renwei2024/nemo-github-issues huggingface dataset:
InterCTC loss and stochastic depth implementation
State: closed | Pull request: yes
Labels: name: ASR

# What does this PR do ?

Adding intermediate CTC loss and stochastic depth as described in https://arxiv.org/abs/2102.03216. 

The current implementation is only for conformer encoder, but I'm not really sure how to write a generic code for this case. Please let me know if you have some ideas here.

**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

To use, specify parameters in the config. E.g., for stochastic depth:
```
model.encoder.stochastic_depth_mode=linear  
model.encoder.stochastic_depth_drop_prob=0.3 
model.encoder.stochastic_depth_start_layer=0
```

For intermediate CTC loss:
```
model.encoder.capture_output_at_layers=[9] 
model.intermediate_loss_weights=[0.3]
```

I've added the docs to ConformerEncoder, but not sure how to add the docs to the new `intermediate_loss_weights` parameter of the `EncDecCTCModel`. Please let me know what's the right place to put those docs in.

# 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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# Additional Information
* Related to # (issue)
from_restore for NM models on HF plus popular properties
State: closed | Pull request: yes
Labels: name: core

name: stale

name: NLP

# What does this PR do ?

Add two popular read-only properties for tensor and pipeline parallelism values.
Change some of the self._cfg to self.cfg and cfg to self.cfg

Adds from_pretrained for NM Models on HF Hub

**Collection**: [Note which collection this PR will affect]
NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

```python
# Add a code snippet demonstrating how to use this 
print(model.tensor_model_parallel_size)
rint(model.pipeline_model_parallel_size)
```

# 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)
Default RNNT loss to int64 targets
State: closed | Pull request: yes
Labels: name: ASR

Signed-off-by: smajumdar <titu1994@gmail.com>

# What does this PR do ?

Defaults the targets of RNNT to int64 to match CTC loss. Should mostly be a cosmetic change, since it is near impossible to train an RNNT with target vocabulary of > 2B tokens, or with seq length > 2B.

**Collection**: [ASR]

# Changelog 
- Update internal casts to int64
- Update tests to force int64 targets

# 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
[Tools] Fix ctc segmentation: exclude audacity files
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**:
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- [ ] 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

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## Who can review?

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# Additional Information
* Related to # (issue)
Added documentation section for ASR datasets from AIStore
State: closed | Pull request: yes
Labels: name: ASR

Signed-off-by: Ante Jukić <ajukic@nvidia.com>

# What does this PR do ?

This PR adds a section in ASR/Datasets to describe how to use a dataset from AIStore.

**Collection**: ASR, Documentation

# Changelog 
- Added a section in ASR/Datasets with a description of using a dataset from AIStore

# 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

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# Additional Information
* Related to # (issue)
Spectrogram Enhancer doesn't generalize to spectrogram lengths unseen during training
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

If one trains TTS spectrogram enhancer (#5565) on short spectrograms, at inference time it doesn't generalize to longer ones. A patch in the beginning gets enhanced but further frames do not.

Example (before, after):
![before](https://user-images.githubusercontent.com/8864149/218520151-230501b1-4e9d-4307-b51e-526440b915c8.png)
![after](https://user-images.githubusercontent.com/8864149/218520199-22187a4b-9cf8-4ac9-b3ce-07c320d3d3b6.png)

**Steps/Code to reproduce bug**

1. Train a spectrogram enhancer
2. Apply it to a spectrogram that's longer than anything from the training set
3. Only a patch in the beginning gets enhanced

**Expected behavior**

The whole spectrogram should have got additional details, not just the first patch
Use module-based k2 import guard
State: closed | Pull request: yes
Labels: name: core

name: ASR

Signed-off-by: Vladimir Bataev <vbataev@nvidia.com>

# What does this PR do ?

Simplify `k2` library import guard: use module-based approach.

**Advantages:**
- less code is required for using guard
- no need to suppress linters (isort, etc.)

**Collection**: [ASR]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

```python
from nemo.core.utils.k2_guard import k2  # import k2 with performing all required checks
```

# 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)
Megatron GPT2 is generating repetitive sentence
State: closed | Pull request: no
Labels: name: bug

name: stale

** P tuning with Megatron GPT2 is generating repetitive sentence**

I am P tuning with Megatron GPT2 model with 100 train and 100 test dataset for a particular activity. I am getting repetitive sentence during inference for most of the samples. 
I have used below things to resolve this issue.
1. Used instruction sentence with sentence(tried without instruction as well)
2. Used a end token after each sentence(tried without end token as well)
3. Used a end token after each label(tried without end token as well)
4. Changed "repetition_penalty" to 0, 0.5, 1000, 10000 etc, however, the issue is still there.

