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import os
import argparse
import yaml
import torch
from audioldm import LatentDiffusion
from audioldm.utils import default_audioldm_config
import time
def make_batch_for_text_to_audio(text, batchsize=2):
text = [text] * batchsize
if batchsize < 2:
print("Warning: Batchsize must be at least 2. Batchsize is set to 2.")
fbank = torch.zeros((batchsize, 1024, 64)) # Not used, here to keep the code format
stft = torch.zeros((batchsize, 1024, 512)) # Not used
waveform = torch.zeros((batchsize, 160000)) # Not used
fname = ["%s.wav" % x for x in range(batchsize)]
batch = (
fbank,
stft,
None,
fname,
waveform,
text,
)
return batch
def text_to_audio(text, batchsize=2, guidance_scale=2.5, n_gen=1, config=None):
if(torch.cuda.is_available()):
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
if(config is not None):
assert type(config) is str
config = yaml.load(open(config, "r"), Loader=yaml.FullLoader)
else:
config = default_audioldm_config()
# config["id"]["version"] = "%s_%s" % (config["id"]["name"], config["id"]["version"])
# Use text as condition instead of using waveform during training
config["model"]["params"]["device"] = device
config["model"]["params"]["cond_stage_key"] = "text"
# No normalization here
latent_diffusion = LatentDiffusion(**config["model"]["params"])
resume_from_checkpoint = "./ckpt/ldm_trimmed.ckpt"
checkpoint = torch.load(resume_from_checkpoint)
latent_diffusion.load_state_dict(checkpoint["state_dict"])
latent_diffusion.eval()
latent_diffusion = latent_diffusion.cuda()
latent_diffusion.cond_stage_model.embed_mode = "text"
batch = make_batch_for_text_to_audio(text, batchsize=batchsize)
with torch.no_grad():
waveform = latent_diffusion.generate_sample(
[batch],
unconditional_guidance_scale=guidance_scale,
n_gen=n_gen,
)
return waveform
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