From ae686e12ab36b0e2b28c13ee51cb0f3ec5c3437e Mon Sep 17 00:00:00 2001 From: Evelina <10428420+ekmb@users.noreply.github.com> Date: Tue, 30 Aug 2022 16:48:48 -0700 Subject: [PATCH] G2P docs (#4841) * g2p docs added Signed-off-by: ekmb * fix references Signed-off-by: ekmb * address review feedback Signed-off-by: ekmb Signed-off-by: ekmb --- docs/source/asr/datasets.rst | 2 +- docs/source/conf.py | 1 + docs/source/index.rst | 9 + docs/source/text_processing/g2p/g2p.rst | 209 ++++++++++++++++++ .../g2p/images/data_labeling_pipeline.png | Bin 0 -> 262187 bytes docs/source/text_processing/intro.rst | 12 + .../text_processing/text_processing_all.bib | 53 +++++ .../g2p/conf/g2p_conformer_ctc.yaml | 4 +- .../g2p/g2p_train_and_evaluate.py | 4 + .../g2p/heteronym_classification_inference.py | 2 + ...ronym_classification_train_and_evaluate.py | 3 + 11 files changed, 296 insertions(+), 3 deletions(-) create mode 100644 docs/source/text_processing/g2p/g2p.rst create mode 100644 docs/source/text_processing/g2p/images/data_labeling_pipeline.png create mode 100644 docs/source/text_processing/intro.rst create mode 100644 docs/source/text_processing/text_processing_all.bib diff --git a/docs/source/asr/datasets.rst b/docs/source/asr/datasets.rst index 8878d66ae739..42a472d61fa9 100644 --- a/docs/source/asr/datasets.rst +++ b/docs/source/asr/datasets.rst @@ -171,7 +171,7 @@ The audio files can be of any format supported by `Pydub `__ ``"Swifts, flushed from chimneys …" → "ˈswɪfts, ˈfɫəʃt ˈfɹəm ˈtʃɪmniz …"``. + +Modern text-to-speech (TTS) models can learn pronunciations from raw text input and its corresponding audio data, +but by relying on grapheme input during training, such models fail to provide a reliable way of correcting wrong pronunciations. As a result, many TTS systems use phonetic input +during training to directly access and correct pronunciations at inference time. G2P systems allow users to enforce the desired pronunciation by providing a phonetic transcript of the input. + +G2P models convert out-of-vocabulary words (OOV), e.g. proper names and loaner words, as well as heteronyms in their phonetic form to improve the quality of the syntesized text. + +*Heteronyms* represent words that have the same spelling but different pronunciations, e.g., “read” in “I will read the book.” vs. “She read her project last week.” A single model that can handle OOVs and heteronyms and replace dictionary lookups can significantly simplify and improve the quality of synthesized speech. + +We support the following G2P models: + +* **ByT5 G2P** a text-to-text model that is based on ByT5 :cite:`g2p--xue2021byt5` neural network model that was originally proposed in :cite:`g2p--vrezavckova2021t5g2p` and :cite:`g2p--zhu2022byt5`. + +* **G2P-Conformer** CTC model - uses a Conformer encoder :cite:`g2p--ggulati2020conformer` followed by a linear decoder; the model is trained with CTC-loss. G2P-Conformer model has about 20 times fewer parameters than the ByT5 model and is a non-autoregressive model that makes it faster during inference. + +The models can be trained using words or sentences as input. +If trained with sentence-level input, the models can handle out-of-vocabulary (OOV) and heteronyms along with unambiguous words in a single pass. +See :ref:`Sentence-level Dataset Preparation Pipeline ` on how to label data for G2P model training. + +Additionally, we support a purpose-built BERT-based classification model for heteronym disambiguation, see :ref:`this ` for details. + +Model Training, Evaluation and Inference +---------------------------------------- + +The section covers both ByT5 and G2P-Conformer models. + +The models take input data in `.json` manifest format, and there should be separate training and validation manifests. +Each line of the manifest should be in the following format: + +.. code:: + + {"text_graphemes": "Swifts, flushed from chimneys.", "text": "ˈswɪfts, ˈfɫəʃt ˈfɹəm ˈtʃɪmniz."