repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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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/g2p/__init__.py | from . import dataio # noqa
from . import homograph # noqa
from . import model # noqa
from .dataio import * # noqa
| 120 | 19.166667 | 31 | 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/__init__.py | """High level processing blocks.
This subpackage gathers higher level blocks, or "lobes".
The classes here may leverage the extended YAML syntax.
"""
| 151 | 24.333333 | 56 | 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/custom_model.py | ../speaker_id/custom_model.py | 29 | 29 | 29 | py |
speechbrain | speechbrain-main/templates/hyperparameter_optimization_speaker_id/mini_librispeech_prepare.py | ../speaker_id/mini_librispeech_prepare.py | 41 | 41 | 41 | 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/mini_librispeech_prepare.py | """
Downloads and creates manifest files for speech recognition with Mini LibriSpeech.
Authors:
* Peter Plantinga, 2021
* Mirco Ravanelli, 2021
"""
import os
import json
import shutil
import logging
from speechbrain.utils.data_utils import get_all_files, download_file
from speechbrain.dataio.dataio import read_audi... | 6,041 | 31.138298 | 86 | 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/Tokenizer/mini_librispeech_prepare.py | ../mini_librispeech_prepare.py | 30 | 30 | 30 | py |
speechbrain | speechbrain-main/templates/speech_recognition/Tokenizer/train.py | #!/usr/bin/env/python3
"""Script for training a BPE tokenizer on the top of CSV or JSON annotation files.
The tokenizer converts words into sub-word units that can be used to train a
language (LM) or an acoustic model (AM).
When doing a speech recognition experiment you have to make
sure that the acoustic and language ... | 1,593 | 31.530612 | 82 | py |
speechbrain | speechbrain-main/templates/speech_recognition/ASR/mini_librispeech_prepare.py | ../mini_librispeech_prepare.py | 30 | 30 | 30 | 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/mini_librispeech_prepare.py | """
Downloads and creates data manifest files for Mini LibriSpeech (spk-id).
For speaker-id, different sentences of the same speaker must appear in train,
validation, and test sets. In this case, these sets are thus derived from
splitting the original training set intothree chunks.
Authors:
* Mirco Ravanelli, 2021
""... | 6,304 | 30.525 | 86 | 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/mini_librispeech_prepare.py | """
Downloads and creates manifest files for Mini LibriSpeech.
Noise is automatically added to samples, managed by the EnvCorrupt class.
Authors:
* Peter Plantinga, 2020
"""
import os
import json
import shutil
import logging
from speechbrain.utils.data_utils import get_all_files, download_file
from speechbrain.datai... | 4,917 | 32.006711 | 86 | 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/prepare_data.py | """
The .csv preperation functions for Binaural-WSJ0Mix.
Author
* Cem Subakan 2020
* Zijian 2022
"""
import os
import csv
def prepare_binaural_wsj0mix(
experiment_name,
datapath,
savepath,
n_spks=2,
skip_prep=False,
fs=8000,
version="min",
):
"""
Prepared binaural wsj2mix if ... | 11,651 | 28.800512 | 110 | 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/prepare_data.py | ../prepare_data.py | 18 | 18 | 18 | 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/convert_to_wav.py | import argparse
import multiprocessing as mp
import wave
from pathlib import Path
from tqdm import tqdm
def convert_to_wav(filepath):
"""
This function converts pcm files to wav files
Arguments
---------
filepath : str
path to the pcm file
Returns
-------
None
"""
w... | 1,034 | 20.122449 | 70 | 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/ksponspeech_prepare.py | ../ksponspeech_prepare.py | 25 | 25 | 25 | 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/Tokenizer/ksponspeech_prepare.py | ../ksponspeech_prepare.py | 25 | 25 | 25 | py |
speechbrain | speechbrain-main/recipes/KsponSpeech/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with ksponspeech.
The tokenizer converts words into sub-word units that can
be used to train a language (LM) or an acoustic model (AM).
When doing a speech recognition experiment you have to make
sure that the acoustic and language models are trained with
th... | 1,966 | 30.725806 | 72 | py |
speechbrain | speechbrain-main/recipes/KsponSpeech/ASR/transformer/ksponspeech_prepare.py | ../../ksponspeech_prepare.py | 28 | 28 | 28 | 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/prepare.py | import os
import shutil
import logging
from speechbrain.dataio.dataio import read_audio, merge_csvs
from speechbrain.utils.data_utils import download_file
try:
import pandas as pd
except ImportError:
err_msg = (
"The optional dependency pandas must be installed to run this recipe.\n"
)
err_msg ... | 7,072 | 36.226316 | 120 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/LM/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/Tokenizer/prepare.py | ../prepare.py | 13 | 13 | 13 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with timers-and-such.
