def __init__(self, int2char, top_paths = 1, beam_width = 200, blank_index = 0, space_idx = 28,
lm_path=None, trie_path=None, dict_path=None, lm_alpha=None, lm_beta1=None, lm_beta2=None):
self.beam_width = beam_width
self.top_n = top_paths
self.labels = ['#']
int2phone = dict()
for digit in int2char:
if digit != 0:
label = bytes.decode(int2char[digit].tostring())
self.labels.append(label)
int2phone[digit] = label
int2phone[0] = '#'
super(BeamDecoder, self).__init__(int2phone, space_idx=space_idx, blank_index=blank_index)
self.label2 = '#123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLM'
try:
from pytorch_ctc import CTCBeamDecoder, Scorer, KenLMScorer
import pytorch_ctc
except ImportError:
raise ImportError("BeamCTCDecoder requires pytorch_ctc package.")
if lm_path is not None:
pytorch_ctc.generate_lm_trie(dict_path, lm_path, trie_path, self.label2, 0, -1)
scorer = KenLMScorer(self.label2, lm_path, trie_path)
scorer.set_lm_weight(lm_alpha)
scorer.set_word_weight(lm_beta1)
scorer.set_valid_word_weight(lm_beta2)
print('hello')
else:
scorer = Scorer()
self._decoder = CTCBeamDecoder(scorer = scorer, labels = self.labels, top_paths = top_paths, beam_width = beam_width, blank_index = blank_index, space_index = space_idx, merge_repeated=False)
def __init__(self,
labels,
beam_width=20,
top_paths=1,
blank_index=0,
space_index=28,
lm_path=None,
trie_path=None,
lm_alpha=None,
lm_beta1=None,
lm_beta2=None):
super(BeamCTCDecoder, self).__init__(labels,
blank_index=blank_index,
space_index=space_index)
self._beam_width = beam_width
self._top_n = top_paths
try:
from pytorch_ctc import CTCBeamDecoder, Scorer, KenLMScorer
except ImportError:
raise ImportError("BeamCTCDecoder requires pytorch_ctc package.")
if lm_path is not None:
scorer = KenLMScorer(labels, lm_path, trie_path)
scorer.set_lm_weight(lm_alpha)
scorer.set_word_weight(lm_beta1)
scorer.set_valid_word_weight(lm_beta2)
else:
scorer = Scorer()
self._decoder = CTCBeamDecoder(scorer,
labels,
top_paths=top_paths,
beam_width=beam_width,
blank_index=blank_index,
space_index=space_index,
merge_repeated=False)
def __init__(self, labels, beam_width=20, top_paths=1, blank_index=0, space_index=28, lm_path=None, trie_path=None,
lm_alpha=None, lm_beta1=None, lm_beta2=None):
super(BeamCTCDecoder, self).__init__(labels, blank_index=blank_index, space_index=space_index)
self._beam_width = beam_width
self._top_n = top_paths
try:
from pytorch_ctc import CTCBeamDecoder, Scorer, KenLMScorer
except ImportError:
raise ImportError("BeamCTCDecoder requires pytorch_ctc package.")
if lm_path is not None:
scorer = KenLMScorer(labels, lm_path, trie_path)
scorer.set_lm_weight(lm_alpha)
scorer.set_word_weight(lm_beta1)
scorer.set_valid_word_weight(lm_beta2)
else:
scorer = Scorer()
self._decoder = CTCBeamDecoder(scorer, labels, top_paths=top_paths, beam_width=beam_width,
blank_index=blank_index, space_index=space_index, merge_repeated=False)
def gen_decoded(feat_list, model_path):
model = set_model_ctc.Layered_RNN(
rnn_input_size=40,
nb_layers=layers,
rnn_hidden_size=hidden_size,
bidirectional=True if num_dirs == 2 else False,
batch_norm=True,
num_classes=61)
model = model.type(gpu_dtype)
model.load_state_dict(torch.load(model_path)) # load model params
model.eval(
) # Put the model in test mode (the opposite of model.train(), essentially)
if decoder_type == 'Greedy':
labels = create_mapping(mapping_file)
decoder = ctc_decode.GreedyDecoder_test(
labels, output='char', space_idx=-1) # setup greedy decoder
if decoder_type == 'Beam':
labels = create_mapping(mapping_file)
scorer = Scorer()
decoder = ctc_decode.BeamDecoder_test(
labels,
scorer,
top_paths=1,
beam_width=200,
output='char',
space_idx=-1) # setup beam decoder without lm
if decoder_type == 'Beam_LM':
labels_symbol = '_123456789abcde~-hij,.|{ofg?!+u}[x]@ABCDEFGHIJKLMNOPQRSTUVWXYZ'
labels_true = create_mapping(mapping_file)
# need to use the fake symbols here for consistency with the trie
scorer = KenLMScorer(labels_symbol,
kenlm_path,
trie_path,
blank_index=0,
space_index=-1)
scorer.set_lm_weight(lm_weight)
scorer.set_word_weight(lm_beta1)
scorer.set_valid_word_weight(lm_beta2)
# need to use the true timit label to convert the decoded position indexes back to phone labels
decoder = ctc_decode.BeamDecoder_test(
labels_true,
scorer,
top_paths=1,
beam_width=200,
output='char',
space_idx=-1) # setup beam decoder with lm
m, v = read_mv(stat_file)
if m is None or v is None:
raise Exception("mean or variance vector does not exist")
with open(feat_list) as f:
with open(out_mlf, 'w') as fw:
fw.write('#!MLF!#\n')
for line in f:
line = line.strip()
if len(line) < 1: continue
print("recognizing file %s" % line)
out_name = '"' + line[:line.rfind('.')] + '.rec' + '"'
fw.write(out_name + '\n')
io = htk_io.fopen(line)
utt_feat = io.getall()
utt_feat -= m # normalize mean
utt_feat /= (np.sqrt(v) + eps) # normalize var
feat_numpy = org_data(utt_feat, skip_frames=5)
feat_tensor = torch.from_numpy(feat_numpy).type(gpu_dtype)
x = Variable(feat_tensor.type(gpu_dtype), volatile=True)
input_sizes_list = [x.size(1)]
x = nn.utils.rnn.pack_padded_sequence(x,
input_sizes_list,
batch_first=True)
probs = model(x, input_sizes_list)
probs = probs.data.cpu()
decoded = decoder.decode(probs, input_sizes_list)[0]
for word in decoded:
fw.write(word + '\n')
fw.write('.\n')
print(' '.join(decoded))