def word2wav(text,PATH='/data/lisa/exp/kumarrit/vctk'):
lib.load_params('cmudict_attention_nmt_best.pkl')
model = nltk.corpus.cmudict.dict()
tknzr = nltk.tokenize.WordPunctTokenizer()
tokens = tknzr.tokenize(text.lower())
char_to_idx = pickle.load(open(PATH+'/phon2code.pkl'))
idx_to_char = {x:y for y,x in char_to_idx.iteritems()}
result = []
for tkn in tokens:
if tkn in ['.',',']:
result += ['pau']
else:
# try:
# result += [re.sub('\d+','',c.lower()) for c in random.choice(model[tkn])]
# except:
# result += [idx_to_char[x] for x in word2phon(tkn)]
result += [idx_to_char[x] for x in word2phon(tkn)]
print result
ctx = np.asarray([36]+[char_to_idx[x] for x in result]+[0]).reshape((1,-1)).astype('int32')
pred_X = predict_fn(
np.asarray([np.random.choice(N_SPEAKERS),]).reshape((1,)).astype('int32'),
ctx
)
generate.generate_wav(pred_X[0],base='generated_sample_16',do_post_filtering=False)
def vocoder2wav(vocoder_frames,
path_to_save=".",
name_prefix="samples",
dataset="vctk",
post_filter=False):
"""
Returns a list of raw audio and a list of (sampled_frequencies, time, specgram_values)
"""
assert (numpy.abs(vocoder_frames).max() <
20.), "Make sure that range of frame values is small"
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
raw_audio = []
audio_spec = []
for i, this_sample in enumerate(vocoder_frames):
generate_wav(this_sample,
path_to_save,
name_prefix + '_' + str(i),
sptk_dir=SPTK_DIR,
world_dir=WORLD_DIR,
norm_info_file=os.path.join(os.environ['FUEL_DATA_PATH'],
dataset, NORM_FILE),
do_post_filtering=post_filter)
rate, data = scipy.io.wavfile.read(
os.path.join(path_to_save, name_prefix + '_' + str(i) + '.wav'))
raw_audio.append(data)
f, t, Sxx = signal.spectrogram(data, rate)
audio_spec.append((f, t, Sxx))
return raw_audio, audio_spec
def char2wav(text,PATH='/data/lisa/exp/kumarrit/vctk'):
lib.load_params('vctk_nmt_best.pkl')
model = nltk.corpus.cmudict.dict()
char_to_idx = pickle.load(open(PATH+'/char2code.pkl'))
result = list(text)
ctx = np.asarray([36]+[char_to_idx[x] for x in result]+[0]).reshape((1,-1)).astype('int32')
phons = nmt_fn(ctx,np.ones_like(ctx).astype('float32')).flatten()
try:
end_idx = np.where(phons==0)[0][0]
phons = phons[:end_idx].tolist()
except:
phons = phons.tolist()
pred_X = predict_fn(
np.asarray([np.random.choice(N_SPEAKERS),]).reshape((1,)).astype('int32'),
np.asarray(phons,dtype=np.int32).reshape((1,-1))
)
generate.generate_wav(pred_X[0],base='generated_sample_16',do_post_filtering=False)
def writewav():
data = np.load('test_X.npy')
out_data = []
for i in xrange(data.shape[0]):
for j in xrange(1,data.shape[1]):
if data[i][j].sum()==0:
break
out_data.append(data[i][:j])
[generate.generate_wav(out_data[i],base='generated_sample_%d'%i,do_post_filtering=False) for i in xrange(data.shape[0])]
data = np.load('train_X.npy')
out_data = []
for i in xrange(data.shape[0]):
for j in xrange(data.shape[1]):
if data[i][j].sum()==0:
break
out_data.append(data[i][:j])
print data.shape
[generate.generate_wav(out_data[i],base='original_sample_%d'%i,do_post_filtering=False) for i in xrange(data.shape[0])]
os.makedirs(to_save_path)
parrot.sampleRnn.sample_raw(
gen_x.swapaxes(0, 1).copy(), features_lengths, args.samples_name,
to_save_path)
print "Successfully sampled raw audio..."
