Update app.py

app.py

@@ -1,3 +1,64 @@
 import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from transformers import AutoConfig, Wav2Vec2Processor, Wav2Vec2FeatureExtractor
+from src.models import Wav2Vec2ForSpeechClassification
 
-gr.Interface.load("models/andromeda01111/Malayalam_SA").launch()
+import librosa
+import IPython.display as ipd
+import numpy as np
+import pandas as pd
+import os
+
+model_name_or_path = "andromeda01111/Malayalam_SA"
+config = AutoConfig.from_pretrained(model_name_or_path)
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
+sampling_rate = feature_extractor.sampling_rate
+model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path)
+
+
+def speech_file_to_array_fn(path, sampling_rate):
+    speech_array, _sampling_rate = torchaudio.load(path)
+    resampler = torchaudio.transforms.Resample(_sampling_rate)
+    speech = resampler(speech_array).squeeze().numpy()
+    return speech
+
+
+def predict(path, sampling_rate):
+    speech = speech_file_to_array_fn(path, sampling_rate)
+    features = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
+
+    input_values = features.input_values
+    attention_mask = features.attention_mask
+
+    with torch.no_grad():
+        logits = model(input_values, attention_mask=attention_mask).logits
+
+    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
+    output_emotion = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
+
+    return output_emotion
+
+
+# Wrapper function for Gradio
+def gradio_predict(audio):
+    predictions = predict(audio)
+    return [f"{pred['Emotion']}: {pred['Score']}" for pred in predictions]
+
+
+# Gradio interface
+emotions = [config.id2label[i] for i in range(len(config.id2label))]
+outputs = [gr.Textbox(label=emotion, interactive=False) for emotion in emotions]
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(label="Upload Audio", type="filepath"),
+    outputs=outputs,
+    title="Emotion Recognition",
+    description="Upload an audio file to predict emotions and their corresponding percentages.",
+)
+
+# Launch the app
+interface.launch()