Jonatasgrosman
Rank: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
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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68.5M
Huggingface
wav2vec2-large-xlsr-53-russian
wav2vec2-large-xlsr-53-russian
The wav2vec2-large-xlsr-53-russian model is an automatic speech recognition (ASR) model trained on large amounts of Russian speech data. It is based on the wav2vec2 architecture and has been fine-tuned specifically for Russian ASR tasks. This model can accurately transcribe spoken Russian into written text, making it useful for various applications such as voice assistants, transcription services, and more.
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376.0K
Huggingface
wav2vec2-large-xlsr-53-polish
wav2vec2-large-xlsr-53-polish
The wav2vec2-large-xlsr-53-polish model is a pre-trained automatic speech recognition (ASR) model specifically designed for the Polish language. Using the Transformer architecture, this model can transcribe spoken Polish audio into written text. It can be fine-tuned on specific ASR tasks or used for general speech-to-text applications in the Polish language.
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178.3K
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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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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56.9K
Huggingface
wav2vec2-large-xlsr-53-arabic
wav2vec2-large-xlsr-53-arabic
The wav2vec2-large-xlsr-53-arabic model is a large-scale automatic speech recognition (ASR) model trained on Arabic speech data. It is based on the wav2vec2 framework and includes 53 different languages and dialects of Arabic. The model is designed to transcribe audio data into text, making it useful for various applications such as transcription services, voice assistants, and more.
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32.0K
Huggingface
wav2vec2-large-xlsr-53-japanese
wav2vec2-large-xlsr-53-japanese
The wav2vec2-large-xlsr-53-japanese model is an Automatic Speech Recognition (ASR) model that has been trained on a large amount of Japanese speech data. ASR is the technology that converts spoken language into written text. This model can be used to transcribe Japanese audio recordings accurately.
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21.1K
Huggingface
wav2vec2-large-xlsr-53-dutch
wav2vec2-large-xlsr-53-dutch
The "wav2vec2-large-xlsr-53-dutch" model is an automatic speech recognition (ASR) model trained on Dutch language data. It is based on the wav2vec2 architecture and uses transformer-based models for speech recognition. ASR models like this one can take audio recordings of spoken language as input and convert them into written transcriptions.
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11.5K
Huggingface
wav2vec2-large-xlsr-53-german
wav2vec2-large-xlsr-53-german
Platform did not provide a description for this model.
$-/run
4.3K
Huggingface
wav2vec2-large-xlsr-53-spanish
wav2vec2-large-xlsr-53-spanish
Platform did not provide a description for this model.
$-/run
2.5K
Huggingface