repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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speechbrain | speechbrain-main/speechbrain/dataio/sampler.py | """PyTorch compatible samplers.
These determine the order of iteration through a dataset.
Authors:
* Aku Rouhe 2020
* Samuele Cornell 2020
* Ralf Leibold 2020
* Artem Ploujnikov 2021
* Andreas Nautsch 2021
"""
import torch
import logging
from operator import itemgetter
from torch.utils.data import (
Ran... | 32,036 | 38.212974 | 132 | py |
speechbrain | speechbrain-main/speechbrain/dataio/batch.py | """Batch collation
Authors
* Aku Rouhe 2020
"""
import collections
import torch
from speechbrain.utils.data_utils import mod_default_collate
from speechbrain.utils.data_utils import recursive_to
from speechbrain.utils.data_utils import batch_pad_right
from torch.utils.data._utils.collate import default_convert
from ... | 9,022 | 32.172794 | 91 | py |
speechbrain | speechbrain-main/speechbrain/dataio/dataloader.py | """PyTorch compatible DataLoaders
Essentially we extend PyTorch DataLoader by adding the ability to save the
data loading state, so that a checkpoint may be saved in the middle of an
epoch.
Example
-------
>>> import torch
>>> from speechbrain.utils.checkpoints import Checkpointer
>>> # An example "dataset" and its l... | 13,097 | 36.637931 | 86 | py |
speechbrain | speechbrain-main/speechbrain/dataio/encoder.py | """Encoding categorical data as integers
Authors
* Samuele Cornell 2020
* Aku Rouhe 2020
"""
import ast
import torch
import collections
import itertools
import logging
import speechbrain as sb
from speechbrain.utils.checkpoints import (
mark_as_saver,
mark_as_loader,
register_checkpoint_hooks,
)
logge... | 39,147 | 34.718978 | 93 | py |
speechbrain | speechbrain-main/speechbrain/dataio/dataset.py | """Dataset examples for loading individual data points
Authors
* Aku Rouhe 2020
* Samuele Cornell 2020
"""
import copy
import contextlib
from types import MethodType
from torch.utils.data import Dataset
from speechbrain.utils.data_pipeline import DataPipeline
from speechbrain.dataio.dataio import load_data_json, ... | 15,593 | 36.30622 | 87 | py |
speechbrain | speechbrain-main/speechbrain/dataio/preprocess.py | """Preprocessors for audio"""
import torch
import functools
from speechbrain.processing.speech_augmentation import Resample
class AudioNormalizer:
"""Normalizes audio into a standard format
Arguments
---------
sample_rate : int
The sampling rate to which the incoming signals should be convert... | 2,293 | 32.735294 | 87 | py |
speechbrain | speechbrain-main/speechbrain/alignment/ctc_segmentation.py | #!/usr/bin/env python3
# 2021, Technische Universität München, Ludwig Kürzinger
"""Perform CTC segmentation to align utterances within audio files.
This uses the ctc-segmentation Python package.
Install it with pip or see the installing instructions in
https://github.com/lumaku/ctc-segmentation
"""
import logging
fro... | 26,318 | 38.577444 | 84 | py |
speechbrain | speechbrain-main/speechbrain/alignment/aligner.py | """
Alignment code
Authors
* Elena Rastorgueva 2020
* Loren Lugosch 2020
"""
import torch
import random
from speechbrain.utils.checkpoints import register_checkpoint_hooks
from speechbrain.utils.checkpoints import mark_as_saver
from speechbrain.utils.checkpoints import mark_as_loader
from speechbrain.utils.data_util... | 52,837 | 34.944218 | 96 | py |
speechbrain | speechbrain-main/speechbrain/utils/edit_distance.py | """Edit distance and WER computation.
Authors
* Aku Rouhe 2020
* Salima Mdhaffar 2021
"""
import collections
EDIT_SYMBOLS = {
"eq": "=", # when tokens are equal
"ins": "I",
"del": "D",
"sub": "S",
}
# NOTE: There is a danger in using mutables as default arguments, as they are
# only initialized ... | 25,986 | 33.788487 | 80 | py |
speechbrain | speechbrain-main/speechbrain/utils/checkpoints.py | """This module implements a checkpoint saver and loader.
A checkpoint in an experiment usually needs to save the state of many different
things: the model parameters, optimizer parameters, what epoch is this, etc.
The save format for a checkpoint is a directory, where each of these separate
saveable things gets its ow... | 45,420 | 36.850833 | 88 | py |
speechbrain | speechbrain-main/speechbrain/utils/profiling.py | """Polymorphic decorators to handle PyTorch profiling and benchmarking.
Author:
* Andreas Nautsch 2022
"""
import numpy as np
from copy import deepcopy
from torch import profiler
from functools import wraps
from typing import Any, Callable, Iterable, Optional
# from typing import List
# from itertools import chai... | 25,566 | 36.653903 | 120 | py |
speechbrain | speechbrain-main/speechbrain/utils/data_utils.py | """This library gathers utilities for data io operation.
