Load lang id any2any training mengruw

README.rst

@@ -5,9 +5,9 @@
   :target: http://www.repostatus.org/#active
   :alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed.
 
-.. |documentation| image:: https://readthedocs.com/projects/nvidia-nemo/badge/?version=main
+.. |documentation| image:: https://readthedocs.com/projects/nvidia-nemo/badge/?version=r1.19.0
   :alt: Documentation
-  :target: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/
+  :target: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/
 
 .. |license| image:: https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg
   :target: https://github.com/NVIDIA/NeMo/blob/master/LICENSE
@@ -25,15 +25,15 @@
   :target: https://pepy.tech/project/nemo-toolkit
   :alt: PyPi total downloads
 
-.. |codeql| image:: https://github.com/nvidia/nemo/actions/workflows/codeql.yml/badge.svg?branch=main&event=push
+.. |codeql| image:: https://github.com/nvidia/nemo/actions/workflows/codeql.yml/badge.svg?branch=r1.19.0&event=push
   :target: https://github.com/nvidia/nemo/actions/workflows/codeql.yml
   :alt: CodeQL
 
 .. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: black
 
-.. _main-readme:
+.. _r1.19.0-readme:
 
 **NVIDIA NeMo**
 ===============
@@ -61,7 +61,7 @@ We have extensive `tutorials <https://docs.nvidia.com/deeplearning/nemo/user-gui
 can all be run on `Google Colab <https://colab.research.google.com>`_.
 
 For advanced users that want to train NeMo models from scratch or finetune existing NeMo models 
-we have a full suite of `example scripts <https://github.com/NVIDIA/NeMo/tree/main/examples>`_ that support multi-GPU/multi-node training.
+we have a full suite of `example scripts <https://github.com/NVIDIA/NeMo/tree/r1.19.0/examples>`_ that support multi-GPU/multi-node training.
 
