renwei2024/nemo-github-issues huggingface dataset:
fix num_workers to 1 (temp fix)
State: closed | Pull request: yes

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Fix VAD struck issue temporarily with num_workers=1

**Collection**: [SD, VAD]

# Changelog 
- config update

# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Bugfix
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O2bert + apex pipeline functions
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 
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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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The reference output is not correct when fine-tuning STT Zh Citrinet1024 Gamma 0.25 Model
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

> my dataset situation:
I only have a dataset with 3Hs of my target accent and I have 2H for train+dev and 1H for valid, and the charset size of it is very small with only 394(It means I have multiple people speaking same lines) (I don't know if this is what causing the problem: poor dataset size and not adequate enough charset)

The reference output from the runtime is not correct.
Following This issue: https://github.com/NVIDIA/NeMo/issues/2725#issue-978965808, I freeze enc. and dec..
But "After several epoch training ,The output reference and predicted are correct" didn't happen.
```bash
# This is just a bunch of meaningless characters, even has some alphabets in it.
[NeMo I 2022-10-22 03:38:50 wer:985] reference:仑兆书偻使仿侬份侣交仑兆书偻使井交X亩且亥亟使佳仰但东伴傀使似侥乘Z使佳
[NeMo I 2022-10-22 03:38:50 wer:986] predicted:难道联美不折者物了明女张找您美的部累吗突的端头在不仅有天离而铁来姑勇华偏而O军
```

```bash
# While following the tutorial step by step can have a correct one on jp..
# Means as an (cultural?) entrance to Japan, the standard Japanese is needed.
# (Don't they just be decoded from unicode in a same way?)
[NeMo I 2022-10-21 09:30:32 wer:985] reference:日本のエスヘラントとしてやはり標準語は必要た
[NeMo I 2022-10-21 09:30:32 wer:986] predicted:郵奈郵奈ょ奈ょ彼加奈施圧息鯨会加会加襞会を圧を圧を赤っ紅奈施奈素米調っ出航米埋裏似樫葉襞浜民た霧営端所坊事備出を樫赤複容紅竹郵沈繊越な素奈所陸霧幼端樫輪み米よ備霧な樫出樫腹飛備紅狩紅樫出素宿敗街暮排樫赤備っな宿敗杯備焦樫要彼事匠リ素赤要宿邦よ樫排を樫紅持紅街備所戻米所解え狩彼演属会住街圧策赤拠防米飛備霧を米狩米赤備複樫紅樫の紅樫航竹戻父所坊証郵拠な複な宿坊飛陸含な樫郵赤彼赤ん赤
```

And my Learning rate drops very fast while train_cer and valid_cer is still very high.(around 1...)

**Steps/Code to reproduce bug**
1. follow the [lang. finetune tutorial](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb#scrollTo=DoWNVNYGOaMX), but doing fine-tuning on zh citrinet with bigger kernel size, skip oov removal, skip argumentation.(Character wise, not the sub-word method in part B)

2. convert the code in notebook to a script and run the script.

> I have my code here.  [run.txt](https://github.com/NVIDIA/NeMo/files/9842185/run.txt)


**Expected behavior**

Supposed to have a readable reference.(And when use the trained model to do transcription, it threw up same sort of meaningless codes too.)

**Environment overview**

 - Environment location: Bare-metal
 - Method of NeMo install: from source(clone and using the reinstall script)

**Environment details**

- OS version Ubuntu 18
- PyTorch version 1.12.1
- Python version 3.8

**Additional context**

Add any other context about the problem here.
Example: GPU model V100 32G x2
[TTS] Fastpitch energy condition and refactoring
State: closed | Pull request: yes

# What does this PR do ?

Adds energy conditioning to FastPitch

**Collection**: [Note which collection this PR will affect]

TTS

# Changelog 
- Add specific line by line info of high level changes in this PR.

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* You can potentially add a usage example below

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# Add a code snippet demonstrating how to use this 
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**PR Type**:
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# Additional Information
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replace ColumnParallelLinear with nn.Linear in export_utils
State: closed | Pull request: yes

Signed-off-by: arendu <adithya.r@gmail.com>

# What does this PR do ?

Make Apex's ColumnParallelLinear export friendly

**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
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# Additional Information
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Add support for Sampled Softmax for RNNT Joint
State: closed | Pull request: yes

# What does this PR do ?

Adds support for Sampled Softmax for RNNT Joint networks via subclass `SampledRNNTJoint`.
Implements the paper [Memory-Efficient Training of RNN-Transducer with Sampled Softmax](https://arxiv.org/abs/2203.16868).

