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Commit 0e0d1e59 authored by David Pollack's avatar David Pollack Committed by Soumith Chintala
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docs update and fixes from pr comments

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docs/requirements.txt
View file @ 0e0d1e59
sphinx
sphinxcontrib-googleanalytics
-e git://github.com/snide/sphinx_rtd_theme.git#egg=sphinx_rtd_theme
-e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
sphinxcontrib.katex
matplotlib
docs/source/_static/img/pytorch-logo-dark.svg
View file @ 0e0d1e59
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docs/source/_static/img/pytorch-logo-flame.svg deleted 100644 → 0
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docs/source/conf.py
View file @ 0e0d1e59
......@@ -22,14 +22,13 @@
# sys.path.insert(0, os.path.abspath('.'))
import torch
import torchaudio
import sphinx_rtd_theme
import pytorch_sphinx_theme
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
needs_sphinx = '1.6'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
......@@ -41,16 +40,24 @@ extensions = [
'sphinx.ext.intersphinx',
'sphinx.ext.todo',
'sphinx.ext.coverage',
'sphinx.ext.mathjax',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'sphinxcontrib.googleanalytics',
'sphinxcontrib.katex',
]
napoleon_use_ivar = True
# katex options
#
#
katex_options = r'''
delimiters : [
{left: "$$", right: "$$", display: true},
{left: "\\(", right: "\\)", display: false},
{left: "\\[", right: "\\]", display: true}
]
'''
googleanalytics_id = 'UA-90545585-1'
googleanalytics_enabled = True
napoleon_use_ivar = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
......@@ -66,8 +73,8 @@ master_doc = 'index'
# General information about the project.
project = 'Torchaudio'
copyright = '2017, Torch Contributors'
author = 'Torch Contributors'
copyright = '2018, Torchaudio Contributors'
author = 'Torchaudio Contributors'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
......@@ -104,14 +111,15 @@ todo_include_todos = True
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
html_theme = 'pytorch_sphinx_theme'
html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
html_theme_options = {
'pytorch_project': 'audio',
'collapse_navigation': False,
'display_version': True,
'logo_only': True,
......@@ -124,19 +132,25 @@ html_logo = '_static/img/pytorch-logo-dark.svg'
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# html_style_path = 'css/pytorch_theme.css'
html_context = {
'css_files': [
'https://fonts.googleapis.com/css?family=Lato',
'_static/css/pytorch_theme.css'
],
}
def setup(app):
# NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value
# and can be moved outside of this function (and the setup(app) function
# can be deleted).
html_css_files = [
'https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css'
]
# In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is
# `add_stylesheet` (deprecated in 1.8).
add_css = getattr(app, 'add_css_file', getattr(app, 'add_stylesheet'))
for css_file in html_css_files:
add_css(css_file)
# -- Options for HTMLHelp output ------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'PyTorchdoc'
htmlhelp_basename = 'TorchAudiodoc'
# -- Options for LaTeX output ---------------------------------------------
......@@ -163,7 +177,7 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'pytorch.tex', 'torchaudio Documentation',
(master_doc, 'pytorch.tex', 'Torchaudio Documentation',
'Torch Contributors', 'manual'),
]
......@@ -173,7 +187,7 @@ latex_documents = [
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'torchaudio', 'torchaudio Documentation',
(master_doc, 'Torchaudio', 'Torchaudio Documentation',
[author], 1)
]
......@@ -184,8 +198,8 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'torchaudio', 'torchaudio Documentation',
author, 'torchaudio', 'One line description of project.',
(master_doc, 'Torchaudio', 'Torchaudio Documentation',
author, 'Torchaudio', 'Load audio files into pytorch tensors.',
'Miscellaneous'),
]
......@@ -193,7 +207,7 @@ texinfo_documents = [
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {
'python': ('https://docs.python.org/', None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'numpy': ('https://docs.scipy.org/doc/numpy/', None),
}
# -- A patch that prevents Sphinx from cross-referencing ivar tags -------
......@@ -246,5 +260,4 @@ def patched_make_field(self, types, domain, items, **kw):
fieldbody = nodes.field_body('', bodynode)
return nodes.field('', fieldname, fieldbody)
TypedField.make_field = patched_make_field
test/test_dataloader.py
View file @ 0e0d1e59
......@@ -18,7 +18,7 @@ class TORCHAUDIODS(Dataset):
self.si.precision = 16
self.E = torchaudio.sox_effects.SoxEffectsChain()
self.E.append_effect_to_chain("rate", [self.si.rate]) # resample to 16000hz
self.E.append_effect_to_chain("channels", [self.si.channels]) # mono singal
self.E.append_effect_to_chain("channels", [self.si.channels]) # mono signal
self.E.append_effect_to_chain("trim", [0, "16000s"]) # first 16000 samples of audio
def __getitem__(self, index):
......@@ -30,7 +30,7 @@ class TORCHAUDIODS(Dataset):
def __len__(self):
return len(self.data)
class Test_LoadSave(unittest.TestCase):
class Test_DataLoader(unittest.TestCase):
def test_1(self):
expected_size = (2, 1, 16000)
ds = TORCHAUDIODS()
......
