bin/audioldm2

#!/usr/bin/python3
import os
import torch
import logging
from audioldm2 import text_to_audio, build_model, save_wave, get_time, read_list
import argparse

os.environ["TOKENIZERS_PARALLELISM"] = "true"
matplotlib_logger = logging.getLogger('matplotlib')
matplotlib_logger.setLevel(logging.WARNING)


CACHE_DIR = os.getenv(
    "AUDIOLDM_CACHE_DIR",
    os.path.join(os.path.expanduser("~"), ".cache/audioldm2"))

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(
    "-tl",
    "--text_list",
    type=str,
    required=False,
    default="",
    help="A file that contains text prompt to the model for audio generation",
)

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

parser.add_argument(
    "--model_name",
    type=str,
    required=False,
    help="The checkpoint you gonna use",
    default="audioldm2-full",
    choices=["audioldm2-full"]
)

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=3.5,
    help="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)",
)

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=0,
    help="Change this value (any integer number) will lead to a different generation result.",
)

args = parser.parse_args()

torch.set_float32_matmul_precision("high")
        
save_path = os.path.join(args.save_path, get_time())

text = args.text
random_seed = args.seed
duration = 10
guidance_scale = args.guidance_scale
n_candidate_gen_per_text = args.n_candidate_gen_per_text

os.makedirs(save_path, exist_ok=True)
audioldm2 = build_model(model_name=args.model_name)

if(args.text_list):
    print("Generate audio based on the text prompts in %s" % args.text_list)
    prompt_todo = read_list(args.text_list)
else: 
    prompt_todo = [text]
    
for text in prompt_todo:
    waveform = text_to_audio(
        audioldm2,
        text,
        seed=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,
    )
        
    save_wave(waveform, save_path, name=text)