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.

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.

Test Inference Examples

Citation

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

@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}
}