python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/__init__.py | |
import torch
import torch.nn as nn
from functools import partial
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5Encoder... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/modules.py |
# PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
# Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn
# Some layers are re-designed for CLAP
import os
os.environ['NUMBA_CACHE_DIR'] = '/tmp/'
import torch
import torch.nn as nn
import torch.nn.functional as F
from torc... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/pann_model.py |
'''
Feature Fusion for Varible-Length Data Processing
AFF/iAFF is referred and modified from https://github.com/YimianDai/open-aff/blob/master/aff_pytorch/aff_net/fusion.py
According to the paper: Yimian Dai et al, Attentional Feature Fusion, IEEE Winter Conference on Applications of Computer Vision, WACV 2021
'''
imp... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/feature_fusion.py |
import hashlib
import os
import urllib
import warnings
from tqdm import tqdm
_RN50 = dict(
openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-q... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/pretrained.py |
__version__ = '0.2.1'
| EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/version.py |
import numpy as np
import torch.nn.functional as F
from torch import nn
from .model import MLPLayers
class LinearProbe(nn.Module):
def __init__(self, model, mlp, freeze, in_ch, out_ch, act=None):
"""
Args:
model: nn.Module
mlp: bool, if True, then use the MLP layer as the l... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/linear_probe.py |
from .factory import list_models, create_model, create_model_and_transforms, add_model_config
from .loss import ClipLoss, gather_features, LPLoss, lp_gather_features, LPMetrics
from .model import CLAP, CLAPTextCfg, CLAPVisionCfg, CLAPAudioCfp, convert_weights_to_fp16, trace_model
from .openai import load_openai_model, ... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/__init__.py |
import json
import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
import torch
from .model import CLAP, convert_weights_to_fp16
from .openai import load_openai_model
from .pretrained import get_pretrained_url, download_pretrained
from .transform import image_transform
_... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/factory.py |
""" CLAP Model
Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
Adapted to the Audio Task.
"""
from collections import OrderedDict
from dataclasses import dataclass
from email.mime import audio
from typing import Tuple, Union, Callable, Optional
import numpy as np... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/model.py |
# Ke Chen
# knutchen@ucsd.edu
# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
# Some layers designed on the model
# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/htsat.py |
""" CLIP tokenizer
Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
import gzip
import html
import os
from functools import lru_cache
from typing import Union, List
import ftfy
import regex as re
import torch
@lru_cache()
def default_bpe():
return os.path.join(o... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/tokenizer.py |
from multiprocessing.sharedctypes import Value
import torch
import torch.distributed.nn
from torch import distributed as dist, nn as nn
from torch.nn import functional as F
import numpy as np
from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
try:
import horovod.torch as hvd
except... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/loss.py |
""" OpenAI pretrained model functions
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
import os
import warnings
from typing import Union, List
import torch
from .model import build_model_from_openai_state_dict
from .pretrained import get_pretrained_url, list_pretr... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/openai.py |
import numpy as np
import torch
from torch import nn as nn
from torchvision.ops.misc import FrozenBatchNorm2d
import logging
import h5py
from tqdm import tqdm
import random
import json
import os
import pathlib
# TODO: (yusong) this not a good place to store those information and does not scale. Need to be fixed later.... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/utils.py |
from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
CenterCrop
def _convert_to_rgb(image):
return image.convert('RGB')
def image_transform(
image_size: int,
is_train: bool,
mean=(0.48145466, 0.4578275, 0.40821073),
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/transform.py |
""" timm model adapter
Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
"""
from collections import OrderedDict
import torch.nn as nn
try:
import timm
from timm.models.layers import Mlp, to_2tuple
from timm.models.layers.attention_pool2d impor... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/timm_model.py |
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
def bert_embeddings(text):
