279 lines
13 KiB
Python
279 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2024 Meta Platforms, Inc. and affiliates, and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Mimi model configuration"""
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import math
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import numpy as np
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class MimiConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`MimiModel`]. It is used to instantiate a
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Mimi model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the
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[kyutai/mimi](https://huggingface.co/kyutai/mimi) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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sampling_rate (`int`, *optional*, defaults to 24000):
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The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
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frame_rate (`float`, *optional*):
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Should be computed from the other parameters, yet kept for backward compatibility.
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audio_channels (`int`, *optional*, defaults to 1):
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Number of channels in the audio data. Either 1 for mono or 2 for stereo.
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hidden_size (`int`, *optional*, defaults to 512):
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Intermediate representation dimension.
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num_filters (`int`, *optional*, defaults to 64):
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Number of convolution kernels of first `MimiConv1d` down sampling layer.
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num_residual_layers (`int`, *optional*, defaults to 1):
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Number of residual layers.
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upsampling_ratios (`Sequence[int]`, *optional*):
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Kernel size and stride ratios. The encoder uses downsampling ratios instead of upsampling ratios, hence it
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will use the ratios in the reverse order to the ones specified here that must match the decoder order.
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If not specified, will defaults to `[8, 6, 5, 4]`
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kernel_size (`int`, *optional*, defaults to 7):
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Kernel size for the initial convolution.
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last_kernel_size (`int`, *optional*, defaults to 3):
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Kernel size for the last convolution layer.
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residual_kernel_size (`int`, *optional*, defaults to 3):
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Kernel size for the residual layers.
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dilation_growth_rate (`int`, *optional*, defaults to 2):
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How much to increase the dilation with each layer.
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use_causal_conv (`bool`, *optional*, defaults to `True`):
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Whether to use fully causal convolution.
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pad_mode (`str`, *optional*, defaults to `"constant"`):
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Padding mode for the convolutions.
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compress (`int`, *optional*, defaults to 2):
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Reduced dimensionality in residual branches.
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trim_right_ratio (`float`, *optional*, defaults to 1.0):
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Ratio for trimming at the right of the transposed convolution under the `use_causal_conv = True` setup. If
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equal to 1.0, it means that all the trimming is done at the right.
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codebook_size (`int`, *optional*, defaults to 2048):
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Number of discret codes in each codebooks.
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codebook_dim (`int`, *optional*, defaults to 256):
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Dimension of the unquantized codebook vectors. If not defined, uses `hidden_size`.
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num_quantizers (`int`, *optional*, defaults to 32):
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Number of quantizer channels, or codebooks, in the quantizer.
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use_conv_shortcut (`bool`, *optional*, defaults to `False`):
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Whether to use a convolutional layer as the 'skip' connection in the `MimiResnetBlock` block. If False,
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an identity function will be used, giving a generic residual connection.
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vector_quantization_hidden_dimension (`int`, *optional*, defaults to 256):
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Intermediate representation dimension in the residual vector quantization space.
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num_semantic_quantizers (`int`, *optional*, defaults to 1):
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Number of semantic quantizer channels, or codebooks, in the semantic quantizer. Must be lower than `num_quantizers`.
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upsample_groups (`int`, *optional*, defaults to 512):
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If `frame_rate!=encodec_frame_rate`, indicates the number of groups used in the upsampling operation to go from one rate to another.
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num_hidden_layers (`int`, *optional*, defaults to 8):
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Number of hidden layers in the Transformer models.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimension of the MLP representations.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
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head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
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The attention head dimension.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 8000):
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The maximum sequence length that this model might ever be used with. Mimi's sliding window attention
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allows sequence of up to 8000 tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the LayerNorm normalization layers.
