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import streamlit as st
import torch
import librosa
from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
from transformers import pipeline
# Title of the app
st.title("Emotion Recognition from Speech")
# Upload audio file
uploaded_file = st.file_uploader("Choose an audio file...", type=["wav"])
# Load the model and feature extractor
model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-er")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-er")
classifier = pipeline("audio-classification", model="superb/hubert-large-superb-er")
if uploaded_file is not None:
# Load and preprocess audio file
speech, sr = librosa.load(uploaded_file, sr=16000, mono=True)
# Display audio player
st.audio(uploaded_file, format='audio/wav')
# Process the audio
inputs = feature_extractor(speech, sampling_rate=16000, padding=True, return_tensors="pt")
# Predict emotion
with torch.no_grad():
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
# Display the result
# st.write("Predicted Emotion:", labels[0])
# Alternatively using the pipeline
results = classifier(uploaded_file, top_k=5)
st.write("Top 5 Predicted Emotions:")
for result in results:
st.write(f"{result['label']}: {result['score']:.4f}")
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