Pandas group by result to columns

I have dataframe like this:

x = pd.DataFrame({
    'audio': ['audio1', 'audio1', 'audio2', 'audio2', 'audio3', 'audio3'],
    'text': ['text1', 'text2', 'text3', 'text4', 'text5', 'text6'],
    'login': ['operator1', 'operator2', 'operator3', 'operator4', 'operator5', 'operator6'] 
})

i'm trying to aggregate it like this:

x1 = x.groupby('audio')['text'].agg(
    [
    ('text1', lambda x : x.iat[0]),
    ('text2', lambda x : x.iat[1]),
    ('leven', lambda x: Levenshtein.distance(x.iat[0], x.iat[1])) #some function works with grouped text
    ]
).reset_index()

and it works but i also need to add grouped logins to row, to make row like this:

audio, text1, text2, leven, login1, login2

I tried something like lambda x : x.ait[0, 1] but it doesnt work

from Pandas group by result to columns