Instructions to use AlexanderMaz/LanguageModel_Fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use AlexanderMaz/LanguageModel_Fusion with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("AlexanderMaz/LanguageModel_Fusion") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
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README.md
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The data is used in project https://github.com/Alexander92-cpu/LanguageModel_Fusion
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Data desciption:
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The data is used in project https://github.com/Alexander92-cpu/LanguageModel_Fusion
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Data desciption:
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- 'asr/stt_en_conformer_transducer_small.nemo' - NeMo ASR pre-trained RNN-T model (https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_small);
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- 'gpt2' - fine-tuned GPT-2 LM model for rescoring (https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2LMHeadModel);
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- 'kenlm/4_ngram_output.bin' - 4-gram language model;
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- 'lstm' - trained from scratch word-level LSTM LM model and the corresponding tokenizer;
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- 'text' - contains text data used for training, validation, and testing.
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