**Collection**: [ASR]

# Changelog 
- Add support for Sampled Softmax in RNNT Joint - allowing huge vocabulary networks at near constant memory cost.
- Add docs for the RNNT decoder and joint modules
- Add tests for Sampled RNNT Joint

# Usage


``` yaml

# Replace the target for model.joint and add new value for `n_samples`
model:
  ...
  joint:
    #  _target_: nemo.collections.asr.modules.RNNTJoint
    _target_: nemo.collections.asr.modules.SampledRNNTJoint
    n_samples: 500
  
  ...

```

# 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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Global batch size support for validation
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)
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**PR Type**:
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# Additional Information
* Related to # (issue)
[TTS] remove the avoidance of circular imports
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

It is no problem to run through below after remove the line.
``` bash
In [2]: from nemo.collections.tts.models.base import SpectrogramGenerator

In [3]: spectrogram_model = SpectrogramGenerator.from_pretrained("tts_en_fastpitch")
100% [......................................................................] 187023360 / 187023360

In [4]: spectrogram_model
Out[4]:
FastPitchModel(
  (mel_loss): MelLoss()
  (pitch_loss): PitchLoss()
  (duration_loss): DurationLoss()
  (aligner): AlignmentEncoder(
    (softmax): Softmax(dim=3)
    (log_softmax): LogSoftmax(dim=3)
    (key_proj): Sequential(
      (0): ConvNorm(
        (conv): PartialConv1d(384, 768, kernel_size=(3,), stride=(1,), padding=(1,))
      )
      (1): ReLU()
      (2): ConvNorm(
        (conv): PartialConv1d(768, 80, kernel_size=(1,), stride=(1,))
```


**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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**PR Type**:
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- [ ] Bugfix
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## Who can review?

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# Additional Information
* Related to # (issue)
[Fix] NoisePerturbation for multi-channel audios
State: closed | Pull request: yes

# What does this PR do ?

Fix the error caused by `noise_gain_db = min(data_rms - noise.rms_db - snr_db, self._max_gain_db)` when handling multi-channel audios. The error was that `min(np.array([1,2,3]), 4)` is invalid since `np.array([1,2,3]) < 4` is ambiguous. To fix it, we first check whether the audio is multi-channel, then we perform the `min()` operation for each channel if it's multi-channel.


**Collection**: ASR



# 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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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
[TTS] Add Mandarin/English Bilingual Recipe for Training Fastpitch Models
State: closed | Pull request: yes

Signed-off-by: Yuekai Zhang <zhangyuekai@foxmail.com>

Add Chinese TTS support with bilingual dataset [SF_bilingual_dataset](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en)

Closed previous #5197 to modify the sign-off info.

@XuesongYang Sorry for the duplicate PR, would you mind reviewing it again? Thank you.
Decoding hangs with pretrained models when runing transcible_speech script
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**
The script freezes and hangs when I run transcrible_speech with conformer transducer pretrained models.
I tried to kill the process and re-run it but it has the same behavior.

The terminal stuck like this:
Transcribing:  50%|███▎      | 1/2 [00:05<00:5, 7.54it/s]
Beam search progress:::  29%|██▎      | 5/17 [00:16<00:01, 8.02it/s]

I tried to follow this solution but it doesn't work because I am using beam search strategy.
https://github.com/NVIDIA/NeMo/issues/3025

**Steps/Code to reproduce bug**

`
class TranscriptionConfig:
    # Required configs
    model_path: Optional[str] = None  # Path to a .nemo file
    pretrained_name: Optional[str] = None  # Name of a pretrained model
    audio_dir: Optional[str] = None  # Path to a directory which contains audio files
    dataset_manifest: Optional[str] = None  # Path to dataset's JSON manifest

    # General configs
    output_filename: Optional[str] = None
    batch_size: int = 32
    num_workers: int = 0

    # Set cudato int to define CUDA device. If 'None', will look for CUDA
    # device anyway, and do inference on CPU only if CUDA device is not found.
    # If `cuda` is a negative number, inference will be on CPU only.
    cuda: Optional[int] = None
    amp: bool = False
    audio_type: str = "mp3"

