Create app.py

app.py

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+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+
+# Load the model and tokenizer from the Hugging Face Model Hub
+model_name = "BrendaTellez/SoundClassificationCNNRNN"
+model = Wav2Vec2ForCTC.from_pretrained(SoundClassificationCNNRNN)
+tokenizer = Wav2Vec2Tokenizer.from_pretrained(SoundClassificationCNNRNN)
+
+# Load the audio file
+audio_file = ""../input/environmental-sound-classification-50/audio/audio/44100/""
+
+# Tokenize the audio file
+inputs = tokenizer(audio_file, return_tensors="pt", padding=True)
+
+# Use the model to classify the audio
+with torch.no_grad():
+    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_class_id = torch.argmax(logits, dim=-1)
+predicted_class_label = tokenizer.decode(predicted_class_id[0])
+
+print(predicted_class_label)