Add requirements.txt, Modifiy app.py

app.py

@@ -1,7 +1,113 @@
+import torch
+import whisper
+import torchaudio
 import gradio as gr
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Define the same model class used during training
+class DialectClassifier(nn.Module):
+    def __init__(self, input_dim, num_classes):
+        super(DialectClassifier, self).__init__()
+        self.fc1 = nn.Linear(input_dim, 128)
+        self.fc2 = nn.Linear(128, 64)
+        self.fc3 = nn.Linear(64, num_classes)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = x.view(x.size(0), -1)  # Flatten the input tensor
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Function to preprocess audio and extract features
+def preprocess_audio(file_path, whisper_model, device):
+    def load_audio(file_path):
+        waveform, sample_rate = torchaudio.load(file_path)
+        if sample_rate != 16000:
+            waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
+
+        # Convert to single channel (mono) if necessary
+        if waveform.shape[0] > 1:
+            waveform = torch.mean(waveform, dim=0, keepdim=True)
+
+        # Pad or trim audio to 30 seconds
+        desired_length = 16000 * 30  # 30 seconds at 16 kHz
+        current_length = waveform.shape[1]
+
+        if current_length < desired_length:
+            # Pad with zeros
+            padding = desired_length - current_length
+            waveform = torch.nn.functional.pad(waveform, (0, padding))
+        elif current_length > desired_length:
+            # Trim to desired length
+            waveform = waveform[:, :desired_length]
+
+        return waveform
+
+    audio = load_audio(file_path)
+    audio = whisper.pad_or_trim(audio.flatten())
+    mel = whisper.log_mel_spectrogram(audio).to_dense()
+
+    with torch.no_grad():
+        mel = mel.unsqueeze(0).to(device)  # Add batch dimension and move to device
+        features = whisper_model.encoder(mel)
+    return features
+
+repo_id = "dipankar53/assamese_dialect_classifier_model"
+model_filename = "dialect_classifier_model.pth"
+model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
+
+label_to_idx = {"Darrangiya Accent": 0, "Kamrupiya Accent": 1, "Upper Assam": 2, "Nalbaria Accent": 3}
+
+# Load Whisper model
+whisper_model = whisper.load_model("medium")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the trained model
+num_classes = len(label_to_idx)
+sample_input = torch.randn(1, 80, 3000).to(device)
+with torch.no_grad():
+  sample_output = whisper_model.encoder(sample_input)
+input_dim = sample_output.view(1, -1).shape[1] # Flatten and get dimension
+
+model = DialectClassifier(input_dim, num_classes)
+model.load_state_dict(torch.load(model_path, map_location=device))
+model.to(device)
+model.eval()
+
+# Function to predict the dialect of a single audio file
+def predict_dialect(audio_path):
+    try:
+        # Preprocess audio and extract features
+        features = preprocess_audio(audio_path, whisper_model, device)
+        features = features.view(1, -1)  # Flatten features
+
+        # Perform prediction
+        with torch.no_grad():
+            outputs = model(features)
+            _, predicted = torch.max(outputs, 1)
+
+        # Map predicted index back to dialect label
+        idx_to_label = {idx: label for label, idx in label_to_idx.items()}
+        predicted_label = idx_to_label[predicted.item()]
+
+        return f"Predicted Dialect: {predicted_label}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=predict_dialect,
+    inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),
+    outputs="text",
+    title="Assamese Dialect Prediction",
+    description="Upload an Assamese audio file to predict its dialect.",
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+whisper
+gradio
+huggingface_hub
+torchaudio