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} | # What does this PR do ?
Fix bug intent slot classification tokenizer to dialogue
**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.
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* Related to # (issue)
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} | # What does this PR do ?
Fix bug intent slot classification tokenizer to dialogue
**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 ?
Fixes a bug with RPE and encode() in token_level_encoder_decoder.
**Collection**: NLP
# Changelog
- Adds `enc_self_attention_relative_position_bias` to the `self.encoder()` call in `megatron_encoder_decoder.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**:
- [ ] 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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} | Fix #4554. There is a typo. Instead of "cartographic", it should be "catastrophic".
# What does this PR do ?
Fix #4554. There is a typo. Instead of "cartographic", it should be "catastrophic".
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Move TTS tokenizers to collections.common.tokenizers so they can be used in cross-collections pipelines.
For implementing ASR/ST model training which generates samples on-the-fly with TTS, we need to use TTS tokenizers. Moving them to collections.common.tokenizers allows to write a wrapper which allows to use them directly in ASR and NLP datasets. New location also seems more logical as:
1) TTS tokenizers are the only tokenizers located outside of collections.common.tokenizers
2) TTS tokenizers are currently located in collections.tts.torch but the only torch function they use is torch.distributed
Ideally, I would recommend refactoring them around TokenizerSpec in the future to have unified implementation of all tokenizers.
**Collection**: [Note which collection this PR will affect]
collections.tts and collections.common.tokenizers
# 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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} | Prompt Learning Notebook Bug Fix
Signed-off-by: Virginia Adams <[email protected]>
Adds back ability to handle dataset example dicts directly passed into the .generate method for prompt learning
**Collection**: NLP
# Changelog
- Updated GPTPromptLearningDataset class
- Updated GPTPromptLearningModel class's generate function
- Updated prompt learning unit test
# Usage
```python
test_examples = [
{"taskname": "intent_and_slot", "utterance": "tell me who will win the next presidential election"},
{"taskname": "intent_and_slot", "utterance": "i would like to pickup a veggie sub with a cookie from subway"},
{"taskname": "intent_and_slot", "utterance": "email happy new year to john"},
{"taskname": "intent_and_slot", "utterance": "set the alarm to seven am for work"},
{"taskname": "sentiment", "sentence": "The products have a low salt and fat content ."},
{"taskname": "sentiment", "sentence": "The agreement is valid for four years ."},
{"taskname": "sentiment", "sentence": "Diluted EPS rose to EUR3 .68 from EUR0 .50 ."},
{"taskname": "sentiment", "sentence": "The company is well positioned in Brazil and Uruguay ."},
{"taskname": "sentiment", "sentence": "Profit before taxes decreased by 9 % to EUR 187.8 mn in the first nine months of 2008 , compared to EUR 207.1 mn a year earlier ."},
]
response = model.generate(inputs=test_examples, length_params=None)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adding support for models trained with full context for cache-aware streaming.
**Collection**: ASR
# Changelog
Adding support for models trained with full context for cache-aware streaming.
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Adds a multispeaker audio session simulator and corresponding documentation and tutorials.
Collections: ASR, Tools
# Changelog
- Adds data simulator
- Adds a manifest_utils file and restructures speaker_utils
- Adds config files for simulating data as well as a tutorial and documentation
# Before your PR is "Ready for review"
**Pre checks**:
- [ x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ x] New Feature
- [ ] Bugfix
- [ x] Documentation
## 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 ?
Move trainer calls for ssl models to training and validation steps only
**Collection**: ASR | {
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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**:
- [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 ?
new PR for previous chinese TN
**Collection**: [NeMo/norm_text_processing/text_normalization]
I think have covered the problems mentioned previously.And I make a new pr to make it clear. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4682 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4682/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4682/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4682/events | https://github.com/NVIDIA/NeMo/pull/4682 | 1,329,711,519 | PR_kwDOC_bI7s48tMvl | 4,682 | [ASR] Add pretrained ASR models for Croatian | {
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
This PR adds pre-trained models for Croatian.
**Collection**: [ASR]
# Changelog
- updated `PretrainedModelInfo` and docs
- minor (cosmetic) modifications to `checkpoint_averaging.py`
# Usage
Tested with the following
```python
from nemo.collections.asr.models import ASRModel
for type in ['ctc', 'transducer']:
model = ASRModel.from_pretrained(f'stt_hr_conformer_{type}_large', map_location='cpu')
print(f'\nmodel_{type}.summarize()')
print(model.summarize())
print()
```
Pretrained models downloaded from NGC.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] 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 | Hello!
I would like to run the train_kenlm.py file but I do not understand all the arguments it requires:
I read the documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html and I ran install_beamsearch_decoders.sh as well, so I have the kenlm bin root but I have no idea how to get the root of the .nemo file and what it is about?
Thanks in advance! | {
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} | # What does this PR do ?
Adds a multispeaker audio session simulator and corresponding documentation and tutorials.
Collections: ASR, Tools
# Changelog
- Adds data simulator
- Adds a manifest_utils file and restructures speaker_utils
- Adds config files for simulating data as well as a tutorial and documentation
# Before your PR is "Ready for review"
**Pre checks**:
- [ x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ x] New Feature
- [ ] Bugfix
- [ x] Documentation
## 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 ?
Apply consistent spline interpolation when generating mel-spectrograms for finetuing hifigan.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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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} | [Speech Data Explorer] An option to explicitly specify the base dir for manifests with relative paths
**Collection**: Tools
# Changelog
- Use `args.audio_base_path` instead of `args.manifest`
- If `args.audio_base_path` is not provided (`None`), then default to the location (parent dir) of the manifest
# Usage
```python
python data_explorer.py /path/to/a/modified/manifest.json --audio-base-path /path/to/audio_data
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Mirror the bugfix from https://github.com/NVIDIA/NeMo/pull/4669
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix fastpitch export
**Collection**: TTS
# Changelog
- Fix fastpitch export
**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 ?
Switching to 2.1.5 pynini version, reverting to pynini install via conda, pip will still be used to Linux systems. Added guard to pytests to skip TN/ITN tests when pynini is not installed.
**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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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do?
Upgrade PTL version to 1.7.2
**Collection**: All
# Changelog
- Default max_steps is -1 now instead of None
- DDPPlugin has been moved from plugins to strategies and renamed as DDPStrategy -> renamed NLPDDPPlugin to NLPDDPStrategy
- override lightning module trainer property to make it compatible with our existing models, since it's been removed from PTL 1.7
- Following args have been removed from Trainer
> - prepare_data_per_node
> - checkpoint_callback - replaced with enable_checkpointing
> - process_position - now part of TQDMprogressbar callback
> - stochastic_weight_avg - now part of callback
> - flush_logs_every_n_steps
> - weights_summary - replaced with callback
> - terminate_on_nan
> - log_gpu_memory - part of DeviceGPUStats callback
> - Progressbar refresh rate - part of TQDMProgressbar callback
TODO:
- [x] remove Cuda override function, once https://github.com/Lightning-AI/lightning/pull/14128 was released as part of 1.7.2
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [x] Documentation | {
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} | Signed-off-by: Virginia Adams <[email protected]>
The T5 prompt learning CI test had some formatting errors keeping the CI tests from running. This should fix them
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Updated Jenkins file
# 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
- [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 type annotations in the `calc_length` function since it causes errors when scripting Conformer models.
**Collection**: ASR
This is the error:
```
[NeMo E 2022-08-03 11:47:46 exportable:116] jit.script() failed!\
Variable 'add_pad' is annotated with type float but is being assigned to a value of type Tensor:
File "/Users/allabana/.virtualenvs/meval/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/subsampling.py", line 149
def calc_length(lengths, padding, kernel_size, stride, ceil_mode, repeat_num=1):
""" Calculates the output length of a Tensor passed through a convolution or max pooling layer"""
add_pad: float = (padding * 2) - kernel_size
~~~~~~~ <--- HERE
one: float = 1.0
for i in range(repeat_num):
```
This is an example usage that causes the error:
```py
pre_trained_model_name = "stt_en_conformer_ctc_small"
model = EncDecCTCModelBPE.from_pretrained(pre_trained_model_name, map_location='cpu')
model.eval()
model.export(f"/tmp/{model._get_name()}.ts", check_trace=True, try_script=True)
```
# Changelog
- Remove type annotations for `add_pad`/`one` in `nemo.collections.asr.parts.submodules.subsampling.calc_length`
- Add type annotations to `calc_length` method args.
# 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
## 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 ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* 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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} | The PR adds the new SOTA model we have for intent classification and slot filling for spoken language understanding.
