renwei2024/nemo-github-issues huggingface dataset:
Fix global batch fit loop
State: closed | Pull request: yes

# What does this PR do ?

Fixes the global batch data fetcher for T5 Glue.

**Collection**: NLP

# Changelog 
- Remove FitLoop abstraction
- Update data fetcher in T5 Glue LightningModule hooks.

# 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

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# Additional Information
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Possible typo in HiFiGan's max_steps value
State: closed | Pull request: no
Labels: name: bug

Is there any particular reason why hifigan's max_steps value is 25,000,000 [here](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/hifigan/hifigan.yaml#L67)  as opposed to 2,500,000 [in the original repo](https://github.com/jik876/hifi-gan/issues/4#issuecomment-719941567)? I've personally had better results with 2,500,000 (model converged way faster). Is it a typo?
fix save_best missing chpt bug, update for setup_tokenizer() changes
State: closed | Pull request: yes

# What does this PR do ?

[NLP] bug fixes for duplex model

**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**:
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation

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# Additional Information
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Multilang asr tutorial
State: closed | Pull request: yes

# What does this PR do ?

Tutorial for inference and training of multilingual ASR models

**Collection**:  ASR

# Changelog 
- Adding the jupyter notebook and a link to it in the readme

# 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**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
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# Additional Information
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Torch conversion for VAD-Diarization pipeline
State: closed | Pull request: yes

Signed-off-by: Taejin Park <tango4j@gmail.com>

# What does this PR do ?

This PR replaces the numpy based functions with torch based functions in VAD-Diarization pipeline. This conversion is motivated for easier conversion from NeMo system to Riva production system. Most of the functions in vad_utils.py and nmesc_clustering.py are compatible with torch.jit.script decorator.   

**Collection**: ASR

# Changelog 
- Converted all the numpy based functions in nmesc_clustering.py, vad_utils.py
- Converted numpy based signal chain in speaker_utils.py
- Changed numpy or python list initialization to torch in clustering_diarizer.py

# Usage
Please refer to [speaker diarization part of the NeMo docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/datasets.html).

```python
python examples/speaker_tasks/diarization/offline_diarization.py \
    diarizer.manifest_filepath="/path/to/ch109_input_manifest.json" \
    diarizer.oracle_num_speakers=2  \ # Since there are exactly 2 speakers per each CH109 session
    diarizer.oracle_vad=True \ # Use oracle VAD extracted from RTTM files.
    diarizer.collar=0.25 \
    diarizer.ignore_overlap=True \
    diarizer.speaker_embeddings.model_path="titanet_large" \
    diarizer.speaker_embeddings.window_length_in_sec=[1.5,1.0,0.5] # Multiscale setting \
    diarizer.speaker_embeddings.shift_length_in_sec=[0.75,0.5,0.25] # Multiscale setting \
    diarizer.speaker_embeddings.parameters.multiscale_weights=[0.33,0.33,0.33] # Equal weights 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [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?

ASR collection contributors.
ability to set log_prediction to false
State: closed | Pull request: yes

# What does this PR do ?

Fixes the bug preventing the user from being able to turn off log_prediction in RNNT models

**Collection**: ASR

# Changelog 
- Add specific line by line info of high level changes in this PR.
- one line change checking to see whether wer.log_predictions = True before turning them on

# Usage
model.wer.log_predictions = False


```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)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to # (issue)
[Draft] [TTS] Fastpitch restructure
State: closed | Pull request: yes

# What does this PR do ?

AudioToCharWithDursF0Dataset class will be deprecated so this PR adds code and config to use old FastPitch checkpoint (checkpoint that was trained using AudioToCharWithDursF0Dataset class which is now deprecated) with new code (TTSDataset) to support inference.

**Collection**: tts.models.fastpitch

# Changelog 
- Adds Warning when using old checkpoint
- Adds config file to be used with old checkpoint

# Usage
* In FastPitchModel.from_pretrained use https://github.com/NVIDIA/NeMo/tree/main/examples/tts/conf/fastpitch_align_old.yaml \
   as config_file to avoid using deprecated AudioToCharWithDursF0Dataset class and run into errors. \

```python
# FastPitchModel.from_pretrained("tts_en_fastpitch", override_config_path="/path/to/fastpitch_align_old.yaml") 
```

# 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?
- [ ] 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

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## Who can review?

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# Additional Information
* Related to # (issue)
Refactor restorefrom
State: closed | Pull request: yes

# What does this PR do ?

Reduce code duplications in restore_from overridden in NLP
Reorganize export files in script/export/

**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?
- [ ] 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

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## Who can review?

