初始化项目，由ModelHub XC社区提供模型

Model: kresnik/wav2vec2-large-xlsr-korean
Source: Original Platform

README.md

@@ -0,0 +1,77 @@
+---
+language: ko
+datasets:
+- kresnik/zeroth_korean
+tags:
+- speech
+- audio
+- automatic-speech-recognition
+license: apache-2.0
+
+model-index:
+- name: 'Wav2Vec2 XLSR Korean'
+  results: 
+    - task:
+        name: Automatic Speech Recognition
+        type: automatic-speech-recognition
+      dataset:
+        name: Zeroth Korean
+        type: kresnik/zeroth_korean
+        args: clean
+      metrics:
+        - name: Test WER
+          type: wer
+          value: 4.74
+        - name: Test CER
+          type: cer
+          value: 1.78
+
+---
+
+
+## Evaluation on Zeroth-Korean ASR corpus
+
+[Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb)
+
+```
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from datasets import load_dataset
+import soundfile as sf
+import torch
+from jiwer import wer
+
+processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean")
+
+model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda')
+
+ds = load_dataset("kresnik/zeroth_korean", "clean")
+
+test_ds = ds['test']
+
+def map_to_array(batch):
+    speech, _ = sf.read(batch["file"])
+    batch["speech"] = speech
+    return batch
+
+test_ds = test_ds.map(map_to_array)
+
+def map_to_pred(batch):
+    inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
+    input_values = inputs.input_values.to("cuda")
+    
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+    batch["transcription"] = transcription
+    return batch
+
+result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"])
+
+print("WER:", wer(result["text"], result["transcription"]))
+
+```
+
+### Expected WER: 4.74%
+### Expected CER: 1.78%