From 6458a32c2862fe7cf4239383b24e40d484ed64c4 Mon Sep 17 00:00:00 2001
From: Lucas Newman <lucas@future.fit>
Date: Wed, 19 Mar 2025 14:48:38 -0700
Subject: [PATCH 1/2] Add EnCodec neural audio codec.

---
 mlx_audio/codec/models/__init__.py         |   1 +
 mlx_audio/codec/models/encodec/__init__.py |   1 +
 mlx_audio/codec/models/encodec/encodec.py  | 743 +++++++++++++++++++++
 mlx_audio/codec/tests/test_encodec.py      |  58 ++
 4 files changed, 803 insertions(+)
 create mode 100644 mlx_audio/codec/models/encodec/__init__.py
 create mode 100644 mlx_audio/codec/models/encodec/encodec.py
 create mode 100644 mlx_audio/codec/tests/test_encodec.py

diff --git a/mlx_audio/codec/models/__init__.py b/mlx_audio/codec/models/__init__.py
index c797ed95..cf26b8da 100644
--- a/mlx_audio/codec/models/__init__.py
+++ b/mlx_audio/codec/models/__init__.py
@@ -1 +1,2 @@
+from .encodec import Encodec
 from .mimi import Mimi
diff --git a/mlx_audio/codec/models/encodec/__init__.py b/mlx_audio/codec/models/encodec/__init__.py
new file mode 100644
index 00000000..3e6ccc2d
--- /dev/null
+++ b/mlx_audio/codec/models/encodec/__init__.py
@@ -0,0 +1 @@
+from .encodec import Encodec
diff --git a/mlx_audio/codec/models/encodec/encodec.py b/mlx_audio/codec/models/encodec/encodec.py
new file mode 100644
index 00000000..59c3f793
--- /dev/null
+++ b/mlx_audio/codec/models/encodec/encodec.py
@@ -0,0 +1,743 @@
+import functools
+import json
+import math
+from pathlib import Path
+from types import SimpleNamespace
+from typing import List, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+from huggingface_hub import snapshot_download
+
+
+def preprocess_audio(
+    raw_audio: Union[mx.array, List[mx.array]],
+    sampling_rate: int = 24000,
+    chunk_length: Optional[int] = None,
+    chunk_stride: Optional[int] = None,
+):
+    r"""
+    Prepare inputs for the EnCodec model.
+
+    Args:
+        raw_audio (mx.array or List[mx.array]): The sequence or batch of
+            sequences to be processed.
+        sampling_rate (int): The sampling rate at which the audio waveform
+            should be digitalized.
+        chunk_length (int, optional): The model's chunk length.
+        chunk_stride (int, optional): The model's chunk stride.
+    """
+    if not isinstance(raw_audio, list):
+        raw_audio = [raw_audio]
+
+    raw_audio = [x[..., None] if x.ndim == 1 else x for x in raw_audio]
+
+    max_length = max(array.shape[0] for array in raw_audio)
+    if chunk_length is not None:
+        max_length += chunk_length - (max_length % chunk_stride)
+
+    inputs = []
+    masks = []
+    for x in raw_audio:
+        length = x.shape[0]
+        mask = mx.ones((length,), dtype=mx.bool_)
+        difference = max_length - length
+        if difference > 0:
+            mask = mx.pad(mask, (0, difference))
+            x = mx.pad(x, ((0, difference), (0, 0)))
+        inputs.append(x)
+        masks.append(mask)
+    return mx.stack(inputs), mx.stack(masks)
+
+
+_lstm_kernel = mx.fast.metal_kernel(
+    name="lstm",
+    input_names=["x", "h_in", "cell", "hidden_size", "time_step", "num_time_steps"],
+    output_names=["hidden_state", "cell_state"],
+    header="""
+    template <typename T>
+    T sigmoid(T x) {
+        auto y = 1 / (1 + metal::exp(-metal::abs(x)));
+        return (x < 0) ? 1 - y : y;
+    }
+    """,
+    source="""
+        uint b = thread_position_in_grid.x;
+        uint d = hidden_size * 4;
+
+        uint elem = b * d + thread_position_in_grid.y;
+        uint index = elem;
+        uint x_index = b * num_time_steps * d + time_step * d + index;
+
+        auto i = sigmoid(h_in[index] + x[x_index]);
+        index += hidden_size;
+        x_index += hidden_size;
+        auto f = sigmoid(h_in[index] + x[x_index]);
+        index += hidden_size;
+        x_index += hidden_size;
+        auto g = metal::precise::tanh(h_in[index] + x[x_index]);
+        index += hidden_size;
+        x_index += hidden_size;
+        auto o = sigmoid(h_in[index] + x[x_index]);
+
+        cell_state[elem] = f * cell[elem] + i * g;
+        hidden_state[elem] = o * metal::precise::tanh(cell_state[elem]);
+    """,
+)
+
+
+def lstm_custom(x, h_in, cell, time_step):
+    assert x.ndim == 3, "Input to LSTM must have 3 dimensions."
