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https://api.github.com/repos/NVIDIA/NeMo/issues/3697 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3697/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3697/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3697/events | https://github.com/NVIDIA/NeMo/issues/3697 | 1,141,015,319 | I_kwDOC_bI7s5EAoMX | 3,697 | Enquiry about the ASR "QuartzNet15x5Base-En" Model | {
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} | null | null | null | Hi, just wondering if the QuartzNet15x5Base-En Model has been trained using the Singapore National Speech Corpus (NSC) and If so, which portions of the corpus was used to train the model.
Thanks. :-) | {
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} | null | null | null | Hello!
I am running 00_NeMo_Primer.
I was running this cell
```
import nemo.collections.asr as nemo_asr
import nemo.collections.tts as nemo_tts
import nemo.collections.nlp as nemo_nlp
```
And got the following error
```
[NeMo W 2022-02-17 15:16:17 experimental:27] Module <class 'nemo.collections.asr.losses.ctc.CTCLoss'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-02-17 15:16:18 experimental:27] Module <class 'nemo.collections.tts.losses.glow_tts_loss.GlowTTSLoss'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-02-17 15:16:18 experimental:27] Module <class 'nemo.collections.tts.modules.glow_tts.TextEncoder'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-02-17 15:16:18 experimental:27] Module <class 'nemo.collections.tts.modules.glow_tts.FlowSpecDecoder'> is experimental, not ready for production and is not fully supported. Use at your own risk.
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/var/folders/6p/q3vy70_j01x7j6tw4kzc_dy00000gp/T/ipykernel_5722/4141817862.py in <module>
1 import nemo.collections.asr as nemo_asr
2 import nemo.collections.tts as nemo_tts
----> 3 import nemo.collections.nlp as nemo_nlp
~/opt/anaconda3/lib/python3.9/site-packages/nemo/collections/nlp/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp import data, models, modules
16 from nemo.package_info import __version__
17
~/opt/anaconda3/lib/python3.9/site-packages/nemo/collections/nlp/data/__init__.py in <module>
19 BertPretrainingPreprocessedDataloader,
20 )
---> 21 from nemo.collections.nlp.data.question_answering_squad.qa_dataset import SquadDataset
22 from nemo.collections.nlp.data.token_classification.token_classification_dataset import (
23 BertTokenClassificationDataset,
~/opt/anaconda3/lib/python3.9/site-packages/nemo/collections/nlp/data/question_answering_squad/qa_dataset.py in <module>
26
27 from nemo.collections.common.parts.utils import _compute_softmax
---> 28 from nemo.collections.nlp.data.question_answering_squad.qa_squad_processing import (
29 SquadProcessor,
30 apply_no_ans_threshold,
~/opt/anaconda3/lib/python3.9/site-packages/nemo/collections/nlp/data/question_answering_squad/qa_squad_processing.py in <module>
20
21 from tqdm import tqdm
---> 22 from transformers.tokenization_bert import BasicTokenizer
23
24 from nemo.collections.nlp.data.data_utils import DataProcessor, is_whitespace, normalize_answer
ModuleNotFoundError: No module named 'transformers.tokenization_bert'
```
The same error was faced by #1566
The suggested fix was to uninstall the latest transformer and revert to 3.5
```
!pip uninstall transformers
!pip install transformers==3.5
```
However, i met this issue when installing transformer 3.5
```
Collecting sentencepiece==0.1.91
Using cached sentencepiece-0.1.91.tar.gz (500 kB)
Preparing metadata (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [5 lines of output]
Package sentencepiece was not found in the pkg-config search path.
Perhaps you should add the directory containing `sentencepiece.pc'
to the PKG_CONFIG_PATH environment variable
No package 'sentencepiece' found
Failed to find sentencepiece pkgconfig
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
```
How do i proceed with the above, or is there another fix where i do not have to revert to transformer 3.5?
Thank you!
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} | # What does this PR do ?
See Design Doc and Milestones for Overarching project at [Nvidia-only access Google Docs](https://docs.google.com/document/d/1IUerv_PLPLgzm5rLEgvHITz_RmK4cKdJKJCV7BXTJ9c/edit?usp=sharing)
Add slot filling prediction capabilities and metrics
**Collection**: NLP
# Changelog
- Integrated the capability of performing intent classification and slot filling jointly using a GPT-style model
- Include metrics for slot filling
- Edited dataset class to improve user customizability in using templates for generation
- Backported IntentSlotClassificationModel into DST module
# 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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"merged_at": "2022-02-18T02:30:14"
} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Adds the GEGLU activation function for T5
**Collection**: NLP
# Changelog
- Updates ParallelMLP to instantiate a new linear layer if the activation is GEGLU.
- Applies the appropriate activation function based on the provided `activation` arg.
# Usage
* You can potentially add a usage example below
Set `model.activation=gelu` or `model.activation=geglu` in `megatron_t5_config.yaml`
# 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 ?
Added an experiment to run fine-tuning on the MNLI dataset, use XNLI dev as validation dataset. The score on the XNLI test is reported.
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} | # What does this PR do ?
Convert Megatron LM checkpoint to NeMo checkpoint. Copy over the weights, optimizer states, LR scheduler states.
Fix the issue of weight decay parameter group assignment.
Tested that the script converts the old Megatron GPT and BERT checkpoint to `.nemo` file fine.
Tested with 40B checkpoint file, the NeMo Megatron can resume from it and continue the training. Need to verify the training curve works fine.
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} | # What does this PR do ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
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## Who can review?
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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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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
lower bound PTL to 1.5.10 and remove last ckpt patch fix in exp manager
**Collection**: core, exp_manager
# Changelog
- lower bound PTL to 1.5.10
- Remove `_save_last_checkpoint` function from exp_manager. PTL fixed issue in 1.5.10
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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<div type='discussions-op-text'>
<sup>Originally posted by **gedefet** February 15, 2022</sup>
Hi All.
<br>
At the end of the FastPitch_Finetuning.ipynb tutorial:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb
<br>
it suggest two different ways to improve performance, one of which is:
<br>
_Mix new speaker data with old speaker data
We recommend to train fastpitch using both old speaker data (LJSpeech in this notebook) and the new speaker data. In this case, please modify the .json when finetuning fastpitch to include speaker information:_
<br>
but it isn't 100% clear how to do that.
<br>
I found another FastPitch_Finetuning.ipynb here:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/tts/FastPitch_Finetuning.ipynb
<br>
Which is different than the other.
<br>
It seems that it has two functions defined to do the mixing, but it seems that this colab is abandoned. Nevertheless, I think that has very useful code, and that has tackled this part of mixing datasets.
<br>
Is this true? Or do I need something more that is not there?Why it is abandoned?
<br>
Thanks!</div> | null | {
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} | null | null | null | Hi all.
The tutorial https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb does not work from scratch.
First, I have to replace NeMo for NVIDIA in the first setup cell for enable it to run:
```
!python -m pip install git+https://github.com/NeMo/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
```
for
```
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
```
Then, I got the following error in the 11th cell:
First commands of the cell:
```
new_speaker_id = 6097
duration_mins = 5
mixing = False
original_speaker_id = "ljspeech"
_ ,last_ckpt = get_best_ckpt("./", new_speaker_id, duration_mins, mixing, original_speaker_id)
print(last_ckpt)
```
Error:
```
AttributeError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/torch/serialization.py](https://localhost:8080/#) in _check_seekable(f)
307 try:
--> 308 f.seek(f.tell())
309 return True
AttributeError: 'NoneType' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
8 frames
[/usr/local/lib/python3.7/dist-packages/torch/serialization.py](https://localhost:8080/#) in raise_err_msg(patterns, e)
302 + " Please pre-load the data into a buffer like io.BytesIO and"
303 + " try to load from it instead.")
--> 304 raise type(e)(msg)
305 raise e
306
AttributeError: 'NoneType' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.
```
Best, | {
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} | null | null | null | In the pretrained Transducer model, the greedy decoding strategy has a max_symbols_per_step parameter equal to 5. I was wondering why is it not "None" and shouldn't the transducer perform better without that parameter? | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Comment out numba install in 22.01 release. It contains numba 0.53.1 which works with RNNT.
**Collection**: [ASR]
# Changelog
- Comment out numba due to numpy conflict.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Updates `.complete` and `.compute_logprobs` to work with pipeline parallelism.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```
python megatron_gpt_eval.py \
--model_file /raid/nemo_files/gpt_126m_tf32_pp2_tp1/megatron_gpt.nemo \
--prompt "Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men" \
--tensor_model_parallel_size 1 \
--pipeline_model_parallel_size 2 \
--tokens_to_generate 64 \
--stop_after_sentence False --precision 32
```
# 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?
- [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.
# Additional Information
* Related to # (issue)
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] | closed | true | null | [] | null | null | 2022-02-15T20:37:38 | 2022-02-16T18:38:24 | 2022-02-16T18:38:24 | CONTRIBUTOR | {
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} | null | null | null | Is there any specific reason that the nemo:1.6.2 container is not yet available on ngc; (https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). The info says the container was modified on Feb. 14, 2022, but the latest tag is still 1.5.1 from Dec. 15, 2021, even though 1.6.2 was released 11 days ago. | null | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds support for Librosa 0.9
**Collection**: [ASR, TTS]
# Changelog
- Update all librosa calls to required kwargs calls
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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# What does this PR do ?
Sets `TRANSOFMERS_OFFLINE=0` for SGD-Gen tests only to avoid issues with model caching.