# text = "Replace me by any text you'd like."
encoded_input = tokenizer(... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/open_clap/bert.py |
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import AutoModel
from .audio import get_audio_encoder
class Projection(nn.Module):
def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
super().__init__()
self.linear1 = nn.Linear(d_in... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/CLAP/clap.py |
from . import clap
from . import audio
from . import utils | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/CLAP/__init__.py |
import argparse
import yaml
import sys
def read_config_as_args(config_path,args=None,is_config_str=False):
return_dict = {}
if config_path is not None:
if is_config_str:
yml_config = yaml.load(config_path, Loader=yaml.FullLoader)
else:
with open(config_path, "r") as f:
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/CLAP/utils.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
def get_audio_encoder(name: str):
if name == "Cnn14":
return Cnn14
else:
raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
class... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/CLAP/audio.py |
import random
import torchaudio
from torch._six import string_classes
import collections
import re
import torch.nn.functional as F
import numpy as np
from transformers import AutoTokenizer
from ldm.modules.encoders.CLAP.utils import read_config_as_args
from ldm.modules.encoders.CLAP.clap import CLAP
import math
import ... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/encoders/CLAP/CLAPWrapper.py |
# -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import numpy as np
import cv2
import tor... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/image_degradation/bsrgan.py |
from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
| EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/image_degradation/__init__.py |
import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
'''
# ----------------------... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/image_degradation/utils_image.py |
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as ... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/image_degradation/bsrgan_light.py |
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/gu... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/diffusionmodules/util.py |
EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/diffusionmodules/__init__.py | |
# pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches th... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/diffusionmodules/model.py |
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/diffusionmodules/custom_openaimodel.py |
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/diffusionmodules/openaimodel.py |
EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/distributions/__init__.py | |
import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
retur... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/distributions/distributions.py |
import sys
import os
import librosa
import numpy as np
import torch
import audio_to_text.captioning.models
import audio_to_text.captioning.models.encoder
import audio_to_text.captioning.models.decoder
import audio_to_text.captioning.utils.train_util as train_util
def load_model(config, checkpoint):
ckpt = torch.l... | EXA-1-master | exa/models/AudioGPT/audio_to_text/inference_waveform.py |
EXA-1-master | exa/models/AudioGPT/audio_to_text/__init__.py | |
EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/__init__.py | |
import math
import torch
class ExponentialDecayScheduler(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, total_iters, final_lrs,
warmup_iters=3000, last_epoch=-1, verbose=False):
self.total_iters = total_iters
self.final_lrs = final_lrs
if not isinstance(self... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/lr_scheduler.py |
import json
from tqdm import tqdm
import logging
import pickle
from collections import Counter
import re
import fire
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
i... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/build_vocab_ltp.py |
import json
from tqdm import tqdm
import logging
import pickle
from collections import Counter
import re
import fire
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
i... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/build_vocab_spacy.py |
import json
from tqdm import tqdm
import logging
import pickle
from collections import Counter
import re
import fire
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/build_vocab.py |
import argparse
import torch
def main(checkpoint):
state_dict = torch.load(checkpoint, map_location="cpu")
if "optimizer" in state_dict:
del state_dict["optimizer"]
if "lr_scheduler" in state_dict:
del state_dict["lr_scheduler"]
torch.save(state_dict, checkpoint)
if __name__ == "__ma... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/remove_optimizer.py |
EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/__init__.py | |
# -*- coding: utf-8 -*-
#!/usr/bin/env python3
import os
