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} | Signed-off-by: stevehuang52 <[email protected]>
This PR fixes the second problem in this [issue 4455](https://github.com/NVIDIA/NeMo/issues/4455).
Previously, the code will fail when there are "/" in the filepaths of tarred datasets. Now we only add the manifest directory to the audio filepath if the resulting file exists, thus fixing the problem.
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} | # What does this PR do ?
Fix the RPE implementation converging slower with loss consistently higher than NeMo APE and Megatron-LM RPE implementation.
NEW: exception is raised when configured with pipelie-parallel-size > 2 and RPE.
**Collection**: NLP
# Changelog
- Changed how RPE parameters are initialized and passed on.
# Usage
* Train with `model.position_embedding_type='relative'` config.
```python
python /home/workspace/NeMo/examples/nlp/language_modeling/megatron_t5_pretraining.py model.position_embedding_type='relative'
```
# 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
## 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 ?
updated indentation
**Collection**: TTS
# 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 ?
- Quick fix to Jenkins CI config for Wav2Vec.
Add a one line overview of what this PR aims to accomplish.
- Halved number of conformer blocks and feedforward parameters to ease up memory requirements on Jenkins
**Collection**: [Note which collection this PR will affect]
- ASR
# Changelog
- Halved transformer layers in wav2vec_ci (12 -> 6)
- Halved inner dimension of transformer encoder. (3072 -> 1536)
- Reduced batch size for wav2vec_ci to 8
# 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)
- [ N] Did you write any new necessary tests?
- [ N] Did you add or update any necessary documentation?
- [ N] 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
- [Y ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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It runs fine. However, while saving results after Epoch 0 it gives this error
In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present
I checked and training step method does have a dictionary. What could be the reason for this?
Im running training on Google Colab (if that helps ) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4465 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4465/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4465/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4465/events | https://github.com/NVIDIA/NeMo/pull/4465 | 1,288,754,116 | PR_kwDOC_bI7s46kaw8 | 4,465 | Add Bucketing support to TarredAudioToClassificationLabelDataset | {
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} | # What does this PR do ?
This PR adds support for bucketing to `TarredAudioToClassificationLabelDataset`.
**Collection**: [ASR]
# Changelog
- Modify `_setup_dataloader_from_config` in `nemo.collections.asr.models.classification_models._EncDecBaseModel` to enable bucketing.
- Adds `pad_id` member to `TarredAudioToClassificationLabelDataset` class.
# 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) -> It does not
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
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https://api.github.com/repos/NVIDIA/NeMo/issues/4464 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4464/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4464/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4464/events | https://github.com/NVIDIA/NeMo/issues/4464 | 1,288,328,671 | I_kwDOC_bI7s5MylXf | 4,464 | Memory consumption by Punctuation model | {
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} | null | null | null | Hi,
I am using PunctuationCapitalizationModel to add punctuation marks in raw text.
When i load the pretrained model it consumes 1427 mib memory approximately. This memory never gets free. The application that i created is flask application and runs in multithreaded environment. For every new request 1427mib memory gets consumed.
Code Snippet:
model = PunctuationCapitalizationModel.from_pretrained("punctuation_en_bert")
model = model.cpu()
texts = [word_data]
processed_texts = model.add_punctuation_capitalization(
texts,
batch_size=128,
max_seq_length=64,
step=8,
margin=16,
)
return str(processed_texts[0])
How to free this memory?
Please help to resolve it. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4463 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4463/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4463/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4463/events | https://github.com/NVIDIA/NeMo/pull/4463 | 1,288,021,730 | PR_kwDOC_bI7s46h-7J | 4,463 | [TN] Bug fix: expand serial coverage of unknown symbol, remove constraints from word graph | {
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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
- [ 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4462 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4462/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4462/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4462/events | https://github.com/NVIDIA/NeMo/pull/4462 | 1,287,956,869 | PR_kwDOC_bI7s46hxIg | 4,462 | r1.10.0 MegaMolBART Compatibility | {
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} | # What does this PR do ?
1. Fixes RegExTokenizer missing attributes.
2. Fixes broken Megatron encoder-decoder LM model ability to receive directly encoder hiddens.
```
MegatronLMEncoderDecoderModel.forward(
self,
encoder_input_ids,
decoder_input_ids,
encoder_attn_mask,
decoder_attn_mask,
token_type_ids=None,
lm_labels=None,
# NEW API
enc_output=None,
enc_output_attn_mask=None,
# NEW API
output_enc_hidden_only=False,
enc_input=None,
):
```
3. Added MegatronLMEncoderDecoderModel.encode method.
4. Improved decoding speed.
**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: Xuesong Yang <[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: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fix some handling of "s" cases in the IPA G2P class.
**Collection**: TTS
# Changelog
- Indexing fixed for cases where there's no apostrophe
- Removed extraneous "t" added for "ts/t's"
- Removed "s" case when there's no apostrophe
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Address QA bugs for German TTS tutorial. Correct PR instead of https://github.com/NVIDIA/NeMo/pull/4449
**Collection**: TTS
# Changelog
- tutorial notebook changed
- learning rate updated to 1e-3 based on https://github.com/NVIDIA/NeMo/pull/4308
**PR Type**: Bugfix
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} | # What does this PR do ?
Update interface with Megatron GPT Prompt Learning model
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [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 ?
Proposed fix for #4455
**Collection**: asr, common
# Changelog
- [Plumb is_tar_manifest throughout dataset code.](https://github.com/NVIDIA/NeMo/commit/b1bc3ab190cbb1e24898cd2af6ff84e2425f69d7)
- [Fix for KeyError.](https://github.com/NVIDIA/NeMo/commit/653d4d755d0132d640305017336ddbf612705aeb)
# Usage
This allow for running ASR training on tar files with keys containing "/".
is_tar_manifest needs to be plumbed through all possible parse_func's in usage today.
# 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
* Related to #4455
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Tar file datasets have two problems today that I am aware of:
1. The filenames inside tar files are *required* not to have "/" or "." in them. Disallowing "." is fine, given that it has special meaning in webdataset. Disallowing "/" causes some difficulties. First, if your key is larger than 255 elements and you convert "/" to "_" (as is done here https://github.com/NVIDIA/NeMo/blob/daf08760dde35433f934a5c38ab293bad3c22825/scripts/speech_recognition/convert_to_tarred_audio_dataset.py#L560, then it is not possible to untar the created tar file. You will get an error like this when running `untar`:
```
File name too long: 'gov_DOT_uscourts_DOT_illappct_DOT_1-07-3566-1-08-0702-1-08-0746-1-08-1373-1-08-1982-1-08-1983-1-08-2151-1-08-2152_gov_DOT_uscourts_DOT_illappct_DOT_1-07-3566-1-08-0702-1-08-0746-1-08-1373-1-08-1982-1-08-1983-1-08-2151-1-08-2152_DOT_2009-11-12_DOT_mp3_00000.flac'
```
This is because linux file names are at most 255 characters: https://serverfault.com/a/9548
2. Suppose that your tar dataset contains the following 4 entries:
```
speaker1/utterance1.wav
speaker1/utterance2.wav
speaker2/utterance1.wav
speaker2/utterance2.wav
```
Today, NeMo will convert the key `speaker1/utterance1.wav` into `${parent_dir_of_manifest}/speaker1/utterance1.wav`. That is to say that it believes that this makes. The code which does that is here:
https://github.com/NVIDIA/NeMo/blob/d8785e022e2ccaf8b02a27ca084aea5acd257b18/nemo/collections/common/parts/preprocessing/manifest.py#L92-L104
This code assumes that the keys of tar files have no "/" in them. A more straightforward way is to pass another argument "is_tar_dataset" into this function to accept tar files appropriately. I will show this below.
