# No language model

This is an end-to-end automatic speech recognition system trained on the CommonVoice Chinese dataset, using wav2vec2.0 and CTC architecture, supporting Chinese speech recognition.

This is an end-to-end automatic speech recognition system trained on the CommonVoice Spanish dataset, using the wav2vec 2.0 pre-trained model combined with a CTC decoder.

This is an automatic speech recognition model for Amharic, trained using wav2vec 2.0 architecture with CTC/Attention mechanism

German ASR model trained on Mozilla Common Voice 6.1, German Wikipedia speech corpus, and m-ailabs corpus, achieving a word error rate of 7.24%

This is an end-to-end automatic speech recognition system trained on the CommonVoice French dataset, utilizing a CRDNN architecture combined with CTC and attention mechanisms.

wav2vec 2.0 speech recognition model trained on the CommonVoice French dataset, using CTC/Attention architecture without requiring a language model

An Estonian automatic speech recognition model fine-tuned based on facebook/wav2vec2-xls-r-300m, trained with approximately 800 hours of diverse data

Turkish speech recognition model based on Wav2Vec2 architecture, fine-tuned on the Common Voice Turkish dataset

This is an end-to-end model for automatic speech recognition in Rwandan, based on the wav2vec 2.0 pre-trained model combined with CTC and attention mechanisms, fine-tuned on the CommonVoice dataset.