| 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): |
| |
| 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" |
| } |
|
|