**Describe the solution you'd like**
I will link to a work-in-progress PR for "A more straightforward way is to pass another argument "is_tar_dataset" into this function to accept tar files appropriately." shortly after creating this issue. Edit: https://github.com/NVIDIA/NeMo/pull/4456
**Describe alternatives you've considered**
I wrote a quick workaround that converts "/" to "_" on the fly after loading the tar file and manifest here: https://github.com/NVIDIA/NeMo/compare/main...galv:tar-remove-slash-workaround
While you could make a "fix" by doing this conversion only when a tarred dataset is being used, this is rather clumsy since the ID changes. This makes debugging and downstream joins hard.
**Additional context**
Why should you make this change? NeMo tarred datasets can currently be created only by its convert_to_tarred_audio_dataset.py script, which cannot be parallelized via something like spark or some other big data processing framework. It is hard for users of big data technology to train on tarred datasets created directly via spark for this reason. Secondly, tar files are kind of the de facto standard by which speech training data is transferred today (see openslr.org, or https://huggingface.co/datasets/MLCommons/peoples_speech). It saves users time and money to be able to train directly on the tar files they have downloaded, instead of having to untar the download, and the retar it with changed names. The existing path basically increases the disk usage required from 2TiB (in the case of People's Speech) to 6TiB.
Additionally, if someone were training on a nemo tarred dataset created by convert_to_tarred_audio_dataset.py and didn't have the underlying data, they would not be able to untar most files simply to listen to them, as their keys will most likely exceed 255 characters in length. This makes observability hard. | {
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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
- [ 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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When I run second cell it gives error on this line
from nemo.collections import nlp as nemo_nlp
And the error is this:
(RuntimeError Traceback (most recent call last)
[<ipython-input-18-051dc782fbfe>](https://localhost:8080/#) in <module>()
11 from tqdm import tqdm
12
---> 13 from nemo.collections import nlp
14 from nemo.utils.exp_manager import exp_manager
16 frames
/usr/local/lib/python3.7/dist-packages/torch/jit/_script.py in script(obj, optimize, _frames_up, _rcb, example_inputs)
RuntimeError:
dropout(Tensor input, float threshold, float value, bool inplace=False) -> (Tensor):
Argument threshold not provided.
:
File "/usr/local/lib/python3.7/dist-packages/megatron/model/transformer.py", line 366
def bias_dropout_add(x, bias, residual, prob, training) :
# type: (Tensor, Tensor, Tensor, float, bool) -> Tensor
out = torch.nn.functional.dropout(x + bias, p=prob, training=training)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
out = residual + out
return out
'bias_dropout_add' is being compiled since it was called from 'bias_dropout_add_fused_train'
File "/usr/local/lib/python3.7/dist-packages/megatron/model/transformer.py", line 380
def bias_dropout_add_fused_train(x, bias, residual, prob) :
# type: (Tensor, Tensor, Tensor, float) -> Tensor
return bias_dropout_add(x, bias, residual, prob, True)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE) | {
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} | null | null | null | I have trained nemo 1.8.2 model, earlier I have used nemo 1.4.0 and used below command to classify the texts
model.classifytext(queries=config.model.infer_samples, batch_size=8, max_seq_length=511)
This time when calling the above command with nemo 1.8.2 I am facing issue as **RuntimeError: expected scalar type Half but found Float**.
Below is the entire log trace:
RuntimeError Traceback (most recent call last)
Input In [20], in <cell line: 1>()
----> 1 model.classifytext(queries=config.model.infer_samples, batch_size=8, max_seq_length=511)
File /opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py:27, in _DecoratorContextManager.__call__.<locals>.decorate_context(*args, **kwargs)
24 @functools.wraps(func)
25 def decorate_context(*args, **kwargs):
26 with self.clone():
---> 27 return func(*args, **kwargs)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py:259, in TextClassificationModel.classifytext(self, queries, batch_size, max_seq_length)
256 for i, batch in enumerate(infer_datalayer):
257 input_ids, input_type_ids, input_mask, subtokens_mask = batch
--> 259 logits = self.forward(
260 input_ids=input_ids.to(device),
261 token_type_ids=input_type_ids.to(device),
262 attention_mask=input_mask.to(device),
263 )
265 preds = tensor2list(torch.argmax(logits, axis=-1))
266 all_preds.extend(preds)
File /opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py:888, in typecheck.__call__(self, wrapped, instance, args, kwargs)
886 # If types are not defined, skip type checks and just call the wrapped method
887 if input_types is None and output_types is None:
--> 888 return wrapped(*args, **kwargs)
890 # Check that all arguments are kwargs
891 if input_types is not None and len(args) > 0:
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py:82, in TextClassificationModel.forward(self, input_ids, attention_mask, token_type_ids)
76 @typecheck()
77 def forward(self, input_ids, attention_mask, token_type_ids):
78 """
79 No special modification required for Lightning, define it as you normally would
80 in the `nn.Module` in vanilla PyTorch.
81 """
---> 82 hidden_states = self.bert_model(
83 input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask
84 )
85 if isinstance(hidden_states, tuple):
86 hidden_states = hidden_states[0]
File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1111, in Module._call_impl(self, *input, **kwargs)
1107 # If we don't have any hooks, we want to skip the rest of the logic in
1108 # this function, and just call forward.
1109 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1110 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1111 return forward_call(*input, **kwargs)
1112 # Do not call functions when jit is used
1113 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py:130, in MegatronBertModel.forward(self, input_ids, attention_mask, token_type_ids, lm_labels)
129 def forward(self, input_ids, attention_mask, token_type_ids, lm_labels=None):
--> 130 output_tensor = self.model(input_ids, attention_mask, token_type_ids=token_type_ids, lm_labels=lm_labels)
131 return output_tensor
File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1111, in Module._call_impl(self, *input, **kwargs)
1107 # If we don't have any hooks, we want to skip the rest of the logic in
1108 # this function, and just call forward.
1109 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1110 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1111 return forward_call(*input, **kwargs)
1112 # Do not call functions when jit is used
1113 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron/bert_model.py:226, in BertModel.forward(self, bert_model_input, attention_mask, token_type_ids, lm_labels)
223 input_ids = bert_model_input
224 position_ids = build_position_ids(input_ids)
--> 226 lm_output = self.language_model(
227 input_ids, position_ids, extended_attention_mask, token_type_ids=token_type_ids
228 )
230 if self.post_process and self.add_binary_head:
231 lm_output, pooled_output = lm_output
File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1111, in Module._call_impl(self, *input, **kwargs)
1107 # If we don't have any hooks, we want to skip the rest of the logic in
1108 # this function, and just call forward.
1109 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1110 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1111 return forward_call(*input, **kwargs)
1112 # Do not call functions when jit is used
1113 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/language_model.py:746, in TransformerLanguageModel.forward(self, enc_input_ids, enc_position_ids, enc_attn_mask, prompt_ids, dec_input_ids, dec_position_ids, dec_attn_mask, enc_dec_attn_mask, token_type_ids, layer_past, get_key_value, pooling_sequence_index, enc_hidden_states, output_enc_hidden_only, encoder_input, set_inference_key_value_memory, inference_max_sequence_len)
744 # encoder.
745 if enc_hidden_states is None:
--> 746 encoder_output = self.encoder(
747 encoder_input,
748 enc_attn_mask,
749 layer_past=layer_past,
750 get_key_value=get_key_value,
751 set_inference_key_value_memory=set_inference_key_value_memory,
752 inference_max_sequence_len=inference_max_sequence_len,
753 )
754 else:
755 encoder_output = enc_hidden_states.to(encoder_input.dtype)
File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1111, in Module._call_impl(self, *input, **kwargs)
1107 # If we don't have any hooks, we want to skip the rest of the logic in
1108 # this function, and just call forward.