norm_info_file = os.path.join(data_dir, args.dataset,
'norm_info_mgc_lf0_vuv_bap_63_MVN.dat')
for idx, this_sample in enumerate(gen_x):
this_sample = this_sample[:features_lengths[idx]]
generate_wav(this_sample,
os.path.join(args.save_dir, 'samples'),
args.samples_name + '_' + str(idx),
sptk_dir=args.sptk_dir,
world_dir=args.world_dir,
norm_info_file=norm_info_file,
do_post_filtering=args.do_post_filtering)
if args.plot_raw:
from scipy.io import wavfile
raw_audio = data_tr['raw_audio'].swapaxes(0, 1)
for idx in range(args.num_samples):
this_raw = numpy.concatenate(
raw_audio[idx])[:80 * int(features_mask_tr.sum(axis=0)[idx])]
wavfile.write(
os.path.join(args.save_dir, 'samples',
'raw_' + args.samples_name + '_' + str(idx) + '.wav'),
16000, this_raw)
def sampler(save_dir, samples_name, do_post_filtering):
test_stream = datasets.parrot_stream(DATASET,
use_speaker=False,
which_sets=('test', ),
batch_size=BATCH_SIZE,
seq_size=10000)
test_iterator = test_stream.get_epoch_iterator()
latents_generated = numpy.random.normal(size=(NUM_REPEAT, LATENT_DIM))
latents_generated = lib.floatX(
numpy.tile(latents_generated, (BATCH_SIZE, 1)))
actual_so_far_raw, mask_raw, text_features_raw, reset = next(test_iterator)
text_features_raw_repeated = numpy.repeat(text_features_raw,
NUM_REPEAT,
axis=1)
samples_so_far = sample_fn(text_features_raw_repeated, latents_generated)
mask_so_far = mask_raw
actual_so_far = actual_so_far_raw.transpose((1, 0, 2))
mask_so_far_repeated = numpy.repeat(mask_so_far, NUM_REPEAT, axis=1)
norm_info_file = os.path.join(data_dir, DATASET,
'norm_info_mgc_lf0_vuv_bap_63_MVN.dat')
if not os.path.exists(os.path.join(save_dir, 'samples')):
os.makedirs(os.path.join(save_dir, 'samples'))
if not os.path.exists(os.path.join(save_dir, 'actual_samples')):
os.makedirs(os.path.join(save_dir, 'actual_samples'))
"""
TODO: Remove this commented section.
"""
for i, this_sample in enumerate(actual_so_far):
this_sample = this_sample[:int(mask_so_far.sum(axis=0)[i])]
generate_wav(this_sample,
os.path.join(save_dir, 'actual_samples'),
samples_name + '_' + str(i),
sptk_dir=SPTK_DIR,
world_dir=WORLD_DIR,
norm_info_file=norm_info_file,
do_post_filtering=do_post_filtering)
for i, this_sample in enumerate(samples_so_far):
this_sample = this_sample[:int(mask_so_far_repeated.sum(axis=0)[i])]
generate_wav(this_sample,
os.path.join(save_dir, 'samples'),
samples_name + '_' + str(i // NUM_REPEAT) + '_latent_' +
str(i % NUM_REPEAT),
sptk_dir=SPTK_DIR,
world_dir=WORLD_DIR,
norm_info_file=norm_info_file,
do_post_filtering=do_post_filtering)
def save(gen,orig,i=0):
generate.generate_wav(gen,base='generated_sample_%d'%i,do_post_filtering=False)
generate.generate_wav(orig,base='original_sample_%d'%i,do_post_filtering=False)
# 10000, sorting_mult=1, labels_type=saved_args.labels_type,
# quantize_features=saved_args.quantized_input)
gen_x, gen_k, gen_w, gen_pi, gen_phi, gen_pi_att = parrot.sample_model(
labels_tr_old, labels_mask_tr_old, features_mask_tr_old,
new_speakers_tr_old, latent_var_tr_old,
LATENT_NUM * args.num_samples)
for j, this_sample in enumerate(gen_x.swapaxes(1, 0)):
this_sample = this_sample[:int(
features_mask_tr_old.sum(axis=0)[j])]
generate_wav(this_sample,
os.path.join(args.save_dir, 'samples',
'adaptation'),
"sample_{}_{}_iters_{}".format(
args.samples_name, j, i),
sptk_dir=args.sptk_dir,
world_dir=args.world_dir,
norm_info_file=norm_info_file,
do_post_filtering=args.do_post_filtering)
if len(costs) != 0:
print "cost at iter {} is {}".format(i,
numpy.mean(costs[-200:]))
print "Successfully sampled the parrot."
if saved_args.quantized_input:
_, dequantize = get_quantizers()
gen_x = dequantize(gen_x)
# import ipdb; ipdb.set_trace()
