Instructions to use Cseti/VibeVoice-ASR_LoRA_Hungarian_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Cseti/VibeVoice-ASR_LoRA_Hungarian_v1 with PEFT:
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- Notebooks
- Google Colab
- Kaggle
VibeVoice-ASR_LoRA_Hungarian_v1
This repository contains a LoRA (Low-Rank Adaptation) adapter for the VibeVoice-ASR model. This fine-tuned version was trained on approximately 500 hours of speech data to enhance its accuracy.
Training
This LoRA was trained on RunPod cloud GPUs.
Performance Comparison
Using 1000 samples from CommonVoice 17 as the evaluation dataset, the following metrics demonstrate a significant improvement over the base model:
| Metric | Base Model (without LoRA) | This Model (with LoRA) |
|---|---|---|
| Raw WER | 53.02% | 19.25% |
| Normalized WER | 48.67% | 15.90% |
Inference
For inference please refer to the official Microsoft repo: https://github.com/microsoft/VibeVoice
Non-Commercial Use Only
Due to the specific licensing and characteristics of the dataset used during the fine-tuning process, this model is prohibited for commercial use. It is intended only for research and evaluation.
Support
Producing and sharing this kind of open-source work requires renting cloud GPUs, which gets expensive quickly. If you find it useful and would like me to keep contributing, your support is very much appreciated:
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Model tree for Cseti/VibeVoice-ASR_LoRA_Hungarian_v1
Base model
microsoft/VibeVoice-ASRCollection including Cseti/VibeVoice-ASR_LoRA_Hungarian_v1
Evaluation results
- Raw WER (with LoRA)self-reported19.250
- Normalized WER (with LoRA)self-reported15.900