Delete app (2).py

app (2).py

@@ -1,45 +0,0 @@
-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()