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
π§ TopIPL: Iterative Pseudo-Labeling for ASR
TopIPL is an iterative pseudo-labeling algorithm for training speech recognition models using both labeled and unlabeled data. It integrates seamlessly into the NeMo ASR pipeline and enables self-training across epochs with minimal manual intervention.
π Key Features
- βοΈ Supports semi-supervised ASR training with dynamic iterative pseudo-label refinement.
- π§ͺ Designed for large-scale training using both labeled and unlabeled speech data.
- π Automatically writes pseudo-labels and updates training configs between iterations.
π¦ Required Components
TopIPL relies on the following components:
[
SDPNeMoRunIPLProcessor]
Commands for running IPL are generated and submitted using SDP processors and NeMo-Run.
See instructions for usage here.Training Callback:
IPLEpochStopperCallback
Add this to your training config underexp_managerto stop training at the end of each epoch, enabling pseudo-label update:
exp_manager:
create_ipl_epoch_stopper_callback: True
ipl_epoch_stopper_callback_params:
stop_every_n_epochs: n # Stop training after every n epochs (default: 1)