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from transformers import pipeline
from transformers import AutoModelForAudioClassification
import gradio as gr
import librosa
import torch
import numpy as np
def classify_audio(audio_file):
model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True)
sr, raw_wav = audio_file
print(audio_file, audio_file[1].dtype)
y = raw_wav.astype(np.float32)
y /= np.max(np.abs(y))
#raw_wav, _ librosa.load(audio_file, sr=16000)
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()
print(str(pred))
return str(pred)
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="Upload an audio file and hit the 'Submit'\
button")
iface.launch()
if __name__ == '__main__':
main()
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