renwei2024/nemo-github-issues huggingface dataset:
fix bug relating to ddp strategy in joint intent slot classification …
State: closed | Pull request: yes

# What does this PR do ?

Fix joint intent slot classification tutorial

**Collection**: nlp

# Changelog 
- Add specific line by line info of high level changes in this PR.

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pin min version as 1.7.2
State: closed | Pull request: yes

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Pin min version for PTL 1.7.2 and remove patch due to .to(cuda) issue with earlier versions. 

**Collection**: Core

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s2s dialog generation custom data processor
State: closed | Pull request: yes

…model

Signed-off-by: Ameya Mahabaleshwarkar <ameyasm1154@gmail.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: nlp

# Changelog 
- Added a data processor for a custom dataset for the DialogueS2SGenerationModel.

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# Additional Information
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[TTS] updated config with a German IPA phoneme tokenizer
State: closed | Pull request: yes

* disabled spline interpolation for beta-binomial distribution by default. Let it generate align prior and save to disks. 
* updated config using a phoneme tokenizer because the final model release was built upon the phoneme tokenizer instead of a mixer of grapheme/phoneme.
  * note that phoneme tokenizer's `encode()` class method was extended to support `c == "\u0303"` for German, but not supported in the previous single speaker German model release.
* use consistent spline interpolation with fastpitch checkpoint when generating mel-spectrograms for hifigan fine-tune. 
  * note: this was already merged in main (https://github.com/NVIDIA/NeMo/pull/4679), but is missing in r1.11.0. Affected file is `scripts/dataset_processing/tts/generate_mels_for_finetune_hifigan.py` (you could ignore this file during your reviewing.)

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

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# Additional Information
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Fixes NLPModel's load from checkpoint due to PTL private function changes
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

Fixes NLPModel's `load_from_checkpoint` because PTL changed `_load_model_state` in 1.7.1.

**Collection**: NLP

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- Add specific line by line info of high level changes in this PR.

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# Additional Information
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Updated docs and doc paths
State: closed | Pull request: yes

Signed-off-by: Virginia Adams <vadams@nvidia.com>

# What does this PR do ?

Updates prompt learning documentation paths and updates some parts of the prompt learning documentation. Addressing https://nvbugs/3748879 [ML-NeMo][A40] Multitask_Prompt_and_PTuning.ipynb contains a broken link and wrong branch

**Collection**: NLP

# Changelog 
- Prompt learning docs
- Prompt learning tutorial
- README.md

# Usage
N/A

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# Additional Information
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Maintain compatibility with py37 new
State: closed | Pull request: yes

# What does this PR do ?

Replace functools.cached_property with functools.lru_cache for compatibility with py37 (which Colab uses).

**Collection**: nlp

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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```python
# Add a code snippet demonstrating how to use this 
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**PR Type**:
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# Additional Information
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Maintain compatibility with py37
State: closed | Pull request: yes

# What does this PR do ?

Replace functools.cached_property with functools.lru_cache for compatibility with py37 (which Colab uses). 

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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```python
# Add a code snippet demonstrating how to use this 
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# Additional Information
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jenkins data simulator fix
State: closed | Pull request: yes

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Fix jenkins CI issue with data simulator section config file

**Collection**: Tools

# Changelog 
- remove single quotes


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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
How to calculate models strides per sec 
State: closed | Pull request: no

How to calculate `models_stride_per_sec` for stt_es_conformer_ctc_large. model.   I am trying to transcribe audio of 1 hr using the 

stt_en_conformer_ctc_large its 0.04

`from nemo.collections.asr.parts.utils.decoder_timestamps_utils import FrameBatchASR_Logits`

is there a better way to stream log audios on the conformer models.
@titu1994
Reverted cahced_property to Python 3.7 friendly version.
State: closed | Pull request: yes

Signed-off-by: Micha Livne <mlivne@cs.toronto.edu>

# What does this PR do ?

Revert to colab Python 3.7friendly version of cached_property

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

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# Additional Information
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publish pretrained itn t5 model for English
State: closed | Pull request: yes

Signed-off-by: Alexandra Antonova <aleksandraa@nvidia.com>

# What does this PR do ?

Add a pretrained model for English itn t5 on NGC

**Collection**: [Note which collection this PR will affect]

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- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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Merge r1.11.0 into main
State: closed | Pull request: yes

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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**Pre checks**:
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# Additional Information
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Prompt learning of Huggingface T5v1.1 converted checkpoints
State: closed | Pull request: yes

# What does this PR do ?

Makes the changes necessary to do prompt learning of T5v1.1-converted checkpoints from HF.

