def test_write_read_multiark(tmpdir, endian, dtype):
path = tmpdir.mkdir("test")
a = np.random.rand(1000, 120).astype(dtype)
b = np.random.rand(10, 120).astype(dtype)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(
path.join("a.ark").strpath,
origin,
scp=path.join("b.scp").strpath,
endian=endian,
)
c = np.random.rand(1000, 120).astype(dtype)
d = np.random.rand(10, 120).astype(dtype)
origin.update({"c": c, "d": d})
with io.open(path.join("b.scp").strpath, "a", encoding="utf-8") as f:
kaldiio.save_ark(path.join("b.ark").strpath, origin, scp=f, endian=endian)
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join("b.scp").strpath, endian=endian).items()
}
_compare(d5, origin)
def test_write_read(tmpdir, shape1, shape2, endian, dtype, max_cache_fd):
path = tmpdir.mkdir("test")
a = np.random.rand(*shape1).astype(dtype)
b = np.random.rand(*shape2).astype(dtype)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(
path.join("a.ark").strpath,
origin,
scp=path.join("b.scp").strpath,
endian=endian,
)
d2 = {k: v for k, v in kaldiio.load_ark(path.join("a.ark").strpath, endian=endian)}
d5 = {
k: v
for k, v in kaldiio.load_scp(
path.join("b.scp").strpath, endian=endian, max_cache_fd=max_cache_fd
).items()
}
with io.open(path.join("a.ark").strpath, "rb") as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd, endian=endian)}
_compare(d2, origin)
_compare(d5, origin)
_compare(d6, origin)
def scp2array_dic(scp_path,
array_dic=None,
ark_path=None,
compression_method=None,
append=False):
"""
read array_dic from ark indexed by scp or
write array_dic to ark while create scp to index
:param scp_path: filepath of scp
:param array_dic: dic of array
:param ark_path: filepath of ark, default is scppath.replace('.scp', '.ark')
:param compression_method: compression method, default=None,
kAutomaticMethod=1, kSpeechFeature=2,
kTwoByteAuto=3,kTwoByteSignedInteger=4, kOneByteAuto=5,
kOneByteUnsignedInteger=6, kOneByteZeroOne=7
:param append: if True, append, else write
:return: dic of numpy array for read while None for write
"""
if array_dic is None:
array_dic = kaldiio.load_scp(scp_path)
return array_dic
else:
if ark_path is None:
ark_path = scp_path.replace(".scp", ".ark")
else:
pass
kaldiio.save_ark(
ark=ark_path,
array_dict=array_dic,
scp=scp_path,
compression_method=compression_method,
append=append,
)
return None
def test_write_read_multiark_sequential(tmpdir, endian):
path = tmpdir.mkdir('test')
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {'a': a, 'b': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
endian=endian)
c = np.random.rand(1000, 120).astype(np.float32)
d = np.random.rand(10, 120).astype(np.float32)
origin.update({'c': c, 'd': d})
with open(path.join('b.scp').strpath, 'a') as f:
kaldiio.save_ark(path.join('b.ark').strpath,
origin,
scp=f,
endian=endian)
d5 = {
k: v
for k, v in kaldiio.load_scp_sequential(path.join('b.scp').strpath,
endian=endian)
}
_compare(d5, origin)
def test_write_read_int32_vector(tmpdir, endian):
path = tmpdir.mkdir('test')
a = np.random.randint(1, 128, 10, dtype=np.int32)
b = np.random.randint(1, 128, 10, dtype=np.int32)
origin = {u'Ï,é,à': a, u'あいうえお': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
endian=endian)
