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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 Wav2Vec2-Conformer model."""
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import math
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import tempfile
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import unittest
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import numpy as np
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from datasets import load_dataset
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from transformers import Wav2Vec2ConformerConfig, is_torch_available
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from transformers.testing_utils import (
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is_flaky,
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require_torch,
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require_torch_accelerator,
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require_torch_fp16,
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slow,
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torch_device,
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)
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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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Wav2Vec2ConformerForAudioFrameClassification,
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Wav2Vec2ConformerForCTC,
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Wav2Vec2ConformerForPreTraining,
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Wav2Vec2ConformerForSequenceClassification,
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Wav2Vec2ConformerForXVector,
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Wav2Vec2ConformerModel,
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Wav2Vec2FeatureExtractor,
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Wav2Vec2Processor,
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)
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from transformers.models.wav2vec2.modeling_wav2vec2 import _sample_negative_indices
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from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
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Wav2Vec2ConformerGumbelVectorQuantizer,
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_compute_mask_indices,
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)
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class Wav2Vec2ConformerModelTester:
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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,
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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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num_adapter_layers=1,
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adapter_stride=2,
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tdnn_dim=(32, 32),
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tdnn_kernel=(5, 3),
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tdnn_dilation=(1, 2),
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xvector_output_dim=32,
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position_embeddings_type="relative",
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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.num_adapter_layers = num_adapter_layers
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self.adapter_stride = adapter_stride
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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.scope = scope
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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.position_embeddings_type = position_embeddings_type
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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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self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
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def prepare_config_and_inputs(self, position_embeddings_type="relative"):
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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 = self.get_config(position_embeddings_type=position_embeddings_type)
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return config, input_values, attention_mask
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def get_config(self, position_embeddings_type="relative"):
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return Wav2Vec2ConformerConfig(
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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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mask_time_prob=self.mask_time_prob,
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mask_time_length=self.mask_time_length,
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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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do_stable_layer_norm=self.do_stable_layer_norm,
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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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num_adapter_layers=self.num_adapter_layers,
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adapter_stride=self.adapter_stride,
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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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position_embeddings_type=position_embeddings_type,
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)
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def create_and_check_model(self, config, input_values, attention_mask):
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model = Wav2Vec2ConformerModel(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_model_with_adapter(self, config, input_values, attention_mask):
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config.add_adapter = True
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model = Wav2Vec2ConformerModel(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.adapter_output_seq_length, self.hidden_size)
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)
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def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask):
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config.add_adapter = True
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config.output_hidden_size = 2 * config.hidden_size
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model = Wav2Vec2ConformerForCTC(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.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size)
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)
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def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
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config.add_adapter = True
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config.output_hidden_size = 8
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model = Wav2Vec2ConformerModel(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,
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(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
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)
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def create_and_check_model_float16(self, config, input_values, attention_mask):
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model = Wav2Vec2ConformerModel(config=config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = Wav2Vec2ConformerModel.from_pretrained(tmpdirname, dtype=torch.float16)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(input_values.type(dtype=torch.float16), 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 check_ctc_loss(self, config, input_values, *args):
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model = Wav2Vec2ConformerForCTC(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 = Wav2Vec2ConformerForSequenceClassification(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 = Wav2Vec2ConformerForCTC(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 = Wav2Vec2ConformerForSequenceClassification(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, input_values, *args):
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config.ctc_zero_infinity = True
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model = Wav2Vec2ConformerForXVector(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_labels_out_of_vocab(self, config, input_values, *args):
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model = Wav2Vec2ConformerForCTC(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 self.parent.assertRaises(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 Wav2Vec2ConformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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Wav2Vec2ConformerForCTC,
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Wav2Vec2ConformerModel,
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Wav2Vec2ConformerForSequenceClassification,
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Wav2Vec2ConformerForPreTraining,
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Wav2Vec2ConformerForAudioFrameClassification,
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Wav2Vec2ConformerForXVector,
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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": Wav2Vec2ConformerForSequenceClassification,
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"automatic-speech-recognition": Wav2Vec2ConformerForCTC,
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"feature-extraction": Wav2Vec2ConformerModel,
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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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||||
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||||
def setUp(self):
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self.model_tester = Wav2Vec2ConformerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=37)
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||||
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||||
def test_config(self):
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||||
self.config_tester.run_common_tests()
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||||
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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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||||
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||||
@is_flaky(
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||||
description="The `codevector_idx` computed with `argmax()` in `Wav2Vec2ConformerGumbelVectorQuantizer.forward` is not stable."
