Instructions to use alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech") model = AutoModelForAudioClassification.from_pretrained("alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# weights
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on
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It achieves the following results on the evaluation set:
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- Loss: 0.0061
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- F1: 0.9993
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# weights
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Librispeech-clean-100 for gender recognition.
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It achieves the following results on the evaluation set:
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- Loss: 0.0061
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- F1: 0.9993
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