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--- |
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language: ja |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: XLSR Wav2Vec2 Japanese Hiragana by Chien Vu |
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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: Common Voice Japanese |
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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: 24.74 |
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- name: Test CER |
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type: cer |
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value: 10.99 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Japanese |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice) and Japanese speech corpus of Saruwatari-lab, University of Tokyo [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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!pip install mecab-python3 |
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!pip install unidic-lite |
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!pip install pykakasi |
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!python -m unidic download |
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import torch |
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import torchaudio |
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import librosa |
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from datasets import load_dataset |
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import MeCab |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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# config |
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wakati = MeCab.Tagger("-Owakati") |
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chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]' |
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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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# load data, processor and model |
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test_dataset = load_dataset("common_voice", "ja", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana") |
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model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana") |
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resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) |
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# Preprocessing the datasets. |
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def speech_file_to_array_fn(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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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(sampling_rate, speech_array).squeeze() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"][:2], 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, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset["sentence"][:2]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Japanese test data of Common Voice. |
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```python |
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!pip install mecab-python3 |
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!pip install unidic-lite |
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!pip install pykakasi |
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!python -m unidic download |
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import torch |
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import librosa |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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import MeCab |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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#config |
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wakati = MeCab.Tagger("-Owakati") |
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chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]' |
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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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# load data, processor and model |
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test_dataset = load_dataset("common_voice", "ja", split="test") |
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wer = load_metric("wer") |
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cer = load_metric("cer") |
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processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana") |
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model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese-hỉragana") |
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model.to("cuda") |
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resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) |
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# Preprocessing the datasets. |
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def speech_file_to_array_fn(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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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(sampling_rate, speech_array).squeeze() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# evaluate function |
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def evaluate(batch): |
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inputs = processor(batch["speech"], 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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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## Test Result |
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**WER:** 24.74%, |
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**CER:** 10.99% |
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## Training |
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The Common Voice `train`, `validation` datasets and Japanese speech corpus datasets were used for training. |
