This model was released on 2021-06-14 and added to Hugging Face Transformers on 2021-06-16.

HuBERT

HuBERT is a self-supervised speech model to cluster aligned target labels for BERT-like prediction loss and applying the prediction loss only over masked regions to force the model to learn both acoustic and language modeling over continuous inputs. It addresses the challenges of multiple sound units per utterance, no lexicon during pre-training, and variable-length sound units without explicit segmentation.

You can find all the original HuBERT checkpoints under the HuBERT collection.

This model was contributed by patrickvonplaten.

Click on the HuBERT models in the right sidebar for more examples of how to apply HuBERT to different audio tasks.

The example below demonstrates how to automatically transcribe speech into text with Pipeline or the AutoModel class.

Pipeline

AutoModel

Quantization

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the weights to 4-bits.

import torch
from transformers import AutoProcessor, AutoModelForCTC, BitsAndBytesConfig
from datasets import load_dataset

bnb_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

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

processor = AutoProcessor.from_pretrained("facebook/hubert-base-ls960")
model = AutoModelForCTC.from_pretrained("facebook/hubert-base-ls960", quantization_config=bnb_config, dtype=torch.float16, device_map="auto", attn_implementation="sdpa")

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)

transcription = processor.batch_decode(predicted_ids)
print(transcription[0])

Notes

HuBERT models expect raw audio input as a 1D float array sampled at 16kHz.

If you want to use a head_mask, use the model with attn_implementation="eager".

model = HubertModel.from_pretrained("facebook/hubert-base-ls960", attn_implementation="eager")

HubertConfig

class transformers.HubertConfig

< source >

( vocab_size = 32 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout = 0.1 activation_dropout = 0.1 attention_dropout = 0.1 feat_proj_layer_norm = True feat_proj_dropout = 0.0 final_dropout = 0.1 layerdrop = 0.1 initializer_range = 0.02 layer_norm_eps = 1e-05 feat_extract_norm = 'group' feat_extract_activation = 'gelu' conv_dim = (512, 512, 512, 512, 512, 512, 512) conv_stride = (5, 2, 2, 2, 2, 2, 2) conv_kernel = (10, 3, 3, 3, 3, 2, 2) conv_bias = False num_conv_pos_embeddings = 128 num_conv_pos_embedding_groups = 16 conv_pos_batch_norm = False do_stable_layer_norm = False apply_spec_augment = True mask_time_prob = 0.05 mask_time_length = 10 mask_time_min_masks = 2 mask_feature_prob = 0.0 mask_feature_length = 10 mask_feature_min_masks = 0 ctc_loss_reduction = 'sum' ctc_zero_infinity = False use_weighted_layer_sum = False classifier_proj_size = 256 pad_token_id = 0 bos_token_id = 1 eos_token_id = 2 **kwargs )

