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Jukebox

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Jukebox

Overview

The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditioned on an artist, genres and lyrics.

The abstract from the paper is the following:

We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples, along with model weights and code.

As shown on the following figure, Jukebox is made of 3 priors which are decoder only models. They follow the architecture described in Generating Long Sequences with Sparse Transformers, modified to support longer context length. First, a autoencoder is used to encode the text lyrics. Next, the first (also called top_prior) prior attends to the last hidden states extracted from the lyrics encoder. The priors are linked to the previous priors respectively via an AudioConditioner module. TheAudioConditioner upsamples the outputs of the previous prior to raw tokens at a certain audio frame per second resolution. The metadata such as artist, genre and timing are passed to each prior, in the form of a start token and positional embedding for the timing data. The hidden states are mapped to the closest codebook vector from the VQVAE in order to convert them to raw audio.

JukeboxModel

This model was contributed by Arthur Zucker. The original code can be found here.

Usage tips

  • This model only supports inference. This is for a few reasons, mostly because it requires a crazy amount of memory to train. Feel free to open a PR and add what’s missing to have a full integration with the hugging face trainer!
  • This model is very slow, and takes 8h to generate a minute long audio using the 5b top prior on a V100 GPU. In order automaticallay handle the device on which the model should execute, use accelerate.
  • Contrary to the paper, the order of the priors goes from 0 to 1 as it felt more intuitive : we sample starting from 0.
  • Primed sampling (conditioning the sampling on raw audio) requires more memory than ancestral sampling and should be used with fp16 set to True.

This model was contributed by Arthur Zucker. The original code can be found here.

JukeboxConfig

class transformers.JukeboxConfig

< >

( vqvae_config = None prior_config_list = None nb_priors = 3 sampling_rate = 44100 timing_dims = 64 min_duration = 0 max_duration = 600.0 max_nb_genres = 5 metadata_conditioning = True **kwargs )

Parameters

  • vqvae_config (JukeboxVQVAEConfig, optional) — Configuration for the JukeboxVQVAE model.
  • prior_config_list (List[JukeboxPriorConfig], optional) — List of the configs for each of the JukeboxPrior of the model. The original architecture uses 3 priors.
  • nb_priors (int, optional, defaults to 3) — Number of prior models that will sequentially sample tokens. Each prior is conditional auto regressive (decoder) model, apart from the top prior, which can include a lyric encoder. The available models were trained using a top prior and 2 upsampler priors.
  • sampling_rate (int, optional, defaults to 44100) — Sampling rate of the raw audio.
  • timing_dims (int, optional, defaults to 64) — Dimensions of the JukeboxRangeEmbedding layer which is equivalent to traditional positional embedding layer. The timing embedding layer converts the absolute and relative position in the currently sampled audio to a tensor of length timing_dims that will be added to the music tokens.
  • min_duration (int, optional, defaults to 0) — Minimum duration of the audios to generate
  • max_duration (float, optional, defaults to 600.0) — Maximum duration of the audios to generate
  • max_nb_genres (int, optional, defaults to 5) — Maximum number of genres that can be used to condition a single sample.
  • metadata_conditioning (bool, optional, defaults to True) — Whether or not to use metadata conditioning, corresponding to the artist, the genre and the min/maximum duration.

This is the configuration class to store the configuration of a JukeboxModel.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information. Instantiating a configuration with the defaults will yield a similar configuration to that of openai/jukebox-1b-lyrics architecture.

The downsampling and stride are used to determine downsampling of the input sequence. For example, downsampling = (5,3), and strides = (2, 2) will downsample the audio by 2^5 = 32 to get the first level of codes, and 2**8 = 256 to get the second level codes. This is mostly true for training the top level prior and the upsamplers.

Example:

>>> from transformers import JukeboxModel, JukeboxConfig

>>> # Initializing a Jukebox configuration
>>> configuration = JukeboxConfig()

>>> # Initializing a model from the configuration
>>> model = JukeboxModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

from_configs

< >

( prior_configs: List vqvae_config: JukeboxVQVAEConfig **kwargs ) → JukeboxConfig

Returns

JukeboxConfig

An instance of a configuration object

Instantiate a JukeboxConfig (or a derived class) from clip text model configuration and clip vision model configuration.

