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---
license: gpl-3.0
language:
- be
tags:
- audio
- speech
- automatic-speech-recognition
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: be
metrics:
- name: Dev WER
type: wer
value: 17.61
- name: Test WER
type: wer
value: 18.7
- name: Dev WER (with LM)
type: wer
value: 11.5
- name: Test WER (with LM)
type: wer
value: 12.4
---
# Automatic Speech Recognition for Belarusian language
Fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on `mozilla-foundation/common_voice_8_0 be` dataset.
`Train`, `Dev`, `Test` splits were used as they are present in the dataset. No additional data was used from `Validated` split,
only 1 voicing of each sentence was used - the way the data was split by [CommonVoice CorporaCreator](https://github.com/common-voice/CorporaCreator).
To build a better model **one can use additional voicings from `Validated` split** for sentences already present in `Train`, `Dev`, `Test` splits,
i.e. enlarge mentioned splits.
Language model was built using [KenLM](https://kheafield.com/code/kenlm/estimation/).
5-gram Language model was built on sentences from `Train + (Other - Dev - Test)` splits of `mozilla-foundation/common_voice_8_0 be` dataset.
Source code is available [here](https://github.com/yks72p/stt_be).
## Run model in a browser
This page contains interactive demo widget that lets you test this model right in a browser.
However, this widget uses Acoustic model only **without** Language model that significantly improves overall performance.
You can play with **full pipeline of Acoustic model + Language model** on the following [spaces page](https://huggingface.co/spaces/ales/wav2vec2-cv-be-lm)
(also works from browser).
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