d2 = {
k: v
for k, v in kaldiio.load_ark(path.join('a.ark').strpath, endian=endian)
}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath,
endian=endian).items()
}
with io.open(path.join('a.ark').strpath, 'rb') as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd, endian=endian)}
_compare(d2, origin)
_compare(d5, origin)
_compare(d6, origin)
def test_write_read_compress(tmpdir, compression_method, endian):
path = tmpdir.mkdir('test')
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {u'Ï,é,à': a, u'あいうえお': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
compression_method=compression_method,
endian=endian)
d2 = {
k: v
for k, v in kaldiio.load_ark(path.join('a.ark').strpath, endian=endian)
}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath,
endian=endian).items()
}
with io.open(path.join('a.ark').strpath, 'rb') as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd, endian=endian)}
_compare_allclose(d2, origin, atol=1e-1)
_compare_allclose(d5, origin, atol=1e-1)
_compare_allclose(d6, origin, atol=1e-1)
def test_write_read(tmpdir, shape1, shape2, endian, dtype):
path = tmpdir.mkdir('test')
a = np.random.rand(*shape1).astype(dtype)
b = np.random.rand(*shape2).astype(dtype)
origin = {u'Ï,é,à': a, u'あいうえお': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
endian=endian)
d2 = {
k: v
for k, v in kaldiio.load_ark(path.join('a.ark').strpath, endian=endian)
}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath,
endian=endian).items()
}
with io.open(path.join('a.ark').strpath, 'rb') as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd, endian=endian)}
_compare(d2, origin)
_compare(d5, origin)
_compare(d6, origin)
def prepareSimData(dataset, audiofiles, destloc, preprocess):
pbar = pkbar.Pbar(name='Preparing SpecImages for ' + dataset + ' (Sim)',
target=len(audiofiles))
for i in range(len(audiofiles)):
audio = audiofiles[i]
audio = audio.strip()
audioName, audioLoc = audio.split()
if dataset == 'Train':
cleanLoc = audioLoc.replace('REVERB_WSJCAM0_tr', 'WSJCAM0')
cleanLoc = cleanLoc.replace('/mc_train', '')
cleanLoc = cleanLoc.replace('_ch1', '')
else:
cleanLoc = audioLoc.replace('far_test', 'cln_test')
cleanLoc = cleanLoc.replace('near_test', 'cln_test')
cleanLoc = cleanLoc.replace('_ch1', '')
reverbPSD, cleanPSD = spectralImages_2D(audioName, audioLoc, cleanLoc,
preprocess)
reverb_arkfile = destloc + '/1ch/' + dataset + '/Sim/' + audioName + '.ark'
clean_arkfile = destloc + '/1ch/' + dataset + '/Clean/' + audioName + '.ark'
Path(os.path.dirname(reverb_arkfile)).mkdir(parents=True,
exist_ok=True)
Path(os.path.dirname(clean_arkfile)).mkdir(parents=True, exist_ok=True)
kaldiio.save_ark(reverb_arkfile,
reverbPSD,
scp=reverb_arkfile.replace('ark', 'scp'))
kaldiio.save_ark(clean_arkfile,
cleanPSD,
scp=clean_arkfile.replace('ark', 'scp'))
pbar.update(i)
os.system('cat ' + destloc + '/1ch/Dev/Sim/*.scp | sort > ' + destloc +
'/1ch/Dev/Sim/wav.scp')
os.system('cat ' + destloc + '/1ch/Dev/Clean/*.scp | sort > ' + destloc +
'/1ch/Dev/Clean/wav.scp')
return
def test_write_read_multiark(tmpdir, endian, dtype):
path = tmpdir.mkdir('test')
a = np.random.rand(1000, 120).astype(dtype)
b = np.random.rand(10, 120).astype(dtype)
