Remove Numpy

app.py

@@ -2,24 +2,21 @@ import gradio as gr
 import spaces
 import torch
 import librosa
-import numpy as np
 from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
 
-
-
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
 model_name = "Hemg/human-emotion-detection"
 feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name).to(device)
 model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name).to(device) 
 
-def preprocess_audio(example):
-    audio_array, sampling_rate = librosa.load(audio_file_path, sr=16000)  # Load and resample to 16kHz
+def preprocess_audio(audio):
+    audio_array, sampling_rate = librosa.load(audio, sr=16000)  # Load and resample to 16kHz
     return {'speech': audio_array, 'sampling_rate': sampling_rate}
 
 @spaces.GPU
 def inference(audio):
-    example = preprocess_audio(audio_file_path)
+    example = preprocess_audio(audio)
     inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)
     inputs = inputs.to(device)  # Move inputs to GPU
     with torch.no_grad():

requirements.txt

@@ -1,5 +1,4 @@
 gradio
 torch
 transformers
-librosa
-numpy
+librosa