Authors
* Mirco Ravanelli 2020
* Aku Rouhe 2020
* Samuele Cornell 2020
"""
import os
import re
import csv
import shutil
import urllib.request
import collections.abc
import torch
import tqdm
import pathlib
import speechbrain as sb
def undo_padding(batch, le... | 17,403 | 28.90378 | 123 | py |
speechbrain | speechbrain-main/speechbrain/utils/logger.py | """Managing the logger, utilities
Author
* Fang-Pen Lin 2012 https://fangpenlin.com/posts/2012/08/26/good-logging-practice-in-python/
* Peter Plantinga 2020
* Aku Rouhe 2020
"""
import sys
import os
import yaml
import tqdm
import logging
import logging.config
import math
import torch
from speechbrain.utils.data_ut... | 5,525 | 27.050761 | 93 | py |
speechbrain | speechbrain-main/speechbrain/utils/_workarounds.py | """This module implements some workarounds for dependencies
Authors
* Aku Rouhe 2022
"""
import torch
import weakref
import warnings
WEAKREF_MARKER = "WEAKREF"
def _cycliclrsaver(obj, path):
state_dict = obj.state_dict()
if state_dict.get("_scale_fn_ref") is not None:
state_dict["_scale_fn_ref"] = ... | 1,188 | 33.970588 | 95 | py |
speechbrain | speechbrain-main/speechbrain/utils/metric_stats.py | """The ``metric_stats`` module provides an abstract class for storing
statistics produced over the course of an experiment and summarizing them.
Authors:
* Peter Plantinga 2020
* Mirco Ravanelli 2020
* Gaelle Laperriere 2021
* Sahar Ghannay 2021
"""
import torch
from joblib import Parallel, delayed
from speechbra... | 31,718 | 33.069817 | 109 | py |
speechbrain | speechbrain-main/speechbrain/utils/distributed.py | """Guard for running certain operations on main process only
Authors:
* Abdel Heba 2020
* Aku Rouhe 2020
"""
import os
import torch
import logging
logger = logging.getLogger(__name__)
def run_on_main(
func,
args=None,
kwargs=None,
post_func=None,
post_args=None,
post_kwargs=None,
run_p... | 6,388 | 33.349462 | 80 | py |
speechbrain | speechbrain-main/speechbrain/utils/bleu.py | """Library for computing the BLEU score
Authors
* Mirco Ravanelli 2021
"""
from speechbrain.utils.metric_stats import MetricStats
def merge_words(sequences):
"""Merge successive words into phrase, putting space between each word
Arguments
---------
sequences : list
Each item contains a lis... | 3,943 | 28 | 112 | py |
speechbrain | speechbrain-main/speechbrain/utils/Accuracy.py | """Calculate accuracy.
Authors
* Jianyuan Zhong 2020
"""
import torch
from speechbrain.dataio.dataio import length_to_mask
def Accuracy(log_probabilities, targets, length=None):
"""Calculates the accuracy for predicted log probabilities and targets in a batch.
Arguments
----------
log_probabilities ... | 2,584 | 29.05814 | 99 | py |
speechbrain | speechbrain-main/speechbrain/utils/torch_audio_backend.py | """Library for checking the torchaudio backend.
Authors
* Mirco Ravanelli 2021
"""
import platform
import logging
import torchaudio
logger = logging.getLogger(__name__)
def check_torchaudio_backend():
"""Checks the torchaudio backend and sets it to soundfile if
windows is detected.
"""
current_syst... | 573 | 23.956522 | 112 | py |
speechbrain | speechbrain-main/speechbrain/utils/train_logger.py | """Loggers for experiment monitoring.
Authors
* Peter Plantinga 2020
"""
import logging
import ruamel.yaml
import torch
import os
logger = logging.getLogger(__name__)
class TrainLogger:
"""Abstract class defining an interface for training loggers."""
def log_stats(
self,
stats_meta,
... | 13,898 | 30.445701 | 168 | py |
speechbrain | speechbrain-main/speechbrain/processing/NMF.py | """Non-negative matrix factorization
Authors
* Cem Subakan
"""
import torch
from speechbrain.processing.features import spectral_magnitude
import speechbrain.processing.features as spf
def spectral_phase(stft, power=2, log=False):
"""Returns the phase of a complex spectrogram.
Arguments
---------
s... | 5,770 | 29.373684 | 103 | py |
speechbrain | speechbrain-main/speechbrain/processing/features.py | """Low-level feature pipeline components
This library gathers functions that compute popular speech features over
batches of data. All the classes are of type nn.Module. This gives the
possibility to have end-to-end differentiability and to backpropagate the
gradient through them. Our functions are a modified versio... | 39,570 | 31.435246 | 81 | py |
speechbrain | speechbrain-main/speechbrain/processing/speech_augmentation.py | """Classes for mutating speech data for data augmentation.
This module provides classes that produce realistic distortions of speech
data for the purpose of training speech processing models. The list of
distortions includes adding noise, adding reverberation, changing speed,
and more. All the classes are of type `tor... | 44,293 | 34.982128 | 81 | py |
speechbrain | speechbrain-main/speechbrain/processing/signal_processing.py | """
Low level signal processing utilities
Authors
* Peter Plantinga 2020
* Francois Grondin 2020
* William Aris 2020
* Samuele Cornell 2020
* Sarthak Yadav 2022
"""
import torch
import math
from packaging import version
def compute_amplitude(waveforms, lengths=None, amp_type="avg", scale="linear"):
"""Compu... | 20,913 | 32.677939 | 123 | py |
speechbrain | speechbrain-main/speechbrain/processing/multi_mic.py | """Multi-microphone components.
This library contains functions for multi-microphone signal processing.
