repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
Hello-Python | Basic/07_dicts.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc
### Dictionaries ###
# Definición
my_dict = dict()
my_other_dict = {}
print(type(my_dict))
print(type(my_other_dict))
my_other_dict = {"Nombre": "Brais",
"Apellido": "Moure", "Edad": 35, 1: "Python"}
my_dict = {
"Nombre": "Brais",
"Apellido":... | 77 | 1,379 |
Hello-Python | Basic/11_classes.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=29327
### Classes ###
# Definición
class MyEmptyPerson:
pass # Para poder dejar la clase vacía
print(MyEmptyPerson)
print(MyEmptyPerson())
# Clase con constructor, funciones y propiedades privadas y públicas
class Person:
def __init__(self, name, surname... | 42 | 988 |
Hello-Python | Basic/05_tuples.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=14711
### Tuples ###
# Definición
my_tuple = tuple()
my_other_tuple = ()
my_tuple = (35, 1.77, "Brais", "Moure", "Brais")
my_other_tuple = (35, 60, 30)
print(my_tuple)
print(type(my_tuple))
# Acceso a elementos y búsqueda
print(my_tuple[0])
print(my_tuple[-1])
# p... | 55 | 1,041 |
Hello-Python | Basic/my_module.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=34583
### Módulo para pruebas ###
def sumValue(numberOne, numberTwo, numberThree):
print(numberOne + numberTwo + numberThree)
def printValue(value):
print(value)
| 11 | 225 |
Hello-Python | Basic/01_variables.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=2938
### Variables ###
my_string_variable = "My String variable"
print(my_string_variable)
my_int_variable = 5
print(my_int_variable)
my_int_to_str_variable = str(my_int_variable)
print(my_int_to_str_variable)
print(type(my_int_to_str_variable))
my_bool_variable = F... | 48 | 1,092 |
Hello-Python | Basic/03_strings.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=8643
### Strings ###
my_string = "Mi String"
my_other_string = 'Mi otro String'
print(len(my_string))
print(len(my_other_string))
print(my_string + " " + my_other_string)
my_new_line_string = "Este es un String\ncon salto de línea"
print(my_new_line_string)
my_tab_s... | 66 | 1,481 |
Hello-Python | Basic/08_conditionals.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=21442
### Conditionals ###
# if
my_condition = False
if my_condition: # Es lo mismo que if my_condition == True:
print("Se ejecuta la condición del if")
my_condition = 5 * 5
if my_condition == 10:
print("Se ejecuta la condición del segundo if")
# if, elif... | 37 | 794 |
Hello-Python | Basic/10_functions.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=26619
### Functions ###
# Definición
def my_function():
print("Esto es una función")
my_function()
my_function()
my_function()
# Función con parámetros de entrada/argumentos
def sum_two_values(first_value: int, second_value):
print(first_value + second_va... | 71 | 1,408 |
Hello-Python | Basic/02_operators.py | .py | # Clase en vídeo: https://youtu.be/Kp4Mvapo5kc?t=5665
### Operadores Aritméticos ###
# Operaciones con enteros
print(3 + 4)
print(3 - 4)
print(3 * 4)
print(3 / 4)
print(10 % 3)
print(10 // 3)
print(2 ** 3)
print(2 ** 3 + 3 - 7 / 1 // 4)
# Operaciones con cadenas de texto
print("Hola " + "Python " + "¿Qué tal?")
prin... | 54 | 1,201 |
Hello-Python | Intermediate/01_list_comprehension.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=3239
### List Comprehension ###
my_original_list = [0, 1, 2, 3, 4, 5, 6, 7]
print(my_original_list)
my_range = range(8)
print(list(my_range))
# Definición
my_list = [i + 1 for i in range(8)]
print(my_list)
my_list = [i * 2 for i in range(8)]
print(my_list)
my_list... | 29 | 471 |
Hello-Python | Intermediate/05_error_types.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=12721
### Error Types ###
# SyntaxError
# print "¡Hola comunidad!" # Descomentar para Error
from math import pi
import math
print("¡Hola comunidad!")
