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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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description_text = "Multi-label (arousal, dominance, valence) Odyssey 2024 Emotion Recognition competition baseline model.<br> \ |
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The model is trained on MSP-Podcast. \ |
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For more details visit: [HuggingFace model page](https://huggingface.co/3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes), \ |
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[paper/soon]() and [GitHub](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main). <br> <br>\ |
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Upload an audio file and hit the 'Submit' button to predict the emotion" |
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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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mean, std = model.config.mean, model.config.std |
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model_sr = model.config.sampling_rate |
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id2label = model.config.id2label |
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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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output = '' |
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if sr != 16000: |
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y = librosa.resample(y, orig_sr=sr, target_sr=model_sr) |
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output += "{} sampling rate is uncompatible, converted to {} as the model was trained on {} sampling rate\n".format(sr, model_sr, model_sr) |
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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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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=description_text) |
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iface.launch() |
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if __name__ == '__main__': |
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main() |
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