I am trying to replicate https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb for generative AI. 

Please let me know the solution for this issue.
Fix hybrid transcribe
State: closed | Pull request: yes
Labels: name: ASR

# What does this PR do ?

Fix transribe function in EncDecHybridRNNTCTCModel.

**Collection**: [Note which collection this PR will affect]
ASR

# Changelog 
- Add variables for transcribe invocation if use_rnnt_decoder.
- Change name of variable (logits_len to encoded_len).
- Change ErrorValue to ValueError in change_decoding_strategy of EncDecHybridRNNTCTCBPEModel.

# 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

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## Who can review?

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# Additional Information
* Related to # (issue)
import missing dependency - gc
State: closed | Pull request: yes
Labels: name: ASR

Signed-off-by: Eduard Maghakyan <edi.maghakyan@gmail.com>

# What does this PR do ?

Fix missing import in a non-CUDA environment. 

**Collection**: ASR collection

# Changelog 
Fix missing import when running in a non-CUDA environment. 

# 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)
[ASR] Training ZeroDivisionError: float division by zero while precision is 16
State: closed | Pull request: no
Labels: name: bug

**Set Precision 16 for fast training**

Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 0.0
Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 0.0
and it gives 
ZeroDivisionError: float division by zero
**Pytorch Lighting Parameters**

devices=-1,  # number of GPUs, -1 would use all available GPUs
strategy='ddp',
amp_level='O1',
precision=16,  
amp_backend='apex'

**Learning Rate 1**

error
`File "/usr/local/lib/python3.8/dist-packages/apex/amp/scaler.py", line 176, in unscale_with_stashed
    out_scale/grads_have_scale,   # 1./scale,
ZeroDivisionError: float division by zero
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.`

**Additional context**

- Nemo Docker version nemo:22.12
- Multi GPU
fast conformer configs and doc
State: closed | Pull request: yes
Labels: name: cherry-pick

name: ASR

Signed-off-by: Dima Rekesh <drekesh@nvidia.com>

# What does this PR do ?
adds fast conformer config files and docs

**Collection**: [Note which collection this PR will affect]
ASR
# Changelog 
- Add specific line by line info of high level changes in this PR.

docs/source/asr/models.rst (added entry for Fast Conformer)
examples/asr/conf/conformer/fastconformer/fastconformer_ctc_bpe.yaml (created)
examples/asr/conf/conformer/fastconformer/fastconformer_transducer_bpe.yaml (created)
# 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. 
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# Additional Information
* Related to # (issue)
Microbatch loader for bert
State: closed | Pull request: yes
Labels: name: core

name: TTS

name: ASR

name: NLP

name: Speaker Tasks

name: CI

name: common

# 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 bug where GPT always enabled distopt overlapped param sync
State: closed | Pull request: yes
Labels: name: bug

name: NLP

# What does this PR do ?

With the changes in https://github.com/NVIDIA/NeMo/pull/5684, I observe that GPT always sets `overlap_param_sync=True`. This PR allows the user to manually override that option, which now `False` by default.

**Collection**: NLP

# Changelog 
- Fix bug where GPT always enabled distopt overlapped param sync

# Usage

When using the `distributed_fused_adam` optimizer, set `overlap_param_sync: True` in the optimizer config.

# 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
* Related to https://github.com/NVIDIA/NeMo/pull/5684
Add some info about FastPitch SSL model
State: closed | Pull request: yes
Labels: name: TTS

Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com>

# What does this PR do ?

Adds a paragraph with some basic information about the SSL FastPitch model. Will add more as the model is finalized and the paper released.

**Collection**: TTS

**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation

# Additional Information
* Related to #5948
Fix Prompt text space issue
State: closed | Pull request: yes
Labels: name: cherry-pick

name: NLP

Signed-off-by: Abhinav Khattar <aklife97@gmail.com>

# What does this PR do ?

Fix space issue in prompt

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
[ASR] Convolution-invariant SDR loss + unit tests
State: closed | Pull request: yes
Labels: name: ASR

# What does this PR do ?

This PR adds a convolution-invariant SDR loss for audio-to-audio tasks.

Reference:
[C. Boeddeker et al., Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation, 2021](https://arxiv.org/abs/2011.15003)

**Collection**: ASR

# Changelog 
- Added CI-SDR loss
- Updated docstrings and simplified implementation of SDRLoss

# 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)
Adds several configurable flags for Megatron GPT models
State: closed | Pull request: yes
Labels: name: NLP

name: CI

# What does this PR do ?

This PR adds the following functionality for Megatron GPT models.

