added a model card

README.md

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+---
+language: ja
+datasets:
+- common_voice
+metrics:
+- wer
+- cer
+model-index:
+- name: wav2vec2-xls-r-300m finetuned on Japanese Hiragana with no word boundaries by Hyungshin Ryu of SLPlab
+  results:
+  - task:
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice Japanese
+      type: common_voice
+      args: ja
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 90.66
+       - name: Test CER
+         type: cer
+         value: 19.35
+---
+# Wav2Vec2-XLS-R-300M-Japanese-Hiragana
+Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Japanese Hiragana characters using the [Common Voice](https://huggingface.co/datasets/common_voice) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
+The sentence outputs do not contain word boundaries. Audio inputs should be sampled at 16kHz.
+## Usage
+The model can be used directly as follows:
+
+```python3
+!pip install mecab-python3
+!pip install unidic-lite
+!pip install pykakasi
+
+
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from datasets import load_dataset, load_metric
+import pykakasi
+import MeCab
+import re
+
+
+# load datasets, processor, and model
+test_dataset = load_dataset("common_voice", "ja", split="test")
+wer = load_metric("wer")
+cer = load_metric("cer")
+PTM = "slplab/wav2vec2-xls-r-300m-japanese-hiragana"
+print("PTM:", PTM)
+processor = Wav2Vec2Processor.from_pretrained(PTM)
+model = Wav2Vec2ForCTC.from_pretrained(PTM)
+device = "cuda"
+model.to(device)
+
+
+# preprocess datasets
+wakati = MeCab.Tagger("-Owakati")
+kakasi = pykakasi.kakasi()
+chars_to_ignore_regex = "[、,。]"
+
+def speech_file_to_array_fn_hiragana_nospace(batch):
+    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).strip()
+    batch["sentence"] = ''.join([d['hira'] for d in kakasi.convert(batch["sentence"])])
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
+    batch["speech"] = resampler(speech_array).squeeze()
+
+    return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn_hiragana_nospace)
+
+
+#evaluate
+def evaluate(batch):
+    inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(inputs.input_values.to(device)).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)
+for i in range(10):
+    print("="*20)
+    print("Prd:", result[i]["pred_strings"])
+    print("Ref:", result[i]["sentence"])
+    
+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"])))
+```
+| Original Text  | Prediction |
+| ------------- | ------------- |
+| この料理は家庭で作れます。 | このりょうりはかていでつくれます |
+| 日本人は、決して、ユーモアと無縁な人種ではなかった。 | にっぽんじんはけしてゆうもあどむえんなじんしゅではなかった |
+| 木村さんに電話を貸してもらいました。 | きむらさんにでんわおかしてもらいました |
+
+## Test Results
+**WER:** 90.66%,
+**CER:** 19.35%
+## Training
+Trained on JSUT and train+valid set of Common Voice Japanese. Tested on test set of Common Voice Japanese.