Back to Zero

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-#import spaces ## For ZeroGPU
+import spaces ## For ZeroGPU
 import torch
 import torchaudio
 from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
@@ -15,7 +15,7 @@ def preprocess_audio(audio):
     resampled_waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)(waveform)
     return {'speech': resampled_waveform.numpy().flatten(), 'sampling_rate': 16000}
 
-#@spaces.GPU ## For ZeroGPU
+@spaces.GPU ## For ZeroGPU
 def inference(audio):
     example = preprocess_audio(audio)
     inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)
@@ -25,7 +25,7 @@ def inference(audio):
     predicted_ids = torch.argmax(logits, dim=-1)
     return model.config.id2label[predicted_ids.item()], logits, predicted_ids   
     
-#@spaces.GPU ## For ZeroGPU
+@spaces.GPU ## For ZeroGPU
 def inference_label(audio):
     example = preprocess_audio(audio)
     inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)