renwei2024/nemo-github-issues huggingface dataset:
ASR confidence bug fix for older Python versions
State: closed | Pull request: yes

Signed-off-by: Aleksandr Laptev <alaptev@nvidia.com>

# What does this PR do ?

Fixes [#(5175)](https://github.com/NVIDIA/NeMo/issues/5175) and removes redundant code left after numerous rebases.

**Collection**: ASR

# 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?

@titu1994

# Additional Information
* Related to [#(5175)](https://github.com/NVIDIA/NeMo/issues/5175)
Model Training 
State: closed | Pull request: no
Labels: name: bug

name: stale

I am training an ru model and I don't know if this process is going well

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)`
`
`
`min_duration = 0.5`
`epochs = 150`
`gpus = 1`
`
`
`params.trainer.devices = gpus`
`params.trainer.max_epochs = epochs`
`params.model.train_ds.manifest_filepath = train_manifest_path`
`params.model.train_ds.min_duration = min_duration`
`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_tr_nemo/"`
`
`
`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)`

There is yaml config:
https://github.com/Project-fil/Project-fil/blob/ee79825609cca9e8b01a9c4eb43b564bb5c2b35d/quartznet_ru.yaml

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: 253 390 
Duration - 5050.82 hours.

I get these files in the "checkpoints" directory and after 10 epochs "wer" never went below 0.9.

`'QuartzNet15x5_ru--val_wer=0.9031-epoch=6.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9067-epoch=8.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9094-epoch=1.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9798-epoch=9-last.ckpt'`

and now

`'QuartzNet15x5_ru--val_wer=0.9031-epoch=6.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9067-epoch=8.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9078-epoch=11.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9798-epoch=19-last.ckpt'`

Please tell me this is a normal learning process or am I doing something wrong.
ONNX export renames 2D parameters, not 1D parameters
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

Exported ONNX graphs retain the names for zero dimensional and one dimensional parameters, but rename any two or more dimensional parameters. This makes writing scripts for later graph manipulation much more tedious and prone to breaking.

**Steps/Code to reproduce bug**

```python3
from nemo.collections.nlp.models.machine_translation import MTEncDecModel

model = MTEncDecModel.from_pretrained('nmt_en_de_transformer12x2')
model.decoder.export('pretrained_decoder.onnx')
```

Open `pretrained_decoder.onnx` in [Netron](https://netron.app/) or some other ONNX graph visualizer. Find and compare the inputs to the nodes `MatMul_106` and `Add_107`.
 - `Add_107` takes 1D tensors as input, and has preserved the name of the parameters from the NeMo model. Its first input is `_decoder.layers.0.first_sub_layer.query_net.bias`.
 - `MatMul_106` requires a 2D tensor as input, and has renamed the parameter from the NeMo model from the quite helpful `_decoder.layers.0.first_sub_layer.query_net.weight` to the generic `onnx::MatMul_636`.

**Expected behavior**

I'd love for it to respect the names of all parameters, which would create a predictable and reliable ONNX graph that can then be further post-processed as needed.

**Environment overview (please complete the following information)**

 - Environment location: Bare-metal
 - Method of NeMo install: `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]` (run today, commit d933ee830d085152fbe07a8fa2b1fa0e493adf0d)

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- PyTorch version: 1.12.1
- Python version: 3.9.0
fix issues with folder names containing nonstandard symbols
State: closed | Pull request: yes

Signed-off-by: Alexandra Antonova <aleksandraa@nvidia.com>

# What does this PR do ?

fix issues with Spoken Wikipedia folder names containing non-standard symbols

**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)
[TTS] Fastpitch energy conditioning
State: closed | Pull request: yes

# What does this PR do ?

Conditions FastPitch with Energy

**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**:
- [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)
Cannot run ASR_with_NeMo.ipynb example
State: closed | Pull request: no
Labels: name: bug

I cannot run the example through CoLab, it stucks in loading in pre-trained model.


# This line will download pre-trained QuartzNet15x5 model from NVIDIA's NGC cloud and instantiate it for you
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")


AttributeError: module 'math' has no attribute 'prod'

If I use v1.12.0 instead main branch, there will be no problem.
[TTS][DOC] update models trained on HifiTTS dataset.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

1. added fastpitch model checkpoint that was trained on hifitts.
2. replaced "single/multiple" speakers with accurate integers.
3. update statistics for hifitts datasets.