+    out_shape = cell.shape
+    return _lstm_kernel(
+        inputs=[x, h_in, cell, out_shape[-1], time_step, x.shape[-2]],
+        output_shapes=[out_shape, out_shape],
+        output_dtypes=[h_in.dtype, h_in.dtype],
+        grid=(x.shape[0], h_in.size // 4, 1),
+        threadgroup=(256, 1, 1),
+    )
+
+
+class LSTM(nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+    ):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.Wx = mx.zeros((4 * hidden_size, input_size))
+        self.Wh = mx.zeros((4 * hidden_size, hidden_size))
+        self.bias = mx.zeros((4 * hidden_size,)) if bias else None
+
+    def __call__(self, x, hidden=None, cell=None):
+        if self.bias is not None:
+            x = mx.addmm(self.bias, x, self.Wx.T)
+        else:
+            x = x @ self.Wx.T
+
+        all_hidden = []
+
+        B = x.shape[0]
+        cell = cell or mx.zeros((B, self.hidden_size), x.dtype)
+        for t in range(x.shape[-2]):
+            if hidden is None:
+                hidden = mx.zeros((B, self.hidden_size * 4), x.dtype)
+            else:
+                hidden = hidden @ self.Wh.T
+            hidden, cell = lstm_custom(x, hidden, cell, t)
+            all_hidden.append(hidden)
+
+        return mx.stack(all_hidden, axis=-2)
+
+
+class EncodecConv1d(nn.Module):
+    """Conv1d with asymmetric or causal padding and normalization."""
+
+    def __init__(
+        self,
+        config,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        self.causal = config.use_causal_conv
+        self.pad_mode = config.pad_mode
+        self.norm_type = config.norm_type
+
+        self.conv = nn.Conv1d(
+            in_channels, out_channels, kernel_size, stride, dilation=dilation
+        )
+        if self.norm_type == "time_group_norm":
+            self.norm = nn.GroupNorm(1, out_channels, pytorch_compatible=True)
+
+        self.stride = stride
+
+        # Effective kernel size with dilations.
+        self.kernel_size = (kernel_size - 1) * dilation + 1
+
+        self.padding_total = kernel_size - stride
+
+    def _get_extra_padding_for_conv1d(
+        self,
+        hidden_states: mx.array,
+    ) -> mx.array:
+        length = hidden_states.shape[1]
+        n_frames = (length - self.kernel_size + self.padding_total) / self.stride + 1
+        n_frames = int(math.ceil(n_frames)) - 1
+        ideal_length = n_frames * self.stride + self.kernel_size - self.padding_total
+        return ideal_length - length
+
+    def _pad1d(
+        self,
+        hidden_states: mx.array,
+        paddings: Tuple[int, int],
+        mode: str = "zero",
+        value: float = 0.0,
+    ):
+        if mode != "reflect":
+            return mx.pad(
+                hidden_states, paddings, mode="constant", constant_values=value
+            )
+
+        length = hidden_states.shape[1]
+        prefix = hidden_states[:, 1 : paddings[0] + 1][:, ::-1]
+        suffix = hidden_states[:, max(length - (paddings[1] + 1), 0) : -1][:, ::-1]
+        return mx.concatenate([prefix, hidden_states, suffix], axis=1)
+
+    def __call__(self, hidden_states):
+        extra_padding = self._get_extra_padding_for_conv1d(hidden_states)
+
+        if self.causal:
+            # Left padding for causal
+            hidden_states = self._pad1d(
+                hidden_states, (self.padding_total, extra_padding), mode=self.pad_mode
+            )
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = self.padding_total // 2
+            padding_left = self.padding_total - padding_right
+            hidden_states = self._pad1d(
+                hidden_states,
+                (padding_left, padding_right + extra_padding),
+                mode=self.pad_mode,
+            )
+
+        hidden_states = self.conv(hidden_states)
+
+        if self.norm_type == "time_group_norm":
+            hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class EncodecConvTranspose1d(nn.Module):
+    """ConvTranspose1d with asymmetric or causal padding and normalization."""
