Update README.md

README.md

@@ -41,10 +41,111 @@ Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav
 
 ## Inference
 ```python
+
+#usage
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+
+model = Wav2Vec2ForCTC.from_pretrained("wav2vec2_large_xlsr_japanese_hiragana")
+processor = Wav2Vec2Processor.from_pretrained("wav2vec2_large_xlsr_japanese_hiragana")
+test_dataset = load_dataset("common_voice", "ja", split="test")
+
+
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()    
+    return batch
+
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset[:2]["sentence"])
 ```
 
 ## Evaluation
 ```python
+
+
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+import pykakasi 
+import MeCab
+
+
+wer = load_metric("wer")
+cer = load_metric("cer")
+
+model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese").to("cuda")
+processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
+test_dataset = load_dataset("common_voice", "ja", split="test")
+
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\�‘、。．！，・―─~｢｣『』\\\\※\[\]\{\}「」〇？…]'
+wakati = MeCab.Tagger("-Owakati")
+kakasi = pykakasi.kakasi()
+kakasi.setMode("J","H")      # kanji to hiragana
+kakasi.setMode("K","H")      # katakana to hiragana
+conv = kakasi.getConverter()
+
+
+FULLWIDTH_TO_HALFWIDTH = str.maketrans(
+    '　０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ！゛＃＄％＆（）＊＋、ー。／：；〈＝〉？＠［］＾＿‘｛｜｝～',
+    ' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&()*+,-./:;<=>?@[]^_`{|}~',
+)
+def fullwidth_to_halfwidth(s):
+    return s.translate(FULLWIDTH_TO_HALFWIDTH)
+
+
+def preprocessData(batch):
+    batch["sentence"] = fullwidth_to_halfwidth(batch["sentence"])
+    batch["sentence"] = re.sub(chars_to_ignore_regex,' ', batch["sentence"]).lower()  #remove special char
+    batch["sentence"] = wakati.parse(batch["sentence"])                              #add space
+    batch["sentence"] = conv.do(batch["sentence"])                                   #covert to hiragana
+    batch["sentence"] = " ".join(batch["sentence"].split())+" "                         #remove multiple space 
+    
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()    
+    return batch
+
+
+test_dataset = test_dataset.map(preprocessData)
+
+
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def evaluate(batch):
+	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+	with torch.no_grad():
+		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+
+	pred_ids = torch.argmax(logits, dim=-1)
+	batch["pred_strings"] = processor.batch_decode(pred_ids)
+	return batch
+
+result = test_dataset.map(evaluate, batched=True, batch_size=8)
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+
+
 ```