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Pop2Piano

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This model was published in HF papers on 2022-11-02 and contributed to Hugging Face Transformers on 2023-08-21.

Pop2Piano

Overview

The Pop2Piano model was proposed in Pop2Piano : Pop Audio-based Piano Cover Generation by Jongho Choi and Kyogu Lee.

Piano covers of pop music are widely enjoyed, but generating them from music is not a trivial task. It requires great expertise with playing piano as well as knowing different characteristics and melodies of a song. With Pop2Piano you can directly generate a cover from a song’s audio waveform. It is the first model to directly generate a piano cover from pop audio without melody and chord extraction modules.

Pop2Piano is an encoder-decoder Transformer model based on T5. The input audio is transformed to its waveform and passed to the encoder, which transforms it to a latent representation. The decoder uses these latent representations to generate token ids in an autoregressive way. Each token id corresponds to one of four different token types: time, velocity, note and ‘special’. The token ids are then decoded to their equivalent MIDI file.

The abstract from the paper is the following:

Piano covers of pop music are enjoyed by many people. However, the task of automatically generating piano covers of pop music is still understudied. This is partly due to the lack of synchronized {Pop, Piano Cover} data pairs, which made it challenging to apply the latest data-intensive deep learning-based methods. To leverage the power of the data-driven approach, we make a large amount of paired and synchronized {Pop, Piano Cover} data using an automated pipeline. In this paper, we present Pop2Piano, a Transformer network that generates piano covers given waveforms of pop music. To the best of our knowledge, this is the first model to generate a piano cover directly from pop audio without using melody and chord extraction modules. We show that Pop2Piano, trained with our dataset, is capable of producing plausible piano covers.

This model was contributed by Susnato Dhar. The original code can be found here.

Usage tips

  • To use Pop2Piano, you will need to install the 🤗 Transformers library, as well as the following third party modules:
pip install pretty-midi==0.2.9 essentia==2.1b6.dev1034 librosa scipy

Please note that you may need to restart your runtime after installation.

  • Pop2Piano is an Encoder-Decoder based model like T5.
  • Pop2Piano can be used to generate midi-audio files for a given audio sequence.
  • Choosing different composers in Pop2PianoForConditionalGeneration.generate() can lead to variety of different results.
  • Setting the sampling rate to 44.1 kHz when loading the audio file can give good performance.
  • Though Pop2Piano was mainly trained on Korean Pop music, it also does pretty well on other Western Pop or Hip Hop songs.

Examples

  • Example using HuggingFace Dataset:
from datasets import load_dataset

from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor


model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano", device_map="auto")
processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")
ds = load_dataset("sweetcocoa/pop2piano_ci", split="test")

inputs = processor(
    audio=ds["audio"][0]["array"], sampling_rate=ds["audio"][0]["sampling_rate"], return_tensors="pt"
)
model_output = model.generate(input_features=inputs["input_features"], composer="composer1")
tokenizer_output = processor.batch_decode(
    token_ids=model_output, feature_extractor_output=inputs
)["pretty_midi_objects"][0]
tokenizer_output.write("./Outputs/midi_output.mid")
  • Example using your own audio file:
import librosa

from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor


audio, sr = librosa.load("<your_audio_file_here>", sr=44100)  # feel free to change the sr to a suitable value.
model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano", device_map="auto")
processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")

inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt").to(model.device)
model_output = model.generate(input_features=inputs["input_features"], composer="composer1")
tokenizer_output = processor.batch_decode(
    token_ids=model_output, feature_extractor_output=inputs
)["pretty_midi_objects"][0]
tokenizer_output.write("./Outputs/midi_output.mid")
  • Example of processing multiple audio files in batch:
import librosa

from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor


# feel free to change the sr to a suitable value.
audio1, sr1 = librosa.load("<your_first_audio_file_here>", sr=44100)
audio2, sr2 = librosa.load("<your_second_audio_file_here>", sr=44100)
model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano", device_map="auto")
processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano")

inputs = processor(audio=[audio1, audio2], sampling_rate=[sr1, sr2], return_attention_mask=True, return_tensors="pt").to(model.device)
# Since we now generating in batch(2 audios) we must pass the attention_mask
model_output = model.generate(
    input_features=inputs["input_features"],
    attention_mask=inputs["attention_mask"],
    composer="composer1",
)
tokenizer_output = processor.batch_decode(
    token_ids=model_output, feature_extractor_output=inputs
)["pretty_midi_objects"]

