# 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 SpeechT5 model.""" import copy import inspect import tempfile import unittest from functools import cached_property from transformers import SpeechT5Config, SpeechT5HifiGanConfig from transformers.testing_utils import ( is_torch_available, require_deterministic_for_xpu, require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from transformers.trainer_utils import set_seed from ...generation.test_utils import GenerationTesterMixin 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 ( SpeechT5ForSpeechToSpeech, SpeechT5ForSpeechToText, SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Model, SpeechT5Processor, ) def prepare_inputs_dict( config, input_ids=None, input_values=None, decoder_input_ids=None, decoder_input_values=None, attention_mask=None, decoder_attention_mask=None, ): if input_ids is not None: encoder_dict = {"input_ids": input_ids} else: encoder_dict = {"input_values": input_values} if decoder_input_ids is not None: decoder_dict = {"decoder_input_ids": decoder_input_ids} else: decoder_dict = {"decoder_input_values": decoder_input_values} return { **encoder_dict, **decoder_dict, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_torch class SpeechT5ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, vocab_size=81, hidden_size=24, num_hidden_layers=2, num_attention_heads=2, intermediate_size=4, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length, self.hidden_size], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) decoder_input_values = floats_tensor([self.batch_size, self.seq_length, self.hidden_size], scale=1.0) decoder_attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() inputs_dict = prepare_inputs_dict( config, input_values=input_values, decoder_input_values=decoder_input_values, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_config(self): return SpeechT5Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, ) def create_and_check_model_forward(self, config, inputs_dict): model = SpeechT5Model(config=config).to(torch_device).eval() input_values = inputs_dict["input_values"] attention_mask = inputs_dict["attention_mask"] decoder_input_values = inputs_dict["decoder_input_values"] result = model(input_values, attention_mask=attention_mask, decoder_input_values=decoder_input_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) @require_torch class SpeechT5ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (SpeechT5Model,) if is_torch_available() else () pipeline_model_mapping = ( {"automatic-speech-recognition": SpeechT5ForSpeechToText, "feature-extraction": SpeechT5Model} if is_torch_available() else {} ) is_encoder_decoder = True test_resize_embeddings = False def setUp(self): self.model_tester = SpeechT5ModelTester(self) self.config_tester = ConfigTester(self, config_class=SpeechT5Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_values", "attention_mask", "decoder_input_values", "decoder_attention_mask", ] expected_arg_names.extend(["encoder_outputs"]) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @unittest.skip(reason="Model has no input_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Model has no input_embeds") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Decoder cannot keep gradients") def test_retain_grad_hidden_states_attentions(self): pass @require_torch class SpeechT5ForSpeechToTextTester: def __init__( self, parent, batch_size=13, encoder_seq_length=1024, # speech is longer decoder_seq_length=7, is_training=False, hidden_size=24, num_hidden_layers=2, num_attention_heads=2, intermediate_size=4, 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, vocab_size=81, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size 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.vocab_size = vocab_size def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.encoder_seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.encoder_seq_length]) decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size).clamp(2) decoder_attention_mask = random_attention_mask([self.batch_size, self.decoder_seq_length]) config = self.get_config() inputs_dict = prepare_inputs_dict( config, input_values=input_values, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_config(self): return SpeechT5Config( hidden_size=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, 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, vocab_size=self.vocab_size, ) def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for stride in self.conv_stride: input_lengths = (input_lengths // stride) - 1 return input_lengths def create_and_check_model_forward(self, config, inputs_dict): model = SpeechT5ForSpeechToText(config=config).to(torch_device).eval() input_values = inputs_dict["input_values"] attention_mask = inputs_dict["attention_mask"] decoder_input_ids = inputs_dict["decoder_input_ids"] result = model(input_values, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.decoder_seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = SpeechT5ForSpeechToText(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, use_cache=True )["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)) @require_torch class