# Latvian DeepSpeech speech recognition model

For more information about the DeepSpeech see https://deepspeech.readthedocs.io/en/r0.9/

This model has two versions:
* The regular (`output_graph.*`) - This is more precise but model files take more space
* The lite (`output_graph-lite.*`) - This is a bit less precise, but model files are smaller

Language model (scorer) has three versions:
* The regular (`kenlm-lv.scorer`) - The biggest and most precise, built with wikipedia, tilde and europarl data 
* The wiki (`kenlm-lv-wiki.scorer`) - Smaller, but still quite comprehensive, built with wikipedia data 
* The wiki (`kenlm-lv-lite.scorer`) - Smallest, built with subset of wikipedia data

Model is trained on:
* https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0
* https://huggingface.co/datasets/google/fleurs

Language model (scorer) is generated from:
* https://huggingface.co/datasets/RaivisDejus/latvian-text

## Recognition precision

### Regular model

Regular model with FULL scorer
* CV 12 only     - WER: 0.150664, CER: 0.044672, loss: 6.493098
* CV 12 + Fleurs - WER: 0.296679, CER: 0.109360, loss: 85.185188

Regular model with WIKI scorer
* CV 12 only     - WER: 0.183172, CER: 0.053100, loss: 6.493098
* CV 12 + Fleurs - WER: 0.321415, CER: 0.114797, loss: 85.185188

Regular model with LITE scorer
* CV 12 only     - WER: 0.230378, CER: 0.064393, loss: 6.493098
* CV 12 + Fleurs - WER: 0.335594, CER: 0.119691, loss: 85.185188

### Lite model
Lite model with FULL scorer
* CV 12 only     - WER: 0.390653, CER: 0.174240, loss: 18.772360
* CV 12 + Fleurs - WER: 0.570163, CER: 0.288619, loss: 147.965317

Lite model with WIKI scorer
* CV 12 only     - WER: 0.429002, CER: 0.186182, loss: 18.772360
* CV 12 + Fleurs - WER: 0.588117, CER: 0.293284, loss: 147.965317

Lite model with LITE scorer
* CV 12 only     - WER: 0.484594, CER: 0.203621, loss: 18.772360
* CV 12 + Fleurs - WER: 0.595669, CER: 0.293980, loss: 147.965317

## Usage

This DeepSpeech model can be used to add voice commands to different systems, like Home Assistant with help of 
[Rhasspy Voice Assistant](https://rhasspy.readthedocs.io/).

Demo is available https://github.com/raivisdejus/DeepSpeechLV
{"language": ["lv"], "license": "cc-by-4.0", "tags": ["deepspeech", "automatic-speech-recognition"], "datasets": ["mozilla-foundation/common_voice_12_0", "RaivisDejus/latvian-text", "google/fleurs"], "metrics": ["wer"]}
tflite deepspeech automatic-speech-recognition lv dataset:mozilla-foundation/common_voice_12_0 dataset:RaivisDejus/latvian-text dataset:google/fleurs license:cc-by-4.0 region:us
TAGS
#tflite #deepspeech #automatic-speech-recognition #lv #dataset-mozilla-foundation/common_voice_12_0 #dataset-RaivisDejus/latvian-text #dataset-google/fleurs #license-cc-by-4.0 #region-us 

# Latvian DeepSpeech speech recognition model

For more information about the DeepSpeech see URL

This model has two versions:
* The regular ('output_graph.*') - This is more precise but model files take more space
* The lite ('output_graph-lite.*') - This is a bit less precise, but model files are smaller

Language model (scorer) has three versions:
* The regular ('URL') - The biggest and most precise, built with wikipedia, tilde and europarl data 
* The wiki ('URL') - Smaller, but still quite comprehensive, built with wikipedia data 
* The wiki ('URL') - Smallest, built with subset of wikipedia data

Model is trained on:
* URL
* URL

Language model (scorer) is generated from:
* URL

## Recognition precision

### Regular model

Regular model with FULL scorer
* CV 12 only     - WER: 0.150664, CER: 0.044672, loss: 6.493098
* CV 12 + Fleurs - WER: 0.296679, CER: 0.109360, loss: 85.185188

