# 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 UniSpeechSat model.""" import math import unittest import numpy as np import pytest from datasets import load_dataset from transformers import UniSpeechSatConfig, 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 ( UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) class UniSpeechSatModelTester: 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, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, tdnn_dim=(32, 32), tdnn_kernel=(3, 3), tdnn_dilation=(1, 1), xvector_output_dim=32, 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.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim 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 UniSpeechSatConfig( 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, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, 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, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, ) 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 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 check_ctc_loss(self, config, input_values, *args): model = UniSpeechSatForCTC(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 = UniSpeechSatForSequenceClassification(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 = UniSpeechSatForCTC(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 = UniSpeechSatForSequenceClassification(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, *args): config.ctc_zero_infinity = True model = UniSpeechSatForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() # use a longer sequence length to account for TDNN temporal downsampling input_values = floats_tensor([self.batch_size, self.seq_length * 2], scale=1.0) 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 = UniSpeechSatForCTC(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 UniSpeechSatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification, UniSpeechSatForAudioFrameClassification, UniSpeechSatForXVector, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": UniSpeechSatForSequenceClassification, "automatic-speech-recognition": UniSpeechSatForCTC, "feature-extraction": UniSpeechSatModel, } if is_torch_available() else {} ) def setUp(self): self.model_tester = UniSpeechSatModelTester(self) self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, 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_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="Model has no input_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Model has input_values instead of input_ids") def test_forward_signature(self): pass @unittest.skip(reason="Model has no tokens embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Model has no input_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, "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 = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", 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 = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", 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 = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus") self.assertIsNotNone(model) @require_torch class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification) if is_torch_available() else () ) def setUp(self): self.model_tester = UniSpeechSatModelTester( self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True ) self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, 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="Model has no input_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Model has input_values instead of input_ids") def test_forward_signature(self): pass @unittest.skip(reason="Model has no tokens embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Model has no input_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, "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 = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", 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 = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", 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_feature_prob_ctc_single_batch(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_feature_prob=0.2, mask_time_length=2, mask_feature_length=2, ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [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, (1, 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 = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large") self.assertIsNotNone(model) @require_torch @require_torchcodec @slow class UniSpeechSatModelIntegrationTest(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)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _load_superb(self, task, num_samples): ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] 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 torch.testing.assert_close( outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, rtol=1e-3, 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 torch.testing.assert_close( outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, rtol=1e-3, atol=1e-3 ) def test_inference_diarization(self): model = UniSpeechSatForAudioFrameClassification.from_pretrained("microsoft/unispeech-sat-base-plus-sd").to( torch_device ) processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sd") input_data = self._load_superb("sd", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) # labels is a one-hot array of shape (num_frames, num_speakers) labels = (outputs.logits > 0).long() # s3prl logits for the same batch expected_logits = torch.tensor( [ [[-5.6119, -5.5845], [-3.7772, -5.4824], [-3.6914, -5.1619], [-4.7560, -5.0496]], [[-6.3785, -4.8365], [-5.5863, -5.4149], [-5.5639, -4.8469], [-6.1511, -4.0052]], [[-6.0355, -3.7414], [-5.5968, -4.8061], [-5.4620, -4.7310], [-5.5864, -4.6078]], [[-5.9493, -4.8963], [-4.4050, -5.4476], [-4.1755, -5.1395], [-4.0272, -4.3705]], ], device=torch_device, ) self.assertEqual(labels[0, :, 0].sum(), 270) self.assertEqual(labels[0, :, 1].sum(), 647) torch.testing.assert_close(outputs.logits[:, :4], expected_logits, rtol=1e-2, atol=1e-2) def test_inference_speaker_verification(self): model = UniSpeechSatForXVector.from_pretrained("microsoft/unispeech-sat-base-plus-sv").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sv") input_data = self._load_superb("si", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) labels = torch.tensor([5, 1, 1, 3], device=torch_device).T with torch.no_grad(): input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) outputs = model(input_values, attention_mask=attention_mask, labels=labels) embeddings = torch.nn.functional.normalize(outputs.embeddings, dim=-1) cosine_sim = torch.nn.CosineSimilarity(dim=-1) # id10002 vs id10002 self.assertAlmostEqual(cosine_sim(embeddings[1], embeddings[2]).item(), 0.9671, 3) # id10006 vs id10002 self.assertAlmostEqual(cosine_sim(embeddings[0], embeddings[1]).item(), 0.4941, 3) # id10002 vs id10004 self.assertAlmostEqual(cosine_sim(embeddings[2], embeddings[3]).item(), 0.5616, 3) self.assertAlmostEqual(outputs.loss.item(), 18.5925, 2)