1. Disable biases.
2. Add different activation functions - geglu, swiglu, reglu, squared relu
3. Different transformer layer configs - pre-ln, post-ln, normformer.
4. RoPE
5. Disable both hidden and attention dropout.
6. RMSNorm Normalization
7. Untie embedding and output layer.

**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. 
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# Additional Information
* Related to # (issue)
replace symbols
State: closed | Pull request: yes
Labels: name: cherry-pick

name: ASR

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Fix Summary name {} is illegal; using {} instead warning
**Collection**: [ASR]

# Changelog 
- replace @ with _


# 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
Vits doc
State: closed | Pull request: yes
Labels: name: TTS

# What does this PR do ?

Update documentation for VITS

**Collection**: [TTS]

  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation


## 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) https://github.com/NVIDIA/NeMo/issues/5948
[TTS] Use denoiser in Riva with: FastPitch+HifiGan
State: closed | Pull request: no
Labels: name: TTS

name: stale

Hi team,
I am using Riva with model TTS FastPitch+HifiGan. I see in the command
riva-build speech_synthesis has parameters:

[--denoiser.max_sequence_idle_microseconds DENOISER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                   [--denoiser.max_batch_size DENOISER.MAX_BATCH_SIZE]
                                   [--denoiser.min_batch_size DENOISER.MIN_BATCH_SIZE]
                                   [--denoiser.opt_batch_size DENOISER.OPT_BATCH_SIZE]
                                   [--denoiser.preferred_batch_size DENOISER.PREFERRED_BATCH_SIZE]
                                   [--denoiser.batching_type DENOISER.BATCHING_TYPE]
                                   [--denoiser.preserve_ordering DENOISER.PRESERVE_ORDERING]
                                   [--denoiser.instance_group_count DENOISER.INSTANCE_GROUP_COUNT]
                                   [--denoiser.max_queue_delay_microseconds DENOISER.MAX_QUEUE_DELAY_MICROSECONDS]
                                   [--denoiser.optimization_graph_level DENOISER.OPTIMIZATION_GRAPH_LEVEL]
                                   [--denoiser.fade_length DENOISER.FADE_LENGTH]
![image](https://user-images.githubusercontent.com/59649953/218043559-ed0e2f4c-a619-43e5-bec3-3ab4629cbaa0.png)


when I don’t set or set the parameters as above, I saw in the config.pbtxt file in the models/tts_postprocessor the parameter use_denoiser=False

![image](https://user-images.githubusercontent.com/59649953/218043598-f8fb935a-9c5d-47b9-8296-cd993e1890cc.png)
Hence any way to use denoiser in Riva!
[G2P] backward compatibility for english tokenizer and bugfix
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 ?

```
added backward compatibility and fixed relevant unit tests.
```
**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/5890
Fix Prompt text space issue
State: closed | Pull request: yes
Labels: name: NLP

Signed-off-by: Abhinav Khattar <aklife97@gmail.com>

# What does this PR do ?

Fix space issue in prompt

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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# Additional Information
* Related to # (issue)
[TTS/TN/G2P] Remove Text Processing from NeMo, move G2P to TTS
State: closed | Pull request: yes
Labels: name: TTS

name: NLP

name: CI

name: common

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: TTS/TN

# 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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**PR Type**:
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# Additional Information
* Related to # (issue)
update container info in readme
State: closed | Pull request: yes

Signed-off-by: fayejf <36722593+fayejf@users.noreply.github.com>

# What does this PR do ?

update container info in readme

**Collection**: core

# Changelog 
- update container info in readme



# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
* Related to # (issue)
[G2P] backward compatibility for english tokenizer and bugfix
State: closed | Pull request: yes


Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

```
added backward compatibility and fixed relevant unit tests.
```
**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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**PR Type**:
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- [x] Bugfix
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# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/5890
[G2P] fixed typos and broken import library.
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**:
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**PR Type**:
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# Additional Information
* Related to # (issue)
[G2P] fixed typos and broken import library.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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**PR Type**:
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# Additional Information
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Adding LDDL support to Bert
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

Add LDDL data loader support to Megatron Bert
**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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**PR Type**:
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# Additional Information
* Related to # (issue)
Implement getKneighborsConnections better
State: closed | Pull request: yes
Labels: name: ASR

Signed-off-by: Dongwoo Arthur Kim <kimdwkimdw@gmail.com>

# What does this PR do ?

Removing `for statement` and optimizing `getKneighborsConnections`

# Changelog 
- https://github.com/NVIDIA/NeMo/pull/5976/files#diff-4188601503d6fb6ce4e990e5148c6c02e57660f95043a149b69b485d492c68b6L314-R318
  - Just simple optimized operation