JukeboxPriorConfig

class transformers.JukeboxPriorConfig

< >

( act_fn = 'quick_gelu' level = 0 alignment_head = 2 alignment_layer = 68 attention_multiplier = 0.25 attention_pattern = 'enc_dec_with_lyrics' attn_dropout = 0 attn_res_scale = False blocks = 64 conv_res_scale = None num_layers = 72 emb_dropout = 0 encoder_config = None encoder_loss_fraction = 0.4 hidden_size = 2048 init_scale = 0.2 is_encoder_decoder = True lyric_vocab_size = 80 mask = False max_duration = 600 max_nb_genres = 1 merged_decoder = True metadata_conditioning = True metadata_dims = [604, 7898] min_duration = 0 mlp_multiplier = 1.0 music_vocab_size = 2048 n_ctx = 6144 n_heads = 2 nb_relevant_lyric_tokens = 384 res_conv_depth = 3 res_conv_width = 128 res_convolution_multiplier = 1 res_dilation_cycle = None res_dilation_growth_rate = 1 res_downs_t = [3, 2, 2] res_strides_t = [2, 2, 2] resid_dropout = 0 sampling_rate = 44100 spread = None timing_dims = 64 zero_out = False **kwargs )

Parameters

  • act_fn (str, optional, defaults to "quick_gelu") — Activation function.
  • alignment_head (int, optional, defaults to 2) — Head that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio alignment
  • alignment_layer (int, optional, defaults to 68) — Index of the layer that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio alignment
  • attention_multiplier (float, optional, defaults to 0.25) — Multiplier coefficient used to define the hidden dimension of the attention layers. 0.25 means that 0.25*width of the model will be used.
  • attention_pattern (str, optional, defaults to "enc_dec_with_lyrics") — Which attention pattern to use for the decoder/
  • attn_dropout (int, optional, defaults to 0) — Dropout probability for the post-attention layer dropout in the decoder.
  • attn_res_scale (bool, optional, defaults to False) — Whether or not to scale the residuals in the attention conditioner block.
  • blocks (int, optional, defaults to 64) — Number of blocks used in the block_attn. A sequence of length seq_len is factored as [blocks, seq_len // blocks] in the JukeboxAttention layer.
  • conv_res_scale (int, optional) — Whether or not to scale the residuals in the conditioner block. Since the top level prior does not have a conditioner, the default value is to None and should not be modified.
  • num_layers (int, optional, defaults to 72) — Number of layers of the transformer architecture.
  • emb_dropout (int, optional, defaults to 0) — Embedding dropout used in the lyric decoder.
  • encoder_config (JukeboxPriorConfig, optional) — Configuration of the encoder which models the prior on the lyrics.
  • encoder_loss_fraction (float, optional, defaults to 0.4) — Multiplication factor used in front of the lyric encoder loss.
  • hidden_size (int, optional, defaults to 2048) — Hidden dimension of the attention layers.
  • init_scale (float, optional, defaults to 0.2) — Initialization scales for the prior modules.
  • is_encoder_decoder (bool, optional, defaults to True) — Whether or not the prior is an encoder-decoder model. In case it is not, and nb_relevant_lyric_tokens is greater than 0, the encoder args should be specified for the lyric encoding.
