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Update TTS tutorial to be consistent with recent code changes.
**Collection**: [TTS]
# Changelog
- Update lower_case and batch_size arguments examples for preprocess_text.py
- Add dataset name to path of logged audio.
# 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)
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} | # What does this PR do ?
Passes layernorm_zero_centered_gamma param properly which fixes layernorm1p issue.
**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)
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} | # What does this PR do ?
- Fix the CustomProgressBar for cases when we are resuming training from a checkpoint. Currently when we are resuming from a checkpoint total value in `iter_num/total` of the progress bar is the number of remaining training steps instead of total training steps which leads to the progress bar displaying` iter_num/?` when iter_num > remaining training steps. This PR fixes the issue by reseting the total with `max_steps`.
- Also, in case of multiple epochs the PR updates the current step number (n) to be the current step number within an epoch (default was current step across an epoch and this can lead to an errored progress bar).
**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.
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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
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SFT model parallel fix for dist ckpt
**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.
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* Related to # (issue)
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Add Blog post for Numba FP16 support in NeMo
**Collection**: [ASR]
# Changelog
- Add blog post
# 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)
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} | # What does this PR do ?
This fixes the learning rate schedule in Megatron models when relying on `max_epochs` instead of `max_steps`.
The issue is that [compute_max_steps](https://github.com/NVIDIA/NeMo/blob/bdea326811ef81d3aff3530b26b67f7a7ce5119c/nemo/core/optim/lr_scheduler.py#L934) is incorrect for Megatron models since `accumulate_grad_batches` is equal to 1 from PTL's perspective, even if we are actually using multiple micro-batches.
**Collection**: nlp
# Changelog
- Fix learning rate schedule in Megatron models when `max_steps` is not set
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
## 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
I considered two potential implementations:
1. (this one) setting `max_steps` directly in `MegatronBaseModel`
2. Setting `t_accumulate_grad_batches` correctly in the scheduler config so that the formula is correct
I didn't go with (2) because it seemed trickier to ensure that all parameters are correct so that the formula works, including with drop_last which [still raises an old warning](https://github.com/NVIDIA/NeMo/blob/a420f9083ea9993152a5221b8875c34470561f13/nemo/core/optim/lr_scheduler.py#L943). It's easier to just compute `max_steps` within `MegatronBaseModel`.
cc @aklife97 | {
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} | # What does this PR do ?
Allow nemo docker to be built on ARM platform
**Collection**: [Note which collection this PR will affect]
# Changelog
- Modular requirements installation
- Triton installation from sdk docker whl file
# Usage
* You can potentially add a usage example below
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
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} | # What does this PR do ?
Adds back a `squeeze(1)` to the Aligner `get_dist()` function.
This should fix a few cells erroring out in the Aligner inference notebook.
See #7490 for more info.
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} | null | null | null | **Describe the bug**
I'm trying to accomplish a seemingly trivial task of saving all checkpoints during training - many hours later after trying many different configurations I still can't do and I see multiple problems
**Steps/Code to reproduce bug**
Here is the relevant part of the config:
```
trainer:
enable_checkpointing: False # checkpointing provided by exp_manager
limit_val_batches: 0
limit_test_batches: 0
exp_manager:
checkpoint_callback_params:
every_n_train_steps: 5
every_n_epochs: null
monitor: null # want all checkpoints,
save_top_k: -1 # save all checkpoints
save_last: True
```
The main doc in PTL is https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#modelcheckpoint
There are multiple problems here, but the key is to get rid of the message as I'm not running eval - only training and be able to save all checkpoints. The message that I get is:
```
Epoch 0, global step 10: '' was not in top -1
```
The PL doc says:
> monitor (Optional[str]) – quantity to monitor. By default it is None which saves a checkpoint only for the last epoch.
if I set it to null it breaks:
```
nemo/nemo/utils/exp_manager.py", line 889, in configure_checkpointing
if "val" in params.monitor:
TypeError: argument of type 'NoneType' is not iterable
```
and looking at PL's source code:
```
def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
if self.save_top_k == 0:
return
# validate metric
if self.monitor is not None:
if self.monitor not in monitor_candidates:
```
it shouldn't go into validation metric if it's set to None, but NeMo doesn't allow me to do that.
Then the PL doc says:
> save_top_k
> (int) – if save_top_k == k, the best k models according to the quantity monitored will be saved. if save_top_k == 0, no models are saved. if save_top_k == -1, all models are saved. Please note that the monitors are checked every every_n_epochs epochs. if save_top_k >= 2 and the callback is called multiple times inside an epoch, the name of the saved file will be appended with a version count starting with v1.
but I still get only the last checkpoint saved,
and this message:
```
Epoch 0, global step 10: 'blah' was not in top -1
```
looks wrong because it shouldn't even go there, as it's set to -1 == meaning do not check.
**Expected behavior**
I need to be able:
1. save all checkpoints
2. while not running eval
Thank you! | {
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} | # What does this PR do ?
As discussed in https://github.com/NVIDIA/NeMo/issues/7495 @titu1994 helped me to make Dllogger to log on rank0 as otherwise the metrics log happens 512 times per step on 512 gpus.
Fixes: https://github.com/NVIDIA/NeMo/issues/7495
**Collection**: [Note which collection this PR will affect]
# Changelog
- make Dllogger to log on rank0 as otherwise the metrics log happens 512 times per step on 512 gpus.
# 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 https://github.com/NVIDIA/NeMo/issues/7495
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} | # What does this PR do ?
the script called default `conf/vits.yaml` config: https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/vits.yaml#L192, which applied `strategy=ddp` by default. When `devices=1` in this tutorial, we should specify `auto` to override `ddp`.
**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
- [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
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} | # What does this PR do ?
SFT model parallel fix for dist ckpt
**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)
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} | # What does this PR do ?
mark the dp inserted example and remove it for 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**:
- [ ] 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.
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* Related to # (issue)
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the script called default `conf/vits.yaml` config: https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/vits.yaml#L192, which applied `strategy=ddp` by default. When `devices=1` in this tutorial, we should specify `auto` to override `ddp`.
**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
- [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)
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} | # What does this PR do ?
evaluates peft outputs.
**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)
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the `examples/asr/speech_pretraining/speech_pre_training.py` is not working properly on the lates main branch, but working in 1.20. At first glance, the error could be related to the upgrade of Pytorch lightning 2.0.
