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kalomaze
2023-08-02 01:13:54 +00:00
committed by github-actions[bot]
parent d688fcf075
commit 18dd5aa102
6 changed files with 994 additions and 530 deletions
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3
LazyImport.py
View File

@@ -1,6 +1,7 @@
from importlib.util import find_spec, LazyLoader, module_from_spec
from sys import modules
def lazyload(name):
if name in modules:
return modules[name]
@@ -10,4 +11,4 @@ def lazyload(name):
module = module_from_spec(spec)
modules[name] = module
loader.exec_module(module)
return module
return module

6
i18n.py
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@@ -1,5 +1,6 @@
import json
def load_language_list(language):
try:
with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
@@ -20,8 +21,10 @@ class I18nAuto:
>>> i18n.print()
Using Language: en_US
"""
def __init__(self, language=None):
from locale import getdefaultlocale
language = language or getdefaultlocale()[0]
if not self._language_exists(language):
language = "en_US"
@@ -32,6 +35,7 @@ class I18nAuto:
@staticmethod
def _language_exists(language):
from os.path import exists
return exists(f"./i18n/{language}.json")
def __call__(self, key):
@@ -40,4 +44,4 @@ class I18nAuto:
def print(self):
"""Prints the language currently in use."""
print(f"Using Language: {self.language}")
print(f"Using Language: {self.language}")

1335
infer-web.py
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File diff suppressed because it is too large Load Diff

95
my_utils.py
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@@ -10,21 +10,24 @@ import random
import csv
platform_stft_mapping = {
'linux': 'stftpitchshift',
'darwin': 'stftpitchshift',
'win32': 'stftpitchshift.exe',
"linux": "stftpitchshift",
"darwin": "stftpitchshift",
"win32": "stftpitchshift.exe",
}
stft = platform_stft_mapping.get(sys.platform)
def CSVutil(file, rw, type, *args):
if type == 'formanting':
if rw == 'r':
if type == "formanting":
if rw == "r":
with open(file) as fileCSVread:
csv_reader = list(csv.reader(fileCSVread))
return (
csv_reader[0][0], csv_reader[0][1], csv_reader[0][2]
) if csv_reader is not None else (lambda: exec('raise ValueError("No data")'))()
(csv_reader[0][0], csv_reader[0][1], csv_reader[0][2])
if csv_reader is not None
else (lambda: exec('raise ValueError("No data")'))()
)
else:
if args:
doformnt = args[0]
@@ -32,65 +35,74 @@ def CSVutil(file, rw, type, *args):
doformnt = False
qfr = args[1] if len(args) > 1 else 1.0
tmb = args[2] if len(args) > 2 else 1.0
with open(file, rw, newline='') as fileCSVwrite:
csv_writer = csv.writer(fileCSVwrite, delimiter=',')
with open(file, rw, newline="") as fileCSVwrite:
csv_writer = csv.writer(fileCSVwrite, delimiter=",")
csv_writer.writerow([doformnt, qfr, tmb])
elif type == 'stop':
elif type == "stop":
stop = args[0] if args else False
with open(file, rw, newline='') as fileCSVwrite:
csv_writer = csv.writer(fileCSVwrite, delimiter=',')
with open(file, rw, newline="") as fileCSVwrite:
csv_writer = csv.writer(fileCSVwrite, delimiter=",")
csv_writer.writerow([stop])
def load_audio(file, sr, DoFormant, Quefrency, Timbre):
converted = False
DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting")
try:
file = (
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
file = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
if (DoFormant.lower() == 'true'):
numerator = round(random.uniform(1,4), 4)
if DoFormant.lower() == "true":
numerator = round(random.uniform(1, 4), 4)
if not file.endswith(".wav"):
if not os.path.isfile(f"{file_formanted}.wav"):
converted = True
#print(f"\nfile = {file}\n")
#print(f"\nfile_formanted = {file_formanted}\n")
# print(f"\nfile = {file}\n")
# print(f"\nfile_formanted = {file_formanted}\n")
converting = (
ffmpeg.input(file_formanted, threads = 0)
ffmpeg.input(file_formanted, threads=0)
.output(f"{RQuote(file_formanted)}.wav")
.run(
cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
cmd=["ffmpeg", "-nostdin"],
capture_stdout=True,
capture_stderr=True,
)
)
else:
pass
file_formanted = f"{file_formanted}.wav" if not file_formanted.endswith(".wav") else file_formanted
file_formanted = (
f"{file_formanted}.wav"
if not file_formanted.endswith(".wav")
else file_formanted
)
print(f" · Formanting {file_formanted}...\n")
command = (
f'{RQuote(stft)} -i "{RQuote(file_formanted)}" -q "{RQuote(Quefrency)}" '
f'-t "{RQuote(Timbre)}" -o "{RQuote(file_formanted)}FORMANTED_{RQuote(str(numerator))}.wav"'
)
os.system(command)
print(f" · Formanted {file_formanted}!\n")
out, _ = (
ffmpeg.input(f"{file_formanted}FORMANTED_{str(numerator)}.wav", threads=0)
ffmpeg.input(
f"{file_formanted}FORMANTED_{str(numerator)}.wav", threads=0
)
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
.run(
cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
)
)
try: os.remove(f"{file_formanted}FORMANTED_{str(numerator)}.wav")
except Exception as e: pass; print(f"couldn't remove formanted type of file due to {e}")
try:
os.remove(f"{file_formanted}FORMANTED_{str(numerator)}.wav")
except Exception as e:
pass
print(f"couldn't remove formanted type of file due to {e}")
else:
out, _ = (
ffmpeg.input(file, threads=0)
@@ -101,12 +113,15 @@ def load_audio(file, sr, DoFormant, Quefrency, Timbre):
)
except Exception as e:
raise RuntimeError(f"Failed to load audio: {e}")
if converted:
try: os.remove(file_formanted)
except Exception as e: pass; print(f"Couldn't remove converted type of file due to {e}")
try:
os.remove(file_formanted)
except Exception as e:
pass
print(f"Couldn't remove converted type of file due to {e}")
converted = False
return np.frombuffer(out, np.float32).flatten()
@@ -119,7 +134,7 @@ def check_audio_duration(file):
probe = ffmpeg.probe(file)
# Extract the duration from the probe result
duration = float(probe['streams'][0]['duration'])
duration = float(probe["streams"][0]["duration"])
# If the duration is less than 0.75 seconds, print the message and exit the loop
if duration < 0.76:
@@ -132,4 +147,4 @@ def check_audio_duration(file):
return True
except Exception as e:
raise RuntimeError(f"Failed to check audio duration: {e}")
raise RuntimeError(f"Failed to check audio duration: {e}")