- [x] Copy transformer modules from `nemo.collections.nlp.modules.common` to `nemo.collections.asr.modules`
- [x] Add models, loss and utils to nemo.collection.asr
- [x] Add example scripts for data downloading and processing
- [x] Add example scripts for training and evaluation
- [x] Refactor model to be independent of EncDecCTCBPE
- [x] Refactor to use built-in SmoothedCrossEntropyLoss
- [x] Write README for example
- [x] Upload model checkpoints to NGC and create model cards
- [x] Update Docs and list_available_models()
- [ ] Create tutorial. // Plan to add in later PR
The NGC model card is [here](https://ngc.nvidia.com/models/nvstaging:nemo:slu_conformer_transformer_large_slurp)
The HF model card is [here](https://huggingface.co/nvidia/slu_conformer_transformer_large_slurp) | {
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Update README docs
**Collection**: [Speaker Tasks]
# Changelog
- update docs on launching fine-tune script
# Usage
-
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [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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} | Added installation of correct apex version.
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds support for RNNT models to compute character level and word level timestamps using similar steps to CTC models.
**Collection**: [ASR]
# Changelog
- Adds char and word level timestamp calculation for character and subword models
# Usage
```python
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytorch_lightning as pl
from omegaconf import OmegaConf, open_dict
from nemo.collections.asr.models import ASRModel
def main():
trainer = pl.Trainer(gpus=[0], precision=32, logger=False)
# Char model
# path = "Path to char model"
# Subword model
path = "Path to subword model"
asr_model = ASRModel.restore_from(path, trainer=trainer, map_location='cpu') # type: EncDecRNNTModel
asr_model.eval()
asr_model.freeze()
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
decoding_cfg = OmegaConf.create({})
with open_dict(decoding_cfg):
decoding_cfg.strategy = "greedy_batch"
decoding_cfg.preserve_alignments = True
decoding_cfg.compute_timestamps = True
decoding_cfg.rnnt_timestamp_type = "all"
asr_model.change_decoding_strategy(decoding_cfg)
hyp, _ = asr_model.transcribe(['/test-other-processed/367-130732-0003.wav'],
batch_size=1, return_hypotheses=True)
print("Evaluating ---")
print()
print("Transcription: ", hyp[0].text)
print()
print("Total number of timesteps :", hyp[0].length)
print()
print("Character/Subword Time-Stamps :", hyp[0].timestep['char'])
print()
print("Word Time-Stamps :", hyp[0].timestep['word'])
print()
if __name__ == '__main__':
main()
```
# 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?
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- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
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] | closed | false | null | [] | null | null | 2022-08-03T01:50:17 | 2022-08-25T14:58:07 | 2022-08-25T14:58:06 | NONE | {
"total": 0,
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} | null | null | null | **Unable to import ASR model from Nemo.Collections **
**import nemo.collections.asr as nemo_asr**
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
** Error Message
Error
ModuleNotFoundError Traceback (most recent call last)
in ()
3 # NeMo's ASR collection - this collections contains complete ASR models and
4 # building blocks (modules) for ASR
----> 5 import nemo.collections.asr as nemo_asr
17 frames
[/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py](https://localhost:8080/#) in ()
27 if _TORCHTEXT_AVAILABLE:
28 if _compare_version("torchtext", operator.ge, "0.9.0"):
---> 29 from torchtext.legacy.data import Batch
30 else:
31 from torchtext.data import Batch
ModuleNotFoundError: No module named 'torchtext.legacy'
**
- Environment location: Collab
- Method of NeMo install: pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- PyTorch version - 1.12
- Python version - 3.7
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Adds support for Enc-Dec models with RPE and PP > 2
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* model.encoder.position_embedding_type="relative"
* model.decoder.position_embedding_type="relative"
* model.pipeline_model_parallel_size=4
* model.pipeline_model_parallel_split_rank=2
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds the fixes from https://github.com/espnet/espnet/pull/4312 for mAES RNNT beam search.
**Collection**: [ASR]
# Changelog
- Add deduplication checks
- Improve speed of mAES beam search
# Usage
```python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytorch_lightning as pl
from omegaconf import OmegaConf, open_dict
from nemo.collections.asr.models import EncDecRNNTBPEModel
def main():
trainer = pl.Trainer(gpus=[1], precision=16, logger=False)
# Model
# path = 'some model path'
asr_model = EncDecRNNTBPEModel.restore_from(path, trainer=trainer, map_location='cpu')
asr_model.eval()
asr_model.freeze()
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
cfg = asr_model.cfg
test_cfg = cfg.test_ds
test_cfg.manifest_filepath = "manifests/test_other.json"
test_cfg.batch_size = 256
asr_model.setup_multiple_test_data(test_cfg)
decoding_cfg = cfg.decoding
with open_dict(decoding_cfg):
decoding_cfg.strategy = "maes"
decoding_cfg.fused_batch_size = -1
decoding_cfg.beam.beam_size = 4
decoding_cfg.beam.tsd_max_sym_exp = 10
decoding_cfg.beam.alsd_max_target_len = 2.5
decoding_cfg.beam.maes_num_steps = 2
decoding_cfg.beam.maes_prefix_alpha = 1
decoding_cfg.beam.maes_expansion_beta = 2
decoding_cfg.beam.maes_expansion_gamma = 2.3
asr_model.change_decoding_strategy(decoding_cfg)
if asr_model.prepare_test(trainer):
trainer.test(asr_model)
if __name__ == '__main__':
main()
```
# 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)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | null | null | null | Hi! I am trying `nvidia/stt_en_conformer_transducer_xlarge` for my ASR system and the performance is impressive. I noticed that there is an option in transcribe function that can support lm_score in output. I wonder if it is possible to generate confidence score for each word or where should I modify the code so that it can show the confidence score for each word. Thanks in advance,
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https://api.github.com/repos/NVIDIA/NeMo/issues/4660 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4660/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4660/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4660/events | https://github.com/NVIDIA/NeMo/pull/4660 | 1,326,278,754 | PR_kwDOC_bI7s48hyh9 | 4,660 | Pin lightning version to be < 1.7.0 | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Pins PTL version to be less than 1.7.0
**Collection**: All
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
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https://api.github.com/repos/NVIDIA/NeMo/issues/4659 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4659/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4659/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4659/events | https://github.com/NVIDIA/NeMo/pull/4659 | 1,326,240,259 | PR_kwDOC_bI7s48hqH_ | 4,659 | Add sentencepiece legacy arg to megatron tokenizer configs | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Makes the sentencepiece legacy arg configurable. This fixes GPT training with sentencepiece.
**Collection**: NLP
# Changelog
- Makes the sentencepiece legacy arg configurable.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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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} | null | null | null | Hello,
I am trying to test a pre-trained conformer transducer, but the reported WER is around 100 %, whereas it is much lower when I use the `transcribe()` method of the model and compute the WER myself. What am I doing wrong?
Script:
```python
import yaml
from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel
import pytorch_lightning as pl
trainer = pl.Trainer(devices=1, accelerator="gpu", max_epochs=50)
config = yaml.load(open("./config/config.yaml"), Loader=yaml.FullLoader)
ct = EncDecRNNTBPEModel.from_pretrained("stt_de_conformer_transducer_large")
ct.setup_test_data(config["data"]["test_ds"])
trainer.test(ct, dataloaders=ct.test_dataloader())
```
Config file:
```yaml
data:
test_ds:
manifest_filepath: data/train_split/debug.txt
sample_rate: 48000
batch_size: 1
shuffle: false
num_workers: 0
pin_memory: true
use_start_end_token: false
``` | {
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} | null | null | null | I was finetuning pretrained citrinet 512 model (Encoder Freeze) for filipino language where i trained a custom tokenizer with vocab size - 1024 and replaced the pretrained citrinet tokenizer section with my custom tokenizer.
`data_dir = "."
!python3 ./scripts/process_asr_text_tokenizer.py
--manifest="{data_dir}/Filipino/train_manifest.json"
--data_root="{data_dir}/complete_tg/tokenizers/tokenizer_1/"
--vocab_size=1024
--tokenizer="spe"
--spe_type="bpe"
--spe_character_coverage=1.0
--log`
Sample vocab -:
▁
##a
##i
##o
##n
##t
##c
##e
##l
##r
##s
##p
##f
##g
##k
##m
##v
This script gives tokenizer.model and vocabulary, In vocab a special character "▁" is present. This is not a normal underscore which is "_".
Can someone explain me why i am getting this special token as due to this special character i am unable to deploy my asr in riva speech with flashlight decoder.
Error got during deployment with flashlight decoder -:
`I0712 13:32:15.565105 94 ctc-decoder-library.cc:23] TRITONBACKEND_ModelInstanceInitialize: nemo_asr_riva_pipeline_filipino-ctc-decoder-cpu-streaming_0 (device 0)
terminate called after throwing an instance of 'std::runtime_error'
what(): [LoadWords] Invalid line: ▁
> Riva waiting for Triton server to load all models...retrying in 1 second
/opt/riva/bin/start-riva: line 4: 94 Aborted (core dumped) tritonserver --log-verbose=0 --strict-model-config=true $model_repos --cuda-memory-pool-byte-size=0:1000000000`
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} | Fixes installation issues in a normal python virtual environment when conda is also present on the same system. Also remove double negation.