    # Recompute model transcription, even if the output folder exists with scores.
    overwrite_transcripts: bool = True

    # Decoding strategy for RNNT models
    rnnt_decoding: RNNTDecodingConfig = RNNTDecodingConfig(fused_batch_size=-1, strategy="beam", beam=beam_decode.BeamRNNTInferConfig(beam_size=2))
`


Here is my decoding config:

`
'decoding': {'strategy': 'beam', 'compute_hypothesis_token_set': False, 'preserve_alignments': None, 'fused_batch_size': -1, 'greedy': {'max_symbols_per_step': 10, 'preserve_alignments': False}, 'beam': {'beam_size': 2, 'search_type': 'default', 'score_norm': True, 'return_best_hypothesis': True, 'tsd_max_sym_exp_per_step': 50, 'alsd_max_target_len': 1.0, 'nsc_max_timesteps_expansion': 1, 'nsc_prefix_alpha': 1, 'maes_num_steps': 2, 'maes_prefix_alpha': 1, 'maes_expansion_gamma': 2.3, 'maes_expansion_beta': 2, 'language_model': None, 'softmax_temperature': 1.0, 'preserve_alignments': False}}
`
[TTS] remove LinVocoder and apply Vocoder as parent class.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

`nemo.collections.tts.model.base.LinVocoder` is a redundant definition because `nemo.collections.tts.model.base.Vocoder` already defined all functions. The only difference is that `LinVocoder` calls `convert_linear_spectrogram_to_audio` while `Vocoder` calls `convert_spectrogram_to_audio`. So it is not necessary to duplicate the definition of Vocoder class.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
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[TTS] deprecate TextToWaveform base class.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

`TextToWaveform` base class, mostly servicing e2e TTS models, is not used anywhere. Deprecate it for now.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
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Add safe dir to jenkinsfile
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**:
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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Remove circular imports
State: closed | Pull request: yes

Signed-off-by: Taejin Park <tango4j@gmail.com>

# What does this PR do ?

This PR fixes the bug in https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3839247
A couple of functions from `speaker_utils.py` was causing a circular import error.
The function calls are removed and tested training/inference based on `DiarizationSpeechLabel` class.

**Collection**: [Note which collection this PR will affect]
ASR

# Changelog 

Removed function imports from `speaker_utils.py` to solve circular import error.

# Usage
* You can potentially add a usage example below

```python

python ./multiscale_diar_decoder.py --config-path='../conf/neural_diarizer' --config-name='msdd_5scl_15_05_50Povl_256x3x32x2.yaml' \
    trainer.devices=1 \
    model.base.diarizer.speaker_embeddings.model_path="titanet_large" \
    model.train_ds.manifest_filepath="<train_manifest_path>" \
    model.validation_ds.manifest_filepath="<dev_manifest_path>" \
    model.train_ds.emb_dir="<train_temp_dir>" \
    model.validation_ds.emb_dir="<dev_temp_dir>" \
    exp_manager.name='sample_train' \
    exp_manager.exp_dir='./msdd_exp'
```

# 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?
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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**:
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- [x] Bugfix
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# Additional Information
* Related to https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3839247
Updates to T0 Dataset and Model
State: closed | Pull request: yes

# What does this PR do ?

Updates the T0 Dataset and Model implementation to use the following:

1. JSONL Text-Memmap datasets (refactored from earlier implementation) + Samples mapping similar to MegatronNMT
2. Megatron Blendable Datasets instead of ad-hoc implementation
3. Megatron batch samplers instead of distributed sampler. With the same train/validation set logic.
4. Support for using HF converted checkpoints (with bos changes, dec_mask fixes etc)

**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?

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# Additional Information
* Related to # (issue)
Update readme intro
State: closed | Pull request: yes

# What does this PR do ?

Updating readme to add links to public models.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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# Additional Information
* Related to # (issue)
Update links
State: closed | Pull request: yes

# What does this PR do ?

Fix two broken links.

