Finetuned Superb SER Model for Spanish

Speech Emotion Recognition Model for handling audios in spanish v5. It can identify Mexican and Chilean accent.

Changes made

1. Replication: Replicates the lesser amount of data to match the other
2. Differentiated Augmentation: Each replication receives a different type of augmentation (Gaussian Noise, Stretching, Pitching)
3. Balance: Changed the training arguments to work with limited GPU resources.

How to use it?

import torch
import librosa
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor

model = Wav2Vec2ForSequenceClassification.from_pretrained("pollitoconpapass/superb-ser-finetuned-spanish-v5")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("pollitoconpapass/superb-ser-finetuned-spanish-v5")

EMOTIONS_DICT = {
  # Modelo Finetuneado
    "LABEL_0": "miedo",
    "LABEL_1": "triste",
    "LABEL_2": "neutral",
    "LABEL_3": "enojo",
    "LABEL_4": "disgusto",
    "LABEL_5": "feliz"
}

def load_audio(file_path):
    speech, _ = librosa.load(file_path, sr=16000, mono=True)
    return speech

audio_path = "/content/your-own-audio.wav"
audio = load_audio(audio_path)

inputs = feature_extractor(audio, sampling_rate=16000, padding=True, return_tensors="pt")
logits = model(**inputs).logits

probs = torch.nn.functional.softmax(logits, dim=-1)[0]

predicted_ids = torch.argmax(logits, dim=-1)
labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
label = labels[0]

resultado = EMOTIONS_DICT[label]
print(f"\nEmoción: {resultado}")
print(f"\nPorcentajes de confianza")
for i, prob in enumerate(probs):
    emotion_label = model.config.id2label[i]
    emotion_name = EMOTIONS_DICT[emotion_label]
    print(f"{emotion_name}: {prob.item()*100:.2f}%")