132 lines
5.3 KiB
Markdown
132 lines
5.3 KiB
Markdown
---
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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 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: 30.84
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- name: Test CER
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type: cer
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value: 17.85
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widget:
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- example_title: Japanese speech corpus sample 1
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src: https://u.pcloud.link/publink/show?code=XZwhAlXZFOtXiqKHMzmYS9wXrCP8Yb7EtRd7
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- example_title: Japanese speech corpus sample 2
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src: https://u.pcloud.link/publink/show?code=XZ6hAlXZ5ccULt0YtrhJFl7LygKg0SJzKX0k
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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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!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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# 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")
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model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
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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"] = 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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!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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# 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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processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
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model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
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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"] = 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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```
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## Test Result
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**WER:** 30.84%,
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**CER:** 17.85%
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## Training
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The Common Voice `train`, `validation` datasets and Japanese speech corpus `basic5000` datasets were used for training.
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