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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/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/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/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 |
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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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 |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/seq2seq/train.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... | 12,912 | 34.869444 | 83 | py |
speechbrain | speechbrain-main/recipes/TIMIT/ASR/CTC/train.py | #!/usr/bin/env python3
"""Recipe for training a phoneme recognizer on TIMIT.
The system relies on a model trained with CTC.
Greedy search is using for validation, while beamsearch
is used at test time to improve the system performance.
To run this recipe, do the following:
> python train.py hparams/train.yaml --data_f... | 9,074 | 33.637405 | 80 | py |
speechbrain | speechbrain-main/recipes/TIMIT/Alignment/train.py | #!/usr/bin/env python3
"""Recipe for training a HMM-DNN alignment system on the TIMIT dataset.
The system is trained can be trained with Viterbi, forward, or CTC loss.
To run this recipe, do the following:
> python train.py hparams/train.yaml --data_folder /path/to/TIMIT
Authors
* Elena Rastorgueva 2020
* Mirco Rav... | 10,757 | 34.272131 | 83 | py |
speechbrain | speechbrain-main/recipes/fluent-speech-commands/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-... | 12,070 | 33.686782 | 125 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/common_voice_prepare.py | """
Data preparation.
Download: https://voice.mozilla.org/en/datasets
Author
------
Titouan Parcollet
Luca Della Libera 2022
Pooneh Mousavi 2022
"""
import os
import csv
import re
import logging
import torchaudio
import unicodedata
from tqdm.contrib import tzip
logger = logging.getLogger(__name__)
def prepare_commo... | 11,693 | 29.854881 | 149 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/self-supervised-learning/wav2vec2/train_hf_wav2vec2.py | #!/usr/bin/env python3
import sys
import torch
import logging
import speechbrain as sb
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils.distributed import run_on_main
"""Recipe for pretraining a wav2vec 2.0 model on CommonVoice EN. Note that it can be
trained with ANY dataset as long ... | 12,890 | 33.746631 | 84 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/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... | 16,161 | 37.028235 | 89 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/ASR/seq2seq/train_with_wav2vec.py | #!/usr/bin/env python3
import sys
import torch
import logging
import speechbrain as sb
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.tokenizers.SentencePiece import SentencePiece
from speechbrain.utils.data_utils import undo_padding
from speechbrain.utils.distributed import run_on_main
""... | 15,201 | 35.719807 | 89 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/ASR/seq2seq/train.py | #!/usr/bin/env python3
import sys
import torch
import logging
import speechbrain as sb
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.tokenizers.SentencePiece import SentencePiece
from speechbrain.utils.data_utils import undo_padding
from speechbrain.utils.distributed import run_on_main
""... | 12,748 | 36.061047 | 89 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/ASR/CTC/train_with_wav2vec.py | #!/usr/bin/env python3
import sys
import torch
import logging
import speechbrain as sb
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.tokenizers.SentencePiece import SentencePiece
from speechbrain.utils.data_utils import undo_padding
from speechbrain.utils.distributed import run_on_main
""... | 14,594 | 36.51928 | 89 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/ASR/transformer/train.py | #!/usr/bin/env python3
"""Recipe for training a Transformer ASR system with CommonVoice
The system employs an encoder, a decoder, and an attention mechanism
between them. Decoding is performed with (CTC/Att joint) beamsearch.
To run this recipe, do the following:
> python train.py hparams/transformer.yaml
With the de... | 16,735 | 35.863436 | 109 | py |
speechbrain | speechbrain-main/recipes/CommonVoice/ASR/transformer/train_with_whisper.py | #!/usr/bin/env python3
"""Recipe for training a whisper-based ASR system with CommonVoice.
The system employs whisper from OpenAI (https://cdn.openai.com/papers/whisper.pdf).
This recipe take the whisper encoder-decoder to fine-tune on.
To run this recipe, do the following:
> python train_with_whisper.py hparams/train... | 11,891 | 34.60479 | 89 | py |
speechbrain | speechbrain-main/recipes/AMI/Diarization/experiment.py | #!/usr/bin/python3
"""This recipe implements diarization system using deep embedding extraction followed by spectral clustering.
To run this recipe:
> python experiment.py hparams/<your_hyperparams_file.yaml>
e.g., python experiment.py hparams/ecapa_tdnn.yaml
Condition: Oracle VAD (speech regions taken from the grou... | 22,324 | 32.172363 | 135 | py |
speechbrain | speechbrain-main/recipes/Switchboard/LM/train.py | #!/usr/bin/env python3
"""Recipe for training a Language Model on Switchboard and Fisher corpus.
