ID_TO_ACCENT = {
    0: "Scottish",
    1: "English",
    2: "Indian",
    3: "Irish",
    4: "Welsh",
    5: "New Zealand",
    6: "Australian",
    7: "South African",
    8: "Canadian",
    9: "NorthernIrish",
    10: "American",
    11: "South East Asia",
    12: "Eastern Europe",
    13: "East Asia",
    14: "Nordic",
    15: "France",
    16: "Southern Europe",
    17: "Germany",
    18: "West Indies",
    19: "Western Africa",
    20: "South Asia",
}

import soundfile as sf
import torch
from scipy import signal

audio_array, sr = sf.read(audio_path)
if audio_array.ndim > 1:
    audio_array = audio_array.mean(axis=1)
if sr != 16000:
    audio_array = signal.resample(audio_array, int(len(audio_array)*16000/sr))
input_features = processor(audio_array, sampling_rate=16000, return_tensors="pt").input_features
output, pooled_embed = model(input_features)
probabilities = torch.nn.functional.softmax(output, dim=1)
predictions = torch.argmax(probabilities, dim=1)
predicted_accent = ID_TO_ACCENT[predictions.item()]
accent_probabilities = {ID_TO_ACCENT[i]: prob.item() for i, prob in enumerate(probabilities[0])}