# Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch Hubert model.""" import math import unittest import pytest from transformers import HubertConfig, is_torch_available from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( HubertForCTC, HubertForSequenceClassification, HubertModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.hubert.modeling_hubert import _compute_mask_indices class HubertModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=2, num_attention_heads=2, hidden_dropout_prob=0.1, # this is most likely not correctly set yet intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, vocab_size=32, do_stable_layer_norm=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.scope = scope output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return HubertConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, do_stable_layer_norm=self.do_stable_layer_norm, ) def create_and_check_model(self, config, input_values, attention_mask): model = HubertModel(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 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 = HubertModel(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 check_ctc_loss(self, config, input_values, *args): model = HubertForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = HubertForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = HubertForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lengths are at least # one shorter than logit lengths to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = HubertForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = HubertForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with pytest.raises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class HubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else () pipeline_model_mapping = ( { "audio-classification": HubertForSequenceClassification, "automatic-speech-recognition": HubertForCTC, "feature-extraction": HubertModel, } if is_torch_available() else {} ) def setUp(self): self.model_tester = HubertModelTester(self) self.config_tester = ConfigTester(self, config_class=HubertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) @unittest.skip(reason="Hubert has no inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Hubert has no inputs_embeds") def test_forward_signature(self): pass # Hubert cannot resize token embeddings # since it has no tokens embeddings @unittest.skip(reason="Hubert has no tokens embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Hubert has no inputs_embeds") def test_model_get_set_embeddings(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # force eager attention to support output attentions config._attn_implementation = "eager" # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = HubertModel.from_pretrained("facebook/hubert-base-ls960") self.assertIsNotNone(model) @require_torch class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else () def setUp(self): self.model_tester = HubertModelTester( self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True ) self.config_tester = ConfigTester(self, config_class=HubertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_batched_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_batch_inference(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) @unittest.skip(reason="Hubert has no inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Hubert has input_values instead of input_ids") def test_forward_signature(self): pass @unittest.skip(reason="Hubert has no tokens embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Hubert has no inputs_embeds") def test_model_get_set_embeddings(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # force eager attention to support output attentions config._attn_implementation = "eager" # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft") self.assertIsNotNone(model) @require_torch class HubertUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) @require_torch @require_torchcodec @slow class HubertModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter( lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _load_superb(self, task, num_samples): from datasets import load_dataset ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] def test_inference_ctc_batched(self): model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft", dtype=torch.float16).to(torch_device) processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft", do_lower_case=True) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.half().to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_keyword_spotting(self): model = HubertForSequenceClassification.from_pretrained( "superb/hubert-base-superb-ks", dtype=torch.float16 ).to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks") input_data = self._load_superb("ks", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) input_values = inputs.input_values.half().to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1) expected_labels = [2, 6, 10, 9] # s3prl logits for the same batch expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], dtype=torch.float16, device=torch_device) self.assertListEqual(predicted_ids.tolist(), expected_labels) torch.testing.assert_close(predicted_logits, expected_logits, rtol=3e-2, atol=3e-2) def test_inference_intent_classification(self): model = HubertForSequenceClassification.from_pretrained( "superb/hubert-base-superb-ic", dtype=torch.float16 ).to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic") input_data = self._load_superb("ic", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) input_values = inputs.input_values.half().to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1) predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1) predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1) expected_labels_action = [1, 0, 4, 3] expected_logits_action = torch.tensor( [5.9052, 12.5865, 4.4840, 10.0240], dtype=torch.float16, device=torch_device ) expected_labels_object = [1, 10, 3, 4] expected_logits_object = torch.tensor( [5.5316, 11.7946, 8.1672, 23.2415], dtype=torch.float16, device=torch_device ) expected_labels_location = [0, 0, 0, 1] expected_logits_location = torch.tensor( [5.2053, 8.9577, 10.0447, 8.1481], dtype=torch.float16, device=torch_device ) self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action) self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object) self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location) # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572 torch.testing.assert_close(predicted_logits_action, expected_logits_action, rtol=3e-1, atol=3e-1) torch.testing.assert_close(predicted_logits_object, expected_logits_object, rtol=3e-1, atol=3e-1) torch.testing.assert_close(predicted_logits_location, expected_logits_location, rtol=3e-1, atol=3e-1) def test_inference_speaker_identification(self): model = HubertForSequenceClassification.from_pretrained( "superb/hubert-base-superb-sid", dtype=torch.float16 ).to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-sid") input_data = self._load_superb("si", 4) output_logits = [] with torch.no_grad(): for example in input_data["speech"]: input = processor(example, return_tensors="pt", padding=True) output = model(input.input_values.half().to(torch_device), attention_mask=None) output_logits.append(output.logits[0]) output_logits = torch.stack(output_logits) predicted_logits, predicted_ids = torch.max(output_logits, dim=-1) expected_labels = [5, 1, 1, 3] # s3prl logits for the same batch expected_logits = torch.tensor( [78231.5547, 123166.6094, 122785.4141, 84851.2969], dtype=torch.float16, device=torch_device ) self.assertListEqual(predicted_ids.tolist(), expected_labels) # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572 torch.testing.assert_close(predicted_logits, expected_logits, rtol=10, atol=10) def test_inference_emotion_recognition(self): model = HubertForSequenceClassification.from_pretrained( "superb/hubert-base-superb-er", dtype=torch.float16 ).to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-er") input_data = self._load_superb("er", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) input_values = inputs.input_values.half().to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1) expected_labels = [1, 1, 2, 2] # s3prl logits for the same batch expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], dtype=torch.float16, device=torch_device) self.assertListEqual(predicted_ids.tolist(), expected_labels) # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572 torch.testing.assert_close(predicted_logits, expected_logits, rtol=1e-1, atol=1e-1) 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 torch.testing.assert_close(outputs[:, :4, :4], expected_outputs_first, rtol=5e-3, atol=5e-3) torch.testing.assert_close(outputs[:, -4:, -4:], expected_outputs_last, rtol=5e-3, atol=5e-3) self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1) def test_inference_hubert_25hz(self): model = HubertModel.from_pretrained("slprl/mhubert-base-25hz").to(torch_device) sample = self._load_datasamples(1) input_speech = torch.tensor(sample[0], dtype=torch.float, device=torch_device).unsqueeze(0) with torch.no_grad(): outputs = model(input_speech, output_hidden_states=True).hidden_states[11] # expected outputs taken from the original textlesslib implementation by: # model = SpeechEncoder.by_name(dense_model_name='mhubert-base-25hz', quantizer_model_name='kmeans', # vocab_size=500, deduplicate=False, need_f0=False) # model(wav)['dense'] expected_outputs_first = torch.tensor( [ [ [0.0267, 0.1776, -0.1706, -0.4559], [-0.2430, -0.2943, -0.1864, -0.1187], [-0.1812, -0.4239, -0.1916, -0.0858], [-0.1495, -0.4758, -0.4036, 0.0302], ] ], device=torch_device, ) expected_outputs_last = torch.tensor( [ [ [0.3366, -0.2734, -0.1415, -0.3055], [0.2329, -0.3580, -0.1421, -0.3197], [0.1631, -0.4301, -0.1965, -0.2956], [0.3342, -0.2185, -0.2253, -0.2363], ] ], device=torch_device, ) expected_output_sum = 1681.7603 torch.testing.assert_close(outputs[:, :4, :4], expected_outputs_first, rtol=5e-3, atol=5e-3) torch.testing.assert_close(outputs[:, -4:, -4:], expected_outputs_last, rtol=5e-3, atol=5e-3) self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1)