Testing

app.py

@@ -1,21 +1,24 @@
 import gradio as gr
 import spaces
 import torch
-import librosa
+#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)
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
 model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name).to(device) 
 
 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}
+    #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):
+    print('hello')
+    '''
     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
@@ -23,12 +26,15 @@ def inference(audio):
         logits = model(**inputs).logits
     predicted_ids = torch.argmax(logits, dim=-1)
     return model.config.id2label[predicted_ids.item()], logits, predicted_ids   # Move tensors back to CPU for further processing
+    '''
 
-iface = gr.Interface(fn=predict_sentiment,
-                     inputs=gr.inputs.Audio(source="microphone", type="filepath"),
-                     outputs="text",
+iface = gr.Interface(fn=inference,
+                     inputs=gr.Audio(type="filepath"),
+                     outputs=[gr.Label(label="Predicted Sentiment"),
+                              gr.JSON(label="Logits"),
+                              gr.JSON(label="Predicted ID")],
                      title="Audio Sentiment Analysis",
                      description="Upload an audio file or record one to analyze sentiment.")
 
 
-iface.launch()
+iface.launch(share=True)

requirements.txt

@@ -1,4 +1,3 @@
-gradio
 torch
 transformers
-librosa
+accelerate