Example
-------
>>> import torch
>>>
>>> from speechbrain.dataio.dataio import read_audio
>>> from speechbrain.processing.features import STFT, ISTFT
>>> from speechbrain.processing.multi_mic import Covariance
>>> ... | 53,438 | 33.836375 | 120 | py |
speechbrain | speechbrain-main/speechbrain/processing/decomposition.py | """
Generalized Eigenvalue Decomposition.
This library contains different methods to adjust the format of
complex Hermitian matrices and find their eigenvectors and
eigenvalues.
Authors
* William Aris 2020
* Francois Grondin 2020
"""
import torch
def gevd(a, b=None):
"""This method computes the eigenvectors ... | 11,655 | 26.818616 | 92 | py |
speechbrain | speechbrain-main/speechbrain/lobes/features.py | """Basic feature pipelines.
Authors
* Mirco Ravanelli 2020
* Peter Plantinga 2020
* Sarthak Yadav 2020
"""
import torch
from speechbrain.processing.features import (
STFT,
spectral_magnitude,
Filterbank,
DCT,
Deltas,
ContextWindow,
)
from speechbrain.nnet.CNN import GaborConv1d
from speechbr... | 14,790 | 32.615909 | 114 | py |
speechbrain | speechbrain-main/speechbrain/lobes/augment.py | """
Combinations of processing algorithms to implement common augmentations.
Examples:
* SpecAugment
* Environmental corruption (noise, reverberation)
Authors
* Peter Plantinga 2020
* Jianyuan Zhong 2020
"""
import os
import torch
import torchaudio
import speechbrain as sb
from speechbrain.utils.data_utils import... | 18,577 | 32.473874 | 102 | py |
speechbrain | speechbrain-main/speechbrain/lobes/downsampling.py | """
Combinations of processing algorithms to implement downsampling methods.
Authors
* Salah Zaiem
"""
import torch
import torchaudio.transforms as T
from speechbrain.nnet.CNN import Conv1d
from speechbrain.nnet.pooling import Pooling1d
class Downsampler(torch.nn.Module):
""" Wrapper for downsampling techniques... | 3,444 | 26.782258 | 80 | py |
speechbrain | speechbrain-main/speechbrain/lobes/beamform_multimic.py | """Beamformer for multi-mic processing.
Authors
* Nauman Dawalatabad
"""
import torch
from speechbrain.processing.features import (
STFT,
ISTFT,
)
from speechbrain.processing.multi_mic import (
Covariance,
GccPhat,
DelaySum,
)
class DelaySum_Beamformer(torch.nn.Module):
"""Generate beamform... | 1,264 | 22.425926 | 81 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/wav2vec.py | """Components necessary to build a wav2vec 2.0 architecture following the
original paper: https://arxiv.org/abs/2006.11477.
Authors
* Rudolf A Braun 2022
* Guillermo Cambara 2022
* Titouan Parcollet 2022
"""
import logging
import torch
import torch.nn.functional as F
import torch.nn as nn
import random
import numpy a... | 12,989 | 32.916449 | 98 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/conv_tasnet.py | """ Implementation of a popular speech separation model.
"""
import torch
import torch.nn as nn
import speechbrain as sb
import torch.nn.functional as F
from speechbrain.processing.signal_processing import overlap_and_add
EPS = 1e-8
class Encoder(nn.Module):
"""This class learns the adaptive frontend for the Co... | 16,379 | 25.721044 | 88 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/MetricGAN.py | """Generator and discriminator used in MetricGAN
Authors:
* Szu-Wei Fu 2020
"""
import torch
import speechbrain as sb
from torch import nn
from torch.nn.utils import spectral_norm
def xavier_init_layer(
in_size, out_size=None, spec_norm=True, layer_type=nn.Linear, **kwargs
):
"Create a layer with spectral no... | 5,148 | 26.832432 | 81 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/MetricGAN_U.py | """Generator and discriminator used in MetricGAN-U
Authors:
* Szu-Wei Fu 2020
"""
import torch
import speechbrain as sb
from torch import nn
from torch.nn.utils import spectral_norm
def xavier_init_layer(
in_size, out_size=None, spec_norm=True, layer_type=nn.Linear, **kwargs
):
"Create a layer with spectral ... | 5,154 | 25.989529 | 81 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/Tacotron2.py | """
Neural network modules for the Tacotron2 end-to-end neural
Text-to-Speech (TTS) model
Authors
* Georges Abous-Rjeili 2021
* Artem Ploujnikov 2021
"""
# This code uses a significant portion of the NVidia implementation, even though it
# has been modified and enhanced
# https://github.com/NVIDIA/DeepLearningExampl... | 59,832 | 30.441408 | 138 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/segan_model.py | """
This file contains two PyTorch modules which together consist of the SEGAN model architecture
(based on the paper: Pascual et al. https://arxiv.org/pdf/1703.09452.pdf).
Modification of the initialization parameters allows the change of the model described in the class project,
such as turning the generator to a VAE... | 8,123 | 31.496 | 108 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/L2I.py | """This file implements the necessary classes and functions to implement Listen-to-Interpret (L2I) interpretation method from https://arxiv.org/abs/2202.11479v2
Authors
* Cem Subakan 2022
* Francesco Paissan 2022
"""
import torch.nn as nn
import torch.nn.functional as F
import torch
from speechbrain.lobes.models.P... | 11,147 | 29.376022 | 160 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/fairseq_wav2vec.py | """This lobe enables the integration of fairseq pretrained wav2vec models.