SpeechT5ForSpeechToTextTest(ModelTesterMixin, unittest.TestCase, GenerationTesterMixin): all_model_classes = (SpeechT5ForSpeechToText,) if is_torch_available() else () is_encoder_decoder = True def setUp(self): self.model_tester = SpeechT5ForSpeechToTextTester(self) self.config_tester = ConfigTester(self, config_class=SpeechT5Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) @unittest.skip(reason="skipped because of dropout") def test_batching_equivalence(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() subsampled_encoder_seq_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths( encoder_seq_length ) subsampled_encoder_key_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths( encoder_key_length ) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_values", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend(["encoder_outputs"]) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) # this model has no inputs_embeds @unittest.skip(reason="Model has no input_embeds") def test_inputs_embeds(self): pass def test_resize_embeddings_untied(self): original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is set to False") original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: self.skipTest(reason="Model cannot untie embeddings") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) def test_resize_tokens_embeddings(self): original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is set to False") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # make sure that decoder_input_ids are resized if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) @unittest.skip(reason="Decoder cannot keep gradients") def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients pass @unittest.skip(reason="Training is not supported yet") def test_training(self): pass @unittest.skip(reason="Training is not supported yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass # 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="Temporarily broken") # TODO (joao, eustache): have a look at this test def test_generate_without_input_ids(self): pass @unittest.skip(reason="Very flaky") # TODO (joao, eustache): have a look at this test def test_generate_continue_from_past_key_values(self): pass @require_torch @require_sentencepiece @require_tokenizers @slow class SpeechT5ForSpeechToTextIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return SpeechT5Processor.from_pretrained("microsoft/speecht5_asr") 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")[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = SpeechT5ForSpeechToText.from_pretrained("microsoft/speecht5_asr") model.to(torch_device) processor = self.default_processor input_speech = self._load_datasamples(1) input_values = processor(audio=input_speech, return_tensors="pt").input_values.to(torch_device) generated_ids = model.generate(input_values) generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel" ] self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS) def test_generation_librispeech_batched(self): model = SpeechT5ForSpeechToText.from_pretrained("microsoft/speecht5_asr") model.to(torch_device) processor = self.default_processor input_speech = self._load_datasamples(4) inputs = processor(audio=input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) generated_ids = model.generate(input_values, attention_mask=attention_mask) generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister quilter's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and rosebeaf looming before us" " similars drawn from eating and its results occur most readily to the mind", "he has grave doubts whether sir frederick latin's work is really greek after all and can discover in it" " but little of rocky ithica", ] self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS) @require_torch class SpeechT5ForTextToSpeechTester: def __init__( self, parent, batch_size=13, encoder_seq_length=7, decoder_seq_length=1024, # speech is longer is_training=False, hidden_size=24, num_hidden_layers=2, num_attention_heads=2, intermediate_size=4, vocab_size=81, num_mel_bins=20, reduction_factor=2, speech_decoder_postnet_layers=2, speech_decoder_postnet_units=32, speech_decoder_prenet_units=32, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.vocab_size = vocab_size self.num_mel_bins = num_mel_bins self.reduction_factor = reduction_factor self.speech_decoder_postnet_layers = speech_decoder_postnet_layers self.speech_decoder_postnet_units = speech_decoder_postnet_units self.speech_decoder_prenet_units = speech_decoder_prenet_units def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size).clamp(2) attention_mask = random_attention_mask([self.batch_size, self.encoder_seq_length]) decoder_input_values = floats_tensor([self.batch_size, self.decoder_seq_length, self.num_mel_bins], scale=1.0) decoder_attention_mask = random_attention_mask([self.batch_size, self.decoder_seq_length]) config = self.get_config() inputs_dict = prepare_inputs_dict( config, input_ids=input_ids, decoder_input_values=decoder_input_values, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_config(self): return