Parameters

vocab_size (int, optional, defaults to 32) — Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling HubertModel. Vocabulary size of the model. Defines the different tokens that can be represented by the inputs_ids passed to the forward method of HubertModel.
hidden_size (int, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int, optional, defaults to 3072) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (str or function, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "selu" and "gelu_new" are supported.
hidden_dropout(float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
activation_dropout (float, optional, defaults to 0.1) — The dropout ratio for activations inside the fully connected layer.
attention_dropout(float, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.
final_dropout (float, optional, defaults to 0.1) — The dropout probability for the final projection layer of Wav2Vec2ForCTC.
layerdrop (float, optional, defaults to 0.1) — The LayerDrop probability. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (float, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.
feat_extract_norm (str, optional, defaults to "group") — The norm to be applied to 1D convolutional layers in feature encoder. One of "group" for group normalization of only the first 1D convolutional layer or "layer" for layer normalization of all 1D convolutional layers.
feat_proj_dropout (float, optional, defaults to 0.0) — The dropout probability for output of the feature encoder.
feat_proj_layer_norm (bool, optional, defaults to True) — Whether to apply LayerNorm to the output of the feature encoder.
feat_extract_activation (str, optional, defaults to “gelu”) -- The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, “gelu”, “relu”, “selu”and“gelu_new”` are supported.
conv_dim (tuple[int], optional, defaults to (512, 512, 512, 512, 512, 512, 512)) — A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature encoder. The length of conv_dim defines the number of 1D convolutional layers.
conv_stride (tuple[int], optional, defaults to (5, 2, 2, 2, 2, 2, 2)) — A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length of conv_stride defines the number of convolutional layers and has to match the length of conv_dim.
conv_kernel (tuple[int], optional, defaults to (10, 3, 3, 3, 3, 3, 3)) — A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The length of conv_kernel defines the number of convolutional layers and has to match the length of conv_dim.
conv_bias (bool, optional, defaults to False) — Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (int, optional, defaults to 128) — Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer.
num_conv_pos_embedding_groups (int, optional, defaults to 16) — Number of groups of 1D convolutional positional embeddings layer.
conv_pos_batch_norm (bool, optional, defaults to False) — Whether to use batch norm instead of weight norm in conv_pos
do_stable_layer_norm (bool, optional, defaults to False) — Whether do apply stable layer norm architecture of the Transformer encoder. do_stable_layer_norm is True corresponds to applying layer norm before the attention layer, whereas do_stable_layer_norm is False corresponds to applying layer norm after the attention layer.
apply_spec_augment (bool, optional, defaults to True) — Whether to apply SpecAugment data augmentation to the outputs of the feature encoder. For reference see SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.
mask_time_prob (float, optional, defaults to 0.05) — Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procedure generates ”mask_time_problen(time_axis)/mask_time_length” independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, mask_time_prob should be `prob_vector_startmask_time_length. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if apply_spec_augment is True`.
mask_time_length (int, optional, defaults to 10) — Length of vector span along the time axis.
mask_time_min_masks (int, optional, defaults to 2), — The minimum number of masks of length mask_feature_length generated along the time axis, each time step, irrespectively of mask_feature_prob. Only relevant if ”mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks”
mask_feature_prob (float, optional, defaults to 0.0) — Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procedure generates ”mask_feature_problen(feature_axis)/mask_time_length” independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, mask_feature_prob should be `prob_vector_startmask_feature_length. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if apply_spec_augment is True`.
mask_feature_length (int, optional, defaults to 10) — Length of vector span along the feature axis.
mask_feature_min_masks (int, optional, defaults to 0), — The minimum number of masks of length mask_feature_length generated along the feature axis, each time step, irrespectively of mask_feature_prob. Only relevant if ”mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks”
ctc_loss_reduction (str, optional, defaults to "sum") — Specifies the reduction to apply to the output of torch.nn.CTCLoss. Only relevant when training an instance of HubertForCTC.
ctc_zero_infinity (bool, optional, defaults to False) — Whether to zero infinite losses and the associated gradients of torch.nn.CTCLoss. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of HubertForCTC.
use_weighted_layer_sum (bool, optional, defaults to False) — Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of HubertForSequenceClassification.
classifier_proj_size (int, optional, defaults to 256) — Dimensionality of the projection before token mean-pooling for classification.

This is the configuration class to store the configuration of a HubertModel. It is used to instantiate an Hubert 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 Hubert facebook/hubert-base-ls960 architecture.

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

Example:

>>> from transformers import HubertModel, HubertConfig

>>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
>>> configuration = HubertConfig()

>>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
>>> model = HubertModel(configuration)

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

HubertModel

class transformers.HubertModel

< source >

( config: HubertConfig )

Parameters

config (HubertConfig) — 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 Hubert Model outputting raw hidden-states without any specific 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

< source >

( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None mask_time_indices: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)

Parameters

input_values (torch.Tensor of shape (batch_size, sequence_length), optional) — Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file into an array of type list[float], a numpy.ndarray or a torch.Tensor, e.g. via the torchcodec library (pip install torchcodec) or the soundfile library (pip install soundfile). To prepare the array into input_values, the AutoProcessor should be used for padding and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
attention_mask (torch.Tensor 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?
mask_time_indices (torch.BoolTensor of shape (batch_size, sequence_length), optional) — Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict masked extracted features in config.proj_codevector_dim space.
output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutput 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 (HubertConfig) and inputs.

last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
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 model at the output of each layer plus the optional initial embedding outputs.
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 after the attention softmax, used to compute the weighted average in the self-attention heads.

The HubertModel 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.

Example:

>>> from transformers import AutoProcessor, HubertModel
>>> from datasets import load_dataset

>>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
>>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")


>>> def map_to_array(example):
...     example["speech"] = example["audio"]["array"]
...     return example


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state

HubertForCTC

class transformers.HubertForCTC

< source >

( config target_lang: typing.Optional[str] = None )

Parameters

config (HubertForCTC) — 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.
target_lang (str, optional) — Language id of adapter weights. Adapter weights are stored in the format adapter..safetensors or adapter..bin. Only relevant when using an instance of HubertForCTC with adapters. Uses ‘eng’ by default.

Hubert Model with a language modeling head on top for Connectionist Temporal Classification (CTC).

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

< source >

( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None ) → transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)

Parameters

input_values (torch.Tensor of shape (batch_size, sequence_length), optional) — Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file into an array of type list[float], a numpy.ndarray or a torch.Tensor, e.g. via the torchcodec library (pip install torchcodec) or the soundfile library (pip install soundfile). To prepare the array into input_values, the AutoProcessor should be used for padding and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
attention_mask (torch.Tensor 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?
output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, target_length), optional) — Labels for connectionist temporal classification. Note that target_length has to be smaller or equal to the sequence length of the output logits. Indices are selected 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 - 1].

Returns

transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutput 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 (HubertConfig) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
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).
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 model at the output of each layer plus the optional initial embedding outputs.
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 after the attention softmax, used to compute the weighted average in the self-attention heads.

The HubertForCTC forward method, overrides the __call__ special method.

Example:

>>> from transformers import AutoProcessor, HubertForCTC
>>> 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("facebook/hubert-base-ls960")
>>> model = HubertForCTC.from_pretrained("facebook/hubert-base-ls960")

>>> # 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)
...

HubertForSequenceClassification

class transformers.HubertForSequenceClassification

< source >

( config )

Parameters

config (HubertForSequenceClassification) — 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.

Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting.

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

< source >

Parameters

input_values (torch.FloatTensor of shape (batch_size, sequence_length)) — Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file into an array of type list[float], a numpy.ndarray or a torch.Tensor, e.g. via the torchcodec library (pip install torchcodec) or the soundfile library (pip install soundfile). To prepare the array into input_values, the AutoProcessor should be used for padding and conversion into a tensor of type torch.FloatTensor. See HubertProcessor.__call__ for details.
attention_mask (torch.Tensor 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?
output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns

transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.SequenceClassifierOutput 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 (HubertConfig) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
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 model at the output of each layer plus the optional initial embedding outputs.
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 after the attention softmax, used to compute the weighted average in the self-attention heads.

The HubertForSequenceClassification forward method, overrides the __call__ special method.

Example of single-label classification:

>>> import torch
>>> from transformers import AutoTokenizer, HubertForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/hubert-base-ls960")
>>> model = HubertForSequenceClassification.from_pretrained("facebook/hubert-base-ls960")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
...

>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = HubertForSequenceClassification.from_pretrained("facebook/hubert-base-ls960", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
...

Example of multi-label classification:

>>> import torch
>>> from transformers import AutoTokenizer, HubertForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/hubert-base-ls960")
>>> model = HubertForSequenceClassification.from_pretrained("facebook/hubert-base-ls960", problem_type="multi_label_classification")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = HubertForSequenceClassification.from_pretrained(
...     "facebook/hubert-base-ls960", num_labels=num_labels, problem_type="multi_label_classification"
... )

>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss

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