Reference: https://arxiv.org/abs/2006.11477
Reference: https://arxiv.org/abs/1904.05862
FairSeq >= 1.0.0 needs to be installed: https://fairseq.readthedocs.io/en/latest/
Authors
* Titouan Parcollet 2021
* Salima Mdhaffar 2021
"""
import tor... | 11,652 | 33.785075 | 105 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/convolution.py | """This is a module to ensemble a convolution (depthwise) encoder with or without residule connection.
Authors
* Jianyuan Zhong 2020
"""
import torch
from speechbrain.nnet.CNN import Conv2d
from speechbrain.nnet.containers import Sequential
from speechbrain.nnet.normalization import LayerNorm
class ConvolutionFront... | 5,520 | 30.369318 | 107 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/ESPnetVGG.py | """This lobes replicate the encoder first introduced in ESPNET v1
source: https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/rnn/encoders.py
Authors
* Titouan Parcollet 2020
"""
import torch
import speechbrain as sb
class ESPnetVGG(sb.nnet.containers.Sequential):
"""This model is a combin... | 3,675 | 29.131148 | 96 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/EnhanceResnet.py | """Wide ResNet for Speech Enhancement.
Author
* Peter Plantinga 2022
"""
import torch
import speechbrain as sb
from speechbrain.processing.features import STFT, ISTFT, spectral_magnitude
class EnhanceResnet(torch.nn.Module):
"""Model for enhancement based on Wide ResNet.
Full model description at: https://... | 7,571 | 30.160494 | 95 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/ContextNet.py | """The SpeechBrain implementation of ContextNet by
https://arxiv.org/pdf/2005.03191.pdf
Authors
* Jianyuan Zhong 2020
"""
import torch
from torch.nn import Dropout
from speechbrain.nnet.CNN import DepthwiseSeparableConv1d, Conv1d
from speechbrain.nnet.linear import Linear
from speechbrain.nnet.pooling import Adaptive... | 9,388 | 30.612795 | 201 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/Xvector.py | """A popular speaker recognition and diarization model.
Authors
* Nauman Dawalatabad 2020
* Mirco Ravanelli 2020
"""
# import os
import torch # noqa: F401
import torch.nn as nn
import speechbrain as sb
from speechbrain.nnet.pooling import StatisticsPooling
from speechbrain.nnet.CNN import Conv1d
from speechbrain.n... | 6,854 | 28.170213 | 77 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/resepformer.py | """Library for the Reseource-Efficient Sepformer.
Authors
* Cem Subakan 2022
"""
import torch
import torch.nn as nn
from speechbrain.lobes.models.dual_path import select_norm
from speechbrain.lobes.models.transformer.Transformer import (
TransformerEncoder,
PositionalEncoding,
get_lookahead_mask,
)
impor... | 21,609 | 29.013889 | 119 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/huggingface_wav2vec.py | """This lobe enables the integration of huggingface pretrained wav2vec2/hubert/wavlm models.
Reference: https://arxiv.org/abs/2006.11477
Reference: https://arxiv.org/abs/1904.05862
Reference: https://arxiv.org/abs/2110.13900
Transformer from HuggingFace needs to be installed:
https://huggingface.co/transformers/instal... | 18,749 | 36.055336 | 123 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/Cnn14.py | """ This file implements the CNN14 model from https://arxiv.org/abs/1912.10211
Authors
* Cem Subakan 2022
* Francesco Paissan 2022
"""
import torch.nn as nn
import torch.nn.functional as F
import torch
def init_layer(layer):
"""Initialize a Linear or Convolutional layer."""
nn.init.xavier_uniform_(layer.... | 7,429 | 30.483051 | 89 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/ECAPA_TDNN.py | """A popular speaker recognition and diarization model.
Authors
* Hwidong Na 2020
"""
# import os
import torch # noqa: F401
import torch.nn as nn
import torch.nn.functional as F
from speechbrain.dataio.dataio import length_to_mask
from speechbrain.nnet.CNN import Conv1d as _Conv1d
from speechbrain.nnet.normalizatio... | 16,703 | 28.050435 | 83 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/VanillaNN.py | """Vanilla Neural Network for simple tests.
Authors
* Elena Rastorgueva 2020
"""
import torch
import speechbrain as sb
class VanillaNN(sb.nnet.containers.Sequential):
"""A simple vanilla Deep Neural Network.
Arguments
---------
activation : torch class
A class used for constructing the activ... | 1,178 | 23.5625 | 60 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/CRDNN.py | """A combination of Convolutional, Recurrent, and Fully-connected networks.
Authors
* Mirco Ravanelli 2020
* Peter Plantinga 2020
* Ju-Chieh Chou 2020
* Titouan Parcollet 2020
* Abdel 2020
"""
import torch
import speechbrain as sb
class CRDNN(sb.nnet.containers.Sequential):
"""This model is a combination of... | 10,521 | 32.724359 | 79 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/HifiGAN.py | """
Neural network modules for the HiFi-GAN: Generative Adversarial Networks for
Efficient and High Fidelity Speech Synthesis
For more details: https://arxiv.org/pdf/2010.05646.pdf
Authors
* Duret Jarod 2021
* Yingzhi WANG 2022
"""
# Adapted from https://github.com/jik876/hifi-gan/ and https://github.com/coqui-ai/... | 37,244 | 28.748403 | 99 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/RNNLM.py | """Implementation of a Recurrent Language Model.
