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.
| Tokenizer | Vocabulary Size | UK-PODS test | MCV-10 test |
|---|---|---|---|
| SentencePiece | 1024 | 0.093 | 0.116 |
References
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