# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!git clone https://github.com/m3hrdadfi/soxan
cd soxan

prediction

~~ import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor

import librosa import IPython.display as ipd import numpy as np import pandas as pd

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition" config = AutoConfig.from_pretrained(model_name_or_path) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) sampling_rate = feature_extractor.sampling_rate model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)

def speech_file_to_array_fn(path, sampling_rate): speech_array, _sampling_rate = torchaudio.load(path) resampler = torchaudio.transforms.Resample(_sampling_rate) speech = resampler(speech_array).squeeze().numpy() return speech

def predict(path, sampling_rate): speech = speech_file_to_array_fn(path, sampling_rate) inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs}

with torch.no_grad():
    logits = model(**inputs).logits

scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs


# prediction

path for a sample

path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' outputs = predict(path, sampling_rate)

[{'Emotion': 'anger', 'Score': '98.3%'}, {'Emotion': 'disgust', 'Score': '0.0%'}, {'Emotion': 'fear', 'Score': '0.4%'}, {'Emotion': 'happiness', 'Score': '0.7%'}, {'Emotion': 'sadness', 'Score': '0.5%'}] ~~~