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| # Import the necessary libraries | |
| from transformers import pipeline | |
| # Initialize the text classification model with a pre-trained model | |
| model_text_emotion = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base") | |
| # Initialize the audio classification model with a pre-trained SER model | |
| model_speech_emotion = pipeline("audio-classification", model="aherzberg/ser_model_fixed_label") | |
| # Initialize the automatic speech recognition model with a pre-trained model that is capable of converting speech to text | |
| model_voice2text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en") | |
| # A function that uses the initialized text classification model to predict the emotion of a given text input | |
| def infere_text_emotion(text): | |
| return model_text_emotion(text)[0]["label"].capitalize() | |
| # A function that uses the initialized audio classification model to predict the emotion of a given speech input | |
| def infere_speech_emotion(text): | |
| # Dict that maps the speech model emotions with the text's ones | |
| emotions_dict = {"angry": "Anger", "disgust": "Disgust", "fear": "Fear", "happy": "Joy", "neutral": "Neutral", "sad": "Sadness"} | |
| inference = model_speech_emotion(text)[0]["label"] | |
| return emotions_dict[inference] | |
| # A function that uses the initialized automatic speech recognition model to convert speech (as an audio file) to text | |
| def infere_voice2text(audio_file): | |
| return model_voice2text(audio_file)["text"] | |