95faabf0a6
* Apply ruff SIM118 fix Signed-off-by: cyy <cyyever@outlook.com> * Apply ruff SIM910 fix Signed-off-by: cyy <cyyever@outlook.com> * Apply ruff SIM101 fix Signed-off-by: cyy <cyyever@outlook.com> * Format code Signed-off-by: cyy <cyyever@outlook.com> * More fixes Signed-off-by: cyy <cyyever@outlook.com> --------- Signed-off-by: cyy <cyyever@outlook.com>
989 lines
48 KiB
Python
989 lines
48 KiB
Python
# Copyright 2024 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 Dac model."""
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import inspect
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import os
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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 Audio, load_dataset
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from parameterized import parameterized
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from transformers import AutoProcessor, DacConfig, DacModel
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from transformers.testing_utils import is_torch_available, require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
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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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@require_torch
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# Copied from transformers.tests.encodec.test_modeling_encodec.EncodecModelTester with Encodec->Dac
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class DacModelTester:
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# Ignore copy
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def __init__(
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self,
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parent,
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batch_size=3,
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num_channels=1,
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is_training=False,
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intermediate_size=1024,
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encoder_hidden_size=16,
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downsampling_ratios=[2, 4, 4],
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decoder_hidden_size=16,
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n_codebooks=6,
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codebook_size=512,
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codebook_dim=4,
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quantizer_dropout=0.0,
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commitment_loss_weight=0.25,
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codebook_loss_weight=1.0,
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sample_rate=16000,
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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.num_channels = num_channels
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self.is_training = is_training
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self.intermediate_size = intermediate_size
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self.sample_rate = sample_rate
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self.encoder_hidden_size = encoder_hidden_size
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self.downsampling_ratios = downsampling_ratios
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self.decoder_hidden_size = decoder_hidden_size
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self.n_codebooks = n_codebooks
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self.codebook_size = codebook_size
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self.codebook_dim = codebook_dim
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self.quantizer_dropout = quantizer_dropout
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self.commitment_loss_weight = commitment_loss_weight
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self.codebook_loss_weight = codebook_loss_weight
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def prepare_config_and_inputs(self):
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input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0)
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config = self.get_config()
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inputs_dict = {"input_values": input_values}
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def prepare_config_and_inputs_for_model_class(self, model_class):
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input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0)
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config = self.get_config()
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inputs_dict = {"input_values": input_values}
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return config, inputs_dict
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# Ignore copy
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def get_config(self):
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return DacConfig(
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encoder_hidden_size=self.encoder_hidden_size,
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downsampling_ratios=self.downsampling_ratios,
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decoder_hidden_size=self.decoder_hidden_size,
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n_codebooks=self.n_codebooks,
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codebook_size=self.codebook_size,
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codebook_dim=self.codebook_dim,
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quantizer_dropout=self.quantizer_dropout,
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commitment_loss_weight=self.commitment_loss_weight,
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codebook_loss_weight=self.codebook_loss_weight,
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)
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# Ignore copy
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def create_and_check_model_forward(self, config, inputs_dict):
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model = DacModel(config=config).to(torch_device).eval()
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input_values = inputs_dict["input_values"]
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result = model(input_values)
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self.parent.assertEqual(result.audio_values.shape, (self.batch_size, self.intermediate_size))
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@require_torch
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# Copied from transformers.tests.encodec.test_modeling_encodec.EncodecModelTest with Encodec->Dac
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class DacModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (DacModel,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_headmasking = False
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test_resize_embeddings = False
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pipeline_model_mapping = {"feature-extraction": DacModel} if is_torch_available() else {}
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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# model does not have attention and does not support returning hidden states
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if "output_attentions" in inputs_dict:
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inputs_dict.pop("output_attentions")
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if "output_hidden_states" in inputs_dict:
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inputs_dict.pop("output_hidden_states")
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return inputs_dict
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def setUp(self):
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self.model_tester = DacModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=DacConfig, hidden_size=37, common_properties=[], has_text_modality=False
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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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def test_model_forward(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_forward(*config_and_inputs)
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# TODO (ydshieh): Although we have a potential cause, it's still strange that this test fails all the time with large differences
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@unittest.skip(reason="Might be caused by `indices` computed with `max()` in `decode_latents`")
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def test_batching_equivalence(self):
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super().test_batching_equivalence()
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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# Ignore copy
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expected_arg_names = ["input_values", "n_quantizers", "return_dict"]
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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@unittest.skip("The DacModel is not transformers based, thus it does not have `inputs_embeds` logics")
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def test_inputs_embeds(self):
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pass
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@unittest.skip("The DacModel is not transformers based, thus it does not have `inputs_embeds` logics")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip("The DacModel is not transformers based, thus it does not have the usual `attention` logic")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip("The DacModel is not transformers based, thus it does not have the usual `attention` logic")
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def test_torchscript_output_attentions(self):
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pass
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@unittest.skip("The DacModel is not transformers based, thus it does not have the usual `hidden_states` logic")
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def test_torchscript_output_hidden_state(self):
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pass
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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configs_no_init.return_dict = False
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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main_input_name = model_class.main_input_name
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try:
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main_input = inputs[main_input_name]
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model(main_input)
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traced_model = torch.jit.trace(model, main_input)
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_model, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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loaded_model_state_dict = loaded_model.state_dict()
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non_persistent_buffers = {}
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for key in loaded_model_state_dict:
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if key not in model_state_dict:
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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if layer_name in loaded_model_state_dict:
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p2 = loaded_model_state_dict[layer_name]
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
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# (Even with this call, there are still memory leak by ~0.04MB)
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self.clear_torch_jit_class_registry()
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@unittest.skip("The DacModel is not transformers based, thus it does not have the usual `attention` logic")
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def test_attention_outputs(self):
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pass
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@unittest.skip("The DacModel is not transformers based, thus it does not have the usual `hidden_states` logic")
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def test_hidden_states_output(self):
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pass
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def test_determinism(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_determinism(first, second):
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# outputs are not tensors but list (since each sequence don't have the same frame_length)
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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if isinstance(first, tuple) and isinstance(second, tuple):
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for tensor1, tensor2 in zip(first, second):
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check_determinism(tensor1, tensor2)
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else:
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check_determinism(first, second)
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def test_model_outputs_equivalence(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def set_nan_tensor_to_zero(t):
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t[t != t] = 0
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return t
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def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
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with torch.no_grad():
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tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
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dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
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def recursive_check(tuple_object, dict_object):
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if isinstance(tuple_object, (list, tuple)):
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif isinstance(tuple_object, dict):
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for tuple_iterable_value, dict_iterable_value in zip(
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tuple_object.values(), dict_object.values()
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):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif tuple_object is None:
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return
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else:
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self.assertTrue(
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torch.allclose(
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set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
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),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
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f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
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),
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)
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recursive_check(tuple_output, dict_output)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs)
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# Ignore copy
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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uniform_init_parms = ["conv", "in_proj", "out_proj", "codebook"]
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if param.requires_grad:
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if any(x in name for x in uniform_init_parms):
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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def test_identity_shortcut(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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config.use_conv_shortcut = False
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self.model_tester.create_and_check_model_forward(config, inputs_dict)
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def normalize(arr):
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norm = np.linalg.norm(arr)
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normalized_arr = arr / norm
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return normalized_arr
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def compute_rmse(arr1, arr2):
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arr1_np = arr1.cpu().numpy().squeeze()
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arr2_np = arr2.cpu().numpy().squeeze()
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max_length = min(arr1.shape[-1], arr2.shape[-1])
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arr1_np = arr1_np[..., :max_length]
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arr2_np = arr2_np[..., :max_length]
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arr1_normalized = normalize(arr1_np)
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arr2_normalized = normalize(arr2_np)
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return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean())
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"""
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Integration tests for DAC.
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Code for reproducing expected outputs can be found here:
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- test_integration: https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-test_dac-py
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- test_batch: https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-test_dac_batch-py
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See https://github.com/huggingface/transformers/pull/39313 for reason behind large tolerance between for encoder
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and decoder outputs (1e-3). In summary, original model uses weight normalization, while Transformers does not. This
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leads to accumulating error. However, this does not affect the quantizer codes, thanks to discretization being
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robust to precision errors. Moreover, codec error is similar between Transformers and original.
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Moreover, here is a script to debug outputs and weights layer-by-layer:
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https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-dac_layer_by_layer_debugging-py
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"""
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# fmt: off
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# -- test_integration
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EXPECTED_PREPROC_SHAPE = {
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"dac_16khz": torch.tensor([1, 1, 93760]),
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"dac_24khz": torch.tensor([1, 1, 140800]),
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"dac_44khz": torch.tensor([1, 1, 258560]),
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}
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EXPECTED_ENC_LOSS = {
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"dac_16khz": 24.889205932617188,
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"dac_24khz": 27.661380767822266,
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"dac_44khz": 23.87179183959961,
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}
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EXPECTED_QUANT_CODES = {
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"dac_16khz": torch.tensor([[[ 804, 25, 536, 52, 68, 867, 388, 653, 484, 706, 301,
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305, 752, 25, 40],
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[ 77, 955, 134, 601, 431, 375, 967, 56, 684, 261, 871,
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552, 232, 341, 228],
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[ 355, 701, 172, 927, 617, 765, 790, 149, 117, 707, 511,
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226, 254, 883, 644],
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[ 184, 85, 828, 54, 211, 1007, 906, 253, 406, 1007, 302,
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577, 644, 330, 601],
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[ 763, 865, 586, 321, 116, 357, 911, 865, 234, 234, 6,
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630, 6, 174, 895],
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[ 454, 241, 67, 622, 487, 426, 749, 833, 639, 900, 372,
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481, 622, 418, 964],
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[ 203, 609, 730, 307, 961, 609, 318, 1011, 747, 949, 343,
|
|
548, 657, 824, 21],
|
|
[ 82, 92, 692, 83, 131, 866, 483, 362, 596, 531, 853,
|
|
121, 404, 512, 373],
|
|
[1003, 260, 431, 460, 827, 927, 81, 76, 444, 298, 168,
|
|
673, 466, 613, 383],
|
|
[ 571, 203, 594, 394, 198, 560, 952, 437, 343, 992, 934,
|
|
316, 497, 123, 305],
|
|
[ 686, 715, 393, 635, 246, 716, 908, 384, 98, 873, 92,
|
|
878, 592, 496, 104],
|
|
[ 721, 502, 606, 204, 993, 428, 176, 395, 617, 323, 342,
|
|
530, 226, 8, 600]]]).to(torch_device),
|
|
"dac_24khz": torch.tensor([[[ 252, 851, 919, 204, 239, 360, 90, 103, 851, 876, 160,
|
|
160, 103, 234, 665],
|
|
[ 908, 658, 479, 556, 847, 265, 496, 32, 847, 773, 623,
|
|
375, 9, 497, 117],
|
|
[ 385, 278, 221, 778, 408, 330, 562, 215, 80, 84, 320,
|
|
728, 931, 470, 944],
|
|
[ 383, 134, 271, 494, 179, 304, 150, 804, 788, 780, 356,
|
|
416, 297, 903, 623],
|
|
[ 487, 263, 414, 947, 608, 810, 140, 74, 372, 129, 417,
|
|
592, 671, 479, 901],
|
|
[ 692, 953, 508, 359, 85, 396, 545, 375, 382, 382, 511,
|
|
382, 383, 643, 134],
|
|
[ 652, 213, 210, 385, 326, 899, 341, 925, 908, 68, 216,
|
|
21, 568, 1008, 635],
|
|
[ 938, 848, 570, 515, 574, 693, 382, 71, 42, 742, 603,
|
|
109, 193, 629, 79],
|
|
[ 847, 101, 874, 894, 384, 832, 378, 658, 1, 487, 976,
|
|
993, 932, 886, 860],
|
|
[ 220, 344, 307, 69, 705, 974, 895, 438, 8, 806, 573,
|
|
690, 543, 709, 303],
|
|
[ 394, 594, 144, 10, 832, 4, 588, 659, 501, 218, 351,
|
|
861, 915, 148, 141],
|
|
[ 447, 763, 930, 894, 196, 668, 528, 862, 70, 598, 136,
|
|
119, 395, 474, 1000],
|
|
[ 677, 178, 637, 874, 471, 113, 23, 534, 333, 6, 821,
|
|
777, 635, 932, 475],
|
|
[ 932, 345, 436, 335, 555, 355, 103, 436, 277, 816, 400,
|
|
356, 73, 23, 450],
|
|
[ 592, 402, 177, 31, 693, 459, 442, 193, 615, 940, 927,
|
|
917, 676, 327, 658],
|
|
[ 192, 458, 540, 808, 626, 340, 290, 700, 190, 345, 381,
|
|
137, 280, 611, 794],
|
|
[ 834, 5, 522, 685, 146, 754, 37, 580, 78, 2, 1008,
|
|
808, 281, 375, 366],
|
|
[ 892, 790, 948, 662, 355, 437, 444, 790, 450, 850, 316,
|
|
529, 385, 480, 178],
|
|
[ 36, 696, 125, 753, 143, 562, 368, 824, 491, 507, 892,
|
|
880, 355, 152, 253],
|
|
[ 934, 829, 457, 261, 668, 1014, 185, 464, 78, 332, 374,
|
|
869, 530, 67, 884],
|
|
[ 567, 914, 334, 38, 313, 744, 6, 210, 489, 867, 200,
|
|
799, 540, 318, 706],
|
|
[ 178, 882, 776, 992, 651, 800, 163, 470, 687, 906, 508,
|
|
260, 36, 783, 64],
|
|
[ 169, 66, 179, 711, 598, 938, 346, 251, 773, 108, 873,
|
|
813, 479, 425, 669],
|
|
[ 981, 692, 143, 589, 224, 282, 86, 712, 689, 907, 586,
|
|
595, 444, 265, 198],
|
|
[ 856, 540, 556, 302, 883, 96, 856, 560, 529, 91, 707,
|
|
286, 142, 553, 252],
|
|
[ 103, 868, 879, 779, 882, 34, 340, 603, 186, 808, 397,
|
|
673, 919, 989, 626],
|
|
[ 933, 215, 775, 747, 842, 836, 744, 272, 604, 202, 288,
|
|
164, 242, 542, 207],
|
|
[ 969, 373, 999, 524, 927, 879, 1017, 14, 526, 385, 478,
|
|
690, 347, 589, 10],
|
|
[ 716, 503, 781, 119, 176, 316, 212, 836, 850, 26, 685,
|
|
973, 606, 796, 593],
|
|
[ 164, 418, 929, 523, 571, 917, 364, 964, 480, 1021, 0,
|
|
994, 876, 887, 379],
|
|
[ 416, 957, 819, 478, 640, 479, 217, 842, 926, 771, 129,
|
|
537, 899, 680, 547],
|
|
[ 623, 596, 332, 517, 947, 376, 699, 918, 1012, 995, 858,
|
|
516, 56, 43, 268]]]).to(torch_device),
|
|
"dac_44khz": torch.tensor([[[ 698, 315, 105, 315, 330, 105, 105, 698, 315, 481, 330,
|
|
93, 629, 315, 105],
|
|
[ 30, 232, 249, 881, 962, 365, 56, 881, 186, 402, 311,
|
|
521, 558, 778, 254],
|
|
[1022, 22, 361, 491, 233, 419, 909, 456, 456, 471, 420,
|
|
569, 455, 491, 16],
|
|
[ 599, 143, 641, 352, 40, 556, 860, 780, 138, 137, 304,
|
|
563, 863, 174, 370],
|
|
[ 485, 350, 242, 555, 174, 581, 666, 744, 559, 810, 127,
|
|
558, 453, 90, 124],
|
|
[ 851, 423, 706, 178, 36, 564, 650, 539, 733, 720, 18,
|
|
265, 619, 545, 581],
|
|
[ 755, 891, 628, 674, 724, 764, 420, 51, 566, 315, 178,
|
|
881, 461, 111, 675],
|
|
[ 52, 995, 512, 139, 538, 666, 1017, 868, 619, 0, 449,
|
|
1005, 982, 106, 139],
|
|
[ 357, 180, 368, 892, 856, 567, 960, 148, 36, 708, 945,
|
|
285, 531, 331, 440]]]).to(torch_device),
|
|
}
|
|
EXPECTED_DEC_OUTPUTS = {
|
|
"dac_16khz": torch.tensor([[ 0.0002, 0.0007, 0.0012, 0.0015, 0.0017, 0.0011, 0.0004, -0.0002,
|
|
-0.0003, 0.0002, 0.0006, 0.0012, 0.0020, 0.0029, 0.0026, 0.0015,
|
|
0.0015, 0.0014, 0.0010, 0.0011, 0.0019, 0.0026, 0.0028, 0.0032,
|
|
0.0040, 0.0031, 0.0022, 0.0025, 0.0020, 0.0010, 0.0001, 0.0001,
|
|
0.0007, 0.0016, 0.0024, 0.0024, 0.0017, 0.0002, -0.0006, -0.0002,
|
|
0.0003, 0.0006, 0.0011, 0.0023, 0.0020, 0.0016, 0.0015, 0.0012,
|
|
0.0005, -0.0003]]).to(torch_device),
|
|
"dac_24khz": torch.tensor([[ 1.8275e-04, 1.8167e-04, -3.1626e-05, -6.4468e-05, 2.1254e-04,
|
|
8.4161e-04, 1.5839e-03, 1.6693e-03, 1.5439e-03, 1.3923e-03,
|
|
1.1167e-03, 6.2019e-04, -1.2014e-04, -5.7301e-04, -1.7829e-04,
|
|
6.0980e-04, 6.7130e-04, 1.6166e-04, -6.9366e-06, 3.1507e-04,
|
|
6.3976e-04, 7.1702e-04, 6.3391e-04, 5.7553e-04, 1.1151e-03,
|
|
1.9032e-03, 1.9737e-03, 1.2812e-03, 5.6187e-04, 3.9073e-04,
|
|
3.8875e-04, 3.0256e-04, 3.8140e-04, 7.6331e-04, 1.3098e-03,
|
|
1.7796e-03, 2.1707e-03, 2.5330e-03, 2.9214e-03, 3.0557e-03,
|
|
2.7402e-03, 2.2303e-03, 1.8196e-03, 1.6796e-03, 1.6199e-03,
|
|
1.0460e-03, 3.5502e-04, 2.8095e-04, 3.8291e-04, 2.2683e-04]]).to(torch_device),
|
|
"dac_44khz": torch.tensor([[ 1.3282e-03, 1.4784e-03, 1.6923e-03, 1.8359e-03, 1.8795e-03,
|
|
1.9519e-03, 1.9145e-03, 1.7839e-03, 1.5222e-03, 1.2423e-03,
|
|
9.9689e-04, 8.4000e-04, 7.6656e-04, 7.7500e-04, 7.7684e-04,
|
|
6.9986e-04, 5.3156e-04, 3.2828e-04, 1.7750e-04, 1.6440e-04,
|
|
2.9904e-04, 5.4582e-04, 8.2008e-04, 1.0400e-03, 1.1518e-03,
|
|
1.1718e-03, 1.1220e-03, 1.0717e-03, 1.0772e-03, 1.1534e-03,
|
|
1.3257e-03, 1.5572e-03, 1.7794e-03, 1.9112e-03, 1.9242e-03,
|
|
1.7837e-03, 1.5347e-03, 1.2386e-03, 9.3313e-04, 6.4671e-04,
|
|
3.5892e-04, 8.4733e-05, -1.6930e-04, -3.9932e-04, -5.8345e-04,
|
|
-6.9382e-04, -7.0792e-04, -5.6856e-04, -2.6751e-04, 1.5914e-04]]).to(torch_device),
|
|
}
|
|
EXPECTED_QUANT_CODEBOOK_LOSS = {
|
|
"dac_16khz": 20.62909698486328,
|
|
"dac_24khz": 22.47393798828125,
|
|
"dac_44khz": 16.229290008544922,
|
|
}
|
|
EXPECTED_CODEC_ERROR = {
|
|
"dac_16khz": 0.003831653157249093,
|
|
"dac_24khz": 0.0025609051808714867,
|
|
"dac_44khz": 0.0007433777209371328,
|
|
}
|
|
# -- test_batch
|
|
EXPECTED_PREPROC_SHAPE_BATCH = {
|
|
"dac_16khz": torch.tensor([2, 1, 113920]),
|
|
"dac_24khz": torch.tensor([2, 1, 170880]),
|
|
"dac_44khz": torch.tensor([2, 1, 313856]),
|
|
}
|
|
EXPECTED_ENC_LOSS_BATCH = {
|
|
"dac_16khz": 20.3460636138916,
|
|
"dac_24khz": 23.54486846923828,
|
|
"dac_44khz": 19.58145523071289,
|
|
}
|
|
EXPECTED_QUANT_CODES_BATCH = {
|
|
"dac_16khz": torch.tensor([[[ 490, 664, 726, 166, 55, 379, 367, 664, 661, 726, 592,
|
|
301, 130, 198, 129],
|
|
[1020, 734, 23, 53, 134, 648, 549, 589, 790, 1000, 420,
|
|
271, 1021, 740, 36],
|
|
[ 701, 344, 955, 19, 927, 212, 212, 667, 212, 627, 837,
|
|
954, 777, 706, 496],
|
|
[ 526, 805, 444, 474, 870, 920, 394, 823, 814, 1021, 319,
|
|
677, 251, 485, 1021],
|
|
[ 721, 134, 280, 439, 287, 77, 175, 902, 973, 412, 548,
|
|
953, 130, 75, 543],
|
|
[ 675, 316, 285, 341, 783, 850, 131, 487, 701, 150, 674,
|
|
730, 900, 481, 498],
|
|
[ 377, 37, 237, 489, 55, 246, 427, 456, 755, 1011, 171,
|
|
631, 695, 576, 804],
|
|
[ 601, 557, 681, 52, 10, 299, 284, 216, 869, 276, 907,
|
|
364, 955, 41, 497],
|
|
[ 465, 553, 697, 59, 701, 195, 335, 225, 896, 804, 240,
|
|
928, 392, 192, 332],
|
|
[ 807, 306, 977, 801, 77, 172, 760, 747, 445, 38, 395,
|
|
31, 924, 724, 835],
|
|
[ 903, 561, 205, 421, 231, 873, 931, 361, 679, 854, 248,
|
|
884, 1011, 857, 248],
|
|
[ 490, 993, 122, 787, 178, 307, 141, 468, 652, 786, 959,
|
|
885, 226, 343, 501]],
|
|
[[ 140, 320, 140, 489, 444, 320, 210, 73, 821, 1004, 388,
|
|
686, 405, 563, 517],
|
|
[ 725, 449, 715, 85, 761, 532, 620, 28, 620, 418, 146,
|
|
532, 418, 453, 565],
|
|
[ 695, 725, 994, 371, 829, 1008, 911, 927, 181, 707, 306,
|
|
337, 254, 577, 857],
|
|
[ 51, 648, 474, 129, 781, 968, 737, 718, 400, 839, 674,
|
|
689, 544, 767, 540],
|
|
[1007, 234, 865, 966, 734, 748, 68, 454, 473, 973, 414,
|
|
586, 618, 6, 612],
|
|
[ 410, 566, 692, 756, 307, 1008, 269, 743, 549, 320, 303,
|
|
729, 507, 741, 362],
|
|
[ 172, 102, 959, 714, 292, 173, 149, 308, 307, 527, 844,
|
|
102, 747, 76, 295],
|
|
[ 656, 144, 994, 245, 686, 925, 48, 356, 126, 418, 112,
|
|
674, 582, 916, 296],
|
|
[ 776, 971, 967, 781, 174, 688, 817, 278, 937, 467, 352,
|
|
463, 530, 804, 619],
|
|
[1009, 284, 966, 907, 397, 875, 279, 643, 878, 315, 734,
|
|
751, 337, 699, 382],
|
|
[ 389, 748, 50, 585, 69, 565, 555, 931, 154, 443, 16,
|
|
139, 905, 172, 361],
|
|
[ 884, 34, 945, 1013, 212, 493, 724, 775, 356, 199, 728,
|
|
552, 755, 223, 378]]]).to(torch_device),
|
|
"dac_24khz": torch.tensor([[[ 234, 322, 826, 360, 204, 208, 766, 826, 458, 322, 919,
|
|
999, 360, 772, 204],
|
|
[ 780, 201, 229, 497, 9, 663, 1002, 243, 556, 300, 781,
|
|
496, 77, 780, 781],
|
|
[ 714, 342, 401, 553, 728, 196, 181, 109, 949, 528, 39,
|
|
558, 180, 5, 197],
|
|
[ 112, 408, 186, 933, 543, 829, 724, 1001, 425, 39, 163,
|
|
517, 986, 348, 653],
|
|
[1001, 207, 671, 551, 742, 231, 870, 577, 353, 1016, 259,
|
|
282, 247, 126, 63],
|
|
[ 924, 59, 799, 739, 771, 568, 280, 673, 639, 1002, 35,
|
|
143, 270, 749, 571],
|
|
[ 310, 982, 904, 666, 819, 67, 161, 373, 945, 871, 597,
|
|
466, 388, 898, 584],
|
|
[ 69, 357, 188, 969, 213, 162, 376, 35, 638, 657, 731,
|
|
991, 625, 833, 801],
|
|
[ 333, 885, 343, 621, 752, 319, 292, 389, 947, 776, 78,
|
|
585, 193, 834, 622],
|
|
[ 958, 144, 680, 819, 303, 832, 56, 683, 366, 996, 609,
|
|
784, 305, 621, 36],
|
|
[ 561, 766, 69, 768, 219, 126, 945, 798, 568, 554, 115,
|
|
245, 31, 384, 167],
|
|
[ 727, 684, 371, 447, 50, 309, 407, 121, 839, 1019, 816,
|
|
423, 604, 489, 738],
|
|
[ 598, 490, 578, 353, 517, 283, 927, 432, 464, 608, 927,
|
|
32, 240, 852, 326],
|
|
[ 337, 226, 450, 862, 549, 799, 887, 925, 392, 841, 539,
|
|
633, 351, 7, 386],
|
|
[ 668, 497, 586, 937, 516, 898, 768, 1014, 420, 173, 116,
|
|
602, 786, 940, 56],
|
|
[ 575, 927, 322, 885, 367, 175, 691, 337, 21, 796, 317,
|
|
826, 109, 604, 54],
|
|
[ 50, 854, 118, 231, 567, 332, 827, 422, 339, 958, 529,
|
|
63, 992, 597, 428],
|
|
[ 480, 619, 605, 598, 912, 1012, 365, 926, 538, 915, 22,
|
|
675, 460, 667, 255],
|
|
[ 578, 373, 355, 92, 920, 454, 979, 536, 645, 442, 783,
|
|
956, 693, 457, 842],
|
|
[1019, 0, 998, 958, 159, 159, 332, 94, 886, 1, 455,
|
|
981, 418, 758, 358],
|
|
[ 698, 843, 1008, 626, 776, 342, 53, 518, 636, 997, 22,
|
|
36, 997, 12, 374],
|
|
[ 904, 408, 802, 456, 645, 899, 15, 447, 857, 265, 185,
|
|
983, 1018, 282, 607],
|
|
[ 459, 467, 461, 358, 389, 792, 385, 678, 50, 888, 63,
|
|
3, 792, 588, 972],
|
|
[ 877, 180, 212, 656, 60, 73, 261, 644, 755, 496, 137,
|
|
948, 879, 361, 863],
|
|
[ 172, 588, 948, 452, 297, 1009, 49, 426, 853, 843, 249,
|
|
957, 1008, 730, 860],
|
|
[ 677, 125, 519, 975, 686, 404, 321, 310, 38, 138, 424,
|
|
457, 98, 736, 1004],
|
|
[ 784, 262, 289, 299, 1022, 170, 865, 869, 951, 839, 100,
|
|
301, 828, 62, 511],
|
|
[ 726, 693, 235, 208, 668, 777, 284, 61, 376, 203, 784,
|
|
101, 344, 587, 736],
|
|
[ 851, 83, 484, 951, 839, 180, 801, 525, 890, 373, 206,
|
|
467, 524, 572, 614],
|
|
[ 48, 297, 674, 895, 740, 179, 782, 242, 721, 815, 85,
|
|
74, 179, 650, 554],
|
|
[ 336, 166, 203, 1021, 89, 991, 410, 518, 1019, 742, 235,
|
|
810, 782, 623, 176],
|
|
[ 110, 999, 360, 260, 278, 582, 921, 470, 242, 667, 21,
|
|
463, 335, 566, 897]],
|
|
[[ 851, 160, 851, 877, 665, 110, 581, 936, 826, 910, 110,
|
|
110, 160, 103, 160],
|
|
[ 325, 342, 722, 260, 549, 617, 508, 0, 221, 631, 846,
|
|
446, 457, 124, 23],
|
|
[ 529, 921, 767, 408, 628, 980, 80, 460, 255, 209, 768,
|
|
255, 773, 759, 861],
|
|
[ 344, 600, 255, 271, 402, 228, 805, 662, 497, 94, 852,
|
|
337, 812, 140, 760],
|
|
[ 415, 423, 322, 337, 599, 703, 520, 332, 377, 539, 511,
|
|
511, 124, 110, 638],
|
|
[ 514, 501, 660, 1014, 678, 77, 563, 793, 608, 464, 405,
|
|
24, 630, 176, 692],
|
|
[ 768, 497, 276, 353, 968, 214, 527, 447, 680, 746, 281,
|
|
972, 681, 708, 907],
|
|
[ 461, 802, 81, 411, 271, 186, 530, 670, 952, 1001, 828,
|
|
270, 568, 74, 606],
|
|
[ 539, 178, 451, 343, 235, 336, 346, 272, 992, 958, 924,
|
|
91, 606, 408, 104],
|
|
[ 668, 629, 817, 872, 526, 369, 889, 265, 297, 140, 229,
|
|
240, 360, 811, 189],
|
|
[ 973, 419, 164, 855, 767, 168, 378, 968, 698, 10, 610,
|
|
297, 236, 976, 668],
|
|
[ 162, 291, 66, 67, 749, 433, 428, 573, 421, 467, 202,
|
|
838, 125, 452, 873],
|
|
[ 5, 949, 393, 322, 563, 679, 306, 467, 779, 326, 624,
|
|
27, 447, 142, 965],
|
|
[ 981, 105, 116, 51, 674, 584, 351, 322, 81, 320, 476,
|
|
527, 668, 212, 944],
|
|
[ 813, 156, 1013, 675, 964, 788, 137, 475, 436, 109, 400,
|
|
899, 599, 820, 746],
|
|
[ 398, 21, 63, 720, 304, 1017, 1009, 889, 475, 619, 684,
|
|
571, 430, 642, 69],
|
|
[ 405, 140, 531, 526, 657, 991, 624, 1014, 818, 256, 300,
|
|
1013, 255, 567, 0],
|
|
[ 153, 469, 23, 553, 210, 812, 327, 527, 251, 406, 38,
|
|
893, 974, 777, 58],
|
|
[ 324, 399, 4, 563, 703, 499, 256, 136, 112, 164, 979,
|
|
524, 975, 596, 520],
|
|
[ 792, 511, 224, 225, 229, 424, 436, 124, 27, 267, 806,
|
|
8, 657, 914, 808],
|
|
[ 595, 491, 993, 961, 722, 756, 937, 723, 195, 991, 436,
|
|
392, 464, 837, 604],
|
|
[ 918, 647, 931, 658, 594, 677, 106, 194, 466, 92, 728,
|
|
575, 302, 864, 930],
|
|
[ 672, 685, 997, 36, 344, 956, 260, 781, 108, 348, 755,
|
|
142, 65, 754, 284],
|
|
[ 327, 987, 859, 525, 115, 551, 384, 202, 10, 669, 84,
|
|
481, 193, 392, 246],
|
|
[ 206, 432, 1018, 954, 534, 350, 902, 30, 428, 701, 913,
|
|
408, 456, 135, 726],
|
|
[ 483, 953, 684, 843, 478, 406, 931, 189, 426, 596, 459,
|
|
34, 306, 140, 22],
|
|
[ 508, 990, 988, 862, 265, 437, 277, 876, 874, 301, 759,
|
|
759, 989, 85, 292],
|
|
[ 586, 487, 860, 525, 90, 436, 15, 475, 625, 714, 697,
|
|
180, 453, 279, 524],
|
|
[ 639, 844, 513, 487, 853, 185, 690, 664, 688, 842, 439,
|
|
1002, 468, 745, 298],
|
|
[ 551, 764, 383, 422, 768, 760, 244, 332, 722, 567, 352,
|
|
654, 579, 1019, 787],
|
|
[ 207, 365, 766, 423, 792, 470, 582, 978, 692, 408, 573,
|
|
19, 314, 471, 587],
|
|
[ 776, 854, 529, 113, 927, 187, 362, 791, 131, 570, 559,
|
|
61, 763, 83, 1015]]]).to(torch_device),
|
|
"dac_44khz": torch.tensor([[[ 330, 315, 315, 619, 481, 315, 197, 315, 315, 105, 481,
|
|
315, 481, 481, 481],
|
|
[ 718, 1007, 929, 6, 906, 944, 402, 750, 675, 854, 336,
|
|
426, 609, 356, 329],
|
|
[ 417, 266, 697, 456, 300, 941, 325, 923, 1022, 605, 991,
|
|
7, 939, 329, 456],
|
|
[ 813, 811, 271, 148, 184, 838, 723, 497, 330, 922, 12,
|
|
333, 918, 963, 285],
|
|
[ 832, 307, 635, 794, 334, 114, 32, 505, 344, 170, 161,
|
|
907, 193, 180, 585],
|
|
[ 91, 941, 912, 1001, 507, 486, 362, 1006, 228, 640, 760,
|
|
215, 577, 633, 371],
|
|
[ 676, 27, 903, 472, 473, 219, 860, 477, 969, 385, 533,
|
|
911, 701, 241, 825],
|
|
[ 326, 399, 116, 443, 605, 373, 534, 199, 748, 538, 516,
|
|
983, 372, 565, 167],
|
|
[ 776, 843, 185, 326, 723, 756, 318, 34, 818, 674, 728,
|
|
554, 721, 369, 267]],
|
|
[[ 578, 698, 330, 330, 330, 578, 330, 801, 330, 330, 330,
|
|
330, 330, 330, 330],
|
|
[ 171, 503, 725, 215, 814, 861, 139, 684, 880, 905, 937,
|
|
418, 359, 190, 823],
|
|
[ 141, 482, 780, 489, 845, 499, 59, 480, 296, 30, 631,
|
|
540, 399, 23, 385],
|
|
[ 402, 837, 216, 116, 535, 456, 1006, 969, 994, 125, 1011,
|
|
285, 851, 832, 197],
|
|
[ 46, 950, 728, 645, 850, 839, 527, 850, 81, 205, 590,
|
|
166, 22, 148, 402],
|
|
[ 98, 758, 474, 941, 217, 667, 681, 109, 719, 824, 162,
|
|
160, 329, 627, 716],
|
|
[ 999, 228, 752, 639, 404, 333, 993, 177, 888, 158, 644,
|
|
221, 1011, 302, 79],
|
|
[ 669, 535, 164, 665, 809, 798, 448, 800, 123, 936, 639,
|
|
361, 353, 402, 160],
|
|
[ 345, 355, 940, 261, 71, 946, 750, 120, 565, 164, 813,
|
|
976, 946, 50, 516]]]).to(torch_device),
|
|
}
|
|
EXPECTED_DEC_OUTPUTS_BATCH = {
|
|
"dac_16khz": torch.tensor([[-1.9537e-04, 1.9159e-04, 3.1591e-04, 2.0804e-04, -3.1973e-05,
|
|
-3.3672e-04, -4.6511e-04, -4.3928e-04, -2.8604e-04, 2.7375e-04,
|
|
8.8118e-04, 1.1193e-03, 1.6241e-03, 1.9374e-03, 1.7826e-03,
|
|
5.9879e-04, -4.4053e-04, -1.3708e-03, -1.9989e-03, -2.0518e-03,
|
|
-1.5591e-03, -4.0491e-04, 6.3700e-04, 1.2456e-03, 1.3381e-03,
|
|
1.2848e-03, 6.0356e-04, 9.4392e-05, -6.1609e-04, -1.3806e-03,
|
|
-1.4977e-03, -9.7825e-04, -3.8692e-04, 5.3131e-04, 1.8666e-03,
|
|
2.3713e-03, 2.1134e-03, 1.4220e-03, 7.3615e-04, -2.5369e-04,
|
|
-9.8636e-04, -1.3868e-03, -1.6701e-03, -1.0521e-03, -6.2109e-04,
|
|
-5.3288e-04, -2.1532e-04, 4.1671e-04, 7.7438e-04, 8.0039e-04],
|
|
[ 6.5413e-05, 3.6614e-04, -1.4457e-03, -2.3634e-04, -3.6627e-04,
|
|
-1.3334e-03, 1.0519e-03, -1.4445e-03, 2.1915e-04, -3.3080e-04,
|
|
-1.3308e-03, 4.8407e-04, 8.6294e-04, -1.7639e-03, 4.2044e-05,