Authors
* Mirco Ravanelli 2020
* Peter Plantinga 2020
* Ju-Chieh Chou 2020
* Titouan Parcollet 2020
* Abdel 2020
"""
import torch
from torch import nn
import speechbrain as sb
class RNNLM(nn.Module):
"""This model is a combination of embedding layer, RNN, DNN... | 3,628 | 28.504065 | 79 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/PIQ.py | """This file implements the necessary classes and functions to implement Posthoc Interpretations via Quantization.
Authors
* Cem Subakan 2023
* Francesco Paissan 2023
"""
import torch
import torch.nn as nn
from torch.autograd import Function
def get_irrelevant_regions(labels, K, num_classes, N_shared=5, stage="TR... | 19,449 | 30.370968 | 273 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/huggingface_whisper.py | """This lobe enables the integration of huggingface pretrained whisper model.
Transformer from HuggingFace needs to be installed:
https://huggingface.co/transformers/installation.html
Authors
* Adel Moumen 2022
* Titouan Parcollet 2022
* Luca Della Libera 2022
"""
import torch
import logging
from torch import nn
... | 12,043 | 35.607903 | 117 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/dual_path.py | """Library to support dual-path speech separation.
Authors
* Cem Subakan 2020
* Mirco Ravanelli 2020
* Samuele Cornell 2020
* Mirko Bronzi 2020
* Jianyuan Zhong 2020
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
from speechbrain.nnet.linear import Linear
from spee... | 42,269 | 28.313454 | 102 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/g2p/dataio.py | """
Data pipeline elements for the G2P pipeline
Authors
* Loren Lugosch 2020
* Mirco Ravanelli 2020
* Artem Ploujnikov 2021 (minor refactoring only)
"""
from functools import reduce
from speechbrain.wordemb.util import expand_to_chars
import speechbrain as sb
import torch
import re
RE_MULTI_SPACE = re.compile(r"\... | 16,894 | 25.153251 | 84 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/g2p/homograph.py | """Tools for homograph disambiguation
Authors
* Artem Ploujnikov 2021
"""
import torch
from torch import nn
class SubsequenceLoss(nn.Module):
"""
A loss function for a specific word in the output, used in
the homograph disambiguation task
The approach is as follows:
1. Arrange only the target wor... | 21,897 | 31.978916 | 118 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/g2p/model.py | """The Attentional RNN model for Grapheme-to-Phoneme
Authors
* Mirco Ravinelli 2021
* Artem Ploujnikov 2021
"""
from speechbrain.lobes.models.transformer.Transformer import (
TransformerInterface,
get_lookahead_mask,
get_key_padding_mask,
)
import torch
from torch import nn
from speechbrain.nnet.linear... | 18,054 | 29.293624 | 119 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/transformer/Transformer.py | """Transformer implementaion in the SpeechBrain style.
Authors
* Jianyuan Zhong 2020
* Samuele Cornell 2021
"""
import math
import torch
import torch.nn as nn
import speechbrain as sb
from typing import Optional
import numpy as np
from .Conformer import ConformerEncoder
from speechbrain.nnet.activations import Swish... | 27,179 | 30.641444 | 119 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/transformer/TransformerSE.py | """CNN Transformer model for SE in the SpeechBrain style.
Authors
* Chien-Feng Liao 2020
"""
import torch # noqa E402
from torch import nn
from speechbrain.nnet.linear import Linear
from speechbrain.lobes.models.transformer.Transformer import (
TransformerInterface,
get_lookahead_mask,
)
class CNNTransforme... | 3,074 | 29.445545 | 92 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/transformer/TransformerLM.py | """An implementation of Transformer Language model.
Authors
* Jianyuan Zhong
* Samuele Cornell
"""
import torch # noqa 42
from torch import nn
from speechbrain.nnet.linear import Linear
from speechbrain.nnet.normalization import LayerNorm
from speechbrain.nnet.containers import ModuleList
from speechbrain.lobes.mo... | 5,248 | 29.876471 | 108 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/transformer/TransformerASR.py | """Transformer for ASR in the SpeechBrain style.
Authors
* Jianyuan Zhong 2020
"""
import torch # noqa 42
from torch import nn
from typing import Optional
from speechbrain.nnet.linear import Linear
from speechbrain.nnet.containers import ModuleList
from speechbrain.lobes.models.transformer.Transformer import (
T... | 12,371 | 34.348571 | 119 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/transformer/Conformer.py | """Conformer implementation.
Authors
* Jianyuan Zhong 2020
* Samuele Cornell 2021
"""
import torch
import torch.nn as nn
from typing import Optional
import speechbrain as sb
import warnings
from speechbrain.nnet.attention import (
RelPosMHAXL,
MultiheadAttention,
PositionalwiseFeedForward,
)
from speech... | 20,245 | 29.127976 | 146 | py |
speechbrain | speechbrain-main/speechbrain/lobes/models/transformer/TransformerST.py | """Transformer for ST in the SpeechBrain sytle.
Authors
* YAO FEI, CHENG 2021
"""
import torch # noqa 42
import logging
from torch import nn
from typing import Optional
from speechbrain.nnet.containers import ModuleList
from speechbrain.lobes.models.transformer.Transformer import (
get_lookahead_mask,
get_k... | 13,931 | 34.360406 | 119 | py |
speechbrain | speechbrain-main/templates/hyperparameter_optimization_speaker_id/train.py | #!/usr/bin/env python3
"""Recipe for training a speaker-id system, with hyperparameter optimization support.
