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| import gradio as gr | |
| import torch | |
| from transformers import pipeline | |
| username = "ardneebwar" ## Complete your username | |
| model_id = f"{username}/wav2vec2-animal-sounds-finetuned-hubert-finetuned-animals" | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline("audio-classification", model=model_id, device=device) | |
| def classify_audio(filepath): | |
| import time | |
| start_time = time.time() | |
| # Assuming `pipe` is your model pipeline for inference | |
| preds = pipe(filepath) | |
| outputs = {} | |
| for p in preds: | |
| outputs[p["label"]] = p["score"] | |
| end_time = time.time() | |
| prediction_time = end_time - start_time | |
| return outputs, prediction_time | |
| title = "🎵 Animal Sound Classifier" | |
| description = """ | |
| Animal Sound Classifier model (Fine-tuned "ntu-spml/distilhubert") | Dataset: ESC-50 from Github (only the animal sounds) | Better to use audios 5 seconds long. | |
| """ | |
| filenames = ['cat.wav'] | |
| filenames = [f"./{f}" for f in filenames] | |
| demo = gr.Interface( | |
| fn=classify_audio, | |
| inputs=gr.Audio(type="filepath", label="Upload your audio file"), | |
| outputs=[gr.Label(label="Predicted Animal Sound"), gr.Number(label="Prediction time (s)")], | |
| title=title, | |
| description=description, | |
| theme="huggingface", | |
| examples=[("cat.wav")], | |
| live=False | |
| ) | |
| demo.launch() |