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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**Pre checks**:
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# Additional Information
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Update r1.11 to new heteronyms list
State: closed | Pull request: yes

# What does this PR do ?

Move over to new heteronyms list based on Wikipedia's [heteronyms short list](https://en.wikipedia.org/wiki/Heteronym_(linguistics)#English) and the [Wikipedia Homograph Data](https://github.com/google-research-datasets/WikipediaHomographData/blob/master/data/wordids.tsv).

**Collection**: TTS

# Changelog 
- Update existing TTS configs to use the new heteronyms list
- Remove outdated heteronyms list
- Add missing pronunciation of "merchandise" to CMUdict
Update to new heteronyms list
State: closed | Pull request: yes

# What does this PR do ?

Move over to new heteronyms list based on Wikipedia's [heteronyms short list](https://en.wikipedia.org/wiki/Heteronym_(linguistics)#English) and the [Wikipedia Homograph Data](https://github.com/google-research-datasets/WikipediaHomographData/blob/master/data/wordids.tsv).

**Collection**: TTS

# Changelog 
- Update existing TTS configs to use the new heteronyms list
- Remove outdated heteronyms list
- Add missing pronunciation of "merchandise" to CMUdict
Add LayerNorm1P, weight decay for LN and unscaled initialization
State: closed | Pull request: yes

# What does this PR do ?

Port Megatron features to match GPT3 convergence

**Collection**: NLP/Megatron

# Changelog 
- Add LayerNorm1P class implementing numerically stable LayerNorm with formula: `(self.weight + 1) * x + self.bias` instead of `self.weight * x + self.bias`
- add option to use weight decay also on LayerNorm weights
- add option to use unscaled residuals initialization

All 3 options are exposed with new hydra flags. With default flag values, the **model behaviour is unchanged**.

# Usage
Override these flags to enable respective features described above:
- model.normalization=layernorm1p
- model.do_layer_norm_weight_decay=1
- model.use_scaled_init_method=0

Each of the flags work independently from each other

# Before your PR is "Ready for review"
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# Additional Information
Checks for trainer.limit_val/test_batches and trainer.val_check_interval
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

Adds checks for trainer.limit_val/test_batches and trainer.val_check_interval

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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**Pre checks**:
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# Additional Information
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Fix megatron p tuning notebook
State: closed | Pull request: yes

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: 
NLP

# Changelog 
- Fix issues with strategy and plugin mix (with PTL 1.7 upgrade)

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bug fix: enable async grad reduction when DP > 1
State: closed | Pull request: yes

# What does this PR do ?

Enable sync gradient reduction only when DP size is larger than 1. 
This fixes the bug that NCCL kernels for grad-AR were issued asynchronously even when DP=1 slowing down compute kernel execution.

**Collection**: [Note which collection this PR will affect]

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# Add a code snippet demonstrating how to use this 
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# Additional Information
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Fix typo in parameter name: save_on_train_end
State: closed | Pull request: yes

Fix a typo in a parameter name. Instead of 'save_on_train_end', the correct parameter name is 'save_nemo_on_train_end'.

Signed-off-by: Miguel Martínez <miguelusque@users.noreply.github.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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# Add a code snippet demonstrating how to use this 
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# Additional Information
* Related to # (issue)
Fix typo in parameter name: save_on_train_end
State: closed | Pull request: yes

# What does this PR do ?

Fixes a typo in a parameter name. Instead of 'save_on_train_end', the correct parameter name is 'save_nemo_on_train_end'.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

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```python
# Add a code snippet demonstrating how to use this 
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## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Multilingual VAD model
State: closed | Pull request: yes

# What does this PR do ?

Release new multilingual VAD model that has been trained on 6 languages.
Find more details and evaluation results in [NGC model card](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet).

