README.md

---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- cer
model-index:
- name: hubert-large-japanese-asr
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Reazonspeech
      type: custom
      args: ja
    metrics:
    - name: Test WER
      type: wer
      value: 40.5197
    - name: Test CER
      type: cer
      value: 23.220979
  - 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: 22.705487
    - name: Test CER
      type: cer
      value: 9.39939
datasets:
- reazon-research/reazonspeech
- mozilla-foundation/common_voice_11_0
language:
- ja
---


<!-- 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. -->

# hubert-large-asr

This model is a fine-tuned version of [rinna/japanese-hubert-large](https://huggingface.co/rinna/japanese-hubert-large) ASR. Initially fine-tuned on the [reazonspeech(small) dataset](https://huggingface.co/datasets/reazon-research/reazonspeech), it was subsequently further fine-tuned on the [common_voice_11_0 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/ja) for ASR tasks.

This model can only predict Hiragana.

## 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

The model was fine-tuned in two main stages, first on the Reazonspeech dataset, followed by the common_voice_11_0 dataset. Details of the training steps and results are as follows:

### Training on Reazonspeech
The initial fine-tuning on the Reazonspeech(small) dataset was carried out with the following performance metrics:

| Step  | Training Loss | Validation Loss | WER    |
|-------|---------------|-----------------|--------|
| 1000  | 12.29880      | 3.610288        | 1.00000|
| 2000  | 3.601800      | 3.505306        | 1.00000|
| 3000  | 2.80300       | 1.948012        | 0.722361|
| 4000  | 1.961500      | 1.545842        | 0.558738|
| 5000  | 1.712000      | 1.420027        | 0.509049|
| 6000  | 1.565500      | 1.235171        | 0.466279|
| 7000  | 1.504900      | 1.160565        | 0.461829|
| 8000  | 1.409800      | 1.088012        | 0.427435|
| 9000  | 1.358800      | 1.097211        | 0.409861|
| 10000 | 1.318600      | 1.062294        | 0.403694|
| 11000 | 1.258500      | 1.026783        | 0.385464|
| 12000 | 1.245100      | 1.024860        | 0.379845|
| 13000 | 1.217700      | 0.985201        | 0.375634|
| 14000 | 1.187900      | 0.977686        | 0.367163|
| 15000 | 1.168100      | 0.978529        | 0.363656|
| 16000 | 1.135800      | 0.965668        | 0.363942|
| 17000 | 1.140600      | 0.953237        | 0.360912|


### Training on common_voice_11_0
After fine-tuning on Reazonspeech, further fine-tuning was performed on the common_voice_11_0 dataset, leading to the following results:

| Step | Training Loss | Validation Loss | WER    |
|------|---------------|-----------------|--------|
| 1000 | 1.08950       | 0.49275         | 0.302035|
| 2000 | 0.86100       | 0.45113         | 0.266950|
| 3000 | 0.76240       | 0.442281        | 0.244981|
| 4000 | 0.70170       | 0.411666        | 0.234287|
| 5000 | 0.66400       | 0.411769        | 0.227942|
| 6000 | 0.63810       | 0.413067        | 0.225690|

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-4
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- num_train_epochs: 10
- 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/hubert-large-japanese-asr')
processor = Wav2Vec2Processor.from_pretrained("TKU410410103/hubert-large-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 reazonspeech(tiny):
- WER: 40.519700%
- CER: 23.220979%

On common_voice_11_0:
- WER: 22.705487%
- CER: 9.399390%

### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu118
- Datasets 2.17.1