211–220 of 232 posts
I'm trying to use this on a 3M mp3 file to test ASR with language code deu, CPU only, and I keep getting this error -- are there limits to the MMS inference? File "fairseq/data/data_utils_fast.pyx", line 30, in fairseq.data.data_utils_fast.batch_by_size_vec assert max_tokens process(args) File "/home/xxx/fairseq/examples/mms/asr/infer/mms_infer.py", line 44, in process
I just wanted to test out the TTS locally on a powerful Ubuntu 22.04 machine, but the process for setting it up seems pretty broken and poorly documented. After 20 minutes of trying I finally gave up since I couldn't get the VITS dependency to build (despite having a fully updated machine with all required compilers). It seems like they never really bother to see if the stuff works on a fresh machine starting from sc…
I wrote a colab to run TTS, it takes some time to install dependencies : https://colab.research.google.com/drive/11tZ1c7kQJ5uKImm9t-C... Overall I got pretty poor results in english and french, I guess it would require some fine-tuning
Also, I used the following to play the sound after it is generated:
from IPython.display import Audio, display display(Audio(output_file, autoplay=True))
I just wanted to test out the TTS locally on a powerful Ubuntu 22.04 machine, but the process for setting it up seems pretty broken and poorly documented. After 20 minutes of trying I finally gave up since I couldn't get the VITS dependency to build (despite having a fully updated machine with all required compilers). It seems like they never really bother to see if the stuff works on a fresh machine starting from sc…
name: fairseq
channels:
- conda-forge
- pytorch
dependencies:
- python=3.9
- Cython==0.29.21
- librosa==0.8.0
- matplotlib=3.3
- numpy=1.19
- scipy==1.5.2
- tensorboard==2.3.0
- pytorch=1
- torchvision=0
- Unidecode==1.1.1
- pip:
- phonemizer==2.2.1
conda create -f environment.yml
conda activate fairseq
cd path/to/vits/monotonic_align
mkdir monotonic_align
python setup.py build_ext --inplace
cd path/to/fairseq
PYTHONPATH=$PYTHONPATH:path/to/vits python examples/mms/tts/infer.py --model-dir checkpoints/eng --wav outputs/eng.wav --txt "As easy as pie"
https://www.ethnologue.com/language/kas/
"Himachal Pradesh state:"
this is obviously wrong so i dont know what else is wrong
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Nah, full-on babelfish is simply not possible. The meaning of the beginning of a sentence can be modified retroactively by the end of the sentence. This means that the Babelfish must either be greatly delayed or awkwardly correct itself every once in a while.
That’s why the babelfish translates brainwaves rather than sounds, which is especially important for communicating with our nonverbal alien neighbors.
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Google Translate is still inferior to ChatGPT 3.5. I suspect this style of model is significantly more expensive to run and Google doesn't want to give it away for free. Really, the only problem with ChatGPT is that it refuses to translate things that go against its nanny programming, which can make it almost worse than useless in some real-life situations.
I tried ChatGPT 3.5 against Google Translate, translating English to Greek, my native language, and they perform almost the same. The text was difficult text of science fiction, fantasy stuff and the results were tolerable. Roughly 50% of the text had to be manually rewritten. Maybe for more casual sentences and not so difficult text, they perform better, i haven't tried. Anyways, they are both better than nothing.
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Wait why is renting a GPU in the cloud not a solution? You can even try multiple options and see which ones are capable enough for your use case. Look into some barebones cloud GPU services, for example Lambda Labs which is significantly cheaper than AWS/GCP but offers basically nothing besides the machine with a GPU. You could even try something like Vast in which people rent out their personal GPU machines for chea…
My annoyance was managing state. I’d have to spend hours installing tools, downloading data, updating code, then when I want to go to bed I have to package it up and store as much as I can on s3 before shutting off the $$ server.
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You can finetune whisper, stable diffusion, and LLM up to about 15B parameters with 24GB VRAM. Which leads you to what hardware to get. Best bang for the $ right now is definitely a used 3090 at ~$700. If you want more than 24GB vram just rent the hardware as it will be cheaper. If you're not willing to drop $700 don't buy anything just rent. I have had decent luck with vast.ai
Can we use 3090 or nvidia gpus in general with mac? do people generally have their windows dekstop for the gpus?