code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def test_bootstrap(random):
"""Test that bootstrapping gives the right answer in dumb cases."""
a_ones = np.ones(10)
n_boot = 5
out1 = algo.bootstrap(a_ones, n_boot=n_boot)
assert_array_equal(out1, np.ones(n_boot))
out2 = algo.bootstrap(a_ones, n_boot=n_boot, func=np.median)
assert_array_equ... | Test that bootstrapping gives the right answer in dumb cases. | test_bootstrap | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_length(random):
"""Test that we get a bootstrap array of the right shape."""
a_norm = np.random.randn(1000)
out = algo.bootstrap(a_norm)
assert len(out) == 10000
n_boot = 100
out = algo.bootstrap(a_norm, n_boot=n_boot)
assert len(out) == n_boot | Test that we get a bootstrap array of the right shape. | test_bootstrap_length | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_range(random):
"""Test that bootstrapping a random array stays within the right range."""
a_norm = np.random.randn(1000)
amin, amax = a_norm.min(), a_norm.max()
out = algo.bootstrap(a_norm)
assert amin <= out.min()
assert amax >= out.max() | Test that bootstrapping a random array stays within the right range. | test_bootstrap_range | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_multiarg(random):
"""Test that bootstrap works with multiple input arrays."""
x = np.vstack([[1, 10] for i in range(10)])
y = np.vstack([[5, 5] for i in range(10)])
def f(x, y):
return np.vstack((x, y)).max(axis=0)
out_actual = algo.bootstrap(x, y, n_boot=2, func=f)
... | Test that bootstrap works with multiple input arrays. | test_bootstrap_multiarg | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_axis(random):
"""Test axis kwarg to bootstrap function."""
x = np.random.randn(10, 20)
n_boot = 100
out_default = algo.bootstrap(x, n_boot=n_boot)
assert out_default.shape == (n_boot,)
out_axis = algo.bootstrap(x, n_boot=n_boot, axis=0)
assert out_axis.shape, (n_boot, x.... | Test axis kwarg to bootstrap function. | test_bootstrap_axis | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_seed(random):
"""Test that we can get reproducible resamples by seeding the RNG."""
data = np.random.randn(50)
seed = 42
boots1 = algo.bootstrap(data, seed=seed)
boots2 = algo.bootstrap(data, seed=seed)
assert_array_equal(boots1, boots2) | Test that we can get reproducible resamples by seeding the RNG. | test_bootstrap_seed | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_ols(random):
"""Test bootstrap of OLS model fit."""
def ols_fit(X, y):
XtXinv = np.linalg.inv(np.dot(X.T, X))
return XtXinv.dot(X.T).dot(y)
X = np.column_stack((np.random.randn(50, 4), np.ones(50)))
w = [2, 4, 0, 3, 5]
y_noisy = np.dot(X, w) + np.random.randn(50) ... | Test bootstrap of OLS model fit. | test_bootstrap_ols | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_units(random):
"""Test that results make sense when passing unit IDs to bootstrap."""
data = np.random.randn(50)
ids = np.repeat(range(10), 5)
bwerr = np.random.normal(0, 2, 10)
bwerr = bwerr[ids]
data_rm = data + bwerr
seed = 77
boots_orig = algo.bootstrap(data_rm, s... | Test that results make sense when passing unit IDs to bootstrap. | test_bootstrap_units | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_string_func():
"""Test that named numpy methods are the same as the numpy function."""
x = np.random.randn(100)
res_a = algo.bootstrap(x, func="mean", seed=0)
res_b = algo.bootstrap(x, func=np.mean, seed=0)
assert np.array_equal(res_a, res_b)
res_a = algo.bootstrap(x, func="... | Test that named numpy methods are the same as the numpy function. | test_bootstrap_string_func | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def test_bootstrap_reproducibility(random):
"""Test that bootstrapping uses the internal random state."""
data = np.random.randn(50)
boots1 = algo.bootstrap(data, seed=100)
boots2 = algo.bootstrap(data, seed=100)
assert_array_equal(boots1, boots2)
random_state1 = np.random.RandomState(200)
... | Test that bootstrapping uses the internal random state. | test_bootstrap_reproducibility | python | mwaskom/seaborn | tests/test_algorithms.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_algorithms.py | BSD-3-Clause |
def get_contour_coords(c, filter_empty=False):
"""Provide compatability for change in contour artist types."""
if isinstance(c, mpl.collections.LineCollection):
# See https://github.com/matplotlib/matplotlib/issues/20906
return c.get_segments()
elif isinstance(c, (mpl.collections.PathCollect... | Provide compatability for change in contour artist types. | get_contour_coords | python | mwaskom/seaborn | tests/test_distributions.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_distributions.py | BSD-3-Clause |
def get_contour_color(c):
"""Provide compatability for change in contour artist types."""
if isinstance(c, mpl.collections.LineCollection):
# See https://github.com/matplotlib/matplotlib/issues/20906
return c.get_color()
elif isinstance(c, (mpl.collections.PathCollection, mpl.contour.QuadCon... | Provide compatability for change in contour artist types. | get_contour_color | python | mwaskom/seaborn | tests/test_distributions.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_distributions.py | BSD-3-Clause |
def integrate(y, x):
""""Simple numerical integration for testing KDE code."""
y = np.asarray(y)
x = np.asarray(x)
dx = np.diff(x)
return (dx * y[:-1] + dx * y[1:]).sum() / 2 | "Simple numerical integration for testing KDE code. | integrate | python | mwaskom/seaborn | tests/test_distributions.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_distributions.py | BSD-3-Clause |
def example_method(self):
"""An example method.