# NameError
language = "Spanish" # Comentar para Error
print(language)
# IndexError
my_list = ["Python", "Swift", "K... | 52 | 1,129 |
Hello-Python | Intermediate/07_regular_expressions.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=19762
### Regular Expressions ###
import re
# match
my_string = "Esta es la lección número 7: Lección llamada Expresiones Regulares"
my_other_string = "Esta no es la lección número 6: Manejo de ficheros"
match = re.match("Esta es la lección", my_string, re.I)
print(... | 79 | 1,862 |
Hello-Python | Intermediate/08_python_package_manager.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=24010
### Python Package Manager ###
# PIP https://pypi.org
# pip install pip
# pip --version
# pip install numpy
import pandas
from mypackage import arithmetics
import requests
import numpy
print(numpy.version.version)
numpy_array = numpy.array([35, 24, 62, 52, 30... | 40 | 683 |
Hello-Python | Intermediate/00_dates.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU
### Dates ###
# Date time
from datetime import timedelta
from datetime import date
from datetime import time
from datetime import datetime
now = datetime.now()
def print_date(date):
print(date.year)
print(date.month)
print(date.day)
print(date.hour)
... | 76 | 1,254 |
Hello-Python | Intermediate/02_challenges.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=4142
### Challenges ###
"""
EL FAMOSO "FIZZ BUZZ”:
Escribe un programa que muestre por consola (con un print) los
números de 1 a 100 (ambos incluidos y con un salto de línea entre
cada impresión), sustituyendo los siguientes:
- Múltiplos de 3 por la palabra "fizz".
- M... | 116 | 2,686 |
Hello-Python | Intermediate/03_lambdas.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=9145
### Lambdas ###
sum_two_values = lambda first_value, second_value: first_value + second_value
print(sum_two_values(2, 4))
multiply_values = lambda first_value, second_value: first_value * second_value - 3
print(multiply_values(2, 4))
def sum_three_values(value):... | 14 | 435 |
Hello-Python | Intermediate/04_higher_order_functions.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=10172
### Higher Order Functions ###
from functools import reduce
def sum_one(value):
return value + 1
def sum_five(value):
return value + 5
def sum_two_values_and_add_value(first_value, second_value, f_sum):
return f_sum(first_value + second_value)
... | 70 | 1,207 |
Hello-Python | Intermediate/06_file_handling.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=15524
### File Handling ###
import xml
import csv
import json
import os
# .txt file
# Leer, escribir y sobrescribir si ya existe
txt_file = open("my_file.txt", "w+")
txt_file.write(
"Mi nombre es Brais\nMi apellido es Moure\n35 años\nY mi lenguaje preferido es P... | 100 | 2,349 |
Hello-Python | Intermediate/mypackage/arithmetics.py | .py | # Clase en vídeo: https://youtu.be/TbcEqkabAWU?t=24010
### Arithmetics ###
def sum_two_values(first_value, second_value):
return first_value + second_value
| 7 | 163 |
MockingBird | gen_voice.py | .py | from models.synthesizer.inference import Synthesizer
from models.encoder import inference as encoder
from models.vocoder.hifigan import inference as gan_vocoder
from pathlib import Path
import numpy as np
import soundfile as sf
import torch
import sys
import os
import re
import cn2an
vocoder = gan_vocoder
def gen_one... | 121 | 4,446 |
MockingBird | train.py | .py | import argparse
def main():
# Arguments
preparser = argparse.ArgumentParser(description=
'Training model.')
preparser.add_argument('--type', type=str,
help='type of training ')
###
paras, _ = preparser.parse_known_args()
if paras.type == "synth":
fr... | 21 | 541 |
MockingBird | run.py | .py | import time
import os
import argparse
import torch
import glob
from pathlib import Path
from tqdm import tqdm
from models.ppg_extractor import load_model
import librosa
import soundfile as sf
from utils.hparams import HpsYaml
from models.encoder.audio import preprocess_wav
from models.encoder import inference as speac... | 142 | 4,626 |
MockingBird | demo_toolbox.py | .py | from pathlib import Path
from control.toolbox import Toolbox
from utils.argutils import print_args
from utils.modelutils import check_model_paths
import argparse
import os
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Runs the toolbox",
formatter_class=argparse.Argument... | 50 | 2,490 |
MockingBird | pre.py | .py | from models.synthesizer.preprocess import create_embeddings, preprocess_dataset, create_emo
from models.synthesizer.hparams import hparams
from pathlib import Path
import argparse
recognized_datasets = [
"aidatatang_200zh",
"aidatatang_200zh_s",
"magicdata",
"aishell3",
"data_aishell"
]
#TODO: add... | 78 | 4,066 |
MockingBird | monotonic_align/setup.py | .py | from distutils.core import setup
from Cython.Build import cythonize
import numpy
setup(
name = 'monotonic_align',
ext_modules = cythonize("core.pyx"),
include_dirs=[numpy.get_include()]
)
| 10 | 195 |
MockingBird | monotonic_align/__init__.py | .py | import numpy as np
import torch
from .monotonic_align.core import maximum_path_c
def maximum_path(neg_cent, mask):
""" Cython optimized version.