**Collection**: ASR

# Changelog 
- Link to new model. 
- VAD+ASR: Tuned postprocessing parameters on 0~20db SNR noisy and clean multilingual ASR data in `examples/asr/conf/vad/vad_inference_postprocessing.yaml`
- VAD in Speaker Diarization: Tuned postprocessing parameters on 11 moved session from CH109 in `examples/speaker_tasks/diarization/conf/offline_diarization.yaml` and `offline_diarization_with_asr.yaml`
- Tutorials and Docs.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Add pynini to Docker container
State: closed | Pull request: yes

# What does this PR do ?

Install `pynini` library in Docker container. 
Currently pynini is not installed in Docker container, which causes TTS models to crash

**Collection**: [Note which collection this PR will affect]
* text processing
* tts

# Changelog 
- install pynini (`Dockerfile`)

# Problem
Run the following code in container built from `r1.11.0` branch:
```python
from nemo.collections.tts.models import FastPitchModel
model = FastPitchModel.from_pretrained("tts_en_fastpitch_multispeaker")
```
*Expected results:* model is loaded correctly
*Actual result:*
```
AttributeError                            Traceback (most recent call last)
File /opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py:583, in _locate(path)
    582 try:
--> 583     obj = getattr(obj, part)
    584 except AttributeError as exc_attr:
AttributeError: module 'nemo_text_processing.text_normalization' has no attribute 'normalize'

During handling of the above exception, another exception occurred:
ModuleNotFoundError                       Traceback (most recent call last)
File /opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py:589, in _locate(path)
    588 try:
--> 589     obj = import_module(mod)
    590     continue
File /opt/conda/lib/python3.8/importlib/__init__.py:127, in import_module(name, package)
    126         level += 1
--> 127 return _bootstrap._gcd_import(name[level:], package, level)
File <frozen importlib._bootstrap>:1014, in _gcd_import(name, package, level)
File <frozen importlib._bootstrap>:991, in _find_and_load(name, import_)
File <frozen importlib._bootstrap>:975, in _find_and_load_unlocked(name, import_)
File <frozen importlib._bootstrap>:671, in _load_unlocked(spec)
File <frozen importlib._bootstrap_external>:843, in exec_module(self, module)
File <frozen importlib._bootstrap>:219, in _call_with_frames_removed(f, *args, **kwds)
File /opt/conda/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py:24, in <module>
     22 from typing import Dict, List, Union
---> 24 import pynini
     25 import regex
ModuleNotFoundError: No module named 'pynini'
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to #4674
Model training
State: closed | Pull request: no
Labels: name: bug

I am training ru model with these inputs data:

My code:

```
import nemo
import nemo.collections.asr as nemo_asr
import pytorch_lightning as pl

from nemo.utils.exp_manager import exp_manager
from omegaconf import DictConfig, OmegaConf

params = OmegaConf.load(model_conf_path)

params.trainer.gpus = 1
params.model.train_ds.manifest_filepath = train_manifest_path
params.model.validation_ds.manifest_filepath = validation_manifest_path
params.model.test_ds.manifest_filepath = test_manifest_path
params.exp_manager.exp_dir = 'path/to/save'

trainer = pl.Trainer(**params.trainer)
model = nemo_asr.models.EncDecCTCModel(cfg=params.model, trainer=trainer)
exp_manager(trainer=trainer, cfg=params.exp_manager)

trainer.fit(model)
trainer.test(model)

```

Train manifest file looks like this:
{"audio_filepath": ""/path/to/wav", "duration": 4.8, "text": "у вас просроченных дней две тысячи сто шестьдесят один дней"}
{"audio_filepath": "/path/to/wav", "duration": 9.1, "text": "выключай выключай давайте следующую свечу других нет сейчас расскажу один анекдот когда"}
Total files: 34786
Duration - 601 hours.

There is yaml config:
https://github.com/Project-fil/Project-fil/blob/main/quartznet_ru.yaml

The training takes place in 4 epoch.
Test_loss - 65.60244750976562 
Test_wer - 0.928061842918396

I get five files in dir 'checkpoints':
```
'QuartzNet15x5_ru--val_wer=0.9281-epoch=4-last.ckpt'
'QuartzNet15x5_ru--val_wer=0.9765-epoch=2.ckpt'  
'QuartzNet15x5_ru--val_wer=0.9594-epoch=3.ckpt'
'QuartzNet15x5_ru--val_wer=0.9281-epoch=4.ckpt'       
 QuartzNet15x5_ru.nemo
```

And when i try to decode wav file, with this code:

