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wav2vec2-large-xlsr-rm-sursilv

Automatic Speech Recognition Transformers PyTorch JAX

wav2vec2 audio speech xlsr-fine-tuning-week Eval Results Inference Endpoints

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patrickvonplaten commited on Mar 29, 2021

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@@ -82,11 +82,13 @@ import re
 test_dataset = load_dataset("common_voice", "rm-sursilv", split="test")
 processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv")
 model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv")
 model.to("cuda")
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\…\«\»\–]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 # Preprocessing the datasets.

 test_dataset = load_dataset("common_voice", "rm-sursilv", split="test")
+wer = load_metric("wer")
 processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv")
 model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv")
 model.to("cuda")
+chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\…\\«\\»\\–]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 # Preprocessing the datasets.