**Error Brief**
```bash
lightning_fabric.utilities.exceptions.MisconfigurationException: `ModelCheckpoint(monitor='val_loss')` could not find the monitored key in the returned metrics: ['learning_rate', 'global_step', 'train_contrastive', 'train_backward_timing in s', 'train_step_timing in s', 'epoch', 'step']. HINT: Did you call `log('val_loss', value)` in the `LightningModule`?
```
**Error Details**
[error_logs.zip](https://github.com/NVIDIA/NeMo/files/12719689/error_logs.zip)
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Adds back a `squeeze(1)` to the Aligner `get_dist()` function.
This should fix a few cells erroring out in the Aligner inference notebook.
See #7490 for more info.
**Collection**:TTS
**PR Type**:
- [ ] New Feature
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} | # What does this PR do ?
This PR added fixes for PTL2.0 related ASR bugs in r1.21.0: Val metrics logging, None dataloader issue
**Collection**:
ASR, Core
# Changelog
- val_dataloader() and test_dataloader() functions in nemo/core/classes/modelPT.py updated to handle cases where dataloader is None
- correct logging of validation metrics in nemo/collections/asr/models/rnnt_models.py and nemo/collections/asr/models/hybrid_rnnt_ctc_models.py
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to [Bug4296343](https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=4296343&cmtNo=), [Bug4296057](https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=4296057&cmtNo=), [Bug4246804](https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=4246804&cmtNo=)
* Fixes issues raised in #7450, #7507
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} | # What does this PR do ?
Fixes a bug in checking the number of ranks before sleeping.
**Collection**: [Core]
# 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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- [ ] 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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} | # What does this PR do ?
Fixes a bug in checking the number of ranks before sleeping.
**Collection**: [Core]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # 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)
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**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)
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} | # What does this PR do ?
The function load_from_checkpoint_dir() in `examples/nlp/language_modeling/megatron_gpt_continue_training.py` has four arguments:
```python
def load_from_checkpoint_dir(cls, cfg, trainer, modify_confg_fn):
```
But the calling side is:
```python
model = load_from_checkpoint_dir(MegatronGPTModel, cfg, trainer, gpt_cfg, modify_confg_fn=_modify_config)
```
The argument `gpt_cfg` is extraneous.
# Changelog
- Remove extraneous argument of load_from_checkpoint_dir()
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@MaximumEntropy @ericharper @ekmb @yzhang123 @VahidooX @vladgets @titu1994 @XuesongYang
# Additional Information
* Related to #7480
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Fixes https://github.com/NVIDIA/NeMo/issues/7460. Exp manager now has a sleep argument to sleep the non rank 0 ranks.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
Fixes https://github.com/NVIDIA/NeMo/issues/7460. Exp manager now has a sleep argument to sleep the non rank 0 ranks.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
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Add a one line overview of what this PR aims to accomplish.
Fixes https://github.com/NVIDIA/NeMo/issues/7460. Exp manager now initializes distributed groups to force a barrier.
# Changelog
- Add specific line by line info of high level changes in this PR.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to # (issue)
#7460
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} | # What does this PR do ?
Fix a typo.
**Collection**: NLP
# Changelog
Fix a typo in the comment part in `nemo/collections/nlp/data/language_modeling/megatron/helpers.cpp`.
# Usage
Do not affect any usage
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I can't figure out how to make the logging not repeat on 512 ranks. Is there a setting to tell nemo to log only on rank 0? at the very least for the iteration logs?
I currently have:
```
exp_manager.create_dllogger_logger: True
```
but I'd happy with any other way.
What I ideally would like is the original Megatron-LM-like style logging where it logs iteration metrics from rank 0.
Thank you!
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] 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)
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} | # What does this PR do ?
fixed default value for `trainer.accelerator` after upgading PTL 2.0.
**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
- [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)
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} | # What does this PR do ?
fixed default value for `trainer.accelerator` after upgading PTL 2.0.
**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
- [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)
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} | # What does this PR do ?
ASR SFT related code changes.
**Collection**: [Note which collection this PR will affect]
NLM
# Changelog
At some places the flow needs to go into an if-else statement based on the task at hand - ASR or TTS.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
The TTS Aligner's `get_dist()` now has an extra dimension (from PR #6806); this PR fixes errors stemming from this in the Aligner notebook.
@rlangman -- was the change from returning a (B, C, T)-dimensional tensor to a (B, 1, C, T)-dimensional one intentional? Do we want to keep that or change it back by adding a squeeze(1)?
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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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?
- [ ] 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)
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Add italian tokenization
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} | # What does this PR do ?
Removes nemo_text_processing as a requirement on ARM.
Some scripts will fail on ARM due to missing nemo_text_processing
**Collection**: TTS
# Changelog
- removes nemo_text_processing as a requirement on arm machines
# Usage
N/A
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
Hello, although #7192 fixed most of our errors, we are still running into some specific cases where the model still fails with the following stack trace.
I also noticed that this error only happens when the batch size is higher than 1, while the model transcribes fine with batch_size 1, although of course being slower with a lot of audio files.
The weird part is that this only seems to happen with a combination of certain audio files, as in transcribing 1 of the 2 files 20 times doesnt give an error.
**Steps/Code to reproduce bug**
Luckily I was able to reproduce this error in the English model.
I used a new docker ( nvcr.io/nvidia/pytorch:23.04-py3 ).
apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
The 2 audio files can be downloaded here: https://easyupload.io/m/980zua
Next I ran the following script:
import nemo
import nemo.collections.asr as nemo_asr
import copy
from omegaconf import DictConfig, OmegaConf, open_dict
from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceConfig
conformer = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="stt_en_conformer_transducer_large")
decoding_config = copy.deepcopy(conformer.cfg.decoding)
confidence_cfg = ConfidenceConfig(preserve_word_confidence=True, preserve_token_confidence=False)
with open_dict(decoding_config):
decoding_config.preserve_alignments = True,
decoding_config.confidence_cfg = confidence_cfg
conformer.change_decoding_strategy(decoding_config)
audio_segment_name_list = [ '/workspace/FailingCuts/12.wav', '/workspace/FailingCuts/13.wav']
hypotheses = conformer.transcribe(paths2audio_files=audio_segment_name_list, return_hypotheses=True, batch_size=2)
**Expected behavior**
Transcription succeeding without errors when using a batch size higher than 1
**Additional context**
Example: GPU model
Ran on a 4090 GPU.
@GNroy, would you be able to look at this again?
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} | # What does this PR do ?
Cache FP8 weight and transpose only at the first micro-batch in each validation and test routine, which is the first micro-batch using the updated weights.
