renwei2024/nemo-github-issues huggingface dataset:
Fix decoding bug for enc-dec models with O2
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

Fixes a bug that causes decoding for enc-dec models with O2 to crash.

**Collection**: NLP

# Changelog 
- Change the module being inspected to determine forward args.

# 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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- [ ] 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**:
- [ ] New Feature
- [ ] Bugfix
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## Who can review?

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# Additional Information
* Related to # (issue)
[TTS] Speaker interpolation in FastPitch
State: closed | Pull request: yes

# What does this PR do ?

Adds support for speaker interpolation in FastPitch

**Collection**: [Note which collection this PR will affect]
tts

# 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

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## Who can review?

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# Additional Information
* Related to # (issue)
[TTS] Adding speaker embedding conditioning in fastpitch
State: closed | Pull request: yes

Signed-off-by: subhankar-ghosh <subhankar2321@gmail.com>

Adds support for conditioning all components of fastpitch on speaker embedding.

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]
tts

# 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?

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# Additional Information
* Related to # (issue)
Add simple pre-commit file
State: closed | Pull request: yes

# What does this PR do ?

Related #4972 

We add a file to stop the failing CI run, but do not include Black until we find a way to resolve the difference in formatting.

# Changelog 
- Add pre-commit ci file.

# 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

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)
Inference with pretrained "stt_en_conformer_ctc_medium" ASR model crashes in python 3.8.10
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

Inference with the medium model specifically causes an error;
![medium_issue](https://user-images.githubusercontent.com/37636054/191699865-d490f214-6a55-4976-9a77-b2a828f64abe.png)

Works totally fine on the same audio file with the following models;

- stt_en_conformer_ctc_small_ls
- stt_en_conformer_ctc_large
- stt_en_conformer_transducer_large

I've so far only encountered the problem in python 3.8.10. In Google Colab and in Python 3.10.4 it works fine.

**Steps/Code to reproduce bug**

In python 3.8.10, 

installed with;

`nemo_toolkit[all] @ git+https://github.com/NVIDIA/NeMo.git@a4ff215ecc9fafd7127223bc21fd50cf023de592"`

Loaded from local pretrained checkpoint (previously downloaded from NGC)

 
`model = nemo_asr.models.ASRModel.restore_from(restore_path=model_path)`
`model.eval()`
`model.freeze()`
`model.preprocessor.featurizer.dither = 0.0`
`model.preprocessor.featurizer.pad_to = 0`

`decoding_cfg = model.cfg.decoding`
`decoding_cfg.preserve_alignments = True`
`decoding_cfg.compute_timestamps = True`
`model.change_decoding_strategy(decoding_cfg)`
`hypothesis = model.transcribe(paths2audio_files=[audio_path_or_url], return_hypotheses=True)[0]`

**Expected behavior**

Inference should finish without "list index out of range"


**Environment details**

- OS version Ubuntu 20.04
- PyTorch version: 1.12.1
- Python version: 3.8.10
Alignment indices for text normalization
State: closed | Pull request: no
Labels: name: feature

I wanted to know if it's possible to get the indices for text normalization for semiotic classes. For example if I have a text
```
I was born in 3/7/1989.
```
which normalized to:
```
I was born in March seventh nineteen eighty nine.
```
The index of begin and end of the text part "3/7/1989" have the correspondence in "March seventh nineteen eighty nine" in the second one.

I want to have a function that give me the indices (start, end) of the input text aligned with the (start, end) in the normalized text.
[TTS] fix broken tutorial for MixerTTS.
State: closed | Pull request: yes
Labels: name: cherry-pick

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**:
- [ ] 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 label models restoring issue from weighted cross entropy
State: closed | Pull request: yes
Labels: name: cherry-pick

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

torch creates state dict of weight in weighted cross entropy and while restoring if weight is None, then it throws error. 
Fixes by introducing weight with all ones. 