SpeechT5Config( hidden_size=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, vocab_size=self.vocab_size, num_mel_bins=self.num_mel_bins, reduction_factor=self.reduction_factor, speech_decoder_postnet_layers=self.speech_decoder_postnet_layers, speech_decoder_postnet_units=self.speech_decoder_postnet_units, speech_decoder_prenet_units=self.speech_decoder_prenet_units, ) def create_and_check_model_forward(self, config, inputs_dict): model = SpeechT5ForTextToSpeech(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] decoder_input_values = inputs_dict["decoder_input_values"] result = model(input_ids, attention_mask=attention_mask, decoder_input_values=decoder_input_values) self.parent.assertEqual( result.spectrogram.shape, (self.batch_size, self.decoder_seq_length * self.reduction_factor, self.num_mel_bins), ) @require_torch class SpeechT5ForTextToSpeechTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (SpeechT5ForTextToSpeech,) if is_torch_available() else () all_generative_model_classes = () is_encoder_decoder = True def setUp(self): self.model_tester = SpeechT5ForTextToSpeechTester(self) self.config_tester = ConfigTester(self, config_class=SpeechT5Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model_can_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) self.assertTrue(model.can_generate()) def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_model_forward_with_labels(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = SpeechT5ForTextToSpeech(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] decoder_attention_mask = inputs_dict["decoder_attention_mask"] labels = inputs_dict["decoder_input_values"] result = model( input_ids, attention_mask=attention_mask, labels=labels, decoder_attention_mask=decoder_attention_mask ) self.assertEqual( result.spectrogram.shape, (self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.num_mel_bins), ) @unittest.skip(reason="Dropout is always present in SpeechT5SpeechDecoderPrenet") def test_decoder_model_past_with_large_inputs(self): pass @unittest.skip(reason="Dropout is always present in SpeechT5SpeechDecoderPrenet") def test_determinism(self): pass @unittest.skip(reason="skipped because there is always dropout in SpeechT5SpeechDecoderPrenet") def test_batching_equivalence(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_values", "decoder_attention_mask", ] expected_arg_names.extend(["encoder_outputs"]) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @unittest.skip(reason="Model has no inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Dropout is always present in SpeechT5SpeechDecoderPrenet") def test_model_outputs_equivalence(self): pass @unittest.skip(reason="Dropout is always present in SpeechT5SpeechDecoderPrenet") def test_save_load(self): pass @unittest.skip(reason="Decoder cannot keep gradients") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="training is not supported yet") def test_training(self): pass @unittest.skip(reason="training is not supported yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass # 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) @require_torch @require_sentencepiece @require_tokenizers @slow class SpeechT5ForTextToSpeechIntegrationTests(unittest.TestCase): @cached_property def default_model(self): return SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts", revision="refs/pr/19").to( torch_device ) @cached_property def default_processor(self): return SpeechT5Processor.from_pretrained("microsoft/speecht5_tts", revision="refs/pr/19") @cached_property def default_vocoder(self): return SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan", revision="refs/pr/1").to(torch_device) def test_generation(self): model = self.default_model processor = self.default_processor input_text = "Mister Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." input_ids = processor(text=input_text, return_tensors="pt").input_ids.to(torch_device) speaker_embeddings = torch.zeros((1, 512), device=torch_device) # Generate speech and validate output dimensions set_seed(555) # Ensure deterministic behavior generated_speech = model.generate_speech(input_ids, speaker_embeddings=speaker_embeddings) num_mel_bins = model.config.num_mel_bins self.assertEqual( generated_speech.shape[1], num_mel_bins, "Generated speech output has an unexpected number of mel bins." ) # Validate generation with additional kwargs using model.generate; # same method than generate_speech set_seed(555) # Reset seed for consistent results generated_speech_with_generate = model.generate( input_ids, attention_mask=None, speaker_embeddings=speaker_embeddings ) self.assertEqual( generated_speech_with_generate.shape, generated_speech.shape, "Shape mismatch between generate_speech and generate methods.", ) @require_deterministic_for_xpu def test_one_to_many_generation(self): model = self.default_model processor = self.default_processor vocoder = self.default_vocoder input_text = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister quilter's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and rosebeaf looming before us", ] inputs = processor(text=input_text, padding="max_length", max_length=128, return_tensors="pt").to(torch_device) speaker_embeddings = torch.zeros((1, 512), device=torch_device) # Generate spectrograms set_seed(555) # Ensure deterministic behavior