transcript.fish VS apertium

I have been working on this podcast transcription project for a couple months and it's been super rewarding.

I listen to a podcast called No Such Thing As A Fish, where some researchers talk about their favorite facts they learned that week. Then they riff on it and are generally smart and funny. I listened to the series so many times that I decided I wanted to listen to the show on shuffle, not at the episode level, but at the fact level.

Since I have been playing around with whisper.cpp in python this seemed like a perfect way to combine some technologies I've been wanting to play with.

I ran whisper over the entire podcast and transcribed all the episodes. I had to do this multiple times because I kept messing up. It eventually took like 7 straight days of my M1 processing to get through ~490 episodes.

4 million words, and an 800Mb SQLite database later, I got the transcriptions done and have put up a nice site for searching through the data.

https://transcript.fish

Now I just need to figure out the rest. Breaking it up into facts. Getting the audio working. Highlighting and linking to words, phrases, etc.

Some cool info about the process so far:

1. The SQLite database is chunked up and stored as static files, and the frontend queries the static files directly using HTTP range requests, so it only downloads a couple hundreds kbs when querying.

2. I've been proper using ChatGPT 3.5 free version to help me write python and SQL. It's been pretty game changing as I feel basically no pain from not knowing what I'm doing.

The code is here: https://github.com/noman-land/transcript.fish

Please help if you know how to get whisper speaker diarization working!! I would really appreciate the help.

Interesting, thanks for the tip! I've never heard of lockdown mode. Unfortunately I don't own an iPhone. Any chance you could post an issue with steps to repro?

https://github.com/noman-land/transcript.fish/issues

Thank you!

This is very cool, looking forward to it! I've been doing the same thing with Spanish Wikipedia articles for a while, using a few lines of Bash + Regex. I was using Apertium for it. https://apertium.org/ It's definitely worse than most ML-based solutions, but it works reliably and fast; you can run it entirely offline. With Spanish translations, the main problem I was facing is lack of vocabulary, so I created https://github.com/phil294/apertium-eng-spa-wiktionary which about doubles the amount of recognized words, albeit with wonky grammar.

I used to keep track of the state of machine translation some years back.

I think the way you measure the success of an automated translation is edit distance, i.e. how many manual edits you need to make to a translated text before you reach some acceptable state. I suppose it's somewhat subjective, but it is possible to construct a benchmark and allow for multiple correct results.

The best resources I knew back then were:

VISL's CG-3 self-reported a competitively low edit distance compared to Google Translate: https://visl.sdu.dk/constraint_grammar.html -- the abstraction unfortunately requires a rather deep knowledge of any one particular language's grammar. It is a convincing argument that in order to beat Google Translate, you want less fuzzy machine learning and more structural analysis. But you also need a PhD in computational linguistics and deep knowledge of each language.

Apertium has an open-source pipeline: https://apertium.org/ -- seems to be much more like an open-source approach with a quality similar to Google Translate (although I don't know if it's better or worse; probably slightly worse in most cases, and with a slightly lower coverage).

For context, I have been contributing CV Creole data to Unicode's CLDR and MediaWiki for a number of years now, but both are mostly manual work. I once considered setting up an Apertium language pair between CV Creole and Portuguese, given the grammatical similarities, but never got around to it.

Lingva is awesome. Also don't forget to check out LibreTranslate and Apertium. They are open source. Apertium can even translate web pages (you need to enter the URL).

#!/bin/bash set -x mkdir -p apertium-src && \ mkdir -p $MTDIR cd apertium-src && \ wget http://ftp.tsukuba.wide.ad.jp/software/gcc/releases/gcc-8.5.0/gcc-8.5.0.tar.gz -O - \ | gzip -dc \ | tar -xf - && \ cd gcc-8.5.0 && \ ./configure --prefix=$MTDIR --disable-multilib && \ make -j $(nproc) && \ make install && \ cd .. || exit 1 cd apertium-src && \ wget https://github.com/unicode-org/icu/releases/download/release-69-1/icu4c-69_1-src.tgz -O - \ | gzip -dc \ | tar -xf - \ && cd icu/source \ && CC=gcc CXX=g++ ./configure --prefix=$MTDIR \ && CC=gcc CXX=g++ make -j $(nproc) \ && CC=gcc CXX=g++ make install \ && cd ../.. \ || exit 1 cd apertium-src && \ svn checkout http://beta.visl.sdu.dk/svn/visl/tools/vislcg3/trunk vislcg3 && \ cd vislcg3 && ./get-boost.sh \ && ./cmake.sh -DCMAKE_INSTALL_PREFIX=$MTDIR \ -DICU_INCLUDE_DIR=$MTDIR/include \ -DICU_LIBRARY=$MTDIR/lib/libicuuc.so \ -DICU_IO_LIBRARY=$MTDIR/lib/libicuio.so \ -DICU_I18N_LIBRARY=$MTDIR/lib/libicui18n.so \ && make -j$(nproc) && \ make install && cd .. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/lttoolbox && \ cd lttoolbox && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium && \ cd apertium && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-lex-tools && \ cd apertium-lex-tools && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-tha && \ cd apertium-tha && ./autogen.sh --prefix=$MTDIR && make && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-tha-eng && \ cd apertium-tha-eng && ./autogen.sh --prefix=$MTDIR && make && make install && cd .. && \ cd .. || exit 1