def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 config.layerdrop = 0.0 model = TFHubertModel(config) input_values = input_values[:3] attention_mask = tf.ones_like(input_values) input_lengths = tf.constant( [input_values.shape[-1] // i for i in [4, 2, 1]]) length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32) # convert values that are over input_lengths to padding input_values = input_values * length_mask attention_mask = attention_mask * length_mask batch_outputs = model(input_values, attention_mask=attention_mask, training=False).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i:i + 1, :input_lengths[i]] output = model(input_slice, training=False).last_hidden_state batch_output = batch_outputs[i:i + 1, :output.shape[1]] self.parent.assertTrue(np.allclose(output, batch_output, atol=1e-3))
def test_model_from_pretrained(self): model = TFHubertModel.from_pretrained("facebook/hubert-large-ls960-ft") self.assertIsNotNone(model)
def create_and_check_model(self, config, input_values, attention_mask): model = TFHubertModel(config) result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size))