---
library_name: transformers
pipeline_tag: audio-classification
tags:
- audio
- music
- speech
- custom-code
license: mit
---

# SMAD CRNN

SMAD is an audio classification model for identifying speech, music, singing,
and non-vocal content in short audio segments. It is designed for lightweight
audio analysis workflows where fast, practical content categorization is
needed.

## Load by model id

```python
import torch
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification

model_id = "duclvQ/smad"

feature_extractor = AutoFeatureExtractor.from_pretrained(
    model_id,
    trust_remote_code=True,
)
model = AutoModelForAudioClassification.from_pretrained(
    model_id,
    trust_remote_code=True,
).eval()

# `audio` must be mono float32 audio sampled at 16 kHz. For longer files, run
# this over 4-second windows.
inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits / model.config.temperature, dim=-1)

label_id = int(probs.argmax(-1)[0])
label = model.config.id2label[label_id]
confidence = float(probs[0, label_id])
```

For arbitrary file paths, load/resample with `librosa`:

```python
import librosa

audio, _ = librosa.load("clip.mp3", sr=16000, mono=True)
```