gchhablani
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wav2vec2-large-xlsr-hu

Automatic Speech Recognition

xlsr-fine-tuning-week

Inference Endpoints

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gchhablani commited on Mar 24, 2021

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8d3e9fb

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1 Parent(s): 16c5a93

Update README.md

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@@ -43,8 +43,8 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 test_dataset = load_dataset("common_voice", "hu", split="test[:2%]")
-processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
-model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
@@ -87,7 +87,7 @@ processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu
 model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu")
 model.to("cuda")
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 # Preprocessing the datasets.

 test_dataset = load_dataset("common_voice", "hu", split="test[:2%]")
+processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu")
+model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu")
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu")
 model.to("cuda")
+chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\–\\…]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 # Preprocessing the datasets.