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| """ | |
| This module provides the implementation of an Audio Projection Model, which is designed for | |
| audio processing tasks. The model takes audio embeddings as input and outputs context tokens | |
| that can be used for various downstream applications, such as audio analysis or synthesis. | |
| The AudioProjModel class is based on the ModelMixin class from the diffusers library, which | |
| provides a foundation for building custom models. This implementation includes multiple linear | |
| layers with ReLU activation functions and a LayerNorm for normalization. | |
| Key Features: | |
| - Audio embedding input with flexible sequence length and block structure. | |
| - Multiple linear layers for feature transformation. | |
| - ReLU activation for non-linear transformation. | |
| - LayerNorm for stabilizing and speeding up training. | |
| - Rearrangement of input embeddings to match the model's expected input shape. | |
| - Customizable number of blocks, channels, and context tokens for adaptability. | |
| The module is structured to be easily integrated into larger systems or used as a standalone | |
| component for audio feature extraction and processing. | |
| Classes: | |
| - AudioProjModel: A class representing the audio projection model with configurable parameters. | |
| Functions: | |
| - (none) | |
| Dependencies: | |
| - torch: For tensor operations and neural network components. | |
| - diffusers: For the ModelMixin base class. | |
| - einops: For tensor rearrangement operations. | |
| """ | |
| import torch | |
| from diffusers import ModelMixin | |
| from einops import rearrange | |
| from torch import nn | |
| class AudioProjModel(ModelMixin): | |
| """Audio Projection Model | |
| This class defines an audio projection model that takes audio embeddings as input | |
| and produces context tokens as output. The model is based on the ModelMixin class | |
| and consists of multiple linear layers and activation functions. It can be used | |
| for various audio processing tasks. | |
| Attributes: | |
| seq_len (int): The length of the audio sequence. | |
| blocks (int): The number of blocks in the audio projection model. | |
| channels (int): The number of channels in the audio projection model. | |
| intermediate_dim (int): The intermediate dimension of the model. | |
| context_tokens (int): The number of context tokens in the output. | |
| output_dim (int): The output dimension of the context tokens. | |
| Methods: | |
| __init__(self, seq_len=5, blocks=12, channels=768, intermediate_dim=512, context_tokens=32, output_dim=768): | |
| Initializes the AudioProjModel with the given parameters. | |
| forward(self, audio_embeds): | |
| Defines the forward pass for the AudioProjModel. | |
| Parameters: | |
| audio_embeds (torch.Tensor): The input audio embeddings with shape (batch_size, video_length, blocks, channels). | |
| Returns: | |
| context_tokens (torch.Tensor): The output context tokens with shape (batch_size, video_length, context_tokens, output_dim). | |
| """ | |
| def __init__( | |
| self, | |
| seq_len=5, | |
| blocks=12, # add a new parameter blocks | |
| channels=768, # add a new parameter channels | |
| intermediate_dim=512, | |
| output_dim=768, | |
| context_tokens=32, | |
| ): | |
| super().__init__() | |
| self.seq_len = seq_len | |
| self.blocks = blocks | |
| self.channels = channels | |
| self.input_dim = ( | |
| seq_len * blocks * channels | |
| ) # update input_dim to be the product of blocks and channels. | |
| self.intermediate_dim = intermediate_dim | |
| self.context_tokens = context_tokens | |
| self.output_dim = output_dim | |
| # define multiple linear layers | |
| self.proj1 = nn.Linear(self.input_dim, intermediate_dim) | |
| self.proj2 = nn.Linear(intermediate_dim, intermediate_dim) | |
| self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim) | |
| self.norm = nn.LayerNorm(output_dim) | |
| def forward(self, audio_embeds): | |
| """ | |
| Defines the forward pass for the AudioProjModel. | |
| Parameters: | |
| audio_embeds (torch.Tensor): The input audio embeddings with shape (batch_size, video_length, blocks, channels). | |
| Returns: | |
| context_tokens (torch.Tensor): The output context tokens with shape (batch_size, video_length, context_tokens, output_dim). | |
| """ | |
| # merge | |
| video_length = audio_embeds.shape[1] | |
| audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") | |
| batch_size, window_size, blocks, channels = audio_embeds.shape | |
| audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) | |
| audio_embeds = torch.relu(self.proj1(audio_embeds)) | |
| audio_embeds = torch.relu(self.proj2(audio_embeds)) | |
| context_tokens = self.proj3(audio_embeds).reshape( | |
| batch_size, self.context_tokens, self.output_dim | |
| ) | |
| context_tokens = self.norm(context_tokens) | |
| context_tokens = rearrange( | |
| context_tokens, "(bz f) m c -> bz f m c", f=video_length | |
| ) | |
| return context_tokens | |