update model card README.md

README.md

@@ -1,152 +1,50 @@
 ---
-language: ja
-datasets:
-- common_voice
-metrics:
-- wer
 tags:
-- audio
-- automatic-speech-recognition
-- speech
-- xlsr-fine-tuning-week
-license: apache-2.0
+- generated_from_trainer
 model-index:
 - name: wav2vec2-live-japanese
-  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%
+  results: []
 ---
 
-# wav2vec2-live-japanese
-
-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"])
-```
+<!-- 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. -->
 
-## 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)
+# wav2vec2-live-japanese
 
+This model was trained from scratch on the None dataset.
 
+## Model description
 
-# 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)
+More information needed
 
-	with torch.no_grad():
-		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+## Intended uses & limitations
 
-	pred_ids = torch.argmax(logits, dim=-1)
-	batch["pred_strings"] = processor.batch_decode(pred_ids)
-	return batch
+More information needed
 
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
+## Training and evaluation data
 
-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"])))
+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