spectrograms, spectrogram_lengths = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, attention_mask=inputs["attention_mask"], return_output_lengths=True, ) # Validate generated spectrogram dimensions expected_batch_size = len(input_text) num_mel_bins = model.config.num_mel_bins actual_batch_size, _, actual_num_mel_bins = spectrograms.shape self.assertEqual(actual_batch_size, expected_batch_size, "Batch size of generated spectrograms is incorrect.") self.assertEqual( actual_num_mel_bins, num_mel_bins, "Number of mel bins in batch generated spectrograms is incorrect." ) # Generate waveforms using the vocoder waveforms = vocoder(spectrograms) waveform_lengths = [int(waveforms.size(1) / max(spectrogram_lengths)) * i for i in spectrogram_lengths] # Validate generation with integrated vocoder set_seed(555) # Reset seed for consistent results waveforms_with_vocoder, waveform_lengths_with_vocoder = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, attention_mask=inputs["attention_mask"], vocoder=vocoder, return_output_lengths=True, ) # Check consistency between waveforms generated with and without standalone vocoder self.assertTrue( torch.allclose(waveforms, waveforms_with_vocoder, atol=1e-8), "Mismatch in waveforms generated with and without the standalone vocoder.", ) self.assertEqual( waveform_lengths, waveform_lengths_with_vocoder, "Waveform lengths differ between standalone and integrated vocoder generation.", ) # Test generation consistency without returning lengths set_seed(555) # Reset seed for consistent results waveforms_with_vocoder_no_lengths = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, attention_mask=inputs["attention_mask"], vocoder=vocoder, return_output_lengths=False, ) # Validate waveform consistency without length information self.assertTrue( torch.allclose(waveforms_with_vocoder_no_lengths, waveforms_with_vocoder, atol=1e-8), "Waveforms differ when generated with and without length information.", ) # Validate batch vs. single instance generation consistency for i, text in enumerate(input_text): inputs = processor(text=text, padding="max_length", max_length=128, return_tensors="pt").to(torch_device) set_seed(555) # Reset seed for consistent results spectrogram = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, ) # Check spectrogram shape consistency self.assertEqual( spectrogram.shape, spectrograms[i][: spectrogram_lengths[i]].shape, "Mismatch in spectrogram shape between batch and single instance generation.", ) # Generate and validate waveform for single instance waveform = vocoder(spectrogram) self.assertEqual( waveform.shape, waveforms[i][: waveform_lengths[i]].shape, "Mismatch in waveform shape between batch and single instance generation.", ) # Check waveform consistency with integrated vocoder set_seed(555) # Reset seed for consistent results waveform_with_integrated_vocoder = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, vocoder=vocoder, ) self.assertTrue( torch.allclose(waveform, waveform_with_integrated_vocoder, atol=1e-8), "Mismatch in waveform between standalone and integrated vocoder for single instance generation.", ) @require_deterministic_for_xpu def test_batch_generation(self): model = self.default_model processor = self.default_processor vocoder = self.default_vocoder input_text = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister quilter's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and rosebeaf looming before us", ] inputs = processor(text=input_text, padding="max_length", max_length=128, return_tensors="pt").to(torch_device) set_seed(555) # Ensure deterministic behavior speaker_embeddings = torch.randn((len(input_text), 512), device=torch_device) # Generate spectrograms set_seed(555) # Reset seed for consistent results spectrograms, spectrogram_lengths = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, attention_mask=inputs["attention_mask"], return_output_lengths=True, ) # Validate generated spectrogram dimensions expected_batch_size = len(input_text) num_mel_bins = model.config.num_mel_bins actual_batch_size, _, actual_num_mel_bins = spectrograms.shape self.assertEqual( actual_batch_size, expected_batch_size, "Batch size of generated spectrograms is incorrect.", ) self.assertEqual( actual_num_mel_bins, num_mel_bins, "Number of mel bins in batch generated spectrograms is incorrect.", ) # Generate waveforms using the vocoder waveforms = vocoder(spectrograms) waveform_lengths = [int(waveforms.size(1) / max(spectrogram_lengths)) * i for i in spectrogram_lengths] # Validate generation with integrated vocoder set_seed(555) # Reset seed for consistent results waveforms_with_vocoder, waveform_lengths_with_vocoder = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, attention_mask=inputs["attention_mask"], vocoder=vocoder, return_output_lengths=True, ) # Check consistency between waveforms generated with and without standalone vocoder self.assertTrue( torch.allclose(waveforms, waveforms_with_vocoder, atol=1e-8), "Mismatch in waveforms generated with and without the standalone vocoder.", ) self.assertEqual( waveform_lengths, waveform_lengths_with_vocoder, "Waveform lengths differ between standalone and integrated vocoder generation.", ) # Test generation