} + +Manifest fields: + +* ``text`` - name of the field in manifest_filepath for ground truth phonemes + +* ``text_graphemes`` - name of the field in manifest_filepath for input grapheme text + +The models can handle input with and without punctuation marks. + +To train ByT5 G2P model and evaluate it after at the end of the training, run: + +.. code:: + + python examples/text_processing/g2p/g2p_train_and_evaluate.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + model.test_ds.manifest_filepath="" \ + trainer.devices=1 \ + do_training=True \ + do_testing=True + +Example of the config file: ``NeMo/examples/text_processing/g2p/conf/t5_g2p.yaml``. + + +To train G2P-Conformer model and evaluate it after at the end of the training, run: + +.. code:: + + python examples/text_processing/g2p/g2p_train_and_evaluate.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + model.test_ds.manifest_filepath="" \ + model.tokenizer.dir= \ + model.tokenizer_grapheme.do_lower=False \ + model.tokenizer_grapheme.add_punctuation=True \ + trainer.devices=1 \ + + do_training=True \ + do_testing=True + +Example of the config file: ``NeMo/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml``. + + +To evaluate a pretrained G2P model, run: + +.. code:: + + python examples/text_processing/g2p/g2p_train_and_evaluate.py \ + # (Optional: --config-path= --config-name=) \ + pretrained_model="" \ + model.test_ds.manifest_filepath="" \ + trainer.devices=1 \ + do_training=False \ + do_testing=True + +To run inference with a pretrained G2P model, run: + +.. code-block:: + + python g2p_inference.py \ + pretrained_model=" \ + manifest_filepath="" \ + output_file="" \ + batch_size=32 \ + num_workers=4 \ + pred_field="pred_text" + +Model's predictions will be saved in `pred_field` of the `output_file`. + +.. _sentence_level_dataset_pipeline: + +Sentence-level Dataset Preparation Pipeline +------------------------------------------- + +Here is the overall overview of the data labeling pipeline for sentence-level G2P model training: + + .. image:: images/data_labeling_pipeline.png + :align: center + :alt: Data labeling pipeline for sentence-level G2P model training + :scale: 70% + +Here we describe the automatic phoneme-labeling process for generating augmented data. The figure below shows the phoneme-labeling steps to prepare data for sentence-level G2P model training. We first convert known unambiguous words to their phonetic pronunciations with dictionary lookups, e.g. CMU dictionary. +Next, we automatically label heteronyms using a RAD-TTS Aligner :cite:`g2p--badlani2022one`. More details on how to disambiguate heteronyms with a pretrained Aligner model could be found in `NeMo/tutorials/tts/Aligner_Inference_Examples.ipynb `__ in `Google's Colab `_. +Finally, we mask-out OOV words with a special masking token, “” in the figure below (note, we use `model.tokenizer_grapheme.unk_token="҂"` symbol during G2P model training.) +Using this unknown token forces a G2P model to produce the same masking token as a phonetic representation during training. During inference, the model generates phoneme predictions for OOV words without emitting the masking token as long as this token is not included in the grapheme input. + + + +.. _bert_heteronym_cl: + +Purpose-built BERT-based classification