def decode(args):
'''RUN DECODING'''
# read training config
idim, odim, train_args = get_model_conf(args.model, args.model_conf)
# show argments
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# define model
tacotron2 = Tacotron2(idim, odim, train_args)
eos = str(tacotron2.idim - 1)
# load trained model parameters
logging.info('reading model parameters from ' + args.model)
torch_load(args.model, tacotron2)
tacotron2.eval()
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
tacotron2 = tacotron2.to(device)
# read json data
with open(args.json, 'rb') as f:
js = json.load(f)['utts']
# chech direcitory
outdir = os.path.dirname(args.out)
if len(outdir) != 0 and not os.path.exists(outdir):
os.makedirs(outdir)
# write to ark and scp file (see https://github.com/vesis84/kaldi-io-for-python)
arkscp = 'ark:| copy-feats --print-args=false ark:- ark,scp:%s.ark,%s.scp' % (
args.out, args.out)
with torch.no_grad(), kaldi_io_py.open_or_fd(arkscp, 'wb') as f:
for idx, utt_id in enumerate(js.keys()):
x = js[utt_id]['output'][0]['tokenid'].split() + [eos]
x = np.fromiter(map(int, x), dtype=np.int64)
x = torch.LongTensor(x).to(device)
# get speaker embedding
if train_args.use_speaker_embedding:
spemb = kaldi_io_py.read_vec_flt(
js[utt_id]['input'][1]['feat'])
spemb = torch.FloatTensor(spemb).to(device)
else:
spemb = None
# decode and write
outs, _, _ = tacotron2.inference(x, args, spemb)
if outs.size(0) == x.size(0) * args.maxlenratio:
logging.warn("output length reaches maximum length (%s)." %
utt_id)
logging.info(
'(%d/%d) %s (size:%d->%d)' %
(idx + 1, len(js.keys()), utt_id, x.size(0), outs.size(0)))
kaldi_io_py.write_mat(f, outs.cpu().numpy(), utt_id)
def decode(args):
'''RUN DECODING'''
# read training config
with open(args.model_conf, 'rb') as f:
logging.info('reading a model config file from ' + args.model_conf)
idim, odim, train_args = pickle.load(f)
# show argments
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# define output activation function
if hasattr(train_args, 'output_activation'):
if train_args.output_activation is None:
output_activation_fn = None
elif hasattr(torch.nn.functional, train_args.output_activation):
output_activation_fn = getattr(torch.nn.functional,
train_args.output_activation)
else:
raise ValueError('there is no such an activation function. (%s)' %
train_args.output_activation)
else:
output_activation_fn = None
# define model
tacotron2 = Tacotron2(
idim=idim,
odim=odim,
spk_embed_dim=train_args.spk_embed_dim if hasattr(
train_args, "spk_embed_dim") else None,
embed_dim=train_args.embed_dim,
elayers=train_args.elayers,
eunits=train_args.eunits,
econv_layers=train_args.econv_layers,
econv_chans=train_args.econv_chans,
econv_filts=train_args.econv_filts,
dlayers=train_args.dlayers,
dunits=train_args.dunits,
prenet_layers=train_args.prenet_layers,
prenet_units=train_args.prenet_units,
postnet_layers=train_args.postnet_layers,
postnet_chans=train_args.postnet_chans,
postnet_filts=train_args.postnet_filts,
adim=train_args.adim,
aconv_chans=train_args.aconv_chans,
aconv_filts=train_args.aconv_filts,
output_activation_fn=output_activation_fn,
cumulate_att_w=train_args.cumulate_att_w,
use_batch_norm=train_args.use_batch_norm,
use_concate=train_args.use_concate,
dropout=train_args.dropout_rate,
zoneout=train_args.zoneout_rate,
threshold=args.threshold,
maxlenratio=args.maxlenratio,
minlenratio=args.minlenratio,
)
eos = str(tacotron2.idim - 1)
# load trained model parameters
logging.info('reading model parameters from ' + args.model)
tacotron2.load_state_dict(
torch.load(args.model, map_location=lambda storage, loc: storage))
tacotron2.eval()
# Set gpu
ngpu = args.ngpu
if ngpu >= 1:
gpu_id = range(ngpu)
logging.info('gpu id: ' + str(gpu_id))
tacotron2.cuda()
else:
gpu_id = [-1]
# read json data
with open(args.json, 'rb') as f:
js = json.load(f)['utts']
