Multitask Speech Model with Wav2Vec2
This repository contains a multitask learning pipeline built on top of Wav2Vec2
, designed to jointly perform:
Automatic Speech Recognition (ASR) (character-level CTC loss)
Speaker Identification
Emotion Recognition
The system is trained on a combination of training dataset with parallel data from speech transcriptions, speaker identification and emotion recognition labels.
📌 Features
Multitask model (Wav2Vec2MultiTasks) with shared Wav2Vec2 encoder and separate heads for:
Speech Recognition (CTC)
Speaker classification
Emotion classification
Custom data preprocessing:
Cleans transcripts (removes punctuation & special characters)
Converts numbers into words
Builds a vocabulary and tokenizer
Filters short/invalid audio
Training, validation, and test splits with collators for CTC.
Evaluation metrics:
Character Error Rate (CER) for character recognition
Accuracy for speaker and emotion classification
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