Fix character whitelist
Browse files- README.md +3 -3
- tokenizer_config.json +8 -1
README.md
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@@ -23,7 +23,7 @@ model-index:
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metrics:
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- name: Test WER
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type: wer
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value:
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---
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# Wav2Vec2-Large-XLSR-53-Dutch
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@@ -87,7 +87,7 @@ processor = Wav2Vec2Processor.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-f
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model = Wav2Vec2ForCTC.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-frisian")
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model.to("cuda")
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\β\%\β\β]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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@@ -117,7 +117,7 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
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print("WER: {:.2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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```
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**Test Result**:
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## Training
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metrics:
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- name: Test WER
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type: wer
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value: 16.25
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---
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# Wav2Vec2-Large-XLSR-53-Dutch
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model = Wav2Vec2ForCTC.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-frisian")
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model.to("cuda")
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\'\β\%\β\β]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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print("WER: {:.2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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```
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**Test Result**: 16.25 %
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## Training
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tokenizer_config.json
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@@ -1 +1,8 @@
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{
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{
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "<pad>",
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"do_lower_case": true,
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"word_delimiter_token": "|"
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}
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