**Collection**: NLP
# Changelog
- Edited Jenkins CI tests to set `TRANSOFMERS_OFFLINE` appropriately.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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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https://api.github.com/repos/NVIDIA/NeMo/issues/3680 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3680/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3680/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3680/events | https://github.com/NVIDIA/NeMo/pull/3680 | 1,138,949,553 | PR_kwDOC_bI7s4y3yeh | 3,680 | [TTS] Update TTS tutorials, Simplification of testing Mixer-TTS and FastPitch | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [TTS]
# Changelog
- Update TTS tutorials
- Simplification of testing Mixer-TTS and FastPitch
- Small fix in FastPitch
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
Closes #3603 #3627 #3628 #3676 | {
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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)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | null | null | null | **Describe the bug**
When I was trying to train Fastpitch from scratch using `fastpitch_align_v1.05.yaml` config, after it spending over 1 hour trying to `Loading dataset`, it threw the error:
```
Traceback (most recent call last):
File "examples/tts/fastpitch.py", line 28, in main
model = FastPitchModel(cfg=cfg.model, trainer=trainer)
File "/root/NeMo/nemo/collections/tts/models/fastpitch.py", line 81, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/root/NeMo/nemo/core/classes/modelPT.py", line 138, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/root/NeMo/nemo/collections/tts/models/fastpitch.py", line 453, in setup_training_data
self._train_dl = self.__setup_dataloader_from_config(cfg)
File "/root/NeMo/nemo/collections/tts/models/fastpitch.py", line 440, in __setup_dataloader_from_config
dataset = instantiate(
File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
return instantiate_node(config, *args, recursive=_recursive_, convert=_convert_)
File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 249, in instantiate_node
return _call_target(_target_, *args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 64, in _call_target
raise type(e)(
File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
return _target_(*args, **kwargs)
File "/root/NeMo/nemo/collections/tts/torch/data.py", line 228, in __init__
librosa.filters.mel(
TypeError: Error instantiating 'nemo.collections.tts.torch.data.TTSDataset' : mel() takes 0 positional arguments but 2 positional arguments (and 3 keyword-only arguments) were given
```
**Steps/Code to reproduce bug**
` python examples/tts/fastpitch.py --config-name=fastpitch_align_v1.05.yaml train_dataset=/root/autodl-tmp/nancy_train.json validation_datasets=/root/autodl-tmp/nancy_val.json sup_data_path=/root/autodl-tmp/nemo-training/nancy_22k/sup_data exp_manager.exp_dir=/root/autodl-tmp/nemo-training/nancy_22k exp_manager.resume_if_exists=True exp_manager.resume_ignore_no_checkpoint=True model.train_ds.dataloader_params.batch_size=24 pitch_mean=199.37802124023438 pitch_std=55.59949493408203`
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: Cloud
- Method of NeMo install: from source
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04
- PyTorch version: 1.10
- Python version: 3.8
**Additional context**
Add any other context about the problem here.
Example: RTX 3090
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I need a small confirmation from your end, please confirm that with the below command the model which is restored is from best checkpoints, as after that I am using the loaded model for predictions with the second command
model = TextClassificationModel.restore_from(restore_path='./trained-model-text-nvidia.nemo')
predicted_class = model.classifytext(queries=config.model.infer_samples, batch_size=2, max_seq_length=511) | {
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} | # What does this PR do ?
Fix bug that causes CI to break due to huggingface cache inconsistency by using local file location
**Collection**: NLP
# Changelog
Previous patch provide a lm_checkpoint location for model to restore_weights, but this method still calls `AutoModel.from_pretrained('gpt2')` before restoring local weights, which can sometimes require cache.
Current patch overcomes this by using `AutoModel.from_pretrained(<local_gpt2_weights_folder>)`, which means that it searches for the local folder directly.
# 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 ?
Fixing the bug in the stateful rnnt decoder. The current code does not keep the last token properly from partial hypothesis to the next hypothesis. If The latest decoding does not produce any tokens, it would not propagate the non-blank last token and just passes blank token to the next hypothesis which is wrong.
# Changelog
- Fixed the bug in the stateful rnnt decoder by keeping the last token and passing that along properly.
**PR Type**:
- [ ] New Feature
- [x ] Bugfix
- [ ] Documentation
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} | … backends
Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Added speaker identification script with cosine and neural classifier backends
**Collection**: speaker_tasks
# Changelog
- replaced speaker_reco_infer.py with speaker_identification_infer.py script
- added conf/speaker_identification_infer.yaml
- Updated documentation and README
# Usage
```bash
python speaker_identification_infer.py data.enrollment_manifest=<path/to/enrollment_manifest> data.test_manifest=<path/to/test_manifest> backend.backend_model=cosine_similarity
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[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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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Fixes folder name for jenkins test.
**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
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} | # What does this PR do ?
This PR fixes prompt tuning to work with the updated pipeline parallel code. In this PR a user can also prompt tune using tensor parallel > 1. More work will need to be done for prompt tuning to be supported for pipeline parallel > 1.
**Collection**: [Note which collection this PR will affect]
BigNLP?
# Changelog
- prompt tuning with tp=1 and pp=1 works again with updated pipeline parallel code
- prompt tuning with tp > 1 works
- prompt tuning works with the updated complete and compute log probs methods
- Added a prompt tuning config file with prompting specific defaults set
- prompt tuning tests are add back
# Usage
```python
python examples/nlp/language_modeling/megatron_gpt_prompt_tuning.py \
--config-name=megatron_prompt_tuning_gpt \
restore_from_path='/prompt-tuning/megatron_gpt.nemo' \
trainer.val_check_interval=2 \
trainer.max_steps=5 \
model.new_prompt_tags=['Winogrande, BoolQ'] \
model.new_prompt_init_text=['logic choose person name, None'] \
model.new_prompt_init_methods=['text, random'] \
model.data.train_ds='/prompt-tuning/wino_bool_prompt_tuning_train.json' \
model.data.valid_ds='/prompt-tuning/wino_bool_prompt_tuning_val.json' \
+model.data.test_ds='/prompt-tuning/wino_bool_prompt_tuning_val.json' \
model.micro_batch_size=2 \
model.global_batch_size=4 \
model.optim.lr=2e-2 \
model.optim.sched.min_lr=2e-3 \
model.optim.sched.warmup_steps=2 \
model.optim.sched.constant_steps=8 \
model.encoder_seq_length=2048
```
# 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
- [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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} | Signed-off-by: smajumdar <[email protected]>
Adds an optional research notebook discussing the Longest Common Subsequence merge algorithm as used for Streaming/Buffered transducer inference.
**Collection**: [ASR]
# Changelog
- Add optional research notebook
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Unblock CI for SGDGEN by locally loading model checkpoint instead of calling from huggingface
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] 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 ?
This PR adds support in BART Megatron model <https://arxiv.org/abs/1910.13461>.
**Collection**: `nemo.collections.nlp.models.language_modeling.megatron_bart_model`
# Usage
```
python /code/examples/nlp/language_modeling/megatron_bart_pretraining.py \
--config-path=/code/examples/nlp/language_modeling/conf \
--config-name=megatron_bart_config \
trainer.num_nodes=1 \
trainer.devices=1 \
trainer.max_epochs=null \
trainer.max_steps=${MAX_STEPS} \
trainer.val_check_interval=${VAL_CHECK_INTERVAL} \
trainer.accumulate_grad_batches=${ACCUMULATE_GRAD_BATCHES} \
trainer.precision=16 \
model.micro_batch_size=${MICRO_BATCH_SIZE} \
model.tensor_model_parallel_size=${TENSOR_MODEL_PARALLEL_SIZE} \
model.max_position_embeddings=${SEQ_LENGTH} \
model.seq_length=${SEQ_LENGTH} \
model.hidden_size=${HIDDEN_SIZE} \
model.num_layers=${NUM_LAYERS} \
model.num_attention_heads=${NUM_ATTENTION_HEADS} \
model.init_method_std=0.015 \
model.data.data_prefix=[1.0,/preproc_data/train/my-t5_test_bert_tokenizer_text_document] \
model.data.seq_length=${SEQ_LENGTH} \
``` | {
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} | # What does this PR do ?
<!-- Add a one line overview of what this PR aims to accomplish. -->
Update Megatron style `BatchSampler`s and `GPTDataset` to prepare data samples in worker processes.
# Changelog
- BatchSamplers return a list of `num_micro_batch x micro_batch_size` indices: https://github.com/NVIDIA/NeMo/compare/main...crcrpar:megatron/update_data_loading?expand=1#diff-a78cf82731c4e37f487037ab45cab0183aac6216ee96e08d3b81ff111495d3caR36 and https://github.com/NVIDIA/NeMo/compare/main...crcrpar:megatron/update_data_loading?expand=1#diff-a78cf82731c4e37f487037ab45cab0183aac6216ee96e08d3b81ff111495d3caR106
- A new arguments of `eos_id` is added to `GPTDataset` in order to change the callsite of `get_ltor_masks_and_position_ids` to `GPTDataset`. [`eos_id`](url) is an attribute of a `Tokenizer` instance.
- `process_micro_batch` is removed in favor of updated `GPTDataset.__getitem__`: https://github.com/NVIDIA/NeMo/compare/main...crcrpar:megatron/update_data_loading?expand=1#diff-e93ccae74f4b67d341676afc9f3c7e2c50f751ec64df84eb3b2a86b62029ef76L456
- `NLPDataConnector` isn't needed thanks to the updated `GPTDataset` and `BatchSampler`s: https://github.com/NVIDIA/NeMo/compare/main...crcrpar:megatron/update_data_loading?expand=1#diff-424bb19c0b7c9dba2aebb3459972c5a8bf8c902dca48a8762f12bc5cde5fea5fL68
# Usage
To utilize worker processes, it'd be better to set `model.data.num_workers` > 0.
See https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader.
<!--
* 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**:
- [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
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} | # What does this PR do ?
Adds support for O2-style mixed-precision training for Megatron LM Encoder-Decoder Models.
**Collection**: NLP
# Changelog
- This PR only changes `megatron_lm_encoder_decoder.py` to add O2 support based on the same changes for GPT.
# Usage
Set `megatron_amp_o2: True` in the `megatron_t5_config.yaml` file.
# 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 a bug in the bucketing which is caused by this PR: https://github.com/NVIDIA/NeMo/pull/3485
Training ASR models with bucketing feature were affected by this.
