transformers/docs/source/en/model_doc/wav2vec2.mdx

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# Wav2Vec2

## Overview

The Wav2Vec2 model was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

The abstract from the paper is the following:

*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
recognition with limited amounts of labeled data.*

Tips:

- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- 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](https://huggingface.co/patrickvonplaten).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Wav2Vec2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

<PipelineTag pipeline="audio-classification"/>

- A notebook on how to [leverage a pretrained Wav2Vec2 model for emotion classification](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb). 🌎
- [`Wav2Vec2ForCTC`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb).
- [Audio classification task guide](../tasks/audio_classification)

<PipelineTag pipeline="automatic-speech-recognition"/>

- A blog post on [boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram).
- A blog post on how to [finetune Wav2Vec2 for English ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-wav2vec2-english).
- A blog post on [finetuning XLS-R for Multi-Lingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2).
- A notebook on how to [create YouTube captions from any video by transcribing audio with Wav2Vec2](https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb). 🌎
- [`Wav2Vec2ForCTC`] is supported by a notebook on [how to finetune a speech recognition model in English](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb), and [how to finetune a speech recognition model in any language](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb).
- [Automatic speech recognition task guide](../tasks/asr)

🚀 Deploy

- A blog post on how to deploy Wav2Vec2 for [Automatic Speech Recogntion with Hugging Face's Transformers & Amazon SageMaker](https://www.philschmid.de/automatic-speech-recognition-sagemaker).

## Wav2Vec2Config

[[autodoc]] Wav2Vec2Config

## Wav2Vec2CTCTokenizer

[[autodoc]] Wav2Vec2CTCTokenizer
    - __call__
    - save_vocabulary
    - decode
    - batch_decode

## Wav2Vec2FeatureExtractor

[[autodoc]] Wav2Vec2FeatureExtractor
    - __call__

## Wav2Vec2Processor

[[autodoc]] Wav2Vec2Processor
    - __call__
    - pad
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode

## Wav2Vec2ProcessorWithLM

[[autodoc]] Wav2Vec2ProcessorWithLM
    - __call__
    - pad
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode

### Decoding multiple audios

If you are planning to decode multiple batches of audios, you should consider using [`~Wav2Vec2ProcessorWithLM.batch_decode`] and passing an instantiated `multiprocessing.Pool`.
Otherwise, [`~Wav2Vec2ProcessorWithLM.batch_decode`] performance will be slower than calling [`~Wav2Vec2ProcessorWithLM.decode`] for each audio individually, as it internally instantiates a new `Pool` for every call. See the example below:

```python
>>> # Let's see how to use a user-managed pool for batch decoding multiple audios
>>> from multiprocessing import get_context
>>> from transformers import AutoTokenizer, AutoProcessor, AutoModelForCTC
>>> from datasets import load_dataset
>>> import datasets
>>> import torch

>>> # import model, feature extractor, tokenizer
>>> model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm").to("cuda")
>>> processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")

>>> # load example dataset
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))


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


>>> # prepare speech data for batch inference
>>> dataset = dataset.map(map_to_array, remove_columns=["audio"])


>>> def map_to_pred(batch, pool):
...     inputs = processor(batch["speech"], sampling_rate=16_000, padding=True, return_tensors="pt")
...     inputs = {k: v.to("cuda") for k, v in inputs.items()}

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

...     transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
...     batch["transcription"] = transcription
...     return batch


>>> # note: pool should be instantiated *after* `Wav2Vec2ProcessorWithLM`.
>>> #       otherwise, the LM won't be available to the pool's sub-processes
>>> # select number of processes and batch_size based on number of CPU cores available and on dataset size
>>> with get_context("fork").Pool(processes=2) as pool:
...     result = dataset.map(
...         map_to_pred, batched=True, batch_size=2, fn_kwargs={"pool": pool}, remove_columns=["speech"]
...     )

>>> result["transcription"][:2]
['MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL', "NOR IS MISTER COULTER'S MANNER LESS INTERESTING THAN HIS MATTER"]
```

## Wav2Vec2 specific outputs

[[autodoc]] models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput

[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput

[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput

[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput

[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput

## Wav2Vec2Model

[[autodoc]] Wav2Vec2Model
    - forward

## Wav2Vec2ForCTC

[[autodoc]] Wav2Vec2ForCTC
    - forward

## Wav2Vec2ForSequenceClassification

[[autodoc]] Wav2Vec2ForSequenceClassification
    - forward

## Wav2Vec2ForAudioFrameClassification

[[autodoc]] Wav2Vec2ForAudioFrameClassification
    - forward

## Wav2Vec2ForXVector

[[autodoc]] Wav2Vec2ForXVector
    - forward

## Wav2Vec2ForPreTraining

[[autodoc]] Wav2Vec2ForPreTraining
    - forward

## TFWav2Vec2Model

[[autodoc]] TFWav2Vec2Model
    - call

## TFWav2Vec2ForCTC

[[autodoc]] TFWav2Vec2ForCTC
    - call

## FlaxWav2Vec2Model

[[autodoc]] FlaxWav2Vec2Model
    - __call__

## FlaxWav2Vec2ForCTC

[[autodoc]] FlaxWav2Vec2ForCTC
    - __call__

## FlaxWav2Vec2ForPreTraining

[[autodoc]] FlaxWav2Vec2ForPreTraining
    - __call__