import sys
import logging
from typing import Callable, Dict, Union
import yaml
import torch
from torch.optim.swa_utils import AveragedModel as torch_average_model
import numpy as np
import pandas as pd
from pprint import pformat
def load_dict_from_csv(csv, cols... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/train_util.py |
import json
from tqdm import tqdm
import re
import fire
def tokenize_caption(input_json: str,
keep_punctuation: bool = False,
host_address: str = None,
character_level: bool = False,
zh: bool = True,
output_json: ... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/tokenize_caption.py |
import json
import random
import argparse
import numpy as np
from tqdm import tqdm
from h5py import File
import sklearn.metrics
random.seed(1)
parser = argparse.ArgumentParser()
parser.add_argument("train_feature", type=str)
parser.add_argument("train_corpus", type=str)
parser.add_argument("pred_feature", type=str)
p... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/predict_nn.py |
from pathlib import Path
import argparse
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument("--input", help="input filename", type=str, nargs="+")
parser.add_argument("--output", help="output result file", default=None)
args = parser.parse_args()
scores = {}
for path in args.input:
with o... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/report_results.py |
import copy
import json
import numpy as np
import fire
def evaluate_annotation(key2refs, scorer):
if scorer.method() == "Bleu":
scores = np.array([ 0.0 for n in range(4) ])
else:
scores = 0
num_cap_per_audio = len(next(iter(key2refs.values())))
for i in range(num_cap_per_audio):
... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/eval_round_robin.py |
import os
import sys
import copy
import pickle
import numpy as np
import pandas as pd
import fire
sys.path.append(os.getcwd())
def coco_score(refs, pred, scorer):
if scorer.method() == "Bleu":
scores = np.array([ 0.0 for n in range(4) ])
else:
scores = 0
num_cap_per_audio = len(refs[list... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/model_eval_diff.py |
# coding=utf-8
#!/usr/bin/env python3
import numpy as np
import pandas as pd
import torch
import gensim
from gensim.models import Word2Vec
from tqdm import tqdm
import fire
import sys
import os
sys.path.append(os.getcwd())
from utils.build_vocab import Vocabulary
def create_embedding(vocab_file: str,
... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/word2vec/create_word_embedding.py |
# coding=utf-8
#!/usr/bin/env python3
import numpy as np
import pandas as pd
import torch
from gensim.models import FastText
from tqdm import tqdm
import fire
import sys
import os
sys.path.append(os.getcwd())
from utils.build_vocab import Vocabulary
def create_embedding(caption_file: str,
vocab_... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/fasttext/create_word_embedding.py |
import pickle
import fire
import numpy as np
import pandas as pd
from tqdm import tqdm
class EmbeddingExtractor(object):
def extract_sentbert(self, caption_file: str, output: str, dev: bool=True, zh: bool=False):
from sentence_transformers import SentenceTransformer
lang2model = {
"zh... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/bert/create_sent_embedding.py |
# -*- coding: utf-8 -*-
import sys
import os
from bert_serving.client import BertClient
import numpy as np
from tqdm import tqdm
import fire
import torch
sys.path.append(os.getcwd())
from utils.build_vocab import Vocabulary
def main(vocab_file: str, output: str, server_hostname: str):
client = BertClient(ip=ser... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/utils/bert/create_word_embedding.py |
# -*- coding: utf-8 -*-
import math
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from .utils import generate_length_mask, init, PositionalEncoding
class BaseDecoder(nn.Module):
"""
Take word/audio embeddings and output the next word probs
Base decoder, cannot be c... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/models/decoder.py |
# -*- coding: utf-8 -*-
import random
import torch
import torch.nn as nn
from .base_model import CaptionModel
from .utils import repeat_tensor
import audio_to_text.captioning.models.decoder
class TransformerModel(CaptionModel):
def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
if not... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/models/transformer_model.py |
from .base_model import *
from .transformer_model import *
| EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/models/__init__.py |
# -*- coding: utf-8 -*-
import math
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchaudio import transforms
from torchlibrosa.augmentation import SpecAugmentation
from .utils import mean_with_lens, max_with_lens, \
init, pack_wrapper, generate_length_mask, PositionalEncod... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/models/encoder.py |
# -*- coding: utf-8 -*-
from typing import Dict
import torch
import torch.nn as nn
from .utils import mean_with_lens, repeat_tensor
class CaptionModel(nn.Module):
"""
Encoder-decoder captioning model.