The following micro-batches within a validation or test routine, use pre-cached FP8 weight and transpose.
# 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)
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Fixes a broken link | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
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# Changelog
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Fix error for gpt sft chat dataset.
**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
- [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.
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* Related to # (issue)
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} | # What does this PR do ?
As described here https://github.com/bastibe/python-soundfile/issues/274 when reading flac files there is bug in libsnd in which it sometimes warps the audio, and as a result sometimes causes an 'Internal psf_fseek() failed.' error when reading.
The only known solution is to read audio as int32 instead of float32. AudioSegment class already has the logic to convert the int32 to float32 [-1, 1] representation during initialization, we just need a flag in segment_from_file() to utilize it.
**Collection**: [TTS]
# Changelog
- Add flag to segment_from_file() to specify dtype to read audio as (the AudioSegment will automatically convert it to float32 afterwards)
- Read audio in VocoderDataset as int32.
- Remove retrial logic which should no longer be necessary.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Disable manual GC during the validation routine. This is valid only when `gc_interval` is set larger than 0.
To disable, set `NEMO_MANUAL_GC_IN_VALIDATION=0`
# 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"
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## Who can review?
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# Additional Information
* Related to # (issue)
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In my environment, the pinned version of setuptools is unavailable (`setuptools==65.5.1`). This is a request to unpin or update to a more recent version (latest is `setuptools==68.2.2`).
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} | null | null | null | **Hydra conf path not found**
I have been trying to run fastpitch_finetune.ipynb and from inside the nemo docker container. While the code ran bug free till inference of fastpitch model, when I am trying to run the steps given to run hifigan_finetune.py it throws an error related to config path
Primary config directory not found.
Check that the config directory '/TTS/conf/hifigan' exists and readable
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Or
path not present in hydra_search_config.
I have been running it on a vm instance with a t4 gpu.
Please reach out if you know why this is happening and how to resolve this.
I have checked hydra directory for this error and tried a few solutions from there the problem is persistent. | {
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} | null | null | null | **Describe the bug**
Hi
I found that using eval_beamsearch_ngram.py to evaluate the language model (LM) could reduce the CER by how much, but I actually found that adding the LM made it worse.
I used pretrain am model=" Conformer-CTC-L-unified_char_ja-JP_6.0.nemo"
and pretrain LM model ="riva_zh_asr_set_2.1_4gram.bin"
my testing data is used commo
I use decoding_mode=greedy and get
`[NeMo I 2023-09-21 15:44:32 eval_beamsearch_ngram:372] Greedy WER/CER = 43.38%/10.48%
`
but when i use
decoding_mode=beamsearch_ngram
decoding_strategy="flashlight"
`[NeMo I 2023-09-21 15:46:57 eval_beamsearch_ngram:237] WER/CER with beam search decoding and N-gram model = 99.80%/24.11%
[NeMo I 2023-09-21 15:46:57 eval_beamsearch_ngram:248] Oracle WER/CER in candidates with perfect LM= 43.36%/10.72%
[NeMo I 2023-09-21 15:46:57 eval_beamsearch_ngram:253] =================================================================================
[NeMo I 2023-09-21 15:46:57 eval_beamsearch_ngram:439] Best WER Candidate = 99.80% :: Beam size = 64, Beam alpha = 1.0, Beam beta = 0.0
[NeMo I 2023-09-21 15:46:57 eval_beamsearch_ngram:444] Best CER Candidate = 24.11% :: Beam size = 64, Beam alpha = 1.0, Beam beta = 0.0
[NeMo I 2023-09-21 15:46:57 eval_beamsearch_ngram:448] =================================================================================`
CER got worse.
**Describe the solution you'd like**
Here is the code I am using
and I found that after each sentence in preds_output_folder, an unrelated English word is appended.
外交与全球性议题E -40.78262926785392
外交与全球性议题L -41.35890473572654
外交与全球性议题I -41.67608906475944
外交与全球性议题A -41.78252627102775
外交与全球性议题D -41.92528642861289
外交与全球性议题J -42.08723165242118
外交与全球性议题K -42.095088259858315
**Steps/Code to reproduce bug**
```
python eval_beamsearch_ngram.py nemo_model_file=/model/zh-TW/stt_zh_citrinet_1024_gamma_0_25.nemo \
input_manifest=/manifests/zh-TW/commonvoice_test_manifest_processed.json \
kenlm_model_file=/model/zh-TW/lm/riva_zh_asr_set_2.1_4gram.bin \
preds_output_folder=./ \
probs_cache_file=null \
beam_width=[20] \
beam_alpha=[1.0] \
beam_beta=[1] \
decoding_mode=beamsearch_ngram \
decoding_strategy="flashlight"
```
**Expected behavior**
How to reduce CER while increasing the LM?
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} | # What does this PR do ?
Cache FP8 weight and transpose only at the first micro-batch in each validation and test routine, which is the first micro-batch using the updated weights.
The following micro-batches within a validation or test routine, use pre-cached FP8 weight and transpose.
# 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)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7469 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7469/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7469/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7469/events | https://github.com/NVIDIA/NeMo/pull/7469 | 1,905,639,088 | PR_kwDOC_bI7s5a0ZEN | 7,469 | Distributed optimizer support for experimental FP8 tensors | {
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} | # What does this PR do ?
This PR integrates with experimental `Float8Tensor`s from the [Transformer Engine float8tensor_experiments branch](https://github.com/sudhakarsingh27/transformerengine/tree/float8tensor_experiments). This allows the model to only store FP8 weight matrices. The distributed optimizer will store an FP32 master copy of the weights and will perform param all-gathers in FP8.
**Collection**: NLP
# Changelog
- Add logic to initialize GPT with FP8 weight matrices
- Add distributed optimizer support for FP8 weight matrices, including FP8 param all-gathers
# Usage
Enable FP8 support:
https://github.com/NVIDIA/NeMo/blob/19a3b7015fe353199af97903df1814e3a470b503/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L169
Use Megatron-core model:
https://github.com/NVIDIA/NeMo/blob/19a3b7015fe353199af97903df1814e3a470b503/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L49
Set the optimizer to `distributed_fused_adam`:
https://github.com/NVIDIA/NeMo/blob/f8be40b75ee1f8437b56fcc9602dc2aaddfb0643/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L228
# 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?
- [x] 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.
Pinging @sudhakarsingh27.
# Additional Information
* Depends on https://github.com/sudhakarsingh27/transformerengine/tree/float8tensor_experiments, but there are import guards to handle the case that it isn't available.
