README.md

---
license: apache-2.0
datasets:
- common_voice
language:
- ja
tags:
- audio
---

# Fine-tuned Japanese Wav2Vec2 model for speech recognition using XLSR-53 large
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using [Common Voice](https://commonvoice.mozilla.org/ja/datasets), [JVS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage
The model can be used directly (without a language model) as follows.

```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "ja"
MODEL_ID = "Ivydata/wav2vec2-large-xlsr-53-japanese"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference: ", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)
```

## Test Result

In the table below I report the Character Error Rate (CER) of the model tested on [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K) dataset.
| Model | CER |
| ------------- | ------------- |
| Ivydata/wav2vec2-large-xlsr-53-japanese | **27.87%** |
| jonatasgrosman/wav2vec2-large-xlsr-53-japanese | 34.18% |
| vumichien/wav2vec2-large-xlsr-japanese | 37.72% |

### Test Inference Examples
| Reference  | Prediction |
| ------------- | ------------- |
| ただ選択するのではなくどう考えて選択をするのか | ただ洗濯するのではなくどう考えて洗択をするのか |
| この巨大な構造物を宇宙に作ることができた人間 | この巨大な構造物を宇宙に作ることができた人間 |
| 何かしら嫌いになっていってしまったわけですよね | 何にかしら気段になっっていってしまったおけどすね |
| そんな僕だからこそ言えることは筋肉を変えれば自分が変わってくるし | んな僕らからこスえることは筋肉を変えれば自分が変わってくし |
| そうするとその言葉を使って未来のイメージを形作っていくことができると | そうするとその言葉を使って未来のイメーージを形作っていことができると |

## Citation
If you want to cite this model you can use this:

```bibtex
@misc{Ivydata2023-wav2vec2-xlsr53-large-japanese,
  title={Fine-tuned Japanese Wav2Vec2 model for speech recognition using XLSR-53 large},
  author={Kosuke Suzuki},
  howpublished={\url{https://huggingface.co/Ivydata/wav2vec2-large-xlsr-53-japanese/}},
  year={2023}
}
```