Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2 facebook/wav2vec2-base-960h architecture. Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information. ... >>> from transformers import Wav2Vec2Config, Wav2Vec2Model >>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration >>> configuration = Wav2Vec2Config() >>> # Initializing a model (with random weights) from the facebook/wav2vec2-base-960h style configuration >>> model = Wav2Vec2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config

Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using Wav2Vec2CTCTokenizer. This model was contributed by patrickvonplaten. class transformers.Wav2Vec2Config(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=0.1, feat_quantizer_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

The suggested decoder could be 4-gram, as it provides huge improvement in performance by fixing the spellling mistakes and grammer issues of CTC and is still faster than Transformer decoders. ... Here is the code to perform offline transcription using Wav2Vec2 model with transformer package.

This model was released on 2020-06-20 and added to Hugging Face Transformers on 2021-02-02. The Wav2Vec2 model was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - transformers/src/transformers/models/wav2vec2/configuration_wav2vec2.py ...

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - transformers/src/transformers/models/wav2vec2/tokenization_wav2vec2.py ...

Wav2Vec2 Model with an XVector feature extraction head on top for tasks like Speaker Verification. ... Calling this function will disable the gradient computation for the feature encoder so that its parameter will ... "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.

Using a novel contrastive pretraining objective, Wav2Vec2 learns powerful speech representations from more than 50.000 hours of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - transformers/src/transformers/models/wav2vec2/processing_wav2vec2.py ...

Example ------- >>> inputs = torch.rand([10, 32000]) >>> model_hub = "facebook/wav2vec2-base-960h" >>> save_path = "savedir" >>> model = Wav2Vec2Pretrain(model_hub, save_path) >>> outputs, _ = model(inputs, wav_lens=None) """ def __init__( self, source, save_path, mask_prob=0.65, mask_length=10, normalize_wav=True, ): super().__init__( source=source, save_path=save_path, for_pretraining=True ) self.mask_prob = mask_prob self.mask_length = mask_length self.normalize_wav = normalize_wav # We check if inputs need to be normalized w.r.t pretrained wav2vec2 · [docs] def forward(self, wav, wav_lens=None): """Takes an input waveform and return its corresponding wav2vec encoding. Arguments --------- wav : torch.Tensor (signal) A batch of audio signals to transform to features.

from transformers import TrainingArguments training_args = TrainingArguments( output_dir="./wav2vec2", group_by_length=True, per_device_train_batch_size=8, gradient_accumulation_steps=2, evaluation_strategy="steps", num_train_epochs=1, fp16=True, save_steps=500, eval_steps=500, logging_steps=500, learning_rate=1e-4, warmup_steps=500, save_total_limit=2, ) Lastly, we would train the model.

September 30, 2021 - The core of wav2vec 2.0 is its Transformer encoder, which takes as input the latent feature vectors and processes it through 12 Transformer blocks for the BASE version of the model, or 24 blocks for the LARGE version.

This model was released on 2023-11-30 and added to Hugging Face Transformers on 2024-01-18. The Wav2Vec2-BERT model was proposed in Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team from Meta AI.

Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using Wav2Vec2Tokenizer (Ref: Hugging Face)

May 25, 2022 - Wav2Vec2 is a transformer-based architecture for ASR tasks and was released in September 2020. The following diagram shows its simplified architecture. For more details, see the original paper.

March 28, 2022 - The proposed fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling back-end using additive angular margin loss allows to obtain deep speaker embedding extractor that is well-generalized across different domains. It is concluded that Contrastive Predictive Coding pretraining scheme efficiently utilizes the power of unlabeled data, and thus opens the door to powerful transformer-based speaker recognition systems.

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. ... >>> 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

Wav2Vec2 models fine-tuned for ASR task can perform feature extraction and classification with one step, but for the sake of the tutorial, we also show how to perform feature extraction here. with torch.inference_mode(): features, _ = model.extract_features(waveform) The returned features is a list of tensors. Each tensor is the output of a transformer layer.

February 15, 2021 - Hugging Face has released Transformers v4.3.0 and it introduces the first Automatic Speech Recognition model to the library: Wav2Vec2

October 27, 2020 - Here is the link to the PR. And if you want to have access to a Wav2Vec2 model, pre-trained on LibriSpeech, it’s as easy as: pip install git+https://github.com/huggingface/transformers