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README.md

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+
+
+
+language:
+- be
+library_name: nemo
+datasets:
+- mozilla-foundation/common_voice_10_0
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- Transducer
+- Conformer
+- Transformer
+- pytorch
+- NeMo
+- hf-asr-leaderboard
 license: cc-by-4.0
+model-index:
+- name: stt_be_conformer_transducer_large
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: MCV_10_be
+      type: mcv_asr
+      config: clean
+      split: test
+      args:
+        language: be
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 3.8
+  
 ---
+# NVIDIA Conformer-RNNT Large (be)
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+| [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--Transducer-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-120M-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-be--Belarusian-lightgrey#model-badge)](#datasets)
+
+
+This model transcribes speech in lowercase Belarusian alphabet including spaces and apostrophes, and is trained on few hundreds of Belarusian speech data.
+It is a "large" variant of Conformer, with around 120 million parameters.
+See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-transducer) for complete architecture details.
+
+
+## Usage
+
+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](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
+
+```
+pip install nemo_toolkit['all']
+```
+
+Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html). 
+
+## Training
+
+The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml).
+
+The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
+
+### Datasets
+All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several hundreds hours of Belarusian speech:
+- Mozilla Common Voice (v10.0)
+## Performance
+Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. WER on dev is 3.8%
+## Limitations
+Since all models are trained on just MCV-10 dataset, 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: Deployment
+
+[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. 
+Additionally, Riva provides: 
+
+* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours 
+* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization 
+* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support 
+
+Although this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva).  
+Check out [Riva live demo](https://developer.nvidia.com/riva#demos). 
+
+## References
+[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
+[2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
+[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+
+## Licence
+
+License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.