Add application file

app.py

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+import whisper
+
+model = whisper.load_model("base")
+
+from transformers import pipeline
+en_fr_translator = pipeline("translation_en_to_fr")
+
+import gradio as gr 
+import time
+
+def transcribe(audio):
+    
+    #time.sleep(3)
+    # load audio and pad/trim it to fit 30 seconds
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to the same device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
+    _, probs = model.detect_language(mel)
+    lang=(f"Detected language: {max(probs, key=probs.get)}")
+
+    
+    
+   
+
+    # decode the audio
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel, options)
+    word= result.text
+    trans = en_fr_translator(word)
+    Trans = trans[0]['translation_text']
+    result=f"{lang}\n{word}\n\nFrench translation: {Trans}"
+    return result
+
+
+gr.Interface(
+title = 'OpenAI Whisper ASR Gradio Web UI',
+fn=transcribe,
+inputs=[
+    gr.inputs.Audio(source="microphone", type="filepath")
+],
+outputs=[
+    "textbox"
+],
+live=True).launch()

requirements.txt

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+git+https://github.com/openai/whisper.git
+transformers