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)