consistency without returning lengths set_seed(555) # Reset seed for consistent results waveforms_with_vocoder_no_lengths = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=speaker_embeddings, attention_mask=inputs["attention_mask"], vocoder=vocoder, return_output_lengths=False, ) # Validate waveform consistency without length information self.assertTrue( torch.allclose(waveforms_with_vocoder_no_lengths, waveforms_with_vocoder, atol=1e-8), "Waveforms differ when generated with and without length information.", ) # Validate batch vs. single instance generation consistency for i, text in enumerate(input_text): inputs = processor(text=text, padding="max_length", max_length=128, return_tensors="pt").to(torch_device) current_speaker_embedding = speaker_embeddings[i].unsqueeze(0) set_seed(555) # Reset seed for consistent results spectrogram = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=current_speaker_embedding, ) # Check spectrogram shape consistency self.assertEqual( spectrogram.shape, spectrograms[i][: spectrogram_lengths[i]].shape, "Mismatch in spectrogram shape between batch and single instance generation.", ) # Generate and validate waveform for single instance waveform = vocoder(spectrogram) self.assertEqual( waveform.shape, waveforms[i][: waveform_lengths[i]].shape, "Mismatch in waveform shape between batch and single instance generation.", ) # Check waveform consistency with integrated vocoder set_seed(555) # Reset seed for consistent results waveform_with_integrated_vocoder = model.generate_speech( input_ids=inputs["input_ids"], speaker_embeddings=current_speaker_embedding, vocoder=vocoder, ) self.assertTrue( torch.allclose(waveform, waveform_with_integrated_vocoder, atol=1e-8), "Mismatch in waveform between standalone and integrated vocoder for single instance generation.", ) @require_torch class SpeechT5ForSpeechToSpeechTester: def __init__( self, parent, batch_size=13, encoder_seq_length=1024, # speech is longer decoder_seq_length=1024, is_training=False, hidden_size=24, num_hidden_layers=2, num_attention_heads=2, intermediate_size=4, 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, vocab_size=81, num_mel_bins=20, reduction_factor=2, speech_decoder_postnet_layers=2, speech_decoder_postnet_units=32, speech_decoder_prenet_units=32, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size 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.vocab_size = vocab_size self.num_mel_bins = num_mel_bins self.reduction_factor = reduction_factor self.speech_decoder_postnet_layers = speech_decoder_postnet_layers self.speech_decoder_postnet_units = speech_decoder_postnet_units self.speech_decoder_prenet_units = speech_decoder_prenet_units def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.encoder_seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.encoder_seq_length]) decoder_input_values = floats_tensor([self.batch_size, self.decoder_seq_length, self.num_mel_bins], scale=1.0) decoder_attention_mask = random_attention_mask([self.batch_size, self.decoder_seq_length]) config = self.get_config() inputs_dict = prepare_inputs_dict( config, input_values=input_values, decoder_input_values=decoder_input_values, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_config(self): return SpeechT5Config( hidden_size=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, 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, vocab_size=self.vocab_size, num_mel_bins=self.num_mel_bins, reduction_factor=self.reduction_factor, speech_decoder_postnet_layers=self.speech_decoder_postnet_layers, speech_decoder_postnet_units=self.speech_decoder_postnet_units, speech_decoder_prenet_units=self.speech_decoder_prenet_units, ) def create_and_check_model_forward(self, config, inputs_dict): model = SpeechT5ForSpeechToSpeech(config=config).to(torch_device).eval() input_values = inputs_dict["input_values"] attention_mask = inputs_dict["attention_mask"] decoder_input_values = inputs_dict["decoder_input_values"] result = model(input_values, attention_mask=attention_mask, decoder_input_values=decoder_input_values) self.parent.assertEqual( result.spectrogram.shape, (self.batch_size, self.decoder_seq_length * self.reduction_factor, self.num_mel_bins), ) @require_torch class SpeechT5ForSpeechToSpeechTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (SpeechT5ForSpeechToSpeech,) if is_torch_available() else () is_encoder_decoder = True test_resize_embeddings = False def setUp(self): self.model_tester = SpeechT5ForSpeechToSpeechTester(self) self.config_tester = ConfigTester(self, config_class=SpeechT5Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_model_forward_with_labels(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = SpeechT5ForSpeechToSpeech(config=config).to(torch_device).eval() input_values = inputs_dict["input_values"] attention_mask = inputs_dict["attention_mask"] decoder_attention_mask = inputs_dict["decoder_attention_mask"] labels = inputs_dict["decoder_input_values"] result = model( input_values, attention_mask=attention_mask, labels=labels, decoder_attention_mask=decoder_attention_mask ) self.assertEqual( result.spectrogram.shape, (self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.num_mel_bins), ) @unittest.skip(reason="There