id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
37,164 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) | null |
37,165 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_decompression(x, C=1):
return np.exp(x) / C | null |
37,166 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C) | null |
37,167 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_decompression_torch(x, C=1):
return torch.exp(x) / C | null |
37,168 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
from torchaudio.transforms import Resample
from .nvSTFT import STFT
from .pcmer import PCmer
def l2_regularization(model, l2_alpha):
l2_loss = []
for module in model.modules():
i... | null |
37,169 | import logging
import multiprocessing
import os
import time
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.cuda.amp import GradScaler, autocast
from torch.nn import functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import Data... | null |
37,170 | import argparse
import json
import os
import re
import wave
from random import shuffle
from loguru import logger
from tqdm import tqdm
import diffusion.logger.utils as du
def get_wav_duration(file_path):
try:
with wave.open(file_path, 'rb') as wav_file:
# 获取音频帧数
n_frames = wav_file.... | null |
37,171 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,172 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,173 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,174 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,175 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,176 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,177 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,178 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,179 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | Freeing up space by deleting saved ckpts Arguments: path_to_models -- Path to the model directory n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth sort_by_time -- True -> chronologically delete ckpts False -> lexicographically delete ckpts |
37,180 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,181 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,182 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,183 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,184 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,185 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,186 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,187 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,188 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,189 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,190 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,191 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,192 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,193 | import argparse
import glob
import json
import logging
import os
import re
import subprocess
import sys
import traceback
from multiprocessing import cpu_count
import faiss
import librosa
import numpy as np
import torch
from scipy.io.wavfile import read
from sklearn.cluster import MiniBatchKMeans
from torch.nn import fu... | null |
37,194 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def resize2d_f0(x, target_len):
source = np.array(x)
source[source < 0.001] = np.nan
target = n... | null |
37,195 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def get_f0(x, p_len,f0_up_key=0):
time_step = 160 / 16000 * 1000
f0_min = 50
f0_max = 1100
... | null |
37,196 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def clean_pitch(input_pitch):
num_nan = np.sum(input_pitch == 1)
if num_nan / len(input_pitch) > 0.... | null |
37,197 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def plt_pitch(input_pitch):
input_pitch = input_pitch.astype(float)
input_pitch[input_pitch == 1] =... | null |
37,198 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def f0_to_pitch(ff):
f0_pitch = 69 + 12 * np.log2(ff / 440)
return f0_pitch | null |
37,199 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def fill_a_to_b(a, b):
if len(a) < len(b):
for _ in range(0, len(b) - len(a)):
a.ap... | null |
37,200 | import io
import logging
import os
import librosa
import numpy as np
import parselmouth
import soundfile
import torch
import torchaudio
import utils
from inference import slicer
from models import SynthesizerTrn
def mkdir(paths: list):
for path in paths:
if not os.path.exists(path):
os.mkdir(pa... | null |
37,201 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,202 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,203 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,204 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,205 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,206 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,207 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,208 | import gc
import hashlib
import io
import json
import logging
import os
import pickle
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
import torch
import torchaudio
import cluster
import utils
from diffusion.unit2mel import load_model_vocoder
from inference import slicer
from mod... | null |
37,209 | import io
import logging
import soundfile
import torch
import torchaudio
from flask import Flask, request, send_file
from flask_cors import CORS
from inference.infer_tool import RealTimeVC, Svc
def voice_change_model():
request_form = request.form
wave_file = request.files.get("sample", None)
# 变调信息
f_... | null |
37,210 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,211 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,212 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,213 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,214 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,215 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,216 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,217 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,218 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,219 |
