(numpy array): audio file path of you want to warping and masking.
      time_masking_para(float): Augmentation parameter, "time mask parameter T"
        If none, default = 27 for LibriSpeech.
      time_mask_num(float): number of time masking lines, "m_T".
        If none, default = 1 for LibriSpeech.

    # Returns
      mel_spectrogram(numpy array): warped and masked mel spectrogram.
    """
    fbank_size = tf.shape(mel_spectrogram)
    n, v = fbank_size[1], fbank_size[2]

    # Step 3 : Time masking
    for i in range(time_mask_num):
        t = tf.random.uniform([], minval=0, maxval=time_masking_para, dtype=tf.int32)
        t0 = tf.random.uniform([], minval=0, maxval=tau-t, dtype=tf.int32)

        # mel_spectrogram[:, t0:t0 + t] = 0
        mask = tf.concat((tf.ones(shape=(1, n-t0-t, v, 1)),
                          tf.zeros(shape=(1, t, v, 1)),
                          tf.ones(shape=(1, t0, v, 1)),
                          ), 1)
        mel_spectrogram = mel_spectrogram * mask
    return tf.cast(mel_spectrogram, dtype=tf.float32)


def spec_augment(mel_spectrogram):

    v = mel_spectrogram.shape[0]
    tau = mel_spectrogram.shape[1]

    warped_mel_spectrogram = sparse_warp(mel_spectrogram)

    warped_frequency_spectrogram = frequency_masking(warped_mel_spectrogram, v=v)

    warped_frequency_time_sepctrogram = time_masking(warped_frequency_spectrogram, tau=tau)

    return warped_frequency_time_sepctrogram


def visualization_spectrogram(mel_spectrogram, title):
    """visualizing first one result of SpecAugment

    # Arguments:
      mel_spectrogram(ndarray): mel_spectrogram to visualize.
      title(String): plot figure's title
    """
    # Show mel-spectrogram using librosa's specshow.
    plt.figure(figsize=(10, 4))
    librosa.display.specshow(librosa.power_to_db(mel_spectrogram[0, :, :, 0], ref=np.max), y_axis='mel', fmax=8000, x_axis='time')
    plt.title(title)
    plt.tight_layout()
    plt.show()


def visualization_tensor_spectrogram(mel_spectrogram, title):
    """visualizing first one result of SpecAugment

    # Arguments:
      mel_spectrogram(ndarray): mel_spectrogram to visualize.
      title(String): plot figure's title
    """

    # Show mel-spectrogram using librosa's specshow.
    plt.figure(figsize=(10, 4))
    librosa.display.specshow(librosa.power_to_db(mel_spectrogram[0, :, :, 0], ref=np.max), y_axis='mel', fmax=8000, x_axis='time')
    # plt.colorbar(format='%+2.0f dB')
    plt.title(title)
    plt.tight_layout()
    plt.show()


================================================
FILE: requirements.txt
================================================
librosa
matplotlib
numpy

================================================
FILE: setup.cfg
================================================
[metadata]
description-file = README.md

================================================
FILE: setup.py
================================================
from setuptools import setup, find_packages

setup(
   name='SpecAugment',
   version='1.2.3',
   description='A implementation of "SpecAugment"',
   url              = 'https://github.com/shelling203/SpecAugment',
   packages         = find_packages(exclude = ['docs', 'tests*']),
   install_requires=['tensorflow', 'librosa', 'matplotlib', 'torch'], #external packages as dependencies
)

