| import torch | |
| from transformers import pipeline | |
| import gradio as gr | |
| MODEL_NAME = "JackismyShephard/whisper-medium.en-finetuned-gtzan" | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="audio-classification", | |
| model=MODEL_NAME, | |
| device=device, | |
| ) | |
| def classify_audio(filepath): | |
| preds = pipe(filepath, top_k = 10) | |
| outputs = {} | |
| for p in preds: | |
| outputs[p["label"]] = p["score"] | |
| return outputs | |
| demo = gr.Interface( | |
| fn=classify_audio, | |
| inputs= gr.Audio(label="Audio file", type="filepath"), | |
| outputs=gr.Label(), | |
| title="Music Genre Classification", | |
| description=( | |
| "Classify long-form audio or microphone inputs with the click of a button! Demo uses the" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to classify audio files" | |
| " of arbitrary length." | |
| ), | |
| examples="./examples", | |
| cache_examples=True, | |
| allow_flagging="never", | |
| ) | |
| demo.launch() |