Automatic Speech Recognition
Transformers
PyTorch
JAX
Vietnamese
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use CuongLD/wav2vec2-large-xlsr-vietnamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CuongLD/wav2vec2-large-xlsr-vietnamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CuongLD/wav2vec2-large-xlsr-vietnamese")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese") model = AutoModelForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
modify readme
Browse files
README.md
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@@ -86,7 +86,7 @@ processor = Wav2Vec2Processor.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietn
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model = Wav2Vec2ForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese")
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model.to("cuda")
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chars_to_ignore_regex = '[\
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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model = Wav2Vec2ForCTC.from_pretrained("CuongLD/wav2vec2-large-xlsr-vietnamese")
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model.to("cuda")
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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