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||||
)
|
||||
def test_batching_equivalence(self, atol=1e-4, rtol=1e-4):
|
||||
super().test_batching_equivalence(atol=atol, rtol=rtol)
|
||||
|
||||
def test_model_with_relative(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative")
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_with_rotary(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary")
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_with_no_rel_pos(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None)
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_with_adapter(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
|
||||
|
||||
def test_model_with_adapter_for_ctc(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs)
|
||||
|
||||
def test_model_with_adapter_proj_dim(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
|
||||
|
||||
@require_torch_accelerator
|
||||
@require_torch_fp16
|
||||
def test_model_float16_with_relative(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative")
|
||||
self.model_tester.create_and_check_model_float16(*config_and_inputs)
|
||||
|
||||
@require_torch_accelerator
|
||||
@require_torch_fp16
|
||||
def test_model_float16_with_rotary(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary")
|
||||
self.model_tester.create_and_check_model_float16(*config_and_inputs)
|
||||
|
||||
def test_ctc_loss_inference(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_ctc_loss(*config_and_inputs)
|
||||
|
||||
def test_seq_classifier_loss_inference(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
|
||||
|
||||
def test_ctc_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_ctc_training(*config_and_inputs)
|
||||
|
||||
def test_seq_classifier_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_seq_classifier_training(*config_and_inputs)
|
||||
|
||||
def test_xvector_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_xvector_training(*config_and_inputs)
|
||||
|
||||
def test_labels_out_of_vocab(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
|
||||
|
||||
@unittest.skip(reason="Wav2Vec2Conformer has not inputs_embeds")
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Wav2Vec2Conformer has input_values instead of input_ids")
|
||||
def test_forward_signature(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Wav2Vec2Conformer has not token embeddings")
|
||||
def test_resize_tokens_embeddings(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Wav2Vec2Conformer has not inputs_embeds")
|
||||
def test_model_get_set_embeddings(self):
|
||||
pass
|
||||
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.output_hidden_states = True
|
||||
config.output_attentions = True
|
||||
|
||||
# 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",
|
||||
"pos_bias_v",
|
||||
"pos_bias_u",
|
||||
"pointwise_conv1",
|
||||
"pointwise_conv2",
|
||||
"feature_projection.projection.weight",
|
||||
"feature_projection.projection.bias",
|
||||
"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, "pos_bias_u") and module.pos_bias_u is not None:
|
||||
module.pos_bias_u.data.fill_(3)
|
||||
if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None:
|
||||
module.pos_bias_v.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 = Wav2Vec2ConformerForCTC.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2
|
||||
)
|
||||
model.to(torch_device).train()
|
||||
processor = Wav2Vec2Processor.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-wav2vec2-conformer", 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 = Wav2Vec2ConformerForCTC.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2
|
||||
)
|
||||
model.to(torch_device).train()
|
||||
processor = Wav2Vec2Processor.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-wav2vec2-conformer", 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 = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
@require_torch
|
||||
class Wav2Vec2ConformerUtilsTest(unittest.TestCase):
|
||||
def test_compute_mask_indices(self):
|
||||
batch_size = 4
|
||||
sequence_length = 60
|
||||
mask_prob = 0.5
|
||||
mask_length = 1
|
||||
|
||||
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
|
||||
|
||||
def test_compute_mask_indices_low_prob(self):
|
||||
# with these settings num_masked_spans=0.5, which means probabilistic rounding
|
||||
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
|
||||
# the other 5 out of 10, cases num_masked_spans=1
|
||||
n_trials = 100
|
||||
batch_size = 4
|
||||
sequence_length = 100
|
||||
mask_prob = 0.05
|
||||
mask_length = 10
|
||||
|
||||
count_dimensions_masked = 0
|
||||
count_dimensions_not_masked = 0
|
||||
|
||||
for _ in range(n_trials):
|
||||
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
num_masks = torch.sum(mask).item()