**Collection**: ASR

# Changelog 
- Fixes by introducing weight with all ones. 

# 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
Improvements to AMI script
State: closed | Pull request: yes

# What does this PR do ?

- Sync AMI evaluation dataset annotations with https://github.com/BUTSpeechFIT/AMI-diarization-setup. This follows the standardization by pyannote
- Add training dataset


**Collection**:
- ASR

# Changelog 
- AMI download script improvements

# 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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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
Introduce pre-commit with pre-commit suggestions
State: closed | Pull request: yes

# What does this PR do?

Currently style checking is done within the `setup.py` and enforced via jenkins.

This is problematic for users who do not have jenkins access and builds fail due to simple style fixes. By using pre-commit CI, we can automate style fixes in PRs meaning less work for users (and have a dedicate CI job for this).

As a side effect of this, due to the old version of black running I had to upgrade the Black version from the pre-release (19) to a stable version (22.*). Due to this there has been some additional style changes. A few summarized below:

- Lots of whitespace removed
- Multiple argument functions are expanded based on length. this is primarily for readability (and hence why the number of lines go up):

i.e 
```python
# old
{'label': NeuralType(tuple('B'), LabelsType()), 'label_length': NeuralType(tuple('B'), LengthsType()),}

# new
{
    'label': NeuralType(tuple('B'), LabelsType()),
    'label_length': NeuralType(tuple('B'), LengthsType()),
}
``` 

# Changelog 
- Introduce pre-commit, update Black version.

# 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
check for active conda environment
State: closed | Pull request: yes
Labels: name: cherry-pick

# What does this PR do ?

Check if conda environment is active rather than binary check 

**Collection**: Core

# Changelog 
- check using CONDA_PREFIX variable (which gets assigned to active conda directory) 

# 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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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
check for active conda environment
State: closed | Pull request: yes

# What does this PR do ?

Check if conda environment is active rather than binary check 

**Collection**: Core

# Changelog 
- check using CONDA_PREFIX variable (which gets assigned to active conda directory) 

# 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
[TTS] Adding speaker interpolation tutorial
State: closed | Pull request: yes

Signed-off-by: subhankar-ghosh <subhankar2321@gmail.com>

# What does this PR do ?

This PR adds Speaker Interpolation tutorial to tutorials/tts

**Collection**: [Note which collection this PR will affect]

None

# 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 label models restoring issue from weighted cross entropy
State: closed | Pull request: yes

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

torch creates state dict of weight in weighted cross entropy and while restoring if weight is None, then it throws error. 
Fixes by introducing weight with all ones. 

**Collection**: ASR

# Changelog 
- Fixes by introducing weight with all ones. 

# 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
Fix cherry pick workflow
State: closed | Pull request: yes
Labels: name: cherry-pick

# 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)
Fix cherry pick workflow
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)
Try reset the cache
State: closed | Pull request: yes
Labels: name: cherry-pick

Signed-off-by: SeanNaren <snarenthiran@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
- [ ] 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)
Try reset the cache
State: closed | Pull request: yes

Signed-off-by: SeanNaren <snarenthiran@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
- [ ] 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 cherry-pick action
State: closed | Pull request: yes

# What does this PR do ?

Automatically cherry-pick release commits to main when PR is merged.

**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)
[TTS] JoC optimization in binarization, aligner loss and FFT - Part 1
State: closed | Pull request: yes
Labels: name: stale

Signed-off-by: subhankar-ghosh <subhankar2321@gmail.com>

# What does this PR do ?

Adds JoC optimization in binarize_attention_parallel, Aligner loss or ForwardSumLoss

**Collection**: [Note which collection this PR will affect]
TTS

# 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

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)
Pipeline Parallel T5 Prompt Learning
State: closed | Pull request: yes

Signed-off-by: Virginia Adams <vadams@nvidia.com>

Adds pipeline parallel T5 prompt learning

**Collection**: NLP

# Changelog 
- Updated T5 prompt learning model class
- Updated base prompt learning model class

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] 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)
Pin Transformers version to fix CI
State: closed | Pull request: yes

# What does this PR do ?

We're seeing failures on main due to the the latest transformers version and our reliance on TRANSFORMERS_OFFLINE in the CI. Running the failed test locally reproduces the error:

```
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 pytest tests/collections/nlp/test_prompt_learning.py::TestMegatronGPTPromptLearningDataset::test_init_prompt_learning_dataset
```

