renwei2024/nemo-github-issues huggingface dataset:
fix wrong word error
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

The docs have an error where the word "cartographic" is used in place of "catastrophic"

**Collection**: ?

# Changelog 
- `docs/source/nlp/prompt_learning.rst` fix word error

# Usage
N/A

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WaveGlow input type fixes
State: closed | Pull request: yes

Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com>

Fix WaveGlow input types to match what gets passed in during training and inference.

**Collection**: TTS

# Changelog 
- Made `z` optional, as it's not passed in during training and inference, and only during export
- Removed extraneous inputs to the WaveGlow module in `convert_spectrogram_to_audio()`
- Added `sigma` to optional inputs for inference, since it is a default argument for the base `convert_spectrogram_to_audio()` function

**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
Megatron Perceiver with tensor and pipeline parallel
State: closed | Pull request: yes

Signed-off-by: Micha Livne <mlivne@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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Add ASR with Adapters Tutorial
State: closed | Pull request: yes

Signed-off-by: smajumdar <smajumdar@nvidia.com>

# What does this PR do ?

Adds a tutorial for ASR with adapters and sub-section for tutorials involving adapters.

**Collection**: [ASR]

**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
[TTS] Fix 44KHz Fastpitch config
State: closed | Pull request: yes

# What does this PR do ?

The config to train Fastpitch at 44KHz was not updated according to the latest code changes in TTS. This PR adds the updated config to train Fastpitch at 44KHz.

**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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Fix/punctuation avoid overwritting tmp files
State: closed | Pull request: yes
Labels: name: bug

# What does this PR do ?

When tarred dataset is created temporary files for text and labels are used. Previously if several scripts were run simultaneously there were risk of race condition because temporary files had hardcoded names.

**Collection**: nlp

# Changelog 
- `tempfile.mksfile()` lib is used
- File creation and usage is wrapped into `try: ... finally: ...` clause.
https://github.com/NVIDIA/NeMo/issues/new/choose
State: closed | Pull request: no
Labels: name: feature

[](https://github.com/NVIDIA/NeMo/blob/650718fd8653d67e5d58eb0ccb3bbc651b48e8c6/CONTRIBUTING.md)**Is your feature request related to a problem? Please describe.**

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

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Provide a code snippet on how new APIs/changes would be used by others.

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Fix a wrong description in offline_diarization_with_asr.yaml
State: closed | Pull request: yes

# What does this PR do ?

Changes a wrong description in offline_diarization_with_asr.yaml

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

Changed the description about asr_based_vad_threshold in offline_diarization_with_asr.yaml.
Fixed multiple typos and grammatical errors.

# 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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Fix cmudict typo: phoneme YI1 -> IY1 in NVME
State: closed | Pull request: yes

Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com>

Corrects entry that mistakenly has "YI1" to "IY1":
Line 133963: NVME  EH1 N V IY1 EH1 M YI1
Fix and add functions in speaker utils
State: closed | Pull request: yes

# What does this PR do ?

This PR fixes bugs for certain cases and adds few functions. Previously, write_rttm2manifest function had following issues:

(1) Offset is not considered
(2) Occasionally, the function was returning the segments outside the given (offset, offset+duration) range.

The newly written part is compared with the original code, and confirmed to 

**Collection**: [Note which collection this PR will affect]

ASR

# Changelog 
- Add specific line by line info of high level changes in this PR.

Added following functions in speaker_utils.py.
These functions are needed to extract oracle VAD stamp from manfiest file, considering the offset and duration.
In addition, these functions are designed for the future use for streaming diarization. 

get_uniq_id_with_dur()
getSubRangeList()
getMinMaxOfRangeList()
getMergedRanges()
int2fl()
fl2int()
combine_int_overlaps()
combine_float_overlaps()
getOverlapRange()
isOverlap()

and modified following functions:
write_rttm2manifest() - to resolve existing issues
segments_manifest_to_subsegments_manifest() - Now, if include_uniq_id = True, the function adds uniq_id to the created subsegment manifest file. This is for using segmentation files for truncated (chopped) diarization dataset, where uniq_id is overlapped if not specified. (One audio file creates multiple 30~40sec utterances and these short utterances should be identifed too.)

Details can be found in the docstring in each function definition.