test/test_transforms.py
View file @ 0e0d1e59
......@@ -20,15 +20,13 @@ class Tester(unittest.TestCase):
audio_orig = self.sig.clone()
result = transforms.Scale()(audio_orig)
self.assertTrue(result.min() >= -1. and result.max() <= 1.,
print("min: {}, max: {}".format(result.min(), result.max())))
self.assertTrue(result.min() >= -1. and result.max() <= 1.)
maxminmax = np.abs(
[audio_orig.min(), audio_orig.max()]).max().astype(np.float)
result = transforms.Scale(factor=maxminmax)(audio_orig)
self.assertTrue((result.min() == -1. or result.max() == 1.) and
result.min() >= -1. and result.max() <= 1.,
print("min: {}, max: {}".format(result.min(), result.max())))
result.min() >= -1. and result.max() <= 1.)
repr_test = transforms.Scale()
repr_test.__repr__()
......@@ -39,21 +37,19 @@ class Tester(unittest.TestCase):
length_orig = audio_orig.size(0)
length_new = int(length_orig * 1.2)
result = transforms.PadTrim(max_len=length_new)(audio_orig)
result = transforms.PadTrim(max_len=length_new, channels_first=False)(audio_orig)
self.assertTrue(result.size(0) == length_new,
print("old size: {}, new size: {}".format(audio_orig.size(0), result.size(0))))
self.assertEqual(result.size(0), length_new)
audio_orig = self.sig.clone()
length_orig = audio_orig.size(0)
length_new = int(length_orig * 0.8)
result = transforms.PadTrim(max_len=length_new)(audio_orig)
result = transforms.PadTrim(max_len=length_new, channels_first=False)(audio_orig)
self.assertTrue(result.size(0) == length_new,
print("old size: {}, new size: {}".format(audio_orig.size(0), result.size(0))))
self.assertEqual(result.size(0), length_new)
repr_test = transforms.PadTrim(max_len=length_new)
repr_test = transforms.PadTrim(max_len=length_new, channels_first=False)
repr_test.__repr__()
def test_downmix_mono(self):
......@@ -67,11 +63,11 @@ class Tester(unittest.TestCase):
self.assertTrue(audio_Stereo.size(1) == 2)
result = transforms.DownmixMono()(audio_Stereo)
result = transforms.DownmixMono(channels_first=False)(audio_Stereo)
self.assertTrue(result.size(1) == 1)
repr_test = transforms.DownmixMono()
repr_test = transforms.DownmixMono(channels_first=False)
repr_test.__repr__()
def test_lc2cl(self):
......@@ -107,7 +103,7 @@ class Tester(unittest.TestCase):
[audio_orig.min(), audio_orig.max()]).max().astype(np.float)
tset = (transforms.Scale(factor=maxminmax),
transforms.PadTrim(max_len=length_new))
transforms.PadTrim(max_len=length_new, channels_first=False))
result = transforms.Compose(tset)(audio_orig)
self.assertTrue(np.abs([result.min(), result.max()]).max() == 1.)
