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import os
import shutil
import gc
import torch
from multiprocessing import cpu_count
from lib.modules import VC
from lib.split_audio import split_silence_nonsilent, adjust_audio_lengths, combine_silence_nonsilent
class Configs:
def __init__(self, device, is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
self.gpu_name = None
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def device_config(self) -> tuple:
if torch.cuda.is_available():
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
#if (
# ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
# or "P40" in self.gpu_name.upper()
# or "1060" in self.gpu_name
# or "1070" in self.gpu_name
# or "1080" in self.gpu_name
# ):
# print("16 series/10 series P40 forced single precision")
# self.is_half = False
# for config_file in ["32k.json", "40k.json", "48k.json"]:
# with open(BASE_DIR / "src" / "configs" / config_file, "r") as f:
# strr = f.read().replace("true", "false")
# with open(BASE_DIR / "src" / "configs" / config_file, "w") as f:
# f.write(strr)
# with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f:
# strr = f.read().replace("3.7", "3.0")
# with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f:
# f.write(strr)
# else:
# self.gpu_name = None
# self.gpu_mem = int(
# torch.cuda.get_device_properties(i_device).total_memory
# / 1024
# / 1024
# / 1024
# + 0.4
# )
# if self.gpu_mem <= 4:
# with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f:
# strr = f.read().replace("3.7", "3.0")
# with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f:
# f.write(strr)
elif torch.backends.mps.is_available():
print("No supported N-card found, use MPS for inference")
self.device = "mps"
else:
print("No supported N-card found, use CPU for inference")
self.device = "cpu"
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G memory config
x_pad = 3
x_query = 10
x_center = 60
x_max = 65
else:
# 5G memory config
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem != None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
return x_pad, x_query, x_center, x_max
def get_model(voice_model):
model_dir = os.path.join(os.getcwd(), "models", voice_model)
model_filename, index_filename = None, None
for file in os.listdir(model_dir):
ext = os.path.splitext(file)[1]
if ext == '.pth':
model_filename = file
if ext == '.index':
index_filename = file
if model_filename is None:
print(f'No model file exists in {models_dir}.')
return None, None
return os.path.join(model_dir, model_filename), os.path.join(model_dir, index_filename) if index_filename else ''
def infer_audio(
model_name,
audio_path,
f0_change=0,
f0_method="rmvpe+",
min_pitch="50",
max_pitch="1100",
crepe_hop_length=128,
index_rate=0.75,
filter_radius=3,
rms_mix_rate=0.25,
protect=0.33,
split_infer=False,
min_silence=500,
silence_threshold=-50,
seek_step=1,
keep_silence=100,
do_formant=False,
quefrency=0,
timbre=1,
f0_autotune=False,
audio_format="wav",
resample_sr=0,
hubert_model_path="assets/hubert/hubert_base.pt",
rmvpe_model_path="assets/rmvpe/rmvpe.pt",
fcpe_model_path="assets/fcpe/fcpe.pt"
):
os.environ["rmvpe_model_path"] = rmvpe_model_path
os.environ["fcpe_model_path"] = fcpe_model_path
configs = Configs('cuda:0', True)
vc = VC(configs)
pth_path, index_path = get_model(model_name)
vc_data = vc.get_vc(pth_path, protect, 0.5)
if split_infer:
inferred_files = []
temp_dir = os.path.join(os.getcwd(), "seperate", "temp")
os.makedirs(temp_dir, exist_ok=True)
print("Splitting audio to silence and nonsilent segments.")
silence_files, nonsilent_files = split_silence_nonsilent(audio_path, min_silence, silence_threshold, seek_step, keep_silence)
print(f"Total silence segments: {len(silence_files)}.\nTotal nonsilent segments: {len(nonsilent_files)}.")
for i, nonsilent_file in enumerate(nonsilent_files):
print(f"Inferring nonsilent audio {i+1}")
inference_info, audio_data, output_path = vc.vc_single(
0,
nonsilent_file,
f0_change,
f0_method,
index_path,
index_path,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
audio_format,
crepe_hop_length,
do_formant,
quefrency,
timbre,
min_pitch,
max_pitch,
f0_autotune,
hubert_model_path
)
if inference_info[0] == "Success.":
print("Inference ran successfully.")
print(inference_info[1])
print("Times:\nnpy: %.2fs f0: %.2fs infer: %.2fs\nTotal time: %.2fs" % (*inference_info[2],))
else:
print(f"An error occurred while processing.\n{inference_info[0]}")
return None
inferred_files.append(output_path)
print("Adjusting inferred audio lengths.")
adjusted_inferred_files = adjust_audio_lengths(nonsilent_files, inferred_files)
print("Combining silence and inferred audios.")
output_count = 1
while True:
output_path = os.path.join(os.getcwd(), "output", f"{os.path.splitext(os.path.basename(audio_path))[0]}{model_name}{f0_method.capitalize()}_{output_count}.{audio_format}")
if not os.path.exists(output_path):
break
output_count += 1
output_path = combine_silence_nonsilent(silence_files, adjusted_inferred_files, keep_silence, output_path)
[shutil.move(inferred_file, temp_dir) for inferred_file in inferred_files]
shutil.rmtree(temp_dir)
else:
inference_info, audio_data, output_path = vc.vc_single(
0,
audio_path,
f0_change,
f0_method,
index_path,
index_path,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
audio_format,
crepe_hop_length,
do_formant,
quefrency,
timbre,
min_pitch,
max_pitch,
f0_autotune,
hubert_model_path
)
if inference_info[0] == "Success.":
print("Inference ran successfully.")
print(inference_info[1])
print("Times:\nnpy: %.2fs f0: %.2fs infer: %.2fs\nTotal time: %.2fs" % (*inference_info[2],))
else:
print(f"An error occurred while processing.\n{inference_info[0]}")
del configs, vc
gc.collect()
return inference_info[0]
del configs, vc
gc.collect()
return output_path |