934 lines
36 KiB
Python
934 lines
36 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 UniSpeechSat model."""
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import math
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import unittest
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import numpy as np
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import pytest
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from datasets import load_dataset
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from transformers import UniSpeechSatConfig, is_torch_available
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from transformers.testing_utils import 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 (
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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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from ...test_pipeline_mixin import PipelineTesterMixin
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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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UniSpeechSatForAudioFrameClassification,
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UniSpeechSatForCTC,
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UniSpeechSatForPreTraining,
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UniSpeechSatForSequenceClassification,
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UniSpeechSatForXVector,
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UniSpeechSatModel,
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Wav2Vec2FeatureExtractor,
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Wav2Vec2Processor,
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)
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class UniSpeechSatModelTester:
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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=2,
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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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mask_time_prob=0.5,
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mask_time_length=2,
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vocab_size=32,
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do_stable_layer_norm=False,
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tdnn_dim=(32, 32),
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tdnn_kernel=(3, 3),
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tdnn_dilation=(1, 1),
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xvector_output_dim=32,
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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.mask_time_prob = mask_time_prob
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self.mask_time_length = mask_time_length
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self.tdnn_dim = tdnn_dim
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self.tdnn_kernel = tdnn_kernel
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self.tdnn_dilation = tdnn_dilation
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self.xvector_output_dim = xvector_output_dim
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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], scale=1.0)
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = self.get_config()
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return config, input_values, attention_mask
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def get_config(self):
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return UniSpeechSatConfig(
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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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mask_time_prob=self.mask_time_prob,
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mask_time_length=self.mask_time_length,
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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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tdnn_dim=self.tdnn_dim,
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tdnn_kernel=self.tdnn_kernel,
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tdnn_dilation=self.tdnn_dilation,
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xvector_output_dim=self.xvector_output_dim,
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)
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def create_and_check_model(self, config, input_values, attention_mask):
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model = UniSpeechSatModel(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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# test does not pass for models making use of `group_norm`
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# check: https://github.com/pytorch/fairseq/issues/3227
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model = UniSpeechSatModel(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 check_ctc_loss(self, config, input_values, *args):
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model = UniSpeechSatForCTC(config=config)
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model.to(torch_device)
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# make sure that dropout is disabled
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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.long)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
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labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
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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
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model.config.ctc_loss_reduction = "sum"
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sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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model.config.ctc_loss_reduction = "mean"
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mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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self.parent.assertTrue(isinstance(sum_loss, float))
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self.parent.assertTrue(isinstance(mean_loss, float))
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def check_seq_classifier_loss(self, config, input_values, *args):
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model = UniSpeechSatForSequenceClassification(config=config)
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model.to(torch_device)
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# make sure that dropout is disabled
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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.long)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
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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
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masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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unmasked_loss = model(input_values, labels=labels).loss.item()
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self.parent.assertTrue(isinstance(masked_loss, float))
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self.parent.assertTrue(isinstance(unmasked_loss, float))
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self.parent.assertTrue(masked_loss != unmasked_loss)
