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| import gradio as gr | |
| import torch | |
| from transformers import pipeline | |
| username = "davidggphy" ## Complete your username | |
| model_id = f"{username}/distilhubert-finetuned-gtzan" | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline("audio-classification", model=model_id, device=device) | |
| # def predict_trunc(filepath): | |
| # preprocessed = pipe.preprocess(filepath) | |
| # truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30) | |
| # model_outputs = pipe.forward(truncated) | |
| # outputs = pipe.postprocess(model_outputs) | |
| # return outputs | |
| def classify_audio(filepath): | |
| """ | |
| Goes from | |
| [{'score': 0.8339303731918335, 'label': 'country'}, | |
| {'score': 0.11914275586605072, 'label': 'rock'},] | |
| to | |
| {"country": 0.8339303731918335, "rock":0.11914275586605072} | |
| """ | |
| preds = pipe(filepath) | |
| # preds = predict_trunc(filepath) | |
| outputs = {} | |
| for p in preds: | |
| outputs[p["label"]] = p["score"] | |
| return outputs | |
| title = "🎵 Music Genre Classifier" | |
| description = """ | |
| demo to showcase the music | |
| classification model that we just trained on the [GTZAN](https://huggingface.co/datasets/marsyas/gtzan) | |
| """ | |
| filenames = ['blues.00098.wav', "disco.00020.wav", "metal.00014.wav", "reggae.00021.wav", "rock.00058.wav"] | |
| filenames = [[f"./{f}"] for f in filenames] | |
| demo = gr.Interface( | |
| fn=classify_audio, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs=gr.outputs.Label(), | |
| title=title, | |
| description=description, | |
| examples=filenames, | |
| ) | |
| demo.launch() | |