Signed-off-by: Anand Joseph <[email protected]>
# What does this PR do ?
Fixes installation issues in a normal python virtual environment when conda is also present on the same system.
# Changelog
- Check if "$CONDA_PREFIX" is set rather than check if "conda" is present.
- Replace double negation "! -z" with "-n" when checking for NVIDIA_PYTORCH_VERSION
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} | # What does this PR do ?
Fixes installation issues in a normal python virtual environment when conda is also present on the same system.
# Changelog
- Check if "$CONDA_PREFIX" is set rather than check if "conda" is present.
- Replace double negation "! -z" with "-n" when checking for NVIDIA_PYTORCH_VERSION
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} | Fixes installation issues in a normal python virtual environment when conda is also present on the same system. Also remove double negation.
# What does this PR do ?
This primarily resolves an issue when we are working in a standard python virtual environment and conda environment is also present but not used. Basically check if $CONDA_PREFIX is set instead of checking if conda is present.
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https://api.github.com/repos/NVIDIA/NeMo/issues/4650 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4650/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4650/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4650/events | https://github.com/NVIDIA/NeMo/pull/4650 | 1,325,217,575 | PR_kwDOC_bI7s48eOex | 4,650 | Mutiscale Diarization Decoder (MSDD) model and module files | {
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
Add multiscale diarization decoder model module, and yaml files.
Additionally, yaml files are also uploaded.
- msdd_models.py
Three classes are included:
(1) ClusterEmbedding
This class is for taking care of cluster-average embeddings by launching clustering diarizer class.
The methods in this class read embedding vectors and clustering output that are written from clustering diarizer class.
(2) EncDecDiarLabelModel
MSDD model class.
(3) OverlapAwareDiarizer
A class that launches Clustering Diarizer and run MSDD infer then evaluate it.
It involves integration of pairwise inference from MSDD and split-infer that breaks down the input to
short sequences.
- msdd_diarizer.py
MSDD Neural model class.
- speaker_utils.py
audio_rttm_map() function is modified to deal with the uniq_id with duration information.
-audio_to_diar_label.py, collections.py
These are couple of very small points where it needs to be changed like: rttm_files -> audio_files
(We need to extract uniq_id when rtttm is not provided for evaluation)
- 4 x yaml files
msdd_5scl_15_05_50Povl_infer.yaml -> for inference
msdd_6scl_30_05_50Povl_infer.yaml -> for inference
msdd_5scl_15_05_50Povl_256x3x32x2.yaml -> for training
msdd_6scl_30_05_50Povl_256x3x32x2.yaml -> for training
The name of these files are up for debate.
* Tutorial and script for training data generation is excluded since it makes PR too big.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* Use the following command to run MSDD.
```python
HYDRA_FULL_ERROR=1 python $BASEPATH/multiscale_diar_decoder_infer.py
--config-path='conf' --config-name='msdd_5scl_15_05_50Povl_infer.yaml' \
diarizer.manifest_filepath=<TEST_MANIFEST> \
diarizer.out_dir=<TEST_EMB_DIR> \
diarizer.msdd_model.model_path=<MSDD_MODEL_PATH>\
```
# 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? - Added unitest for msdd_model. Will add unit-test for audio_to_diar_label once micro diarization dataset is ready.
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds / propagates language id when creating a tarred [multilingual] dataset from non-tarred
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
One line change in the convert_to_tarred_audio_dataset.py script
# Usage
* You can potentially add a usage example below
No change to user interface.
```python
# Add a code snippet demonstrating how to use this
```
python convert_to_tarred_audio_dataset.py <params>
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Updates URL of github page source to `gh-pages-src`.
# NOTE
Push all changes to docs to this branch only.
**Collection**: [Docs]
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# What does this PR do ?
Try to force the github action to have adequate permissions | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Check if github page deployment works
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} | # What does this PR do ?
Adds a github action to build the website for nvidia.github.io/NeMo/ on every push.
**Collection**: [Note which collection this PR will affect]
# 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 ?
Modernize and update NeMo Github.io website.
**Collection**: [Docs]
# Changelog
- Replace previous redirect with mkdocs for material based website
# Usage
- To build the website locally to see the contents, run `bash build_docs.sh` from the `gh-pages` branch.
- In order to add posts, follow the steps in the template inside `docs/blogs/template.md`
# 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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} | # What does this PR do ?
It adds the muTransfer capablity to do RETRO model pretraining.
1. Included the mutransfer codes from MS github.
2. Add shape calculation script.
3. Add the mutransfer pretraining script.
4. Make sure it uses fused_softmax padding mode because of the customized masks
5. Include a CI test to make sure the model training performance matches.
It also fix a bug that PTL grad sync is not called.
To use muTransfer for other models (GPT, T5, BERT etc), you can follow the same pattern.
1. register muadam or muadamw optimizer.
2. swap to use MuReadout to to convert model_d to logistics
3. Use the similar parameters init method in the mutransfer_pretrain file.
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} | null | null | null | Hello.
Is any script for training Mixer-TTS-X?
I've used it in the model select inference example, and I really like how it sounds. Better than FastPitch and MixerTTS, in my opinion. Also in the paper shows a higher MOS.
I think it would be extremely useful.
Thanks,
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} | # What does this PR do ?
Update pynini from `2.1.4` to `2.1.5` to fix MacOS build
**Collection**: [Text Processing]
# Changelog
- Update pynini from `2.1.4` to `2.1.5`
# Reason
Pynini 2.1.4 can't be installed on MacOS (Intel processor, Conda, python 3.8). Using version `2.1.5` fixes the issue.
```
Building wheels for collected packages: pynini
Building wheel for pynini (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> [55 lines of output]
...
building '_pywrapfst' extension
creating build/temp.macosx-10.9-x86_64-3.8
creating build/temp.macosx-10.9-x86_64-3.8/extensions
clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /<user_dir>/anaconda3/envs/ml38/include -fPIC -O2 -isystem /<user_dir>/anaconda3/envs/ml38/include -I/<user_dir>/anaconda3/envs/ml38/include/python3.8 -c extensions/_pywrapfst.cpp -o build/temp.macosx-10.9-x86_64-3.8/extensions/_pywrapfst.o -std=c++17 -Wno-register -Wno-deprecated-declarations -Wno-unused-function -Wno-unused-local-typedefs -funsigned-char -stdlib=libc++ -mmacosx-version-min=10.7
extensions/_pywrapfst.cpp:753:10: fatal error: 'fst/types.h' file not found
#include <fst/types.h>
^~~~~~~~~~~~~
1 error generated.
error: command 'clang' failed with exit status 1
[end of output]
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@yzhang123
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 ?
If several `ds_item`s are used for validation or testing and `cache_dir` is set, then pickled features for different features overwrite each other.
**Collection**: nlp
# Changelog
- if `cache_dir` is set, then an absolute path to dataset is appended to `cache_dir` to avoid collisions
- Both text and labels file names are incorporated into pickled features file name. This is done to avoid collisions in rare cases when there are different labels files for 1 input text file.
- I also changed token classification pickled features name
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} | null | null | null | Hello, so I have tried training conformer for a couple of times with different audio augmentations but the results seem the same. On training and test audio model performs well, but on my recording or any other audio which was not in the test/training set model performs horribly. I am using ffprobe for validating the encoding sample rate and a number of channels. all audios have Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, 1 channels, s16, 256 kb/s this properties. My question is, am I doing something wrong or why does conformer lack **generalization**?
```
# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding.
# Architecture and training config:
# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file.
# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one.
#
# +-------------+---------+---------+----------+------------+-----+
# | Model | d_model | n_heads | n_layers | time_masks | lr |
# +=============+=========+========+===========+============+=====+
# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 |
# +-------------+---------+--------+-----------+------------+-----+
# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 |
# +-------------+---------+--------+-----------+------------+-----+
# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 |
# +---------------------------------------------------------------+
#
# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2
# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence.