```
first_path = path/to/QuartzNet15x5_ru--val_wer=0.9281-epoch=4-last.ckpt  # or another *.ckpt
restored_model = nemo_asr.models.EncDecCTCModel.load_from_checkpoint(first_path)
rt = restored_model.transcribe([aud_ru])
print(rt)
```

I get this: 
['просротеяне две тстистоиеяьдесят тоде нде']
and 
['аич д брдие еча скожеден ниду']
instead:
['у вас просроченных дней две тысячи сто шестьдесят один дней']
and
['выключай выключай давайте следующую свечу других нет сейчас расскажу один анекдот когда']

All files that I decode are decrypted incorrectly
Am I doing something wrong or not enough hours to learn?
Intent slot model onnx export test
State: closed | Pull request: yes

# What does this PR do ?

Intent slot model onnx export test

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Intent slot model onnx export test
State: closed | Pull request: yes

# What does this PR do ?

Update Intent Slot model ONNX Export Test

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Fix pynini install in TTS tutorials
State: closed | Pull request: yes

Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com>

# What does this PR do ?

Update TTS tutorials to install pynini with the new script.

**Collection**: TTS

**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

# Additional Information
* Related to #4674
Add psutils to mock imports
State: closed | Pull request: yes

Signed-off-by: ericharper <complex451@gmail.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Which NeMo Docker Container is working good
State: closed | Pull request: no
Labels: name: bug

i use nvcr.io/nvidia/nemo:22.05, and git master

try test .py

test .py code content only

import nemo.collections.asr as nemo_asr

had a error about

```
Traceback (most recent call last):
  File "test.py", line 1, in <module>
    import nemo.collections.asr as nemo_asr
  File "/code/nemo/collections/asr/__init__.py", line 15, in <module>
    from nemo.collections.asr import data, losses, models, modules
  File "/code/nemo/collections/asr/models/__init__.py", line 16, in <module>
    from nemo.collections.asr.models.classification_models import EncDecClassificationModel
  File "/code/nemo/collections/asr/models/classification_models.py", line 27, in <module>
    from pytorch_lightning.metrics.regression import MeanAbsoluteError, MeanSquaredError
ModuleNotFoundError: No module named 'pytorch_lightning.metrics'
```
i try install pip install pytorch-lightning==1.2.2

had another error about
```
Traceback (most recent call last):
  File "test.py", line 1, in <module>
    import nemo.collections.asr as nemo_asr
  File "/code/nemo/collections/asr/__init__.py", line 15, in <module>
    from nemo.collections.asr import data, losses, models, modules
  File "/code/nemo/collections/asr/models/__init__.py", line 16, in <module>
    from nemo.collections.asr.models.classification_models import EncDecClassificationModel
  File "/code/nemo/collections/asr/models/classification_models.py", line 31, in <module>
    from nemo.collections.asr.parts.features import WaveformFeaturizer
  File "/code/nemo/collections/asr/parts/features.py", line 44, in <module>
    from torch_stft import STFT
ModuleNotFoundError: No module named 'torch_stft'
```

i try install pip install torch-stft

now is work, but I don't know if there will be the next problem
add eMHA interface
State: closed | Pull request: yes

# What does this PR do ?
Add the interface to enable TransformerEngine/eMHA at NeMo

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
**training.model.use_emha=True**
Should be used with **training.model.transformer_engine=True**

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
[TTS] bugfix for missing configs.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [x] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Fixing outputs of infer() and use of NeMo length regulator helper
State: closed | Pull request: yes

Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>

# What does this PR do ?

Making small adjustments to exported methods to match Riva expectations.

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
adding decoder memory for faster inference - MegatronNMT
State: closed | Pull request: yes
Labels: name: stale

Signed-off-by: David Mosallanezhad <dmosallanezh@nvidia.com>

# What does this PR do ?

adding decoder memory for faster inference - MegatronNMT

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Change Num Partitions size expansion fix
State: closed | Pull request: yes

# What does this PR do ?

Fixes model disk size expansion in Change Num Partitions

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)