For a tutorial on hyperparameter optimization, refer to this tutorial:
https://colab.research.google.com/drive/1b-5EOjZC7M9RvfWZ0Pq0HMV0KmQKu730#scrollTo=lJup9mNnYw_0
The template can use used as a
basic exam... | 13,148 | 35.935393 | 95 | py |
speechbrain | speechbrain-main/templates/speech_recognition/LM/custom_model.py | """
This file contains a very simple PyTorch module to use for language modeling.
To replace this model, change the `!new:` tag in the hyperparameter file
to refer to a built-in SpeechBrain model or another file containing
a custom PyTorch module. Instead of this simple model, we suggest using one
of the following bui... | 2,590 | 27.163043 | 78 | py |
speechbrain | speechbrain-main/templates/speech_recognition/LM/train.py | #!/usr/bin/env python3
"""Recipe for training a language model with a given text corpus.
> python train.py RNNLM.yaml
To run this recipe, you need to first install the Huggingface dataset:
> pip install datasets
Authors
* Ju-Chieh Chou 2020
* Jianyuan Zhong 2021
* Mirco Ravanelli 2021
"""
import sys
import loggi... | 9,669 | 32.344828 | 80 | py |
speechbrain | speechbrain-main/templates/speech_recognition/ASR/train.py | #!/usr/bin/env/python3
"""Recipe for training a sequence-to-sequence ASR system with mini-librispeech.
The system employs an encoder, a decoder, and an attention mechanism
between them. Decoding is performed with beam search coupled with a neural
language model.
To run this recipe, do the following:
> python train.py ... | 17,800 | 37.364224 | 85 | py |
speechbrain | speechbrain-main/templates/speaker_id/custom_model.py | """
This file contains a very simple TDNN module to use for speaker-id.
To replace this model, change the `!new:` tag in the hyperparameter file
to refer to a built-in SpeechBrain model or another file containing
a custom PyTorch module.
Authors
* Nauman Dawalatabad 2020
* Mirco Ravanelli 2020
"""
import torch #... | 5,638 | 29.814208 | 77 | py |
speechbrain | speechbrain-main/templates/speaker_id/train.py | #!/usr/bin/env python3
"""Recipe for training a speaker-id system. The template can use used as a
basic example for any signal classification task such as language_id,
emotion recognition, command classification, etc. The proposed task classifies
28 speakers using Mini Librispeech. This task is very easy. In a real
sce... | 12,410 | 35.289474 | 80 | py |
speechbrain | speechbrain-main/templates/enhancement/custom_model.py | """
This file contains a very simple PyTorch module to use for enhancement.
To replace this model, change the `!new:` tag in the hyperparameter file
to refer to a built-in SpeechBrain model or another file containing
a custom PyTorch module.
Authors
* Peter Plantinga 2021
"""
import torch
class CustomModel(torch.n... | 1,992 | 30.140625 | 79 | py |
speechbrain | speechbrain-main/templates/enhancement/train.py | #!/usr/bin/env/python3
"""Recipe for training a speech enhancement system with spectral masking.
To run this recipe, do the following:
> python train.py train.yaml --data_folder /path/to/save/mini_librispeech
To read the code, first scroll to the bottom to see the "main" code.
This gives a high-level overview of what... | 11,127 | 34.552716 | 80 | py |
speechbrain | speechbrain-main/recipes/BinauralWSJ0Mix/separation/dynamic_mixing.py | import speechbrain as sb
import numpy as np
import torch
import torchaudio
import glob
import os
import random
from speechbrain.processing.signal_processing import rescale
from speechbrain.dataio.batch import PaddedBatch
from scipy.signal import fftconvolve
"""
The functions to implement Dynamic Mixing For SpeechSepar... | 7,597 | 33.694064 | 85 | py |
speechbrain | speechbrain-main/recipes/BinauralWSJ0Mix/separation/train.py | #!/usr/bin/env/python3
"""Recipe for training a neural speech separation system on binaural wsjmix the
dataset. The system employs an encoder, a decoder, and a masking network.
To run this recipe, do the following:
> python train.py hparams/convtasnet-parallel.yaml
--data_folder yourpath/binaural-wsj0m... | 32,509 | 37.023392 | 113 | py |
speechbrain | speechbrain-main/recipes/KsponSpeech/ksponspeech_prepare.py | """
Data preparation.
Download: https://aihub.or.kr/aidata/105/download
Author
------
Dongwon Kim, Dongwoo Kim 2021
"""
import csv
import logging
import os
import re
import torchaudio
from speechbrain.dataio.dataio import load_pkl, merge_csvs, save_pkl
from speechbrain.utils.data_utils import get_all_files
logger ... | 11,619 | 26.213115 | 80 | py |
speechbrain | speechbrain-main/recipes/KsponSpeech/LM/train.py | #!/usr/bin/env python3
"""Recipe for training a Language Model with ksponspeech train-965.2
transcript and lm_corpus.
To run this recipe, do the following:
> pip install datasets
> python train.py hparams/<hparam_file>.yaml \
--data_folder <local_path_to_librispeech_dataset>
Authors
* Jianyuan Zhong 2021
* Ju-C... | 7,234 | 32.967136 | 80 | py |
speechbrain | speechbrain-main/recipes/KsponSpeech/ASR/transformer/train.py | #!/usr/bin/env python3
"""Recipe for training a Transformer ASR system with KsponSpeech.