Parameters
----------
a : str
A method parameter.
""" | An example method.
Parameters
----------
a : str
A method parameter.
| example_method | python | mwaskom/seaborn | tests/test_docstrings.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_docstrings.py | BSD-3-Clause |
def example_func():
"""An example function.
Parameters
----------
a : str
A function parameter.
""" | An example function.
Parameters
----------
a : str
A function parameter.
| example_func | python | mwaskom/seaborn | tests/test_docstrings.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_docstrings.py | BSD-3-Clause |
def test_mask_limits(self):
"""Make sure masked cells are not used to calculate extremes"""
kws = self.default_kws.copy()
mask = self.x_norm > 0
kws['mask'] = mask
p = mat._HeatMapper(self.x_norm, **kws)
assert p.vmax == np.ma.array(self.x_norm, mask=mask).max()
... | Make sure masked cells are not used to calculate extremes | test_mask_limits | python | mwaskom/seaborn | tests/test_matrix.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_matrix.py | BSD-3-Clause |
def has_verdana():
"""Helper to verify if Verdana font is present"""
# This import is relatively lengthy, so to prevent its import for
# testing other tests in this module not requiring this knowledge,
# import font_manager here
import matplotlib.font_manager as mplfm
try:
verdana_font =... | Helper to verify if Verdana font is present | has_verdana | python | mwaskom/seaborn | tests/test_rcmod.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_rcmod.py | BSD-3-Clause |
def _network(t=None, url="https://github.com"):
"""
Decorator that will skip a test if `url` is unreachable.
Parameters
----------
t : function, optional
url : str, optional
"""
if t is None:
return lambda x: _network(x, url=url)
def wrapper(*args, **kwargs):
# att... |
Decorator that will skip a test if `url` is unreachable.
Parameters
----------
t : function, optional
url : str, optional
| _network | python | mwaskom/seaborn | tests/test_utils.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_utils.py | BSD-3-Clause |
def test_to_utf8(s, exp):
"""Test the to_utf8 function: object to string"""
u = utils.to_utf8(s)
assert isinstance(u, str)
assert u == exp | Test the to_utf8 function: object to string | test_to_utf8 | python | mwaskom/seaborn | tests/test_utils.py | https://github.com/mwaskom/seaborn/blob/master/tests/test_utils.py | BSD-3-Clause |
def process(self, audio_data, last=False):
"""
Add audio data and process it
params:
audio_data: audio data in numpy array
last: whether this is the last chunk of data
returns:
Processed audio data, returns None if no split point is found
"""
... |
Add audio data and process it
params:
audio_data: audio data in numpy array
last: whether this is the last chunk of data
returns:
Processed audio data, returns None if no split point is found
| process | python | THUDM/GLM-4-Voice | audio_process.py | https://github.com/THUDM/GLM-4-Voice/blob/master/audio_process.py | Apache-2.0 |
def _find_silence_boundary(self, audio):
"""
Find the starting point of silence boundary in audio
"""
# Convert audio to decibels
db = librosa.amplitude_to_db(np.abs(audio), ref=np.max)
# Find points below threshold
silence_points = np.where(db < self.thr... |
Find the starting point of silence boundary in audio
| _find_silence_boundary | python | THUDM/GLM-4-Voice | audio_process.py | https://github.com/THUDM/GLM-4-Voice/blob/master/audio_process.py | Apache-2.0 |
def _find_silence_end(self, start_point):
"""
Find the end point of silence segment
"""
db = librosa.amplitude_to_db(np.abs(self.buffer[start_point:]), ref=np.max)
silence_points = np.where(db >= self.threshold_db)[0]
if len(silence_points) == 0:
retu... |
Find the end point of silence segment
| _find_silence_end | python | THUDM/GLM-4-Voice | audio_process.py | https://github.com/THUDM/GLM-4-Voice/blob/master/audio_process.py | Apache-2.0 |
def __iter__(self):
""" Return an iterator over the source dataset processed by the
given processor.
