| ---
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| license: mit
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| library_name: pytorch
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| pipeline_tag: audio-classification
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| language:
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| - sr
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| - en
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| datasets:
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| - declare-lab/meld
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| - seac
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| metrics:
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| - accuracy
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| - weighted-f1
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| tags:
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| - emotion-recognition
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| - speech-emotion-recognition
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| - audio
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| - wav2vec2
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| - transfer-learning
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| - meld
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| - seac
|
| ---
|
|
|
| # Audio Emotion Recognition (MELD → SEAC, Audio-only)
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|
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| ## Overview
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|
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| This model performs **speech emotion recognition from audio only**.
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|
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| It uses a **pretrained Wav2Vec2 encoder (frozen)** as a feature extractor,
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| followed by a lightweight classification head.
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|
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| The model was:
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|
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| - **Pretrained on:** MELD (English conversational emotions)
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| - **Fine-tuned on:** SEAC (Serbian emotional speech)
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| - **Task:** 5-class emotion classification from speech audio
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|
|
| ---
|
|
|
| ## Emotions
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|
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| The model predicts:
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|
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| - neutral
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| - joy
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| - anger
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| - sadness
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| - fear
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|
|
| ---
|
|
|
| ## Architecture
|
|
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| - **Encoder:** `facebook/wav2vec2-base` (frozen)
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| - **Pooling:** Mean pooling over temporal hidden states
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| - **Classifier:** Fully connected classification head
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| - **Training strategy:** Transfer learning (classifier-only fine-tuning)
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|
|
| ---
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|
|
| ## Transfer Learning Setup
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|
|
| **Stage 1 – Pretraining (MELD)**
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| - Audio-only emotion classification
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|
|
| **Stage 2 – Fine-tuning (SEAC)**
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| - Encoder frozen
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| - Only classification head updated
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|
|
| ---
|
|
|
| ## Evaluation (SEAC Test Set)
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|
|
| | Metric | Score |
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| |---------------|-------|
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| | Accuracy | **0.7107** |
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| | Weighted F1 | **0.7130** |
|
|
|
| ---
|
|
|
| ## Notes
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|
|
| - Sampling rate: 16 kHz
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| - Mean temporal pooling is used to obtain utterance-level embeddings.
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| - The released weights include only the classification head.
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| The encoder is loaded from `facebook/wav2vec2-base`.
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|
|
| --- |
