remove inheritance

algorithmic_efficiency/workloads/librispeech_deepspeech/workload.py

@@ -1,7 +1,7 @@
 from algorithmic_efficiency.workloads.librispeech_conformer import workload
 
 
-class BaseDeepspeechLibrispeechWorkload(workload.BaseLibrispeechWorkload):
+class BaseDeepspeechLibrispeechWorkload(spec.Workload):
 
   @property
   def validation_target_value(self) -> float:
@@ -11,6 +11,50 @@ def validation_target_value(self) -> float:
   def test_target_value(self) -> float:
     return 0.073397
 
+  def has_reached_test_target(self, eval_result: Dict[str, float]) -> bool:
+    return eval_result['test/wer'] < self.test_target_value
+
+  @property
+  def loss_type(self) -> spec.LossType:
+    return spec.LossType.CTC_LOSS
+
+  @property
+  def num_train_examples(self) -> int:
+    return 263840
+
+  @property
+  def num_eval_train_examples(self) -> int:
+    # Round up from num_validation_examples (which is the default for
+    # num_eval_train_examples) to the next multiple of eval_batch_size, so that
+    # we don't have to extract the correctly sized subset of the training data.
+    rounded_up_multiple = math.ceil(self.num_validation_examples /
+                                    self.eval_batch_size)
+    return rounded_up_multiple * self.eval_batch_size
+
+  @property
+  def num_validation_examples(self) -> int:
+    return 5348
+
+  @property
+  def num_test_examples(self) -> int:
+    return 2472
+
+  @property
+  def eval_batch_size(self) -> int:
+    return 256
+
+  @property
+  def train_mean(self):
+    raise NotImplementedError
+
+  @property
+  def train_stddev(self):
+    raise NotImplementedError
+
+  @property
+  def eval_period_time_sec(self) -> int:
+    return 24 * 60
+
   @property
   def step_hint(self) -> int:
     """Max num steps the baseline algo was given to reach the target."""