* Depends on https://github.com/NVIDIA/apex/pull/1723
* Depends on https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/tree/run_fp8_poc_with_nemo/
* Behavior is unchanged for non-FP8 models.
* If any FP8 params are detected, overlapping is disabled for non-FP8 grad reductions and param all-gathers. | {
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} | # What does this PR do ?
Make audio codec losses more configurable, and add STFT and SI-SDR loss definitions.
**Collection**: [TTS]
# Changelog
- Rename encodec.yaml to encodec_24000.yaml to include sampling rate, and modify parameters to better match the original paper.
- Add audio_codec_24000.yaml where we can start creating config for optimized NeMo audio codec recipe.
- Separate out aggregate mel loss into separate L1 and L2 losses.
- Add parameter to control mel dimension for different resolutions.
- Implement multi-resolution log STFT loss.
- Implement SI-SDR loss, being a slightly modified version of the torchmetrics implementation but with masking support.
- Make all losses optional during train_step, only computing and optimizing on them if their loss_scale is non-zero. Except L1 mel loss which I expect will always be used, so I can use it as the logged "t_loss" value.
- Compute all losses during validation step for monitoring and validation.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
# Additional Information
The new configuration places no weight on time domain loss, scales the mel dimension proportional to the resolution (like DAC), and adds STFT loss (like univnet) to further improve accuracy.
These are what the validation losses look like when training with different losses: mel fixed dimension, mel scaled dimension, STFT, and (mel scaled & STFT).
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} | # What does this PR do ?
Disable manual GC during the validation routine. This is valid only when `gc_interval` is set larger than 0.
To disable, set `NEMO_MANUAL_GC_IN_VALIDATION=0`
# 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)
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} | # What does this PR do ?
1. Make Tacotron2 able to be exported to ONNX format files.
2. Create an example of how to use these ONNX files to do inference
The code is mainly learned from [NVIDIA/DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/exports/export_tacotron2_onnx.py)
# Changelog
- Add support for Tacotron2 model to export to onnx format
# Usage
For testing, we could install necessary packages, check out this PR and run the example script:
```bash
pip install onnxruntime
cd NeMo
PYTHONPATH=. python3 examples/tts/export/infer_tacotron2_onnx.py
```
# 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?
@blisc @XuesongYang
# Additional Information
* Related to #6999
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1. Make Tacotron2 able to be exported to ONNX format files.
2. Create an example of how to use these ONNX files to do inference
The code is mainly learned from [NVIDIA/DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/exports/export_tacotron2_onnx.py)
# Changelog
- Add support for Tacotron2 model to export to onnx format
# Usage
For testing, we could install necessary packages, check out this PR and run the example script:
```bash
pip install onnx onnxruntime
cd NeMo
PYTHONPATH=. python3 examples/tts/export/infer_tacotron2_onnx.py
```
# 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?
# Additional Information
* Related to #6999
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} | # What does this PR do ?
Replace assert with warning if we don't have enough token to truncate.
**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
- [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)
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Modify callbacks to save artifacts into dataset specific folders, instead of saving artifacts from all datasets into 1 folder. This helps organization when training on many datasets, and also handles the case when multiple datasets have files with the same name.
**Collection**: [TTS]
# Changelog
- Pass dataset names through TTS data loaders
- Save artifacts under epoch_{x}/{dataset_name}/
- Add initial logging for FastPitch and HiFi-GAN recipes
# 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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} | # What does this PR do ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
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**Pre checks**:
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https://api.github.com/repos/NVIDIA/NeMo/issues/7460 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7460/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7460/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7460/events | https://github.com/NVIDIA/NeMo/issues/7460 | 1,903,700,310 | I_kwDOC_bI7s5xeClW | 7,460 | The moving of files in `exp_manager` may cause crashes in other processes | {
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} | null | null | null | **Describe the bug**
`exp_manager()` calls `check_resume()` which moves current files to a `run_x` folder ([link to code](https://github.com/NVIDIA/NeMo/blob/cfb1f449831a9a0309aab71187e3192dbd26d1a7/nemo/utils/exp_manager.py#L630))
However, this may cause issues due to these files being accessed by other processes, here's an example of a stack trace:
```
File "my_script.py", line 94, in main
trainer.fit(model)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 565, in _fit_impl
ckpt_path = self._checkpoint_connector._select_ckpt_path(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 108, in _select_ckpt_path
ckpt_path = self._parse_ckpt_path(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 121, in _parse_ckpt_path
if ckpt_path is None and SLURMEnvironment.detect() and self._hpc_resume_path is not None:
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 60, in _hpc_resume_path
max_version = self.__max_ckpt_version_in_folder(dir_path_hpc, "hpc_ckpt_")
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 537, in __max_ckpt_version_in_folder
files = [os.path.basename(f["name"]) for f in fs.listdir(uri)]
File "/usr/local/lib/python3.10/dist-packages/fsspec/spec.py", line 1448, in listdir
return self.ls(path, detail=detail, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/fsspec/implementations/local.py", line 64, in ls
return [self.info(f) for f in it]
File "/usr/local/lib/python3.10/dist-packages/fsspec/implementations/local.py", line 64, in <listcomp>
return [self.info(f) for f in it]
File "/usr/local/lib/python3.10/dist-packages/fsspec/implementations/local.py", line 75, in info
out = path.stat(follow_symlinks=False)
FileNotFoundError: [Errno 2] No such file or directory: '/path/to/log_dir/nemo_log_globalrank-81_localrank-1.txt'
```
Note that in this example `trainer.fit(model)` is called *after* `exp_manager()`, but since execution is asynchronous across processes, one process may run it while rank 0 is still moving files around.
**Steps/Code to reproduce bug**
This is a bit tricky since it's somewhat random and also depends on how fast your filesystem is.
**Environment overview (please complete the following information)**
- Environment location: Docker (recent image)
**Additional context**
To fix this, my first suggestion would be to add some kind of barrier around the code that moves files. It's a bit tricky though since at this point `torch.distributed` is not initialized, and I'm not even sure if we can tell how many processes are running. I haven't given it much thought yet but please let me know if you have a better idea!