# With weight_decay=0.0, learning rate may need to get reduced to 2.0.
# You may find more info about Conformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-ctc
# Pre-trained models of Conformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html
# The checkpoint of the large model trained on LibriSpeech with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls
name: "Conformer-CTC-BPE"
model:
sample_rate: 16000
log_prediction: true # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
skip_nan_grad: false
train_ds:
augmentor:
white_noise:
prob: 0.3
min_level: -50
max_level: -10
impulse:
prob: 0.3
manifest_path: /home/ubuntu/ASR/NeMo/scripts/dataset_processing/RIR_DATA/processed/rir.json
shift:
prob: 0.3
min_shift_ms: -5.0
max_shift_ms: 5.0
#time_stretch:
#prob: 0.4
#min_speed_rate: 0.9
#max_speed_rate: 1.1
#num_rates: 5
#n_fft: 512
#gain:
#prob: 0.4
#min_gain_dbfs: -10
#max_gain_dbfs: 10
#========================
#transcode_aug:
#prob: 0.3
#speed:
#prob: 0.3
#resample_type: 'kaiser_best'
#num_rates: 5
#sr: 16000
manifest_filepath: ???
sample_rate: ${model.sample_rate}
batch_size: 32 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: ???
sample_rate: ${model.sample_rate}
batch_size: 32 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
test_ds:
manifest_filepath: null
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
# recommend small vocab size of 128 or 256 when using 4x sub-sampling
# you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
tokenizer:
dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
#rect_masks: 5 # Number of rectangles to cut from any given spectrogram
#rect_freq: 50 # Max cut of size 50 along the frequency dimension
#rect_time: 120 # Max cut of size 120 along the time dimension
# SpecAugment parameters
freq_masks: 2 # Cut two frequency bands
freq_width: 27 # ... of width 15 at maximum
time_masks: 5 # Cut out 10 time bands
time_width: 0.05 # ... of width 25 at maximum
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 16
d_model: 176
# Sub-sampling params
subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory
subsampling_factor: 4 # must be power of 2
subsampling_conv_channels: -1 # -1 sets it to d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 4 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
conv_norm_type: 'batch_norm' # batch_norm or layer_norm
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
optim:
name: adamw
lr: 5.0
# optimizer arguments
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 0
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 10000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 1000
max_steps: null # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
accumulate_grad_batches: 16
gradient_clip_val: 0.0
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
log_every_n_steps: 10 # Interval of logging.
progress_bar_refresh_rate: 10
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
# you need to set these two to True to continue the training
resume_if_exists: false
resume_ignore_no_checkpoint: false
# You may use this section to create a W&B logger
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
```
tokenizer was trained like this:
python $NEMO_ROOT/scripts/tokenizers/process_asr_text_tokenizer.py \
--manifest=/home/ubuntu/data/train.ds \
--data_root="tokenizer/" \
--vocab_size=128 \
--tokenizer="spe" \
--no_lower_case \
--spe_type="unigram" \
--spe_character_coverage=1.0 \
--log
```
So I guess, what I am really asking is, with what configuration would the model generalize better on unseen data, because right now for some unknown reason for me model drastically overfits on training/ "training like" data and on any other audio it's impossible two get a single world right...
sidenotes: If I use 16 precision most of the time loss becomes non because of rir I guess otherwise it trains well but still has generalization issues. accumulate_grad_batches was increased cause I am training on only 1 gpu with 16 gigs of vram | {
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} | [] | closed | false | null | [] | null | null | 2022-08-01T05:41:22 | 2022-08-05T13:57:08 | 2022-08-05T13:57:07 | CONTRIBUTOR | {
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} | # What does this PR do ?
PR for chinese TN part in text_normalization
**Collection**: NeMo/norm_text_processing/text_normalization
Chinese Text Normalization
1. How To Use
python normalize.py --lang "zh" --text "text to be normalized"
2. TN Pipeline
There are 3 components in TN pipeline:
pre-processing (before tagger)
non-standard word normalization
post-processing (after verbalizer)
2.1 Pre-Processing
Char Width Conversion (全角 -> 半角)
苹果CEO宣布发布新IPHONE -> 苹果CEO宣布发布新IPHONE
他说:“我们已经吃过了!”。 -> 他说:"我们已经吃过了!".
covers English letters, digits, punctuations and some symbols
the complete mapping table data/char/fullwidth_to_halfwidth.tsv
Blacklist (Removal)
Sometime you may want to remove certain things like interjections/fillers "啊", "呃" etc
呃这个呃啊我不知道 -> 这个我不知道
customizable via data/blacklist/interjections.tsv
2.2 Non-Standard-Words(NSW) normalization
Numbers
共465篇,约315万字 -> 共四百六十五篇,约三百一十五万字
共计6.42万人 -> 共计六点四二万人
同比升高0.6个百分点 -> 同比升高零点六个百分点
Fraction
总量的1/5以上 -> 总量的五分之一以上
相当于头发丝的1/16 -> 相当于头发丝的十六分之一
3/2是一个假分数 -> 二分之三是一个假分数
Percentage
同比增长6.3% -> 同比增长百分之六点三
增幅0.4% -> 增幅百分之零点四
Date
2002/01/28 -> 二零零二年一月二十八日
2002-01-28 -> 二零零二年一月二十八日
2002.01.28 -> 二零零二年一月二十八日
2002/01 -> 二零零二年一月
Time
8月16号12:00之前 -> 八月十六号十二点之前
我是5:02开始的 -> 我是五点零二分开始的
于5:35:36发射 -> 于五点三十五分三十六秒发射
8:00 a.m. 准时开会 -> 八点 a m 准时开会
Math
比分定格在78:96 -> 比分定格在七十八比九十六
计算-2的绝对值是2 -> 计算负二的绝对值是二
±2的平方都是4 -> 正负二的平方都是四
Money
价格是¥13.5 -> 价格是十三点五元
价格是$13.5 -> 价格是十三点五美元
价格是A$13.5 -> 价格是十三点五澳元
价格是HKD13.5 -> 价格是十三点五港元
Measure
重达25kg -> 二十五千克
最高气温38°C -> 最高气温三十八摄氏度
实际面积120m² -> 实际面积一百二十平方米
渲染速度10ms一帧 -> 渲染速度十毫秒一帧
Number series (phone, mobile numbers)
可以打我手机13501234567 -> 可以打我手机一三五零一二三四五六七
可以拨打12306来咨询 -> 可以拨打一二三零六来咨询
Erhua(儿化音) Removal
这儿有只鸟儿 -> 这有只鸟
这事儿好办 -> 这事好办
我儿子喜欢这地儿 -> 我儿子喜欢这地
erhua whitelist is customizable via data/erhua/whitelist.tsv
Whitelist(Replacement)
a set of user-defined hard mapping, i.e. exact-string matching & replacement
CEO -> C E O
GPU -> G P U
O2O -> O to O
B2B -> B to B
customizable via data/whitelist/default.tsv
2.3 Post-Processing
Punctuation Removal
If enabled, punctuations are removed.
Uppercase or Lowercase Conversion
If enabled, English letters are converted to uppercases / lowercases
Out-Of-Vocabulary(OOV) Tagger
If enabled, OOV chars are tagged with <oov> and </oov>, e.g.:
我们안녕 -> 我们<oov>안</oov><oov>녕</oov>
雪の花 -> 雪<oov>の</oov>花
default charset (national standard) [通用规范汉字表](https://zh.wikipedia.org/wiki/%E9%80%9A%E7%94%A8%E8%A7%84%E8%8C%83%E6%B1%89%E5%AD%97%E8%A1%A8)
you can extend charset via data/char/charset_extension.tsv
3. Credits
Author: Zhenxiang MA @ Tsinghua University
Advisors: [SpeechColab](https://github.com/SpeechColab) organization
The authors of this work would like to thank:
The authors of foundational libraries like OpenFst & Pynini
NeMo team and NeMo open-source community
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} | # What does this PR do ?
This is a work-in-progress for a model and data set that performs multilingual punctuation restoration, true casing, and sentence boundary detection. See `Usage` below for a demo of what this PR does.
Some key features:
* Resolves most of the issues mentioned in #3819
* Emphasis on multi-lingual; data set handles the nuances of each language.
* Language-agnostic inference. Inputs do not need language labels, and batches can contain multiple languages. Text is processed in its native script (e.g., Chinese is processed without spaces, for Spanish we can predict inverted punctuation, etc.).
* User can train with plain-text files as input; all "art work" is done by the pre-processor at training time
**Collection**: NLP
# Changelog
No changes to existing NeMo code, only additions.
# Usage
See current example model on the [HF hub](https://huggingface.co/1-800-BAD-CODE/punct_cap_seg_22lang1pass_xlmr).
# 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
## 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 discussion #3819
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# What does this PR do ?
When model was restored from checkpoint, several datasets were not working for testing and validation.
**Collection**: nlp
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https://api.github.com/repos/NVIDIA/NeMo/issues/4635 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4635/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4635/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4635/events | https://github.com/NVIDIA/NeMo/issues/4635 | 1,322,924,469 | I_kwDOC_bI7s5O2jm1 | 4,635 | Can't redefine method: __filter_banks_getter on class: __torch__.nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures | {
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} | null | null | null | **Steps/Code to reproduce bug**
I'm trying to repeat the model deployment [instruction ](https://github.com/pytorch/android-demo-app/blob/master/SpeechRecognition/create_wav2vec2.py) on Android.
