ah now its okay

app.py

@@ -1,10 +1,6 @@
-from multilingual_translation import text_to_text_generation
-from utils import lang_ids, data_scraping
 import whisper
 import gradio as gr
 
-lang_list = list(lang_ids.keys())
-model_list = data_scraping()
 model = whisper.load_model("small")
 
 def transcribe(audio):
@@ -24,28 +20,9 @@ def transcribe(audio):
     # decode the audio
     options = whisper.DecodingOptions(fp16 = False)
     result = whisper.decode(model, mel, options)
-
-    finalResult = text_to_text_generation(prompt=result.text, model_id='facebook/m2m100_418M', device='cpu',target_lang='English')
-    return finalResult
-
-# api endpoint to return the transcription in EN as a json response
-
-# @app.route('/transcribe', methods=['POST'])
-# def transcribe_api():
-#     if request.method == 'POST':
-#         audio = request.files['audio']
-#         audio = audio.read()
-#         audio = io.BytesIO(audio)
-#         audio = whisper.load_audio(audio)
-#         audio = whisper.pad_or_trim(audio)
-#         mel = whisper.log_mel_spectrogram(audio).to(model.device)
-#         _, probs = model.detect_language(mel)
-#         print(f"Detected language: {max(probs, key=probs.get)}")
-#         options = whisper.DecodingOptions(fp16 = False)
-#         result = whisper.decode(model, mel, options)
-#         return jsonify(result)
-
-
+    # translate the result to english
+    newresult = whisper.translate(result, "en")
+    return newresult.text
     
     
  
@@ -58,6 +35,4 @@ gr.Interface(
     outputs=[
         "textbox"
     ],
-    live=True).launch(debug=True, enable_queue=True)
-
-# output = gr.outputs.Textbox(label="Output Text")
+    live=True).launch()

requirements.txt

@@ -1,6 +1,2 @@
-torch
-beautifulsoup4==4.11.2
-multilingual_translation==0.0.5
-requests==2.28.1
 tensorflow
 git+https://github.com/openai/whisper.git