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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](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()
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