# Low CER performance

Denmark's state-of-the-art speech recognition model trained by Alvenir, based on the CoRal-v2 dataset, supporting multiple Danish dialects

A wav2vec 2.0 Base model fine-tuned on the large-scale Japanese automatic speech recognition corpus ReazonSpeech v2.0, suitable for Japanese automatic speech recognition tasks.

Japanese speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampled audio input

Estonian speech recognition model fine-tuned from OpenAI Whisper-large-v2, developed by Tallinn University of Technology, trained on approximately 1,200 hours of data

This is a French automatic speech recognition model based on the XLS-R 1B architecture, fine-tuned on multiple French speech datasets.

This is an automatic speech recognition model fine-tuned on Estonian datasets based on the facebook/wav2vec2-xls-r-1b model.

This is a Catalan speech recognition model fine-tuned from facebook/wav2vec2-xls-r-300m, trained on multiple Catalan datasets.

This is an automatic speech recognition model fine-tuned on the Slovenian language dataset (MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL) based on the facebook/wav2vec2-xls-r-300m model.

Hindi speech recognition model fine-tuned from facebook/wav2vec2-xls-r-300m, trained on Openslr multilingual corpus and Common Voice 7.0 dataset

This is an automatic speech recognition model fine-tuned on the Central Kurdish dialect based on the facebook/wav2vec2-xls-r-300m model, trained on the Common Voice 8.0 dataset.

This is an automatic speech recognition (ASR) model fine-tuned on Kazakh (KK) speech datasets based on the facebook/wav2vec2-xls-r-300m model.