LingoNaut Language Assistant. Multilingual Studying with an… | by Nate Cibik | Feb, 2024

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Picture by creator utilizing DALL-E 3.


Whisper is an open-source speech-to-text mannequin supplied by OpenAI. There are 5 mannequin sizes out there in each English-focused and multilingual varieties to select from, relying on the complexity of the applying and desired accuracy-efficiency tradeoff. Whisper is an end-to-end speech-to-text framework that makes use of an encoder-decoder transformer structure working on enter audio cut up into 30-second chunks and transformed right into a log-Mel spectrogram. The community is skilled on a number of speech processing duties, together with multilingual speech recognition, speech translation, spoken language identification, and voice exercise detection.

Diagram of Whisper structure from the analysis paper.

For this challenge, two walkie-talkie buttons can be found to the consumer: one which sends their common English-language inquiries to the bot via the lighter, quicker “base” mannequin, and a second which deploys the bigger “medium” multilingual mannequin that may distinguish between dozens of languages and precisely transcribe accurately pronounced statements. Within the context of language studying, this leads the consumer to focus very intently on their pronunciation, accelerating the training course of. A chart of the out there Whisper fashions is proven under:

Chart from


There exists quite a lot of extremely helpful open-source language mannequin interfaces, all catering to totally different use instances with various ranges of complexity for setup and use. Among the many most generally recognized are the oobabooga text-gen webui, with arguably essentially the most flexibility and under-the-hood management, llama.cpp, which initially centered on optimized deployment of quantized fashions on smaller CPU-only units however has since expanded to serving different {hardware} varieties, and the streamlined interface chosen for this challenge (constructed on high of llama.cpp): Ollama.

Ollama focuses on simplicity and effectivity, working within the background and able to serving a number of fashions concurrently on small {hardware}, rapidly shifting fashions out and in of reminiscence as wanted to serve their requests. As an alternative of specializing in lower-level instruments like fine-tuning, Ollama excels at easy set up, environment friendly runtime, an excellent unfold of ready-to-use fashions, and instruments for importing pretrained mannequin weights. The deal with effectivity and ease makes Ollama the pure selection for LLM interface in a challenge like LingoNaut, for the reason that consumer doesn’t want to recollect to shut their session to release sources, as Ollama will routinely handle this within the background when the app will not be in use. Additional, the prepared entry to performant, quantized fashions within the library is ideal for frictionless improvement of LLM functions like LingoNaut.

Whereas Ollama will not be technically constructed for Home windows, it’s simple for Home windows customers to put in it on Home windows Subsystem for Linux (WSL), then talk with the server from their Home windows functions. With WSL put in, open a Linux terminal and enter the one-liner Ollama set up command. As soon as the set up finishes, merely run “ollama serve” within the Linux terminal, and you’ll then talk together with your Ollama server from any Python script in your Home windows machine. 🐸 TTS

TTS is a fully-loaded text-to-speech library out there for non-commercial use, with paid business licenses out there. The library has skilled notable recognition, with 3k forks and 26.6k stars on GitHub as of the time of this writing, and it’s clear why: the library works just like the Ollama of the text-to-speech area, offering a unified interface for accessing a various array of performant fashions which cowl quite a lot of use instances (for instance: offering a multi-speaker, multilingual mannequin for this challenge), thrilling options comparable to voice cloning, and controls over the pace and emotional tone of transcriptions.

The TTS library gives an in depth number of text-to-speech fashions, together with the illustrious Fairseq fashions from Fb analysis’s Massively Multilingual Speech (MMS) challenge. For LingoNaut, the crew’s personal XTTS mannequin turned out to be the proper selection, because it generates high-quality speech in a number of languages seamlessly. Though the mannequin does have a “language” enter parameter, I discovered that even leaving this set to “en” for English and easily passing textual content in different languages nonetheless ends in trustworthy multilingual era with largely appropriate pronunciations.

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