wav2vec2-base-finetuned-iemocap-acted-to-natural-emotion
This model is part of our ICASSP 2026 paper: Test-Time Adaptation Methods for Speech Emotion Recognition.
Model Description
Wav2Vec2 fine-tuned on IEMOCAP for Task2 (scripted to improvised domain shift)
- Base Model: facebook/wav2vec2-base
- Dataset: IEMOCAP
- Task: Task2 - scripted to improvised domain shift
- Emotions: angry, happy, neutral, sad
- Number of Classes: 4
Usage
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import torch
import torchaudio
# Load model and processor
model_checkpoint = "LincolnD/wav2vec2-base-finetuned-iemocap-acted-to-natural-emotion"
processor = AutoFeatureExtractor.from_pretrained(model_checkpoint)
model = AutoModelForAudioClassification.from_pretrained(model_checkpoint)
# Load and process audio
audio_path = "path/to/your/audio.wav"
waveform, sample_rate = torchaudio.load(audio_path)
# Resample to 16kHz if needed
if sample_rate != 16000:
resampler = torchaudio.transforms.Resample(sample_rate, 16000)
waveform = resampler(waveform)
# Process and predict
inputs = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
emotion_id = predictions.item()
emotion = model.config.id2label[emotion_id]
print(f"Predicted emotion: {emotion}")
Training Details
This model was fine-tuned from the pre-trained Wav2Vec2 base model on the IEMOCAP dataset.
Training Data
- Dataset: IEMOCAP
- Emotions: 4 classes
- Sampling Rate: 16kHz
Intended Use
This model is designed for use in Test-Time Adaptation (TTA) experiments as part of our research on adapting speech emotion recognition systems to new domains and speakers.
Evaluation
For detailed evaluation results and comparison with various TTA methods, please refer to our paper.
Model Card Authors
LincolnD
License
MIT License - See repository for details.