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--- |
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language: |
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- hy |
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license: cc-by-nc-4.0 |
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library_name: nemo |
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datasets: |
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- mozilla-foundation/common_voice_17_0_Armenianlibrispeech_asr |
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- mozilla-foundation/common_voice_7_0 |
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- vctk |
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- fisher_corpus |
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- Switchboard-1 |
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- WSJ-0 |
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- WSJ-1 |
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- National-Singapore-Corpus-Part-1 |
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- National-Singapore-Corpus-Part-6 |
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- facebook/multilingual_librispeech |
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thumbnail: null |
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tags: |
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- automatic-speech-recognition |
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- speech |
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- audio |
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- low-resource-languages |
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- CTC |
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- Conformer |
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- Transformer |
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- NeMo |
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- pytorch |
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model-index: |
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- name: stt_arm_conformer_ctc_large |
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results: [] |
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--- |
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## Model Overview |
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This model is a fine-tuned version of the NVIDIA NeMo Conformer CTC large model, adapted for transcribing Armenian speech. |
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## NVIDIA NeMo: Training |
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To train, fine-tune, or play with the model, you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend installing it after you've installed the latest Pytorch version. |
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``` |
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pip install nemo_toolkit['all'] |
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``` |
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## How to Use this Model |
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The model is available for use in the NeMo toolkit, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. |
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### Automatically instantiate the model |
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```python |
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import nemo.collections.asr as nemo_asr |
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asr_model = nemo_asr.models.ASRModel.from_pretrained("Yeroyan/stt_arm_conformer_ctc_large") |
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``` |
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### Transcribing using Python |
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First, let's get a sample |
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``` |
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wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav |
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``` |
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Then simply do: |
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``` |
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asr_model.transcribe(['2086-149220-0033.wav']) |
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``` |
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### Transcribing many audio files |
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```shell |
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python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="Yeroyan/stt_arm_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" |
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``` |
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### Input |
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This model accepts 16000 KHz Mono-channel Audio (wav files) as input. |
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### Output |
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This model provides transcribed speech as a string for a given audio sample. |
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## Model Architecture |
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The model uses a Conformer Convolutional Neural Network architecture with CTC loss for speech recognition. |
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## Training |
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This model was originally trained on diverse English speech datasets and fine-tuned on a dataset comprising Armenian speech (100epochs) |
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### Datasets |
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The model was fine-tuned on the Armenian dataset from the Common Voice corpus, version 17.0 (Mozilla Foundation). |
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For dataset processing, we have used the following fork: [NeMo-Speech-Data-Processor](https://github.com/Ara-Yeroyan/NeMo-speech-data-processor/tree/armenian_mcv) |
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## Performance |
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| Version | Tokenizer | Vocabulary Size | MCV Test WER | MCV Test WER (no punctuation) | Train Dataset | |
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|---------|---------------|-----------------|--------------|-------------------------------|---------------| |
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| 1.6.0 | SentencePiece | 128 | 15.0% | 12.44% | MCV v17 | |
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| | Unigram | | | | (Armenian) | |
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## Limitations |
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- Eastern Armenian |
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- Need to replace "եւ" with "և" after each prediction (tokenizer does not contain "և" symbol which is unique linguistic exceptions as it does not have an uppercase version) |
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## References |
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[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
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[2] [Enhancing ASR on low-resource languages (paper)](https://drive.google.com/file/d/1bMETu9M7FGXFeR4P5InXzT1y6rMLjbF0/view?usp=sharing) |
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