<!--Copyright 2021 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# Wav2Vec2

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64, 
">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## 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.*

This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).

Note: Meta (FAIR) released a new version of [Wav2Vec2-BERT 2.0](https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert) - it's pretrained on 4.5M hours of audio. We especially recommend using it for fine-tuning tasks, e.g. as per [this guide](https://huggingface.co/blog/fine-tune-w2v2-bert).

## Usage 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`].

## Using Flash Attention 2

Flash Attention 2 is an faster, optimized version of the model.

### Installation 

First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the [official documentation](https://github.com/Dao-AILab/flash-attention#installation-and-features). If your hardware is not compatible with Flash Attention 2, you can still benefit from attention kernel optimisations through Better Transformer support covered [above](https://huggingface.co/docs/transformers/main/en/model_doc/bark#using-better-transformer).

Next, [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) the latest version of Flash Attention 2:

```bash
pip install -U flash-attn --no-build-isolation
```

### Usage

To load a model using Flash Attention 2, we can pass the argument `attn_implementation="flash_attention_2"` to [`.from_pretrained`](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained). We'll also load the model in half-precision (e.g. `torch.float16`), since it results in almost no degradation to audio quality but significantly lower memory usage and faster inference:

```python
>>> from transformers import Wav2Vec2Model

model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to(device)
...
```

### Expected speedups

Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of the `facebook/wav2vec2-large-960h-lv60-self` model and the flash-attention-2 and sdpa (scale-dot-product-attention) versions. . We show the average speedup obtained on the `librispeech_asr` `clean` validation split: 


<div style="text-align: center">
<img src="https://huggingface.co/datasets/kamilakesbi/transformers_image_doc/resolve/main/data/Wav2Vec2_speedup.png">
</div>



## 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 Recognition 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
    - set_target_lang

## 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

<frameworkcontent>
<pt>

## Wav2Vec2Model

[[autodoc]] Wav2Vec2Model
    - forward

## Wav2Vec2ForCTC

[[autodoc]] Wav2Vec2ForCTC
    - forward
    - load_adapter

## Wav2Vec2ForSequenceClassification

[[autodoc]] Wav2Vec2ForSequenceClassification
    - forward

## Wav2Vec2ForAudioFrameClassification

[[autodoc]] Wav2Vec2ForAudioFrameClassification
    - forward

## Wav2Vec2ForXVector

[[autodoc]] Wav2Vec2ForXVector
    - forward

## Wav2Vec2ForPreTraining

[[autodoc]] Wav2Vec2ForPreTraining
    - forward

</pt>
<tf>

## TFWav2Vec2Model

[[autodoc]] TFWav2Vec2Model
    - call

## TFWav2Vec2ForSequenceClassification

[[autodoc]] TFWav2Vec2ForSequenceClassification
    - call

## TFWav2Vec2ForCTC

[[autodoc]] TFWav2Vec2ForCTC
    - call

</tf>
<jax>

## FlaxWav2Vec2Model

[[autodoc]] FlaxWav2Vec2Model
    - __call__

## FlaxWav2Vec2ForCTC

[[autodoc]] FlaxWav2Vec2ForCTC
    - __call__

## FlaxWav2Vec2ForPreTraining

[[autodoc]] FlaxWav2Vec2ForPreTraining
    - __call__

</jax>
</frameworkcontent>