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# Additional Information
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Update package info
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 
```

# 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

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## Who can review?

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# Additional Information
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Stateless timer fix for PTL 1.6
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

Fixes a stateless timer restore issue because PTL 1.6 changed their Timer API

**Collection**: all

# Changelog 
- Override `save_state_dict()` and `load_state_dict()` to not load time information.

# Usage
N/A

# 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

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# Additional Information
* Related to # (issue)
pretrained fastpitch not loading
State: closed | Pull request: no
Labels: name: bug

an error appears:

ypeError                                 Traceback (most recent call last)
[<ipython-input-6-f04173251243>](https://localhost:8080/#) in <module>()
---> 19 fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch")

8 frames
[/usr/local/lib/python3.7/dist-packages/yaml/__init__.py](https://localhost:8080/#) in dump(data, stream, Dumper, **kwds)
    198     Emit YAML parsing events into a stream.
    199     If stream is None, return the produced string instead.
--> 200     """
    201     getvalue = None
    202     if stream is None:

TypeError: dump_all() got an unexpected keyword argument 'sort_keys'
Speech transcription with background music
State: closed | Pull request: no

Hi,

I've been testing the NeMo transcription engine for some time now and I came across a question. In the case of voice with background music, how would NeMo behave?
Would I need to train with audios with background music? Or would that be a problem?

An example, a person narrating some text and an audio in the background. I would like to have the result of what the person is saying.
json.dumps and non English characters
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

NeMo uses the `json.dumps` function to save dictionaries to JSON files. `json.dumps` is problematic when there are non-English characters involved. For example, I work with the Slovenian language and `json.dumps` will printout `\u0161` instead of `č` at all locations. To resolve this you need to use the `ensure_ascii=False` option for `json.dumps`. I saw that at a couple of location NeMo already uses this but at the majority locations it does not.

**Steps/Code to reproduce bug**

Run `examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py` or a similar transcription script with a model for a language that has non-English characters and inspect the transcript.

**Expected behavior**

Non-English characters should be printed properly in outputs and not as a unicode number.

**Environment overview (please complete the following information)**

NeMo container 22.01.

**Proposed solution**

Add `ensure_ascii=False` to all `json.dumps` calls in NeMo. If you are interested in this, I can prepare a PR.
fixes to the cleanup flag
State: closed | Pull request: yes

Signed-off-by: Dima Rekesh <bmwshop@gmail.com>

# What does this PR do ?
bugfix for the cleanup flag 

Add a one line overview of what this PR aims to accomplish.
bugfix for the cleanup flag 

**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?

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# Additional Information
* Related to # (issue)
added stt_en_conformer_transducer_large_ls to NGC
State: closed | Pull request: yes

Signed-off-by: Vahid <vnoroozi@nvidia.com>

# What does this PR do ?

Added stt_en_conformer_transducer_large_ls  to NGC. A Conformer-Transducer-Large just trained on LS.

**Collection**: ASR

# Changelog 
- Added a new model stt_en_conformer_transducer_large_ls to NGC (A Conformer-Transducer-Large just trained on LS)

**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
Megatron legacy conversion support
State: closed | Pull request: yes

Signed-off-by: Ramanathan Arunachalam <rarunachalam@nvidia.com>

# What does this PR do ?
Fix bugs in MegatronBert export to support models trained on NeMo < 1.5
Refactor code for NLP models forward functions based on Bert
Fix a bug in Intent Slot classification on path join
Modify export.py to showcase exporting legacy MegatronBert NLP models

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?
- [ ] 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)
NMT GRPC sever URL fix
State: closed | Pull request: yes

# What does this PR do ?

Makes server URL generic for NMT GRPC  and not specific to being on localhost

**Collection**: Tools

# Changelog 
- Accept server URL directly instead of just a localhost port in `client.py`

# Usage

