mirror of
https://github.com/huggingface/transformers.git
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e4bacf6614
* make SpeechT5 model by copying Wav2Vec2 * add paper to docs * whoops added docs in wrong file * remove SpeechT5Tokenizer + put CTC back in the name * remove deprecated class * remove unused docstring * delete SpeechT5FeatureExtractor, use Wav2Vec2FeatureExtractor instead * remove classes we don't need right now * initial stab at speech encoder prenet * add more speech encoder prenet stuff * improve SpeechEncoderPrenet * add encoder (not finished yet) * add relative position bias to self-attention * add encoder CTC layers * fix formatting * add decoder from BART, doesn't work yet * make it work with generate loop * wrap the encoder into a speech encoder class * wrap the decoder in a text decoder class * changed my mind * changed my mind again ;-) * load decoder weights, make it work * add weights for text decoder postnet * add SpeechT5ForCTC model that uses only the encoder * clean up EncoderLayer and DecoderLayer * implement _init_weights in SpeechT5PreTrainedModel * cleanup config + Encoder and Decoder * add head + cross attention masks * improve doc comments * fixup * more cleanup * more fixup * TextDecoderPrenet works now, thanks Kendall * add CTC loss * add placeholders for other pre/postnets * add type annotation * fix freeze_feature_encoder * set padding tokens to 0 in decoder attention mask * encoder attention mask downsampling * remove features_pen calculation * disable the padding tokens thing again * fixup * more fixup * code review fixes * rename encoder/decoder wrapper classes * allow checkpoints to be loaded into SpeechT5Model * put encoder into wrapper for CTC model * clean up conversion script * add encoder for TTS model * add speech decoder prenet * add speech decoder post-net * attempt to reconstruct the generation loop * add speech generation loop * clean up generate_speech * small tweaks * fix forward pass * enable always dropout on speech decoder prenet * sort declaration * rename models * fixup * fix copies * more fixup * make consistency checker happy * add Seq2SeqSpectrogramOutput class * doc comments * quick note about loss and labels * add HiFi-GAN implementation (from Speech2Speech PR) * rename file * add vocoder to TTS model * improve vocoder * working on tokenizer * more better tokenizer * add CTC tokenizer * fix decode and batch_code in CTC tokenizer * fix processor * two processors and feature extractors * use SpeechT5WaveformFeatureExtractor instead of Wav2Vec2 * cleanup * more cleanup * even more fixup * notebooks * fix log-mel spectrograms * support reduction factor * fixup * shift spectrograms to right to create decoder inputs * return correct labels * add labels for stop token prediction * fix doc comments * fixup * remove SpeechT5ForPreTraining * more fixup * update copyright headers * add usage examples * add SpeechT5ProcessorForCTC * fixup * push unofficial checkpoints to hub * initial version of tokenizer unit tests * add slow test * fix failing tests * tests for CTC tokenizer * finish CTC tokenizer tests * processor tests * initial test for feature extractors * tests for spectrogram feature extractor * fixup * more fixup * add decorators * require speech for tests * modeling tests * more tests for ASR model * fix imports * add fake tests for the other models * fixup * remove jupyter notebooks * add missing SpeechT5Model tests * add missing tests for SpeechT5ForCTC * add missing tests for SpeechT5ForTextToSpeech * sort tests by name * fix Hi-Fi GAN tests * fixup * add speech-to-speech model * refactor duplicate speech generation code * add processor for SpeechToSpeech model * add usage example * add tests for speech-to-speech model * fixup * enable gradient checkpointing for SpeechT5FeatureEncoder * code review * push_to_hub now takes repo_id * improve doc comments for HiFi-GAN config * add missing test * add integration tests * make number of layers in speech decoder prenet configurable * rename variable * rename variables * add auto classes for TTS and S2S * REMOVE CTC!!! * S2S processor does not support save/load_pretrained * fixup * these models are now in an auto mapping * fix doc links * rename HiFiGAN to HifiGan, remove separate config file * REMOVE auto classes * there can be only one * fixup * replace assert * reformat * feature extractor can process input and target at same time * update checkpoint names * fix commit hash
1548 lines
62 KiB
Python
1548 lines
62 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch SpeechT5 model. """
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import copy
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import inspect
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import tempfile
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import unittest
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from transformers import SpeechT5Config, SpeechT5HifiGanConfig
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from transformers.testing_utils import (
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is_torch_available,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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require_torchaudio,
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slow,
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torch_device,
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)
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from transformers.utils import cached_property
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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if is_torch_available():
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import torch
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from transformers import (
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SpeechT5ForSpeechToSpeech,
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SpeechT5ForSpeechToText,
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SpeechT5ForTextToSpeech,
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SpeechT5HifiGan,
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SpeechT5Model,
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SpeechT5Processor,
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)
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def prepare_inputs_dict(
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config,
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input_ids=None,
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input_values=None,
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decoder_input_ids=None,
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decoder_input_values=None,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_mask=None,
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cross_attn_head_mask=None,
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):
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if input_ids is not None:
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encoder_dict = {"input_ids": input_ids}
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else:
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encoder_dict = {"input_values": input_values}
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if decoder_input_ids is not None:
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decoder_dict = {"decoder_input_ids": decoder_input_ids}
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else:
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decoder_dict = {"decoder_input_values": decoder_input_values}
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if head_mask is None:
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head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
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if decoder_head_mask is None:
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decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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if cross_attn_head_mask is None:
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cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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return {
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**encoder_dict,
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**decoder_dict,
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"attention_mask": attention_mask,
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"decoder_attention_mask": decoder_attention_mask,
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"head_mask": head_mask,
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"decoder_head_mask": decoder_head_mask,
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"cross_attn_head_mask": cross_attn_head_mask,
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}
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@require_torch
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class SpeechT5ModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=False,
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hidden_size=24,
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num_hidden_layers=4,
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num_attention_heads=2,
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intermediate_size=4,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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def prepare_config_and_inputs(self):
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input_values = floats_tensor([self.batch_size, self.seq_length, self.hidden_size], scale=1.0)
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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decoder_input_values = floats_tensor([self.batch_size, self.seq_length, self.hidden_size], scale=1.0)
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decoder_attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = self.get_config()
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inputs_dict = prepare_inputs_dict(
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config,
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input_values=input_values,
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decoder_input_values=decoder_input_values,
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attention_mask=attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def get_config(self):
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return SpeechT5Config(
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hidden_size=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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)
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def create_and_check_model_forward(self, config, inputs_dict):
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model = SpeechT5Model(config=config).to(torch_device).eval()
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input_values = inputs_dict["input_values"]
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attention_mask = inputs_dict["attention_mask"]
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decoder_input_values = inputs_dict["decoder_input_values"]
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result = model(input_values, attention_mask=attention_mask, decoder_input_values=decoder_input_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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@require_torch
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class SpeechT5ModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (SpeechT5Model,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_headmasking = False
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test_resize_embeddings = False
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input_name = "input_values"
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def setUp(self):
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self.model_tester = SpeechT5ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=SpeechT5Config, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model_forward(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_forward(*config_and_inputs)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = [
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"input_values",
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"attention_mask",
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"decoder_input_values",
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"decoder_attention_mask",
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]
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expected_arg_names.extend(
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["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
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if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
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else ["encoder_outputs"]
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)
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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# this model has no inputs_embeds
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def test_inputs_embeds(self):
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pass
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# this model has no input embeddings
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def test_model_common_attributes(self):
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pass
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def test_retain_grad_hidden_states_attentions(self):
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# decoder cannot keep gradients
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pass
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@slow
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def test_torchscript_output_attentions(self):
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# disabled because this model doesn't have decoder_input_ids
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pass
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@slow
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def test_torchscript_output_hidden_state(self):
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# disabled because this model doesn't have decoder_input_ids
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pass
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@slow
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def test_torchscript_simple(self):
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# disabled because this model doesn't have decoder_input_ids
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pass
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@require_torch