 For scaling NeMo LLM training on Slurm clusters or public clouds, please see the `NVIDIA NeMo Megatron Launcher <https://github.com/NVIDIA/NeMo-Megatron-Launcher>`_.
 The NM launcher has extensive recipes, scripts, utilities, and documentation for training NeMo LLMs and also has an `Autoconfigurator <https://github.com/NVIDIA/NeMo-Megatron-Launcher#53-using-autoconfigurator-to-find-the-optimal-configuration>`_ 
@@ -74,7 +74,7 @@ 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>`_
+    * `Automatic Speech Recognition (ASR) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/intro.html>`_
         * Supported ASR models: `<https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html>`_
             * Jasper, QuartzNet, CitriNet, ContextNet
             * Conformer-CTC, Conformer-Transducer, FastConformer-CTC, FastConformer-Transducer
@@ -88,42 +88,42 @@ Key Features
         * Streaming/Buffered ASR (CTC/Transducer) - `Chunked Inference Examples <https://github.com/NVIDIA/NeMo/tree/stable/examples/asr/asr_chunked_inference>`_
         * Cache-aware Streaming Conformer - `<https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer>`_
         * Beam Search decoding
-        * `Language Modelling for ASR <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html>`_: N-gram LM in fusion with Beam Search decoding, Neural Rescoring with Transformer
-        * `Support of long audios for Conformer with memory efficient local attention <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html#inference-on-long-audio>`_
-    * `Speech Classification, Speech Command Recognition and Language Identification <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_classification/intro.html>`_: MatchboxNet (Command Recognition), AmberNet (LangID)
+        * `Language Modelling for ASR <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/asr_language_modeling.html>`_: N-gram LM in fusion with Beam Search decoding, Neural Rescoring with Transformer
+        * `Support of long audios for Conformer with memory efficient local attention <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/results.html#inference-on-long-audio>`_
+    * `Speech Classification, Speech Command Recognition and Language Identification <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/speech_classification/intro.html>`_: MatchboxNet (Command Recognition), AmberNet (LangID)
     * `Voice activity Detection (VAD) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speech_classification/models.html#marblenet-vad>`_: MarbleNet
         * ASR with VAD Inference - `Example <https://github.com/NVIDIA/NeMo/tree/stable/examples/asr/asr_vad>`_
-    * `Speaker Recognition <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/intro.html>`_: TitaNet, ECAPA_TDNN, SpeakerNet
-    * `Speaker Diarization <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/intro.html>`_
+    * `Speaker Recognition <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/speaker_recognition/intro.html>`_: TitaNet, ECAPA_TDNN, SpeakerNet
+    * `Speaker Diarization <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/speaker_diarization/intro.html>`_
         * Clustering Diarizer: TitaNet, ECAPA_TDNN, SpeakerNet
         * Neural Diarizer: MSDD (Multi-scale Diarization Decoder)
-    * `Speech Intent Detection and Slot Filling <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_intent_slot/intro.html>`_: Conformer-Transformer
+    * `Speech Intent Detection and Slot Filling <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/asr/speech_intent_slot/intro.html>`_: Conformer-Transformer
     * `Pretrained models on different languages. <https://ngc.nvidia.com/catalog/collections/nvidia:nemo_asr>`_: English, Spanish, German, Russian, Chinese, French, Italian, Polish, ...
     * `NGC collection of pre-trained speech processing models. <https://ngc.nvidia.com/catalog/collections/nvidia:nemo_asr>`_
 * Natural Language Processing
     * `NeMo Megatron pre-training of Large Language Models <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html>`_
-    * `Neural Machine Translation (NMT) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/machine_translation/machine_translation.html>`_
-    * `Punctuation and Capitalization <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/punctuation_and_capitalization.html>`_
-    * `Token classification (named entity recognition) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/token_classification.html>`_
-    * `Text classification <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_classification.html>`_
-    * `Joint Intent and Slot Classification <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/joint_intent_slot.html>`_
-    * `Question answering <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/question_answering.html>`_
-    * `GLUE benchmark <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/glue_benchmark.html>`_
-    * `Information retrieval <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/information_retrieval.html>`_
-    * `Entity Linking <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/entity_linking.html>`_
-    * `Dialogue State Tracking <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/sgd_qa.html>`_
-    * `Prompt Learning <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/prompt_learning.html>`_
+    * `Neural Machine Translation (NMT) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/machine_translation/machine_translation.html>`_
+    * `Punctuation and Capitalization <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/punctuation_and_capitalization.html>`_
+    * `Token classification (named entity recognition) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/token_classification.html>`_
+    * `Text classification <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/text_classification.html>`_
+    * `Joint Intent and Slot Classification <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/joint_intent_slot.html>`_
+    * `Question answering <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/question_answering.html>`_
+    * `GLUE benchmark <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/glue_benchmark.html>`_
+    * `Information retrieval <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/information_retrieval.html>`_
+    * `Entity Linking <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/entity_linking.html>`_
+    * `Dialogue State Tracking <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/sgd_qa.html>`_
+    * `Prompt Learning <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/nemo_megatron/prompt_learning.html>`_
     * `NGC collection of pre-trained NLP models. <https://ngc.nvidia.com/catalog/collections/nvidia:nemo_nlp>`_
     * `Synthetic Tabular Data Generation <https://developer.nvidia.com/blog/generating-synthetic-data-with-transformers-a-solution-for-enterprise-data-challenges/>`_
-* `Speech synthesis (TTS) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/intro.html#>`_
+* `Speech synthesis (TTS) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/tts/intro.html#>`_
     * Spectrogram generation: Tacotron2, GlowTTS, TalkNet, FastPitch, FastSpeech2, Mixer-TTS, Mixer-TTS-X
     * Vocoders: WaveGlow, SqueezeWave, UniGlow, MelGAN, HiFiGAN, UnivNet
     * End-to-end speech generation: FastPitch_HifiGan_E2E, FastSpeech2_HifiGan_E2E, VITS
     * `NGC collection of pre-trained TTS models. <https://ngc.nvidia.com/catalog/collections/nvidia:nemo_tts>`_
 * `Tools <https://github.com/NVIDIA/NeMo/tree/stable/tools>`_
-    * `Text Processing (text normalization and inverse text normalization) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_normalization/intro.html>`_
-    * `CTC-Segmentation tool <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tools/ctc_segmentation.html>`_
-    * `Speech Data Explorer <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tools/speech_data_explorer.html>`_: a dash-based tool for interactive exploration of ASR/TTS datasets
+    * `Text Processing (text normalization and inverse text normalization) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/nlp/text_normalization/intro.html>`_
+    * `CTC-Segmentation tool <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/tools/ctc_segmentation.html>`_
+    * `Speech Data Explorer <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/tools/speech_data_explorer.html>`_: a dash-based tool for interactive exploration of ASR/TTS datasets
     * `Speech Data Processor <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/tools/speech_data_processor.html>`_
 