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def check_ctc_training(self, config, input_values, *args):
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config.ctc_zero_infinity = True
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model = UniSpeechSatForCTC(config=config)
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model.to(torch_device)
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model.train()
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# freeze feature encoder
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model.freeze_feature_encoder()
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input_values = input_values[:3]
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
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labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
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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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if max_length_labels[i] < labels.shape[-1]:
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# it's important that we make sure that target lengths are at least
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# one shorter than logit lengths to prevent -inf
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labels[i, max_length_labels[i] - 1 :] = -100
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loss = model(input_values, labels=labels).loss
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self.parent.assertFalse(torch.isinf(loss).item())
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loss.backward()
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def check_seq_classifier_training(self, config, input_values, *args):
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config.ctc_zero_infinity = True
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model = UniSpeechSatForSequenceClassification(config=config)
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model.to(torch_device)
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model.train()
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# freeze everything but the classification head
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model.freeze_base_model()
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input_values = input_values[:3]
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
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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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loss = model(input_values, labels=labels).loss
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self.parent.assertFalse(torch.isinf(loss).item())
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loss.backward()
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def check_xvector_training(self, config, *args):
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config.ctc_zero_infinity = True
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model = UniSpeechSatForXVector(config=config)
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model.to(torch_device)
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model.train()
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# freeze everything but the classification head
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model.freeze_base_model()
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# use a longer sequence length to account for TDNN temporal downsampling
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input_values = floats_tensor([self.batch_size, self.seq_length * 2], scale=1.0)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
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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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loss = model(input_values, labels=labels).loss
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self.parent.assertFalse(torch.isinf(loss).item())
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loss.backward()
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def check_labels_out_of_vocab(self, config, input_values, *args):
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model = UniSpeechSatForCTC(config)
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model.to(torch_device)
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model.train()
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input_values = input_values[:3]
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
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labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
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with pytest.raises(ValueError):
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model(input_values, labels=labels)
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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 UniSpeechSatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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UniSpeechSatForCTC,
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UniSpeechSatForPreTraining,
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UniSpeechSatModel,
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UniSpeechSatForSequenceClassification,
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UniSpeechSatForAudioFrameClassification,
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UniSpeechSatForXVector,
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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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pipeline_model_mapping = (
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{
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"audio-classification": UniSpeechSatForSequenceClassification,
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"automatic-speech-recognition": UniSpeechSatForCTC,
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"feature-extraction": UniSpeechSatModel,
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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 = UniSpeechSatModelTester(self)
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self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, 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_ctc_loss_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_ctc_loss(*config_and_inputs)
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def test_seq_classifier_loss_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_seq_classifier_loss(*config_and_inputs)
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def test_ctc_train(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_ctc_training(*config_and_inputs)
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def test_seq_classifier_train(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_seq_classifier_training(*config_and_inputs)
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def test_xvector_train(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_xvector_training(*config_and_inputs)
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def test_labels_out_of_vocab(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
