# WavLM ## Overview The WavLM model was proposed in [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://huggingface.co/papers/2110.13900) 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 unsupervisedly 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.* Relevant checkpoints can be found under https://huggingface.co/models?other=wavlm. This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be found [here](https://github.com/microsoft/unilm/tree/master/wavlm). ## Usage tips - WavLM is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please use [Wav2Vec2Processor](/docs/transformers/v5.5.2/en/model_doc/wav2vec2#transformers.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](/docs/transformers/v5.5.2/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer). - WavLM performs especially well on speaker verification, speaker identification, and speaker diarization tasks. ## Resources - [Audio classification task guide](../tasks/audio_classification) - [Automatic speech recognition task guide](../tasks/asr) ## WavLMConfig[[transformers.WavLMConfig]] #### transformers.WavLMConfig[[transformers.WavLMConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/configuration_wavlm.py#L27) This is the configuration class to store the configuration of a WavLMModel. It is used to instantiate a 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 [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.5.2/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.5.2/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python ``` Example: ```python >>> 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 ``` **Parameters:** vocab_size (`int`, *optional*, defaults to `32`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`. hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder. intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. hidden_dropout (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. activation_dropout (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout ratio for activations inside the fully connected layer. attention_dropout (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout ratio for the attention probabilities. feat_proj_dropout (`float`, *optional*, defaults to 0.0) : The dropout probability for output of the feature encoder. final_dropout (`float`, *optional*, defaults to 0.1) : The dropout probability for the final projection layer of [WavLMForCTC](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMForCTC). layerdrop (`Union[float, int]`, *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-05`) : 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_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. num_buckets (`int`, *optional*, defaults to 320) : The number of buckets to use for each attention layer max_bucket_distance (`int`, *optional*, defaults to 800) : Maximum bucket distance 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](https://huggingface.co/papers/1904.08779). mask_time_prob (`float`, *optional*, defaults to 0.05) : Probability 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 >> 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("microsoft/wavlm-base") >>> model = WavLMForCTC.from_pretrained("microsoft/wavlm-base") >>> # 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) ... ``` **Parameters:** config ([WavLMForCTC](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMForCTC)) : 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()](/docs/transformers/v5.5.2/en/main_classes/model#transformers.PreTrainedModel.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 [WavLMForCTC](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMForCTC) with adapters. Uses 'eng' by default. **Returns:** `[CausalLMOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput) or `tuple(torch.FloatTensor)`` A [CausalLMOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs. ## WavLMForSequenceClassification[[transformers.WavLMForSequenceClassification]] #### transformers.WavLMForSequenceClassification[[transformers.WavLMForSequenceClassification]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/modeling_wavlm.py#L1254) WavLM Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting. This model inherits from [PreTrainedModel](/docs/transformers/v5.5.2/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WavLMForSequenceClassification.forwardhttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/modeling_wavlm.py#L1287[{"name": "input_values", "val": ": torch.Tensor | None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "labels", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ""}]- **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](/docs/transformers/v5.5.2/en/model_doc/auto#transformers.AutoProcessor) should be used for padding and conversion into a tensor of type `torch.FloatTensor`. See `WavLMProcessor.__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?](../glossary#attention-mask) - **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](/docs/transformers/v5.5.2/en/main_classes/output#transformers.utils.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).0[SequenceClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`A [SequenceClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs. The [WavLMForSequenceClassification](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.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. - **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. Example of single-label classification: ```python >>> import torch >>> from transformers import AutoTokenizer, WavLMForSequenceClassification >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/wavlm-base") >>> model = WavLMForSequenceClassification.from_pretrained("microsoft/wavlm-base") >>> 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 = WavLMForSequenceClassification.from_pretrained("microsoft/wavlm-base", num_labels=num_labels) >>> labels = torch.tensor([1]) >>> loss = model(**inputs, labels=labels).loss >>> round(loss.item(), 2) ... ``` Example of multi-label classification: ```python >>> import torch >>> from transformers import AutoTokenizer, WavLMForSequenceClassification >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/wavlm-base") >>> model = WavLMForSequenceClassification.from_pretrained("microsoft/wavlm-base", 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 = WavLMForSequenceClassification.from_pretrained( ... "microsoft/wavlm-base", 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 ``` **Parameters:** config ([WavLMForSequenceClassification](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMForSequenceClassification)) : 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()](/docs/transformers/v5.5.2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[SequenceClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`` A [SequenceClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs. ## WavLMForAudioFrameClassification[[transformers.WavLMForAudioFrameClassification]] #### transformers.WavLMForAudioFrameClassification[[transformers.WavLMForAudioFrameClassification]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/modeling_wavlm.py#L1359) The Wavlm Model with a frame classification head on top for tasks like Speaker Diarization. This model inherits from [PreTrainedModel](/docs/transformers/v5.5.2/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WavLMForAudioFrameClassification.forwardhttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/modeling_wavlm.py#L1391[{"name": "input_values", "val": ": torch.Tensor | None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "labels", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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](/docs/transformers/v5.5.2/en/model_doc/auto#transformers.AutoProcessor) should be used for padding and conversion into a tensor of type `torch.FloatTensor`. See `WavLMProcessor.__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?](../glossary#attention-mask) - **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). - **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](/docs/transformers/v5.5.2/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[TokenClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`A [TokenClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs. The [WavLMForAudioFrameClassification](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.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. - **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. Example: ```python >>> 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") >>> model = WavLMForAudioFrameClassification.from_pretrained("microsoft/wavlm-base") >>> # 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() ... ``` **Parameters:** config ([WavLMForAudioFrameClassification](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification)) : 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()](/docs/transformers/v5.5.2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[TokenClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`` A [TokenClassifierOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs. ## WavLMForXVector[[transformers.WavLMForXVector]] #### transformers.WavLMForXVector[[transformers.WavLMForXVector]] [Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/modeling_wavlm.py#L1513) WavLM Model with an XVector feature extraction head on top for tasks like Speaker Verification. This model inherits from [PreTrainedModel](/docs/transformers/v5.5.2/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.WavLMForXVector.forwardhttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/wavlm/modeling_wavlm.py#L1563[{"name": "input_values", "val": ": torch.Tensor | None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "labels", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ""}]- **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](/docs/transformers/v5.5.2/en/model_doc/auto#transformers.AutoProcessor) should be used for padding and conversion into a tensor of type `torch.FloatTensor`. See `WavLMProcessor.__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?](../glossary#attention-mask) - **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](/docs/transformers/v5.5.2/en/main_classes/output#transformers.utils.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).0[XVectorOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.XVectorOutput) or `tuple(torch.FloatTensor)`A [XVectorOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs. The [WavLMForXVector](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.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. - **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. Example: ```python >>> 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") >>> model = WavLMForXVector.from_pretrained("microsoft/wavlm-base") >>> # 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 >> round(similarity.item(), 2) ... ``` **Parameters:** config ([WavLMForXVector](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMForXVector)) : 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()](/docs/transformers/v5.5.2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[XVectorOutput](/docs/transformers/v5.5.2/en/main_classes/output#transformers.modeling_outputs.XVectorOutput) or `tuple(torch.FloatTensor)`` A [XVectorOutput](/docs/transformers/v5.5.2/en/main_classes/output#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](/docs/transformers/v5.5.2/en/model_doc/wavlm#transformers.WavLMConfig)) and inputs.