Edit: actually, at least on SLURM we can tell how many processes there are from `trainer.num_nodes * trainer.numdevices`. We can't always assume this is the case because if for instance we run it locally, then PTL will spawn additional processes *after* `exp_manager()` is called (but in that case there should be no risk of such conflict between processes). I am thus planning to submit a SLURM-only fix, with a filesystem-based synchronization between processes. If anyone has a better idea please chime in! | {
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} | * new frame_confidence and alignments lists are now always created after the while loop
Signed-off-by: Aleksandr Laptev <[email protected]>
* tests added
Signed-off-by: Aleksandr Laptev <[email protected]>
---------
Signed-off-by: Aleksandr Laptev <[email protected]>
(cherry picked from commit 6012ca2f4034cadb2f3186899ffc9ef8490472cb)
# 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)
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} | null | null | null | **Describe the bug**
When running an experiment where the model is using tensor parallelism, the `exp_manager.resume_if_exists` option fails to load the last checkpoint when resuming the experiment.
**Steps/Code to reproduce bug**
* Launch an experiment with a TP>1 model and stop it after saving the first checkpoint
* Try to resume the experiment, and notice the warning:
```
`[NeMo W 2023-09-19 01:42:57 exp_manager:591] There were no checkpoints found in checkpoint_dir or no
checkpoint folder at checkpoint_dir: /path/to/checkpoints. Training from scratch.`
```
**Expected behavior**
It should resume from the last checkpoint saved.
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: from Docker image (was taken from a recent nightly build)
**Additional context**
At first glance I suspect the issue came from https://github.com/NVIDIA/NeMo/pull/7116/files due to changing `rglob` into `glob` (cc @ericharper) ==> since the .ckpt files are under the `mp_rank_xy` subfolders, they are not found by `glob`.
(if the fix is just to change it back to `rglob` I'm happy to submit a PR, but I assume there's a reason why it was changed) | {
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} | # What does this PR do ?
Fix a typo.
**Collection**: NLP
# Changelog
Fix a typo in the comment part in `nemo/collections/nlp/data/language_modeling/megatron/helpers.cpp`.
# Usage
Do not affect any usage
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
With PTL2.0, returning None from the *_dataloader methods is no longer supported (reference - https://github.com/Lightning-AI/lightning/issues/17154). This change causes an issue with ASR decoding when we don't provide a valid trainer.validation_ds.manifest_filepath or trainer.test_ds.manifest_filepath. For example running the following code:
`python NeMo/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py --config-path=../conf/citrinet/ --config-name=citrinet_1024.yaml model.tokenizer.dir=data/tokenizer/tokenizer_bpe_v1024/ model.tokenizer.type="bpe" model.train_ds.manifest_filepath=data/LibriSpeech/train_clean_100.json model.validation_ds.manifest_filepath=data/LibriSpeech/dev_clean.json model.train_ds.batch_size=16 +accelerator="gpu" +strategy="ddp" +devices=1 trainer.max_epochs=1 exp_manager.checkpoint_callback_params.monitor="train_loss"`
throws the error -
`TypeError: An invalid dataloader was returned from EncDecCTCModelBPE.val_dataloader(). Found None.`
The following PR fixes this issue by making the default return value of val_dataloader and test_dataloader as `[]`
**Collection**: All
# Changelog
```
def val_dataloader(self):
if self._validation_dl is not None:
return self._validation_dl
else: # None dataloader no longer supported in PTL2.0
return []
def test_dataloader(self):
if self._test_dl is not None:
return self._test_dl
else: # None dataloader no longer supported in PTL2.0
return []
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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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.
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* Related to # (issue)
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} | # What does this PR do ?
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# 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?
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- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | When I change the ddp to auto, I can train, but I get a new error with the following error. What do you guys suggest please?
error executing job with overrides: []
Traceback (most recent call last):
File "/home/zhengbeida/code/NeMo-main/NeMo-main/NeMo-main/examples/slu/speech_intent_slot/run_speech_intent_slot_train.py", line 119, in main
trainer.fit(model)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 520, in fit
call._call_and_handle_interrupt(
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 559, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 935, in _run
results = self._run_stage()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 978, in _run_stage
self.fit_loop.run()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 201, in run
self.advance()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 354, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 134, in run
self.on_advance_end()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 248, in on_advance_end
self.val_loop.run()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 174, in _decorator
return loop_run(self, *args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 122, in run
return self.on_run_end()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 258, in on_run_end
self._on_evaluation_end()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 303, in _on_evaluation_end
call._call_callback_hooks(trainer, hook_name, *args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 190, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 311, in on_validation_end
self._save_topk_checkpoint(trainer, monitor_candidates)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 358, in _save_topk_checkpoint
raise MisconfigurationException(m)
lightning_fabric.utilities.exceptions.MisconfigurationException: `ModelCheckpoint(monitor='val_wer')` could not find the monitored key in the returned metrics: ['train_loss', 'learning_rate_g0', 'learning_rate_g1', 'train_backward_timing', 'train_step_timing', 'training_batch_wer', 'epoch', 'step']. HINT: Did you call `log('val_wer', value)` in the `LightningModule`?
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. | {
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} | null | null | null | **Describe the bug**
Hello and, first of all, thank you for your work! I have the following problem:
While training a Fastconformer Hybrid RNNT-CTC model, I perform multi-validation on several datasets at the end of each epoch. The reported results on these datasets at validation, greatly differ from the results obtained when using **speech_to_text_eval.py** (or inherently the **transcribe_speech.py** script). They differ by a margin of +-2% WER on almost all datasets, some having smaller differences than others. (for default RNNT decoding) When I trained the normal fastconformer_transducer model, I haven't experienced such big gaps in WER between training and offline inference.
I have tried providing the model as it is to the _model_path_ argument or firstly changed the decoder to rnnt and then load it, but I obtained same results in both cases. I am also aware of the _dither_ argument in the _preprocessor_ from other issues, and tried changing it to **0**, but didn't obtain any change in the results.
I have trained the model using bfloat16, but at inference i can't use bfloat16 as I am using a **CPU only** machine, might this be a reason for such a great difference? Should I use a different script for Hybrid models?
Differences for dataset 1: (10.93% train vs 11.68% inf)
Differences for dataset 2: (12.84% train vs 14.08% inf)
Differences for dataset 3: (3.149% train vs 2.94% inf)
**Steps/Code to reproduce bug**
Config file used for training:
https://drive.google.com/file/d/1S4eLgDMSsRoLYYxqey-kPOTOHjZfwCOA/view?usp=drive_link
Script used:
```
python3 speech_to_text_eval.py model_path=<path_to_hybrid_model> dataset_manifest=<path_to_manifest_file> output_filename=<path_to_out_json>
```
The **transcribe_speech.py** and **speech_to_text_eval.py** are unchanged. The **config for the fastconformer hybrid** model is almost the same as the normal one.
