This model leverages updated Mel-Frequency Cepstral Coefficients (MFCC) features to perform robust audio analysis. It is designed for tasks such as audio classification or speech recognition, capturing spectral properties of audio signals even in noisy conditions.

Intended Use

Primary Use: Audio classification, speech recognition, or any audio analysis tasks.
Target Users: Researchers, developers, and hobbyists working in audio processing and machine learning.
Out-of-Scope Use: Not intended for real-time processing in highly dynamic environments without further adaptation or for applications requiring precise speech-to-text conversion in multiple languages.

Model Architecture

Base Architecture: (e.g., Convolutional Neural Network, Recurrent Neural Network, Transformer, etc.)
Input: Preprocessed audio signals represented as updated MFCC features.
Output: Depending on the task, the model outputs class probabilities or transcriptions.

Training Data

Dataset(s): (CREMA-D, RAVDESS)
Preprocessing: Audio normalization, MFCC extraction parameters (e.g., number of coefficients, window size, hop length).
Splits: Details on training, validation, and testing splits.
Augmentation: (Apply random pitch shifting and noise addition)

Evaluation Metrics

Accuracy:
Precision/Recall/F1-Score:
Additional Metrics: (e.g., ROC-AUC, confusion matrices, etc.)
Benchmarking: (Optional – describe how your model compares against baselines.)

Limitations

Sensitivity to very high levels of background noise.
Potential performance degradation on audio types not represented in the training data.
(Any other model-specific limitations or failure modes.)

Ethical Considerations

Ensure privacy and consent when processing audio data.
Consider potential biases if the training data is not diverse.
Avoid deploying in contexts where misclassifications could have serious consequences without thorough validation.

How to Use

Below is an example code snippet to load and use the model:

python

from transformers import AutoModel, AutoTokenizer

# Replace 'username/updated-mfcc-model' with your model's path on Hugging Face
model = AutoModel.from_pretrained("username/updated-mfcc-model")
tokenizer = AutoTokenizer.from_pretrained("username/updated-mfcc-model")

# Example: processing an audio file
# audio_input = ... (your audio processing code to extract MFCC features)
# outputs = model(audio_input)
# print(outputs)

Sharath45/SPEECH_EMOTION_RECOGNITION

Updated MFCC Model

Model Description