```python
from torch.utils.mobile_optimizer import optimize_for_mobile
pretrained_model = nemo_asr.models.EncDecCTCModel.load_from_checkpoint(
"my_model.ckpt",
map_location="cpu"
)
pretrained_model.eval()
pretrained_model.encoder.freeze()
pretrained_model.decoder.freeze()
quantized_model = torch.quantization.quantize_dynamic(
pretrained_model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8, inplace=True
)
scripted_model = torch.jit.script(quantized_model)
# optimized_model = optimize_for_mobile(scripted_model)
```
But torch.jit.script returns an error:
> RuntimeError: Can't redefine method: __filter_banks_getter on class: __torch__.nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures (of Python compilation unit at: ...)
Can you advise how to fix this?
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I am trying to train a TTS model. I was looking for the VITS model, but I couldn't find it in your repository. Do you intend to add the VITS model to the collection of TTS models? And if you intend to add this model, when will this process happen? | {
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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
```
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} | null | null | null | **Describe the bug**
`WordTokenizer` should construct a vocab out of the words in a string, e.g., ['A word here'] -> ['A','word','here']. Instead, in constructs a character-based vocabulary due to its re-use of the `CharTokenizer` code. Additionally, if a properly constructed vocab is attempted to be loaded, it fails due to the error checking that is again inherited from `CharTokenizer`.
**Steps/Code to reproduce bug**
For constructing vocab:
```python
>>> from nemo.collections.common.tokenizers import WordTokenizer
>>> WordTokenizer.build_vocab('/tmp/vocab-georgea0729.txt', 'A word goes here')
>>> with open('/tmp/vocab-georgea0729.txt') as fh:
... print(fh.readlines())
...
['{}\n', "' '\n", "'e'\n", "'o'\n", "'r'\n", "'A'\n", "'w'\n", "'d'\n", "'g'\n", "'s'\n", "'h'\n"]
>>> tokenizer = WordTokenizer('/tmp/vocab-georgea0729.txt')
>>> tokenizer.text_to_tokens('A word goes here')
['A', None, None, None]
>>> tokenizer.text_to_tokens('A e w o h')
['A', 'e', 'w', 'o', 'h']
```
For Loading vocab:
```python
>>> with open('/tmp/georgea-manual-vocab0729.txt', 'w') as fh:
... fh.write("{}\n'A'\n'word'\n'goes'\n'here'\n")
...
28
>>> tokenizer = WordTokenizer('/tmp/georgea-manual-vocab0729.txt')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/common/tokenizers/word_tokenizer.py", line 49, in __init__
super().__init__(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/common/tokenizers/char_tokenizer.py", line 134, in __init__
self.check_token_from_file(token, vocab_file, i)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/common/tokenizers/char_tokenizer.py", line 241, in check_token_from_file
raise ValueError(
ValueError: Each line in vocabulary have to be a Python string literal containing 1 character. Encountered 'word' on line 1 in file /tmp/georgea-manual-vocab0729.txt.
>>>
```
**Expected behavior**
```python
>>> from nemo.collections.common.tokenizers import WordTokenizer
>>> WordTokenizer.build_vocab('/tmp/vocab-georgea0729.txt', 'A word goes here')
>>> with open('/tmp/vocab-georgea0729.txt') as fh:
... print(fh.readlines())
...
["{}\n", "'A'\n", "'word'\n", "'goes'\n", "'here'\n"]
>>> tokenizer = WordTokenizer('/tmp/vocab-georgea0729.txt')
>>> tokenizer.text_to_tokens('A word goes here')
['A', 'word', 'goes', 'here']
>>> tokenizer.text_to_tokens('A e w o h')
['A', None, None, None, None]
```
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} | # What does this PR do ?
This PR fixes #4627 by setting the initial vocab of `RegExTokenizer` to the supplied special tokens.
**Collection**: Common
# Changelog
- Sets the initial vocab of `RegExTokenier` to the supplied special tokens.
# 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?
@michalivne @titu1994
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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"merged_at": "2022-08-02T08:58:29"
} | # What does this PR do ?
Fix ITN rules for Portuguese (time).
Fix the case when the text is "às três horas" (at three o'clock) to force to have "horas" (like o'clock) instead of "às três" (at three).
Collection: [Text Processing]
# 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
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 | Hi,
I am trying to compute word level timestamps with CTCBPEDecoder and able to get the Hypotheses with timestep dictionary. But the dictionary has start_offset and end_offset for each word. I would like to know the offset value and stride multplication factor for computing final timestamp. Formulation for it would be great.
Here is a sample hypothesis object with timestep dictionary:
`[Hypothesis(score=tensor(-1.0438), y_sequence=tensor([[-5.6491e+01, -1.8834e+01, -1.4659e+01, ..., -2.6665e+01,
-3.1068e+01, -2.8610e-06],
[-5.7072e+01, -1.8568e+01, -1.7445e+01, ..., -2.4695e+01,
-3.3793e+01, -5.9605e-07],
[-5.7386e+01, -1.8189e+01, -1.9202e+01, ..., -2.3949e+01,
-3.3627e+01, -8.3446e-07],
...,
[-5.5325e+01, -1.4839e+01, -1.9966e+01, ..., -2.1065e+01,
-3.3125e+01, -1.4305e-05],
[-5.6287e+01, -1.4943e+01, -2.2234e+01, ..., -2.3138e+01,
-3.3042e+01, -7.3909e-06],
[-5.2566e+01, -1.3785e+01, -2.1438e+01, ..., -2.2653e+01,
-2.8750e+01, -1.4543e-05]], device='cuda:0'), text="well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very like the old portrait", dec_out=None, dec_state=None, timestep={'timestep': [12, 15, 19, 21, 23, 24, 25, 27, 29, 30, 31, 33, 36, 37, 39, 41, 44, 46, 47, 50, 52, 56, 57, 59, 61, 63, 64, 66, 67, 68, 69, 70, 71, 72, 74, 83, 85, 87, 90, 92, 94, 96, 97, 100, 102, 105, 107, 110, 125, 130, 135, 136, 138, 140, 141, 143, 146, 148, 150, 152, 158, 161, 163, 164, 167, 169, 171, 172, 174, 176], 'char': [{'char': 'we', 'start_offset': 11, 'end_offset': 12}, {'char': 'll', 'start_offset': 12, 'end_offset': 15}, {'char': 'i', 'start_offset': 15, 'end_offset': 19}, {'char': 'do', 'start_offset': 19, 'end_offset': 21}, {'char': 'n', 'start_offset': 21, 'end_offset': 23}, {'char': "'", 'start_offset': 23, 'end_offset': 24}, {'char': 't', 'start_offset': 24, 'end_offset': 25}, {'char': 'w', 'start_offset': 25, 'end_offset': 27}, {'char': 'i', 'start_offset': 27, 'end_offset': 29}, {'char': 's', 'start_offset': 29, 'end_offset': 30}, {'char': 'h', 'start_offset': 30, 'end_offset': 31}, {'char': 'to', 'start_offset': 31, 'end_offset': 33}, {'char': 's', 'start_offset': 33, 'end_offset': 36}, {'char': 'e', 'start_offset': 36, 'end_offset': 37}, {'char': 'e', 'start_offset': 37, 'end_offset': 39}, {'char': 'it', 'start_offset': 39, 'end_offset': 41}, {'char': 'a', 'start_offset': 41, 'end_offset': 44}, {'char': 'n', 'start_offset': 44, 'end_offset': 46}, {'char': 'y', 'start_offset': 46, 'end_offset': 47}, {'char': 'mo', 'start_offset': 47, 'end_offset': 50}, {'char': 're', 'start_offset': 50, 'end_offset': 52}, {'char': 'o', 'start_offset': 52, 'end_offset': 56}, {'char': 'b', 'start_offset': 56, 'end_offset': 57}, {'char': 's', 'start_offset': 57, 'end_offset': 59}, {'char': 'er', 'start_offset': 59, 'end_offset': 61}, {'char': 've', 'start_offset': 61, 'end_offset': 63}, {'char': 'd', 'start_offset': 63, 'end_offset': 64}, {'char': 'p', 'start_offset': 64, 'end_offset': 66}, {'char': 'h', 'start_offset': 66, 'end_offset': 67}, {'char': 'o', 'start_offset': 67, 'end_offset': 69}, {'char': 'e', 'start_offset': 69, 'end_offset': 70}, {'char': 'b', 'start_offset': 70, 'end_offset': 72}, {'char': 'e', 'start_offset': 72, 'end_offset': 74}, {'char': 't', 'start_offset': 74, 'end_offset': 83}, {'char': 'ur', 'start_offset': 83, 'end_offset': 85}, {'char': 'n', 'start_offset': 85, 'end_offset': 87}, {'char': 'ing', 'start_offset': 87, 'end_offset': 90}, {'char': 'a', 'start_offset': 90, 'end_offset': 92}, {'char': 'w', 'start_offset': 92, 'end_offset': 94}, {'char': 'a', 'start_offset': 94, 'end_offset': 96}, {'char': 'y', 'start_offset': 96, 'end_offset': 97}, {'char': 'he', 'start_offset': 97, 'end_offset': 100}, {'char': 'r', 'start_offset': 100, 'end_offset': 102}, {'char': 'e', 'start_offset': 102, 'end_offset': 105}, {'char': 'y', 'start_offset': 105, 'end_offset': 107}, {'char': 'es', 'start_offset': 107, 'end_offset': 110}, {'char': 'it', 'start_offset': 