**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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[TTS] add Chinese TTS
State: closed | Pull request: yes

# What does this PR do ?

Add Chinese TTS support with bilingual dataset [SF_bilingual_dataset](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en)

**Collection**: TTS


# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you add or update any necessary documentation?
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
[ASR] AudioToAudio datasets and related test
State: closed | Pull request: yes
Labels: name: feature

name: ASR

name: common

# What does this PR do ?

This is a draft PR of datasets for different audio-to-audio tasks.

**Datasets**

- `BaseAudioDataset`: Abstract base class.
- `AudioToTargetDataset`: A dataset for audio-to-audio tasks where the goal is to use an input signal to recover the corresponding target signal.
- `AudioToTargetWithReferenceDataset`: A dataset for audio-to-audio tasks where the goal is to use an input signal to recover the corresponding target signal and an additional reference signal is available.
- `AudioToTargetWithEmbeddingDataset`: A dataset for audio-to-audio tasks where the goal is to use an input signal to recover the corresponding target signal and an additional embedding signal. It is assumed that the embedding is in a form of a vector.

**Tests**

Multiple tests are implemented in `test_asr_datasets.py` in class `TestAudioDatasets`. These tests include

- `test_audio_to_target_dataset`: multiple tests for `AudioToTargetDataset`
- `test_audio_to_target_dataset_with_target_list`: tests specifically a scenario where target is provides as a list of files
- `test_audio_to_target_with_reference_dataset`: tests for `AudioToTargetWithReferenceDataset`
- `test_audio_to_target_with_embedding_dataset`: tests for `AudioToTargetWithEmbeddingDataset`

Tests can be started using the following command
```
pytest tests/collections/asr/test_asr_datasets.py::TestAudioDatasets
```

**Collection**: ASR

# Changelog 
- Added datasets in `audio_to_audio.py`
- Added unit tests in `test_asr_datasets.py` and `test_audio_utils.py`
- Added an option in `AudioSegment. segment_from_file` to use a fixed offset

# Usage

Usage is illustrated in unit tests, which can be executed using

```python
pytest tests/collections/asr/test_asr_datasets.py::TestAudioDatasets
```

# 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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- [ ] Bugfix
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# Additional Information
* Related to # (issue)
P&C tutorial, inference script
State: closed | Pull request: yes

Signed-off-by: Matvei Novikov <mattyson.so@gmail.com>

# What does this PR do ?

Added tutorial and inference script for lexical audio P&C model

**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)
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  - [ ] 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)
checks for accepted adapter type at module level
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?
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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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## Who can review?

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# Additional Information
* Related to # (issue)
fix to t5 adapter and ia3 eval scripts
State: closed | Pull request: yes

Signed-off-by: arendu <adithya.r@gmail.com>

# What does this PR do ?
The `predict_step` for T5 adapters and IA3 returns a dictionary with different keys (#4746)  . This PR updates to the eval scripts to use the new keys.

**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)
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [X] Bugfix
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# Additional Information
* Related to # (issue)
[TTS] Fix TTS Primer image markup
State: closed | Pull request: yes

Signed-off-by: Ryan <rlangman@nvidia.com>

# What does this PR do ?

Fixes markup issues with github code rendering.

**Collection**: [TTS]

# Changelog 

- Changed image links from "tts_primer" branch to "main" branch.
- Removed <figure> and rearranged some tags around images to ensure that they render in github.
- Fixed 1 wikipedia link by replacing closing parenthesis ")" with "%29".

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
Add fully torch.jit.script-able speaker clustering module
State: closed | Pull request: yes

# What does this PR do ?

The main goal of this PR is adding a fully torch.script-able clustering function.
**Convert entire clustering module to jit.scriptabe module**: For this purpose, the clustering code employed torch.module class so that clustering system
can be converted to torch.jit.script.  
**Convert all string variables to float**: As a side-effect, all the arguments that are passed to clustering module should be torch.Tensor.float, 
so the string-formatted arguments in diarization pipeline is all changed to torch.Tensor.float.


After this PR is merged, NeMo and Riva will be sharing an identical speaker clustering code.
Input and output is torch.Tensor, and by default, all operations are processed on GPU.

Also, integer type annotation should be torch.LongTensor if you want to convert to torch.script.


**Collection**: [Note which collection this PR will affect]

# Changelog 

- Changed COSclustering function -> forward() and changed class to use torch.module.
- Fixed docstrings.
- Removed unused class variables 
- Replace eigvalsh (only calculate eig vals) instead eigsh (full eig-decomp) which makes computation a bit faster 
- Timestamps for clustering is now using float instead using string format.

