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---
language:
- ja
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
- cer
model-index:
- name: uniTKU-hubert-japanese-asr
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: common_voice_11_0
type: common_voice
args: ja
metrics:
- type: wer
value: 27.511982
name: Test WER
- type: cer
value: 11.563649
name: Test CER
---
# uniTKU-hubert-japanese-asr
This model was fine-tuned on a dataset provided by uniTKU, and it has maintained the original performance metrics on the [common_voice_11_0 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/ja).
This model can only predict Hiragana.
## Training Procedure
Fine-tuning on the uniTKU dataset led to the following results:
| Step | Training Loss | Validation Loss | WER |
|-------|---------------|-----------------|--------|
| 100 | 1.127100 | 1.089644 | 0.668508|
| 200 | 0.873500 | 0.682353 | 0.508287|
| 300 | 0.786200 | 0.482965 | 0.397790|
| 400 | 0.670400 | 0.345377 | 0.381215|
| 500 | 0.719500 | 0.387554 | 0.337017|
| 600 | 0.707700 | 0.371083 | 0.292818|
| 700 | 0.658300 | 0.236447 | 0.243094|
| 800 | 0.611100 | 0.207679 | 0.193370|
### Training hyperparameters
The training hyperparameters remained consistent throughout the fine-tuning process:
- learning_rate: 1e-4
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 2
- max_steps: 800
- lr_scheduler_type: linear
### How to evaluate the model
```python
from transformers import HubertForCTC, Wav2Vec2Processor
from datasets import load_dataset
import torch
import torchaudio
import librosa
import numpy as np
import re
import MeCab
import pykakasi
from evaluate import load
model = HubertForCTC.from_pretrained('TKU410410103/uniTKU-hubert-japanese-asr')
processor = Wav2Vec2Processor.from_pretrained("TKU410410103/uniTKU-hubert-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 uniTKU Dataset:
- WER: 19.003370%
- CER: 11.027523%
On common_voice_11_0:
- WER: 27.511982%
- CER: 11.563649%
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu118
- Datasets 2.17.1 |