110, 'end_offset': 125}, {'char': 'is', 'start_offset': 125, 'end_offset': 130}, {'char': 'c', 'start_offset': 130, 'end_offset': 135}, {'char': 'er', 'start_offset': 135, 'end_offset': 136}, {'char': 't', 'start_offset': 136, 'end_offset': 138}, {'char': 'a', 'start_offset': 138, 'end_offset': 140}, {'char': 'in', 'start_offset': 140, 'end_offset': 141}, {'char': 'ly', 'start_offset': 141, 'end_offset': 143}, {'char': '', 'start_offset': 143, 'end_offset': 146}, {'char': 'ver', 'start_offset': 146, 'end_offset': 148}, {'char': 'y', 'start_offset': 148, 'end_offset': 150}, {'char': 'like', 'start_offset': 150, 'end_offset': 152}, {'char': 'the', 'start_offset': 152, 'end_offset': 158}, {'char': 'o', 'start_offset': 158, 'end_offset': 161}, {'char': 'l', 'start_offset': 161, 'end_offset': 163}, {'char': 'd', 'start_offset': 163, 'end_offset': 164}, {'char': 'p', 'start_offset': 164, 'end_offset': 167}, {'char': 'or', 'start_offset': 167, 'end_offset': 169}, {'char': 't', 'start_offset': 169, 'end_offset': 171}, {'char': 'ra', 'start_offset': 171, 'end_offset': 174}, {'char': 'it', 'start_offset': 174, 'end_offset': 176}], 'word': [{'word': 'well', 'start_offset': 11, 'end_offset': 19}, {'word': 'i', 'start_offset': 19, 'end_offset': 21}, {'word': "don't", 'start_offset': 25, 'end_offset': 27}, {'word': 'wish', 'start_offset': 31, 'end_offset': 33}, {'word': 'to', 'start_offset': 33, 'end_offset': 36}, {'word': 'see', 'start_offset': 39, 'end_offset': 41}, {'word': 'it', 'start_offset': 41, 'end_offset': 44}, {'word': 'any', 'start_offset': 47, 'end_offset': 50}, {'word': 'more', 'start_offset': 52, 'end_offset': 56}, {'word': 'observed', 'start_offset': 64, 'end_offset': 66}, {'word': 'phoebe', 'start_offset': 74, 'end_offset': 83}, {'word': 'turning', 'start_offset': 90, 'end_offset': 92}, {'word': 'away', 'start_offset': 97, 'end_offset': 100}, {'word': 'her', 'start_offset': 102, 'end_offset': 105}, {'word': 'eyes', 'start_offset': 110, 'end_offset': 125}, {'word': 'it', 'start_offset': 125, 'end_offset': 130}, {'word': 'is', 'start_offset': 130, 'end_offset': 135}, {'word': 'certainly', 'start_offset': 143, 'end_offset': 146}, {'word': 'very', 'start_offset': 150, 'end_offset': 152}, {'word': 'like', 'start_offset': 152, 'end_offset': 158}, {'word': 'the', 'start_offset': 158, 'end_offset': 161}, {'word': 'old', 'start_offset': 164, 'end_offset': 167}, {'word': 'portrait', 'start_offset': 164, 'end_offset': 176}]}, alignments=tensor([[-5.6491e+01, -1.8834e+01, -1.4659e+01, ..., -2.6665e+01,
-3.1068e+01, -2.8610e-06],
[-5.7072e+01, -1.8568e+01, -1.7445e+01, ..., -2.4695e+01,
-3.3793e+01, -5.9605e-07],
[-5.7386e+01, -1.8189e+01, -1.9202e+01, ..., -2.3949e+01,
-3.3627e+01, -8.3446e-07],
...,
[-5.5325e+01, -1.4839e+01, -1.9966e+01, ..., -2.1065e+01,
-3.3125e+01, -1.4305e-05],
[-5.6287e+01, -1.4943e+01, -2.2234e+01, ..., -2.3138e+01,
-3.3042e+01, -7.3909e-06],
[-5.2566e+01, -1.3785e+01, -2.1438e+01, ..., -2.2653e+01,
-2.8750e+01, -1.4543e-05]], device='cuda:0'), length=tensor(186, dtype=torch.int32), y=None, lm_state=None, lm_scores=None, tokens=None, last_token=None)]`
Thanks in advance
Sagar
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} | Signed-off-by: smajumdar <[email protected]>
Fix HF test where model card might not contain info
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Core]
# 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)
- [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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | The ID's of the default special tokens in RegexTokenizer are set here:
https://github.com/NVIDIA/NeMo/blob/72d78d832c6332ccc7f44811fb777eb7044df1d8/nemo/collections/common/tokenizers/regex_tokenizer.py#L75-L82
However, the constructor, allows custom special tokens:
https://github.com/NVIDIA/NeMo/blob/72d78d832c6332ccc7f44811fb777eb7044df1d8/nemo/collections/common/tokenizers/regex_tokenizer.py#L42-L51
So this could cause issues (specifically extra unknowns) when text is converted to tokens, then to ids, then back. | {
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} | # What does this PR do ?
Add inference support for Megatron T5 in Dialogue module
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Small fix for ignoring overlap median filtering and use shift > 0.01 in generating speech segments efficiently.
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Adds G2P models for OOV and heteronyms.
**Collection**: [Text Processing]
# Changelog
G2P models for OOV and heteronyms.
# 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
- [ ] 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 ?
Fix ITN rules for Portuguese (date and time).
In addiction, tests for these rules were added.
Collection: [Text Processing]
# 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
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: Iztok Lebar Bajec <[email protected]>
# What does this PR do ?
While waiting for #3819 to be finished, upgrade of current punctuation capitalization model with support for lowercasing, uppercasing, and capitalisation.
**Collection**: NLP/PC
# Changelog
Modified the capitalisation decision from pure capitalisation, as soon as `capit_label` differs from noop (`O`), to one where the operation is based on three classes: lowercase (`L`), uppercase (`U`) and capitalize (`C`).
Warning: Due to the `class_label` previously used for capitalisation (`U`) and the way the decision was implemented prior to this PR, this change becomes a breaking change. Models trained prior to this PR will result in returning `all caps` instead of `capitalising` selected words. A retrain will, however, provide additional functionality.
# Usage
* You can potentially add a usage example below
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
@PeganovAnton | {
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} | null | null | null | Is it possible to extract the sentence_piece tokeniser (.model, .vocab) file from the .nemo format?
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https://api.github.com/repos/NVIDIA/NeMo/issues/4619 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4619/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4619/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4619/events | https://github.com/NVIDIA/NeMo/pull/4619 | 1,318,832,100 | PR_kwDOC_bI7s48JJ2_ | 4,619 | Support listing Hugging Face model info | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Add new apis to filter and obtain NeMo hugging face models via code.
**Collection**: [Core]
# Changelog
- Adds two methods `get_hf_model_filter()` and `search_huggingface_models(model_filter=None)` to ModelPT.
- First method returns a basic ModelFilter object, can be overridden by the user. Used as default to next method if nothing is provided.
- Add support for metadata attributes to the filter to modify the result list of ModelInfo.
- `list_available_models_on_hf()` lists the nemo models that satisfy the filter.
# Usage
```python
from nemo.core import ModelPT
# You can replace <DomainSubclass> with any subclass of ModelPT.
# Get default ModelFilter
filt = <DomainSubclass>.get_hf_model_filter()
# Make any modifications to the filter as necessary
filt.language = [...]
filt.task = ...
filt.tags = [...]
# Add any metadata to the filter as needed
filt.limit_results = 5
# Obtain model info
model_infos = <DomainSubclass>.search_huggingface_models(model_filter=filt)
# Browse through cards and select an appropriate one
card = model_infos[0]
# Restore model using `modelId` of the card.
model = ModelPT.from_pretrained(card.modelId)
```
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4618 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4618/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4618/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4618/events | https://github.com/NVIDIA/NeMo/issues/4618 | 1,318,740,003 | I_kwDOC_bI7s5OmmAj | 4,618 | Cuda Out of memory | {
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} | null | null | null | Hi,
I am trying to train a conformer-transducer model on LibriSpeech but I am getting out of memory error. I am using the `conformer_transducer_bpe.yaml` config to initialize a `EncDecRNNTBPEModel` class (also tried the `char` version) with `batch_size=8` on a `NVIDIA K80 GPU` with 12GB of memory and this is the error I am getting:
`...
