---
license: apache-2.0
datasets:
- keremberke/license-plate-object-detection
language:
- ru
- pl
- en
- zh
- tk
- ar
- es
- el
- fr
- ae
metrics:
- bertscore
base_model:
- yainage90/fashion-object-detection
new_version: yainage90/fashion-object-detection
pipeline_tag: zero-shot-classification
library_name: transformers
tags:
- Suno
---
# felguk-suno-or-people
[](https://hf.co/felguk)
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.
---
## Model Details
- **Model Name:** `felguk-suno-or-people`
- **Task:** Audio Classification
- **Input:** Audio clip (WAV format)
- **Output:** Classification label (`suno` or `people`)
---
## Usage
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:
```bash
pip install transformers datasets
```
## load model
```bash
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import torch
# Load the model and feature extractor
model = AutoModelForAudioClassification.from_pretrained("Felguk/Felguk-suno-or-people")
feature_extractor = AutoFeatureExtractor.from_pretrained("Felguk/Felguk-suno-or-people")
```
```bash
from datasets import load_dataset, Audio
# Load an example audio file (replace with your own file)
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")
```
```bash
# 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}")