 
@@ -139,10 +139,10 @@ Requirements
 Documentation
 -------------
 
-.. |main| image:: https://readthedocs.com/projects/nvidia-nemo/badge/?version=main
+.. |r1.19.0| image:: https://readthedocs.com/projects/nvidia-nemo/badge/?version=r1.19.0
   :alt: Documentation Status
   :scale: 100%
-  :target: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/
+  :target: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.19.0/
 
 .. |stable| image:: https://readthedocs.com/projects/nvidia-nemo/badge/?version=stable
   :alt: Documentation Status
@@ -152,7 +152,7 @@ Documentation
 +---------+-------------+------------------------------------------------------------------------------------------------------------------------------------------+
 | Version | Status      | Description                                                                                                                              |
 +=========+=============+==========================================================================================================================================+
-| Latest  | |main|      | `Documentation of the latest (i.e. main) branch. <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/>`_                  |
+| Latest  | |r1.19.0|      | `Documentation of the latest (i.e. main) branch. <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/>`_                  |
 +---------+-------------+------------------------------------------------------------------------------------------------------------------------------------------+
 | Stable  | |stable|    | `Documentation of the stable (i.e. most recent release) branch. <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/>`_ |
 +---------+-------------+------------------------------------------------------------------------------------------------------------------------------------------+
@@ -263,7 +263,7 @@ packaging is also needed:
 
 .. code-block:: bash
 
-  pip install -y packaging
+  pip install packaging
 
 
 Transformer Engine

docs/source/_static/css/custom.css

@@ -255,7 +255,7 @@ article ul {
 	}
 }
 
-@media (min-width: 1400px) {
+@media (min-width: none) {
 	body {
 		font-size: 18px;
 	}

docs/source/conf.py

@@ -28,7 +28,6 @@
 
 sys.path.insert(0, os.path.abspath("../.."))
 sys.path.insert(0, os.path.abspath("../../nemo"))
-sys.path.insert(0, os.path.abspath("../../nemo_text_processing"))
 
 from package_info import __version__
 
@@ -47,18 +46,20 @@
     'hydra',  # hydra-core in requirements, hydra during import
     'dateutil',  # part of core python
     'transformers.tokenization_bert',  # has ., troublesome for this regex
-    'megatron',  # megatron-lm in requirements, megatron in import
     'sklearn',  # scikit_learn in requirements, sklearn in import
     'nemo_text_processing.inverse_text_normalization',  # Not installed automatically
     'nemo_text_processing.text_normalization',  # Not installed automatically
     'attr',  # attrdict in requirements, attr in import
     'torchmetrics',  # inherited from PTL
     'lightning_utilities',  # inherited from PTL
     'apex',
+    'megatron.core',
+    'transformer_engine',
     'joblib',  # inherited from optional code
     'IPython',
     'ipadic',
     'psutil',
+    'regex',
 ]
 
 _skipped_autodoc_mock_imports = ['wrapt', 'numpy']