# coding=utf-8 # Copyright 2023 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. """EnCodec model configuration""" import math from typing import Optional import numpy as np from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class EncodecConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of an [`EncodecModel`]. It is used to instantiate a Encodec 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 [facebook/encodec_24khz](https://huggingface.co/facebook/encodec_24khz) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: target_bandwidths (`List[float]`, *optional*, defaults to `[1.5, 3.0, 6.0, 12.0, 24.0]`): The range of diffent bandwiths the model can encode audio with. sampling_rate (`int`, *optional*, defaults to 24000): The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz). audio_channels (`int`, *optional*, defaults to 1): Number of channels in the audio data. Either 1 for mono or 2 for stereo. normalize (`bool`, *optional*, defaults to `False`): Whether the audio shall be normalized when passed. chunk_length_s (`float`, *optional*): If defined the audio is pre-processed into chunks of lengths `chunk_length_s` and then encoded. overlap (`float`, *optional*): Defines the overlap between each chunk. It is used to compute the `chunk_stride` using the following formulae : `int((1.0 - self.overlap) * self.chunk_length)`. hidden_size (`int`, *optional*, defaults to 128): Intermediate representation dimension. num_filters (`int`, *optional*, defaults to 32): Number of convolution kernels of first `EncodecConv1d` down sampling layer. num_residual_layers (`int`, *optional*, defaults to 1): Number of residual layers. upsampling_ratios (`Sequence[int]` , *optional*, defaults to `[8, 5, 4, 2]`): 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. norm_type (`str`, *optional*, defaults to `"weight_norm"`): Normalization method. Should be in `["weight_norm", "time_group_norm"]` kernel_size (`int`, *optional*, defaults to 7): Kernel size for the initial convolution. last_kernel_size (`int`, *optional*, defaults to 7): 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 `"reflect"`): Padding mode for the convolutions. compress (`int`, *optional*, defaults to 2): Reduced dimensionality in residual branches (from Demucs v3). num_lstm_layers (`int`, *optional*, defaults to 2): Number of LSTM layers at the end of the encoder. 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 1024): Number of discret codes that make up VQVAE. codebook_dim (`int`, *optional*): Dimension of the codebook vectors. If not defined, uses `hidden_size`. use_conv_shortcut (`bool`, *optional*, defaults to `True`): Whether to use a convolutional layer as the 'skip' connection in the `EncodecResnetBlock` block. If False, an identity function will be used, giving a generic residual connection. Example: ```python >>> from transformers import EncodecModel, EncodecConfig >>> # Initializing a "facebook/encodec_24khz" style configuration >>> configuration = EncodecConfig() >>> # Initializing a model (with random weights) from the "facebook/encodec_24khz" style configuration >>> model = EncodecModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "encodec" def __init__( self, target_bandwidths=[1.5, 3.0, 6.0, 12.0, 24.0], sampling_rate=24_000, audio_channels=1, normalize=False, chunk_length_s=None, overlap=None, hidden_size=128, num_filters=32, num_residual_layers=1, upsampling_ratios=[8, 5, 4, 2], norm_type="weight_norm", kernel_size=7, last_kernel_size=7, residual_kernel_size=3, dilation_growth_rate=2, use_causal_conv=True, pad_mode="reflect", compress=2, num_lstm_layers=2, trim_right_ratio=1.0, codebook_size=1024, codebook_dim=None, use_conv_shortcut=True, **kwargs, ): self.target_bandwidths = target_bandwidths self.sampling_rate = sampling_rate self.audio_channels = audio_channels self.normalize = normalize self.chunk_length_s = chunk_length_s self.overlap = overlap self.hidden_size = hidden_size self.num_filters = num_filters self.num_residual_layers = num_residual_layers self.upsampling_ratios = upsampling_ratios self.norm_type = norm_type 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.num_lstm_layers = num_lstm_layers 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.use_conv_shortcut = use_conv_shortcut if self.norm_type not in ["weight_norm", "time_group_norm"]: raise ValueError( f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' ) super().__init__(**kwargs) # This is a property because you might want to change the chunk_length_s on the fly @property def chunk_length(self) -> Optional[int]: if self.chunk_length_s is None: return None else: return int(self.chunk_length_s * self.sampling_rate) # This is a property because you might want to change the chunk_length_s on the fly @property def chunk_stride(self) -> Optional[int]: if self.chunk_length_s is None or self.overlap is None: return None else: return max(1, int((1.0 - self.overlap) * self.chunk_length)) @property def frame_rate(self) -> int: hop_length = np.prod(self.upsampling_ratios) return math.ceil(self.sampling_rate / hop_length) @property def num_quantizers(self) -> int: return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)) __all__ = ["EncodecConfig"]