is always dropout in SpeechT5SpeechDecoderPrenet") def test_decoder_model_past_with_large_inputs(self): pass @unittest.skip(reason="There is always dropout in SpeechT5SpeechDecoderPrenet") def test_determinism(self): pass @unittest.skip(reason="skipped because there is always dropout in SpeechT5SpeechDecoderPrenet") def test_batching_equivalence(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() subsampled_encoder_seq_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths( encoder_seq_length ) subsampled_encoder_key_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths( encoder_key_length ) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_values", "attention_mask", "decoder_input_values", "decoder_attention_mask", ] expected_arg_names.extend(["encoder_outputs"]) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @unittest.skip(reason="Model has no input_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Model has no input_embeds") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Dropout is always present in SpeechT5SpeechDecoderPrenet") def test_model_outputs_equivalence(self): pass @unittest.skip(reason="Decoder cannot keep gradients") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="Dropout is always present in SpeechT5SpeechDecoderPrenet") def test_save_load(self): pass @unittest.skip(reason="Training is not supported yet") def test_training(self): pass @unittest.skip(reason="Training is not supported yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass # 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) @require_torch @require_sentencepiece @require_tokenizers @slow class SpeechT5ForSpeechToSpeechIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return SpeechT5Processor.from_pretrained("microsoft/speecht5_vc") 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")[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = SpeechT5ForSpeechToSpeech.from_pretrained("microsoft/speecht5_vc") model.to(torch_device) processor = self.default_processor input_speech = self._load_datasamples(1) input_values = processor(audio=input_speech, return_tensors="pt").input_values.to(torch_device) speaker_embeddings = torch.zeros((1, 512), device=torch_device) generated_speech = model.generate_speech(input_values, speaker_embeddings=speaker_embeddings) self.assertEqual(generated_speech.shape[1], model.config.num_mel_bins) self.assertGreaterEqual(generated_speech.shape[0], 300) self.assertLessEqual(generated_speech.shape[0], 310) class SpeechT5HifiGanTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, num_mel_bins=20, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.num_mel_bins = num_mel_bins def prepare_config_and_inputs(self): input_values = floats_tensor([self.seq_length, self.num_mel_bins], scale=1.0) config = self.get_config() return config, input_values def get_config(self): return SpeechT5HifiGanConfig( model_in_dim=self.num_mel_bins, upsample_initial_channel=32, ) def create_and_check_model(self, config, input_values): model = SpeechT5HifiGan(config=config).to(torch_device).eval() result = model(input_values) self.parent.assertEqual(result.shape, (self.seq_length * 256,)) def prepare_config_and_inputs_for_common(self): config, input_values = self.prepare_config_and_inputs() inputs_dict = {"spectrogram": input_values} return config, inputs_dict @require_torch class SpeechT5HifiGanTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (SpeechT5HifiGan,) if is_torch_available() else () test_resize_embeddings = False test_resize_position_embeddings = False test_mismatched_shapes = False test_missing_keys = False is_encoder_decoder = False has_attentions = False def setUp(self): self.model_tester = SpeechT5HifiGanTester(self) self.config_tester = ConfigTester(self, config_class=SpeechT5HifiGanConfig) def test_config(self): self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_from_and_save_pretrained_subfolder() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() 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_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "spectrogram", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @unittest.skip(reason="Model does not output hidden states") def test_hidden_states_output(self): pass @unittest.skip(reason="Model has no input_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Model has no input_embeds") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Model does not support all arguments tested") def test_model_outputs_equivalence(self): pass @unittest.skip(reason="Model does not output hidden states") def test_retain_grad_hidden_states_attentions(self): pass def test_batched_inputs_outputs(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() batched_inputs = inputs["spectrogram"].unsqueeze(0).repeat(2, 1, 1) with torch.no_grad(): batched_outputs = model(batched_inputs.to(torch_device)) self.assertEqual( batched_inputs.shape[0], batched_outputs.shape[0], msg="Got different batch dims for input and output" ) def test_unbatched_inputs_outputs(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(inputs["spectrogram"].to(torch_device)) self.assertTrue(outputs.dim() == 1, msg="Got un-batched inputs but batched output")