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nemo |
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cc-by-4.0 |
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This model transcribes speech into lowercase Cyrillic alphabet including space, and is trained on around 1636 hours of Russian speech data. It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters. See the model architecture section and NeMo documentation for complete architecture details.
The model is available for use in the NeMo toolkit [3], 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']
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_ru_conformer_transducer_large")
Simply do:
asr_model.transcribe(['<your_audio>.wav'])
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/stt_ru_conformer_transducer_large"
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
This model accepts 16 kHz mono-channel Audio (wav files) as input.
This model provides transcribed speech as a string for a given audio sample.
Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding. You may find more info on the detail of this model here: Conformer-Transducer Model.
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.
The vocabulary we use contains 33 characters:
[' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я']
Rare symbols with diacritics were replaced during preprocessing.
The tokenizers for these models were built using the text transcripts of the train set with this script.
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of more than a thousand hours of Russian speech:
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
Version | Tokenizer | Vocabulary Size | MCV 10.0 dev | MCV 10.0 test | GOLOS-crowd test | GOLOS-farfield test | RuLS test | Train Dataset |
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1.13.0 | SentencePiece Unigram | 1024 | 3.5 | 4.0 | 2.7 | 7.6 | 12.0 | NeMo ASRSET |
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides:
Although this model isn’t supported yet by Riva, the list of supported models is here.
Check out Riva live demo.
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