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Ivydata/
wav2vec2-large-xlsr-53-japanese

Name: wav2vec2-large-xlsr-53-japanese
Rating: 2.5 (25 reviews)

transformers

pytorch

wav2vec2

automatic-speech-recognition

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Fine-tuned Japanese Wav2Vec2 model for speech recognition using XLSR-53 large

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using Common Voice, JVS and 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 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}
}