
 
WavLM
	
Overview
	
The WavLM model was proposed in WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen,
Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu,
Michael Zeng, Furu Wei.
The abstract from the paper is the following:
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been
attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker
identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is
challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks.
WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity
preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on
recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where
additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up
the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB
benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.
Tips:
WavLM is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please use
Wav2Vec2Processor for the feature extraction.
WavLM model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
using Wav2Vec2CTCTokenizer.
WavLM performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
Relevant checkpoints can be found under https://huggingface.co/models?other=wavlm.
This model was contributed by patrickvonplaten. The Authors’ code can be
found here.
Documentation resources
	
Audio classification task guide
Automatic speech recognition task guide
WavLMConfig
	
class transformers.WavLMConfig
<
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_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
num_buckets = 320
max_bucket_distance = 800
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
num_codevectors_per_group = 320
num_codevector_groups = 2
contrastive_logits_temperature = 0.1
num_negatives = 100
codevector_dim = 256
proj_codevector_dim = 256
diversity_loss_weight = 0.1
ctc_loss_reduction = 'mean'
ctc_zero_infinity = False
use_weighted_layer_sum = False
classifier_proj_size = 256
tdnn_dim = (512, 512, 512, 512, 1500)
tdnn_kernel = (5, 3, 3, 1, 1)
tdnn_dilation = (1, 2, 3, 1, 1)
xvector_output_dim = 512
num_ctc_classes = 80
pad_token_id = 0
bos_token_id = 1
eos_token_id = 2
add_adapter = False
adapter_kernel_size = 3
adapter_stride = 2
num_adapter_layers = 3
output_hidden_size = None
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 32) —
Vocabulary size of the WavLM model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling WavLMModel. Vocabulary size of the model. Defines the different tokens
that can be represented by the inputs_ids passed to the forward method of WavLMModel.
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.
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 WavLMForCTC.
layerdrop (float, optional, defaults to 0.1) —
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/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_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] or List[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] or List[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] or List[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.
do_stable_layer_norm (bool, optional, defaults to False) —
Whether to 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) —
Propability of each feature vector along the time axis to be chosen as the start of the vector span to be
masked. Approximately mask_time_prob * sequence_length // mask_time_length feature vectors will be masked
along the time axis. 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) —
Propability of each feature vector along the feature axis to be chosen as the start of the vector span to
be masked. Approximately mask_time_prob * hidden_size // mask_time_length feature vectors will be masked
along the time axis. 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.
num_codevectors_per_group (int, optional, defaults to 320) —
Number of entries in each quantization codebook (group).
num_codevector_groups (int, optional, defaults to 2) —
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (float, optional, defaults to 0.1) —
The temperature kappa in the contrastive loss.
num_negatives (int, optional, defaults to 100) —
Number of negative samples for the contrastive loss.
codevector_dim (int, optional, defaults to 256) —
Dimensionality of the quantized feature vectors.
proj_codevector_dim (int, optional, defaults to 256) —
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (int, optional, defaults to 0.1) —
The weight of the codebook diversity loss component.
ctc_loss_reduction (str, optional, defaults to "mean") —
Specifies the reduction to apply to the output of torch.nn.CTCLoss. Only relevant when training an
instance of WavLMForCTC.
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 WavLMForCTC.
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 WavLMForSequenceClassification.
classifier_proj_size (int, optional, defaults to 256) —
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (Tuple[int] or List[int], optional, defaults to (512, 512, 512, 512, 1500)) —
A tuple of integers defining the number of output channels of each 1D convolutional layer in the TDNN
module of the XVector model. The length of tdnn_dim defines the number of TDNN layers.
tdnn_kernel (Tuple[int] or List[int], optional, defaults to (5, 3, 3, 1, 1)) —
A tuple of integers defining the kernel size of each 1D convolutional layer in the TDNN module of the
XVector model. The length of tdnn_kernel has to match the length of tdnn_dim.
tdnn_dilation (Tuple[int] or List[int], optional, defaults to (1, 2, 3, 1, 1)) —
A tuple of integers defining the dilation factor of each 1D convolutional layer in TDNN module of the
XVector model. The length of tdnn_dilation has to match the length of tdnn_dim.
xvector_output_dim (int, optional, defaults to 512) —
Dimensionality of the XVector embedding vectors.
add_adapter (bool, optional, defaults to False) —
Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for
warm-starting Wav2Vec2 for SpeechEncoderDecoder models.
adapter_kernel_size (int, optional, defaults to 3) —
Kernel size of the convolutional layers in the adapter network. Only relevant if add_adapter is True.
adapter_stride (int, optional, defaults to 2) —
Stride of the convolutional layers in the adapter network. Only relevant if add_adapter is True.
num_adapter_layers (int, optional, defaults to 3) —
Number of convolutional layers that should be used in the adapter network. Only relevant if add_adapter is True.
output_hidden_size (int, optional) —
Dimensionality of the encoder output layer. If not defined, this defaults to hidden-size. Only relevant
if add_adapter is True.
This is the configuration class to store the configuration of a WavLMModel. It is used to instantiate an WavLM
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 WavLM
microsoft/wavlm-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
Example:
	Copied
 from transformers import WavLMConfig, WavLMModel
 # Initializing a WavLM facebook/wavlm-base-960h style configuration
 configuration = WavLMConfig()
 # Initializing a model (with random weights) from the facebook/wavlm-base-960h style configuration
 model = WavLMModel(configuration)
 # Accessing the model configuration
 configuration = model.config
WavLMModel
	
