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8d8f544 | 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 | import torch
import librosa
import numpy as np
from transformers import AutoFeatureExtractor, Wav2Vec2ForSequenceClassification
import argparse
import os
class EmotionPredictor:
def __init__(self, model_path="models/wav2vec2-finetuned"):
"""
Initializes the predictor with the fine-tuned model.
"""
print(f"Loading model from: {model_path}...")
# Load config and model
try:
self.feature_extractor = AutoFeatureExtractor.from_pretrained(model_path)
self.model = Wav2Vec2ForSequenceClassification.from_pretrained(model_path)
self.model.eval() # Set to inference mode
# Extract labels from config
self.id2label = self.model.config.id2label
print(f"Model loaded. Labels: {list(self.id2label.values())}")
except Exception as e:
print(f"Failed to load model: {e}")
raise
def predict(self, audio_path):
"""
Predicts emotion from a single audio file.
"""
# 1. Load Audio
try:
speech, sr = librosa.load(audio_path, sr=16000)
except Exception as e:
return {"error": f"Could not load audio: {e}"}
# 2. Preprocess
inputs = self.feature_extractor(
speech,
sampling_rate=16000,
padding=True,
return_tensors="pt"
)
# 3. Inference
with torch.no_grad():
logits = self.model(inputs.input_values).logits
# 4. Post-processing
probabilities = torch.nn.functional.softmax(logits, dim=-1)
predicted_id = torch.argmax(logits, dim=-1).item()
confidence = probabilities[0][predicted_id].item()
predicted_label = self.id2label[predicted_id]
return {
"emotion": predicted_label,
"confidence": f"{confidence:.2%}",
"probabilities": {
self.id2label[i]: f"{probabilities[0][i].item():.2%}"
for i in range(len(self.id2label))
}
}
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Predict emotion from audio file")
parser.add_argument("--file", type=str, help="Path to .wav file")
args = parser.parse_args()
if args.file:
predictor = EmotionPredictor()
result = predictor.predict(args.file)
print("\nPrediction Result:")
print(f"Emotion: {result.get('emotion', 'Error')}")
print(f"Confidence: {result.get('confidence', 'N/A')}")
print(result)
else:
print("Please provide a file path using --file")
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