from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer
from datasets import Dataset, Audio
import torch
import pandas as pd

ds = pd.read_csv('CSV_DATA.csv')
ds = ds.dropna(how='any', axis=0)

test = Dataset.from_pandas(skn_test)
test = test.cast_column("AUDIO_PATH_COLUMN", Audio(sampling_rate=16000))

tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("okuparinen/LIA_300m_simple", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
model = Wav2Vec2ForCTC.from_pretrained("okuparinen/LIA_300m_simple").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("okuparinen/LIA_300m_simple", tokenizer=tokenizer)

def prepare_dataset(batch):
    audio = batch["AUDIO_PATH"]
    batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
    batch["input_length"] = len(batch["input_values"])

    return batch

test_ready = test.map(prepare_dataset, remove_columns=test.column_names)

length = len(test)
predictions = []

for i in range(0, length, 1):
    input_dict = processor(test_ready[i]["input_values"], return_tensors="pt", padding=True)
    logits = model(input_dict.input_values.to("cuda")).logits
    
    pred_ids = torch.argmax(logits, dim=-1)[0]
    
    prediction = processor.decode(pred_ids)
    predictions.append(prediction)

with open("OUTFILE.txt", "w") as f_pred:
    for line in predictions:
        f_pred.write(line + '\n')