origin = {u'Ï,é,à': a, u'あいうえお': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
endian=endian)
c = np.random.rand(1000, 120).astype(dtype)
d = np.random.rand(10, 120).astype(dtype)
origin.update({u'c': c, u'd': d})
with io.open(path.join('b.scp').strpath, 'a', encoding='utf-8') as f:
kaldiio.save_ark(path.join('b.ark').strpath,
origin,
scp=f,
endian=endian)
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath,
endian=endian).items()
}
_compare(d5, origin)
def extract(scp_path, model, name=None, path='./', dif_pairs=20, sam_pairs=-1):
name = name if name is not None else \
os.path.basename(scp_path).split('\.')[0]
loder = KaldiLoader(scp_path=scp_path)
meta_dict = {}
key_list = []
# same pairs
for spk in loder.spk_list:
utt_list = get_utt_list(loder.raw_labels, loder.spk2indexes[spk])
key_list.clear()
for utt1 in utt_list:
for utt2 in utt_list:
key = "{}#{}".format(utt1, utt2)
key_list.append(key)
sam_key_pairs = random.sample(
key_list,
sam_pairs if sam_pairs != -1 else min(len(key_list), sam_pairs))
for key in sam_key_pairs:
utt1, utt2 = key.split('#')
utt_xvec1 = loder.datas.get(utt1)
utt_xvec2 = loder.datas.get(utt2)
val = forward(utt_xvec1, utt_xvec2, model)
meta_dict[key] = val
# different pairs
for spk1, spk2 in combinations(loder.spk_list, 2):
utt_list1 = get_utt_list(loder.raw_labels, loder.spk2indexes[spk1])
utt_list2 = get_utt_list(loder.raw_labels, loder.spk2indexes[spk2])
key_list.clear()
for utt1 in utt_list1:
for utt2 in utt_list2:
key = "{}#{}".format(utt1, utt2)
key_list.append(key)
dif_key_pairs = random.sample(
key_list,
dif_pairs if dif_pairs != -1 else min(len(key_list), dif_pairs))
for key in dif_key_pairs:
utt1, utt2 = key.split('#')
utt_xvec1 = loder.datas.get(utt1)
utt_xvec2 = loder.datas.get(utt2)
val = forward(utt_xvec1, utt_xvec2, model)
meta_dict[key] = val
ko.save_ark(ark=os.path.join(path, '{}.ark'.format(name)),
array_dict=meta_dict,
scp=os.path.join(path, '{}.scp'.format(name)))
def __call__(self, key, value_dict):
# Concatenate the rate to the signal to save it
rate = value_dict.get("rate", 0)
array = value_dict["x"]
array = np.concatenate((rate * np.ones((1, *array.shape[1:])), array),
axis=0)
kaldiio.save_ark(self.writer, {key: array},
scp=self.writer_scp,
compression_method=self.compression_method)
def evaluation(model, prior, loader, epoch, args, task='dev'):
"""forward model with PyTorch and run decoding with Kaldi
Args:
model (nn.Module): dnn model
prior (array): prior probability $\mathcal{P}(HMMstate)$
loader (dataloader): data generator
epoch (int): current epoch
args (arguments): parameters for decoding
task (str): evaluation for dev set or test set
Returns:
output, err (str): runtime log
total_loss / total_utt (float): average loss
total_acc / total_utt (float): average accuracy
"""
# forward model
model.eval()
likelihood_dict = {}
total_loss = 0.
total_acc = 0.
total_utt = 0
for (utt_id, utt_feat, utt_align) in loader:
utt_feat = torch.from_numpy(utt_feat)
utt_align = torch.from_numpy(utt_align)
log_probs = model(utt_feat)
loss, acc = cal_loss_acc(log_probs, utt_align, -1)
total_loss += loss.item()
total_acc += acc
total_utt += len(utt_id)
likelihood = log_probs[0, :, :].data.cpu().numpy().reshape(-1, args.cdphones_num) \