Unfortunately, I can't provide the datasets I trained/evaluated on, but I also tried to reproduce the experiments on Common Voice Spanish 7.0 dataset (cv-corpus-7.0-2021-07-21), but with a much smaller difference between the results..
Results on dev-set: (6.96% train vs 6.86% inf)
**Expected behavior**
Same or similar results ( difference of 0.1-0.2% WER ) between validation and offline inference (when using transcribe_speech.py) for consistent training.
**Environment details**
- pytorch-lightning 1.9.4
- torch 2.1.0a0+fe05266
- torch-tensorrt 1.4.0.dev0
- torchaudio 2.1.0
- torchmetrics 0.11.4
- torchvision 0.15.0a0
- python 3.8.10
**Environment overview (please complete the following information)**
- Tested offline inference using the main branch code of NeMo repo at 18.09.2023 .
**Additional context**
Trained on A100 gpu with bf16 precision, inference on CPU only. I am also using online noise and speed augmentations while training. I initialiazed the Hybrid model from a transducer pretrained model, but I obtained the same behavior (big differences in WER) while also initializing from a hybrid model.
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When I try to reproduce it, it comes up with the following error.Do you guys have any good suggestions, please?
Error executing job with overrides: []
self._run(model, ckpt_path=ckpt_path)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 935, in _run
results = self._run_stage()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 978, in _run_stage
self.fit_loop.run()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 201, in run
self.advance()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 354, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 133, in run
self.advance(data_fetcher)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 218, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 185, in run
self._optimizer_step(kwargs.get("batch_idx", 0), closure)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 261, in _optimizer_step
call._call_lightning_module_hook(
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 142, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1266, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 158, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 257, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 224, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 114, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/optimizer.py", line 33, in _use_grad
ret = func(self, *args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/adamw.py", line 148, in step
loss = closure()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 101, in _wrap_closure
closure_result = closure()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 140, in __call__
self._result = self.closure(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 126, in closure
step_output = self._step_fn()
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 308, in _training_step
training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 288, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 329, in training_step
return self.model(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1139, in forward
if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, either by setting the string value `strategy='ddp_find_unused_parameters_true'` or by setting the flag in the strategy with `strategy=DDPStrategy(find_unused_parameters=True)`.
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
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But according to https://web.archive.org/web/20170119114252/http://www.itl.nist.gov/iad/mig/tests/rt/2009/docs/rt09-meeting-eval-plan-v2.pdf it should be:
```
<SOURCE><SP><CHANNEL><SP> <BEG-TIME><SP><DURATION><SP><TOKEN><SP>
<CONF><SP><TYPE><SP><SPEAKER><NEWLINE>
``` | {
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} | # What does this PR do?
When running the below snippet from the [document](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_tacotron2) on a machine with GPU
```python
# Load Tacotron2
from nemo.collections.tts.models import Tacotron2Model
spec_generator = Tacotron2Model.from_pretrained("tts_en_tacotron2")
# Load vocoder
from nemo.collections.tts.models import HifiGanModel
vocoder = HifiGanModel.from_pretrained(model_name="tts_en_hifigan")
# Generate audio
import soundfile as sf
import torch
with torch.no_grad():
parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
# Save the audio to disk in a file called speech.wav
if isinstance(audio, torch.Tensor):
audio = audio.to('cpu').numpy()
sf.write("speech.wav", audio.T, 22050, format="WAV")
```
It works well.
But if change it to using CPU:
```python
spec_generator = Tacotron2Model.from_pretrained("tts_en_tacotron2").to("cpu")
...
vocoder = HifiGanModel.from_pretrained(model_name="tts_en_hifigan").to("cpu")
```
The snippet will report the error:
```
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
```
The reason is the code directly sets input to GPU when it's available.
# Changelog
- Fix checking of cuda/cpu device for inputs Decoder
# Usage
```python
# Load Tacotron2
from nemo.collections.tts.models import Tacotron2Model
spec_generator = Tacotron2Model.from_pretrained("tts_en_tacotron2").to("cpu")
# Load vocoder
from nemo.collections.tts.models import HifiGanModel
vocoder = HifiGanModel.from_pretrained(model_name="tts_en_hifigan").to("cpu")
# Generate audio
import soundfile as sf
import torch
with torch.no_grad():
parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
# Save the audio to disk in a file called speech.wav
if isinstance(audio, torch.Tensor):
audio = audio.to('cpu').numpy()
sf.write("speech.wav", audio.T, 22050, format="WAV")
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
@blisc @okuchaiev @titu1994
# Additional Information
This fix is learned from the [PyTorch forum](https://discuss.pytorch.org/t/how-to-check-if-model-is-on-cuda/180/4?u=robindong)
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} | null | null | null | So i found this example from nvidia:
https://pytorch.org/hub/nvidia_deeplearningexamples_fastpitch/
I reimplemented it in my environment, but I am little confused, because the title says "Fastpitch 2". Is this the same as the Fastpitch models in the NGC catalogue (for example this: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_es_multispeaker_fastpitchhifigan) architecture wise?
There is also this model for riva which is called Fastpitch 1.1: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/speechsynthesis_english_fastpitch
Beside multiple speakers and language I do not understand what the difference between this models is (Except riva being faster of course). Can someone elaborate this naming convention. Should I use the Fastpitch 2 model from the example and retrain it for my purposes to get the best performance or are the fine tuned models in the NGC catalogue already fastpitch2 models? | {
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} | I think the correct option for
```bash
python <NeMo base path>/scripts/speaker_tasks/create_alignment_manifest.py \
--input_manifest_filepath <Path to train_clean_100.json manifest file> \
--base_alignment_path <Path to LibriSpeech_Alignments directory> \
--output_path train-clean-100-align.json \
--ctm_output_directory ./ctm_out \
--libri_dataset_split train-clean-100
```
is `--output_manifest_filepath` not `--output_path` in the current version. | {
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} | # What does this PR do ?
I believe this PR fixes the issue of FFTransformerDecoder altering the input tensor by adding positional embeddings in-place.
**Collection**: TTS
# Changelog
- change `inp += pos_emb` to `inp = inp + pos_emb` to prevent changing the input tensor
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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```
ValueError: You selected an invalid strategy name: `strategy=None`. It must be either a string or an instance of `pytorch_lightning.strategies.Strategy`. Example choices: auto, ddp, ddp_spawn, deepspeed, ... Find a complete list of options in our documentation at https://lightning.ai/
```
A clear and concise description of what the bug is.
