uk-pods-conformer

Usage

The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.

pip install nemo_toolkit['all']

Automatically instantiate the model

from nemo.collections.asr.models import EncDecCTCModelBPE
asr_model = EncDecCTCModelBPE.from_pretrained("taras-sereda/uk-pods-conformer")

Transcribing using Python

First, let's get a sample

wget "https://huggingface.co/datasets/taras-sereda/uk-pods/resolve/main/example/e934c3e4-c37b-4607-98a8-22cdff933e4a_0266.wav?download=true" -O e934c3e4-c37b-4607-98a8-22cdff933e4a_0266.wav

Then simply do:

asr_model.transcribe(['e934c3e4-c37b-4607-98a8-22cdff933e4a_0266.wav'])

Input

This model accepts 16000 kHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Model Architecture

Conformer-CTC model is a non-autoregressive variant of Conformer model [2] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: Conformer-CTC Model.

Datasets

This model has been trained using a combination of 2 datasets:

- UK-PODS [3] train dataset: This dataset comprises 46 hours of conversational speech collected from Ukrainian podcasts.
- Validated Mozilla Common Voice Corpus 10.0: (excluding dev and test data) dataset that includes 50.1 hours of Ukrainian speech.

Performance

Performances of the ASR model is reported in terms of Word Error Rate (WER) with greedy decoding.

References

• [1] NVIDIA NeMo Toolkit

• [2] Conformer: Convolution-augmented Transformer for Speech Recognition

• [3] UK-PODS