1109 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1110 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1111 return forward_call(*input, **kwargs)
1112 # Do not call functions when jit is used
1113 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/transformer.py:1095, in ParallelTransformer.forward(self, hidden_states, attention_mask, layer_past, get_key_value, encoder_output, enc_dec_attn_mask, set_inference_key_value_memory, inference_max_sequence_len)
1092 if self.post_process:
1093 # Reverting data format change [s b h] --> [b s h].
1094 hidden_states = hidden_states.transpose(0, 1).contiguous()
-> 1095 output = self.final_layernorm(hidden_states)
1096 else:
1097 output = hidden_states
File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1111, in Module._call_impl(self, *input, **kwargs)
1107 # If we don't have any hooks, we want to skip the rest of the logic in
1108 # this function, and just call forward.
1109 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1110 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1111 return forward_call(*input, **kwargs)
1112 # Do not call functions when jit is used
1113 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.8/site-packages/apex/normalization/fused_layer_norm.py:414, in MixedFusedLayerNorm.forward(self, input)
412 if not input.is_cuda:
413 return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
--> 414 return mixed_dtype_fused_layer_norm_affine(input, self.weight, self.bias, self.normalized_shape, self.eps)
File /opt/conda/lib/python3.8/site-packages/apex/normalization/fused_layer_norm.py:183, in mixed_dtype_fused_layer_norm_affine(input, weight, bias, normalized_shape, eps)
181 args = _cast_if_autocast_enabled(input, weight, bias, normalized_shape, eps)
182 with torch.cuda.amp.autocast(enabled=False):
--> 183 return FusedLayerNormAffineMixedDtypesFunction.apply(*args)
File /opt/conda/lib/python3.8/site-packages/apex/normalization/fused_layer_norm.py:96, in FusedLayerNormAffineMixedDtypesFunction.forward(ctx, input, weight, bias, normalized_shape, eps)
94 weight_ = weight.contiguous()
95 bias_ = bias.contiguous()
---> 96 output, mean, invvar = fused_layer_norm_cuda.forward_affine_mixed_dtypes(
97 input_, ctx.normalized_shape, weight_, bias_, ctx.eps
98 )
99 ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
100 return output
RuntimeError: expected scalar type Half but found Float | {
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} | null | null | null | **Describe the bug**
Trying to run `megatron_nmt_training.py`, with `conf/aayn_base_megatron.yml` initialization crashes with the following trace:
```python
Traceback (most recent call last):
File "examples/nlp/machine_translation/megatron_nmt_training.py", line 95, in <module>
main()
File "/workspace/nemo/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "examples/nlp/machine_translation/megatron_nmt_training.py", line 68, in main
MTDataPreproc(cfg=cfg.model, trainer=trainer)
File "/workspace/nemo/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py", line 172, in __init__
encoder_model_name=cfg.encoder.get('model_name'),
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 357, in __getattr__
self._format_and_raise(key=key, value=None, cause=e)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/base.py", line 190, in _format_and_raise
format_and_raise(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/_utils.py", line 738, in format_and_raise
_raise(ex, cause)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/_utils.py", line 716, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set end OC_CAUSE=1 for full backtrace
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 351, in __getattr__
return self._get_impl(key=key, default_value=_DEFAULT_MARKER_)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 438, in _get_impl
node = self._get_node(key=key, throw_on_missing_key=True)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 465, in _get_node
self._validate_get(key)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 166, in _validate_get
self._format_and_raise(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/base.py", line 190, in _format_and_raise
format_and_raise(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/_utils.py", line 818, in format_and_raise
_raise(ex, cause)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/omegaconf/_utils.py", line 716, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set end OC_CAUSE=1 for full backtrace
omegaconf.errors.ConfigAttributeError: Key 'encoder' is not in struct
full_key: model.encoder
object_type=dict
```
@MaximumEntropy Tracking down the source, the offending line seems to be https://github.com/NVIDIA/NeMo/blob/09be885e3f96ccded893dabe5985ffa22736f111/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py#L172
as the `encoder` property is defined in all other NMT yaml files, but not in `aayn_base_megatron.yml`. This is true also for `decoder`. Hance the following lines will lead to a crash
https://github.com/NVIDIA/NeMo/blob/09be885e3f96ccded893dabe5985ffa22736f111/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py#L172
https://github.com/NVIDIA/NeMo/blob/09be885e3f96ccded893dabe5985ffa22736f111/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py#L227
https://github.com/NVIDIA/NeMo/blob/09be885e3f96ccded893dabe5985ffa22736f111/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py#L177
https://github.com/NVIDIA/NeMo/blob/09be885e3f96ccded893dabe5985ffa22736f111/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py#L232
**Environment details**
- `nemo:1.9.0` built on `pytorch:22.05-py3` with `./reinstall.sh`
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} | Signed-off-by: Jocelyn Huang <[email protected]>
Co-authored-by: Eric Harper <[email protected]>
# What does this PR do ?
related to https://github.com/NVIDIA/NeMo/pull/4446, which has been merged to r1.10.0.
**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
# Add a code snippet demonstrating how to use this
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Address QA bugs for German TTS tutorial
**Collection**: TTS
# Changelog
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
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**PR Type**:
- [ ] New Feature
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} | Signed-off-by: Virginia Adams <[email protected]>
Bug fixes for prompt learning notebook after pipeline parallel addition
https://nvbugs/3681543 [ML-NeMo][A30] Multitask_Prompt_and_PTuning.ipynb branch name not 'r1.10.0' and failed with ValueError if manually changed to the r1.10.0 branch
Add a one line overview of what this PR aims to accomplish.
**Collection**: BigNLP
# Changelog
- Updated tutorial notebook
# Usage
Run Jupiter notebook
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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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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[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: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fix typo in the default `max_steps` for HiFi-GAN configs (see #4431).
**Collection**: TTS
# Changelog
- Changed default steps from 25M to 2.5M
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Raised in discussion #4431
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} | # What does this PR do ?
Unify intent slot util function file locations
**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
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] 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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Decrease font size on tables to fit more content
**Collection**: None
# Changelog
- Decrease font size on tables to fit more content
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | Signed-off-by: Xuesong Yang <[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
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ 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 table overflow handling. In particular, this caused issues on the [Scores page](https://github.com/NVIDIA/NeMo/blob/r1.10.0/docs/source/asr/scores.rst)
**Collection**: None
# Changelog
- Setup section to overflow horizontally
- Add scrolling to tables placed under `section` tags
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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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} | # What does this PR do ?
Fix the RPE implementation converging slower with loss consistently higher than NeMo APE and Megatron-LM RPE implementation.
**Collection**: NLP
# Changelog
- Changed how RPE parameters are initialized and passed on.
# Usage
* Train with `model.position_embedding_type='relative'` config.
```python
python /home/workspace/NeMo/examples/nlp/language_modeling/megatron_t5_pretraining.py model.position_embedding_type='relative'
```
# 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
## 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 | Does Nemo support multi-channel?, So far I have only seen single-channel inputs.
If not which is the best way I can go about adding support for it.
Any reason why Is not supported?
Thanks | {
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} | # What does this PR do ?
Fix the `gpt_eval` argument in the text | {
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} | # What does this PR do ?
Update dialogue tutorial version
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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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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 ?
relate to https://github.com/NVIDIA/NeMo/pull/4392
**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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# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
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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 ?
related to https://github.com/NVIDIA/NeMo/pull/4392
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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# What does this PR do ?
related to https://github.com/NVIDIA/NeMo/pull/4388
**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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} | null | null | null | Hi, I get `StopIteration` exception here:
https://github.com/NVIDIA/NeMo/blob/41f27a5e68934761b05dd373e7b288aa798858e5/nemo/collections/asr/modules/conformer_encoder.py#L231
I call this method from `forward_for_export` here:
https://github.com/NVIDIA/NeMo/blob/41f27a5e68934761b05dd373e7b288aa798858e5/nemo/collections/asr/modules/conformer_encoder.py#L249
I found that `self.parameters()` is zero length. Found this happens when I use multiple GPUs in DP mode. When I run the code on one GPU I get `self.parameters() > 0`.
Issue about it: https://github.com/pytorch/pytorch/issues/40457
Maybe it would be better if the device was passed into the method, like so:
```
def set_max_audio_length(self, max_audio_length, device='cpu'):
....
```
Because it's strange to get such problems when getting a device, when it can be easily passed as a parameter.
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I want to change the Model according to the BLS input. But BLS requires the same input bach size. So I have to make input like ["model1", "model1"]. If my batch size is 100, should I send 100 same data like ["model1", "model1", "model1"].
**Describe the solution you'd like**
It would be nice to have the feature to send metadata other than batch data. | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Try updating jerkins tests
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fixes paths on ASR Adapter tutorial
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | At word level, i can extract the timestamps, how can i also extract a confidence level or a score from this word transcription?
i was following this tutorial: https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Offline_ASR.ipynb | null | {
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Re-apply the fixes to the Tacotron 2 tutorial from #4209. Need to open this PR since those fixes never made it to main and the 1.10 branch.
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* See #4209 for original bugfix info. | {
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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
Fix import error in Offline_ASR_with_VAD_for_CTC_models in r1.10
**Collection**: ASR
# Changelog
- change `main` to `$BRANCH `
# 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?
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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.
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https://api.github.com/repos/NVIDIA/NeMo/issues/4419 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4419/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4419/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4419/events | https://github.com/NVIDIA/NeMo/pull/4419 | 1,280,271,427 | PR_kwDOC_bI7s46IKmJ | 4,419 | Add T5 in prompt learning documentations | {
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} | Signed-off-by: Jiaqi Zeng <[email protected]>
# What does this PR do ?
Update the prompt learning documentations.
**Collection**: [NLP]
# Changelog
- Add T5-specific details
- Fix some typos
# 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.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4418 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4418/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4418/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4418/events | https://github.com/NVIDIA/NeMo/issues/4418 | 1,280,101,523 | I_kwDOC_bI7s5MTMyT | 4,418 | RuntimeError Fastpitch Training: Timed out initializing process group in store based barrier on rank: 0 | {
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} | null | null | null | I am training a fastpitch model and when I use large data i.e. 21,000 audio files to trainset, I get the following error.
`15247it [58:54, 4.53it/s]Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00)
14572it [58:55, 3.74it/s]Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00)
15334it [59:14, 4.30it/s]Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00)
13797it [59:18, 3.78it/s]Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00)
13836it [59:28, 3.86it/s]Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00)
15461it [59:45, 4.33it/s]Waiting in store based barrier to initialize process group for rank: 0, key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00)
13911it [59:47, 3.76it/s][NeMo W 2022-06-22 12:50:01 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py:420: UserWarning: Error handling mechanism for deadlock detection is uninitialized. Skipping check.
rank_zero_warn("Error handling mechanism for deadlock detection is uninitialized. Skipping check.")
Error executing job with overrides: ['train_dataset=6670_manifest_other_train.json', 'validation_datasets=6670_manifest_other_dev.json']
15213it [59:40, 4.36it/s]Traceback (most recent call last):
File "fastpitch_align.py", line 31, in main
trainer.fit(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 768, in fit
self._call_and_handle_interrupt(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 719, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1169, in _run
self.strategy.setup_environment()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 151, in setup_environment
self.setup_distributed()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 191, in setup_distributed
init_dist_connection(self.cluster_environment, self._process_group_backend)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py", line 355, in init_dist_connection
torch.distributed.init_process_group(torch_distributed_backend, rank=global_rank, world_size=world_size, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 610, in init_process_group
_store_based_barrier(rank, store, timeout)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 246, in _store_based_barrier
raise RuntimeError(
RuntimeError: Timed out initializing process group in store based barrier on rank: 0, for key: store_based_barrier_key:1 (world_size=7, worker_count=1, timeout=0:30:00
)`
I am training this on
- 7xA100(80G)
- PyTorch Nvidia container v21.12-py3
- Nemo 1.9.0
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https://api.github.com/repos/NVIDIA/NeMo/issues/4417 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4417/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4417/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4417/events | https://github.com/NVIDIA/NeMo/pull/4417 | 1,279,517,402 | PR_kwDOC_bI7s46FlrZ | 4,417 | Update with new conformer checkpoints. | {
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} | # What does this PR do ?
Update the English conformer checkpoints with models trained on NeMo ASRSET 3.0.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Updated the English conformer checkpoints
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} | # What does this PR do ?
Adds SqueezeFormer model to ASR collections
**Collection**: [ASR]
# Changelog
- Adds prototype configs, code and model level support for Squeezeformer models
- Checkpoints may be provided at a later date after debugging smaller sized models.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix typos in docs.
**Collection**: [Documentation]
# Changelog
- Add specific line by line info of high level changes in this PR.
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Fixed a bug in calling the ctc_decoder_predictions_tensor method from the WER objects instead of WER.decoding.
# Changelog
- Changed all the wer.ctc_decoder_predictions_tensor to wer.decoding.ctc_decoder_predictions_tensor .
**PR Type**:
- [ ] New Feature
- [x ] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/4413 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4413/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4413/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4413/events | https://github.com/NVIDIA/NeMo/pull/4413 | 1,279,142,385 | PR_kwDOC_bI7s46ERt_ | 4,413 | [TTS][DE] Multi-speaker fastpitch model training recipe on HUI-Audio-Corpus-German | {
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} | # What does this PR do ?
* created a dataset preparation recipe for HUI-ACG.
* re-write `get_data.py` to support multiprocessing and decouple text normalization.
* added a script to convert graphemes into phonemes.
* created a model config for multi-speaker Fastpitch model training.
**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
```bash
# Download the dataset, normalize texts, create train/val/test splits of JSON manifests.
$ python scripts/dataset_processing/tts/hui_acg/get_data.py \
--data-root /home/xueyang/datasets \
--normalize-text
$ ls /home/xueyang/datasets/HUI-Audio-Corpus-German-clean/*_text_normed.json
test_manifest_text_normed.json train_manifest_text_normed.json val_manifest_text_normed.json
# run phonemizer to convert graphemes into phonemes, resulting in new files with name like train_manifest_text_normed_phonemes.json
$ python scripts/dataset_processing/tts/hui_acg/phonemizer.py \
--json-manifests /home/xueyang/datasets/HUI-Audio-Corpus-German-clean/train_manifest_text_normed.json \
/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/val_manifest_text_normed.json \
/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/test_manifest_text_normed.json \
--preserve-punctuation
$ ls /home/xueyang/datasets/HUI-Audio-Corpus-German-clean/*_phonemes.json
test_manifest_text_normed_phonemes.json train_manifest_text_normed_phonemes.json val_manifest_text_normed_phonemes.json
$ python scripts/dataset_processing/tts/extract_sup_data.py \
--config-path hui_acg/ds_conf \
--config-name ds_for_fastpitch_align.yaml \
manifest_filepath=/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/Friedrich_Clean/train_manifest_text_normed_phonemes.json \
sup_data_path=/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/sup_data_phonemes
# you would see the information in the terminal
-- Set up ENVs...
a867b5ef4cd08fe0db216937b2d3e767fe71b3e7
-- Started ...
[NeMo I 2022-06-17 15:15:57 tokenize_and_classify:81] Creating ClassifyFst grammars. This might take some time...
