Create app.py

Yo it's a tested version.....

app.py

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+!pip install git+https://github.com/openai/whisper.git
+!pip install transformers
+# Installs the openai-whisper package from its GitHub repository.
+
+import gradio as gr
+import torch
+import whisper # Now you should be able to import the whisper module
+import warnings
+import os
+import librosa
+import numpy as np
+from transformers import pipeline
+
+warnings.filterwarnings('ignore')
+
+MODEL_NAME = "openai/whisper-small"
+BATCH_SIZE = 8
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+# Whisper for transcription
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device
+)
+
+# Emotion classifier for text-based classification
+emotion_classifier = pipeline("text-classification", model='MilaNLProc/xlm-emo-t', return_all_scores=True)
+
+# Function to extract prosodic features using librosa
+def extract_audio_features(audio_file):
+    y, sr = librosa.load(audio_file)
+    
+    # Pitch (Fundamental Frequency)
+    pitches, magnitudes = librosa.core.piptrack(y=y, sr=sr)
+    pitch = np.mean([pitches[i][magnitudes[i] > 0] for i in range(len(pitches)) if len(pitches[i][magnitudes[i] > 0]) > 0])
+
+    # Intensity (RMS)
+    rms = np.mean(librosa.feature.rms(y=y))
+
+    # Loudness (Using the perceptual C-weighting of the signal)
+    S = np.abs(librosa.stft(y))**2
+    loudness = np.mean(librosa.perceptual_weighting(S, freqs=librosa.fft_frequencies(sr=sr)))
+
+    return {
+        "pitch": pitch,
+        "rms": rms,
+        "loudness": loudness
+    }
+
+# Function to transcribe and classify emotions (dual-pipeline)
+def translate_and_classify(audio):
+    # Step 1: Transcribe audio to text using Whisper
+    text_result = pipe(audio, batch_size=BATCH_SIZE)["text"]
+    
+    # Step 2: Extract prosodic features from the audio using librosa
+    prosodic_features = extract_audio_features(audio)
+    
+    # Step 3: Use the emotion classifier on the transcribed text
+    emotion = emotion_classifier(text_result)
+    detected_emotion = {}
+    for emotion_item in emotion[0]:
+        detected_emotion[emotion_item["label"]] = emotion_item["score"]
+
+    # Combine prosodic features and text-based emotion detection
+    combined_result = {
+        "transcription": text_result,
+        "text_based_emotion": detected_emotion,
+        "prosody": prosodic_features
+    }
+    
+    return combined_result["transcription"], combined_result["text_based_emotion"], combined_result["prosody"]
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """# Emotion Detection from Speech
+    
+    ##### Detection of anger, sadness, joy, fear in speech using OpenAI Whisper, XLM-RoBERTa, and prosodic features (pitch, loudness, intensity)
+    """)
+
+    with gr.Column():
+        with gr.Tab("Record Audio"):
+            audio_input_r = gr.Audio(label='Record Audio Input', sources=["microphone"], type="filepath")
+            transcribe_audio_r = gr.Button('Transcribe')
+
+        with gr.Tab("Upload Audio as File"):
+            audio_input_u = gr.Audio(label='Upload Audio', sources=["upload"], type="filepath")
+            transcribe_audio_u = gr.Button('Transcribe')
+
+        with gr.Row():
+            transcript_output = gr.Textbox(label="Transcription", lines=3)
+            emotion_output = gr.Label(label="Detected Emotion from Text")