# Before your PR is "Ready for review"
**Pre checks**:
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Binarized memmap dataloader for Megatron NMT, Inference and checkpoint -> nemo
State: closed | Pull request: yes

# What does this PR do ?

1. Adds megatron memory-mapped dataloaders to NMT.
2. Inference script/config with a translate() method.

**Collection**: NLP

# Changelog 
- Add a new dataset class for megatron memmap dataset.
- Add an inference script with the associated yaml config.
- Change the `use_tarred_dataset` arg to a generic `dataset_type` arg that can take `[text, tarred, bin_memmap, text_memmap]`

# Usage
* Set the following in the yaml config

- `dataset_type: bin_memmap`.

# 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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[TN] update default for SH 
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**:
- [ ] 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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- [X] Bugfix
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# Additional Information
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High value for WER using Citrinet1024
State: closed | Pull request: no

Hi, thanks for your great tutorial. 
I fine-tuned Citrinet 1024 using a Persian audio data set. The WER starts from 4, and the loss value is about 500. But I did the same with Citrinet 256, the WER starts from 0.99 and after each epoch decreases. Why in Citrinet 1024 I faced a large value for WER?

**Environment overview**

- Environment location: Google Colab

- Method of NeMo install: !pip install nemo_toolkit[asr]

- NeMo version: main


**Environment details**

- OS version : "Ubuntu20.04.3 LTS"
Fine-tuning Conformer with Persian language
State: closed | Pull request: no

Hi, would you please share with me a collab notebook file for fine-tuning conformer_ctc_bpe? I want to use it for the Persian language. when I use the` ASR_CTC_Language_Finetuning.ipynb` notebook I got the desired result with all Citrinet models, but I can not use this notebook file for any of the CTC Conformer models. For example when I use `stt_en_conformer_ctc_large` model in the following part `results = analyse_ctc_failures_in_model(model)` I have this error:

`
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
    data.append(next(self.dataset_iter))
  File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 226, in map
    for sample in data:
  File "/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/data/audio_to_text.py", line 671, in __next__
    audio_bytes, audio_filename = next(self.iterator)
  File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 282, in to_tuple
    for sample in data:
  File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 240, in rename
    for sample in data:
  File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 187, in shuffle
    for sample in data:
  File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 153, in group_by_keys
    for fname, value in data:
  File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 131, in tar_file_expander
    for source in data:
  File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 86, in url_opener
    if handler(exn):
  File "/usr/local/lib/python3.7/dist-packages/webdataset/utils.py", line 12, in reraise_exception
    raise exn
  File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 82, in url_opener
    stream = gopen.gopen(sample["url"], **kw)
  File "/usr/local/lib/python3.7/dist-packages/webdataset/gopen.py", line 192, in gopen
    return open(url, mode, buffering=bufsize)
FileNotFoundError: [Errno 2] No such file or directory: '/data/NeMo_ASR_SET/English/v2.0/train/audio_2568.tar'
`

How should I solve this error?

Thanks
unifying to extract mel spectrogram using nemo.collections.asr.module…
State: closed | Pull request: yes

# What does this PR do ?

unifying to extract mel spectrogram using `nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor`.

**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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Runtime audio segment sampling for SSL 
State: closed | Pull request: yes

# What does this PR do ?

Adds the ability to
1. Randomly sample audio segments of specified length from input during run-time
2. Fraction based mask count specification in MaskedPatchAugmentation
3. Enable flac file support

**Collection**: ASR

# Changelog 
- Updated `MaskedPatchAugmentation` in `audio_preprocessing.py`
- Added `RandomSegmentPertubation` in `perturn.py`
- Enabled `flac` file flag in `audio_to_text.py`


# 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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- [ ] Bugfix
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# Additional Information
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Check implicit grad acc in GLUE dataset building
State: closed | Pull request: yes

# What does this PR do ?

Fixes a bug where doing implicit grad acc on your validation data would not check on GLUE/XNLI dataset building. This would only happen in generic finetuning.

**Collection**: NLP

# Changelog 
- Add implicit gradacc checks in MegatronT5GLUEModel

# 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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Configure T5 finetuning metrics
State: closed | Pull request: yes

# What does this PR do ?

Adds the ability to

1. Specify the validation metric you want to use while fine-tuning GLUE/XNLI or custom datasets
2. Write model predictions on the validation and test sets to a file even with data parallel > 0.

