mistral-pl-owca
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wav2vec2-large-xlsr-53-arabic
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the train and validation splits of Common Voice 6.1 and Arabic Speech Corpus. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-chinese-zh-cn
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Chinese using the train and validation splits of Common Voice 6.1, CSS10 and ST-CMDS. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-dutch
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-english
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on English using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-finnish
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-french
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on French using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-german
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on German using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-greek
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-hungarian
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-italian
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Italian using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-japanese
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the train and validation splits of Common Voice 6.1, CSS10 and JSUT. When using this model, make sure that your speech input is sampled at 16kHz. In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-10). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
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View details ↗wav2vec2-large-xlsr-53-persian
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Persian using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-polish
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-large-xlsr-53-portuguese
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz.
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View details ↗wav2vec2-large-xlsr-53-russian
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz.
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View details ↗wav2vec2-large-xlsr-53-spanish
jonatasgrosmanFine-tuned facebook/wav2vec2-large-xlsr-53 on Spanish using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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View details ↗wav2vec2-xls-r-1b-english
jonatasgrosman基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wav2vec2-xls-r-1b-french
jonatasgrosman基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wav2vec2-xls-r-1b-german
jonatasgrosman基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wav2vec2-xls-r-1b-portuguese
jonatasgrosmanFine-tuned facebook/wav2vec2-xls-r-1b on Portuguese using the train and validation splits of Common Voice 8.0, CORAA, Multilingual TEDx, and Multilingual LibriSpeech. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :) Using the HuggingSound library:
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View details ↗wav2vec2-xls-r-1b-russian
jonatasgrosman基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wav2vec2-xls-r-1b-spanish
jonatasgrosman基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗swift15-gsq-rco-iq3s-mtp
jonathan-golden基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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