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/recipes/UrbanSound8k/SoundClassification/train.py | #!/usr/bin/python3
"""Recipe for training sound class embeddings (e.g, xvectors) using the UrbanSound8k.
We employ an encoder followed by a sound classifier.
To run this recipe, use the following command:
> python train_class_embeddings.py {hyperparameter_file}
Using your own hyperparameter file or one of the followi... | 16,914 | 36.175824 | 115 | py |
speechbrain | speechbrain-main/recipes/CommonLanguage/common_language_prepare.py | """
Data preparation of CommonLangauge dataset for LID.
Download: https://zenodo.org/record/5036977#.YNo1mHVKg5k
Author
------
Pavlo Ruban 2021
"""
import os
import csv
import logging
import torchaudio
from tqdm.contrib import tzip
from speechbrain.utils.data_utils import get_all_files
logger = logging.getLogger(__... | 7,969 | 24.876623 | 94 | py |
speechbrain | speechbrain-main/recipes/CommonLanguage/lang_id/common_language_prepare.py | ../common_language_prepare.py | 29 | 29 | 29 | py |
speechbrain | speechbrain-main/recipes/CommonLanguage/lang_id/train.py | #!/usr/bin/env python3
import os
import sys
import torch
import logging
import torchaudio
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from common_language_prepare import prepare_common_language
"""Recipe for training a LID system with CommonLanguage.
To run this recipe, do the following:
> pytho... | 10,881 | 33.328076 | 80 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/prepare_data.py | """
The functions to create the .csv files for Aishell1Mix
Author
* Cem Subakan 2020
"""
import os
import csv
import tarfile
import zipfile
import glob
import tqdm.contrib.concurrent
import soundfile as sf
import functools
from pysndfx import AudioEffectsChain
from urllib.request import urlretrieve
def prepare_ais... | 12,102 | 30.600522 | 80 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/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
... | 7,142 | 30.46696 | 93 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/separation/prepare_data.py | ../prepare_data.py | 18 | 18 | 18 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/separation/train.py | #!/usr/bin/env/python3
"""Recipe for training a neural speech separation system on Aishell1Mix2/3 datasets.
The system employs an encoder, a decoder, and a masking network.
To run this recipe, do the following:
> python train.py hparams/sepformer-aishell1mix2.yaml
> python train.py hparams/sepformer-aishell1mix3.yaml
... | 25,295 | 36.090909 | 108 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/separation/scripts/create_aishell1mix_metadata.py | import argparse
import os
import random
import warnings
import numpy as np
import pandas as pd
import pyloudnorm as pyln
import soundfile as sf
from tqdm import tqdm
# Global parameters
# eps secures log and division
EPS = 1e-10
# max amplitude in sources and mixtures
MAX_AMP = 0.9
# In aishell1 all the sources are a... | 17,357 | 35.012448 | 80 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/separation/scripts/create_aishell1mix_from_metadata.py | import os
import argparse
import soundfile as sf
import pandas as pd
import numpy as np
import functools
from scipy.signal import resample_poly
import tqdm.contrib.concurrent
import glob
import shutil
# eps secures log and division
EPS = 1e-10
# Rate of the sources in aishell1
RATE = 16000
parser = argparse.ArgumentP... | 16,794 | 30.392523 | 95 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/separation/scripts/create_aishell1_metadata.py | import os
import argparse
import soundfile as sf
import pandas as pd
import glob
from tqdm import tqdm
# Global parameter
# We will filter out files shorter than that
NUMBER_OF_SECONDS = 3
# In aishell1 all the sources are at 16K Hz
RATE = 16000
# Command line arguments
parser = argparse.ArgumentParser()
parser.add_a... | 5,508 | 32.797546 | 79 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/separation/scripts/create_wham_metadata.py | import os
import argparse
import soundfile as sf
import pandas as pd
import glob
from tqdm import tqdm
# Global parameter
# We will filter out files shorter than that
NUMBER_OF_SECONDS = 3
# In WHAM! all the sources are at 16K Hz
RATE = 16000
# Command line arguments
parser = argparse.ArgumentParser()
parser.add_argu... | 3,893 | 33.460177 | 79 | py |
speechbrain | speechbrain-main/recipes/Aishell1Mix/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... | 2,781 | 27.10101 | 80 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/librispeech_prepare.py | """
Data preparation.