"""
assert self.source is not None
assert callable(self.f)
return self.f(iter(self.source), *self.args, **self.kw) | Return an iterator over the source dataset processed by the
given processor.
| __iter__ | python | THUDM/GLM-4-Voice | cosyvoice/dataset/dataset.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/dataset.py | Apache-2.0 |
def sample(self, data):
""" Sample data according to rank/world_size/num_workers
Args:
data(List): input data list
Returns:
List: data list after sample
"""
data = list(range(len(data)))
# force datalist even
if self.parti... | Sample data according to rank/world_size/num_workers
Args:
data(List): input data list
Returns:
List: data list after sample
| sample | python | THUDM/GLM-4-Voice | cosyvoice/dataset/dataset.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/dataset.py | Apache-2.0 |
def Dataset(data_list_file,
data_pipeline,
mode='train',
shuffle=True,
partition=True,
tts_file='',
prompt_utt2data=''):
""" Construct dataset from arguments
We have two shuffle stage in the Dataset. The first is global
shuffle... | Construct dataset from arguments
We have two shuffle stage in the Dataset. The first is global
shuffle at shards tar/raw file level. The second is global shuffle
at training samples level.
Args:
data_type(str): raw/shard
tokenizer (BaseTokenizer): tokenizer to ... | Dataset | python | THUDM/GLM-4-Voice | cosyvoice/dataset/dataset.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/dataset.py | Apache-2.0 |
def parquet_opener(data, mode='train', tts_data={}):
""" Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
"""
for sample in data:
assert 'src' in samp... | Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
| parquet_opener | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def cosy_jsonl_opener(data, mode='train', tts_data={}):
""" Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
"""
for sample in data:
assert 'src' in s... | Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
| cosy_jsonl_opener | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def cosy_jsonl_opener_vq0918_nopool(data, mode='train', tts_data={}):
""" Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
"""
for sample in data:
ass... | Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
| cosy_jsonl_opener_vq0918_nopool | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def cosy_jsonl_opener_vq0918_pool2(data, mode='train', tts_data={}):
""" Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
"""
for sample in data:
asse... | Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
| cosy_jsonl_opener_vq0918_pool2 | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def cosy_jsonl_opener_vq0918_pool4(data, mode='train', tts_data={}):
""" Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
"""
for sample in data:
asse... | Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
| cosy_jsonl_opener_vq0918_pool4 | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def cosy_jsonl_opener_vq0918_pool8(data, mode='train', tts_data={}):
""" Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
"""
for sample in data:
asse... | Give url or local file, return file descriptor
Inplace operation.
Args:
data(Iterable[str]): url or local file list
Returns:
Iterable[{src, stream}]
| cosy_jsonl_opener_vq0918_pool8 | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def filter(data,
max_length=10240,
min_length=10,
token_max_length=200,
token_min_length=1,
min_output_input_ratio=0.0005,
max_output_input_ratio=1,
mode='train'):
""" Filter sample according to feature and label length
Inplace ope... | Filter sample according to feature and label length
Inplace operation.
Args::
data: Iterable[{key, wav, label, sample_rate}]
max_length: drop utterance which is greater than max_length(10ms)
min_length: drop utterance which is less than min_length(10ms)
... | filter | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def filter_speech_token(data,
max_length=10240,
min_length=10,
token_max_length=5000,
token_min_length=1,
min_output_input_ratio=0.0005,
max_output_input_ratio=30,
mode='train'):
""" Filter sample according to feature and label length
... | Filter sample according to feature and label length
Inplace operation.
Args::
data: Iterable[{key, wav, label, sample_rate}]
max_length: drop utterance which is greater than max_length(10ms)
min_length: drop utterance which is less than min_length(10ms)
... | filter_speech_token | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def resample(data, resample_rate=22050, min_sample_rate=16000, mode='train'):
""" Resample data.
Inplace operation.
Args:
data: Iterable[{key, wav, label, sample_rate}]
resample_rate: target resample rate
Returns:
Iterable[{key, wav, label, sample_rate}]... | Resample data.
Inplace operation.
Args:
data: Iterable[{key, wav, label, sample_rate}]
resample_rate: target resample rate
Returns:
Iterable[{key, wav, label, sample_rate}]
| resample | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def compute_fbank(data,
feat_extractor,
mode='train'):
""" Extract fbank
Args:
data: Iterable[{key, wav, label, sample_rate}]
Returns:
Iterable[{key, feat, label}]
"""
for sample in data:
assert 'sample_rate' in sample
... | Extract fbank
Args:
data: Iterable[{key, wav, label, sample_rate}]
Returns:
Iterable[{key, feat, label}]
| compute_fbank | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def parse_embedding(data, normalize, mode='train'):
""" Parse utt_embedding/spk_embedding
Args:
data: Iterable[{key, wav, label, sample_rate}]
Returns:
Iterable[{key, feat, label}]
"""
for sample in data:
sample['utt_embedding'] = torch.tensor(sample['utt_em... | Parse utt_embedding/spk_embedding
Args:
data: Iterable[{key, wav, label, sample_rate}]
Returns:
Iterable[{key, feat, label}]
| parse_embedding | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def tokenize(data, get_tokenizer, allowed_special, mode='train'):
""" Decode text to chars or BPE
Inplace operation
Args:
data: Iterable[{key, wav, txt, sample_rate}]
Returns:
Iterable[{key, wav, txt, tokens, label, sample_rate}]
"""
tokenizer = get_tokenize... | Decode text to chars or BPE
Inplace operation
Args:
data: Iterable[{key, wav, txt, sample_rate}]
Returns:
Iterable[{key, wav, txt, tokens, label, sample_rate}]
| tokenize | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def shuffle(data, shuffle_size=10000, mode='train'):
""" Local shuffle the data
Args:
data: Iterable[{key, feat, label}]
shuffle_size: buffer size for shuffle
Returns:
Iterable[{key, feat, label}]
"""
buf = []
for sample in data:
buf.append(s... | Local shuffle the data
Args:
data: Iterable[{key, feat, label}]
shuffle_size: buffer size for shuffle
Returns:
Iterable[{key, feat, label}]
| shuffle | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def sort(data, sort_size=500, mode='train'):
""" Sort the data by feature length.
