# Common Voice dataset

This model is a speech recognition model fine-tuned on the Tamil Common Voice 17.0 dataset based on OpenAI's Whisper Small, with a Word Error Rate (WER) of 43.23%.

This is a Whisper-small speech recognition model fine-tuned on French datasets, reducing the word error rate by 6.793 percentage points compared to the baseline model.

A speech recognition model fine-tuned on the Polish Common Voice 17.0 dataset based on OpenAI Whisper-base

A Cantonese automatic speech recognition model fine-tuned on Whisper v3, trained on the Common Voice 17 dataset

A Whisper small speech recognition model fine-tuned on the Common Voice 17.0 Marathi dataset, based on simran14/mr-model-h

Amharic speech recognition model fine-tuned on the common_voice_16_1 dataset based on facebook/wav2vec2-xls-r-300m

This model is a speech processing model fine - tuned on the common_voice_albanian dataset based on facebook/wav2vec2-xls-r-300m, suitable for Albanian - related tasks.

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53, specifically designed for automatic speech recognition tasks in Swahili.

A Dhivehi (official language of Maldives) automatic speech recognition model fine-tuned based on OpenAI Whisper-small, trained on Common Voice 13 dataset

The Whisper (small) model fine-tuned by the Hezar team based on the Persian part of the Common Voice dataset, which can be used for automatic speech recognition tasks.

This is a Bengali automatic speech recognition model based on the Whisper small architecture, fine-tuned on approximately 400 hours of Mozilla Common Voice dataset with a word error rate of 4.58%

Persian automatic speech recognition model based on Whisper architecture, fine-tuned on Common Voice 11.0 Persian dataset

This model is a fine-tuned speech recognition model based on OpenAI's Whisper Large V2 on the Kazakh Common Voice 11.0 dataset

A speech recognition model fine-tuned on Spanish dataset based on OpenAI Whisper-tiny

A Persian automatic speech recognition model fine-tuned from facebook/hubert-large-ll60k, trained using the Common Voice 7.0 Persian dataset.

A Swedish automatic speech recognition model fine-tuned from microsoft/wavlm-large, suitable for 16kHz sampled audio input.

An Italian automatic speech recognition model fine-tuned based on microsoft/wavlm-large, trained using the Common Voice 7.0 Italian dataset.

Fine-tuned from a randomly initialized wav2vec2 model for Italian speech recognition tasks, trained on the training split of Common Voice 7.0 (Italian).

An Italian automatic speech recognition model fine-tuned based on the facebook/wav2vec2-large-xlsr-53 model, trained using the Common Voice 7.0 Italian dataset.

An Italian automatic speech recognition model fine-tuned based on facebook/wav2vec2-large-lv60, trained using the Common Voice 7.0 Italian dataset.

A Thai speech recognition model fine-tuned from facebook/hubert-large-ll60k, trained on data from Common Voice 7.0

An English speech recognition model fine-tuned based on facebook/wav2vec2-large-nl-voxpopuli, trained using the Common Voice 7.0 training set.

This model is a Tamil speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-xls-r-300m

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

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base for Persian language, supporting Persian speech-to-text tasks.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base for Persian language datasets, primarily used for Persian speech-to-text tasks.

This is a Turkish speech recognition model based on the wav2vec2 architecture, fine-tuned on the Common Voice dataset with a word error rate (WER) of 0.3634.

An automatic speech recognition model fine-tuned on Marathi speech datasets based on Facebook's XLS-R-300M model

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

A Lithuanian automatic speech recognition model trained on the Common Voice dataset, implemented using the wav2vec2 architecture and fairseq framework.

This model is a Persian speech recognition model fine-tuned based on facebook/wav2vec2-base, suitable for Persian speech-to-text tasks.

An automatic speech recognition system fine-tuned on the Common Voice 8 Belarusian dataset based on facebook/wav2vec2-base model

This model is an automatic speech recognition (ASR) model fine-tuned on the Turkish COMMON_VOICE dataset based on facebook/wav2vec2-large-xlsr-53, achieving a word error rate (WER) of 33.05% on the evaluation set.

Automatic speech recognition model fine-tuned on Mozilla Common Voice Portuguese dataset based on facebook/wav2vec2-xls-r-300m

An Arabic speech recognition model fine-tuned from facebook/wav2vec2-xls-r-300m on the Common Voice Arabic dataset

This is an automatic speech recognition model fine-tuned on the Greek Common Voice 8 dataset, based on the facebook/wav2vec2-xls-r-300m model.

A speech recognition model fine-tuned on the COMMON_VOICE - AB dataset based on facebook/wav2vec2-large-xlsr-53

This model is a fine-tuned automatic speech recognition model based on facebook/wav2vec2-xls-r-1b on Lithuanian dataset

This model is an automatic speech recognition (ASR) model fine-tuned on the COMMON_VOICE SV-SE dataset based on facebook/wav2vec2-large-xlsr-53, supporting Swedish speech recognition.

A fine-tuned model based on facebook/wav2vec2-large-xlsr-53 for Kinyarwanda, capable of predicting apostrophes in marked pronouns and vowel-initial word contractions