model for heteronym disambiguation +-------------------------------------------------------------------------- + +HeteronymClassificationModel is a BERT-based :cite:`g2p--ddevlin2018bert` model represents a token classification model and can handle multiple heteronyms at once. The model takes a sentence as an input, and then for every word, it selects a heteronym option out of the available forms. +We mask irrelevant forms to disregard the model’s predictions for non-ambiguous words. E.g., given the input “The Poems are simple to read and easy to comprehend.” the model scores possible {READ_PRESENT and READ_PAST} options for the word “read”. +Possible heteronym forms are extracted from the WikipediaHomographData :cite:`g2p--gorman2018improving`. + +The model expects input to be in `.json` manifest format, where is line contains at least the following fields: + +.. code:: + + {"text_graphemes": "Oxygen is less able to diffuse into the blood, leading to hypoxia.", "start_end": [23, 30], "homograph_span": "diffuse", "word_id": "diffuse_vrb"} + +Manifest fields: + +* `text_graphemes` - input sentence + +* `start_end` - beginning and end of the heteronym span in the input sentence + +* `homograph_span` - heteronym word in the sentence + +* `word_id` - heteronym label, e.g., word `diffuse` has the following possible labels: `diffuse_vrb` and `diffuse_adj`. See `https://github.com/google-research-datasets/WikipediaHomographData/blob/master/data/wordids.tsv `__ for more details. + +To convert the WikipediaHomographData to `.json` format suitable for the HeteronymClassificationModel training, run: + +.. code-block:: + + # WikipediaHomographData could be downloaded from `https://github.com/google-research-datasets/WikipediaHomographData `__. + + python NeMo/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py \ + --data_folder=/WikipediaHomographData-master/data/eval/ + --output=eval.json + python NeMo/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py \ + --data_folder=/WikipediaHomographData-master/data/train/ + --output=train.json + +To train and evaluate the model, run: + +.. code-block:: + + python heteronym_classification_train_and_evaluate.py \ + train_manifest=" \ + validation_manifest=" \ + model.encoder.pretrained="" \ + model.wordids= \ + do_training=True \ + do_testing=True + + +To run inference with a pretrained HeteronymClassificationModel, run: + +.. code-block:: + + python heteronym_classification_inference.py \ + manifest="" \ + pretrained_model="" \ + output_file="" + +Note, if the input manifest contains target "word_id", evaluation will be also performed. During inference, the model predicts heteronym `word_id` and saves predictions in `"pred_text"` field of the `output_file`: + +.. code:: + + {"text_graphemes": "Oxygen is less able to diffuse into the blood, leading to hypoxia.", "pred_text": "diffuse_vrb", "start_end": [23, 30], "homograph_span": "diffuse", "word_id": "diffuse_vrb"} + + +Requirements +------------ + +G2P requires NeMo NLP and ASR collections installed. See `Installation instructions `__ for more details. + + +References +---------- + +.. bibliography:: ../text_processing_all.bib + :style: plain + :labelprefix: g2p- + :keyprefix: g2p-- diff --git a/docs/source/text_processing/g2p/images/data_labeling_pipeline.png b/docs/source/text_processing/g2p/images/data_labeling_pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..002b63246c953131b4206acb41aca73df9197a5c GIT binary patch literal 262187 zcma&N1yo!+69A06Q;NG5cXuchr#Qvk-Q5;9q z=xAqZVQm5i_ANX)6;?