# Changelog
Changed the way the length of a dataset is retrieved to fix the bug in the bucketing.
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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} | Signed-off-by: Subhankar Ghosh <[email protected]>
# What does this PR do ?
Adds SpeakerID to `process_transcript` method of libritts/get_data.py
# Changelog
Gets speakerID from the path because the path to every manifest has the speakerID.
Adds the speakerID to the entry as another (key,value) pair.
# Usage
Doesn't change how libritts/get_data.py will be called. The manifests will contain speaker field.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hello,
I'm training a model on Tacotron2. I tried little datasets, large datasets, but always I get the same messages.
Reached max decoder steps XXXX
By default there are 1000 steps. I tried the default configuration and also I tried greater values.
I thought it was caused because the labels property in tacotron2.yaml. But I grouped all the letters in my dataset and every letter is on the "labels" property.
Also I tried adding a dot at the end of each sentence. Absolutely all of the sentences are between 1 and 10 seconds.
The dataset is all in spanish. I tried to train minutes, hours, etc. I mean in the json file I tried from 10000 registers to 100 registers.
When training it always notice Reached max decoder steps XXXX
Do you know why is it happening? Do you know how to solve it?
Thank you!
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} | # What does this PR do ?
Adds scripts 1) download and prepare noise data from Demand database and 2) to add noise to an audio dataset at various SNRs
**Collection**: [ASR]
# Changelog
- Added script to download and prepare manifest for noise data from Demand noise database.
- Added script to add noise to an audio dataset at various SNRs
# 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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} | null | null | null | I am trying the NEMO_Primer from this GitHub repository. I have tried downloading the model on Google Colab however if I try to run the demo on Jupiter notebook locally, the model cant be downloaded. Please see image to see error. This error pops up when passing a pre-trained model name and instantiate a model directly from the cloud:
citrinet = nemo_asr.models.EncDecCTCModelBPE.from_pretrained('stt_en_citrinet_512')
or
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_quartznet15x5")
<img width="972" alt="Screenshot 2022-02-14 at 11 10 41 AM" src="https://user-images.githubusercontent.com/90296566/153793932-26dc0258-0225-4542-9322-bc7a496b4059.png">
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] | null | null | 2022-02-13T00:24:29 | 2022-05-09T06:37:49 | 2022-02-16T09:06:36 | NONE | {
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} | null | null | null | Hello,
I Installed a clean Ubuntu 20.04, installed nvidia drivers 440 for RTX 3080. Then I followed the instructions to train a model with Tacotron2.
numpy fails with the next error:
valueerror: numpy.ndarray size changed, may indicate binary incompatibility. expected 96 from c header, got 88 from pyobject
I tried the default version of numpy and also I tried pip install --upgrade numpy==1.21.0
I really don'tknow how to solve the problem and I write this to ask for help.
Do you know how to solve it?
Thank you!
David
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Adds the ability to use the prefix-LM objective for T5 which reuses the GPT dataset class that already exists.
**Collection**: NLP
# Changelog
- Adds at T5LMAdaptedDataset class that inherits from GPTDataset that just splits a text sequence into encoder and decoder components
- Re-uses the existing build_train_valid_test_datasets functions that exist for BERT and T5 for this class.
- Moves some dataset utils to a new file to prevent circular imports between gpt_dataset.py and dataset_utils.py.
# Usage
Add the `dataset_type` flag to either `t5` or `t5_prefix_lm` in `megatron_t5_config.yaml`.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Speed up CUDA RNNT loss
**Collection**: [ASR]
# Changelog
- Fix issue with data movement of costs tensor from llForward to pytorch data view in numba
- This alone costs a linear loop (scaling with batch size) that is roughly 10x the kernel costs themselves.
- Fix by writing a small kernel to copy the data and update the costs.
# 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?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: ekmb <[email protected]>
# 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?
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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 ?
Update readme to include streaming inference directory for ASR.
**Collection**: [ASR]
# Changelog
- Update readme.md to contain link to streaming / buffered asr
- Update URL in tutorial for streaming / buffered transducer models
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
* adds onnx support for speaker models
**Collection**: [speaker_tasks]
# Changelog
- Added forward_for_export function
- Fixed previous asr onnx exportable tests
# Usage
To generate onnx speaker model from titanet nemo
```bash
python export.py <path/to/titanet-l.nemo> <path/to/titanet-l.onnx>
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
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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} | Signed-off-by: sam1373 <[email protected]>
# What does this PR do ?
Add ASR ssl tutorial to the tutorial docs page.
**Collection**: ASR
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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 | I am testing out the NVIDIA NeMo tutorial for ASR_with_Nemo.ipynb on google colab. The dataset in the demo from an4 works fine for me. However when I try to upload a .wav file from my local pc, the code returns an error "RuntimeError: Error opening '020010009.wav': File contains data in an unknown format." This is odd because I tried uploading a .sph file and use their code to convert the .sph file into a .wav file and it outputs the same error. I opened up the .sph file from the an4 dataset and opened up my .sph file and even though both are .sph files, the file content looks different. Seems like the demo only works on the an4 audio dataset and nothing else. Can anyone help? | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Allows configuring the mean n-gram size for T5 span masking when using a geometric distribution.
**Collection**: NLP
# Changelog
- Used the fact that the expectation of the geometric distribution is 1/p to set p appropriately for a given expected value of n-gram size.
# Usage
To get a mean n-gram size of 3 while masking (like suggested in the T5 paper), thee data configuration for T5 can look like this:
```
max_ngram_size: ${seq_length_dec}
mean_ngram_size: 3
geometric_dist: True
permutation: False
whole_work_masking: True
favor_longer_ngrams: False
```
```python
# N/A same run script as T5, just requires changing the yaml config like above.
```
# 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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} | # What does this PR do ?
Update analytics code.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] 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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} | # What does this PR do ?
Update adobe analytics code.
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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} | # What does this PR do ?
This PR adds a function that writes cluster label. This cluster label (speaker label for each segment) is needed to provide estimated segment label for overlap-aware diarization module or target-speaker ASR.
**Collection**: asr (but actually speaker_tasks)
# Changelog
- Removed unnecessary line in speaker_utils.py
- Added a line that adds segment info and cluster label
- Added lines that write files to speaker_outputs.
# Usage
Please refer to [Diarization docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/datasets.html)
# 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
## 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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} | Added num workers, cause without that argument any training goes really slow
# What does this PR do ?
reduced training time
# Changelog
- Add specific line by line info of high level changes in this PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] Bugfix
## 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
* Found out that training on 1.6.0 starts with num workers == 0.
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} | Signed-off-by: Oktai Tatanov <[email protected]>
# What does this PR do ?
**Collection**: [tts]
# Changelog
- refactoring of Aligner code and config, replacing deprecated datasets with `TTSDataset`
- adding three methods to AlignmentEncoder which can be useful for Neural G2P research
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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This PR adds a function that writes cluster label. This cluster label (speaker label for each segment) is needed to provide estimated segment label for overlap-aware diarization module or target-speaker ASR. | {
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} | Signed-off-by: Aleksandr Laptev <[email protected]>
# What does this PR do ?
This PR fixes the version mismatch between torchaudio and torch (0.10 vs 1.11) in Dockerfile.
If the version of torchaudio does not match the version of torch, then it loses its CUDA backend and the following errors may occur:
```
NotImplementedError: Could not run 'torchaudio::rnnt_loss' with arguments from the 'CUDA' backend.
```
# Changelog
Dockerfile
- `--branch release/0.10` -> `--branch release/0.11`
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} | null | null | null | Thank you for sharing a great tool.
I'm wondering if I can make a contribution to Nemo sharing the pretrained models trained with Ksponspeech dataset which is an open-domain Korean dialog corpus with benchmark scores, configs, and related codes.
If so, please let me know how to share the pretrained models. | {
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} | # What does this PR do ?
Adds Text Normalization for spanish language to nemo_text_normalization
**Collection**: Nemo Text Normalization
# Changelog
- Adds Text Normalization for Spanish language. Verbalizers and classifiers are available for following classes
- - Cardinal
- - Decimal
- - Ordinal
- - Fraction
- - Money
- - Measure
- - Date
- - Time
- - Electronic
- - Whitelist
Also includes a localization option of es-amer, which changes formatting rules to accommodate tendencies in Central American orthography. (e.g. Use of periods to group cardinals instead of commas - as is customary for other Spanish speaking locales.)
Includes updated es_pytests for text normalization and edits to export_grammar.sh and normalize.py to allow deployment. All tests have passed in nemo docker environment.
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [Y ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [Y ] Did you write any new necessary tests?
- [Y ] Did you add or update any necessary documentation?
- [ ]Y 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**:
- [ Y] New Feature
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
- * N. B. Sparrowhawk deployment is altering white-space following periods, so TN is unable to manage both am/pm along with time zones. That is -> 10 a.m. est will cause an error. However, 10.00 h est is stable. This issue is Sparrowhawk related so have been unable to debug.
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} | # Addition of FastPitch training tutorial
Please have one of the following:
* New feature: (short feature description) FastPitch training tutorial.
This PR adds FastPitch tutorial to the same notebook as MixerTTS as both generate spectrogram. This tutorial goes through the data download, preprocess, FastPitch import and training pipeline.
Tutorial was added with careful consideration to make minimal changes to MixerTTS tutorial.