# Since we now have 2 generated MIDI files
tokenizer_output[0].write("./Outputs/midi_output1.mid")
tokenizer_output[1].write("./Outputs/midi_output2.mid")
  • Example of processing multiple audio files in batch (Using Pop2PianoFeatureExtractor and Pop2PianoTokenizer):
import librosa

from transformers import Pop2PianoFeatureExtractor, Pop2PianoForConditionalGeneration, Pop2PianoTokenizer


# feel free to change the sr to a suitable value.
audio1, sr1 = librosa.load("<your_first_audio_file_here>", sr=44100)
audio2, sr2 = librosa.load("<your_second_audio_file_here>", sr=44100)
model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano", device_map="auto")
feature_extractor = Pop2PianoFeatureExtractor.from_pretrained("sweetcocoa/pop2piano")
tokenizer = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano")

inputs = feature_extractor(
    audio=[audio1, audio2],
    sampling_rate=[sr1, sr2],
    return_attention_mask=True,
    return_tensors="pt",
)
# Since we now generating in batch(2 audios) we must pass the attention_mask
model_output = model.generate(
    input_features=inputs["input_features"],
    attention_mask=inputs["attention_mask"],
    composer="composer1",
)
tokenizer_output = tokenizer.batch_decode(
    token_ids=model_output, feature_extractor_output=inputs
)["pretty_midi_objects"]

# Since we now have 2 generated MIDI files
tokenizer_output[0].write("./Outputs/midi_output1.mid")
tokenizer_output[1].write("./Outputs/midi_output2.mid")

Pop2PianoConfig

class transformers.Pop2PianoConfig

< >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: str | torch.dtype | None = Nonechunk_size_feed_forward: int = 0id2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: Literal['regression', 'single_label_classification', 'multi_label_classification'] | None = Noneis_encoder_decoder: bool = Truevocab_size: int = 2400composer_vocab_size: int = 21d_model: int = 512d_kv: int = 64d_ff: int = 2048num_layers: int = 6num_decoder_layers: int | None = Nonenum_heads: int = 8relative_attention_num_buckets: int = 32relative_attention_max_distance: int = 128dropout_rate: float | int = 0.1layer_norm_epsilon: float = 1e-06initializer_factor: float = 1.0feed_forward_proj: str = 'gated-gelu'use_cache: bool = Truepad_token_id: int | None = 0eos_token_id: int | list[int] | None = 1dense_act_fn: str = 'relu'is_decoder: bool = Falsetie_word_embeddings: bool = True )

Parameters

  • is_encoder_decoder (bool, optional, defaults to True) — Whether the model is used as an encoder/decoder or not.
  • vocab_size (int, optional, defaults to 2400) — Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.
  • composer_vocab_size (int, optional, defaults to 21) — Denotes the number of composers.
  • d_model (int, optional, defaults to 512) — Size of the encoder layers and the pooler layer.
  • d_kv (int, optional, defaults to 64) — Size of the key, query, value projections per attention head. The inner_dim of the projection layer will be defined as num_heads * d_kv.
  • d_ff (int, optional, defaults to 2048) — Dimension of the MLP representations.
  • num_layers (int, optional, defaults to 6) — Number of hidden layers in the Transformer decoder.
  • num_decoder_layers (int, optional) — Number of hidden layers in the Transformer decoder. Will use the same value as num_layers if not set.
  • num_heads (int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer decoder.
  • relative_attention_num_buckets (int, optional, defaults to 32) — The number of buckets to use for each attention layer.
  • relative_attention_max_distance (int, optional, defaults to 128) — The maximum distance of the longer sequences for the bucket separation.
  • dropout_rate (Union[float, int], optional, defaults to 0.1) — The ratio for all dropout layers.
  • layer_norm_epsilon (float, optional, defaults to 1e-06) — The epsilon used by the layer normalization layers.
  • initializer_factor (float, optional, defaults to 1.0) — A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).
  • feed_forward_proj (string, optional, defaults to "gated-gelu") — Type of feed forward layer to be used. Should be one of "relu" or "gated-gelu".
  • use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True or when the model is a decoder-only generative model.
  • pad_token_id (int, optional, defaults to 0) — Token id used for padding in the vocabulary.
  • eos_token_id (Union[int, list[int]], optional, defaults to 1) — Token id used for end-of-stream in the vocabulary.
  • dense_act_fn (string, optional, defaults to "relu") — Type of Activation Function to be used in Pop2PianoDenseActDense and in Pop2PianoDenseGatedActDense.
  • is_decoder (bool, optional, defaults to False) — Whether the model is used as a decoder or not. If False, the model is used as an encoder.
  • tie_word_embeddings (bool, optional, defaults to True) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.

