AudioCraft provides a modular implementation of conditioning modules that can be used with the language model to condition the generation. The codebase was developed in order to easily extend the set of modules currently supported to easily develop new ways of controlling the generation.
For now, we support 3 main types of conditioning within AudioCraft:
The Language Model relies on 2 core components that handle processing information:
ConditionProvider class, that maps metadata to processed conditions, leveraging
all the defined conditioners for the given task.ConditionFuser class, that takes preprocessed conditions and properly fuse the
conditioning embedding to the language model inputs following a given fusing strategy.Different conditioners (for text, waveform, joint embeddings…) are provided as torch modules in AudioCraft and are used internally in the language model to process the conditioning signals and feed them to the language model.
The BaseConditioner torch module is the base implementation for all conditioners in AudioCraft.
Each conditioner is expected to implement 2 methods:
tokenize method that is used as a preprocessing method that contains all processing
that can lead to synchronization points (e.g. BPE tokenization with transfer to the GPU).
The output of the tokenize method will then be used to feed the forward method.forward method that takes the output of the tokenize method and contains the core computation
to obtain the conditioning embedding along with a mask indicating valid indices (e.g. padding tokens).The ConditionProvider prepares and provides conditions given a dictionary of conditioners.
Conditioners are specified as a dictionary of attributes and the corresponding conditioner providing the processing logic for the given attribute.
Similarly to the conditioners, the condition provider works in two steps to avoid synchronization points:
tokenize method that takes a list of conditioning attributes for the batch,
and runs all tokenize steps for the set of conditioners.forward method that takes the output of the tokenize step and runs all the forward steps
for the set of conditioners.The list of conditioning attributes is passed as a list of ConditioningAttributes
that is presented just below.
Once all conditioning signals have been extracted and processed by the ConditionProvider
as dense embeddings, they remain to be passed to the language model along with the original
language model inputs.
The ConditionFuser handles specifically the logic to combine the different conditions
to the actual model input, supporting different strategies to combine them.
One can therefore define different strategies to combine or fuse the condition to the input, in particular:
prepend strategy,sum strategy,cross strategy,input_interpolate strategy.The ConditioningAttributes dataclass is the base class for metadata
containing all attributes used for conditioning the language model.
It currently supports the following types of attributes:
These different types of attributes are the attributes that are processed by the different conditioners.
ConditioningAttributes are extracted from metadata loaded along the audio in the datasets,
provided that the metadata used by the dataset implements the SegmentWithAttributes abstraction.
All metadata-enabled datasets to use for conditioning in AudioCraft inherits
the audiocraft.data.info_dataset.InfoAudioDataset class
and the corresponding metadata inherits and implements the SegmentWithAttributes abstraction.
Refer to the audiocraft.data.music_dataset.MusicAudioDataset
class as an example.
All text conditioners are expected to inherit from the TextConditioner class.
AudioCraft currently provides two text conditioners:
LUTConditioner that relies on look-up-table of embeddings learned at train time,
and relying on either no tokenizer or a spacy tokenizer. This conditioner is particularly
useful for simple experiments and categorical labels.T5Conditioner that relies on a
pre-trained T5 model
frozen or fine-tuned at train time to extract the text embeddings.All waveform conditioners are expected to inherit from the WaveformConditioner class and
consist of a conditioning method that takes a waveform as input. The waveform conditioner
must implement the logic to extract the embedding from the waveform and define the downsampling
factor from the waveform to the resulting embedding.
The ChromaStemConditioner conditioner is a waveform conditioner for the chroma features
conditioning used by MusicGen. It takes a given waveform, extracts relevant stems for melody
(namely all non drums and bass stems) using a
pre-trained Demucs model
and then extracts the chromagram bins from the remaining mix of stems.
We finally provide support for conditioning based on joint text and audio embeddings through
the JointEmbeddingConditioner class and the CLAPEmbeddingConditioner that implements such
a conditioning method relying on a pretrained CLAP model.
We provide a Classifier Free Guidance implementation in AudioCraft. With the classifier free guidance dropout, all attributes are dropped with the same probability.
We further provide an attribute dropout strategy. Unlike the classifier free guidance dropout, the attribute dropout drops given attributes with a defined probability, allowing the model not to expect all conditioning signals to be provided at once.
Conditioners that require some heavy computation on the waveform can be cached, in particular
the ChromaStemConditioner or CLAPEmbeddingConditioner. You just need to provide the
cache_path parameter to them. We recommend running dummy jobs for filling up the cache quickly.
An example is provided in the musicgen.musicgen_melody_32khz grid.