|
||||
|
||||
if num_masks > 0:
|
||||
count_dimensions_masked += 1
|
||||
else:
|
||||
count_dimensions_not_masked += 1
|
||||
|
||||
# as we test for at least 10 masked dimension and at least
|
||||
# 10 non-masked dimension, this test could fail with probability:
|
||||
# P(100 coin flips, at most 9 heads) = 1.66e-18
|
||||
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
|
||||
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
|
||||
|
||||
def test_compute_mask_indices_overlap(self):
|
||||
batch_size = 4
|
||||
sequence_length = 80
|
||||
mask_prob = 0.5
|
||||
mask_length = 4
|
||||
|
||||
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
|
||||
for batch_sum in mask.sum(axis=-1):
|
||||
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
|
||||
|
||||
def test_compute_mask_indices_attn_mask_overlap(self):
|
||||
batch_size = 4
|
||||
sequence_length = 80
|
||||
mask_prob = 0.5
|
||||
mask_length = 4
|
||||
|
||||
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
|
||||
attention_mask[:2, sequence_length // 2 :] = 0
|
||||
|
||||
mask = _compute_mask_indices(
|
||||
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
|
||||
)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
for batch_sum in mask.sum(axis=-1):
|
||||
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
|
||||
|
||||
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
|
||||
|
||||
def test_compute_mask_indices_short_audio(self):
|
||||
batch_size = 4
|
||||
sequence_length = 100
|
||||
mask_prob = 0.05
|
||||
mask_length = 10
|
||||
|
||||
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
|
||||
# force one example to be heavily padded
|
||||
attention_mask[0, 5:] = 0
|
||||
|
||||
mask = _compute_mask_indices(
|
||||
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
|
||||
)
|
||||
|
||||
# make sure that non-padded examples cannot be padded
|
||||
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
|
||||
|
||||
def test_compute_perplexity(self):
|
||||
probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100
|
||||
|
||||
ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs)
|
||||
self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3)
|
||||
|
||||
# mask half of the input
|
||||
mask = torch.ones((2,), device=torch_device, dtype=torch.bool)
|
||||
mask[0] = 0
|
||||
|
||||
ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask)
|
||||
self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3)
|
||||
|
||||
def test_sample_negatives(self):
|
||||
batch_size = 2
|
||||
sequence_length = 10
|
||||
hidden_size = 4
|
||||
num_negatives = 3
|
||||
|
||||
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
|
||||
sequence_length, hidden_size
|
||||
) # each value in vector consists of same value
|
||||
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
|
||||
|
||||
# sample negative indices
|
||||
sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None)
|
||||
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
|
||||
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
|
||||
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
|
||||
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
|
||||
|
||||
# make sure no negatively sampled vector is actually a positive one
|
||||
for negative in negatives:
|
||||
self.assertTrue(((negative - features) == 0).sum() == 0.0)
|
||||
|
||||
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
|
||||
self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
|
||||
|
||||
def test_sample_negatives_with_mask(self):
|
||||
batch_size = 2
|
||||
sequence_length = 10
|
||||
hidden_size = 4
|
||||
num_negatives = 3
|
||||
|
||||
# second half of last input tensor is padded
|
||||
mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
|
||||
mask[-1, sequence_length // 2 :] = 0
|
||||
|
||||
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
|
||||
sequence_length, hidden_size
|
||||
) # each value in vector consists of same value
|
||||
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
|
||||
|
||||
# replace masked feature vectors with -100 to test that those are not sampled
|
||||
features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100)
|
||||
|
||||
# sample negative indices
|
||||
sampled_negative_indices = _sample_negative_indices(
|
||||
(batch_size, sequence_length), num_negatives, mask.cpu().numpy()
|
||||
)
|
||||
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
|
||||
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
|
||||
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
|
||||
|
||||
self.assertTrue((negatives >= 0).all().item())
|
||||
|
||||
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
|
||||
|
||||
# make sure no negatively sampled vector is actually a positive one
|