Download: http://www.openslr.org/12
Author
------
Mirco Ravanelli, Ju-Chieh Chou, Loren Lugosch 2020
"""
import os
import csv
import random
from collections import Counter
import logging
import torchaudio
from tqdm.contrib import tzip
from speechbrain.utils.data_utils import download_file, get_... | 12,515 | 27.905312 | 87 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/LM/librispeech_prepare.py | ../librispeech_prepare.py | 25 | 25 | 25 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/LM/dataset.py | # coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 3,836 | 32.365217 | 138 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/LM/train.py | #!/usr/bin/env python3
"""Recipe for training a Language Model with librispeech train-960 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-Chieh Cho... | 7,083 | 31.645161 | 94 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/G2P/evaluate.py | """Recipe for evaluating a grapheme-to-phoneme system with librispeech lexicon.
The script may be use in isolation or in combination with Orion to fit
hyperparameters that do not require model retraining (e.g. Beam Search)
"""
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.batch import PaddedBatch
f... | 14,699 | 34.083532 | 88 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/G2P/librispeech_prepare.py | ../librispeech_prepare.py | 25 | 25 | 25 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/G2P/train_lm.py | #!/usr/bin/env python3
"""Recipe for training a language model with a phoneme model.
> 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
* Artem Ploujnikov 2021
"""
im... | 7,998 | 30.996 | 80 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/G2P/train.py | #!/usr/bin/env/python3
"""Recipe for training a grapheme-to-phoneme system with one of the available datasets.
See README.md for more details
Authors
* Loren Lugosch 2020
* Mirco Ravanelli 2020
* Artem Ploujnikov 2021
"""
from speechbrain.dataio.dataset import (
FilteredSortedDynamicItemDataset,
DynamicIte... | 43,545 | 32.887938 | 87 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/G2P/tokenizer_prepare.py | """A script to prepare annotations for tokenizers
"""
import json
import os
import re
import datasets
from speechbrain.lobes.models.g2p.dataio import build_token_char_map
MULTI_SPACE = re.compile(r"\s{2,}")
def phn2txt(phn, phoneme_map):
"""Encodes phonemes using a character map for use with SentencePiece
... | 2,512 | 26.315217 | 77 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/self-supervised-learning/wav2vec2/librispeech_prepare.py | ../../librispeech_prepare.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/self-supervised-learning/wav2vec2/train_sb_wav2vec2.py | #!/usr/bin/env python3
"""Recipe for pretraining wav2vec2 (https://arxiv.org/abs/2006.11477).
See config file for model definition.
See the readme of the recipe for advices on the pretraining that may appear
a bit challenging depending on your available resources.
To run this recipe call python train.py hparams/train_... | 13,411 | 33.836364 | 111 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/Tokenizer/librispeech_prepare.py | ../librispeech_prepare.py | 25 | 25 | 25 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with librispeech.
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... | 1,917 | 30.442623 | 72 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/transducer/librispeech_prepare.py | ../../librispeech_prepare.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/transducer/train.py | #!/usr/bin/env/python3
"""Recipe for training a Transducer ASR system with librispeech.
The system employs an encoder, a decoder, and an joint network
between them. Decoding is performed with beamsearch coupled with a neural
language model.
To run this recipe, do the following:
> python train.py hparams/train.yaml
Wi... | 16,705 | 38.124122 | 89 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/seq2seq/librispeech_prepare.py | ../../librispeech_prepare.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/seq2seq/train.py | #!/usr/bin/env/python3
"""Recipe for training a sequence-to-sequence ASR system with librispeech.
The system employs an encoder, a decoder, and an attention mechanism
between them. Decoding is performed with beamsearch coupled with a neural
language model.