|
|
2.0936e-04, -2.9692e-03, 8.7512e-04, 1.3507e-03, 2.0057e-03,
|
|
-5.5121e-04, 1.3708e-03, -3.1085e-05, -2.6315e-03, -6.7661e-04,
|
|
6.2430e-04, 8.3580e-04, -1.5940e-03, 3.3061e-03, 1.3702e-03,
|
|
-1.7913e-03, -4.0576e-05, -5.5106e-04, -9.3050e-04, -2.3780e-03,
|
|
-5.3527e-04, 1.5840e-03, -1.4020e-03, 1.2090e-03, 6.0580e-04,
|
|
-1.8049e-03, 3.5135e-05, -3.0823e-03, 5.0042e-04, -1.1099e-03,
|
|
1.1512e-04, 3.3324e-03, -1.7616e-03, 5.2421e-04, -1.3589e-03]]).to(torch_device),
|
|
"dac_24khz": torch.tensor([[ 2.5545e-04, 8.9353e-05, -4.1158e-04, -6.1750e-04, -5.9480e-04,
|
|
-5.6071e-04, -5.2090e-04, -4.2821e-04, -1.4335e-04, -6.9339e-05,
|
|
-9.0480e-05, 6.5549e-05, 7.5300e-05, 1.9337e-07, 2.0931e-04,
|
|
4.1511e-04, 1.1008e-04, -1.6662e-04, 4.9021e-05, 4.0946e-04,
|
|
4.3870e-04, 3.9847e-04, 4.1346e-04, 2.3158e-04, 2.4527e-04,
|
|
4.4284e-04, 3.8170e-04, 1.2579e-04, -4.0307e-05, -2.8757e-04,
|
|
-8.5801e-04, -1.4023e-03, -1.5856e-03, -1.5326e-03, -1.5314e-03,
|
|
-1.4345e-03, -1.0435e-03, -5.2566e-04, 2.8071e-05, 5.4406e-04,
|
|
8.9030e-04, 1.0047e-03, 1.0342e-03, 9.4115e-04, 6.8876e-04,
|
|
3.2003e-04, -7.9418e-05, -4.0320e-04, -5.7941e-04, -7.3025e-04],
|
|
[-4.7845e-04, 3.8872e-04, 4.0155e-04, 3.6504e-04, 1.5022e-03,
|
|
1.2856e-03, -1.8015e-04, -7.2616e-05, 6.3906e-04, -1.1491e-03,
|
|
-2.7369e-03, -1.5336e-03, -8.2313e-04, -1.6791e-03, -9.4759e-06,
|
|
2.3807e-03, -2.2854e-04, -2.9693e-03, 2.9812e-04, 2.7258e-03,
|
|
-3.8019e-04, -2.2031e-03, -3.6195e-04, -6.6059e-04, -2.0270e-03,
|
|
-9.9469e-05, 5.4256e-04, -3.3896e-03, -3.9328e-03, 5.6228e-04,
|
|
1.1226e-03, -1.0931e-03, 1.0939e-03, 2.9646e-03, -4.1916e-04,
|
|
-1.8292e-03, 1.0766e-03, 2.3094e-04, -3.4554e-03, -2.0085e-03,
|
|
5.9608e-04, -1.3147e-03, -1.3603e-03, 1.8352e-03, 4.6342e-04,
|
|
-2.6805e-03, -1.3435e-05, 2.8397e-03, 1.0937e-04, -1.7540e-03]]).to(torch_device),
|
|
"dac_44khz": torch.tensor([[-4.8139e-04, -2.2367e-04, 3.1570e-06, 1.6349e-04, 2.6632e-04,
|
|
3.9803e-04, 5.3275e-04, 7.0730e-04, 8.0937e-04, 9.2120e-04,
|
|
1.0271e-03, 1.0728e-03, 1.0603e-03, 1.0328e-03, 9.8452e-04,
|
|
8.4670e-04, 6.5249e-04, 4.2936e-04, 1.9743e-04, -4.4033e-06,
|
|
-1.5679e-04, -2.3475e-04, -2.6826e-04, -2.6645e-04, -2.9844e-04,
|
|
-3.6448e-04, -4.6388e-04, -5.5712e-04, -6.4478e-04, -7.0090e-04,
|
|
-7.1978e-04, -6.8389e-04, -6.1487e-04, -4.9192e-04, -3.1528e-04,
|
|
-1.3920e-04, 1.6591e-05, 1.4938e-04, 2.6723e-04, 4.0855e-04,
|
|
6.0641e-04, 8.1632e-04, 9.6742e-04, 1.0481e-03, 1.0581e-03,
|
|
1.0213e-03, 9.3807e-04, 8.1994e-04, 6.9299e-04, 5.8774e-04],
|
|
[ 7.2770e-04, 8.2807e-04, 3.7124e-04, -4.1002e-04, -8.7899e-04,
|
|
-6.0642e-04, 2.0435e-04, 1.0668e-03, 1.3318e-03, 7.8307e-04,
|
|
-3.2117e-04, -1.3448e-03, -1.6520e-03, -1.0778e-03, 2.4146e-05,
|
|
9.8221e-04, 1.2399e-03, 7.6147e-04, -2.2230e-05, -4.7415e-04,
|
|
-1.4114e-04, 8.9560e-04, 1.9897e-03, 2.4969e-03, 2.0585e-03,
|
|
1.0263e-03, 1.5015e-04, 9.2623e-05, 7.8239e-04, 1.3270e-03,
|
|
7.3531e-04, -1.1100e-03, -3.1865e-03, -3.9610e-03, -2.6410e-03,
|
|
-6.5765e-06, 1.9960e-03, 1.7654e-03, -5.9006e-04, -3.2932e-03,
|
|
-4.2902e-03, -2.8423e-03, -6.7126e-05, 2.0438e-03, 2.2075e-03,
|
|
8.8849e-04, -3.6330e-04, -3.9405e-04, 6.1344e-04, 1.4316e-03]]).to(torch_device),
|
|
}
|
|
EXPECTED_QUANT_CODEBOOK_LOSS_BATCH = {
|
|
"dac_16khz": 20.685312271118164,
|
|
"dac_24khz": 23.66303253173828,
|
|
"dac_44khz": 16.129348754882812,
|
|
}
|
|
EXPECTED_CODEC_ERROR_BATCH = {
|
|
"dac_16khz": 0.0019726448226720095,
|
|
"dac_24khz": 0.0013017073506489396,
|
|
"dac_44khz": 0.0003825263702310622,
|
|
}
|
|
# fmt: on
|
|
|
|
|
|
@slow
|
|
@require_torch
|
|
class DacIntegrationTest(unittest.TestCase):
|
|
@parameterized.expand([(model_name,) for model_name in EXPECTED_PREPROC_SHAPE.keys()])
|
|
def test_integration(self, model_name):
|
|
# load model and processor
|
|
model_id = f"descript/{model_name}"
|
|
model = DacModel.from_pretrained(model_id, force_download=True).to(torch_device).eval()
|
|
processor = AutoProcessor.from_pretrained(model_id)
|
|
|
|
# load audio sample
|
|
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
|
|
audio_sample = librispeech_dummy[0]["audio"]["array"]
|
|
|
|
# check on processor audio shape
|
|
inputs = processor(
|
|
raw_audio=audio_sample,
|
|
sampling_rate=processor.sampling_rate,
|
|
return_tensors="pt",
|
|
).to(torch_device)
|
|
torch.equal(torch.tensor(inputs["input_values"].shape), EXPECTED_PREPROC_SHAPE[model_name])
|
|
|
|
with torch.no_grad():
|
|
# compare encoder loss
|
|
encoder_outputs = model.encode(inputs["input_values"])
|
|
torch.testing.assert_close(
|
|
EXPECTED_ENC_LOSS[model_name], encoder_outputs[0].squeeze().item(), rtol=1e-3, atol=1e-3
|
|
)
|
|
|
|
# compare quantizer outputs
|
|
quantizer_outputs = model.quantizer(encoder_outputs[1])
|
|
torch.testing.assert_close(
|
|
EXPECTED_QUANT_CODES[model_name],
|
|
quantizer_outputs[1][..., : EXPECTED_QUANT_CODES[model_name].shape[-1]],
|
|
rtol=1e-6,
|
|
atol=1e-6,
|
|
)
|
|
torch.testing.assert_close(
|
|
EXPECTED_QUANT_CODEBOOK_LOSS[model_name], quantizer_outputs[4].squeeze().item(), rtol=1e-6, atol=1e-6
|
|
)
|
|
|
|
# compare decoder outputs
|
|
decoded_outputs = model.decode(encoder_outputs[1])
|
|
torch.testing.assert_close(
|
|
EXPECTED_DEC_OUTPUTS[model_name],
|
|
decoded_outputs["audio_values"][..., : EXPECTED_DEC_OUTPUTS[model_name].shape[-1]],
|
|
rtol=1e-3,
|
|
atol=1e-3,
|
|
)
|
|
|
|
# compare codec error / lossiness
|
|
codec_err = compute_rmse(decoded_outputs["audio_values"], inputs["input_values"])
|
|
torch.testing.assert_close(EXPECTED_CODEC_ERROR[model_name], codec_err, rtol=1e-5, atol=1e-5)
|
|
|
|
# make sure forward and decode gives same result
|
|
enc_dec = model(inputs["input_values"])[1]
|
|
torch.testing.assert_close(decoded_outputs["audio_values"], enc_dec, rtol=1e-6, atol=1e-6)
|
|
|
|
@parameterized.expand([(model_name,) for model_name in EXPECTED_PREPROC_SHAPE_BATCH.keys()])
|
|
def test_integration_batch(self, model_name):
|
|
# load model and processor
|
|
model_id = f"descript/{model_name}"
|
|
model = DacModel.from_pretrained(model_id).to(torch_device)
|
|
processor = AutoProcessor.from_pretrained(model_id)
|
|
|
|
# load audio samples
|
|
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
|
|
audio_samples = [np.array([audio_sample["array"]])[0] for audio_sample in librispeech_dummy[-2:]["audio"]]
|
|
|
|
# check on processor audio shape
|
|
inputs = processor(
|
|
raw_audio=audio_samples,
|
|
sampling_rate=processor.sampling_rate,
|
|
truncation=False,
|
|
return_tensors="pt",
|
|
).to(torch_device)
|
|
torch.equal(torch.tensor(inputs["input_values"].shape), EXPECTED_PREPROC_SHAPE_BATCH[model_name])
|
|
|
|
with torch.no_grad():
|
|
# compare encoder loss
|
|
encoder_outputs = model.encode(inputs["input_values"])
|
|
torch.testing.assert_close(
|
|
EXPECTED_ENC_LOSS_BATCH[model_name], encoder_outputs[0].mean().item(), rtol=1e-3, atol=1e-3
|
|
)
|
|
|
|
# compare quantizer outputs
|
|
quantizer_outputs = model.quantizer(encoder_outputs[1])
|
|
torch.testing.assert_close(
|
|
EXPECTED_QUANT_CODES_BATCH[model_name],
|
|
quantizer_outputs[1][..., : EXPECTED_QUANT_CODES_BATCH[model_name].shape[-1]],
|
|
rtol=1e-6,
|
|
atol=1e-6,
|
|
)
|
|
torch.testing.assert_close(
|
|
EXPECTED_QUANT_CODEBOOK_LOSS_BATCH[model_name],
|
|
quantizer_outputs[4].mean().item(),
|
|
rtol=1e-6,
|
|
atol=1e-6,
|
|
)
|
|
|
|
# compare decoder outputs
|
|
decoded_outputs = model.decode(encoder_outputs[1])
|
|
torch.testing.assert_close(
|
|
EXPECTED_DEC_OUTPUTS_BATCH[model_name],
|
|
decoded_outputs["audio_values"][..., : EXPECTED_DEC_OUTPUTS_BATCH[model_name].shape[-1]],
|
|
rtol=1e-3,
|
|
atol=1e-3,
|
|
)
|
|
|
|
# compare codec error / lossiness
|
|
codec_err = compute_rmse(decoded_outputs["audio_values"], inputs["input_values"])
|
|
torch.testing.assert_close(EXPECTED_CODEC_ERROR_BATCH[model_name], codec_err, rtol=1e-6, atol=1e-6)
|
|
|
|
# make sure forward and decode gives same result
|
|
enc_dec = model(inputs["input_values"])[1]
|
|
torch.testing.assert_close(decoded_outputs["audio_values"], enc_dec, rtol=1e-6, atol=1e-6)
|