389 lines
15 KiB
Python
389 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2021 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 Wav2Vec2 model. """
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import math
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import unittest
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from tests.test_modeling_common import floats_tensor, random_attention_mask
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from transformers import is_torch_available
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from transformers.testing_utils import require_datasets, require_soundfile, require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, _config_zero_init
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if is_torch_available():
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import torch
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from transformers import Wav2Vec2Config, Wav2Vec2ForCTC, Wav2Vec2ForMaskedLM, Wav2Vec2Model, Wav2Vec2Tokenizer
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class Wav2Vec2ModelTester:
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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=1024, # speech is longer
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is_training=False,
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hidden_size=16,
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feat_extract_norm="group",
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feat_extract_dropout=0.0,
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feat_extract_activation="gelu",
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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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num_hidden_layers=4,
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num_attention_heads=2,
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hidden_dropout_prob=0.1, # this is most likely not correctly set yet
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intermediate_size=20,
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layer_norm_eps=1e-5,
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hidden_act="gelu",
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initializer_range=0.02,
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vocab_size=32,
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do_stable_layer_norm=False,
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scope=None,
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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.feat_extract_norm = feat_extract_norm
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self.feat_extract_dropout = feat_extract_dropout
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self.feat_extract_activation = feat_extract_activation
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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.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_dropout_prob = hidden_dropout_prob
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self.intermediate_size = intermediate_size
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.vocab_size = vocab_size
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self.do_stable_layer_norm = do_stable_layer_norm
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self.scope = scope
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output_seq_length = self.seq_length
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for kernel, stride in zip(self.conv_kernel, self.conv_stride):
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output_seq_length = (output_seq_length - (kernel - 1)) / stride
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self.output_seq_length = int(math.ceil(output_seq_length))
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self.encoder_seq_length = self.output_seq_length
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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.vocab_size)
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = Wav2Vec2Config(
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hidden_size=self.hidden_size,
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feat_extract_norm=self.feat_extract_norm,
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feat_extract_dropout=self.feat_extract_dropout,
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feat_extract_activation=self.feat_extract_activation,
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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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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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hidden_dropout_prob=self.hidden_dropout_prob,
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intermediate_size=self.intermediate_size,
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layer_norm_eps=self.layer_norm_eps,
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hidden_act=self.hidden_act,
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initializer_range=self.initializer_range,
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vocab_size=self.vocab_size,
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)
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return config, input_values, attention_mask
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def create_and_check_model(self, config, input_values, attention_mask):
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model = Wav2Vec2Model(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
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)
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def create_and_check_batch_inference(self, config, input_values, *args):
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# Not sure how to make this test pass at the moment. Batched input yields
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# same results as official fairseq implementation, but gives different results
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# depending on whether batched input is used or not
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# check: https://github.com/pytorch/fairseq/issues/3227
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model = Wav2Vec2Model(config=config)
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model.to(torch_device)
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model.eval()
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input_values = input_values[:3]
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attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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# pad input
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for i in range(len(input_lengths)):
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input_values[i, input_lengths[i] :] = 0.0
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attention_mask[i, input_lengths[i] :] = 0.0
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batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
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for i in range(input_values.shape[0]):
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input_slice = input_values[i : i + 1, : input_lengths[i]]
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output = model(input_slice).last_hidden_state
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batch_output = batch_outputs[i : i + 1, : output.shape[1]]
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self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
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def prepare_config_and_inputs_for_common(self):
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config, input_values, attention_mask = self.prepare_config_and_inputs()
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inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_torch
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class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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Wav2Vec2Model,
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Wav2Vec2ForMaskedLM,
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)
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if is_torch_available()
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else ()
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)
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test_pruning = False
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test_headmasking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = Wav2Vec2ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, 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(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(*config_and_inputs)
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# Wav2Vec2 has no inputs_embeds
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def test_inputs_embeds(self):
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pass
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# `input_ids` is renamed to `input_values`
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def test_forward_signature(self):
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pass
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# Wav2Vec2 cannot resize token embeddings
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# since it has no tokens embeddings
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def test_resize_tokens_embeddings(self):
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pass
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# Wav2Vec2 has no inputs_embeds
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# and thus the `get_input_embeddings` fn
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# is not implemented
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def test_model_common_attributes(self):
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pass
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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if "conv.weight" in name:
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
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msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
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)
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else:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
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)
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@slow
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def test_model_from_pretrained(self):
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model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsNotNone(model)
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@require_torch
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class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForCTC) if is_torch_available() else ()
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test_pruning = False
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test_headmasking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = Wav2Vec2ModelTester(
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self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True
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)
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self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, 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(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(*config_and_inputs)
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def test_batched_inference(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_batch_inference(*config_and_inputs)
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# Wav2Vec2 has no inputs_embeds
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def test_inputs_embeds(self):
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pass
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# `input_ids` is renamed to `input_values`
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def test_forward_signature(self):
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pass
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# Wav2Vec2 cannot resize token embeddings
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# since it has no tokens embeddings
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def test_resize_tokens_embeddings(self):
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pass
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# Wav2Vec2 has no inputs_embeds
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# and thus the `get_input_embeddings` fn
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# is not implemented
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def test_model_common_attributes(self):
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pass
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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if "conv.weight" in name:
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
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msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
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)
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else:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
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)
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@slow
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def test_model_from_pretrained(self):
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model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsNotNone(model)
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@require_torch
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@slow
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@require_datasets
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@require_soundfile
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class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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def _load_datasamples(self, num_samples):
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from datasets import load_dataset
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import soundfile as sf
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# map files to raw
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def map_to_array(batch):
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speech, _ = sf.read(batch["file"])
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batch["speech"] = speech
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return batch
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ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
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ds = ds.select(range(num_samples)).map(map_to_array)
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return ds["speech"][:num_samples]
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def test_inference_ctc_normal(self):
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
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model.to(torch_device)
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
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input_speech = self._load_datasamples(1)
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input_values = tokenizer(input_speech, return_tensors="pt").input_values.to(torch_device)
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with torch.no_grad():
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logits = model(input_values).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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predicted_trans = tokenizer.batch_decode(predicted_ids)
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EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
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self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
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def test_inference_ctc_normal_batched(self):
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
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model.to(torch_device)
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
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input_speech = self._load_datasamples(2)
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inputs = tokenizer(input_speech, return_tensors="pt", padding=True, truncation=True)
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input_values = inputs.input_values.to(torch_device)
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with torch.no_grad():
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logits = model(input_values).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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predicted_trans = tokenizer.batch_decode(predicted_ids)
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EXPECTED_TRANSCRIPTIONS = [
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"a man said to the universe sir i exist",
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"sweat covered brion's body trickling into the tight lowing cloth that was the only garment he wore",
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]
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self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
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def test_inference_ctc_robust_batched(self):
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to(torch_device)
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
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input_speech = self._load_datasamples(4)
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inputs = tokenizer(input_speech, return_tensors="pt", padding=True, truncation=True)
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input_values = inputs.input_values.to(torch_device)
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attention_mask = inputs.attention_mask.to(torch_device)
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with torch.no_grad():
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logits = model(input_values, attention_mask=attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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predicted_trans = tokenizer.batch_decode(predicted_ids)
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EXPECTED_TRANSCRIPTIONS = [
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"a man said to the universe sir i exist",
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"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
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"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with the thousands of spectators were trivialities not worth thinking about",
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"his instant panic was followed by a small sharp blow high on his chest",
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]
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self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
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