79
tensorlowest.py
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@@ -6,11 +6,12 @@ from re import search as RSearch
import pandas as pd
from ast import literal_eval as LEval
weights_dir = 'weights/'
weights_dir = "weights/"
def find_biggest_tensorboard(tensordir):
try:
files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
files = [f for f in os.listdir(tensordir) if f.endswith(".0")]
if not files:
print("No files with the '.0' extension found!")
return
@@ -32,92 +33,96 @@ def find_biggest_tensorboard(tensordir):
print("Couldn't find your model!")
return
def main(model_name, save_freq, lastmdls):
global lowestval_weight_dir, scl
tensordir = os.path.join('logs', model_name)
tensordir = os.path.join("logs", model_name)
lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
latest_file = find_biggest_tensorboard(tensordir)
if latest_file is None:
print("Couldn't find a valid tensorboard file!")
return
tfile = os.path.join(tensordir, latest_file)
ea = event_accumulator.EventAccumulator(tfile,
ea = event_accumulator.EventAccumulator(
tfile,
size_guidance={
event_accumulator.COMPRESSED_HISTOGRAMS: 500,
event_accumulator.IMAGES: 4,
event_accumulator.AUDIO: 4,
event_accumulator.SCALARS: 0,
event_accumulator.HISTOGRAMS: 1,
})
event_accumulator.COMPRESSED_HISTOGRAMS: 500,
event_accumulator.IMAGES: 4,
event_accumulator.AUDIO: 4,
event_accumulator.SCALARS: 0,
event_accumulator.HISTOGRAMS: 1,
},
)
ea.Reload()
ea.Tags()
scl = ea.Scalars('loss/g/total')
scl = ea.Scalars("loss/g/total")
listwstep = {}
for val in scl:
if (val.step // save_freq) * save_freq in [val.step for val in scl]:
listwstep[float(val.value)] = (val.step // save_freq) * save_freq
lowest_vals = sorted(listwstep.keys())[:lastmdls]
sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
sorted_dict = {
value: step for value, step in listwstep.items() if value in lowest_vals
}
return sorted_dict
def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
os.makedirs(lowestval_weight_dir, exist_ok=True)
logdir = []
files = []
lbldict = {
'Values': {},
'Names': {}
}
lbldict = {"Values": {}, "Names": {}}
weights_dir_path = os.path.join(weights_dir, "")
low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
try:
file_dict = LEval(file_dict)
except Exception as e:
except Exception as e:
print(f"Error! {e}")
return f"Couldn't load tensorboard file! {e}"
weights = [f for f in os.scandir(weights_dir)]
for key, value in file_dict.items():
pattern = fr"^{model_name}_.*_s{value}\.pth$"
matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
pattern = rf"^{model_name}_.*_s{value}\.pth$"
matching_weights = [
f.name for f in weights if f.is_file() and RSearch(pattern, f.name)
]
for weight in matching_weights:
source_path = weights_dir_path + weight
destination_path = os.path.join(lowestval_weight_dir, weight)
copy2(source_path, destination_path)
logdir.append(f"File = {weight} Value: {key}, Step: {value}")
lbldict['Names'][weight] = weight
lbldict['Values'][weight] = key
lbldict["Names"][weight] = weight
lbldict["Values"][weight] = key
files.append(low_val_path + weight)
print(f"File = {weight} Value: {key}, Step: {value}")
yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
yield ("\n".join(logdir), files, pd.DataFrame(lbldict))
return "".join(logdir), files, pd.DataFrame(lbldict)
return ''.join(logdir), files, pd.DataFrame(lbldict)
if __name__ == "__main__":
model = str(input("Enter the name of the model: "))
sav_freq = int(input("Enter save frequency of the model: "))
ds = main(model, sav_freq)
if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
if ds:
selectweights(model, ds, weights_dir, lowestval_weight_dir)

6
train/utils.py
View File

@@ -271,9 +271,11 @@ def load_wav_to_torch(full_path):
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding='utf-8') as f:
with open(filename, encoding="utf-8") as f:
filepaths_and_text = [line.strip().split(split) for line in f]
filepaths_and_text = [item for item in filepaths_and_text if len(item) == 5] # ensure there are 5 items.
filepaths_and_text = [
item for item in filepaths_and_text if len(item) == 5
] # ensure there are 5 items.
return filepaths_and_text