To run this recipe, do the following:
> python train.py hparam... | 15,618 | 35.92435 | 89 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/CTC/librispeech_prepare.py | ../../librispeech_prepare.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/CTC/train_with_wav2vec.py | #!/usr/bin/env/python3
"""Recipe for training a wav2vec-based ctc ASR system with librispeech.
The system employs wav2vec as its encoder. Decoding is performed with
ctc greedy decoder.
To run this recipe, do the following:
> python train_with_wav2vec.py hparams/train_{hf,sb}_wav2vec.yaml
The neural network is trained o... | 16,312 | 36.415138 | 89 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/CTC/train_with_whisper.py | #!/usr/bin/env/python3
"""Recipe for training a whisper-based ctc ASR system with librispeech.
The system employs whisper from OpenAI (https://cdn.openai.com/papers/whisper.pdf).
This recipe take only the whisper encoder and add a DNN + CTC to fine-tune.
If you want to use the full whisper system, please refer to the ... | 13,226 | 35.238356 | 89 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/transformer/librispeech_prepare.py | ../../librispeech_prepare.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/transformer/train.py | #!/usr/bin/env python3
"""Recipe for training a Transformer ASR system with librispeech.
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... | 19,105 | 36.171206 | 105 | py |
speechbrain | speechbrain-main/recipes/LibriSpeech/ASR/transformer/train_with_whisper.py | #!/usr/bin/env python3
"""Recipe for training a whisper-based ASR system with librispeech.
The system employs whisper from OpenAI (https://cdn.openai.com/papers/whisper.pdf).
This recipe take the whisper encoder-decoder to fine-tune on the NLL.
If you want to only use the whisper encoder system, please refer to the re... | 11,771 | 34.457831 | 89 | py |
speechbrain | speechbrain-main/recipes/MEDIA/media_prepare.py | """
Data preparation.
Download:
https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/
https://catalogue.elra.info/en-us/repository/browse/ELRA-E0024/
https://drive.google.com/drive/u/1/folders/1z2zFZp3c0NYLFaUhhghhBakGcFdXVRyf
See README.md for more info.
Author
------
Gaelle Laperriere 2023
"""
import xml.... | 36,330 | 28.465531 | 262 | py |
speechbrain | speechbrain-main/recipes/MEDIA/SLU/CTC/media_prepare.py | ../../media_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/MEDIA/SLU/CTC/train_hf_wav2vec.py | #!/usr/bin/env python3
"""
Recipe for training a CTC based SLU system with Media.
The system employs a wav2vec2 model and a decoder.
To run this recipe, do the following:
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
With the default hyperparameters, the system employs a VanillaNN encoder.
The neur... | 13,866 | 34.284987 | 83 | py |
speechbrain | speechbrain-main/recipes/MEDIA/ASR/CTC/media_prepare.py | ../../media_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/MEDIA/ASR/CTC/train_hf_wav2vec.py | #!/usr/bin/env python3
"""
Recipe for training a CTC based ASR system with Media.
The system employs a wav2vec2 model and a decoder.
To run this recipe, do the following:
> python train_with_wav2vec.py hparams/train_with_wav2vec.yaml
With the default hyperparameters, the system employs a VanillaNN encoder.
