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--- |
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language: ko |
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datasets: |
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- kresnik/zeroth_korean |
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tags: |
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- speech |
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- audio |
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- automatic-speech-recognition |
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license: apache-2.0 |
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--- |
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## Evaluation on Zeroth-Korean ASR corpus |
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[Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb) |
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``` |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from datasets import load_dataset |
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import soundfile as sf |
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import torch |
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from jiwer import wer |
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processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean") |
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model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda') |
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ds = load_dataset("kresnik/zeroth_korean", "clean") |
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test_ds = ds['test'] |
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def map_to_array(batch): |
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speech, _ = sf.read(batch["file"]) |
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batch["speech"] = speech |
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return batch |
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test_ds = test_ds.map(map_to_array) |
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def map_to_pred(batch): |
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inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") |
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input_values = inputs.input_values.to("cuda") |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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batch["transcription"] = transcription |
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return batch |
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result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"]) |
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print("WER:", wer(result["text"], result["transcription"])) |
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``` |
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### Expected WER: 7.43% |
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### Expected CER: 2.5% |