[NeMo I 2022-06-17 15:16:15 data:186] Loading dataset from /data/HUI-Audio-Corpus-German-clean/train_manifest_text_normed_phonemes.json.
[NeMo I 2022-06-17 15:16:22 data:221] Loaded dataset with 173622 files.
[NeMo I 2022-06-17 15:16:22 data:223] Dataset contains 403.30 hours.
[NeMo I 2022-06-17 15:16:22 data:321] Pruned 0 files. Final dataset contains 173622 files
[NeMo I 2022-06-17 15:16:22 data:323] Pruned 0.00 hours. Final dataset contains 403.30 hours.
Processing /home/xueyang/datasets/HUI-Audio-Corpus-German-clean/train_manifest_text_normed_phonemes.json:
PITCH_MEAN=155.3916473388672, PITCH_STD=102.26656341552734
PITCH_MIN=65.4063949584961, PITCH_MAX=2093.004638671875
# train FastPitch model. copy & paste PITCH_MEAN, PITCH_STD, PITCH_MIN, and PITCH_MAX to the params.
$ python examples/tts/fastpitch.py \
--config-path conf/de \
--config-name fastpitch_align_44100 \
model.train_ds.dataloader_params.num_workers=16 \
model.validation_ds.dataloader_params.num_workers=16 \
model.train_ds.dataloader_params.batch_size=24 \
model.validation_ds.dataloader_params.batch_size=24 \
train_dataset=/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/train_manifest_text_normed_phonemes.json \
validation_datasets=/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/val_manifest_text_normed_phonemes.json \
sup_data_path=/home/xueyang/datasets/HUI-Audio-Corpus-German-clean/sup_data_phonemes \
pitch_mean=155.3916473388672 \
pitch_std=102.26656341552734 \
pitch_fmin=65.4063949584961 \
pitch_fmax=2093.004638671875 \
trainer.devices=2 \
trainer.num_nodes=1 \
trainer.accumulate_grad_batches=2 \
trainer.log_every_n_steps=10 \
exp_manager.exp_dir=/home/xueyang/experiments/GermanTTS/multi_spk_tts_de/fastpitch-train/results \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
+exp_manager.create_wandb_logger=True \
+exp_manager.wandb_logger_kwargs.name=fastpitch-train \
+exp_manager.wandb_logger_kwargs.project=multi_spk_tts_de
```
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4412 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4412/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4412/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4412/events | https://github.com/NVIDIA/NeMo/pull/4412 | 1,279,075,079 | PR_kwDOC_bI7s46ECSW | 4,412 | Add ASR Scores to Docs | {
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} | # What does this PR do ?
Adds current ASR model scores to "scores" page.
**Collection**: [ASR]
# Changelog
- Adds the scores of all ASR models from NGC to docs.
# Usage
* You can potentially add a usage example below
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Add O2 suport to RETRO.
Fix bugs to make sure post-ln work.
Fix bug to make sure cyclicLR work. | {
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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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I am working on training the TTS model using Fastpitch architecture using Librivox 44khz dataset. I was getting issues in the trained model and looking for possible solutions online. but now I saw that in the 1.9.0 release of Nemo, you have removed the 44100 sample rate model configs. Can someone share the reason for that? | {
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Polish the batch matmul code, make it more pytorchnic
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} | null | null | null | **Describe the bug**
Hi, I am trying to follow the steps underlines here: https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/recognition
but when i launch : python speaker_reco_finetune.py --pretrained_model='/notebooks/data/atc_tenant/asr/smancha5/Nemo_speaker_training/titanet_l/titanet-l.nemo' --finetune_config_file='titanet-large.yaml'
It gives me this error:
[NeMo I 2022-06-21 09:06:08 features:200] PADDING: 16
[NeMo I 2022-06-21 09:06:08 label_models:100] loss is Angular Softmax
[NeMo I 2022-06-21 09:06:11 save_restore_connector:243] Model EncDecSpeakerLabelModel was successfully restored from /notebooks/data/atc_tenant/asr/smancha5/Nemo_speaker_training/titanet_l/titanet-l.nemo.
[NeMo I 2022-06-21 09:06:11 label_models:370] Setting up data loaders with manifests provided from model_config
[NeMo I 2022-06-21 09:06:11 collections:290] Filtered duration for loading collection is 0.000000.
[NeMo I 2022-06-21 09:06:11 collections:294] # 16585 files loaded accounting to # 304 labels
[NeMo W 2022-06-21 09:06:11 label_models:133] Total number of 304 found in all the manifest files.
File "speaker_reco_finetune.py", line 94, in <module>
main()
File "speaker_reco_finetune.py", line 79, in main
speaker_model.setup_finetune_model(finetune_config.model)
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 373, in setup_finetune_model
self.setup_training_data(model_config.train_ds)
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 201, in setup_training_data
self._train_dl = self.__setup_dataloader_from_config(config=train_data_layer_config)
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 138, in __setup_dataloader_from_config
augmentor = process_augmentations(config['augmentor'])
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/perturb.py", line 890, in process_augmentations
augmentation = perturbation_types[augment_name](**augment_kwargs)
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/perturb.py", line 403, in __init__
self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True)
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/collections.py", line 214,in __init__
for item in manifest.item_iter(manifests_files):
File "/home/smancha5/.local/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py", line 69, in item_iter
for manifest_file in manifests_files:
TypeError: 'NoneType' object is not iterable
**Steps/Code to reproduce bug**
python speaker_reco_finetune.py --pretrained_model='/notebooks/data/atc_tenant/asr/smancha5/Nemo_speaker_training/titanet_l/titanet-l.nemo' --finetune_config_file='titanet-large.yaml'
**Expected behavior**
finetuning of the speaker recog model.
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: clone https://github.com/NVIDIA/NeMo.git && cd /NeMo && bash reinstall.sh
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
nvcr.io/nvidia/pytorch:22.03-py3
If NVIDIA docker image is used you don't need to specify these.
nvcr.io/nvidia/pytorch:22.03-py3
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | null | null | null | **Describe the bug**
```
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-16-358abbfc674b> in <module>
----> 1 from nemo.collections.nlp.models import PunctuationCapitalizationModel
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp import data, losses, models, modules
16 from nemo.package_info import __version__
17
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/__init__.py in <module>
14
15
---> 16 from nemo.collections.nlp.models.duplex_text_normalization import (
17 DuplexDecoderModel,
18 DuplexTaggerModel,
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/duplex_text_normalization/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp.models.duplex_text_normalization.duplex_decoder import DuplexDecoderModel
16 from nemo.collections.nlp.models.duplex_text_normalization.duplex_tagger import DuplexTaggerModel
17 from nemo.collections.nlp.models.duplex_text_normalization.duplex_tn import (
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py in <module>
37
38 try:
---> 39 from nemo_text_processing.text_normalization.normalize_with_audio import NormalizerWithAudio
40
41 PYNINI_AVAILABLE = True
/opt/conda/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize_with_audio.py in <module>
25
26 try:
---> 27 from nemo.collections.asr.metrics.wer import word_error_rate
28 from nemo.collections.asr.models import ASRModel
29
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.asr import data, losses, models, modules
16 from nemo.package_info import __version__
17
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/__init__.py in <module>
15 from nemo.collections.asr.models.asr_model import ASRModel
16 from nemo.collections.asr.models.classification_models import EncDecClassificationModel
---> 17 from nemo.collections.asr.models.clustering_diarizer import ClusteringDiarizer
18 from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE
19 from nemo.collections.asr.models.ctc_models import EncDecCTCModel
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py in <module>
29 from nemo.collections.asr.models.classification_models import EncDecClassificationModel
30 from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel
---> 31 from nemo.collections.asr.parts.mixins.mixins import DiarizationMixin
32 from nemo.collections.asr.parts.utils.speaker_utils import (
33 audio_rttm_map,
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/mixins/__init__.py in <module>
14
15 from nemo.collections.asr.parts.mixins.asr_adapter_mixins import ASRAdapterModelMixin
---> 16 from nemo.collections.asr.parts.mixins.mixins import (
17 ASRAdapterModelMixin,
18 ASRBPEMixin,
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/mixins/mixins.py in <module>
20
21 from nemo.collections.asr.parts.mixins.asr_adapter_mixins import ASRAdapterModelMixin
---> 22 from nemo.collections.asr.parts.utils import asr_module_utils
23 from nemo.collections.common import tokenizers
24 from nemo.utils import logging
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/asr_module_utils.py in <module>
17 from omegaconf import DictConfig, open_dict
18
---> 19 from nemo.collections.asr.modules import conv_asr
20 from nemo.collections.asr.parts.submodules import jasper
21 from nemo.utils import logging
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.asr.modules.audio_preprocessing import (
16 AudioToMelSpectrogramPreprocessor,
17 AudioToMFCCPreprocessor,
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/audio_preprocessing.py in <module>
39
40 try:
---> 41 import torchaudio
42 import torchaudio.functional
43 import torchaudio.transforms
/opt/conda/lib/python3.8/site-packages/torchaudio/__init__.py in <module>
----> 1 from torchaudio import _extension # noqa: F401
2 from torchaudio import (
3 compliance,
4 datasets,
5 functional,
/opt/conda/lib/python3.8/site-packages/torchaudio/_extension.py in <module>
25
26
---> 27 _init_extension()
/opt/conda/lib/python3.8/site-packages/torchaudio/_extension.py in _init_extension()
19 # which depends on `libtorchaudio` and dynamic loader will handle it for us.
