Instructions to use RayyanAhmed9477/speech-emotion-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use RayyanAhmed9477/speech-emotion-analyzer with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://RayyanAhmed9477/speech-emotion-analyzer") - Notebooks
- Google Colab
- Kaggle
Speech Emotion Analyzer Model
This is a Keras model trained for speech emotion recognition.
Model Details
The model is a Convolutional Neural Network (CNN) trained on audio features (Mel Spectrograms) to classify speech into the following emotion categories:
angry, disgust, fear, happy, neutral, sad, surprise
Usage
To use this model, you can load it using TensorFlow/Keras:
import tensorflow as tf
from huggingface_hub import hf_hub_download
repo_id = "RayyanAhmed9477/speech-emotion-analyzer"
filename = "cnn_emotion_model.keras"
# Download the model file
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
# Load the model
model = tf.keras.models.load_model(model_path)
# Now you can use the model for prediction
# (You'll need to implement feature extraction similar to the original app)
Features
- Data loading and preprocessing using the RAVDESS dataset
- Feature extraction using librosa (MFCCs and spectrograms)
- Neural network models (CNN) implemented with TensorFlow/Keras
- Model training with early stopping and comprehensive evaluation metrics
- Hyperparameter optimization
- Downloads last month
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