Wav2vec Vm Finetune

🚀 wav2vec-vm-finetune

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m for voicemail detection. It can distinguish between voicemail greetings and live human responses using call recording data.

🚀 Quick Start

This model is a fine - tuned version of 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.

✨ Features

• Automated Voicemail Detection: Ideal for AI - powered call assistants to detect voicemails automatically.
• Response Filtering: Useful in customer service and sales call automation for filtering voicemail responses.

📚 Documentation

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

• Intended Uses:
  • Automated voicemail detection in AI - powered call assistants.
  • Filtering voicemail responses in customer service and sales call automation.
• Limitations:
  • Only trained 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

📄 License

This project is under the Apache - 2.0 license.