neg_cent: [b, t_t, t_s]
mask: [b, t_t, t_s]
"""
device = neg_cent.device
dtype = neg_cent.dtype
neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
pat... | 20 | 612 |
MockingBird | control/toolbox/__init__.py | .py | from control.toolbox.ui import UI
from models.encoder import inference as encoder
from models.synthesizer.inference import Synthesizer
from models.vocoder.wavernn import inference as rnn_vocoder
from models.vocoder.hifigan import inference as gan_vocoder
from models.vocoder.fregan import inference as fgan_vocoder
from ... | 477 | 19,342 |
MockingBird | control/toolbox/ui.py | .py | from PyQt5.QtCore import Qt, QStringListModel
from PyQt5 import QtGui
from PyQt5.QtWidgets import *
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from models.encoder.inference import plot_embedding_as_heatmap
from control.toolbox.utterance import Uttera... | 701 | 28,772 |
MockingBird | control/cli/encoder_train.py | .py | from utils.argutils import print_args
from models.encoder.train import train
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Trains the speaker encoder. You must have run encoder_preprocess.py first.",
formatter_class=argparse.Argu... | 47 | 2,350 |
MockingBird | control/cli/vocoder_preprocess.py | .py | from models.synthesizer.synthesize import run_synthesis
from models.synthesizer.hparams import hparams
from utils.argutils import print_args
import argparse
import os
if __name__ == "__main__":
class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
pass
p... | 60 | 2,792 |
MockingBird | control/cli/ppg2mel_train.py | .py | import sys
import torch
import argparse
import numpy as np
from utils.hparams import HpsYaml
from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
# For reproducibility, comment these may speed up training
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def main(... | 68 | 3,015 |
MockingBird | control/cli/train_ppg2mel.py | .py | import sys
import torch
import argparse
import numpy as np
from utils.hparams import HpsYaml
from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
# For reproducibility, comment these may speed up training
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def main(... | 67 | 3,014 |
MockingBird | control/cli/vocoder_train.py | .py | from utils.argutils import print_args
from models.vocoder.wavernn.train import train
from models.vocoder.hifigan.train import train as train_hifigan
from models.vocoder.fregan.train import train as train_fregan
from utils.util import AttrDict
from pathlib import Path
import argparse
import json
import torch
import torc... | 92 | 4,512 |
MockingBird | control/cli/pre4ppg.py | .py | from pathlib import Path
import argparse
from models.ppg2mel.preprocess import preprocess_dataset
from pathlib import Path
import argparse
recognized_datasets = [
"aidatatang_200zh",
"aidatatang_200zh_s", # sample
]
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="P... | 50 | 2,411 |
MockingBird | control/cli/synthesizer_train.py | .py | from models.synthesizer.hparams import hparams
from models.synthesizer.train import train
from utils.argutils import print_args
import argparse
def new_train():
parser = argparse.ArgumentParser()
parser.add_argument("run_id", type=str, help= \
"Name for this model instance. If a model state from the sa... | 40 | 1,931 |
MockingBird | control/cli/encoder_preprocess.py | .py | import argparse
from pathlib import Path
from models.encoder.preprocess import (preprocess_aidatatang_200zh,
preprocess_librispeech, preprocess_voxceleb1,
preprocess_voxceleb2)
from utils.argutils import print_args
if __name__ == "__main__":
class My... | 65 | 3,032 |
MockingBird | control/mkgui/app.py | .py | from pydantic import BaseModel, Field
import os
from pathlib import Path
from enum import Enum
from models.encoder import inference as encoder
import librosa
from scipy.io.wavfile import write
import re
import numpy as np
