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ff7b988 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | import io
import logging
import warnings
from contextlib import redirect_stdout
import jukebox.hparams
import jukebox.make_models
import jukebox.utils.dist_utils
import librosa
import numpy as np
import torch
from ..utils import decode_audio, get_approximate_audio_length
from .base import Representation
_SAMPLE_RATE = 44100
_FRAME_HOP_SIZE = 128
_MIN_LENGTH_SAMPLES = (60 * _SAMPLE_RATE) + 16
_MAX_LENGTH_SAMPLES = (600 * _SAMPLE_RATE) - 96
_CHUNK_FRAMES = 8192
_CHUNK_SAMPLES = _CHUNK_FRAMES * _FRAME_HOP_SIZE
_SINGLETON = None
def init_jukebox_singleton(model="5b", num_layers=53, log=True):
global _SINGLETON
if _SINGLETON is None:
# Set up device
with redirect_stdout(io.StringIO()) as s:
rank, local_rank, device = jukebox.utils.dist_utils.setup_dist_from_mpi()
if log:
logging.info(s.getvalue())
# Set up hyperparams
hps = jukebox.hparams.Hyperparams()
hps.sr = _SAMPLE_RATE
hps.n_samples = 3 if model == "5b_lyrics" else 8
hps.name = "samples"
chunk_size = 16 if model == "5b_lyrics" else 32
max_batch_size = 3 if model == "5b_lyrics" else 16
hps.levels = 3
hps.hop_fraction = [0.5, 0.5, 0.125]
# Load VQVAE
vqvae, *priors = jukebox.make_models.MODELS[model]
with redirect_stdout(io.StringIO()) as s:
vqvae = jukebox.make_models.make_vqvae(
jukebox.hparams.setup_hparams(
vqvae, dict(sample_length=_CHUNK_SAMPLES)
),
device,
)
if log:
logging.info(s.getvalue())
# Set up language model
if num_layers is not None:
overrides = dict(prior_depth=num_layers)
else:
overrides = dict()
with redirect_stdout(io.StringIO()) as s:
lm = jukebox.make_models.make_prior(
jukebox.hparams.setup_hparams(priors[-1], overrides), vqvae, device
)
if log:
logging.info(s.getvalue())
lm.prior.only_encode = True
_SINGLETON = (model, num_layers, hps, vqvae, lm, device)
else:
if (model, num_layers) != _SINGLETON[:2]:
raise Exception("Jukebox can only be initialized once")
return _SINGLETON
class Jukebox(Representation):
def __init__(self, num_layers=53, fp16=False, log=True):
# NOTE: Layer 53 is the deepest that fit on a commodity 12GB card
(
_,
_,
self.hps,
self.vqvae,
self.lm,
self.device,
) = init_jukebox_singleton(model="5b", num_layers=num_layers, log=log)
self.fp16 = fp16
@classmethod
def decode_audio(cls, audio_path, offset=0.0, duration=None):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
audio, sr = librosa.load(
audio_path, sr=None, mono=False, offset=offset, duration=duration
)
if audio.ndim == 1:
audio = audio[np.newaxis, :]
audio = np.swapaxes(audio, 0, 1)
audio = np.mean(audio, axis=1, keepdims=False)
if sr != _SAMPLE_RATE:
audio = librosa.resample(audio, orig_sr=sr, target_sr=_SAMPLE_RATE, res_type="kaiser_best")
if audio.shape[0] > 0:
norm_factor = np.abs(audio).max()
if norm_factor > 0:
audio /= norm_factor
return audio
def _codify_audio(
self, audio, tqdm=lambda x: x, window_size=_CHUNK_SAMPLES, pad=True
):