The neur... | 12,920 | 33.827493 | 83 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/dynamic_mixing_wham.py | ../../WHAMandWHAMR/separation/dynamic_mixing.py | 47 | 47 | 47 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/create_whamr_rirs.py | ../../WHAMandWHAMR/meta/create_whamr_rirs.py | 44 | 44 | 44 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/train_wham.py | ../../WHAMandWHAMR/separation/train.py | 38 | 38 | 38 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/preprocess_dynamic_mixing_librimix.py | ../../LibriMix/meta/preprocess_dynamic_mixing.py | 48 | 48 | 48 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/dynamic_mixing_librimix.py | ../../LibriMix/separation/dynamic_mixing.py | 43 | 43 | 43 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/preprocess_dynamic_mixing_wham.py | ../../WHAMandWHAMR/meta/preprocess_dynamic_mixing.py | 52 | 52 | 52 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/prepare_data_librimix.py | ../../LibriMix/prepare_data.py | 30 | 30 | 30 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/prepare_data_wham.py | ../../WHAMandWHAMR/prepare_data.py | 34 | 34 | 34 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/wham_room.py | ../../WHAMandWHAMR/meta/wham_room.py | 36 | 36 | 36 | py |
speechbrain | speechbrain-main/recipes/REAL-M/sisnr-estimation/train.py | #!/usr/bin/env/python3
"""
Recipe for training a Blind SI-SNR estimator
Authors:
* Cem Subakan 2021
* Mirco Ravanelli 2021
* Samuele Cornell 2021
"""
import os
import sys
import torch
import speechbrain as sb
import speechbrain.nnet.schedulers as schedulers
from speechbrain.utils.distributed import run_on_main
fro... | 29,744 | 35.631773 | 152 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/prepare_data.py | """
Author
* Cem Subakan 2020
The .csv preperation functions for WSJ0-Mix.
"""
import os
import csv
def prepare_wham_whamr_csv(
datapath, savepath, skip_prep=False, fs=8000, task="separation"
):
"""
Prepares the csv files for wham or whamr dataset
Arguments:
----------
datapath (str) :... | 5,989 | 28.653465 | 98 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/separation/create_whamr_rirs.py | ../meta/create_whamr_rirs.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/separation/dynamic_mixing.py | import speechbrain as sb
import numpy as np
import torch
import torchaudio
import glob
import os
from pathlib import Path
import random
from speechbrain.processing.signal_processing import rescale
from speechbrain.dataio.batch import PaddedBatch
"""
The functions to implement Dynamic Mixing For SpeechSeparation
Autho... | 7,212 | 30.915929 | 107 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/separation/prepare_data.py | ../prepare_data.py | 18 | 18 | 18 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/separation/wham_room.py | ../meta/wham_room.py | 20 | 20 | 20 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/separation/train.py | #!/usr/bin/env/python3
"""Recipe for training a neural speech separation system on WHAM! and WHAMR!
datasets. The system employs an encoder, a decoder, and a masking network.
To run this recipe, do the following:
> python train.py hparams/sepformer-wham.yaml --data_folder /your_path/wham_original
> python train.py hpa... | 25,297 | 36.039531 | 108 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/meta/create_whamr_rirs.py | """
Adapted from the original WHAMR script to obtain the Room Impulse ResponsesRoom Impulse Responses
Authors
* Cem Subakan 2021
"""
import os
import pandas as pd
import argparse
import torchaudio
from wham_room import WhamRoom
from scipy.signal import resample_poly
import torch
from speechbrain.pretrained.fetchi... | 4,052 | 27.542254 | 97 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/meta/rir_constants.py | NUM_BANDS = 4
SNR_THRESH = -6.0
PRE_NOISE_SECONDS = 2.0
SAMPLERATE = 16000
MAX_SAMPLE_AMP = 0.95
MIN_SNR_DB = -3.0
MAX_SNR_DB = 6.0
PRE_NOISE_SAMPLES = PRE_NOISE_SECONDS * SAMPLERATE
| 183 | 19.444444 | 50 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/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/WHAMandWHAMR/meta/wham_room.py | import numpy as np
import pyroomacoustics as pra
from pyroomacoustics.parameters import constants
from scipy.signal import resample_poly
class WhamRoom(pra.room.ShoeBox):
"""
This class is taken from the original WHAMR! scripts.
The original script can be found in
http://wham.whisper.ai/
This cla... | 3,202 | 26.612069 | 90 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/enhancement/create_whamr_rirs.py | ../meta/create_whamr_rirs.py | 28 | 28 | 28 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/enhancement/dynamic_mixing.py | import speechbrain as sb
import numpy as np
import torch
import torchaudio
import glob
import os
from pathlib import Path
import random
from speechbrain.processing.signal_processing import rescale
from speechbrain.dataio.batch import PaddedBatch
"""
The functions to implement Dynamic Mixing For SpeechSeparation
Autho... | 7,223 | 30.964602 | 107 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/enhancement/prepare_data.py | ../prepare_data.py | 18 | 18 | 18 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/enhancement/preprocess_dynamic_mixing.py | ../meta/preprocess_dynamic_mixing.py | 36 | 36 | 36 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/enhancement/wham_room.py | ../meta/wham_room.py | 20 | 20 | 20 | py |
speechbrain | speechbrain-main/recipes/WHAMandWHAMR/enhancement/train.py | #!/usr/bin/env/python3
"""Recipe for training a neural speech separation system on WHAM! and WHAMR!
datasets. The system employs an encoder, a decoder, and a masking network.
