File size: 2,411 Bytes
5957fc2 a2b10f4 947cc57 d781426 a2b10f4 d781426 a2b10f4 98c0bb4 a2b10f4 fd03154 a2b10f4 ac8c48e a2b10f4 ac8c48e a2b10f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import base64
import io
import numpy as np
import librosa
import torch
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
THRESHOLD = 0.75
device = "cpu"
class EndpointHandler:
def __init__(self, model_dir):
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_dir)
self.model = Wav2Vec2ForSequenceClassification.from_pretrained(
model_dir,
low_cpu_mem_usage=True
).to(device)
self.model.eval()
def load_mp3_from_base64(self, b64):
audio_bytes = base64.b64decode(b64)
with io.BytesIO(audio_bytes) as f:
y, sr = librosa.load(f, sr=16000)
return y
def predict_chunked(self, y):
chunk_len = 16000
probs = []
for start in range(0, len(y), chunk_len):
chunk = y[start:start + chunk_len]
if len(chunk) < 4000:
continue
inputs = self.feature_extractor(
chunk,
sampling_rate=16000,
return_tensors="pt"
)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
logits = self.model(**inputs).logits
p = torch.softmax(logits, dim=1)[0][1].item()
probs.append(p)
return float(np.mean(probs)) if probs else 0.0
def __call__(self, data):
# Accept both formats
if "inputs" in data:
req = data["inputs"]
else:
req = data
language = req.get("language")
audio_base64 = req.get("audioBase64")
y = self.load_mp3_from_base64(audio_base64)
ai_prob = self.predict_chunked(y)
if ai_prob >= THRESHOLD:
return {
"status": "success",
"language": language,
"classification": "AI_GENERATED",
"confidenceScore": round(ai_prob, 4),
"explanation": "Unnatural pitch consistency and synthetic speech patterns detected"
}
else:
return {
"status": "success",
"language": language,
"classification": "HUMAN",
"confidenceScore": round(1 - ai_prob, 4),
"explanation": "Natural pitch variation and human speech characteristics detected"
}
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