def local_model_refresh_fn():
choices = scan_local_models()
return gr.Dropdown.update(choices=choices) | null |
37,220 | import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices... | null |
37,221 | from time import time
import numpy as np
import pynvml
import torch
from torch.nn.functional import normalize
The provided code snippet includes necessary dependencies for implementing the `_kpp` function. Write a Python function `def _kpp(data: torch.Tensor, k: int, sample_size: int = -1)` to solve the following prob... | Picks k points in the data based on the kmeans++ method. Parameters ---------- data : torch.Tensor Expect a rank 1 or 2 array. Rank 1 is assumed to describe 1-D data, rank 2 multidimensional data, in which case one row is one observation. k : int Number of samples to generate. sample_size : int sample data to avoid mem... |
37,222 | import argparse
import logging
import os
import time
from pathlib import Path
import numpy as np
import torch
import tqdm
from kmeans import KMeansGPU
from sklearn.cluster import KMeans, MiniBatchKMeans
logger = logging.getLogger(__name__)
class KMeansGPU:
'''
Kmeans clustering algorithm implemented with PyTorch
... | null |
37,223 | import asyncio
import random
import sys
import edge_tts
from edge_tts import VoicesManager
from langdetect import DetectorFactory, detect
TEXT = sys.argv[1]
LANG = detect(TEXT) if sys.argv[2] == "Auto" else sys.argv[2]
RATE = sys.argv[3]
VOLUME = sys.argv[4]
GENDER = sys.argv[5] if len(sys.argv) == 6 else None
OUTPUT_F... | null |
37,224 | import argparse
import torch
from loguru import logger
from torch.optim import lr_scheduler
from diffusion.data_loaders import get_data_loaders
from diffusion.logger import utils
from diffusion.solver import train
from diffusion.unit2mel import Unit2Mel
from diffusion.vocoder import Vocoder
The provided code snippet i... | Parse command-line arguments. |
37,225 | import time
import librosa
import numpy as np
import torch
from torch import autocast
from torch.cuda.amp import GradScaler
from diffusion.logger import utils
from diffusion.logger.saver import Saver
def test(args, model, vocoder, loader_test, saver):
print(' [*] testing...')
model.eval()
# losses
test_... | null |
37,226 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `model_wrapper` function. Write a Python function `def model_wrapper( model, noise_schedule, model_type="noise", model_kwargs={}, guidance_type="uncond", condition=None, unconditional_con... | Create a wrapper function for the noise prediction model. |
37,227 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `interpolate_fn` function. Write a Python function `def interpolate_fn(x, xp, yp)` to solve the following problem:
A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a different... | A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd). The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) Args: x: PyTorch tensor with sha... |
37,228 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `expand_dims` function. Write a Python function `def expand_dims(v, dims)` to solve the following problem:
Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns... | Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns: a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. |
37,229 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def exists(x):
return x is not None
def default(val, d):
if exists(... | null |
37,230 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def extract(a, t):
return a[t].reshape((1, 1, 1, 1)) | null |
37,231 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def noise_like(shape, device, repeat=False):
def repeat_noise():
... | null |
37,232 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing ... | linear schedule |
37,233 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing ... | cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ |
37,234 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def extract_1(a, t):
return a[t].reshape((1, 1, 1, 1)) | null |
37,235 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def predict_stage0(noise_pred, noise_pred_prev):
return (noise_pred + n... | null |
37,236 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def predict_stage1(noise_pred, noise_list):
return (noise_pred * 3
... | null |
37,237 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def predict_stage2(noise_pred, noise_list):
return (noise_pred * 23
... | null |
37,238 | import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def predict_stage3(noise_pred, noise_list):
return (noise_pred * 55
... | null |
37,239 | import json
import os
import torch
import yaml
class DotDict(dict):
def __getattr__(*args):
def load_config(path_config):
with open(path_config, "r") as config:
args = yaml.safe_load(config)
args = DotDict(args)
# print(args)
return args | null |
37,240 | import json
import os
import torch
import yaml
def save_config(path_config,config):
config = dict(config)
with open(path_config, "w") as f:
yaml.dump(config, f) | null |
37,241 | import json
import os
import torch
import yaml
def to_json(path_params, path_json):
params = torch.load(path_params, map_location=torch.device('cpu'))
raw_state_dict = {}
for k, v in params.items():
val = v.flatten().numpy().tolist()
raw_state_dict[k] = val
with open(path_json, 'w') as... | null |
37,242 | import json
import os
import torch
import yaml
def convert_tensor_to_numpy(tensor, is_squeeze=True):
if is_squeeze:
tensor = tensor.squeeze()
