Jonatasgrosman

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Average Model Cost: $0.0000

Number of Runs: 69,234,307

Models by this creator

wav2vec2-large-xlsr-53-english

wav2vec2-large-xlsr-53-english

jonatasgrosman

The model wav2vec2-large-xlsr-53-english is an automatic speech recognition (ASR) model designed to convert spoken language into written text. It is trained using the wav2vec2 architecture and the Cross-lingual Speaker Representations (XLSR) method. The model is specifically trained for the English language and is capable of accurately transcribing speech for various applications such as transcription services, voice assistants, and voice command recognition systems.

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$-/run

68.5M

Huggingface

wav2vec2-large-xlsr-53-portuguese

wav2vec2-large-xlsr-53-portuguese

The wav2vec2-large-xlsr-53-portuguese model is an automatic speech recognition (ASR) model that is trained to convert spoken Portuguese language into written text. It is based on the wav2vec2.0 framework and has been fine-tuned specifically for Portuguese. The model achieves state-of-the-art results on the Common Voice dataset for Portuguese. It can be used for various applications requiring speech-to-text conversion in Portuguese.

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$-/run

78.0K

Huggingface

wav2vec2-large-xlsr-53-chinese-zh-cn

wav2vec2-large-xlsr-53-chinese-zh-cn

The wav2vec2-large-xlsr-53-chinese-zh-cn model is a fine-tuned automatic speech recognition (ASR) model specifically trained for Chinese speech recognition tasks. It is based on the XLSR-53 large model architecture and has been fine-tuned using the training and validation data from Common Voice 6.1, CSS10, and ST-CMDS datasets. The model is designed to handle speech inputs sampled at 16kHz. It can be used for ASR tasks in Chinese without the need for a language model. The model has been evaluated using the Chinese test data from Common Voice and provides Word Error Rate (WER) and Character Error Rate (CER) metrics for evaluation purposes. The model can be cited using the provided citation information.

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$-/run

56.9K

Huggingface

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