from transformers import pipeline from transformers import AutoModelForAudioClassification import gradio as gr import librosa import torch import numpy as np description_text = "Multi-label (arousal, dominance, valence) Odyssey 2024 Emotion Recognition competition baseline model.
\ The model is trained on MSP-Podcast. \ For more details visit: [HuggingFace](https://huggingface.co/3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes), \ [paper/soon]() and [GitHub](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main).

\ Upload an audio file and hit the 'Submit' button to predict the emotion" def classify_audio(audio_file): model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True) mean, std = model.config.mean, model.config.std model_sr = model.config.sampling_rate id2label = model.config.id2label sr, raw_wav = audio_file y = raw_wav.astype(np.float32, order='C') / np.iinfo(raw_wav.dtype).max output = '' if sr != 16000: y = librosa.resample(y, orig_sr=sr, target_sr=model_sr) output += "{} sampling rate is uncompatible, converted to {} as the model was trained on {} sampling rate\n".format(sr, model_sr, model_sr) norm_wav = (y - mean) / (std+0.000001) mask = torch.ones(1, len(norm_wav)) wavs = torch.tensor(norm_wav).unsqueeze(0) pred = model(wavs, mask).detach().numpy() for att_i, att_val in enumerate(pred[0]): output += "{}: \t{:0.4f}\n".format(id2label[att_i], att_val) return output def main(): iface = gr.Interface(fn=classify_audio, inputs=gr.Audio(sources=["upload", "microphone"], label="Audio file"), outputs=gr.Text(), title="Speech Emotion Recognition App", description=description_text) iface.launch() if __name__ == '__main__': main()