44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
|
import os
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import lm_decoder
|
||
|
|
|
||
|
|
decode_opts = lm_decoder.DecodeOptions(
|
||
|
|
7000, # max_active
|
||
|
|
200, # min_active
|
||
|
|
17., # beam
|
||
|
|
8., # lattice_beam
|
||
|
|
1.0, # acoustic_scale
|
||
|
|
0.98, # ctc_blank_skip_threshold
|
||
|
|
10 # nbest
|
||
|
|
)
|
||
|
|
|
||
|
|
model_path = '/oak/stanford/groups/shenoy/stfan/code/nptlrig2/LanguageModelDecoder/examples/handwriting/s0/3gram_no_prune/data/lang_test'
|
||
|
|
decode_resource = lm_decoder.DecodeResource(
|
||
|
|
os.path.join(model_path, 'TLG.fst'),
|
||
|
|
"",
|
||
|
|
"",
|
||
|
|
os.path.join(model_path, 'words.txt'),
|
||
|
|
""
|
||
|
|
)
|
||
|
|
decoder = lm_decoder.BrainSpeechDecoder(decode_resource, decode_opts)
|
||
|
|
|
||
|
|
# Load handwriting RNN logits output
|
||
|
|
logits = np.load('test_logits.npy')
|
||
|
|
print(logits.shape)
|
||
|
|
|
||
|
|
# Rearrange logits to Kaldi character order
|
||
|
|
# [ctc_blank, ">", ",", "?", "~", "'", a, b, ..., z]
|
||
|
|
char_range = list(range(0, 26))
|
||
|
|
logits = logits[:, :, [31] + [26, 27, 30, 29, 28] + char_range]
|
||
|
|
|
||
|
|
# Decode
|
||
|
|
for i in range(logits.shape[0]):
|
||
|
|
lm_decoder.DecodeNumpy(decoder, logits[i])
|
||
|
|
decoder.FinishDecoding()
|
||
|
|
if len(decoder.result()) > 0:
|
||
|
|
print(decoder.result()[0].sentence)
|
||
|
|
else:
|
||
|
|
print("No result")
|
||
|
|
decoder.Reset()
|