20 if path.exists():
---> 21 torch.ops.load_library(path)
22 torch.classes.load_library(path)
23 # This import is for initializing the methods registered via PyBind11
/opt/conda/lib/python3.8/site-packages/torch/_ops.py in load_library(self, path)
108 # static (global) initialization code in order to register custom
109 # operators with the JIT.
--> 110 ctypes.CDLL(path)
111 self.loaded_libraries.add(path)
112
/opt/conda/lib/python3.8/ctypes/__init__.py in __init__(self, name, mode, handle, use_errno, use_last_error, winmode)
371
372 if handle is None:
--> 373 self._handle = _dlopen(self._name, mode)
374 else:
375 self._handle = handle
OSError: libtorch_cuda_cpp.so: cannot open shared object file: No such file or directory
```
**Steps/Code to reproduce bug**
When running this:
from nemo.collections.nlp.models import PunctuationCapitalizationModel
**Expected behavior**
I expect to be able to import and use the PunctuationCapitalizationModel
NB: When I don't install torchaudio, then I'm able to import and use the PunctuationCapitalizationModel normally. But after I install torchaudio, then it doesn't work and gives the error above. (And I need torchaudio for other things in my application)
**Environment overview (please complete the following information)**
- Environment location: AWS Sagemaker studio, torch 1.10 GPU container
- Method of NeMo install: pip install
(requirements.txt at bottom)
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version - Ubuntu 20.04.4 LTS
- PyTorch version - 1.10.2+cu113 (and torchaudio 0.10.2+cu113)
- Python version - 3.8.10
**Additional context**
requirements.txt:
numpy
pandas
librosa
datasets
transformers>=4.16.2
nvgpu
nemo_toolkit[all]
--extra-index-url https://download.pytorch.org/whl/cu113
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} | # What does this PR do ?
Fix hanging/deadlock issue caused by multiprocessing in SD tutorial and add tdqm that informs ETA to reduce false alarm of hanging.
reflected review in https://github.com/NVIDIA/NeMo/pull/4378
**Collection**: ASR
# Changelog
Cherry pick updates to r1.10.0 https://github.com/NVIDIA/NeMo/pull/4317, (modify the code in place here to avoid sign-off issue)
Add instruction and set num_workers=1 in SD tutorial as a workaround for python multiprocessing would hang with python in some cases.
Add tqdm that informs ETA for VAD processing
# 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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cfg = copy.deepcopy(asr_model.confg)
OmegaConf.set_struct(cfg.preprocessor, False)
# some changes for streaming scenario
cfg.preprocessor.dither = 0.0
cfg.preprocessor.pad_to = 0
cfg.preprocessor.normalize = "None"
Also while streaming thought mic I was normalizing the audio by dividing it by audio / (1 << 15) (had seen this is some tutorial)
Model Used.: ctcconformerbpe small | null | {
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Fixes import error in rare cases
**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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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Fix an import error for some models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hello!
I'm new to ASR (AI in general) and I'm struggling to fine-tune the Conformer CTC (small) on the [UASpeech dataset](http://www.isle.illinois.edu/sst/data/UASpeech/).
If I try to fine-tune the model from the NeMo pre-trained checkpoint, the loss starts around 40 and jumps to 4.14e+03 in the next step then some value around e+05 and stays in that range. Val_wer is nearly a constant 100%. It occasionally predicts long sequences of repeated characters (e.g. `iiiiiiiiiiiiiiiiiiiiiiiiii`). I'm wondering if I'm making some mistake when I change the vocabulary. I use the `process_asr_text_tokenizer.py` script giving 455 as the vocab size (the number of unique words in the dataset) to create the vocabulary then pass it to `model.cfg["train_ds"]["manifest_filepath"]`. I also set
```
model.cfg["decoder"]["vocabulary"] = []
model.cfg["decoder"]["num_classes"] = 455
```
Here's my `model.cfg`:
```
{'sample_rate': 16000,
'log_prediction': True,
'ctc_reduction': 'mean_batch',
'train_ds':
{'manifest_filepath': path_to_custom_train_set,
'sample_rate': 16000,
'batch_size': 16,
'shuffle': True,
'num_workers': 8,
'pin_memory': True,
'use_start_end_token': False,
'trim_silence': False,
'max_duration': 16.7,
'min_duration': 0.1,
'is_tarred': False,
'tarred_audio_filepaths': None,
'shuffle_n': 2048,
'bucketing_strategy': 'synced_randomized',
'bucketing_batch_size': None},
'validation_ds':
{'manifest_filepath': path_to_custom_dataset_val_set,
'sample_rate': 16000,
'batch_size': 16,
'shuffle': False,
'num_workers': 8,
'pin_memory': True,
'use_start_end_token': False},
'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': ''},
'tokenizer':
{'dir': path_to_custom_tokenizer_spe_bpe,
'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': 2,
'freq_width': 27,
'time_width': 100},
'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': 455,
'vocabulary': []},
'optim':
{'name': 'adamw',
'lr': 5,
'betas': [0.9,
0.98],
'weight_decay': 0,
'sched':
{'name': 'NoamAnnealing',
'd_model': 176,
'warmup_steps': 10000,
'warmup_ratio': None,
'min_lr': 1e-06}
},
'init_weights_from_model': '/exp_init//ngc_checkpoints/small/ls_d176_adamlr2_wd0_aug5x0.05_wu10k_stridc176_ctc_2k_wpe128_untied_newrel.nemo',
'target': 'nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE'}
```
I'm thinking that there might be an issue with the `init_weights_from_model` having a different vocabulary size because when I try to perform evaluation on the checkpoint I fine-tuned (by calling `model = nemo_asr.models.EncDecCTCModelBPE.load_from_checkpoint(path_to_checkpoint)`), I get the following error where the mismatch corresponds with the old and new vocabulary sizes.
