---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
- speech-recognition
- audio-classification
- voicemail-detection
model-index:
- name: wav2vec-vm-finetune
  results: []
language:
- en
metrics:
- accuracy
---

# 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