mms-1b-all-AmhLM

Model Summary

Well I made a KenLM model that works with Meta's <a href="https://huggingface.co/facebook/mms-1b-all">Massively Multilingual Speech (MMS)</a> to improve ASR transcriptions in the Amharic language. The model is based on the Amharic Common Crawl corpus dating from Jan-Dec of 2018 (<a href="https://data.statmt.org/cc-100/">link</a>). It seems to improve my ASR transcriptions considerably well, but of course I don't expect this LM to improve the WER for amharic transcriptions to the level spoken of in the MMS paper (around 32%). To do that, a larger Amharic corpus would be needed, and I have no clue how to compile one myself. For reference, the one I used is merely 837MB; the <a href="https://scontent.fphx1-2.fna.fbcdn.net/v/t39.8562-6/348827959_6967534189927933_6819186233244071998_n.pdf?_nc_cat=104&ccb=1-7&_nc_sid=ad8a9d&_nc_ohc=APrxV5RqnpwAX_XU5Kf&_nc_ht=scontent.fphx1-2.fna&oh=00_AfDOEKZa4CJrwLrCdt4RwjMVJUCO6Fe3XmpEtjKxtRwUpg&oe=649BD6C2">MMS paper</a> suggests using a corpus of > 5GB. <br />

Getting Started

To use this LM-boosted processor via the Transformers library, utilize the "Wav2Vec2ProcessorWithLM" class instead of "Wav2Vec2Processor". Here's a quick example of how I use it: <br />

from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM


# load pretrained model
model = Wav2Vec2ForCTC.from_pretrained("facebook/mms-1b-all")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("jlonsako/mms-1b-all-AmhLM")

model.load_adapter("amh")

input_values = processor("insert audio file path", sampling_rate=16_000, return_tensors="pt").input_values

with torch.no_grad():
  logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
print(transcription[0])

Limitations

I would love to post stats like WER and BLEU scores on out-of-framework datasets, but considering the fact that I'm a C# web developer by trade, I have no clue how to perform those tests. I just want to make this available for anyone who wants to test MMS performance with an AMH LM, and also for my own use. Happy testing!

Final Notes

I hope it goes without saying that this repository inherits licenses from Meta MMS, meaning this repo can only be used for research purposes. Personally, I am using this to build a simple video transcription app for Amharic videos generated by my church. If you wanna know more or would like to help make this LM better (seeing as I am clueless), feel free to reach out to me at my email: [email protected]. <br /> -Joshua Lonsako