The tokenizer coverts semantics into sub-word units that can
be used to train a language (LM) or an acoustic model (AM).
To run this recipe, do the following:
> python train.py hparams/tokenizer_bpe51.yaml
Authors
* Abdel Heba 2021
... | 1,549 | 26.678571 | 72 | py |
speechbrain | speechbrain-main/recipes/timers-and-such/decoupled/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/prepare.py | import os
import jsonlines
from speechbrain.dataio.dataio import read_audio, merge_csvs
from speechbrain.utils.data_utils import download_file
import shutil
try:
import pandas as pd
except ImportError:
err_msg = (
"The optional dependency pandas must be installed to run this recipe.\n"
)
err_ms... | 6,258 | 35.389535 | 120 | py |
speechbrain | speechbrain-main/recipes/SLURP/Tokenizer/prepare.py | ../prepare.py | 13 | 13 | 13 | py |
speechbrain | speechbrain-main/recipes/SLURP/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with SLURP.
The tokenizer coverts semantics into sub-word units that can
be used to train a language (LM) or an acoustic model (AM).
To run this recipe, do the following:
> python train.py hyperparams/tokenizer_bpe51.yaml
Authors
* Abdel Heba 2021
* Mir... | 1,501 | 26.309091 | 72 | py |
speechbrain | speechbrain-main/recipes/SLURP/NLU/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/IEMOCAP/emotion_recognition/iemocap_prepare.py | """
Downloads and creates data manifest files for IEMOCAP
(https://paperswithcode.com/dataset/iemocap).
Authors:
* Mirco Ravanelli, 2021
* Modified by Pierre-Yves Yanni, 2021
* Abdel Heba, 2021
* Yingzhi Wang, 2022
"""
import os
import sys
import re
import json
import random
import logging
from speechbrain.dataio... | 10,788 | 30.363372 | 148 | py |
speechbrain | speechbrain-main/recipes/LibriMix/prepare_data.py | """
The functions to create the .csv files for LibriMix
Author
* Cem Subakan 2020
"""
import os
import csv
def prepare_librimix(
datapath,
savepath,
n_spks=2,
skip_prep=False,
librimix_addnoise=False,
fs=8000,
):
"""
Prepare .csv files for librimix
Arguments:
----------
... | 6,470 | 29.814286 | 80 | 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/prepare_data.py | ../prepare_data.py | 18 | 18 | 18 | 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/classification/confusion_matrix_fig.py | #!/usr/bin/env python3
"""Helper to create Confusion Matrix figure
Authors
* David Whipps 2021
* Ala Eddine Limame 2021
"""
import numpy as np
import matplotlib.pyplot as plt
import itertools
def create_cm_fig(cm, display_labels):
"""Creates confusion matrix plot.
Arguments
---------
cm : np.ndar... | 1,622 | 24.359375 | 77 | py |
speechbrain | speechbrain-main/recipes/ESC50/classification/esc50_prepare.py | ../esc50_prepare.py | 19 | 19 | 19 | 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/esc50_prepare.py | ../esc50_prepare.py | 19 | 19 | 19 | 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/aishell_prepare.py | import os
import shutil
import logging
from speechbrain.dataio.dataio import read_audio
from speechbrain.utils.data_utils import download_file
import glob
import csv
logger = logging.getLogger(__name__)
def prepare_aishell(data_folder, save_folder, skip_prep=False):
"""
This function prepares the AISHELL-1 d... | 3,368 | 31.708738 | 120 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/Tokenizer/aishell_prepare.py | ../aishell_prepare.py | 21 | 21 | 21 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/Tokenizer/pretrained.py | """
Pre-trained Tokenizer for inference.
Authors
* Mirco Ravanelli 2020
* Abdel Heba 2020
"""
import os
from speechbrain.utils.data_utils import download_file
import sentencepiece as spm
class tokenizer:
"""Downloads and loads the pretrained tokenizer.
Arguments
---------
tokenizer_file : str
... | 934 | 23.605263 | 73 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with AISHELL-1.
The tokenizer coverts transcripts into sub-word units that can
be used to train a language (LM) or an acoustic model (AM).
To run this recipe, do the following:
> python train.py hparams/tokenizer_bpe5000.yaml
Authors
* Abdel Heba 2021
*... | 1,480 | 26.425926 | 72 | py |
speechbrain | speechbrain-main/recipes/AISHELL-1/ASR/seq2seq/aishell_prepare.py | ../../aishell_prepare.py | 24 | 24 | 24 | 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/aishell_prepare.py | ../../aishell_prepare.py | 24 | 24 | 24 | 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/aishell_prepare.py | ../../aishell_prepare.py | 24 | 24 | 24 | 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 |
speechbrain | speechbrain-main/recipes/LJSpeech/TTS/ljspeech_prepare.py | """
LJspeech data preparation.
