transformers/pr_37082/en/model_doc/wav2vec2-bert.md

Wav2Vec2-BERT

Overview

The Wav2Vec2-BERT model was proposed in Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team from Meta AI.

This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification.

The official results of the model can be found in Section 3.2.1 of the paper.

The abstract from the paper is the following:

Recent advancements in automatic speech translation have dramatically expanded language coverage, improved multimodal capabilities, and enabled a wide range of tasks and functionalities. That said, large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion. First, we contribute an improved version of the massively multilingual and multimodal SeamlessM4T model—SeamlessM4T v2. This newer model, incorporating an updated UnitY2 framework, was trained on more low-resource language data. The expanded version of SeamlessAlign adds 114,800 hours of automatically aligned data for a total of 76 languages. SeamlessM4T v2 provides the foundation on which our two newest models, SeamlessExpressive and SeamlessStreaming, are initiated. SeamlessExpressive enables translation that preserves vocal styles and prosody. Compared to previous efforts in expressive speech research, our work addresses certain underexplored aspects of prosody, such as speech rate and pauses, while also preserving the style of one's voice. As for SeamlessStreaming, our model leverages the Efficient Monotonic Multihead Attention (EMMA) mechanism to generate low-latency target translations without waiting for complete source utterances. As the first of its kind, SeamlessStreaming enables simultaneous speech-to-speech/text translation for multiple source and target languages. To understand the performance of these models, we combined novel and modified versions of existing automatic metrics to evaluate prosody, latency, and robustness. For human evaluations, we adapted existing protocols tailored for measuring the most relevant attributes in the preservation of meaning, naturalness, and expressivity. To ensure that our models can be used safely and responsibly, we implemented the first known red-teaming effort for multimodal machine translation, a system for the detection and mitigation of added toxicity, a systematic evaluation of gender bias, and an inaudible localized watermarking mechanism designed to dampen the impact of deepfakes. Consequently, we bring major components from SeamlessExpressive and SeamlessStreaming together to form Seamless, the first publicly available system that unlocks expressive cross-lingual communication in real-time. In sum, Seamless gives us a pivotal look at the technical foundation needed to turn the Universal Speech Translator from a science fiction concept into a real-world technology. Finally, contributions in this work—including models, code, and a watermark detector—are publicly released and accessible at the link below.

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

Usage tips

• Wav2Vec2-BERT follows the same architecture as Wav2Vec2-Conformer, but employs a causal depthwise convolutional layer and uses as input a mel-spectrogram representation of the audio instead of the raw waveform.
• Wav2Vec2-BERT can use either no relative position embeddings, Shaw-like position embeddings, Transformer-XL-like position embeddings, or rotary position embeddings by setting the correct config.position_embeddings_type.
• Wav2Vec2-BERT also introduces a Conformer-based adapter network instead of a simple convolutional network.

Resources

• Wav2Vec2BertForCTC is supported by this example script.

• You can also adapt these notebooks on how to finetune a speech recognition model in English, and how to finetune a speech recognition model in any language.

• Wav2Vec2BertForSequenceClassification can be used by adapting this example script.

• See also: Audio classification task guide

Wav2Vec2BertConfig[[transformers.Wav2Vec2BertConfig]]

transformers.Wav2Vec2BertConfig[[transformers.Wav2Vec2BertConfig]]

Source

This is the configuration class to store the configuration of a Wav2Vec2BertModel. It is used to instantiate an Wav2Vec2Bert 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 Wav2Vec2Bert facebook/wav2vec2-bert-rel-pos-large 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 Wav2Vec2BertConfig, Wav2Vec2BertModel

>>> # Initializing a Wav2Vec2Bert facebook/wav2vec2-bert-rel-pos-large style configuration
>>> configuration = Wav2Vec2BertConfig()

>>> # Initializing a model (with random weights) from the facebook/wav2vec2-bert-rel-pos-large style configuration
>>> model = Wav2Vec2BertModel(configuration)

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

Parameters:

vocab_size (int, optional) : Vocabulary size of the Wav2Vec2Bert model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling Wav2Vec2BertModel. Vocabulary size of the model. Defines the different tokens that can be represented by the inputs_ids passed to the forward method of Wav2Vec2BertModel.

hidden_size (int, optional, defaults to 1024) : Dimensionality of the encoder layers and the pooler layer.

num_hidden_layers (int, optional, defaults to 24) : Number of hidden layers in the Transformer encoder.

num_attention_heads (int, optional, defaults to 16) : Number of attention heads for each attention layer in the Transformer encoder.

intermediate_size (int, optional, defaults to 4096) : Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.

feature_projection_input_dim (int, optional, defaults to 160) : Input dimension of this model, i.e the dimension after processing input audios with SeamlessM4TFeatureExtractor or Wav2Vec2BertProcessor.

hidden_act (str or function, optional, defaults to "swish") : The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "selu", "swish" and "gelu_new" are supported.

hidden_dropout (float, optional, defaults to 0.0) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

activation_dropout (float, optional, defaults to 0.0) : The dropout ratio for activations inside the fully connected layer.

attention_dropout (float, optional, defaults to 0.0) : The dropout ratio for the attention probabilities.

feat_proj_dropout (float, optional, defaults to 0.0) : The dropout probability for the feature projection.

final_dropout (float, optional, defaults to 0.1) : The dropout probability for the final projection layer of Wav2Vec2BertForCTC.

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-05) : The epsilon used by the layer normalization layers.

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_prob*len(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_start*mask_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 >> from transformers import AutoProcessor, Wav2Vec2BertForCTC

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/wav2vec2-bert-rel-pos-large") model = Wav2Vec2BertForCTC.from_pretrained("facebook/wav2vec2-bert-rel-pos-large")

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 ([Wav2Vec2BertForCTC](/docs/transformers/pr_37082/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForCTC)) : 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/pr_37082/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 [UniSpeechSatForCTC](/docs/transformers/pr_37082/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC) with adapters. Uses 'eng' by default.

**Returns:**

`[transformers.modeling_outputs.CausalLMOutput](/docs/transformers/pr_37082/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput) or `tuple(torch.FloatTensor)``

A [transformers.modeling_outputs.CausalLMOutput](/docs/transformers/pr_37082/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 ([Wav2Vec2BertConfig](/docs/transformers/pr_37082/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig)) 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.

## Wav2Vec2BertForSequenceClassification[[transformers.Wav2Vec2BertForSequenceClassification]]

#### transformers.Wav2Vec2BertForSequenceClassification[[transformers.Wav2Vec2BertForSequenceClassification]]

[Source](https://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py#L1158)

Wav2Vec2Bert 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/pr_37082/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.Wav2Vec2BertForSequenceClassification.forwardhttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py#L1184[{"name": "input_features", "val": ": typing.Optional[torch.Tensor]"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Optional[torch.Tensor] = None"}]- **input_features** (`torch.Tensor` of shape `(batch_size, sequence_length, feature_dim)`, *optional*) --
  The tensors corresponding to the input audio features. Audio features can be obtained using
  [Wav2Vec2FeatureExtractor](/docs/transformers/pr_37082/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor). See [Wav2Vec2FeatureExtractor.__call__()](/docs/transformers/pr_37082/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor.__call__) for details ([Wav2Vec2Processor](/docs/transformers/pr_37082/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) uses
  [Wav2Vec2FeatureExtractor](/docs/transformers/pr_37082/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor) for processing audios).
- **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/pr_37082/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[transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_37082/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_37082/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 ([Wav2Vec2BertConfig](/docs/transformers/pr_37082/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig)) 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 [Wav2Vec2BertForSequenceClassification](/docs/transformers/pr_37082/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForSequenceClassification) 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 of single-label classification:

```python
>>> import torch
>>> from transformers import AutoTokenizer, Wav2Vec2BertForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-bert-rel-pos-large")
>>> model = Wav2Vec2BertForSequenceClassification.from_pretrained("facebook/wav2vec2-bert-rel-pos-large")

>>> 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 = Wav2Vec2BertForSequenceClassification.from_pretrained("facebook/wav2vec2-bert-rel-pos-large", 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, Wav2Vec2BertForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-bert-rel-pos-large")
>>> model = Wav2Vec2BertForSequenceClassification.from_pretrained("facebook/wav2vec2-bert-rel-pos-large", 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 = Wav2Vec2BertForSequenceClassification.from_pretrained(
...     "facebook/wav2vec2-bert-rel-pos-large", 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 (Wav2Vec2BertForSequenceClassification) : 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.

Returns:

[transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_37082/en/main_classes/output#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 (Wav2Vec2BertConfig) 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.

Wav2Vec2BertForAudioFrameClassification[[transformers.Wav2Vec2BertForAudioFrameClassification]]

transformers.Wav2Vec2BertForAudioFrameClassification[[transformers.Wav2Vec2BertForAudioFrameClassification]]

Source

The Wav2Vec2 Bert Model with a frame classification head on top for tasks like Speaker Diarization.

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.

forwardtransformers.Wav2Vec2BertForAudioFrameClassification.forwardhttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py#L1274[{"name": "input_features", "val": ": typing.Optional[torch.Tensor]"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]- input_features (torch.Tensor of shape (batch_size, sequence_length, feature_dim), optional) -- The tensors corresponding to the input audio features. Audio features can be obtained using Wav2Vec2FeatureExtractor. See Wav2Vec2FeatureExtractor.call() for details (Wav2Vec2Processor uses Wav2Vec2FeatureExtractor for processing audios).

• 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?

• 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 instead of a plain tuple.0transformers.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 (Wav2Vec2BertConfig) 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 Wav2Vec2BertForAudioFrameClassification 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 AutoFeatureExtractor, Wav2Vec2BertForAudioFrameClassification
>>> 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("facebook/wav2vec2-bert-rel-pos-large")
>>> model = Wav2Vec2BertForAudioFrameClassification.from_pretrained("facebook/wav2vec2-bert-rel-pos-large")

>>> # 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 (Wav2Vec2BertForAudioFrameClassification) : 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.

Returns:

[transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_37082/en/main_classes/output#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 (Wav2Vec2BertConfig) 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.

Wav2Vec2BertForXVector[[transformers.Wav2Vec2BertForXVector]]

transformers.Wav2Vec2BertForXVector[[transformers.Wav2Vec2BertForXVector]]

Source

Wav2Vec2Bert Model with an XVector feature extraction head on top for tasks like Speaker Verification.

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.

forwardtransformers.Wav2Vec2BertForXVector.forwardhttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py#L1432[{"name": "input_features", "val": ": typing.Optional[torch.Tensor]"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Optional[torch.Tensor] = None"}]- input_features (torch.Tensor of shape (batch_size, sequence_length, feature_dim), optional) -- The tensors corresponding to the input audio features. Audio features can be obtained using Wav2Vec2FeatureExtractor. See Wav2Vec2FeatureExtractor.call() for details (Wav2Vec2Processor uses Wav2Vec2FeatureExtractor for processing audios).

• 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).0transformers.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 (Wav2Vec2BertConfig) 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 Wav2Vec2BertForXVector 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 AutoFeatureExtractor, Wav2Vec2BertForXVector
>>> 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("facebook/wav2vec2-bert-rel-pos-large")
>>> model = Wav2Vec2BertForXVector.from_pretrained("facebook/wav2vec2-bert-rel-pos-large")

>>> # 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 (Wav2Vec2BertForXVector) : 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.

Returns:

[transformers.modeling_outputs.XVectorOutput](/docs/transformers/pr_37082/en/main_classes/output#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 (Wav2Vec2BertConfig) 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.