# Usage
* You can potentially add a usage example below

```python
  python neural_diarizer/multiscale_diar_decoder_infer.py \
    diarizer.manifest_filepath=<path to manifest file> \
    diarizer.out_dir='demo_output' \
    diarizer.speaker_embeddings.parameters.save_embeddings=False \
    diarizer.vad.model_path=<pretrained model name or path to .nemo> \
    diarizer.speaker_embeddings.model_path=<pretrained speaker embedding model name or path to .nemo> \
    diarizer.msdd_model.model_path=<pretrained MSDD model name or path .nemo> \
```

# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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## Who can review?

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# Additional Information
* Related to # (issue)
returned the counter of deletions and insertions
State: closed | Pull request: yes

Signed-off-by: George Zelenfroynd <gzelenfroind@nvidia.com>

# What does this PR do ?
returned the counter of deletions and insertions

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)
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- [ ] 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**:
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
* Related to # (issue)
Scoring and Analysis of Adapter Experiments
State: closed | Pull request: yes

# What does this PR do ?

Added the script for scoring and analysis of the adapter experiments.

**Collection**: asr

# Changelog 
- Created a new script: `examples/asr/asr_adapters/scoring_and_analysis.py`
- Updated README: `examples/asr/asr_adapters/README.md`

# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation

## Who can review?

Anyone in the NeMo ASR Team
Fixed bug where decoding strategy not being updated in transcribe speech
State: closed | Pull request: yes

Signed-off-by: Shantanu Acharya <shantanua@nvidia.com>

# What does this PR do ?

In transcribe_speech.py, the `RNNTDecodingConfig` object was being created again thereby overriding the params specified by the user.

**Collection**: asr

# 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

## Who can review?

Anyone in the asr team.
Error encountered when trying to fine-tune STT Zh Quartznet15x5 on accented dataset.
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

```bash
# When loading model
Model stt_zh_quartznet15x5 was not found. Check cls.list_available_models() for the list of all available models.
```

```bash
# When directly trying transcribing
Error executing job with overrides: ['pretrained_name=stt_zh_quartznet15x5', 'audio_dir=****']
Traceback (most recent call last):
  File "examples/asr/transcribe_speech.py", line 155, in main
    asr_model = ASRModel.from_pretrained(
  File "/****/NeMo/nemo/core/classes/common.py", line 846, in from_pretrained
    class_, nemo_model_file_in_cache = cls._get_ngc_pretrained_model_info(
  File "/****/NeMo/nemo/core/classes/common.py", line 898, in _get_ngc_pretrained_model_info
    raise FileNotFoundError(
FileNotFoundError: Model stt_zh_quartznet15x5 was not found. Check cls.list_available_models() for the list of all available models.
```

**Steps/Code to reproduce bug**

1. follow the steps to run the model in its [HP](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_zh_quartznet15x5)

**Expected behavior**

Should works fine?

**Environment overview (please complete the following information)**

 - Environment location: Baremetal
 - Method of NeMo install: pip from source (Pull from GitHub following the instructions)

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 18 cuda 11.3 cudnn8.3.2_0
- PyTorch version 1.12.1
- Python version 3.8

**Additional context**

GPU model: 3090
Fix absolute path in GPT Adapter CI tests
State: closed | Pull request: yes

Signed-off-by: arendu <adithya.r@gmail.com>

Fixes abs paths in GPT Adapter CI tests. Follow-up of #5183 

**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)
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- [ ] 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)
relative paths for T5 adapter CI tests
State: closed | Pull request: yes

Signed-off-by: arendu <adithya.r@gmail.com>

# What does this PR do ?

update CI tests for T5 to save/load/delete relative paths using `exp_manager.exp_dir`.

**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

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## Who can review?

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# Additional Information
* Related to # (issue)
Update docs with Comparison tool info, and slightly change .sh for ea…
State: closed | Pull request: yes

…se of use

Signed-off-by: George Zelenfroynd <gzelenfroind@nvidia.com>

# What does this PR do ?
describes new features in documentation

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]
ASR

# 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?

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# Additional Information
* Related to # (issue)