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from transformers import pipeline |
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from transformers import AutoModelForAudioClassification |
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import gradio as gr |
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import librosa |
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import torch |
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import numpy as np |
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mean, std = -8.278621631819787e-05, 0.08485510250851999 |
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id2label = {0: 'arousal', 1: 'dominance', 2: 'valence'} |
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def classify_audio(audio_file): |
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model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True) |
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sr, raw_wav = audio_file |
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y = raw_wav.astype(np.float32, order='C') / np.iinfo(raw_wav.dtype).max |
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norm_wav = (y - mean) / (std+0.000001) |
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mask = torch.ones(1, len(norm_wav)) |
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wavs = torch.tensor(norm_wav).unsqueeze(0) |
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pred = model(wavs, mask).detach().numpy() |
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output = '' |
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if sr != 16000: |
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output += "{} sampling rate is uncompatible. The model was trained on {} sampleing rate\n".format(sr, 16000) |
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for att_i, att_val in enumerate(pred[0]): |
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output += "{}: \t{:0.4f}\n".format(id2label[att_i], att_val) |
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return output |
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def main(): |
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iface = gr.Interface(fn=classify_audio, inputs=gr.Audio(sources=["upload", "microphone"], label="Audio file"), |
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outputs=gr.Text(), title="Speech Emotion Recognition App", |
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description="Upload an audio file and hit the 'Submit'\ |
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button") |
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iface.launch() |
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if __name__ == '__main__': |
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main() |
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