To run this recipe, do the following:
> pip install datasets
> python train.py hparams/<params>.yaml
Authors
* Jianyuan Zhong 2021
* Ju-Chieh Chou 2020
* Dominik Wagner 2022
"""
import sys
import logging
import torch
f... | 7,050 | 32.417062 | 93 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/seq2seq/train.py | #!/usr/bin/env/python3
"""Recipe for training a sequence-to-sequence ASR system with Switchboard.
The system employs an encoder, a decoder, and an attention mechanism
between them. Decoding is performed with beamsearch.
To run this recipe, do the following:
> python train.py hparams/train_BPE1000.yaml
With the defaul... | 17,124 | 34.901468 | 89 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/CTC/train_with_wav2vec.py | #!/usr/bin/env python3
import functools
import os
import sys
from pathlib import Path
import torch
import logging
import speechbrain as sb
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.tokenizers.SentencePiece import SentencePiece
from speechbrain.utils.data_utils import undo_padding
fro... | 15,887 | 34.623318 | 116 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/transformer/train.py | #!/usr/bin/env python3
"""Recipe for training a Transformer ASR system with Switchboard.
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... | 20,245 | 35.677536 | 105 | py |
speechbrain | speechbrain-main/recipes/Google-speech-commands/prepare_GSC.py | """
Data preparation for Google Speech Commands v0.02.
Download: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
Author
------
David Raby-Pepin 2021
"""
import os
from os import walk
import glob
import shutil
import logging
import torch
import re
import hashlib
import copy
import numpy as np
from s... | 11,265 | 30.915014 | 119 | py |
speechbrain | speechbrain-main/recipes/Google-speech-commands/train.py | #!/usr/bin/python3
"""Recipe for training a classifier using the
Google Speech Commands v0.02 Dataset.
To run this recipe, use the following command:
> python train.py {hyperparameter_file}
Using your own hyperparameter file or one of the following:
hyperparams/xvect.yaml (xvector system)
Author
* Mirco Rava... | 10,834 | 31.056213 | 80 | py |
speechbrain | speechbrain-main/recipes/IWSLT22_lowresource/train.py | #!/usr/bin/env python3
"""Recipe for fine-tuning a wav2vec model for the ST task (no transcriptions).
Author
* Marcely Zanon Boito, 2022
"""
import sys
import torch
import logging
import speechbrain as sb
from speechbrain.tokenizers.SentencePiece import SentencePiece
from speechbrain.utils.distributed import run_on_... | 16,143 | 36.284065 | 100 | py |
speechbrain | speechbrain-main/recipes/Voicebank/voicebank_prepare.py | # -*- coding: utf-8 -*-
"""
Data preparation.
Download and resample, use ``download_vctk`` below.
https://datashare.is.ed.ac.uk/handle/10283/2791
Authors:
* Szu-Wei Fu, 2020
* Peter Plantinga, 2020
"""
import os
import json
import string
import urllib
import shutil
import logging
import tempfile
import torchaudio
... | 14,761 | 30.14346 | 80 | py |
speechbrain | speechbrain-main/recipes/Voicebank/enhance/SEGAN/train.py | #!/usr/bin/env/python3
"""Recipe for training a speech enhancement system with the Voicebank dataset based on the SEGAN model architecture.
(based on the paper: Pascual et al. https://arxiv.org/pdf/1703.09452.pdf).
To run this recipe, do the following:
> python train.py hparams/train.yaml
Authors
* Francis Carter 20... | 17,802 | 33.568932 | 116 | py |
speechbrain | speechbrain-main/recipes/Voicebank/enhance/MetricGAN/train.py | #!/usr/bin/env/python3
"""
Recipe for training a speech enhancement system with the Voicebank dataset.
To run this recipe, do the following:
> python train.py hparams/{hyperparam_file}.yaml
Authors
* Szu-Wei Fu 2020
* Peter Plantinga 2021
"""
import os
import sys
import shutil
import pickle
import torch
import tor... | 22,525 | 35.687296 | 80 | py |
speechbrain | speechbrain-main/recipes/Voicebank/enhance/spectral_mask/train.py | #!/usr/bin/env/python3
"""Recipe for training a speech enhancement system with the Voicebank dataset.
To run this recipe, do the following:
> python train.py hparams/{hyperparam_file}.yaml
Authors
* Szu-Wei Fu 2020
"""
import os
import sys
import torch
import torchaudio
import speechbrain as sb
from pesq import pesq... | 9,102 | 33.481061 | 84 | py |
speechbrain | speechbrain-main/recipes/Voicebank/enhance/waveform_map/train.py | #!/usr/bin/env/python3
"""Recipe for training a waveform-based speech enhancement
system with the Voicebank dataset.
To run this recipe, do the following:
> python train.py hparams/{hyperparam_file}.yaml
Authors
* Szu-Wei Fu 2020
"""
import os
import sys
import torch
import torchaudio
import speechbrain as sb
from p... | 8,228 | 33.145228 | 83 | py |
speechbrain | speechbrain-main/recipes/Voicebank/enhance/MetricGAN-U/voicebank_prepare.py | # -*- coding: utf-8 -*-
"""
Data preparation.