**Collection**: NLP

# Changelog 
- Modify setup_metric in MegatronT5FinetuneModel to configure metrics based on what the user provides in their config
- Update inference step and inference epoch ends to log with this metric accoridngly.
- Update yaml files to reflect these changes.

# Usage
model.data.validation_ds.metric_name='spearman_corr_coeff'
model.data.validation_ds.write_predictions_to_file=True
model.data.validation_ds.output_file_path_prefix='/path/to/model_predictions'

# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
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Tutorial on ITN with Thutmose tagger and small fixes
State: closed | Pull request: yes

1. Add tutorial on ITN with Thutmose tagger. 2. Fix multiple spaces in the output

Signed-off-by: Alexandra Antonova <aleksandraa@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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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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Fix register artifacts
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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  - [ ] 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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Fix register artifacts
State: closed | Pull request: yes

Signed-off-by: Ramanathan Arunachalam <rarunachalam@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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**PR Type**:
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- [ x] Bugfix
- [ ] Documentation

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# Additional Information
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Add Module-level Adapters, Save-Restore and tests
State: closed | Pull request: yes

# What does this PR do ?

Adds significant functionality to adapters, allowing multiple module-specific adapters, save and restore of just the adapters themselves rather than the full model (saving several hundred megabytes-to-gigabytes), and a suite of tests for the new functionality.

**Collection**: [Core, ASR]

# Changelog 
- Adds support for both "global" and "module" specific adapters. Module specific adapters allow for targetted adapters to certain modules, independent of the others.
- Adds support for saving and restoring just the adapter modules, independent of the original model weights. This allows savings of significant disk storage, since just the adapters are usually a tiny fraction of the param count of the models.
- Adds a battery of tests for the new capabilities.
- Add support for dropout to adapter modules
- Add support for stochastic dropout to all adapters via ResidualAddStrategy.

# Usage

Subclass implementations can chose between global adapters, module adapters or both. A basic implementation of Adapters for a toy model is provided in the `tests/core/mixins/adapters/test_adapter_model_mixin.py`

```python
# Global adapters (adds "abc" adapter to all supported modules - encoder+decoder+others1)
model.add_adapter(name="abc", cfg=cfg) 

# Module adapters 
model.add_adapter(name="decoder:abc", cfg=cfg)

# Save all of the adapters (only the adapters themselves)
model.save_adapters("adapters.pt", name=None)
OR
model.save_adapters("adapters.pt", name={global or module adapter name})

# Restore one-or-all adapters (only the adapters themselves)
new_model.load_adapters("adapters.pt", name=None)
OR
new_model.load_adapters("adapters.pt", name={exact module name from state dict}, 
                        map_location='cpu', strict=True)
```

# 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
add augmentation to label models
State: closed | Pull request: yes

Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>

# What does this PR do ?

Add spec augmentation to label models 

**Collection**: ASR

# Changelog 
- edit label_models.py to include spec augmentation

**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
Dialogue tasks unit test
State: closed | Pull request: yes

# What does this PR do ?

Add unit test to dialogue tasks and some refactor to facilitate unit test

**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
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Cannot select speaker while inferencing on models trained on latest FastPitch_Finetuning.ipynb version
State: closed | Pull request: no
Labels: name: bug

Hi all.

I'm trying to generate audio with Inference_DurationPitchControl.ipynb. I've trained the FastPitch model with both LJSpeech and a custom dataset on the latest release (r1.8.2) with FastPitch_Finetuning (https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb). Also, I've models previously trained on a previous version of this script (r1.6.2).

The problem is using the new models, the voice is always the voice of the LJSpeech woman, regardless the speaker selection I choose. Nevertheless, there is still a subtle difference in the generated audio if I choose one speaker or another.

If I load one of the 'old' models, I can select the speaker without a problem.

