--- 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](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. ## 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 ### 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% ### How to use the model ```python from transformers import HubertForCTC, Wav2Vec2Processor from datasets import load_dataset 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)) ``` ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu118 - Datasets 2.17.1