Regular model with WIKI scorer
* CV 12 only     - WER: 0.183172, CER: 0.053100, loss: 6.493098
* CV 12 + Fleurs - WER: 0.321415, CER: 0.114797, loss: 85.185188

Regular model with LITE scorer
* CV 12 only     - WER: 0.230378, CER: 0.064393, loss: 6.493098
* CV 12 + Fleurs - WER: 0.335594, CER: 0.119691, loss: 85.185188

### Lite model
Lite model with FULL scorer
* CV 12 only     - WER: 0.390653, CER: 0.174240, loss: 18.772360
* CV 12 + Fleurs - WER: 0.570163, CER: 0.288619, loss: 147.965317

Lite model with WIKI scorer
* CV 12 only     - WER: 0.429002, CER: 0.186182, loss: 18.772360
* CV 12 + Fleurs - WER: 0.588117, CER: 0.293284, loss: 147.965317

Lite model with LITE scorer
* CV 12 only     - WER: 0.484594, CER: 0.203621, loss: 18.772360
* CV 12 + Fleurs - WER: 0.595669, CER: 0.293980, loss: 147.965317

## Usage

This DeepSpeech model can be used to add voice commands to different systems, like Home Assistant with help of 
Rhasspy Voice Assistant.

Demo is available URL
# Latvian DeepSpeech speech recognition model

For more information about the DeepSpeech see URL

This model has two versions:
* The regular ('output_graph.*') - This is more precise but model files take more space
* The lite ('output_graph-lite.*') - This is a bit less precise, but model files are smaller

Language model (scorer) has three versions:
* The regular ('URL') - The biggest and most precise, built with wikipedia, tilde and europarl data 
* The wiki ('URL') - Smaller, but still quite comprehensive, built with wikipedia data 
* The wiki ('URL') - Smallest, built with subset of wikipedia data

Model is trained on:
* URL
* URL

Language model (scorer) is generated from:
* URL ## Recognition precision ### Regular model

Regular model with FULL scorer
* CV 12 only     - WER: 0.150664, CER: 0.044672, loss: 6.493098
* CV 12 + Fleurs - WER: 0.296679, CER: 0.109360, loss: 85.185188

Regular model with WIKI scorer
* CV 12 only     - WER: 0.183172, CER: 0.053100, loss: 6.493098
* CV 12 + Fleurs - WER: 0.321415, CER: 0.114797, loss: 85.185188

Regular model with LITE scorer
* CV 12 only     - WER: 0.230378, CER: 0.064393, loss: 6.493098
* CV 12 + Fleurs - WER: 0.335594, CER: 0.119691, loss: 85.185188 ### Lite model
Lite model with FULL scorer
* CV 12 only     - WER: 0.390653, CER: 0.174240, loss: 18.772360
* CV 12 + Fleurs - WER: 0.570163, CER: 0.288619, loss: 147.965317

Lite model with WIKI scorer
* CV 12 only     - WER: 0.429002, CER: 0.186182, loss: 18.772360
* CV 12 + Fleurs - WER: 0.588117, CER: 0.293284, loss: 147.965317

Lite model with LITE scorer
* CV 12 only     - WER: 0.484594, CER: 0.203621, loss: 18.772360
* CV 12 + Fleurs - WER: 0.595669, CER: 0.293980, loss: 147.965317 ## Usage

This DeepSpeech model can be used to add voice commands to different systems, like Home Assistant with help of 
Rhasspy Voice Assistant.

Demo is available URL
TAGS
#tflite #deepspeech #automatic-speech-recognition #lv #dataset-mozilla-foundation/common_voice_12_0 #dataset-RaivisDejus/latvian-text #dataset-google/fleurs #license-cc-by-4.0 #region-us 
 # Latvian DeepSpeech speech recognition model

For more information about the DeepSpeech see URL

This model has two versions:
* The regular ('output_graph.*') - This is more precise but model files take more space
* The lite ('output_graph-lite.*') - This is a bit less precise, but model files are smaller

Language model (scorer) has three versions:
* The regular ('URL') - The biggest and most precise, built with wikipedia, tilde and europarl data 
* The wiki ('URL') - Smaller, but still quite comprehensive, built with wikipedia data 
* The wiki ('URL') - Smallest, built with subset of wikipedia data