```
RuntimeError: Error(s) in loading state_dict for EncDecCTCModelBPE:
size mismatch for decoder.decoder_layers.0.weight: copying a param with shape torch.Size([129, 176, 1]) from checkpoint, the shape in current model is torch.Size([456, 176, 1]).
size mismatch for decoder.decoder_layers.0.bias: copying a param with shape torch.Size([129]) from checkpoint, the shape in current model is torch.Size([456]).
```
The loss explosion doesn't happen if I start training from scratch (using a config file). The loss starts around 400 but decreases rapidly to 11/12 within the first epoch then plateaus. After 60 epochs, the predictions still do not seem to improve (mostly random single letters), but maybe it just needs more training? Val_wer fluctuates between 99.9 and 100%.
1. Any idea as to what I'm doing wrong here? It's good to note that the dataset is of impaired speech but it doesn't seem to be responsible for the loss issue since it doesn't happen when the model is trained from scratch.
2. When you use the tokenizer script, is it typical to do it just for the train manifest or a union of both? I'm only doing it for the train set to have a good "in the wild" evaluation.
3. When you have a small dataset, do you typically still need 2 distinct val and test sets? I use the same one for evaluation since it doesn't affect training, and I've noticed that existing papers do not mention a third split.
Thank you so much in advance!
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] | null | null | 2022-06-18T18:35:23 | 2022-06-18T22:45:58 | 2022-06-18T22:45:56 | CONTRIBUTOR | {
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"merged_at": "2022-06-18T22:45:56"
} | # What does this PR do ?
Removes race condition from 2 Punctuation and Capitalization tests. These tests were using files with fixed names
and thus if such a test had 2 replicas on same machine a race condition could occur.
**Collection**: nlp
# Additional Information
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"merged_at": "2022-06-18T00:38:18"
} | # 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.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Add a line referencing NVIDIA AI Platform.
**Collection**: [Docs, Core]
**PR Type**:
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} | # What does this PR do ?
1. Trains Megatron-based NMT models based on maximum number of samples.
2. Added support in `text_memmap` and `csv_memmap` in Megatron encoder-decoder models (T5, BART, UL2)
**Collection**: NLP
# Usage
Add to command line
```
model.data.data_impl=text_mmap \
+model.data.data_impl_kwargs.newline_int=10 \
+model.data.data_impl_kwargs.header_lines=0 \
+model.data.data_impl_kwargs.workers=null \
+model.data.data_impl_kwargs.sort_dataset_paths=False
```
```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 ?
Fixes Wav2Vec configs so compatible with contrastive loss. (Adds MaskedAugmentation to pretrain models.) Removes Wav2Vec from experimental folder and moves wav2vec configs to SSL folder for consistency.
**Collection**: [Note which collection this PR will affect]
- ASR
# Changelog
- Wav2Vec Pretrain (Base and Large): replaces SpecAug with MaskedPatchAugmentation. Adds appropriate contrastive loss flags.
- Deleted Wav2Vec folder from `examples/experimental`
- Moved Wav2Vec configs to `conf/SSL`
# 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**:
- [ Y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N] Did you write any new necessary tests?
- [ N] Did you add or update any necessary documentation?
- [ N] 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
- [Y ] 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 ?
Fix artifact restore
**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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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
- Reconfigures micro-batch sizes for validation datasets on validation start in Megatron NMT
**Collection**: NLP
# Changelog
- Add an `on_validation_epoch_start()` hook to reconfigure batch sizes.
# 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 ?
pitch, voiced_mask, and prob_voiced are return values for the func `librosa.pyin`, but we always returned the value of `voiced_mask` after going through `load_from_dir()` no matter which one of the three variables were requested.
**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
```bash
(nemo) xueyang@aiapps-031622:~/workspace/NeMo$ pytest --cpu tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist
A valid `test_data.tar.gz` test archive (7343147B) found in the `/home/xueyang/workspace/NeMo/tests/.data` folder.
/home/xueyang/miniconda3/envs/nemo/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
Setting numba compat : True
====================================================================================== test session starts ======================================================================================
platform linux -- Python 3.8.13, pytest-7.1.1, pluggy-1.0.0 -- /home/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /home/xueyang/workspace/NeMo, configfile: setup.cfg
plugins: hydra-core-1.1.1, anyio-3.6.1
collected 4 items
tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types0-output_indices0] PASSED [ 25%]
tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types1-output_indices1] PASSED [ 50%]
tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types2-output_indices2] PASSED [ 75%]
tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types3-output_indices3] PASSED [100%]
======================================================================================= slowest durations =======================================================================================
14.02s call tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types0-output_indices0]
14.00s call tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types3-output_indices3]
13.99s call tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types2-output_indices2]
13.90s call tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_save_voiced_items_if_pt_file_not_exist[sup_data_types1-output_indices1]
(8 durations < 0.005s hidden. Use -vv to show these durations.)
====================================================================================== 4 passed in 57.14s =======================================================================================
```
# 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)
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**PR Type**:
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- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Implement prompt learning for T5 models.
**Collection**: [NLP]
# Changelog
- A base prompt learning dataset class (will support both T5 and GPT in the future).
- A T5-specific prompt learning dataset class.
- A base prompt learning model class (will support both T5 and GPT in the future).
- A T5-specific prompt learning model class.
- Scripts for training and inference.
- Config files for training and inference.
- Temporarily fix a bug in T5 decode function to support prompt learning inference.
# Usage
```
python megatron_t5_prompt_learning.py --config-name=megatron_t5_prompt_learning.yaml
python megatron_t5_prompt_learning_eval.py t5_model_file=models/megatron_t5_220m_f16.nemo virtual_prompt_model_file=models/p_tuning_sent_t5.nemo
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
When training an TransformerXL model by following the script transformer_lm.py, I get the following error:
For this, I use the same train, eval and test json files as I use for the CTC conformer models which train fine. I use this in combination with a tokenizer trained with the following command:
`python /workspace/NeMo/scripts/process_asr_text_tokenizer.py --manifest=/workspace/NeMo/data/DataFrames/Baseline/Train/train.json --data_root=/workspace/Tokenizer_AllData_1024 --vocab_size=1024 --tokenizer="spe" --spe_type="unigram" --spe_character_coverage=1.0 --no_lower_case --log`
Again, the conformer CTC trained fine with this tokenizer.
Since this doesn't seem to be a python error, I can't figure out where it goes wrong.
Cant these tokenizers be used in combination with transformerXL?
Tokenizer config:
The goal is to do ASR -> Beam search N-Gram -> TransformerXL
**Steps/Code to reproduce bug**
Build tokenizer -> change paths in conf/transformer_lm_config.yaml -> python transformer_lm.py
**Expected behavior**
TransformerXL model trains without problems
**Environment overview (please complete the following information)**
```
docker run --gpus all -it --shm-size=8g -p 8888:8888 -p 0.0.0.0:6006:6006 --name TransformerXL --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:22.05-py3
Installed NeMo with git clone and ./reinstall.sh
```
Python 3.8.13
Torch: 1.12.0a0+8a1a93a
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} | # What does this PR do ?
Hide the right side bar
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
1. This bug blocks modeling using speaker IDs because `speaker_ids` (or saying `_` in this context) should be placed before `voiced_masks`.
2. `P_voiceds` was also not correct because it aggregates `voiced_mask` instead of `p_voiced`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
`nmesc_clustering.py`
- This PR adds an option named "maj_vote_spk_count" to NMESC clustering module.
Originally, speaker number was obtained from one p-value.
In the new implementation, maj_vote_spk_count = True, the system takes majority vote on
all p-values that are included.
This feature almost halves the speaker counting error for 1.5sec window setting.
(CH109, 1.5/0.75 win/shift, speaker counting error becomes 12.8% -> 7.3%)
Default will be False for now. Yaml file will be later updated with other updates to provide the best performance.