from control.mkgui.base.components.types import FileContent
from models.vocoder.hifigan import inf... | 151 | 6,088 |
MockingBird | control/mkgui/preprocess.py | .py | from pydantic import BaseModel, Field
import os
from pathlib import Path
from enum import Enum
from typing import Any, Tuple
# Constants
EXT_MODELS_DIRT = f"data{os.sep}ckpt{os.sep}ppg_extractor"
ENC_MODELS_DIRT = f"data{os.sep}ckpt{os.sep}encoder"
if os.path.isdir(EXT_MODELS_DIRT):
extractors = Enum('extr... | 96 | 3,297 |
MockingBird | control/mkgui/train.py | .py | from pydantic import BaseModel, Field
import os
from pathlib import Path
from enum import Enum
from typing import Any
from models.synthesizer.hparams import hparams
from models.synthesizer.train import train as synt_train
# Constants
SYN_MODELS_DIRT = f"data{os.sep}ckpt{os.sep}synthesizer"
ENC_MODELS_DIRT = f"data{os.... | 106 | 3,648 |
MockingBird | control/mkgui/train_vc.py | .py | from pydantic import BaseModel, Field
import os
from pathlib import Path
from enum import Enum
from typing import Any, Tuple
import numpy as np
from utils.hparams import HpsYaml
from utils.util import AttrDict
import torch
# Constants
EXT_MODELS_DIRT = f"data{os.sep}ckpt{os.sep}ppg_extractor"
CONV_MODELS_DIRT = f"data... | 155 | 5,531 |
MockingBird | control/mkgui/app_vc.py | .py | import os
from enum import Enum
from pathlib import Path
from typing import Any, Tuple
import librosa
import matplotlib.pyplot as plt
import torch
from pydantic import BaseModel, Field
from scipy.io.wavfile import write
import models.ppg2mel as Convertor
import models.ppg_extractor as Extractor
from control.mkgui.bas... | 166 | 7,093 |
MockingBird | control/mkgui/base/core.py | .py | import importlib
import inspect
import re
from typing import Any, Callable, Type, Union, get_type_hints
from pydantic import BaseModel, parse_raw_as
from pydantic.tools import parse_obj_as
def name_to_title(name: str) -> str:
"""Converts a camelCase or snake_case name to title case."""
# If camelCase -> conv... | 204 | 6,269 |
MockingBird | control/mkgui/base/api/fastapi_utils.py | .py | """Collection of utilities for FastAPI apps."""
import inspect
from typing import Any, Type
from fastapi import FastAPI, Form
from pydantic import BaseModel
def as_form(cls: Type[BaseModel]) -> Any:
"""Adds an as_form class method to decorated models.
The as_form class method can be used with FastAPI endpo... | 103 | 3,397 |
MockingBird | control/mkgui/base/components/types.py | .py | import base64
from typing import Any, Dict, overload
class FileContent(str):
def as_bytes(self) -> bytes:
return base64.b64decode(self, validate=True)
def as_str(self) -> str:
return self.as_bytes().decode()
@classmethod
def __modify_schema__(cls, field_schema: Dict[str, Any]) -> Non... | 47 | 1,550 |
MockingBird | control/mkgui/base/components/outputs.py | .py | from typing import List
from pydantic import BaseModel
class ScoredLabel(BaseModel):
label: str
score: float
class ClassificationOutput(BaseModel):
__root__: List[ScoredLabel]
def __iter__(self): # type: ignore
return iter(self.__root__)
def __getitem__(self, item): # type: ignore
... | 44 | 1,186 |
MockingBird | control/mkgui/base/ui/streamlit_ui.py | .py | import datetime
import inspect
import mimetypes
import sys
from os import getcwd, unlink, path
from platform import system
from tempfile import NamedTemporaryFile
from typing import Any, Callable, Dict, List, Type
from PIL import Image
import pandas as pd
import streamlit as st
from fastapi.encoders import jsonable_en... | 934 | 36,541 |
MockingBird | control/mkgui/base/ui/streamlit_utils.py | .py | CUSTOM_STREAMLIT_CSS = """
div[data-testid="stBlock"] button {
width: 100% !important;
margin-bottom: 20px !important;
border-color: #bfbfbf !important;
}
section[data-testid="stSidebar"] div {
max-width: 10rem;
}
pre code {
white-space: pre-wrap;
}
"""
| 14 | 266 |
MockingBird | control/mkgui/base/ui/schema_utils.py | .py | from typing import Dict