# Usage
Nothing to be different as before.

# Before your PR is "Ready for review"
**Pre checks**:
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This is a part of ASR, diarizer.
@titu1994 @redoctopus, @jbalam-nv, or @okuchaiev

# Additional Information
* Related to # (issue)
replace symbols
State: closed | Pull request: yes
Labels: name: ASR

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Fix Summary name {} is illegal; using {} instead warning
**Collection**: [ASR]

# Changelog 
- replace @ with _


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**Pre checks**:
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Results differ for different codecs and bitrate
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

I am getting different diarization results when I modify the codec and bitrate of a video and then convert that into an audio file.

**Steps/Code to reproduce bug**

Take any yt video
Download in 2 different resolutions(1080p and 480p)
Convert into audio file and run speaker diarization.
Now, convert the lower res video into MPEG4 format using ffmpeg -i <inputfile> <outputfile>
Results for this will change
Manipulating codec will also change results

**Expected behavior**

Same results always or always increasing bitrate should give better resuts
fast conformer configs and doc
State: closed | Pull request: yes
Labels: name: ASR

Signed-off-by: Dima Rekesh <drekesh@nvidia.com>

# What does this PR do ?
adds fast conformer config files and docs

**Collection**: [Note which collection this PR will affect]
ASR
# Changelog 
- Add specific line by line info of high level changes in this PR.

docs/source/asr/models.rst (added entry for Fast Conformer)
examples/asr/conf/conformer/fastconformer/fastconformer_ctc_bpe.yaml (created)
examples/asr/conf/conformer/fastconformer/fastconformer_transducer_bpe.yaml (created)
# 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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# Additional Information
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fix broken link
State: closed | Pull request: yes

Signed-off-by: ericharper <complex451@gmail.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
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# Additional Information
* Related to # (issue)
Added list_available_models
State: closed | Pull request: yes
Labels: name: TTS

Signed-off-by: Evgeniy Shabalin <36159472+treacker@users.noreply.github.com>

# What does this PR do ?

Added LJSpeech model to list_available_models

**Collection**: [TTS]

# Changelog 
- Add specific line by line info of high level changes in this PR.

  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation


## Who can review?

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# Additional Information
* Related to # (issue)
Questions regarding VAD fine-tuning.
State: closed | Pull request: no

Hello,

I would like to fine-tune the vad multilingual marblenet model using my custom speaker diarization dataset. At the moment, I read the following tutorials:
1. https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/06_Voice_Activiy_Detection.ipynb#scrollTo=Z7FxGpSE_FyM
2. https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb
3. https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb

However, I am not sure yet how to adapt these tutorials to the marblenet model. Most of my questions are related to the data preparation and preprocessing step:
1. I read in the marblenet paper that the nn takes a fixed length of 64 dimensional melfilterbank features from 0.63s length segments. Does this mean that I should extract speech and non speech segments of length 0.63s?
2. Should the labels be: "speech" and "non-speech"?
3. Is there any script that takes care of this data preparation and preprocessing part? Or is there any new tutorial similar to the ASR tutorial?
4. Can I find the preprocessing tools such as SpecAugment or SpecCutOut in the nemo toolkit?

Thank you in advance!
The hyperlink to the examples page is wrong
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

The hyperlink to the examples page is unfortunately pointed at update_readme_into branch(which does not exist anymore) instead of the main branch

**Steps/Code to reproduce bug**

Click on the hyperlink in 
For advanced users that want to train NeMo models from scratch or finetune existing NeMo models we have a full suite of [example scripts](https://github.com/NVIDIA/NeMo/tree/update_readme_into/examples) that support multi-GPU/multi-node training.


**Expected behavior**

Should link to (https://github.com/NVIDIA/NeMo/examples) i think
Fastconformer config
State: closed | Pull request: yes
Labels: name: core

name: TTS

name: ASR

name: NLP

name: Speaker Tasks

name: CI

name: common

# What does this PR do ?

Adds config files for Fast Conformer

**Collection**: [Note which collection this PR will affect]
ASR

# Changelog 
- Two config files and description of Fast Conformer in models.html

# 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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# Additional Information
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Update PUBLICATIONS.md
State: closed | Pull request: yes

# What does this PR do ?

Update the papers in PUBLICATIONS.md

**Collection**: [Docs]

# Changelog 
- Adds papers from ASR, TTS, TN/ITN, and SD

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**Pre checks**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation