diff --git a/conftest.py b/conftest.py
index 0fab7ce3db27..4181220b44b0 100644
--- a/conftest.py
+++ b/conftest.py
@@ -89,7 +89,7 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "torch_compile_test: mark test which tests torch compile functionality")
     config.addinivalue_line("markers", "torch_export_test: mark test which tests torch export functionality")
 
-    os.environ['DISABLE_SAFETENSORS_CONVERSION'] = 'true'
+    os.environ["DISABLE_SAFETENSORS_CONVERSION"] = "true"
 
 
 def pytest_collection_modifyitems(items):
diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 27aeafab612f..c2864b2c83a5 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -973,6 +973,8 @@
         title: Whisper
       - local: model_doc/xcodec
         title: X-Codec
+      - local: model_doc/xcodec2
+        title: X-Codec2
       - local: model_doc/xls_r
         title: XLS-R
       - local: model_doc/xlsr_wav2vec2
diff --git a/docs/source/en/model_doc/xcodec2.md b/docs/source/en/model_doc/xcodec2.md
new file mode 100644
index 000000000000..d37a745c86b0
--- /dev/null
+++ b/docs/source/en/model_doc/xcodec2.md
@@ -0,0 +1,89 @@
+<!--Copyright 2025 The HuggingFace 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2025-02-06 and added to Hugging Face Transformers on 2025-04-29.*
+
+# X-Codec2
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The X-Codec2 model was proposed in [Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis](https://huggingface.co/papers/2502.04128).
+
+X-Codec2 is a neural audio codec designed to improve speech synthesis and general audio generation for large language model (LLM) pipelines. It extends the original X-Codec by refining how semantic and acoustic information is integrated and tokenized, enabling efficient and high-fidelity audio representation.
+
+Its architecture is based on [X-Codec](./xcodec) with several major differences:
+
+- **Unified Semantic-Acoustic Tokenization**: X-Codec2 fuses outputs from a semantic encoder (e.g., Wav2Vec2-BERT) and an acoustic encoder into a single embedding, capturing both high-level meaning (e.g., text content, emotion) and low-level audio details (e.g., timbre).
+- **Single-Stage Vector Quantization (VQ)**: Unlike the multi-layer residual VQ in most approaches (e.g., [X-Codec](./xcodec), [DAC](./dac), [EnCodec](./encodec)), X-Codec2 uses a single-layer Feature-Space Quantization (FSQ) for stability and compatibility with causal, autoregressive LLMs.
+- **Semantic Supervision During Training**: It adds a semantic reconstruction loss, ensuring that the discrete tokens preserve meaningful linguistic and emotional information — crucial for TTS tasks.
+- **Transformer-Friendly Design**: The 1D token structure of X-Codec2 naturally aligns with the autoregressive modeling in LLMs like LLaMA, improving training efficiency and downstream compatibility.
+
+## Usage example 
+
+Here is a quick example of how to encode and decode an audio using this model:
+
+```python 
+>>> import torch
+>>> from datasets import Audio, load_dataset
+>>> from transformers import AutoFeatureExtractor, Xcodec2Model
+
+>>> torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+>>> # load model and feature extractor
+>>> model_id = "hf-audio/xcodec2"
+>>> model = Xcodec2Model.from_pretrained(model_id).to(torch_device).eval()
+>>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+>>> # load data
+>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
+>>> audio = dataset[0]["audio"]["array"]
+
+>>> # prepare data
+>>> inputs = feature_extractor(audio=audio, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt").to(torch_device)
+
+>>> # encoder and decoder
+>>> audio_codes = model.encode(**inputs).audio_codes
+>>> audio_values = model.decode(audio_codes).audio_values
+>>> # or the equivalent with a forward pass
+>>> model_output = model(**inputs)
+>>> audio_codes = model_output.audio_codes
+>>> audio_values = model_output.audio_values
+```
+
+This model was contributed by [Steven Zheng](https://huggingface.co/Steveeeeeeen) and [Eric Bezzam](https://huggingface.co/bezzam).
+The original code can be found [here](https://github.com/zhenye234/X-Codec-2.0).
+
+
+## Xcodec2Config
+
+[[autodoc]] Xcodec2Config
+
+## Xcodec2FeatureExtractor
+
+[[autodoc]] Xcodec2FeatureExtractor
+    - __call__
+
+## Xcodec2Model
+
+[[autodoc]] Xcodec2Model
+    - decode
+    - encode
+    - forward
diff --git a/src/transformers/audio_utils.py b/src/transformers/audio_utils.py
index df4bbf1ca604..4c4baadcd781 100644
--- a/src/transformers/audio_utils.py
+++ b/src/transformers/audio_utils.py
@@ -33,12 +33,17 @@
     is_librosa_available,
     is_numpy_array,
     is_soundfile_available,
+    is_torch_available,
     is_torch_tensor,
     is_torchcodec_available,
     requires_backends,
 )
 
 
+if is_torch_available():
+    import torch
+    import torch.nn.functional as F
+
 if TYPE_CHECKING:
     import torch
 
@@ -1032,6 +1037,131 @@ def spectrogram_batch(
     return spectrogram_list
 
 
+def spectrogram_torch(
+    waveform_list: list["torch.Tensor"],
+    window: "torch.Tensor",
+    frame_length: int,
+    hop_length: int,
+    fft_length: Optional[int] = None,
+    power: float = 1.0,
+    center: bool = True,
+    pad_mode: str = "reflect",
+    onesided: bool = True,
+    dither: float = 0.0,
+    preemphasis: Optional[float] = None,
+    mel_filters: Optional["torch.Tensor"] = None,
+    mel_floor: float = 1e-10,
+    log_mel: Optional[str] = None,
+    reference: float = 1.0,
+    min_value: float = 1e-10,
+    db_range: Optional[float] = None,
+    remove_dc_offset: Optional[bool] = False,
+    device: str = "cpu",
+    dtype: str = "float32",
+):
+    """
+    PyTorch version of spectrogram_batch().
+
+    For spectrogram computation, tensors are promoted to `torch.float64` for better precision,
+    and returned according to `dtype`.
+    """
+
+    window_length = len(window)
+    if fft_length is None:
+        fft_length = frame_length
+    if frame_length > fft_length:
+        raise ValueError(f"frame_length ({frame_length}) may not be larger than fft_length ({fft_length})")
+    if window_length != frame_length:
+        raise ValueError(f"window_length ({window_length}) must equal frame_length ({frame_length})")
+    if hop_length <= 0:
+        raise ValueError("hop_length must be greater than zero")
+    if dtype not in ["float16", "float32", "float64"]:
+        raise ValueError(f"dtype must be one of 'float16', 'float32', 'float64', got {dtype}")
+    dtype = getattr(torch, dtype)
+
+    # Convert list of waveforms → padded tensor [B, T]
+    max_len = max(w.shape[-1] for w in waveform_list)
+    padded_waveforms = torch.stack([F.pad(w, (0, max_len - w.shape[-1]), value=0.0) for w in waveform_list]).to(
+        device=device, dtype=torch.float64
+    )
+
+    # Optional centering (reflect pad)
+    if center:
+        pad_amt = frame_length // 2
+        padded_waveforms = F.pad(padded_waveforms, (pad_amt, pad_amt), mode=pad_mode)
+
+    B, T = padded_waveforms.shape
+    num_frames = 1 + (T - frame_length) // hop_length
+    fft_func = torch.fft.rfft if onesided else torch.fft.fft
+    num_bins = (fft_length // 2 + 1) if onesided else fft_length
+
+    # Promote to float64 for better precision
+    window = window.to(device=device, dtype=torch.float64)
+    mel_filters = mel_filters.to(device=device, dtype=torch.float64) if mel_filters is not None else None
+
+    # Create output buffer
+    spectrogram = torch.empty((B, num_frames, num_bins), dtype=torch.complex128, device=device)
+    buffer = torch.zeros((B, fft_length), dtype=torch.float64, device=device)
+
+    for frame_idx in range(num_frames):
+        t0 = frame_idx * hop_length
+        buffer[:, :frame_length] = padded_waveforms[:, t0 : t0 + frame_length]
+
+        # Dither
+        if dither != 0.0:
+            buffer[:, :frame_length] += dither * torch.randn_like(buffer[:, :frame_length])
+
+        # DC offset removal
+        if remove_dc_offset:
+            buffer[:, :frame_length] -= buffer[:, :frame_length].mean(dim=1, keepdim=True)
+
+        # Preemphasis
+        if preemphasis is not None:
+            buffer[:, 1:frame_length] -= preemphasis * buffer[:, : frame_length - 1]
+            buffer[:, 0] *= 1 - preemphasis
+
+        # Apply window
+        buffer[:, :frame_length] *= window
+
+        # FFT
+        spectrogram[:, frame_idx] = fft_func(buffer, n=fft_length)
+
+    # Magnitude / power
+    if power is not None:
+        spectrogram = torch.abs(spectrogram).pow(power)
+
+    # Mel projection
+    if mel_filters is not None:
+        # spectrogram: [batch, num_frames, num_bins], mel_filters: [num_bins, num_mels]
+        if mel_filters.shape[0] != spectrogram.shape[-1]:
+            raise ValueError(
+                f"Mel filter input bins ({mel_filters.shape[0]}) must match spectrogram frequency bins ({spectrogram.shape[-1]}). "
+                f"Please check that mel_filters were designed for fft_length={spectrogram.shape[-1] * 2 - 2 if spectrogram.shape[-1] > 1 else spectrogram.shape[-1]}."
+            )
+        spectrogram = torch.matmul(spectrogram, mel_filters)
+        spectrogram = torch.maximum(spectrogram, torch.tensor(mel_floor, device=device))
+
+    # Log scaling
+    if power is not None and log_mel is not None:
+        if log_mel == "log":
+            spectrogram = torch.log(torch.clamp(spectrogram, min=min_value))
+        elif log_mel == "log10":
+            spectrogram = torch.log10(torch.clamp(spectrogram, min=min_value))
+        elif log_mel == "dB":
+            ref = torch.tensor(reference, device=device)
+            spectrogram = 10.0 * torch.log10(torch.clamp(spectrogram / ref, min=min_value))
+            if db_range is not None:
+                max_val = spectrogram.amax(dim=-1, keepdim=True)
+                spectrogram = torch.maximum(spectrogram, max_val - db_range)
+        else:
+            raise ValueError(f"Unknown log_mel option: {log_mel}")
+
+    # Return list of [num_bins, num_frames_i]
+    true_frames = [1 + (w.shape[-1] - frame_length) // hop_length for w in waveform_list]
+    spec_list = [spectrogram[i, :n, :].T.to(dtype) for i, n in enumerate(true_frames)]
+    return spec_list
+
+
 def power_to_db(
     spectrogram: np.ndarray,
     reference: float = 1.0,
diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py
index c721f24a506d..14353bcad0a2 100644
--- a/src/transformers/models/__init__.py
+++ b/src/transformers/models/__init__.py
@@ -377,6 +377,7 @@
     from .whisper import *
     from .x_clip import *
     from .xcodec import *
+    from .xcodec2 import *
     from .xglm import *
     from .xlm import *
     from .xlm_roberta import *
diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py
index c641645d5656..9fad70fa9669 100644
--- a/src/transformers/models/auto/configuration_auto.py
+++ b/src/transformers/models/auto/configuration_auto.py
@@ -444,6 +444,7 @@
         ("whisper", "WhisperConfig"),
         ("xclip", "XCLIPConfig"),
         ("xcodec", "XcodecConfig"),
+        ("xcodec2", "Xcodec2Config"),
         ("xglm", "XGLMConfig"),
         ("xlm", "XLMConfig"),
         ("xlm-prophetnet", "XLMProphetNetConfig"),
@@ -904,6 +905,7 @@
         ("whisper", "Whisper"),
         ("xclip", "X-CLIP"),
         ("xcodec", "X-CODEC"),
+        ("xcodec2", "X-CODEC2"),
         ("xglm", "XGLM"),
         ("xlm", "XLM"),
         ("xlm-prophetnet", "XLM-ProphetNet"),
diff --git a/src/transformers/models/auto/feature_extraction_auto.py b/src/transformers/models/auto/feature_extraction_auto.py
index 0d3dab2e8fd8..a4e811568fb1 100644
--- a/src/transformers/models/auto/feature_extraction_auto.py
+++ b/src/transformers/models/auto/feature_extraction_auto.py
@@ -73,6 +73,7 @@
         ("wavlm", "Wav2Vec2FeatureExtractor"),
         ("whisper", "WhisperFeatureExtractor"),
         ("xcodec", "DacFeatureExtractor"),
+        ("xcodec2", "Xcodec2FeatureExtractor"),
     ]
 )
 
diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py
index 298834bebe93..7d992c6ce0cd 100644
--- a/src/transformers/models/auto/modeling_auto.py
+++ b/src/transformers/models/auto/modeling_auto.py
@@ -423,6 +423,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("whisper", "WhisperModel"),
         ("xclip", "XCLIPModel"),
         ("xcodec", "XcodecModel"),
+        ("xcodec2", "Xcodec2Model"),
         ("xglm", "XGLMModel"),
         ("xlm", "XLMModel"),
         ("xlm-prophetnet", "XLMProphetNetModel"),
diff --git a/src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py b/src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py
index d0a8a07b3a9e..eecd5675a158 100644
--- a/src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py
+++ b/src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py
@@ -117,8 +117,7 @@ def _extract_fbank_features(
         waveform: np.ndarray,
     ) -> np.ndarray:
         """
-        Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs
-        and hence the waveform should not be normalized before feature extraction.
+        Get mel-filter bank features using Numpy method to mimic Kaldi.
         """
         # by default, it extracts the left channel if stereo
         if len(waveform.shape) == 2:
diff --git a/src/transformers/models/xcodec/modeling_xcodec.py b/src/transformers/models/xcodec/modeling_xcodec.py
index 4e1d376a3d08..54e4480266fc 100644
--- a/src/transformers/models/xcodec/modeling_xcodec.py
+++ b/src/transformers/models/xcodec/modeling_xcodec.py
@@ -558,7 +558,7 @@ def forward(
 
         >>> inputs = feature_extractor(raw_audio=audio_sample, return_tensors="pt")
 
-        >>> outputs = model(**inputs)
+        >>> outputs = model(inputs["input_values"])
         >>> audio_codes = outputs.audio_codes
         >>> audio_values = outputs.audio_values
         ```
diff --git a/src/transformers/models/xcodec2/__init__.py b/src/transformers/models/xcodec2/__init__.py
new file mode 100644
index 000000000000..0e099a8b1449
--- /dev/null
+++ b/src/transformers/models/xcodec2/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2025 The HuggingFace 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.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_xcodec2 import *
+    from .feature_extraction_xcodec2 import *
+    from .modeling_xcodec2 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/xcodec2/configuration_xcodec2.py b/src/transformers/models/xcodec2/configuration_xcodec2.py
new file mode 100644
index 000000000000..3b2aad43aa69
--- /dev/null
+++ b/src/transformers/models/xcodec2/configuration_xcodec2.py
@@ -0,0 +1,200 @@
+# coding=utf-8
+# Copyright 2025 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.
+"""Xcodec2 model configuration"""
+
+import math
+
+import numpy as np
+
+from transformers import AutoConfig, Wav2Vec2BertConfig
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Xcodec2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of an [`Xcodec2Model`]. It is used to instantiate a
+    Xcodec2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the
+    [HKUSTAudio/xcodec2](https://huggingface.co/HKUSTAudio/xcodec2) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        encoder_hidden_size (`int`, *optional*, defaults to 1024):
+            Hidden size for the audio encoder model.
+        downsampling_ratios (`list[int]`, *optional*, defaults to `[2, 2, 4, 4, 5]`):
+            Ratios for downsampling in the encoder.
+        decoder_hidden_size (`int`, *optional*, defaults to 1024):
+            Hidden size for the audio decoder model.
+        semantic_model_config (`Union[Dict, Wav2Vec2BertConfig]`, *optional*):
+            An instance of the configuration object for the semantic (Wav2Vec2BertConfig) model.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        sampling_rate (`int`, *optional*, defaults to 16000):
+            The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for the model.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            Number of key value heads for the model.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer decoder.
+        resnet_dropout (`float`, *optional*, defaults to 0.1):
+            Dropout rate for the ResNet blocks in the decoder.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            Dropout rate for the attention layer.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the attention layer.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            Epsilon for RMS normalization.
+        head_dim (`int`, *optional*, defaults to 64):
+            Head dimension for the model.
+        vq_dim (`int`, *optional*, defaults to 2048):
+            Dimension for the VQ codebook.
+        vq_levels (`list[int]`, *optional*, defaults to `[4, 4, 4, 4, 4, 4, 4, 4]`):
+            Levels for the VQ codebook.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the rotary position embeddings.
+        istft_padding (`str`, *optional*, defaults to `"same"`):
+            Padding type for the iSTFT in the decoder. Should be one of `"center"` or `"same"`.
+    """
+
+    model_type = "xcodec2"
+
+    sub_configs = {
+        "semantic_model_config": Wav2Vec2BertConfig,
+    }
+
+    def __init__(
+        self,
+        encoder_hidden_size=1024,
+        downsampling_ratios=[2, 2, 4, 4, 5],
+        decoder_hidden_size=1024,
+        semantic_model_config=None,
+        initializer_range=0.02,
+        sampling_rate=16000,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        num_hidden_layers=12,
+        resnet_dropout=0.1,
+        attention_dropout=0.0,
+        attention_bias=False,
+        hidden_act="silu",
+        rms_norm_eps=1e-6,
+        head_dim=64,
+        vq_dim=2048,
+        vq_levels=[4, 4, 4, 4, 4, 4, 4, 4],
+        max_position_embeddings=4096,
+        rope_theta=10000.0,
+        istft_padding="same",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.encoder_hidden_size = encoder_hidden_size
+        self.downsampling_ratios = downsampling_ratios
+
+        self.semantic_model_id = "facebook/w2v-bert-2.0"  # needed for feature extractor
+        if semantic_model_config is None:
+            self.semantic_model_config = Wav2Vec2BertConfig()
+        elif isinstance(semantic_model_config, dict):
+            if "_name_or_path" in semantic_model_config:
+                # If the config is a path, load it using AutoConfig
+                self.semantic_model_config = AutoConfig.from_pretrained(semantic_model_config["_name_or_path"])
+                self.semantic_model_id = semantic_model_config["_name_or_path"]
+            else:
+                # assume HubertConfig as probably created from scratch
+                logger.warning(
+                    "Could not determine semantic model type from config architecture. Defaulting to `Wav2Vec2BertConfig`."
+                )
+                self.semantic_model_config = Wav2Vec2BertConfig(**semantic_model_config)
+        elif isinstance(semantic_model_config, Wav2Vec2BertConfig):
+            self.semantic_model_config = semantic_model_config
+        else:
+            raise ValueError(
+                f"semantic_model_config must be a dict or Wav2Vec2BertConfig instance, but got {type(semantic_model_config)}"
+            )
+
+        self.initializer_range = initializer_range
+        self.sampling_rate = sampling_rate
+
+        # decoder parameters, which has hybrid ResNet-Transformer architecture
+        self.decoder_hidden_size = decoder_hidden_size
+        self.head_dim = head_dim
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.resnet_dropout = resnet_dropout
+        self.attention_dropout = attention_dropout
+        self.attention_bias = attention_bias
+        self.hidden_act = hidden_act
+        self.rms_norm_eps = rms_norm_eps
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        # -- Vocos vocoder parameters
+        self.istft_padding = istft_padding
+
+        # single codebook VQ is main feature of Xcodec2
+        self.num_quantizers = 1
+        self.vq_dim = vq_dim
+        self.vq_levels = vq_levels
+
+    @property
+    def frame_rate(self) -> int:
+        return math.ceil(self.sampling_rate / self.hop_length)
+
+    @property
+    def semantic_hidden_size(self) -> int:
+        return self.semantic_model_config.hidden_size
+
+    @property
+    def intermediate_size(self) -> int:
+        # Semantic and acoustic features are combined for a "fused feature embedding"
+        # See Encoder section on p. 3 of https://arxiv.org/pdf/2502.04128
+        return self.encoder_hidden_size + self.semantic_hidden_size
+
+    @property
+    def hop_length(self) -> int:
+        return int(np.prod(self.downsampling_ratios))
+
+    @property
+    def n_fft(self) -> int:
+        # For ISTFT in Vocos decoder
+        return self.hop_length * 4
+
+    @property
+    def hidden_size(self) -> int:
+        # For Transformer used in decoder
+        # See Decoder > Acoustic Reconstruction on p. 3 of https://arxiv.org/pdf/2502.04128
+        return self.decoder_hidden_size
+
+    @property
+    def codebook_size(self) -> int:
+        return int(np.prod(self.vq_levels))
+
+    @property
+    def codebook_dim(self) -> int:
+        return len(self.vq_levels)
+
+
+__all__ = ["Xcodec2Config"]
diff --git a/src/transformers/models/xcodec2/convert_xcodec2_checkpoint_to_pytorch.py b/src/transformers/models/xcodec2/convert_xcodec2_checkpoint_to_pytorch.py
new file mode 100644
index 000000000000..b01bd7826be6
--- /dev/null
+++ b/src/transformers/models/xcodec2/convert_xcodec2_checkpoint_to_pytorch.py
@@ -0,0 +1,316 @@
+# coding=utf-8
+# Copyright 2025 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.
+"""Convert Xcodec2 checkpoints."""
+
+import argparse
+import json
+import re
+
+import safetensors
+import torch
+
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    Xcodec2Config,
+    Xcodec2FeatureExtractor,
+    Xcodec2Model,
+    logging,
+)
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers.models.xcodec2")
+
+
+def assert_param_count(model_1, model_2):
+    count_1 = sum(p[1].numel() for p in model_1.named_parameters() if "final_proj" not in p[0])
+    count_2 = sum(p[1].numel() for p in model_2.named_parameters() if "final_proj" not in p[0])
+    assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}"
+
+
+def param_count(model):
+    return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0])
+
+
+def permute_for_rope(input_tensor, n_heads, dim1, dim2):
+    """
+    When you go from the complex ROPE formulation to sin and cos one, you need
+    to permute the query and key weights (to avoid doing it on the fly)
+    """
+    input_tensor = input_tensor.reshape(dim1, dim2)
+    input_tensor = input_tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2)
+    input_tensor = input_tensor.transpose(1, 2).reshape(dim1, dim2)
+    return input_tensor
+
+
+def _convert_model(state_dict, hf_model):
+    tensors = {}
+
+    for old_k in state_dict.keys():
+        # update to new names used in `Xcodec2Model` in modeling_xcodec2.py
+        # old names can be found here: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py#L12
+        if "CodecEnc" in old_k:
+            k = old_k.replace("CodecEnc", "acoustic_encoder")
+
+            # Handle initial convolutional layer (conv_blocks.0 -> initial_conv)
+            if "conv_blocks.0" in k:
+                k = k.replace("conv_blocks.0", "initial_conv")
+
+            # Handle final layers (conv_final_block.0 -> final_activation, conv_final_block.1 -> final_conv)
+            elif "conv_final_block.0" in k:
+                k = k.replace("conv_final_block.0", "final_activation")
+            elif "conv_final_block.1" in k:
+                k = k.replace("conv_final_block.1", "final_conv")
+
+            # Handle encoder blocks (conv_blocks.1, conv_blocks.2, etc. -> encoder_blocks.0, encoder_blocks.1, etc.)
+            elif "conv_blocks." in k:
+                # Extract the block index (subtracting 1 because initial_conv replaced conv_blocks.0)
+                conv_block_pattern = r"acoustic_encoder\.conv_blocks\.(\d+)(.*)"
+                match = re.match(conv_block_pattern, k)
+                if match:
+                    block_idx = int(match.group(1))
+                    suffix = match.group(2)
+                    # Adjust index (subtract 1 because we moved conv_blocks.0 to initial_conv)
+                    k = f"acoustic_encoder.encoder_blocks.{block_idx - 1}{suffix}"
+
+            # Handle the ResidualUnit structure changes (nested blocks)
+            # We're looking for patterns like "block.X.block.Y" where Y is 0-3
+            nested_block_pattern = r"(.*\.block\.\d+)\.block\.(\d+)(.*)"
+            match = re.match(nested_block_pattern, k)
+            if match:
+                prefix = match.group(1)  # Everything before .block.Y
+                subblock_idx = int(match.group(2))  # Y value (0-3)
+                suffix = match.group(3)  # Everything after .block.Y
+
+                # Map to the corresponding component in ResidualUnit
+                if subblock_idx == 0:
+                    k = f"{prefix}.activation1{suffix}"
+                elif subblock_idx == 1:
+                    k = f"{prefix}.conv1{suffix}"
+                elif subblock_idx == 2:
+                    k = f"{prefix}.activation2{suffix}"
+                elif subblock_idx == 3:
+                    k = f"{prefix}.conv2{suffix}"
+
+            # Handle the EncoderBlock structure changes
+            # We're looking for patterns like "encoder_blocks.X.block.Y" where Y is 0-2 (residual units), 3 (activation), or 4 (conv)
+            encoder_block_pattern = r"(acoustic_encoder\.encoder_blocks\.\d+)\.block\.(\d+)(.*)"
+            match = re.match(encoder_block_pattern, k)
+            if match:
+                prefix = match.group(1)  # Everything before .block.Y (conv_blocks.X)
+                block_idx = int(match.group(2))  # Y value (0-4)
+                suffix = match.group(3)  # Everything after .block.Y
+
+                # Map to the corresponding component in EncoderBlock
+                if block_idx < 3:  # First 3 are residual units (typically 0, 1, 2)
+                    k = f"{prefix}.residual_units.{block_idx}{suffix}"
+                elif block_idx == 3:  # This is the activation
+                    k = f"{prefix}.activation{suffix}"
+                elif block_idx == 4:  # This is the final conv
+                    k = f"{prefix}.conv{suffix}"
+        elif "generator" in old_k:
+            k = old_k.replace("generator", "decoder")
+
+            # Handle prior_net -> prior_blocks conversion
+            if "backbone.prior_net.0." in k:
+                k = k.replace("backbone.prior_net.0.", "backbone.prior_blocks.0.")
+            elif "backbone.prior_net.1." in k:
+                k = k.replace("backbone.prior_net.1.", "backbone.prior_blocks.1.")
+
+            # Handle post_net -> post_blocks conversion
+            elif "backbone.post_net.0." in k:
+                k = k.replace("backbone.post_net.0.", "backbone.post_blocks.0.")
+            elif "backbone.post_net.1." in k:
+                k = k.replace("backbone.post_net.1.", "backbone.post_blocks.1.")
+
+            # Handle special cases for decoder.backbone.transformers
+            elif "backbone.transformers" in k:
+                if "c_attn" in k:
+                    # split into 3 tensors
+                    c_attn_weight = state_dict[old_k]
+                    W_q, W_k, W_v = torch.chunk(c_attn_weight, chunks=3, dim=0)
+
+                    n_heads = hf_model.config.num_attention_heads
+                    W_q = permute_for_rope(W_q, n_heads, W_q.shape[0], W_q.shape[1])
+                    W_k = permute_for_rope(W_k, n_heads, W_k.shape[0], W_k.shape[1])
+                    k_mod = k.replace("att.", "self_attn.")
+                    tensors[k_mod.replace("c_attn", "q_proj")] = W_q
+                    tensors[k_mod.replace("c_attn", "k_proj")] = W_k
+                    tensors[k_mod.replace("c_attn", "v_proj")] = W_v
+                    continue  # Skip the rest of the loop for this key
+                elif "c_proj" in k:
+                    k = k.replace(".att.c_proj", ".self_attn.o_proj")
+                elif "att_norm" in k:
+                    k = k.replace("att_norm", "input_layernorm")
+                elif "ffn_norm" in k:
+                    k = k.replace("ffn_norm", "post_attention_layernorm")
+        elif "SemanticEncoder_module" in old_k:
+            k = old_k.replace("SemanticEncoder_module", "semantic_encoder")
+
+            # Handle residual_blocks -> individual modules conversion
+            if "residual_blocks.0." in k:
+                k = k.replace("residual_blocks.0.", "act1.")
+            elif "residual_blocks.1." in k:
+                k = k.replace("residual_blocks.1.", "conv1.")
+            elif "residual_blocks.2." in k:
+                k = k.replace("residual_blocks.2.", "act2.")
+            elif "residual_blocks.3." in k:
+                k = k.replace("residual_blocks.3.", "conv2.")
+        else:
+            k = old_k
+
+        # copy over to new state dict
+        tensors[k] = state_dict[old_k]
+
+    state_dict = tensors
+    extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys())
+    missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys())
+    if len(extra_keys) != 0:
+        raise ValueError(f"{len(extra_keys)} extra keys found: {extra_keys}")
+    if len(missing_keys) != 0:
+        raise ValueError(f"{len(missing_keys)} missing keys found: {missing_keys}")
+    hf_model.load_state_dict(state_dict, strict=True)
+    n_params = param_count(hf_model)
+
+    logger.info(f"model loaded: {round(n_params / 1e6, 1)}M params")
+
+    del state_dict
+
+    return hf_model
+
+
+@torch.no_grad()
+def convert_checkpoint(
+    checkpoint_path,
+    pytorch_dump_folder_path=None,
+    config_path=None,
+    repo_id=None,
+):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+
+    semantic_model_id = "facebook/w2v-bert-2.0"
+
+    # load json
+    if config_path is not None:
+        with open(config_path, "r") as f:
+            model_config = json.load(f)
+        # # NOTE: `semantic_hidden_size` not needed as inside `semantic_model_config.hidden_size` (below)
+        # semantic_hidden_size = model_config["semantic_hidden_size"]
+        encoder_hidden_size = model_config["codec_encoder_hidden_size"]
+        decoder_hidden_size = model_config["codec_decoder_hidden_size"]
+        # # NOTE: not needed as always used: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py#L35
+        # use_vocos = model_config["use_vocos"]
+    else:
+        # default to https://huggingface.co/HKUSTAudio/xcodec2/blob/main/config.json
+        encoder_hidden_size = 1024
+        decoder_hidden_size = 1024
+
+    # create config
+    # -- use hardcoded semantic model: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py#L19
+    semantic_model_config = AutoConfig.from_pretrained(semantic_model_id, output_hidden_states=True)
+
+    config = Xcodec2Config(
+        encoder_hidden_size=encoder_hidden_size,
+        decoder_hidden_size=decoder_hidden_size,
+        semantic_model_config=semantic_model_config,
+    )
+
+    # create model
+    if not torch.cuda.is_available():
+        raise ValueError("Run this script on a machine with a GPU for weight norm layers to be correctly copied.")
+    torch_device = "cuda"
+    model = Xcodec2Model(config).to(torch_device).eval()
+
+    original_checkpoint = safetensors.torch.load_file(checkpoint_path)
+    if "best_state" in original_checkpoint:
+        # we might have a training state saved, in which case discard the yaml results and just retain the weights
+        original_checkpoint = original_checkpoint["best_state"]
+
+    # add weight norm, convert, and remove weight norm
+    model.apply_weight_norm()
+    model = _convert_model(original_checkpoint, model)
+    model.remove_weight_norm()
+
+    # create feature extractor
+    dac_id = "descript/dac_16khz"
+    semantic_model_id = "facebook/w2v-bert-2.0"
+    dac_fe = AutoFeatureExtractor.from_pretrained(dac_id)
+    semantic_fe = AutoFeatureExtractor.from_pretrained(semantic_model_id)
+    if semantic_fe.sampling_rate != dac_fe.sampling_rate:
+        raise ValueError(
+            f"Sampling rates for DAC and semantic feature extractor must match, got {dac_fe.sampling_rate} and {semantic_fe.sampling_rate}."
+        )
+    feature_extractor = Xcodec2FeatureExtractor(
+        feature_size=semantic_fe.feature_size,
+        sampling_rate=semantic_fe.sampling_rate,
+        num_mel_bins=semantic_fe.num_mel_bins,
+        padding_value=semantic_fe.padding_value,
+        stride=semantic_fe.stride,
+        n_channels=dac_fe.feature_size,
+        hop_length=dac_fe.hop_length,
+        pre_padding_value=dac_fe.padding_value,
+    )
+
+    # save and upload
+    if pytorch_dump_folder_path is not None:
+        model.save_pretrained(pytorch_dump_folder_path)
+        feature_extractor.save_pretrained(pytorch_dump_folder_path)
+
+    if repo_id:
+        print("Pushing to the hub...")
+        feature_extractor.push_to_hub(repo_id)
+        model.push_to_hub(repo_id)
+
+
+"""
+While there is training code on GitHub: https://github.com/zhenye234/X-Codec-2.0
+Modeling code on the Hub is used by xcodec2 (pip) package: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py
+
+Given their example usage: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py
+It seems best to use the config and weights from the Hub.
+
+Conversion example usage:
+```
+# download model and config
+wget https://huggingface.co/HKUSTAudio/xcodec2/resolve/main/config.json -P /raid/eric/xcodec2_original
+wget https://huggingface.co/HKUSTAudio/xcodec2/resolve/main/model.safetensors -P /raid/eric/xcodec2_original
+
+# run conversion
+python src/transformers/models/xcodec2/convert_xcodec2_checkpoint_to_pytorch.py \
+    --checkpoint_path /raid/eric/xcodec2_original/model.safetensors \
+    --config_path /raid/eric/xcodec2_original/config.json \
+    --push_to_hub hf-audio/xcodec2
+```
+
+"""
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
+    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
+    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+    parser.add_argument(
+        "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+    )
+
+    args = parser.parse_args()
+    convert_checkpoint(
+        args.checkpoint_path,
+        args.pytorch_dump_folder_path,
+        args.config_path,
+        args.push_to_hub,
+    )
diff --git a/src/transformers/models/xcodec2/feature_extraction_xcodec2.py b/src/transformers/models/xcodec2/feature_extraction_xcodec2.py
new file mode 100644
index 000000000000..e45ae6fce048
--- /dev/null
+++ b/src/transformers/models/xcodec2/feature_extraction_xcodec2.py
@@ -0,0 +1,387 @@
+# coding=utf-8
+# Copyright 2025 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.
+"""Feature extractor class for Xcodec2 model."""
+
+import copy
+from typing import Any, Optional, Union
+
+import numpy as np
+
+from ... import is_torch_available
+from ...audio_utils import (
+    AudioInput,
+    make_list_of_audio,
+    mel_filter_bank,
+    spectrogram_batch,
+    spectrogram_torch,
+    window_function,
+)
+from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+from ...feature_extraction_utils import BatchFeature
+from ...utils import PaddingStrategy, TensorType, logging
+
+
+if is_torch_available():
+    import torch
+
+
+logger = logging.get_logger(__name__)
+
+
+class Xcodec2FeatureExtractor(SequenceFeatureExtractor):
+    r"""
+    Constructs a Xcodec2 feature extractor.
+
+    This feature extractor inherits from [`SequenceFeatureExtractor`] which contains most of the main methods. Users
+    should refer to this superclass for more information regarding those methods.
+
+    This class extracts mel-filter bank features for the semantic encoder, and also returns padded audio for the
+    acoustic encoder.
+
+    Args:
+        feature_size (`int`, *optional*, defaults to 80):
+            The feature dimension of the extracted features.
+        sampling_rate (`int`, *optional*, defaults to 16000):
+            The sample rate at which the audio files should be digitalized expressed in hertz (Hz).
+        n_fft (`int`, *optional*, defaults to 512):
+            FFT length for STFT.
+        window_length (`int`, *optional*, defaults to 400):
+            Length of window used in FFT.
+        num_mel_bins (`int`, *optional*, defaults to 80):
+            Number of Mel-frequency bins.
+        padding_value (`float`, *optional*, defaults to 1.0):
+            The value that is used to fill the padding vectors for the mel spectrogram.
+        stride (`int`, *optional*, defaults to 2):
+            Stride used to reshape audios from shape (batch_size,num_frames,num_mel_bins) to
+            (batch_size,num_frames//stride,num_mel_bins*stride). Namely grouping adjacent frames.
+        n_channels (`int`, *optional*, defaults to 1):
+            Number of channels in the input audio.
+        spec_hop_length (`int`, *optional*, defaults to 160):
+            Hop length used for computing Mel spectrogram.
+        hop_length (`int`, *optional*, defaults to 320):
+            Number of audio samples encoded per frame. Equivalent to product of downsampling ratios.
+        pre_padding_value (`float`, *optional*, defaults to 0.0):
+            The value that is used to fill the padding vectors for the input audio (before computing the spectrogram).
+    """
+
+    model_input_names = ["audio_spectrogram", "audio", "padding_mask"]
+
+    def __init__(
+        self,
+        feature_size=80,
+        sampling_rate=16000,
+        n_fft=512,
+        window_length=400,
+        num_mel_bins=80,
+        padding_value=1.0,
+        stride=2,
+        n_channels=1,
+        spec_hop_length=160,
+        hop_length=320,
+        pre_padding_value=0.0,
+        **kwargs,
+    ):
+        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
+
+        # For DAC-like padding before Mel feature extraction
+        self.n_channels = n_channels
+        self.hop_length = hop_length
+        self.pre_padding_value = pre_padding_value
+        self.initial_padder = SequenceFeatureExtractor(
+            feature_size=n_channels,
+            sampling_rate=sampling_rate,
+            padding_value=pre_padding_value,
+            **kwargs,
+        )
+        self.initial_padder.model_input_names = ["audio", "padding_mask"]
+
+        # filter bank like SeamlessM4T
+        self.n_fft = n_fft
+        self.num_mel_bins = num_mel_bins
+        self.stride = stride
+        self.window_length = window_length
+        self.mel_floor = 1.192092955078125e-07
+        self.preemphasis = 0.97
+        self.spec_hop_length = spec_hop_length
+        self.spec_power = 2.0
+        self.spec_center = False
+        self.spec_remove_dc_offset = True
+        self.mel_filters = mel_filter_bank(
+            num_frequency_bins=self.n_fft // 2 + 1,
+            num_mel_filters=self.num_mel_bins,
+            min_frequency=20,
+            max_frequency=self.sampling_rate // 2,
+            sampling_rate=self.sampling_rate,
+            norm=None,
+            mel_scale="kaldi",
+            triangularize_in_mel_space=True,
+        )
+        self.window = window_function(self.window_length, "povey", periodic=False)
+
+    def _extract_fbank_features_numpy(
+        self,
+        audio_list: list[np.ndarray],
+    ) -> list[np.ndarray]:
+        """
+        Batch version of mel-filter bank feature extraction for improved efficiency for a batch.
+        """
+        # Process audio: apply Kaldi scaling
+        processed_audio = []
+        for audio in audio_list:
+            audio = np.squeeze(audio) * (2**15)  # Kaldi compliance: 16-bit signed integers
+            processed_audio.append(audio)
+
+        # Use batch spectrogram processing
+        features_list = spectrogram_batch(
+            processed_audio,
+            self.window,
+            frame_length=self.window_length,
+            hop_length=self.spec_hop_length,
+            fft_length=self.n_fft,
+            power=self.spec_power,
+            center=self.spec_center,
+            preemphasis=self.preemphasis,
+            mel_filters=self.mel_filters,
+            log_mel="log",
+            mel_floor=self.mel_floor,
+            remove_dc_offset=self.spec_remove_dc_offset,
+        )
+
+        # Transpose each feature matrix to match expected format
+        return [features.T for features in features_list]
+
+    def _extract_fbank_features_torch(
+        self,
+        audio: np.ndarray,
+        device: str = "cpu",
+    ) -> list[np.ndarray]:
+        """
+        torch-based mel-filter bank feature extraction.
+        """
+        audio = torch.from_numpy(audio).to(device)
+        window = torch.from_numpy(self.window).to(device)
+        mel_filters = torch.from_numpy(self.mel_filters).to(device)
+
+        # To list of tensors for `spectrogram_torch`
+        processed_waveforms = []
+        for waveform in audio:
+            waveform = waveform.squeeze() * (2**15)  # Kaldi compliance: 16-bit signed integers
+            processed_waveforms.append(waveform)
+
+        # Use batch spectrogram processing
+        features_list = spectrogram_torch(
+            processed_waveforms,
+            window,
+            frame_length=self.window_length,
+            hop_length=self.spec_hop_length,
+            fft_length=self.n_fft,
+            power=self.spec_power,
+            center=self.spec_center,
+            preemphasis=self.preemphasis,
+            mel_filters=mel_filters,
+            log_mel="log",
+            mel_floor=self.mel_floor,
+            remove_dc_offset=self.spec_remove_dc_offset,
+        )
+
+        # Transpose each feature matrix to match expected format
+        return [features.T for features in features_list]
+
+    def __call__(
+        self,
+        audio: AudioInput,
+        padding: Union[bool, str, PaddingStrategy] = True,
+        max_length: Optional[int] = None,
+        truncation: bool = False,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        sampling_rate: Optional[int] = None,
+        do_normalize_per_mel_bins: Optional[bool] = True,
+        use_torch: bool = False,
+        device: str = "cpu",
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Main method to featurize and prepare for the model one or several sequence(s).
+
+        Args:
+            audio (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                Numpy array or torch tensor with shape (num_channels, sequence_length). A list of such arrays or
+                tensors can also be provided for a batch of inputs.
+            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+                Select a strategy to pad the returned sequences (according to the model's padding side and padding
+                index) among:
+
+                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                  sequence if provided).
+                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+                  acceptable input length for the model if that argument is not provided.
+                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                  lengths).
+            pad_to_multiple_of (`int`, *optional*, defaults to 2):
+                If set will pad the sequence to a multiple of the provided value.
+
+                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
+            max_length (`int`, *optional*):
+                Maximum length of the returned list and optionally padding length (see above).
+            truncation (`bool`):
+                Activates truncation to cut input sequences longer than *max_length* to *max_length*.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors instead of list of python integers. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return Numpy `np.ndarray` objects.
+            sampling_rate (`int`, *optional*):
+                The sample rate at which the `audio` input was sampled. It is strongly recommended to pass
+                `sampling_rate` at the forward call to prevent silent errors.
+            do_normalize_per_mel_bins (`bool`, *optional*, defaults to `True`):
+                Whether or not to zero-mean unit-variance normalize the input per mel-channel.
+            use_torch (`bool`, *optional*, defaults to `True`):
+                Whether or not to use torch for mel-filter bank feature extraction. If `False`, a numpy
+            device (`str`, *optional*, defaults to `"cpu"`):
+                Device for PyTorch tensors when using torch.
+            kwargs (*optional*):
+                Remaining dictionary of keyword arguments that will be passed to the tokenizer or the feature
+                extractor.
+        """
+        if sampling_rate is not None:
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
+                    f" {self.sampling_rate}. Please make sure that the provided `audio` input was sampled with"
+                    f" {self.sampling_rate} and not {sampling_rate}."
+                )
+        else:
+            logger.warning(
+                f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
+        # ensure batch
+        audio = make_list_of_audio(audio)
+
+        # DAC-like padding
+        for _, example in enumerate(audio):
+            if example.ndim > 2:
+                raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}")
+            if self.feature_size == 1 and example.ndim != 1:
+                raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels")
+            if self.feature_size == 2:
+                raise ValueError("Stereo audio isn't supported for now")
+
+        input_values = BatchFeature({"audio": audio})
+        padded_inputs = self.initial_padder.pad(
+            input_values,
+            max_length=max_length,
+            truncation=truncation,
+            padding=padding,
+            return_attention_mask=padding,
+            pad_to_multiple_of=self.hop_length,
+            return_tensors="np",
+        )
+        if padding:
+            padded_inputs["padding_mask"] = padded_inputs.pop("attention_mask")
+
+        # Add channel dimension: (batch_size, sequence_length) -> (batch_size, 1, sequence_length)
+        padded_inputs["audio"] = padded_inputs["audio"][:, np.newaxis, :]
+
+        # Xcodec2 processing between DAC and SeamlessM4T feature extractors
+        # See: https://github.com/huggingface/transformers/pull/37868#discussion_r2382396644
+        # 1) redundant padding inside modeling of PyPI version (xcodec2==0.1.3)
+        # probably accidental on their part, but it is needed to get same results
+        # since their logic pads even if input is multiple of hop length
+        audio_seq_len = padded_inputs["audio"].shape[-1]
+        hop_padding = self.hop_length - (audio_seq_len % self.hop_length)
+        padded_inputs["audio"] = np.pad(
+            padded_inputs["audio"],
+            ((0, 0), (0, 0), (0, hop_padding)),  # only pad time dim
+            mode="constant",
+            constant_values=0.0,
+        )
+        padded_inputs["padding_mask"] = np.pad(
+            padded_inputs["padding_mask"],
+            ((0, 0), (0, hop_padding)),
+            mode="constant",
+            constant_values=0.0,
+        )
+
+        # 2) padding before semantic model feature extractor (i.e. that of SeamlessM4TFeatureExtractor)
+        semantic_padding = self.hop_length // 2
+        semantic_input = np.pad(
+            padded_inputs["audio"],
+            ((0, 0), (0, 0), (semantic_padding, semantic_padding)),  # only pad time dim
+            mode="constant",
+            constant_values=0.0,
+        )
+
+        # Compute Mel Spectrogram like in SeamlessM4TFeatureExtractor
+        if use_torch:
+            if not is_torch_available():
+                raise ImportError("`use_torch=True` requires PyTorch to be installed.")
+            mel_features = self._extract_fbank_features_torch(semantic_input, device=device)
+            mel_features = [x.cpu().numpy() for x in mel_features]
+        else:
+            semantic_input = [np.asarray(speech, dtype=np.float32) for speech in semantic_input]
+            mel_features = self._extract_fbank_features_numpy(semantic_input)
+
+        if do_normalize_per_mel_bins:
+            # torch defaults to ddof=1, and numpy defaults to ddof=0
+            mel_features = [(x - x.mean(0)) / np.sqrt(x.var(0, ddof=1) + 1e-7) for x in mel_features]
+        encoded_inputs = BatchFeature({"audio_spectrogram": mel_features})
+        padded_mel = self.pad(
+            encoded_inputs,
+            padding=padding,
+            max_length=max_length,
+            truncation=truncation,
+            pad_to_multiple_of=self.stride,
+            return_attention_mask=False,
+            return_tensors="np",
+        )
+        # Process mel features with stride reshaping
+        audio_spectrogram = padded_mel["audio_spectrogram"]
+        batch_size, num_frames, num_mel_channels = audio_spectrogram.shape
+
+        # Trim frames to be divisible by stride and reshape to group adjacent frames (features * stride)
+        trimmed_frames = num_frames - (num_frames % self.stride)
+        audio_spectrogram = audio_spectrogram[:, :trimmed_frames, :].reshape(
+            batch_size, trimmed_frames // self.stride, num_mel_channels * self.stride
+        )
+
+        # Combine output from DAC-like padding and SeamlessM4T feature extractor
+        padded_inputs["audio_spectrogram"] = audio_spectrogram
+
+        if return_tensors is not None:
+            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+        return padded_inputs
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Serializes this instance to a Python dictionary. Returns:
+            `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
+        """
+        output = copy.deepcopy(self.__dict__)
+        output["feature_extractor_type"] = self.__class__.__name__
+        if "mel_filters" in output:
+            del output["mel_filters"]
+        if "initial_padder" in output:
+            del output["initial_padder"]
+        if "window" in output:
+            del output["window"]
+        return output
+
+
+__all__ = ["Xcodec2FeatureExtractor"]
diff --git a/src/transformers/models/xcodec2/modeling_xcodec2.py b/src/transformers/models/xcodec2/modeling_xcodec2.py
new file mode 100644
index 000000000000..d99559c1e3f1
--- /dev/null
+++ b/src/transformers/models/xcodec2/modeling_xcodec2.py
@@ -0,0 +1,1813 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/xcodec2/modular_xcodec2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_xcodec2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 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.
+
+import math
+import random
+from contextlib import nullcontext
+from dataclasses import dataclass
+from functools import partial, wraps
+from math import ceil
+from typing import Callable, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import int32, nn, sin, sinc
+from torch.amp import autocast
+from torch.nn import Module, Parameter
+
+from transformers.activations import ACT2FN
+
+from ...cache_utils import Cache
+from ...integrations import use_kernel_forward_from_hub
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+    ModelOutput,
+    TransformersKwargs,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from ...utils.deprecation import deprecate_kwarg
+from ..auto import AutoModel
+from .configuration_xcodec2 import Xcodec2Config
+
+
+# General docstring
+_CONFIG_FOR_DOC = "Xcodec2Config"
+
+
+@dataclass
+class Xcodec2Output(ModelOutput):
+    """
+    Args:
+        audio_values (`torch.FloatTensor` of shape `(batch_size, 1, sequence_length)`, *optional*):
+            Decoded audio waveform values in the time domain, obtained using the decoder
+            part of Xcodec2. These represent the reconstructed audio signal.
+        audio_codes (`torch.LongTensor` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Discrete code embeddings computed using `model.encode`. These are the quantized
+            representations of the input audio used for further processing or generation.
+        quantized_representation (`torch.Tensor` of shape `(batch_size, dimension, time_steps)`):
+            Quantized continuous representation of input's embedding.
+        codes_padding_mask (`torch.int32` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Downsampled `padding_mask` for indicating valid audio codes in `audio_codes`.
+    """
+
+    audio_values: Optional[torch.FloatTensor] = None
+    audio_codes: Optional[torch.LongTensor] = None
+    quantized_representation: Optional[torch.Tensor] = None
+    codes_padding_mask: Optional[torch.Tensor] = None
+
+
+@dataclass
+class Xcodec2EncoderOutput(ModelOutput):
+    """
+    Args:
+        audio_codes (`torch.LongTensor` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Discrete code embeddings computed using `model.encode`. These represent
+            the compressed, quantized form of the input audio signal that can be
+            used for storage, transmission, or generation.
+        quantized_representation (`torch.Tensor` of shape `(batch_size, dimension, time_steps)`):
+            Quantized continuous representation of input's embedding.
+        codes_padding_mask (`torch.int32` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Downsampled `padding_mask` for indicating valid audio codes in `audio_codes`.
+
+    """
+
+    audio_codes: Optional[torch.LongTensor] = None
+    quantized_representation: Optional[torch.Tensor] = None
+    codes_padding_mask: Optional[torch.Tensor] = None
+
+
+@dataclass
+class Xcodec2DecoderOutput(ModelOutput):
+    """
+    Args:
+        audio_values (`torch.FloatTensor` of shape `(batch_size, 1, segment_length)`, *optional*):
+            Decoded audio waveform values in the time domain, obtained by converting
+            the discrete codes back into continuous audio signals. This represents
+            the reconstructed audio that can be played back.
+    """
+
+    audio_values: Optional[torch.FloatTensor] = None
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+# Default `unsqueeze_dim=2`
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=2):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 2):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+class Xcodec2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Xcodec2Config, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = False
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Xcodec2RotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: Xcodec2Config, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Unlike LlamaMLP, does not have `gate_proj` layer
+class Xcodec2MLP(nn.Module):
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+        dim = config.hidden_size
+
+        self.fc1 = nn.Linear(dim, 4 * dim, bias=False)
+        self.activation = ACT2FN[config.hidden_act]
+        self.fc2 = nn.Linear(4 * dim, dim, bias=False)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.activation(x)
+        x = self.fc2(x)
+        return x
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Xcodec2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Xcodec2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Xcodec2DecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Xcodec2Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = Xcodec2Attention(config=config, layer_idx=layer_idx)
+
+        self.mlp = Xcodec2MLP(config)
+        self.input_layernorm = Xcodec2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Xcodec2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Xcodec2SnakeBeta(nn.Module):
+    """
+    A modified Snake function which uses separate parameters for the magnitude and frequency of the periodic components.
+
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+
+    Parameters:
+        - dim: The number of input features
+        - logscale: Whether to use logarithmic scaling for alpha and beta (default: False)
+
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+
+    Examples:
+        >>> activation = Xcodec2SnakeBeta(256)
+        >>> x = torch.randn(1, 256, 100)  # (batch, channels, time)
+        >>> y = activation(x)
+    """
+
+    def __init__(self, dim, logscale=True):
+        """
+        Args:
+            dim: Shape of the input features (channels dimension)
+            logscale: Whether to use log scale initialization (default: True)
+                            If True, alpha and beta are initialized to zeros
+                            If False, alpha and beta are initialized to ones
+
+        Note:
+            - alpha controls the frequency of the periodic components
+            - beta controls the magnitude of the periodic components
+        """
+        super().__init__()
+        self.dim = dim
+
+        # initialize alpha
+        self.logscale = logscale
+        if self.logscale:  # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(dim))
+            self.beta = Parameter(torch.zeros(dim))
+        else:  # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(dim))
+            self.beta = Parameter(torch.ones(dim))
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        """
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        """
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
+    """
+    Creates a Kaiser-windowed sinc filter for low-pass filtering.
+
+    Args:
+        cutoff: Normalized cutoff frequency (0 to 0.5, where 0.5 is the Nyquist frequency)
+        half_width: Transition bandwidth parameter controlling filter roll-off
+        kernel_size: Size of the filter kernel
+
+    Returns:
+        torch.Tensor: A 1D filter kernel of shape (1, 1, kernel_size)
+    """
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+
+    # For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    """
+    Used by `DownSample1d`.
+    """
+
+    def __init__(
+        self,
+        cutoff=0.5,
+        half_width=0.6,
+        stride: int = 1,
+        padding: bool = True,
+        padding_mode: str = "replicate",
+        kernel_size: int = 12,
+    ):
+        super().__init__()
+        if cutoff < -0.0:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    # input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+        return out
+
+
+class UpSample1d(nn.Module):
+    """
+    1D upsampling module using transposed convolution with a sinc filter.
+
+    This module performs anti-aliased upsampling by first padding the input signal,
+    then applying a transposed convolution with a Kaiser-windowed sinc filter,
+    and finally trimming the resulting signal to remove boundary effects.
+
+    Args:
+        ratio (int): Upsampling ratio (default: 2)
+        kernel_size (int, optional): Size of the filter kernel.
+            If None, it's automatically calculated as 6 * ratio.
+    """
+
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size)
+        self.register_buffer("filter", filter)
+
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        x = self.ratio * F.conv_transpose1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+        x = x[..., self.pad_left : -self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    """
+    1D downsampling module using a low-pass filter.
+
+    This module performs anti-aliased downsampling by applying a low-pass filter
+    with an appropriate cutoff frequency followed by strided convolution.
+
+    Args:
+        ratio (int): Downsampling ratio (default: 2)
+        kernel_size (int, optional): Size of the filter kernel.
+            If None, it's automatically calculated as 6 * ratio.
+    """
+
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio, half_width=0.6 / ratio, stride=ratio, kernel_size=self.kernel_size
+        )
+
+    def forward(self, x):
+        x = self.lowpass(x)
+
+        return x
+
+
+class Activation1d(nn.Module):
+    """
+    A module that applies activation function with up-sampling and down-sampling.
+
+    This module first up-samples the input signal, applies an activation function,
+    and then down-samples the result. This architecture allows for more complex
+    non-linear transformations of the signal.
+
+    Args:
+        activation (`torch.nn.Module`):
+            The activation function module to apply (e.g., nn.ReLU(), nn.LeakyReLU()).
+        up_ratio (`int`, *optional*, defaults to 2):
+            The up-sampling ratio for increasing temporal resolution before activation.
+        down_ratio (`int`, *optional*, defaults to 2):
+            The down-sampling ratio for decreasing temporal resolution after activation.
+        up_kernel_size (`int`, *optional*, defaults to 12):
+            The kernel size used in the up-sampling operation.
+        down_kernel_size (`int`, *optional*, defaults to 12):
+            The kernel size used in the down-sampling operation.
+    """
+
+    def __init__(
+        self, activation, up_ratio: int = 2, down_ratio: int = 2, up_kernel_size: int = 12, down_kernel_size: int = 12
+    ):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x
+
+
+class ResidualUnit(nn.Module):
+    """
+    A residual unit that combines dilated convolutions with activation functions.
+
+    This unit performs a series of operations while maintaining a residual connection:
+    1. First activation with up/down-sampling
+    2. Dilated convolution with kernel size 7
+    3. Second activation with up/down-sampling
+    4. 1x1 convolution for channel mixing
+    5. Addition of the input (residual connection)
+
+    Args:
+        dim (`int`, *optional*, defaults to 16):
+            The number of channels in the input and output tensors.
+        dilation (`int`, *optional*, defaults to 1):
+            The dilation rate for the main convolution operation. Higher values
+            increase the receptive field without increasing parameter count.
+    """
+
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+        pad = ((7 - 1) * dilation) // 2
+        self.activation1 = Activation1d(activation=Xcodec2SnakeBeta(dim, logscale=True))
+        self.conv1 = nn.Conv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad)
+        self.activation2 = Activation1d(activation=Xcodec2SnakeBeta(dim, logscale=True))
+        self.conv2 = nn.Conv1d(dim, dim, kernel_size=1)
+
+    def forward(self, x):
+        residual = x
+        x = self.activation1(x)
+        x = self.conv1(x)
+        x = self.activation2(x)
+        x = self.conv2(x)
+        return residual + x
+
+
+class EncoderBlock(nn.Module):
+    """
+    Encoder block for the Xcodec2 acoustic encoder.
+
+    This block consists of:
+    1. A series of residual units with different dilation rates
+    2. An activation function with upsampling and downsampling
+    3. A strided convolution to change the dimension and downsample if stride > 1
+
+    Args:
+        dim (int): Dimension of the output features. Input dimension is assumed to be dim // 2
+        stride (int): Stride for the final convolution, controls downsampling factor
+        dilations (tuple): Dilation rates for the residual units
+    """
+
+    def __init__(self, dim: int = 16, stride: int = 1, dilations=(1, 3, 9)):
+        super().__init__()
+        self.residual_units = nn.ModuleList([ResidualUnit(dim // 2, dilation=d) for d in dilations])
+        self.activation = Activation1d(activation=Xcodec2SnakeBeta(dim // 2, logscale=True))
+        self.conv = nn.Conv1d(
+            dim // 2,
+            dim,
+            kernel_size=2 * stride,
+            stride=stride,
+            padding=stride // 2 + stride % 2,
+        )
+
+    def forward(self, x):
+        for residual_unit in self.residual_units:
+            x = residual_unit(x)
+        x = self.activation(x)
+        x = self.conv(x)
+        return x
+
+
+class Xcodec2CodecEncoder(nn.Module):
+    """
+    The encoder component of the Xcodec2 model for audio encoding.
+
+    This encoder transforms raw audio waveforms into latent representations:
+    1. Initial convolution to process the raw audio input
+    2. Series of encoder blocks with progressively increasing feature dimensions and downsampling
+    3. Final activation and projection to the target hidden dimension
+
+    Args:
+        d_model (`int`, *optional*, defaults to 48):
+            Initial dimension of the model. This will be doubled at each encoder block.
+        downsampling_ratios (`List[int]`, *optional*, defaults to [2, 2, 4, 4, 5]):
+            List of downsampling ratios for each encoder block. The product of these values
+            determines the total temporal compression factor.
+        dilations (`tuple`, *optional*, defaults to (1, 3, 9)):
+            Tuple of dilation rates used in the residual units within each encoder block.
+        hidden_dim (`int`, *optional*, defaults to 1024):
+            Dimension of the final latent representation after encoding.
+    """
+
+    def __init__(
+        self,
+        d_model=48,
+        downsampling_ratios=[2, 2, 4, 4, 5],
+        dilations=(1, 3, 9),
+        hidden_dim=1024,
+    ):
+        super().__init__()
+
+        self.initial_conv = nn.Conv1d(1, d_model, kernel_size=7, padding=3)
+
+        self.encoder_blocks = nn.ModuleList()
+        for i, stride in enumerate(downsampling_ratios):
+            d_model *= 2
+            self.encoder_blocks.append(EncoderBlock(d_model, stride=stride, dilations=dilations))
+
+        self.final_activation = Activation1d(activation=Xcodec2SnakeBeta(d_model, logscale=True))
+        self.final_conv = nn.Conv1d(d_model, hidden_dim, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        """
+        Forward pass of the Xcodec2 encoder.
+
+        Args:
+            x (`torch.Tensor` of shape `(batch_size, 1, sequence_length)`):
+                Input audio waveform tensor. Expected to be a mono audio signal with shape
+                (batch_size, 1, sequence_length).
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, compressed_length, hidden_dim)`:
+                Encoded audio representation with temporal dimension compressed by the product
+                of all downsampling_ratios. The output tensor is transposed to have the
+                sequence dimension second (batch_size, seq_len, features) for compatibility
+                with transformer-based models.
+        """
+        # Initial convolution
+        x = self.initial_conv(x)
+
+        # Apply all encoder blocks
+        for encoder_block in self.encoder_blocks:
+            x = encoder_block(x)
+
+        # Final processing
+        x = self.final_activation(x)
+        x = self.final_conv(x)
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class ResNetBlock(nn.Module):
+    """
+    A basic residual block for 1D convolutional networks.
+
+    This block consists of:
+    1. GroupNorm + SiLU-like activation (x * sigmoid(x)) + Conv1d
+    2. GroupNorm + SiLU-like activation + Dropout + Conv1d
+    3. Residual connection, with optional projection if input and output channels differ
+
+    Args:
+        in_channels: Number of input channels
+        out_channels: Number of output channels (defaults to in_channels if None)
+        dropout: Dropout probability (default: 0.1)
+    """
+
+    def __init__(self, *, in_channels, out_channels=None, dropout=0.1):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.conv1 = torch.nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = torch.nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = h * torch.sigmoid(h)
+        h = self.conv1(h)
+        h = self.norm2(h)
+        h = h * torch.sigmoid(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class Xcodec2VocosBackbone(nn.Module):
+    """
+    Hybrid ResNet-Transformer architecture.
+
+    Architecture overview:
+    - Input embedding: A 1D convolution (kernel size 7, padding 3) projects raw features
+      into the model's hidden dimension.
+    - Prior ResNet blocks: Two ResNet-style residual blocks operate on the hidden states
+      to provide early nonlinear feature processing.
+    - Rotary embeddings: Rotary positional embeddings (RoPE) are initialized to provide
+      position-dependent information to the Transformer layers.
+    - Transformer stack: A sequence of Xcodec2DecoderLayer modules applies self-attention
+      with rotary embeddings, interleaved with MLPs, to model long-range dependencies.
+    - Normalization: A final LayerNorm is applied to stabilize training and outputs.
+    - Post ResNet blocks: Two additional ResNet-style blocks refine the representation
+      after the Transformer stack.
+
+    This design combines convolutional inductive biases (via ResNet-style blocks and the
+    initial Conv1d embedding) with the global sequence modeling capabilities of
+    Transformers, making it suitable for sequence data such as audio or other temporal
+    signals.
+    """
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+
+        self.embed = nn.Conv1d(config.hidden_size, config.hidden_size, kernel_size=7, padding=3)
+        block_in = config.hidden_size
+        dropout = config.resnet_dropout
+
+        self.prior_blocks = nn.ModuleList(
+            [
+                ResNetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout),
+                ResNetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout),
+            ]
+        )
+
+        # Initialize rotary embeddings
+        self.position_ids = torch.arange(config.num_attention_heads).unsqueeze(0)
+        self.rotary_emb = Xcodec2RotaryEmbedding(config=config)
+
+        # Create transformer layers
+        self.transformers = nn.ModuleList(
+            [Xcodec2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-6)
+
+        self.post_blocks = nn.ModuleList(
+            [
+                ResNetBlock(
+                    in_channels=config.hidden_size,
+                    out_channels=config.hidden_size,
+                    dropout=dropout,
+                ),
+                ResNetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout),
+            ]
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the Xcodec2VocosBackbone.
+
+        The processing flow includes:
+        1. Initial embedding via 1D convolution
+        2. Processing through ResNet blocks
+        3. Transformer processing with rotary position embeddings
+        4. Final ResNet blocks and normalization
+
+        Args:
+            x (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Input tensor containing features to process.
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`:
+                Processed features with the same shape as the input but transformed
+                through the backbone architecture.
+        """
+        # Handle initial transformations
+        x = x.transpose(1, 2)
+        x = self.embed(x)
+
+        # Process through prior_blocks
+        for block in self.prior_blocks:
+            x = block(x)
+
+        x = x.transpose(1, 2)  # [batch, seq_len, hidden_dim]
+
+        # Generate rotary embeddings
+        position_embeddings = self.rotary_emb(x, self.position_ids.to(x.device))
+
+        # Apply transformer layers with position embeddings
+        for layer in self.transformers:
+            x = layer(
+                x,
+                position_embeddings=position_embeddings,
+            )
+
+        # Handle final transformations
+        x = x.transpose(1, 2)
+
+        # Process through post_blocks
+        for block in self.post_blocks:
+            x = block(x)
+
+        x = x.transpose(1, 2)
+        x = self.final_layer_norm(x)
+
+        return x
+
+
+class Xcodec2ISTFT(nn.Module):
+    """
+    As in original Vocos code:
+    https://github.com/gemelo-ai/vocos/blob/c859e3b7b534f3776a357983029d34170ddd6fc3/vocos/spectral_ops.py#L7
+
+    Custom ISTFT implementation to support "same" padding as in Vocos.
+    """
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+        if config.istft_padding not in ["center", "same"]:
+            raise ValueError("Padding must be 'center' or 'same'.")
+        self.padding = config.istft_padding
+        self.n_fft = config.n_fft
+        self.hop_length = config.hop_length
+        self.win_length = getattr(config, "win_length", config.n_fft)
+        window = torch.hann_window(self.win_length)
+        self.register_buffer("window", window)
+
+    def forward(self, spec: torch.Tensor) -> torch.Tensor:
+        """
+        Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram.
+
+        Args:
+            spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size,
+                  is the number of frequency bins, and T is the number of time frames.
+
+        Returns:
+            Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal.
+        """
+        if spec.dim() != 3:
+            raise ValueError("Expected a 3D tensor as input")
+
+        if self.padding == "center":
+            # Fallback to pytorch native implementation
+            return torch.istft(spec, self.n_fft, self.hop_length, self.win_length, self.window, center=True)
+
+        elif self.padding == "same":
+            # Custom implementation from Vocos codebase
+            pad = (self.win_length - self.hop_length) // 2
+            n_frames = spec.shape[-1]
+
+            # Inverse FFT
+            ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward")
+            ifft = ifft * self.window[None, :, None]
+
+            # Overlap and Add
+            output_size = (n_frames - 1) * self.hop_length + self.win_length
+            y = F.fold(
+                ifft,
+                output_size=(1, output_size),
+                kernel_size=(1, self.win_length),
+                stride=(1, self.hop_length),
+            )[:, 0, 0, pad:-pad]
+
+            # Window envelope
+            window_sq = self.window.square().expand(1, n_frames, -1).transpose(1, 2)
+            window_envelope = F.fold(
+                window_sq,
+                output_size=(1, output_size),
+                kernel_size=(1, self.win_length),
+                stride=(1, self.hop_length),
+            ).squeeze()[pad:-pad]
+
+            # Normalize
+            if not (window_envelope > 1e-11).all():
+                raise ValueError("Window envelope values are too small (<=1e-11)")
+            return y / window_envelope
+
+        else:
+            raise ValueError("Padding must be 'center' or 'same'.")
+
+
+class Xcodec2ISTFTHead(nn.Module):
+    """
+    As in original Vocos code:
+    https://github.com/gemelo-ai/vocos/blob/c859e3b7b534f3776a357983029d34170ddd6fc3/vocos/heads.py#L26
+    - Projects the hidden states to STFT coefficients (magnitude and phase)
+    - Applies ISTFT to reconstruct the time-domain audio signal
+    """
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+        self.out = torch.nn.Linear(config.hidden_size, config.n_fft + 2)
+        self.istft = Xcodec2ISTFT(config)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass of the ISTFTHead module.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
+                        L is the sequence length, and H denotes the model dimension.
+
+        Returns:
+            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
+            Tensor: Predicted STFT coefficients of shape (B, L, N+2), where N is the number of frequency bins.
+        """
+        x_pred = self.out(x).transpose(1, 2)
+        mag, p = x_pred.chunk(2, dim=1)
+        mag = torch.exp(mag)
+        mag = torch.clip(mag, max=1e2)  # safeguard to prevent excessively large magnitudes
+        # wrapping happens here. These two lines produce real and imaginary value
+        spectrogram_real = torch.cos(p)
+        spectrogram_imag = torch.sin(p)
+        spectrogram_complex = mag * (spectrogram_real + 1j * spectrogram_imag)
+        audio = self.istft(spectrogram_complex)
+        return audio
+
+
+def round_ste(z):
+    """
+    Round with straight through gradients.
+    Used in `Xcodec2FSQ`.
+    """
+    zhat = z.round()
+    return z + (zhat - z).detach()
+
+
+def maybe(fn):
+    """
+    Used in `Xcodec2FSQ`.
+    """
+
+    @wraps(fn)
+    def inner(x, *args, **kwargs):
+        if x is None:
+            return x
+        return fn(x, *args, **kwargs)
+
+    return inner
+
+
+class Xcodec2FSQ(Module):
+    """
+    Copied from https://github.com/lucidrains/vector-quantize-pytorch/blob/fe903ce2ae9c125ace849576aa6d09c5cec21fe4/vector_quantize_pytorch/finite_scalar_quantization.py#L61
+    Simply changed asserts to raise ValueErrors.
+    """
+
+    def __init__(
+        self,
+        levels: list[int],
+        dim: Optional[int] = None,
+        num_codebooks=1,
+        keep_num_codebooks_dim: Optional[bool] = None,
+        scale: Optional[float] = None,
+        allowed_dtypes: tuple[torch.dtype, ...] = (torch.float32, torch.float64),
+        channel_first: bool = False,
+        projection_has_bias: bool = True,
+        return_indices=True,
+        force_quantization_f32=True,
+        preserve_symmetry: bool = False,
+        noise_dropout=0.0,
+    ):
+        super().__init__()
+
+        _levels = torch.tensor(levels, dtype=int32)
+        self.register_buffer("_levels", _levels, persistent=False)
+
+        _basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dim=0, dtype=int32)
+        self.register_buffer("_basis", _basis, persistent=False)
+
+        self.scale = scale
+
+        self.preserve_symmetry = preserve_symmetry
+        self.noise_dropout = noise_dropout
+
+        codebook_dim = len(levels)
+        self.codebook_dim = codebook_dim
+
+        effective_codebook_dim = codebook_dim * num_codebooks
+        self.num_codebooks = num_codebooks
+        self.effective_codebook_dim = effective_codebook_dim
+
+        keep_num_codebooks_dim = keep_num_codebooks_dim if keep_num_codebooks_dim is not None else num_codebooks > 1
+        if num_codebooks > 1 and not keep_num_codebooks_dim:
+            raise ValueError("When num_codebooks > 1, keep_num_codebooks_dim must be True")
+        self.keep_num_codebooks_dim = keep_num_codebooks_dim
+
+        self.dim = dim if dim is not None else len(_levels) * num_codebooks
+
+        self.channel_first = channel_first
+
+        has_projections = self.dim != effective_codebook_dim
+        self.project_in = (
+            nn.Linear(self.dim, effective_codebook_dim, bias=projection_has_bias) if has_projections else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(effective_codebook_dim, self.dim, bias=projection_has_bias) if has_projections else nn.Identity()
+        )
+
+        self.has_projections = has_projections
+
+        self.return_indices = return_indices
+        if return_indices:
+            self.codebook_size = math.prod(levels)
+            implicit_codebook = self._indices_to_codes(torch.arange(self.codebook_size))
+            self.register_buffer("implicit_codebook", implicit_codebook, persistent=False)
+
+        self.allowed_dtypes = allowed_dtypes
+        self.force_quantization_f32 = force_quantization_f32
+
+    def bound(self, z, eps: float = 1e-3):
+        half_l = (self._levels - 1) * (1 + eps) / 2
+        offset = torch.where(self._levels % 2 == 0, 0.5, 0.0)
+        shift = (offset / half_l).atanh()
+        return (z + shift).tanh() * half_l - offset
+
+    def quantize(self, z):
+        _, _, noise_dropout, preserve_symmetry, half_width = (
+            z.shape[0],
+            z.device,
+            self.noise_dropout,
+            self.preserve_symmetry,
+            (self._levels // 2),
+        )
+        bound_fn = self.symmetry_preserving_bound if preserve_symmetry else self.bound
+
+        bounded_z = bound_fn(z)
+        if self.training and noise_dropout > 0.0:
+            offset_mask = torch.bernoulli(torch.full_like(bounded_z, noise_dropout)).bool()
+            offset = torch.rand_like(bounded_z) - 0.5
+            bounded_z = torch.where(offset_mask, bounded_z + offset, bounded_z)
+
+        return round_ste(bounded_z) / half_width
+
+    def _scale_and_shift(self, zhat_normalized):
+        half_width = self._levels // 2
+        return (zhat_normalized * half_width) + half_width
+
+    def _scale_and_shift_inverse(self, zhat):
+        half_width = self._levels // 2
+        return (zhat - half_width) / half_width
+
+    def _indices_to_codes(self, indices):
+        level_indices = self.indices_to_level_indices(indices)
+        codes = self._scale_and_shift_inverse(level_indices)
+        return codes
+
+    def codes_to_indices(self, zhat):
+        """Converts a `code` to an index in the codebook."""
+        if zhat.shape[-1] != self.codebook_dim:
+            raise ValueError(f"Expected zhat to have shape [-1, {self.codebook_dim}], but got {zhat.shape}")
+        zhat = self._scale_and_shift(zhat)
+        return (zhat * self._basis).sum(dim=-1).to(int32)
+
+    def indices_to_level_indices(self, indices):
+        """Converts indices to indices at each level, perhaps needed for a transformer with factorized embeddings"""
+        indices = indices.unsqueeze(-1)
+        codes_non_centered = (indices // self._basis) % self._levels
+        return codes_non_centered
+
+    def indices_to_codes(self, indices):
+        """Inverse of `codes_to_indices`."""
+        if indices is None:
+            raise ValueError("indices cannot be None")
+
+        codes = self._indices_to_codes(indices)
+
+        if self.keep_num_codebooks_dim:
+            codes = codes.reshape(*codes.shape[:-2], -1)
+
+        codes = self.project_out(codes)
+
+        return codes
+
+    def forward(self, z):
+        """
+        einstein notation
+        b - batch
+        n - sequence (or flattened spatial dimensions)
+        d - feature dimension
+        c - number of codebook dim
+        """
+
+        if z.shape[-1] != self.dim:
+            raise ValueError(f"expected dimension of {self.dim} but found dimension of {z.shape[-1]}")
+
+        z = self.project_in(z)
+
+        # z = rearrange(z, 'b n (c d) -> b n c d', c = self.num_codebooks)
+        b, n, cd = z.shape  # (b, n, c·d)
+        c = self.num_codebooks
+        d = cd // c  # infer the per-codebook dimension
+
+        z = z.view(b, n, c, d)  # now (b, n, c, d)
+        # whether to force quantization step to be full precision or not
+
+        force_f32 = self.force_quantization_f32
+        quantization_context = partial(autocast, "cuda", enabled=False) if force_f32 else nullcontext
+        with quantization_context():
+            orig_dtype = z.dtype
+
+            if force_f32 and orig_dtype not in self.allowed_dtypes:
+                z = z.float()
+
+            codes = self.quantize(z)
+
+            # returning indices could be optional
+            indices = None
+
+            if self.return_indices:
+                indices = self.codes_to_indices(codes)
+
+            # codes = rearrange(codes, 'b n c d -> b n (c d)')
+            codes = codes.flatten(start_dim=2)
+            codes = codes.to(orig_dtype)
+
+        # project out
+        out = self.project_out(codes)
+        if not self.keep_num_codebooks_dim and self.return_indices:
+            indices = maybe(lambda t, *_, **__: t.squeeze(-1))(indices)
+
+        # return quantized output and indices
+        return out, indices
+
+
+def get_maybe_sync_seed(device, max_size=10_000):
+    """
+    Used in `Xcodec2ResidualFSQ`.
+    """
+    rand_int = torch.randint(0, max_size, (), device=device)
+
+    if dist.is_initialized() and dist.get_world_size() > 1:
+        dist.all_reduce(rand_int)
+
+    return rand_int.item()
+
+
+class Xcodec2ResidualFSQ(Module):
+    """
+    Copied from https://github.com/lucidrains/vector-quantize-pytorch/blob/fe903ce2ae9c125ace849576aa6d09c5cec21fe4/vector_quantize_pytorch/residual_fsq.py#L49
+    Simply changed asserts to raise ValueErrors.
+    """
+
+    def __init__(
+        self,
+        *,
+        levels: list[int],
+        num_quantizers,
+        dim: Optional[int] = None,
+        is_channel_first=False,
+        quantize_dropout=False,
+        quantize_dropout_cutoff_index=0,
+        quantize_dropout_multiple_of=1,
+        soft_clamp_input_value=None,
+        **kwargs,
+    ):
+        super().__init__()
+        codebook_dim = len(levels)
+
+        dim = codebook_dim if dim is None else dim
+
+        requires_projection = codebook_dim != dim
+        self.project_in = nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()
+        self.project_out = nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
+        self.has_projections = requires_projection
+
+        self.is_channel_first = is_channel_first
+        self.num_quantizers = num_quantizers
+        self.soft_clamp_input_value = soft_clamp_input_value
+        self.levels = levels
+        self.layers = nn.ModuleList([])
+
+        levels_tensor = torch.Tensor(levels)
+
+        scales = []
+
+        for ind in range(num_quantizers):
+            scales.append((levels_tensor - 1) ** -ind)
+
+            fsq = Xcodec2FSQ(levels=levels, dim=codebook_dim, **kwargs)
+
+            self.layers.append(fsq)
+
+        self.codebook_size = self.layers[0].codebook_size
+
+        self.register_buffer("scales", torch.stack(scales), persistent=False)
+
+        self.quantize_dropout = quantize_dropout and num_quantizers > 1
+
+        if quantize_dropout_cutoff_index < 0:
+            raise ValueError("quantize_dropout_cutoff_index must be greater than or equal to 0")
+
+        self.quantize_dropout_cutoff_index = quantize_dropout_cutoff_index
+        self.quantize_dropout_multiple_of = quantize_dropout_multiple_of
+
+    @property
+    def codebooks(self):
+        codebooks = [layer.implicit_codebook for layer in self.layers]
+        codebooks = torch.stack(codebooks, dim=0)
+        return codebooks
+
+    def get_codes_from_indices(self, indices):
+        _, quantize_dim = indices.shape[0], indices.shape[-1]
+        b, *spatial_dims, q = indices.shape
+        indices_packed = indices.reshape(b, -1, q)
+        ps = (tuple(spatial_dims),)
+
+        if quantize_dim < self.num_quantizers:
+            if not self.quantize_dropout > 0.0:
+                raise ValueError(
+                    "quantize dropout must be greater than 0 if you wish to reconstruct from a signal with less fine quantizations"
+                )
+            indices_packed = F.pad(indices_packed, (0, self.num_quantizers - quantize_dim), value=-1)
+
+        mask = indices_packed == -1
+        indices_proc = indices_packed.masked_fill(mask, 0)
+
+        indices_permuted = indices_proc.permute(2, 0, 1)
+
+        selected_codes = [self.codebooks[qi][indices_permuted[qi]] for qi in range(self.num_quantizers)]
+
+        all_codes = torch.stack(selected_codes, dim=0)
+
+        mask_permuted = mask.permute(2, 0, 1).unsqueeze(-1)
+        all_codes = all_codes.masked_fill(mask_permuted, 0.0)
+
+        scales_reshaped = self.scales.view(self.num_quantizers, 1, 1, -1)
+        all_codes = all_codes * scales_reshaped
+
+        spatial_shape = tuple(ps[0])
+        q, b, _, d = all_codes.shape
+
+        all_codes = all_codes.reshape(q, b, *spatial_shape, d)
+        return all_codes
+
+    def get_output_from_indices(self, indices):
+        codes = self.get_codes_from_indices(indices)
+        codes_summed = torch.sum(codes, dim=0)
+        return self.project_out(codes_summed)
+
+    def forward(self, x, return_all_codes=False, rand_quantize_dropout_fixed_seed=None):
+        num_quant, quant_dropout_multiple_of, device = self.num_quantizers, self.quantize_dropout_multiple_of, x.device
+
+        x = self.project_in(x)
+
+        if self.soft_clamp_input_value is not None:
+            clamp_value = self.soft_clamp_input_value
+            x = (x / clamp_value).tanh() * clamp_value
+
+        quantized_out = 0.0
+        residual = self.layers[0].bound(x)
+
+        all_indices = []
+
+        should_quantize_dropout = self.training and self.quantize_dropout and torch.is_grad_enabled()
+
+        if should_quantize_dropout:
+            if rand_quantize_dropout_fixed_seed is None:
+                rand_quantize_dropout_fixed_seed = get_maybe_sync_seed(device)
+
+            rand = random.Random(rand_quantize_dropout_fixed_seed)
+
+            rand_quantize_dropout_index = rand.randrange(self.quantize_dropout_cutoff_index, num_quant)
+
+            if quant_dropout_multiple_of != 1:
+                rand_quantize_dropout_index = ceil((rand_quantize_dropout_index + 1) / quant_dropout_multiple_of) - 1
+
+            null_indices = torch.full(x.shape[:2], -1.0, device=device, dtype=torch.long)
+
+        with autocast("cuda", enabled=False):
+            for quantizer_index, (layer, scale) in enumerate(zip(self.layers, self.scales)):
+                if should_quantize_dropout and quantizer_index > rand_quantize_dropout_index:
+                    all_indices.append(null_indices)
+                    continue
+
+                quantized, indices = layer(residual / scale)
+
+                quantized = quantized * scale
+
+                residual = residual - quantized.detach()
+                quantized_out = quantized_out + quantized
+
+                all_indices.append(indices)
+
+        quantized_out = self.project_out(quantized_out.to(x.dtype))
+
+        all_indices = torch.stack(all_indices, dim=-1)
+
+        ret = (quantized_out, all_indices)
+
+        if not return_all_codes:
+            return ret
+
+        all_codes = self.get_codes_from_indices(all_indices)
+
+        return (*ret, all_codes)
+
+
+class Xcodec2CodecDecoderVocos(nn.Module):
+    def __init__(
+        self,
+        config: Xcodec2Config,
+    ):
+        super().__init__()
+        self.quantizer = Xcodec2ResidualFSQ(
+            dim=config.vq_dim, levels=config.vq_levels, num_quantizers=config.num_quantizers
+        )
+        self.backbone = Xcodec2VocosBackbone(config=config)
+        self.head = Xcodec2ISTFTHead(config=config)
+
+    def quantize(self, x):
+        """
+        Quantize input features using the vector quantizer.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (B, D, L) where B is batch size,
+                              D is feature dimension, and L is sequence length.
+
+        Returns:
+            Tuple of (quantized_tensor, quantization_indices)
+            - quantized_tensor: The quantized representation with shape (B, L, D)
+            - quantization_indices: The indices in the codebook with shape (B, L, Q)
+              where Q is the number of quantizers
+        """
+        x = x.permute(0, 2, 1)
+        x, q = self.quantizer(x)
+        x = x.permute(0, 2, 1)
+        q = q.permute(0, 2, 1)
+        return x, q
+
+    def forward(self, x):
+        """
+        x (torch.Tensor):
+            Projected audio codes to reconstruct as audio.
+
+        Returns:
+            audio_waveform: The reconstructed audio waveform with shape (B, 1, T).
+        """
+        x = self.backbone(x)
+        x = self.head(x).unsqueeze(1)
+        return x
+
+
+class Xcodec2SemanticEncoder(nn.Module):
+    """
+    Maps input features of pre-trained semantic model to semantic embedding.
+    """
+
+    def __init__(
+        self,
+        input_channels: int,
+        code_dim: int,
+        encode_channels: int,
+        kernel_size: int = 3,
+        bias: bool = True,
+    ):
+        super().__init__()
+
+        # Initial convolution, maps input_channels to encode_channels
+        self.initial_conv = nn.Conv1d(
+            in_channels=input_channels,
+            out_channels=encode_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=False,
+        )
+
+        # Residual block with two convolutional layers
+        self.act1 = nn.ReLU(inplace=True)
+        self.conv1 = nn.Conv1d(
+            encode_channels,
+            encode_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=bias,
+        )
+        self.act2 = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv1d(
+            encode_channels,
+            encode_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=bias,
+        )
+
+        # Final convolution, maps encode_channels to code_dim
+        self.final_conv = nn.Conv1d(
+            in_channels=encode_channels,
+            out_channels=code_dim,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=False,
+        )
+
+    def forward(self, x):
+        x = self.initial_conv(x)
+        residual = x
+        x = self.act1(x)
+        x = self.conv1(x)
+        x = self.act2(x)
+        x = self.conv2(x)
+        x = x + residual
+        x = self.final_conv(x)
+        return x
+
+
+class Xcodec2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
+    """
+
+    config_class = Xcodec2Config
+    base_model_prefix = "xcodec2"
+    main_input_name = "audio"
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Conv1d)):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif (
+            isinstance(module, (nn.GroupNorm, nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d))
+            or "LayerNorm" in module.__class__.__name__
+            or "RMSNorm" in module.__class__.__name__
+        ):
+            # Norms can exist without weights (in which case they are None from torch primitives)
+            if hasattr(module, "weight") and module.weight is not None:
+                module.weight.data.fill_(1.0)
+            if hasattr(module, "bias") and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, Xcodec2SnakeBeta):
+            # Initialize alpha and beta based on the logscale setting
+            if module.logscale:
+                # Log scale alphas initialized to zeros
+                module.alpha.data.zero_()
+                module.beta.data.zero_()
+            else:
+                # Linear scale alphas initialized to ones
+                module.alpha.data.fill_(1.0)
+                module.beta.data.fill_(1.0)
+
+
+XCODEC2_INPUTS_DOCSTRING = r"""
+    args:
+        audio (`torch.FloatTensor` of shape `(batch_size, channels, num_samples)`):
+            Audio waveform.
+        audio_spectrogram (`torch.FloatTensor` of shape `(batch_size, mel_bins, time_steps)`):
+            Mel spectrogram.
+        return_dict (`bool`, *optional*):
+            whether to return a `Xcodec2Output` or a plain tuple.
+"""
+
+
+# Taking inspiration form modeling code of original authors: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py
+@add_start_docstrings(
+    "The Xcodec2 neural audio codec model.",
+    XCODEC2_INPUTS_DOCSTRING,
+)
+class Xcodec2Model(Xcodec2PreTrainedModel):
+    config_class = Xcodec2Config
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__(config)
+
+        self.hop_length = config.hop_length  # needed for padding
+        self.semantic_model = AutoModel.from_config(config.semantic_model_config).eval()
+        # Adaptor of semantic model embedding
+        self.semantic_encoder = Xcodec2SemanticEncoder(
+            config.semantic_hidden_size, config.semantic_hidden_size, config.semantic_hidden_size
+        )
+
+        self.acoustic_encoder = Xcodec2CodecEncoder(
+            downsampling_ratios=config.downsampling_ratios, hidden_dim=config.encoder_hidden_size
+        )
+        self.decoder = Xcodec2CodecDecoderVocos(config=config)
+        self.fc_prior = nn.Linear(config.intermediate_size, config.intermediate_size)
+        self.fc_post_a = nn.Linear(config.intermediate_size, config.decoder_hidden_size)
+
+        self.post_init()
+
+    def apply_weight_norm(self, legacy=True):
+        weight_norm = nn.utils.weight_norm
+        if hasattr(nn.utils.parametrizations, "weight_norm") and not legacy:
+            weight_norm = nn.utils.parametrizations.weight_norm
+
+        # Weight norm was only applied in acoustic encoder of original model
+        # -- to initial_conv
+        weight_norm(self.acoustic_encoder.initial_conv)
+
+        # -- to encoder blocks
+        for encoder_block in self.acoustic_encoder.encoder_blocks:
+            # -- to each residual unit in the block
+            for residual_unit in encoder_block.residual_units:
+                weight_norm(residual_unit.conv1)
+                weight_norm(residual_unit.conv2)
+            # -- to the final conv in the encoder block
+            weight_norm(encoder_block.conv)
+
+        # -- to final_conv
+        weight_norm(self.acoustic_encoder.final_conv)
+
+    def remove_weight_norm(self, legacy=True):
+        """Remove weight normalization from layers that have it applied via apply_weight_norm."""
+        remove_weight_norm = nn.utils.remove_weight_norm
+        if hasattr(nn.utils.parametrizations, "weight_norm") and not legacy:
+            remove_weight_norm = torch.nn.utils.parametrize.remove_parametrizations
+
+        # Remove weight norm from acoustic_encoder
+        # -- from initial_conv
+        try:
+            remove_weight_norm(self.acoustic_encoder.initial_conv)
+        except (ValueError, RuntimeError):
+            raise ValueError("Not able to remove weight norm. Have you run `apply_weight_norm?`")
+
+        # -- from encoder blocks
+        for encoder_block in self.acoustic_encoder.encoder_blocks:
+            # -- from each residual unit in the block
+            for residual_unit in encoder_block.residual_units:
+                remove_weight_norm(residual_unit.conv1)
+                remove_weight_norm(residual_unit.conv2)
+            # -- from the final conv in the encoder block
+            remove_weight_norm(encoder_block.conv)
+
+        # -- from final_conv
+        remove_weight_norm(self.acoustic_encoder.final_conv)
+
+    def encode(
+        self,
+        audio: torch.Tensor,
+        audio_spectrogram: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Xcodec2EncoderOutput]:
+        """
+        Encodes the input audio waveform into discrete codes.
+
+        Args:
+            audio (`torch.Tensor` of shape `(batch_size, 1, sequence_length)`):
+                Input audio waveform.
+            audio_spectrogram (`torch.Tensor` of shape `(batch_size, mel_bins, time_steps)`):
+                Input audio mel spectrogram for semantic encoding.
+            padding_mask (`torch.Tensor` of shape `(batch_size, 1, sequence_length)`):
+                Padding mask used to pad `audio`.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+            `audio_codes` of shape `[batch_size, 1, frames]`, the discrete encoded codes for the input audio waveform.
+            `quantized_representation` of shape `[batch_size, hidden_size, frames]`, the continuous quantized
+                representation after quantization.
+            `codes_padding_mask` of shape `[batch_size, 1, frames]`, downsampled `padding_mask` for indicating valid
+                audio codes in `audio_codes`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # 1) Semantic embedding: 16th layer of pretrained model: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py#L64
+        semantic_output = self.semantic_model(audio_spectrogram, output_hidden_states=True)
+        semantic_hidden_16 = semantic_output.hidden_states[16]
+        semantic_hidden_16 = semantic_hidden_16.transpose(1, 2)
+        semantic_encoded = self.semantic_encoder(semantic_hidden_16)
+
+        # 2) Get acoustic embedding
+        vq_emb = self.acoustic_encoder(audio)
+        vq_emb = vq_emb.transpose(1, 2)
+
+        # 3) Concat embeddings and apply final layers
+        if vq_emb.shape[-1] != semantic_encoded.shape[-1]:
+            min_len = min(vq_emb.shape[-1], semantic_encoded.shape[-1])
+            vq_emb = vq_emb[:, :, :min_len]
+            semantic_encoded = semantic_encoded[:, :, :min_len]
+        concat_emb = torch.cat([semantic_encoded, vq_emb], dim=1)
+        concat_emb = self.fc_prior(concat_emb.transpose(1, 2)).transpose(1, 2)
+
+        # 4) Get codes for decoder
+        quantized_representation, audio_codes = self.decoder.quantize(concat_emb)
+
+        # If provided, compute corresponding padding mask for audio codes
+        codes_padding_mask = None
+        if padding_mask is not None:
+            # Expected token length, as in: https://github.com/zhenye234/X-Codec-2.0/blob/ccbbf340ff143dfa6a0ea7cd61ec34a8ba2f1c3d/inference_save_code.py#L89
+            audio_length = padding_mask.sum(dim=-1, keepdim=True).cpu()
+            token_length = audio_length // self.hop_length
+            codes_padding_mask = torch.zeros(audio_codes.shape, dtype=padding_mask.dtype)
+            idx = torch.arange(audio_codes.shape[-1]).view(1, -1)
+            codes_padding_mask = (idx < token_length).to(padding_mask.dtype).to(padding_mask.device)
+
+        if not return_dict:
+            return audio_codes, quantized_representation, codes_padding_mask
+
+        return Xcodec2EncoderOutput(
+            audio_codes=audio_codes,
+            quantized_representation=quantized_representation,
+            codes_padding_mask=codes_padding_mask,
+        )
+
+    def decode(
+        self,
+        audio_codes,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Xcodec2DecoderOutput]:
+        """
+        Decodes the given frames into an output audio waveform.
+
+        Note that the output might be a bit bigger than the input. In that case, any extra steps at the end can be
+        trimmed.
+
+        Args:
+            audio_codes (`torch.LongTensor`  of shape `(batch_size, 1, codes_length)`):
+                Discrete code indices computed using `model.encode`.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+            Decoded audio values of shape `(batch_size, 1, num_samples)` obtained using the decoder part of Xcodec2.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        vq_post_emb = self.decoder.quantizer.get_output_from_indices(audio_codes.transpose(1, 2))
+        vq_post_emb = self.fc_post_a(vq_post_emb)
+        recon_audio = self.decoder(vq_post_emb)
+
+        if not return_dict:
+            return recon_audio
+
+        return Xcodec2DecoderOutput(
+            audio_values=recon_audio,
+        )
+
+    @add_start_docstrings_to_model_forward(XCODEC2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=Xcodec2Output, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        audio: torch.Tensor,
+        audio_spectrogram: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Xcodec2Output]:
+        r"""
+        Returns:
+            `Xcodec2Output` or `tuple(torch.FloatTensor, torch.LongTensor, torch.FloatTensor)`:
+            - `audio_values` (`torch.FloatTensor` of shape `(batch_size, 1, num_samples)`):
+                Reconstructed audio waveform.
+            - `audio_codes` (`torch.LongTensor` of shape `(batch_size, 1, codes_length)`):
+                Discrete code indices computed using `model.encode`.
+            - `quantized_representation` (`torch.FloatTensor` of shape `(batch_size, hidden_size, frames)`):
+                The continuous quantized representation after quantization.
+            - `codes_padding_mask` (`torch.int32` of shape `(batch_size, 1, codes_length)`):
+                Downsampled `padding_mask` for indicating valid audio codes in `audio_codes`.
+
+        Examples:
+
+        ```python
+        >>> from datasets import load_dataset
+        >>> from transformers import AutoFeatureExtractor, Xcodec2Model
+
+        >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> audio = dataset["train"]["audio"][0]["array"]
+
+        >>> model_id = "hf-audio/xcodec2"
+        >>> model = Xcodec2Model.from_pretrained(model_id)
+        >>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+        >>> inputs = feature_extractor(audio=audio, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> audio_codes = outputs.audio_codes
+        >>> audio_values = outputs.audio_values
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # for truncating output audio to original length
+        length = audio.shape[-1]
+
+        audio_codes, quantized_representation, codes_padding_mask = self.encode(
+            audio, audio_spectrogram=audio_spectrogram, padding_mask=padding_mask, return_dict=False
+        )
+        audio_values = self.decode(audio_codes, return_dict=False)[..., :length]
+
+        if not return_dict:
+            return (audio_values, audio_codes, quantized_representation, codes_padding_mask)
+
+        return Xcodec2Output(
+            audio_values=audio_values,
+            audio_codes=audio_codes,
+            quantized_representation=quantized_representation,
+            codes_padding_mask=codes_padding_mask,
+        )
+
+
+__all__ = ["Xcodec2Model", "Xcodec2PreTrainedModel"]
diff --git a/src/transformers/models/xcodec2/modular_xcodec2.py b/src/transformers/models/xcodec2/modular_xcodec2.py
new file mode 100644
index 000000000000..57151d434009
--- /dev/null
+++ b/src/transformers/models/xcodec2/modular_xcodec2.py
@@ -0,0 +1,1642 @@
+# coding=utf-8
+# Copyright 2025 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.
+
+import math
+import random
+from contextlib import nullcontext
+from dataclasses import dataclass
+from functools import partial, wraps
+from math import ceil
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import int32, nn, sin, sinc
+from torch.amp import autocast
+from torch.nn import Module, Parameter
+
+from transformers.activations import ACT2FN
+
+from ...modeling_utils import PreTrainedModel
+from ...utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_torch_flex_attn_available,
+    logging,
+    replace_return_docstrings,
+)
+from ..auto import AutoModel
+from ..llama.modeling_llama import (
+    LlamaAttention,
+    LlamaDecoderLayer,
+    LlamaRotaryEmbedding,
+)
+from .configuration_xcodec2 import Xcodec2Config
+
+
+# General docstring
+_CONFIG_FOR_DOC = "Xcodec2Config"
+
+
+@dataclass
+class Xcodec2Output(ModelOutput):
+    """
+    Args:
+        audio_values (`torch.FloatTensor` of shape `(batch_size, 1, sequence_length)`, *optional*):
+            Decoded audio waveform values in the time domain, obtained using the decoder
+            part of Xcodec2. These represent the reconstructed audio signal.
+        audio_codes (`torch.LongTensor` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Discrete code embeddings computed using `model.encode`. These are the quantized
+            representations of the input audio used for further processing or generation.
+        quantized_representation (`torch.Tensor` of shape `(batch_size, dimension, time_steps)`):
+            Quantized continuous representation of input's embedding.
+        codes_padding_mask (`torch.int32` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Downsampled `padding_mask` for indicating valid audio codes in `audio_codes`.
+    """
+
+    audio_values: Optional[torch.FloatTensor] = None
+    audio_codes: Optional[torch.LongTensor] = None
+    quantized_representation: Optional[torch.Tensor] = None
+    codes_padding_mask: Optional[torch.Tensor] = None
+
+
+@dataclass
+class Xcodec2EncoderOutput(ModelOutput):
+    """
+    Args:
+        audio_codes (`torch.LongTensor` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Discrete code embeddings computed using `model.encode`. These represent
+            the compressed, quantized form of the input audio signal that can be
+            used for storage, transmission, or generation.
+        quantized_representation (`torch.Tensor` of shape `(batch_size, dimension, time_steps)`):
+            Quantized continuous representation of input's embedding.
+        codes_padding_mask (`torch.int32` of shape `(batch_size, 1, codes_length)`, *optional*):
+            Downsampled `padding_mask` for indicating valid audio codes in `audio_codes`.
+
+    """
+
+    audio_codes: Optional[torch.LongTensor] = None
+    quantized_representation: Optional[torch.Tensor] = None
+    codes_padding_mask: Optional[torch.Tensor] = None
+
+
+@dataclass
+class Xcodec2DecoderOutput(ModelOutput):
+    """
+    Args:
+        audio_values (`torch.FloatTensor` of shape `(batch_size, 1, segment_length)`, *optional*):
+            Decoded audio waveform values in the time domain, obtained by converting
+            the discrete codes back into continuous audio signals. This represents
+            the reconstructed audio that can be played back.
+    """
+
+    audio_values: Optional[torch.FloatTensor] = None
+
+
+if is_torch_flex_attn_available():
+    pass
+
+
+logger = logging.get_logger(__name__)
+
+
+# Default `unsqueeze_dim=2`
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=2):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 2):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# See here for their attention implementation:
+# https://huggingface.co/HKUSTAudio/xcodec2/blob/main/vq/bs_roformer5.py
+class Xcodec2Attention(LlamaAttention):
+    def __init__(self, config: Xcodec2Config, layer_idx: int):
+        super().__init__(config, layer_idx=layer_idx)
+        self.is_causal = False
+
+
+class Xcodec2RotaryEmbedding(LlamaRotaryEmbedding):
+    pass
+
+
+# Unlike LlamaMLP, does not have `gate_proj` layer
+class Xcodec2MLP(nn.Module):
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+        dim = config.hidden_size
+
+        self.fc1 = nn.Linear(dim, 4 * dim, bias=False)
+        self.activation = ACT2FN[config.hidden_act]
+        self.fc2 = nn.Linear(4 * dim, dim, bias=False)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.activation(x)
+        x = self.fc2(x)
+        return x
+
+
+# Original Transformer block: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/vq/bs_roformer5.py#L91
+class Xcodec2DecoderLayer(LlamaDecoderLayer):
+    pass
+
+
+class Xcodec2SnakeBeta(nn.Module):
+    """
+    A modified Snake function which uses separate parameters for the magnitude and frequency of the periodic components.
+
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+
+    Parameters:
+        - dim: The number of input features
+        - logscale: Whether to use logarithmic scaling for alpha and beta (default: False)
+
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+
+    Examples:
+        >>> activation = Xcodec2SnakeBeta(256)
+        >>> x = torch.randn(1, 256, 100)  # (batch, channels, time)
+        >>> y = activation(x)
+    """
+
+    def __init__(self, dim, logscale=True):
+        """
+        Args:
+            dim: Shape of the input features (channels dimension)
+            logscale: Whether to use log scale initialization (default: True)
+                            If True, alpha and beta are initialized to zeros
+                            If False, alpha and beta are initialized to ones
+
+        Note:
+            - alpha controls the frequency of the periodic components
+            - beta controls the magnitude of the periodic components
+        """
+        super().__init__()
+        self.dim = dim
+
+        # initialize alpha
+        self.logscale = logscale
+        if self.logscale:  # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(dim))
+            self.beta = Parameter(torch.zeros(dim))
+        else:  # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(dim))
+            self.beta = Parameter(torch.ones(dim))
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        """
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        """
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
+    """
+    Creates a Kaiser-windowed sinc filter for low-pass filtering.
+
+    Args:
+        cutoff: Normalized cutoff frequency (0 to 0.5, where 0.5 is the Nyquist frequency)
+        half_width: Transition bandwidth parameter controlling filter roll-off
+        kernel_size: Size of the filter kernel
+
+    Returns:
+        torch.Tensor: A 1D filter kernel of shape (1, 1, kernel_size)
+    """
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+
+    # For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    """
+    Used by `DownSample1d`.
+    """
+
+    def __init__(
+        self,
+        cutoff=0.5,
+        half_width=0.6,
+        stride: int = 1,
+        padding: bool = True,
+        padding_mode: str = "replicate",
+        kernel_size: int = 12,
+    ):
+        super().__init__()
+        if cutoff < -0.0:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    # input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+        return out
+
+
+class UpSample1d(nn.Module):
+    """
+    1D upsampling module using transposed convolution with a sinc filter.
+
+    This module performs anti-aliased upsampling by first padding the input signal,
+    then applying a transposed convolution with a Kaiser-windowed sinc filter,
+    and finally trimming the resulting signal to remove boundary effects.
+
+    Args:
+        ratio (int): Upsampling ratio (default: 2)
+        kernel_size (int, optional): Size of the filter kernel.
+            If None, it's automatically calculated as 6 * ratio.
+    """
+
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size)
+        self.register_buffer("filter", filter)
+
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        x = self.ratio * F.conv_transpose1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+        x = x[..., self.pad_left : -self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    """
+    1D downsampling module using a low-pass filter.
+
+    This module performs anti-aliased downsampling by applying a low-pass filter
+    with an appropriate cutoff frequency followed by strided convolution.
+
+    Args:
+        ratio (int): Downsampling ratio (default: 2)
+        kernel_size (int, optional): Size of the filter kernel.
+            If None, it's automatically calculated as 6 * ratio.
+    """
+
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio, half_width=0.6 / ratio, stride=ratio, kernel_size=self.kernel_size
+        )
+
+    def forward(self, x):
+        x = self.lowpass(x)
+
+        return x
+
+
+class Activation1d(nn.Module):
+    """
+    A module that applies activation function with up-sampling and down-sampling.
+
+    This module first up-samples the input signal, applies an activation function,
+    and then down-samples the result. This architecture allows for more complex
+    non-linear transformations of the signal.
+
+    Args:
+        activation (`torch.nn.Module`):
+            The activation function module to apply (e.g., nn.ReLU(), nn.LeakyReLU()).
+        up_ratio (`int`, *optional*, defaults to 2):
+            The up-sampling ratio for increasing temporal resolution before activation.
+        down_ratio (`int`, *optional*, defaults to 2):
+            The down-sampling ratio for decreasing temporal resolution after activation.
+        up_kernel_size (`int`, *optional*, defaults to 12):
+            The kernel size used in the up-sampling operation.
+        down_kernel_size (`int`, *optional*, defaults to 12):
+            The kernel size used in the down-sampling operation.
+    """
+
+    def __init__(
+        self, activation, up_ratio: int = 2, down_ratio: int = 2, up_kernel_size: int = 12, down_kernel_size: int = 12
+    ):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x
+
+
+class ResidualUnit(nn.Module):
+    """
+    A residual unit that combines dilated convolutions with activation functions.
+
+    This unit performs a series of operations while maintaining a residual connection:
+    1. First activation with up/down-sampling
+    2. Dilated convolution with kernel size 7
+    3. Second activation with up/down-sampling
+    4. 1x1 convolution for channel mixing
+    5. Addition of the input (residual connection)
+
+    Args:
+        dim (`int`, *optional*, defaults to 16):
+            The number of channels in the input and output tensors.
+        dilation (`int`, *optional*, defaults to 1):
+            The dilation rate for the main convolution operation. Higher values
+            increase the receptive field without increasing parameter count.
+    """
+
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+        pad = ((7 - 1) * dilation) // 2
+        self.activation1 = Activation1d(activation=Xcodec2SnakeBeta(dim, logscale=True))
+        self.conv1 = nn.Conv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad)
+        self.activation2 = Activation1d(activation=Xcodec2SnakeBeta(dim, logscale=True))
+        self.conv2 = nn.Conv1d(dim, dim, kernel_size=1)
+
+    def forward(self, x):
+        residual = x
+        x = self.activation1(x)
+        x = self.conv1(x)
+        x = self.activation2(x)
+        x = self.conv2(x)
+        return residual + x
+
+
+class EncoderBlock(nn.Module):
+    """
+    Encoder block for the Xcodec2 acoustic encoder.
+
+    This block consists of:
+    1. A series of residual units with different dilation rates
+    2. An activation function with upsampling and downsampling
+    3. A strided convolution to change the dimension and downsample if stride > 1
+
+    Args:
+        dim (int): Dimension of the output features. Input dimension is assumed to be dim // 2
+        stride (int): Stride for the final convolution, controls downsampling factor
+        dilations (tuple): Dilation rates for the residual units
+    """
+
+    def __init__(self, dim: int = 16, stride: int = 1, dilations=(1, 3, 9)):
+        super().__init__()
+        self.residual_units = nn.ModuleList([ResidualUnit(dim // 2, dilation=d) for d in dilations])
+        self.activation = Activation1d(activation=Xcodec2SnakeBeta(dim // 2, logscale=True))
+        self.conv = nn.Conv1d(
+            dim // 2,
+            dim,
+            kernel_size=2 * stride,
+            stride=stride,
+            padding=stride // 2 + stride % 2,
+        )
+
+    def forward(self, x):
+        for residual_unit in self.residual_units:
+            x = residual_unit(x)
+        x = self.activation(x)
+        x = self.conv(x)
+        return x
+
+
+class Xcodec2CodecEncoder(nn.Module):
+    """
+    The encoder component of the Xcodec2 model for audio encoding.
+
+    This encoder transforms raw audio waveforms into latent representations:
+    1. Initial convolution to process the raw audio input
+    2. Series of encoder blocks with progressively increasing feature dimensions and downsampling
+    3. Final activation and projection to the target hidden dimension
+
+    Args:
+        d_model (`int`, *optional*, defaults to 48):
+            Initial dimension of the model. This will be doubled at each encoder block.
+        downsampling_ratios (`List[int]`, *optional*, defaults to [2, 2, 4, 4, 5]):
+            List of downsampling ratios for each encoder block. The product of these values
+            determines the total temporal compression factor.
+        dilations (`tuple`, *optional*, defaults to (1, 3, 9)):
+            Tuple of dilation rates used in the residual units within each encoder block.
+        hidden_dim (`int`, *optional*, defaults to 1024):
+            Dimension of the final latent representation after encoding.
+    """
+
+    def __init__(
+        self,
+        d_model=48,
+        downsampling_ratios=[2, 2, 4, 4, 5],
+        dilations=(1, 3, 9),
+        hidden_dim=1024,
+    ):
+        super().__init__()
+
+        self.initial_conv = nn.Conv1d(1, d_model, kernel_size=7, padding=3)
+
+        self.encoder_blocks = nn.ModuleList()
+        for i, stride in enumerate(downsampling_ratios):
+            d_model *= 2
+            self.encoder_blocks.append(EncoderBlock(d_model, stride=stride, dilations=dilations))
+
+        self.final_activation = Activation1d(activation=Xcodec2SnakeBeta(d_model, logscale=True))
+        self.final_conv = nn.Conv1d(d_model, hidden_dim, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        """
+        Forward pass of the Xcodec2 encoder.
+
+        Args:
+            x (`torch.Tensor` of shape `(batch_size, 1, sequence_length)`):
+                Input audio waveform tensor. Expected to be a mono audio signal with shape
+                (batch_size, 1, sequence_length).
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, compressed_length, hidden_dim)`:
+                Encoded audio representation with temporal dimension compressed by the product
+                of all downsampling_ratios. The output tensor is transposed to have the
+                sequence dimension second (batch_size, seq_len, features) for compatibility
+                with transformer-based models.
+        """
+        # Initial convolution
+        x = self.initial_conv(x)
+
+        # Apply all encoder blocks
+        for encoder_block in self.encoder_blocks:
+            x = encoder_block(x)
+
+        # Final processing
+        x = self.final_activation(x)
+        x = self.final_conv(x)
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class ResNetBlock(nn.Module):
+    """
+    A basic residual block for 1D convolutional networks.
+
+    This block consists of:
+    1. GroupNorm + SiLU-like activation (x * sigmoid(x)) + Conv1d
+    2. GroupNorm + SiLU-like activation + Dropout + Conv1d
+    3. Residual connection, with optional projection if input and output channels differ
+
+    Args:
+        in_channels: Number of input channels
+        out_channels: Number of output channels (defaults to in_channels if None)
+        dropout: Dropout probability (default: 0.1)
+    """
+
+    def __init__(self, *, in_channels, out_channels=None, dropout=0.1):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.conv1 = torch.nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = torch.nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = h * torch.sigmoid(h)
+        h = self.conv1(h)
+        h = self.norm2(h)
+        h = h * torch.sigmoid(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class Xcodec2VocosBackbone(nn.Module):
+    """
+    Hybrid ResNet-Transformer architecture.
+
+    Architecture overview:
+    - Input embedding: A 1D convolution (kernel size 7, padding 3) projects raw features
+      into the model's hidden dimension.
+    - Prior ResNet blocks: Two ResNet-style residual blocks operate on the hidden states
+      to provide early nonlinear feature processing.
+    - Rotary embeddings: Rotary positional embeddings (RoPE) are initialized to provide
+      position-dependent information to the Transformer layers.
+    - Transformer stack: A sequence of Xcodec2DecoderLayer modules applies self-attention
+      with rotary embeddings, interleaved with MLPs, to model long-range dependencies.
+    - Normalization: A final LayerNorm is applied to stabilize training and outputs.
+    - Post ResNet blocks: Two additional ResNet-style blocks refine the representation
+      after the Transformer stack.
+
+    This design combines convolutional inductive biases (via ResNet-style blocks and the
+    initial Conv1d embedding) with the global sequence modeling capabilities of
+    Transformers, making it suitable for sequence data such as audio or other temporal
+    signals.
+    """
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+
+        self.embed = nn.Conv1d(config.hidden_size, config.hidden_size, kernel_size=7, padding=3)
+        block_in = config.hidden_size
+        dropout = config.resnet_dropout
+
+        self.prior_blocks = nn.ModuleList(
+            [
+                ResNetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout),
+                ResNetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout),
+            ]
+        )
+
+        # Initialize rotary embeddings
+        self.position_ids = torch.arange(config.num_attention_heads).unsqueeze(0)
+        self.rotary_emb = Xcodec2RotaryEmbedding(config=config)
+
+        # Create transformer layers
+        self.transformers = nn.ModuleList(
+            [Xcodec2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-6)
+
+        self.post_blocks = nn.ModuleList(
+            [
+                ResNetBlock(
+                    in_channels=config.hidden_size,
+                    out_channels=config.hidden_size,
+                    dropout=dropout,
+                ),
+                ResNetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout),
+            ]
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the Xcodec2VocosBackbone.
+
+        The processing flow includes:
+        1. Initial embedding via 1D convolution
+        2. Processing through ResNet blocks
+        3. Transformer processing with rotary position embeddings
+        4. Final ResNet blocks and normalization
+
+        Args:
+            x (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Input tensor containing features to process.
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`:
+                Processed features with the same shape as the input but transformed
+                through the backbone architecture.
+        """
+        # Handle initial transformations
+        x = x.transpose(1, 2)
+        x = self.embed(x)
+
+        # Process through prior_blocks
+        for block in self.prior_blocks:
+            x = block(x)
+
+        x = x.transpose(1, 2)  # [batch, seq_len, hidden_dim]
+
+        # Generate rotary embeddings
+        position_embeddings = self.rotary_emb(x, self.position_ids.to(x.device))
+
+        # Apply transformer layers with position embeddings
+        for layer in self.transformers:
+            x = layer(
+                x,
+                position_embeddings=position_embeddings,
+            )
+
+        # Handle final transformations
+        x = x.transpose(1, 2)
+
+        # Process through post_blocks
+        for block in self.post_blocks:
+            x = block(x)
+
+        x = x.transpose(1, 2)
+        x = self.final_layer_norm(x)
+
+        return x
+
+
+class Xcodec2ISTFT(nn.Module):
+    """
+    As in original Vocos code:
+    https://github.com/gemelo-ai/vocos/blob/c859e3b7b534f3776a357983029d34170ddd6fc3/vocos/spectral_ops.py#L7
+
+    Custom ISTFT implementation to support "same" padding as in Vocos.
+    """
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+        if config.istft_padding not in ["center", "same"]:
+            raise ValueError("Padding must be 'center' or 'same'.")
+        self.padding = config.istft_padding
+        self.n_fft = config.n_fft
+        self.hop_length = config.hop_length
+        self.win_length = getattr(config, "win_length", config.n_fft)
+        window = torch.hann_window(self.win_length)
+        self.register_buffer("window", window)
+
+    def forward(self, spec: torch.Tensor) -> torch.Tensor:
+        """
+        Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram.
+
+        Args:
+            spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size,
+                  is the number of frequency bins, and T is the number of time frames.
+
+        Returns:
+            Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal.
+        """
+        if spec.dim() != 3:
+            raise ValueError("Expected a 3D tensor as input")
+
+        if self.padding == "center":
+            # Fallback to pytorch native implementation
+            return torch.istft(spec, self.n_fft, self.hop_length, self.win_length, self.window, center=True)
+
+        elif self.padding == "same":
+            # Custom implementation from Vocos codebase
+            pad = (self.win_length - self.hop_length) // 2
+            n_frames = spec.shape[-1]
+
+            # Inverse FFT
+            ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward")
+            ifft = ifft * self.window[None, :, None]
+
+            # Overlap and Add
+            output_size = (n_frames - 1) * self.hop_length + self.win_length
+            y = F.fold(
+                ifft,
+                output_size=(1, output_size),
+                kernel_size=(1, self.win_length),
+                stride=(1, self.hop_length),
+            )[:, 0, 0, pad:-pad]
+
+            # Window envelope
+            window_sq = self.window.square().expand(1, n_frames, -1).transpose(1, 2)
+            window_envelope = F.fold(
+                window_sq,
+                output_size=(1, output_size),
+                kernel_size=(1, self.win_length),
+                stride=(1, self.hop_length),
+            ).squeeze()[pad:-pad]
+
+            # Normalize
+            if not (window_envelope > 1e-11).all():
+                raise ValueError("Window envelope values are too small (<=1e-11)")
+            return y / window_envelope
+
+        else:
+            raise ValueError("Padding must be 'center' or 'same'.")
+
+
+class Xcodec2ISTFTHead(nn.Module):
+    """
+    As in original Vocos code:
+    https://github.com/gemelo-ai/vocos/blob/c859e3b7b534f3776a357983029d34170ddd6fc3/vocos/heads.py#L26
+    - Projects the hidden states to STFT coefficients (magnitude and phase)
+    - Applies ISTFT to reconstruct the time-domain audio signal
+    """
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__()
+        self.out = torch.nn.Linear(config.hidden_size, config.n_fft + 2)
+        self.istft = Xcodec2ISTFT(config)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass of the ISTFTHead module.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
+                        L is the sequence length, and H denotes the model dimension.
+
+        Returns:
+            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
+            Tensor: Predicted STFT coefficients of shape (B, L, N+2), where N is the number of frequency bins.
+        """
+        x_pred = self.out(x).transpose(1, 2)
+        mag, p = x_pred.chunk(2, dim=1)
+        mag = torch.exp(mag)
+        mag = torch.clip(mag, max=1e2)  # safeguard to prevent excessively large magnitudes
+        # wrapping happens here. These two lines produce real and imaginary value
+        spectrogram_real = torch.cos(p)
+        spectrogram_imag = torch.sin(p)
+        spectrogram_complex = mag * (spectrogram_real + 1j * spectrogram_imag)
+        audio = self.istft(spectrogram_complex)
+        return audio
+
+
+def round_ste(z):
+    """
+    Round with straight through gradients.
+    Used in `Xcodec2FSQ`.
+    """
+    zhat = z.round()
+    return z + (zhat - z).detach()
+
+
+def floor_ste(z):
+    """
+    Floor with straight through gradients.
+    Used in `Xcodec2FSQ`.
+    """
+    zhat = z.floor()
+    return z + (zhat - z).detach()
+
+
+def maybe(fn):
+    """
+    Used in `Xcodec2FSQ`.
+    """
+
+    @wraps(fn)
+    def inner(x, *args, **kwargs):
+        if x is None:
+            return x
+        return fn(x, *args, **kwargs)
+
+    return inner
+
+
+def get_maybe_sync_seed(device, max_size=10_000):
+    """
+    Used in `Xcodec2ResidualFSQ`.
+    """
+    rand_int = torch.randint(0, max_size, (), device=device)
+
+    if dist.is_initialized() and dist.get_world_size() > 1:
+        dist.all_reduce(rand_int)
+
+    return rand_int.item()
+
+
+class Xcodec2FSQ(Module):
+    """
+    Copied from https://github.com/lucidrains/vector-quantize-pytorch/blob/fe903ce2ae9c125ace849576aa6d09c5cec21fe4/vector_quantize_pytorch/finite_scalar_quantization.py#L61
+    Simply changed asserts to raise ValueErrors.
+    """
+
+    def __init__(
+        self,
+        levels: list[int],
+        dim: Optional[int] = None,
+        num_codebooks=1,
+        keep_num_codebooks_dim: Optional[bool] = None,
+        scale: Optional[float] = None,
+        allowed_dtypes: tuple[torch.dtype, ...] = (torch.float32, torch.float64),
+        channel_first: bool = False,
+        projection_has_bias: bool = True,
+        return_indices=True,
+        force_quantization_f32=True,
+        preserve_symmetry: bool = False,
+        noise_dropout=0.0,
+    ):
+        super().__init__()
+
+        _levels = torch.tensor(levels, dtype=int32)
+        self.register_buffer("_levels", _levels, persistent=False)
+
+        _basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dim=0, dtype=int32)
+        self.register_buffer("_basis", _basis, persistent=False)
+
+        self.scale = scale
+
+        self.preserve_symmetry = preserve_symmetry
+        self.noise_dropout = noise_dropout
+
+        codebook_dim = len(levels)
+        self.codebook_dim = codebook_dim
+
+        effective_codebook_dim = codebook_dim * num_codebooks
+        self.num_codebooks = num_codebooks
+        self.effective_codebook_dim = effective_codebook_dim
+
+        keep_num_codebooks_dim = keep_num_codebooks_dim if keep_num_codebooks_dim is not None else num_codebooks > 1
+        if num_codebooks > 1 and not keep_num_codebooks_dim:
+            raise ValueError("When num_codebooks > 1, keep_num_codebooks_dim must be True")
+        self.keep_num_codebooks_dim = keep_num_codebooks_dim
+
+        self.dim = dim if dim is not None else len(_levels) * num_codebooks
+
+        self.channel_first = channel_first
+
+        has_projections = self.dim != effective_codebook_dim
+        self.project_in = (
+            nn.Linear(self.dim, effective_codebook_dim, bias=projection_has_bias) if has_projections else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(effective_codebook_dim, self.dim, bias=projection_has_bias) if has_projections else nn.Identity()
+        )
+
+        self.has_projections = has_projections
+
+        self.return_indices = return_indices
+        if return_indices:
+            self.codebook_size = math.prod(levels)
+            implicit_codebook = self._indices_to_codes(torch.arange(self.codebook_size))
+            self.register_buffer("implicit_codebook", implicit_codebook, persistent=False)
+
+        self.allowed_dtypes = allowed_dtypes
+        self.force_quantization_f32 = force_quantization_f32
+
+    def bound(self, z, eps: float = 1e-3):
+        half_l = (self._levels - 1) * (1 + eps) / 2
+        offset = torch.where(self._levels % 2 == 0, 0.5, 0.0)
+        shift = (offset / half_l).atanh()
+        return (z + shift).tanh() * half_l - offset
+
+    def quantize(self, z):
+        _, _, noise_dropout, preserve_symmetry, half_width = (
+            z.shape[0],
+            z.device,
+            self.noise_dropout,
+            self.preserve_symmetry,
+            (self._levels // 2),
+        )
+        bound_fn = self.symmetry_preserving_bound if preserve_symmetry else self.bound
+
+        bounded_z = bound_fn(z)
+        if self.training and noise_dropout > 0.0:
+            offset_mask = torch.bernoulli(torch.full_like(bounded_z, noise_dropout)).bool()
+            offset = torch.rand_like(bounded_z) - 0.5
+            bounded_z = torch.where(offset_mask, bounded_z + offset, bounded_z)
+
+        return round_ste(bounded_z) / half_width
+
+    def _scale_and_shift(self, zhat_normalized):
+        half_width = self._levels // 2
+        return (zhat_normalized * half_width) + half_width
+
+    def _scale_and_shift_inverse(self, zhat):
+        half_width = self._levels // 2
+        return (zhat - half_width) / half_width
+
+    def _indices_to_codes(self, indices):
+        level_indices = self.indices_to_level_indices(indices)
+        codes = self._scale_and_shift_inverse(level_indices)
+        return codes
+
+    def codes_to_indices(self, zhat):
+        """Converts a `code` to an index in the codebook."""
+        if zhat.shape[-1] != self.codebook_dim:
+            raise ValueError(f"Expected zhat to have shape [-1, {self.codebook_dim}], but got {zhat.shape}")
+        zhat = self._scale_and_shift(zhat)
+        return (zhat * self._basis).sum(dim=-1).to(int32)
+
+    def indices_to_level_indices(self, indices):
+        """Converts indices to indices at each level, perhaps needed for a transformer with factorized embeddings"""
+        indices = indices.unsqueeze(-1)
+        codes_non_centered = (indices // self._basis) % self._levels
+        return codes_non_centered
+
+    def indices_to_codes(self, indices):
+        """Inverse of `codes_to_indices`."""
+        if indices is None:
+            raise ValueError("indices cannot be None")
+
+        codes = self._indices_to_codes(indices)
+
+        if self.keep_num_codebooks_dim:
+            codes = codes.reshape(*codes.shape[:-2], -1)
+
+        codes = self.project_out(codes)
+
+        return codes
+
+    def forward(self, z):
+        """
+        einstein notation
+        b - batch
+        n - sequence (or flattened spatial dimensions)
+        d - feature dimension
+        c - number of codebook dim
+        """
+
+        if z.shape[-1] != self.dim:
+            raise ValueError(f"expected dimension of {self.dim} but found dimension of {z.shape[-1]}")
+
+        z = self.project_in(z)
+
+        # z = rearrange(z, 'b n (c d) -> b n c d', c = self.num_codebooks)
+        b, n, cd = z.shape  # (b, n, c·d)
+        c = self.num_codebooks
+        d = cd // c  # infer the per-codebook dimension
+
+        z = z.view(b, n, c, d)  # now (b, n, c, d)
+        # whether to force quantization step to be full precision or not
+
+        force_f32 = self.force_quantization_f32
+        quantization_context = partial(autocast, "cuda", enabled=False) if force_f32 else nullcontext
+        with quantization_context():
+            orig_dtype = z.dtype
+
+            if force_f32 and orig_dtype not in self.allowed_dtypes:
+                z = z.float()
+
+            codes = self.quantize(z)
+
+            # returning indices could be optional
+            indices = None
+
+            if self.return_indices:
+                indices = self.codes_to_indices(codes)
+
+            # codes = rearrange(codes, 'b n c d -> b n (c d)')
+            codes = codes.flatten(start_dim=2)
+            codes = codes.to(orig_dtype)
+
+        # project out
+        out = self.project_out(codes)
+        if not self.keep_num_codebooks_dim and self.return_indices:
+            indices = maybe(lambda t, *_, **__: t.squeeze(-1))(indices)
+
+        # return quantized output and indices
+        return out, indices
+
+
+class Xcodec2ResidualFSQ(Module):
+    """
+    Copied from https://github.com/lucidrains/vector-quantize-pytorch/blob/fe903ce2ae9c125ace849576aa6d09c5cec21fe4/vector_quantize_pytorch/residual_fsq.py#L49
+    Simply changed asserts to raise ValueErrors.
+    """
+
+    def __init__(
+        self,
+        *,
+        levels: list[int],
+        num_quantizers,
+        dim: Optional[int] = None,
+        is_channel_first=False,
+        quantize_dropout=False,
+        quantize_dropout_cutoff_index=0,
+        quantize_dropout_multiple_of=1,
+        soft_clamp_input_value=None,
+        **kwargs,
+    ):
+        super().__init__()
+        codebook_dim = len(levels)
+
+        dim = codebook_dim if dim is None else dim
+
+        requires_projection = codebook_dim != dim
+        self.project_in = nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()
+        self.project_out = nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
+        self.has_projections = requires_projection
+
+        self.is_channel_first = is_channel_first
+        self.num_quantizers = num_quantizers
+        self.soft_clamp_input_value = soft_clamp_input_value
+        self.levels = levels
+        self.layers = nn.ModuleList([])
+
+        levels_tensor = torch.Tensor(levels)
+
+        scales = []
+
+        for ind in range(num_quantizers):
+            scales.append((levels_tensor - 1) ** -ind)
+
+            fsq = Xcodec2FSQ(levels=levels, dim=codebook_dim, **kwargs)
+
+            self.layers.append(fsq)
+
+        self.codebook_size = self.layers[0].codebook_size
+
+        self.register_buffer("scales", torch.stack(scales), persistent=False)
+
+        self.quantize_dropout = quantize_dropout and num_quantizers > 1
+
+        if quantize_dropout_cutoff_index < 0:
+            raise ValueError("quantize_dropout_cutoff_index must be greater than or equal to 0")
+
+        self.quantize_dropout_cutoff_index = quantize_dropout_cutoff_index
+        self.quantize_dropout_multiple_of = quantize_dropout_multiple_of
+
+    @property
+    def codebooks(self):
+        codebooks = [layer.implicit_codebook for layer in self.layers]
+        codebooks = torch.stack(codebooks, dim=0)
+        return codebooks
+
+    def get_codes_from_indices(self, indices):
+        _, quantize_dim = indices.shape[0], indices.shape[-1]
+        b, *spatial_dims, q = indices.shape
+        indices_packed = indices.reshape(b, -1, q)
+        ps = (tuple(spatial_dims),)
+
+        if quantize_dim < self.num_quantizers:
+            if not self.quantize_dropout > 0.0:
+                raise ValueError(
+                    "quantize dropout must be greater than 0 if you wish to reconstruct from a signal with less fine quantizations"
+                )
+            indices_packed = F.pad(indices_packed, (0, self.num_quantizers - quantize_dim), value=-1)
+
+        mask = indices_packed == -1
+        indices_proc = indices_packed.masked_fill(mask, 0)
+
+        indices_permuted = indices_proc.permute(2, 0, 1)
+
+        selected_codes = [self.codebooks[qi][indices_permuted[qi]] for qi in range(self.num_quantizers)]
+
+        all_codes = torch.stack(selected_codes, dim=0)
+
+        mask_permuted = mask.permute(2, 0, 1).unsqueeze(-1)
+        all_codes = all_codes.masked_fill(mask_permuted, 0.0)
+
+        scales_reshaped = self.scales.view(self.num_quantizers, 1, 1, -1)
+        all_codes = all_codes * scales_reshaped
+
+        spatial_shape = tuple(ps[0])
+        q, b, _, d = all_codes.shape
+
+        all_codes = all_codes.reshape(q, b, *spatial_shape, d)
+        return all_codes
+
+    def get_output_from_indices(self, indices):
+        codes = self.get_codes_from_indices(indices)
+        codes_summed = torch.sum(codes, dim=0)
+        return self.project_out(codes_summed)
+
+    def forward(self, x, return_all_codes=False, rand_quantize_dropout_fixed_seed=None):
+        num_quant, quant_dropout_multiple_of, device = self.num_quantizers, self.quantize_dropout_multiple_of, x.device
+
+        x = self.project_in(x)
+
+        if self.soft_clamp_input_value is not None:
+            clamp_value = self.soft_clamp_input_value
+            x = (x / clamp_value).tanh() * clamp_value
+
+        quantized_out = 0.0
+        residual = self.layers[0].bound(x)
+
+        all_indices = []
+
+        should_quantize_dropout = self.training and self.quantize_dropout and torch.is_grad_enabled()
+
+        if should_quantize_dropout:
+            if rand_quantize_dropout_fixed_seed is None:
+                rand_quantize_dropout_fixed_seed = get_maybe_sync_seed(device)
+
+            rand = random.Random(rand_quantize_dropout_fixed_seed)
+
+            rand_quantize_dropout_index = rand.randrange(self.quantize_dropout_cutoff_index, num_quant)
+
+            if quant_dropout_multiple_of != 1:
+                rand_quantize_dropout_index = ceil((rand_quantize_dropout_index + 1) / quant_dropout_multiple_of) - 1
+
+            null_indices = torch.full(x.shape[:2], -1.0, device=device, dtype=torch.long)
+
+        with autocast("cuda", enabled=False):
+            for quantizer_index, (layer, scale) in enumerate(zip(self.layers, self.scales)):
+                if should_quantize_dropout and quantizer_index > rand_quantize_dropout_index:
+                    all_indices.append(null_indices)
+                    continue
+
+                quantized, indices = layer(residual / scale)
+
+                quantized = quantized * scale
+
+                residual = residual - quantized.detach()
+                quantized_out = quantized_out + quantized
+
+                all_indices.append(indices)
+
+        quantized_out = self.project_out(quantized_out.to(x.dtype))
+
+        all_indices = torch.stack(all_indices, dim=-1)
+
+        ret = (quantized_out, all_indices)
+
+        if not return_all_codes:
+            return ret
+
+        all_codes = self.get_codes_from_indices(all_indices)
+
+        return (*ret, all_codes)
+
+
+class Xcodec2CodecDecoderVocos(nn.Module):
+    def __init__(
+        self,
+        config: Xcodec2Config,
+    ):
+        super().__init__()
+        self.quantizer = Xcodec2ResidualFSQ(
+            dim=config.vq_dim, levels=config.vq_levels, num_quantizers=config.num_quantizers
+        )
+        self.backbone = Xcodec2VocosBackbone(config=config)
+        self.head = Xcodec2ISTFTHead(config=config)
+
+    def quantize(self, x):
+        """
+        Quantize input features using the vector quantizer.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (B, D, L) where B is batch size,
+                              D is feature dimension, and L is sequence length.
+
+        Returns:
+            Tuple of (quantized_tensor, quantization_indices)
+            - quantized_tensor: The quantized representation with shape (B, L, D)
+            - quantization_indices: The indices in the codebook with shape (B, L, Q)
+              where Q is the number of quantizers
+        """
+        x = x.permute(0, 2, 1)
+        x, q = self.quantizer(x)
+        x = x.permute(0, 2, 1)
+        q = q.permute(0, 2, 1)
+        return x, q
+
+    def forward(self, x):
+        """
+        x (torch.Tensor):
+            Projected audio codes to reconstruct as audio.
+
+        Returns:
+            audio_waveform: The reconstructed audio waveform with shape (B, 1, T).
+        """
+        x = self.backbone(x)
+        x = self.head(x).unsqueeze(1)
+        return x
+
+
+class Xcodec2SemanticEncoder(nn.Module):
+    """
+    Maps input features of pre-trained semantic model to semantic embedding.
+    """
+
+    def __init__(
+        self,
+        input_channels: int,
+        code_dim: int,
+        encode_channels: int,
+        kernel_size: int = 3,
+        bias: bool = True,
+    ):
+        super().__init__()
+
+        # Initial convolution, maps input_channels to encode_channels
+        self.initial_conv = nn.Conv1d(
+            in_channels=input_channels,
+            out_channels=encode_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=False,
+        )
+
+        # Residual block with two convolutional layers
+        self.act1 = nn.ReLU(inplace=True)
+        self.conv1 = nn.Conv1d(
+            encode_channels,
+            encode_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=bias,
+        )
+        self.act2 = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv1d(
+            encode_channels,
+            encode_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=bias,
+        )
+
+        # Final convolution, maps encode_channels to code_dim
+        self.final_conv = nn.Conv1d(
+            in_channels=encode_channels,
+            out_channels=code_dim,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            bias=False,
+        )
+
+    def forward(self, x):
+        x = self.initial_conv(x)
+        residual = x
+        x = self.act1(x)
+        x = self.conv1(x)
+        x = self.act2(x)
+        x = self.conv2(x)
+        x = x + residual
+        x = self.final_conv(x)
+        return x
+
+
+class Xcodec2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
+    """
+
+    config_class = Xcodec2Config
+    base_model_prefix = "xcodec2"
+    main_input_name = "audio"
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Conv1d)):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif (
+            isinstance(module, (nn.GroupNorm, nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d))
+            or "LayerNorm" in module.__class__.__name__
+            or "RMSNorm" in module.__class__.__name__
+        ):
+            # Norms can exist without weights (in which case they are None from torch primitives)
+            if hasattr(module, "weight") and module.weight is not None:
+                module.weight.data.fill_(1.0)
+            if hasattr(module, "bias") and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, Xcodec2SnakeBeta):
+            # Initialize alpha and beta based on the logscale setting
+            if module.logscale:
+                # Log scale alphas initialized to zeros
+                module.alpha.data.zero_()
+                module.beta.data.zero_()
+            else:
+                # Linear scale alphas initialized to ones
+                module.alpha.data.fill_(1.0)
+                module.beta.data.fill_(1.0)
+
+
+XCODEC2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+    Parameters:
+        config ([`Xcodec2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+XCODEC2_INPUTS_DOCSTRING = r"""
+    args:
+        audio (`torch.FloatTensor` of shape `(batch_size, channels, num_samples)`):
+            Audio waveform.
+        audio_spectrogram (`torch.FloatTensor` of shape `(batch_size, mel_bins, time_steps)`):
+            Mel spectrogram.
+        return_dict (`bool`, *optional*):
+            whether to return a `Xcodec2Output` or a plain tuple.
+"""
+
+
+# Taking inspiration form modeling code of original authors: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py
+@add_start_docstrings(
+    "The Xcodec2 neural audio codec model.",
+    XCODEC2_INPUTS_DOCSTRING,
+)
+class Xcodec2Model(Xcodec2PreTrainedModel):
+    config_class = Xcodec2Config
+
+    def __init__(self, config: Xcodec2Config):
+        super().__init__(config)
+
+        self.hop_length = config.hop_length  # needed for padding
+        self.semantic_model = AutoModel.from_config(config.semantic_model_config).eval()
+        # Adaptor of semantic model embedding
+        self.semantic_encoder = Xcodec2SemanticEncoder(
+            config.semantic_hidden_size, config.semantic_hidden_size, config.semantic_hidden_size
+        )
+
+        self.acoustic_encoder = Xcodec2CodecEncoder(
+            downsampling_ratios=config.downsampling_ratios, hidden_dim=config.encoder_hidden_size
+        )
+        self.decoder = Xcodec2CodecDecoderVocos(config=config)
+        self.fc_prior = nn.Linear(config.intermediate_size, config.intermediate_size)
+        self.fc_post_a = nn.Linear(config.intermediate_size, config.decoder_hidden_size)
+
+        self.post_init()
+
+    def apply_weight_norm(self, legacy=True):
+        weight_norm = nn.utils.weight_norm
+        if hasattr(nn.utils.parametrizations, "weight_norm") and not legacy:
+            weight_norm = nn.utils.parametrizations.weight_norm
+
+        # Weight norm was only applied in acoustic encoder of original model
+        # -- to initial_conv
+        weight_norm(self.acoustic_encoder.initial_conv)
+
+        # -- to encoder blocks
+        for encoder_block in self.acoustic_encoder.encoder_blocks:
+            # -- to each residual unit in the block
+            for residual_unit in encoder_block.residual_units:
+                weight_norm(residual_unit.conv1)
+                weight_norm(residual_unit.conv2)
+            # -- to the final conv in the encoder block
+            weight_norm(encoder_block.conv)
+
+        # -- to final_conv
+        weight_norm(self.acoustic_encoder.final_conv)
+
+    def remove_weight_norm(self, legacy=True):
+        """Remove weight normalization from layers that have it applied via apply_weight_norm."""
+        remove_weight_norm = nn.utils.remove_weight_norm
+        if hasattr(nn.utils.parametrizations, "weight_norm") and not legacy:
+            remove_weight_norm = torch.nn.utils.parametrize.remove_parametrizations
+
+        # Remove weight norm from acoustic_encoder
+        # -- from initial_conv
+        try:
+            remove_weight_norm(self.acoustic_encoder.initial_conv)
+        except (ValueError, RuntimeError):
+            raise ValueError("Not able to remove weight norm. Have you run `apply_weight_norm?`")
+
+        # -- from encoder blocks
+        for encoder_block in self.acoustic_encoder.encoder_blocks:
+            # -- from each residual unit in the block
+            for residual_unit in encoder_block.residual_units:
+                remove_weight_norm(residual_unit.conv1)
+                remove_weight_norm(residual_unit.conv2)
+            # -- from the final conv in the encoder block
+            remove_weight_norm(encoder_block.conv)
+
+        # -- from final_conv
+        remove_weight_norm(self.acoustic_encoder.final_conv)
+
+    def encode(
+        self,
+        audio: torch.Tensor,
+        audio_spectrogram: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Xcodec2EncoderOutput]:
+        """
+        Encodes the input audio waveform into discrete codes.
+
+        Args:
+            audio (`torch.Tensor` of shape `(batch_size, 1, sequence_length)`):
+                Input audio waveform.
+            audio_spectrogram (`torch.Tensor` of shape `(batch_size, mel_bins, time_steps)`):
+                Input audio mel spectrogram for semantic encoding.
+            padding_mask (`torch.Tensor` of shape `(batch_size, 1, sequence_length)`):
+                Padding mask used to pad `audio`.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+            `audio_codes` of shape `[batch_size, 1, frames]`, the discrete encoded codes for the input audio waveform.
+            `quantized_representation` of shape `[batch_size, hidden_size, frames]`, the continuous quantized
+                representation after quantization.
+            `codes_padding_mask` of shape `[batch_size, 1, frames]`, downsampled `padding_mask` for indicating valid
+                audio codes in `audio_codes`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # 1) Semantic embedding: 16th layer of pretrained model: https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py#L64
+        semantic_output = self.semantic_model(audio_spectrogram, output_hidden_states=True)
+        semantic_hidden_16 = semantic_output.hidden_states[16]
+        semantic_hidden_16 = semantic_hidden_16.transpose(1, 2)
+        semantic_encoded = self.semantic_encoder(semantic_hidden_16)
+
+        # 2) Get acoustic embedding
+        vq_emb = self.acoustic_encoder(audio)
+        vq_emb = vq_emb.transpose(1, 2)
+
+        # 3) Concat embeddings and apply final layers
+        if vq_emb.shape[-1] != semantic_encoded.shape[-1]:
+            min_len = min(vq_emb.shape[-1], semantic_encoded.shape[-1])
+            vq_emb = vq_emb[:, :, :min_len]
+            semantic_encoded = semantic_encoded[:, :, :min_len]
+        concat_emb = torch.cat([semantic_encoded, vq_emb], dim=1)
+        concat_emb = self.fc_prior(concat_emb.transpose(1, 2)).transpose(1, 2)
+
+        # 4) Get codes for decoder
+        quantized_representation, audio_codes = self.decoder.quantize(concat_emb)
+
+        # If provided, compute corresponding padding mask for audio codes
+        codes_padding_mask = None
+        if padding_mask is not None:
+            # Expected token length, as in: https://github.com/zhenye234/X-Codec-2.0/blob/ccbbf340ff143dfa6a0ea7cd61ec34a8ba2f1c3d/inference_save_code.py#L89
+            audio_length = padding_mask.sum(dim=-1, keepdim=True).cpu()
+            token_length = audio_length // self.hop_length
+            codes_padding_mask = torch.zeros(audio_codes.shape, dtype=padding_mask.dtype)
+            idx = torch.arange(audio_codes.shape[-1]).view(1, -1)
+            codes_padding_mask = (idx < token_length).to(padding_mask.dtype).to(padding_mask.device)
+
+        if not return_dict:
+            return audio_codes, quantized_representation, codes_padding_mask
+
+        return Xcodec2EncoderOutput(
+            audio_codes=audio_codes,
+            quantized_representation=quantized_representation,
+            codes_padding_mask=codes_padding_mask,
+        )
+
+    def decode(
+        self,
+        audio_codes,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Xcodec2DecoderOutput]:
+        """
+        Decodes the given frames into an output audio waveform.
+
+        Note that the output might be a bit bigger than the input. In that case, any extra steps at the end can be
+        trimmed.
+
+        Args:
+            audio_codes (`torch.LongTensor`  of shape `(batch_size, 1, codes_length)`):
+                Discrete code indices computed using `model.encode`.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+            Decoded audio values of shape `(batch_size, 1, num_samples)` obtained using the decoder part of Xcodec2.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        vq_post_emb = self.decoder.quantizer.get_output_from_indices(audio_codes.transpose(1, 2))
+        vq_post_emb = self.fc_post_a(vq_post_emb)
+        recon_audio = self.decoder(vq_post_emb)
+
+        if not return_dict:
+            return recon_audio
+
+        return Xcodec2DecoderOutput(
+            audio_values=recon_audio,
+        )
+
+    @add_start_docstrings_to_model_forward(XCODEC2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=Xcodec2Output, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        audio: torch.Tensor,
+        audio_spectrogram: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Xcodec2Output]:
+        r"""
+        Returns:
+            `Xcodec2Output` or `tuple(torch.FloatTensor, torch.LongTensor, torch.FloatTensor)`:
+            - `audio_values` (`torch.FloatTensor` of shape `(batch_size, 1, num_samples)`):
+                Reconstructed audio waveform.
+            - `audio_codes` (`torch.LongTensor` of shape `(batch_size, 1, codes_length)`):
+                Discrete code indices computed using `model.encode`.
+            - `quantized_representation` (`torch.FloatTensor` of shape `(batch_size, hidden_size, frames)`):
+                The continuous quantized representation after quantization.
+            - `codes_padding_mask` (`torch.int32` of shape `(batch_size, 1, codes_length)`):
+                Downsampled `padding_mask` for indicating valid audio codes in `audio_codes`.
+
+        Examples:
+
+        ```python
+        >>> from datasets import load_dataset
+        >>> from transformers import AutoFeatureExtractor, Xcodec2Model
+
+        >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> audio = dataset["train"]["audio"][0]["array"]
+
+        >>> model_id = "hf-audio/xcodec2"
+        >>> model = Xcodec2Model.from_pretrained(model_id)
+        >>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+        >>> inputs = feature_extractor(audio=audio, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> audio_codes = outputs.audio_codes
+        >>> audio_values = outputs.audio_values
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # for truncating output audio to original length
+        length = audio.shape[-1]
+
+        audio_codes, quantized_representation, codes_padding_mask = self.encode(
+            audio, audio_spectrogram=audio_spectrogram, padding_mask=padding_mask, return_dict=False
+        )
+        audio_values = self.decode(audio_codes, return_dict=False)[..., :length]
+
+        if not return_dict:
+            return (audio_values, audio_codes, quantized_representation, codes_padding_mask)
+
+        return Xcodec2Output(
+            audio_values=audio_values,
+            audio_codes=audio_codes,
+            quantized_representation=quantized_representation,
+            codes_padding_mask=codes_padding_mask,
+        )
+
+
+__all__ = ["Xcodec2Model", "Xcodec2PreTrainedModel"]
diff --git a/tests/fixtures/xcodec2/expected_results_feature_extractor.json b/tests/fixtures/xcodec2/expected_results_feature_extractor.json
new file mode 100644
index 000000000000..2d7c56dd8103
--- /dev/null
+++ b/tests/fixtures/xcodec2/expected_results_feature_extractor.json
@@ -0,0 +1 @@
+{"audio_spectrogram": [-1.3513745069503784, -1.2938611507415771, -1.3042515516281128, -1.24922776222229, -1.1503021717071533, -1.092949628829956, -1.1691689491271973, -1.269083857536316, -1.30203115940094, -1.3397775888442993, -1.7783260345458984, -1.666186809539795, -1.5241273641586304, -1.308205246925354, -1.0876264572143555, -1.0227214097976685, -1.1668208837509155, -1.122986078262329, -0.911615788936615, -0.9979852437973022, -1.0430642366409302, -0.9802085161209106, -0.8771641254425049, -0.8030658960342407, -0.8125691413879395, -1.0069810152053833, -1.3551208972930908, -1.0124515295028687, -1.0286870002746582, -1.2922166585922241, -0.9571806788444519, -1.1323972940444946, -1.1946827173233032, -0.7315863966941833, -0.5566411018371582, -0.050126075744628906, 0.12781931459903717, -0.059717196971178055, -0.4141717255115509, -0.5882015228271484, -0.6041294932365417, -0.6762491464614868, -0.6562864184379578, -0.3303810954093933, -0.6363117694854736, -0.5720179080963135, -0.5896602272987366, -0.8763933181762695, -0.9099599123001099, -0.5604336261749268], "shape": [1, 294, 160]}
\ No newline at end of file
diff --git a/tests/fixtures/xcodec2/expected_results_single.json b/tests/fixtures/xcodec2/expected_results_single.json
new file mode 100644
index 000000000000..d6cd5f9b1fda
--- /dev/null
+++ b/tests/fixtures/xcodec2/expected_results_single.json
@@ -0,0 +1 @@
+{"audio_codes": [39221, 36132, 48388, 4919, 23836, 40229, 40501, 40244, 21788, 11574, 22808, 48178, 44340, 48392, 65463, 56596, 60453, 60756, 60986, 48470, 64866, 48438, 48682, 44342, 48873, 65193, 65062, 59151, 58430, 50132, 47153, 25252, 5837, 27213, 64551, 7073, 23430, 53960, 53856, 53309, 65085, 28514, 33730, 58178, 27541, 7940, 54486, 35733, 24487, 25051, 11559, 21479, 45745, 6759, 23496, 53400, 53044, 52186, 49572, 49758, 49846, 56735, 52660, 44701, 61604, 54938, 26721, 33481, 35531, 56922, 36546, 34574, 61725, 46028, 15513, 30861, 29508, 9096, 38864, 49718, 64955, 48633, 56183, 29633, 23298, 15235, 32517, 20595, 61267, 65191, 21569, 9970, 1429, 8136, 28294, 3783, 34714, 28416, 57770, 29363, 61523, 45170, 61807, 29109, 24921, 25235, 60195, 4425, 8275, 21458, 63027, 47118, 11532, 40206, 6983, 21850, 37481, 57919, 57711, 61979, 62335, 49881, 27042, 28708, 4941, 34458, 48243, 29269, 9139, 21386, 54807, 48171, 24434, 37824, 12118, 30541, 41123, 38806, 18439, 37824, 35478, 53248, 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1.8095202904078178e-05, 3.2897096389206126e-05, 3.7320885894587263e-05, 4.727980194729753e-05, 5.290500484989025e-05, -2.8115052828070475e-06, -1.3802901776216459e-05, 4.530415026238188e-05, 1.7122993085649796e-05, -9.676883564679883e-06, 2.922193380072713e-05, 4.709900531452149e-05, -1.484695530962199e-05, 2.679826684470754e-05, 9.708025027066469e-05, 5.909429819439538e-05, -5.998295364406658e-06, -1.1865397027577274e-05, 1.7596781617612578e-05, -1.2678569873969536e-06, -6.15744102105964e-06, 4.26633378083352e-05, 2.9731247195741162e-05, 6.973560084588826e-05, 3.9487546018790454e-05, 2.4334209228982218e-05, 4.423264545039274e-05, 2.5397337594768032e-05, 6.611580465687439e-05, 5.363776654121466e-05, 1.5007488400442526e-05, 1.9821469322778285e-05, 8.46496332087554e-05, 4.378660014481284e-05, 1.3526245311368257e-05, 1.730803523969371e-05, 5.702128692064434e-05, 3.02631869999459e-05, -7.282471415237524e-06, 1.4821678632870317e-05, 2.608516297186725e-05, 3.226366243325174e-05, 2.518865221645683e-05, 5.748319745180197e-05, 6.051031959941611e-06, 1.5820383850950748e-05, 2.6422590963193215e-05, 1.4281895346357487e-05, 1.2238286217325367e-05, 2.142549965356011e-05, 3.1012539693620056e-05, 5.359622446121648e-05, 1.306949070567498e-05, 1.7006108464556746e-05, 1.283238179894397e-05, 2.43848844547756e-05, 1.44485720738885e-05, 7.087912308634259e-06, 1.066919412551215e-05, -1.073473958967952e-05, -6.255760354179074e-07, 3.017030394403264e-05, -1.4621239188272739e-06, 1.316684574703686e-06, 4.970231748302467e-05, 6.46350645183702e-06, -1.393776256009005e-05, 2.311663411092013e-05, 3.583731813705526e-05, -8.6126428868738e-06, 2.8714077870972687e-06, 3.9388909499393776e-05, 3.239805664634332e-05, 3.427654519327916e-05, -3.9051392377587035e-06, 2.4153943741112016e-05, 1.1452852959337179e-05, 4.283535963622853e-05, 4.775746856466867e-05, 9.00692157301819e-06, 4.212437033856986e-06, 4.902469663647935e-05, 5.788989710708847e-06, 1.1606864973146003e-05, 4.884762893198058e-05, 4.017079481855035e-05, -5.2404233429115266e-05, 7.1430868047173135e-06, 1.412287019775249e-05, -1.8386897863820195e-05, 6.623175977438223e-06, 3.2066000130726025e-05, 8.945029890128353e-07, -4.3174437450943515e-05, 9.859092642727774e-06, 1.3530512660508975e-05, 3.828808985417709e-05, 3.328847526518075e-07, 2.133529596903827e-05, 2.5960745915654115e-05, -8.993079973151907e-06, 1.7902983017847873e-05, 2.360173675697297e-05, 2.6887766580330208e-05, 1.0035324521595612e-05, -3.361020935699344e-05, -6.272725840972271e-07, -5.5440677897422574e-06, 1.9816350686596707e-05, 2.079080331895966e-05, 9.54076199377596e-07, -1.648782563279383e-05, -1.2546671314339619e-05, -6.24781978331157e-06, 2.4020204136832035e-07, -3.125521743641002e-06, 2.0122970454394817e-06, -8.070192052400671e-06, 2.1962454411550425e-05, 1.635234002606012e-05, -1.8212925851912587e-06, -1.3866550034435932e-05, -1.1139512025692966e-05, 1.743959182931576e-05, -4.145139882894e-06, 2.181683885282837e-05, 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-2.2242915292736143e-05, 1.2956193131685723e-05, 1.2466058251447976e-05, -1.376189447910292e-05, 8.690984941495117e-06, 6.187911367305787e-06, 1.5360497854999267e-05, -2.23272800212726e-05, 2.6416266337037086e-05, 6.5767549131123815e-06, 2.387172389717307e-05, 1.0150239177164622e-05, 1.7448004427933483e-06, -6.841509275545832e-06, 1.5967107174219564e-05, 8.569980991524062e-07, -6.699851837765891e-06, 4.936896584695205e-05, 7.5849898166779894e-06, -1.812741538742557e-05, 3.585818558349274e-05, 1.212889219459612e-05, -1.1472254300315399e-05, -1.0502439181436785e-05, 1.3019885955145583e-05, 1.3114803323333035e-06, 6.218148882908281e-06, 3.362887218827382e-05, 7.668036232644226e-06, 1.9371962480363436e-05, 2.4029121050261892e-05, 2.220365968241822e-05, -4.550365247268928e-06, 2.5444594939472154e-05, 9.348879757453687e-06, 1.918475936690811e-05, 2.381552621955052e-05, 1.3444263458950445e-05, 2.8519629267975688e-05, 4.836974767385982e-06, 2.4932805899879895e-05, -1.6781788872322068e-05, -1.1302043276373297e-05, 1.5783432900207117e-05, 1.5598385289194994e-05, -1.6964646420092322e-05, 1.4932981684978586e-05, 1.893633270810824e-05, 5.723793037759606e-06, 5.375571163312998e-06, 6.9530547079921234e-06, 1.5188787074293941e-05, 5.124418748891912e-06, 3.660247602965683e-05, 2.6472949684830382e-05, 2.4471595679642633e-05, 1.1032266229449306e-05, 7.576215011795284e-06, 6.854513230791781e-06, 2.406009480182547e-05], "codec_error": 0.00273896474391222}
\ No newline at end of file
diff --git a/tests/models/dac/test_feature_extraction_dac.py b/tests/models/dac/test_feature_extraction_dac.py
index c995485d3311..40be753ae1c2 100644
--- a/tests/models/dac/test_feature_extraction_dac.py
+++ b/tests/models/dac/test_feature_extraction_dac.py
@@ -49,7 +49,7 @@ def floats_list(shape, scale=1.0, rng=None, name=None):
 
 
 @require_torch
-# Copied from transformers.tests.encodec.test_feature_extraction_dac.EncodecFeatureExtractionTester with Encodec->Dac
+# Copied from transformers.tests.encodec.test_feature_extraction_encodec.EncodecFeatureExtractionTester with Encodec->Dac
 class DacFeatureExtractionTester:
     # Ignore copy
     def __init__(
@@ -102,7 +102,7 @@ def _flatten(list_of_lists):
 
 
 @require_torch
-# Copied from transformers.tests.encodec.test_feature_extraction_dac.EnCodecFeatureExtractionTest with Encodec->Dac
+# Copied from transformers.tests.encodec.test_feature_extraction_encodec.EnCodecFeatureExtractionTest with Encodec->Dac
 class DacFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
     feature_extraction_class = DacFeatureExtractor
 
diff --git a/tests/models/xcodec2/__init__.py b/tests/models/xcodec2/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/xcodec2/test_feature_extraction_xcodec2.py b/tests/models/xcodec2/test_feature_extraction_xcodec2.py
new file mode 100644
index 000000000000..3e435972d53d
--- /dev/null
+++ b/tests/models/xcodec2/test_feature_extraction_xcodec2.py
@@ -0,0 +1,213 @@
+# Copyright 2024 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 Xcodec2 feature extractor."""
+
+import itertools
+import json
+import random
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+from transformers import Xcodec2FeatureExtractor
+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_torch_available():
+    import torch
+
+
+global_rng = random.Random()
+
+
+# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
+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 Xcodec2FeatureExtractionTester:
+    # Ignore copy
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size=80,  # number of mel bins
+        n_channels=1,
+        sampling_rate=16000,
+        spec_hop_length=160,
+        hop_length=320,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.spec_hop_length = spec_hop_length
+        self.hop_length = hop_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.feature_size = feature_size
+        self.n_channels = n_channels
+        self.sampling_rate = sampling_rate
+
+    # Ignore copy
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "sampling_rate": self.sampling_rate,
+            "hop_length": self.hop_length,
+            "spec_hop_length": self.spec_hop_length,
+            "n_channels": self.n_channels,
+            # Model usses 1.0, but 0.0 needed to pass `test_padding_from_array`
+            # -- SeamlessM4TFeatureExtractionTester does the same
+            "padding_value": 0.0,
+        }
+
+    # Copied from transformers.tests.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTester.prepare_inputs_for_common
+    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:
+            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
+        else:
+            # make sure that inputs increase in size
+            speech_inputs = [
+                floats_list((x, self.feature_size))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+        if numpify:
+            speech_inputs = [np.asarray(x) for x in speech_inputs]
+        return speech_inputs
+
+
+@require_torch
+class Xcodec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = Xcodec2FeatureExtractor
+
+    def setUp(self):
+        self.feat_extract_tester = Xcodec2FeatureExtractionTester(self)
+
+    def test_call(self):
+        TOL = 1e-6
+
+        # 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]
+        torch_audio_inputs = [torch.tensor(audio_input) for audio_input in audio_inputs]
+
+        # Test not batched input
+        sampling_rate = self.feat_extract_tester.sampling_rate
+        encoded_sequences_1 = feat_extract(torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np")
+        encoded_sequences_2 = feat_extract(np_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np")
+        # -- use torch for mel features
+        encoded_sequences_3 = feat_extract(
+            torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", use_torch=True, device="cpu"
+        )
+        self.assertTrue(np.allclose(encoded_sequences_1.audio, encoded_sequences_2.audio, atol=TOL))
+        self.assertTrue(
+            np.allclose(encoded_sequences_1.audio_spectrogram, encoded_sequences_2.audio_spectrogram, atol=TOL)
+        )
+        self.assertTrue(
+            np.allclose(encoded_sequences_1.audio_spectrogram, encoded_sequences_3.audio_spectrogram, atol=TOL)
+        )
+
+        # Test using GPU
+        if torch.cuda.is_available():
+            encoded_sequences_3 = feat_extract(
+                torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", use_torch=True, device="cuda"
+            )
+            self.assertTrue(np.allclose(encoded_sequences_1.audio, encoded_sequences_3.audio, atol=TOL))
+            self.assertTrue(
+                np.allclose(encoded_sequences_1.audio_spectrogram, encoded_sequences_3.audio_spectrogram, atol=TOL)
+            )
+
+        # Test batched
+        encoded_sequences_1 = feat_extract(
+            torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np"
+        )
+        encoded_sequences_2 = feat_extract(
+            np_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np"
+        )
+        encoded_sequences_3 = feat_extract(
+            torch_audio_inputs,
+            sampling_rate=sampling_rate,
+            padding=True,
+            return_tensors="np",
+            use_torch=True,
+            device="cpu",
+        )
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1.audio, encoded_sequences_2.audio):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=TOL))
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1.audio_spectrogram, encoded_sequences_2.audio_spectrogram):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=TOL))
+        for enc_seq_1, enc_seq_3 in zip(encoded_sequences_1.audio_spectrogram, encoded_sequences_3.audio_spectrogram):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_3, atol=TOL))
+
+    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, 32).astype(np.float64)
+        py_audio_inputs = np_audio_inputs.tolist()
+
+        for inputs in [py_audio_inputs, np_audio_inputs]:
+            np_processed = feature_extractor.pad([{"audio_spectrogram": inputs}], return_tensors="np")
+            self.assertTrue(np_processed.audio_spectrogram.dtype == np.float32)
+            pt_processed = feature_extractor.pad([{"audio_spectrogram": inputs}], return_tensors="pt")
+            self.assertTrue(pt_processed.audio_spectrogram.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")[:num_samples]["audio"]
+
+        return [x["array"] for x in audio_samples]
+
+    def test_integration(self):
+        # Reproducer: https://gist.github.com/ebezzam/909243aa00934cab1c1aad3d6a161580#file-xcodec2_feature_extraction-py
+        RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/xcodec2/expected_results_feature_extractor.json"
+        with open(RESULTS_PATH, "r") as f:
+            expected_results = json.load(f)
+        # expected_results = expected_results[0]
+        EXPECTED_INPUT_VALUES = torch.tensor(expected_results["audio_spectrogram"])
+        EXPECTED_SHAPE = tuple(expected_results["shape"])
+
+        input_audio = self._load_datasamples(1)
+        feature_extractor = Xcodec2FeatureExtractor()
+        input_values = feature_extractor(input_audio, return_tensors="pt")["audio_spectrogram"]
+        self.assertEqual(input_values.shape, EXPECTED_SHAPE)
+        torch.testing.assert_close(input_values[0, 0, : EXPECTED_INPUT_VALUES.shape[-1]], EXPECTED_INPUT_VALUES)
+
+    # Ignore copy
+    @unittest.skip("Xcodec2 doesn't support stereo input")
+    def test_integration_stereo(self):
+        pass
diff --git a/tests/models/xcodec2/test_modeling_xcodec2.py b/tests/models/xcodec2/test_modeling_xcodec2.py
new file mode 100644
index 000000000000..3d8dc0fe2f64
--- /dev/null
+++ b/tests/models/xcodec2/test_modeling_xcodec2.py
@@ -0,0 +1,475 @@
+# Copyright 2025 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 Xcodec2 model."""
+
+import inspect
+import json
+import os
+import tempfile
+import unittest
+from pathlib import Path
+
+import numpy as np
+from datasets import Audio, load_dataset
+
+from tests.test_configuration_common import ConfigTester
+from tests.test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
+from transformers import AutoFeatureExtractor, Xcodec2Config
+from transformers.testing_utils import (
+    is_torch_available,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import Xcodec2Model
+
+
+@require_torch
+class Xcodec2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=4,
+        num_channels=1,
+        sample_rate=16000,
+        hop_length=320,
+        num_mel_bins=80,
+        mel_hop_length=160,
+        stride=2,
+        is_training=False,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.sample_rate = sample_rate
+        self.is_training = is_training
+        self.hop_length = hop_length
+        self.num_samples = hop_length * 4  # feature extractor will pad to multiple of hop_length
+        self.num_channels = num_channels
+        self.num_mel_bins = num_mel_bins
+        self.stride = stride
+        self.mel_hop_length = mel_hop_length
+
+    def prepare_config_and_inputs(self):
+        audio = floats_tensor([self.batch_size, self.num_channels, self.num_samples], scale=1.0)
+        audio_spectrogram = floats_tensor(
+            [self.batch_size, self.num_samples // self.mel_hop_length, self.num_mel_bins * self.stride], scale=1.0
+        )
+        config = self.get_config()
+        inputs_dict = {"audio": audio, "audio_spectrogram": audio_spectrogram}
+        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 prepare_config_and_inputs_for_model_class(self, model_class):
+        config, inputs_dict = self.prepare_config_and_inputs()
+        return config, inputs_dict
+
+    def get_config(self):
+        return Xcodec2Config(
+            sampling_rate=self.sample_rate,
+            audio_channels=self.num_channels,
+        )
+
+    def create_and_check_model_forward(self, config, inputs_dict):
+        model = Xcodec2Model(config=config).to(torch_device).eval()
+        audio = inputs_dict["audio"]
+        audio_spectrogram = inputs_dict["audio_spectrogram"]
+        result = model(audio, audio_spectrogram)
+        self.parent.assertEqual(
+            result.audio_values.shape,
+            (self.batch_size, self.num_channels, self.num_samples),
+        )
+
+
+@require_torch
+class Xcodec2ModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (Xcodec2Model,) if is_torch_available() else ()
+    is_encoder_decoder = True
+    test_pruning = False
+    test_headmasking = False
+    test_resize_embeddings = False
+    test_torchscript = False
+    test_can_init_all_missing_weights = False  # Semantic model weights come from pretrained model
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        # model 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 = Xcodec2ModelTester(self)
+        self.config_tester = ConfigTester(
+            # self, config_class=Xcodec2Config, common_properties=[], has_text_modality=False
+            self,
+            config_class=Xcodec2Config,
+            encoder_hidden_size=37,
+            decoder_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 = ["audio", "audio_spectrogram", "padding_mask", "return_dict"]
+            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
+
+    def test_gradient_checkpointing_backward_compatibility(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            if not model_class.supports_gradient_checkpointing:
+                continue
+
+            config.text_encoder.gradient_checkpointing = True
+            config.audio_encoder.gradient_checkpointing = True
+            config.decoder.gradient_checkpointing = True
+            model = model_class(config)
+            self.assertTrue(model.is_gradient_checkpointing)
+
+    @unittest.skip(reason="We cannot configure to output a smaller model.")
+    def test_model_is_small(self):
+        pass
+
+    @unittest.skip(reason="The XcodecModel does not have `inputs_embeds` logics")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="The XcodecModel does not have `inputs_embeds` logics")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="The XcodecModel does not have the usual `attention` logic")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="The XcodecModel does not have the usual `attention` logic")
+    def test_torchscript_output_attentions(self):
+        pass
+
+    @unittest.skip(reason="The XcodecModel does not have the usual `hidden_states` logic")
+    def test_torchscript_output_hidden_state(self):
+        pass
+
+    # Copied from transformers.tests.encodec.test_modeling_encodec.EncodecModelTest._create_and_check_torchscript
+    def _create_and_check_torchscript(self, config, inputs_dict):
+        if not self.test_torchscript:
+            self.skipTest(reason="test_torchscript is set to False")
+
+        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)
+
+            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(reason="The Xcodec2Model does not have the usual `attention` logic")
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip(reason="The Xcodec2Model does not have the usual `hidden_states` logic")
+    def test_hidden_states_output(self):
+        pass
+
+    # Copied from transformers.tests.encodec.test_modeling_encodec.EncodecModelTest.test_determinism
+    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)
+
+    # Copied from transformers.tests.encodec.test_modeling_encodecEncodecModelTest.test_model_outputs_equivalence
+    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)
+
+                self.assertTrue(isinstance(tuple_output, tuple))
+                self.assertTrue(isinstance(dict_output, dict))
+
+                for tuple_value, dict_value in zip(tuple_output, dict_output.values()):
+                    self.assertTrue(
+                        torch.allclose(
+                            set_nan_tensor_to_zero(tuple_value), set_nan_tensor_to_zero(dict_value), atol=1e-5
+                        ),
+                        msg=(
+                            "Tuple and dict output are not equal. Difference:"
+                            f" {torch.max(torch.abs(tuple_value - dict_value))}. Tuple has `nan`:"
+                            f" {torch.isnan(tuple_value).any()} and `inf`: {torch.isinf(tuple_value)}. Dict has"
+                            f" `nan`: {torch.isnan(dict_value).any()} and `inf`: {torch.isinf(dict_value)}."
+                        ),
+                    )
+
+        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():
+                # skipping the parametrizations original0 tensor
+                if name == "semantic_model.encoder.pos_conv_embed.conv.parametrizations.weight.original0":
+                    continue
+
+                uniform_init_parms = ["conv"]
+
+                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_class.__name__} seems not properly initialized",
+                        )
+
+    @unittest.skip(reason="The Xcodec2Model does not Flash attention")
+    def test_flash_attn_2_inference_equivalence(self):
+        pass
+
+    @unittest.skip(reason="The Xcodec2Model does not Flash attention")
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        pass
+
+    @unittest.skip(reason="The Xcodec2Model does not have support dynamic compile yet")
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+
+"""
+Integration tests for Xcodec2
+
+Code for reproducing expected outputs can be found here:
+https://gist.github.com/ebezzam/909243aa00934cab1c1aad3d6a161580#file-xcodec2_integration-py
+
+PyPI model does not support batch inference.
+
+"""
+
+
+# Copied from transformers.tests.encodec.test_modeling_encodec.normalize
+def normalize(arr):
+    norm = np.linalg.norm(arr)
+    normalized_arr = arr / norm
+    return normalized_arr
+
+
+# Copied from transformers.tests.encodec.test_modeling_encodec.compute_rmse
+def compute_rmse(arr1, arr2):
+    arr1_np = arr1.cpu().numpy().squeeze()
+    arr2_np = arr2.cpu().numpy().squeeze()
+    max_length = min(arr1.shape[-1], arr2.shape[-1])
+    arr1_np = arr1_np[..., :max_length]
+    arr2_np = arr2_np[..., :max_length]
+    arr1_normalized = normalize(arr1_np)
+    arr2_normalized = normalize(arr2_np)
+    return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean())
+
+
+@slow
+@require_torch
+class Xcodec2IntegrationTest(unittest.TestCase):
+    def test_integration(self):
+        # load expected results
+        results_path = Path(__file__).parent.parent.parent / "fixtures/xcodec2/expected_results_single.json"
+        with open(results_path, "r") as f:
+            raw_data = json.load(f)
+        exp_code = torch.tensor(raw_data["audio_codes"])
+        exp_recon = torch.tensor(raw_data["recon_wav"])
+        exp_codec_error = float(raw_data["codec_error"])
+
+        # load model
+        model_id = "hf-audio/xcodec2"
+        model = Xcodec2Model.from_pretrained(model_id).to(torch_device).eval()
+        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+        # load data
+        dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
+        audio = dataset[0]["audio"]["array"]
+        inputs = feature_extractor(
+            audio=audio,
+            sampling_rate=feature_extractor.sampling_rate,
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            # compare discrete codes
+            enc_tol = 0  # exact match
+            audio_codes = model.encode(inputs["audio"], inputs["audio_spectrogram"], return_dict=False)[0]
+            torch.testing.assert_close(
+                audio_codes.squeeze().cpu().to(exp_code.dtype),
+                exp_code,
+                rtol=enc_tol,
+                atol=enc_tol,
+            )
+
+            # compare recon
+            # NOTE (ebezzam) different transformer implementation, in particular ROPE leads to numerical differences
+            # their ROPE implementation is interleaved: `torchtune.modules.RotaryPositionalEmbeddings`
+            dec_tol = 1e-3
+            dec = model.decode(audio_codes, return_dict=False)
+            torch.testing.assert_close(
+                dec.squeeze().cpu(),
+                exp_recon,
+                rtol=dec_tol,
+                atol=dec_tol,
+            )
+
+            # compare codec error
+            err_tol = 1e-5
+            codec_error = compute_rmse(inputs["audio"], dec).item()
+            torch.testing.assert_close(codec_error, exp_codec_error, rtol=err_tol, atol=err_tol)
+
+            # make sure forward and decode gives same result
+            round_trip_tol = 0
+            enc_dec = model(inputs["audio"], inputs["audio_spectrogram"]).audio_values
+            torch.testing.assert_close(
+                dec[..., : enc_dec.shape[-1]], enc_dec, rtol=round_trip_tol, atol=round_trip_tol
+            )