File "/home/ec2-user/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/modules/rnnt.py", line 992, in joint
res = self.joint_net(inp) # [B, T, U, V + 1]
File "/home/ec2-user/anaconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ec2-user/anaconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/home/ec2-user/anaconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ec2-user/anaconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: CUDA out of memory. Tried to allocate 2.16 GiB (GPU 0; 11.17 GiB total capacity; 8.88 GiB already allocated; 1.76 GiB free; 9.01 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF`
I managed to train a `EncDecCTCModelBPE` model with much larger number of parameters on the same dataset with a larger batch size but I am not sure why I am getting memory error for a smaller model. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4617 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4617/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4617/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4617/events | https://github.com/NVIDIA/NeMo/issues/4617 | 1,318,682,787 | I_kwDOC_bI7s5OmYCj | 4,617 | Using PyTorch Lightning Quantization Aware Training callback | {
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} | null | null | null | Can we use PL's QuantizationAwareTraining and ModelPruning callbacks during NeMo model training?
To quickly test this, I tried to add the QAT callback to the trainer in speech_to_text_ctc.py script to train a quick Quartznet5x5 model:
```
callbacks = [QuantizationAwareTraining(qconfig='qnnpack')]
trainer = pl.Trainer(callbacks=callbacks, **cfg.trainer)
```
and I get the following error:
```
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/ytuncel/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/ytuncel/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/ytuncel/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/ytuncel/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/ytuncel/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/ytuncel/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/ytuncel/tiny_asr/src/tiny_asr/script.py", line 108, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "/home/ytuncel/tiny_asr/src/tiny_asr/script.py", line 103, in main
trainer.fit(asr_model)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in fit
self._call_and_handle_interrupt(
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 723, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run
results = self._run_stage()
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage
return self._run_train()
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1345, in _run_train
self._run_sanity_check()
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1413, in _run_sanity_check
val_loop.run()
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 154, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 128, in advance
output = self._evaluation_step(**kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 226, in _evaluation_step
output = self.trainer._call_strategy_hook("validation_step", *kwargs.values())
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1765, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 344, in validation_step
return self.model.validation_step(*args, **kwargs)
File "/home/ytuncel/miniconda3/envs/asr/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 621, in validation_step
log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len)
TypeError: wrapper() got an unexpected keyword argument 'input_signal'
```
Are there any examples that show how we can use the Pruning and QAT tools in pytorch with NeMo? | {
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} | # What does this PR do ?
Fix a bug in the prior calculation used in the TTS aligner
**Collection**: [TTS]
# Changelog
- Fix off-by-1 bug in prior calculation
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
The prior here is supposed to help the TTS model align by teaching it to place more weight on phonemes along the diagonal, with it always starting on the first phoneme and ending on the last phoneme.
The current logic produces a prior that looks like this, which awkwardly ends earlier than expected with maximum weight on the last phoneme before the end of the sequence:

After this change, the prior looks like this:

I think the intuition is that we model the attention as a binomial distribution (eg. coin flips with head being success, tail failure) where each success results in the attention moving on to the next phoneme. To reach the end of a sequence of N phonemes, you only need (N - 1) successes. | {
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} | # What does this PR do ?
Add a LossMisconfigurationException that provides more context on how to fix certain issues that can occur during training.
**Collection**: ASR
# Changelog
- Add a `LossMisconfigurationException` for `ContrastiveLoss`
# 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
## 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 #4587
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https://api.github.com/repos/NVIDIA/NeMo/issues/4614 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4614/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4614/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4614/events | https://github.com/NVIDIA/NeMo/pull/4614 | 1,318,643,927 | PR_kwDOC_bI7s48IiNP | 4,614 | Improve normalize_batch ValueError message | {
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} | # What does this PR do ?
Improve `normalize_batch` error message with suggestions on how to fix.
Sometimes audio files with zero/small duration sneak by as files with a non-zero duration field, causing this error.
**Collection**: ASR
# Changelog
* Improve `normalize_batch` `ValueError` message with suggestions on how to fix
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
* Adds utility option to save input and output tensors for VAD model inference useful for testing and comparison in isolation from pre/post processing
* Adds support to disable median filtering for VAD post processing, (`None`, `False`, `''`)
**Collection**: ASR
# Changelog
- Add VAD options to save artifacts and disable median filtering
# Usage
examples/asr/conf/vad/vad_inference_postprocessing.yaml
```yaml
vad:
parameters:
smoothing: None
save_artifacts: False
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] 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 ?
Bug fix for Audio-based TN when "hundred" is optional, new method to split text into sentences before TN
**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**:
- [ X] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds speech separation model to NeMo.
**Collection**: ASR
# Changelog
- High level script for speech separation `examples/speech_tasks/separation/speech_separation.py`
- Sample config file `examples/speech_tasks/separation/conf/sep_transformer.yaml`
- Separation model `nemo/collections/asr/models/ss_model.py`
- Pre-processor `AudioToFeaturesConvPreprocessor` in `nemo/collections/asr/modules/audio_preprocessing.py`
- Decoder `nemo/collections/asr/modules/ss_decoder.py`
- Loss `nemo/collections/asr/losses/ss_losses/si_snr.py`
- Dataset `nemo/collections/asr/data/audio_to_audio.py`
- Inference `EncDecSpeechSeparationModel.extract_sources()` in `nemo/collections/asr/models/ss_model.py`
# Usage
```python
# load model
cfg = {'init_from_nemo_model': '<path_to_nemo_ckpt>'}
cfg = OmegaConf.create(cfg)
model_cfg = ASRModel.restore_from(restore_path=cfg.init_from_nemo_model, return_config=True)
sep_model = EncDecSpeechSeparationModel(cfg=model_cfg)
sep_model.maybe_init_from_pretrained_checkpoint(cfg, map_location='cpu')
# run inference
paths2audio_files=[<list_of_audio_mixtures_to_separate>]
sep_model.extract_sources(
paths2audio_files = paths2audio_files,
save_dir='<path_to_save_separated_sources>',
orig_sr=16000,
num_sources=2,
batch_size=1,
num_workers=1,
)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add support for subword time stamping using CTC decoding strategy.
**Collection**: [ASR]
# Changelog
- Fix mistake in selection of word boundaries for subword models
- Update tutorial with subword time stamp computation.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Updates the default values of the warmup and the layer norm in the example adapter config file. It also adds the option to specify tags as well as offline logging of wandb.
**Collection**: asr
# Changelog
File modified: `examples/asr/conf/asr_adapters/asr_adaptation.yaml`
- Adapters now use pre-norm by default
- The default number of warmup steps is set to 10% of the training set
- Added two parameters: `offline` and `tags` within the `wandb_logger_kwargs` config to allow specifying offline logging and tags for each run respectively.
# Before your PR is “Ready for review”
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven’t finished some of the above items you can still open “Draft” PR.
## Who can review?
Anyone in NeMo ASR. | {
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} | # What does this PR do ?
Updates the default values of the warmup and the layer norm in the example adapter config file. It also adds the option to specify tags as well as offline logging of wandb.
**Collection**: asr
# Changelog
File modified: `examples/asr/conf/asr_adapters/asr_adaptation.yaml`
- Adapters now use pre-norm by default
- The default number of warmup steps is set to 10% of the training set
- Added two parameters: `offline` and `tags` within the `wandb_logger_kwargs` config to allow specifying offline logging and tags for each run respectively.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in NeMo ASR.
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} | # What does this PR do ?
Adds a script and a tutorial for using a trained RAD-TTS Aligner as a G2P disambiguation tool.
**Collection**: TTS
# Changelog
- Created the `aligner_g2p.py` script for disambiguating heteronym pronunciations
- Added a new TTS Aligner example tutorial, which includes visualizations and demonstration of how heteronym disambiguation works
# Usage
For the disambiguation script:
```
python aligner_g2p.py \
--model=<model_path> \
--manifest=<manifest_path> \
--out=<output_json_path> \
--confidence=0.02 \
--verbose
```
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4603 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4603/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4603/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4603/events | https://github.com/NVIDIA/NeMo/pull/4603 | 1,317,459,219 | PR_kwDOC_bI7s48Eo0w | 4,603 | 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4602 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4602/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4602/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4602/events | https://github.com/NVIDIA/NeMo/pull/4602 | 1,317,420,671 | PR_kwDOC_bI7s48EggC | 4,602 | Label Smoothing in VocabParallelCrossEntropy | {
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"merged_at": "2022-08-04T07:13:05"
} | # What does this PR do ?