```
python tools/nmt_grpc_service/client.py --source_language en --target_language de --text "This is a sentence." --server_url 1.1.1.1:50052
```

# 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?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
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# Additional Information
* Related to # (issue)
Megatron legacy conversion support
State: closed | Pull request: yes

# What does this PR do ?

Fix bugs in MegatronBert export to support models trained on NeMo < 1.5
Refactor code for NLP models forward functions based on Bert
Fix a bug in Intent Slot classification on path join
Modify export.py to showcase exporting legacy MegatronBert NLP models

**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?
- [ ] 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?

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# Additional Information
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Tacotron2 ipynb not working. Hydra issue.
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

When running the tutorial notebook for Tacotron2 training in Google Colab. A Hydra issue is raised:
`Traceback (most recent call last):
File "[tacotron2.py](http://tacotron2.py/)", line 44, in <module>
main()  # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 103, in wrapper
strict=None,
TypeError: _run_hydra() got an unexpected keyword argument 'strict'`

**Steps/Code to reproduce bug**

Running each cell of the Tacotron2 tutorial notebook in a new Colab session. NOTE: Lines in the first cell were uncommented as per the instructions. 

**Expected behavior**

Training to begin without error on the example dataset provided.

**Environment overview (please complete the following information)**

 - Environment location: Collab
 - Method of NeMo install: As per instructions within notebook. Please specify exact commands you used to install.
Add a missing line to ASR_with_NeMo.ipynb
State: closed | Pull request: yes

# What does this PR do ?

Add a missing line to inference step. The WER is different with and without the line.

**Collection**: ASR

# Changelog 
- Add a missing line to inference step in ASR_with_NeMo.ipynb
- Deleted lines were handled automatically by PyCharm.

# Usage
* N/A

# 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)
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- [ ] 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
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Multilang asr models
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 
```

# 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?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
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Ranking merge
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 
```

# 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
- [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)

- [x] ci tests are passing
- [x] SH tests are passing
- [ ] with LM matches baseline numbers
Add domain groups to changelog builder
State: closed | Pull request: yes

# What does this PR do ?

Adds some block configs to group together PRs under each domain when building the changelog.

**Collection**: [docs]

# Usage

After building the changelog, each domain will have a dangling `</details>` tag in the raw markdown, move that to the end of the domains PR list to group them together.

# 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
- [x] Documentation
Ptl requirements bound
State: closed | Pull request: yes

# What does this PR do ?

Bounds PTL since pypi PTL 1.6 is out.

**Collection**: All

# Changelog 
- Change PTL requirements file.

# Usage
N/A

# 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)
Comment gpt resume ci test
State: closed | Pull request: yes

# What does this PR do ?

Comments out GPT CI tests for resuming training temporarily due to hanging.

**Collection**: NLP

# Changelog 
- Comments out GPT CI resume tests

# Usage
N/A

# 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)
Update NeMo CI to 22.03
State: closed | Pull request: yes

# What does this PR do ?

Updates NeMo CI to the PyTorch 22.03 container

**Collection**: All

# Changelog 
- Changes the Jenkinsfile with the path to the new container.

# Usage
N/A

# 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)
`helpers.cpp:25:10: fatal error: pybind11/pybind11.h: No such file or directory` error when calling `compile_helper`
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

```python
from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import compile_helper
compile_helper()
```
Throws the following error:
```
/usr/bin/python3: No module named pybind11
g++ -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color  helpers.cpp -o helpers.cpython-38-x86_64-linux-gnu.so
helpers.cpp:25:10: fatal error: pybind11/pybind11.h: No such file or directory
   25 | #include <pybind11/pybind11.h>
      |          ^~~~~~~~~~~~~~~~~~~~~
compilation terminated.
make: *** [Makefile:23: helpers.cpython-38-x86_64-linux-gnu.so] Error 1
[NeMo E 2022-03-29 09:34:40 dataset_utils:77] Making C++ dataset helpers module failed, exiting.
```
**Expected behavior**

Should not throw an error.

**Environment overview (please complete the following information)**

 - Environment location: metal
 - Method of NeMo install: from source

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.2
- PyTorch version: 1.10.0+cu113
- Python version: 3.8
JSON output from diarization now includes sentences. Optimized senten…
State: closed | Pull request: yes

…ce generation code

Signed-off-by: Jure Demsar <demsarjure@gmail.com>

# What does this PR do ?

Diarization with ASR JSON output now also includes sentences. This is a continuation of https://github.com/NVIDIA/NeMo/pull/3791, which I accidentally closed when cleaning up the repo.

**Collection**: [Note which collection this PR will affect]

ASR

# Changelog 

Diarization with ASR JSON output now also includes sentences.

# Usage

You can use the `examples/speaker_tasks/diarization/offline_diarization.py` script and check the output JSON file (`.../pred_rttms/uniq_name.json`).

# 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? **No.**
- [x] Did you add or update any necessary documentation? **No.**
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)? **No.**
- [x] Reviewer: Does the PR have correct import guards for all optional libraries? **N/A.**

**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?

@titu1994, @redoctopus, @jbalam-nv, or @okuchaiev

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 #3708 (https://github.com/NVIDIA/NeMo/issues/3708)

When implementing I used an approach that changes the original codebase as little as possible. In other words, I tried to be non-invasive. After reviewing the existing code for sentences (`string_out`) preparation in `make_json_output()` of `/nemo/collections/asr/parts/utils/diarization_utils.py` I feel like the whole process could be done much more efficiently and elegantly. Currently, the sentence preparation is appending words to a string where the beginning of the string are the time stamps. When the ending time stamp needs to be corrected the code then uses string splitting to extract and replace/correct the end time value. This is quite clunky and inefficient. It would be much more elegant if time stamps and the sentences were stored and built within a dictionary and then printed out at the end.
Get 404 client error when I execute example in /blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

In website https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb， I run the code step by step, and i get the 404 client error on the last line of chapter "Setting up a NeMo Experiment"

**Steps/Code to reproduce bug**

model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer) in chapter "[Setting up a NeMo Experiment](url)"

**Environment overview (please complete the following information)**

 - Method of NeMo install:  Google Colaboratory, 
 
**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- python 3.7.13
- pytorch 1.10.0 
- cuda 11.1
Megatron support
State: closed | Pull request: yes

# What does this PR do ?

Add MegatronBert support for more NLP models; 
Add support for finetuning a downstream task which used MegatronBert as pretrained model

Fix RIVA and ONNX export issues of Bert and MegatronBert (This is coming from https://github.com/NVIDIA/NeMo/pull/3883 PR)
The change in order of arguments won't break usage of anyone unless they deliberately turned off parameters check and passed arguments in positional fashion. ONNX export is one of the places that does that internally in PyTorch and that's why we need to make that order consistent across HF and Nemo BERT models and NLPModel that uses those modules as input_module.

NLPModel __init__() refactoring ( https://github.com/NVIDIA/NeMo/pull/3887 ) 
This is done to reduce code duplication and make it manageable. Each class has their own minor config differences and registers own artifacts, and those subtle inconsistencies break assumptions downstream (e.g. in Riva)

**Collection**: [Note which collection this PR will affect]

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**:
- [ 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [x ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x ] 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)
improvements to commonvoice data script
State: closed | Pull request: yes

Signed-off-by: Dima Rekesh <bmwshop@gmail.com>

# What does this PR do ?
improvements to et_commonvoice_data.py: logging, handling of overwrites, tgz cleanup 

**Collection**: ASR
# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