||||
for negative in negatives:
|
||||
self.assertTrue(((negative - features) == 0).sum() == 0.0)
|
||||
|
||||
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
|
||||
self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
|
||||
|
||||
|
||||
@require_torch
|
||||
@slow
|
||||
class Wav2Vec2ConformerModelIntegrationTest(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)])
|
||||
speech_samples = speech_samples[:num_samples]["audio"]
|
||||
|
||||
return [x["array"] for x in speech_samples]
|
||||
|
||||
def test_inference_ctc_normal_batched_rel_pos(self):
|
||||
model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft")
|
||||
model.to(torch_device)
|
||||
processor = Wav2Vec2Processor.from_pretrained(
|
||||
"facebook/wav2vec2-conformer-rel-pos-large-960h-ft", do_lower_case=True
|
||||
)
|
||||
|
||||
input_speech = self._load_datasamples(2)
|
||||
|
||||
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
input_values = inputs.input_values.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(input_values).logits
|
||||
|
||||
predicted_ids = torch.argmax(logits, dim=-1)
|
||||
predicted_trans = processor.batch_decode(predicted_ids)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"a man said to the universe sir i exist",
|
||||
"sweat covered brion's body trickling into the tight loincloth that was the only garment he wore",
|
||||
]
|
||||
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
||||
|
||||
def test_inference_ctc_normal_batched_rope(self):
|
||||
model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft")
|
||||
model.to(torch_device)
|
||||
processor = Wav2Vec2Processor.from_pretrained(
|
||||
"facebook/wav2vec2-conformer-rope-large-960h-ft", do_lower_case=True
|
||||
)
|
||||
|
||||
input_speech = self._load_datasamples(2)
|
||||
|
||||
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
input_values = inputs.input_values.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(input_values).logits
|
||||
|
||||
predicted_ids = torch.argmax(logits, dim=-1)
|
||||
predicted_trans = processor.batch_decode(predicted_ids)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"a man said to the universe sir i exist",
|
||||
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
|
||||
]
|
||||
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
||||
|
||||
def test_inference_pretrained(self):
|
||||
model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
|
||||
model.to(torch_device)
|
||||
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
||||
"facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True
|
||||
)
|
||||
input_speech = self._load_datasamples(2)
|
||||
|
||||
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
batch_size = inputs_dict["input_values"].shape[0]
|
||||
feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
|
||||
|
||||
features_shape = (batch_size, feature_seq_length)
|
||||
|
||||
torch.manual_seed(0)
|
||||
mask_time_indices = _compute_mask_indices(
|
||||
features_shape,
|
||||
model.config.mask_time_prob,
|
||||
model.config.mask_time_length,
|
||||
min_masks=2,
|
||||
)
|
||||
mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(
|
||||
inputs_dict.input_values.to(torch_device),
|
||||
attention_mask=inputs_dict.attention_mask.to(torch_device),
|
||||
mask_time_indices=mask_time_indices,
|
||||
)
|
||||
|
||||
# compute cosine similarity
|
||||
cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)
|
||||
|
||||
# retrieve cosine sim of masked features
|
||||
cosine_sim_masked = cosine_sim[mask_time_indices]
|
||||
|
||||
# ... now compare to randomly initialized model
|
||||
|
||||
config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
|
||||
model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval()
|
||||
|
||||
with torch.no_grad():
|
||||
outputs_rand = model_rand(
|
||||
inputs_dict.input_values.to(torch_device),
|
||||
attention_mask=inputs_dict.attention_mask.to(torch_device),
|
||||
mask_time_indices=mask_time_indices,
|
||||
)
|
||||
|
||||
# compute cosine similarity
|
||||
cosine_sim_rand = torch.cosine_similarity(
|
||||
outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1
|
||||
)
|
||||
|
||||
# retrieve cosine sim of masked features
|
||||
cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices]
|
||||
|
||||
# a pretrained wav2vec2_conformer model has learned to predict the quantized latent states
|
||||
# => the cosine similarity between quantized states and predicted states > 0.5
|
||||
# a random wav2vec2_conformer model has not learned to predict the quantized latent states
|
||||
# => the cosine similarity between quantized states and predicted states is very likely < 0.1
|
||||
self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)
|
||||
Reference in New Issue
Block a user