class transformers.WavLMModel
<
source
>
(
config: WavLMConfig
)
Parameters 
config (WavLMConfig) — 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 WavLM Model transformer outputting raw hidden-states without any specific head on top.
WavLM was proposed in WavLM: Unified Speech Representation Learning with Labeled and Unlabeled
Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong Huang.
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 etc.).
This model is a PyTorch torch.nn.Module sub-class. 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.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor)
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] or a numpy.ndarray, e.g. via 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.LongTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and 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?
attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, attention_mask should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values should simply be padded with 0 and passed without attention_mask. Be aware that these
models also yield slightly different results depending on whether input_values is padded or not.
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.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Wav2Vec2BaseModelOutput 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 (WavLMConfig) 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.
extract_features (torch.FloatTensor of shape (batch_size, sequence_length, conv_dim[-1])) — Sequence of extracted feature vectors of the last convolutional 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 + 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 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 WavLMModel 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:
	Copied
 from transformers import AutoProcessor, WavLMModel
 import torch
 from datasets import load_dataset
 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("patrickvonplaten/wavlm-libri-clean-100h-base-plus")
 model = WavLMModel.from_pretrained("patrickvonplaten/wavlm-libri-clean-100h-base-plus")
 # audio file is decoded on the fly
 inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 292, 768]
WavLMForCTC
	
class transformers.WavLMForCTC
<
source
>
(
config
target_lang: typing.Optional[str] = None
)
Parameters 
config (WavLMConfig) — 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.
WavLM Model with a language modeling head on top for Connectionist Temporal Classification (CTC).
WavLM was proposed in WavLM: Unified Speech Representation Learning with Labeled and Unlabeled
Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong Huang.
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 etc.).
This model is a PyTorch torch.nn.Module sub-class. 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.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] or a numpy.ndarray, e.g. via 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.LongTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and 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?
attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, attention_mask should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values should simply be padded with 0 and passed without attention_mask. Be aware that these
models also yield slightly different results depending on whether input_values is padded or not.
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 (WavLMConfig) 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 WavLMForCTC 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:
	Copied
 from transformers import AutoProcessor, WavLMForCTC
 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("patrickvonplaten/wavlm-libri-clean-100h-base-plus")
 model = WavLMForCTC.from_pretrained("patrickvonplaten/wavlm-libri-clean-100h-base-plus")
 # 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]
'mister quilter is the aposle of the middle classes and we are glad to welcome his gospel'
 inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids
 # compute loss
 loss = model(**inputs).loss
 round(loss.item(), 2)
12.51
WavLMForSequenceClassification
	
class transformers.WavLMForSequenceClassification
<
source
>
(
config
)
Parameters 
config (WavLMConfig) — 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.
WavLM Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
WavLM was proposed in WavLM: Unified Speech Representation Learning with Labeled and Unlabeled
Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong Huang.
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 etc.).
This model is a PyTorch torch.nn.Module sub-class. 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.SequenceClassifierOutput or tuple(torch.FloatTensor)
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] or a numpy.ndarray, e.g. via 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.LongTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and 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?
attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, attention_mask should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values should simply be padded with 0 and passed without attention_mask. Be aware that these
models also yield slightly different results depending on whether input_values is padded or not.
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 (WavLMConfig) 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 WavLMForSequenceClassification 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:
	Copied
 from transformers import AutoFeatureExtractor, WavLMForSequenceClassification
 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
 feature_extractor = AutoFeatureExtractor.from_pretrained("patrickvonplaten/wavlm-libri-clean-100h-base-plus")
 model = WavLMForSequenceClassification.from_pretrained("patrickvonplaten/wavlm-libri-clean-100h-base-plus")
 # audio file is decoded on the fly
 inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_ids = torch.argmax(logits, dim=-1).item()
 predicted_label = model.config.id2label[predicted_class_ids]
 # compute loss - target_label is e.g. "down"
 target_label = model.config.id2label[0]
 inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
 loss = model(**inputs).loss
WavLMForAudioFrameClassification
	
class transformers.WavLMForAudioFrameClassification
<
source
>
(
config
)
Parameters 
config (WavLMConfig) — 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.
WavLM Model with a frame classification head on top for tasks like Speaker Diarization.
WavLM was proposed in WavLM: Unified Speech Representation Learning with Labeled and Unlabeled
Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong Huang.
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 etc.).
This model is a PyTorch torch.nn.Module sub-class. 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
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
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] or a numpy.ndarray, e.g. via 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.LongTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and 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?
attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, attention_mask should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values should simply be padded with 0 and passed without attention_mask. Be aware that these
models also yield slightly different results depending on whether input_values is padded or not.
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.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput 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 (WavLMConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification 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 WavLMForAudioFrameClassification 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:
	Copied
 from transformers import AutoFeatureExtractor, WavLMForAudioFrameClassification
 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
 feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/wavlm-base-plus-sd")
 model = WavLMForAudioFrameClassification.from_pretrained("microsoft/wavlm-base-plus-sd")
 # audio file is decoded on the fly
 inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=sampling_rate)
 with torch.no_grad():
...     logits = model(**inputs).logits
 probabilities = torch.sigmoid(logits[0])
 # labels is a one-hot array of shape (num_frames, num_speakers)
 labels = (probabilities > 0.5).long()
 labels[0].tolist()
[0, 0]
WavLMForXVector
	
class transformers.WavLMForXVector
<
source
>
(
config
)
Parameters 
config (WavLMConfig) — 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.
WavLM Model with an XVector feature extraction head on top for tasks like Speaker Verification.
WavLM was proposed in WavLM: Unified Speech Representation Learning with Labeled and Unlabeled
Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong Huang.
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 etc.).
This model is a PyTorch torch.nn.Module sub-class. 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.XVectorOutput or tuple(torch.FloatTensor)
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] or a numpy.ndarray, e.g. via 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.LongTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and 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?
attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, attention_mask should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values should simply be padded with 0 and passed without attention_mask. Be aware that these
models also yield slightly different results depending on whether input_values is padded or not.
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.XVectorOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.XVectorOutput 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 (WavLMConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, config.xvector_output_dim)) — Classification hidden states before AMSoftmax.
embeddings (torch.FloatTensor of shape (batch_size, config.xvector_output_dim)) — Utterance embeddings used for vector similarity-based retrieval.
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 + 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 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 WavLMForXVector 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:
	Copied
 from transformers import AutoFeatureExtractor, WavLMForXVector
 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
 feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/wavlm-base-plus-sv")
 model = WavLMForXVector.from_pretrained("microsoft/wavlm-base-plus-sv")
 # audio file is decoded on the fly
 inputs = feature_extractor(
...     [d["array"] for d in dataset[:2]["audio"]], sampling_rate=sampling_rate, return_tensors="pt", padding=True
... )
 with torch.no_grad():
...     embeddings = model(**inputs).embeddings
 embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
 # the resulting embeddings can be used for cosine similarity-based retrieval
 cosine_sim = torch.nn.CosineSimilarity(dim=-1)
 similarity = cosine_sim(embeddings[0], embeddings[1])
 threshold = 0.7  # the optimal threshold is dataset-dependent
 if similarity < threshold:
...     print("Speakers are not the same!")
 round(similarity.item(), 2)
0.97
←Wav2Vec2Phoneme
Whisper→
WavLM
Overview
Documentation resources
WavLMConfig
WavLMModel
WavLMForCTC
WavLMForSequenceClassification
WavLMForAudioFrameClassification
WavLMForXVector