```python
# python get_commonvoice_data.py --num_workers=4 --language es --log --files_to_process test.tsv dev.tsv
```

# 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)
doc links update
State: closed | Pull request: yes

Signed-off-by: ekmb <ebakhturina@nvidia.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
- [ 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)
Upgrade to PTL 1.6.0
State: closed | Pull request: yes

# What does this PR do ?

This PR will have fixes needed for PTL 1.6.0. The PR will first install PTL 1.6.0rc0 via the Jenkinsfile. Then when PTL 1.6.0 is on PYPI, PTL will be installed via .reinstall.sh. Do not merge this PR until PTL 1.6.0 is on PYPI.

**Collection**: Core

# 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)
Additional Japanese processor for NMT that uses MeCab segmentation. Fix for BLEU in one-many NMT
State: closed | Pull request: yes

# What does this PR do ?

1. It implements a processor `JaMeCabProcessor` to train and evaluate models where Japanese is segmented with MeCab
2. Fixes a bug in one-many translation BLEU score eval. Old code would not index into self.tgt_language to select the appropriate language.

**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)
Adding cache-aware streaming Conformer with look-ahead support
State: closed | Pull request: yes

# What does this PR do ?

Adding cache-aware streaming Conformer training and inference with look-ahead support. It is achieved by training a model with limited effective right context and then perform the streaming with activation caching support. Limiting the right context would reduce the accuracy in compare to the an offline model but it gives better accuracy and significantly higher throughput by dropping duplicates in the computations which happens in buffered-based streaming.Large right context decreases the WER while increasing the latency.

It supports the three following modes:
1-fully causal model with zero look-ahead with zero latency
2-regular look-ahead
3-chunk-aware look-ahead with small duplication in computations.

It supports both Conformer-CTC and Conformer-Transducer and they can get trained with regular scripts but the configs files in the following folder:
NeMo/examples/asr/conf/conformer/streaming/

A model trained in streaming mode can get evaluated with the following script:
NeMo/examples/asr/conf/conformer/streaming/speech_to_text_streaming_infer.py

This script would simulate the streaming inference for a single audio or a manifest of audio files. Streaming can be done in multi-streaming mode (batched inference) for the manifest file to speed up the streaming. It can also compare the results with offline evaluation and report the differences in both the WER and models' outputs.

The accuracy of the model in both the offline evaluation and streaming is going to be exactly the same. In offline mode, the whole audio is passed through the model while in streaming audio is passed chunk by chunk.

# Changelog 
- Added frame-wise streaming Conformer models with look-ahead support and caching mechanism for streaming inference.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```
  
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
Bert init refactor
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 
```

# 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)