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import os |
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from inference import Evaluator |
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import argparse |
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from utils.YParams import YParams |
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import torch |
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import gradio as gr |
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def read_markdown_file(path): |
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with open(path, 'r', encoding='utf-8') as file: |
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return file.read() |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--yaml_config", default='config.yaml', type=str) |
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parser.add_argument("--config", default='resnet_0.7', type=str) |
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args = parser.parse_args() |
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params = YParams(os.path.abspath(args.yaml_config), args.config) |
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try: |
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params.device = torch.device(torch.cuda.current_device()) |
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except: |
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params.device = "cpu" |
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expDir = "ckpts/resnet_0.7/150classes_alldata_cliplength30" |
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params['checkpoint_path'] = os.path.join(expDir, 'training_checkpoints/ckpt.tar') |
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params['best_checkpoint_path'] = os.path.join(expDir, 'training_checkpoints/best_ckpt.tar') |
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evaluator = Evaluator(params) |
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with gr.Blocks() as demo: |
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with gr.Tab("Classifier"): |
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gr.Interface( |
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title="Carnatic Raga Classifier", |
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description="**Welcome!** This is a deep-learning based raga classifier. Simply upload or record an audio clip to test it out (uploading is more reliable). \n", |
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article = "**Get in Touch:** Feel free to reach out to [me](https://sanjeevraja.com/) via email (sanjeevr AT berkeley DOT edu) with any questions or feedback! ", |
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fn=evaluator.inference, |
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inputs=[ |
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gr.Slider(minimum = 1, maximum = 150, value = 5, label = "Number of displayed ragas", info = "Choose number of top predictions to display"), |
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gr.Audio() |
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], |
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outputs="label", |
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allow_flagging = False |
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) |
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with gr.Tab("About"): |
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gr.Markdown(read_markdown_file('about.md')) |
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gr.Image('site/tsne.jpeg', height = 800, width=800) |
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demo.launch() |
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