"""
pad_idx = 0
start_idx = 1
end_idx = 2
max_length = 20
def __init__(self, enc... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/models/base_model.py |
import math
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence
def sort_pack_padded_sequence(input, lengths):
sorted_lengths, indices = torch.sort(lengths, descending=True)
tmp = pack_padded_sequence(input[indices], ... | EXA-1-master | exa/models/AudioGPT/audio_to_text/captioning/models/utils.py |
import librosa
import librosa.filters
import math
import numpy as np
import scipy.io.wavfile
def load_wav(path):
max_length = 32000 * 10
wav = librosa.core.load(path, sr=32000)[0]
if len(wav) > max_length:
audio = wav[0:max_length]
# pad audio to max length, 10s for AudioCaps
if len(wav) <... | EXA-1-master | exa/models/AudioGPT/sound_extraction/utils/wav_io.py |
import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.signal import get_window
import librosa.util as librosa_util
from librosa.util import pad_center, tiny
# from audio_processing import window_sumsquare
def window_sumsquare(window, n_frames, hop_length=512, wi... | EXA-1-master | exa/models/AudioGPT/sound_extraction/utils/stft.py |
import torch
import numpy as np
def add_noise_and_scale(front, noise, snr_l=0, snr_h=0, scale_lower=1.0, scale_upper=1.0):
"""
:param front: front-head audio, like vocal [samples,channel], will be normlized so any scale will be fine
:param noise: noise, [samples,channel], any scale
:param snr_l: Option... | EXA-1-master | exa/models/AudioGPT/sound_extraction/utils/create_mixtures.py |
import torch
import torch.nn as nn
class Film(nn.Module):
def __init__(self, channels, cond_embedding_dim):
super(Film, self).__init__()
self.linear = nn.Sequential(
nn.Linear(cond_embedding_dim, channels * 2),
nn.ReLU(inplace=True),
nn.Linear(channels * 2, chann... | EXA-1-master | exa/models/AudioGPT/sound_extraction/model/film.py |
from .modules import *
import numpy as np
class UNetRes_FiLM(nn.Module):
def __init__(self, channels, cond_embedding_dim, nsrc=1):
super(UNetRes_FiLM, self).__init__()
activation = 'relu'
momentum = 0.01
self.nsrc = nsrc
self.channels = channels
self.downsample_rati... | EXA-1-master | exa/models/AudioGPT/sound_extraction/model/resunet_film.py |
import torch
import torch.nn as nn
from transformers import *
import warnings
warnings.filterwarnings('ignore')
# pretrained model name: (model class, model tokenizer, output dimension, token style)
MODELS = {
'prajjwal1/bert-mini': (BertModel, BertTokenizer),
}
class Text_Encoder(nn.Module):
def __init__(self... | EXA-1-master | exa/models/AudioGPT/sound_extraction/model/text_encoder.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .film import Film
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, activation, momentum):
super(ConvBlock, self).__init__()
self.activation = activation
padding = (kern... | EXA-1-master | exa/models/AudioGPT/sound_extraction/model/modules.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .text_encoder import Text_Encoder
from .resunet_film import UNetRes_FiLM
class LASSNet(nn.Module):
def __init__(self, device='cuda'):
super(LASSNet, self).__init__()
self.text_embedder = Text_Encoder(device)
self.UNet =... | EXA-1-master | exa/models/AudioGPT/sound_extraction/model/LASSNet.py |
"""
Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class TimeWarperFunction(th.autograd.Function):
... | EXA-1-master | exa/models/AudioGPT/mono2binaural/src/warping.py |
import numpy as np
import scipy.linalg
from scipy.spatial.transform import Rotation as R
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from src.warping import GeometricTimeWarper, MonotoneTimeWarper
from src.utils import Net
class GeometricWarper(nn.Module):
def __init__(self, sampling_... | EXA-1-master | exa/models/AudioGPT/mono2binaural/src/models.py |
"""
Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import numpy as np
import torch as th
#import torchaudio as ta
class Net(th.nn.Module):
def __init__(self, model_name... | EXA-1-master | exa/models/AudioGPT/mono2binaural/src/utils.py |
EXA-1-master | exa/models/AudioGPT/audio_detection/__init__.py | |
EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/__init__.py | |
import numpy as np
import csv
sample_rate = 32000
clip_samples = sample_rate * 10 # Audio clips are 10-second
# Load label
with open('./audio_detection/audio_infer/metadata/class_labels_indices.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
lines = list(reader)
labels = []
ids = [] # Each labe... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/config.py |
import sys
class ExceptionHook:
instance = None
def __call__(self, *args, **kwargs):
if self.instance is None:
from IPython.core import ultratb
self.instance = ultratb.FormattedTB(mode='Plain',
color_scheme='Linux', call_pdb=1)
return self.instance(*args... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/crash.py |
import argparse
import csv
import os
from utilities import create_folder
def dcase2017task4(args):
"""Create black list. Black list is a list of audio ids that will be
skipped in training.
