nvidia
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parakeet-tdt_ctc-1.1b

Automatic Speech Recognition

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nithinraok commited on May 7

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@@ -233,7 +233,7 @@ This model uses a Hybrid FastConformer-TDT-CTC architecture. FastConformer [1] i
 ## Training
-The NeMo toolkit [3] was used for finetuning this model for 20,000 steps over `parakeet-tdt-1.1` model. This model is trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_hybrid_rnnt_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fastconformer_hybrid_transducer_ctc_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).

 ## Training
+The NeMo toolkit [3] was used for finetuning this model for 20,000 steps over `parakeet-tdt-1.1` model. This model is trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_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).