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/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/timit_prepare.py | ../../timit_prepare.py | 22 | 22 | 22 | 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/timit_prepare.py | ../timit_prepare.py | 19 | 19 | 19 | 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/prepare.py | import os
import logging
from speechbrain.dataio.dataio import read_audio
try:
import pandas as pd
except ImportError:
err_msg = (
"The optional dependency pandas must be installed to run this recipe.\n"
)
err_msg += "Install using `pip install pandas`.\n"
raise ImportError(err_msg)
logger... | 2,812 | 26.048077 | 80 | py |
speechbrain | speechbrain-main/recipes/fluent-speech-commands/Tokenizer/prepare.py | ../prepare.py | 13 | 13 | 13 | py |
speechbrain | speechbrain-main/recipes/fluent-speech-commands/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer for Fluent Speech Commands.
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... | 1,446 | 25.796296 | 72 | py |
speechbrain | speechbrain-main/recipes/fluent-speech-commands/direct/prepare.py | ../prepare.py | 13 | 13 | 13 | 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/common_voice_prepare.py | ../../common_voice_prepare.py | 29 | 29 | 29 | 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/common_voice_prepare.py | ../../common_voice_prepare.py | 29 | 29 | 29 | 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/common_voice_prepare.py | ../../common_voice_prepare.py | 29 | 29 | 29 | 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/common_voice_prepare.py | ../../common_voice_prepare.py | 29 | 29 | 29 | 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/common_voice_prepare.py | ../../common_voice_prepare.py | 29 | 29 | 29 | 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/ami_prepare.py | """
Data preparation.
Download: http://groups.inf.ed.ac.uk/ami/download/
Prepares metadata files (JSON) from manual annotations "segments/" using RTTM format (Oracle VAD).
"""
import os
import logging
import xml.etree.ElementTree as et
import glob
import json
from ami_splits import get_AMI_split
from speechbrain.da... | 16,456 | 28.921818 | 101 | py |
speechbrain | speechbrain-main/recipes/AMI/ami_splits.py | """
AMI corpus contained 100 hours of meeting recording.
This script returns the standard train, dev and eval split for AMI corpus.
For more information on dataset please refer to http://groups.inf.ed.ac.uk/ami/corpus/datasets.shtml
"""
ALLOWED_OPTIONS = ["scenario_only", "full_corpus", "full_corpus_asr"]
def get_AM... | 4,914 | 21.442922 | 100 | py |
speechbrain | speechbrain-main/recipes/AMI/Diarization/ami_prepare.py | ../ami_prepare.py | 17 | 17 | 17 | 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/switchboard_prepare.py | """
This script prepares the data of the switchboard-1 release 2 corpus (LDC97S62).
Optionally, the Fisher corpus transcripts (LDC2004T19 and LDC2005T19) can be added to
the CSVs for Tokenizer and LM training.
The test set is based on the eval2000/Hub 5 data (LDC2002S09/LDC2002T43).
The datasets can be obtained from:
... | 42,755 | 33.122905 | 114 | py |
speechbrain | speechbrain-main/recipes/Switchboard/LM/switchboard_prepare.py | ../switchboard_prepare.py | 25 | 25 | 25 | 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/Tokenizer/switchboard_prepare.py | ../switchboard_prepare.py | 25 | 25 | 25 | py |
speechbrain | speechbrain-main/recipes/Switchboard/Tokenizer/train.py | #!/usr/bin/env/python3
"""Recipe for training a BPE tokenizer with Switchboard.
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... | 1,910 | 30.85 | 72 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/normalize_util.py | """
This script provides some utility functions that can be used
during inference of ASR models.
The intended use is to import `normalize_words` after decoding and tokenization.
Note that this will only work, when UPPERCASE letters are used throughout the recipe.
Author
------
Dominik Wagner 2022
"""
import re
import... | 8,294 | 38.5 | 95 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/seq2seq/switchboard_prepare.py | ../../switchboard_prepare.py | 28 | 28 | 28 | 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/seq2seq/normalize_util.py | ../normalize_util.py | 20 | 20 | 20 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/CTC/switchboard_prepare.py | ../../switchboard_prepare.py | 28 | 28 | 28 | 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/CTC/normalize_util.py | ../normalize_util.py | 20 | 20 | 20 | py |
speechbrain | speechbrain-main/recipes/Switchboard/ASR/transformer/switchboard_prepare.py | ../../switchboard_prepare.py | 28 | 28 | 28 | 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/Switchboard/ASR/transformer/normalize_util.py | ../normalize_util.py | 20 | 20 | 20 | 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/prepare_iwslt22.py | #!/usr/bin/env python3
"""
Tamasheq-French data processing.
