Ser W2v2 Finetuned

--- language: - fr license: cc-by-nc-sa-4.0 library_name: transformers tags: - audio - speech-emotion-recognition - wav2vec2 - regression - pytorch datasets: - gemep metrics: - ccc - mae pipeline_tag: audio-classification base_model: - audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim --- # SER Wav2Vec2 Finetuned on GEMEP (French) This model is a fine-tuned version of the [audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim) for **Speech Emotion Recognition (SER)** in French. It specifically targets the prediction of emotional dimensions: **Valence, Arousal, and Dominance (VAD)**. ## Model Description The model was fine-tuned using the **GEMEP corpus** (Geneva Multimodal Expression Portrayal), which contains pseudo-sentences uttered by professional actors. For the purpose of the associated research paper, we collected new annotations for Valence, Arousal, and Dominance through a dedicated user study. The target scores are the mean values of these human annotations. - **Base Model:** [Wav2Vec2-Large-Robust-12](https://huggingface.co/facebook/wav2vec2-large-robust) - **Pre-trained weights by:** [audeering](https://huggingface.co/audeering) - **Language:** French (Pseudo-speech) - **Task:** Dimensional Emotion Regression (VAD) ## Usage To use this model, you need the `transformers` library and the specific processing logic used by the Audeering architecture. ```python from transformers import AutoModel, AutoFeatureExtractor import torch import torch.nn as nn # Load model and processor model_name = "rosalied/ser-w2v2-finetuned" processor = AutoFeatureExtractor.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) # Example: Inference on a 16kHz audio array # input_values = processor(audio_array, sampling_rate=16000, return_tensors="pt").input_values # with torch.no_grad(): # outputs = model(input_values) # # The outputs represent [Arousal, Dominance, Valence]

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