# NOTE: Ugly API for legacy test case.
hop_size = _CHUNK_SAMPLES
hop_size_frames = window_size // _FRAME_HOP_SIZE
result = []
for i in tqdm(list(range(0, audio.shape[0], hop_size))):
context = audio[i : i + window_size]
if pad and context.shape[0] < window_size:
context = np.pad(context, (0, window_size - context.shape[0]))
with torch.no_grad():
context = torch.tensor(
context, dtype=torch.float32, device=self.device
).view(1, -1, 1)
context_codified = self.vqvae.encode(context)[-1].view(-1).cpu().numpy()
context_codified = context_codified[:hop_size_frames]
result.append(context_codified)
return np.concatenate(result, axis=0)
def codify_audio(self, audio, tqdm=lambda x: x):
return self._codify_audio(audio, tqdm=tqdm)
def lm_activations(
self,
audio_codified,
metadata_offset_seconds=0.0,
metadata_total_length_seconds=None,
metadata_artist=None,
metadata_genre=None,
metadata_lyrics=None,
tqdm=lambda x: x,
):
hop_size = _CHUNK_FRAMES
window_size = _CHUNK_FRAMES
if audio_codified.shape[0] % _CHUNK_FRAMES != 0:
raise ValueError()
# Compute metadata offset
metadata_initial_offset = int(metadata_offset_seconds * _SAMPLE_RATE)
metadata_initial_offset = (
metadata_initial_offset // _FRAME_HOP_SIZE
) * _FRAME_HOP_SIZE
assert metadata_initial_offset % _FRAME_HOP_SIZE == 0
if metadata_initial_offset < 0:
raise ValueError()
# Compute metadata total length
if metadata_total_length_seconds is None:
metadata_total_length = audio_codified.shape[0] * _FRAME_HOP_SIZE
else:
metadata_total_length = int(metadata_total_length_seconds * _SAMPLE_RATE)
metadata_total_length = max(metadata_total_length, _MIN_LENGTH_SAMPLES)
metadata_total_length = min(metadata_total_length, _MAX_LENGTH_SAMPLES)
metadata_total_length = (
metadata_total_length // _FRAME_HOP_SIZE
) * _FRAME_HOP_SIZE
assert metadata_total_length % _FRAME_HOP_SIZE == 0
assert metadata_total_length >= _MIN_LENGTH_SAMPLES
assert metadata_total_length <= _MAX_LENGTH_SAMPLES
result = []
for i in tqdm(list(range(0, audio_codified.shape[0], hop_size))):
# Select context window
context = audio_codified[i : i + window_size]
metadata_offset = metadata_initial_offset + i * _FRAME_HOP_SIZE
metadata_offset = min(
metadata_offset,
metadata_total_length - (context.shape[0] * _FRAME_HOP_SIZE),
)
metadata_offset = max(metadata_offset, 0)
assert metadata_offset % _FRAME_HOP_SIZE == 0
with torch.no_grad():
# Context
x = torch.tensor(context, dtype=torch.int64, device=self.device).view(
1, -1
)
# Conditioning info
meta = dict(
artist="unknown" if metadata_artist is None else metadata_artist,
genre="unknown" if metadata_genre is None else metadata_genre,
total_length=metadata_total_length,
offset=metadata_offset,
lyrics="Placeholder lyrics which do not affect 5b"
if metadata_lyrics is None
else metadata_lyrics,
)
metas = [meta] * self.hps.n_samples
labels = [None, None, self.lm.labeller.get_batch_labels(metas, "cuda")]
x_cond, y_cond, _ = self.lm.get_cond(None, self.lm.get_y(labels[-1], 0))
x_cond = x_cond[:1]
y_cond = y_cond[:1]
# Extract activations
activations = (
self.lm.prior.forward(
x, x_cond=x_cond, y_cond=y_cond, fp16=self.fp16
)
.cpu()
.numpy()
)
if self.fp16:
activations = activations.astype(np.float16)
result.append(activations[0])
# Clear memory
del x
del labels
del x_cond
del y_cond
torch.cuda.empty_cache()
return np.concatenate(result, axis=0)
def __call__(self, audio_path, offset=0.0, duration=None):
audio = self.decode_audio(audio_path, offset=offset, duration=duration)
if offset == 0.0 and duration is None:
total_length = audio.shape[0] / _SAMPLE_RATE
else:
total_length = get_approximate_audio_length(audio_path)
codified_audio = self.codify_audio(audio)
activations = self.lm_activations(
codified_audio,
metadata_offset_seconds=offset,
metadata_total_length_seconds=total_length,
)
activations = activations[: int(audio.shape[0] / _FRAME_HOP_SIZE)]
rate = _SAMPLE_RATE / _FRAME_HOP_SIZE
return rate, activations
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