Upload 13 files

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+artifact.wav filter=lfs diff=lfs merge=lfs -text
+extra_hystole.wav filter=lfs diff=lfs merge=lfs -text
+FYP_Model filter=lfs diff=lfs merge=lfs -text
+model.safetensors.crdownload filter=lfs diff=lfs merge=lfs -text

FYP_Model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efd8f2304cff40ac674bf246b58682ef1e617f9e66a11372efffdf90b7ffe99e
+size 57901174

app.py

@@ -0,0 +1,111 @@
+import streamlit as st
+from transformers import pipeline
+import torchaudio
+from config import MODEL_ID
+
+# Load the model and pipeline using the model_id variable
+pipe = pipeline("audio-classification", model=MODEL_ID)
+
+def classify_audio(filepath):
+    preds = pipe(filepath)
+    outputs = {"normal": 0.0, "artifact": 0.0, "murmur": 0.0}
+    for p in preds:
+        label = p["label"]
+        # Simplify the labels and accumulate the scores
+        if "artifact" in label:
+            outputs["artifact"] += p["score"]
+        elif "murmur" in label:
+            outputs["murmur"] += p["score"]
+        elif "extra" in label or "Normal" in label:
+            outputs["normal"] += p["score"]
+    return outputs
+
+# Streamlit app layout
+st.title("Heartbeat Sound Classification")
+
+# Theme selection
+theme = st.sidebar.selectbox(
+    "Select Theme",
+    ["Light Green", "Light Blue"]
+)
+
+# Add custom CSS for styling based on the selected theme
+if theme == "Light Green":
+    st.markdown(
+        """
+        <style>
+        body, .stApp {
+            background-color: #e8f5e9; /* Light green background */
+        }
+        .stApp {
+            color: #004d40; /* Dark green text */
+        }
+        .stButton > button, .stFileUpload > div {
+            background-color: #004d40; /* Dark green button and file uploader background */
+            color: white; /* White text */
+        }
+        .stButton > button:hover, .stFileUpload > div:hover {
+            background-color: #00332c; /* Darker green on hover */
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+elif theme == "Light Blue":
+    st.markdown(
+        """
+        <style>
+        body, .stApp {
+            background-color: #e0f7fa; /* Light blue background */
+        }
+        .stApp {
+            color: #006064; /* Dark blue text */
+        }
+        .stButton > button, .stFileUpload > div {
+            background-color: #006064; /* Dark blue button and file uploader background */
+            color: white; /* White text */
+        }
+        .stButton > button:hover, .stFileUpload > div:hover {
+            background-color: #004d40; /* Darker blue on hover */
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+
+# File uploader for audio files
+uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3"])
+
+if uploaded_file is not None:
+    st.subheader("Uploaded Audio File")
+    # Load and display the audio file
+    audio_bytes = uploaded_file.read()
+    st.audio(audio_bytes, format='audio/wav')
+
+    # Save the uploaded file to a temporary location
+    with open("temp_audio_file.wav", "wb") as f:
+        f.write(audio_bytes)
+    
+    # Classify the audio file
+    st.write("Classifying the audio...")
+    results = classify_audio("temp_audio_file.wav")
+    
+    # Display the classification results in a dedicated output box
+    st.subheader("Classification Results")
+    results_box = st.empty()
+    results_str = "\n".join([f"{label}: {score:.2f}" for label, score in results.items()])
+    results_box.text(results_str)
+
+# Sample Audio Files for classification
+st.write("Sample Audio Files:")
+examples = ['normal.wav', 'murmur.wav', 'extra_systole.wav', 'extra_hystole.wav', 'artifact.wav']
+for example in examples:
+    if st.button(example):
+        st.subheader(f"Sample Audio: {example}")
+        audio_bytes = open(example, 'rb').read()
+        st.audio(audio_bytes, format='audio/wav')
+        results = classify_audio(example)
+        st.write("Results:")
+        results_str = "\n".join([f"{label}: {score:.2f}" for label, score in results.items()])
+        st.text(results_str)

artifact.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3dac4681a1f493576bf6855b6f621aeabdb71ed9e1fd8cc20023c450f51711b
+size 793878

config.json

@@ -0,0 +1,86 @@
+{
+  "_name_or_path": "ntu-spml/distilhubert",
+  "activation_dropout": 0.1,
+  "apply_spec_augment": false,
+  "architectures": [
+    "HubertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "do_stable_layer_norm": false,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.0,
+  "feat_proj_layer_norm": false,
+  "final_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Atraining_artifact_aug",
+    "1": "Atraining_extrahls_aug",
+    "2": "Btraining_extrasystole_aug",
+    "3": "Btraining_murmur_aug",
+    "4": "Normal_aug"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Atraining_artifact_aug": "0",
+    "Atraining_extrahls_aug": "1",
+    "Btraining_extrasystole_aug": "2",
+    "Btraining_murmur_aug": "3",
+    "Normal_aug": "4"
+  },
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "hubert",
+  "num_attention_heads": 12,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32
+}

config.py

@@ -0,0 +1,2 @@
+# config.py
+MODEL_ID = "arham061/distilhubert-finetuned-PASCAL_Dataset_Augmented"

events.out.tfevents.1710064702.c9211bb32db6.1776.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2908d1146b5efb8e524f6c7338fff5015aca93c816ccb9d7a83449cd35631699
+size 71812

extra_hystole.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:277eede8e722df5d8a013a7d919618e39c3b0f3f3a00ddd843b20cd960b6baa5
+size 793878

model.safetensors.crdownload

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f78f8ea5438daf850dd513a2b6f3775fc45641702fea46ff18ee05ea76a3ad6
+size 62000965

preprocessor_config.json

@@ -0,0 +1,9 @@
+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

requirements (1).txt

@@ -0,0 +1,4 @@
+streamlit
+transformers
+torch
+torchaudio