renwei2024/nemo-github-issues huggingface dataset:
Nemo model for Brazillian Portuguese
State: closed | Pull request: no
Labels: name: feature

**Is your feature request related to a problem? Please describe.**

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electronic and whitelist refactor
State: closed | Pull request: yes

# What does this PR do ?

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Prompt tuning bug fix
State: closed | Pull request: yes

# What does this PR do ?

Automatically assigns prompt ids to prompt tags if no prompt ids were specified in the config. This fixes a backward compatibility bug with models that were prompt tuned before this NeMo release.

**Collection**: [Note which collection this PR will affect]

# Changelog 
Changed the init function for the Megatron GPT Class

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```python
# Add a code snippet demonstrating how to use this 
```

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Prompt Tuning Backwards Compatibility and Multitask Prompt Tuning Bug Fix
State: closed | Pull request: yes

# What does this PR do ?

Automatically assigns prompt ids to prompt tags if no prompt ids were specified in the config. This fixes a backward compatibility bug with models that were prompt tuned before this NeMo release. 

**Collection**: [Note which collection this PR will affect]

# Changelog 
Changed the init function for the Megatron GPT Class

# Usage
Same as before

```python
# Add a code snippet demonstrating how to use this 
```

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Prompt-Tuning-Documentation
State: closed | Pull request: yes

# What does this PR do ?

Updates prompt tuning documentation

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Only changed the megatron.rst file

# Usage
* Documentation 

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# Additional Information
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mT5 whole word masking and T5 finetuning config fixes
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

This PR makes two fixes to T5 before r1.7.0 (a) Sets async grad allreduce to false for bf16 O2 (b) undoes whole word masking logic for sentencepiece

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* N/A

```python
# Add a code snippet demonstrating how to use this 
```

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Ranking refactor y
State: closed | Pull request: yes

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# Usage
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```python
# Add a code snippet demonstrating how to use this 
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**PR Type**:
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- [ ] Bugfix
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# Additional Information
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range, serial, measure, time
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.

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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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Merge r1.7.0 main
State: closed | Pull request: yes

# What does this PR do ?

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# Changelog 
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# Usage
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```python
# Add a code snippet demonstrating how to use this 
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# Additional Information
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Bug in decoder_timestamps_utils.py, class ASR_TIMESTAMPS
State: closed | Pull request: no

Hi, 

method `def load_LM_for_CTC_decoder` of the `ASR_TIMESTAMPS` class in the `decoder_timestamps_utils.py` has a bug. When checking the type of the ASR model, 'EncDecCTCModelBPE' should be checked first and then 'EncDecCTCModel'. Otherwise 'EncDecCTCModelBPE' models will always be treated as `EncDecCTCModel` models.
  
https://github.com/NVIDIA/NeMo/blob/431234ae08db3fe241a387f527d3b1ce26af4c10/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py#L390
Update to 22.02
State: closed | Pull request: yes

# What does this PR do ?

This PR updates NeMo to use the NVIDIA NGC PyTorch 22.02 container.

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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**:
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Update package info
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.

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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**:
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**PR Type**:
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- [ ] Bugfix
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# Additional Information
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Update Tacotron2_Training.ipynb
State: closed | Pull request: yes

Signed-off-by: Jason <jasoli@nvidia.com>

# 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**:
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**PR Type**:
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# Additional Information
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Question : accumulate_grad_batches seems not working
State: closed | Pull request: no
Labels: name: bug

Hi guys,

I'm trying to train conformer rnnt model with accumulating several batches for gradient. For that, I specified it in the trainer as follow : 
```
    trainer = pl.Trainer(gpus=gpus, max_epochs=nb_epochs, distributed_backend="ddp", precision=16, accumulate_grad_batches=32)
```
but it seems like nothing changes. and the training evolution is the same as without adding accumulate_grad_batches parameter.
Do you have any idea how to solve it ?

With kind regards,
WIP: Add language modelling support for transducer models
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Add language modelling support for transducer models. It enables using N-gram LM with Transducer models. It also enables the neural rescoring feature for Transducer models.

# 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 
```
 
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
Making gender assignment random for cardinals, fractions, and decimal…
State: closed | Pull request: yes

…s. Also making fixes to measure so sounds more fluent.

