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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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- cer |
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model-index: |
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- name: hubert-large-japanese-asr |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Reazonspeech |
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type: custom |
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args: ja |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 40.5197 |
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- name: Test CER |
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type: cer |
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value: 23.220979 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_11_0 |
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type: common_voice |
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args: ja |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 22.705487 |
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- name: Test CER |
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type: cer |
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value: 9.39939 |
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datasets: |
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- reazon-research/reazonspeech |
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- mozilla-foundation/common_voice_11_0 |
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language: |
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- ja |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hubert-large-asr |
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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. |
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This model can only predict Hiragana. |
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## Acknowledgments |
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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). |
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## Training procedure |
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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: |
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### Training on Reazonspeech |
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The initial fine-tuning on the Reazonspeech(small) dataset was carried out with the following performance metrics: |
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| Step | Training Loss | Validation Loss | WER | |
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|-------|---------------|-----------------|--------| |
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| 1000 | 12.29880 | 3.610288 | 1.00000| |
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| 2000 | 3.601800 | 3.505306 | 1.00000| |
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| 3000 | 2.80300 | 1.948012 | 0.722361| |
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| 4000 | 1.961500 | 1.545842 | 0.558738| |
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| 5000 | 1.712000 | 1.420027 | 0.509049| |
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| 6000 | 1.565500 | 1.235171 | 0.466279| |
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| 7000 | 1.504900 | 1.160565 | 0.461829| |
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| 8000 | 1.409800 | 1.088012 | 0.427435| |
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| 9000 | 1.358800 | 1.097211 | 0.409861| |
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| 10000 | 1.318600 | 1.062294 | 0.403694| |
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| 11000 | 1.258500 | 1.026783 | 0.385464| |
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| 12000 | 1.245100 | 1.024860 | 0.379845| |
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| 13000 | 1.217700 | 0.985201 | 0.375634| |
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| 14000 | 1.187900 | 0.977686 | 0.367163| |
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| 15000 | 1.168100 | 0.978529 | 0.363656| |
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| 16000 | 1.135800 | 0.965668 | 0.363942| |
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| 17000 | 1.140600 | 0.953237 | 0.360912| |
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### Training on common_voice_11_0 |
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After fine-tuning on Reazonspeech, further fine-tuning was performed on the common_voice_11_0 dataset, leading to the following results: |
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| Step | Training Loss | Validation Loss | WER | |
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|------|---------------|-----------------|--------| |
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| 1000 | 1.08950 | 0.49275 | 0.302035| |
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| 2000 | 0.86100 | 0.45113 | 0.266950| |
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| 3000 | 0.76240 | 0.442281 | 0.244981| |
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| 4000 | 0.70170 | 0.411666 | 0.234287| |
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| 5000 | 0.66400 | 0.411769 | 0.227942| |
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| 6000 | 0.63810 | 0.413067 | 0.225690| |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-4 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- num_train_epochs: 10 |
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- lr_scheduler_type: linear |
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### How to evaluate the model |
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```python |
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from transformers import HubertForCTC, Wav2Vec2Processor |
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from datasets import load_dataset |
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import torch |
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import torchaudio |
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import librosa |
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import numpy as np |
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import re |
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import MeCab |
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import pykakasi |
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from evaluate import load |
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model = HubertForCTC.from_pretrained('TKU410410103/hubert-large-japanese-asr') |
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processor = Wav2Vec2Processor.from_pretrained("TKU410410103/hubert-large-japanese-asr") |
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# load dataset |
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test_dataset = load_dataset('mozilla-foundation/common_voice_11_0', 'ja', split='test') |
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remove_columns = [col for col in test_dataset.column_names if col not in ['audio', 'sentence']] |
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test_dataset = test_dataset.remove_columns(remove_columns) |
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# resample |
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def process_waveforms(batch): |
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speech_arrays = [] |
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sampling_rates = [] |
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for audio_path in batch['audio']: |
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speech_array, _ = torchaudio.load(audio_path['path']) |
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speech_array_resampled = librosa.resample(np.asarray(speech_array[0].numpy()), orig_sr=48000, target_sr=16000) |
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speech_arrays.append(speech_array_resampled) |
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sampling_rates.append(16000) |
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batch["array"] = speech_arrays |
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batch["sampling_rate"] = sampling_rates |
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return batch |
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# hiragana |
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CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", |
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"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", |
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"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", |
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"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", |
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"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"] |
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chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" |
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wakati = MeCab.Tagger("-Owakati") |
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kakasi = pykakasi.kakasi() |
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kakasi.setMode("J","H") |
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kakasi.setMode("K","H") |
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kakasi.setMode("r","Hepburn") |
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conv = kakasi.getConverter() |
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def prepare_char(batch): |
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batch["sentence"] = conv.do(wakati.parse(batch["sentence"]).strip()) |
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batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip() |
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return batch |
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resampled_eval_dataset = test_dataset.map(process_waveforms, batched=True, batch_size=50, num_proc=4) |
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eval_dataset = resampled_eval_dataset.map(prepare_char, num_proc=4) |
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# begin the evaluation process |
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wer = load("wer") |
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cer = load("cer") |
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def evaluate(batch): |
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inputs = processor(batch["array"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to(device), attention_mask=inputs.attention_mask.to(device)).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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columns_to_remove = [column for column in eval_dataset.column_names if column != "sentence"] |
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batch_size = 16 |
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result = eval_dataset.map(evaluate, remove_columns=columns_to_remove, batched=True, batch_size=batch_size) |
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wer_result = wer.compute(predictions=result["pred_strings"], references=result["sentence"]) |
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cer_result = cer.compute(predictions=result["pred_strings"], references=result["sentence"]) |
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print("WER: {:2f}%".format(100 * wer_result)) |
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print("CER: {:2f}%".format(100 * cer_result)) |
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``` |
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### Test results |
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The final model was evaluated as follows: |
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On reazonspeech(tiny): |
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- WER: 40.519700% |
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- CER: 23.220979% |
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On common_voice_11_0: |
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- WER: 22.705487% |
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- CER: 9.399390% |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.1 |
