infer.py

import onnxruntime
import torch

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')

sentence = "Kupil sem bicikel in mu zamenjal stol.".lower()

ort_session = onnxruntime.InferenceSession("g2p_t5.onnx", providers=["CPUExecutionProvider"])


def g2p(sentence, onnx_session, tokenizer):
    input_ids = [sentence]
    input_encoding = tokenizer(
        input_ids, padding='longest', max_length=512, truncation=True, return_tensors='pt',
    )
    input_ids, attention_mask = input_encoding.input_ids, input_encoding.attention_mask
    ort_inputs = {'input_ids': input_ids.numpy()}
    ort_outs = ort_session.run(None, ort_inputs)
    generated_ids = [ort_outs[0]]
    generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
    return generated_texts

    
result = g2p(sentence, ort_session, tokenizer)
print(result)