metadata
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
hubert-large-asr
This model is a fine-tuned version of rinna/japanese-hubert-large ASR. Initially fine-tuned on the reazonspeech(small) dataset, it was subsequently further fine-tuned on the common_voice_11_0 dataset 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.
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
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