Update README.md

README.md

@@ -1,50 +1,112 @@
 ---
+language: ja
+datasets:
+- common_voice
+metrics:
+- wer
 tags:
-- generated_from_trainer
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
 model-index:
 - name: wav2vec2-live-japanese
-  results: []
+  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: 22.08%
+       - name: Test CER
+         type: cer
+         value: 10.08%
 ---
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # wav2vec2-live-japanese
-
-This model was trained from scratch on the None dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0003
-- train_batch_size: 3
-- eval_batch_size: 2
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 6
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- num_epochs: 50
-- mixed_precision_training: Native AMP
-
-### Framework versions
-
-- Transformers 4.11.2
-- Pytorch 1.9.1
-- Datasets 1.11.0
-- Tokenizers 0.10.3
+https://github.com/ttop32/wav2vec2-live-japanese-translator    
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese hiragana using the 
+- [common_voice](https://huggingface.co/datasets/common_voice)     
+- [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut)     
+- [CSS10](https://github.com/Kyubyong/css10)     
+- [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K)     
+- [JVS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus)
+## Inference
+```python
+#usage
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese")
+processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
+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"])))
+```