delete app2.py

app2.py

@@ -1,35 +0,0 @@
-import os
-import torch
-import torchaudio
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
-
-# Set the Hugging Face API token
-os.environ["HUGGINGFACE_TOKEN"] = "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"
-
-# Load the pre-trained model and tokenizer
-model_name = "BrendaTellez/sounds2"
-model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=True)
-tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name, use_auth_token=True)
-
-# Get the audio file from the user
-file_path = input("Enter the path to the audio file: ")
-
-# Load the audio file using torchaudio
-waveform, sample_rate = torchaudio.load(file_path)
-
-# Resample the audio to match the sample rate expected by the model
-if waveform.shape[0] != model.config.sample_rate:
-    resampler = torchaudio.transforms.Resample(waveform.shape[1], model.config.sample_rate)
-    waveform = resampler(waveform)
-
-# Tokenize the audio using the model's tokenizer
-inputs = tokenizer(waveform.numpy(), 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(f"The audio file is classified as: {predicted_class_label}")