Model is trained on:
* URL
* URL

Language model (scorer) is generated from:
* URL ## Recognition precision ### Regular model

Regular model with FULL scorer
* CV 12 only     - WER: 0.150664, CER: 0.044672, loss: 6.493098
* CV 12 + Fleurs - WER: 0.296679, CER: 0.109360, loss: 85.185188

Regular model with WIKI scorer
* CV 12 only     - WER: 0.183172, CER: 0.053100, loss: 6.493098
* CV 12 + Fleurs - WER: 0.321415, CER: 0.114797, loss: 85.185188

Regular model with LITE scorer
* CV 12 only     - WER: 0.230378, CER: 0.064393, loss: 6.493098
* CV 12 + Fleurs - WER: 0.335594, CER: 0.119691, loss: 85.185188 ### Lite model
Lite model with FULL scorer
* CV 12 only     - WER: 0.390653, CER: 0.174240, loss: 18.772360
* CV 12 + Fleurs - WER: 0.570163, CER: 0.288619, loss: 147.965317

Lite model with WIKI scorer
* CV 12 only     - WER: 0.429002, CER: 0.186182, loss: 18.772360
* CV 12 + Fleurs - WER: 0.588117, CER: 0.293284, loss: 147.965317

Lite model with LITE scorer
* CV 12 only     - WER: 0.484594, CER: 0.203621, loss: 18.772360
* CV 12 + Fleurs - WER: 0.595669, CER: 0.293980, loss: 147.965317 ## Usage

This DeepSpeech model can be used to add voice commands to different systems, like Home Assistant with help of 
Rhasspy Voice Assistant.

Demo is available URL
TAGS
#tflite #deepspeech #automatic-speech-recognition #lv #dataset-mozilla-foundation/common_voice_12_0 #dataset-RaivisDejus/latvian-text #dataset-google/fleurs #license-cc-by-4.0 #region-us 
# Latvian DeepSpeech speech recognition model

For more information about the DeepSpeech see URL

This model has two versions:
* The regular ('output_graph.*') - This is more precise but model files take more space
* The lite ('output_graph-lite.*') - This is a bit less precise, but model files are smaller

Language model (scorer) has three versions:
* The regular ('URL') - The biggest and most precise, built with wikipedia, tilde and europarl data 
* The wiki ('URL') - Smaller, but still quite comprehensive, built with wikipedia data 
* The wiki ('URL') - Smallest, built with subset of wikipedia data

Model is trained on:
* URL
* URL

Language model (scorer) is generated from:
* URL## Recognition precision### Regular model

Regular model with FULL scorer
* CV 12 only     - WER: 0.150664, CER: 0.044672, loss: 6.493098
* CV 12 + Fleurs - WER: 0.296679, CER: 0.109360, loss: 85.185188

Regular model with WIKI scorer
* CV 12 only     - WER: 0.183172, CER: 0.053100, loss: 6.493098
* CV 12 + Fleurs - WER: 0.321415, CER: 0.114797, loss: 85.185188

Regular model with LITE scorer
* CV 12 only     - WER: 0.230378, CER: 0.064393, loss: 6.493098
* CV 12 + Fleurs - WER: 0.335594, CER: 0.119691, loss: 85.185188### Lite model
Lite model with FULL scorer
* CV 12 only     - WER: 0.390653, CER: 0.174240, loss: 18.772360
* CV 12 + Fleurs - WER: 0.570163, CER: 0.288619, loss: 147.965317

Lite model with WIKI scorer
* CV 12 only     - WER: 0.429002, CER: 0.186182, loss: 18.772360
* CV 12 + Fleurs - WER: 0.588117, CER: 0.293284, loss: 147.965317

Lite model with LITE scorer
* CV 12 only     - WER: 0.484594, CER: 0.203621, loss: 18.772360
* CV 12 + Fleurs - WER: 0.595669, CER: 0.293980, loss: 147.965317## Usage

This DeepSpeech model can be used to add voice commands to different systems, like Home Assistant with help of 
Rhasspy Voice Assistant.

Demo is available URL