The system employs an encoder, a decoder, and an attention mechanism
between them. Decoding is performed with (CTC/Att joint) beamsearch
coupled with a neural language model.
To run this recipe, do the following:
> python train.py... | 17,980 | 36.696017 | 80 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/LM/train.py | #!/usr/bin/env/python3
"""
Recipe for Timers and Such LM training.
Run using:
> python train.py hparams/train.yaml
Authors
* Loren Lugosch 2020
"""
import sys
import torch
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils.distributed import run_on_main
# Define training proc... | 8,041 | 32.648536 | 83 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/decoupled/train.py | #!/usr/bin/env/python3
"""
Recipe for "decoupled" (speech -> ASR -> text -> NLU -> semantics) SLU.
The NLU part is trained on the ground truth transcripts, and at test time
we use the ASR to transcribe the audio and use that transcript as the input to the NLU.
Run using:
> python train.py hparams/train.yaml
Authors
... | 13,448 | 32.125616 | 89 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/direct/train_with_wav2vec2.py | #!/usr/bin/env/python3
"""
Recipe for "direct" (speech -> semantics) SLU with wav2vec2.0_based transfer learning.
We encode input waveforms into features using a wav2vec2.0 model pretrained on ASR from HuggingFace (facebook/wav2vec2-base-960h),
then feed the features into a seq2seq model to map them to semantics.
(Ad... | 13,849 | 32.373494 | 130 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/direct/train.py | #!/usr/bin/env/python3
"""
Recipe for "direct" (speech -> semantics) SLU with ASR-based transfer learning.
We encode input waveforms into features using a model trained on LibriSpeech,
then feed the features into a seq2seq model to map them to semantics.
(Adapted from the LibriSpeech seq2seq ASR recipe written by Ju-... | 13,273 | 32.605063 | 125 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/multistage/train.py | #!/usr/bin/env/python3
"""
Recipe for "multistage" (speech -> ASR -> text -> NLU -> semantics) SLU.
We transcribe each minibatch using a model trained on LibriSpeech,
then feed the transcriptions into a seq2seq model to map them to semantics.
(The transcriptions could be done offline to make training faster;
the bene... | 14,030 | 33.138686 | 117 | py |
speechbrain | speechbrain-main/recipes/VoxLingua107/lang_id/create_wds_shards.py | ################################################################################
#
# Converts the unzipped <LANG_ID>/<VIDEO---0000.000-0000.000.wav> folder
# structure of VoxLingua107 into a WebDataset format
#
# Author(s): Tanel Alumäe, Nik Vaessen
######################################################################... | 5,210 | 27.47541 | 81 | py |
speechbrain | speechbrain-main/recipes/VoxLingua107/lang_id/train.py | #!/usr/bin/python3
"""Recipe for training language embeddings using the VoxLingua107 Dataset.
This recipe is heavily inspired by this: https://github.com/nikvaessen/speechbrain/tree/sharded-voxceleb/my-recipes/SpeakerRec
To run this recipe, use the following command:
> python train_lang_embeddings_wds.py {hyperparame... | 8,757 | 30.390681 | 126 | py |
speechbrain | speechbrain-main/recipes/SLURP/NLU/train.py | #!/usr/bin/env/python3
"""
Text-only NLU recipe. This recipes takes the golden ASR
transcriptions and tries to estimate the semantics on
the top of that.
Authors
* Loren Lugosch, Mirco Ravanelli 2020
"""
import sys
import torch
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils... | 12,701 | 33.895604 | 96 | py |
speechbrain | speechbrain-main/recipes/SLURP/direct/train_with_wav2vec2.py | #!/usr/bin/env/python3
"""
Recipe for "direct" (speech -> semantics) SLU.
We encode input waveforms into features using the wav2vec2/HuBert model,
then feed the features into a seq2seq model to map them to semantics.
(Adapted from the LibriSpeech seq2seq ASR recipe written by Ju-Chieh Chou, Mirco Ravanelli, Abdel Heba,... | 13,958 | 35.163212 | 125 | py |
speechbrain | speechbrain-main/recipes/SLURP/direct/train.py | #!/usr/bin/env/python3
"""
Recipe for "direct" (speech -> semantics) SLU with ASR-based transfer learning.
We encode input waveforms into features using a model trained on LibriSpeech,
then feed the features into a seq2seq model to map them to semantics.
(Adapted from the LibriSpeech seq2seq ASR recipe written by Ju-... | 13,228 | 35.144809 | 125 | py |
speechbrain | speechbrain-main/recipes/IEMOCAP/emotion_recognition/train_with_wav2vec2.py | #!/usr/bin/env python3
"""Recipe for training an emotion recognition system from speech data only using IEMOCAP.
The system classifies 4 emotions ( anger, happiness, sadness, neutrality) with wav2vec2.
To run this recipe, do the following:
> python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml --data_folder ... | 10,890 | 35.182724 | 108 | py |
speechbrain | speechbrain-main/recipes/IEMOCAP/emotion_recognition/train.py | #!/usr/bin/env python3
"""Recipe for training an emotion recognition system from speech data only using IEMOCAP.
The system classifies 4 emotions ( anger, happiness, sadness, neutrality)
with an ECAPA-TDNN model.