# chech direcitory
outdir = os.path.dirname(args.out)
if len(outdir) != 0 and not os.path.exists(outdir):
os.makedirs(outdir)
# check the use of embedding
# TODO(kan-bayashi): need to remove in the future
if hasattr(train_args, "spk_embed_dim"):
if train_args.spk_embed_dim is not None:
train_args.use_speaker_embedding = True
else:
train_args.use_speaker_embedding = False
else:
train_args.use_speaker_embedding = False
# TODO(kan-bayashi): need to be fixed in pytorch v4
if not torch_is_old:
torch.set_grad_enabled(False)
# write to ark and scp file (see https://github.com/vesis84/kaldi-io-for-python)
arkscp = 'ark:| copy-feats --print-args=false ark:- ark,scp:%s.ark,%s.scp' % (
args.out, args.out)
with kaldi_io_py.open_or_fd(arkscp, 'wb') as f:
for idx, utt_id in enumerate(js.keys()):
x = js[utt_id]['output'][0]['tokenid'].split() + [eos]
x = np.fromiter(map(int, x), dtype=np.int64)
x = torch.from_numpy(x)
if args.ngpu > 0:
x = x.cuda()
# TODO(kan-bayashi): need to be fixed in pytorch v4
if torch_is_old:
x = Variable(x, volatile=True)
# get speaker embedding
if train_args.use_speaker_embedding:
spemb = kaldi_io_py.read_vec_flt(
js[utt_id]['input'][1]['feat'])
spemb = torch.from_numpy(spemb)
# TODO(kan-bayashi): need to be fixed in pytorch v4
if torch_is_old:
spemb = Variable(spemb, volatile=True)
if args.ngpu > 0:
spemb = spemb.cuda()
else:
spemb = None
# decode and write
outs, _, _ = tacotron2.inference(x, spemb)
if outs.size(0) == x.size(0) * args.maxlenratio:
logging.warn("output length reaches maximum length (%s)." %
utt_id)
logging.info(
'(%d/%d) %s (size:%d->%d)' %
(idx + 1, len(js.keys()), utt_id, x.size(0), outs.size(0)))
kaldi_io_py.write_mat(f, outs.data.cpu().numpy(), utt_id)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--fs',
type=int,
default=22050,
help='Sampling frequency')
parser.add_argument('--fmax',
type=int,
default=None,
nargs='?',
help='Maximum frequency')
parser.add_argument('--fmin',
type=int,
default=None,
nargs='?',
help='Minimum frequency')
parser.add_argument('--n_mels',
type=int,
default=80,
help='Number of mel basis')
parser.add_argument('--n_fft',
type=int,
default=1024,
help='FFT length in point')
parser.add_argument('--n_shift',
type=int,
default=512,
help='Shift length in point')
parser.add_argument('--window',
type=str,
default='hann',
choices=['hann', 'hamming'],
help='Type of window')
parser.add_argument('scp', type=str, help='WAV scp files')
parser.add_argument('out', type=str, help='Output file id')
args = parser.parse_args()
# logging info
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
)
# load scp
with open(args.scp, 'r') as f:
scp = [x.replace('\n', '').split() for x in f.readlines()]
# chech direcitory
outdir = os.path.dirname(args.out)
if len(outdir) != 0 and not os.path.exists(outdir):
os.makedirs(outdir)
# write to ark and scp file (see https://github.com/vesis84/kaldi-io-for-python)
arkscp = 'ark:| copy-feats --print-args=false ark:- ark,scp:%s.ark,%s.scp' % (
args.out, args.out)
# extract feature and then write as ark with scp format
with kaldi_io_py.open_or_fd(arkscp, 'wb') as f:
for idx, (utt_id, path) in enumerate(scp, 1):
x, fs = librosa.core.load(path, sr=None)
assert fs == args.fs
lmspc = logmelspectrogram(x=x,
fs=args.fs,
n_mels=args.n_mels,
n_fft=args.n_fft,
n_shift=args.n_shift,
window=args.window,
fmin=args.fmin,
fmax=args.fmax)
logging.info("(%d/%d) %s" % (idx, len(scp), utt_id))
kaldi_io_py.write_mat(f, lmspc, utt_id)
def encode(args):
'''Get ASR encoded representations...probably for xvectors'''
# seed setting
torch.manual_seed(args.seed)
# read training config
idim, odim, odim_adv, train_args = get_model_conf(args.model,
args.model_conf)
# load trained model parameters
logging.info('reading model parameters from ' + args.model)
e2e = E2E(idim, odim, train_args, odim_adv=odim_adv)
model = Loss(e2e, train_args.mtlalpha)
if train_args.rnnlm is not None:
