language_folder

.gitattributes

@@ -63,4 +63,3 @@ loquacious_small_train.csv filter=lfs diff=lfs merge=lfs -text
 loquacious_medium_train.csv filter=lfs diff=lfs merge=lfs -text
 loquacious_large_train.csv filter=lfs diff=lfs merge=lfs -text
 loquacious_dev.csv filter=lfs diff=lfs merge=lfs -text
-loquacious.sequitur_g2p.model_5 filter=lfs diff=lfs merge=lfs -text

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README.md

@@ -185,21 +185,6 @@ Some of the above datasets, in particular People's Speech, Yodas and CommonVoice
 
 Audios are embedded as raw bytes (can be decoded by soundfile). We chunked and created smaller audio files from long ones based on start and stop supervision from the different manifests of the datasets (this is necessary for HuggingFace). Language ID with a [SpeechBrain language ID model](https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa) was performed on Yodas.
 
-### Count-based Language Models and Lexicon
-
-The dataset includes three ARPA-format count-based language models trained on the text of the *train.large* subset of Loquacious:
-
-| LM  | Size | Binary Size | Dev. Perplexity |
-| --- | --- | --- | --- |
-| 3-gram pruned |   331MB | 721MB | 222 |
-| 4-gram pruned |   538MB | 1.2GB | 202 |
-| 4-gram unpruned | 2.4GB | 4.7GB | 193 |
-
-Each language model is limited to a vocabulary containing 216k words.
-
-We also provide a pronunciation lexicon using ARPA-style phonemes containing one or multiple pronunciations for each of the words in the vocabulary.
-The original pronunciations are based on [CMUDict 0.7b](http://www.speech.cs.cmu.edu/cgi-bin/cmudict). Missing pronunciations were generated using [Sequitur](https://github.com/sequitur-g2p/sequitur-g2p), for which we also provide the trained G2P model.
-
 #### Referencing the Loquacious Set and SpeechBrain
 
 ```

loquacious.sequitur_g2p.model_5

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