def infer():
tensor_global_step = tf.train.get_or_create_global_step()
model_infer = args.Model(
tensor_global_step,
encoder=args.model.encoder.type,
decoder=args.model.decoder.type,
is_train=False,
args=args)
dataset_dev = args.dataset_test if args.dataset_test else args.dataset_dev
saver = tf.train.Saver(max_to_keep=40)
size_variables()
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
config.log_device_placement = False
with tf.train.MonitoredTrainingSession(config=config) as sess:
checkpoint = tf.train.latest_checkpoint(args.dirs.checkpoint_init)
saver.restore(sess, checkpoint)
total_cer_dist = 0
total_cer_len = 0
total_wer_dist = 0
total_wer_len = 0
with open(args.dir_model.name+'_decode.txt', 'w') as fw:
for sample in tqdm(dataset_dev):
if not sample:
continue
dict_feed = {model_infer.list_pl[0]: np.expand_dims(sample['feature'], axis=0),
model_infer.list_pl[1]: np.array([len(sample['feature'])])}
sample_id, shape_batch, _ = sess.run(model_infer.list_run, feed_dict=dict_feed)
# decoded, sample_id, decoded_sparse = sess.run(model_infer.list_run, feed_dict=dict_feed)
res_txt = array2text(sample_id[0], args.data.unit, args.idx2token, eos_idx=args.eos_idx, min_idx=0, max_idx=args.dim_output-1)
# align_txt = array2text(alignment[0], args.data.unit, args.idx2token, min_idx=0, max_idx=args.dim_output-1)
ref_txt = array2text(sample['label'], args.data.unit, args.idx2token, eos_idx=args.eos_idx, min_idx=0, max_idx=args.dim_output-1)
list_res_char = list(res_txt)
list_ref_char = list(ref_txt)
list_res_word = res_txt.split()
list_ref_word = ref_txt.split()
cer_dist = ed.eval(list_res_char, list_ref_char)
cer_len = len(list_ref_char)
wer_dist = ed.eval(list_res_word, list_ref_word)
wer_len = len(list_ref_word)
total_cer_dist += cer_dist
total_cer_len += cer_len
total_wer_dist += wer_dist
total_wer_len += wer_len
if cer_len == 0:
cer_len = 1000
wer_len = 1000
if wer_dist/wer_len > 0:
fw.write('id:\t{} \nres:\t{}\nref:\t{}\n\n'.format(sample['id'], res_txt, ref_txt))
logging.info('current cer: {:.3f}, wer: {:.3f};\tall cer {:.3f}, wer: {:.3f}'.format(
cer_dist/cer_len, wer_dist/wer_len, total_cer_dist/total_cer_len, total_wer_dist/total_wer_len))
logging.info('dev CER {:.3f}: WER: {:.3f}'.format(total_cer_dist/total_cer_len, total_wer_dist/total_wer_len))
def decode_test(step, sample, model, sess, unit, args):
# sample = dataset_dev[0]
dict_feed = {
model.list_pl[0]: np.expand_dims(sample['feature'], axis=0),
model.list_pl[1]: np.array([len(sample['feature'])])
}
(decoded_ctc, decoded), shape_sample, _ = sess.run(model.list_run,
feed_dict=dict_feed)
res_ctc_txt = array2text(decoded_ctc[0], unit, args.idx2phone,
args.phone2idx)
res_txt = array2text(decoded[0], unit, args.idx2token, args.token2idx)
ref_txt = array2text(sample['label'], unit, args.idx2token, args.token2idx)
logging.warning('length: {}, \nres_ctc: \n{}\nres: \n{}\nref: \n{}'.format(
shape_sample[1], res_ctc_txt, res_txt, ref_txt))
def showing_csv_data():
from dataProcessing.dataHelper import ASR_csv_DataSet
dataset_train = ASR_csv_DataSet(
list_files=[args.dirs.train.data],
args=args,
_shuffle=False,
transform=False)
ref_txt = array2text(dataset_train[0]['label'], args.data.unit, args.idx2token)
print(ref_txt)
def decode_test(step,
sample,
model,
sess,
unit,
idx2token,
eos_idx=None,
min_idx=0,
max_idx=None):
# sample = dataset_dev[0]
dict_feed = {
model.list_pl[0]: np.expand_dims(sample['feature'], axis=0),
model.list_pl[1]: np.array([len(sample['feature'])])
}
sampled_id, shape_sample, _ = sess.run(model.list_run, feed_dict=dict_feed)
res_txt = array2text(sampled_id[0], unit, idx2token, eos_idx, min_idx,
max_idx)
ref_txt = array2text(sample['label'], unit, idx2token, eos_idx, min_idx,
max_idx)
logging.warning('length: {}, res: \n{}\nref: \n{}'.format(
shape_sample[1], res_txt, ref_txt))
def infer_lm():
tensor_global_step = tf.train.get_or_create_global_step()
dataset_dev = args.dataset_test if args.dataset_test else args.dataset_dev
model_lm = args.Model_LM(tensor_global_step,
training=False,
args=args.args_lm)
args.lm_obj = model_lm
saver_lm = tf.train.Saver(model_lm.variables())
args.top_scope = tf.get_variable_scope() # top-level scope
args.lm_scope = model_lm.decoder.scope
model_infer = args.Model(tensor_global_step,
encoder=args.model.encoder.type,
decoder=args.model.decoder.type,
training=False,
args=args)
saver = tf.train.Saver(model_infer.variables())
size_variables()
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