%g`Lpn)*Ku@;zkkwH#@%OOX*h;Y$Eqr#7~g1EsGzX$$%4q4 zKdXof<4%L4LrY2u4+LsdAx9vqI19r-n-I`1DW=1E_l&*Xzw5oXxi~%W@;oNBZjAGS z`(uouN3!Wtg0X(pOn~#V^VIf+Abk9^6@bzO0jod0E!{s>Ri*Dg6!q5H)(V>|<+JL@ zy3pzOuJ<)~FxBw`99{lEsyQ95k<==QF2xw!pEqI&ImIlT8i>-v4zEPgBbJ!SJDx+$ z;}J0}tt|K*ogRP;-j&EhAVmvijNd0edGLqKVaNb(HOyTUy{8$@%O0A5sOOt5GZUNB zF-0tib;MI7cB;5}_2{k>cd=vkg_ZP`plC67Q0>h-|Ic@ZbljSBf`K72 zL<(^9Fg3}EN@^dAxXiD^h@xg1Lm4+Yh5_^>o*~UjYCTA|+=3!E?YM}6X53$o22%!y 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zXk{o7*#UmY7x#cEpn!H!c&liCUy;3DEo!B@uIOlAFEB*>FBxcmnnmM5p54Lo3CA_L zgH~DGpEjn0AOiyiH^>V}fBK>@2~dg16A83O<=SACobqLOFe~Ow|~lG> z3dsW~^VBQv6HR>y=wB5_zk3B!zfUMAkWZSmGhg+tD0=Oe?6iPju*>MYC9mg=tb_`y z!f#-X*R@2fVT02Jv`F{;$qLv0$(=fvD=QPDiF*>&J8vn~ojd3q2xmQhvuuEz%*oeL zcw%Z3z@Fxmv_%fmae_6!x%Ia6cBE#D8BwE?)5Ieh$zGtIxShcMlXK?W!`*D%@#_4B z9>+f<8&KsqsOqD_v3iB22De7TE?^;9223RwULNZkomJ5V+C@6zdF8(f?>Favj*}Y$I(9?;BawH)rL4U9 z(_GK>XP#G9?s*NG+PgOXVS^Z<3$XKMYM#vtGg3pyfSAxP)AsxJ&)DCua@0A4k4B+I zr%k=HPQ1Dg|s=a+3H`dOx$~cNJNt9&1_`tPyRP>|GO-$G(D|`zE3yj|KGL$XW2`d zu)}EaJIA-Zzd@Y;Q1Qn~{HTF3fJ$^yLtu4`fAISkm;bx4`27cv|G(aN;Jd@1+=8J| Rl_Iz}?ke3^EP`A3{SPsQkK6zN literal 0 HcmV?d00001 diff --git a/docs/source/text_processing/intro.rst b/docs/source/text_processing/intro.rst new file mode 100644 index 000000000000..40f78ee0650b --- /dev/null +++ b/docs/source/text_processing/intro.rst @@ -0,0 +1,12 @@ +NeMo Text Processing +==================== + +NeMo provides a set of models for text processing input and/or output of Automatic Speech Recognitions (ASR) and Text-to-Speech (TTS) models: \ +`https://github.com/NVIDIA/NeMo/tree/main/nemo_text_processing `__ . + +.. toctree:: + :maxdepth: 1 + + g2p + + diff --git a/docs/source/text_processing/text_processing_all.bib b/docs/source/text_processing/text_processing_all.bib new file mode 100644 index 000000000000..412d3ee21be6 --- /dev/null +++ b/docs/source/text_processing/text_processing_all.bib @@ -0,0 +1,53 @@ +@article{xue2021byt5, + title={ByT5: Towards a token-free future with pre-trained byte-to-byte models 2021}, + author={Xue, Linting and Barua, Aditya and Constant, Noah and Al-Rfou, Rami and Narang, Sharan and Kale, Mihir and Roberts, Adam and Raffel, Colin}, + journal={arXiv preprint arXiv:2105.13626}, + year={2021} +} + +@article{vrezavckova2021t5g2p, + title={T5g2p: Using text-to-text transfer transformer for grapheme-to-phoneme conversion}, + author={{\v{R}}ez{\'a}{\v{c}}kov{\'a}, Mark{\'e}ta and {\v{S}}vec, Jan and Tihelka, Daniel}, + year={2021}, + journal={International Speech Communication Association} +} + +@article{zhu2022byt5, + title={ByT5 model for massively multilingual grapheme-to-phoneme conversion}, + author={Zhu, Jian and Zhang, Cong and Jurgens, David}, + journal={arXiv preprint arXiv:2204.03067}, + year={2022} +} + +@article{ggulati2020conformer, + title={Conformer: Convolution-augmented transformer for speech recognition}, + author={Gulati, Anmol and Qin, James and Chiu, Chung-Cheng and Parmar, Niki and Zhang, Yu and Yu, Jiahui and Han, Wei and Wang, Shibo and Zhang, Zhengdong and Wu, Yonghui and others}, + journal={arXiv preprint arXiv:2005.08100}, + year={2020} +} + +@article{ddevlin2018bert, + title={Bert: Pre-training of deep bidirectional transformers for language understanding}, + author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, + journal={arXiv preprint arXiv:1810.04805}, + year={2018} +} + +@inproceedings{gorman2018improving, + title={Improving homograph disambiguation with supervised machine learning}, + author={Gorman, Kyle and Mazovetskiy, Gleb and Nikolaev, Vitaly}, + booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, + year={2018} +} + + +@inproceedings{badlani2022one, + title={One