Push model using huggingface_hub.

README.md

@@ -9,43 +9,7 @@ tags:
 - speaker-identification
 ---
 
-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
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: https://huggingface.co/asadullah797/ssl-semi-multitask
+- Paper: [More Information Needed]
+- Docs: https://github.com/asadullah797/ssl_semi-multitask/blob/main/README.md

model.safetensors

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