fix asr output name

delta/models/asr_model.py

@@ -244,7 +244,7 @@ def build(self):
  x)
 
  # Output layer with softmax
- x = TimeDistributed(Dense(self._vocab_size))(x)
+ x = TimeDistributed(Dense(self._vocab_size), name="outputs")(x)
 
  input_length = Input(name='input_length', shape=[], dtype='int64')
  labels = Input(name='targets', shape=[None], dtype='int32')

egs/hkust/asr/v1/conf/asr-ctc.yml

@@ -36,7 +36,7 @@ data:
  batch_strategy: auto # strategy to count maximum size of batch(support 4 values: "auto", "seq", "frame", "bin")
  batch_mode: true # ture, user control batch; false, `generate` will yeild one example
  num_parallel_calls: 10
- num_prefetch_batch: 2
+ num_prefetch_batch: 20
  shuffle_buffer_size: 2000
  need_shuffle: true
  sortagrad: true
@@ -58,10 +58,6 @@ model:
 
 solver:
  name: AsrSolver
- adversarial:
- enable: false # whether to using adversiral training
- adv_alpha: 0.5 # adviseral alpha of loss
- adv_epslion: 0.1 # adviseral example epslion
  model_average:
  enable: false # use average model
  var_avg_decay: 0.99 # the decay rate of varaibles
@@ -96,29 +92,13 @@ solver:
  cals:
  - name: AccuracyCal
  arguments: null
- - name: ConfusionMatrixCal
- arguments: null
- - name: PrecisionCal
- arguments:
- average: 'binary'
- - name: RecallCal
- arguments:
- average: 'binary'
- - name: F1ScoreCal
- arguments:
- average: 'binary'
  postproc:
  enbale: false
  name: EmoPostProc
  log_verbose: false
  eval: true # compute metrics
  infer: true # get predict results
  pred_path: null # None for `model_path`/infer, dumps infer output to this dir
- thresholds:
- - 0.5
- smoothing:
- enable: true
- count: 2
  saver:
  model_path: "exp/asr-ctc/ckpt"
  max_to_keep: 10

egs/mini_an4/asr/v1/conf/asr-ctc.yml

@@ -36,7 +36,7 @@ data:
  batch_strategy: auto # strategy to count maximum size of batch(support 4 values: "auto", "seq", "frame", "bin")
  batch_mode: true # ture, user control batch; false, `generate` will yeild one example
  num_parallel_calls: 10
- num_prefetch_batch: 2
+ num_prefetch_batch: 20
  shuffle_buffer_size: 2000
  need_shuffle: true
  sortagrad: true
@@ -58,10 +58,6 @@ model:
 
 solver:
  name: AsrSolver
- adversarial:
- enable: false # whether to using adversiral training
- adv_alpha: 0.5 # adviseral alpha of loss
- adv_epslion: 0.1 # adviseral example epslion
  model_average:
  enable: false # use average model
  var_avg_decay: 0.99 # the decay rate of varaibles
@@ -96,29 +92,13 @@ solver:
  cals:
  - name: AccuracyCal
  arguments: null
- - name: ConfusionMatrixCal
- arguments: null
- - name: PrecisionCal
- arguments:
- average: 'binary'
- - name: RecallCal
- arguments:
- average: 'binary'
- - name: F1ScoreCal
- arguments:
- average: 'binary'
  postproc:
  enbale: false
  name: EmoPostProc
  log_verbose: false
  eval: true # compute metrics
  infer: true # get predict results
  pred_path: null # None for `model_path`/infer, dumps infer output to this dir
- thresholds:
- - 0.5
- smoothing:
- enable: true
- count: 2
  saver:
  model_path: "exp/asr-ctc/ckpt"
  max_to_keep: 10