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Running
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Zero
File size: 3,101 Bytes
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import soundfile as sf
import json
import torch
from tqdm import tqdm
from huggingface_hub import snapshot_download
from tango.models import AudioDiffusion, DDPMScheduler
from tango.audioldm.audio.stft import TacotronSTFT
from tango.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")
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.([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))
tango = Tango("declare-lab/tango2")
prompt = "An audience cheering and clapping"
audio = tango.generate(prompt)
sf.write(f"{prompt}.wav", audio, samplerate=16000) |