## Before your PR is "Ready for review"
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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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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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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# Additional Information
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} | null | null | null | **Describe the bug**
I tried to fine tune the hifigan model according to `FastPitch_Finetuning.ipynb`, but I encountered the following error:
```
Traceback (most recent call last):
File "examples/tts/hifigan_finetune.py", line 28, in main
trainer.fit(model)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
self._dispatch()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
self.training_type_plugin.start_training(self)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
self._results = trainer.run_stage()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
return self._run_train()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1311, in _run_train
self._run_sanity_check(self.lightning_module)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1375, in _run_sanity_check
self._evaluation_loop.run()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
dl_outputs = self.epoch_loop.run(dataloader, dataloader_idx, dl_max_batches, self.num_dataloaders)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 122, in advance
output = self._evaluation_step(batch, batch_idx, dataloader_idx)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 217, in _evaluation_step
output = self.trainer.accelerator.validation_step(step_kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 236, in validation_step
return self.training_type_plugin.validation_step(*step_kwargs.values())
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py", line 444, in validation_step
return self.model(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 886, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 92, in forward
output = self.module.validation_step(*inputs, **kwargs)
File "/root/NeMo/nemo/collections/tts/models/hifigan.py", line 226, in validation_step
audio, audio_len, audio_mel = batch
ValueError: not enough values to unpack (expected 3, got 2)
```
**Steps/Code to reproduce bug**
Generate the mel files according to the notebook and run `python examples/tts/hifigan_finetune.py --config_name=hifigan.yaml model.train_ds.dataloader_params.batch_size=32 model.max_steps=1000 ~model.sched model.optim.lr=0.0001 train_dataset=./hifigan_train_ft.json validation_datasets=./hifigan_val_ft.json exp_manager.exp_dir=hifigan_ft +init_from_nemo_model=tts_hifigan.nemo trainer.check_val_every_n_epoch=10 model/train_ds=train_ds_finetune`
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Environment location: Cloud: [AutoDL](https://www.autodl.com/)
- Method of NeMo install: From source, `git clone` and `pip install .[tts]`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
PyTorch 1.10.0, Python 3.8, Cuda 11.3
**Additional context**
Add any other context about the problem here.
GPU: RTX 3090 * 2
VRAM: 24GB
CPU: 30 Cores AMD EPYC 7543 32-Core Processor
RAM: 160GB | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3627 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3627/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3627/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3627/events | https://github.com/NVIDIA/NeMo/issues/3627 | 1,127,049,718 | I_kwDOC_bI7s5DLWn2 | 3,627 | FastPitch fine-tunning using TDataset | {
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} | null | null | null | The tutorial on FastPitch fine-tunning (`NeMo/tutorials/tts/`) is based on the `fastpitch_align.yaml` and `AudioToCharWithPriorAndPitchDataset`, which will soon be deprecated. Is there any guidance how to finetune a pretrained FastPitch model based on the `fastpitch_align_v1.05.yaml` and thus `TDataset`? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3626 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3626/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3626/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3626/events | https://github.com/NVIDIA/NeMo/pull/3626 | 1,126,808,389 | PR_kwDOC_bI7s4yNmsX | 3,626 | Remove compression from .nemo files | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/3626",
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} | # What does this PR do ?
This PR removes compression from .nemo files.
The compression is not desirable because:
* It takes awfully long to compress multi-billion parameter language models
* It does not actually save much space. For example a large CTC conformer uncompressed is 464Mb vs 431Mb compressed
This is a change to core save_to/restore_from functionality of all models.
**It is backwards compatible - older checkpoints (with compression) will still work**
# Usage
* Usage remains exactly the same.
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Removes @experimental decorator in the diarization related files.
Signed-off-by: Taejin Park <[email protected]>
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
With the following YAML:
```
exp_manager:
exp_dir: null
name: ContextNet-8x-Stride-RNNT
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: val_wer
mode: min
save_top_k: 3
always_save_nemo: true
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
entity: null
resume_if_exists: false
resume_ignore_no_checkpoint: false
```
NeMo saves both .ckpt files and a .nemo file.
It is not 100% clear whether the best .CKPT file is what is used to save the .NEMO file, or whether the last .CKPT is used.
I'm guessing from a perusal of [exp_manager.py](https://github.com/NVIDIA/NeMo/blob/ddcc2a6c4eabc74b6b5021da05ff67d6d38ccf68/nemo/utils/exp_manager.py) that it saves the best model as the .NEMO file, but I can't say for sure by looking at the code.
[This document ](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/core/core.html#experiment-manager)is not clear and doesn't seem to mention all the keywords I see in the Python code.
**Describe the solution you'd like**
Please update the documentation to include all keywords related to exp_manager.py and which states clearly whether the best CKPT or last CKPT is saved in the training run's NEMO file.
**Hmmm**
I'm trying to verify this by comparing an element of the state dict of each of the CKPT files with the same element from the NEMO file and **none** of the CKPTs matches the NEMO file. Is this to be expected? So running
```
from glob import glob
ckpts=glob('./nemo_experiments/ContextNet-8x-Stride-RNNT/2022-02-07_18-13-37/checkpoints/*.ckpt')
model.restore_from('./nemo_experiments/ContextNet-8x-Stride-RNNT/2022-02-07_18-13-37/checkpoints/ContextNet-8x-Stride-RNNT.nemo')
sd1=model.state_dict();
for ckpt in ckpts:
print("=====================================================\n\n\n")
model2.load_from_checkpoint(ckpt);
sd2=model2.state_dict();
print(ckpt)
print('NEMO')
print(sd1['encoder.encoder.0.mconv.0.conv.weight'][0:3])
print('CKPT')
print(sd2['encoder.encoder.0.mconv.0.conv.weight'][0:3])
```
gives me (after deleting all the logging noise from NeMo):
```
./nemo_experiments/ContextNet-8x-Stride-RNNT/2022-02-07_18-13-37/checkpoints/ContextNet-8x-Stride-RNNT--val_wer=0.1228-epoch=1.ckpt
NEMO
tensor([[[-0.3809, -0.0407, 0.5199, 0.0178, -0.8887]],
[[-0.0082, -0.7741, 0.4612, -0.2517, -0.0893]],
[[-0.2904, -0.7611, -0.0239, 0.3230, 0.4082]]])
CKPT
tensor([[[ 0.2958, -0.5322, -0.6329, 0.0123, 0.5627]],
[[ 0.2680, 0.1954, 0.4858, 0.1461, -0.7119]],
[[ 0.7032, 0.3157, -0.5824, -0.7284, -0.6969]]])
=====================================================
./nemo_experiments/ContextNet-8x-Stride-RNNT/2022-02-07_18-13-37/checkpoints/ContextNet-8x-Stride-RNNT--val_wer=0.1735-epoch=19-last.ckpt
NEMO
tensor([[[-0.3809, -0.0407, 0.5199, 0.0178, -0.8887]],
[[-0.0082, -0.7741, 0.4612, -0.2517, -0.0893]],
[[-0.2904, -0.7611, -0.0239, 0.3230, 0.4082]]])
CKPT
tensor([[[ 0.2958, -0.5322, -0.6329, 0.0123, 0.5627]],
[[ 0.2680, 0.1954, 0.4858, 0.1461, -0.7119]],
[[ 0.7032, 0.3157, -0.5824, -0.7284, -0.6969]]])
=====================================================
./nemo_experiments/ContextNet-8x-Stride-RNNT/2022-02-07_18-13-37/checkpoints/ContextNet-8x-Stride-RNNT--val_wer=0.1306-epoch=0.ckpt
NEMO
tensor([[[-0.3809, -0.0407, 0.5199, 0.0178, -0.8887]],
[[-0.0082, -0.7741, 0.4612, -0.2517, -0.0893]],
[[-0.2904, -0.7611, -0.0239, 0.3230, 0.4082]]])
CKPT
tensor([[[ 0.2958, -0.5322, -0.6329, 0.0123, 0.5627]],
[[ 0.2680, 0.1954, 0.4858, 0.1461, -0.7119]],
[[ 0.7032, 0.3157, -0.5824, -0.7284, -0.6969]]])
=====================================================
./nemo_experiments/ContextNet-8x-Stride-RNNT/2022-02-07_18-13-37/checkpoints/ContextNet-8x-Stride-RNNT--val_wer=0.1365-epoch=2.ckpt
NEMO
tensor([[[-0.3809, -0.0407, 0.5199, 0.0178, -0.8887]],
[[-0.0082, -0.7741, 0.4612, -0.2517, -0.0893]],
[[-0.2904, -0.7611, -0.0239, 0.3230, 0.4082]]])
CKPT
tensor([[[ 0.2958, -0.5322, -0.6329, 0.0123, 0.5627]],
[[ 0.2680, 0.1954, 0.4858, 0.1461, -0.7119]],
[[ 0.7032, 0.3157, -0.5824, -0.7284, -0.6969]]])
Also a little odd that all of the CKPTs for this element are the same.
**PPS**
If I just run EVAL but not TRAIN on the best CKPT and the NEMO file, they come out the same, so even the weights under the hood aren't the same, I guess they are the same model. It would be nice to know why the weights above are different though. | {
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} | # What does this PR do ?
It generalizes the P-tuning method to support various NLP tasks including text-cls, NER, text summation, Q/A etc. It supports multiple task with one prompt encoder model. It also includes a tutorial notebook to show how to use it.
# Changelog
- Added the decoder for the p-tuning model so it can generate any length of text.
- The prompt encoder can be conditioned on the task-tags, so it supports multiple tasks simultaneously.
- Added the p-tune dataset. The user just need to write their own data processor to generate customized prompts for different tasks.
# Usage
Check the notebook `https://github.com/NVIDIA/NeMo/blob/generalized_gpt_ptuning/tutorials/nlp/PTune_multiple_NLP_tasks.ipynb`
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Updates the documentation for fine-tuning an ASR model.
**Collection**: [Docs]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] 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?
