metadata
language:
- ja
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-base-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: 14.177284
name: Test WER
- type: cer
value: 6.462501
name: Test CER
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Reazonspeech
type: custom
args: ja
metrics:
- name: Test WER
type: wer
value: 40.864413
- name: Test CER
type: cer
value: 29.367348
wav2vec2-base-asr
This model is a fine-tuned version of rinna/japanese-wav2vec2-base 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
Fine-tuning on the common_voice_11_0 dataset led to the following results:
| Step | Training Loss | Validation Loss | WER |
|---|---|---|---|
| 1000 | 6.088100 | 3.452597 | 1.000000 |
| 2000 | 2.816600 | 0.756278 | 0.263624 |
| 3000 | 0.837600 | 0.471486 | 0.185915 |
| 4000 | 0.624900 | 0.420854 | 0.159801 |
| 5000 | 0.533300 | 0.392494 | 0.149141 |
| 6000 | 0.490000 | 0.394669 | 0.144826 |
| 7000 | 0.441600 | 0.379999 | 0.141807 |
Training hyperparameters
The training hyperparameters remained consistent throughout the fine-tuning process:
- learning_rate: 1e-4
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- num_train_epochs: 20
- warmup_steps: 2000
- lr_scheduler_type: linear
How to evaluate the model
from transformers import Wav2Vec2ForCTC, 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 = Wav2Vec2ForCTC.from_pretrained('TKU410410103/wav2vec2-base-japanese-asr')
processor = Wav2Vec2Processor.from_pretrained("TKU410410103/wav2vec2-base-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 common_voice_11_0:
- WER: 14.177284%
- CER: 6.462501%
On reazonspeech(tiny):
- WER: 40.864413%
- CER: 29.367348%
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu118
- Datasets 2.17.1