| 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) | |