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readme.md

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+### Whisper API Test 
+The python script simply uses the Whisper API as the backend and Gradio web app framework as the frontend 
+
+### Prerequisite Packages
+```bash
+pip install gradio 
+pip install git+https://github.com/openai/whisper.git 
+pip install -U pip setuptools wheel
+pip install -U spacy
+python -m spacy download en_core_web_sm
+python -m spacy download xx_ent_wiki_sm
+pip install spacy-fastlang
+```
+ffmpeg should also be installed 
+```bash 
+# on Ubuntu or Debian
+sudo apt update && sudo apt install ffmpeg
+
+# on Arch Linux
+sudo pacman -S ffmpeg
+
+# on MacOS using Homebrew (https://brew.sh/)
+brew install ffmpeg
+
+# on Windows using Chocolatey (https://chocolatey.org/)
+choco install ffmpeg
+
+# on Windows using Scoop (https://scoop.sh/)
+scoop install ffmpeg
+```

whisperAPITest.py

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+import whisper
+import gradio as gr
+import spacy_fastlang
+import spacy
+import seaborn as sns 
+import matplotlib.pyplot as plt 
+import pandas as pd 
+#import pymongo_get_database
+nlp = spacy.load('en_core_web_sm') 
+nlp.add_pipe('language_detector')
+def find_frequency_and_percentage(items):
+    frequency = {}
+    total_items = len(items)
+    for item in items:
+        if item in frequency:
+            frequency[item] += 1
+        else:
+            frequency[item] = 1
+    for item, count in frequency.items():
+        percentage = (count / total_items) * 100
+        print(f"{item}: {percentage:.2f}%")
+    return frequency,total_items
+def whisperbackend(audiopath, audiopath2): 
+    if(audiopath == None): 
+        audiopath = audiopath2
+    model = whisper.load_model("large") 
+    audio = whisper.load_audio(audiopath)
+    audio = whisper.pad_or_trim(audio)
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    _, probs = model.detect_language(mel)
+    lang = max(probs, key=probs.get)
+    result = model.transcribe(audiopath)['text'] 
+    options = dict(language='ar',beam_size=5,best_of=5)
+    translate_options = dict(task="translate",**options)
+    translation = model.transcribe(audiopath,**translate_options)['text']
+    result_list = result.split() 
+    result_lang = [] 
+    for i in result_list: 
+        doc = nlp(i) 
+        result_lang.append(doc._.language)
+    freq_list, total_items = find_frequency_and_percentage(result_lang)
+    freq_keys = freq_list.keys() 
+    freq_values = freq_list.values() 
+    freq_df = pd.DataFrame({'Languages':freq_keys,'Percentages':freq_values}) 
+    freq_df['Percentages'] = freq_df['Percentages'].apply(lambda x: (x / total_items) *100)
+    if lang == 'en': 
+        lang = 'English'
+    elif lang == 'ar': 
+        lang = 'Arabic'
+    #pymongo_get_database.create_document(translation)
+    fig = (sns.barplot(freq_df,x='Languages',y='Percentages')).get_figure()
+    return fig, "The detected language is: \n"+lang+"\n Audio transcription: \n"+result+'\n'+translation
+with gr.Blocks() as demo:  
+    with gr.Tab("Input & Translation"):
+        audio_input1 = gr.Audio(source="microphone",type="filepath") 
+        audio_input2 = gr.Audio(type="filepath") 
+        text_output1 = gr.Textbox() 
+        button1 = gr.Button("Process Audio") 
+    with gr.Tab("Visualization"):
+        image_output1 = gr.Plot(width=350,height=300) 
+    button1.click(whisperbackend,inputs=[audio_input1,audio_input2],
+                  outputs=[image_output1,text_output1])
+demo.launch(share=True)