Sort is used after shuffle and before batch, so we can group
utts with similar lengths into a batch, and `sort_size` should
be less than `shuffle_size`
Args:
data: Iterable[{key, feat, labe... | Sort the data by feature length.
Sort is used after shuffle and before batch, so we can group
utts with similar lengths into a batch, and `sort_size` should
be less than `shuffle_size`
Args:
data: Iterable[{key, feat, label}]
sort_size: buffer size for sort
... | sort | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def static_batch(data, batch_size=16):
""" Static batch the data by `batch_size`
Args:
data: Iterable[{key, feat, label}]
batch_size: batch size
Returns:
Iterable[List[{key, feat, label}]]
"""
buf = []
for sample in data:
buf.append(sample)
... | Static batch the data by `batch_size`
Args:
data: Iterable[{key, feat, label}]
batch_size: batch size
Returns:
Iterable[List[{key, feat, label}]]
| static_batch | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def dynamic_batch(data, max_frames_in_batch=12000, mode='train'):
""" Dynamic batch the data until the total frames in batch
reach `max_frames_in_batch`
Args:
data: Iterable[{key, feat, label}]
max_frames_in_batch: max_frames in one batch
Returns:
Iterab... | Dynamic batch the data until the total frames in batch
reach `max_frames_in_batch`
Args:
data: Iterable[{key, feat, label}]
max_frames_in_batch: max_frames in one batch
Returns:
Iterable[List[{key, feat, label}]]
| dynamic_batch | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def padding(data, use_spk_embedding, mode='train'):
""" Padding the data into training data
Args:
data: Iterable[List[{key, feat, label}]]
Returns:
Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
"""
for sample in data:
assert isinstance(s... | Padding the data into training data
Args:
data: Iterable[List[{key, feat, label}]]
Returns:
Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
| padding | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def padding_speech_token(data, use_spk_embedding, mode='train'):
""" Padding the data into training data
Args:
data: Iterable[List[{key, feat, label}]]
Returns:
Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
"""
for sample in data:
assert... | Padding the data into training data
Args:
data: Iterable[List[{key, feat, label}]]
Returns:
Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
| padding_speech_token | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def padding_speech_token_spk(data, use_spk_embedding, mode='train'):
""" Padding the data into training data
Args:
data: Iterable[List[{key, feat, label}]]
Returns:
Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
"""
for sample in data:
as... | Padding the data into training data
Args:
data: Iterable[List[{key, feat, label}]]
Returns:
Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
| padding_speech_token_spk | python | THUDM/GLM-4-Voice | cosyvoice/dataset/processor.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/dataset/processor.py | Apache-2.0 |
def __init__(
self,
in_channels,
out_channels,
channels=(256, 256),
dropout=0.05,
attention_head_dim=64,
n_blocks=1,
num_mid_blocks=2,
num_heads=4,
act_fn="snake",
):
"""
This decoder requires an input with the same shap... |
This decoder requires an input with the same shape of the target. So, if your text content
is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
| __init__ | python | THUDM/GLM-4-Voice | cosyvoice/flow/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/decoder.py | Apache-2.0 |
def forward(self, x, mask, mu, t, spks=None, cond=None):
"""Forward pass of the UNet1DConditional model.
Args:
x (torch.Tensor): shape (batch_size, in_channels, time)
mask (_type_): shape (batch_size, 1, time)
t (_type_): shape (batch_size)
spks (_type_, ... | Forward pass of the UNet1DConditional model.