Signed-off-by: Bonham79 <travismbartley@gmail.com>

# What does this PR do ?
Updates tn_es to make gender randomized.

Add a one line overview of what this PR aims to accomplish.
This will update some minor aspects of tn_es to lower error rate.

**Collection**: [Nemo Text Processing]

# Changelog 
- Cardinal and Decimal have randomized gender assignment
- Fraction has randomized gender assignment for integer portion
- Measure now has more fluent normalization of fractional amounts. (Note, due to Sparrowhawk architecture it can sound slightly awkward: 1 1/2  l-> "uno y medio litros" as opposed to more common "uno litro y medio"). Change would require rewriting of how measure verbalizer manages fraction tags.
- Non-deterministic strings now permit apocope for "una". This arises when the number precedes a noun with stressed "a" sound. Since this is hard to determine which FST setup, this is left to normalize_with_audio to manage. 

# Usage

# Before your PR is "Ready for review"
**Pre checks**:
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- [ y] Bugfix
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# Additional Information
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CUDA error in Speaker Diarization in nmesc_clustering.py - error: CUSOLVER_STATUS_INTERNAL_ERROR
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**
It is very strange , only one audio file has this error when calling speaker diariation function, Error Message as below:

Error:cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR, when calling `cusolverDnSsyevd(handle, jobz, uplo, n, A, lda, W, work, lwork, info)`

**Steps/Code to reproduce bug**
I tracked to the root of error, which is in the function (nmesc_clustering.py), when call eigh(laplacian) with cuda (cuda device =1). 

def eigDecompose(laplacian, cuda, device=None):  
    print(len(laplacian))
    if TORCH_EIGN:
        if cuda:
            if device is None:
                device = torch.cuda.current_device()            
            
            laplacian = torch.from_numpy(laplacian).float().to(f"cuda:{device}")
        else:
            laplacian = torch.from_numpy(laplacian).float()
                
        **lambdas, diffusion_map = eigh(laplacian)**
        lambdas = lambdas.cpu().numpy()
        diffusion_map = diffusion_map.cpu().numpy()
    else:
        lambdas, diffusion_map = eigh(laplacian)

    return lambdas, diffusion_map

**Environment overview (please complete the following information)**
Linux environment, 2 gpus, using gpu 1

**Additional context**
the error happens when laplacian shape is 592x592, while other audio files (no error) with laplacian shape of odd dimensions, not sure if it is the reason.
Duplex model inference fix, money encoder fix
State: closed | Pull request: yes

Signed-off-by: ekmb <ebakhturina@nvidia.com>

# What does this PR do ?
Bug fixes for Duplex inference and WFST non-ascii error

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?
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**PR Type**:
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- [ X] Bugfix
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# Additional Information
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Yuya/megatron t5 glue eval
State: closed | Pull request: yes

Signed-off-by: Yu Yao <yuya@nvidia.com>

# What does this PR do ?
Add megatron t5 glue eval-only script 

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
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# Additional Information
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T5 pipeline parallel
State: closed | Pull request: yes

# What does this PR do ?

Adds pipeline parallel training support to T5.

**Collection**: NLP

# Changelog 
- TBD

# Usage

Set `pipeline_model_parallel_size=2/4` in `megatron_t5_config.yaml`.

# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
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punct process bug fix
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 
```

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**Pre checks**:
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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Bugfix for GPT eval 
State: closed | Pull request: yes

# What does this PR do ?

We fix the case where batch_size > 1 and also fix a typo.


**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**:
- [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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

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# Additional Information
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[TTS] Fix bugs in E2E TTS, Mixer-TTS and FastPitch
State: closed | Pull request: yes

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [TTS]

# Changelog 
- Fix bugs in E2E TTS, Mixer-TTS and FastPitch 

**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
adding ranking baseline determ
State: closed | Pull request: yes

Signed-off-by: Yang Zhang <yangzhang@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?
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# Additional Information
* Related to # (issue)
fix consumed samples calculation + PTune Model bugs
State: closed | Pull request: yes

This PR will load the initial consumed samples from the checkpoint file names. The consumed samples will be calculated based on the relative training steps and global batch size.
The T5 doesn't seem to calculate the consumed samples correctly if it supports pipeline parallel. 
```python
    def compute_consumed_samples(self, global_relative_step=0):
        app_state = AppState()
        consumed_samples = (
            self.init_consumed_samples
            + global_relative_step
            * app_state.data_parallel_size
            * self._cfg.micro_batch_size
            * self.trainer.accumulate_grad_batches
        )
        return int(consumed_samples)
```
Need to confirm we will replace `self.trainer.accumulate_grad_batches` with `get_num_microbatches()`
TN docs update
State: closed | Pull request: yes

# What does this PR do ?

Text normalization docs improvements:
- added quick start
- merged all docs related to TN/ITN
- fixed references issues
- added docs for audio-based TN

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

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Update branch in Jenkinsfile
State: closed | Pull request: yes

Signed-off-by: ericharper <complex451@gmail.com>

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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Test grad accumulation for RNNT loss
State: closed | Pull request: yes

Signed-off-by: smajumdar <titu1994@gmail.com>

# What does this PR do ?

Adds a test to check if grad accumulation works with Numba CUDA/CPU RNNT Loss.

**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 
pytest tests/collections/asr/numba --relax_numba_compat
```

# 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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [x] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
Can we use ASR pre-trained models with SSL pre-training code?
State: closed | Pull request: no
Labels: name: bug

Hi,
I have used the [SSL pre-training script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py) as follows:
```sh
python speech_pre_training.py \
    --config-path="../conf/ssl/citrinet/" \
    --config-name="citrinet_ssl_1024" \
    model.train_ds.manifest_filepath="./train_LS_manifest.json" \
    model.validation_ds.manifest_filepath="./test_LS_manifest.json" \
    trainer.gpus=1 \
    trainer.accelerator="ddp" \
    trainer.max_epochs=100 \
    trainer.log_every_n_steps=100 \
    trainer.accumulate_grad_batches=1 \
    trainer.check_val_every_n_epoch=1 \
    trainer.precision=32 \
    model.train_ds.batch_size=12 \
    model.validation_ds.batch_size=10 \
    model.spec_augment.freq_masks=4 \
    model.spec_augment.time_masks=10 \
    model.spec_augment.freq_width=27 \
    model.spec_augment.time_width=0.05 \
    model.optim.name="novograd" \
    model.optim.lr=0.05 \
    model.optim.betas=[0.8,0.25] \
    model.optim.weight_decay=0.001 \
    model.optim.sched.warmup_steps=5000 \
    model.optim.sched.min_lr=1e-9 \
    exp_manager.exp_dir="ssl_experiments/" \
    exp_manager.name="SSL-Citrinet1024" \
    exp_manager.create_tensorboard_logger=True \
    exp_manager.resume_if_exists=True \
    exp_manager.resume_ignore_no_checkpoint=True \
    exp_manager.create_wandb_logger=False \
    +init_from_pretrained_model="stt_en_citrinet_1024"
```
However, I have faced an error when the PyTorch lightening attempts to load the pre-trained `stt_en_citrinet_1024` weights (_i.e.,_ `+init_from_pretrained_model="stt_en_citrinet_1024"`). The error refers to the [line 61](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py#L61) of code (`maybe_init_from_pretrained_checkpoint` method). I have investigated and I realized that the `maybe_init_from_pretrained_checkpoint` method has the internal attribute of `strict=False` [ref](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/core/core.html?highlight=maybe_init_from_pretrained_checkpoint#nemo.core.ModelPT.maybe_init_from_pretrained_checkpoint). So, would you please help me that can I load the pre-trained wights? If yes, How I can do that?
Best
Question : Conformer RNNT finetuning 
State: closed | Pull request: no

Hi guys, 

I am trying to finetune STT En Conformer RNNT for spanish. the problem is the following : when I try to change the tokenizer, the model is just such as training from scratch and it takes too much time for converging ( in reality it doesn't converge :p ). Does any one have an explanation, an idea or a trick to bypass this problem.