To run this recipe, do the following:
> python train.py hparams/sepformer-wham.yaml --data_folder /your_path/wham_original
> python train.py hpa... | 29,072 | 36.465206 | 108 | py |
speechbrain | speechbrain-main/recipes/LibriParty/VAD/commonlanguage_prepare.py | import os
import logging
import torchaudio
import speechbrain as sb
from speechbrain.utils.data_utils import get_all_files
logger = logging.getLogger(__name__)
COMMON_LANGUAGE_URL = (
"https://zenodo.org/record/5036977/files/CommonLanguage.tar.gz?download=1"
)
def prepare_commonlanguage(folder, csv_file, max_no... | 3,586 | 36.757895 | 78 | py |
speechbrain | speechbrain-main/recipes/LibriParty/VAD/data_augment.py | """This library is used to create data on-the-fly for VAD.
Authors
* Mirco Ravanelli 2020
"""
import torch
import torchaudio
import random
# fade-in/fade-out definition
fade_in = torchaudio.transforms.Fade(fade_in_len=1000, fade_out_len=0)
fade_out = torchaudio.transforms.Fade(fade_in_len=0, fade_out_len=1000)
de... | 13,096 | 29.45814 | 83 | py |
speechbrain | speechbrain-main/recipes/LibriParty/VAD/musan_prepare.py | import os
import logging
import torchaudio
import speechbrain as sb
from speechbrain.utils.data_utils import get_all_files
logger = logging.getLogger(__name__)
def prepare_musan(folder, music_csv, noise_csv, speech_csv, max_noise_len=None):
"""Prepare the musan dataset (music, noise, speech).
Arguments
... | 3,700 | 37.154639 | 80 | py |
speechbrain | speechbrain-main/recipes/LibriParty/VAD/train.py | #!/usr/bin/env python3
"""
Recipe for training a Voice Activity Detection (VAD) model on LibriParty.
This code heavily relis on data augmentation with external datasets.
(e.g, open_rir, musan, CommonLanguge is used as well).
Make sure you download all the datasets before staring the experiment:
- LibriParty: https://d... | 9,259 | 30.931034 | 96 | py |
speechbrain | speechbrain-main/recipes/LibriParty/VAD/libriparty_prepare.py | """ This script prepares the data-manifest files (in JSON format)
for training and testing a Voice Activity Detection system with the
LibriParty dataset.
The dataset contains sequences of 1-minutes of LibiSpeech sentences
corrupted by noise and reverberation. The dataset can be downloaded
from here:
https://drive.goo... | 9,595 | 30.155844 | 82 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/get_dataset_from_metadata.py | """
LibriParty Dataset creation by using official metadata.
Author
------
Samuele Cornell, 2020
Mirco Ravanelli, 2020
"""
import os
import sys
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils.data_utils import download_file
from local.create_mixtures_from_metadata import create... | 1,357 | 27.291667 | 77 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/download_required_data.py | """
Source datasets downloading script for LibriParty.
Author
------
Samuele Cornell, 2020
"""
import argparse
import os
from speechbrain.utils.data_utils import download_file
from local.resample_folder import resample_folder
LIBRISPEECH_URLS = [
"http://www.openslr.org/resources/12/test-clean.tar.gz",
"http... | 3,072 | 36.47561 | 168 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/create_custom_dataset.py | """
Custom LibriParty creation script with user specified parameters.
