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import streamlit as st
from transformers import HubertForSequenceClassification, HubertConfig, Wav2Vec2FeatureExtractor
import torch
import soundfile as sf
# Load model and tokenizer
model_name = "model_hubert_finetuned_nopeft.pth" # Replace with your model path or Hugging Face model hub path
config = HubertConfig.from_pretrained(model_name)
config.id2label = {0: 'neu', 1: 'hap', 2: 'ang', 3: 'sad', 4: 'dis', 5: 'sur', 6: 'fea', 7: 'cal'}
config.label2id = {"neu": 0, "hap": 1, "ang": 2, "sad": 3, "dis": 4, "sur": 5, "fea": 6, "cal": 7}
config.num_labels = 8 # Set it to the number of classes in your SER task
# Load the pre-trained model with the modified configuration
model = HubertForSequenceClassification.from_pretrained(model_name, config=config, ignore_mismatched_sizes=True)
model.to('cuda' if torch.cuda.is_available() else 'cpu')
model.eval()
# Load feature extractor
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-er")
st.title("Speech Emotion Recognition Model")
uploaded_file = st.file_uploader("Upload an audio file", type=["wav"])
if uploaded_file is not None:
# Load audio file
audio_input, sampling_rate = sf.read(uploaded_file)
# Preprocess audio input
inputs = feature_extractor(audio_input, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: value.to('cuda' if torch.cuda.is_available() else 'cpu') for key, value in inputs.items()}
# Get prediction
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
predicted_class = torch.argmax(probabilities, dim=1).item()
# Display prediction
st.write(f"Predicted class: {config.id2label[predicted_class]}")
st.write(f"Class probabilities: {probabilities}")
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