- prior
likelihood_dict[utt_id[0]] = likelihood
# decoding is time consuming, especially at the early stages of training,
# so we only do this for the last epoch.
if args.epochs - epoch < 2:
# write likelihood to ark-files
if not os.path.exists(args.output_dir + '/decode_' + task):
os.makedirs(args.output_dir + '/decode_' + task)
ark_file = os.path.join(args.output_dir, 'decode_' + task,
'ep' + str(epoch) + task + '_likelihood.ark')
kaldiio.save_ark(ark_file, likelihood_dict)
# run kaldi for decoding
ground_truth_dir = args.output_dir + '/../data/text_' + task
cmd_decode = 'cd kaldi_decoding_script && ./decode_dnn.sh --kaldi-root ' + args.kaldi_root + ' ' + \
args.graph_dir + ' ' + ground_truth_dir + ' ' + \
args.gmmhmm + ' ' + ark_file + ' && cd ..'
output, err = run_shell(cmd_decode)
else:
output = "".encode("utf-8")
err = "".encode("utf-8")
return output, err, total_loss / total_utt, total_acc / total_utt
def main_concatenate(audio_dir, visual_dir, store_dir, ji=None, nj=None):
audio_loader = scp2array_dic(
scp_path=os.path.join(audio_dir, "feats.scp"),
array_dic=None,
ark_path=None,
compression_method=None,
append=False,
)
visual_npz_dic = {}
with codecs.open(os.path.join(visual_dir, "embedding.scp"), "r") as handle:
lines_content = handle.readlines()
for line_content in [
*map(lambda x: x[:-1] if x[-1] in ["\n"] else x, lines_content)
]:
key, path = line_content.split(" ")
visual_npz_dic[key] = path
common_keys = [*(set(audio_loader.keys()) & set(visual_npz_dic.keys()))]
store_scp = os.path.abspath(
os.path.join(store_dir, "raw_av_embedding.{}.scp".format(ji))
)
store_ark = os.path.abspath(
os.path.join(store_dir, "raw_av_embedding.{}.ark".format(ji))
)
for key_idx in tqdm(
range(len(common_keys)), leave=True, desc="0" if ji is None else str(ji)
):
if ji is None:
processing_token = True
else:
if key_idx % nj == ji:
processing_token = True
else:
processing_token = False
if processing_token:
key = common_keys[key_idx]
audio_array = audio_loader[key]
visual_array = np.load(visual_npz_dic[key])["data"][0]
expend_visual_array = np.stack(
[visual_array for _ in range(4)], axis=-1
).reshape(-1, visual_array.shape[-1])
expend_visual_array = expend_visual_array[1:]
expend_visual_array = expend_visual_array[: audio_array.shape[0]]
audio_visual_array = np.concatenate(
[audio_array, expend_visual_array], axis=-1
)
kaldiio.save_ark(
ark=store_ark,
array_dict={key: audio_visual_array},
scp=store_scp,
append=True,
)
return None
def test_write_read_ascii(tmpdir):
path = tmpdir.mkdir("test")
a = np.random.rand(10, 10).astype(np.float32)
b = np.random.rand(5, 35).astype(np.float32)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(
path.join("a.ark").strpath, origin, scp=path.join("a.scp").strpath, text=True
)
d2 = {k: v for k, v in kaldiio.load_ark(path.join("a.ark").strpath)}
d5 = {k: v for k, v in kaldiio.load_scp(path.join("a.scp").strpath).items()}
_compare_allclose(d2, origin)
_compare_allclose(d5, origin)
def test_write_compressed_arks(tmpdir, compression_method):
# Assume arks dir existing at the same directory
ark0 = dict(
kaldiio.load_ark(
os.path.join(os.path.dirname(__file__), 'arks', 'test.ark')))
path = tmpdir.mkdir('test').join('c.ark').strpath
kaldiio.save_ark(path, ark0, compression_method=compression_method)
arkc = dict(kaldiio.load_ark(path))
arkc_valid = dict(
kaldiio.load_ark(
os.path.join(os.path.dirname(__file__), 'arks',
'test.cm{}.ark'.format(compression_method))))
_compare_allclose(arkc, arkc_valid, atol=1e-4)
def test_write_load_ascii(tmpdir):
