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--- |
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license: cc-by-4.0 |
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language: |
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- qu |
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metrics: |
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- cer |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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--- |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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import torch |
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import torchaudio |
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# load model and processor |
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processor = Wav2Vec2Processor.from_pretrained("ivangtorre/wav2vec2-xls-r-300m-quechua") |
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model = Wav2Vec2ForCTC.from_pretrained("ivangtorre/wav2vec2-xls-r-300m-quechua") |
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# load dummy dataset and read soundfiles |
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file = torchaudio.load("quechua000573.wav") |
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# retrieve logits |
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logits = model(file[0]).logits |
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# take argmax and decode |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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print("HF prediction: ", transcription) |
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``` |
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## Citation |
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```bibtex |
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@misc{grosman2021xlsr-1b-russian, |
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title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {R}ussian}, |
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author={Grosman, Jonatas}, |
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howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-russian}}, |
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year={2022} |
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} |
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``` |
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