**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
- [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)
fix for bug in bignlp
State: closed | Pull request: yes

Signed-off-by: arendu <adithya.r@gmail.com>

# What does this PR do ?

Fixes error in t5 bignlp scripts evaluation state.

**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)
Move to optimizer based EMA implementation
State: closed | Pull request: yes
Labels: name: feature

name: common

# What does this PR do ?

Move to using the EMA Optimizer. This improves performance, supports offloading to CPU!

# Changelog 
- Move to EMA Optimizer implementation

# 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?
- [ ] 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)
Facing an issue while infer a prompt-tuned model "TypeError: megatron_gpt_generate() takes 5 positional arguments but 6 were given" 
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

While we try to infer/use a prompt-tuned model we are getting an error "TypeError: megatron_gpt_generate() takes 5 positional arguments but 6 were given" 

**Steps/Code to reproduce bug**
We have followed below notebook for prompt-tuning:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb

While we tried to use prompt-tuned model using following lines of code: 
model.generate(inputs=test_examples, length_params=None)

**Expected behavior**

It should give the result of given prompt.

**Environment overview (please complete the following information)**

 - Environment location: Google Colab Pro with GPU and High RAM
 - Method of NeMo install: [from source]. !python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}#egg=nemo_toolkit[all]
 
**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 18.04.6 LTS
- PyTorch version: 1.12.1+cu113
- Python version: Python 3.7.14
[Speaker Diarization + ASR] Is NeMo Speaker Diarization + ASR task using an End-to-End model?
State: closed | Pull request: no
Labels: name: feature

Recently, I demonstrated NeMo's Speaker Diarization + ASR task (nemo/examples/speaker_tasks/diarization/offline_diarization_with_asr.py) to my Professor, but he asked me if that end-to-end? 
According to his question, I assume he means NeMo Speaker Diarization + ASR task not using an End-to-End model.
Is that correct? if that is, he hopes I manifest NeMo's Speaker Diarization + ASR task to an End-to-End model, that is SAD, Speaker embedding model, ASR model should be mixed into one model.
Sorry about my stupid question..
reduced to 14 inactive days to be stale for PRs.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

Let's make room of CI tests for more active PRs. Reducing to 14 days from 30 inactive days. 

**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)
Support TorchScript export for Squeezeformer
State: closed | Pull request: yes

Signed-off-by: smajumdar <titu1994@gmail.com>

# What does this PR do ?

Adds torchscript export to Squeezeformer

**Collection**: [ASR]

# Changelog 
- Add torchscript export testing
- Add torchscript export for Squeezeformer

# 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
POSIX portable filename character set violation in NeMo repo
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

Error while downloading and uploading files

With use of an Object store with S3 services, errors are thrown due to file naming conventions that do not adhere to the POSIX filename character set. The example below shows two filenames that are causing and error with container persistence on the Object Store. The “(“ and the “)” characters are the cause of the error but a comprehensive search of the repo file names would be necessary in order to find all of the potential issues.

**Expected behavior**

File should be uploaded or downloaded from Object Storage without an issues

Filenames should only contain the characters of the POSIX portable file name character set outlined below.
              Uppercase A to Z.
              Lowercase a to z.
              Numbers 0 to 9.
              Period ( . )
              Underscore (_)
              Hyphen (-)

**Environment overview (please complete the following information)**

Object Storage with S3 services

**Environment details**



**Additional context**
5405 | 5h 1m 38s | Uploading: NeMo/tutorials/text_processing/Text_(Inverse)_Normalization.ipynb
5406 | 5h 1m 38s | Error while downloading file: '/NeMo/tutorials/text_processing/Text_(Inverse)_Normalization.ipynb'
5407 | 5h 1m 38s | Error while downloading file: '/NeMo/tutorials/nlp/Non_English_Downstream_Tasks_(NER).ipynb'
Fix BF16 test
State: closed | Pull request: yes

Signed-off-by: SeanNaren <snarenthiran@nvidia.com>

# What does this PR do ?

Fixes the failing test on main regarding BF16. Thanks @ericharper 


# 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)
[Tools][ASR] Tool for generating data using simulated RIRs
State: closed | Pull request: yes

Signed-off-by: Ante Jukić <ajukic@nvidia.com>

# What does this PR do ?

This PR adds a tool for generating a dataset using RIRs, speech, and noise.