path = tmpdir.mkdir('test')
a = np.random.rand(10, 10).astype(np.float32)
b = np.random.rand(5, 35).astype(np.float32)
origin = {'a': a, 'b': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('a.scp').strpath,
text=True)
d2 = {k: v for k, v in kaldiio.load_ark(path.join('a.ark').strpath)}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('a.scp').strpath).items()
}
_compare_allclose(d2, origin)
_compare_allclose(d5, origin)
def write_expanded_feature(raw_mfcc_and_pitch_file, output_data_dir):
expanded_features = {}
for utt, features in kaldiio.load_ark(raw_mfcc_and_pitch_file):
num_frames = len(features)
target_emotion_column = np.full((num_frames, 1),
get_target_emotion(utt))
expanded_feature = np.append(features, target_emotion_column, 1)
expanded_features[utt] = expanded_feature
(_, split, _) = raw_mfcc_and_pitch_file.split('.', 2)
kaldiio.save_ark(
os.path.join(output_data_dir,
'mfcc_pitch_and_target_emotion.%s.ark' % (split)),
expanded_features,
scp=os.path.join(output_data_dir,
'mfcc_pitch_and_target_emotion.%s.scp' % (split)))
def test_write_read_sequential(tmpdir, endian):
path = tmpdir.mkdir('test')
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {u'Ï,é,à': a, u'あいうえお': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
endian=endian)
d5 = {
k: v
for k, v in kaldiio.load_scp_sequential(path.join('b.scp').strpath,
endian=endian)
}
_compare(d5, origin)
def test_write_read_int32_vector_ascii(tmpdir):
path = tmpdir.mkdir("test")
a = np.random.randint(1, 128, 10, dtype=np.int32)
b = np.random.randint(1, 128, 10, dtype=np.int32)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(
path.join("a.ark").strpath, origin, scp=path.join("b.scp").strpath, text=True
)
d2 = {k: v for k, v in kaldiio.load_ark(path.join("a.ark").strpath)}
d5 = {k: v for k, v in kaldiio.load_scp(path.join("b.scp").strpath).items()}
with io.open(path.join("a.ark").strpath, "rb") as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd)}
_compare_allclose(d2, origin)
_compare_allclose(d5, origin)
_compare_allclose(d6, origin)
def main(args):
with kaldiio.ReadHelper(f'scp:{args.emb_in}') as reader:
x_vector_u = {utt:embd for utt, embd in reader}
R = np.load(args.rotation)
# Convert from dictionaries to numpy arrays
u_out, u_out_label = (
np.array([x_vector_u[i] for i in x_vector_u]),
np.array([i for i in x_vector_u]),
)
_, _, emb, emb_label = frontend(args, np.zeros((512,512)), np.zeros((512,)), u_out, u_out_label)
R_emb = np.dot(emb, R)
scp_data = {utt:embd for utt, embd in zip(emb_label, R_emb)}
kaldiio.save_ark(f'{args.emb_out}/transformed_xvector.ark', scp_data, scp=f'{args.emb_out}/transformed_xvector.scp')
def prepareRealData(dataset, audiofiles, destloc, preprocess):
pbar = pkbar.Pbar(name='Preparing SpecImages for ' + dataset + ' (Real)',
target=len(audiofiles))
for i in range(len(audiofiles)):
audio = audiofiles[i]
audio = audio.strip()
audioName, audioLoc = audio.split()
reverbPSD = spectralImages_1D(audioName, audioLoc, preprocess)
reverb_arkfile = destloc + '/1ch/Dev/Real/' + audioName + '.ark'
Path(os.path.dirname(reverb_arkfile)).mkdir(parents=True,
exist_ok=True)
kaldiio.save_ark(reverb_arkfile,
reverbPSD,
scp=reverb_arkfile.replace('ark', 'scp'))
pbar.update(i)
os.system('cat ' + destloc + '/1ch/Dev/Real/*.scp | sort > ' + destloc +
'/1ch/Dev/Real/wav.scp')
return
def test_write_read_sequential(tmpdir, endian):
path = tmpdir.mkdir("test")
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(
path.join("a.ark").strpath,
origin,
scp=path.join("b.scp").strpath,
