---
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
---

# Korla/Wav2Vec2BertForCTC-hsb

## Model Description

**Wav2Vec2BertForCTC-hsb** is a fine-tuned [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) model with a BERT-style character classification head, adapted for **Upper Sorbian** automatic speech recognition (ASR). This model has been fine-tuned for CTC (Connectionist Temporal Classification) loss and is capable of transcribing audio in the Upper Sorbian language.

## Usage

This model can be used for speech-to-text tasks on Upper Sorbian audio.

An optional **5-gram language model (`5gram.bin`)** is provided for decoding with an external LM scorer. This n-gram model was trained on a corpus of **Upper Sorbian Holy Masses**, which can help improve decoding accuracy for religious or formal speech domains.

## Training Data

The model was fine-tuned on a dataset provided by the **Foundation for the Sorbian People**, which consists of high-quality recordings and transcripts in Upper Sorbian. The dataset includes diverse speakers and speech conditions, ensuring a robust acoustic model.

## Language Model

- **Name:** `5gram.bin`
- **Type:** 5-gram character-level KenLM language model
- **Domain:** Upper Sorbian religious speech (Holy Masses)
- **Usage:** For decoding with tools such as [CTCDecoder](https://github.com/parlance/ctcdecode).

## Limitations

- The model's accuracy may degrade on informal or highly dialectal speech not represented in the training data.  
- The language model is domain-specific (religious speech) and may bias decoding toward that context.  
- The model supports only **Upper Sorbian**, not Lower Sorbian or other Slavic languages.

## How to Use
For normal use (without LM) you can load the model into a pipeline.

To use the 5-gram language model for decoding, use the pyctcdecode library.

## Citation
Please cite as:
```bibtex
@misc{korla_wav2vec2bertforctc_hsb,
  author       = {Karl Baier},
  title        = {Wav2Vec2BertForCTC-hsb},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/Korla/Wav2Vec2BertForCTC-hsb}},
}
```