Here I paste the code that I've used for training the new models on the latest FastPitch_Finetuning script (I've changed batch size to 16):

```
!(python fastpitch_finetune.py --config-name=fastpitch_align_v1.05.yaml \
train_dataset=./train.json \
validation_datasets=./val.json \
sup_data_path=./fastpitch_sup_data \
phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.01 \
heteronyms_path=tts_dataset_files/heteronyms-030921 \
whitelist_path=tts_dataset_files/lj_speech.tsv \
exp_manager.exp_dir=$OUTPUT_CHEKPOINTS \
+init_from_nemo_model=./tts_en_fastpitch_align.nemo \
+trainer.max_steps=$max_steps ~trainer.max_epochs \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=16 model.validation_ds.dataloader_params.batch_size=16 \
model.n_speakers=2 model.pitch_mean=121.9 model.pitch_std=20 \
model.pitch_fmin=65 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
+model.text_tokenizer.add_blank_at=true \
)
```

Here is the code of the old models training:

```
!(python fastpitch_finetune.py --config-name=fastpitch_align.yaml \
train_dataset=./train.json \
validation_datasets=./val.json \
+init_from_nemo_model=./tts_en_fastpitch_align.nemo \
+trainer.max_steps=$max_steps ~trainer.max_epochs trainer.check_val_every_n_epoch=25 \
prior_folder=./PriorsCustom model.train_ds.dataloader_params.batch_size=24 \
model.validation_ds.dataloader_params.batch_size=24 \
exp_manager.exp_dir=$OUTPUT_CHEKPOINTS \
model.n_speakers=2 model.pitch_avg=90 model.pitch_std=20 \
model.pitch_fmin=65 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
)
```

Thank you very much.
Thutmose tagger fixes and tutorial
State: closed | Pull request: yes

# What does this PR do ?

Add tutorial for Thutmose Tagger

**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)
Restored tests previously disabled for 22.03 base
State: closed | Pull request: yes

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

# What does this PR do ?

Added WFST graph to handle punctuation after verbalization step, parallel normalization, bug fixes

**Collection**: [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 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ X] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
[TTS] fix for #4012 and #4100
State: closed | Pull request: yes
Labels: name: stale

Signed-off-by: Iztok Lebar Bajec <ilb@fri.uni-lj.si>

# 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
- [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)
[TTS] fix for #4105 and #4100
State: closed | Pull request: yes
Labels: name: stale

Signed-off-by: Iztok Lebar Bajec <ilb@fri.uni-lj.si>

# 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
- [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)
scripts/export.py fails with `--device=cpu`
State: closed | Pull request: no
Labels: name: bug

name: TTS

name: stale

Regardless of the model being exported the script fails with
```python3
  File "scripts/export.py", line 160, in nemo_export
    raise e
  File "scripts/export.py", line 139, in nemo_export
    output_example = forward_method(model)(*input_list, **input_dict)
  File "/data/nemo_main/nemo/collections/tts/models/hifigan.py", line 425, in forward_for_export
    return self.generator(x=spec)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1111, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/nemo_main/nemo/collections/tts/modules/hifigan_modules.py", line 245, in forward
    x = self.conv_pre(x)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1118, in _call_impl
    result = hook(self, input)
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py", line 64, in __call__
    setattr(module, self.name, self.compute_weight(module))
  File "/opt/conda/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py", line 24, in compute_weight
    return _weight_norm(v, g, self.dim)
RuntimeError: Inference tensors do not track version counter.
```
Tested with `nemo:1.9.0rc0` based on `pytorch:22.04-py3`. Related to #4100
asr_based_vad_threshold isn't what it's supposed to be
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

In [asr_with_diarization config](https://github.com/NVIDIA/NeMo/blob/2aa68822bbfb8e7e8c221817b66de28928d6443b/examples/speaker_tasks/diarization/conf/offline_diarization_with_asr.yaml) the threshold is described as follows:
```yml
parameters:
      asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
      asr_based_vad_threshold: 50 # threshold (multiple of 10ms) for ignoring the gap between two words when generating VAD timestamps using ASR based VAD.
```

However in [ASR_DIAR_OFFLINE.get_speech_labels_from_decoded_prediction](https://github.com/NVIDIA/NeMo/blob/2aa68822bbfb8e7e8c221817b66de28928d6443b/nemo/collections/asr/parts/utils/diarization_utils.py#L185) it is used as a value in seconds:
```python
  if word_ts[count][0] - word_ts[count - 1][1] <= self.nonspeech_threshold:
```

So, the default config defines minimum distance between words as 50 seconds, which usually results in the vad manifest being just one line with start and end time of the audio file.