......
torchaudio/__init__.py
View file @ 0e0d1e59
......@@ -34,17 +34,18 @@ def load(filepath,
If `callable`, then the output is passed as a parameter
to the given function, then the output is divided by
the result.
channels_first (bool): Set channels first or length first in result. Default: ``True``
num_frames (int, optional): number of frames to load. 0 to load everything after the offset.
offset (int, optional): number of frames from the start of the file to begin data loading.
signalinfo (sox_signalinfo_t, optional): a sox_signalinfo_t type, which could be helpful if the
audio type cannot be automatically determine
audio type cannot be automatically determined
encodinginfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the
audio type cannot be automatically determined
filetype (str, optional): a filetype or extension to be set if sox cannot determine it automatically
Returns: tuple(Tensor, int)
- Tensor: output Tensor of size `[C x L]` or `[L x C]` where L is the number of audio frames, C is the number of channels
- int: the sample-rate of the audio (as listed in the metadata of the file)
- int: the sample rate of the audio (as listed in the metadata of the file)
Example::
......@@ -113,8 +114,9 @@ def save_encinfo(filepath,
filepath (string): path to audio file
src (Tensor): an input 2D Tensor of shape `[C x L]` or `[L x C]` where L is
the number of audio frames, C is the number of channels
channels_first (bool): Set channels first or length first in result. Default: ``True``
signalinfo (sox_signalinfo_t): a sox_signalinfo_t type, which could be helpful if the
audio type cannot be automatically determine
audio type cannot be automatically determined
encodinginfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the
audio type cannot be automatically determined
filetype (str, optional): a filetype or extension to be set if sox cannot determine it automatically
......
torchaudio/sox_effects.py
View file @ 0e0d1e59
......@@ -28,6 +28,48 @@ def SoxEffect():
class SoxEffectsChain(object):
"""SoX effects chain class.
Args:
normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31`
(assumes signed 32-bit audio), and normalizes to `[0, 1]`.
If `number`, then output is divided by that number
If `callable`, then the output is passed as a parameter
to the given function, then the output is divided by
the result.
channels_first (bool, optional): Set channels first or length first in result. Default: ``True``
out_siginfo (sox_signalinfo_t, optional): a sox_signalinfo_t type, which could be helpful if the
audio type cannot be automatically determined
out_encinfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the
audio type cannot be automatically determined
filetype (str, optional): a filetype or extension to be set if sox cannot determine it automatically
Returns: tuple(Tensor, int)
- Tensor: output Tensor of size `[C x L]` or `[L x C]` where L is the number of audio frames, C is the number of channels
- int: the sample rate of the audio (as listed in the metadata of the file)
Example::
class MyDataset(Dataset):
def __init__(self, audiodir_path):
self.data = [fn for fn in os.listdir(audiodir_path)]
self.E = torchaudio.sox_effects.SoxEffectsChain()
self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz
self.E.append_effect_to_chain("channels", ["1"]) # mono signal
def __getitem__(self, index):
fn = self.data[index]
self.E.set_input_file(fn)
x, sr = self.E.sox_build_flow_effects()
return x, sr
def __len__(self):
return len(self.data)
>>> torchaudio.initialize_sox()
>>> ds = MyDataset(path_to_audio_files)
>>> for sig, sr in ds:
>>> [do something here]
>>> torchaudio.shutdown_sox()
"""
EFFECTS_AVAILABLE = set(effect_names())
......
torchaudio/transforms.py
View file @ 0e0d1e59
......@@ -20,12 +20,6 @@ def _check_is_variable(tensor):
return tensor, is_variable
def _tlog10(x):
"""Pytorch Log10
"""
return torch.log(x) / torch.log(x.new([10]))
class Compose(object):
"""Composes several transforms together.