Args:
x (torch.Tensor): shape (batch_size, in_channels, time)
mask (_type_): shape (batch_size, 1, time)
t (_type_): shape (batch_size)
spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None... | forward | python | THUDM/GLM-4-Voice | cosyvoice/flow/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/decoder.py | Apache-2.0 |
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
"""Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, me... | Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
n_timesteps (int): number of diffusion steps
temperatur... | forward | python | THUDM/GLM-4-Voice | cosyvoice/flow/flow_matching.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/flow_matching.py | Apache-2.0 |
def solve_euler(self, x, t_span, mu, mask, spks, cond):
"""
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
... |
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
ma... | solve_euler | python | THUDM/GLM-4-Voice | cosyvoice/flow/flow_matching.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/flow_matching.py | Apache-2.0 |
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
"""Computes diffusion loss
Args:
x1 (torch.Tensor): Target
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): target mask
shape: (batch_size, 1, mel_timesteps)
m... | Computes diffusion loss
Args:
x1 (torch.Tensor): Target
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): target mask
shape: (batch_size, 1, mel_timesteps)
mu (torch.Tensor): output of encoder
shape: (batch_size,... | compute_loss | python | THUDM/GLM-4-Voice | cosyvoice/flow/flow_matching.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/flow_matching.py | Apache-2.0 |
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
"""Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, me... | Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
n_timesteps (int): number of diffusion steps
temperatur... | forward | python | THUDM/GLM-4-Voice | cosyvoice/flow/flow_matching_dit.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/flow_matching_dit.py | Apache-2.0 |
def solve_euler(self, x, t_span, mu, mask, spks, cond):
"""
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise torch.Size([1, 80, 621])
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor):... |
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise torch.Size([1, 80, 621])
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel... | solve_euler | python | THUDM/GLM-4-Voice | cosyvoice/flow/flow_matching_dit.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/flow_matching_dit.py | Apache-2.0 |
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
"""Computes diffusion loss
Args:
x1 (torch.Tensor): Target
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): target mask
shape: (batch_size, 1, mel_timesteps)
m... | Computes diffusion loss
Args:
x1 (torch.Tensor): Target
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): target mask
shape: (batch_size, 1, mel_timesteps)
mu (torch.Tensor): output of encoder
shape: (batch_size,... | compute_loss | python | THUDM/GLM-4-Voice | cosyvoice/flow/flow_matching_dit.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/flow_matching_dit.py | Apache-2.0 |
def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
"""Pad for a convolution to make sure that the last window is full.
Extra padding is added at the end. This is required to ensure that we can rebuild
an output of the same length, as otherwise, even with padding, som... | Pad for a convolution to make sure that the last window is full.
Extra padding is added at the end. This is required to ensure that we can rebuild
an output of the same length, as otherwise, even with padding, some time steps
might get removed.
For instance, with total padding = 4, kernel size = 4, stri... | pad_for_conv1d | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/adp.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/adp.py | Apache-2.0 |
def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
"""Tiny wrapper around F.pad, just to allow for reflect padding on small input.
If this is the case, we insert extra 0 padding to the right before the reflection happen.
"""
length = x.shape[-1]
padd... | Tiny wrapper around F.pad, just to allow for reflect padding on small input.
If this is the case, we insert extra 0 padding to the right before the reflection happen.
| pad1d | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/adp.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/adp.py | Apache-2.0 |
def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
"""Remove padding from x, handling properly zero padding. Only for 1d!"""
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
assert (padding_left + padding_right) <= x.shape[-1]... | Remove padding from x, handling properly zero padding. Only for 1d! | unpad1d | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/adp.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/adp.py | Apache-2.0 |
def get_channels(
self, channels_list: Optional[Sequence[Tensor]] = None, layer: int = 0
) -> Optional[Tensor]:
"""Gets context channels at `layer` and checks that shape is correct"""
use_context_channels = self.use_context_channels and self.has_context[layer]
if not use_context_chan... | Gets context channels at `layer` and checks that shape is correct | get_channels | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/adp.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/adp.py | Apache-2.0 |
def get_mapping(
self, time: Optional[Tensor] = None, features: Optional[Tensor] = None
) -> Optional[Tensor]:
"""Combines context time features and features into mapping"""
items, mapping = [], None
# Compute time features
if self.use_context_time:
assert_message... | Combines context time features and features into mapping | get_mapping | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/adp.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/adp.py | Apache-2.0 |
def sample_discrete_euler(model, x, steps, sigma_max=1, **extra_args):
"""Draws samples from a model given starting noise. Euler method"""
# Make tensor of ones to broadcast the single t values
ts = x.new_ones([x.shape[0]])
# Create the noise schedule
t = torch.linspace(sigma_max, 0, steps + 1)
... | Draws samples from a model given starting noise. Euler method | sample_discrete_euler | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/sampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/sampling.py | Apache-2.0 |
def sample(model, x, steps, eta, **extra_args):
"""Draws samples from a model given starting noise. v-diffusion"""
ts = x.new_ones([x.shape[0]])
# Create the noise schedule
t = torch.linspace(1, 0, steps + 1)[:-1]
alphas, sigmas = get_alphas_sigmas(t)
# The sampling loop
for i in trange(s... | Draws samples from a model given starting noise. v-diffusion | sample | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/sampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/sampling.py | Apache-2.0 |
def __init__(self, dim, bias=False, fix_scale=False):
"""
bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
"""
super().__init__()
if fix_scale:
self.register_buffer("gamma", torch.ones(dim))
el... |
bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
| __init__ | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/transformer.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/transformer.py | Apache-2.0 |
def __init__(self, dim, bias=False, fix_scale=False):
"""
bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
"""
super().__init__()
if fix_scale:
self.register_buffer("gamma", torch.ones(dim))
el... |
bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
| __init__ | python | THUDM/GLM-4-Voice | cosyvoice/flow/stable/transformer_use_mask.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/flow/stable/transformer_use_mask.py | Apache-2.0 |
def forward(self, x):
"""
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
"""
# source for harmonic branch
with torch.no_grad():
s... |
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
| forward | python | THUDM/GLM-4-Voice | cosyvoice/hifigan/generator.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/hifigan/generator.py | Apache-2.0 |
def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
'''
Initialization.