if tensor.requires_grad:
tensor = tensor.detach()
if tensor.is_cuda:
tensor = tensor.cpu()
return tensor.numpy() | null |
37,243 | import json
import os
import torch
import yaml
def traverse_dir(
root_dir,
extensions,
amount=None,
str_include=None,
str_exclude=None,
is_pure=False,
is_sort=False,
is_ext=True):
file_list = []
cnt = 0
for root, _, files in os.walk(root_dir):
... | null |
37,244 | import os
import random
import librosa
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from utils import repeat_expand_2d
def traverse_dir(
root_dir,
extensions,
amount=None,
str_include=None,
str_exclude=None,
is_pure=False,
... | null |
37,245 | import os
import random
import librosa
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from utils import repeat_expand_2d
class AudioDataset(Dataset):
def __init__(
self,
filelists,
waveform_sec,
hop_size,
sample... | null |
37,246 | from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isf... | null |
37,247 | from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
def extract(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape... | null |
37,248 | from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
def noise_like(shape, device, repeat=False):
def repeat_noise():
return torch.randn((1, *shape[1:]), device=d... | null |
37,249 | from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `linear_beta_schedule` function. Write a P... | linear schedule |
37,250 | from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `cosine_beta_schedule` function. Write a P... | cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ |
37,251 | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import yaml
from diffusion_onnx import GaussianDiffusion
class DotDict(dict):
def __getattr__(*args):
val = dict.get(*args)
return DotDict(val) if type(val) is dict else val
__set... | null |
37,252 | import os
import numpy as np
import torch
import torch.nn as nn
import yaml
from .diffusion import GaussianDiffusion
from .vocoder import Vocoder
from .wavenet import WaveNet
class DotDict(dict):
def __getattr__(*args):
val = dict.get(*args)
return DotDict(val) if type(val) is dict... | null |
37,255 | import argparse
import concurrent.futures
import os
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import cpu_count
import librosa
import numpy as np
from rich.progress import track
from scipy.io import wavfile
def process(item):
spkdir, wav_name, args = item
speaker = spkdir.replace("\... | null |
37,256 | import math
import warnings
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Parameter
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return (
0.5 * x * (1... | Returns the activation function corresponding to `activation` |
37,257 | import math
import warnings
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__... | Initialize the weights specific to the BERT Model. This overrides the default initializations depending on the specified arguments. 1. If normal_init_linear_weights is set then weights of linear layer will be initialized using the normal distribution and bais will be set to the specified value. 2. If normal_init_embed_... |
37,258 | import math
import warnings
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Parameter
The provided code snippet includes necessary dependencies for implementing the `quant_noise` function. Write a Python function `def quant_noise(m... | Wraps modules and applies quantization noise to the weights for subsequent quantization with Iterative Product Quantization as described in "Training with Quantization Noise for Extreme Model Compression" Args: - module: nn.Module - p: amount of Quantization Noise - block_size: size of the blocks for subsequent quantiz... |
37,259 | import logging
import math
from typing import List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from vencoder.wavlm.modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GLU_Linear,
GradMultiply,
MultiheadAttention,
... | Computes random mask spans for a given shape Args: shape: the the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements mask_prob: probability for each token t... |
37,260 | import copy
import random
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as t_func
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
def _compute_mask(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
... | null |
37,261 | import copy
import random
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as t_func
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
class HubertSoft(Hubert):
def __init__(self):
super().__init__()
def units(self, wav: torch.Tens... | r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`. Args: path (str): path of a pretrained model |
37,263 | import copy
import random
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as t_func
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
class HubertSoft(Hubert):
def __init__(self):
super().__init__()
def units(self, wav: torch.Tens... | r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`. Args: path (str): path of a pretrained model |
37,264 | import json
import os
import sys
import zlib
from typing import Callable, TextIO
system_encoding = sys.getdefaultencoding()
if system_encoding != "utf-8":
else:
def make_safe(string):
# replaces any character not representable using the system default encoding with an '?',
# avoiding UnicodeEncodeError... | null |
37,265 | import json
import os
import sys
import zlib
from typing import Callable, TextIO
def make_safe(string):
# utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
return string | null |
37,266 | import json
import os
import sys
import zlib
from typing import Callable, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.