Wav2vec2 Base Finetuned Ravdess Personalization

--- language: en tags: - audio - speech-emotion-recognition - wav2vec2 - RAVDESS - test-time-adaptation license: mit library_name: transformers datasets: - xbgoose/ravdess --- # wav2vec2-base-finetuned-ravdess-personalization This model is part of our ICASSP 2026 paper: **Test-Time Adaptation Methods for Speech Emotion Recognition**. ## Model Description Wav2Vec2 fine-tuned on RAVDESS for Task1 (intra-corpus personalization) - **Base Model**: facebook/wav2vec2-base - **Dataset**: RAVDESS - **Task**: Task1 - intra-corpus personalization - **Emotions**: neutral, calm, happy, sad, angry, fearful, disgust, surprised - **Number of Classes**: 8 ## Usage ```python from transformers import AutoModelForAudioClassification, AutoFeatureExtractor import torch import torchaudio # Load model and processor model_checkpoint = "LincolnD/wav2vec2-base-finetuned-ravdess-personalization" 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 RAVDESS dataset. ### Training Data - **Dataset**: RAVDESS - **Emotions**: 8 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.

Source context: 111 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo LincolnD/wav2vec2-base-finetuned-ravdess-personalization