To reproduce :
- Running notebook on strategy = None with pytorch lightning version 2.0.7
-https://github.com/NVIDIA/NeMo/blame/83d6614fbf29cf885f3bc36233f6e3758ba8f1e3/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb#L478
- Run https://github.com/NVIDIA/NeMo/blame/83d6614fbf29cf885f3bc36233f6e3758ba8f1e3/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb#L494
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
**Expected behavior**
https://github.com/NVIDIA/NeMo/blame/83d6614fbf29cf885f3bc36233f6e3758ba8f1e3/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb#L494 pytorch trainer should be able to configured without issue.
**Environment overview (please complete the following information)**
- Environment location: AWS
- Method of NeMo install: pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]
**Environment details**
- PyTorch version
**Additional context**
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* Prevent split, tokenize, and truncation if not exceed `max_seq_length`.
* Remove `_` if we don't have leading space before.
**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)
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} | # What does this PR do ?
Add dynamic position interpolation for `seq length > learned length`
Learned length is `pre-trained max_position_embeddings * seq_len_interpolation_factor`
**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**:
- [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)
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} | # What does this PR do ?
Renames confidence `measure` back to `method` and removes warnings.
**Collection**: [Note which collection this PR will affect]
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
```
# 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?
@Kipok
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} | # What does this PR do ?
Conversion script for HuggingFace Falcon checkpoint (1b-rw/7b-rw/7b/40b/180b) to NeMo format
Support Falcon models in mcore.
**Collection**: NLP
# Changelog
- add conversion script
# Usage
* See docstring in the script added.
# 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
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)
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} | null | null | null | Hello, I am currently training the Conformer-CTC model for ASR.
I am interested in how logit prediction changes throughout training, and I am wondering if there is any way to modify the code so that my model saves logits and targets data as a pickle file for the end of each validation step. I am aware that I can append each (logits, targets) in `validation_step_outputs` and then save them in `on_validation_epoch_end()`. However, I am a bit scared to overwrite the function in `ctc_bpe_models.py`. It would be great if there is a safer way to handle such a situation. | {
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} | # What does this PR do?
When running `python scripts/dataset_processing/tts/ljspeech/get_data.py --data-root <your_local_dataset_root>` to get the data of LJSpeech as the [document](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/datasets.html#ljspeech), it reported error:
```
FileNotFoundError: [Errno 2] No such file or directory: 'lj_speech.tsv extracted from the readme file in the dataset. You can also download the file from https://github.com/NVIDIA/NeMo-text-processing/blob/main/nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv'
```
Solution: The default value of argument `--whitelist-path` of `get_data.py` should be `None` (so the get_data.py will automatically download the lj_speech.tsv) instead of a long string.
# Changelog
- Fix failure of ljspeech's get_data.py.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
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} | # What does this PR do ?
Draft fix for #7428
**Collection**: [nlp]
# Changelog
- Fix handling of `limit_val_batches` which could lead to crashes or computing validation error on less data than intended
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Fixes #7428
This is a draft as I am unsure this is the "right" way to fix it, and more work is needed to finish it. However, I would like to request feedback from NeMo devs to know whether this is heading in the right direction. Some things to think about:
* Probably that other classes need to be modified as well, beyond MegatronGPTModel
* Changing the length of the validation dataloader might have unintended consequences
* The MegatronPretrainingRandomSampler might need some fix as well
* I think (at first glance) that the dataloaders are not properly implementing `drop_last` since it seems to be based on `micro_batch_times_data_parallel_size` rather than the global batch size (I didn't try to fix it in this PR)
* I haven't tested at all my fix for the float `limit_val_batches`
* Does the test set also need something similar?
* ... (this is definitely rushed => there's a good chance I may have missed more stuff) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7428 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7428/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7428/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7428/events | https://github.com/NVIDIA/NeMo/issues/7428 | 1,893,631,285 | I_kwDOC_bI7s5w3oU1 | 7,428 | Validation does not work as expected due to incorrect handling of `limit_val_batches` | {
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} | null | null | null | **Describe the bug**
There are at least two issues with how `limit_val_batches` is handled in the current master branch:
1. `trainer.limit_val_batches` is multiplied by `get_num_microbatches()` [here](https://github.com/NVIDIA/NeMo/blob/e3365ead822b4355a6391dd458d3457c441252ac/nemo/collections/nlp/models/language_modeling/megatron_base_model.py#L213C40-L213C47) even if it is a float (e.g., 1.0), which can make the code crash immediately after [here](https://github.com/NVIDIA/NeMo/blob/e3365ead822b4355a6391dd458d3457c441252ac/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L993) because we end up with a float > 1.0
2. Because the length of the validation dataloader is computed in terms of global batches ([here](https://github.com/NVIDIA/NeMo/blob/e3365ead822b4355a6391dd458d3457c441252ac/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py#L85)), PyTorch Lightning will cap the number of validation steps to this number if `limit_val_batches * get_num_microbatches()` is higher, and we won't do the intended number of validation steps. Worse, this can lead to a crash due to incomplete batches.
Here's an example of what can go wrong because of (2):
* Consider the case where the validation set has 127 samples, `global_batch_size=16`, `micro_batch_size=2`, `data_parallel_size=2` and `limit_val_batches=6`
* In that case we have `get_num_microbatches() == 4`
* The multiplication of `limit_val_batches` by `get_num_microbatches()` sets `trainer.limit_val_batches=24`
* The length of the validation dataloader is `127 // 16 == 7`
* As a result, instead of processing 24 validation microbatches, we only process 7. This ends up causing a `StopIteration` in-between the `on_validation_batch_start` and `on_validation_batch_end` callbacks, which is caught by PTL but prevents the validation_batch timer from stopping properly, which triggers a crash on the next validation batch (`RuntimeError: Cannot start timer = 'validation_step_timing' since it is already active`)
**Steps/Code to reproduce bug**
I'm seeing this issue with custom code that I can't easily share now, but it should be easy to repro with any megatron GPT training loop.
**Environment overview (please complete the following information)**
- Environment location: Docker (I think the image is taken from a recent nightly build)
- Method of NeMo install: I tried both the version in my Docker image and the current master
**Additional context**
I'll put up a draft PR with a potential direction to solve these issues (see #7429) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7427 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7427/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7427/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7427/events | https://github.com/NVIDIA/NeMo/pull/7427 | 1,893,580,029 | PR_kwDOC_bI7s5aL1Sk | 7,427 | Fix CustomProgressBar for resume | {
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} | # What does this PR do ?