To run this recipe, do the following:
> python train.py hparams/train.yaml --data_folder /path/to/IEMOCAP... | 13,084 | 34.080429 | 89 | py |
speechbrain | speechbrain-main/recipes/LibriMix/separation/dynamic_mixing.py | import speechbrain as sb
import numpy as np
import torch
import torchaudio
import glob
import os
from speechbrain.dataio.batch import PaddedBatch
from tqdm import tqdm
import warnings
import pyloudnorm
import random
"""
The functions to implement Dynamic Mixing For SpeechSeparation
Authors
* Samuele Cornell 2021
... | 7,257 | 30.284483 | 93 | py |
speechbrain | speechbrain-main/recipes/LibriMix/separation/train.py | #!/usr/bin/env/python3
"""Recipe for training a neural speech separation system on Libri2/3Mix datasets.
The system employs an encoder, a decoder, and a masking network.
To run this recipe, do the following:
> python train.py hparams/sepformer-libri2mix.yaml
> python train.py hparams/sepformer-libri3mix.yaml
The exp... | 25,102 | 35.754026 | 108 | py |
speechbrain | speechbrain-main/recipes/LibriMix/meta/preprocess_dynamic_mixing.py | """
This script allows to resample a folder which contains audio files.
The files are parsed recursively. An exact copy of the folder is created,
with same structure but contained resampled audio files.
Resampling is performed by using sox through torchaudio.
Author
------
Samuele Cornell, 2020
"""
import os
import ar... | 2,732 | 27.175258 | 80 | py |
speechbrain | speechbrain-main/recipes/ESC50/esc50_prepare.py | """
Creates data manifest files for ESC50
If the data does not exist in the specified --data_folder, we download the data automatically.
https://urbansounddataset.weebly.com/urbansound8k.htm://github.com/karolpiczak/ESC-50
Authors:
* Cem Subakan 2022, 2023
* Francesco Paissan 2022, 2023
Adapted from the Urbansoun... | 12,776 | 33.814714 | 160 | py |
speechbrain | speechbrain-main/recipes/ESC50/classification/train_classifier.py | #!/usr/bin/python3
"""Recipe to train a classifier on ESC50 data
We employ an encoder followed by a sound classifier.
To run this recipe, use the following command:
> python train_classifier.py hparams/cnn14.yaml --data_folder yourpath/ESC-50-master
Authors
* Cem Subakan 2022, 2023
* Francesco Paissan 2022, 2... | 15,164 | 35.454327 | 92 | py |
speechbrain | speechbrain-main/recipes/ESC50/interpret/train_l2i.py | #!/usr/bin/python3
"""This recipe to train L2I (https://arxiv.org/abs/2202.11479) to interepret audio classifiers.
Authors
* Cem Subakan 2022, 2023
* Francesco Paissan 2022, 2023
"""
import os
import sys
import torch
import torchaudio
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speec... | 24,425 | 35.026549 | 95 | py |
speechbrain | speechbrain-main/recipes/ESC50/interpret/train_piq.py | #!/usr/bin/python3
"""This recipe to train PIQ to interepret audio classifiers.
Authors
* Cem Subakan 2022, 2023
* Francesco Paissan 2022, 2023
"""
import os
import sys
import torch
import torchaudio
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils.distributed import run... | 26,143 | 33.627815 | 92 | py |
speechbrain | speechbrain-main/recipes/ESC50/interpret/train_nmf.py | #!/usr/bin/python3
"""The recipe to train an NMF model with amortized inference on ESC50 data.
To run this recipe, use the following command:
> python train_nmf.py hparams/nmf.yaml --data_folder /yourpath/ESC-50-master
Authors
* Cem Subakan 2022, 2023
* Francesco Paissan 2022, 2023
"""
import sys
import tor... | 4,783 | 32.222222 | 86 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/ASR/seq2seq/train.py | #!/usr/bin/env/python3
"""
AISHELL-1 seq2seq model recipe. (Adapted from the LibriSpeech recipe.)
"""
import sys
import torch
import logging
import speechbrain as sb
from speechbrain.utils.distributed import run_on_main
from hyperpyyaml import load_hyperpyyaml
logger = logging.getLogger(__name__)
# Define trainin... | 13,009 | 34.162162 | 89 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/ASR/CTC/train_with_wav2vec.py | #!/usr/bin/env/python3
"""AISHELL-1 CTC recipe.
The system employs a wav2vec2 encoder and a CTC decoder.
Decoding is performed with greedy decoding.
To run this recipe, do the following:
> python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml
With the default hyperparameters, the system employs a pretrained ... | 13,335 | 33.282776 | 89 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/ASR/transformer/train_with_wav2vect.py | #!/usr/bin/env/python3
"""
AISHELL-1 transformer model recipe. (Adapted from the LibriSpeech recipe.).
It is designed to work with wav2vec2 pre-training.
"""
import sys
import torch
import logging
import speechbrain as sb
from speechbrain.utils.distributed import run_on_main
from hyperpyyaml import load_hyperpyyaml
... | 17,879 | 35.341463 | 94 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/ASR/transformer/train.py | #!/usr/bin/env/python3
"""
AISHELL-1 transformer model recipe. (Adapted from the LibriSpeech recipe.)
"""
import sys
import torch
import logging
import speechbrain as sb
from speechbrain.utils.distributed import run_on_main
from hyperpyyaml import load_hyperpyyaml
logger = logging.getLogger(__name__)
# Define tra... | 17,133 | 35.147679 | 94 | py |
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