Download and resample, use ``download_vctk`` below.
https://datashare.is.ed.ac.uk/handle/10283/2791
Authors:
* Szu-Wei Fu, 2020
* Peter Plantinga, 2020
"""
import os
import json
import string
import urllib
import shutil
import logging
import tempfile
import torchaudio
... | 14,812 | 30.054507 | 80 | py |
speechbrain | speechbrain-main/recipes/Voicebank/enhance/MetricGAN-U/train.py | #!/usr/bin/env/python3
"""
Recipe for training MetricGAN-U (Unsupervised) with the Voicebank dataset.
To run this recipe, do the following:
> python train.py hparams/{hyperparam_file}.yaml
Authors
* Szu-Wei Fu 2021/09
"""
import os
import sys
import shutil
import torch
import torchaudio
import speechbrain as sb
imp... | 27,522 | 34.331194 | 103 | py |
speechbrain | speechbrain-main/recipes/Voicebank/MTL/ASR_enhance/train.py | #!/usr/bin/env python3
"""Recipe for multi-task learning, using seq2seq and enhancement objectives.
To run this recipe, do the following:
> python train.py hparams/{config file} --data_folder /path/to/noisy-vctk
There's three provided files for three stages of training:
> python train.py hparams/pretrain_perceptual.y... | 21,897 | 37.826241 | 82 | py |
speechbrain | speechbrain-main/recipes/Voicebank/ASR/CTC/train.py | # /usr/bin/env python3
"""Recipe for doing ASR with phoneme targets and CTC loss on Voicebank
To run this recipe, do the following:
> python train.py hparams/{hyperparameter file} --data_folder /path/to/noisy-vctk
Use your own hyperparameter file or the provided `hyperparams.yaml`
To use noisy inputs, change `input_... | 7,651 | 33.781818 | 81 | py |
speechbrain | speechbrain-main/recipes/Voicebank/dereverb/spectral_mask/train.py | #!/usr/bin/env/python3
"""Recipe for training a speech enhancement system with the Voicebank dataset.
To run this recipe, do the following:
> python train.py hparams/{hyperparam_file}.yaml
Authors
* Szu-Wei Fu 2020
"""
import os
import sys
import torch
import torchaudio
import speechbrain as sb
from pesq import pesq... | 9,295 | 33.686567 | 84 | py |
speechbrain | speechbrain-main/recipes/Voicebank/dereverb/MetricGAN-U/train.py | #!/usr/bin/env/python3
"""
Recipe for training MetricGAN-U (Unsupervised) with the Voicebank dataset.
To run this recipe, do the following:
> python train.py hparams/{hyperparam_file}.yaml
Authors
* Szu-Wei Fu 2021/09
"""
import os
import sys
import shutil
import torch
import torchaudio
import speechbrain as sb
imp... | 28,023 | 34.60864 | 103 | py |
speechbrain | speechbrain-main/recipes/LibriTTS/libritts_prepare.py | from speechbrain.utils.data_utils import get_all_files, download_file
from speechbrain.processing.speech_augmentation import Resample
import json
import os
import shutil
import random
import logging
import torchaudio
logger = logging.getLogger(__name__)
LIBRITTS_URL_PREFIX = "https://www.openslr.org/resources/60/"
d... | 7,710 | 33.424107 | 115 | py |
speechbrain | speechbrain-main/recipes/LibriTTS/vocoder/hifigan/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/LibriTTS
Authors
* Duret Jarod 2021
* Yingzhi WANG 2022
* Pradnya Kandarkar 2022
... | 15,335 | 35.688995 | 90 | py |
speechbrain | speechbrain-main/recipes/DVoice/dvoice_prepare.py | """
Data preparation.