# set rnnlm. external rnnlm is used for recognition.
model.predictor.rnnlm = rnnlm
torch_load(args.model, model)
e2e.recog_args = args
# gpu
if args.ngpu == 1:
gpu_id = range(args.ngpu)
logging.info('gpu id: ' + str(gpu_id))
model.cuda()
arkscp = 'ark:| copy-feats --print-args=false ark:- ark,scp:%s.ark,%s.scp' % (
args.feats_out, args.feats_out)
if args.batchsize == 0:
with torch.no_grad():
with kaldi_io_py.open_or_fd(arkscp,
'wb') as f, open(args.feats_in,
'rb') as f2:
lines = f2.read().splitlines()
for idx, line in enumerate(lines, 1):
line = line.strip().split()
name = line[0]
logging.info('(%d/%d) decoding ' + name, idx, len(lines))
feat = kaldi_io_py.read_mat(line[1])
rep = e2e.erep(feat)
logging.info('Rep shape: %s', rep.shape)
kaldi_io_py.write_mat(f, rep, name)
else:
try:
from itertools import zip_longest as zip_longest
except Exception:
from itertools import izip_longest as zip_longest
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# Create json object for batch processing
logging.info("Creating json for batch processing...")
js = {}
with open(args.feats_in, 'rb') as f:
lines = f.read().splitlines()
for line in lines:
line = line.strip().split()
name = line[0]
featpath = line[1]
feat_shape = kaldi_io_py.read_mat(featpath).shape
js[name] = {'feat': featpath, 'shape': feat_shape}
# sort data
logging.info("Sorting data for batch processing...")
keys = list(js.keys())
feat_lens = [js[key]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)),
key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
with kaldi_io_py.open_or_fd(arkscp, 'wb') as f:
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
feats = [
kaldi_io_py.read_mat(js[name]['feat'])
for name in names
]
reps, replens = e2e.erep_batch(feats)
print(reps.shape, replens)
for i, rep in enumerate(reps):
name = names[i]
kaldi_io_py.write_mat(f, rep, name)
def tts_decode(args):
'''RUN DECODING'''
# read training config
# idim, odim, train_args = get_model_conf(args.model, args.model_conf)
# seed setting
torch.manual_seed(args.seed)
# show argments
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# read training config
with open(args.model_conf, "rb") as f:
logging.info('reading a model config file from' + args.model_conf)
idim_asr, odim_asr, train_args = pickle.load(f)
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# specify model architecture
logging.info('reading model parameters from' + args.model)
e2e_asr = E2E(idim_asr, odim_asr, train_args)
logging.info(e2e_asr)
asr_loss = Loss(e2e_asr, train_args.mtlalpha)
# specify model architecture for TTS
# reverse input and output dimension
tts_loss = setup_tts_loss(odim_asr, idim_asr - 3, train_args)
logging.info(tts_loss)
# define loss
model = ASRTTSLoss(asr_loss, tts_loss, train_args)
def cpu_loader(storage, location):
return storage
def remove_dataparallel(state_dict):
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if k.startswith("module."):
k = k[7:]
new_state_dict[k] = v
return new_state_dict
model.load_state_dict(
remove_dataparallel(torch.load(args.model, map_location=cpu_loader)))
# define model
tacotron2 = Tacotron2(idim, odim, train_args)
eos = str(tacotron2.idim - 1)
# load trained model parameters
logging.info('reading model parameters from ' + args.model)
torch_load(args.model, tacotron2)
tacotron2.eval()