"""
# Augments & parameters
workspace = args.workspace
# Black list from DCASE 2017 Task 4
t... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/create_black_list.py |
import numpy as np
import argparse
import csv
import os
import glob
import datetime
import time
import logging
import h5py
import librosa
from utilities import (create_folder, get_filename, create_logging,
float32_to_int16, pad_or_truncate, read_metadata)
import config
def split_unbalanced_csv_to_partial_csvs(a... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/dataset.py |
import numpy as np
import argparse
import csv
import os
import glob
import datetime
import time
import logging
import h5py
import librosa
from utilities import create_folder, get_sub_filepaths
import config
def create_indexes(args):
"""Create indexes a for dataloader to read for training. When users have
a ... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/create_indexes.py |
import os
import sys
import numpy as np
import argparse
import h5py
import time
import _pickle as cPickle
import _pickle
import matplotlib.pyplot as plt
import csv
from sklearn import metrics
from utilities import (create_folder, get_filename, d_prime)
import config
def _load_metrics0(filename, sample_rate, window_s... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/plot_statistics.py |
import os
import sys
import numpy as np
import argparse
import h5py
import time
import pickle
import matplotlib.pyplot as plt
import csv
from sklearn import metrics
from utilities import (create_folder, get_filename, d_prime)
import config
def load_statistics(statistics_path):
statistics_dict = pickle.load(open(... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/plot_for_paper.py |
import os
import logging
import h5py
import soundfile
import librosa
import numpy as np
import pandas as pd
from scipy import stats
import datetime
import pickle
def create_folder(fd):
if not os.path.exists(fd):
os.makedirs(fd)
def get_filename(path):
path = os.path.realpath(path)
... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/utilities.py |
import numpy as np
import h5py
import csv
import time
import logging
from utilities import int16_to_float32
def read_black_list(black_list_csv):
"""Read audio names from black list.
"""
with open(black_list_csv, 'r') as fr:
reader = csv.reader(fr)
lines = list(reader)
black_list_nam... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/utils/data_generator.py |
import numpy as np
import time
import torch
import torch.nn as nn
def move_data_to_device(x, device):
if 'float' in str(x.dtype):
x = torch.Tensor(x)
elif 'int' in str(x.dtype):
x = torch.LongTensor(x)
else:
return x
return x.to(device)
def do_mixup(x, mixup_lambda):
"""... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/pytorch_utils.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
from audio_infer.pytorch.pytorch_utils import do_mixup, interpolate, pad_framewise_output
import os
import sys
import math
import numpy a... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/models.py |
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import librosa
import matplotlib.pyplot as plt
import torch
from utilities import create_folder, get_filename
from models import *
from pytorch_utils import move_data_to_device
import config
def audio_tag... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/inference.py |
from sklearn import metrics
from pytorch_utils import forward
class Evaluator(object):
def __init__(self, model):
"""Evaluator.
Args:
model: object
"""
self.model = model
def evaluate(self, data_loader):
"""Forward evaluation data and calculate stat... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/evaluate.py |
import torch
import torch.nn.functional as F
def clip_bce(output_dict, target_dict):
"""Binary crossentropy loss.