`diarizer.clustering.parameters.maj_vote_spk_count=False`
- This PR also adds error handling for extreamly short segments (1~10 segments)
when creating p_value_list, the minimum length is now forcibly assigned so that we can avoid having an empty list.
`speaker_utils.py`
- Removed getMinMaxOfRangeList() function
- Also "RTTM label has been truncated since start is greater than duration of audio file" warning message is not appropriate, thus removed.
- Also, added error handling for empty audio clips.
Empty audio clips happen when (1) VAD returns nothing (2) duration is 0 in manifest file.
For these cases, uniq ids are removed from self.AUDIO_RTTM_MAP dictionary to avoid the following errors.
- The modified change is tested with less than 1 second sample and silence sample, and then all together in one manifest file.
`clustering_diarizer.py`
- Error handling for empty audio file has been added. The empty uniq_id is removed from the AUDIO_RTTM_MAP dictionary.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
This PR adds mode function in NMEanalysis() function.
This implementation adds slightly more memory but no difference in time complexity except torch.mode operation.
# Usage
Please refer to [this document.](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_diarization/datasets.html#evaluation-on-benchmark-datasets)
# Before your PR is "Ready for review"
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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)
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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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} | Signed-off-by: Subhankar Ghosh <[email protected]>
# What does this PR do ?
FastPitch multi-speaker code was setup such that speaker embeddings would only condition the encoder. This PR changes that and conditions encoder, pitch predictor, duration predictor and decoder on the speaker embeddings. Through experimentation this approach has been seen to produce better quality speech in a multi-speaker setting. This PR does not affect single speaker scenario.
**Collection**: [Note which collection this PR will affect]
tts/modules
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Fix the bug of T5 decoding given input embeddings.
**Collection**: [NLP]
# Changelog
- Updating the way of passing arguments to avoid argument mismatching.
- Delete an argument which is not being utilized.
# 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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} | # What does this PR do ?
Fixes typos in the tutorial notebooks
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | …to encoder was overwriting leaf history. Wasn't caught on previous torch versions.
Signed-off-by: tbartley94 <[email protected]>
# What does this PR do ?
Fixes bug on backward pass with wav2vec module. See https://github.com/NVIDIA/NeMo/issues/4362
**Collection**: [Note which collection this PR will affect]
- ASR
# Changelog
- Addition of convolutional position changed from inplace operation to standard variable assignment.
# 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**:
- [Y ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N] Did you write any new necessary tests?
- [ N] Did you add or update any necessary documentation?
- [ N] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ Y] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [Y ] 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: Xiaowei Ren <[email protected]>
# What does this PR do ?
Remove redundant bias expand. The bias should be broadcast automatically.
# Changelog
- deleted redundant code in transformer.py
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [x] 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?
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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 ?
Turns WandB logger off by default
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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 sequence parallelism and selective activation checkpointing.
This PR currently depends on a fork of Apex: https://github.com/NVIDIA/apex/pull/1396
**Collection**: NLP
# Changelog
- Update GPT config
- Update parallel_lm_logits
- Add CoreAttention
- This is the block that gets recomputed when using selective activation checkpointing
- Update ParallelAttention to work with CoreAttention
- Update ParallelTransformerLayer
- Layer norm calls to use sequence parallel
- Update ParallelTransformer
- Update args
- Update final layer norm
- Add rng contex to forward when using sequence parallel
- Remove transposes
- Update Pooler
- Gather from sequence parallel regions
- Update embedding
- Add sequence_parallel flag
- add transpose in forward
- scatter for dropout
- Update TransformerLanguageModel
- Update GPTModel
- Add transposes
- All-reduce layer norm params when using sequence parallel
- Add sequence parallel arg to apex calls
- Update configs and arg checking for activation checkpointing
# Usage
```
# enable sequence parallelism
model.sequence_parallel=True
# enable selective activation checkpointing
model.activations_checkpoint_granularity=selective
# all transformer layers have CoreAttention checkpointed when using selective
model.activations_checkpoint_method=null
model.activations_checkpoint_num_layers=null
```
# 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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} | Signed-off-by: Shantanu Acharya <[email protected]>
# What does this PR do ?
A user can now specify the name of the directory to save the wandb logs by setting the value `exp_manager.wandb_logger_kwargs.save_dir` in the config.
**Collection**: [utils]
# Changelog
Files changed: `nemo/utils/exp_manager.py` (line: 670-671)
```
if wandb_kwargs.get('save_dir', None) is None:
wandb_kwargs['save_dir'] = exp_dir
os.makedirs(wandb_kwargs['save_dir'], exist_ok=True)
wandb_logger = WandbLogger(version=version, **wandb_kwargs)
```
# 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**:
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- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in NeMo
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} | # What does this PR do ?
Fix hanging/deadlock issue caused by multiprocessing in SD tutorial
**Collection**: ASR
# Changelog
- Cherry pick updates to r1.10.0 https://github.com/NVIDIA/NeMo/pull/4317-
Add instruction and set num_workers=1 in SD tutorial as a workaround for python multiprocessing would hang with python in some cases
# 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: MaximumEntropy <[email protected]>
# What does this PR do ?
Refactors bias activation fusion logic and adds the config option to the remaining yaml files.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Adds/replaces `bias_gelu_fusion` with `bias_activation_fusion` in remaining yaml configs.
- Refactors some of the logic within `transformer.py` and `lm_encoder_decoder.py`.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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 currently looking for a way to apply a state-of-the-art inverse text normalization method on Danish.
I installed and tested NeMo for English and it works quite well. In the paper it is also mentioned that it is possible to apply the idea to a different language.
But I cannot see any documentation/guide to be able to do so. Could you please help me out? | null | {
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# What does this PR do ?
fixes invalid_ids error on Megatron decode function by changing invalid tokens to <unk>
**Collection**: NLP
# Changelog
- small bug fix
# 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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fixes invalid_ids error on Megatron decode function
**Collection**: NLP
# Changelog
- filter invalid tokens from the predicted tokens
# Usage
N/A
# 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: smajumdar <[email protected]>
# What does this PR do ?
Fix issue with adapters where config is a dataclass and cannot be iterated on correctly.
**Collection**: [ASR]
# Changelog
- Add a check and force cast to Dict Config for input to the function.
# 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 ?
fixes invalid_ids error on Megatron decode function
**Collection**: NLP
# Changelog
- filter invalid tokens from the predicted tokens
# Usage
N/A
# 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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} | # What does this PR do ?
fixes invalid_ids error on Megatron decode function
**Collection**: NLP
# Changelog
- filter invalid tokens from the predicted tokens
# Usage
N/A
# 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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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Core]
# Changelog
- Adds documentation about NeMo adapters.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
`is_phoneme may not exist in JSON files if only considering chars as inputs.`
**Collection**: [Note which collection this PR will affect]
`nemo.collections.tts.torch.data`
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Remove depreciated comments and implement unimplemented features
**Collection**: NLP
# Changelog
- Removed mentions of `data_dir` from config as it doesn't exists anymore and changed it to `ds_item`
- Added the ability to add new punctuation marks to data preparation script `examples/nlp/token_classification/data/prepare_data_for_punctuation_capitalization.py `
- Removed unnecessary file extension check in `nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py`
# 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**:
- [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: smajumdar <[email protected]>
# What does this PR do ?
Fixes trainer/global_step in wandb logs
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Core]
# Changelog
- Adds wandb as a dependency specifically for core
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
Sets headscale=False by default in ParallelAttention
**Collection**: [Note which collection this PR will affect]
# Changelog
- Sets headscale=False by default in ParallelAttention
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] 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: ekmb <[email protected]>
# What does this PR do ?
Remove torch.distributed.is_initialized() guard from the dataset as it always returns False. Try to load features only if the pkl file exists, else create the cache file.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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