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)
