Beta
Explore
Marketplace
Neural Labs
Chat
Wallet
Docs
audio_meld_seac_finetuned – AI Model by PetraMicanovic | AlphaNeural AI
You can deploy this model and start earning money today!
PetraMicanovic
/
audio_meld_seac_finetuned
like
0
pytorch
emotion-recognition
speech-emotion-recognition
audio
wav2vec2
transfer-learning
meld
seac
audio-classification
sr
en
declare-lab/meld
seac
mit
us
Views
No views yet
Model card
Files and Versions
Community
API
Deploy
Audio Emotion Recognition (MELD → SEAC, Audio-only)
Overview
This model performs
speech emotion recognition from audio only
.
It uses a
pretrained Wav2Vec2 encoder (frozen)
as a feature extractor, followed by a lightweight classification head.
The model was:
Pretrained on:
MELD (English conversational emotions)
Fine-tuned on:
SEAC (Serbian emotional speech)
Task:
5-class emotion classification from speech audio
Emotions
The model predicts:
neutral
joy
anger
sadness
fear
Architecture
Encoder:
facebook/wav2vec2-base
(frozen)
Pooling:
Mean pooling over temporal hidden states
Classifier:
Fully connected classification head
Training strategy:
Transfer learning (classifier-only fine-tuning)
Transfer Learning Setup
Stage 1 – Pretraining (MELD)
Audio-only emotion classification
Stage 2 – Fine-tuning (SEAC)
Encoder frozen
Only classification head updated
Evaluation (SEAC Test Set)
Metric
Score
Accuracy
0.7107
Weighted F1
0.7130
Notes
Sampling rate: 16 kHz
Mean temporal pooling is used to obtain utterance-level embeddings.
The released weights include only the classification head. The encoder is loaded from
facebook/wav2vec2-base
.