# 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
- [x] Bugfix
- [ ] Documentation
ValueError: `cfg` must have `tokenizer` config to create a tokenizer !
State: closed | Pull request: no
Labels: name: bug

Training or Fine-Tuning an Acoustic Model：
Model fine-tuning is a set of techniques that makes fine adjustments to a pre-existing model using new data, so as to make it adapt to new situations while also retaining its original capabilities.
so I download this  model:speechtotext_zh_cn_conformer.tlt and  modidy the evaluate.yaml on test_ds.labels to the Mandarin then use this api:
!tao speech_to_text_citrinet evaluate \
     -e $SPECS_DIR/speech_to_text_citrinet/evaluate.yaml \
     -g 1 \
     -k $KEY \
     -m $RESULTS_DIR/speechtotext_zh_cn_conformer.tlt \
     -r $RESULTS_DIR/citrinet/evaluate \
     test_ds.manifest_filepath=$DATA_DIR/train.json
but i got this error 
raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !")
ValueError: `cfg` must have `tokenizer` config to create a tokenizer !
Add docs tutorial on kinyarwanda asr
State: closed | Pull request: yes

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if logic error 
State: closed | Pull request: no

https://github.com/NVIDIA/NeMo/blob/e856e9732af79a6ed4bffaa3d709bfa387799587/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py#L399

  if 'EncDecCTCModel' in str(type(asr_model)):
      labels = asr_model.decoder.vocabulary
  elif 'EncDecCTCModelBPE' in str(type(asr_model)):
      vocab = asr_model.tokenizer.tokenizer.get_vocab()
      labels = list(vocab.keys())
      labels[0] = "<unk>"

I found that
'EncDecCTCModel' is in 'EncDecCTCModelBPE'

so I think the order should be changed.
Import pycuda.autoprimaryctx or pycuda.autoinit to init pycuda execut…
State: closed | Pull request: yes

…ion environment

Signed-off-by: Jin Li <liji@nvidia.com>

# What does this PR do ?

Import pycuda.autoprimaryctx or pycuda.autoinit to init pycuda execution environment to fix "invalid device context - no currently active context?" issue

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- Add specific line by line info of high level changes in this PR.

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```python
# Add a code snippet demonstrating how to use this 
```

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[TTS] Condition FastPitch on Energy
State: closed | Pull request: yes

# What does this PR do ?

Adds support for Conditioning FastPitch model on Energy

**Collection**: [Note which collection this PR will affect]

nemo/collections/tts/

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- Add specific line by line info of high level changes in this PR.

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```python
# Add a code snippet demonstrating how to use this 
```

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[TTS] fix broken tutorial for MixerTTS.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

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# Add a code snippet demonstrating how to use this 
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[TTS] fix broken tutorial for MixerTTS.
State: closed | Pull request: yes

# What does this PR do ?


**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

adding missing metadata.

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* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
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RADTTS model changes to accommodate export with batch size > 1
State: closed | Pull request: yes

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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Checkpoint averaging class fix
State: closed | Pull request: yes

# What does this PR do ?

This PR adds flag `--class_path` to checkpoint averaging script to allow explicit class path (e..g, `module.submodule.class`).

Example:

```
NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py \
  --class_path  nemo.collections.nlp.models.language_modeling.megatron_bert_model.MegatronBertModel \
  my_model.nemo
```

**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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- [ ] 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?
  
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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Simple numbers in Text Normalization
State: closed | Pull request: no

Hello everyone, 
I'm looking to Text Normalization, Inverse Text Normalization to be more specific, and I saw something "weird".
For simples numbers, like "Um" (equivalent "one") returns "Um" instead "1", should  this be normal?
The ITN normalize with some prefix or suffix, or can inverse normalize numbers in general?

![image](https://user-images.githubusercontent.com/94904861/191030298-0ac5053a-f6fa-4283-8b2a-99e1329c9fe1.png)
Wrong inference for repeated word in RNNTs
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

I'm building an ASR with Conformer Transducer with "beam" decoding strategy beam_size of 2 but when I inference some audio contains a repeat number (telephone, ID..) the ASR model is often missing the number.
For example: 
one one one two two three three  => one one two three
nine nine nine nine  => nine nine
...

