def test_assert_different_length_batch_generation():
# prepare batch
batch = 4
length = 32
x = np.random.randint(0, 256, size=(batch, 1))
h = np.random.randn(batch, 28, length)
length_list = sorted(
list(np.random.randint(length // 2, length - 1, batch)))
with torch.no_grad():
net = WaveNet(256, 28, 4, 4, 10, 3, 2)
net.apply(initialize)
net.eval()
# sample-by-sample generation
gen1_list = []
for x_, h_, length in zip(x, h, length_list):
batch_x = torch.from_numpy(np.expand_dims(x_, 0)).long()
batch_h = torch.from_numpy(np.expand_dims(h_, 0)).float()
gen1 = net.fast_generate(batch_x, batch_h, length, 1, "argmax")
gen1_list += [gen1]
# batch generation
batch_x = torch.from_numpy(x).long()
batch_h = torch.from_numpy(h).float()
gen2_list = net.batch_fast_generate(batch_x, batch_h, length_list, 1,
"argmax")
# assertion
for gen1, gen2 in zip(gen1_list, gen2_list):
np.testing.assert_array_equal(gen1, gen2)
def test_generate():
batch = 2
x = np.random.randint(0, 256, size=(batch, 1))
h = np.random.randn(batch, 28, 10)
length = h.shape[-1] - 1
with torch.no_grad():
net = WaveNet(256, 28, 4, 4, 10, 3, 2)
net.apply(initialize)
net.eval()
for x_, h_ in zip(x, h):
batch_x = torch.from_numpy(np.expand_dims(x_, 0)).long()
batch_h = torch.from_numpy(np.expand_dims(h_, 0)).float()
net.generate(batch_x, batch_h, length, 1, "sampling")
net.fast_generate(batch_x, batch_h, length, 1, "sampling")
batch_x = torch.from_numpy(x).long()
batch_h = torch.from_numpy(h).float()
net.batch_fast_generate(batch_x, batch_h, [length] * batch, 1, "sampling")
def gpu_decode(feat_list, gpu):
# set default gpu and do not track gradient
torch.cuda.set_device(gpu)
torch.set_grad_enabled(False)
# define model and load parameters
if config.use_upsampling_layer:
upsampling_factor = config.upsampling_factor
else:
upsampling_factor = 0
model = WaveNet(n_quantize=config.n_quantize,
n_aux=config.n_aux,
n_resch=config.n_resch,
n_skipch=config.n_skipch,
dilation_depth=config.dilation_depth,
dilation_repeat=config.dilation_repeat,
kernel_size=config.kernel_size,
upsampling_factor=upsampling_factor)
model.load_state_dict(
torch.load(args.checkpoint,
map_location=lambda storage, loc: storage)["model"])
model.eval()
model.cuda()
# define generator
generator = decode_generator(
feat_list,
batch_size=args.batch_size,
feature_type=config.feature_type,
wav_transform=wav_transform,
feat_transform=feat_transform,
upsampling_factor=config.upsampling_factor,
use_upsampling_layer=config.use_upsampling_layer,
use_speaker_code=config.use_speaker_code)
# decode
if args.batch_size > 1:
for feat_ids, (batch_x, batch_h, n_samples_list) in generator:
logging.info("decoding start")
samples_list = model.batch_fast_generate(
batch_x, batch_h, n_samples_list, args.intervals)
for feat_id, samples in zip(feat_ids, samples_list):
wav = decode_mu_law(samples, config.n_quantize)
sf.write(args.outdir + "/" + feat_id + ".wav", wav,
args.fs, "PCM_16")
logging.info("wrote %s.wav in %s." %
(feat_id, args.outdir))
else:
for feat_id, (x, h, n_samples) in generator:
logging.info("decoding %s (length = %d)" %
(feat_id, n_samples))
samples = model.fast_generate(x, h, n_samples, args.intervals)
wav = decode_mu_law(samples, config.n_quantize)
sf.write(args.outdir + "/" + feat_id + ".wav", wav, args.fs,
"PCM_16")
logging.info("wrote %s.wav in %s." % (feat_id, args.outdir))
def main():
"""RUN DECODING."""
parser = argparse.ArgumentParser()
# decode setting
parser.add_argument("--seed_waveforms",
required=True,
type=str,
help="directory or list of wav files")
parser.add_argument("--checkpoint",
required=True,
type=str,
help="model file")
parser.add_argument("--outdir",
required=True,
type=str,
help="directory to save generated samples")
parser.add_argument("--config",
default=None,
type=str,
help="configure file")
parser.add_argument("--fs", default=16000, type=int, help="sampling rate")
parser.add_argument("--batch_size",
default=1,
type=int,
help="number of batches to decode of batch_length")
parser.add_argument("--speaker_code", type=int, help="speaker code")
# other setting
parser.add_argument("--intervals",
default=1000,
type=int,
help="log interval")
parser.add_argument("--seed", default=1, type=int, help="seed number")
parser.add_argument("--verbose", default=1, type=int, help="log level")
args = parser.parse_args()
# set log level
if args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(
level=logging.WARNING,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show arguments
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# check arguments
if args.config is None:
args.config = os.path.dirname(args.checkpoint) + "/model.conf"
if not os.path.exists(args.config):
raise FileNotFoundError("config file is missing (%s)." % (args.config))
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
if os.path.isdir(args.seed_waveforms):
filenames = sorted(
find_files(args.seed_waveforms, "*.wav", use_dir_name=False))
wav_list = [
args.seed_waveforms + "/" + filename for filename in filenames
]
elif os.path.isfile(args.seed_waveforms):
wav_list = read_txt(args.seed_waveforms)
else:
logging.error("--seed_waveforms should be directory or list.")
sys.exit(1)
# if speaker code is specified then filter out all other speakers if files conform to wave set pattern
if not args.speaker_code is None:
try:
wav_list = [
file for file in wav_list
if parse_wave_file_name(file)[1] == args.speaker_code
]
except ValueError:
# files not in wave set format, then original list will be kept
pass
# fix seed
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# fix slow computation of dilated conv
# https://github.com/pytorch/pytorch/issues/15054#issuecomment-450191923
torch.backends.cudnn.benchmark = True
# load config
config = torch.load(args.config)
wav_transform = transforms.Compose(
[lambda x: encode_mu_law(x, config.n_quantize)])
# set default gpu and do not track gradient
torch.set_grad_enabled(False)
# define model and load parameters
model = WaveNet(n_quantize=config.n_quantize,
n_aux=1,
n_resch=config.n_resch,
n_skipch=config.n_skipch,
dilation_depth=config.dilation_depth,
dilation_repeat=config.dilation_repeat,
kernel_size=config.kernel_size)
checkpoint = torch.load(args.checkpoint,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint["model"])
model.eval()
if torch.cuda.is_available():
model.cuda()
# extract train iterations
train_iterations = checkpoint["iterations"]
# choose seed sample
wav_id = np.random.randint(0, high=len(wav_list))
wav_file = wav_list[wav_id]
seed_x, _rate = sf.read(wav_file, dtype=np.float32)
assert args.fs == _rate, f"expected sample rate is {args.fs}, {wav_file} is {_rate}"
tot_sample_length = config.batch_length + model.receptive_field
max_sample_id = seed_x.shape[0] - tot_sample_length
assert max_sample_id >= 0
sample_id = np.random.randint(0, max_sample_id)
seed_sample = seed_x[sample_id:sample_id + tot_sample_length]
# take code from filename if none
speaker_code = parse_wave_file_name(
wav_file)[1] if args.speaker_code is None else args.speaker_code
# log decoding info
output_fn_info = f"I{train_iterations}-R{args.seed}-S{speaker_code}-W{Path(wav_file).stem}"
length_sec = args.batch_size * config.batch_length / args.fs
logging.info(
f"generating {args.batch_size} batches of {config.batch_length} samples = {length_sec} seconds"
f" into {output_fn_info} set")
# write seed sample
sf.write(args.outdir + f"/seed_sample-{output_fn_info}.wav", seed_sample,
args.fs, "PCM_16")
logging.info("wrote seed_sample.wav in %s." % args.outdir)
# decode
new_samples = args.batch_size * config.batch_length
x, h = decode_from_wav(seed_sample,
new_samples,
wav_transform=wav_transform,
speaker_code=speaker_code)
def save_samples(samples, file_name):
wav_data = decode_mu_law(samples, config.n_quantize)
sf.write(file_name, wav_data, args.fs, "PCM_16")
def progress_callback(samples, no_samples, elapsed):
save_samples(samples,
args.outdir + "/" + f"decoded.t.all-{output_fn_info}.wav")
save_samples(samples[-no_samples:],
args.outdir + "/" + f"decoded.t.new-{output_fn_info}.wav")
logging.info("decoding (length = %d)" % h.shape[2])
samples = model.fast_generate(x,
h,
new_samples,
args.intervals,
callback=progress_callback)
# samples = model.generate(x, h, new_samples, args.intervals)
logging.info(f"decoded {len(seed_sample)}")
save_samples(samples, args.outdir + "/" + f"decoded-{output_fn_info}.wav")
logging.info("wrote decoded.wav in %s." % args.outdir)
def test_assert_fast_generation():
# get batch
batch = 2
x = np.random.randint(0, 256, size=(batch, 1))
h = np.random.randn(batch, 28, 32)
length = h.shape[-1] - 1
with torch.no_grad():
# --------------------------------------------------------
# define model without upsampling and with kernel size = 2
# --------------------------------------------------------
net = WaveNet(256, 28, 4, 4, 10, 3, 2)
net.apply(initialize)
net.eval()
# sample-by-sample generation
gen1_list = []
gen2_list = []
for x_, h_ in zip(x, h):
batch_x = torch.from_numpy(np.expand_dims(x_, 0)).long()
batch_h = torch.from_numpy(np.expand_dims(h_, 0)).float()
gen1 = net.generate(batch_x, batch_h, length, 1, "argmax")
gen2 = net.fast_generate(batch_x, batch_h, length, 1, "argmax")
np.testing.assert_array_equal(gen1, gen2)
gen1_list += [gen1]
gen2_list += [gen2]
gen1 = np.stack(gen1_list)
gen2 = np.stack(gen2_list)
np.testing.assert_array_equal(gen1, gen2)
# batch generation
batch_x = torch.from_numpy(x).long()
batch_h = torch.from_numpy(h).float()
gen3_list = net.batch_fast_generate(batch_x, batch_h, [length] * batch,
1, "argmax")
gen3 = np.stack(gen3_list)
np.testing.assert_array_equal(gen3, gen2)
# --------------------------------------------------------
# define model without upsampling and with kernel size = 3
# --------------------------------------------------------
net = WaveNet(256, 28, 4, 4, 10, 3, 3)
net.apply(initialize)
net.eval()
# sample-by-sample generation
gen1_list = []
gen2_list = []
for x_, h_ in zip(x, h):
batch_x = torch.from_numpy(np.expand_dims(x_, 0)).long()
batch_h = torch.from_numpy(np.expand_dims(h_, 0)).float()
gen1 = net.generate(batch_x, batch_h, length, 1, "argmax")
gen2 = net.fast_generate(batch_x, batch_h, length, 1, "argmax")
np.testing.assert_array_equal(gen1, gen2)
gen1_list += [gen1]
gen2_list += [gen2]
gen1 = np.stack(gen1_list)
gen2 = np.stack(gen2_list)
np.testing.assert_array_equal(gen1, gen2)
# batch generation
batch_x = torch.from_numpy(x).long()
batch_h = torch.from_numpy(h).float()
gen3_list = net.batch_fast_generate(batch_x, batch_h, [length] * batch,
1, "argmax")
gen3 = np.stack(gen3_list)
np.testing.assert_array_equal(gen3, gen2)
# get batch
batch = 2
upsampling_factor = 10
x = np.random.randint(0, 256, size=(batch, 1))
h = np.random.randn(batch, 28, 3)
length = h.shape[-1] * upsampling_factor - 1
# -----------------------------------------------------
# define model with upsampling and with kernel size = 2
# -----------------------------------------------------
net = WaveNet(256, 28, 4, 4, 10, 3, 2, upsampling_factor)
net.apply(initialize)
net.eval()
# sample-by-sample generation
gen1_list = []
gen2_list = []
for x_, h_ in zip(x, h):
batch_x = torch.from_numpy(np.expand_dims(x_, 0)).long()
batch_h = torch.from_numpy(np.expand_dims(h_, 0)).float()
gen1 = net.generate(batch_x, batch_h, length, 1, "argmax")
gen2 = net.fast_generate(batch_x, batch_h, length, 1, "argmax")
np.testing.assert_array_equal(gen1, gen2)
gen1_list += [gen1]
gen2_list += [gen2]
gen1 = np.stack(gen1_list)
gen2 = np.stack(gen2_list)
np.testing.assert_array_equal(gen1, gen2)
# batch generation
batch_x = torch.from_numpy(x).long()
batch_h = torch.from_numpy(h).float()
gen3_list = net.batch_fast_generate(batch_x, batch_h, [length] * batch,
1, "argmax")
gen3 = np.stack(gen3_list)
np.testing.assert_array_equal(gen3, gen2)
# -----------------------------------------------------
# define model with upsampling and with kernel size = 3
# -----------------------------------------------------
net = WaveNet(256, 28, 4, 4, 10, 3, 2, upsampling_factor)
net.apply(initialize)
net.eval()
# sample-by-sample generation
gen1_list = []
gen2_list = []
for x_, h_ in zip(x, h):
batch_x = torch.from_numpy(np.expand_dims(x_, 0)).long()
batch_h = torch.from_numpy(np.expand_dims(h_, 0)).float()
gen1 = net.generate(batch_x, batch_h, length, 1, "argmax")
gen2 = net.fast_generate(batch_x, batch_h, length, 1, "argmax")
np.testing.assert_array_equal(gen1, gen2)
gen1_list += [gen1]
gen2_list += [gen2]
gen1 = np.stack(gen1_list)
gen2 = np.stack(gen2_list)
np.testing.assert_array_equal(gen1, gen2)
# batch generation
batch_x = torch.from_numpy(x).long()
batch_h = torch.from_numpy(h).float()
gen3_list = net.batch_fast_generate(batch_x, batch_h, [length] * batch,
1, "argmax")
gen3 = np.stack(gen3_list)
np.testing.assert_array_equal(gen3, gen2)
