
Wav2Vec2
Overview
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.
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.
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].

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.

A notebook on how to leverage a pretrained Wav2Vec2 model for emotion classification. 🌎
[Wav2Vec2ForCTC] is supported by this example script and notebook.
Audio classification task guide

A blog post on boosting Wav2Vec2 with n-grams in 🤗 Transformers.
A blog post on how to finetune Wav2Vec2 for English ASR with 🤗 Transformers.
A blog post on finetuning XLS-R for Multi-Lingual ASR with 🤗 Transformers.
A notebook on how to create YouTube captions from any video by transcribing audio with Wav2Vec2. 🌎
[Wav2Vec2ForCTC] is supported by a notebook on how to finetune a speech recognition model in English, and how to finetune a speech recognition model in any language.
Automatic speech recognition task guide

🚀 Deploy

A blog post on how to deploy Wav2Vec2 for Automatic Speech Recognition with Hugging Face's Transformers & Amazon 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:
thon

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
    - load_adapter
Wav2Vec2ForSequenceClassification
[[autodoc]] Wav2Vec2ForSequenceClassification
    - forward
Wav2Vec2ForAudioFrameClassification
[[autodoc]] Wav2Vec2ForAudioFrameClassification
    - forward
Wav2Vec2ForXVector
[[autodoc]] Wav2Vec2ForXVector
    - forward
Wav2Vec2ForPreTraining
[[autodoc]] Wav2Vec2ForPreTraining
    - forward

TFWav2Vec2Model
[[autodoc]] TFWav2Vec2Model
    - call
TFWav2Vec2ForSequenceClassification
[[autodoc]] TFWav2Vec2ForSequenceClassification
    - call
TFWav2Vec2ForCTC
[[autodoc]] TFWav2Vec2ForCTC
    - call

FlaxWav2Vec2Model
[[autodoc]] FlaxWav2Vec2Model
    - call
FlaxWav2Vec2ForCTC
[[autodoc]] FlaxWav2Vec2ForCTC
    - call
FlaxWav2Vec2ForPreTraining
[[autodoc]] FlaxWav2Vec2ForPreTraining
    - call

