Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,363 Bytes
9a6dac6 |
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 |
import json
import torch
from tqdm import tqdm
from huggingface_hub import snapshot_download
from models import AudioDiffusion, DDPMScheduler, DDIMScheduler
from audioldm.audio.stft import TacotronSTFT
from audioldm.variational_autoencoder import AutoencoderKL
class Tango:
def __init__(self, name="declare-lab/tango", device="cuda:0"):
path = snapshot_download(repo_id=name)
vae_config = json.load(open("{}/vae_config.json".format(path)))
stft_config = json.load(open("{}/stft_config.json".format(path)))
main_config = json.load(open("{}/main_config.json".format(path)))
self.vae = AutoencoderKL(**vae_config).to(device)
self.stft = TacotronSTFT(**stft_config).to(device)
self.model = AudioDiffusion(**main_config).to(device)
vae_weights = torch.load(
"{}/pytorch_model_vae.bin".format(path), map_location=device
)
stft_weights = torch.load(
"{}/pytorch_model_stft.bin".format(path), map_location=device
)
main_weights = torch.load(
"{}/pytorch_model_main.bin".format(path), map_location=device
)
self.vae.load_state_dict(vae_weights)
self.stft.load_state_dict(stft_weights)
self.model.load_state_dict(main_weights)
print("Successfully loaded checkpoint from:", name)
self.vae.eval()
self.stft.eval()
self.model.eval()
# self.scheduler = DDPMScheduler.from_pretrained(
# main_config["scheduler_name"], subfolder="scheduler"
# )
self.scheduler = DDIMScheduler.from_pretrained(
main_config["scheduler_name"], subfolder="scheduler"
)
def chunks(self, lst, n):
"""Yield successive n-sized chunks from a list."""
for i in range(0, len(lst), n):
yield lst[i : i + n]
def generate(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
"""Genrate audio for a single prompt string."""
with torch.no_grad():
latents = self.model.inference(
[prompt],
self.scheduler,
steps,
guidance,
samples,
disable_progress=disable_progress,
)
mel = self.vae.decode_first_stage(latents)
wave = self.vae.decode_to_waveform(mel)
return wave[0]
def generate_for_batch(
self,
prompts,
steps=100,
guidance=3,
samples=1,
batch_size=8,
disable_progress=True,
):
"""Genrate audio for a list of prompt strings."""
outputs = []
for k in tqdm(range(0, len(prompts), batch_size)):
batch = prompts[k : k + batch_size]
with torch.no_grad():
latents = self.model.inference(
batch,
self.scheduler,
steps,
guidance,
samples,
disable_progress=disable_progress,
)
mel = self.vae.decode_first_stage(latents)
wave = self.vae.decode_to_waveform(mel)
outputs += [item for item in wave]
if samples == 1:
return outputs
else:
return list(self.chunks(outputs, samples))
|