# Low Word Error Rate (WER)

Ukrainian speech recognition model fine-tuned on Yehor/openstt-uk dataset, based on facebook/w2v-bert-2.0

This is a French automatic speech recognition (ASR) model fine-tuned on the wav2vec2-FR-7K-large model, trained on over 2,200 hours of French speech data.

A Luxembourgish speech recognition model fine-tuned from the wav2vec 2.0 large XLSR-53 checkpoint, trained with 842 hours of unlabeled and 14 hours of labeled data, integrated with a 5-gram language model

This is a Dutch automatic speech recognition (ASR) model fine-tuned based on the XLS-R 1 billion parameter model, trained on multiple datasets including Common Voice 8.0, supporting 16kHz sampling rate audio input.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-large-960h, specifically optimized for cryptocurrency-related podcast content.

Finnish automatic speech recognition (ASR) model fine-tuned based on Facebook's wav2vec2-xls-r-300m, trained with 275.6 hours of Finnish data, supports decoding with KenLM language model

Catalan speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, trained on Common Voice and parliamentary speech datasets

A Dutch speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, trained on the Common Voice and CSS10 datasets, supporting 16kHz audio input.

A Swedish automatic speech recognition model fine-tuned based on the VoxRex large model, supporting 16kHz sampling rate audio input

This is an automatic speech recognition (ASR) model for Hindi, which performs excellently on datasets such as Common Voice.

This model is an automatic speech recognition model fine-tuned on the Romansh-Sursilvan dialect dataset based on facebook/wav2vec2-xls-r-300m, achieving a 24.09% Word Error Rate (WER) on the Common Voice 8 test set.

German speech recognition model fine-tuned on the wav2vec-large-xlsr-53 framework, achieving 11.26% word error rate on the CommonVoice German test set

A Spanish automatic speech recognition (ASR) model fine-tuned from the facebook/wav2vec2-large-xlsr-53 model, trained on the Common Voice Spanish dataset, supporting speech input at 16kHz sampling rate.