Update app.py

app.py

@@ -1,3 +1,5 @@
+import os
+os.system("pip install git+https://github.com/openai/whisper.git")
 import evaluate
 from evaluate.utils import launch_gradio_widget
 import gradio as gr
@@ -25,11 +27,18 @@ emotion_dict = {
 def classify_toxicity(audio_file, text_input, classify_anxiety):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
-        whisper_module = evaluate.load("whisper")
-        transcription_results = whisper_module.compute(uploaded=audio_file)
-     
+        '''whisper_model = WhisperModel.from_pretrained("openai/whisper-base")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
+        transcription_results = whisper_model.compute(uploaded=audio_file)
+         '''
+        audio = whisper.load_audio(audio_file)
+        mel = whisper.log_mel_spectrogram(audio).to(model.device)
+        _, probs = model.detect_language(mel)    
+        options = whisper.DecodingOptions(fp16 = False)
+        result = whisper.decode(model, mel, options)
         # Extract the transcribed text
-        transcribed_text = transcription_results["transcription"]
+        # transcribed_text = transcription_results["transcription"]
+        transcribed_text = resut.text
         
         #### Emotion classification ####
         emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")