def test_inference_distilhubert(self):
model = HubertModel.from_pretrained("ntu-spml/distilhubert").to(
torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained(
"ntu-spml/distilhubert")
# TODO: can't test on batched inputs due to incompatible padding https://github.com/pytorch/fairseq/pull/3572
input_speech = self._load_datasamples(1)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
with torch.no_grad():
outputs = model(input_values).last_hidden_state
# expected outputs taken from the original SEW implementation
expected_outputs_first = torch.tensor(
[[
[-0.3505, 0.1167, 0.0608, 0.1294],
[-0.3085, 0.0481, 0.1106, 0.0955],
[-0.3107, -0.0391, 0.0739, 0.1360],
[-0.2385, -0.1795, -0.0928, 0.2389],
]],
device=torch_device,
)
expected_outputs_last = torch.tensor(
[[
[-0.0732, 0.0255, 0.0529, -0.1372],
[-0.0812, 0.1259, 0.0564, -0.0438],
[-0.0054, 0.0758, -0.0002, -0.1617],
[0.0133, -0.0320, -0.0687, 0.0062],
]],
device=torch_device,
)
expected_output_sum = -3776.0730
self.assertTrue(
torch.allclose(outputs[:, :4, :4],
expected_outputs_first,
atol=5e-3))
self.assertTrue(
torch.allclose(outputs[:, -4:, -4:],
expected_outputs_last,
atol=5e-3))
self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1)
def test_model_from_pretrained(self):
model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
self.assertIsNotNone(model)