Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,516 Bytes
8c92a11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from argparse import ArgumentParser
import os
from models.codec.facodec.facodec_inference import FAcodecInference
from utils.util import load_config
import torch
def build_inference(args, cfg):
supported_inference = {
"FAcodec": FAcodecInference,
}
inference_class = supported_inference[cfg.model_type]
inference = inference_class(args, cfg)
return inference
def cuda_relevant(deterministic=False):
torch.cuda.empty_cache()
# TF32 on Ampere and above
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.enabled = True
torch.backends.cudnn.allow_tf32 = True
# Deterministic
torch.backends.cudnn.deterministic = deterministic
torch.backends.cudnn.benchmark = not deterministic
torch.use_deterministic_algorithms(deterministic)
def build_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--config",
type=str,
required=True,
help="JSON/YAML file for configurations.",
)
parser.add_argument(
"--checkpoint_path",
type=str,
default=None,
help="Acoustic model checkpoint directory. If a directory is given, "
"search for the latest checkpoint dir in the directory. If a specific "
"checkpoint dir is given, directly load the checkpoint.",
)
parser.add_argument(
"--source",
type=str,
required=True,
help="Path to the source audio file",
)
parser.add_argument(
"--reference",
type=str,
default=None,
help="Path to the reference audio file, passing an",
)
parser.add_argument(
"--output_dir",
type=str,
default=None,
help="Output dir for saving generated results",
)
return parser
def main():
# Parse arguments
parser = build_parser()
args = parser.parse_args()
print(args)
# Parse config
cfg = load_config(args.config)
# CUDA settings
cuda_relevant()
# Build inference
inferencer = build_inference(args, cfg)
# Run inference
_ = inferencer.inference(args.source, args.output_dir)
# Run voice conversion
if args.reference is not None:
_ = inferencer.voice_conversion(args.source, args.reference, args.output_dir)
if __name__ == "__main__":
main()
|