......@@ -92,29 +86,35 @@ class PadTrim(object):
"""Pad/Trim a 1d-Tensor (Signal or Labels)
Args:
tensor (Tensor): Tensor of audio of size (Samples x Channels)
tensor (Tensor): Tensor of audio of size (n x c) or (c x n)
max_len (int): Length to which the tensor will be padded
channels_first (bool): Pad for channels first tensors. Default: `True`
"""
def __init__(self, max_len, fill_value=0):
def __init__(self, max_len, fill_value=0, channels_first=True):
self.max_len = max_len
self.fill_value = fill_value
self.len_dim, self.ch_dim = int(channels_first), int(not channels_first)
def __call__(self, tensor):
"""
Returns:
Tensor: (max_len x Channels)
Tensor: (c x Ln or (n x c)
"""
if self.max_len > tensor.size(0):
pad = torch.ones((self.max_len - tensor.size(0),
tensor.size(1))) * self.fill_value
pad = pad.type_as(tensor)
tensor = torch.cat((tensor, pad), dim=0)
elif self.max_len < tensor.size(0):
tensor = tensor[:self.max_len, :]
assert tensor.size(self.ch_dim) < 128, \
"Too many channels ({}) detected, look at channels_first param.".format(tensor.size(self.ch_dim))
if self.max_len > tensor.size(self.len_dim):
padding_size = [self.max_len - tensor.size(self.len_dim) if i == self.len_dim
else tensor.size(self.ch_dim)
for i in range(2)]
pad = torch.empty(padding_size, dtype=tensor.dtype).fill_(self.fill_value)
tensor = torch.cat((tensor, pad), dim=self.len_dim)
elif self.max_len < tensor.size(self.len_dim):
tensor = tensor.narrow(self.len_dim, 0, self.max_len)
return tensor
def __repr__(self):
......@@ -122,25 +122,26 @@ class PadTrim(object):
class DownmixMono(object):
"""Downmix any stereo signals to mono
"""Downmix any stereo signals to mono. Consider using a `SoxEffectsChain` with
the `channels` effect instead of this transformation.
Inputs:
tensor (Tensor): Tensor of audio of size (Samples x Channels)
tensor (Tensor): Tensor of audio of size (c x n) or (n x c)
channels_first (bool): Downmix across channels dimension. Default: `True`
Returns:
tensor (Tensor) (Samples x 1):
"""
def __init__(self):
pass
def __init__(self, channels_first=None):
self.ch_dim = int(not channels_first)
def __call__(self, tensor):
if isinstance(tensor, (torch.LongTensor, torch.IntTensor)):
tensor = tensor.float()
if tensor.size(1) > 1:
tensor = torch.mean(tensor.float(), 1, True)
tensor = torch.mean(tensor, self.ch_dim, True)
return tensor
def __repr__(self):
......@@ -148,8 +149,7 @@ class DownmixMono(object):
class LC2CL(object):
"""Permute a 2d tensor from samples (Length) x Channels to Channels x
samples (Length)
"""Permute a 2d tensor from samples (n x c) to (c x n)
"""
def __call__(self, tensor):
......@@ -162,7 +162,6 @@ class LC2CL(object):
tensor (Tensor): Tensor of audio signal with shape (CxL)
"""
return tensor.transpose(0, 1).contiguous()
def __repr__(self):
......@@ -292,7 +291,7 @@ class SPEC2DB(object):
def __call__(self, spec):
spec, is_variable = _check_is_variable(spec)
spec_db = self.multiplier * _tlog10(spec / spec.max()) # power -> dB
spec_db = self.multiplier * torch.log10(spec / spec.max()) # power -> dB
if self.top_db is not None:
spec_db = torch.max(spec_db, spec_db.new([self.top_db]))
return spec_db if is_variable else spec_db.data
......@@ -320,7 +319,6 @@ class MEL2(object):
Example:
>>> sig, sr = torchaudio.load("test.wav", normalization=True)
>>> sig = transforms.LC2CL()(sig) # (n, c) -> (c, n)
>>> spec_mel = transforms.MEL2(sr)(sig) # (c, l, m)
"""
def __init__(self, sr=16000, ws=400, hop=None, n_fft=None,
......@@ -406,8 +404,8 @@ class MEL(object):
class BLC2CBL(object):
"""Permute a 3d tensor from Bands x samples (Length) x Channels to Channels x
Bands x samples (Length)
"""Permute a 3d tensor from Bands x Sample length x Channels to Channels x
Bands x Samples length
"""
def __call__(self, tensor):
......
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