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# UniSpeechSat 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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# UniSpeechSat 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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# UniSpeechSat 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_retain_grad_hidden_states_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = True
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# no need to test all models as different heads yield the same functionality
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model_class = self.all_model_classes[0]
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model = model_class(config)
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model.to(torch_device)
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# set layer drop to 0
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model.config.layerdrop = 0.0
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input_values = inputs_dict["input_values"]
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input_lengths = torch.tensor(
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[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
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)
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output_lengths = model._get_feat_extract_output_lengths(input_lengths)
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labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
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inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
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inputs_dict["labels"] = labels
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outputs = model(**inputs_dict)
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output = outputs[0]
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# Encoder-/Decoder-only models
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hidden_states = outputs.hidden_states[0]
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attentions = outputs.attentions[0]
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hidden_states.retain_grad()
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attentions.retain_grad()
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output.flatten()[0].backward(retain_graph=True)
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self.assertIsNotNone(hidden_states.grad)
|
|
self.assertIsNotNone(attentions.grad)
|
|
|
|
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",
|
|
"conv.parametrizations.weight",
|
|
"masked_spec_embed",
|
|
"codevectors",
|
|
"quantizer.weight_proj.weight",
|
|
"project_hid.weight",
|
|
"project_hid.bias",
|
|
"project_q.weight",
|
|
"project_q.bias",
|
|
"feature_projection.projection.weight",
|
|
"feature_projection.projection.bias",
|
|
"label_embeddings_concat",
|
|
"objective.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",
|
|
)
|
|
|
|
# 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, "codevectors") and module.codevectors is not None:
|
|
module.codevectors.data.fill_(3)
|
|
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
|
|
module.masked_spec_embed.data.fill_(3)
|
|
|
|
def test_mask_feature_prob_ctc(self):
|
|
model = UniSpeechSatForCTC.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2
|
|
)
|
|
model.to(torch_device).train()
|
|
processor = Wav2Vec2Processor.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True
|
|
)
|
|
|
|
batch_duration_in_seconds = [1, 3, 2, 6]
|
|
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
|
|
|
batch = processor(
|
|
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
|
)
|
|
logits = model(
|
|
input_values=batch["input_values"].to(torch_device),
|
|
attention_mask=batch["attention_mask"].to(torch_device),
|
|
).logits
|
|
|
|
self.assertEqual(logits.shape, (4, 1498, 32))
|
|
|
|
def test_mask_time_prob_ctc(self):
|
|
model = UniSpeechSatForCTC.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2
|
|
)
|
|
model.to(torch_device).train()
|
|
processor = Wav2Vec2Processor.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True
|
|
)
|
|
|
|
batch_duration_in_seconds = [1, 3, 2, 6]
|
|
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
|
|
|
batch = processor(
|
|
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
|
)
|
|
|
|
logits = model(
|
|
input_values=batch["input_values"].to(torch_device),
|
|
attention_mask=batch["attention_mask"].to(torch_device),
|
|
).logits
|
|
|
|
self.assertEqual(logits.shape, (4, 1498, 32))
|
|
|
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus")
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
test_pruning = False
|
|
test_headmasking = False
|
|
test_torchscript = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = UniSpeechSatModelTester(
|
|
self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True
|
|
)
|
|
self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, hidden_size=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
def test_batched_inference(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_batch_inference(*config_and_inputs)
|
|
|
|
def test_ctc_loss_inference(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_ctc_loss(*config_and_inputs)
|
|
|
|
def test_seq_classifier_loss_inference(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
|
|
|
|
def test_ctc_train(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_ctc_training(*config_and_inputs)
|
|
|
|
def test_seq_classifier_train(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_seq_classifier_training(*config_and_inputs)
|
|
|
|
def test_labels_out_of_vocab(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
|
|
|
|
# UniSpeechSat has no inputs_embeds
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
# `input_ids` is renamed to `input_values`
|
|
def test_forward_signature(self):
|
|
pass
|
|
|
|
# UniSpeechSat cannot resize token embeddings
|
|
# since it has no tokens embeddings
|
|
def test_resize_tokens_embeddings(self):
|
|
pass
|
|
|
|
# UniSpeechSat has no inputs_embeds
|
|
# and thus the `get_input_embeddings` fn
|
|
# is not implemented
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
config.output_attentions = True
|
|
|
|
# no need to test all models as different heads yield the same functionality
|
|
model_class = self.all_model_classes[0]
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
# set layer drop to 0
|
|
model.config.layerdrop = 0.0
|
|
|
|
input_values = inputs_dict["input_values"]
|
|
|
|
input_lengths = torch.tensor(
|
|
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
|
|
)
|
|
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
|
|
|
|
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
|
|
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
|
|
inputs_dict["labels"] = labels
|
|
|
|
outputs = model(**inputs_dict)
|
|
|
|
output = outputs[0]
|
|
|
|
# Encoder-/Decoder-only models
|
|
hidden_states = outputs.hidden_states[0]
|
|
attentions = outputs.attentions[0]
|
|
|
|
hidden_states.retain_grad()
|
|
attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(hidden_states.grad)
|
|
self.assertIsNotNone(attentions.grad)
|
|
|
|
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",
|
|
"conv.parametrizations.weight",
|
|
"masked_spec_embed",
|
|
"codevectors",
|
|
"quantizer.weight_proj.weight",
|
|
"project_hid.weight",
|
|
"project_hid.bias",
|
|
"project_q.weight",
|
|
"project_q.bias",
|
|
"feature_projection.projection.weight",
|
|
"feature_projection.projection.bias",
|
|
"label_embeddings_concat",
|
|
"objective.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",
|
|
)
|
|
|
|