- [ ] 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 | 2022-02-07T04:31:00 | 2022-02-18T15:55:53 | 2022-02-18T15:55:53 | NONE | {
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} | null | null | null | **Describe the bug**
When I tried to train / finetune FastPitch, the following error shows up:
```
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
self._dispatch()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
self.training_type_plugin.start_training(self)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
self._results = trainer.run_stage()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
return self._run_train()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1319, in _run_train
self.fit_loop.run()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 234, in advance
self.epoch_loop.run(data_fetcher)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 193, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 215, in advance
result = self._run_optimization(
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 277, in _run_optimization
self.trainer.fit_loop.epoch_loop.batch_loop._update_running_loss(result.loss)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 127, in _update_running_loss
self.accumulated_loss.append(current_loss)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/supporters.py", line 78, in append
x = x.to(self.memory)
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "examples/tts/fastpitch_finetune.py", line 37, in main
trainer.fit(model)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 698, in _call_and_handle_interrupt
self.training_type_plugin.reconciliate_processes(traceback.format_exc())
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py", line 533, in reconciliate_processes
raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
pytorch_lightning.utilities.exceptions.DeadlockDetectedException: DeadLock detected from rank: 0
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
self._dispatch()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
self.training_type_plugin.start_training(self)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
self._results = trainer.run_stage()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
return self._run_train()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1319, in _run_train
self.fit_loop.run()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 234, in advance
self.epoch_loop.run(data_fetcher)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 193, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 145, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 215, in advance
result = self._run_optimization(
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 277, in _run_optimization
self.trainer.fit_loop.epoch_loop.batch_loop._update_running_loss(result.loss)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 127, in _update_running_loss
self.accumulated_loss.append(current_loss)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/supporters.py", line 78, in append
x = x.to(self.memory)
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```
**Steps/Code to reproduce bug**
I tried to train a FastPitch model on the Nancy Corpus, the above error showed up at first with the cmd: `examples/tts/fastpitch.py --config-name=fastpitch_align.yaml train_dataset=/root/autodl-tmp/nancy_train.json validation_datasets=/root/autodl-tmp/nancy_val.json prior_folder=/root/autodl-tmp/nemo-training/nancy_22k/prior exp_manager.exp_dir=/root/autodl-tmp/nemo-training/nancy_22k +exp_manager.resume_if_exists=True +exp_manager.resume_ignore_no_checkpoint=True model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24`, then I changed the `exp_dir`
, it started training.
Then I tried to finetune the resulting model on my own dataset with cmd `python examples/tts/fastpitch_finetune.py --config-name=fastpitch_align.yaml train_dataset=/root/autodl-tmp/male-speakers--with--nancy_train.json validation_datasets=/root/autodl-tmp/male-speakers--with--nancy_val.json +init_from_nemo_model=/root/autodl-tmp/nemo-training/nancy_fastpitch.nemo prior_folder=/root/autodl-tmp/nemo-training/multi-males/prior_multi-males model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 exp_manager.exp_dir=/root/autodl-tmp/nemo-training/multi-males model.n_speakers=5 model.pitch_avg=121.9 model.pitch_std=37.1 model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=2e-4 ~model.optim.sched`, the error showed up again, and this time changing `exp_dir` does not help.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: Cloud: [AutoDL](https://www.autodl.com/)
- Method of NeMo install: From source, `git clone` and `pip install .[tts]`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
PyTorch 1.10.0, Python 3.8, Cuda 11.3
**Additional context**
Add any other context about the problem here.
GPU: RTX 3090 * 2
VRAM: 24GB
CPU: 30 Cores AMD EPYC 7543 32-Core Processor
RAM: 160GB | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3619 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3619/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3619/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3619/events | https://github.com/NVIDIA/NeMo/issues/3619 | 1,125,310,731 | I_kwDOC_bI7s5DEuEL | 3,619 | nemo2riva tts_en_fastpitch fails if not using nemo:1.5.1 | {
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] | null | null | 2022-02-06T20:37:25 | 2022-04-06T00:05:12 | 2022-04-06T00:05:12 | CONTRIBUTOR | {
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} | null | null | null | Bear with me, this is going to be a lengthy one :)
Riva: 1.8.0-beta
Nemo: 1.5.1, 1.6.2, head
Model: [tts_en_fastpitch_align.nemo](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_fastpitch)
Related to #3613, could also be relevant for #3609, #3605 and #3567
Working example
- start off of `nvcr.io/nvidia/nemo:1.5.1`
- install the Riva toolset
- call `nemo2riva` to convert `tts_en_fastpitch_align.nemo` to `riva` format
Non working example 1:
- start off of `nvcr.io/nvidia/nemo:1.5.1`
- clone the NeMo repository, checkout tag `v1.6.2` or `1.7.0` and upgrade with `./reinstall.sh`
- install Riva toolset
- call `nemo2riva` to convert `tts_en_fastpitch_align.nemo` to `riva` format
crashes with
```
E0206 16:37:38.504896 140528981497664 cookbook.py:71] Export failed. Please make sure your NeMo model class (nemo.collections.tts.models.fastpitch.FastPitchModel) has working export() and that you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/opt/conda/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cli/nemo2riva.py", line 50, in nemo2riva
Nemo2Riva(args)
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/convert.py", line 65, in Nemo2Riva
save_archive(obj=model, save_path=riva_out, cfg=cfg, artifacts=artifacts, metadata=manifest['metadata'])
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cookbook.py", line 76, in save_archive
raise e
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cookbook.py", line 69, in save_archive
_, descriptions = obj.export(export_file, check_trace=cfg.args.runtime_check)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/exportable.py", line 179, in export
output_example = tuple(self.forward(*input_list, **input_dict))
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/models/fastpitch.py", line 497, in forward_for_export
return self.fastpitch.infer(text=text, pitch=pitch, pace=pace, speaker=speaker)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/modules/fastpitch.py", line 300, in infer
dec_out, _ = self.decoder(input=len_regulated, seq_lens=dec_lens)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/modules/transformer.py", line 215, in forward
return self._forward(input, mask_from_lens(seq_lens).unsqueeze(2), conditioning)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/modules/transformer.py", line 223, in _forward
out = layer(out, mask=mask)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/modules/transformer.py", line 171, in forward
output = self.dec_attn(dec_inp, attn_mask=~mask.squeeze(2))
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/modules/transformer.py", line 113, in forward
return self._forward(inp, attn_mask)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/tts/modules/transformer.py", line 142, in _forward
attn_prob = F.softmax(attn_score, dim=2)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/functional.py", line 1779, in softmax
ret = input.softmax(dim)
RuntimeError: CUDA out of memory. Tried to allocate 9.30 GiB (GPU 0; 15.78 GiB total capacity; 12.18 GiB already allocated; 2.05 GiB free; 12.30 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
```
Non working example 2:
- clone the NeMo repository, checkout tag `v1.6.2`
- use nemo Dockerfile to create container
- install Riva toolset in container
- call `nemo2riva` to convert `tts_en_fastpitch_align.nemo` to `riva` format
crashes with the same error as example 1
Non working example 3:
- clone the NeMo repository
- use nemo Dockerfile to create container from head (as a difference to previous examples this time the container is based on pytorch:22.01)
- install Riva toolset in container (requires an upgrade to Numba=0.55)
- call `nemo2riva` to convert `tts_en_fastpitch_align.nemo` to `riva` format
crashes with
```
/opt/conda/lib/python3.8/site-packages/torch/_masked/__init__.py:223: UserWarning: Failed to initialize NumPy: module compiled against API version 0xf but this version of numpy is 0xe (Triggered internally at /opt/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:68.)
example_input = torch.tensor([[-3, -2, -1], [0, 1, 2]])
Traceback (most recent call last):
File "/opt/conda/bin/nemo2riva", line 5, in <module>
from nemo2riva.cli import nemo2riva
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/__init__.py", line 12, in <module>
from nemo2riva.convert import Nemo2Riva
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/convert.py", line 18, in <module>
from nemo2riva.cookbook import save_archive
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cookbook.py", line 10, in <module>
from nemo.core import Exportable, ModelPT
File "/opt/conda/lib/python3.8/site-packages/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/__init__.py", line 18, in <module>
import pytorch_lightning
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/__init__.py", line 20, in <module>
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/callbacks/__init__.py", line 14, in <module>
from pytorch_lightning.callbacks.base import Callback
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/callbacks/base.py", line 26, in <module>
from pytorch_lightning.utilities.types import STEP_OUTPUT
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/types.py", line 25, in <module>
from torchmetrics import Metric
File "/opt/conda/lib/python3.8/site-packages/torchmetrics/__init__.py", line 14, in <module>
from torchmetrics import functional # noqa: E402
File "/opt/conda/lib/python3.8/site-packages/torchmetrics/functional/__init__.py", line 14, in <module>
from torchmetrics.functional.audio.pit import permutation_invariant_training, pit, pit_permutate
File "/opt/conda/lib/python3.8/site-packages/torchmetrics/functional/audio/__init__.py", line 26, in <module>
from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality # noqa: F401
File "/opt/conda/lib/python3.8/site-packages/torchmetrics/functional/audio/pesq.py", line 20, in <module>
import pesq as pesq_backend
File "/opt/conda/lib/python3.8/site-packages/pesq/__init__.py", line 6, in <module>
from .cypesq import cypesq, cypesq_retvals, cypesq_error_message as pesq_error_message
File "pesq/cypesq.pyx", line 1, in init cypesq
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
Googling for the UserWarning leads to https://github.com/numba/numba/issues/7754, but the installed numpy is 1.21.5.
What is behind all this ... We added support to `fastpitch.py` (based on the soon to be deprecated `fastpitch_align.yml` not `1.05`) for a non-English language that we are working with. Our starting point was `NeMo:1.6.0`, but after finishing and successfully training a model we discovered that if we want to deploy it using Riva, we first have to back port all of our changes. Since the old `yml` will become deprecated only in `NeMo:1.8.0`, it perhaps should still be supported for conversion to riva format.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3617 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3617/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3617/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3617/events | https://github.com/NVIDIA/NeMo/issues/3617 | 1,125,220,497 | I_kwDOC_bI7s5DEYCR | 3,617 | Question: validation_ds for En Conformer-CTC Small LibriSpeech configuration. | {
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} | null | null | null | this is a question about the pre-trained [model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_small_ls).
I downloaded the model and used the following command to check the training-related conf.
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="<path_of_pretrained_model>")
asr_model._cfg
```
And I checked the following.
