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app.py

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+import whisper
+import gradio as gr 
+import time
+model = whisper.load_model("base")
+#from transformers import pipeline
+#es_en_translator = pipeline("translation_es_to_en")
+
+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)
+    print(f"Detected language: {max(probs, key=probs.get)}")
+    #lang = LANGUAGES[language]
+    #lang=(f"Detected language: {lang}")
+
+
+    # decode the audio
+    options = whisper.DecodingOptions(fp16 = False)#,task= "translate")
+    result = whisper.decode(model, mel, options)
+    #word= result.text
+    #trans = es_en_translator(word)
+    #Trans = trans[0]['translation_text']
+    #result=f"{lang}\n{word}\n\nEnglish translation: {Trans}"
+    return result.text
+    
+    
+ 
+gr.Interface(
+    title = 'SPEECH TO TEXT', 
+    fn=transcribe, 
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath")
+    ],
+    outputs=[
+        "textbox"
+    ],
+    live=True).launch()

requirements.txt

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