Author
------
Marcely Zanon Boito 2022
"""
import json
import os
def write_json(json_file_name, data):
with open(json_file_name, mode="w", encoding="utf-8") as output_file:
json.dump(
data,
output_file,
ensur... | 1,873 | 23.986667 | 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/voicebank_prepare.py | ../../voicebank_prepare.py | 26 | 26 | 26 | 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/voicebank_prepare.py | ../../voicebank_prepare.py | 26 | 26 | 26 | 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/voicebank_prepare.py | ../../voicebank_prepare.py | 26 | 26 | 26 | 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/voicebank_prepare.py | ../../voicebank_prepare.py | 26 | 26 | 26 | 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/composite_eval.py | """Composite objective enhancement scores in Python (CSIG, CBAK, COVL)
Taken from https://github.com/facebookresearch/denoiser/blob/master/scripts/matlab_eval.py
Authors
* adiyoss (https://github.com/adiyoss)
"""
from scipy.linalg import toeplitz
from tqdm import tqdm
from pesq import pesq
import librosa
import nump... | 15,193 | 33.531818 | 90 | py |
speechbrain | speechbrain-main/recipes/Voicebank/MTL/ASR_enhance/voicebank_prepare.py | ../../voicebank_prepare.py | 26 | 26 | 26 | 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/voicebank_prepare.py | ../../voicebank_prepare.py | 26 | 26 | 26 | 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/voicebank_revb_prepare.py | # -*- coding: utf-8 -*-
"""
Data preparation.
First "Manually" Download VoiceBank-SLR [1] from:
https://bio-asplab.citi.sinica.edu.tw/Opensource.html#VB-SLR
[1] “MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech”
Authors:
* Szu-Wei Fu, 2020
* Peter Plantinga, 20... | 9,316 | 28.958199 | 108 | 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/voicebank_revb_prepare.py | # -*- coding: utf-8 -*-
"""
Data preparation.
First "Manually" Download VoiceBank-SLR [1] from:
https://bio-asplab.citi.sinica.edu.tw/Opensource.html#VB-SLR
[1] “MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech”
Authors:
* Szu-Wei Fu, 2020
* Peter Plantinga, 20... | 9,317 | 28.865385 | 108 | 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/libritts_prepare.py | ../../libritts_prepare.py | 25 | 25 | 25 | 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/recipes/DVoice/ASR/CTC/dvoice_prepare.py | ../../dvoice_prepare.py | 23 | 23 | 23 | py |
speechbrain | speechbrain-main/tests/__init__.py | """ Availing scripts for testing to be imported, i.e., code in tests/utils.
"""
| 80 | 26 | 75 | py |
speechbrain | speechbrain-main/tests/consistency/test_recipe.py | """Tests for checking the recipes and their files.
Authors
* Mirco Ravanelli 2022
"""
import os
import csv
from speechbrain.utils.data_utils import get_all_files, get_list_from_csv
__skip_list = ["README.md", "setup"]
def test_recipe_list(
search_folder="recipes",
hparam_ext=[".yaml"],
hparam_field="H... | 6,945 | 31.157407 | 104 | py |
speechbrain | speechbrain-main/tests/consistency/test_yaml.py | """Consistency check between yaml files and script files.
Authors
* Mirco Ravanelli 2022
"""
import os
import csv
from tests.consistency.test_recipe import __skip_list
from tests.utils.check_yaml import check_yaml_vs_script
def test_yaml_script_consistency(recipe_folder="tests/recipes"):
"""This test checks the... | 1,717 | 30.814815 | 92 | py |
speechbrain | speechbrain-main/tests/consistency/test_HF_repo.py | """Library for the HuggingFace (HF) repositories.
Authors
* Mirco Ravanelli 2022
* Andreas Nautsch 2022
"""
import os
import csv
from speechbrain.utils.data_utils import download_file
def run_HF_check(
recipe_folder="tests/recipes", field="HF_repo", output_folder="HF_repos",
):
"""Checks if the code report... | 3,526 | 27.443548 | 89 | py |
speechbrain | speechbrain-main/tests/consistency/test_docstrings.py | """Tests for checking the docstrings of functions and classes.