- Fix the CustomProgressBar for cases when we are resuming training from a checkpoint. Currently when we are resuming from a checkpoint total value in `iter_num/total` of the progress bar is the number of remaining training steps instead of total training steps which leads to the progress bar displaying` iter_num/?` when iter_num > remaining training steps. This PR fixes the issue by reseting the total with `max_steps`.
- Also, in case of multiple epochs the PR updates the current step number (n) to be the current step number within an epoch (default was current step across an epoch and this can lead to an errored progress bar).
**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)
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} | null | null | null | **Describe the bug**
I cannot use the "STT En FastConformer Hybrid Transducer-CTC Large Streaming 80ms". First of all, the NGC catalog instructions don't work. But more importantly, there is a shape mismatch error "size mismatch for encoder.pre_encode.out.weight: copying a param with shape torch.Size([512, 2816]) from checkpoint, the shape in current model is torch.Size([512, 2560])." even when I work around that.
**Steps/Code to reproduce bug**
First of all, the instructions at https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_80ms, namely:
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="stt_en_fastconformer_hybrid_large_streaming_80ms")
```
Fails with the following:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/dgalvez/scratch/code/asr/riva-asrlib-decoder/NeMo/nemo/core/classes/common.py", line 848, in from_pretrained
class_, nemo_model_file_in_cache = cls._get_ngc_pretrained_model_info(
File "/home/dgalvez/scratch/code/asr/riva-asrlib-decoder/NeMo/nemo/core/classes/common.py", line 900, in _get_ngc_pretrained_model_info
raise FileNotFoundError(
FileNotFoundError: Model stt_en_fastconformer_hybrid_large_streaming_80ms was not found. Check cls.list_available_models() for the list of all available models.
```
I tried to work around this by
```
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_hybrid_large_streaming_80ms/versions/1.20.0/zip -O stt_en_fastconformer_hybrid_large_streaming_80ms_1.20.0.zip
unzip stt_en_fastconformer_hybrid_large_streaming_80ms_1.20.0.zip
python -c "import nemo.collections.asr as nemo_asr; model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.restore_from('stt_en_fastconformer_hybrid_large_streaming_80ms.nemo')"
```
Fails with:
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/dgalvez/scratch/code/asr/riva-asrlib-decoder/NeMo/nemo/core/classes/modelPT.py", line 434, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/dgalvez/scratch/code/asr/riva-asrlib-decoder/NeMo/nemo/core/connectors/save_restore_connector.py", line 246, in restore_from
self.load_instance_with_state_dict(instance, state_dict, strict)
File "/home/dgalvez/scratch/code/asr/riva-asrlib-decoder/NeMo/nemo/core/connectors/save_restore_connector.py", line 201, in load_instance_with_state_dict
instance.load_state_dict(state_dict, strict=strict)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1667, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for EncDecHybridRNNTCTCBPEModel:
size mismatch for encoder.pre_encode.out.weight: copying a param with shape torch.Size([512, 2816]) from checkpoint, the shape in current model is torch.Size([512, 2560]).
```
**Expected behavior**
The model should be able to be constructed like other pretrained models uploaded to NGC.. | {
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} | # What does this PR do ?
Conversion script for HuggingFace StarCoder checkpoint to NeMo format.
**Collection**: NLP
# Changelog
- Add conversion script
- Update Jenkinsfile
# Usage
See docstring in the script added.
# 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
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. | {
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} | null | null | null | **Describe the bug**
The implementation of Global Style Tokens (GSTs) in FastPitch introduced in #6417 does not follow the prescription of the original [paper](https://arxiv.org/abs/1803.09017). In particular, the difference lies in the choice of the reference audio for a given training/validation sample `<text, ground_truth_audio>`:
- In the original paper, it is recommended to use the ground-truth audio as the reference audio
- In the NeMo implementation, the reference audio is chosen at random among audio samples from the same speaker
I discovered this after training a FastPitch model with GSTs and observing that the choice of the reference audio at inference time would have virtually no impact at all. More precisely, given a text T and **n** _different_ reference audios R_1, ..., R_n, passing <T, R_i> for any i in [1, n] would result in almost the exact same audio A. I say "almost" because the files appeared different when compared using `diff`, but they had the _exact same length_ and they sounded _exactly the same_ to the ear.
So what I did was to modify the [code](https://github.com/NVIDIA/NeMo/blob/v1.20.0/nemo/collections/tts/data/dataset.py#L704) that's responsible for selecting the reference audio in `nemo.collections.tts.data.dataset.TTSDataset.__getitem__`, effectively changing this:
`reference_audio = self.featurizer.process(self.data[reference_index]["audio_filepath"], ...)`
to this:
`reference_audio = self.featurizer.process(sample["audio_filepath"], ...)`
which is exactly what the GST paper prescribes. In this case, I observed that the choice of the reference audio at inference time _did_ make a difference, however small (although this is probably due to the fact that the training set I used was not very varied in terms of speaking styles).
My intuition is that if an audio A1 is used to compute the style embedding that is then used to condition the generation of the Mel spectrogram for another audio A2, the model will learn to ignore that style embedding because the information that can be extracted from A1 is useless to generate the spectrogram for A2. You could argue that this isn't quite true because A1 and A2 come from the same speaker, hence you could extract speaker information from A1 to generate the spectrogram for A2, but FastPitch already contains a SpeakerLookup module that takes care of encoding speaker information.
**Steps/Code to reproduce bug**
1. Train a FastPitch model using GSTs, for example using the following configuration file: `examples/tts/conf/fastpitch_align_44100_adapter.yaml`
2. Perform multiple inference steps where the input text is always the same, but the reference audio is different
3. Generate audios using a vocoder such as HiFi-GAN
**Expected behavior**
Choosing different reference audios at inference time results in different audios produced as output.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04.2
- PyTorch version: 2.0.1
- Python version: 3.8
**Additional context**
Add any other context about the problem here.
GPU model: RTX 3090 Ti
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} | # What does this PR do ?
Conversion script for HuggingFace StarCoder checkpoint to NeMo format.
**Collection**: NLP
# Changelog
- Add conversion script
- Update Jenkinsfile
# Usage
See docstring in the script added.
# 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
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. | {
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Check NEMO_PATH
# Usage
* You can potentially add a usage example below
Check NEMO_PATH and exit with alert if it does not exist
**PR Type**:
- [V] New Feature
# Additional Information
* Related to #7320
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enables layer selection for ia3.
**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
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# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
This updates the conversion script to work with dist ckpt for GPT.
**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"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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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
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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.
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* Related to # (issue)
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