| 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.<br> \ | |
| The model is trained on MSP-Podcast. \ | |
| For more details visit: [HuggingFace model page](https://huggingface.co/3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes), \ | |
| [paper/soon]() and [GitHub](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main). <br> <br>\ | |
| 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() | |