Authors
* Mirco Ravanelli 2022
"""
from tests.utils.check_docstrings import check_docstrings
def test_recipe_list(base_folder="."):
check_folders = ["speechbrain", "tools", "templates"]
assert check_docstrings(base_folder, check_folders)
| 313 | 25.166667 | 62 | py |
speechbrain | speechbrain-main/tests/unittests/test_dataset.py | def test_dynamic_item_dataset():
from speechbrain.dataio.dataset import DynamicItemDataset
import operator
data = {
"utt1": {"foo": -1, "bar": 0, "text": "hello world"},
"utt2": {"foo": 1, "bar": 2, "text": "how are you world"},
"utt3": {"foo": 3, "bar": 4, "text": "where are you wo... | 3,451 | 37.786517 | 82 | 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_superpowers.py | import sys
import pytest
@pytest.mark.skipif(
sys.platform.startswith("win"),
reason="shell tools not necessarily available on Windows",
)
def test_run_shell():
from speechbrain.utils.superpowers import run_shell
out, err, code = run_shell("echo -n hello")
assert out.decode() == "hello"
asser... | 684 | 26.4 | 71 | 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_counting.py | def test_pad_ends():
from speechbrain.lm.counting import pad_ends
assert next(pad_ends(["a", "b", "c"])) == "<s>"
assert next(pad_ends(["a", "b", "c"], pad_left=False)) == "a"
assert list(pad_ends(["a", "b", "c"], pad_left=False))[-1] == "</s>"
assert list(pad_ends([], pad_left=False))
assert l... | 1,040 | 33.7 | 75 | py |
speechbrain | speechbrain-main/tests/unittests/test_edit_distance.py | def test_accumulatable_wer_stats():
from speechbrain.utils.edit_distance import accumulatable_wer_stats
refs = [[[1, 2, 3], [4, 5, 6]], [[7, 8], [9]]]
hyps = [[[1, 2, 4], [5, 6]], [[7, 8], [10]]]
# Test basic functionality:
stats = accumulatable_wer_stats(refs[0], hyps[0])
assert stats["WER"] =... | 2,313 | 35.730159 | 71 | 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 |
speechbrain | speechbrain-main/tests/unittests/test_checkpoints.py | import pytest
def test_checkpointer(tmpdir, device):
from speechbrain.utils.checkpoints import Checkpointer
import torch
class Recoverable(torch.nn.Module):
def __init__(self, param):
super().__init__()
self.param = torch.nn.Parameter(torch.tensor([param]))
def fo... | 14,332 | 35.940722 | 93 | py |
speechbrain | speechbrain-main/tests/unittests/test_embedding.py | import torch
def test_embedding(device):
from speechbrain.nnet.embedding import Embedding
# create one hot vector and consider blank as zero vector
embedding_dim = 39
blank_id = 39
size_dict = 40
emb = Embedding(
num_embeddings=size_dict, consider_as_one_hot=True, blank_id=blank_id
... | 783 | 26.034483 | 78 | py |
speechbrain | speechbrain-main/tests/unittests/test_ngram_lm.py | def test_backofff_ngram_lm():
from speechbrain.lm.ngram import BackoffNgramLM
import math
HALF = math.log(0.5)
ngrams = {
1: {tuple(): {"a": HALF, "b": HALF}},
2: {("a",): {"a": HALF, "b": HALF}, ("b",): {"a": HALF}},
}
backoffs = {1: {("b",): 0.0}}
lm = BackoffNgramLM(ngram... | 901 | 32.407407 | 65 | py |
speechbrain | speechbrain-main/tests/unittests/test_epoch_loop.py | def test_epoch_loop_recovery(tmpdir):
from speechbrain.utils.checkpoints import Checkpointer
from speechbrain.utils.epoch_loop import EpochCounter
epoch_counter = EpochCounter(2)
recoverer = Checkpointer(tmpdir, {"epoch": epoch_counter})
for epoch in epoch_counter:
assert epoch == 1
... | 1,315 | 32.74359 | 62 | py |
speechbrain | speechbrain-main/tests/unittests/test_dependency_graph.py | import pytest
def test_dependency_graph():
from speechbrain.utils.depgraph import (
DependencyGraph,
CircularDependencyError,
)
dg = DependencyGraph()
# a->b->c
dg.add_edge("b", "c")
dg.add_edge("a", "b")
assert dg.is_valid()
eval_order = [node.key for node in dg.get_e... | 2,357 | 30.026316 | 73 | py |
speechbrain | speechbrain-main/tests/unittests/test_activations.py | import torch
import torch.nn
def test_softmax(device):
from speechbrain.nnet.activations import Softmax
inputs = torch.tensor([1, 2, 3], device=device).float()
act = Softmax(apply_log=False)
outputs = act(inputs)
assert torch.argmax(outputs) == 2
assert torch.jit.trace(act, inputs)
| 312 | 19.866667 | 59 | py |
speechbrain | speechbrain-main/tests/unittests/test_batching.py | import pytest
import torch
import numpy as np
def test_batch_pad_right_to(device):
from speechbrain.utils.data_utils import batch_pad_right
import random
n_channels = 40
batch_lens = [1, 5]
for b in batch_lens:
rand_lens = [random.randint(10, 53) for x in range(b)]
tensors = [
... | 2,404 | 28.329268 | 77 | py |
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