Update README.md

README.md

@@ -17,4 +17,39 @@ Futher fine-tuned [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek
 
 When using this model, make sure that your speech input is sampled at 16kHz.
 
-The script used for training can be found here: https://github.com/hyyoka/wav2vec2-korean-senior
+The script used for training can be found here: https://github.com/hyyoka/wav2vec2-korean-senior
+
+
+### Inference
+
+``` py
+import torchaudio
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import re
+
+def clean_up(transcription):
+    hangul = re.compile('[^ ㄱ-ㅣ가-힣]+')
+    result = hangul.sub('', transcription)
+    return result
+
+model_name "hyyoka/wav2vec2-xlsr-korean-senior"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+speech_array, sampling_rate = torchaudio.load(wav_file)
+feat = processor(speech_array[0], 
+                            sampling_rate=16000, 
+                            padding=True,
+                            max_length=800000, 
+                            truncation=True,
+                            return_attention_mask=True,
+                            return_tensors="pt",
+                            pad_token_id=49
+                            )
+input = {'input_values': feat['input_values'],'attention_mask':feat['attention_mask']}
+
+outputs = model(**input, output_attentions=True)
+logits = outputs.logits
+predicted_ids = logits.argmax(axis=-1)
+transcription = processor.decode(predicted_ids[0])
+stt_result = clean_up(transcription)
+```