With best regards,
num_workers default number change
State: closed | Pull request: yes

Signed-off-by: George Zelenfroynd gzelenfroind@nvidia.com
# What does this PR do ?

added lines to asr models that have no num_worker param, its absence caused slow training

**Collection**: ASR


# Changelog 
- Adds lines in files of asr models

# 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

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## Who can review?

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# Additional Information
* Related to # (issue)
ignore speakers with few utterances
State: closed | Pull request: yes

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Added feature for ignoring samples from speakers with very few utterances

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Added a helper function for ignoring samples from speakers with very few utterances
- Made code run on multiple processes 


# 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)
Polylang asr
State: closed | Pull request: yes

# What does this PR do ?

Adds the ability to aggregate multiple tokenizers for multi-lingual ASR training and inference

**Collection**: ASR

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
Add the 'lang' field to each entry in your manifest file -- e.g. to add the same language (es) to all entries in a file, you could do cat tarred_audio_manifest.json | jq -c ‘. += {“lang”:“es”}’
Change the tokenizer config to the agg type, specifying the tokenizer for each language, e.g.
tokenizer:
  type: agg
  langs:
    en:
      dir: ???
      type: bpe
    es:
      dir: ???
      type: bpe

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [X ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ X] Did you write any new necessary tests?
- [ X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [X ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to # (issue)
Update libritts get data. audio-based normalization
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?

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# Additional Information
* Related to # (issue)
Multilang asr
State: closed | Pull request: yes

# What does this PR do ?

- Adds the ability to aggregate multiple tokenizers for multi-lingual ASR training and inference

**Collection**: [ASR]

# Changelog 


# Usage
* Add the 'lang' field to each entry in your manifest file -- e.g. to add the same language (`es`) to all entries in a file, you could do  `cat tarred_audio_manifest.json | jq -c ‘. += {“lang”:“es”}’`
* Change the tokenizer config to the `agg` type, specifying the tokenizer for each language, e.g.

```
tokenizer:
  type: agg
  tokenizers:
    en:
      dir: ???
      type: bpe
    es:
      dir: ???
      type: bpe
```


```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?

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# Additional Information
* Related to # (issue)
ASR SSL Update
State: closed | Pull request: yes

# What does this PR do ?

Update contrastive ssl for asr

**Collection**: ASR

# Changelog 
- add options to contrastive loss implementation
- add slightly different spec augmentation for ssl
- update corresponding configs for conformer, citrinet, contextnet ssl

**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
[TTS] Fix bugs in VocoderDataset and TTSDataset
State: closed | Pull request: yes

**Collection**: [TTS]

# Changelog 
- Fix bug with random sampling and `numpy` to `torch` converting in `__getitem__`
- Fix bug with `phoneme_probability` field for `EnglishCharsTokenizer`
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
[WIP] Masking changes
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

Minor issue with checking indexes when adding special tokens to SPM.

Figure out what is going on with sentencepiece and whole word masking.

**Collection**: [Note which collection this PR will affect]

# Changelog 

- Revert `is_start_piece()`.
- Check if id is >=0 while adding special tokens and not just > 0.

# Usage
* You can potentially add a usage example below

N/A 

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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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
- [ ] Documentation

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# Additional Information
* Related to # (issue)
Updated Speech Data Explorer
State: closed | Pull request: yes

# What does this PR do ?

Adds support of recent versions of Dash and librosa. Adds new features (metrics, export, UI).

**Collection**: Tools

# Changelog 
- Ported SDE to Plotly Dash 2.1, librosa 0.9
- Added new metrics (insertions, deletions, diff)
- Added support for downloading sorted/filtered vocabulary
- Updated UI

# 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
- [X] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to #3395