Update README.md

README.md

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 ---
 license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+- cer
+model-index:
+- name: wav2vec2-base-japanese-asr
+  results:
+  - task:
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_11_0
+      type: common_voice
+      args: ja
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 
+    - name: Test CER
+      type: cer
+      value: 
+datasets:
+- mozilla-foundation/common_voice_11_0
+language:
+- ja
 ---
+
+# wav2vec2-base-asr
+
+This model is a fine-tuned version of [rinna/japanese-wav2vec2-base](https://huggingface.co/rinna/japanese-wav2vec2-base) on the [common_voice_11_0 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/ja) for ASR tasks.
+
+## Acknowledgments
+
+This model's fine-tuning approach was inspired by and references the training methodology used in [vumichien/wav2vec2-large-xlsr-japanese-hiragana](https://huggingface.co/vumichien/wav2vec2-large-xlsr-japanese-hiragana).
+
+## Training Procedure
+
+Fine-tuning on the common_voice_11_0 dataset led to the following results:
+
+
+### Training hyperparameters
+
+The training hyperparameters remained consistent throughout the fine-tuning process:
+
+- learning_rate: 1e-4
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- num_train_epochs: 30
+- lr_scheduler_type: linear
+
+### How to evaluate the model
+
+```python
+from transformers import HubertForCTC, Wav2Vec2Processor
+from datasets import load_dataset
+import torchaudio
+import librosa
+import numpy as np
+import re
+import MeCab
+import pykakasi
+from evaluate import load
+
+model = Wav2vec2ForCTC.from_pretrained('TKU410410103/wav2vec2-base-japanese-asr')
+processor = Wav2Vec2Processor.from_pretrained("TKU410410103/wav2vec2-base-japanese-asr")
+
+# load dataset
+test_dataset = load_dataset('mozilla-foundation/common_voice_11_0', 'ja', split='test')
+remove_columns = [col for col in test_dataset.column_names if col not in ['audio', 'sentence']]
+test_dataset = test_dataset.remove_columns(remove_columns)
+
+# resample
+def process_waveforms(batch):
+    speech_arrays = []
+    sampling_rates = []
+
+    for audio_path in batch['audio']:
+        speech_array, _ = torchaudio.load(audio_path['path'])
+        speech_array_resampled = librosa.resample(np.asarray(speech_array[0].numpy()), orig_sr=48000, target_sr=16000)
+        speech_arrays.append(speech_array_resampled)
+        sampling_rates.append(16000)
+
+    batch["array"] = speech_arrays
+    batch["sampling_rate"] = sampling_rates
+
+    return batch
+
+# hiragana
+CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
+          "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
+          "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
+          "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
+          "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
+chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
+
+wakati = MeCab.Tagger("-Owakati")
+kakasi = pykakasi.kakasi()
+kakasi.setMode("J","H")
+kakasi.setMode("K","H")
+kakasi.setMode("r","Hepburn")
+conv = kakasi.getConverter()
+
+def prepare_char(batch):
+    batch["sentence"] = conv.do(wakati.parse(batch["sentence"]).strip())
+    batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
+    return batch
+
+
+resampled_eval_dataset = test_dataset.map(process_waveforms, batched=True, batch_size=50, num_proc=4)
+eval_dataset = resampled_eval_dataset.map(prepare_char, num_proc=4)
+
+# begin the evaluation process
+wer = load("wer")
+cer = load("cer")
+
+def evaluate(batch):
+    inputs = processor(batch["array"], sampling_rate=16_000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(inputs.input_values.to(device), attention_mask=inputs.attention_mask.to(device)).logits
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+    return batch
+
+columns_to_remove = [column for column in eval_dataset.column_names if column != "sentence"]
+batch_size = 16
+result = eval_dataset.map(evaluate, remove_columns=columns_to_remove, batched=True, batch_size=batch_size)
+
+wer_result = wer.compute(predictions=result["pred_strings"], references=result["sentence"])
+cer_result = cer.compute(predictions=result["pred_strings"], references=result["sentence"])
+
+print("WER: {:2f}%".format(100 * wer_result))
+print("CER: {:2f}%".format(100 * cer_result))
+```
+
+### Test results
+The final model was evaluated as follows:
+
+On common_voice_11_0:
+- WER: 
+- CER: 
+  
+### Framework versions
+
+- Transformers 4.39.1
+- Pytorch 2.2.1+cu118
+- Datasets 2.17.1