1. Enable your Google Cloud Platform (GCP) speech-to-Text API. 2. Enable your GCP Natural Language API. 3. Create or reuse a GCP Service Account. 4. Create a call center audio file (WAV extensions work best). 5. Create a bucket in GCP Storage. 6. Upload your audio WAV file to your GCP Storage bucket (your audio file must be different from the one used in class). 7. Extract the text from your audio file. 8. Run a sentiment analysis process on the extracted audio text. 9. Store each speech-extracted record in a No-SQL database like Firestore. Your database must have these keys in each document: 1. wav_filename (e.g. "1.wav") 2. sentence_number (e.g. 1) 3. sentence_text (e.g. "Thanks for calling XYZ my name is John") 4. sentiment (e.g. 0.87) 5. magnitude (e.g. 1.2) 6. transcription_date (e.g. "2023-01-28") 10. Visualize your findings. If you do not want to use GCP, you can use another cloud provider or package that will generate similar results