**Collection**: ASR

# Changelog 
- Added `RIRMixGenerator` in `asr/data/data_simulation.py`
- Added unit tests in `tests/collections/asr/test_asr_data_simulation.py`
- Added an example config and a readme file in `tools/rir_corpus_generator`
- Updated `README.md` in `tools/rir_corpus_generator`


# Usage
The tool can be used as

```python
python rir_mix_generator.py output_dir=OUTPUT_DIR
```
where `OUTPUT_DIR` is a path to the output directory. This will use the default configuration in `conf/rir_mix.yaml`.

The output will be structured as

```
OUTPUT_DIR
+--{train, dev, test}
|	+--*_mic.wav
|	+--*_target_reverberant.wav
|	+--*_target_anechoic.wav
|	+--*_noise.wav
|	+--*_interference.wav
+--config.yaml
+--{train, dev, test}_manifest.json
+--{train, dev, test}_info.png
```

Each directory, e.g, `{train, dev, test}`, corresponds to a subset of data and contain `*.wav` files with audio signals. Corresponding `*_manifest.yaml` files contain metadata for each subset.

# 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)
[DRAFT] [TTS] New Speaker Adaptation using adapters
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

New speaker adaptation using adapters in TTS FastPitch model.

**Collection**: [Note which collection this PR will affect]

TTS collection

# 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**:
- [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)
Fix loading checkpoints from dirpath if it's specified
State: closed | Pull request: yes

Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com>

# What does this PR do ?

Fixes checkpoint loading if `exp_manager.checkpoint_callback_params.dirpath` is specified.

**Collection**: Utils

# Changelog 
- Adds a check for whether `dirpath` has been set, and passes it in to `check_resume()` to use instead of `<log_dir>/checkpoints` for checkpoint restoration
- Unit tests added for restoring from `dirpath`
- Tested load/store for both `dirpath` being set and not (using `<log_dir>/checkpoints`) and both now work

# Usage

The following should now save to AND load from the specified `ckpt_dir`.

```bash
python examples/tts/fastpitch.py \
train_dataset=train.json \
validation_datasets=val.json \
trainer.devices=1 \
trainer.max_epochs=5 \
+exp_manager.checkpoint_callback_params.dirpath=ckpt_dir/ \
+exp_manager.explicit_log_dir=logdir/ \
exp_manager.resume_if_exists=true \
exp_manager.resume_ignore_no_checkpoint=true \
trainer.check_val_every_n_epoch=1
```

**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

# Additional Information
* Related to #5136
Fix NMT Eval Sampler
State: closed | Pull request: yes

# What does this PR do ?

Current Eval Dataloader gives incorrect len #4808 
Fix NMT Eval Sampler to get correct dataset

**Collection**: NMT

# 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 errors in speaker diarization nemo docs
State: closed | Pull request: yes

# What does this PR do ?

Fix some minor errors and spacing errors in speaker diarization docs.

**Collection**: [Note which collection this PR will affect]
ASR

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

This is documentation.

# 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?
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# Additional Information
* Related to # (issue)
How to limit the diarization in a long audio file?
State: closed | Pull request: no
Labels: name: bug

name: feature

Hello, developers!
I've got an issue and was wondering if you could help me.

My goal: reducing the time of diarization if an audio file is longer than 10 minutes.

My manifest json file:
> 'audio_filepath': /home/audio_file.wav,
> 'offset': 0,
> 'duration': 600,
> 'label': 'infer',
> 'text': '-',
> 'num_speakers': 2,
> 'rttm_filepath': None,
> 'uem_filepath': None

I tried to set up 'offset' and 'duration' in the manifest file, but it didn't work properly and It still diarized the whole audio file

Is there a possibility to limit the diarization?

I'll appreciate any help
Speech Dataset Processor
State: closed | Pull request: yes

Signed-off-by: Elena Rastorgueva <erastorgueva@nvidia.com>

# What does this PR do ?

Adding the Speech Dataset Processor tool.

