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