Download: https://dvoice.ma/
Author
------
Abdou Mohamed Naira 2022
"""
import os
import csv
import re
import logging
import torchaudio
import unicodedata
from tqdm.contrib import tzip
import random
import pandas as pd
from tqdm import tqdm
import numpy as np
import glob
logger = logging.getLog... | 15,401 | 29.804 | 80 | py |
speechbrain | speechbrain-main/recipes/DVoice/ASR/CTC/train_with_wav2vec2.py | #!/usr/bin/env python3
import sys
import torch
import logging
import speechbrain as sb
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.tokenizers.SentencePiece import SentencePiece
from speechbrain.utils.data_utils import undo_padding
from speechbrain.utils.distributed import run_on_main
""... | 14,424 | 36.273902 | 105 | py |
speechbrain | speechbrain-main/tests/unittests/test_normalization.py | import torch
import torch.nn
def test_BatchNorm1d(device):
from speechbrain.nnet.normalization import BatchNorm1d
input = torch.randn(100, 10, device=device) + 2.0
norm = BatchNorm1d(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = o... | 5,689 | 28.635417 | 76 | py |
speechbrain | speechbrain-main/tests/unittests/test_core.py | def test_parse_arguments():
from speechbrain.core import parse_arguments
filename, run_opts, overrides = parse_arguments(
["params.yaml", "--device=cpu", "--seed=3", "--data_folder", "TIMIT"]
)
assert filename == "params.yaml"
assert run_opts["device"] == "cpu"
assert overrides == "seed... | 1,475 | 35 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_samplers.py | import torch
def test_ConcatDatasetBatchSampler(device):
from torch.utils.data import TensorDataset, ConcatDataset, DataLoader
from speechbrain.dataio.sampler import (
ReproducibleRandomSampler,
ConcatDatasetBatchSampler,
)
import numpy as np
datasets = []
for i in range(3):
... | 1,418 | 27.959184 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_pretrainer.py | def test_pretrainer(tmpdir, device):
import torch
from torch.nn import Linear
# save a model in tmpdir/original/model.ckpt
first_model = Linear(32, 32).to(device)
pretrained_dir = tmpdir / "original"
pretrained_dir.mkdir()
with open(pretrained_dir / "model.ckpt", "wb") as fo:
torch.... | 843 | 35.695652 | 79 | py |
speechbrain | speechbrain-main/tests/unittests/test_dropout.py | import torch
import torch.nn
def test_dropout(device):
from speechbrain.nnet.dropout import Dropout2d
inputs = torch.rand([4, 10, 32], device=device)
drop = Dropout2d(drop_rate=0.0).to(device)
outputs = drop(inputs)
assert torch.all(torch.eq(inputs, outputs))
drop = Dropout2d(drop_rate=1.0)... | 497 | 22.714286 | 67 | py |
speechbrain | speechbrain-main/tests/unittests/test_augment.py | import os
import torch
from speechbrain.dataio.dataio import write_audio
def test_add_noise(tmpdir, device):
from speechbrain.processing.speech_augmentation import AddNoise
# Test concatenation of batches
wav_a = torch.sin(torch.arange(8000.0, device=device)).unsqueeze(0)
a_len = torch.ones(1, device... | 8,419 | 38.345794 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_categorical_encoder.py | import pytest
def test_categorical_encoder(device):
from speechbrain.dataio.encoder import CategoricalEncoder
encoder = CategoricalEncoder()
encoder.update_from_iterable("abcd")
integers = encoder.encode_sequence("dcba")
assert all(isinstance(i, int) for i in integers)
assert encoder.is_conti... | 8,005 | 35.557078 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_RNN.py | import torch
import torch.nn
from collections import OrderedDict
def test_RNN(device):
from speechbrain.nnet.RNN import RNN, GRU, LSTM, LiGRU, QuasiRNN, RNNCell
# Check RNN
inputs = torch.randn(4, 2, 7, device=device)
net = RNN(
hidden_size=5,
input_shape=inputs.shape,
num_la... | 5,091 | 29.130178 | 79 | py |
speechbrain | speechbrain-main/tests/unittests/test_features.py | import torch
def test_deltas(device):
from speechbrain.processing.features import Deltas
size = torch.Size([10, 101, 20], device=device)
inp = torch.ones(size, device=device)
compute_deltas = Deltas(input_size=20).to(device)
out = torch.zeros(size, device=device)
assert torch.sum(compute_del... | 4,139 | 30.12782 | 72 | py |
speechbrain | speechbrain-main/tests/unittests/test_profiling.py | def test_profile_class(device):
import torch
from torch.optim import SGD
from speechbrain.core import Brain
from speechbrain.utils.profiling import profile
@profile
class SimpleBrain(Brain):
def compute_forward(self, batch, stage):
return self.modules.model(batch[0])
... | 81,815 | 78.820488 | 185 | py |
speechbrain | speechbrain-main/tests/unittests/test_multi_mic.py | import torch
def test_gccphat(device):
from speechbrain.processing.features import STFT
from speechbrain.processing.multi_mic import Covariance, GccPhat
# Creating the test signal
fs = 16000
delay = 60
sig = torch.randn([10, fs], device=device)
sig_delayed = torch.cat(
(torch.z... | 916 | 24.472222 | 70 | py |
speechbrain | speechbrain-main/tests/unittests/test_signal_processing.py | import torch
def test_normalize(device):
from speechbrain.processing.signal_processing import compute_amplitude
from speechbrain.processing.signal_processing import rescale
import random
import numpy as np
for scale in ["dB", "linear"]:
for amp_type in ["peak", "avg"]:
for te... | 1,035 | 32.419355 | 77 | py |
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