[WIP] add EnCodec model (#23655)

* boilerplate stuff

* messing around with the feature extractor

* fix feature extractor

* unit tests for feature extractor

* rename speech to audio

* quick-and-dirty import of Meta's code

* import weights (sort of)

* cleaning up

* more cleaning up

* move encoder/decoder args into config

* cleanup model

* rename EnCodec -> Encodec

* RVQ parameters in config

* add slow test

* add lstm init and test_init

* Add save & load

* finish EncodecModel

* remove decoder_input_values as they are ont used anywhere (not removed from doc yet)

* fix test feature extraction model name

* Add better slow test

* Fix tests

* some fixup and cleaning

* Improve further

* cleaning up quantizer

* fix up conversion script

* test don't pass, _encode_fram does not work

* update tests with output per encode and decode

* more cleanup

* rename _codebook

* remove old config cruft

* ratios & hop_length

* use ModuleList instead of Sequential

* clean up resnet block

* update types

* update tests

* fixup

* quick cleanup

* fix padding

* more styl,ing

* add patrick feedback

* fix copies

* fixup

* fix lstm

* fix shape issues

* fixup

* rename conv layers

* fixup

* fix decoding

* small conv refactoring

* remove norm_params

* simplify conv layers

* rename conv layers

* stuff

* Clean up

* Add padding logic

use padding mask

small conv refactoring

remove norm_params

simplify conv layers

rename conv layers

stuff

add batched test

update

Clean up

merge and update for padding

fix padding

fixup

* clean up more

* clean up more

* More clean ups

* cleanup convolutions

* typo

* fix typos

* fixup

* build PR doc?

* start refactoring docstring

* fix don't pad when no strid and chunk

* update docstring

* update docstring

* nits

* update going to lunch

* update config and model

* fix broken testse (becaue of the config changes)

* fix scale computation

* fixu[

* only return dict if speciefied or if config returns it

* remove todos

* update defaults in config

* update conversion script

* fix doctest

* more docstring + fixup

* nits on batched_tests

* more nits

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* update basxed on review

* fix update

* updaet tests

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fixup

* add overlap and chunl_length_s

* cleanup feature extraction

* teste edge cases truncation and padding

* correct processor values

* update config encodec, nits

* fix tests

* fixup

* fix 24Hz test

* elle tests are green

* fix fixup

* Apply suggestions from code review

* revert readme changes

* fixup

* add example

* use facebook checkpoints

* fix typo

* no pipeline tests

* use slef.pad everywhere we can

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* update based on review

* update

* update mdx

* fix bug and tests

* fixup

* fix doctest

* remove comment

* more nits

* add more coverage for `test_truncation_and_padding`

* fixup

* add last test

* fix text

* nits

* Update tests/models/encodec/test_modeling_encodec.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* take care of the last comments

* typo

* fix test

* nits

* fixup

* Update src/transformers/models/encodec/feature_extraction_encodec.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: arthur.zucker@gmail.com <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/utils/dummy_pt_objects.py

@@ -2655,6 +2655,23 @@ def load_tf_weights_in_electra(*args, **kwargs):
     requires_backends(load_tf_weights_in_electra, ["torch"])
 
 
+ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class EncodecModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class EncodecPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class EncoderDecoderModel(metaclass=DummyObject):
     _backends = ["torch"]
 

src/transformers/utils/dummy_speech_objects.py

@@ -9,6 +9,13 @@ class ASTFeatureExtractor(metaclass=DummyObject):
         requires_backends(self, ["speech"])
 
 
+class EncodecFeatureExtractor(metaclass=DummyObject):
+    _backends = ["speech"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["speech"])
+
+
 class MCTCTFeatureExtractor(metaclass=DummyObject):
     _backends = ["speech"]
 

tests/models/encodec/test_feature_extraction_encodec.py

+# coding=utf-8
+# Copyright 2021-2023 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for the EnCodec feature extractor."""
+
+import itertools
+import random
+import unittest
+
+import numpy as np
+
+from transformers import is_speech_available
+from transformers.testing_utils import require_torch
+from transformers.utils.import_utils import is_torch_available
+
+from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
+
+
+if is_speech_available():
+    from transformers import EncodecFeatureExtractor
+
+if is_torch_available():
+    import torch
+
+
+global_rng = random.Random()
+
+
+def floats_list(shape, scale=1.0, rng=None, name=None):
+    """Creates a random float32 tensor"""
+    if rng is None:
+        rng = global_rng
+
+    values = []
+    for batch_idx in range(shape[0]):
+        values.append([])
+        for _ in range(shape[1]):
+            values[-1].append(rng.random() * scale)
+
+    return values
+
+
+@require_torch
+class EnCodecFeatureExtractionTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size=1,
+        padding_value=0.0,
+        sampling_rate=24000,
+        return_attention_mask=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.feature_size = feature_size
+        self.padding_value = padding_value
+        self.sampling_rate = sampling_rate
+        self.return_attention_mask = return_attention_mask
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "padding_value": self.padding_value,
+            "sampling_rate": self.sampling_rate,
+            "return_attention_mask": self.return_attention_mask,
+        }
+
+    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
+        def _flatten(list_of_lists):
+            return list(itertools.chain(*list_of_lists))
+
+        if equal_length:
+            audio_inputs = floats_list((self.batch_size, self.max_seq_length))
+        else:
+            # make sure that inputs increase in size
+            audio_inputs = [
+                _flatten(floats_list((x, self.feature_size)))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+
+        if numpify:
+            audio_inputs = [np.asarray(x) for x in audio_inputs]
+
+        return audio_inputs
+
+
+@require_torch
+class EnCodecFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = EncodecFeatureExtractor if is_speech_available() else None
+
+    def setUp(self):
+        self.feat_extract_tester = EnCodecFeatureExtractionTester(self)
+
+    def test_call(self):
+        # Tests that all call wrap to encode_plus and batch_encode_plus
+        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        # create three inputs of length 800, 1000, and 1200
+        audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs]
+
+        # Test not batched input
+        encoded_sequences_1 = feat_extract(audio_inputs[0], return_tensors="np").input_values
+        encoded_sequences_2 = feat_extract(np_audio_inputs[0], return_tensors="np").input_values
+        self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
+
+        # Test batched
+        encoded_sequences_1 = feat_extract(audio_inputs, padding=True, return_tensors="np").input_values
+        encoded_sequences_2 = feat_extract(np_audio_inputs, padding=True, return_tensors="np").input_values
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+    def test_double_precision_pad(self):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_audio_inputs = np.random.rand(100).astype(np.float64)
+        py_audio_inputs = np_audio_inputs.tolist()
+
+        for inputs in [py_audio_inputs, np_audio_inputs]:
+            np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
+            self.assertTrue(np_processed.input_values.dtype == np.float32)
+            pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
+            self.assertTrue(pt_processed.input_values.dtype == torch.float32)
+
+    def _load_datasamples(self, num_samples):
+        from datasets import load_dataset
+
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        # automatic decoding with librispeech
+        audio_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
+
+        return [x["array"] for x in audio_samples]
+
+    def test_integration(self):
+        # fmt: off
+        EXPECTED_INPUT_VALUES = torch.tensor(
+            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
+             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
+             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
+             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
+             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
+             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]
+        )
+        # fmt: on
+        input_audio = self._load_datasamples(1)
+        feature_extractor = EncodecFeatureExtractor()
+        input_values = feature_extractor(input_audio, return_tensors="pt").input_values
+        self.assertEquals(input_values.shape, (1, 1, 93680))
+        self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-6))
+
+    def test_integration_stereo(self):
+        # fmt: off
+        EXPECTED_INPUT_VALUES = torch.tensor(
+            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
+             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
+             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
+             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
+             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
+             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]
+        )
+        # fmt: on
+        input_audio = self._load_datasamples(1)
+        input_audio = [np.tile(input_audio[0][None], reps=(2, 1))]
+        input_audio[0][1] *= 0.5
+        feature_extractor = EncodecFeatureExtractor(feature_size=2)
+        input_values = feature_extractor(input_audio, return_tensors="pt").input_values
+        self.assertEquals(input_values.shape, (1, 2, 93680))
+        self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-6))
+        self.assertTrue(torch.allclose(input_values[0, 1, :30], EXPECTED_INPUT_VALUES * 0.5, atol=1e-6))
+
+    def test_truncation_and_padding(self):
+        input_audio = self._load_datasamples(2)
+        # would be easier if the stride was like
+        feature_extractor = EncodecFeatureExtractor(feature_size=1, chunk_length_s=1, overlap=0.01)
+
+        # pad and trunc raise an error ?
+        with self.assertRaisesRegex(
+            ValueError,
+            "^Both padding and truncation were set. Make sure you only set one.$",
+        ):
+            truncated_outputs = feature_extractor(
+                input_audio, padding="max_length", truncation=True, return_tensors="pt"
+            ).input_values
+
+        # truncate to chunk
+        truncated_outputs = feature_extractor(input_audio, truncation=True, return_tensors="pt").input_values
+        self.assertEquals(truncated_outputs.shape, (2, 1, 71520))  # 2 chunks
+
+        # force truncate to max_length
+        truncated_outputs = feature_extractor(
+            input_audio, truncation=True, max_length=48000, return_tensors="pt"
+        ).input_values
+        self.assertEquals(truncated_outputs.shape, (2, 1, 48000))
+
+        # pad to chunk
+        padded_outputs = feature_extractor(input_audio, padding=True, return_tensors="pt").input_values
+        self.assertEquals(padded_outputs.shape, (2, 1, 95280))
+
+        # pad to chunk
+        truncated_outputs = feature_extractor(input_audio, return_tensors="pt").input_values
+        self.assertEquals(truncated_outputs.shape, (2, 1, 95280))
+
+        # force pad to max length
+        truncated_outputs = feature_extractor(
+            input_audio, padding="max_length", max_length=100000, return_tensors="pt"
+        ).input_values
+        self.assertEquals(truncated_outputs.shape, (2, 1, 100000))
+
+        # force no pad
+        with self.assertRaisesRegex(
+            ValueError,
+            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+        ):
+            truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
+
+        truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values
+        self.assertEquals(truncated_outputs.shape, (1, 1, 93680))
+
+        # no pad if no chunk_length_s
+        feature_extractor.chunk_length_s = None
+        with self.assertRaisesRegex(
+            ValueError,
+            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+        ):
+            truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
+
+        truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values
+        self.assertEquals(truncated_outputs.shape, (1, 1, 93680))
+
+        # no pad if no overlap
+        feature_extractor.chunk_length_s = 2
+        feature_extractor.overlap = None
+        with self.assertRaisesRegex(
+            ValueError,
+            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+        ):
+            truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
+
+        truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values
+        self.assertEquals(truncated_outputs.shape, (1, 1, 93680))

tests/models/encodec/test_modeling_encodec.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the PyTorch Encodec model. """
+
+import copy
+import inspect
+import os
+import tempfile
+import unittest
+from typing import Dict, List, Tuple
+
+import numpy as np
+from datasets import Audio, load_dataset
+
+from transformers import AutoProcessor, EncodecConfig
+from transformers.testing_utils import (
+    is_torch_available,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    _config_zero_init,
+    floats_tensor,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import EncodecModel
+
+
+def prepare_inputs_dict(
+    config,
+    input_ids=None,
+    input_values=None,
+    decoder_input_ids=None,
+    attention_mask=None,
+    decoder_attention_mask=None,
+    head_mask=None,
+    decoder_head_mask=None,
+    cross_attn_head_mask=None,
+):
+    if input_ids is not None:
+        encoder_dict = {"input_ids": input_ids}
+    else:
+        encoder_dict = {"input_values": input_values}
+
+    decoder_dict = {"decoder_input_ids": decoder_input_ids} if decoder_input_ids is not None else {}
+
+    return {**encoder_dict, **decoder_dict}
+
+
+@require_torch
+class EncodecModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        num_channels=2,
+        is_training=False,
+        num_hidden_layers=4,
+        intermediate_size=40,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+
+        self.num_hidden_layers = num_hidden_layers
+        self.intermediate_size = intermediate_size
+
+    def prepare_config_and_inputs(self):
+        input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0)
+        config = self.get_config()
+        inputs_dict = {"input_values": input_values}
+        return config, inputs_dict
+
+    def prepare_config_and_inputs_for_common(self):
+        config, inputs_dict = self.prepare_config_and_inputs()
+        return config, inputs_dict
+
+    def get_config(self):
+        return EncodecConfig(audio_channels=self.num_channels, chunk_in_sec=None)
+
+    def create_and_check_model_forward(self, config, inputs_dict):
+        model = EncodecModel(config=config).to(torch_device).eval()
+
+        input_values = inputs_dict["input_values"]
+        result = model(input_values)
+        self.parent.assertEqual(
+            result.audio_values.shape, (self.batch_size, self.num_channels, self.intermediate_size)
+        )
+
+
+@require_torch
+class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (EncodecModel,) if is_torch_available() else ()
+    is_encoder_decoder = True
+    test_pruning = False
+    test_headmasking = False
+    test_resize_embeddings = False
+    pipeline_model_mapping = {}
+    input_name = "input_values"
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        # model does not have attention and does not support returning hidden states
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+        if "output_attentions" in inputs_dict:
+            inputs_dict.pop("output_attentions")
+        if "output_hidden_states" in inputs_dict:
+            inputs_dict.pop("output_hidden_states")
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = EncodecModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=EncodecConfig, hidden_size=37, common_properties=[], has_text_modality=False
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model_forward(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model_forward(*config_and_inputs)
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["input_values", "padding_mask", "bandwidth"]
+            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have `inputs_embeds` logics")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have `inputs_embeds` logics")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have the usual `attention` logic")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have the usual `attention` logic")
+    def test_torchscript_output_attentions(self):
+        pass
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have the usual `hidden_states` logic")
+    def test_torchscript_output_hidden_state(self):
+        pass
+
+    def _create_and_check_torchscript(self, config, inputs_dict):
+        if not self.test_torchscript:
+            return
+
+        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
+        configs_no_init.torchscript = True
+        configs_no_init.return_dict = False
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            model.to(torch_device)
+            model.eval()
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+
+            main_input_name = model_class.main_input_name
+
+            try:
+                main_input = inputs[main_input_name]
+                model(main_input)
+                traced_model = torch.jit.trace(model, main_input)
+            except RuntimeError:
+                self.fail("Couldn't trace module.")
+
+            with tempfile.TemporaryDirectory() as tmp_dir_name:
+                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
+
+                try:
+                    torch.jit.save(traced_model, pt_file_name)
+                except Exception:
+                    self.fail("Couldn't save module.")
+
+                try:
+                    loaded_model = torch.jit.load(pt_file_name)
+                except Exception:
+                    self.fail("Couldn't load module.")
+
+            model.to(torch_device)
+            model.eval()
+
+            loaded_model.to(torch_device)
+            loaded_model.eval()
+
+            model_state_dict = model.state_dict()
+            loaded_model_state_dict = loaded_model.state_dict()
+
+            non_persistent_buffers = {}
+            for key in loaded_model_state_dict.keys():
+                if key not in model_state_dict.keys():
+                    non_persistent_buffers[key] = loaded_model_state_dict[key]
+
+            loaded_model_state_dict = {
+                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
+            }
+
+            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
+
+            model_buffers = list(model.buffers())
+            for non_persistent_buffer in non_persistent_buffers.values():
+                found_buffer = False
+                for i, model_buffer in enumerate(model_buffers):
+                    if torch.equal(non_persistent_buffer, model_buffer):
+                        found_buffer = True
+                        break
+
+                self.assertTrue(found_buffer)
+                model_buffers.pop(i)
+
+            models_equal = True
+            for layer_name, p1 in model_state_dict.items():
+                if layer_name in loaded_model_state_dict:
+                    p2 = loaded_model_state_dict[layer_name]
+                    if p1.data.ne(p2.data).sum() > 0:
+                        models_equal = False
+
+            self.assertTrue(models_equal)
+
+            # Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
+            # (Even with this call, there are still memory leak by ~0.04MB)
+            self.clear_torch_jit_class_registry()
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have the usual `attention` logic")
+    def test_attention_outputs(self):
+        pass
+
+    def test_feed_forward_chunking(self):
+        (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            torch.manual_seed(0)
+            config = copy.deepcopy(original_config)
+            config.chunk_length_s = None
+            config.overlap = None
+            config.sampling_rate = 10
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+            inputs["input_values"] = inputs["input_values"].repeat(1, 1, 10)
+
+            hidden_states_no_chunk = model(**inputs)[0]
+
+            torch.manual_seed(0)
+            config.chunk_length_s = 1
+            config.overlap = 0
+            config.sampling_rate = 10
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            hidden_states_with_chunk = model(**inputs)[0]
+            self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
+
+    @unittest.skip("The EncodecModel is not transformers based, thus it does not have the usual `hidden_states` logic")
+    def test_hidden_states_output(self):
+        pass
+
+    def test_determinism(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def check_determinism(first, second):
+            # outputs are not tensors but list (since each sequence don't have the same frame_length)
+            out_1 = first.cpu().numpy()
+            out_2 = second.cpu().numpy()
+            out_1 = out_1[~np.isnan(out_1)]
+            out_2 = out_2[~np.isnan(out_2)]
+            max_diff = np.amax(np.abs(out_1 - out_2))
+            self.assertLessEqual(max_diff, 1e-5)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
+                second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
+
+            if isinstance(first, tuple) and isinstance(second, tuple):
+                for tensor1, tensor2 in zip(first, second):
+                    check_determinism(tensor1, tensor2)
+            else:
+                check_determinism(first, second)
+
+    def test_model_outputs_equivalence(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def set_nan_tensor_to_zero(t):
+            t[t != t] = 0
+            return t
+
+        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
+            with torch.no_grad():
+                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
+                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs)
+
+                def recursive_check(tuple_object, dict_object):
+                    if isinstance(tuple_object, (List, Tuple)):
+                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
+                            recursive_check(tuple_iterable_value, dict_iterable_value)
+                    elif isinstance(tuple_object, Dict):
+                        for tuple_iterable_value, dict_iterable_value in zip(
+                            tuple_object.values(), dict_object.values()
+                        ):
+                            recursive_check(tuple_iterable_value, dict_iterable_value)
+                    elif tuple_object is None:
+                        return
+                    else:
+                        self.assertTrue(
+                            torch.allclose(
+                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
+                            ),
+                            msg=(
+                                "Tuple and dict output are not equal. Difference:"
+                                f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
+                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
+                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
+                            ),
+                        )
+
+                recursive_check(tuple_output, dict_output)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+    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"]
+                ignore_init = ["lstm"]
+                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",
+                        )
+                    elif not any([x in name for x in ignore_init]):
+                        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",
+                        )
+
+
+def normalize(arr):
+    norm = np.linalg.norm(arr)
+    normalized_arr = arr / norm
+    return normalized_arr
+
+
+def compute_rmse(arr1, arr2):
+    arr1_normalized = normalize(arr1)
+    arr2_normalized = normalize(arr2)
+    return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean())
+
+
+@slow
+@require_torch
+class EncodecIntegrationTest(unittest.TestCase):
+    def test_integration_24kHz(self):
+        expected_rmse = {
+            "1.5": 0.0025,
+            "24.0": 0.0015,
+        }
+        expected_codesums = {
+            "1.5": [367184],
+            "24.0": [6648961],
+        }
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        model_id = "facebook/encodec_24khz"
+
+        model = EncodecModel.from_pretrained(model_id).to(torch_device)
+        processor = AutoProcessor.from_pretrained(model_id)
+
+        librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
+        audio_sample = librispeech_dummy[-1]["audio"]["array"]
+
+        inputs = processor(
+            raw_audio=audio_sample,
+            sampling_rate=processor.sampling_rate,
+            return_tensors="pt",
+        ).to(torch_device)
+
+        for bandwidth, expected_rmse in expected_rmse.items():
+            with torch.no_grad():
+                # use max bandwith for best possible reconstruction
+                encoder_outputs = model.encode(inputs["input_values"], bandwidth=float(bandwidth))
+
+                audio_code_sums = [a[0].sum().cpu().item() for a in encoder_outputs[0]]
+
+                # make sure audio encoded codes are correct
+                self.assertListEqual(audio_code_sums, expected_codesums[bandwidth])
+
+                audio_codes, scales = encoder_outputs.to_tuple()
+                input_values_dec = model.decode(audio_codes, scales, inputs["padding_mask"])[0]
+                input_values_enc_dec = model(
+                    inputs["input_values"], inputs["padding_mask"], bandwidth=float(bandwidth)
+                )[-1]
+
+            # make sure forward and decode gives same result
+            self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, atol=1e-3))
+
+            # make sure shape matches
+            self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape)
+
+            arr = inputs["input_values"][0].cpu().numpy()
+            arr_enc_dec = input_values_enc_dec[0].cpu().numpy()
+
+            # make sure audios are more or less equal
+            # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0
+            rmse = compute_rmse(arr, arr_enc_dec)
+            self.assertTrue(rmse < expected_rmse)
+
+    def test_integration_48kHz(self):
+        expected_rmse = {
+            "3.0": 0.001,
+            "24.0": 0.0005,
+        }
+        expected_codesums = {
+            "3.0": [142174, 147901, 154090, 178965, 161879],
+            "24.0": [1561048, 1284593, 1278330, 1487220, 1659404],
+        }
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        model_id = "facebook/encodec_48khz"
+
+        model = EncodecModel.from_pretrained(model_id).to(torch_device)
+        model = model.eval()
+        processor = AutoProcessor.from_pretrained(model_id)
+
+        librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
+        audio_sample = librispeech_dummy[-1]["audio"]["array"]
+
+        # transform mono to stereo
+        audio_sample = np.array([audio_sample, audio_sample])
+
+        inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt").to(
+            torch_device
+        )
+
+        for bandwidth, expected_rmse in expected_rmse.items():
+            with torch.no_grad():
+                # use max bandwith for best possible reconstruction
+                encoder_outputs = model.encode(
+                    inputs["input_values"], inputs["padding_mask"], bandwidth=float(bandwidth), return_dict=False
+                )
+                audio_code_sums = [a[0].sum().cpu().item() for a in encoder_outputs[0]]
+
+                # make sure audio encoded codes are correct
+                self.assertListEqual(audio_code_sums, expected_codesums[bandwidth])
+                audio_codes, scales = encoder_outputs
+                input_values_dec = model.decode(audio_codes, scales, inputs["padding_mask"])[0]
+                input_values_enc_dec = model(
+                    inputs["input_values"], inputs["padding_mask"], bandwidth=float(bandwidth)
+                )[-1]
+
+            # make sure forward and decode gives same result
+            self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, atol=1e-3))
+
+            # make sure shape matches
+            self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape)
+
+            arr = inputs["input_values"][0].cpu().numpy()
+            arr_enc_dec = input_values_enc_dec[0].cpu().numpy()
+
+            # make sure audios are more or less equal
+            # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0
+            rmse = compute_rmse(arr, arr_enc_dec)
+            self.assertTrue(rmse < expected_rmse)
+
+    def test_batch_48kHz(self):
+        expected_rmse = {
+            "3.0": 0.001,
+            "24.0": 0.0005,
+        }
+        expected_codesums = {
+            "3.0": [
+                [71689, 78549, 75644, 88889, 73100, 82509, 71449, 82835],
+                [84427, 82356, 75809, 52509, 80137, 87672, 87436, 70456],
+            ],
+            "24.0": [
+                [71689, 78549, 75644, 88889, 73100, 82509, 71449, 82835],
+                [84427, 82356, 75809, 52509, 80137, 87672, 87436, 70456],
+            ],
+        }
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        model_id = "facebook/encodec_48khz"
+
+        model = EncodecModel.from_pretrained(model_id).to(torch_device)
+        processor = AutoProcessor.from_pretrained(model_id, chunk_length_s=1, overlap=0.01)
+
+        librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
+
+        audio_samples = [
+            np.array([audio_sample["array"], audio_sample["array"]])
+            for audio_sample in librispeech_dummy[-2:]["audio"]
+        ]
+
+        inputs = processor(raw_audio=audio_samples, sampling_rate=processor.sampling_rate, return_tensors="pt")
+        input_values = inputs["input_values"].to(torch_device)
+        for bandwidth, expected_rmse in expected_rmse.items():
+            with torch.no_grad():
+                # use max bandwith for best possible reconstruction
+                encoder_outputs = model.encode(input_values, bandwidth=float(bandwidth), return_dict=False)
+                audio_code_sums_0 = [a[0][0].sum().cpu().item() for a in encoder_outputs[0]]
+                audio_code_sums_1 = [a[0][1].sum().cpu().item() for a in encoder_outputs[0]]
+
+                # make sure audio encoded codes are correct
+                self.assertListEqual(audio_code_sums_0, expected_codesums[bandwidth][0])
+                self.assertListEqual(audio_code_sums_1, expected_codesums[bandwidth][1])
+
+                audio_codes, scales = encoder_outputs
+                input_values_dec = model.decode(audio_codes, scales)[0]
+                input_values_enc_dec = model(input_values, bandwidth=float(bandwidth))[-1]
+
+            # make sure forward and decode gives same result
+            self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, atol=1e-3))
+
+            # make sure shape matches
+            self.assertTrue(input_values.shape == input_values_enc_dec.shape)
+
+            arr = input_values[0].cpu().numpy()
+            arr_enc_dec = input_values_enc_dec[0].cpu().numpy()
+
+            # make sure audios are more or less equal
+            # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0
+            rmse = compute_rmse(arr, arr_enc_dec)
+            self.assertTrue(rmse < expected_rmse)

utils/check_config_attributes.py

@@ -31,6 +31,8 @@ transformers = direct_transformers_import(PATH_TO_TRANSFORMERS)
 CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING
 
 SPECIAL_CASES_TO_ALLOW = {
+    # used to compute the property `self.chunk_length`
+    "EncodecConfig": ["overlap"],
     # used as `self.bert_model = BertModel(config, ...)`
     "DPRConfig": True,
     # not used in modeling files, but it's an important information

utils/documentation_tests.txt

@@ -83,6 +83,7 @@ src/transformers/models/electra/configuration_electra.py
 src/transformers/models/electra/modeling_electra.py
 src/transformers/models/electra/modeling_tf_electra.py
 src/transformers/models/efficientformer/modeling_tf_efficientformer.py
+src/transformers/models/encodec/modeling_encodec.py
 src/transformers/models/ernie/configuration_ernie.py
 src/transformers/models/ernie_m/configuration_ernie_m.py
 src/transformers/models/ernie_m/modeling_ernie_m.py
@@ -395,6 +396,7 @@ src/transformers/models/deit/feature_extraction_deit.py
 src/transformers/models/detr/feature_extraction_detr.py
 src/transformers/models/donut/feature_extraction_donut.py
 src/transformers/models/dpt/feature_extraction_dpt.py
+src/transformers/models/encodec/feature_extraction_encodec.py
 src/transformers/models/flava/feature_extraction_flava.py
 src/transformers/models/glpn/feature_extraction_glpn.py
 src/transformers/models/imagegpt/feature_extraction_imagegpt.py