config.log_device_placement = False
with tf.train.MonitoredTrainingSession(config=config) as sess:
checkpoint = tf.train.latest_checkpoint(args.dirs.checkpoint_init)
checkpoint_lm = tf.train.latest_checkpoint(args.dirs.lm_checkpoint)
saver.restore(sess, checkpoint)
saver_lm.restore(sess, checkpoint_lm)
total_cer_dist = 0
total_cer_len = 0
total_wer_dist = 0
total_wer_len = 0
with open(args.dir_model.name + '_decode.txt', 'w') as fw:
# with open('/mnt/lustre/xushuang/easton/projects/asr-tf/exp/aishell/lm_acc.txt', 'w') as fw:
for sample in tqdm(dataset_dev):
if not sample:
continue
dict_feed = {
model_infer.list_pl[0]:
np.expand_dims(sample['feature'], axis=0),
model_infer.list_pl[1]:
np.array([len(sample['feature'])])
}
sample_id, shape_batch, beam_decoded = sess.run(
model_infer.list_run, feed_dict=dict_feed)
# decoded, sample_id, decoded_sparse = sess.run(model_infer.list_run, feed_dict=dict_feed)
res_txt = array2text(sample_id[0],
args.data.unit,
args.idx2token,
min_idx=0,
max_idx=args.dim_output - 1)
ref_txt = array2text(sample['label'],
args.data.unit,
args.idx2token,
min_idx=0,
max_idx=args.dim_output - 1)
list_res_char = list(res_txt)
list_ref_char = list(ref_txt)
list_res_word = res_txt.split()
list_ref_word = ref_txt.split()
cer_dist = ed.eval(list_res_char, list_ref_char)
cer_len = len(list_ref_char)
wer_dist = ed.eval(list_res_word, list_ref_word)
wer_len = len(list_ref_word)
total_cer_dist += cer_dist
total_cer_len += cer_len
total_wer_dist += wer_dist
total_wer_len += wer_len
if cer_len == 0:
cer_len = 1000
wer_len = 1000
if wer_dist / wer_len > 0:
print('ref ', ref_txt)
for i, decoded, score, rerank_score in zip(
range(10), beam_decoded[0][0], beam_decoded[1][0],
beam_decoded[2][0]):
candidate = array2text(decoded,
args.data.unit,
args.idx2token,
min_idx=0,
max_idx=args.dim_output - 1)
print('res', i, candidate, score, rerank_score)
fw.write('res: {}; ref: {}\n'.format(
candidate, ref_txt))
fw.write('id:\t{} \nres:\t{}\nref:\t{}\n\n'.format(
sample['id'], res_txt, ref_txt))
logging.info(
'current cer: {:.3f}, wer: {:.3f};\tall cer {:.3f}, wer: {:.3f}'
.format(cer_dist / cer_len, wer_dist / wer_len,
total_cer_dist / total_cer_len,
total_wer_dist / total_wer_len))
logging.info('dev CER {:.3f}: WER: {:.3f}'.format(
total_cer_dist / total_cer_len, total_wer_dist / total_wer_len))
def infer():
tensor_global_step = tf.train.get_or_create_global_step()
model_infer = args.Model(tensor_global_step,
encoder=args.model.encoder.type,
decoder=args.model.decoder.type,
training=False,
args=args)
dataset_dev = args.dataset_test
saver = tf.train.Saver(max_to_keep=1)
size_variables()
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
config.log_device_placement = False
with tf.train.MonitoredTrainingSession(config=config) as sess:
saver.restore(sess, args.dirs.checkpoint)
total_cer_dist = 0
total_cer_len = 0
total_wer_dist = 0
total_wer_len = 0
with open('outputs/decoded.txt', 'w') as fw:
for i, sample in enumerate(dataset_dev):
if not sample:
continue
dict_feed = {
model_infer.list_pl[0]:
np.expand_dims(sample['feature'], axis=0),
model_infer.list_pl[1]:
np.array([len(sample['feature'])])
}
sample_id, shape_batch, _ = sess.run(model_infer.list_run,
feed_dict=dict_feed)
# decoded, sample_id, decoded_sparse = sess.run(model_infer.list_run, feed_dict=dict_feed)
res_txt = array2text(sample_id[0], args.data.unit,
args.idx2token, args.token2idx)
ref_txt = array2text(sample['label'], args.data.unit,
args.idx2token, args.token2idx)
list_res_char = list(res_txt)
list_ref_char = list(ref_txt)
list_res_word = res_txt.split()
list_ref_word = ref_txt.split()
cer_dist = ed.eval(list_res_char, list_ref_char)
cer_len = len(list_ref_char)
wer_dist = ed.eval(list_res_word, list_ref_word)
wer_len = len(list_ref_word)
res_len = len(list_res_word)
total_cer_dist += cer_dist
total_cer_len += cer_len
total_wer_dist += wer_dist
total_wer_len += wer_len
if cer_len == 0:
cer_len = 1000
wer_len = 1000
if wer_dist / wer_len > 0:
fw.write('uttid:\t{} \nres:\t{}\nref:\t{}\n\n'.format(
sample['uttid'], res_txt, ref_txt))
sys.stdout.write(
'\rcurrent cer: {:.3f}, wer: {:.3f} res/ref: {:.3f};\tall cer {:.3f}, wer: {:.3f} {}/{} {:.2f}%'
.format(cer_dist / cer_len, wer_dist / wer_len,
res_len / wer_len, total_cer_dist / total_cer_len,
total_wer_dist / total_wer_len, i,
len(dataset_dev), i / len(dataset_dev) * 100))
sys.stdout.flush()
logging.info('dev CER {:.3f}: WER: {:.3f}'.format(
total_cer_dist / total_cer_len, total_wer_dist / total_wer_len))
