/doc/models.md

Models

This is the list of models compatible with Vosk-API.

To add a new model here create an issue on Github.

Model	Size	Accuracy	Notes
English
vosk-model-en-us-aspire-0.2	1.4G	TBD	Trained on Fisher + more or less recent LM. Should be pretty good for generic US English transcription
vosk-model-small-en-us-0.3	36M	TBD	Lightweight wideband model for Android and RPi
Chinese
vosk-model-cn-0.1.zip	195M	TBD	Big narrowband Chinese model for server processing
vosk-model-small-cn-0.3	32M	TBD	Lightweight wideband model for Android and RPi
Russian
vosk-model-ru-0.10.zip	2.5G	TBD	Big narrowband Russian model for server processing
vosk-model-small-ru-0.4	39M	TBD	Lightweight wideband model for Android and RPi
French
vosk-model-small-fr-pguyot-0.3	39M	TBD	Lightweight wideband model for Android and RPi trained by Paul Guyot
German
tuda-de	566M	TBD	Wideband server model from tuda-de
vosk-model-small-de-zamia-0.3	49M	TBD	Lightweight wideband model for Android and RPi
Spanish
vosk-model-small-es-0.3	33M	TBD	Lightweight wideband model for Android and RPi
Portuguese
vosk-model-small-pt-0.3	31M	TBD	Lightweight wideband model for Android and RPi
Greek
vosk-model-el-gr-0.6.zip	1.1G	TBD	Big narrowband Greek model for server processing, not extremely accurate though
Turkish
vosk-model-small-tr-0.3	35M	TBD	Lightweight wideband model for Android and RPi
Vietnamese
vosk-model-small-vn-0.3	32M	TBD	Lightweight wideband model for Android and RPi
Speaker identification model
vosk-model-spk-0.3	13M	TBD	Model for speaker identification, should work for all languages

Other models

Other places where you can check for models which might be compatible:

• http://kaldi-asr.org/models.html - variety of models from Kaldi - librispeech, aspire, chinese models
• https://github.com/daanzu/kaldi-active-grammar/blob/master/docs/models.md - Big dictation models
• http://zamia-speech.org/asr/ - German and English model from Zamia
• https://github.com/pguyot/zamia-speech/releases - French models for Zamia
• https://github.com/opensource-spraakherkenning-nl/Kaldi_NL - Dutch model
• https://montreal-forced-aligner.readthedocs.io/en/latest/pretrained_models.html (GMM models, not compatible but might be still useful)
• https://github.com/goodatlas/zeroth - Korean Kaldi (just a recipe and data to train)
• https://github.com/undertheseanlp/automatic_speech_recognition - Vietnamese Kaldi project

Training your own model

You can train your model with Kaldi toolkit. The training is pretty standard - you need tdnn nnet3 model with ivectors. You can check mini_librispeech recipe for details. Some notes on training:

• For smaller mobile models watch number of parameters
• Train the model without pitch. It might be helpful for small amount of data, but for large database it doesn't give the advantage but complicates the processing and increases response time.
• Train ivector of dim 30 instead of standard 100 to save memory of mobile models.

Model structure

Once you trained the model arrange the files according to the following layout (see en-us-aspire for details):

• am/final.mdl - acoustic model
• conf/mfcc.conf - mfcc config file. Make sure you take mfcc_hires.conf version if you are using hires model (most external ones)
• conf/model.conf - provide default decoding beams and silence phones. you have to create this file yourself, it is not present in kaldi model
• ivector/final.dubm - take ivector files from ivector extractor
• ivector/final.ie
• ivector/final.mat
• ivector/splice.conf
• ivector/global_cmvn.stats
• ivector/online_cmvn.conf
• graph/phones/word_boundary.int - from the graph
• graph/HCLG.fst - this is the decoding graph, if you are not using lookahead
• graph/HCLr.fst - use Gr.fst and HCLr.fst instead of one big HCLG.fst if you want to run rescoring
• graph/Gr.fst
• graph/phones.txt - from the graph
• graph/words.txt - from the graph
• rescore/G.carpa - carpa rescoring is optional but helpful in big models. Usually located inside data/lang_test_rescore
• rescore/G.fst - also optional if you want to use rescoring