INPUT:
- in_features: shape of the input
- alpha: trainable parameter
alpha is initialized to 1 by default, higher values = higher-frequency.
... |
Initialization.
INPUT:
- in_features: shape of the input
- alpha: trainable parameter
alpha is initialized to 1 by default, higher values = higher-frequency.
alpha will be trained along with the rest of your model.
| __init__ | python | THUDM/GLM-4-Voice | cosyvoice/transformer/activation.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/activation.py | Apache-2.0 |
def forward_qkv(
self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Transform query, key and value.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor ... | Transform query, key and value.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
Returns:
torch.Tensor: Transformed query tenso... | forward_qkv | python | THUDM/GLM-4-Voice | cosyvoice/transformer/attention.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/attention.py | Apache-2.0 |
def forward_attention(
self,
value: torch.Tensor,
scores: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
) -> torch.Tensor:
"""Compute attention context vector.
Args:
value (torch.Tensor): Transformed value, size
... | Compute attention context vector.
Args:
value (torch.Tensor): Transformed value, size
(#batch, n_head, time2, d_k).
scores (torch.Tensor): Attention score, size
(#batch, n_head, time1, time2).
mask (torch.Tensor): Mask, size (#batch, 1, time2)... | forward_attention | python | THUDM/GLM-4-Voice | cosyvoice/transformer/attention.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/attention.py | Apache-2.0 |
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
pos_emb: torch.Tensor = torch.empty(0),
cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
) -> Tuple[torch.Tensor, torch... | Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor): Mask tensor (#batch, 1, time... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/attention.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/attention.py | Apache-2.0 |
def rel_shift(self, x):
"""Compute relative positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
time1 means the length of query vector.
Returns:
torch.Tensor: Output tensor.
"""
zero_pad = torch.zeros((... | Compute relative positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
time1 means the length of query vector.
Returns:
torch.Tensor: Output tensor.
| rel_shift | python | THUDM/GLM-4-Voice | cosyvoice/transformer/attention.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/attention.py | Apache-2.0 |
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
pos_emb: torch.Tensor = torch.empty(0),
cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
) -> Tuple[torch.Tensor, torch... | Compute 'Scaled Dot Product Attention' with rel. positional encoding.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/attention.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/attention.py | Apache-2.0 |
def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
"""Compute relative positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
time1 means the length of query vector.
Returns:
torch.Tensor: Output tensor.
"""
... | Compute relative positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
time1 means the length of query vector.
Returns:
torch.Tensor: Output tensor.
| rel_shift | python | THUDM/GLM-4-Voice | cosyvoice/transformer/attention.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/attention.py | Apache-2.0 |
def __init__(self,
channels: int,
kernel_size: int = 15,
activation: nn.Module = nn.ReLU(),
norm: str = "batch_norm",
causal: bool = False,
bias: bool = True):
"""Construct an ConvolutionModule object.
... | Construct an ConvolutionModule object.
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernel size of conv layers.
causal (int): Whether use causal convolution or not
| __init__ | python | THUDM/GLM-4-Voice | cosyvoice/transformer/convolution.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/convolution.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
cache: torch.Tensor = torch.zeros((0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute convolution module.
Args:
x (torch.Tensor): Input tensor ... | Compute convolution module.
Args:
x (torch.Tensor): Input tensor (#batch, time, channels).
mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
(0, 0, 0) means fake mask.
cache (torch.Tensor): left context cache, it is only
used i... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/convolution.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/convolution.py | Apache-2.0 |
def forward(
self,
memory: torch.Tensor,
memory_mask: torch.Tensor,
ys_in_pad: torch.Tensor,
ys_in_lens: torch.Tensor,
r_ys_in_pad: torch.Tensor = torch.empty(0),
reverse_weight: float = 0.0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""For... | Forward decoder.
Args:
memory: encoded memory, float32 (batch, maxlen_in, feat)
memory_mask: encoder memory mask, (batch, 1, maxlen_in)
ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
ys_in_lens: input lengths of this batch (batch)
r_ys_i... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/decoder.py | Apache-2.0 |
def forward_one_step(
self,
memory: torch.Tensor,
memory_mask: torch.Tensor,
tgt: torch.Tensor,
tgt_mask: torch.Tensor,
cache: Optional[List[torch.Tensor]] = None,
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""Forward one step.
