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Universal multilingual automatic speech transcription into IPA
I just ran:
python3.11 -m pip install --upgrade pip
pip install -r requirements.txt
And the requirements fetch fails on torch
mismatch, any ideas how to fix (I am no python expert yet):
¤ pip install -r requirements.txt
Collecting transformers@ git+https://github.com/huggingface/transformers@799cea64ac1029d66e9e58f18bc6f47892270723 (from -r requirements.txt (line 14))
Cloning https://github.com/huggingface/transformers (to revision 799cea64ac1029d66e9e58f18bc6f47892270723) to /private/var/folders/8x/z26qdb3x465gmtqr6z4x8hgm0000gn/T/pip-install-7i6y5txl/transformers_e094f57325924970b06bcd4058e74de3
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /private/var/folders/8x/z26qdb3x465gmtqr6z4x8hgm0000gn/T/pip-install-7i6y5txl/transformers_e094f57325924970b06bcd4058e74de3
Running command git rev-parse -q --verify 'sha^799cea64ac1029d66e9e58f18bc6f47892270723'
Running command git fetch -q https://github.com/huggingface/transformers 799cea64ac1029d66e9e58f18bc6f47892270723
Running command git checkout -q 799cea64ac1029d66e9e58f18bc6f47892270723
Resolved https://github.com/huggingface/transformers to commit 799cea64ac1029d66e9e58f18bc6f47892270723
Installing build dependencies ... done
Getting requirements to build wheel ... done
Installing backend dependencies ... done
Preparing metadata (pyproject.toml) ... done
Collecting datasets==2.9.0 (from -r requirements.txt (line 1))
Using cached datasets-2.9.0-py3-none-any.whl.metadata (19 kB)
Collecting epitran==1.24 (from -r requirements.txt (line 2))
Using cached epitran-1.24-py2.py3-none-any.whl.metadata (34 kB)
Collecting ffmpeg-python==0.2.0 (from -r requirements.txt (line 3))
Using cached ffmpeg_python-0.2.0-py3-none-any.whl.metadata (1.7 kB)
Collecting jiwer==2.5.1 (from -r requirements.txt (line 4))
Using cached jiwer-2.5.1-py3-none-any.whl.metadata (11 kB)
Collecting librosa==0.9.2 (from -r requirements.txt (line 5))
Using cached librosa-0.9.2-py3-none-any.whl.metadata (8.2 kB)
Collecting mecab-python3==1.0.6 (from -r requirements.txt (line 6))
Using cached mecab_python3-1.0.6-cp311-cp311-macosx_10_9_universal2.whl.metadata (6.1 kB)
Collecting pandas==1.5.2 (from -r requirements.txt (line 7))
Using cached pandas-1.5.2-cp311-cp311-macosx_11_0_arm64.whl.metadata (11 kB)
Collecting panphon==0.20.0 (from -r requirements.txt (line 8))
Using cached panphon-0.20.0-py2.py3-none-any.whl.metadata (15 kB)
Collecting pyarrow==8.0.0 (from -r requirements.txt (line 9))
Using cached pyarrow-8.0.0.tar.gz (846 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: pydub==0.25.1 in /opt/homebrew/lib/python3.11/site-packages (from -r requirements.txt (line 10)) (0.25.1)
Collecting romkan==0.2.1 (from -r requirements.txt (line 11))
Using cached romkan-0.2.1.tar.gz (10 kB)
Preparing metadata (setup.py) ... done
ERROR: Could not find a version that satisfies the requirement torch==1.11.0 (from versions: 2.0.0, 2.0.1, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.2.2)
ERROR: No matching distribution found for torch==1.11.0
Thanks! Looking forward to testing this out and following along your paper.
Hi, I reached out to you via email just now FYI, just introducing myself basically, but wanted to follow up here.
I am interested in languages with interesting phonemic patterns. Basically I just would like to cover all the ground of the pronunciation possibilities, in as short a word list as possible.
How far is multipa from supporting all these features? What would it take (what work is there to be done to support these features)? Perhaps I could help in some way.
I can't tell but looking briefly at the code it appears this is a text transcription 2 IPA library, not an audio to IPA, is that correcft? Do you have an audio to IPA library elsewhere if correct? If not, what am I missing? (I am looking at things like finnish_to_ipa.py for example).
Basically, how do I do audio to IPA, given your paper 😁 ❤️ 🚀
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