
# 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).

{"language": ["be"], "license": "gpl-3.0", "tags": ["audio", "speech", "automatic-speech-recognition"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "be"}, "metrics": [{"type": "wer", "value": 17.61, "name": "Dev WER"}, {"type": "wer", "value": 18.7, "name": "Test WER"}, {"type": "wer", "value": 11.5, "name": "Dev WER (with LM)"}, {"type": "wer", "value": 12.4, "name": "Test WER (with LM)"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech be dataset:mozilla-foundation/common_voice_8_0 license:gpl-3.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #be #dataset-mozilla-foundation/common_voice_8_0 #license-gpl-3.0 #model-index #endpoints_compatible #has_space #region-us 


# Automatic Speech Recognition for Belarusian language

Fine-tuned version of 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. 
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. 
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.

## 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
(also works from browser).

# Automatic Speech Recognition for Belarusian language

Fine-tuned version of 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. 
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. 
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. ## 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
(also works from browser).
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #be #dataset-mozilla-foundation/common_voice_8_0 #license-gpl-3.0 #model-index #endpoints_compatible #has_space #region-us 
 # Automatic Speech Recognition for Belarusian language

Fine-tuned version of 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. 
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. 
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. ## 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
(also works from browser).
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #be #dataset-mozilla-foundation/common_voice_8_0 #license-gpl-3.0 #model-index #endpoints_compatible #has_space #region-us 
# Automatic Speech Recognition for Belarusian language

Fine-tuned version of 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. 
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. 
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.## 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
(also works from browser).