Update app.py

app.py

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+import re
+import gradio as gr
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, pipeline
+import torch
+
+# Load Whisper model for transcription
+whisper_model_name = "openai/whisper-large"  # You can use base, small, medium, or large based on your needs
+processor = WhisperProcessor.from_pretrained(whisper_model_name)
+model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name)
+
+# Initialize the language detection model (using zero-shot classification for language detection)
+lang_detect_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# Function to transcribe audio to text using Whisper model
+def transcribe_audio(audio_file):
+    # Prepare input features for Whisper
+    input_features = processor(audio_file, return_tensors="pt")
+    # Generate transcription
+    generated_ids = model.generate(input_features["input_features"])
+    transcription = processor.decode(generated_ids[0], skip_special_tokens=True)
+    return transcription
+
+# Function to detect the language of the transcription using zero-shot classification
+def detect_language(text):
+    result = lang_detect_model(text, candidate_labels=["en", "fr", "es", "de", "it", "pt", "zh", "ja", "ar", "hi"])
+    return result['labels'][0], result['scores'][0]  # Return the detected language and score
+
+# Cleanup function to remove filler words and clean the transcription
+def cleanup_text(text):
+    # Remove filler words like "uh", "um", etc.
+    text = re.sub(r'\b(uh|um|like|you know|so|actually|basically)\b', '', text, flags=re.IGNORECASE)
+    # Remove extra spaces
+    text = re.sub(r'\s+', ' ', text)
+    # Strip leading and trailing spaces
+    text = text.strip()
+    # Capitalize the first letter
+    text = text.capitalize()
+    return text
+
+# Main function to process the audio and detect language
+def process_audio(audio_file):
+    transcription = transcribe_audio(audio_file)  # Transcribe audio to text
+    lang, score = detect_language(transcription)  # Detect the language of the transcription
+    cleaned_text = cleanup_text(transcription)  # Clean up the transcription
+    return cleaned_text, lang, score  # Return cleaned transcription, language, and confidence score
+
+# Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(label="Record your voice", type="filepath", scale=1)  # Input for audio file
+            output_text = gr.Textbox(label="Transcription", scale=1)  # Output text for transcription
+            output_lang = gr.Textbox(label="Detected Language", scale=1)  # Output text for detected language
+            output_score = gr.Textbox(label="Confidence Score", scale=1)  # Output confidence score
+            process_btn = gr.Button("Process Audio")  # Button to process audio
+
+        process_btn.click(fn=process_audio, inputs=[audio_input], outputs=[output_text, output_lang, output_score])
+
+    demo.launch(debug=True)