+
+    def __init__(
+        self,
+        config,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+    ):
+        super().__init__()
+        self.causal = config.use_causal_conv
+        self.trim_right_ratio = config.trim_right_ratio
+        self.norm_type = config.norm_type
+        self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride)
+        if config.norm_type == "time_group_norm":
+            self.norm = nn.GroupNorm(1, out_channels, pytorch_compatible=True)
+        self.padding_total = kernel_size - stride
+
+    def __call__(self, hidden_states):
+        hidden_states = self.conv(hidden_states)
+
+        if self.norm_type == "time_group_norm":
+            hidden_states = self.norm(hidden_states)
+
+        if self.causal:
+            padding_right = math.ceil(self.padding_total * self.trim_right_ratio)
+        else:
+            padding_right = self.padding_total // 2
+
+        padding_left = self.padding_total - padding_right
+
+        end = hidden_states.shape[1] - padding_right
+        hidden_states = hidden_states[:, padding_left:end, :]
+        return hidden_states
+
+
+class EncodecLSTM(nn.Module):
+    def __init__(self, config, dimension):
+        super().__init__()
+        self.lstm = [LSTM(dimension, dimension) for _ in range(config.num_lstm_layers)]
+
+    def __call__(self, hidden_states):
+        h = hidden_states
+        for lstm in self.lstm:
+            h = lstm(h)
+        return h + hidden_states
+
+
+class EncodecResnetBlock(nn.Module):
+    """
+    Residual block from SEANet model as used by EnCodec.
+    """
+
+    def __init__(self, config, dim: int, dilations: List[int]):
+        super().__init__()
+        kernel_sizes = (config.residual_kernel_size, 1)
+        if len(kernel_sizes) != len(dilations):
+            raise ValueError("Number of kernel sizes should match number of dilations")
+
+        hidden = dim // config.compress
+        block = []
+        for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)):
+            in_chs = dim if i == 0 else hidden
+            out_chs = dim if i == len(kernel_sizes) - 1 else hidden
+            block += [nn.ELU()]
+            block += [
+                EncodecConv1d(config, in_chs, out_chs, kernel_size, dilation=dilation)
+            ]
+        self.block = block
+
+        if getattr(config, "use_conv_shortcut", True):
+            self.shortcut = EncodecConv1d(config, dim, dim, kernel_size=1)
+        else:
+            self.shortcut = nn.Identity()
+
+    def __call__(self, hidden_states):
+        residual = hidden_states
+        for layer in self.block:
+            hidden_states = layer(hidden_states)
+
+        return self.shortcut(residual) + hidden_states
+
+
+class EncodecEncoder(nn.Module):
+    """SEANet encoder as used by EnCodec."""
+
+    def __init__(self, config):
+        super().__init__()
+        model = [
+            EncodecConv1d(
+                config, config.audio_channels, config.num_filters, config.kernel_size
+            )
+        ]
+        scaling = 1
+
+        for ratio in reversed(config.upsampling_ratios):
+            current_scale = scaling * config.num_filters
+            for j in range(config.num_residual_layers):
+                model += [
+                    EncodecResnetBlock(
+                        config, current_scale, [config.dilation_growth_rate**j, 1]
+                    )
+                ]
+            model += [nn.ELU()]
+            model += [
+                EncodecConv1d(
+                    config,
+                    current_scale,
+                    current_scale * 2,
+                    kernel_size=ratio * 2,
+                    stride=ratio,
+                )
+            ]
+            scaling *= 2
+
+        model += [EncodecLSTM(config, scaling * config.num_filters)]
+        model += [nn.ELU()]
+        model += [
+            EncodecConv1d(
+                config,
+                scaling * config.num_filters,
+                config.hidden_size,
+                config.last_kernel_size,
+            )
+        ]
+
+        self.layers = model
+
+    def __call__(self, hidden_states):
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+        return hidden_states
+
+
+class EncodecDecoder(nn.Module):
+    """SEANet decoder as used by EnCodec."""
+
+    def __init__(self, config):
+        super().__init__()
+        scaling = int(2 ** len(config.upsampling_ratios))
+        model = [
+            EncodecConv1d(
+                config,
+                config.hidden_size,
+                scaling * config.num_filters,
+                config.kernel_size,
+            )
+        ]
+
+        model += [EncodecLSTM(config, scaling * config.num_filters)]
+
+        for ratio in config.upsampling_ratios:
+            current_scale = scaling * config.num_filters
+            model += [nn.ELU()]
+            model += [
+                EncodecConvTranspose1d(
+                    config,
+                    current_scale,
+                    current_scale // 2,
+                    kernel_size=ratio * 2,
+                    stride=ratio,
+                )
+            ]
+            for j in range(config.num_residual_layers):
+                model += [
+                    EncodecResnetBlock(
+                        config, current_scale // 2, (config.dilation_growth_rate**j, 1)
+                    )
+                ]
+            scaling //= 2
+
+        model += [nn.ELU()]
+        model += [
+            EncodecConv1d(
+                config,
+                config.num_filters,
+                config.audio_channels,
+                config.last_kernel_size,
+            )
+        ]
+        self.layers = model
+
+    def __call__(self, hidden_states):
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+        return hidden_states
+
+
+class EncodecEuclideanCodebook(nn.Module):
+    """Codebook with Euclidean distance."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.embed = mx.zeros((config.codebook_size, config.codebook_dim))
+
+    def quantize(self, hidden_states):
+        embed = self.embed.T
+        scaled_states = hidden_states.square().sum(axis=1, keepdims=True)
+        dist = -(
+            scaled_states
+            - 2 * hidden_states @ embed
+            + embed.square().sum(axis=0, keepdims=True)
+        )
+        embed_ind = dist.argmax(axis=-1)
+        return embed_ind
+
+    def encode(self, hidden_states):
+        shape = hidden_states.shape
+        hidden_states = hidden_states.reshape((-1, shape[-1]))
+        embed_ind = self.quantize(hidden_states)
+        embed_ind = embed_ind.reshape(*shape[:-1])
+        return embed_ind
+
+    def decode(self, embed_ind):
+        return self.embed[embed_ind]
+
+
+class EncodecVectorQuantization(nn.Module):
+    """
+    Vector quantization implementation. Currently supports only euclidean distance.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.codebook = EncodecEuclideanCodebook(config)
+
+    def encode(self, hidden_states):
+        return self.codebook.encode(hidden_states)
+
+    def decode(self, embed_ind):
+        return self.codebook.decode(embed_ind)
+
+
+class EncodecResidualVectorQuantizer(nn.Module):
+    """Residual Vector Quantizer."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.codebook_size = config.codebook_size
+
+        hop_length = np.prod(config.upsampling_ratios)
+        self.frame_rate = math.ceil(config.sampling_rate / hop_length)
+        self.num_quantizers = int(
+            1000 * config.target_bandwidths[-1] // (self.frame_rate * 10)
+        )
+        self.layers = [
+            EncodecVectorQuantization(config) for _ in range(self.num_quantizers)
+        ]
+
+    def get_num_quantizers_for_bandwidth(
+        self, bandwidth: Optional[float] = None
+    ) -> int:
+        """Return num_quantizers based on specified target bandwidth."""
+        bw_per_q = math.log2(self.codebook_size) * self.frame_rate
+        num_quantizers = self.num_quantizers
+        if bandwidth is not None and bandwidth > 0.0:
+            num_quantizers = int(max(1, math.floor(bandwidth * 1000 / bw_per_q)))
+        return num_quantizers
+
+    def encode(
+        self, embeddings: mx.array, bandwidth: Optional[float] = None
+    ) -> mx.array:
+        """
+        Encode a given input array with the specified frame rate at the given
+        bandwidth. The RVQ encode method sets the appropriate number of
+        quantizers to use and returns indices for each quantizer.
+        """
+        num_quantizers = self.get_num_quantizers_for_bandwidth(bandwidth)
+        residual = embeddings
+        all_indices = []
+        for layer in self.layers[:num_quantizers]:
+            indices = layer.encode(residual)
+            quantized = layer.decode(indices)
+            residual = residual - quantized
+            all_indices.append(indices)
+        out_indices = mx.stack(all_indices, axis=1)
+        return out_indices
+
+    def decode(self, codes: mx.array) -> mx.array:
+        """Decode the given codes to the quantized representation."""
+        quantized_out = None
+        for i, indices in enumerate(codes.split(codes.shape[1], axis=1)):
+            layer = self.layers[i]
+            quantized = layer.decode(indices.squeeze(1))
+            if quantized_out is None:
+                quantized_out = quantized
+            else:
+                quantized_out = quantized + quantized_out
+        return quantized_out
+
+
+class Encodec(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = SimpleNamespace(**config)
+        self.encoder = EncodecEncoder(self.config)
+        self.decoder = EncodecDecoder(self.config)
+        self.quantizer = EncodecResidualVectorQuantizer(self.config)
+
+    def _encode_frame(
+        self, input_values: mx.array, bandwidth: float, padding_mask: mx.array
+    ) -> Tuple[mx.array, Optional[mx.array]]:
+        """
+        Encodes the given input using the underlying VQVAE.
+        """
+        length = input_values.shape[1]
+        duration = length / self.config.sampling_rate
+
+        if (
+            self.config.chunk_length_s is not None
+            and duration > 1e-5 + self.config.chunk_length_s
+        ):
+            raise RuntimeError(
+                f"Duration of frame ({duration}) is longer than chunk {self.config.chunk_length_s}"
+            )
+
+        scale = None
+        if self.config.normalize:
+            # if the padding is non zero
+            input_values = input_values * padding_mask[..., None]
+            mono = mx.sum(input_values, axis=2, keepdims=True) / input_values.shape[2]
+            scale = mono.square().mean(axis=1, keepdims=True).sqrt() + 1e-8
+            input_values = input_values / scale
+
+        embeddings = self.encoder(input_values)
+        codes = self.quantizer.encode(embeddings, bandwidth)
+        return codes, scale
+
+    def encode(
+        self,
+        input_values: mx.array,
+        padding_mask: mx.array = None,
+        bandwidth: Optional[float] = None,
+    ) -> Tuple[mx.array, Optional[mx.array]]:
+        """
+        Encodes the input audio waveform into discrete codes.
+
+        Args:
+            input_values (mx.array): The input audio waveform with shape
+                ``(batch_size, channels, sequence_length)``.
+            padding_mask (mx.array): Padding mask used to pad the ``input_values``.
+            bandwidth (float, optional): The target bandwidth. Must be one of
+                ``config.target_bandwidths``. If ``None``, uses the smallest
+                possible bandwidth. bandwidth is represented as a thousandth of
+                what it is, e.g. 6kbps bandwidth is represented as bandwidth == 6.0
+
+        Returns:
+            A list of frames containing the discrete encoded codes for the
+            input audio waveform, along with rescaling factors for each chunk
+            when ``config.normalize==True``. Each frame is a tuple ``(codebook,
+            scale)``, with ``codebook`` of shape ``(batch_size, num_codebooks,
+            frames)``.
+        """
+
+        if bandwidth is None:
+            bandwidth = self.config.target_bandwidths[0]
+        if bandwidth not in self.config.target_bandwidths:
+            raise ValueError(
+                f"This model doesn't support the bandwidth {bandwidth}. Select one of {self.config.target_bandwidths}."
+            )
+
+        _, input_length, channels = input_values.shape
+
+        if channels < 1 or channels > 2:
+            raise ValueError(
+                f"Number of audio channels must be 1 or 2, but got {channels}"
+            )
+
+        chunk_length = self.chunk_length
+        if chunk_length is None:
+            chunk_length = input_length
+            stride = input_length
+        else:
+            stride = self.chunk_stride
+
+        if padding_mask is None:
+            padding_mask = mx.ones(input_values.shape[:2], dtype=mx.bool_)
+        encoded_frames = []
+        scales = []
+
+        print(
+            f"Encoding {input_length} samples into {chunk_length} samples with stride {stride}."
+        )
+        step = chunk_length - stride
+        if (input_length % stride) != step:
+            raise ValueError(
+                "The input length is not properly padded for batched chunked encoding. Make sure to pad the input correctly."
+            )
+
+        for offset in range(0, input_length - step, stride):
+            mask = padding_mask[:, offset : offset + chunk_length].astype(mx.bool_)
+            frame = input_values[:, offset : offset + chunk_length]
+            encoded_frame, scale = self._encode_frame(frame, bandwidth, mask)
+            encoded_frames.append(encoded_frame)
+            scales.append(scale)
+
+        encoded_frames = mx.stack(encoded_frames)
+
+        return (encoded_frames, scales)
+
+    @staticmethod
+    def _linear_overlap_add(frames: List[mx.array], stride: int):
+        if len(frames) == 0:
+            raise ValueError("`frames` cannot be an empty list.")
+
+        dtype = frames[0].dtype
+        N, frame_length, C = frames[0].shape
+        total_size = stride * (len(frames) - 1) + frames[-1].shape[1]
+
+        time_vec = mx.linspace(0, 1, frame_length + 2, dtype=dtype)[1:-1]
+        weight = 0.5 - (time_vec - 0.5).abs()
+
+        weight = weight[:, None]
+        sum_weight = mx.zeros((total_size, 1), dtype=dtype)
+        out = mx.zeros((N, total_size, C), dtype=dtype)
+        offset = 0
+
+        for frame in frames:
+            frame_length = frame.shape[1]
+            out[:, offset : offset + frame_length] += weight[:frame_length] * frame
+            sum_weight[offset : offset + frame_length] += weight[:frame_length]
+            offset += stride
+
+        return out / sum_weight
+
+    def _decode_frame(
+        self, codes: mx.array, scale: Optional[mx.array] = None
+    ) -> mx.array:
+        embeddings = self.quantizer.decode(codes)
+        outputs = self.decoder(embeddings)
+        if scale is not None:
+            outputs = outputs * scale
+        return outputs
+
+    @property
+    def channels(self):
+        return self.config.audio_channels
+
+    @property
+    def sampling_rate(self):
+        return self.config.sampling_rate
+
+    @property
+    def chunk_length(self):
+        if self.config.chunk_length_s is None:
+            return None
+        else:
+            return int(self.config.chunk_length_s * self.config.sampling_rate)
+
+    @property
+    def chunk_stride(self):
+        if self.config.chunk_length_s is None or self.config.overlap is None:
+            return None
+        else:
+            return max(1, int((1.0 - self.config.overlap) * self.chunk_length))
+
+    @classmethod
+    def from_pretrained(cls, path_or_repo: str):
+        """
+        Load the model and audo preprocessor.
+        """
+        path = Path(path_or_repo)
+        if not path.exists():
+            path = Path(
+                snapshot_download(
+                    repo_id=path_or_repo,
+                    allow_patterns=["*.json", "*.safetensors", "*.model"],
+                )
+            )
+
+        with open(path / "config.json", "r") as f:
+            config = json.load(f)
+
+        model = cls(config)
+        model.load_weights(str(path / "model.safetensors"))
+        processor = functools.partial(
+            preprocess_audio,
+            sampling_rate=config.sampling_rate,
+            chunk_length=model.chunk_length,
+            chunk_stride=model.chunk_stride,
+        )
+        mx.eval(model)
+        return model, processor
+
+    def decode(
+        self,
+        audio_codes: mx.array,
+        audio_scales: Union[mx.array, List[mx.array]],
+        padding_mask: Optional[mx.array] = None,
+    ) -> Tuple[mx.array, mx.array]:
+        """
+        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 should be trimmed.
+
+        Args:
+            audio_codes (mx.array): Discret code embeddings of shape
+                ``(batch_size, nb_chunks, chunk_length)``.
+            audio_scales (mx.array): Scaling factor for each input.
+            padding_mask (mx.array): Padding mask.
+        """
+        chunk_length = self.chunk_length
+        if chunk_length is None:
+            if audio_codes.shape[1] != 1:
+                raise ValueError(f"Expected one frame, got {len(audio_codes)}")
+            audio_values = self._decode_frame(audio_codes[:, 0], audio_scales[0])
+        else:
+            decoded_frames = []
+
+            for frame, scale in zip(audio_codes, audio_scales):
+                frames = self._decode_frame(frame, scale)
+                decoded_frames.append(frames)
+
+            audio_values = self._linear_overlap_add(
+                decoded_frames, self.chunk_stride or 1
+            )
+
+        # truncate based on padding mask
+        if padding_mask is not None and padding_mask.shape[1] < audio_values.shape[1]:
+            audio_values = audio_values[:, : padding_mask.shape[1]]
+        return audio_values
diff --git a/mlx_audio/codec/tests/test_encodec.py b/mlx_audio/codec/tests/test_encodec.py
new file mode 100644
index 00000000..83286b57
--- /dev/null
+++ b/mlx_audio/codec/tests/test_encodec.py
@@ -0,0 +1,58 @@
+import unittest
+
+import mlx.core as mx
+
+from ..models.encodec import Encodec
+
+config = {
+    "audio_channels": 1,
+    "chunk_length_s": None,
+    "codebook_dim": 128,
+    "codebook_size": 1024,
+    "compress": 2,
+    "dilation_growth_rate": 2,
+    "hidden_size": 128,
+    "kernel_size": 7,
+    "last_kernel_size": 7,
+    "model_type": "encodec",
+    "norm_type": "weight_norm",
+    "normalize": False,
+    "num_filters": 32,
+    "num_lstm_layers": 2,
+    "num_residual_layers": 1,
+    "overlap": None,
+    "pad_mode": "reflect",
+    "residual_kernel_size": 3,
+    "sampling_rate": 24000,
+    "target_bandwidths": [1.5, 3.0, 6.0, 12.0, 24.0],
+    "trim_right_ratio": 1.0,
+    "upsampling_ratios": [8, 5, 4, 2],
+    "use_causal_conv": True,
+}
+
+
+class TesEncodec(unittest.TestCase):
+    """Test EnCodec model encoding and decoding."""
+
+    def test_encodec_24khz(self):
+        model = Encodec(config)
+
+        audio = mx.zeros((1, 120_000, 1))
+
+        # default bandwidth
+        (codes, scales) = model.encode(audio)
+        self.assertEqual(codes.shape, (1, 1, 2, 375))
+
+        audio_out = model.decode(codes, scales)
+        self.assertEqual(audio_out.shape, (1, 120_000, 1))
+
+        # 6kbps bandwidth
+        (codes, scales) = model.encode(audio, bandwidth=6)
+        self.assertEqual(codes.shape, (1, 1, 8, 375))
+
+        audio_out = model.decode(codes, scales)
+        self.assertEqual(audio_out.shape, (1, 120_000, 1))
+
+
+if __name__ == "__main__":
+    unittest.main()

From 1ee0dd73165dbb63dbbb32b4822804ec91859cff Mon Sep 17 00:00:00 2001
From: Lucas Newman <lucas@future.fit>
Date: Wed, 19 Mar 2025 15:03:01 -0700
Subject: [PATCH 2/2] Remove debug log.

---
 mlx_audio/codec/models/encodec/encodec.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/mlx_audio/codec/models/encodec/encodec.py b/mlx_audio/codec/models/encodec/encodec.py
index 59c3f793..c8747d35 100644
--- a/mlx_audio/codec/models/encodec/encodec.py
+++ b/mlx_audio/codec/models/encodec/encodec.py
@@ -599,9 +599,6 @@ def encode(
         encoded_frames = []
         scales = []
 
-        print(
-            f"Encoding {input_length} samples into {chunk_length} samples with stride {stride}."
-        )
         step = chunk_length - stride
         if (input_length % stride) != step:
             raise ValueError(