Create app.py

app.py

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+
+# Import necessary libraries
+import gradio as gr
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+from transformers import WhisperModel, WhisperFeatureExtractor
+import datasets
+from datasets import load_dataset, DatasetDict, Audio
+from huggingface_hub import PyTorchModelHubMixin
+
+# Define data class
+class SpeechInferenceDataset(Dataset):
+    def __init__(self, audio_data, text_processor):
+        self.audio_data = audio_data
+        self.text_processor = text_processor
+
+    def __len__(self):
+        return len(self.audio_data)
+
+    def __getitem__(self, index):
+        inputs = self.text_processor(self.audio_data[index]["audio"]["array"],
+                                     return_tensors="pt",
+                                     sampling_rate=self.audio_data[index]["audio"]["sampling_rate"])
+        input_features = inputs.input_features
+        # Assuming 'encoder' is defined or available in the scope
+        decoder_input_ids = torch.tensor([[1, 1]]) * encoder.config.decoder_start_token_id
+        return input_features, decoder_input_ids
+
+# Define model class
+class SpeechClassifier(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, config):
+        super(SpeechClassifier, self).__init__()
+        self.encoder = WhisperModel.from_pretrained(config["encoder"])
+        self.classifier = nn.Sequential(
+            nn.Linear(self.encoder.config.hidden_size, 4096),
+            nn.ReLU(),
+            nn.Linear(4096, 2048),
+            nn.ReLU(),
+            nn.Linear(2048, 1024),
+            nn.ReLU(),
+            nn.Linear(1024, 512),
+            nn.ReLU(),
+            nn.Linear(512, config["num_labels"])
+        )
+
+    def forward(self, input_features, decoder_input_ids):
+        outputs = self.encoder(input_features, decoder_input_ids=decoder_input_ids)
+        pooled_output = outputs['last_hidden_state'][:, 0, :]
+        logits = self.classifier(pooled_output)
+        return logits
+
+# Prepare data function
+def prepare_data(audio_file_path, model_checkpoint="openai/whisper-base"):
+    feature_extractor = WhisperFeatureExtractor.from_pretrained(model_checkpoint)
+    inference_data = datasets.Dataset.from_dict({"path": [audio_file_path], "audio": [audio_file_path]}).cast_column("audio", Audio(sampling_rate=16_000))
+    inference_dataset = SpeechInferenceDataset(inference_data, feature_extractor)
+    inference_loader = DataLoader(inference_dataset, batch_size=1, shuffle=False)
+    input_features, decoder_input_ids = next(iter(inference_loader))
+    # Ensure 'device' is defined or replace with 'torch.device("cpu")' if GPU is not available
+    input_features = input_features.squeeze(1).to(device)
+    decoder_input_ids = decoder_input_ids.squeeze(1).to(device)
+    return input_features, decoder_input_ids
+
+# Prediction function
+def predict(audio_file_path, config={"encoder": "openai/whisper-base", "num_labels": 2}):
+    input_features, decoder_input_ids = prepare_data(audio_file_path)
+    model = SpeechClassifier(config)
+    model.eval()
+    with torch.no_grad():
+        logits = model(input_features, decoder_input_ids)
+        predicted_ids = int(torch.argmax(logits, dim=-1))
+    return predicted_ids
+
+# Gradio Interface function
+def gradio_interface(uploaded_file):
+    with open(uploaded_file.name, "wb") as f:
+        f.write(uploaded_file.read())
+    prediction = predict(uploaded_file.name)
+    label = "Hypernasality Detected" if prediction == 1 else "No Hypernasality Detected"
+    return label
+
+# Create and launch Gradio Interface with File upload input
+iface = gr.Interface(fn=gradio_interface, 
+                     inputs=gr.inputs.File(label="Upload Audio File"), 
+                     outputs="text")
+iface.launch()