"""
return F.binary_cross_entropy(
output_dict['clipwise_output'], target_dict['target'])
def get_loss_func(loss_type):
if loss_type == 'clip_bce':
return clip_bce | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/losses.py |
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import h5py
import math
import time
import logging
import matplotlib.pyplot as plt
import torch
torch.backends.cudnn.benchmark=True
torch.manual_seed(0)
import torch.nn as nn
import torch.nn.functional as ... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/finetune_template.py |
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import time
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from utilities import (create_folder, get_filename, creat... | EXA-1-master | exa/models/AudioGPT/audio_detection/audio_infer/pytorch/main.py |
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2021/3/9 16:33
# @Author : dongchao yang
# @File : train.py
from itertools import zip_longest
import numpy as np
from scipy import ndimage
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
from torchlibrosa.augmentation import ... | EXA-1-master | exa/models/AudioGPT/audio_detection/target_sound_detection/src/models.py |
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2021/3/9 16:33
# @Author : dongchao yang
# @File : train.py
import collections
import sys
from loguru import logger
from pprint import pformat
import numpy as np
import pandas as pd
import scipy
import six
import sklearn.preprocessing as pre
import torch... | EXA-1-master | exa/models/AudioGPT/audio_detection/target_sound_detection/src/utils.py |
from setuptools import setup, find_packages
setup(
name = 'recurrent-memory-transformer-pytorch',
packages = find_packages(exclude=[]),
version = '0.2.2',
license='MIT',
description = 'Recurrent Memory Transformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description... | EXA-1-master | exa/models/recurrent-memory-transformer-pytorch/setup.py |
import gzip
import random
import tqdm
import numpy as np
import torch
from torch.optim import Adam
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
from recurrent_memory_transformer_pytorch import RecurrentMemoryTransformer, RecurrentMemoryTransformerWrapper
# constants
NUM_BATC... | EXA-1-master | exa/models/recurrent-memory-transformer-pytorch/train.py |
from recurrent_memory_transformer_pytorch.recurrent_memory_transformer import RecurrentMemoryTransformer, RecurrentMemoryTransformerWrapper
| EXA-1-master | exa/models/recurrent-memory-transformer-pytorch/recurrent_memory_transformer_pytorch/__init__.py |
from collections import namedtuple
from functools import wraps
from packaging import version
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
# constants
Config = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
# ... | EXA-1-master | exa/models/recurrent-memory-transformer-pytorch/recurrent_memory_transformer_pytorch/attend.py |
import math
from functools import partial
from itertools import zip_longest
from contextlib import nullcontext
from typing import Optional, List
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat, pack, unpack
from recurrent_memory_transformer_pytorch.atten... | EXA-1-master | exa/models/recurrent-memory-transformer-pytorch/recurrent_memory_transformer_pytorch/recurrent_memory_transformer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import codecs
import os
import platform
import re
from glob import glob
import setuptools
from setuptools import Extension
from setuptools.command.build_ext import build_ext
def clean_html(raw_html):
cleanr = re.compile("<.*?>")
clea... | EXA-1-master | exa/models/mmf-main/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
| EXA-1-master | exa/models/mmf-main/tools/__init__.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import lib as sweep
from lib import hyperparam
def get_grid(args):
max_update = 22000
return [
hyperparam("run_type", "train_val"),
hyperparam("config", "projects/visual_bert/configs/vqa2/defaults.yaml"),
# hyp... | EXA-1-master | exa/models/mmf-main/tools/sweeps/sweep_visual_bert.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Copied from fairseq. Mostly written by @myleott. Adapted accordingly for mmf
import datetime
import itertools
import os
import random
import shlex
import shutil
import subprocess
from collections import OrderedDict
from glob import glob
from mmf.utils.general impor... | EXA-1-master | exa/models/mmf-main/tools/sweeps/lib/slurm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Copied from fairseq. Mostly written by @myleott. Adapted accordingly for mmf
import argparse
import datetime
import json
import os
import socket
# if argv is None, we will read from sys.argv (invoke params)
def get_args(argv=None):
parser = argparse.ArgumentPa... | EXA-1-master | exa/models/mmf-main/tools/sweeps/lib/__init__.py |
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