This is only used... | Forward one step.
This is only used for decoding.
Args:
memory: encoded memory, float32 (batch, maxlen_in, feat)
memory_mask: encoded memory mask, (batch, 1, maxlen_in)
tgt: input token ids, int64 (batch, maxlen_out)
tgt_mask: input token mask, (batc... | forward_one_step | python | THUDM/GLM-4-Voice | cosyvoice/transformer/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/decoder.py | Apache-2.0 |
def tie_or_clone_weights(self, jit_mode: bool = True):
"""Tie or clone module weights (between word_emb and output_layer)
depending of whether we are using TorchScript or not"""
if not self.use_output_layer:
return
if jit_mode:
logging.info("clone emb.weight t... | Tie or clone module weights (between word_emb and output_layer)
depending of whether we are using TorchScript or not | tie_or_clone_weights | python | THUDM/GLM-4-Voice | cosyvoice/transformer/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/decoder.py | Apache-2.0 |
def forward(
self,
memory: torch.Tensor,
memory_mask: torch.Tensor,
ys_in_pad: torch.Tensor,
ys_in_lens: torch.Tensor,
r_ys_in_pad: torch.Tensor,
reverse_weight: float = 0.0,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Forward decoder.
... | Forward decoder.
Args:
memory: encoded memory, float32 (batch, maxlen_in, feat)
memory_mask: encoder memory mask, (batch, 1, maxlen_in)
ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
ys_in_lens: input lengths of this batch (batch)
r_ys_i... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/decoder.py | Apache-2.0 |
def forward_one_step(
self,
memory: torch.Tensor,
memory_mask: torch.Tensor,
tgt: torch.Tensor,
tgt_mask: torch.Tensor,
cache: Optional[List[torch.Tensor]] = None,
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""Forward one step.
This is only used... | Forward one step.
This is only used for decoding.
Args:
memory: encoded memory, float32 (batch, maxlen_in, feat)
memory_mask: encoded memory mask, (batch, 1, maxlen_in)
tgt: input token ids, int64 (batch, maxlen_out)
tgt_mask: input token mask, (batc... | forward_one_step | python | THUDM/GLM-4-Voice | cosyvoice/transformer/decoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/decoder.py | Apache-2.0 |
def forward(
self,
tgt: torch.Tensor,
tgt_mask: torch.Tensor,
memory: torch.Tensor,
memory_mask: torch.Tensor,
cache: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Compute decoded features.
Args:
... | Compute decoded features.
Args:
tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
tgt_mask (torch.Tensor): Mask for input tensor
(#batch, maxlen_out).
memory (torch.Tensor): Encoded memory
(#batch, maxlen_in, size).
memo... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/decoder_layer.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/decoder_layer.py | Apache-2.0 |
def forward(self,
x: torch.Tensor,
offset: Union[int, torch.Tensor] = 0) \
-> Tuple[torch.Tensor, torch.Tensor]:
"""Add positional encoding.
Args:
x (torch.Tensor): Input. Its shape is (batch, time, ...)
offset (int, torch.tensor): pos... | Add positional encoding.
Args:
x (torch.Tensor): Input. Its shape is (batch, time, ...)
offset (int, torch.tensor): position offset
Returns:
torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
torch.Tensor: for compatibility to RelPositionalEnc... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/embedding.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/embedding.py | Apache-2.0 |
def position_encoding(self,
offset: Union[int, torch.Tensor],
size: int,
apply_dropout: bool = True) -> torch.Tensor:
""" For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the wh... | For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, but will call this function several times with
increasing input size in a streaming scenario, so the dropout will
be applied several times... | position_encoding | python | THUDM/GLM-4-Voice | cosyvoice/transformer/embedding.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/embedding.py | Apache-2.0 |
def forward(self,
x: torch.Tensor,
offset: Union[int, torch.Tensor] = 0) \
-> Tuple[torch.Tensor, torch.Tensor]:
"""Compute positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Enc... | Compute positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
torch.Tensor: Positional embedding tensor (1, time, `*`).
| forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/embedding.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/embedding.py | Apache-2.0 |
def forward(self,
x: torch.Tensor,
offset: Union[int, torch.Tensor] = 0) \
-> Tuple[torch.Tensor, torch.Tensor]:
""" Just return zero vector for interface compatibility
"""
pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device)
return s... | Just return zero vector for interface compatibility
| forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/embedding.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/embedding.py | Apache-2.0 |
def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0):
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x)
x = x ... | Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
| forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/embedding.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/embedding.py | Apache-2.0 |
def position_encoding(self,
offset: Union[int, torch.Tensor],
size: int) -> torch.Tensor:
""" For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, b... | For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, but will call this function several times with
increasing input size in a streaming scenario, so the dropout will
be applied several times... | position_encoding | python | THUDM/GLM-4-Voice | cosyvoice/transformer/embedding.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/embedding.py | Apache-2.0 |
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
inpu... |
Args:
input_size (int): input dim
output_size (int): dimension of attention
attention_heads (int): the number of heads of multi head attention
linear_units (int): the hidden units number of position-wise feed
forward
num_blocks (int): ... | __init__ | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def forward(
self,
xs: torch.Tensor,
xs_lens: torch.Tensor,
decoding_chunk_size: int = 0,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Embed positions in tensor.
