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import torch
import librosa
import numpy as np
from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
# =====================
# CONFIG
# =====================
MODEL_DIR = "exported_audio_model"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
SR = 16000
MAX_SAMPLES = 8 * SR # 8 seconds
# =====================
# LOAD MODEL + PROCESSOR (ONCE)
# =====================
processor = Wav2Vec2Processor.from_pretrained(MODEL_DIR)
model = Wav2Vec2ForSequenceClassification.from_pretrained(MODEL_DIR)
model.to(DEVICE)
model.eval()
# =====================
# PREDICT FUNCTION
# =====================
def predict_audio(wav_path):
# Load audio
audio, sr = librosa.load(wav_path, sr=SR, mono=True)
# Truncate if needed
if len(audio) > MAX_SAMPLES:
audio = audio[:MAX_SAMPLES]
# Processor handles padding
inputs = processor(
audio,
sampling_rate=SR,
return_tensors="pt",
padding=True,
return_attention_mask=True
)
input_values = inputs.input_values.to(DEVICE)
attention_mask = inputs.attention_mask.to(DEVICE)
with torch.no_grad():
outputs = model(
input_values=input_values,
attention_mask=attention_mask
)
probs = torch.softmax(outputs.logits, dim=1)[0]
pred_id = torch.argmax(probs).item()
label = model.config.id2label[pred_id]
confidence = probs[pred_id].item() * 100
return label, round(confidence, 2)
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