Adds label smoothing for VocabParallelCrossEntropy
**Collection**: [Note which collection this PR will affect]
# Changelog
- Adds the label smoothing arg to VocabParallelCrossEntropy
# Usage
* `model.label_smoothing=0.1`
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4601 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4601/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4601/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4601/events | https://github.com/NVIDIA/NeMo/issues/4601 | 1,317,407,192 | I_kwDOC_bI7s5OhgnY | 4,601 | Key Error when using conformer-transducer-bpe | {
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"total": 0,
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} | null | null | null | Hi,
I am trying to train a conformer-transducer ASR model with BERT tokenizer. I am using `conformer_transducer_bpe.yaml` as the base of my config with few changes to set the tokenizer. When running it, I get the following error:
```
raise ex.with_traceback(sys.exc_info()[2]) # set end OC_CAUSE=1 for full backtrace
omegaconf.errors.ConfigKeyError: Key 'num_classes' is not in struct
full_key: decoder.num_classes
object_type=dict
```
so I decided to set the decode.num_classes to -1 but then I get this error:
```
raise InstantiationException(msg) from e
hydra.errors.InstantiationException: Error in call to target 'nemo.collections.asr.modules.rnnt.RNNTDecoder':
TypeError("__init__() got an unexpected keyword argument 'num_classes'")
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4600 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4600/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4600/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4600/events | https://github.com/NVIDIA/NeMo/pull/4600 | 1,317,312,747 | PR_kwDOC_bI7s48EJnB | 4,600 | Dividing generative question-answering CI tests | {
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} | Signed-off-by: Ameya Mahabaleshwarkar <[email protected]>
# What does this PR do ?
Split CI tests for generative question-answering into multiple stages to reduce memory usage
**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**:
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} | # What does this PR do ?
Trying to return model output with lengths is not exporting correctly.
**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?
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/4598 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4598/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4598/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4598/events | https://github.com/NVIDIA/NeMo/pull/4598 | 1,317,163,273 | PR_kwDOC_bI7s48DqYI | 4,598 | T0 model and dataset | {
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} | # What does this PR do ?
Adds the T0 Dataset and Model classes.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Adds the T0 dataset and model class files
- Repurposes the generic fine-tuning run script for T0
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | null | null | null | Hi all!
I've come across this [issue](https://github.com/NVIDIA/NeMo/issues/234) from 2020, which clearly shows that non-English TTS had some issues at that time. Has the state of things changed since then?
I need to train Tacotron2 for Russian, but I face so many troubles in preprocessing stage. Mainly with text preprocessing. Unfortunately, I couldn't find any decent tutorials on how to train a model for a non-English dataset.
Can anyone help? | null | {
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} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
Add two ASR models for Kinyarwanda (ctc and transducer large), add tagger-based ITN model for Russian
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Implemented the fused softmax GPU kernel that can be a drop-in replacement of the `apex`'s FusedScaleMaskSoftmax`. It addresses the following issues:
1. Removed the sequence length limitation. It works with tensor of any length.
2. Fix the `grad_input` modification issue https://github.com/NVIDIA/apex/issues/1430
3. Fix the problem when all keys are masked https://github.com/NVIDIA/apex/issues/1390
It uses the torch.cppextension to build the kernel dynamically like MegatronLM repo does. The kernels will be saved in the `/tmp` directory so it can be used in slurm with enroot clusters too.
It includes a unit test to cover a few cases.
The performance is comparable to the apex one. Here is a training step time graph when it is tested with training a RETRO model.

The blue curve is pytorch softmax. The bottom red curve is apex fused kernel and the middle red curve the fused kernel in this PR.
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Following older answers for closed issues: https://github.com/NVIDIA/NeMo/issues/2307 and the tutorial: https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Streaming_ASR.ipynb.
Any updates on Nemo supporting LM with long audio (streaming)?
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} | # What does this PR do ?
Use DALI pipelines as part of `ssl_models`
**Collection**: ASR
# Changelog
- Add DALI char dataset support to SSL model
**PR Type**:
- New Feature
Tested manually by running 1 validation step on a local dataset.
## 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 #4591
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I noticed `SpeechEncDecSelfSupervisedModel`'s don't check if `use_dali` is set in their `_setup_dataloader_from_config()` method.
Is that intended? I noticed some performance improvements using Dali and the error traces it returns are much more helpful than the typical audio dataset ones.
**Describe the solution you'd like**
Just add
```py
device = 'gpu' if torch.cuda.is_available() else 'cpu'
if config.get('use_dali', False):
device_id = self.local_rank if device == 'gpu' else None
dataset = audio_to_text_dataset.get_dali_bpe_dataset(
config=config,
tokenizer=self.tokenizer,
shuffle=shuffle,
device_id=device_id,
global_rank=self.global_rank,
world_size=self.world_size,
preprocessor_cfg=self._cfg.preprocessor,
)
return dataset
```
to `_setup_dataloader_from_config` in `SpeechEncDecSelfSupervisedModel`.
I can open a PR if this if fine, but wanted to see if there was an explicit reason not to include it.
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} | null | null | null | Hi, I trained a conformer CTC BPE model. Now I wanna convert it to TensorRT.
Is it possible or not?
If yes, how can I do it?
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} | null | null | null | **Describe the bug**
After a few steps when pretraining a `SpeechEncDecSelfSupervisedModel`, training fails with the following error
```sh
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 468, in training_step
loss_value, loss_val_dict = self.decoder_loss_step(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 450, in decoder_loss_step
current_loss_value = current_loss(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1129, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 187, in forward
out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1])
RuntimeError: shape '[16, -1, 128]' is invalid for input of size 547712
```
**Steps/Code to reproduce bug**
I used the [speech_pre_training.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py) script with the default configuration for the conformer [here](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ssl/conformer/conformer_ssl.yaml).
I only modified `train_ds.max_duration: 25.0`.
I also tried the config in the [Self_Supervised_Pre_Training.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Self_Supervised_Pre_Training.ipynb) notebook and I got the same error.
**Expected behavior**
Training should process normally and/or a more detailed explanation why the reshape failed and what parameters to change.
**Environment overview (please complete the following information)**
- Environment location: Docker (`nvcr.io/nvidia/pytorch:22.05-py3`)
- Method of NeMo install: `pip install 'nemo_toolkit[all]==1.10.0'`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
```sh
docker run --rm -it --gpus all --ipc=host --env-file .env train
```
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} | Signed-off-by: Faris Abdullah Alasmary <[email protected]>
# What does this PR do ?
Fix a bug in LSTM decoder while using bidirectional LSTM
**Collection**: ASR
# Changelog
- Adding one line of code to check if the argument `bidirectional` is True and multiply `lstm_hidden_size` by 2 since the output vector of both directions have the same size and they are concatenated to form the final output of the LSTM model.
# 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.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
If the duration of an audio file is <8s (unsure exactly, need further testing), then `sample_negatives` will throw the following error:
```sh
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 491, in validation_step
loss_value, _ = self.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len, targets, target_lengths)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 450, in decoder_loss_step
current_loss_value = current_loss(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1129, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 205, in forward
negatives, _ = self.sample_negatives(targets_masked_only, targets_masked_only.size(0)) # T'xBxC # T'
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 139, in sample_negatives
neg_idxs = torch.multinomial(torch.ones((num, high), device=y.device), self.num_negatives)
RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement
```
If you haven't read the docs or understand the code/loss function, then it might not be clear that you need to adjust the `min_duration` or `num_negatives`
**Steps/Code to reproduce bug**
Run any of the examples in [`speech_pretraining`](https://github.com/NVIDIA/NeMo/tree/main/examples/asr/speech_pretraining) with `train/validation_ds.min_duration < 8.0`
**Expected behavior**
While this is expected to fail, I would like to propose that we catch this error and replace with something more...constructive.
I can open a PR, but wanted to see if the maintainers are open to the suggestion.
My suggestion is to re-raise with a `NeMoBaseException` (or make a new one? `LossMisconfigurationError`?) with the suggestion to either adjust `num_negative` or the `min_duration`.
Edit: I realized I opened this as a bug, but it should've been a feature suggestion. | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
normalize() has a `500` word limit per here: https://github.com/NVIDIA/NeMo/blob/main/nemo_text_processing/text_normalization/normalize.py#L255
This is not documented in the documentation pages as far as I can see.
**Describe the solution you'd like**
Documentation should make this requirement clear up front, so when developing a pipeline which uses nemo text normalization one is not surprised by crashes when are large input comes through. Instead developers could split into sentences knowing it is a requirement of this method.
Bonus: explain why there is a 500 word limit.
**Describe alternatives you've considered**
Seems like a text normalizer model would know where sentence bounds are to be able to operate, so the normalizer could split into sentences itself and process the sentences, removing this limitation. | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a bug in setup_metric() when fine-tuning without a specified test set.
**Collection**: NLP
# Changelog
- Use the provided data_cfg to instantiate a metric rather than test_ds.
# 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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