Wav2vec Vm Finetune Onnx

--- library_name: transformers license: apache-2.0 base_model: - facebook/wav2vec2-xls-r-300m - jakeBland/wav2vec-vm-finetune tags: - generated_from_trainer - speech-recognition - audio-classification - voicemail-detection model-index: - name: wav2vec-vm-finetune results: [] language: - en metrics: - accuracy --- Original Repo: https://huggingface.co/jakeBland/wav2vec-vm-finetune # wav2vec-vm-finetune This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for **voicemail detection**. It is trained on a dataset of call recordings to distinguish between **voicemail greetings** and **live human responses**. ## Model description This model builds on **wav2vec2-xls-r-300m**, a self-supervised speech model trained on large-scale multilingual data. We fine-tuned it on the first two seconds of a call. ## Intended uses & limitations - Automated voicemail detection in AI-powered call assistants. - Filtering voicemail responses in customer service and sales call automation. - Only trianed on the English language. - Assumes the voicemail track is isolated and contains no audio from the caller. - Designed for the first two seconds of audio when calling a voicemail. ## Training and evaluation data The model was trained on a proprietary dataset of call recordings, labeled as: - **Live human responses** - **Voicemail greetings** The dataset includes diverse voicemail recordings across multiple types to improve generalization. ## Evaluation metrics The model achieved: - **98% accuracy** on voicemail detection. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.48.2 - Pytorch 2.5.1+cu124 - Datasets 1.18.3 - Tokenizers 0.21.0

Source context: 6 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo abdofallah/wav2vec-vm-finetune-onnx