Args:
xs: padded input tensor (B, T, D)
... | Embed positions in tensor.
Args:
xs: padded input tensor (B, T, D)
xs_lens: input length (B)
decoding_chunk_size: decoding chunk size for dynamic chunk
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def forward_chunk(
self,
xs: torch.Tensor,
offset: int,
required_cache_size: int,
att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
) -> Tuple... | Forward just one chunk
Args:
xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
where `time == (chunk_size - 1) * subsample_rate + subsample.right_context + 1`
offset (int): current offset in encoder output time stamp
re... | forward_chunk | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def forward_chunk_by_chunk(
self,
xs: torch.Tensor,
decoding_chunk_size: int,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
""" Forward input chunk by chunk with chunk_size like a streaming
fashion
Here we should pay special ... | Forward input chunk by chunk with chunk_size like a streaming
fashion
Here we should pay special attention to computation cache in the
streaming style forward chunk by chunk. Three things should be taken
into account for computation in the current network:
1. transforme... | forward_chunk_by_chunk | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
inpu... | Construct TransformerEncoder
See Encoder for the meaning of each parameter.
| __init__ | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
inpu... | Construct ConformerEncoder
Args:
input_size to use_dynamic_chunk, see in BaseEncoder
positionwise_conv_kernel_size (int): Kernel size of positionwise
conv1d layer.
macaron_style (bool): Whether to use macaron style for
positionwise layer.
... | __init__ | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
inpu... | Construct ConformerEncoder
Args:
input_size to use_dynamic_chunk, see in BaseEncoder
positionwise_conv_kernel_size (int): Kernel size of positionwise
conv1d layer.
macaron_style (bool): Whether to use macaron style for
positionwise layer.
... | __init__ | python | THUDM/GLM-4-Voice | cosyvoice/transformer/encoder.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/encoder.py | Apache-2.0 |
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""Compute loss between x and target.
The model outputs and data labels tensors are flatten to
(batch*seqlen, class) shape and a mask is applied to the
padding part which should not be calculated for loss.
... | Compute loss between x and target.
The model outputs and data labels tensors are flatten to
(batch*seqlen, class) shape and a mask is applied to the
padding part which should not be calculated for loss.
Args:
x (torch.Tensor): prediction (batch, seqlen, class)
t... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/label_smoothing_loss.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/label_smoothing_loss.py | Apache-2.0 |
def forward(self, xs: torch.Tensor) -> torch.Tensor:
"""Foward function.
Args:
xs: input tensor (B, L, D)
Returns:
output tensor, (B, L, D)
"""
B, L, D = xs.size(
) # batch size, sequence length, embedding dimension (idim)
xs = xs.view(-1... | Foward function.
Args:
xs: input tensor (B, L, D)
Returns:
output tensor, (B, L, D)
| forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/positionwise_feed_forward.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/positionwise_feed_forward.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Input x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): ... | Input x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: linear input tensor (#batch, time', odim),
where time' = time .
torch.Tensor: linear input mas... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Input x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): ... | Input x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: linear input tensor (#batch, time', odim),
where time' = time .
torch.Tensor: linear input mas... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tenso... | Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 2.
torch.Tensor: Subsampled... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tenso... | Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 4.
torch.Tensor: Subsampled... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor... | Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 6.
torch.Tensor: Subsampled ... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tenso... | Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 8.
torch.Tensor: Subsampled... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
offset: Union[int, torch.Tensor] = 0
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Input x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): ... | Input x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: linear input tensor (#batch, time', odim),
where time' = time .
torch.Tensor: linear input mas... | forward | python | THUDM/GLM-4-Voice | cosyvoice/transformer/subsampling.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/transformer/subsampling.py | Apache-2.0 |
def pad_list(xs: List[torch.Tensor], pad_value: int):
"""Perform padding for the list of tensors.
Args:
xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
pad_value (float): Value for padding.
Returns:
Tensor: Padded tensor (B, Tmax, `*`).
Examples:
... | Perform padding for the list of tensors.
Args:
xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
pad_value (float): Value for padding.
Returns:
Tensor: Padded tensor (B, Tmax, `*`).
Examples:
>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
... | pad_list | python | THUDM/GLM-4-Voice | cosyvoice/utils/common.py | https://github.com/THUDM/GLM-4-Voice/blob/master/cosyvoice/utils/common.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.