i need to verify this pretrained model on another datasets xith multiple wave file how to arrange the code

README.md

@@ -15,20 +15,8 @@ pipeline_tag: audio-classification
 
 # Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
 
-Please note that this model is for research purpose only.
-A commercial license for a model
-that has been trained on much more data
-can be acquired with [audEERING](https://www.audeering.com/products/devaice/).
-The model expects a raw audio signal as input,
-and outputs predictions for arousal, dominance and valence in a range of approximately 0...1.
-In addition,
-it provides the pooled states of the last transformer layer.
-The model was created by fine-tuning
-[Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust)
-on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7).
-The model was pruned from 24 to 12 transformer layers before fine-tuning.
-An [ONNX](https://onnx.ai/) export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127).
-Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
+The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
+Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
 
 # Usage
 
@@ -96,7 +84,7 @@ class EmotionModel(Wav2Vec2PreTrainedModel):
 device = 'cpu'
 model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
 processor = Wav2Vec2Processor.from_pretrained(model_name)
-model = EmotionModel.from_pretrained(model_name).to(device)
+model = EmotionModel.from_pretrained(model_name)
 
 # dummy signal
 sampling_rate = 16000