def resolve_reference(reference: str, references: Dict) -> Dict:
return references[reference.split("/")[-1]]
def get_single_reference_item(property: Dict, references: Dict) -> Dict:
# Ref can either be directly in the properties or the first element of allOf
reference = property.... | 136 | 3,765 |
MockingBird | utils/f0_utils.py | .py | import logging
import numpy as np
import pyworld
from scipy.interpolate import interp1d
from scipy.signal import firwin, get_window, lfilter
def compute_mean_std(lf0):
nonzero_indices = np.nonzero(lf0)
mean = np.mean(lf0[nonzero_indices])
std = np.std(lf0[nonzero_indices])
return mean, std
def compu... | 125 | 3,607 |
MockingBird | utils/util.py | .py | import matplotlib
from torch.nn import functional as F
import torch
matplotlib.use('Agg')
import time
class Timer():
''' Timer for recording training time distribution. '''
def __init__(self):
self.prev_t = time.time()
self.clear()
def set(self):
self.prev_t = time.time()
def... | 147 | 4,371 |
MockingBird | utils/data_load.py | .py | import random
import numpy as np
import torch
from utils.f0_utils import get_cont_lf0
import resampy
from .audio_utils import MAX_WAV_VALUE, load_wav, mel_spectrogram
from librosa.util import normalize
import os
SAMPLE_RATE=16000
def read_fids(fid_list_f):
with open(fid_list_f, 'r') as f:
fids = [l.strip... | 215 | 8,253 |
MockingBird | utils/loss.py | .py | import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
loss += torch.mean(torch.abs(rl - gl))
return loss*2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses =... | 54 | 1,210 |
MockingBird | utils/audio_utils.py | .py | import numpy as np
import torch
import torch.utils.data
from scipy.io.wavfile import read
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
mel_basis = {}
hann_window = {}
def load_wav(full_path):
sampling_rate, data = read(full_path)
return data, sampling_rate
def load_wav_to_torch(... | 99 | 3,452 |
MockingBird | utils/modelutils.py | .py | from pathlib import Path
def check_model_paths(encoder_path: Path, synthesizer_path: Path, vocoder_path: Path):
# This function tests the model paths and makes sure at least one is valid.
if encoder_path.is_file() or encoder_path.is_dir():
return
if synthesizer_path.is_file() or synthesizer_path.is... | 17 | 767 |
MockingBird | utils/logmmse.py | .py | # The MIT License (MIT)
#
# Copyright (c) 2015 braindead
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modif... | 248 | 9,181 |
MockingBird | utils/hparams.py | .py | import yaml
import json
import ast
def load_hparams_json(filename):
with open(filename, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
def load_hparams_yaml(filename):
stream = open(filename, 'r')
docs = yaml.safe_load_all(stream)
... | 111 | 3,436 |
MockingBird | skills/speak/scripts/render_timeline.py | .py | #!/usr/bin/env python3
"""Timeline mode: render SRT to timeline-accurate audio.
Supports two backends:
- kokoro (default): local CLI, uses ffmpeg atempo for duration matching
- noiz: cloud API with server-side duration forcing, emotion, voice cloning
Parses SRT, resolves per-segment voice config from a voice-map ... | 487 | 16,730 |
MockingBird | skills/speak/scripts/text_to_srt.py | .py | #!/usr/bin/env python3
"""Convert plain text to SRT with auto-estimated timings.
Splits text into sentences, estimates duration per sentence based on
character-per-second rate, and writes a valid SRT file.
"""
import argparse
import re
import sys
from pathlib import Path
from typing import List, Tuple
SENTENCE_SPLIT... | 116 | 3,559 |
MockingBird | skills/speak/scripts/noiz_tts.py | .py | #!/usr/bin/env python3
"""Simple TTS via Noiz API (no timeline).
Supports direct text or text-file input, optional emotion enhancement,
voice cloning via reference audio, and emotion parameters.
Use kokoro-tts CLI directly for the Kokoro backend (no wrapper needed).