endian=endian,
)
d5 = {
k: v
for k, v in kaldiio.load_scp_sequential(
path.join("b.scp").strpath, endian=endian
)
}
_compare(d5, origin)
def test_write_load(tmpdir):
path = tmpdir.mkdir('test')
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {'a': a, 'b': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath)
d2 = {k: v for k, v in kaldiio.load_ark(path.join('a.ark').strpath)}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath).items()
}
with open(path.join('a.ark').strpath, 'rb') as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd)}
_compare(d2, origin)
_compare(d5, origin)
_compare(d6, origin)
def extract():
model = load_model()
model.eval()
transform = Transpose2D()
dataset = KaldiFeatDataset(root=args.root, transform=transform)
loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=args.pin_memory)
utt2emb = {}
for data, utt in tqdm(loader):
with torch.no_grad():
data = data.to(device)
emb = model(data)[0].cpu().numpy()
if args.norm:
emb = emb / linalg.norm(emb)
utt2emb[utt[0]] = emb
kaldiio.save_ark(args.output + '.ark', utt2emb, args.output + '.scp')
def test_write_read_int32_vector_ascii(tmpdir):
path = tmpdir.mkdir('test')
a = np.random.randint(1, 128, 10, dtype=np.int32)
b = np.random.randint(1, 128, 10, dtype=np.int32)
origin = {'a': a, 'b': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath,
text=True)
d2 = {k: v for k, v in kaldiio.load_ark(path.join('a.ark').strpath)}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath).items()
}
with open(path.join('a.ark').strpath, 'r') as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd)}
_compare_allclose(d2, origin)
_compare_allclose(d5, origin)
_compare_allclose(d6, origin)
def test_append_mode(tmpdir):
path = tmpdir.mkdir("test")
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(path.join("a.ark").strpath, origin, scp=path.join("b.scp").strpath)
kaldiio.save_ark(
path.join("a2.ark").strpath,
{"Ï,é,à": a},
scp=path.join("b2.scp").strpath,
append=True,
)
kaldiio.save_ark(
path.join("a2.ark").strpath,
{"あいうえお": b},
scp=path.join("b2.scp").strpath,
append=True,
)
d1 = {k: v for k, v in kaldiio.load_ark(path.join("a.ark").strpath)}
d2 = {k: v for k, v in kaldiio.load_scp(path.join("b.scp").strpath).items()}
d3 = {k: v for k, v in kaldiio.load_ark(path.join("a2.ark").strpath)}
d4 = {k: v for k, v in kaldiio.load_scp(path.join("b2.scp").strpath).items()}
_compare(d1, origin)
_compare(d2, origin)
_compare(d3, origin)
_compare(d4, origin)
def test_append_mode(tmpdir):
path = tmpdir.mkdir('test')
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {'a': a, 'b': b}
kaldiio.save_ark(path.join('a.ark').strpath,
origin,
scp=path.join('b.scp').strpath)
kaldiio.save_ark(path.join('a2.ark').strpath, {'a': a},
scp=path.join('b2.scp').strpath,
append=True)
kaldiio.save_ark(path.join('a2.ark').strpath, {'b': b},
scp=path.join('b2.scp').strpath,
append=True)
d1 = {k: v for k, v in kaldiio.load_ark(path.join('a.ark').strpath)}
d2 = {
k: v
for k, v in kaldiio.load_scp(path.join('b.scp').strpath).items()
}
d3 = {k: v for k, v in kaldiio.load_ark(path.join('a2.ark').strpath)}
d4 = {
k: v
for k, v in kaldiio.load_scp(path.join('b2.scp').strpath).items()
}
_compare(d1, origin)
_compare(d2, origin)
_compare(d3, origin)
_compare(d4, origin)
def write_kaldi_matrix(folder, model, loader, output_dir):
"""
Args:
folder: str
model: pytorch model
loader: torch.utils.data.Dataloader
output_dir: str
"""
if not os.path.exists(output_dir):
print(
"Destination folder for storage\n of .ark , .scp files doesn't exist!!"