================================================
FILE: tests/spec_augment_test_TF.py
================================================
# Copyright 2019 RnD at Spoon Radio
#
# 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.
# ==============================================================================
"""SpecAugment test"""

import argparse
import librosa
from SpecAugment import spec_augment_tensorflow
import os, sys
import numpy as np
# sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))

parser = argparse.ArgumentParser(description='Spec Augment')
parser.add_argument('--audio-path', default='../data/61-70968-0002.wav',
                    help='The audio file.')
parser.add_argument('--time-warp-para', default=80,
                    help='time warp parameter W')
parser.add_argument('--frequency-mask-para', default=100,
                    help='frequency mask parameter F')
parser.add_argument('--time-mask-para', default=27,
                    help='time mask parameter T')
parser.add_argument('--masking-line-number', default=1,
                    help='masking line number')

args = parser.parse_args()
audio_path = args.audio_path
time_warping_para = args.time_warp_para
time_masking_para = args.frequency_mask_para
frequency_masking_para = args.time_mask_para
masking_line_number = args.masking_line_number

if __name__ == "__main__":

    # Step 0 : load audio file, extract mel spectrogram
    audio, sampling_rate = librosa.load(audio_path)
    mel_spectrogram = librosa.feature.melspectrogram(y=audio,
                                                     sr=sampling_rate,
                                                     n_mels=256,
                                                     hop_length=128,
                                                     fmax=8000)

    # reshape spectrogram shape to [batch_size, time, frequency, 1]
    shape = mel_spectrogram.shape
    mel_spectrogram = np.reshape(mel_spectrogram, (-1, shape[0], shape[1], 1))

    # Show Raw mel-spectrogram
    spec_augment_tensorflow.visualization_spectrogram(mel_spectrogram=mel_spectrogram,
                                                      title="Raw Mel Spectrogram")

    # Show time warped & masked spectrogram
    spec_augment_tensorflow.visualization_tensor_spectrogram(mel_spectrogram=spec_augment_tensorflow.spec_augment(mel_spectrogram),
                                                      title="tensorflow Warped & Masked Mel Spectrogram")


================================================
FILE: tests/spec_augment_test_pytorch.py
================================================
# Copyright 2019 RnD at Spoon Radio
#
# 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.
# ==============================================================================
"""SpecAugment test"""

import argparse
import librosa
import numpy as np
import torch
from SpecAugment import spec_augment_pytorch
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))

parser = argparse.ArgumentParser(description='Spec Augment')
parser.add_argument('--audio-path', default='../data/61-70968-0002.wav',
                    help='The audio file.')
parser.add_argument('--time-warp-para', default=80,
                    help='time warp parameter W')
parser.add_argument('--frequency-mask-para', default=100,
                    help='frequency mask parameter F')
parser.add_argument('--time-mask-para', default=27,
                    help='time mask parameter T')
parser.add_argument('--masking-line-number', default=1,
                    help='masking line number')

args = parser.parse_args()
audio_path = args.audio_path
time_warping_para = args.time_warp_para
time_masking_para = args.frequency_mask_para
frequency_masking_para = args.time_mask_para
masking_line_number = args.masking_line_number

if __name__ == "__main__":

    # Step 0 : load audio file, extract mel spectrogram
    audio, sampling_rate = librosa.load(audio_path)
    mel_spectrogram = librosa.feature.melspectrogram(y=audio,
                                                     sr=sampling_rate,
                                                     n_mels=256,
                                                     hop_length=128,
                                                     fmax=8000)

    # reshape spectrogram shape to [batch_size, time, frequency]
    shape = mel_spectrogram.shape
    mel_spectrogram = np.reshape(mel_spectrogram, (-1, shape[0], shape[1]))
    mel_spectrogram = torch.from_numpy(mel_spectrogram)

    # Show Raw mel-spectrogram
    spec_augment_pytorch.visualization_spectrogram(mel_spectrogram=mel_spectrogram,
                                                      title="Raw Mel Spectrogram")

    # Calculate SpecAugment pytorch
    warped_masked_spectrogram = spec_augment_pytorch.spec_augment(mel_spectrogram=mel_spectrogram)

    # Show time warped & masked spectrogram
    spec_augment_pytorch.visualization_spectrogram(mel_spectrogram=warped_masked_spectrogram,
                                                      title="pytorch Warped & Masked Mel Spectrogram")