"""
import argparse
import base64
import binascii
im... | 202 | 6,490 |
MockingBird | models/synthesizer/inference.py | .py | import torch
from models.synthesizer import audio
from models.synthesizer.hparams import hparams
from models.synthesizer.models.tacotron import Tacotron
from models.synthesizer.utils.symbols import symbols
from models.synthesizer.utils.text import text_to_sequence
from models.vocoder.display import simple_table
from pa... | 186 | 7,979 |
MockingBird | models/synthesizer/preprocess.py | .py | from multiprocessing.pool import Pool
from functools import partial
from itertools import chain
from pathlib import Path
from tqdm import tqdm
import numpy as np
from models.encoder import inference as encoder
from models.synthesizer.preprocess_audio import preprocess_general, extract_emo
from models.synthesizer.prep... | 157 | 7,383 |
MockingBird | models/synthesizer/vits_dataset.py | .py | import os
import random
import numpy as np
import torch.nn.functional as F
import torch
import torch.utils.data
from utils.audio_utils import load_wav_to_torch, spectrogram
from utils.util import intersperse
from models.synthesizer.utils.text import text_to_sequence
"""Multi speaker version"""
class VitsDataset(torc... | 263 | 10,031 |
MockingBird | models/synthesizer/train_vits.py | .py | import os
from loguru import logger
import torch
import glob
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, G... | 394 | 16,830 |
MockingBird | models/synthesizer/train.py | .py | import torch
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from models.synthesizer import audio
from models.synthesizer.models.tacotron import Tacotron
from models.synthesizer.synthesizer_dataset import SynthesizerDatase... | 317 | 14,597 |
MockingBird | models/synthesizer/gst_hyperparameters.py | .py | class GSTHyperparameters():
E = 512
# reference encoder
ref_enc_filters = [32, 32, 64, 64, 128, 128]
# style token layer
token_num = 10
# token_emb_size = 256
num_heads = 8
n_mels = 256 # Number of Mel banks to generate
| 14 | 261 |
MockingBird | models/synthesizer/preprocess_audio.py | .py | import librosa
import numpy as np
from models.encoder import inference as encoder
from utils import logmmse
from models.synthesizer import audio
from pathlib import Path
from pypinyin import Style
from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
from pypinyin.converter import DefaultConverter
from pypin... | 132 | 5,582 |
MockingBird | models/synthesizer/preprocess_transcript.py | .py | def preprocess_transcript_aishell3(dict_info, dict_transcript):
for v in dict_transcript:
if not v:
continue
v = v.strip().replace("\n","").replace("\t"," ").split(" ")
transList = []
for i in range(2, len(v), 2):
transList.append(v[i])
dict_info[v[0]]... | 18 | 596 |
MockingBird | models/synthesizer/audio.py | .py | import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
import soundfile as sf
def load_wav(path, sr):
return librosa.core.load(path, sr=sr)[0]
def save_wav(wav, path, sr):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
#proposed by @dsmiller
wavf... | 207 | 7,790 |
MockingBird | models/synthesizer/synthesize.py | .py | import torch
from torch.utils.data import DataLoader
from models.synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
from models.synthesizer.models.tacotron import Tacotron
from models.synthesizer.utils.symbols import symbols
import numpy as np
from pathlib import Path
from tqdm import tqdm
i... | 97 | 4,265 |
MockingBird | models/synthesizer/synthesizer_dataset.py | .py | import torch
from torch.utils.data import Dataset
import numpy as np
from pathlib import Path
from models.synthesizer.utils.text import text_to_sequence
class SynthesizerDataset(Dataset):
def __init__(self, metadata_fpath: Path, mel_dir: Path, embed_dir: Path, hparams):
print("Using inputs from:\n\t%s\n\t... | 94 | 3,460 |
MockingBird | models/synthesizer/hparams.py | .py | from utils.hparams import HParams
hparams = HParams(
### Signal Processing (used in both synthesizer and vocoder)
sample_rate = 16000,
n_fft = 1024, # filter_length
num_mels = 80,
hop_size = 256, # Tacotron uses 12.5 ms frame shift (set to sample_rate... | 79 | 4,565 |
MockingBird | models/synthesizer/utils/cleaners.py | .py | """
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You"ll typically want to use:
1. "english_cleaners" for English text
2. "tr... | 89 | 2,419 |
MockingBird | models/synthesizer/utils/symbols.py | .py | """
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run
through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details.