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class SpeechT5ForSpeechToTextTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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encoder_seq_length=1024, # speech is longer
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decoder_seq_length=7,
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is_training=False,
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hidden_size=24,
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num_hidden_layers=4,
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num_attention_heads=2,
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intermediate_size=4,
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conv_dim=(32, 32, 32),
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conv_stride=(4, 4, 4),
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conv_kernel=(8, 8, 8),
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conv_bias=False,
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num_conv_pos_embeddings=16,
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num_conv_pos_embedding_groups=2,
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vocab_size=81,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.encoder_seq_length = encoder_seq_length
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self.decoder_seq_length = decoder_seq_length
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.conv_dim = conv_dim
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self.conv_stride = conv_stride
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self.conv_kernel = conv_kernel
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self.conv_bias = conv_bias
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self.num_conv_pos_embeddings = num_conv_pos_embeddings
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self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
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self.vocab_size = vocab_size
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def prepare_config_and_inputs(self):
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input_values = floats_tensor([self.batch_size, self.encoder_seq_length], scale=1.0)
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attention_mask = random_attention_mask([self.batch_size, self.encoder_seq_length])
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decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size).clamp(2)
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decoder_attention_mask = random_attention_mask([self.batch_size, self.decoder_seq_length])
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config = self.get_config()
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inputs_dict = prepare_inputs_dict(
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config,
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input_values=input_values,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def get_config(self):
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return SpeechT5Config(
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hidden_size=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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conv_dim=self.conv_dim,
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conv_stride=self.conv_stride,
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conv_kernel=self.conv_kernel,
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conv_bias=self.conv_bias,
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num_conv_pos_embeddings=self.num_conv_pos_embeddings,
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num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
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vocab_size=self.vocab_size,
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)
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def create_and_check_model_forward(self, config, inputs_dict):
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model = SpeechT5ForSpeechToText(config=config).to(torch_device).eval()
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input_values = inputs_dict["input_values"]
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attention_mask = inputs_dict["attention_mask"]
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decoder_input_ids = inputs_dict["decoder_input_ids"]
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result = model(input_values, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.decoder_seq_length, self.vocab_size))
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def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
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model = SpeechT5ForSpeechToText(config=config).get_decoder().to(torch_device).eval()
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input_ids = inputs_dict["decoder_input_ids"]
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attention_mask = inputs_dict["decoder_attention_mask"]
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
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@require_torch
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class SpeechT5ForSpeechToTextTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (SpeechT5ForSpeechToText,) if is_torch_available() else ()
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all_generative_model_classes = (SpeechT5ForSpeechToText,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_headmasking = False
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input_name = "input_values"
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def setUp(self):
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self.model_tester = SpeechT5ForSpeechToTextTester(self)
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self.config_tester = ConfigTester(self, config_class=SpeechT5Config, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_save_load_strict(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
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self.assertEqual(info["missing_keys"], [])
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def test_model_forward(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_forward(*config_and_inputs)
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def test_decoder_model_past_with_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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subsampled_encoder_seq_length = model.speecht5.encoder.prenet._get_feat_extract_output_lengths(
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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(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["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)
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
uniform_init_parms = [
|
|
"conv.weight",
|
|
"masked_spec_embed",
|
|
"feature_projection.projection.weight",
|
|
"feature_projection.projection.bias",
|
|
]
|
|
if param.requires_grad:
|
|
if any([x in name for x in uniform_init_parms]):
|
|
self.assertTrue(
|
|
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
# this model has no inputs_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:
|
|
return
|
|
|
|
original_config.tie_word_embeddings = False
|
|
|
|
# if model cannot untied embeddings -> leave test
|
|
if original_config.tie_word_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config).to(torch_device)
|
|
|
|
# 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:
|
|
return