This is the configuration class to store the configuration of a Pop2PianoModel. It is used to instantiate a Pop2Piano 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 sweetcocoa/pop2piano

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

Pop2PianoFeatureExtractor

class transformers.Pop2PianoFeatureExtractor

< >

( sampling_rate: int = 22050padding_value: int = 0window_size: int = 4096hop_length: int = 1024min_frequency: float = 10.0feature_size: int = 512num_bars: int = 2**kwargs )

Parameters

  • sampling_rate (int, optional, defaults to 22050) — Target Sampling rate of audio signal. It’s the sampling rate that we forward to the model.
  • padding_value (int, optional, defaults to 0) — Padding value used to pad the audio. Should correspond to silences.
  • window_size (int, optional, defaults to 4096) — Length of the window in samples to which the Fourier transform is applied.
  • hop_length (int, optional, defaults to 1024) — Step size between each window of the waveform, in samples.
  • min_frequency (float, optional, defaults to 10.0) — Lowest frequency that will be used in the log-mel spectrogram.
  • feature_size (int, optional, defaults to 512) — The feature dimension of the extracted features.
  • num_bars (int, optional, defaults to 2) — Determines interval between each sequence.

Constructs a Pop2Piano feature extractor.

This feature extractor inherits from SequenceFeatureExtractor which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

This class extracts rhythm and preprocesses the audio before it is passed to the model. First the audio is passed to RhythmExtractor2013 algorithm which extracts the beat_times, beat positions and estimates their confidence as well as tempo in bpm, then beat_times is interpolated and to get beatsteps. Later we calculate extrapolated_beatsteps from it to be used in tokenizer. On the other hand audio is resampled to self.sampling_rate and preprocessed and then log mel spectogram is computed from that to be used in our transformer model.

__call__

< >

( audio: numpy.ndarray | list[float] | list[numpy.ndarray] | list[list[float]]sampling_rate: int | list[int]steps_per_beat: int = 2resample: bool | None = Truereturn_attention_mask: bool | None = Falsereturn_tensors: str | transformers.utils.generic.TensorType | None = None**kwargs )

Parameters

  • audio (np.ndarray, List) — The audio or batch of audio to be processed. Each audio can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values.
  • sampling_rate (int) — The sampling rate at which the audio input was sampled. It is strongly recommended to pass sampling_rate at the forward call to prevent silent errors.
  • steps_per_beat (int, optional, defaults to 2) — This is used in interpolating beat_times.
  • resample (bool, optional, defaults to True) — Determines whether to resample the audio to sampling_rate or not before processing. Must be True during inference.
  • return_attention_mask (bool optional, defaults to False) — Denotes if attention_mask for input_features, beatsteps and extrapolated_beatstep will be given as output or not. Automatically set to True for batched inputs.
  • return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return Numpy np.ndarray objects. If nothing is specified, it will return list of np.ndarray arrays.

Main method to featurize and prepare for the model.

Pop2PianoForConditionalGeneration

class transformers.Pop2PianoForConditionalGeneration

< >

( config: Pop2PianoConfig )

Parameters

  • config (Pop2PianoConfig) — 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.

Pop2Piano Model with a language modeling head on top.

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

< >

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonedecoder_input_ids: typing.Optional[torch.LongTensor] = Nonedecoder_attention_mask: typing.Optional[torch.BoolTensor] = Noneencoder_outputs: tuple[tuple[torch.Tensor]] | None = Nonepast_key_values: transformers.cache_utils.Cache | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Noneinput_features: typing.Optional[torch.FloatTensor] = Nonedecoder_inputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: bool | None = None**kwargs: Unpack ) → Seq2SeqLMOutput or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Pop2Piano is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for detail. What are input IDs? To know more on how to prepare input_ids for pretraining take a look a Pop2Piano Training.
  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are decoder input IDs? Pop2Piano uses the pad_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values). To know more on how to prepare
  • decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
  • encoder_outputs (tuple[tuple[torch.Tensor]], optional) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • input_features (torch.FloatTensor of shape (batch_size, sequence_length, feature_dim), optional) — The tensors corresponding to the input audio features. Audio features can be obtained using Pop2PianoFeatureExtractor. See Pop2PianoFeatureExtractor.call() for details (Pop2PianoProcessor uses Pop2PianoFeatureExtractor for processing audios).
  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) — Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

    If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.

  • labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in [-100, 0, ..., config.vocab_size - 1]. All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]
  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

Returns

Seq2SeqLMOutput or tuple(torch.FloatTensor)

A Seq2SeqLMOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Pop2PianoConfig) and inputs.

The Pop2PianoForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • past_key_values (EncoderDecoderCache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a EncoderDecoderCache instance. For more details, see our kv cache guide.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.

  • decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.

  • encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

  • encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import AutoProcessor, Pop2PianoForConditionalGeneration
>>> from datasets import load_dataset
>>> import torch

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = AutoProcessor.from_pretrained("sweetcocoa/pop2piano")
>>> model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano")

>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)

>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
...

>>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids

>>> # compute loss
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)
...

generate

< >

( input_featuresattention_mask = Nonecomposer = 'composer1'generation_config = None**kwargs ) → ModelOutput or torch.LongTensor

Parameters

  • input_features (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — This is the featurized version of audio generated by Pop2PianoFeatureExtractor.
  • attention_mask — For batched generation input_features are padded to have the same shape across all examples. attention_mask helps to determine which areas were padded and which were not.
    • 1 for tokens that are not padded,
    • 0 for tokens that are padded.
  • composer (str, optional, defaults to "composer1") — This value is passed to Pop2PianoConcatEmbeddingToMel to generate different embeddings for each "composer". Please make sure that the composer value is present in composer_to_feature_token in generation_config. For an example please see https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json .
  • generation_config (~generation.GenerationConfig, optional) — The generation configuration to be used as base parametrization for the generation call. **kwargs passed to generate matching the attributes of generation_config will override them. If generation_config is not provided, the default will be used, which had the following loading priority: 1) from the generation_config.json model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit GenerationConfig’s default values, whose documentation should be checked to parameterize generation.
  • kwargs — Ad hoc parametrization of generate_config and/or additional model-specific kwargs that will be forwarded to the forward function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with decoder_.

Returns

ModelOutput or torch.LongTensor

A ModelOutput (if return_dict_in_generate=True or when config.return_dict_in_generate=True) or a torch.FloatTensor. Since Pop2Piano is an encoder-decoder model (model.config.is_encoder_decoder=True), the possible ModelOutput types are:

Generates token ids for midi outputs.

Most generation-controlling parameters are set in generation_config which, if not passed, will be set to the model’s default generation configuration. You can override any generation_config by passing the corresponding parameters to generate(), e.g. .generate(inputs, num_beams=4, do_sample=True). For an overview of generation strategies and code examples, check out the following guide.

Pop2PianoTokenizer

class transformers.Pop2PianoTokenizer

< >

( vocabdefault_velocity = 77num_bars = 2unk_token = '-1'eos_token = '1'pad_token = '0'bos_token = '2'**kwargs )

Parameters

  • vocab (str) — Path to the vocab file which contains the vocabulary.
  • default_velocity (int, optional, defaults to 77) — Determines the default velocity to be used while creating midi Notes.
  • num_bars (int, optional, defaults to 2) — Determines cutoff_time_idx in for each token.
  • unk_token (str or tokenizers.AddedToken, optional, defaults to "-1") — 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.
  • eos_token (str or tokenizers.AddedToken, optional, defaults to 1) — The end of sequence token.
  • pad_token (str or tokenizers.AddedToken, optional, defaults to 0) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation.
  • bos_token (str or tokenizers.AddedToken, optional, defaults to 2) — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

Constructs a Pop2Piano tokenizer. This tokenizer does not require training.

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

__call__

< >

( notes: numpy.ndarray | list[pretty_midi.Note] | list[list[pretty_midi.Note]]padding: bool | str | transformers.utils.generic.PaddingStrategy = Falsetruncation: bool | str | transformers.tokenization_utils_base.TruncationStrategy | None = Nonemax_length: int | None = Nonepad_to_multiple_of: int | None = Nonereturn_attention_mask: bool | None = Nonereturn_tensors: str | transformers.utils.generic.TensorType | None = Noneverbose: bool = True**kwargs )

Parameters

  • notes (numpy.ndarray of shape [batch_size, max_sequence_length, 4] or list of pretty_midi.Note objects) — This represents the midi notes.

    If notes is a numpy.ndarray:

    • Each sequence must have 4 values, they are onset idx, offset idx, pitch and velocity. If notes is a list containing pretty_midi.Note objects:
    • Each sequence must have 4 attributes, they are start, end, pitch and velocity.
  • padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:

    • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
    • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
    • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
  • truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
  • max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
  • return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

    What are attention masks?

  • return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:

    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return Numpy np.ndarray objects.
  • verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings.

This is the __call__ method for Pop2PianoTokenizer. It converts the midi notes to the transformer generated token ids.

Pop2PianoProcessor

class transformers.Pop2PianoProcessor

< >

( feature_extractortokenizer )

Parameters

  • feature_extractor (Pop2PianoFeatureExtractor) — The feature extractor is a required input.
  • tokenizer (tokenizer_class) — The tokenizer is a required input.

Constructs a Pop2PianoProcessor which wraps a feature extractor and a tokenizer into a single processor.

Pop2PianoProcessor offers all the functionalities of Pop2PianoFeatureExtractor and tokenizer_class. See the ~Pop2PianoFeatureExtractor and ~tokenizer_class for more information.

__call__

< >

( audio: numpy.ndarray | list[float] | list[numpy.ndarray] = Nonesampling_rate: int | list[int] | None = Nonesteps_per_beat: int = 2resample: bool | None = Truenotes: list | transformers.utils.generic.TensorType = Nonepadding: bool | str | transformers.utils.generic.PaddingStrategy = Falsetruncation: bool | str | transformers.tokenization_utils_base.TruncationStrategy = Nonemax_length: int | None = Nonepad_to_multiple_of: int | None = Noneverbose: bool = True**kwargs )

Parameters

  • audio (Union[numpy.ndarray, list[float], list[numpy.ndarray]], optional) — The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio.
  • sampling_rate (int or list[int], optional) — The sampling rate of the input audio in Hz. This should match the sampling rate used by the feature extractor. If not provided, the default sampling rate from the processor configuration will be used.
  • steps_per_beat (int, optional, defaults to 2) — The number of time steps per musical beat. This parameter controls the temporal resolution of the musical representation. A higher value provides finer temporal granularity but increases the sequence length. Used when processing audio to extract musical features.
  • resample (bool, optional, defaults to True) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
  • notes (list or TensorType, optional) — Pre-extracted musical notes in MIDI format. When provided, the processor skips audio feature extraction and directly processes the notes through the tokenizer. Each note should be represented as a list or tensor containing pitch, velocity, and timing information.
  • padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:

    • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence is provided).
    • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
    • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
  • truncation (bool, str or TruncationStrategy, optional) — Activates and controls truncation. Accepts the following values:

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
  • max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

    If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. Requires padding to be activated. This is especially useful to enable using Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
  • verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:

    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
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