"""
# from . import cmudict
_pad = "_"
_eos = "~... | 19 | 779 |
MockingBird | models/synthesizer/utils/plot.py | .py | import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
MATPLOTLIB_FLAG = False
def split_title_line(title_text, max_words=5):
"""
A function that splits any string based on specific character
(returning it with the string), with maximum number of words on it
"""
seq = title_te... | 168 | 4,974 |
MockingBird | models/synthesizer/utils/text.py | .py | from .symbols import symbols
from . import cleaners
import re
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
# Regular expression matching text enclosed in curly braces:
_curly_re = re.compile(r"(.*?)\{(.... | 75 | 2,162 |
MockingBird | models/synthesizer/utils/numbers.py | .py | import re
import inflect
_inflect = inflect.engine()
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
_number_re = ... | 69 | 2,116 |
MockingBird | models/synthesizer/models/wav2emo.py | .py | import torch
import torch.nn as nn
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
class RegressionHead(nn.Module):
r"""Classification head."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_s... | 51 | 1,300 |
MockingBird | models/synthesizer/models/vits.py | .py | import math
import torch
from torch import nn
from torch.nn import functional as F
from loguru import logger
from .sublayer.vits_modules import *
import monotonic_align
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from utils.util import... | 529 | 19,198 |
MockingBird | models/synthesizer/models/base.py | .py | import torch
import torch.nn as nn
import imp
import numpy as np
class Base(nn.Module):
def __init__(self, stop_threshold):
super().__init__()
self.init_model()
self.num_params()
self.register_buffer("step", torch.zeros(1, dtype=torch.long))
self.register_buffer("stop_thre... | 78 | 2,600 |
MockingBird | models/synthesizer/models/tacotron.py | .py | import torch
import torch.nn as nn
from .sublayer.global_style_token import GlobalStyleToken
from .sublayer.pre_net import PreNet
from .sublayer.cbhg import CBHG
from .sublayer.lsa import LSA
from .base import Base
from models.synthesizer.gst_hyperparameters import GSTHyperparameters as gst_hp
from models.synthesizer.h... | 299 | 14,355 |
MockingBird | models/synthesizer/models/sublayer/vits_modules.py | .py | import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d
from torch.nn.utils import weight_norm, remove_weight_norm
from utils.util import init_weights, get_padding, convert_pad_shape, convert_pad_shape, subsequent_mask, fused_add_tanh_sigmoid_multiply
from .common... | 676 | 24,696 |
MockingBird | models/synthesizer/models/sublayer/global_style_token.py | .py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as tFunctional
from models.synthesizer.gst_hyperparameters import GSTHyperparameters as hp
from models.synthesizer.hparams import hparams
class GlobalStyleToken(nn.Module):
"""
inputs: style mel spectrograms [batch_size... | 146 | 5,381 |
MockingBird | models/synthesizer/models/sublayer/cbhg.py | .py | import torch
import torch.nn as nn
from .common.batch_norm_conv import BatchNormConv
from .common.highway_network import HighwayNetwork
class CBHG(nn.Module):
def __init__(self, K, in_channels, channels, proj_channels, num_highways):
super().__init__()
# List of all rnns to call `flatten_parameter... | 86 | 2,917 |
MockingBird | models/synthesizer/models/sublayer/pre_net.py | .py | import torch.nn as nn
import torch.nn.functional as F
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
def forward(... | 28 | 788 |
MockingBird | models/synthesizer/models/sublayer/lsa.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
class LSA(nn.Module):
def __init__(self, attn_dim, kernel_size=31, filters=32):
super().__init__()
self.conv = nn.Conv1d(1, filters, padding=(kernel_size - 1) // 2, kernel_size=kernel_size, bias=True)
self.L = nn.Linear(filt... | 43 | 1,609 |
MockingBird | models/synthesizer/models/sublayer/common/transforms.py | .py | import torch
from torch.nn import functional as F
import numpy as np
DEFAULT_MIN_BIN_WIDTH = 1e-3
DEFAULT_MIN_BIN_HEIGHT = 1e-3
DEFAULT_MIN_DERIVATIVE = 1e-3
def piecewise_rational_quadratic_transform(inputs,
unnormalized_widths,
... | 194 | 8,490 |
MockingBird | models/synthesizer/models/sublayer/common/highway_network.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
self.W1.bias.data.fill_(0.)
