# Copyright 2022 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 Wav2Vec2-Conformer model.""" import math import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import Wav2Vec2ConformerConfig, is_torch_available from transformers.testing_utils import ( is_flaky, require_torch, require_torch_accelerator, require_torch_fp16, 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 ( Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.wav2vec2.modeling_wav2vec2 import _sample_negative_indices from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import ( Wav2Vec2ConformerGumbelVectorQuantizer, _compute_mask_indices, ) class Wav2Vec2ConformerModelTester: 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, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, num_adapter_layers=1, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, position_embeddings_type="relative", 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.num_adapter_layers = num_adapter_layers self.adapter_stride = adapter_stride self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.scope = scope self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.position_embeddings_type = position_embeddings_type 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 self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1 def prepare_config_and_inputs(self, position_embeddings_type="relative"): input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config(position_embeddings_type=position_embeddings_type) return config, input_values, attention_mask def get_config(self, position_embeddings_type="relative"): return Wav2Vec2ConformerConfig( 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, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, 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, do_stable_layer_norm=self.do_stable_layer_norm, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, num_adapter_layers=self.num_adapter_layers, adapter_stride=self.adapter_stride, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, position_embeddings_type=position_embeddings_type, ) def create_and_check_model(self, config, input_values, attention_mask): model = Wav2Vec2ConformerModel(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_model_with_adapter(self, config, input_values, attention_mask): config.add_adapter = True model = Wav2Vec2ConformerModel(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.adapter_output_seq_length, self.hidden_size) ) def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 2 * config.hidden_size model = Wav2Vec2ConformerForCTC(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size) ) def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Wav2Vec2ConformerModel(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.adapter_output_seq_length, config.output_hidden_size), ) def create_and_check_model_float16(self, config, input_values, attention_mask): model = Wav2Vec2ConformerModel(config=config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = Wav2Vec2ConformerModel.from_pretrained(tmpdirname, dtype=torch.float16) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_values.type(dtype=torch.float16), attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def check_ctc_loss(self, config, input_values, *args): model = Wav2Vec2ConformerForCTC(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 = Wav2Vec2ConformerForSequenceClassification(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 = Wav2Vec2ConformerForCTC(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 = Wav2Vec2ConformerForSequenceClassification(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_xvector_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ConformerForXVector(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 = Wav2Vec2ConformerForCTC(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 self.parent.assertRaises(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 Wav2Vec2ConformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( Wav2Vec2ConformerForCTC, Wav2Vec2ConformerModel, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForXVector, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": Wav2Vec2ConformerForSequenceClassification, "automatic-speech-recognition": Wav2Vec2ConformerForCTC, "feature-extraction": Wav2Vec2ConformerModel, } if is_torch_available() else {} ) test_can_init_all_missing_weights_before_2023 = True def setUp(self): self.model_tester = Wav2Vec2ConformerModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=32) 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) @is_flaky( description="The `codevector_idx` computed with `argmax()` in `Wav2Vec2ConformerGumbelVectorQuantizer.forward` is not stable." ) def test_batching_equivalence(self, atol=1e-4, rtol=1e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) def test_model_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_no_rel_pos(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter(*config_and_inputs) def test_model_with_adapter_for_ctc(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs) def test_model_with_adapter_proj_dim(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs) @require_torch_accelerator @require_torch_fp16 def test_model_float16_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model_float16(*config_and_inputs) @require_torch_accelerator @require_torch_fp16 def test_model_float16_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model_float16(*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_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_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="Wav2Vec2Conformer has not inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip( reason="Wav2Vec2Conformer uses relative position embeddings that produce a dense attention bias incompatible with Flash Attention" ) def test_sdpa_can_dispatch_on_flash(self): pass @unittest.skip(reason="Wav2Vec2Conformer has input_values instead of input_ids") def test_forward_signature(self): pass @unittest.skip(reason="Wav2Vec2Conformer has not token embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Wav2Vec2Conformer has not 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 # 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, "pos_bias_u") and module.pos_bias_u is not None: module.pos_bias_u.data.fill_(3) if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None: module.pos_bias_v.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = Wav2Vec2ConformerForCTC.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") self.assertIsNotNone(model) @require_torch class Wav2Vec2ConformerUtilsTest(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_low_prob(self): # with these settings num_masked_spans=0.5, which means probabilistic rounding # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in # the other 5 out of 10, cases num_masked_spans=1 n_trials = 100 batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 count_dimensions_masked = 0 count_dimensions_not_masked = 0 for _ in range(n_trials): mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) num_masks = torch.sum(mask).item() if num_masks > 0: count_dimensions_masked += 1 else: count_dimensions_not_masked += 1 # as we test for at least 10 masked dimension and at least # 10 non-masked dimension, this test could fail with probability: # P(100 coin flips, at most 9 heads) = 1.66e-18 self.assertGreater(count_dimensions_masked, int(n_trials * 0.1)) self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1)) 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) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask ) mask = torch.from_numpy(mask).to(torch_device) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_mask_indices_short_audio(self): batch_size = 4 sequence_length = 100 mask_prob = 0.05 mask_length = 10 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) # force one example to be heavily padded attention_mask[0, 5:] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2 ) # make sure that non-padded examples cannot be padded self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any()) def test_compute_perplexity(self): probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs) self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3) # mask half of the input mask = torch.ones((2,), device=torch_device, dtype=torch.bool) mask[0] = 0 ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask) self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3) def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consists of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # sample negative indices sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consists of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100) # sample negative indices sampled_negative_indices = _sample_negative_indices( (batch_size, sequence_length), num_negatives, mask.cpu().numpy() ) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @slow class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): 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)]) speech_samples = speech_samples[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_ctc_normal_batched_rel_pos(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large-960h-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.to(torch_device) with torch.no_grad(): logits = model(input_values).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 loincloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_normal_batched_rope(self): model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained( "facebook/wav2vec2-conformer-rope-large-960h-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.to(torch_device) with torch.no_grad(): logits = model(input_values).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_pretrained(self): model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) batch_size = inputs_dict["input_values"].shape[0] feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1])) features_shape = (batch_size, feature_seq_length) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, min_masks=2, ) mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # ... now compare to randomly initialized model config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval() with torch.no_grad(): outputs_rand = model_rand( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim_rand = torch.cosine_similarity( outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1 ) # retrieve cosine sim of masked features cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices] # a pretrained wav2vec2_conformer model has learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states > 0.5 # a random wav2vec2_conformer model has not learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states is very likely < 0.1 self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)