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)