>{'sample_rate': 16000, 'log_prediction': True, 'ctc_reduction': 'mean_batch', 'train_ds': {'manifest_filepath': '/data/Librispeech_SP_Tarred/tarred_audio_manifest.json', 'sample_rate': 16000, 'batch_size': 32, 'shuffle': True, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False, 'trim_silence': False, 'max_duration': 16.7, 'min_duration': 0.1, 'shuffle_n': 2048, 'is_tarred': True, 'tarred_audio_filepaths': '/data/Librispeech_SP_Tarred/audio__OP_0..511_CL_.tar'}, 'validation_ds': {'manifest_filepath': ['/manifests/librispeech/librivox-dev-other.json', '/manifests/librispeech/librivox-dev-clean.json', '/manifests/librispeech/librivox-test-other.json', '/manifests/librispeech/librivox-test-clean.json'], 'sample_rate': 16000, 'batch_size': 32, 'shuffle': False, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False, 'is_tarred': False, 'tarred_audio_filepaths': '/data/LibriSpeech/eval__OP_0..1023_CL_.tar'}, 'test_ds': {'manifest_filepath': ['/manifests/librispeech/librivox-dev-other.json', '/manifests/librispeech/librivox-dev-clean.json', '/manifests/librispeech/librivox-test-other.json', '/manifests/librispeech/librivox-test-clean.json'], 'sample_rate': 16000, 'batch_size': 32, 'shuffle': False, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False, 'is_tarred': False, 'tarred_audio_filepaths': '/data/LibriSpeech/eval__OP_0..1023_CL_.tar'}, 'tokenizer': {'dir': '/tokenizers/librispeech/experimental/tokenizer_spe_unigram_v128_max0/', 'type': 'bpe'}, 'preprocessor': {'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_size': 0.025, 'window_stride': 0.01, 'window': 'hann', 'features': 80, 'n_fft': 512, 'log': True, 'frame_splicing': 1, 'dither': 1e-05, 'pad_to': 0, 'pad_value': 0.0}, 'spec_augment': {'_target_': 'nemo.collections.asr.modules.SpectrogramAugmentation', 'freq_masks': 2, 'time_masks': 5, 'freq_width': 27, 'time_width': 0.05}, 'encoder': {'_target_': 'nemo.collections.asr.modules.ConformerEncoder', 'feat_in': 80, 'feat_out': -1, 'n_layers': 16, 'd_model': 176, 'subsampling': 'striding', 'subsampling_factor': 4, 'subsampling_conv_channels': 176, 'ff_expansion_factor': 4, 'self_attention_model': 'rel_pos', 'n_heads': 4, 'att_context_size': [-1, -1], 'xscaling': True, 'untie_biases': True, 'pos_emb_max_len': 5000, 'conv_kernel_size': 31, 'dropout': 0.1, 'dropout_emb': 0.0, 'dropout_att': 0.1}, 'decoder': {'_target_': 'nemo.collections.asr.modules.ConvASRDecoder', 'feat_in': 176, 'num_classes': 128, 'vocabulary': ['<unk>', 'e', 's', '▁', 't', 'a', 'o', 'i', '▁the', 'd', 'l', 'n', '▁a', 'm', 'y', 'u', '▁s', 'p', 'ed', 'c', '▁and', 're', '▁to', '▁of', 'r', 'w', 'ing', '▁w', 'h', '▁p', '▁c', 'er', 'f', 'k', 'ar', '▁in', '▁f', '▁b', 'g', 'an', 'in', '▁i', 'en', '▁he', 'le', '▁g', 'or', 'll', 'b', '▁be', 'ro', 'st', 'on', '▁d', 'v', 'ly', 'ce', 'ur', 'es', '▁that', '▁o', 'us', '▁was', '▁it', '▁th', 've', 'ch', 'un', 'al', '▁t', '▁ma', 'ri', '▁you', '▁on', 'ver', 'ent', '▁for', '▁re', 'ra', "'", '▁his', 'ir', 'ter', '▁with', '▁her', 'it', 'th', '▁mo', '▁me', '▁ha', '▁e', '▁as', 'tion', '▁had', '▁not', '▁no', '▁do', 'ther', '▁but', '▁st', '▁she', '▁is', 'igh', '▁ho', '▁lo', 'ng', '▁him', '▁an', 'ck', 'j', 'ugh', '▁de', '▁li', '▁mi', '▁la', '▁my', '▁con', '▁have', '▁this', '▁which', 'q', '▁up', '▁said', '▁from', '▁who', '▁ex', 'x', 'z']}, 'optim': {'name': 'adamw', 'lr': 5.0, 'betas': [0.9, 0.98], 'weight_decay': 0.001, 'sched': {'name': 'NoamAnnealing', 'd_model': 176, 'warmup_steps': 10000, 'warmup_ratio': None, 'min_lr': 1e-06}}, 'target': 'nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE'},
I paid attention to the next part.
>'validation_ds': {'manifest_filepath': ['/manifests/librispeech/librivox-dev-other.json', '/manifests/librispeech/librivox-dev-clean.json', '/manifests/librispeech/librivox-test-other.json', '/manifests/librispeech/librivox-test-clean.json'],
Did you use data other than LS-dev for LS training?
Or, is it a default value of the model not used in real training? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3616 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3616/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3616/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3616/events | https://github.com/NVIDIA/NeMo/pull/3616 | 1,125,167,909 | PR_kwDOC_bI7s4yIU6X | 3,616 | Clarifying documentation | {
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} | [] | closed | false | null | [] | null | null | 2022-02-06T10:44:57 | 2022-02-07T18:50:51 | 2022-02-07T18:50:50 | CONTRIBUTOR | {
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} | # What does this PR do ?
Updated Optimizers & schedulers documentation.
**Collection**: all
# Changelog
- minor fix to documentation
# 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
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[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 #3549
Signed-off-by: Iztok Lebar Bajec [email protected]
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https://api.github.com/repos/NVIDIA/NeMo/issues/3615 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3615/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3615/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3615/events | https://github.com/NVIDIA/NeMo/pull/3615 | 1,125,156,748 | PR_kwDOC_bI7s4yIS0w | 3,615 | [TTS] Fix UnivNet LibriTTS pretrained location | {
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"merged_at": "2022-02-08T12:52:32"
} | # What does this PR do ?
Mini-PR :) - Fixes the NGC location of the UnivNet LibriTTS pretrained model.
**Collection**: TTS
# Changelog
- Fixes the NGC location of the UnivNet LibriTTS pretrained model.
# 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?
- [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**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/3614 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3614/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3614/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3614/events | https://github.com/NVIDIA/NeMo/pull/3614 | 1,125,037,574 | PR_kwDOC_bI7s4yH8sM | 3,614 | Update numba if NVIDIA_PYTORCH_VERSION not correct | {
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} | Update/install numba if `NVIDIA_PYTORCH_VERSION` is not equal to the one in `Dockerfile`. Resolves #3613
Signed-off-by: Iztok Lebar Bajec <[email protected]>
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```
ERROR: Cannot uninstall 'llvmlite'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```
`pip install -e` on the other hand succeeds installing `nemo:1.7.0rc0` and `numpy:1.22.2`, the rest of the packages remain untouched.
It seems that `./reinstall.sh` which used to work fine, a week or so ago when following the same procedure to upgrade to `nemo:1.6.0rc` redeveloped issue #841. The solution remains the same, first call
```
pip install --ignore-installed llvmlite
```
followed by `./reinstall.sh`. In this case, apart `llvml`, the following packages are updated
```
ftfy-6.0.3 nemo-toolkit-1.7.0rc0 numba-0.55.1 pytorch-lightning-1.5.9 sacrebleu-2.0.0 setuptools-59.5.0
```
Interestingly `numpy` in this case is left at `1.21.5`. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3612 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3612/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3612/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3612/events | https://github.com/NVIDIA/NeMo/issues/3612 | 1,124,907,000 | I_kwDOC_bI7s5DDLf4 | 3,612 | using NGC containers [ NeMo/Riva] there is no nemo2riva | {
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docker run --runtime=nvidia -it --rm -v --shm-size=16g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:1.5.1
```
```
oot@adbd9847582b:/workspace/nemo# pip3 install nvidia-pyindex
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: nvidia-pyindex in /opt/conda/lib/python3.8/site-packages (1.0.9)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
root@adbd9847582b:/workspace/nemo# pip3 install nemo2riva
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
ERROR: Could not find a version that satisfies the requirement nemo2riva (from versions: none)
ERROR: No matching distribution found for nemo2riva
root@adbd9847582b:/workspace/nemo# pip3 install nemo2riva-1.9.0-beta-py3-none-any.whl
WARNING: Requirement 'nemo2riva-1.9.0-beta-py3-none-any.whl' looks like a filename, but the file does not exist
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
ERROR: nemo2riva-1.9.0-beta-py3-none-any.whl is not a supported wheel on this platform.
```
where do I get the tool?
Also tried with
```
nvcr.io/nvidia/riva/riva-speech:1.8.0-beta-server
``` | {
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} | Signed-off-by: ericharper <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix wheel build with new syntax.
**Collection**: [Bugfix]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"merged_at": "2022-02-17T01:31:01"
} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
Reproduce and fix failing tests inside of the 22.01 container in [PR 3571 ](https://github.com/NVIDIA/NeMo/pull/3571)
**Collection**: ASR label dataloader and tarred label dataset
# Changelog
- Fix failing tests due to 22.01 container upgrade
# Usage
* You can potentially add a usage example below
None
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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 in PR 3571 )](https://github.com/NVIDIA/NeMo/pull/3571)
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} | null | null | null | Weights and Biases is causing some GPT-3 runs to fail. This seems to occur randomly. When launching multiple identical jobs, the first two can fail because of this and the third one works.
