def test_inference_encoder_large(self): model = UniSpeechSatModel.from_pretrained( "microsoft/unispeech-sat-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-large-xlsr-53") input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # fmt: off expected_hidden_states_slice = torch.tensor( [[[-0.1172, -0.0797], [-0.0012, 0.0213]], [[-0.1225, -0.1277], [-0.0668, -0.0585]]], device=torch_device, ) # fmt: on self.assertTrue( torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3))
def test_inference_encoder_base(self): model = UniSpeechSatModel.from_pretrained( "microsoft/unispeech-sat-base-plus") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base", return_attention_mask=True) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # fmt: off expected_hidden_states_slice = torch.tensor( [[[-0.0743, 0.1384], [-0.0845, 0.1704]], [[-0.0954, 0.1936], [-0.1123, 0.2095]]], device=torch_device, ) # fmt: on self.assertTrue( torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3))
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 model = UniSpeechSatModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i]:] = 0.0 attention_mask[i, input_lengths[i]:] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).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).last_hidden_state batch_output = batch_outputs[i:i + 1, :output.shape[1]] self.parent.assertTrue( torch.allclose(output, batch_output, atol=1e-3))
def create_and_check_model(self, config, input_values, attention_mask): model = UniSpeechSatModel(config=config) model.to(torch_device) model.eval() 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))
def test_model_from_pretrained(self): model = UniSpeechSatModel.from_pretrained( "microsoft/unispeech-sat-large") self.assertIsNotNone(model)