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import os
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import torch
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import logging
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from audiosr import super_resolution, build_model, save_wave, get_time, read_list
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import argparse
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os.environ["TOKENIZERS_PARALLELISM"] = "true"
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matplotlib_logger = logging.getLogger('matplotlib')
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matplotlib_logger.setLevel(logging.WARNING)
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-i",
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"--input_audio_file",
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type=str,
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required=False,
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help="Input audio file for audio super resolution",
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)
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parser.add_argument(
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"-il",
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"--input_file_list",
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type=str,
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required=False,
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default="",
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help="A file that contains all audio files that need to perform audio super resolution",
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)
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parser.add_argument(
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"-s",
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"--save_path",
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type=str,
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required=False,
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help="The path to save model output",
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default="./output",
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)
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parser.add_argument(
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"--model_name",
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type=str,
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required=False,
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help="The checkpoint you gonna use",
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default="basic",
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choices=["basic","speech"]
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)
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parser.add_argument(
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"-d",
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"--device",
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type=str,
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required=False,
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help="The device for computation. If not specified, the script will automatically choose the device based on your environment.",
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default="auto",
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)
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parser.add_argument(
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"--ddim_steps",
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type=int,
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required=False,
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default=50,
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help="The sampling step for DDIM",
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)
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parser.add_argument(
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"-gs",
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"--guidance_scale",
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type=float,
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required=False,
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default=3.5,
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help="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)",
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)
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parser.add_argument(
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"--seed",
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type=int,
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required=False,
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default=42,
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help="Change this value (any integer number) will lead to a different generation result.",
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)
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parser.add_argument(
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"--suffix",
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type=str,
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required=False,
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help="Suffix for the output file",
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default="_AudioSR_Processed_48K",
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)
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args = parser.parse_args()
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torch.set_float32_matmul_precision("high")
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save_path = os.path.join(args.save_path, get_time())
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assert args.input_file_list is not None or args.input_audio_file is not None,"Please provide either a list of audio files or a single audio file"
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input_file = args.input_audio_file
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random_seed = args.seed
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sample_rate=48000
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latent_t_per_second=12.8
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guidance_scale = args.guidance_scale
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os.makedirs(save_path, exist_ok=True)
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audiosr = build_model(model_name=args.model_name, device=args.device)
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if(args.input_file_list):
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print("Generate audio based on the text prompts in %s" % args.input_file_list)
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files_todo = read_list(args.input_file_list)
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else:
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files_todo = [input_file]
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for input_file in files_todo:
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name = os.path.splitext(os.path.basename(input_file))[0] + args.suffix
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waveform = super_resolution(
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audiosr,
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input_file,
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seed=random_seed,
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guidance_scale=guidance_scale,
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ddim_steps=args.ddim_steps,
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latent_t_per_second=latent_t_per_second
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)
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save_wave(waveform, inputpath=input_file, savepath=save_path, name=name, samplerate=sample_rate)
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