Author
------
Samuele Cornell, 2020
"""
import os
import sys
import json
import random
import numpy as np
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils.data_utils import get_all_files
from local.create_mi... | 3,971 | 30.03125 | 79 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/local/resample_folder.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 a... | 2,091 | 29.318841 | 79 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/local/create_mixtures_metadata.py | """
This file contains functions to create json metadata used to create
mixtures which simulate a multi-party conversation in a noisy scenario.
Author
------
Samuele Cornell, 2020
"""
import numpy as np
from pathlib import Path
import json
import os
from tqdm import tqdm
import torchaudio
def _read_metadata(file_p... | 7,619 | 35.285714 | 80 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/local/create_mixtures_from_metadata.py | """
This file contains functions to create mixtures given json metadata.
The mixtures simulate a multi-party conversation in a noisy scenario.
Author
------
Samuele Cornell, 2020
"""
import os
import torch
import json
import numpy as np
import torchaudio
from speechbrain.processing.signal_processing import rescale, ... | 6,540 | 33.246073 | 80 | py |
speechbrain | speechbrain-main/recipes/LibriParty/generate_dataset/local/__init__.py | 0 | 0 | 0 | py | |
speechbrain | speechbrain-main/recipes/WSJ0Mix/prepare_data.py | """
The .csv preperation functions for WSJ0-Mix.
Author
* Cem Subakan 2020
"""
import os
import csv
def prepare_wsjmix(
datapath,
savepath,
n_spks=2,
skip_prep=False,
librimix_addnoise=False,
fs=8000,
):
"""
Prepared wsj2mix if n_spks=2 and wsj3mix if n_spks=3.
Arguments:
... | 7,573 | 30.427386 | 102 | py |
speechbrain | speechbrain-main/recipes/WSJ0Mix/separation/dynamic_mixing.py | import speechbrain as sb
import numpy as np
import torch
import torchaudio
import glob
import os
from pathlib import Path
import random
from speechbrain.processing.signal_processing import rescale
from speechbrain.dataio.batch import PaddedBatch
"""
The functions to implement Dynamic Mixing For SpeechSeparation
Autho... | 7,309 | 32.378995 | 93 | py |
speechbrain | speechbrain-main/recipes/WSJ0Mix/separation/prepare_data.py | ../prepare_data.py | 18 | 18 | 18 | py |
speechbrain | speechbrain-main/recipes/WSJ0Mix/separation/preprocess_dynamic_mixing.py | ../meta/preprocess_dynamic_mixing.py | 36 | 36 | 36 | py |
speechbrain | speechbrain-main/recipes/WSJ0Mix/separation/train.py | #!/usr/bin/env/python3
"""Recipe for training a neural speech separation system on 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/sepformer.yaml
> python train.py hparams/dualpath_rnn.yaml
> python train.py hparams/co... | 23,418 | 35.706897 | 108 | py |
speechbrain | speechbrain-main/recipes/WSJ0Mix/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/VoxCeleb/voxceleb_prepare.py | """
Data preparation.
Download: http://www.robots.ox.ac.uk/~vgg/data/voxceleb/
"""
import os
import csv
import logging
import glob
import random
import shutil
import sys # noqa F401
import numpy as np
import torch
import torchaudio
from tqdm.contrib import tqdm
from speechbrain.dataio.dataio import (
load_pkl,
... | 16,001 | 29.192453 | 103 | py |
speechbrain | speechbrain-main/recipes/VoxCeleb/SpeakerRec/train_speaker_embeddings.py | #!/usr/bin/python3
"""Recipe for training speaker embeddings (e.g, xvectors) using the VoxCeleb Dataset.
We employ an encoder followed by a speaker classifier.
To run this recipe, use the following command:
> python train_speaker_embeddings.py {hyperparameter_file}
Using your own hyperparameter file or one of the fol... | 8,833 | 33.108108 | 85 | py |
speechbrain | speechbrain-main/recipes/VoxCeleb/SpeakerRec/speaker_verification_cosine.py | #!/usr/bin/python3
"""Recipe for training a speaker verification system based on cosine distance.
The cosine distance is computed on the top of pre-trained embeddings.
