Super resolution example? about audioldm HOT 13 OPEN

#!/usr/bin/python3
import os
from audioldm import text_to_audio, style_transfer, build_model, save_wave, get_time, super_resolution_and_inpainting
import argparse

CACHE_DIR = os.getenv(
    "AUDIOLDM_CACHE_DIR",
    os.path.join(os.path.expanduser("~"), ".cache/audioldm"))

parser = argparse.ArgumentParser()

parser.add_argument(
    "-t",
    "--text",
    type=str,
    required=False,
    default="",
    help="Text prompt to the model for audio generation",
)

parser.add_argument(
    "-f",
    "--file_path",
    type=str,
    required=False,
    default=None,
    help="(--mode transfer): Original audio file for style transfer; Or (--mode generation): the guidance audio file for generating simialr audio",
)

parser.add_argument(
    "--transfer_strength",
    type=float,
    required=False,
    default=0.5,
    help="A value between 0 and 1. 0 means original audio without transfer, 1 means completely transfer to the audio indicated by text",
)

parser.add_argument(
    "-s",
    "--save_path",
    type=str,
    required=False,
    help="The path to save model output",
    default="./output",
)

parser.add_argument(
    "-ckpt",
    "--ckpt_path",
    type=str,
    required=False,
    help="The path to the pretrained .ckpt model",
    default=os.path.join(
                CACHE_DIR,
                "audioldm-s-full.ckpt",
            ),
)

parser.add_argument(
    "-b",
    "--batchsize",
    type=int,
    required=False,
    default=1,
    help="Generate how many samples at the same time",
)

parser.add_argument(
    "--ddim_steps",
    type=int,
    required=False,
    default=200,
    help="The sampling step for DDIM",
)

parser.add_argument(
    "-gs",
    "--guidance_scale",
    type=float,
    required=False,
    default=2.5,
    help="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)",
)

parser.add_argument(
    "-dur",
    "--duration",
    type=float,
    required=False,
    default=10.0,
    help="The duration of the samples",
)

parser.add_argument(
    "-n",
    "--n_candidate_gen_per_text",
    type=int,
    required=False,
    default=3,
    help="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation",
)

parser.add_argument(
    "--seed",
    type=int,
    required=False,
    default=42,
    help="Change this value (any integer number) will lead to a different generation result.",
)

args = parser.parse_args()
assert args.duration % 2.5 == 0, "Duration must be a multiple of 2.5"

mode = "super_resolution_and_inpainting"
        
save_path = os.path.join(args.save_path, mode)

if(args.file_path is not None):
    save_path = os.path.join(save_path, os.path.basename(args.file_path.split(".")[0]))

text = args.text
random_seed = args.seed
duration = args.duration
guidance_scale = args.guidance_scale
n_candidate_gen_per_text = args.n_candidate_gen_per_text

os.makedirs(save_path, exist_ok=True)
audioldm = build_model(ckpt_path=args.ckpt_path)

waveform = super_resolution_and_inpainting(
    audioldm,
    text,
    args.file_path,
    random_seed,
    duration=duration,
    guidance_scale=guidance_scale,
    ddim_steps=args.ddim_steps,
    n_candidate_gen_per_text=n_candidate_gen_per_text,
    batchsize=args.batchsize,
    time_mask_ratio_start_and_end=(0.10, 0.15), # regenerate the 10% to 15% of the time steps in the spectrogram
    # time_mask_ratio_start_and_end=(1.0, 1.0), # no inpainting
    # freq_mask_ratio_start_and_end=(0.75, 1.0), # regenerate the higher 75% to 100% mel bins
    freq_mask_ratio_start_and_end=(1.0, 1.0), # no super-resolution
)


save_wave(waveform, save_path, name="%s_%s" % (get_time(), text))