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 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_model_from_pretrained(self): model = UniSpeechSatModel.from_pretrained( "microsoft/unispeech-sat-large") self.assertIsNotNone(model)