def run(**kwargs):
from jukebox.utils.dist_utils import setup_dist_from_mpi
model = "1b_lyrics"
port = 29500
rank, local_rank, device = setup_dist_from_mpi(port=port)
hps = Hyperparams()
hps.sr = 44100
hps.n_samples = 1
hps.name = kwargs["sample_name"]
chunk_size = 32
max_batch_size = 16
hps.levels = 3
hps.hop_fraction = [.5,.5,.125]
vqvae, *priors = MODELS[model]
vqvae = make_vqvae(setup_hparams(vqvae, dict(sample_length = 1048576)), device)
top_prior = make_prior(setup_hparams(priors[-1], dict()), vqvae, device)
sample_length_in_seconds = kwargs["sample_length"]
hps.sample_length = (int(sample_length_in_seconds*hps.sr)//top_prior.raw_to_tokens)*top_prior.raw_to_tokens
assert hps.sample_length >= top_prior.n_ctx*top_prior.raw_to_tokens, f'Please choose a larger sampling rate'
metas = [dict(
artist = kwargs["artist"],
genre = kwargs["genre"],
total_length = hps.sample_length,
offset = 0,
lyrics = kwargs["lyrics"],
),
] * hps.n_samples
labels = [None, None, top_prior.labeller.get_batch_labels(metas, 'cuda')]
sampling_temperature = .98
lower_batch_size = 16
max_batch_size = 16
lower_level_chunk_size = 32
chunk_size = 32
sampling_kwargs = [
dict(temp=.99, fp16=True, max_batch_size=lower_batch_size,
chunk_size=lower_level_chunk_size),
dict(temp=0.99, fp16=True, max_batch_size=lower_batch_size,
chunk_size=lower_level_chunk_size),
dict(temp=sampling_temperature, fp16=True,
max_batch_size=max_batch_size, chunk_size=chunk_size)
]
zs = [t.zeros(hps.n_samples,0,dtype=t.long, device='cuda') for _ in range(len(priors))]
zs = _sample(zs, labels, sampling_kwargs, [None, None, top_prior], [2], hps)
del top_prior
empty_cache()
top_prior=None
upsamplers = [make_prior(setup_hparams(prior, dict()), vqvae, 'cpu') for prior in priors[:-1]]
labels[:2] = [prior.labeller.get_batch_labels(metas, 'cuda') for prior in upsamplers]
zs = upsample(zs, labels, sampling_kwargs, [*upsamplers, top_prior], hps)
from IPython.display import Audio
from jukebox.make_models import make_vqvae, make_prior, MODELS, make_model
from jukebox.hparams import Hyperparams, setup_hparams
from jukebox.sample import sample_single_window, _sample, \
sample_partial_window, upsample, \
load_prompts
from jukebox.utils.dist_utils import setup_dist_from_mpi
from jukebox.utils.torch_utils import empty_cache
port = random.randint(10000, 20000)
rank, local_rank, device = setup_dist_from_mpi(port=port)
model = "5b_lyrics" # or "1b_lyrics"
hps = Hyperparams()
hps.sr = 44100
hps.n_samples = 3 if model == '5b_lyrics' else 16
# Specifies the directory to save the sample in.
# We set this to the Google Drive mount point.
if len(sys.argv) > 1:
this_run_slug = sys.argv[1]
else:
this_run_slug = "co_compose_synth2"
hps.name = '/home/robin/google-drive/samples/' + this_run_slug + '/'
meta = pickle.load(open(f'{hps.name}meta.p', "rb"))
hps.sample_length = 1048576 if model == "5b_lyrics" else 786432
chunk_size = 16 if model == "5b_lyrics" else 32
max_batch_size = 3 if model == "5b_lyrics" else 16
