Automatic Speech Recognition
NeMo
Finnish
asr
speech-recognition
canary-v2
kenlm
finnish
Eval Results (legacy)
Instructions to use RASMUS/Finnish-ASR-Canary-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use RASMUS/Finnish-ASR-Canary-v2 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("RASMUS/Finnish-ASR-Canary-v2") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
File size: 980 Bytes
cde7fe4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | Dataset Creation Tool Based on CTC-Segmentation
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This tool provides functionality to align long audio files with the corresponding transcripts and split them into shorter fragments
that are suitable for an Automatic Speech Recognition (ASR) model training.
More details could be found in `NeMo/tutorials/tools/CTC_Segmentation_Tutorial.ipynb <https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/tools/CTC_Segmentation_Tutorial.ipynb>`__ (can be executed with `Google's Colab <https://colab.research.google.com/notebooks/intro.ipynb>`_).
The tool is based on the `CTC-Segmentation <https://github.com/lumaku/ctc-segmentation>`__ package and
`CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition
<https://arxiv.org/abs/2007.09127>`__ :cite:`tools-kurzinger2020ctc`
References
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.. bibliography:: tools_all.bib
:style: plain
:labelprefix: TOOLS
:keyprefix: tools-
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