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use_cache (`bool`, *optional*, defaults to `False`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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use_streaming (`bool`, *optional*, defaults to `False`):
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Whether to use streaming mode. If `True`, the model encode method will return the padding cache that can be used in a subsequent call to the encode method.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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sliding_window (`int`, *optional*, defaults to 250):
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Sliding window attention window size. If not specified, will default to `250`.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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layer_scale_initial_scale (`float`, *optional*, defaults to 0.01):
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Initiale scale of the residual rescaling operation done in the Transformer models.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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Example:
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```python
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>>> from transformers import MimiModel, MimiConfig
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>>> # Initializing a "kyutai/mimi" style configuration
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>>> configuration = MimiConfig()
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>>> # Initializing a model (with random weights) from the "kyutai/mimi" style configuration
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>>> model = MimiModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mimi"
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def __init__(
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self,
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sampling_rate=24_000,
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frame_rate=None,
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audio_channels=1,
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hidden_size=512,
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num_filters=64,
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num_residual_layers=1,
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upsampling_ratios=None,
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kernel_size=7,
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last_kernel_size=3,
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residual_kernel_size=3,
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dilation_growth_rate=2,
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use_causal_conv=True,
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pad_mode="constant",
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compress=2,
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trim_right_ratio=1.0,
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codebook_size=2048,
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codebook_dim=256,
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num_quantizers=32,
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use_conv_shortcut=False,
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vector_quantization_hidden_dimension=256,
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num_semantic_quantizers=1,
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upsample_groups=512,
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num_hidden_layers=8,
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intermediate_size=2048,
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num_attention_heads=8,
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num_key_value_heads=8,
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head_dim=None,
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hidden_act="gelu",
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max_position_embeddings=8000,
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initializer_range=0.02,
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norm_eps=1e-5,
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use_cache=False,
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use_streaming=False,
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rope_theta=10000.0,
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sliding_window=250,
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attention_dropout=0.0,
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layer_scale_initial_scale=0.01,
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attention_bias=False,
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**kwargs,
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):
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self.sampling_rate = sampling_rate
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self.audio_channels = audio_channels
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self.hidden_size = hidden_size
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self.num_filters = num_filters
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self.num_residual_layers = num_residual_layers
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self.upsampling_ratios = upsampling_ratios if upsampling_ratios else [8, 6, 5, 4]
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self.kernel_size = kernel_size
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self.last_kernel_size = last_kernel_size
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self.residual_kernel_size = residual_kernel_size
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self.dilation_growth_rate = dilation_growth_rate
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self.use_causal_conv = use_causal_conv
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self.pad_mode = pad_mode
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self.compress = compress
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self.trim_right_ratio = trim_right_ratio
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self.codebook_size = codebook_size
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self.codebook_dim = codebook_dim if codebook_dim is not None else hidden_size
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self.num_quantizers = num_quantizers
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self.use_conv_shortcut = use_conv_shortcut
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self.vector_quantization_hidden_dimension = vector_quantization_hidden_dimension
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self.upsample_groups = upsample_groups
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.norm_eps = norm_eps
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self.use_cache = use_cache
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self.use_streaming = use_streaming
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self.rope_theta = rope_theta
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self.sliding_window = sliding_window
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self.attention_dropout = attention_dropout
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self.head_dim = head_dim or hidden_size // num_attention_heads
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self.layer_scale_initial_scale = layer_scale_initial_scale
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self.attention_bias = attention_bias
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# Handle backward compatibility for frame_rate:
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# If frame_rate is explicitly provided, use it (backward compatibility)
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# Otherwise, compute it from other parameters (correctly)
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if frame_rate is not None:
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self._frame_rate = frame_rate
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else:
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self._frame_rate = None
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if num_semantic_quantizers >= self.num_quantizers:
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raise ValueError(
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f"The number of semantic quantizers should be lower than the total number of quantizers {self.num_quantizers}, but is currently {num_semantic_quantizers}."
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)
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self.num_semantic_quantizers = num_semantic_quantizers
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super().__init__(**kwargs)
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@property
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def encodec_frame_rate(self) -> int:
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hop_length = np.prod(self.upsampling_ratios)
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return math.ceil(self.sampling_rate / hop_length)
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@property
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def num_codebooks(self) -> int:
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# alias to num_quantizers
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return self.num_quantizers
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@property
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def frame_size(self) -> int:
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# 1. we need each encoder conv stride
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# first conv
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strides = [1]
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# layer convs
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for ratio in reversed(self.upsampling_ratios):
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for j in range(self.num_residual_layers):
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len_kernel_sizes = len(self.residual_kernel_size) if isinstance(self.residual_kernel_size, list) else 1
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strides.extend([1] * (len_kernel_sizes + 1))
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if self.use_conv_shortcut: # skip connection
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strides.append(1)
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strides.append(ratio)
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# last conv
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strides.append(1)
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# downsampling layer
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strides.append(2)
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return math.prod(strides)
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@property
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def frame_rate(self) -> float:
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# handle backward compatibility
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if self._frame_rate is not None:
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return self._frame_rate
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return self.sampling_rate / self.frame_size
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__all__ = ["MimiConfig"]
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