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
tacotron2 = tacotron2.to(device)
# read json data
with open(args.json, 'rb') as f:
js = json.load(f)['utts']
# chech direcitory
outdir = os.path.dirname(args.out)
if len(outdir) != 0 and not os.path.exists(outdir):
os.makedirs(outdir)
# write to ark and scp file (see https://github.com/vesis84/kaldi-io-for-python)
arkscp = 'ark:| copy-feats --print-args=false ark:- ark,scp:%s.ark,%s.scp' % (
args.out, args.out)
with torch.no_grad(), kaldi_io_py.open_or_fd(arkscp, 'wb') as f:
for idx, utt_id in enumerate(js.keys()):
x = js[utt_id]['output'][0]['tokenid'].split() + [eos]
x = np.fromiter(map(int, x), dtype=np.int64)
x = torch.LongTensor(x).to(device)
# get speaker embedding
if train_args.use_speaker_embedding:
spemb = kaldi_io_py.read_vec_flt(
js[utt_id]['input'][1]['feat'])
spemb = torch.FloatTensor(spemb).to(device)
else:
spemb = None
# decode and write
outs, _, _ = tacotron2.inference(x, args, spemb)
if outs.size(0) == x.size(0) * args.maxlenratio:
logging.warn("output length reaches maximum length (%s)." %
utt_id)
logging.info(
'(%d/%d) %s (size:%d->%d)' %
(idx + 1, len(js.keys()), utt_id, x.size(0), outs.size(0)))
kaldi_io_py.write_mat(f, outs.cpu().numpy(), utt_id)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--fs', type=int, help='Sampling frequency')
parser.add_argument('--n_fft',
type=int,
default=1024,
help='FFT length in point')
parser.add_argument('--n_shift',
type=int,
default=512,
help='Shift length in point')
parser.add_argument('--win_length',
type=int,
default=None,
nargs='?',
help='Analisys window length in point')
parser.add_argument('--window',
type=str,
default='hann',
choices=['hann', 'hamming'],
help='Type of window')
parser.add_argument('--write_utt2num_frames',
type=strtobool,
default=True,
help='Whether to write utt2num file')
parser.add_argument('scp', type=str, help='WAV scp files')
parser.add_argument('out', type=str, help='Output file id')
args = parser.parse_args()
# logging info
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
)
# load scp
with open(args.scp, 'r') as f:
scp = [x.replace('\n', '').split() for x in f.readlines()]
if len(scp[0]) != 2:
utt_ids = [scp_[0] for scp_ in scp]
paths = [scp_[-2] for scp_ in scp]
scp = [[utt_id, path] for utt_id, path in zip(utt_ids, paths)]
# chech direcitory
outdir = os.path.dirname(args.out)
if len(outdir) != 0 and not os.path.exists(outdir):
os.makedirs(outdir)
# write to ark and scp file (see https://github.com/vesis84/kaldi-io-for-python)
if args.write_utt2num_frames:
job_id = "." + args.out.split(".")[-1] if args.out.split(
".")[-1].isdigit() else ""
arkscp = (
'ark:| copy-feats --print-args=false --write-num-frames=ark,t:%s '
'ark:- ark,scp:%s.ark,%s.scp') % (os.path.dirname(
args.out) + "/utt2num_frames" + job_id, args.out, args.out)
else:
arkscp = 'ark:| copy-feats --print-args=false ark:- ark,scp:%s.ark,%s.scp' % (
args.out, args.out)
# extract feature and then write as ark with scp format
with kaldi_io_py.open_or_fd(arkscp, 'wb') as f:
for idx, (utt_id, path) in enumerate(scp, 1):
x, fs = sf.read(path)
assert fs == args.fs
spc = spectrogram(x=x,
fs=args.fs,
n_fft=args.n_fft,
n_shift=args.n_shift,
win_length=args.win_length,
window=args.window)
logging.info("(%d/%d) %s" % (idx, len(scp), utt_id))
kaldi_io_py.write_mat(f, spc, utt_id)