# 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, "codevectors") and module.codevectors is not None:
|
|
module.codevectors.data.fill_(3)
|
|
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
|
|
module.masked_spec_embed.data.fill_(3)
|
|
|
|
def test_mask_feature_prob_ctc(self):
|
|
model = UniSpeechSatForCTC.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2
|
|
)
|
|
model.to(torch_device).train()
|
|
processor = Wav2Vec2Processor.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True
|
|
)
|
|
|
|
batch_duration_in_seconds = [1, 3, 2, 6]
|
|
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
|
|
|
batch = processor(
|
|
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
|
)
|
|
|
|
logits = model(
|
|
input_values=batch["input_values"].to(torch_device),
|
|
attention_mask=batch["attention_mask"].to(torch_device),
|
|
).logits
|
|
|
|
self.assertEqual(logits.shape, (4, 1498, 32))
|
|
|
|
def test_mask_time_prob_ctc(self):
|
|
model = UniSpeechSatForCTC.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2
|
|
)
|
|
model.to(torch_device).train()
|
|
processor = Wav2Vec2Processor.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True
|
|
)
|
|
|
|
batch_duration_in_seconds = [1, 3, 2, 6]
|
|
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
|
|
|
batch = processor(
|
|
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
|
)
|
|
|
|
logits = model(
|
|
input_values=batch["input_values"].to(torch_device),
|
|
attention_mask=batch["attention_mask"].to(torch_device),
|
|
).logits
|
|
|
|
self.assertEqual(logits.shape, (4, 1498, 32))
|
|
|
|
def test_mask_time_feature_prob_ctc_single_batch(self):
|
|
model = UniSpeechSatForCTC.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat",
|
|
mask_time_prob=0.2,
|
|
mask_feature_prob=0.2,
|
|
mask_time_length=2,
|
|
mask_feature_length=2,
|
|
)
|
|
model.to(torch_device).train()
|
|
processor = Wav2Vec2Processor.from_pretrained(
|
|
"hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True
|
|
)
|
|
|
|
batch_duration_in_seconds = [6]
|
|
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
|
|
|
batch = processor(
|
|
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
|
)
|
|
|
|
logits = model(
|
|
input_values=batch["input_values"].to(torch_device),
|
|
attention_mask=batch["attention_mask"].to(torch_device),
|
|
).logits
|
|
|
|
self.assertEqual(logits.shape, (1, 1498, 32))
|
|
|
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large")
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
@require_soundfile
|
|
@slow
|
|
class UniSpeechSatModelIntegrationTest(unittest.TestCase):
|
|
def _load_datasamples(self, num_samples):
|
|
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
# automatic decoding with librispeech
|
|
speech_samples = ds.sort("id").filter(
|
|
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
|
|
)[:num_samples]["audio"]
|
|
|
|
return [x["array"] for x in speech_samples]
|
|
|
|
def _load_superb(self, task, num_samples):
|
|
ds = load_dataset("anton-l/superb_dummy", task, split="test")
|
|
|
|
return ds[:num_samples]
|
|
|
|
def test_inference_encoder_base(self):
|
|
model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus")
|
|
model.to(torch_device)
|
|
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
|
"facebook/wav2vec2-base", return_attention_mask=True
|
|
)
|
|
input_speech = self._load_datasamples(2)
|
|
|
|
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(
|
|
inputs_dict.input_values.to(torch_device),
|
|
attention_mask=inputs_dict.attention_mask.to(torch_device),
|
|
)
|
|
|
|
# fmt: off
|
|
expected_hidden_states_slice = torch.tensor(
|
|
[[[-0.0743, 0.1384],
|
|
[-0.0845, 0.1704]],
|
|
[[-0.0954, 0.1936],
|
|
[-0.1123, 0.2095]]],
|
|
device=torch_device,
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3))
|
|
|
|
def test_inference_encoder_large(self):
|
|
model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large")
|
|
model.to(torch_device)
|
|
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-xlsr-53")
|
|
input_speech = self._load_datasamples(2)
|
|
|
|
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(
|
|
inputs_dict.input_values.to(torch_device),
|
|
attention_mask=inputs_dict.attention_mask.to(torch_device),
|
|
)
|
|
|
|
# fmt: off
|
|
expected_hidden_states_slice = torch.tensor(
|
|
[[[-0.1172, -0.0797],
|
|
[-0.0012, 0.0213]],
|
|
[[-0.1225, -0.1277],
|
|
[-0.0668, -0.0585]]],
|
|
device=torch_device,
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3))
|
|
|
|
def test_inference_diarization(self):
|
|
model = UniSpeechSatForAudioFrameClassification.from_pretrained("microsoft/unispeech-sat-base-plus-sd").to(
|
|
torch_device
|
|
)
|
|
processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sd")
|
|
input_data = self._load_superb("sd", 4)
|
|
inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000)
|
|
|
|
input_values = inputs.input_values.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(input_values, attention_mask=attention_mask)
|
|
# labels is a one-hot array of shape (num_frames, num_speakers)
|
|
labels = (outputs.logits > 0).long()
|
|
|
|
# s3prl logits for the same batch
|
|
expected_logits = torch.tensor(
|
|
[
|
|
[[-5.6119, -5.5845], [-3.7772, -5.4824], [-3.6914, -5.1619], [-4.7560, -5.0496]],
|
|
[[-6.3785, -4.8365], [-5.5863, -5.4149], [-5.5639, -4.8469], [-6.1511, -4.0052]],
|
|
[[-6.0355, -3.7414], [-5.5968, -4.8061], [-5.4620, -4.7310], [-5.5864, -4.6078]],
|
|
[[-5.9493, -4.8963], [-4.4050, -5.4476], [-4.1755, -5.1395], [-4.0272, -4.3705]],
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],
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|
device=torch_device,
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|
)
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|
self.assertEqual(labels[0, :, 0].sum(), 270)
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self.assertEqual(labels[0, :, 1].sum(), 647)
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self.assertTrue(torch.allclose(outputs.logits[:, :4], expected_logits, atol=1e-2))
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|
|
|
def test_inference_speaker_verification(self):
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|
model = UniSpeechSatForXVector.from_pretrained("microsoft/unispeech-sat-base-plus-sv").to(torch_device)
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|
processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sv")
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|
input_data = self._load_superb("si", 4)
|
|
|
|
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
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|
labels = torch.tensor([5, 1, 1, 3], device=torch_device).T
|
|
|
|
with torch.no_grad():
|
|
input_values = inputs.input_values.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
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|
outputs = model(input_values, attention_mask=attention_mask, labels=labels)
|
|
embeddings = torch.nn.functional.normalize(outputs.embeddings, dim=-1)
|
|
|
|
cosine_sim = torch.nn.CosineSimilarity(dim=-1)
|
|
# id10002 vs id10002
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|
self.assertAlmostEqual(cosine_sim(embeddings[1], embeddings[2]).item(), 0.9671, 3)
|
|
# id10006 vs id10002
|
|
self.assertAlmostEqual(cosine_sim(embeddings[0], embeddings[1]).item(), 0.4941, 3)
|
|
# id10002 vs id10004
|
|
self.assertAlmostEqual(cosine_sim(embeddings[2], embeddings[3]).item(), 0.5616, 3)
|
|
|
|
self.assertAlmostEqual(outputs.loss.item(), 18.5925, 2)
|