**Collection**: Tools

# 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)
Fixes some args that were not removed properly for multilingual Megatron NMT
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

Fixes some args that were not removed properly for multilingual megatron NMT.

**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)
Fix get_samples function 
State: closed | Pull request: yes

# What does this PR do ?

Update and remove unnecessary parts in `get_samples` function. "int" type setting is no longer needed. 

Removed `get_samples` function from [decoder_timestamps_utils.py](https://github.com/NVIDIA/NeMo/pull/5141/files#diff-6e1688943c976f39bdee5fed0c8948f57c88411cfb69548b7fece4b9f7720912) since it is almost the same as get_samples function in [streaming_utils.py](https://github.com/NVIDIA/NeMo/blob/a17a69dea4f8768c0c444573c5a5c499c1e6e2ef/nemo/collections/asr/parts/utils/streaming_utils.py)


**Collection**: 
ASR

# Changelog 

- Remove unnecessary `int16` dtype and float conversion lines
- Keep the output data format as float
- Update librosa resample argument requirements

# Usage

Check out streaming_asr tutorial notebook.

# 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?
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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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## Who can review?

Anyone in the community is free to review the PR once the checks have passed.
gpt ia3 CI tests
State: closed | Pull request: yes

Signed-off-by: arendu <adithya.r@gmail.com>

# What does this PR do ?
- Added CI tests for GPT based IA3 training and inference.


**Collection**: [NLP]

# 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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- [ ] 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

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## Who can review?

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# Additional Information
* Related to # (issue)
Merge r1.12.0 main
State: closed | Pull request: yes

# What does this PR do ?

Merge bugfixes and doc updates from code frozen branch to main.

**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)
Add scripts for converting Spoken Wikipedia to asr dataset
State: closed | Pull request: yes

Signed-off-by: Alexandra Antonova <aleksandraa@nvidia.com>

# What does this PR do ?

Scripts for converting Spoken Wikipedia to an asr dataset (download, preprocessing, ctc-segmentation, filtering).

**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)
refactor TTS documentation organization and add new contents.
State: closed | Pull request: yes

* refactor TTS documentation organization and add new content.
* fix asr api bug.
* fix broken links.
* fix unexpected indentation errors.
* fixed unexpected indentation.
* fixed broken paper reference.
* fixed cross-references and typos.
* fixed toctree errors.
* removed duplicate references.
* replace 'main' branch to 'stable'.

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
- [ ] 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)
checkpoint_dir with checkpoint manager is not being set corrector while resuming training
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

When I stopped fine-tuning the FastPitch model and wanted to restart training, I noticed it checks for the last checkpoint in the wrong directory (I'm settings the checkpoints directory outside of the logs).
So, in my case the checkpoints are being saved at `models/fastpitch/checkpoints` but it is trying to search in `models/fastpitch/logs/checkpoints`.
This is due to the hard-coded path value here: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/exp_manager.py#L424, so inside `check_resume()`:
```py
    checkpoint_dir = Path(Path(log_dir) / "checkpoints")
```
While the correct path is being set here: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/exp_manager.py#L889, so inside `configure_checkpointing()`:
```py
    if params.dirpath is None:
        params.dirpath = Path(log_dir / 'checkpoints')
```
it correctly checks if `dirpath` is set (it is in my case) and only otherwise defaults to the `checkpoints` folder inside of the `logs` folder.

I did not fill in the rest of the template of the bug report as this is a question about a specific code piece, not related to any hardware or scenario.
[TTS] Add SpanishCharsTokenizer
State: closed | Pull request: yes

Signed-off-by: Ryan <rlangman@nvidia.com>

# What does this PR do ?

Add a Spanish language tokenizer for doing grapheme-only TTS training.