Download: https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
Authors
* Yingzhi WANG 2022
"""
import os
import csv
import json
import logging
import random
from speechbrain.dataio.dataio import (
load_pkl,
save_pkl,
)
logger = logging.getLogger(__name__)
OPT_FILE = "opt... | 7,584 | 28.285714 | 80 | py |
speechbrain | speechbrain-main/recipes/LJSpeech/TTS/tacotron2/ljspeech_prepare.py | ../ljspeech_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/LJSpeech/TTS/tacotron2/train.py | # -*- coding: utf-8 -*-
"""
Recipe for training the Tacotron Text-To-Speech model, an end-to-end
neural text-to-speech (TTS) system
To run this recipe, do the following:
# python train.py --device=cuda:0 --max_grad_norm=1.0 --data_folder=/your_folder/LJSpeech-1.1 hparams/train.yaml
to infer simply load saved mod... | 13,447 | 32.53616 | 113 | py |
speechbrain | speechbrain-main/recipes/LJSpeech/TTS/vocoder/hifi_gan/ljspeech_prepare.py | ../../ljspeech_prepare.py | 25 | 25 | 25 | py |
speechbrain | speechbrain-main/recipes/LJSpeech/TTS/vocoder/hifi_gan/train.py | #!/usr/bin/env python3
"""Recipe for training a hifi-gan vocoder.
For more details about hifi-gan: https://arxiv.org/pdf/2010.05646.pdf
To run this recipe, do the following:
> python train.py hparams/train.yaml --data_folder /path/to/LJspeech
Authors
* Duret Jarod 2021
* Yingzhi WANG 2022
"""
import sys
import tor... | 14,460 | 34.618227 | 90 | py |
speechbrain | speechbrain-main/recipes/Fisher-Callhome-Spanish/fisher_callhome_prepare.py | """
Data preparation
Author
-----
YAO-FEI, CHENG 2021
"""
import os
import re
import json
import string
import logging
import subprocess
from typing import List
from dataclasses import dataclass, field
import torch
import torchaudio
from tqdm import tqdm
from speechbrain.utils.data_utils import get_all_files
from ... | 25,525 | 33.682065 | 114 | py |
speechbrain | speechbrain-main/recipes/Fisher-Callhome-Spanish/Tokenizer/fisher_callhome_prepare.py | ../fisher_callhome_prepare.py | 29 | 29 | 29 | py |
speechbrain | speechbrain-main/recipes/Fisher-Callhome-Spanish/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with librispeech.
The tokenizer coverts words into sub-word units that can
be used to train a language (LM) or an acoustic model (AM).
When doing a speech recognition experiment you have to make
sure that the acoustic and language models are trained with
the... | 1,495 | 31.521739 | 72 | py |
speechbrain | speechbrain-main/recipes/Fisher-Callhome-Spanish/ST/transformer/train.py | #!/usr/bin/env/python3
"""Recipe for training a Transformer based ST system with Fisher-Callhome.
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 hpara... | 23,243 | 35.778481 | 91 | py |
speechbrain | speechbrain-main/recipes/UrbanSound8k/urbansound8k_prepare.py | """
Creates data manifest files from UrbanSound8k, suitable for use in SpeechBrain.
https://urbansounddataset.weebly.com/urbansound8k.html
From the authors of UrbanSound8k:
1. Don't reshuffle the data! Use the predefined 10 folds and perform 10-fold (not 5-fold) cross validation
The experiments conducted by vast maj... | 14,774 | 37.476563 | 122 | py |
speechbrain | speechbrain-main/recipes/UrbanSound8k/SoundClassification/urbansound8k_prepare.py | ../urbansound8k_prepare.py | 26 | 26 | 26 | py |
speechbrain | speechbrain-main/recipes/UrbanSound8k/SoundClassification/confusion_matrix_fig.py | #!/usr/bin/env python3
"""Helper to create Confusion Matrix figure
Authors
* David Whipps 2021
* Ala Eddine Limame 2021
"""
import numpy as np
import matplotlib.pyplot as plt
import itertools
def create_cm_fig(cm, display_labels):
fig = plt.figure(figsize=cm.shape, dpi=50, facecolor="w", edgecolor="k")
a... | 1,359 | 26.2 | 77 | py |
speechbrain | speechbrain-main/recipes/UrbanSound8k/SoundClassification/custom_model.py | """
This file contains a very simple TDNN module to use for sound class identification.
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
* David Whipps 2021
* Ala Eddine Limame 2021
Adapted... | 5,731 | 29.489362 | 83 | py |
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