|
|
|
|
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)
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
# decoder cannot keep gradients
|
|
pass
|
|
|
|
# training is not supported yet
|
|
def test_training(self):
|
|
pass
|
|
|
|
def test_training_gradient_checkpointing(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.data.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.data.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_torchaudio
|
|
@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").select(range(num_samples))[: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=4,
|
|
num_attention_heads=2,
|
|
intermediate_size=4,
|
|
vocab_size=81,
|
|
num_mel_bins=20,
|
|
reduction_factor=2,
|
|
):
|
|
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
|
|
|
|
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,
|
|
)
|
|
|
|
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 = (SpeechT5ForTextToSpeech,) if is_torch_available() else ()
|
|
is_encoder_decoder = True
|
|
test_pruning = False
|
|
test_headmasking = False
|
|
|
|
input_name = "input_ids"
|
|
|
|
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_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"], [])
|
|
|
|
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)
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_decoder_model_past_with_large_inputs(self):
|
|
pass
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_determinism(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(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["encoder_outputs"]
|
|
)
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
uniform_init_parms = [
|
|
"conv.weight",
|
|
]
|
|
if param.requires_grad:
|
|
if any([x in name for x in uniform_init_parms]):
|
|
self.assertTrue(
|
|
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
# this model has no inputs_embeds
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_model_outputs_equivalence(self):
|
|
pass
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_save_load(self):
|
|
pass
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
# decoder cannot keep gradients
|
|
pass
|
|
|
|
@slow
|
|
def test_torchscript_output_attentions(self):
|
|
# disabled because this model doesn't have decoder_input_ids
|
|
pass
|
|
|
|
@slow
|
|
def test_torchscript_output_hidden_state(self):
|
|
# disabled because this model doesn't have decoder_input_ids
|
|
pass
|
|
|
|
@slow
|
|
def test_torchscript_simple(self):
|
|
# disabled because this model doesn't have decoder_input_ids
|
|
pass
|
|
|
|
# training is not supported yet
|
|
def test_training(self):
|
|
pass
|
|
|
|
def test_training_gradient_checkpointing(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.data.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.data.fill_(3)
|
|
|
|
|
|
@require_torch
|
|
@require_torchaudio
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
@slow
|
|
class SpeechT5ForTextToSpeechIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def default_processor(self):
|
|
return SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
|
|
|
|
def test_generation(self):
|
|
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
|
|
model.to(torch_device)
|
|
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)
|
|
|
|
generated_speech = model.generate_speech(input_ids)
|
|
self.assertEqual(generated_speech.shape, (1800, model.config.num_mel_bins))
|
|
|
|
|
|
@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=4,
|
|
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,
|
|
):
|
|
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
|
|
|
|
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,
|
|
)
|
|
|
|
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 ()
|
|
all_generative_model_classes = (SpeechT5ForSpeechToSpeech,) if is_torch_available() else ()
|
|
is_encoder_decoder = True
|
|
test_pruning = False
|
|
test_headmasking = False
|
|
test_resize_embeddings = False
|
|
|
|
input_name = "input_values"
|
|
|
|
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"], [])
|
|
|
|
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)
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_decoder_model_past_with_large_inputs(self):
|
|
pass
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_determinism(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(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(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["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)
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
uniform_init_parms = [
|
|
"conv.weight",
|
|
"masked_spec_embed",
|
|
"feature_projection.projection.weight",
|
|
"feature_projection.projection.bias",
|
|
]
|
|
if param.requires_grad:
|
|
if any([x in name for x in uniform_init_parms]):
|
|
self.assertTrue(
|
|
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
# this model has no inputs_embeds
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
# this model has no input embeddings
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_model_outputs_equivalence(self):
|
|
pass
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
# decoder cannot keep gradients
|
|
pass
|
|
|
|
# skipped because there is always dropout in SpeechT5SpeechDecoderPrenet
|
|
def test_save_load(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_torchscript_output_attentions(self):
|
|
# disabled because this model doesn't have decoder_input_ids
|
|
pass
|
|
|
|
@slow
|
|
def test_torchscript_output_hidden_state(self):
|
|
# disabled because this model doesn't have decoder_input_ids
|
|
pass
|
|
|
|
@slow
|
|
def test_torchscript_simple(self):
|
|
# disabled because this model doesn't have decoder_input_ids
|
|
pass
|
|
|
|
# training is not supported yet
|
|
def test_training(self):
|
|
pass
|
|
|
|
def test_training_gradient_checkpointing(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.data.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.data.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_torchaudio
|
|
@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").select(range(num_samples))[: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))
|
|
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,
|
|
)
|
|
|
|
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_torchscript = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_resize_position_embeddings = False
|
|
test_head_masking = False
|
|
test_mismatched_shapes = False
|
|
test_missing_keys = False
|
|
test_model_parallel = False
|
|
is_encoder_decoder = False
|
|
has_attentions = False
|
|
|
|
input_name = "spectrogram"
|
|
|
|
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)
|
|
|
|
# this model does not output hidden states
|
|
def test_hidden_states_output(self):
|
|
pass
|
|
|
|
# skip
|
|
def test_initialization(self):
|
|
pass
|
|
|
|
# this model has no inputs_embeds
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
# this model has no input embeddings
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
# skip as this model doesn't support all arguments tested
|
|
def test_model_outputs_equivalence(self):
|
|
pass
|
|
|
|
# this model does not output hidden states
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
# skip because it fails on automapping of SpeechT5HifiGanConfig
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
# skip because it fails on automapping of SpeechT5HifiGanConfig
|
|
def test_save_load_fast_init_to_base(self):
|
|
pass
|