**Collection**: [TTS]

# Changelog 
- Add SpanishCharsTokenizer
- Add unit tests for English, German, and Spanish grapheme tokenizers.

# Usage

```python
from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import SpanishCharsTokenizer

text = "¿Cuál es su nombre?"

tokenizer = SpanishCharsTokenizer(
    pad_with_space=True,
)

tokens = tokenizer(text)
graphemes = tokenizer.decode(tokens)
graphemes = graphemes.replace('|', '')

# [0, 54, 3, 21, 27, 12, 0, 5, 19, 0, 19, 21, 0, 14, 15, 13, 2, 18, 5, 38, 0]
print(tokens)
# ¿cuál es su nombre? 
print(graphemes)
```

# 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
Adding global batch support and O2 for Megatron BERT
State: closed | Pull request: yes

# What does this PR do ?

Adds global batch size and O2 support for bert model

**NLP**: 

# Changelog 
- Add global batch size support

# 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)
val_loss of new model tacotron2 tts for persian language don't decreased
State: closed | Pull request: no
Labels: name: bug

name: stale

I trained new model tacotron2 ( TTS ) with new data for persian language
but my val_loss is not normally

![Screenshot from 2022-10-11 01-36-20](https://user-images.githubusercontent.com/21003446/194959489-d45cd934-01be-415f-8743-55aaa74fc035.png)

I continued this training till 300 epochs but my val_loss was not normally decreased and it is constantly fluctuating

sample of one data in train or validation json file:
`{"audio_filepath": "./wav/4.wav", "duration": 5.6750625, "is_phoneme": 1, "text": "gAme sevvOm d%r b/rnamerizI  $enAxte moqe@iyy%te fe@lIye hozE @%st  ", "normalized_text": "gAme sevvOm d%r b/rnamerizI  $enAxte moqe@iyy%te fe@lIye hozE @%st  "}`

text is phoneticized but with uncommon method!
data is for persian(Farsi) language and I didn't use persian alphabet and I don't know what is my problem!
the output of my model is unclear!
loss diagram is normally but val_loss no
Failed to run ONNX squeezeformer
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

Squeezeformer model succesfuly exported
But after running on Squeezeformer.encoder.input_example t producess dimmentions mismatch error 

**Steps/Code to reproduce bug**

```
python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_squeezeformer_ctc_xsmall_ls", map_location="cpu")
asr_model.export("squeezeformer.onnx", onnx_opset_version=15)

import onnxruntime as ort
ort_sess = ort.InferenceSession('squeezeformer.onnx')

processed_signal, processed_signal_len = asr_model.encoder.input_example()
outputs = ort_sess.run(None, {'audio_signal': processed_signal.numpy(),
                              'length':processed_signal_len.numpy()})
```

**Error**
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running MatMul node. Name:'MatMul_3906' Status Message: matmul_helper.h:61 Compute MatMul dimension mismatch
```
**Environment details**

nemo_toolkit - 1.12.0rc0
[Chinese text normalization] speed up graph building
State: closed | Pull request: yes

Signed-off-by: 彭震东 <275331498@qq.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)
Changed the URL for the Mozilla S3 bucket
State: closed | Pull request: yes

Signed-off-by: Maicon Domingues [dominguesm@outlook.com](mailto:dominguesm@outlook.com)

# What does this PR do ?

Changes the URL used to download Common Voice datasets, thus allowing the latest versions to be downloaded in any language.

**Collection**: asr

# Changelog 

The change allows any version and language of the Common Voice dataset to be downloaded. Directly pointing to the Bucket S3 made available by Mozilla.

It is worth mentioning that I have been following and using this URL for at least 4 months, so I believe the change is safe.

As the contract for using the `get_commonvoice_data.py` file has remained the same, no impact is expected on examples using this script.

# Usage
* You can potentially add a usage example below

```sh
!python scripts/dataset_processing/get_commonvoice_data.py \
    --data_root "datasets/$LANGUAGE/" \
    --manifest_dir=$manifest_dir \
    --sample_rate=16000 \
    --n_channels=1 \
    --version="cv-corpus-11.0-2022-09-21" \
    --language="pt" \
    --log \
    --files_to_process 'train.tsv' 'dev.tsv' 'test.tsv'
```

# 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**:
- [x] 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)
Add Squeezeformer CTC model checkpoints on Librispeech
State: closed | Pull request: yes

Signed-off-by: smajumdar <titu1994@gmail.com>

# What does this PR do ?

Adds Squeezeformer CTC checkpoints

**Collection**: [ASR]

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_squeezeformer_ctc_large_ls")
```

# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation