Could anyone recommend a speech recognition library for python 3 which is completely offline and free? If so could you also add steps to installing this library. I have tried pocketsphinx but the live speech recognition is too inaccurate for what I would like.
2 Answers
You can try Kaldi, it is not easy to install, but it is more accurate than pocketsphinx. For local decoding you can use something like
https://github.com/alphacep/vosk-server/
the model is here http://kaldi-asr.org/models/m1
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Thank you, I'll give it another go installing on Ubuntu because at this point I can't get it to work on Mac OS. If I can install Kaldi then I'll try the websocket. Thank you, if it works I will mark as answered.– user56979Aug 28, 2019 at 1:02
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I can't get it to work. I installed Kaldi from here jrmeyer.github.io/asr/2016/01/26/Installing-Kaldi.html but the websocket can't find the directory
voice dataand won't build. So no idea anymore.– user56979Aug 31, 2019 at 1:42 -
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OK thank you. I got past that error and then about five others but there is another one I'm stuck on. This is what I get...
D1Ev' can not be used when making a shared object; recompile with -fPIC/usr/bin/ld: final link failed: Bad valuecollect2: error: ld returned 1 exit status. I get it while running make for the web-socket.– user56979Sep 1, 2019 at 1:10 -
OpenAI's Whisper (off-line, MIT license, Python 3.9, CLI) yields some highly accurate transcription. To use (tested on Ubuntu 20.04 x64 LTS):
conda create -y --name whisperpy39 python==3.9
conda activate whisperpy39
pip install git+https://github.com/openai/whisper.git
sudo apt update && sudo apt install ffmpeg
whisper recording.wav
whisper recording.wav --model large
If using an Nvidia 3090 GPU, add the following after conda activate whisperpy39
pip install -f https://download.pytorch.org/whl/torch_stable.html
conda install pytorch==1.10.1 torchvision torchaudio cudatoolkit=11.0 -c pytorch
Performance info below.
Model inference time:
| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
|---|---|---|---|---|---|
| tiny | 39 M | tiny.en |
tiny |
~1 GB | ~32x |
| base | 74 M | base.en |
base |
~1 GB | ~16x |
| small | 244 M | small.en |
small |
~2 GB | ~6x |
| medium | 769 M | medium.en |
medium |
~5 GB | ~2x |
| large | 1550 M | N/A | large |
~10 GB | 1x |
WER on several corpus from https://cdn.openai.com/papers/whisper.pdf:
WER on several languages from https://github.com/openai/whisper/blob/main/language-breakdown.svg:
answered Sep 23 at 2:05
