Felguk Suno Or People

🚀 felguk-suno-or-people

This model is designed to classify audio clips into two categories: "Suno" music or "People" music, which can be used for music recommendation, genre classification, and more.

🚀 Quick Start

This model is designed to classify audio clips into two categories: "Suno" music or "People" music. It is trained on a dataset containing examples of both types of music and can be used for various applications such as music recommendation, genre classification, and more.

✨ Features

• Multilingual Support: Supports multiple languages including Russian (ru), Polish (pl), English (en), Chinese (zh), Turkmen (tk), Arabic (ar), Spanish (es), Greek (el), French (fr), and Emirati (ae).
• Metrics: Uses bertscore as a metric.
• Base Model: Built upon yainage90/fashion-object-detection.
• Pipeline Tag: Suitable for zero-shot-classification.
• Library Name: Utilizes the transformers library.

📦 Installation

This model is not currently available via third - party inference providers or the Hugging Face Inference API. However, you can easily use it locally by following the steps below.

Step 1: Install Required Libraries

Make sure you have the transformers and datasets libraries installed:

pip install transformers datasets

💻 Usage Examples

Basic Usage

# Load the model and feature extractor
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import torch

model = AutoModelForAudioClassification.from_pretrained("Felguk/Felguk-suno-or-people")
feature_extractor = AutoFeatureExtractor.from_pretrained("Felguk/Felguk-suno-or-people")

# Load an example audio file (replace with your own file)
from datasets import load_dataset, Audio
dataset = load_dataset("common_voice", "en", split="train", streaming=True)
audio_sample = next(iter(dataset))["audio"]

# Preprocess the audio
inputs = feature_extractor(audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt")

# Perform inference
with torch.no_grad():
    logits = model(**inputs).logits

# Get the predicted label
predicted_class_id = logits.argmax().item()
label = model.config.id2label[predicted_class_id]
print(f"Predicted label: {label}")

📚 Documentation

Model Details

Other Information

📄 License

This project is licensed under the apache-2.0 license.