TTS alignment to rule them all}, + author={Badlani, Rohan and {\L}a{\'n}cucki, Adrian and Shih, Kevin J and Valle, Rafael and Ping, Wei and Catanzaro, Bryan}, + booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, + pages={6092--6096}, + year={2022}, + organization={IEEE} +} + + diff --git a/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml b/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml index bc28f621ca99..72d069b2ba70 100644 --- a/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml +++ b/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml @@ -27,7 +27,7 @@ model: feat_in: ${model.embedding.d_model} feat_out: -1 # you may set it if you need different output size other than the default d_model n_layers: 16 - d_model: 256 + d_model: 176 # Sub-sampling params subsampling: null # vggnet or striding, vggnet may give better results but needs more memory @@ -39,7 +39,7 @@ model: # Multi-headed Attention Module's params self_attention_model: rel_pos # rel_pos or abs_pos - n_heads: 8 # may need to be lower for smaller d_models + n_heads: 4 # may need to be lower for smaller d_models # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention att_context_size: [ -1, -1 ] # -1 means unlimited context xscaling: true # scales up the input embeddings by sqrt(d_model) diff --git a/examples/text_processing/g2p/g2p_train_and_evaluate.py b/examples/text_processing/g2p/g2p_train_and_evaluate.py index 4f7e1102200b..395b82c3f073 100644 --- a/examples/text_processing/g2p/g2p_train_and_evaluate.py +++ b/examples/text_processing/g2p/g2p_train_and_evaluate.py @@ -38,6 +38,8 @@ trainer.devices=1 \ do_training=True \ do_testing=True + + Example of the config file: NeMo/examples/text_processing/g2p/conf/t5_g2p.yaml # Training Conformer-G2P Model and evaluation at the end of training: python examples/text_processing/g2p/g2p_train_and_evaluate.py \ @@ -50,6 +52,8 @@ do_training=True \ do_testing=True + Example of the config file: NeMo/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml + # Run evaluation of the pretrained model: python examples/text_processing/g2p/g2p_train_and_evaluate.py \ # (Optional: --config-path= --config-name=) \ diff --git a/examples/text_processing/g2p/heteronym_classification_inference.py b/examples/text_processing/g2p/heteronym_classification_inference.py index b0047093eb52..5e9afbb08fd1 100644 --- a/examples/text_processing/g2p/heteronym_classification_inference.py +++ b/examples/text_processing/g2p/heteronym_classification_inference.py @@ -31,6 +31,8 @@ This script runs inference with HeteronymClassificationModel If the input manifest contains target "word_id", evaluation will be also performed. +To prepare dataset, see NeMo/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py + python heteronym_classification_inference.py \ manifest="" \ pretrained_model="" \ diff --git a/examples/text_processing/g2p/heteronym_classification_train_and_evaluate.py b/examples/text_processing/g2p/heteronym_classification_train_and_evaluate.py index a28021fe8b83..456d5ed32d5c 100644 --- a/examples/text_processing/g2p/heteronym_classification_train_and_evaluate.py +++ b/examples/text_processing/g2p/heteronym_classification_train_and_evaluate.py @@ -27,11 +27,14 @@ """ This script runs training and evaluation of HeteronymClassificationModel +To prepare dataset, see NeMo/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py + To run training and testing: python heteronym_classification_train_and_evaluate.py \ train_manifest=" \ validation_manifest=" \ model.encoder.pretrained="" \ + model.wordids= \ do_training=True \ do_testing=True """