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README.md
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metrics:
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- name: Test WER
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type: wer
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value: 41.
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---
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# Wav2Vec2-Large-XLSR-53-Mongolian
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice)
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model_name = "sammy786/wav2vec2-large-xlsr-mongolian"
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device = "cuda"
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chars_to_ignore_regex = '[
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
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processor = Wav2Vec2Processor.from_pretrained(model_name)
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batch["target"] = batch["sentence"]
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return batch
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result = ds.map(map_to_pred, batched=True, batch_size=
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wer = load_metric("wer")
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print(wer.compute(predictions=result["predicted"], references=result["target"]))
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```
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**Test Result**: 41.
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metrics:
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- name: Test WER
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type: wer
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value: 41.92
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---
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# Wav2Vec2-Large-XLSR-53-Mongolian
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice)
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model_name = "sammy786/wav2vec2-large-xlsr-mongolian"
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device = "cuda"
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chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�]'
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model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
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processor = Wav2Vec2Processor.from_pretrained(model_name)
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batch["target"] = batch["sentence"]
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return batch
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result = ds.map(map_to_pred, batched=True, batch_size=8, remove_columns=list(ds.features.keys()))
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wer = load_metric("wer")
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print(wer.compute(predictions=result["predicted"], references=result["target"]))
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```
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**Test Result**: 41.92 %
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