  • mask (bool, optional, defaults to False) — Whether or not to mask the previous positions in the attention.
  • max_duration (int, optional, defaults to 600) — Maximum supported duration of the generated song in seconds.
  • max_nb_genres (int, optional, defaults to 1) — Maximum number of genres that can be used to condition the model.
  • merged_decoder (bool, optional, defaults to True) — Whether or not the decoder and the encoder inputs are merged. This is used for the separated encoder-decoder architecture
  • metadata_conditioning (bool, optional, defaults to True) — Whether or not to condition on the artist and genre metadata.
  • metadata_dims (List[int], optional, defaults to [604, 7898]) — Number of genres and the number of artists that were used to train the embedding layers of the prior models.
  • min_duration (int, optional, defaults to 0) — Minimum duration of the generated audio on which the model was trained.
  • mlp_multiplier (float, optional, defaults to 1.0) — Multiplier coefficient used to define the hidden dimension of the MLP layers. 0.25 means that 0.25*width of the model will be used.
  • music_vocab_size (int, optional, defaults to 2048) — Number of different music tokens. Should be similar to the JukeboxVQVAEConfig.nb_discrete_codes.
  • n_ctx (int, optional, defaults to 6144) — Number of context tokens for each prior. The context tokens are the music tokens that are attended to when generating music tokens.
  • n_heads (int, optional, defaults to 2) — Number of attention heads.
  • nb_relevant_lyric_tokens (int, optional, defaults to 384) — Number of lyric tokens that are used when sampling a single window of length n_ctx
  • res_conv_depth (int, optional, defaults to 3) — Depth of the JukeboxDecoderConvBock used to upsample the previously sampled audio in the JukeboxMusicTokenConditioner.
  • res_conv_width (int, optional, defaults to 128) — Width of the JukeboxDecoderConvBock used to upsample the previously sampled audio in the JukeboxMusicTokenConditioner.
  • res_convolution_multiplier (int, optional, defaults to 1) — Multiplier used to scale the hidden_dim of the JukeboxResConv1DBlock.
  • res_dilation_cycle (int, optional) — Dilation cycle used to define the JukeboxMusicTokenConditioner. Usually similar to the ones used in the corresponding level of the VQVAE. The first prior does not use it as it is not conditioned on upper level tokens.
  • res_dilation_growth_rate (int, optional, defaults to 1) — Dilation grow rate used between each convolutionnal block of the JukeboxMusicTokenConditioner
  • res_downs_t (List[int], optional, defaults to [3, 2, 2]) — Downsampling rates used in the audio conditioning network
  • res_strides_t (List[int], optional, defaults to [2, 2, 2]) — Striding used in the audio conditioning network
  • resid_dropout (int, optional, defaults to 0) — Residual dropout used in the attention pattern.
  • sampling_rate (int, optional, defaults to 44100) — Sampling rate used for training.
  • spread (int, optional) — Spread used in the summary_spread_attention pattern
  • timing_dims (int, optional, defaults to 64) — Dimension of the timing embedding.
  • zero_out (bool, optional, defaults to False) — Whether or not to zero out convolution weights when initializing.

This is the configuration class to store the configuration of a JukeboxPrior. It is used to instantiate a JukeboxPrior according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the top level prior from the [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox -1b-lyrics) architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

JukeboxVQVAEConfig

class transformers.JukeboxVQVAEConfig

< >

( act_fn = 'relu' nb_discrete_codes = 2048 commit = 0.02 conv_input_shape = 1 conv_res_scale = False embed_dim = 64 hop_fraction = [0.125, 0.5, 0.5] levels = 3 lmu = 0.99 multipliers = [2, 1, 1] res_conv_depth = 4 res_conv_width = 32 res_convolution_multiplier = 1 res_dilation_cycle = None res_dilation_growth_rate = 3 res_downs_t = [3, 2, 2] res_strides_t = [2, 2, 2] sample_length = 1058304 init_scale = 0.2 zero_out = False **kwargs )

Parameters

  • act_fn (str, optional, defaults to "relu") — Activation function of the model.
  • nb_discrete_codes (int, optional, defaults to 2048) — Number of codes of the VQVAE.
  • commit (float, optional, defaults to 0.02) — Commit loss multiplier.
  • conv_input_shape (int, optional, defaults to 1) — Number of audio channels.
  • conv_res_scale (bool, optional, defaults to False) — Whether or not to scale the residuals of the JukeboxResConv1DBlock.
  • embed_dim (int, optional, defaults to 64) — Embedding dimension of the codebook vectors.
  • hop_fraction (List[int], optional, defaults to [0.125, 0.5, 0.5]) — Fraction of non-intersecting window used when continuing the sampling process.
  • levels (int, optional, defaults to 3) — Number of hierarchical levels that used in the VQVAE.
  • lmu (float, optional, defaults to 0.99) — Used in the codebook update, exponential moving average coefficient. For more detail refer to Appendix A.1 of the original VQVAE paper
  • multipliers (List[int], optional, defaults to [2, 1, 1]) — Depth and width multipliers used for each level. Used on the res_conv_width and res_conv_depth
  • res_conv_depth (int, optional, defaults to 4) — Depth of the encoder and decoder block. If no multipliers are used, this is the same for each level.
  • res_conv_width (int, optional, defaults to 32) — Width of the encoder and decoder block. If no multipliers are used, this is the same for each level.
  • res_convolution_multiplier (int, optional, defaults to 1) — Scaling factor of the hidden dimension used in the JukeboxResConv1DBlock.
  • res_dilation_cycle (int, optional) — Dilation cycle value used in the JukeboxResnet. If an int is used, each new Conv1 block will have a depth reduced by a power of res_dilation_cycle.
  • res_dilation_growth_rate (int, optional, defaults to 3) — Resnet dilation growth rate used in the VQVAE (dilation_growth_rate ** depth)
  • res_downs_t (List[int], optional, defaults to [3, 2, 2]) — Downsampling rate for each level of the hierarchical VQ-VAE.
  • res_strides_t (List[int], optional, defaults to [2, 2, 2]) — Stride used for each level of the hierarchical VQ-VAE.
  • sample_length (int, optional, defaults to 1058304) — Provides the max input shape of the VQVAE. Is used to compute the input shape of each level.
  • init_scale (float, optional, defaults to 0.2) — Initialization scale.
  • zero_out (bool, optional, defaults to False) — Whether or not to zero out convolution weights when initializing.

This is the configuration class to store the configuration of a JukeboxVQVAE. It is used to instantiate a JukeboxVQVAE according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the VQVAE from openai/jukebox-1b-lyrics architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

JukeboxTokenizer

class transformers.JukeboxTokenizer

< >

( artists_file genres_file lyrics_file version = ['v3', 'v2', 'v2'] max_n_lyric_tokens = 512 n_genres = 5 unk_token = '<|endoftext|>' **kwargs )

Parameters

  • artists_file (str) — Path to the vocabulary file which contains a mapping between artists and ids. The default file supports both “v2” and “v3”
  • genres_file (str) — Path to the vocabulary file which contain a mapping between genres and ids.
  • lyrics_file (str) — Path to the vocabulary file which contains the accepted characters for the lyrics tokenization.
  • version (List[str], optional, default to ["v3", "v2", "v2"]) — List of the tokenizer versions. The 5b-lyrics’s top level prior model was trained using v3 instead of v2.
  • n_genres (int, optional, defaults to 1) — Maximum number of genres to use for composition.
  • max_n_lyric_tokens (int, optional, defaults to 512) — Maximum number of lyric tokens to keep.
  • unk_token (str, optional, defaults to "<|endoftext|>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

Constructs a Jukebox tokenizer. Jukebox can be conditioned on 3 different inputs :

  • Artists, unique ids are associated to each artist from the provided dictionary.
  • Genres, unique ids are associated to each genre from the provided dictionary.
  • Lyrics, character based tokenization. Must be initialized with the list of characters that are inside the vocabulary.

This tokenizer does not require training. It should be able to process a different number of inputs: as the conditioning of the model can be done on the three different queries. If None is provided, defaults values will be used.:

Depending on the number of genres on which the model should be conditioned (n_genres).

>>> from transformers import JukeboxTokenizer

>>> tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
>>> tokenizer("Alan Jackson", "Country Rock", "old town road")["input_ids"]
[tensor([[   0,    0,    0, 6785,  546,   41,   38,   30,   76,   46,   41,   49,
           40,   76,   44,   41,   27,   30]]), tensor([[  0,   0,   0, 145,   0]]), tensor([[  0,   0,   0, 145,   0]])]

You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

If nothing is provided, the genres and the artist will either be selected randomly or set to None

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to: this superclass for more information regarding those methods.

However the code does not allow that and only supports composing from various genres.

save_vocabulary

< >

( save_directory: str filename_prefix: Optional = None )

Parameters

  • save_directory (str) — A path to the directory where to saved. It will be created if it doesn’t exist.
  • filename_prefix (Optional[str], optional) — A prefix to add to the names of the files saved by the tokenizer.

Saves the tokenizer’s vocabulary dictionary to the provided save_directory.

JukeboxModel

class transformers.JukeboxModel

< >

( config )

Parameters

  • config (JukeboxConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare JUKEBOX Model used for music generation. 4 sampling techniques are supported : primed_sample, upsample, continue_sample and ancestral_sample. It does not have a forward method as the training is not end to end. If you want to fine-tune the model, it is recommended to use the JukeboxPrior class and train each prior individually.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

ancestral_sample

< >

( labels n_samples = 1 **sampling_kwargs )

Parameters

  • labels (List[torch.LongTensor]) — List of length n_sample, and shape (self.levels, 4 + self.config.max_nb_genre + lyric_sequence_length) metadata such as artist_id, genre_id and the full list of lyric tokens which are used to condition the generation.
  • n_samples (int, optional, default to 1) — Number of samples to be generated in parallel.

Generates music tokens based on the provided labels. Will start at the desired prior level and automatically upsample the sequence. If you want to create the audio, you should call model.decode(tokens)`, which will use the VQ-VAE decoder to convert the music tokens to raw audio.

Example:

>>> from transformers import AutoTokenizer, JukeboxModel, set_seed

>>> model = JukeboxModel.from_pretrained("openai/jukebox-1b-lyrics", min_duration=0).eval()
>>> tokenizer = AutoTokenizer.from_pretrained("openai/jukebox-1b-lyrics")

>>> lyrics = "Hey, are you awake? Can you talk to me?"
>>> artist = "Zac Brown Band"
>>> genre = "Country"
>>> metas = tokenizer(artist=artist, genres=genre, lyrics=lyrics)
>>> set_seed(0)
>>> music_tokens = model.ancestral_sample(metas.input_ids, sample_length=400)

>>> with torch.no_grad():
...     model.decode(music_tokens)[:, :10].squeeze(-1)
tensor([[-0.0219, -0.0679, -0.1050, -0.1203, -0.1271, -0.0936, -0.0396, -0.0405,
    -0.0818, -0.0697]])

primed_sample

< >

( raw_audio labels **sampling_kwargs )

Parameters

  • raw_audio (List[torch.Tensor] of length n_samples ) — A list of raw audio that will be used as conditioning information for each samples that will be generated.
  • labels (List[torch.LongTensor] of length n_sample, and shape (self.levels, self.config.max_nb_genre + lyric_sequence_length) — List of metadata such as artist_id, genre_id and the full list of lyric tokens which are used to condition the generation.
  • sampling_kwargs (Dict[Any]) — Various additional sampling arguments that are used by the _sample function. A detail list of the arguments can bee seen in the _sample function documentation.

Generate a raw audio conditioned on the provided raw_audio which is used as conditioning at each of the generation levels. The audio is encoded to music tokens using the 3 levels of the VQ-VAE. These tokens are used: as conditioning for each level, which means that no ancestral sampling is required.

continue_sample

< >

( music_tokens labels **sampling_kwargs )

Parameters

  • music_tokens (List[torch.LongTensor] of length self.levels ) — A sequence of music tokens which will be used as context to continue the sampling process. Should have self.levels tensors, each corresponding to the generation at a certain level.
  • labels (List[torch.LongTensor] of length n_sample, and shape (self.levels, self.config.max_nb_genre + lyric_sequence_length) — List of metadata such as artist_id, genre_id and the full list of lyric tokens which are used to condition the generation.
  • sampling_kwargs (Dict[Any]) — Various additional sampling arguments that are used by the _sample function. A detail list of the arguments can bee seen in the _sample function documentation.

Generates a continuation of the previously generated tokens.

upsample

< >

( music_tokens labels **sampling_kwargs )

Parameters

  • music_tokens (List[torch.LongTensor] of length self.levels ) — A sequence of music tokens which will be used as context to continue the sampling process. Should have self.levels tensors, each corresponding to the generation at a certain level.
  • labels (List[torch.LongTensor] of length n_sample, and shape (self.levels, self.config.max_nb_genre + lyric_sequence_length) — List of metadata such as artist_id, genre_id and the full list of lyric tokens which are used to condition the generation.
  • sampling_kwargs (Dict[Any]) — Various additional sampling arguments that are used by the _sample function. A detail list of the arguments can bee seen in the _sample function documentation.

Upsamples a sequence of music tokens using the prior at level level.

_sample

< >

( music_tokens labels sample_levels metas = None chunk_size = 32 sampling_temperature = 0.98 lower_batch_size = 16 max_batch_size = 16 sample_length_in_seconds = 24 compute_alignments = False sample_tokens = None offset = 0 save_results = True sample_length = None )

Parameters

  • music_tokens (List[torch.LongTensor]) — A sequence of music tokens of length self.levels which will be used as context to continue the sampling process. Should have self.levels tensors, each corresponding to the generation at a certain level.
  • labels (List[torch.LongTensor]) — List of length n_sample, and shape (self.levels, 4 + self.config.max_nb_genre + lyric_sequence_length) metadata such as artist_id, genre_id and the full list of lyric tokens which are used to condition the generation.
  • sample_levels (List[int]) — List of the desired levels at which the sampling will be done. A level is equivalent to the index of the prior in the list of priors
  • metas (List[Any], optional) — Metadatas used to generate the labels
  • chunk_size (int, optional, defaults to 32) — Size of a chunk of audio, used to fill up the memory in chuncks to prevent OOM erros. Bigger chunks means faster memory filling but more consumption.
  • sampling_temperature (float, optional, defaults to 0.98) — Temperature used to ajust the randomness of the sampling.
  • lower_batch_size (int, optional, defaults to 16) — Maximum batch size for the lower level priors
  • max_batch_size (int, optional, defaults to 16) — Maximum batch size for the top level priors
  • sample_length_in_seconds (int, optional, defaults to 24) — Desired length of the generation in seconds
  • compute_alignments (bool, optional, defaults to False) — Whether or not to compute the alignment between the lyrics and the audio using the top_prior
  • sample_tokens (int, optional) — Precise number of tokens that should be sampled at each level. This is mostly useful for running dummy experiments
  • offset (int, optional, defaults to 0) — Audio offset used as conditioning, corresponds to the starting sample in the music. If the offset is greater than 0, the lyrics will be shifted take that intoaccount
  • save_results (bool, optional, defaults to True) — Whether or not to save the intermediate results. If True, will generate a folder named with the start time.
  • sample_length (int, optional) — Desired length of the generation in samples.

Core sampling function used to generate music tokens. Iterates over the provided list of levels, while saving the generated raw audio at each step.

Returns: torch.Tensor

Example:

>>> from transformers import AutoTokenizer, JukeboxModel, set_seed
>>> import torch

>>> metas = dict(artist="Zac Brown Band", genres="Country", lyrics="I met a traveller from an antique land")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
>>> model = JukeboxModel.from_pretrained("openai/jukebox-1b-lyrics", min_duration=0).eval()

>>> labels = tokenizer(**metas)["input_ids"]
>>> set_seed(0)
>>> zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)]
>>> zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False)
>>> zs[0]
tensor([[1853, 1369, 1150, 1869, 1379, 1789,  519,  710, 1306, 1100, 1229,  519,
      353, 1306, 1379, 1053,  519,  653, 1631, 1467, 1229, 1229,   10, 1647,
     1254, 1229, 1306, 1528, 1789,  216, 1631, 1434,  653,  475, 1150, 1528,
     1804,  541, 1804, 1434]])

JukeboxPrior

class transformers.JukeboxPrior

< >

( config: JukeboxPriorConfig level = None nb_priors = 3 vqvae_encoder = None vqvae_decoder = None )

Parameters

  • config (JukeboxPriorConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
  • level (int, optional) — Current level of the Prior. Should be in range [0,nb_priors].
  • nb_priors (int, optional, defaults to 3) — Total number of priors.
  • vqvae_encoder (Callable, optional) — Encoding method of the VQVAE encoder used in the forward pass of the model. Passing functions instead of the vqvae module to avoid getting the parameters.
  • vqvae_decoder (Callable, optional) — Decoding method of the VQVAE decoder used in the forward pass of the model. Passing functions instead of the vqvae module to avoid getting the parameters.

The JukeboxPrior class, which is a wrapper around the various conditioning and the transformer. JukeboxPrior can be seen as language models trained on music. They model the next music token prediction task. If a (lyric) encoderù is defined, it also models the next character` prediction on the lyrics. Can be conditionned on timing, artist, genre, lyrics and codes from lower-levels Priors.

sample

< >

( n_samples music_tokens = None music_tokens_conds = None metadata = None temp = 1.0 top_k = 0 top_p = 0.0 chunk_size = None sample_tokens = None )

Parameters

  • n_samples (int) — Number of samples to generate.
  • music_tokens (List[torch.LongTensor], optional) — Previously gemerated tokens at the current level. Used as context for the generation.
  • music_tokens_conds (List[torch.FloatTensor], optional) — Upper-level music tokens generated by the previous prior model. Is None if the generation is not conditionned on the upper-level tokens.
  • metadata (List[torch.LongTensor], optional) — List containing the metatdata tensor with the artist, genre and the lyric tokens.
  • temp (float, optional, defaults to 1.0) — Sampling temperature.
  • top_k (int, optional, defaults to 0) — Top k probabilities used for filtering.
  • top_p (float, optional, defaults to 0.0) — Top p probabilities used for filtering.
  • chunk_size (int, optional) — Size of the chunks used to prepare the cache of the transformer.
  • sample_tokens (int, optional) — Number of tokens to sample.

Ancestral/Prime sampling a window of tokens using the provided conditioning and metadatas.

forward

< >

( hidden_states: Tensor metadata: Optional decode: Optional = False get_preds: Optional = False )

Parameters

  • hidden_states (torch.Tensor) — Hidden states which should be raw audio
  • metadata (List[torch.LongTensor], optional) — List containing the metadata conditioning tensorwith the lyric and the metadata tokens.
  • decode (bool, optional, defaults to False) — Whether or not to decode the encoded to tokens.
  • get_preds (bool, optional, defaults to False) — Whether or not to return the actual predicitons of the model.

Encode the hidden states using the vqvae encoder, and then predicts the next token in the forward_tokens function. The loss is the sum of the encoder loss and the decoder loss.

JukeboxVQVAE

class transformers.JukeboxVQVAE

< >

( config: JukeboxVQVAEConfig )

Parameters

  • config (JukeboxConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The Hierarchical VQ-VAE model used in Jukebox. This model follows the Hierarchical VQVAE paper from Will Williams, Sam Ringer, Tom Ash, John Hughes, David MacLeod, Jamie Dougherty.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( raw_audio: FloatTensor )

Parameters

  • raw_audio (torch.FloatTensor) — Audio input which will be encoded and decoded.

Forward pass of the VQ-VAE, encodes the raw_audio to latent states, which are then decoded for each level. The commit loss, which ensure that the encoder’s computed embeddings are close to the codebook vectors, is computed.

Example:

>>> from transformers import JukeboxVQVAE, set_seed
>>> import torch

>>> model = JukeboxVQVAE.from_pretrained("openai/jukebox-1b-lyrics").eval()
>>> set_seed(0)
>>> zs = [torch.randint(100, (4, 1))]
>>> model.decode(zs).shape
torch.Size([4, 8, 1])

encode

< >

( input_audio start_level = 0 end_level = None bs_chunks = 1 )

Parameters

  • input_audio (torch.Tensor) — Raw audio which will be encoded to its discrete representation using the codebook. The closest code form the codebook will be computed for each sequence of samples.
  • start_level (int, optional, defaults to 0) — Level at which the encoding process will start. Default to 0.
  • end_level (int, optional) — Level at which the encoding process will start. Default to None.
  • bs_chunks (int, optional, defaults to 1) — Number of chunks of raw audio to process at the same time.

Transforms the input_audio to a discrete representation made out of music_tokens.

decode

< >

( music_tokens start_level = 0 end_level = None bs_chunks = 1 )

Parameters

  • music_tokens (torch.LongTensor) — Tensor of music tokens which will be decoded to raw audio by using the codebook. Each music token should be an index to a corresponding code vector in the codebook.
  • start_level (int, optional) — Level at which the decoding process will start. Default to 0.
  • end_level (int, optional) — Level at which the decoding process will start. Default to None.
  • bs_chunks (int, optional) — Number of chunks to process at the same time.

Transforms the input music_tokens to their raw_audio representation.

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