Delete inference.py

inference.py

@@ -1,37 +0,0 @@
-# inference.py
-import torch
-import numpy as np
-import librosa
-from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
-
-# Load model and feature extractor
-model_name = "path_or_hub_id_of_your_finetuned_model"  # e.g. "username/wav2vec2-accuracy-classifier"
-model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
-
-# Put model in eval mode
-model.eval()
-
-def predict_accuracy_level(audio_path: str):
-    # 1. Load raw audio using librosa (or similar)
-    speech, sr = librosa.load(audio_path, sr=16000)  # match your model’s sample rate
-
-    # 2. Extract features
-    inputs = feature_extractor(
-        speech, 
-        sampling_rate=16000, 
-        return_tensors="pt", 
-        padding=True
-    )
-
-    # 3. Forward pass
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        predicted_id = torch.argmax(logits, dim=-1).item()
-
-    # 4. Convert predicted_id to your accuracy scale
-    #    If 0..7 is your model’s internal label, you might map it back to 3..10
-    #    Or keep it as 0..7, whichever you prefer
-    accuracy_level = predicted_id + 3  # example if your model outputs 0..7
-    return accuracy_level