Traceback:
> Traceback (most recent call last):
File "/opt/conda/bin/wandb", line 8, in <module>
sys.exit(cli())
File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1128, in __call__
return self.main(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1053, in main
rv = self.invoke(ctx)
File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1659, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1395, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 754, in invoke
return __callback(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/wandb/cli/cli.py", line 86, in wrapper
return func(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/wandb/cli/cli.py", line 235, in login
wandb.login(relogin=relogin, key=key, anonymous=anon_mode, host=host, force=True)
File "/opt/conda/lib/python3.8/site-packages/wandb/sdk/wandb_login.py", line 65, in login
configured = _login(**kwargs)
File "/opt/conda/lib/python3.8/site-packages/wandb/sdk/wandb_login.py", line 268, in _login
wlogin.configure_api_key(key)
File "/opt/conda/lib/python3.8/site-packages/wandb/sdk/wandb_login.py", line 154, in configure_api_key
apikey.write_key(self._settings, key)
File "/opt/conda/lib/python3.8/site-packages/wandb/sdk/lib/apikey.py", line 223, in write_key
api.clear_setting("anonymous", globally=True, persist=True)
File "/opt/conda/lib/python3.8/site-packages/wandb/apis/internal.py", line 75, in clear_setting
return self.api.clear_setting(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/wandb/apis/internal.py", line 19, in api
self._api = InternalApi(*self._api_args, **self._api_kwargs)
File "/opt/conda/lib/python3.8/site-packages/wandb/sdk/internal/internal_api.py", line 78, in __init__
self._settings = Settings(
File "/opt/conda/lib/python3.8/site-packages/wandb/old/settings.py", line 23, in __init__
self._global_settings.read([Settings._global_path()])
File "/opt/conda/lib/python3.8/configparser.py", line 697, in read
self._read(fp, filename)
File "/opt/conda/lib/python3.8/configparser.py", line 1082, in _read
raise MissingSectionHeaderError(fpname, lineno, line)
configparser.MissingSectionHeaderError: File contains no section headers.
file: '/root/.config/wandb/settings', line: 1
'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
wandb: Appending key for api.wandb.ai to your netrc file: /root/.netrc
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"merged_at": "2022-02-04T20:18:19"
} | # What does this PR do ?
Add env variable to force transformers to run offline during CI
**Collection**: [bugfix]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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**
NeMo ASR grammar seems to be incorrect on any version except v1.4.0.
Besides the odd behavior with the grammar we can also notice that Punctuation takes much longer (100x~) in v1.4.0 compared to other versions that worked for my setup which leaves me with the only choice of using v1.4.0 for transcriptions and v1.6.0 for punctuation, eg:
```
NeMo Toolkit v1.4.0
Punctuation took: 13.06 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very like the old portrait."]
```
```
NeMo Toolkit v1.6.0
Punctuation took: 0.08 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
```
**Steps/Code to reproduce bug**
`pip install Cython`
```
pip install nemo_toolkit[all]==1.6.1
pip install nemo_toolkit[all]==1.6.0
pip install nemo_toolkit[all]==1.5.1
pip install nemo_toolkit[all]==1.4.0
```
I simply ran one of these every time I wanted to switch a version
**Expected behavior**
Let's inspect the debug log that I repeatedly tested multiple times on different versions of NeMo on the same wave file:
https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
If you will notice, only version 1.4.0 will get the word `like` right while all the others will sort of "make up" the word `likt` that doesn't really exists in any known dictionary, besides names, which I get the complexity of the manner, but am curios what "changed" or more like what got messed up so we can now see this behavior:
**NeMo Toolkit v1.4.0**
```
NeMo Toolkit v1.4.0
Init took: 27.58 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.14it/s]
Transcriptions took: 0.93 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very like the old portrait"]
Punctuation took: 13.77 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very like the old portrait."]
Everything took: 42.28 seconds
NeMo Toolkit v1.4.0
Init took: 29.06 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.15s/it]
Transcriptions took: 1.22 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very like the old portrait"]
Punctuation took: 12.90 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very like the old portrait."]
Everything took: 43.18 seconds
NeMo Toolkit v1.4.0
Init took: 28.26 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.29it/s]
Transcriptions took: 0.83 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very like the old portrait"]
Punctuation took: 12.20 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very like the old portrait."]
Everything took: 41.30 seconds
NeMo Toolkit v1.4.0
Init took: 27.88 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.16it/s]
Transcriptions took: 0.92 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very like the old portrait"]
Punctuation took: 13.06 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very like the old portrait."]
Everything took: 41.86 seconds
```
**NeMo Toolkit v1.5.1**
```
NeMo Toolkit v1.5.1
Init took: 24.72 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.00s/it]
Transcriptions took: 1.09 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 11.76batch/s]
Punctuation took: 0.10 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 25.91 seconds
NeMo Toolkit v1.5.1
Init took: 24.43 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.08it/s]
Transcriptions took: 1.01 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 11.36batch/s]
Punctuation took: 0.10 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 25.54 seconds
NeMo Toolkit v1.5.1
Init took: 24.63 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.06it/s]
Transcriptions took: 1.03 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 12.99batch/s]
Punctuation took: 0.09 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 25.76 seconds
NeMo Toolkit v1.5.1
Init took: 23.22 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.10it/s]
Transcriptions took: 0.99 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 14.29batch/s]
Punctuation took: 0.08 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 24.30 seconds
```
**NeMo Toolkit v1.6.0**
```
NeMo Toolkit v1.6.0
Init took: 22.43 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.27it/s]
Transcriptions took: 0.87 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.38batch/s]
Punctuation took: 0.07 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 23.37 seconds
NeMo Toolkit v1.6.0
Init took: 22.57 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.25it/s]
Transcriptions took: 0.88 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.38batch/s]
Punctuation took: 0.07 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 23.53 seconds
NeMo Toolkit v1.6.0
Init took: 22.14 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.22it/s]
Transcriptions took: 0.91 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.63batch/s]
Punctuation took: 0.07 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 23.12 seconds
NeMo Toolkit v1.6.0
Init took: 22.27 seconds
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.26it/s]
Transcriptions took: 0.88 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.15batch/s]
Punctuation took: 0.08 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 23.22 seconds
```
**NeMo Toolkit v1.6.1**
```
NeMo Toolkit v1.6.1
Init took: 37.51 seconds
Transcribing: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.30s/it]
Transcriptions took: 1.36 seconds
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
100%|█████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.54s/batch]
Punctuation took: 1.58 seconds
["Well, I don't wish to see it any more, observed Phoebe, turning away her eyes. It is certainly very likt the old portrait."]
Everything took: 40.45 seconds
```
**Environment overview**
- Environment location: Local
- Method of NeMo install: pip install. as can be seen above.
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Latest
- PyTorch version: as required by each nemo package that I installed on a fresh machine using the commands above ^^
- Python version: 3.7.11 (also tested and got the same behavior on: 3.8.12 and 3.9.7)
- Python versions that didn't run it were: 2.7.18, 3.5.6, 3.6.13, 3.10.0
**Additional context**
I installed apex for cpu using:
```
pip install -v --disable-pip-version-check --no-cache-dir git+https://github.com/NVIDIA/apex
```
(TORCH_CUDA_ARCH_LIST="compute capability")
The following NeMo versions were not able to run at all using the same spec:
```
v1.3.0 and below (tested until v1.0.0)
v1.5.0
```
**Example Code:** (Fixed + added full snippet below)
```
import time
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
if __name__ == "__main__":
print("NeMo Toolkit vX.Y.Z")
start = time.time()
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained("stt_en_quartznet15x5")
punctuation = nemo_nlp.models.PunctuationCapitalizationModel.from_pretrained(model_name='punctuation_en_distilbert')
print("Init took: " + '%.2f' % (time.time() - start) + " seconds")
start = time.time()
transcriptions = asr_model.transcribe(['2086-149220-0033.wav'])
print("Transcription took: " + '%.2f' % (time.time() - start) + " seconds")
start = time.time()
res = punctuation.add_punctuation_capitalization(queries=transcriptions)
print("Punctuation took: " + '%.2f' % (time.time() - start) + " seconds")
print(transcriptions)
print(res)
``` | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds current nemo version to all nemo files generated.
# Note
The version will point to the *earliest* known version of NeMo that was compatible with this model. This does not preclude later versions being compatible/incompatible with the checkpoint, so it is up to the user to determine which version would work best, but it can be assumed that no version prior to the noted version will support the checkpoint.
**Collection**: [Core]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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**Collection**: [DOC]
# Changelog
- fix typo in README
- fix minimal version for SciPy (it is necessary to use `scipy.stats`
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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Trying to run tts/tutorials/fastpitch_finetune.ipynb from Colab, the first cell give the following error:
`Collecting nemo_toolkit[tts]
Cloning https://github.com/NeMo/NeMo.git (to revision r1.6.1) to /tmp/pip-install-dikw_cg4/nemo-toolkit_dde310e4647c4385a9c5f59aa12c3537
Running command git clone -q https://github.com/NeMo/NeMo.git /tmp/pip-install-dikw_cg4/nemo-toolkit_dde310e4647c4385a9c5f59aa12c3537
fatal: could not read Username for 'https://github.com/': No such device or address
WARNING: Discarding git+https://github.com/NeMo/[email protected]#egg=nemo_toolkit[tts]. Command errored out with exit status 128: git clone -q https://github.com/NeMo/NeMo.git /tmp/pip-install-dikw_cg4/nemo-toolkit_dde310e4647c4385a9c5f59aa12c3537 Check the logs for full command output.