I thought it was because of the decoding strategy so I tried to change decoding strategy to "greedy" but so many repeated words are generated (eg: one one two two => one one oneo one one two two two)

I also try ASR with Adapters by training only audio with repeated number only but I doesn't solve problem well

**Expected behavior**

ASR model should generate enough number even the number or words are repeated

**Environment details**
-OS version - Ubuntu 18.04.5 LTS
-PyTorch version - 1.10.1
-Python version - 3.8.11

**Others**
-Decoding strategy: beam decoding with beam_size: 2

How can I solve this problem?
Thanks!
Scheduler function while fine-tuning ASR
State: closed | Pull request: no
Labels: name: stale

While fine-tuning ctc asr model the lr used is in range of 0.025 to 0.001 (according to examples In notebooks). But the scheduler further reduces the lr. 

when I set lr = 0.025 
the max_lr I see after warmup is 1 e -6,  its not 0.025 

so should we only use scheduler while training from scratch and not fine-tuning ?
[TTS] Condition All Components of FastPitch on Speaker Embeddings
State: closed | Pull request: yes

# What does this PR do ?

This PR does the following:
1) Adds capability to condition decoder, pitch and duration predictor of FastPitch and Aligner on speaker embeddings.
2) Adds capability to condition aligner with any kind of embedding.

**Collection**: [Note which collection this PR will affect]

nemo/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**:
- [ ] 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

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Question
State: closed | Pull request: no
Labels: name: stale

I had a question and I was wondering if I could get a bit of help since I'm new to things here.
I noticed that the ASR models have been trained on 16k examples and expect a test example to be at the same sampling rate.

1. I have a higher sampling rate example eg. 24kHz and I downsample it to 16kHz and want to get the encoder outputs. Is there a way to verify the encoder outputs are correct?
One high-level sanity check I can think of is if I could get the frame-level transcriptions on the downsampled 16kHz signal and compare with what I hear in the audio approximately at that point of time.
where is the script_to_bpe.py?
State: closed | Pull request: no
Labels: name: bug

name: stale

<img width="1181" alt="image" src="https://user-images.githubusercontent.com/23027105/190307749-802cdf06-8e44-4d0c-b845-9d257159edc7.png">
<img width="1051" alt="image" src="https://user-images.githubusercontent.com/23027105/190307823-d77bd5f8-82c0-4d2f-8e94-e5b598d67338.png">
Adding minor fixes 
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.
it changes training config files structure to not depend on training or validation config setting
Revert back Radam instead of using pytorch implementation
unsorted lens in RADTTS Encoder

**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 examples/tts/radtts.py trainer.max_epochs=400 export_dir="/dir" model.optim.lr=0.00005 model.train_ds.dataloader_params.batch_size=16 model.validation_ds.dataloader_params.batch_size=16 train_dataset="/Training_data.json" validation_datasets="Validation_data.json" pitch_mean=202.67527982176898 pitch_std=54.12737646048864  sample_rate=44100 n_window_size=2048 n_window_stride=512 highfreq=null n_fft=2048 sup_data_path="dir_for sup_data" 
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
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Update megatron interface to dialogue
State: closed | Pull request: yes

# What does this PR do ?

1. Increase speed of rank candidates
2. Include support for p tuning for Megatron GPT
3. Include support for TP> 1 model (i.e. 5B Megatron GPT )
4. Update generate_candidate interface for Megatron GPT based on unified text generation method
5. Included custom collate_fn to remove excess padding in batch for Dialogue GPT Classification Dataset

**Collection**: nlp

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- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
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- [ ] Bugfix
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Update docs link for `prompt_learning` tutorial
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Update docs link for `prompt_learning` tutorial

**Collection**: NLP

# 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?
  
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[TTS] Add NeMo TTS Primer Tutorial
State: closed | Pull request: yes

# What does this PR do ?

Add a primer document to our TTS tutorials covering general introductory material related on models and running them in NeMo.

**Collection**: TTS

# Changelog 
- Add TTS Primer notebook
- Add images directory (similar to ASR tutorials)
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation