July 25, 2025 - wav2vec2 · pretraining · speech · arxiv: 2006.11477 · License: apache-2.0 · Model card Files Files and versions · xet Community · 11 · Deploy · Use this model · Facebook's Wav2Vec2 · The base model pretrained on 16kHz sampled speech audio.

July 25, 2025 - wav2vec2 · pretraining · speech · arxiv: 2006.11477 · License: apache-2.0 · Model card Files Files and versions · xet Community · 2 · Deploy · Use this model · Facebook's Wav2Vec2 · The base model pretrained on 16kHz sampled speech audio.

Facebook AI is releasing code and models for wav2vec 2.0, a self-supervised algorithm that enables automatic speech recognition models with just 10 minutes of transcribed speech data.

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January 16, 2024 - from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"]) print("WER:", wer(result["text"], result["transcription"]))

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A live speech recognition using Facebooks wav2vec 2.0 model. - oliverguhr/wav2vec2-live

August 5, 2022 - # !pip install transformers # !pip install datasets import soundfile as sf import torch from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # load pretrained model processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") model = ...

Wav2Vec2.0-base is a model, which pre-trained to learn speech representations on unlabeled data as described in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations paper and fine-tuned for speech recognition task ...

Facebook's Wav2Vec2 is a large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. The model was trained with a Self-Training objective.

Audio transcription model for converting English speech audio to English text

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October 22, 2020 - We show for the first time that ... 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....

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June 2, 2021 - Wav2Vec2 can thus, allow for improved automatic speech recognition for many more languages and domains with much less annotated data but State of the art results. Thank you for reading!

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>>> from torchaudio.models.wav2vec2.utils import import_huggingface_model >>> >>> original = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> model = import_huggingface_model(original) >>> >>> waveforms, _ = torchaudio.load("audio.wav") >>> logits, _ = model(waveforms)

February 15, 2021 - And Hugging Face has no plans to stop its growing applications. Hugging Face just dropped the State-of-the-art Natural Language Processing library Transformers v4.30 and it has extended its reach to Speech Recognition by adding one of the leading Automatic Speech Recognition models by Facebook called the Wav2Vec2.

# Url for xlsr wav2vec2 #wav2vec2_hub: C:\Users\nathk\Documents\TacoProject\brain\speechbrain\xlsr_53_56k.pt wav2vec2_hub: facebook/wav2vec2-large-xlsr-53 #wav2vec2_hub: C:/Users/nathk/Documents/TacoProject/brain/speechbrain/recipes/DVoice/ASR/CTC/results/wav2vec2_ctc_FONGBE/1250/save/wav2vec2_checkpoint/config.json # Data files data_folder: /localscratch/ALFFA_PUBLIC/ASR/FONGBE/data train_csv_file: !ref <data_folder>/train.csv