)
sys.exit(1)
with torch.no_grad():
for idx, img in enumerate(loader):
img = img.view(img.size(0), -1).cuda()
output = model(img)
lt_vec = model.lant_vec.cpu()
feats_np = lt_vec.numpy()
# initially --> s1_v1_u32 , to --> s01_u32.ark to match audio name files
#print(folder)
#s1 --> s01
if folder.index("_") - folder.index("s") == 2:
folder = "s0" + folder.split("s")[1]
#s01_v1_u32 --> s01_u32
tokens = folder.split("_")
folder = tokens[0] + "_" + tokens[2]
#print(folder)
scp_file = output_dir + "/" + folder + ".scp"
ark_file = output_dir + "/" + folder + ".ark"
write_dict = {}
write_dict[folder] = feats_np
kaldiio.save_ark(ark_file, write_dict, scp=scp_file)
def test_write_read_compress(tmpdir, compression_method, endian):
path = tmpdir.mkdir("test")
a = np.random.rand(1000, 120).astype(np.float32)
b = np.random.rand(10, 120).astype(np.float32)
origin = {"Ï,é,à": a, "あいうえお": b}
kaldiio.save_ark(
path.join("a.ark").strpath,
origin,
scp=path.join("b.scp").strpath,
compression_method=compression_method,
endian=endian,
)
d2 = {k: v for k, v in kaldiio.load_ark(path.join("a.ark").strpath, endian=endian)}
d5 = {
k: v
for k, v in kaldiio.load_scp(path.join("b.scp").strpath, endian=endian).items()
}
with io.open(path.join("a.ark").strpath, "rb") as fd:
d6 = {k: v for k, v in kaldiio.load_ark(fd, endian=endian)}
_compare_allclose(d2, origin, atol=1e-1)
_compare_allclose(d5, origin, atol=1e-1)
_compare_allclose(d6, origin, atol=1e-1)
def gen_dummy_data_dir(data_dir,
num_spk,
num_utt_per_spk,
feat_len=100,
feat_dim=40):
''' Generate a dummy data directory and return its meta. '''
os.makedirs(data_dir, exist_ok=True)
meta = kaldi_dir.KaldiMetaData()
feats = {}
vads = {}
for spk_idx in range(num_spk):
for utt_idx in range(num_utt_per_spk):
spk = str(spk_idx)
utt = '%s_%d' % (spk, utt_idx)
utt_meta = kaldi_dir.Utt()
feat_mat = np.ones((feat_len, feat_dim), dtype='float32')
feats[utt] = feat_mat
utt_meta.featlen = feat_len
vad_mat = np.ones((feat_len, ), dtype='float32')
vads[utt] = vad_mat
utt_meta.spk = spk
meta.utts[utt] = utt_meta
meta.collect_spks_from_utts()
meta.dump(data_dir, True)
feats_ark_path = os.path.join(data_dir, 'feats.ark')
feats_scp_path = os.path.join(data_dir, 'feats.scp')
kaldiio.save_ark(feats_ark_path, feats, scp=feats_scp_path, text=True)
vad_ark_path = os.path.join(data_dir, 'vad.ark')
vad_scp_path = os.path.join(data_dir, 'vad.scp')
kaldiio.save_ark(vad_ark_path, vads, scp=vad_scp_path, text=True)
loaded_meta = kaldi_dir.KaldiMetaData()
loaded_meta.load(data_dir)
return loaded_meta