**Steps/Code to reproduce bug**

```python
from nemo.collections.asr.parts.utils.diarization_utils import ASR_DIAR_OFFLINE
import json

with open("test_manifest.json",'w') as f:
    f.write(json.dumps({'audio_filepath':"test.wav"}))
cfg_diarizer = {
    "manifest_filepath":"test_manifest.json",
    "asr":{"parameters":{"asr_based_vad_threshold":50,"fix_word_ts_with_VAD":False},"ctc_decoder_parameters":{},
          "realigning_lm_parameters":{}},    
    
    "out_dir":"."
}
asr_diar_offline = ASR_DIAR_OFFLINE(**cfg_diarizer)

fake_ts = {'test.wav': [[0.0, 20.], [40., 45.]]}


asr_diar_offline.save_VAD_labels_list(fake_ts)
!cat {asr_diar_offline.VAD_RTTM_MAP['test.wav']['rttm_filepath']}
```

**Expected behavior**
Two time ranges 0-20 and 40-45 are split in the vad rttm
Tacotron2 retrain
State: closed | Pull request: yes

Refactoring Tacotron2 to use new TTSDataset class

**Collection**: [Note which collection this PR will affect]

# Changelog 
 nemo.collections.asr.data.audio_to_text.AudioToCharDataset -> nemo.collections.tts.torch.data.TTSDataset

# 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.
Add path_prefix to concat to the begining of audio_file
State: closed | Pull request: yes

# What does this PR do ?

Add path_prefix to concat to the begining of audio_file.

**Collection**: ASR collection

# Changelog 
- Add path_prefix to concat to the begining of audio_file.

# Usage
* You can use path_prefix on the cloud platforms like AWS Sagemaker, where you don't know the full path of the data before the training process is started. 
* Ones training is started you get path of data from environment variables and provide to path_prefix script parameter

```python
# python3 examples/asr/asr_ctc/speech_to_text_ctc.py +model.train_ds.path_prefix=your/data/path +model.validation_ds.path_prefix=your/data/path
```

**PR Type**:
-  New Feature
Add CharParser for Cyrillic letters
State: closed | Pull request: yes

Signed-off-by: Карпов Николай Вячеславович <karpnv@yandex.ru>

# What does this PR do ?

Add RUCharParser to parse Cyrillic letters

**Collection**: ASR collection

# Changelog 
- Add RUCharParser to parse Cyrillic letters
- Add config with cyrillic letters examples/asr/conf/quartznet/quartznet_15x5_ru.yaml
- Use RUCharParser as "parser: ru" in train_ds, validation_ds, test_ds

# Usage
* With quartznet_15x5_ru.yaml you can train acoustic model using Golos dataset 

```python
# python3 examples/asr/asr_ctc/speech_to_text_ctc.py -cn quartznet/quartznet_15x5_ru.yaml
```
Runtime error when exporting FastPitch model to ONNX
State: closed | Pull request: no
Labels: name: bug

name: TTS

name: stale

I tried to export a FastPitch model I trained to ONNX with [export.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/export.py).
The command I used was:
`python scripts/export.py "/home/xxx/TTSs/Nemo-models/nancy_fastpitch-44k-new-v3.nemo" "nancy_fastpitch-44k-new-v3.onnx" --runtime-check --device="cpu" --autocast`
But it produced an error:
```
Traceback (most recent call last):
  File "scripts/export.py", line 169, in <module>
    nemo_export(sys.argv[1:])
  File "scripts/export.py", line 158, in nemo_export
    raise e
  File "scripts/export.py", line 139, in nemo_export
    output_example = forward_method(model)(*input_list, **input_dict)
  File "/mnt/d/TTSs/NeMo/nemo/collections/tts/models/fastpitch.py", line 576, in forward_for_export
    return self.fastpitch.infer(text=text, pitch=pitch, pace=pace, speaker=speaker)
  File "/mnt/d/TTSs/NeMo/nemo/collections/tts/modules/fastpitch.py", line 288, in infer
    enc_out, enc_mask = self.encoder(input=text, conditioning=spk_emb)
  File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/d/TTSs/NeMo/nemo/collections/tts/modules/transformer.py", line 262, in forward
    return self._forward(self.word_emb(input), (input != self.padding_idx).unsqueeze(2), conditioning)  # (B, L, 1)
  File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/modules/sparse.py", line 158, in forward
    return F.embedding(
  File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/functional.py", line 2044, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Inference tensors do not track version counter.
```
when it was `Getting output example`.

What am I missing? Thank you!