# 16kHz audio processing

Low-bitrate speech codec based on focal modulation network, supporting 16 kHz speech encoding

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 for audio emotion detection, capable of recognizing 7 emotional states

Whisper-Tiny is a small automatic speech recognition (ASR) model developed by OpenAI, designed for speech-to-text tasks and suitable for Unity environments.

This is a model fine-tuned for the task of French speech to phoneme conversion, based on the facebook/wav2vec2-base-fr-voxpopuli-v2 model and trained using the Common Voice v13 dataset.

Vietnamese automatic speech recognition model based on wav2vec2 architecture, pre-trained with 13,000 hours of unlabeled YouTube audio and fine-tuned on 250 hours of labeled data

Wav2Vec2 Conformer model fine-tuned on 100 hours of Librispeech data, incorporating rotary position embedding technology

This model is a large-scale speech recognition model based on the Wav2Vec2 architecture, pre-trained and fine-tuned on 10 minutes of data from Libri-Light and Librispeech, using self-training objectives, suitable for 16kHz sampled speech audio.

Data2Vec is a general self-supervised learning framework applicable to speech, vision, and language tasks. This large audio model is pre-trained and fine-tuned on 10 minutes of Librispeech data, suitable for 16kHz sampled speech audio.

Data2Vec-Audio-Large is a large model pre-trained on 16kHz sampled speech audio using a self-supervised learning framework, suitable for tasks such as speech recognition.

A Ukrainian automatic speech recognition (ASR) model fine-tuned from facebook/wav2vec2-large-xlsr-53, trained on the Common Voice dataset.

Data2Vec is a general self-supervised learning framework applicable to speech, vision, and language tasks. This audio base model was pre-trained and fine-tuned on 100 hours of Librispeech audio data.

Hubert is a self-supervised learning-based speech representation model that learns joint acoustic and linguistic representations of speech through BERT-like predictive loss.

This is an automatic speech recognition model fine-tuned on the Turkish Common Voice dataset based on the facebook/wav2vec2-large-xlsr-53 model, achieving a test WER of 21.13%.

This is a speech model based on Facebook's Wav2Vec2 architecture, specifically pretrained for Slovenian (sl) using 11.3k hours of unlabeled data from the VoxPopuli corpus.

Wav2Vec2 base model pretrained on Portuguese VoxPopuli corpus, suitable for speech recognition tasks

Facebook's Wav2Vec2 large model, pre-trained on 88.99999999999999 hours of unlabeled data from the Slavic language VoxPopuli corpus.

Facebook's Wav2Vec2 large model, pre-trained on 27.5 hours of unlabeled data from the Baltic language subset of the VoxPopuli corpus.

Polish Wav2Vec2 base model trained on VoxPopuli corpus, suitable for speech recognition tasks

An automatic speech recognition model fine-tuned on Greek language based on facebook/wav2vec2-large-xlsr-53

A fine-tuned speech recognition model for the Romansh Vallader dialect based on facebook/wav2vec2-large-xlsr-53, achieving a word error rate of 32.89%

Facebook's Wav2Vec2 large model, pretrained only on 101.5 hours of unlabeled data from the Romance language VoxPopuli corpus, suitable for speech recognition tasks.

Facebook's Wav2Vec2 large model, pretrained exclusively on unlabeled data from the VoxPopuli corpus for Maltese (mt), suitable for speech recognition tasks.

Indonesian automatic speech recognition (ASR) model fine-tuned on the XLSR architecture, trained on the Common Voice Indonesian dataset

HuBERT is a self-supervised speech representation learning model that learns speech features through BERT-like prediction loss, suitable for tasks such as speech recognition.

Wav2Vec2 base model pretrained on Slovak data from the VoxPopuli corpus, suitable for speech recognition tasks.

A speech model based on Facebook's Wav2Vec2 architecture, specifically pre-trained for Swedish using 16.3k hours of unlabeled data from the VoxPopuli corpus.

Wav2Vec2 base model pretrained on the VoxPopuli corpus, specialized for Czech speech processing

A speech model based on Facebook's Wav2Vec2 architecture, specifically pre-trained for Finnish, suitable for speech recognition tasks.

This is a French speech recognition model fine-tuned from the XLSR-53 large model, trained on the Common Voice dataset, supporting high-accuracy French speech-to-text conversion.

A fine-tuned automatic speech recognition model for Persian (Farsi) based on facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampled audio input.

This is an automatic speech recognition (ASR) model fine-tuned on the Georgian language based on the facebook/wav2vec2-large-xlsr-53 model, trained using the Common Voice dataset.

A speech model based on Facebook's Wav2Vec2 framework, specifically pretrained for Estonian

A Portuguese automatic speech recognition (ASR) model fine-tuned from facebook/wav2vec2-large-xlsr-53, achieving a 17.22% word error rate (WER) on the Common Voice Portuguese dataset

A Wav2Vec2 base model pre-trained on 24.1k hours of unlabeled English data from the VoxPopuli corpus, suitable for speech recognition tasks.

A speech recognition model fine-tuned on the Tatar Common Voice dataset based on Facebook's wav2vec2-large-xlsr-53 model

A German speech pretrained model based on Facebook's Wav2Vec2 architecture, pretrained using 23.2k unlabeled German data from the VoxPopuli corpus.

A speech model based on Facebook's Wav2Vec2 architecture, specifically pretrained for Dutch using 19.0k unlabeled data from the VoxPopuli corpus.

A Romanian speech recognition model fine-tuned based on facebook/wav2vec2-xls-r-300m, trained on Common Voice 8.0 and Romanian speech synthesis datasets, ranked first in Romanian speech recognition in the HuggingFace Robust Speech Challenge.

Wav2Vec2 large speech model pre-trained on 42.5 hours of unannotated Uralic language data from the VoxPopuli corpus

This is a speech model based on Facebook's Wav2Vec2 architecture, specifically pretrained for Lithuanian using 14.4k unlabeled data from the VoxPopuli corpus.