def test_predict(self, art_warning, audio_data):
try:
test_input = audio_data
lingvo = TensorFlowLingvoASR()
predictions = lingvo.predict(test_input, batch_size=2)
assert predictions.shape == test_input.shape
assert isinstance(predictions[0], np.str_)
except ARTTestException as e:
art_warning(e)
def test_predict(self, art_warning, audio_data):
try:
import tensorflow.compat.v1 as tf1
tf1.reset_default_graph()
test_input = audio_data
lingvo = TensorFlowLingvoASR()
predictions = lingvo.predict(test_input, batch_size=2)
assert predictions.shape == test_input.shape
assert isinstance(predictions[0], np.str_)
except ARTTestException as e:
art_warning(e)
def test_predict(self, art_warning):
try:
transcripts = list()
audios = list()
for filename, sample in self.samples.items():
file_path = get_file(filename, sample["uri"])
_, audio = read(file_path)
audios.append(audio)
transcripts.append(sample["transcript"])
audio_batch = np.array(audios, dtype=object)
lingvo = TensorFlowLingvoASR()
prediction = lingvo.predict(audio_batch, batch_size=1)
assert prediction[0] == transcripts[0]
except ARTTestException as e:
art_warning(e)