🚀 wav2vec2-live-japanese
This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 on Japanese hiragana, aiming to provide high - quality automatic speech recognition for Japanese.
🚀 Quick Start
This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 on Japanese hiragana using multiple datasets:
You can access the project on GitHub: https://github.com/ttop32/wav2vec2-live-japanese-translator
💻 Usage Examples
Basic Usage
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese")
processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
test_dataset = load_dataset("common_voice", "ja", split="test")
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["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)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
Advanced Usage
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import pykakasi
import MeCab
wer = load_metric("wer")
cer = load_metric("cer")
model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
test_dataset = load_dataset("common_voice", "ja", split="test")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\�‘、。.!,・―─~「」『』\\\\※\[\]\{\}「」〇?…]'
wakati = MeCab.Tagger("-Owakati")
kakasi = pykakasi.kakasi()
kakasi.setMode("J","H")
kakasi.setMode("K","H")
conv = kakasi.getConverter()
FULLWIDTH_TO_HALFWIDTH = str.maketrans(
' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!゛#$%&()*+、ー。/:;〈=〉?@[]^_‘{|}~',
' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&()*+,-./:;<=>?@[]^_`{|}~',
)
def fullwidth_to_halfwidth(s):
return s.translate(FULLWIDTH_TO_HALFWIDTH)
def preprocessData(batch):
batch["sentence"] = fullwidth_to_halfwidth(batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex,' ', batch["sentence"]).lower()
batch["sentence"] = wakati.parse(batch["sentence"])
batch["sentence"] = conv.do(batch["sentence"])
batch["sentence"] = " ".join(batch["sentence"].split())+" "
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()
return batch
test_dataset = test_dataset.map(preprocessData)
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
📄 License
This project is licensed under the Apache 2.0 license.
📚 Documentation
Property |
Details |
Model Type |
wav2vec2-live-japanese |
Training Data |
common_voice, JSUT, CSS10, TEDxJP - 10K, JVS, JSSS |
Metrics |
WER, CER |
Task |
Automatic Speech Recognition |
Dataset |
Common Voice Japanese |
Test WER |
21.48% |
Test CER |
9.82% |