The pre-trained model is automatically downloaded from the web if not specified.
This recipe is designed to work on a single GPU.
To run this recipe, r... | 9,905 | 33.515679 | 85 | py |
speechbrain | speechbrain-main/recipes/VoxCeleb/SpeakerRec/speaker_verification_plda.py | #!/usr/bin/python3
"""Recipe for training a speaker verification system based on PLDA using the voxceleb dataset.
The system employs a pre-trained model followed by a PLDA transformation.
The pre-trained model is automatically downloaded from the web if not specified.
To run this recipe, run the following command:
... | 12,496 | 32.414439 | 94 | py |
speechbrain | speechbrain-main/recipes/VoxCeleb/SpeakerRec/voxceleb_prepare.py | ../voxceleb_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/TIMIT/timit_prepare.py | """
Data preparation.
Download: https://catalog.ldc.upenn.edu/LDC93S1
Authors
* Mirco Ravanelli 2020
* Elena Rastorgueva 2020
"""
import os
import json
import logging
from speechbrain.utils.data_utils import get_all_files
from speechbrain.dataio.dataio import read_audio
logger = logging.getLogger(__name__)
SAMPLERA... | 15,072 | 26.809963 | 83 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/transducer/train_wav2vec.py | #!/usr/bin/env/python3
"""Recipe for training a phoneme recognizer with
Transducer loss on the TIMIT dataset.
To run this recipe, do the following:
> python train.py hparams/train.yaml --data_folder /path/to/TIMIT
Authors
* Abdel Heba 2020
* Mirco Ravanelli 2020
* Ju-Chieh Chou 2020
"""
import os
import sys
impor... | 12,544 | 32.632708 | 83 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/transducer/timit_prepare.py | ../../timit_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/transducer/train.py | #!/usr/bin/env/python3
"""Recipe for training a phoneme recognizer with
Transducer loss on the TIMIT dataset.
To run this recipe, do the following:
> python train.py hparams/train.yaml --data_folder /path/to/TIMIT
Authors
* Abdel Heba 2020
* Mirco Ravanelli 2020
* Ju-Chieh Chou 2020
"""
import os
import sys
impor... | 10,118 | 33.301695 | 83 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq_knowledge_distillation/train_kd.py | #!/usr/bin/env python3
"""Recipe for doing ASR with phoneme targets and joint seq2seq
and CTC loss on the TIMIT dataset following a knowledge distillation scheme as
reported in " Distilling Knowledge from Ensembles of Acoustic Models for Joint
CTC-Attention End-to-End Speech Recognition", Yan Gao et al.
To run this r... | 18,522 | 34.081439 | 81 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq_knowledge_distillation/timit_prepare.py | ../../timit_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq_knowledge_distillation/save_teachers.py | #!/usr/bin/env python3
"""Recipe for doing ASR with phoneme targets and joint seq2seq
and CTC loss on the TIMIT dataset following a knowledge distillation scheme as
reported in " Distilling Knowledge from Ensembles of Acoustic Models for Joint
CTC-Attention End-to-End Speech Recognition", Yan Gao et al.
To run this r... | 14,025 | 34.329975 | 152 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq_knowledge_distillation/train_teacher.py | #!/usr/bin/env python3
"""Recipe for doing ASR with phoneme targets and joint seq2seq
and CTC loss on the TIMIT dataset following a knowledge distillation scheme as
reported in " Distilling Knowledge from Ensembles of Acoustic Models for Joint
CTC-Attention End-to-End Speech Recognition", Yan Gao et al.
To run this re... | 11,903 | 34.323442 | 80 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq/timit_prepare.py | ../../timit_prepare.py | 22 | 22 | 22 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq/train_with_wav2vec2.py | #!/usr/bin/env python3
"""Recipe for training a phoneme recognizer on TIMIT.
The system relies on an encoder, a decoder, and attention mechanisms between them.
Training is done with NLL. CTC loss is also added on the top of the encoder.
Greedy search is using for validation, while beamsearch is used at test time to
imp... | 13,829 | 33.575 | 83 | py |
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