def forward(self, x):
x1 = self.W... | 18 | 438 |
MockingBird | models/synthesizer/models/sublayer/common/batch_norm_conv.py | .py | import torch.nn as nn
import torch.nn.functional as F
class BatchNormConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel, relu=True):
super().__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False)
self.bnorm = nn.Batc... | 14 | 497 |
MockingBird | models/encoder/inference.py | .py | from models.encoder.params_data import *
from models.encoder.model import SpeakerEncoder
from models.encoder.audio import preprocess_wav # We want to expose this function from here
from matplotlib import cm
from models.encoder import audio
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
im... | 196 | 8,661 |
MockingBird | models/encoder/preprocess.py | .py | from multiprocess.pool import ThreadPool
from models.encoder.params_data import *
from models.encoder.config import librispeech_datasets, anglophone_nationalites
from datetime import datetime
from models.encoder import audio
from pathlib import Path
from tqdm import tqdm
import numpy as np
class DatasetLog:
"""
... | 185 | 8,200 |
MockingBird | models/encoder/train.py | .py | from models.encoder.visualizations import Visualizations
from models.encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset
from models.encoder.params_model import *
from models.encoder.model import SpeakerEncoder
from utils.profiler import Profiler
from pathlib import Path
import torch
... | 124 | 5,102 |
MockingBird | models/encoder/model.py | .py | from models.encoder.params_model import *
from models.encoder.params_data import *
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve
from torch.nn.utils import clip_grad_norm_
from scipy.optimize import brentq
from torch import nn
import numpy as np
import torch
class SpeakerEncoder(nn.Modu... | 136 | 6,326 |
MockingBird | models/encoder/audio.py | .py | from scipy.ndimage.morphology import binary_dilation
from models.encoder.params_data import *
from pathlib import Path
from typing import Optional, Union
from warnings import warn
import numpy as np
import librosa
import struct
try:
import webrtcvad
except:
warn("Unable to import 'webrtcvad'. This package enab... | 118 | 4,706 |
MockingBird | models/encoder/visualizations.py | .py | from models.encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
from datetime import datetime
from time import perf_counter as timer
import matplotlib.pyplot as plt
import numpy as np
# import webbrowser
import visdom
import umap
colormap = np.array([
[76, 255, 0],
[0, 127, 70],... | 178 | 6,677 |
MockingBird | models/encoder/data_objects/speaker.py | .py | from models.encoder.data_objects.random_cycler import RandomCycler
from models.encoder.data_objects.utterance import Utterance
from pathlib import Path
# Contains the set of utterances of a single speaker
class Speaker:
def __init__(self, root: Path):
self.root = root
self.name = root.name
... | 41 | 1,852 |
MockingBird | models/encoder/data_objects/speaker_verification_dataset.py | .py | from models.encoder.data_objects.random_cycler import RandomCycler
from models.encoder.data_objects.speaker_batch import SpeakerBatch
from models.encoder.data_objects.speaker import Speaker
from models.encoder.params_data import partials_n_frames
from torch.utils.data import Dataset, DataLoader
from pathlib import Path... | 56 | 2,174 |
MockingBird | models/encoder/data_objects/speaker_batch.py | .py | import numpy as np
from typing import List
from models.encoder.data_objects.speaker import Speaker
class SpeakerBatch:
def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
self.speakers = speakers
self.partials = {s: s.random_partial(utterances_per_speaker, n_fra... | 13 | 623 |
MockingBird | models/vocoder/display.py | .py | import matplotlib.pyplot as plt
import time
import numpy as np
import sys
def progbar(i, n, size=16):
done = (i * size) // n
bar = ''
for i in range(size):
bar += '█' if i <= done else '░'
return bar
def stream(message) :
try:
sys.stdout.write("\r{%s}" % message)
except:
... | 129 | 3,159 |
MockingBird | models/vocoder/vocoder_dataset.py | .py | from torch.utils.data import Dataset
from pathlib import Path
from models.vocoder.wavernn import audio
import models.vocoder.wavernn.hparams as hp
import numpy as np
import torch
class VocoderDataset(Dataset):
def __init__(self, metadata_fpath: Path, mel_dir: Path, wav_dir: Path):
print("Using inputs from... | 84 | 3,144 |
MockingBird | models/vocoder/wavernn/inference.py | .py | from models.vocoder.wavernn.models.fatchord_version import WaveRNN
from models.vocoder.wavernn import hparams as hp
import torch
_model = None # type: WaveRNN
def load_model(weights_fpath, verbose=True):
global _model, _device
if verbose:
print("Building Wave-RNN")
_model = WaveRNN(
... | 65 | 1,834 |
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