add conformer configs for hat model #6372
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Signed-off-by: andrusenkoau <[email protected]>
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It looks fine, i wonder if we want to create a another directory for it such as asr/conf/conformer_hat since it is technically a different model.
I am fine with this approach too but would like @VahidooX to review / comment on what his preference are for Conformer HAT.
It might be preferable to have a subdirectory conf/conformer/hat/conformer_hat_*.yaml ?
Looks good to me as long as they have their own separate folder under conformer. |
Signed-off-by: andrusenkoau <[email protected]>
Signed-off-by: andrusenkoau <[email protected]>
Signed-off-by: andrusenkoau <[email protected]>
Signed-off-by: andrusenkoau <[email protected]>
Signed-off-by: andrusenkoau <[email protected]>
| @@ -75,7 +75,7 @@ Key Features | |||
| * Speech processing | |||
| * `HuggingFace Space for Audio Transcription (File, Microphone and YouTube) <https://huggingface.co/spaces/smajumdar/nemo_multilingual_language_id>`_ | |||
| * `Automatic Speech Recognition (ASR) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/intro.html>`_ | |||
| * Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC, FastConformer-CTC, FastConformer-Transducer... | |||
| * Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC, FastConformer-CTC, FastConformer-Transducer, Conformer-HAT... | |||
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Would you please also update the following statement to have Hybrid ASR:
Supports CTC, Transducer/RNNT and Hybrid losses/decoders
docs/source/asr/models.rst
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| .. _Conformer-HAT_model: | ||
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| Conformer-HAT (Hybrid Autoregressive Transducer) | ||
| -------------------------------------- |
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The lines should have the same size as the title.
| Conformer HAT model (do not confuse it with Hybrid-Transducer-CTC) is a modification of Conformer-Transducer model based on `Google paper <https://arxiv.org/abs/2003.07705>`_. | ||
| The main idea is to separate labels and blank score predictions, which allows to estimate the internal LM probabilities during decoding. | ||
| When external LM is available for inference, the internal LM can be subtracted from HAT model prediction in beamsearch decoding to improve external LM efficiency. | ||
| It can be helpful in the case of text-only adaptation for new domains. |
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How can users use this feature?
Do the current LM scripts support it?
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By default Conformer HAT model works in decoding time as a standard Transducer model with the same interface. However, if you have an external ngram LM you can use scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram_transducer.py script. The new updated version of the script is under reviewing -- #6370
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@VahidooX -- could you approve the PR if everything is OK?
Signed-off-by: andrusenkoau <[email protected]>
Signed-off-by: andrusenkoau <[email protected]>
* add conformer configs for hat model Signed-off-by: andrusenkoau <[email protected]> Signed-off-by: hsiehjackson <[email protected]>
What does this PR do ?
Add conformer char and bpe configs for hat model (https://arxiv.org/abs/2003.07705)
Collection: [ASR]
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