ERROR: Could not find a version that satisfies the requirement nemo-toolkit (unavailable) (from versions: 0.11.0b6.linux-x86_64, 0.8, 0.8.1, 0.8.2, 0.9.0, 0.10.0b0, 0.10.0b1, 0.10.0b2, 0.10.0b3, 0.10.0b4, 0.10.0b5, 0.10.0b6, 0.10.0b7, 0.10.0b8, 0.10.0b9, 0.10.0b10, 0.10.0, 0.10.1, 0.11.0b1, 0.11.0b2, 0.11.0b3, 0.11.0b4, 0.11.0b5, 0.11.0b8, 0.11.0b10, 0.11.0b11, 0.11.0b12, 0.11.0b14, 0.11.0, 1.0.0a4, 1.0.0b0, 1.0.0b1, 1.0.0b2, 1.0.0b3, 1.0.0rc1, 1.0.0, 1.0.1, 1.0.2, 1.1.0, 1.2.0, 1.3.0, 1.4.0, 1.5.0, 1.5.1, 1.6.0, 1.6.1)
ERROR: No matching distribution found for nemo-toolkit (unavailable)`
In addition, I've been using fastpitch_finetune.ipynb with my own dataset since months ago (Branch Main), and now I cannot run it anymore **since 3 days ago**. It gives me an error when finetuning hifigan in line 32 (sorry for the long output):
`env: HYDRA_FULL_ERROR=1 [NeMo W 2022-02-04 14:08:27 optimizers:52] Apex was not found. Using the lamb or fused_adam optimizer will error out. ################################################################################ ### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk ### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile) ### (or run as: KALDI_ROOT=<your_path> python <your_script>.py) ################################################################################ [NeMo W 2022-02-04 14:08:28 experimental:28] Module <function get_argmin_mat at 0x7f6324ecccb0> is experimental, not ready for production and is not fully supported. Use at your own risk. [NeMo W 2022-02-04 14:08:28 experimental:28] Module <function getMultiScaleCosAffinityMatrix at 0x7f6324eccd40> is experimental, not ready for production and is not fully supported. Use at your own risk. [NeMo W 2022-02-04 14:08:28 experimental:28] Module <function parse_scale_configs at 0x7f6324ecc950> is experimental, not ready for production and is not fully supported. Use at your own risk. [NeMo W 2022-02-04 14:08:28 experimental:28] Module <function get_embs_and_timestamps at 0x7f6324ecc290> is experimental, not ready for production and is not fully supported. Use at your own risk. [NeMo W 2022-02-04 14:08:28 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'hifigan.yaml': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information warnings.warn(msg, UserWarning) Traceback (most recent call last): File "hifigan_finetune.py", line 32, in <module> main() # noqa pylint: disable=no-value-for-parameter File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper config_name=config_name, File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 378, in _run_hydra lambda: hydra.run( File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 214, in run_and_report raise ex File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 211, in run_and_report return func() File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 381, in <lambda> overrides=args.overrides, File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 92, in run run_mode=RunMode.RUN, File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 563, in compose_config from_shell=from_shell, File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/config_loader_impl.py", line 145, in load_configuration from_shell=from_shell, File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/config_loader_impl.py", line 262, in _load_configuration_impl ConfigLoaderImpl._apply_overrides_to_config(config_overrides, cfg) File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/config_loader_impl.py", line 342, in _apply_overrides_to_config f"Could not delete from config. '{override.key_or_group}' does not exist." hydra.errors.ConfigCompositionException: Could not delete from config. 'model.sched' does not exist.`
**Steps/Code to reproduce bug**
Simply trying to execute the latest tts/tutorials/fastpitch_finetune.ipynb.
**Environment details**
Google Colab and Colab PRO +
Any help on this will be appreciated.
Thanks! | {
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} | # What does this PR do ?
This PR adds support for buffered RNNT evaluation, so that models can be evaluated on audio of any length by models which are normally restricted to small audio duration.
**Collection**: [ASR]
# Changelog
- Refactor the asr_chunked_inference into ctc and rnnt sub folders
- Add speech_to_text_buffered_infer_rnnt.py script to perform RNNT buffered inference using two approaches.
- "Middle token" algorithm
- "Longest common subsequence merge" algorithm
- Refactor RNNT greedy batched decoding to significantly improve decoding speed after training.
- This now is automatically applied right after performing `RNNTModel.change_decoding_strategy()`
# Usage
* You can potentially add a usage example below
```sh
Script to perform buffered inference using RNNT models.
# Middle Token merge algorithm
python speech_to_text_buffered_infer_rnnt.py \
--asr_model="<Path to a nemo model>" \
--test_manifest="<Path to a JSON manifest>" \
--model_stride=4 \
--output_path="." \
--total_buffer_in_secs=10.0 \
--chunk_len_in_ms=8000 \
--device="cuda:0" \
--batch_size=128
# Longer Common Subsequence (LCS) Merge algorithm
python speech_to_text_buffered_infer_rnnt.py \
--asr_model="<Path to a nemo model>" \
--test_manifest="<Path to a JSON manifest>" \
--model_stride=4 \
--output_path="." \
--use_lcs_merge \
--lcs_alignment_dir=<OPTIONAL: Some path to store the LCS alignments> \
--total_buffer_in_secs=10.0 \
--chunk_len_in_ms=8000 \
--device="cuda:0" \
--batch_size=128
# NOTE:
You can use `DEBUG=1 python speech_to_text_buffered_infer_rnnt.py ...` to print out the
ground truth text and predictions of the model.
```
# 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?
- [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
# Additional Information
* Notebook explaining the parts of the script will be released shortly.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Updates from old `python setup.py bdist_wheel` syntax to `pip build`.
**Collection**: [bugfix]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | An issue with regard to preprocessing (Moses, Jieba, etc.) in the MTEncDecModel:
If the language code is supplied to `MTEncDecModel.translate()` , preprocessing will occur. On the other hand, if the language code is not supplied ('ignore'), the directional tokens ('\<es\>', etc.) cannot be appended. This makes it impossible to have a multilingual model that does not apply preprocessing, which is relatively slow.
It would be a sufficient workaround to refactor how this method decides to do preprocessing (e.g., `preprocess=False`), but it should probably be an attribute of the model itself whether preprocessing is needed. When using a model from NGC, how would a user know whether to use `source_lang="ignore"` or `source_lang="en"`?
It looks like there are a few other places in the model where pre- and post-processing are deeply-entrenched as well. It would be nice to see all of this separated so a user can instantiate a model with `pre_and_post_process=False` and take control of how the text is processed. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3597 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3597/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3597/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3597/events | https://github.com/NVIDIA/NeMo/pull/3597 | 1,123,476,374 | PR_kwDOC_bI7s4yC9gy | 3,597 | [TTS] Convert entry from UTF-16 to UTF-8 | {
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# What does this PR do ?
Updates the DÉJÀ entry in CMUdict to the correct encoding (UTF-8), as mentioned in #3594.
**Collection**: TTS
# Changelog
Bytes `D\xc9J\xc0` updated to `D\xc3\x89J\xc3\x80`.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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I have about 100 hours of pre-processed data for male and female speakers. There is some overlap in the lines the speakers are reciting.
I was wondering if having different speakers saying the same exact text would negatively impact the training of an automatic speech recognition model? Would I have to remove the audio and text files where the speakers say the same thing? Each speaker sounds quite different from the other.
Thank you,
Enrico | {
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} | # Overview
This change adds [Variational Inference with adversarial learning (VITS)](https://github.com/jaywalnut310/vits) to the collection of TTS models.
VITS uses the `monotonic_align` module, which requires Cython to be installed prior to building (i.e. run `pip install cython` before running `./reinstall.sh`). | {
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} | Signed-off-by: Oktai Tatanov <[email protected]>
# What does this PR do ?
**Collection**: [tts]
# Changelog
- Fix bug in NVIDIA's CMUDict: word `INTERNATIONAL` used non-existent phoneme
- Fix incorrect usage of TTSDataset in some files
**PR Type**:
- [x] Bugfix
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} | # What does this PR do ?
Fixes a typo in fastpitch_align_v1.05 config that leads to high pitch loss values on validation.
**Collection**: TTS
**PR Type**:
- [x] Bugfix
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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# Before your PR is "Ready for review"
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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
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 ?
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.
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Upgrade pytorch_lightning trainer flags that are going to be deprecated in future
**Collection**: Core and All collections yaml files
# Changelog
- Update pytorch_lightning deprecated variables, namely:
> - accelerator to the trainer now takes the type of accelerator device than distributed type
> - distributed type is handled by strategy flag
> - previously: accelerator='ddp' now accelerator='gpu' strategy='ddp'
> - trainer flag checkpoint_callback is renamed to enable_checkpointing
> - ModelCheckpoint argument evert_n_val_epochs renamed to every_n_epochs
> - Update `trainer.gpus` to `trainer.devices` ( With this change setting devices=0 doesnt auto select gpu instead need to make accelerator='cpu' and also note accelerator=cpu now will only accept >0 as values for devices)
> - `examples/asr/transcribe_speech.py` script modified to remove cuda option. Script automatically searches for GPU and if its not present then falls back to CPU
> - `checkpointing_callback` -> `enable_checkpointing`
> - `flush_logs_every_n_steps` removed from trainer flags
> - `sync_batchnorm` and `num_nodes` removed from NLPDPPPlugins
# 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
-
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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?
- [ ] 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 ?
Add a one line overview of what this PR aims to accomplish.
Merge bugfixes from r1.6.1 into main.
**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 ?
Closes #3509 on the NeMo side. Requires a fix in Riva as well.
**Collection**: TTS
# Changelog
- There was a name collusion with audio that resulted in a onnx dynamic axes error
- input_example was changed to be compliant with the new spec of (return dict,) as opposed to the older return dict
**PR Type**:
- [x] Bugfix | {
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} | **Collection**: [tts]
# Changelog
- refactoring of HiFi-GAN code and config, replacing deprecated datasets with `VocoderDataset`
- refactoring of UnivNet code and config, replacing deprecated datasets with `VocoderDataset`, add exportability (it works correctly only for TorchScript)
- refactoring of WaveGlow code and config, replacing deprecated datasets with `VocoderDataset`
- minor refactoring of FastPitch code
- minor refactoring of Mixer-TTS code
- fixing small bugs in Exportable
**PR Type**:
- [x] New Feature
- [x] Bugfix | {
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