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**Collection**: [Note which collection this PR will affect]
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# Usage
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# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] 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 ?
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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.
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* Related to # (issue)
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**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Add dataset 'AISHELL-3' from OpenSLR for training FastPitch model.
# Changelog
- Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS
# Usage
1. create directories
```
mkdir data_aishell3 mani_aishell3 sup_aishell3
```
2. install NeMo packages ([doc](https://github.com/NVIDIA/NeMo#installation))
3. run scripts to get data, process data and train the model from the data
```
python3 scripts/dataset_processing/tts/aishell3/get_data.py \
--data-root data_aishell3 \
--val-size 0.1 \
--test-size 0.2 \
--seed-for-ds-split 100 \
--manifests-path mani_aishell3
python3 scripts/dataset_processing/tts/extract_sup_data.py \
--config-path aishell3/ds_conf \
--config-name ds_for_fastpitch_align.yaml \
manifest_filepath=mani_aishell3/train_manifest.json \
sup_data_path=sup_aishell3
python3 examples/tts/fastpitch.py --config-path conf/zh/ \
--config-name fastpitch_align_multispeaker_22050.yaml \
model.train_ds.dataloader_params.batch_size=32 \
model.validation_ds.dataloader_params.batch_size=8 \
train_dataset=mani_aishell3/train_manifest.json \
validation_datasets=mani_aishell3/val_manifest.json \
sup_data_path=sup_aishell3 \
exp_manager.exp_dir=result \
trainer.max_epochs=160 \
trainer.check_val_every_n_epoch=1 \
pitch_mean=214.61354064941406 \
pitch_std=64.61677551269531 \
+exp_manager.create_wandb_logger=true \
+exp_manager.wandb_logger_kwargs.name="tutorial" \
+exp_manager.wandb_logger_kwargs.project="aishell"
```
# Sample
The FastPitch model trained by AISHELL-3 dataset is [here](https://github.com/RobinDong/nemo_models/raw/main/models/aishell3.nemo). You can download it and run the snippet below:
```python
from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder
from nemo.collections.tts.models import FastPitchModel, HifiGanModel
text = "这些新一代的中央处理器不只效能惊人，价格也十分有竞争力。"
device = "cpu"
fastpitch_model = FastPitchModel.restore_from("~/Downloads/aishell3.nemo").eval().to(device)
model = HifiGanModel.from_pretrained(model_name="tts_zh_hifigan_sfspeech").eval().to(device)
# Normalize the text and convert it into individual phonemes/tokens.
tokens = fastpitch_model.parse(text)
# Generate spectrogram from text
spectrogram = fastpitch_model.generate_spectrogram(tokens=tokens, speaker=5)
# Invert the spectrogram into audio samples
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
# Convert output from pytorch tensor to numpy array
audio = audio.cpu().detach().numpy()
import soundfile as sf
sf.write("sample.wav", audio.T, 22050, format="WAV")
```
The `sample.wav` is here: [aishell3.zip](https://github.com/NVIDIA/NeMo/files/12567483/aishell3.zip)
Change the speaker id to 1863 `speaker=1863` would make a male sound.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@blisc @okuchaiev | {
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} | # What does this PR do ?
Add dummy userbuf config files for hybrd config build.
# 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 ?
Since loss_mask doesn't work for sequence parallel, we need to default it to False. Any config running sequence parallel will fail.
If you want to use loss_mask, set `+model.use_loss_mask=True`.
See #7395 for a proper fix.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# 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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**Collection**: tutorials
# Changelog
- new Google Colab tutorial on Speech Data Explorer
# Usage
The tutorial can be executed in Google Colab environment.
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**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 ?
Fixes a bug where calling foward_only with `self.mcore=False `results in an error, for `MegatronGPTModel`
If we run self.mcore_gpt = False , and we also want to do just a forward pass without generation, the length of the batch is 3
This causes an error since `set_inference_key_value_memory `and `inference_max_sequence_len `are not set
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Update Core Commit
**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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Pass knob to enable SHARP to M-Core
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
- Added description of supported positional embedding types in GPT and T5 models
- Added description of positional interpolation
- Added information about Flash attention | {
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} | # What does this PR do ?
The optimizer `apex.distributed_fused_adam` is a high-performance adam optimizer implementation. It provides a bucket function to update the gradient aggregation to improve performance. However, bad bucket configuration can lead to GB-level memory loss. This PR realizes the automatic search for the optimal bucket size for GPT model, maximizing the bucket size while minimizing memory loss, thereby maintaining excellent optimizer performance.
**Collection**: NLP
# Changelog
- add bucket_cap_mb automatic search function
- add unit test
# Usage
When using distributed_fused_adam, you can configure bucket_cap_mb to auto to enable this function.
```yaml
...
optim:
name: distributed_fused_adam
lr: 0.0001
weight_decay: 0.1
betas:
- 0.9
- 0.95
bucket_cap_mb: auto
overlap_grad_sync: true
overlap_param_sync: true
contiguous_grad_buffer: true
sched:
name: CosineAnnealing
warmup_steps: 107
constant_steps: 11873
min_lr: 1.0e-05
...
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Help me review this PR, thank you! @MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, @okuchaiev
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} | # What does this PR do ?
When running `tutorials/asr/Online_Offline_Speech_Commands_Demo.ipynb` in Google Colab, it failed at the last code block because of lacking package `einops`:
So add a install command for it.
# Changelog
- Fix missing pip package 'einops' in 'Online_Offline_Speech_Commands_Demo.ipynb'
# 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?
@titu1994, @redoctopus, @jbalam-nv
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} | # What does this PR do ?
When running `tutorials/asr/Online_Offline_Speech_Commands_Demo.ipynb` in Google Colab, it failed to install `pyaudio` and the following step `import pyaudio as pa` break.
Removing `sox` from `apt-get install` command would solve this failure. And even if we remove `sox` explicitly in this command, it will be successfully installed through following command `python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[asr]`.
# Changelog
- Fix the failure of install pyaudio
# 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?
@titu1994, @redoctopus, @jbalam-nv
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} | # What does this PR do ?
When you do not enable mcore or use loss mask, sequence parallel will not work properly. This PR fixes this problem.
**Collection**: NLP
# Changelog
- Modify post_language_model_processing to make sequence-parallel and loss-mask compatible.
- Mark the parameters of layernorms with sequence-parallel so that sequence-parallel can be calculated correctly on layernorms.
- For the correctness check of sequence-parallel(unit test), the wikitext dataset has been added.
# Usage
* This is a feature compatibility fix, you can now use sequence-parallel without enabling mcore.
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Please help review, thank you! @MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, @okuchaiev
# Additional Information
The new unit test will start the PyTorch TCP Store and may conflict with other unit tests. Please use an independent process for unit testing.
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} | # What does this PR do ?
Fix some bugs related to `cfg.precision`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- use `self.cfg.precision` instead of `cfg.precision` in bert model
- use `self.torch_dtype` instead of `self.cfg.precision` where applicable
# 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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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)
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- [ ] 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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} | jp-JP ITN creating PR .
# What does this PR do ?
This PR is to update information for new jp-JP ITN support.
Add a one line overview of what this PR aims to accomplish.
To update the chart/table where the languages with support are listed.
**Collection**: [Note which collection this PR will affect]
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* You can potentially add a usage example below
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} | # What does this PR do ?
Pass knob to enable SHARP to M-Core
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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**Pre checks**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | null | null | null | **Describe the bug**
I follow the sample code in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_zh_fastpitch_hifigan_sfspeech) to generate chinese TTS audio
but get a bed result
I also try other models (e.g. english, german and spanish), but it works fine
**Steps/Code to reproduce bug**
```
# Load spectrogram generator
from nemo.collections.tts.models import FastPitchModel
spec_generator = FastPitchModel.from_pretrained(model_name="tts_zh_fastpitch_sfspeech")
# Load Vocoder
from nemo.collections.tts.models import HifiGanModel
model = HifiGanModel.from_pretrained(model_name="tts_zh_hifigan_sfspeech")
# Generate audio
import soundfile as sf
import torch
with torch.no_grad():
parsed = spec_generator.parse("这些新一代的CPU不只效能惊人。")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
if isinstance(audio, torch.Tensor):
audio = audio.to('cpu').numpy()
# Save the audio to disk in a file called speech.wav
sf.write("speech.wav", audio.T, 22050, format='WAV')
```
**Environment details**
OS: ubuntu 20.04
NeMo : brench 1.20.0
python : 3.8
pytorch : 2.0.1
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Add a one line overview of what this PR aims to accomplish.
This is a temp fix before PEFT refactor. (I wrote similar logic in #7308 )
**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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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"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Adds support for bias conversion in TP PP Script for swiglu supported modls
**Collection**: [NLP]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Converts UL2. dataset to use GPT baseclass
**Collection**: [NLP]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
This PR adds an option to log artifacts before fit starts, for log input data only once.
**Collection**: TTS
# Changelog
- Adds `initial_log` to `ArtifactGenerator::generate_artifacts`
- Adds `_log_artifacts` to `LoggingCallback`
- Adds `on_fit_start` to `LoggingCallback`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
In megatron_change_num_partitions.py there is dedicated code to handle splitting some layers with fast GLU activations:
* https://github.com/NVIDIA/NeMo/blob/c5ef26cafa935f9966ab3f074248913aef7fa509/examples/nlp/language_modeling/megatron_change_num_partitions.py#L236
* https://github.com/NVIDIA/NeMo/blob/c5ef26cafa935f9966ab3f074248913aef7fa509/examples/nlp/language_modeling/megatron_change_num_partitions.py#L278
However, this code seems to only handle the `.weight` part, not the `.bias` part. And it seems like the bias would also require some special handling: https://github.com/NVIDIA/NeMo/blob/8508e07a20edbfac6e37fdf252c036f4e2215654/nemo/collections/nlp/modules/common/megatron/mlp.py#L220
**Steps/Code to reproduce bug**
I haven't actually tried to trigger a bug because of this -- I am opening this issue because I suspect there is a bug.
**Expected behavior**
I would have expected the `.bias` to be handled as well (or a `NotImplementedError` exception if it is known that this is not supported yet).
**Additional context**
Tagging people who dealt with this code: @titu1994 / @hsiehjackson | {
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} | # What does this PR do ?
Fixes the megatron_gpt_peft_eval.py script.
Fix megatron_gpt_peft_eval.py.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Use MegatronGPTPEFTModel instead of MegatronGPTSFTModel when PEFT modules exist.
- Remove the dependency on having a test_ds when in server mode.
- Add inference parameters from the script instead of test_ds when running in server mode.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@arendu
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RNN-T confidence and alignment bugfix.
Fixes https://github.com/NVIDIA/NeMo/issues/7192
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- New `frame_confidence` and `alignments` lists are now always created after the while loop.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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## Who can review?
@titu1994 @hainan-xv
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/issues/7192
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Hi, I am trying to use a latest PyTorch (build from source from the master branch). But when I run the NeMo training script, I meet with the following error:
```
File "/usr/local/lib/python3.10/dist-packages/apex/contrib/optimizers/distributed_fused_adam.py", line 1602, in _local_grad_norm
self.grad_sync()
File "/usr/local/lib/python3.10/dist-packages/apex/contrib/optimizers/distributed_fused_adam.py", line 1572, in grad_sync
self._force_bucket_grad_sync()
File "/usr/local/lib/python3.10/dist-packages/apex/contrib/optimizers/distributed_fused_adam.py", line 1241, in _force_bucket_grad_sync
self._finish_bucket_grad_sync()
File "/usr/local/lib/python3.10/dist-packages/apex/contrib/optimizers/distributed_fused_adam.py", line 1394, in _finish_bucket_grad_sync
bucket.sync_wait()
File "/usr/local/lib/python3.10/dist-packages/apex/contrib/optimizers/distributed_fused_adam.py", line 361, in sync_wait
self.sync_request.wait()
File "/workspace/pytorch/torch/distributed/distributed_c10d.py", line 1650, in __getattribute__
raise RuntimeError(f"Illegal to call {name} on IllegalWork object")
RuntimeError: Illegal to call wait on IllegalWork object
```
I have found that this error is related to `distributed_fused_adam` under the data parallel setting. When I change the optimizer to `fused_adam`, the training script can run successfully.
My training script can run successfully under the default PyTorch from nvcr.io/nvidia/pytorch:23.06-py3
**Environment details**
My docker is based on nvcr.io/nvidia/pytorch:23.06-py3
PyTorch: build from source on the master branch (commit: e471c12a016a46d7b8d1c8c83a38021f08fce078)
Apex commit: 4e1ae43f7f7ac69113ef426dd15f37123f0a2ed3
I am also trying to update apex to 7995de18677295c5edeeab082179edbfdb6ee16a following README but seemingly it is not correctly built and it would raise `ValueError: Cannot resolve optimizer 'distributed_fused_adam'.` I have reflected this issue on https://github.com/NVIDIA/apex/issues/1722 | {
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} | # What does this PR do ?
Adding MoE parameter passing for BERT model.
**Collection**: NLP/Language Models/BERT
# Changelog
- `bert_model.py` adding MoE parameters, passing them to `language_model`
- `megatron_bert_model.py` adding and passing MoE parameters
- `language_model.py` adding and passing MoE parameters
# 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 ?
When following the [document](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/datasets.html#sfspeech-chinese-english-bilingual-speech) to get dataset `SFSpeech Chinese/English Bilingual Speech`, the first command
```bash
python scripts/dataset_processing/tts/sfbilingual/get_data.py \
--data-root <your_local_dataset_root> \
--val-size 0.1 \
--test-size 0.2 \
--seed-for-ds-split 100
```
directly raise error:
```
Traceback (most recent call last):
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 122, in <module>
main()
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 116, in main
__process_data(
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 91, in __process_data
entries = __process_transcript(dataset_path)
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 65, in __process_transcript
with open(file_path / "text_SF.txt", encoding="utf-8") as fin:
FileNotFoundError: [Errno 2] No such file or directory: 'tryit/text_SF.txt'
```
The reason: `scripts/dataset_processing/tts/sfbilingual/get_data.py` actually doesn't download the dataset. The `SFSpeech Chinese/English Bilingual Speech` could only be downloaded through [Nvidia NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en).
# Changelog
- Add steps in the document of dataset SFSpeech to download by ngc-cli tool at first
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@blisc @okuchaiev @titu1994 @XuesongYang | {
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} | # What does this PR do ?
When running notebook `tutorials/asr/Online_Offline_Speech_Commands_Demo.ipynb` of `main` branch in the Google Colab, it will report the error:
```
TypeError Traceback (most recent call last)
[<ipython-input-18-1dea7463f5dd>](https://localhost:8080/#) in <cell line: 5>()
3 CHANNELS = 1
4 audio, sample_rate = librosa.load(wave_file, sr=SAMPLE_RATE)
----> 5 dur = librosa.get_duration(audio)
6 print(dur)
TypeError: get_duration() takes 0 positional arguments but 1 was given
```
The calling of `librosa.get_duration(audio)` is wrong. This PR is fixing it.
# Changelog
- Fix the error of calling librosa.get_duration() in notebook.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] 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?
@titu1994, @redoctopus, @jbalam-nv
# Additional Information
* Related to #7179
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- Add typecheck to forward methods
- Fix various typos in input/output types
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**Collection**: [Note which collection this PR will affect]
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# Usage
* You can potentially add a usage example below
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# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**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.
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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 ?
2.0.8 introduces an issue with the progress bar when using max_steps
**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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- [ ] 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
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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): https://github.com/NVIDIA/NeMo/issues/7349
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} | # What does this PR do ?
Revert the commented line.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
intermediate output tensor is [s,b,h] while the final output is [b,s,h]
**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 ?
I observed our Dockerfile had constrained `numba>=0.57.1` (see [here](https://github.com/NVIDIA/NeMo/blob/main/Dockerfile#L86)) while `reinstall.sh` still pins `NUMBA_VERSION=0.55` (see [here](https://github.com/NVIDIA/NeMo/blob/main/reinstall.sh#L23)). When I run `reinstall.sh` from the latest main, it will raise installation error for `llvmlite` (see error below). Once I pinned `NUMBA_VERSION=0.57.1`, and run `reinstall.sh` again, the problem is resolved.
```bash
Attempting uninstall: llvmlite
Found existing installation: llvmlite 0.38.1
ERROR: Cannot uninstall 'llvmlite'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```
**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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] | closed | false | null | [] | null | null | 2023-09-04T10:15:33 | 2023-09-06T05:44:55 | 2023-09-06T05:44:55 | CONTRIBUTOR | {
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} | # What does this PR do ?
When following the [document](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/datasets.html#sfspeech-chinese-english-bilingual-speech) to get dataset `SFSpeech Chinese/English Bilingual Speech`, the first command
```bash
python scripts/dataset_processing/tts/sfbilingual/get_data.py \
--data-root <your_local_dataset_root> \
--val-size 0.1 \
--test-size 0.2 \
--seed-for-ds-split 100
```
directly raise error:
```
Traceback (most recent call last):
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 122, in <module>
main()
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 116, in main
__process_data(
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 91, in __process_data
entries = __process_transcript(dataset_path)
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 65, in __process_transcript
with open(file_path / "text_SF.txt", encoding="utf-8") as fin:
FileNotFoundError: [Errno 2] No such file or directory: 'tryit/text_SF.txt'
```
The reason: `scripts/dataset_processing/tts/sfbilingual/get_data.py` actually doesn't download the dataset. The `SFSpeech Chinese/English Bilingual Speech` could only be downloaded through [Nvidia NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en).
# Changelog
- Add steps in the document of dataset SFSpeech to download by ngc-cli tool at first
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@blisc @okuchaiev
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} | # What does this PR do ?
When following the [document](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/datasets.html#sfspeech-chinese-english-bilingual-speech) to get dataset `SFSpeech Chinese/English Bilingual Speech`, the first command
```bash
python scripts/dataset_processing/tts/sfbilingual/get_data.py \
--data-root <your_local_dataset_root> \
--val-size 0.1 \
--test-size 0.2 \
--seed-for-ds-split 100
```
directly raise error:
```
Traceback (most recent call last):
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 122, in <module>
main()
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 116, in main
__process_data(
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 91, in __process_data
entries = __process_transcript(dataset_path)
File "/home/xxx/NeMo/scripts/dataset_processing/tts/sfbilingual/get_data.py", line 65, in __process_transcript
with open(file_path / "text_SF.txt", encoding="utf-8") as fin:
FileNotFoundError: [Errno 2] No such file or directory: 'tryit/text_SF.txt'
```
The reason: `scripts/dataset_processing/tts/sfbilingual/get_data.py` actually doesn't download the dataset. The `SFSpeech Chinese/English Bilingual Speech` could only be downloaded through [Nvidia NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en).
# Changelog
- Add steps in the document of dataset SFSpeech to download by ngc-cli tool at first
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@blisc @okuchaiev
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"merged_at": "2023-09-01T23:13:32"
} | # 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 ?
https://github.com/NVIDIA/apex/pull/1721 adds distributed optimizer support for multiple dtypes for the grad reduce-scatters and param all-gathers. This has two main applications in NeMo:
- For GPT, most grads can be reduced in BF16 but embedding grads need to be reduced in FP32 to avoid learning issues.
- For FP8 support, weight matrices can be stored in FP8 while most other parameters (e.g. biases, layernorm params, embeddings) are in BF16. In upcoming work, we would like to handle FP8 and BF16 param all-gathers in the same optimizer.
This approach is much nicer than the old approach for mixed BF16/FP32 grad reductions (https://github.com/NVIDIA/NeMo/pull/5920), and is also compatible with the distributed checkpoint format (https://github.com/NVIDIA/NeMo/pull/7116, https://github.com/NVIDIA/NeMo/pull/7281). I confirm I've been able to save and load models with different parallel configs and grad reduction dtypes.
**Collection**: NLP
# Changelog
- Remove old distopt infrastructure for mixed BF16/FP32 grad reductions
- Add type hints to distributed optimizer wrapper
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/f8be40b75ee1f8437b56fcc9602dc2aaddfb0643/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L228
Configure the optimizer with `grad_sync_dtype: bf16`.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Depends on https://github.com/NVIDIA/apex/pull/1721
* Will break checkpoints for models that are using mixed BF16/FP32 grad reductions | {
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} | # What does this PR do ?
Add an option to run amp O2 in gpt_eval/inference
**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 ?
Optionally, if state_dict contains tensors in the model's buffers, load them as well.
**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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Change neural type of `tokens` from `Index` to `TokenIndex`
**Collection**: [TTS]
# Changelog
- Change token type to TokenIndex
- Cleanup unused import and file.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Mcore maintains both weights of embedding and output layers. When share_embeddings_and_output_weights=true, weight of output layer is not registered as _with_fp32_optimizer, then the [allreduce](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L752) op hangs .
**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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Error response from daemon: Get https://nvcr.io/v2/: net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers)
How can i sovle this problem ? | {
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} | # What does this PR do ?
Upgrading to the latest PyTorch container.
**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 ?
This PR fixes memory deallocation for indexed_dataset when dataset is first created (currently it happens only when loaded).
Thanks to @hillst for finding the bug!
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
When training on a single node, multi-gpu setup using SLURM, there's a warning that pops up that I cannot figure out:
```
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
```
**Steps/Code to reproduce bug**
Using the [ASR fine-tuning example](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_finetune.py) in a `sbatch` script:
```sh
[...]
srun python speech_to_text_finetune.py
[...]
```
It produces the following:
```
[...]
Using bfloat16 Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
Running in `fast_dev_run` mode: will run the requested loop using 20 batch(es). Logging and checkpointing is suppressed.
Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/8
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/8
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/8
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
[W socket.cpp:601] [c10d] The client socket has failed to connect to [ip-26-0-146-37]:22456 (errno: 22 - Invalid argument).
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/8
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 8 processes
----------------------------------------------------------------------------------------------------
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
| Name | Type | Params
------------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | encoder | ConformerEncoder | 121 M
2 | decoder | ConvASRDecoder | 66.2 K
3 | loss | CTCLoss | 0
4 | spec_augmentation | SpectrogramAugmentation | 0
5 | _wer | WERBPE | 0
------------------------------------------------------------------------
[...]
```
**Expected behavior**
Distributed training to work without any issues or slowdowns.
**Environment overview (please complete the following information)**
- Environment location: SLURM on AWS
- Method of NeMo install: `pip install nemo_toolkit[asr]`
**Environment details**
- OS version: `Deep Learning Base GPU AMI (Ubuntu 20.04) ` (AWS)
- PyTorch version: `2.0.1+cu117`
- Python version: `3.8.10`
**Additional context**
GPU model: 8 A100
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} | null | null | null | Hi, I am trying to use N-gram language model with Hybrid Transducer CTC large. but i am getting an error.
I loaded model using the below code.
```import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="stt_en_fastconformer_hybrid_large_pc")
```
I am following this [https://github.com/NVIDIA/NeMo/blob/acbd88257f20e776c09f5015b8a793e1bcfa584d/tutorials/asr/Offline_ASR.ipynb](tutorial) to add language model .
```lm_path="/content/5gram_correct.arpa"
beam_search_lm = nemo_asr.modules.BeamSearchDecoderWithLM(
vocab=list(asr_model.decoder.vocabulary),
beam_width=16,
alpha=1, beta=1.5,
lm_path=lm_path,
num_cpus=max(os.cpu_count(), 1),
input_tensor=False)
```
but i am getting this error.
`AttributeError: 'RNNTDecoder' object has no attribute 'vocabulary'`
is there any way to use N-gram language model with Hybrid Transducer CTC large? | {
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} | null | null | null | **Describe the bug**
cannot import name 'JobStatus' from 'hydra.core.utils'.
I have cloned Nemo git repository on my vm instance and am trying to resolve this import issue
**Execute Fastpitch_fintetune.ipynb in import cell**
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} | null | null | null | Do rnnt-bpe models support word boosting? If not, is there any library that may implement this?
I am also interested if beam search + n-gram modelling for rnnt supports timestamp extraction/confidence estimate for words?
Thank you in advance!! | {
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Basically I want to provide 2 wav files as input and have the output be a single wav file that contains the speech from one file with the voice from the other file.
I went through the documentation for examples and tutorials and could not find how I could achieve this. | {
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} | null | null | null | **Describe the bug**
It is not possible to install the requirements of the NLP component because the fasttext wheel cannot be build any more. However, there is a fork called fasttext-wheel that can be installed with pip. I have tested it extensively and it works with NeMo
**Steps/Code to reproduce bug**
```
pip install -r requirement/requirements_nlp.txt
```
**Expected behavior**
To be able to install NLP component of the NeMo toolkit without having to hack the requirements_nlp file each time.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip and source
```
pip install "git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]"
```
or (from source)
```
pip install -e ".[all]"
```
**Environment details**
- OS version : Manjaro Linux (current)
- PyTorch version: Nightly builds with Cuda 12.1 (pip)
- Python version: 3.8.18 (NeMo does not work with more recent Python versions)
**Additional context**
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} | # What does this PR do ?
Save_to a .nemo file with distributed checkpoint and restore_from a .nemo file with dist ckpt.
This makes it so that, for example, eval can be done with different model parallel configs on the fly.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Remove apex, trainer, torch.distributed dependencies when run inference on single gpu.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
A demo colab:
https://colab.research.google.com/drive/1kMoBxNzTB8W0dmDzzWaMhl3WgYYXfh8N?usp=sharing
# Before your PR is "Ready for review"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Fixes broken configs (PTL 2.0): move parameter `resume_from_checkpoint` to `exp_manager`, as implemented in #7159
**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"
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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add bf16 inference support and fix seq_len stft issue
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# Changelog
- torch.floor_divide will make sure to not go into round off issues at higher precisions
- add support for amp_dtype during transcribing
# Usage
```bash
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```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [x] Bugfix
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} | # What does this PR do ?
Move model precision copy to MegatronBaseModel to reduce boilerplate.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Remove `cfg.model.precision` = `cfg.trainer.precision` copy from several examples
- Set precision in `MegatronBaseModel` which all Megatron models inherit from
- Fix some mapping of PTL 2.0 precision to torch datatypes that were exposed by above
# 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
- [X] Refactor
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fix resume from checkpoint behavior to have lower priority than experiment dir.
**Collection**: [Note which collection this PR will affect]
# Changelog
- resume from checkpoint behavior
- reorganize `check_resume` for prints
- disable `TRANSFORMERS_OFFLINE` for some tests that were failing
# 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.
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} | # What does this PR do ?
Fix issue related to this PR https://github.com/NVIDIA/NeMo/pull/7200/
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | I tried to run the colab notebook from the stable realse(`BRANCH='r1.20.0'`):https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb
On the step for initializing the model:
```
model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
```
I got this error:
```
[NeMo E 2023-08-28 16:39:54 common:506] Model instantiation failed!
Target class: nemo.collections.nlp.models.language_modeling.megatron_bert_model.MegatronBertModel
Error(s): name 'set_logging_level' is not defined
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/common.py", line 485, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py", line 89, in __init__
super().__init__(cfg, trainer=trainer, no_lm_init=False)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 123, in __init__
initialize_model_parallel_for_nemo(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/modules/common/megatron/megatron_init.py", line 141, in initialize_model_parallel_for_nemo
set_logging_level(apex_transformer_log_level)
NameError: name 'set_logging_level' is not defined
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
[<ipython-input-25-4645443f78bc>](https://localhost:8080/#) in <cell line: 1>()
----> 1 model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
````
BTW, I always got dependencies error for installing NeMo on Colab. Currently, I just ignore it.
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
ipython 7.34.0 requires jedi>=0.16, which is not installed.
cvxpy 1.3.2 requires setuptools>65.5.1, but you have setuptools 65.5.1 which is incompatible.
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} | # What does this PR do ?
Adding an option to disable `bias_dropout_add_fusion`, same as for GPT model [here](https://github.com/NVIDIA/NeMo/blob/455d2dfb3479cd30a7857db3c2e2864a921488b8/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L362)
**Collection**: Language Modeling -> BERT
# Changelog
- `bert_model.py:188` - adding the flag to the `bert_model` constructor
- `bert_model.py:236` - passing the flag to the `language_model` creation
- `megatron_bert_model.py:184` - passing the flag to the `bert_model` based on config
# Usage
* `bias_dropout_add_fusion: <BOOLEAN>` can now be set in BERT model config
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
tried to run: NeMo/tutorials/00_NeMo_Primer.ipynb
got error message after this command:
import nemo.collections.asr as nemo_asr
**Environment details**
environment: jetson agx orin, jetpack 5.1.2, in a docker: [dustynv/nemo:r35.3.1](https://hub.docker.com/r/dustynv/nemo/tags)
[error_message.txt](https://github.com/NVIDIA/NeMo/files/12450309/error_message.txt)
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} | # What does this PR do ?
Add a new "cv" branch for lip reading task and everything related to computer vision
**Collection**: cv
# Changelog
- new cv branch
- 6 new cv models in cv/models: VisualEncDecCTCModelBPE, VisualEncDecCTCModel, VisualEncDecHybridRNNTCTCBPEModel, VisualEncDecHybridRNNTCTCModel, VisualEncDecRNNTBPEModel, VisualEncDecRNNTModel
- cv datasets in cv/data: VideoToBPEDataset, VideoToCharDataset, TarredVideoToBPEDataset
- cv modules in cv/modules: ResNetVideoFrontEnd, LinearProjectionVideoFrontEnd, VideoAugmentation, VideoPreprocessor
- video featurizer in cv/parts/preprocessing
# Usage
* You can potentially add a usage example below
```python
import nemo.collections.cv as nemo_cv
from omegaconf import OmegaConf
config_path = "model_config.yaml"
params = OmegaConf.load(config_path)
# hybrid CTC/RNN-T BPE lip reading model
model = nemo_cv.models.VisualEncDecHybridRNNTCTCBPEModel(cfg=params.model)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Description**
While using NeMo in google golab in CPU mode the GPU spike to 100% never comes down. the same issue is in the TPU mode Also the RAM spiked to 100%
**Steps to reproduce bug**
Try to run this collab file, the same issue will be seen
````
https://colab.research.google.com/drive/1uO2cgu5wjZqEUY1wnlHCe0npe3XRDTka?usp=sharing
````
**Expected behavior**
A clear and concise description of what you expected to happen.
- Environment Location: Collab
- Method of NeMo install: While running this code
````
parsed = spec_generator.parse(input_text)
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
````
**Environment details**
Otherwise, please provide:
- OS version
- PyTorch Version
````
pytorch-lightning==1.9.4
torch @ https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=a7a49d459bf4862f64f7bc1a68beccf8881c2fa9f3e0569608e16ba6f85ebf7b
````
- Python 3.10.12
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Run tests after what is failing on main
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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check other tests after what is currently failing on main
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**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
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**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 ?
O2=True changes the names of the expected keys after merging.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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# Usage
* You can potentially add a usage example below
```python
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```
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Add some configuration to TTS preprocessing to make them more usable.
Most important, adding `batch_size` to text normalization multi-processing which I have found to make it about 10x faster than using the default 'auto' batch size in joblib. I do not fully understand why, but the documentation indicates that large batch sizes work for small, fast tasks:
> batch_size: int or 'auto', default: 'auto'
> The number of atomic tasks to dispatch at once to each
> worker. When individual evaluations are very fast, dispatching
> calls to workers can be slower than sequential computation because
> of the overhead. Batching fast computations together can mitigate
> this.
**Collection**: [TTS]
# Changelog
- Add `batch_size` parameter to `preprocess_text.py`
- Add input and output field parameters to `preprocess_text.py`
- Fix boolean flag parsing for `lower_case` for `preprocess_text.py`
- Modify `compute_feature_stats.py` so that it can take a list of manifests, making it easy to compute pitch/energy stats across multiple datasets.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | File "/home/mid01/anaconda3/envs/py3.8/lib/python3.8/site-packages/nemo/collections/asr/modules/beam_search_decoder.py", line 64, in __init__
from ctc_decoders import Scorer, ctc_beam_search_decoder_batch
ModuleNotFoundError: No module named 'ctc_decoders'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "decoder_testing.py", line 38, in <module>
beam_search = nemo_asr.modules.BeamSearchDecoderWithLM(
File "/home/mid01/anaconda3/envs/py3.8/lib/python3.8/site-packages/nemo/collections/asr/modules/beam_search_decoder.py", line 66, in __init__
raise ModuleNotFoundError(
ModuleNotFoundError: BeamSearchDecoderWithLM requires the installation of ctc_decoders from scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh
My python version is 3.8 as https://github.com/NVIDIA/NeMo/issues/6690 said.
And I also tried
conda create --name nemo python==3.8.10
conda activate nemo
```
# install NeMo
sudo apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
# install ctc-decoders
./scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh
```
but
./scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh: line 26: cd: /workspace/nemo: No such file or directory
error occured then I get GPG error, No module named 'swig_decoders' ..
still doesn't work.
I just want to get BeamSearchDecoder's result.
Please help me.
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} | # What does this PR do ?
1) Catch the end of `dataloader_iter` to exit gracefully from `validation_step` without having to prefetch all `microbatches` in a step which was being done previously. This is a temporary piece of code that is needed until we have lightning's fix that takes care of catching the end of `dataloader_iter`
2) Set `limit_val_batches` and `num_sanity_val_steps` for `pretraining` to be a multiple of num of `microbatches`. `limit_val_batches` is reconfigured so that we run as many global batches or validation_steps as the user entered `limit_val_batches`.
**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/7317 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7317/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7317/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7317/events | https://github.com/NVIDIA/NeMo/pull/7317 | 1,865,887,514 | PR_kwDOC_bI7s5Yu7Z5 | 7,317 | [ASR] Multichannel mask estimator with flex number of channels | {
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} | # What does this PR do ?
This PR adds a multichannel mask estimator which can process flexible number of channels.
It also fixes some bugs related to PTL 2.0 and librosa version.
**Collection**: ASR
# Changelog
- added example config `beamforming_flex_channels.yaml`
- add `max_utts` `process_audio.py`
- updated `librosa.get_duration` to use `path`, since `filename` is deprecated
- bumped `librosa` requirement to `0.10.0` in `requirements_asr.py`
- added `MaskEstimatorFlexChannels` in `audio_modules.py`
- added various multichannel modules in `multichannel_modules.py`, including `ParametricMultichannelWienerFiler` and `ReferenceChannelEstimatorSNR`
- added unit tests for multichannel modules
# 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.
# Additional Information
* Related to # (issue)
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```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
in
----> 1 import nemo.collections.asr as nemo_asr
2
3 asr_models = [model for model in dir(nemo_asr.models) if model.endswith('Model')]
4 asr_models
[~/.local/lib/python3.8/site-packages/nemo/collections/asr/__init__.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/collections/asr/__init__.py) in
13 # limitations under the License.
14
---> 15 from nemo.collections.asr import data, losses, models, modules
16 from nemo.package_info import __version__
17
[~/.local/lib/python3.8/site-packages/nemo/collections/asr/losses/__init__.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/collections/asr/losses/__init__.py) in
13 # limitations under the License.
14
---> 15 from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
16 from nemo.collections.asr.losses.audio_losses import SDRLoss
17 from nemo.collections.asr.losses.ctc import CTCLoss
[~/.local/lib/python3.8/site-packages/nemo/collections/asr/losses/angularloss.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/collections/asr/losses/angularloss.py) in
16 import torch
17
---> 18 from nemo.core.classes import Loss, Typing, typecheck
19 from nemo.core.neural_types import LabelsType, LogitsType, LossType, NeuralType
20
[~/.local/lib/python3.8/site-packages/nemo/core/__init__.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/core/__init__.py) in
14
15 import nemo.core.neural_types
---> 16 from nemo.core.classes import *
[~/.local/lib/python3.8/site-packages/nemo/core/classes/__init__.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/core/classes/__init__.py) in
28 )
29 from nemo.core.classes.dataset import Dataset, IterableDataset
---> 30 from nemo.core.classes.exportable import Exportable, ExportFormat
31 from nemo.core.classes.loss import Loss
32 from nemo.core.classes.mixins import access_mixins, adapter_mixins
[~/.local/lib/python3.8/site-packages/nemo/core/classes/exportable.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/core/classes/exportable.py) in
21 from nemo.core.utils.neural_type_utils import get_dynamic_axes, get_io_names
22 from nemo.utils import logging
---> 23 from nemo.utils.export_utils import (
24 ExportFormat,
25 augment_filename,
[~/.local/lib/python3.8/site-packages/nemo/utils/export_utils.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/nemo/utils/export_utils.py) in
18 from typing import Callable, Dict, Optional, Type
19
---> 20 import onnx
21 import torch
22 import torch.nn as nn
[~/.local/lib/python3.8/site-packages/onnx/__init__.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/onnx/__init__.py) in
18 import onnx.checker # noqa
19 import onnx.defs # noqa
---> 20 import onnx.helper # noqa
21 import onnx.utils # noqa
22
[~/.local/lib/python3.8/site-packages/onnx/helper.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/onnx/helper.py) in
15 TypeProto, SequenceProto, MapProto, IR_VERSION, TrainingInfoProto
16 from onnx import defs
---> 17 from onnx import mapping
18 from onnx.mapping import STORAGE_TENSOR_TYPE_TO_FIELD
19 from typing import Text, Sequence, Any, Optional, Dict, Union, TypeVar, Callable, Tuple, List, cast
[~/.local/lib/python3.8/site-packages/onnx/mapping.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/onnx/mapping.py) in
25 int(TensorProto.UINT32): np.dtype('uint32'),
26 int(TensorProto.UINT64): np.dtype('uint64'),
---> 27 int(TensorProto.STRING): np.dtype(np.object)
28 }
29
[~/.local/lib/python3.8/site-packages/numpy/__init__.py](https://vscode-remote+ssh-002dremote-002bapex-002dlogin-002ecmkl-002eac-002eth.vscode-resource.vscode-cdn.net/home/ekapolc/workspaces/yuat/diarization/~/.local/lib/python3.8/site-packages/numpy/__init__.py) in __getattr__(attr)
303
304 if attr in __former_attrs__:
--> 305 raise AttributeError(__former_attrs__[attr])
306
307 # Importing Tester requires importing all of UnitTest which is not a
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
**Environment overview
Ran through slurm with the option '#srun --container-image=nvcr.io/nvidia/nemo:23.06'.
No additional modification to the environment.
**Additional context**
GPU model: A100
`import nemo` worked just fine, the error occurred when importing `nemo.collections.asr`
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https://api.github.com/repos/NVIDIA/NeMo/issues/7315 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7315/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7315/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7315/events | https://github.com/NVIDIA/NeMo/issues/7315 | 1,865,580,253 | I_kwDOC_bI7s5vMn7d | 7,315 | nlp language modeling transformer lm raises value error with tarred dataset | {
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} | [] | closed | false | null | [] | null | null | 2023-08-24T17:19:23 | 2023-08-29T11:59:30 | 2023-08-29T11:59:30 | NONE | {
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} | null | null | null | I followed the steps in this[ language modeling documentation ](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/language_modeling.html#sentence-dataset-construction) but when I run the[ training script ](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/transformer_lm.py) it raises a value error which seemingly is related to using a tarred dataset instead of a normal SentenceDataset in the config file (it works with plain txt but since I have a large dataset I should be using the tarred format)
**my config file:**
name: TransformerLanguageModel
do_training: true # set to False if only preprocessing data
is_tarred: true
model:
label_smoothing: 0.0
preproc_out_dir: "" # path to store data preprocessing outputs
train_ds:
use_tarred_dataset: true
tar_files: "tarred/trainTarred/lm-batches.tokens.2048._OP_1..1866_CL_."
metadata_file: "tarred/trainTarred/metadata.json"
shard_strategy: scatter
tar_shuffle_n: 256
validation_ds:
use_tarred_dataset: true
tar_files: "tarred/validTarred/lm-batches.tokens.2048._OP_1..455_CL_."
metadata_file: "tarred/validTarred/metadata.json"
shard_strategy: scatter
tar_shuffle_n: 256
test_ds:
use_tarred_dataset: true
tar_files: "tarred/testTarred/lm-batches.tokens.2048._OP_1..204_CL_."
metadata_file: "tarred/testTarred/metadata.json"
shard_strategy: scatter
tar_shuffle_n: 256
optim:
name: adam
lr: 0.001
betas:
- 0.9
- 0.98
weight_decay: 0.0
sched:
name: InverseSquareRootAnnealing
min_lr: 0.0
last_epoch: -1
warmup_ratio: 0.1
tokenizer:
tokenizer_name: yttm
tokenizer_model: "tokenizer.model"
vocab_file: "persian_tokenizer.vocab"
special_tokens:
- "<bos>"
- "<eos>"
# training_sample_size: null # valid for sentencepiece tokenizer
encoder:
library: nemo
model_name: null
pretrained: false
max_sequence_length: 512
num_token_types: 0
embedding_dropout: 0.1
learn_positional_encodings: false
hidden_size: 512
num_layers: 6
inner_size: 2048
num_attention_heads: 8
ffn_dropout: 0.1
attn_score_dropout: 0.1
attn_layer_dropout: 0.1
hidden_act: relu
mask_future: true
pre_ln: false
head:
num_layers: 1
use_transformer_init: true
activation: relu
log_softmax: true
dropout: 0.0
trainer:
devices: 3
num_nodes: 1
max_epochs: 100
precision: 16-mixed
accelerator: gpu
strategy: ddp
enable_checkpointing: false
logger: false
log_every_n_steps: 50
check_val_every_n_epoch: 1
benchmark: false
exp_manager:
name: TransformerLM
files_to_copy: []
**Error traceback:**
Error executing job with overrides: []
Traceback (most recent call last):
File "/model_training.py", line 36, in <module>
main()
File "-/python3.10/site-packages/nemo/core/config/hydra_runner.py", line 126, in wrapper
_run_hydra(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
lambda: hydra.run(
File "-/python3.10/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "-/python3.10/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "-/python3.10/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/home/roshan/ariana/model_training.py", line 32, in main
trainer.fit(transformer_lm)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 529, in fit
call._call_and_handle_interrupt(
File "-/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 41, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 91, in launch
return function(*args, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 568, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 973, in _run
results = self._run_stage()
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1014, in _run_stage
self._run_sanity_check()
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1043, in _run_sanity_check
val_loop.run()
File "-/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 177, in _decorator
return loop_run(self, *args, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 108, in run
batch, batch_idx, dataloader_idx = next(data_fetcher)
File "-/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 136, in __next__
self._fetch_next_batch(self.dataloader_iter)
File "-/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 150, in _fetch_next_batch
batch = next(iterator)
File "-/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 284, in __next__
out = next(self._iterator)
File "-/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 123, in __next__
out = next(self.iterators[0])
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "-/python3.10/site-packages/torch/_utils.py", line 644, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "-/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "-/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "-/python3.10/site-packages/webdataset/pipeline.py", line 64, in iterator
for sample in self.iterator1():
File "-/python3.10/site-packages/webdataset/pipeline.py", line 58, in iterator1
source = self.invoke(step, source)
File "-/python3.10/site-packages/webdataset/pipeline.py", line 52, in invoke
raise ValueError(f"{f}: not a valid pipeline stage")
ValueError: None: not a valid pipeline stage
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/roshan/ariana/model_training.py", line 36, in <module>
main()
File "-/python3.10/site-packages/nemo/core/config/hydra_runner.py", line 126, in wrapper
_run_hydra(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
lambda: hydra.run(
File "-/python3.10/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "-/python3.10/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "-/python3.10/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/home/roshan/ariana/model_training.py", line 32, in main
trainer.fit(transformer_lm)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 529, in fit
call._call_and_handle_interrupt(
File "-/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 41, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 91, in launch
return function(*args, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 568, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 973, in _run
results = self._run_stage()
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1014, in _run_stage
self._run_sanity_check()
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1043, in _run_sanity_check
val_loop.run()
File "-/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 177, in _decorator
return loop_run(self, *args, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 108, in run
batch, batch_idx, dataloader_idx = next(data_fetcher)
File "-/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 136, in __next__
self._fetch_next_batch(self.dataloader_iter)
File "-/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 150, in _fetch_next_batch
batch = next(iterator)
File "-/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 284, in __next__
out = next(self._iterator)
File "-/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 123, in __next__
out = next(self.iterators[0])
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "-/python3.10/site-packages/torch/_utils.py", line 644, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "-/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "-/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "-/python3.10/site-packages/webdataset/pipeline.py", line 64, in iterator
for sample in self.iterator1():
File "-/python3.10/site-packages/webdataset/pipeline.py", line 58, in iterator1
source = self.invoke(step, source)
File "-/python3.10/site-packages/webdataset/pipeline.py", line 52, in invoke
raise ValueError(f"{f}: not a valid pipeline stage")
ValueError: None: not a valid pipeline stage
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/roshan/ariana/model_training.py", line 36, in <module>
main()
File "-/python3.10/site-packages/nemo/core/config/hydra_runner.py", line 126, in wrapper
_run_hydra(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "-/python3.10/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
lambda: hydra.run(
File "-/python3.10/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "-/python3.10/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "-/python3.10/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/home/roshan/ariana/model_training.py", line 32, in main
trainer.fit(transformer_lm)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 529, in fit
call._call_and_handle_interrupt(
File "-/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 41, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 91, in launch
return function(*args, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 568, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 973, in _run
results = self._run_stage()
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1014, in _run_stage
self._run_sanity_check()
File "-/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1043, in _run_sanity_check
val_loop.run()
File "-/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 177, in _decorator
return loop_run(self, *args, **kwargs)
File "-/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 108, in run
batch, batch_idx, dataloader_idx = next(data_fetcher)
File "-/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 136, in __next__
self._fetch_next_batch(self.dataloader_iter)
File "-/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 150, in _fetch_next_batch
batch = next(iterator)
File "-/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 284, in __next__
out = next(self._iterator)
File "-/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 123, in __next__
out = next(self.iterators[0])
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "-/python3.10/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "-/python3.10/site-packages/torch/_utils.py", line 644, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "-/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "-/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "-/python3.10/site-packages/webdataset/pipeline.py", line 64, in iterator
for sample in self.iterator1():
File "-/python3.10/site-packages/webdataset/pipeline.py", line 58, in iterator1
source = self.invoke(step, source)
File "-/python3.10/site-packages/webdataset/pipeline.py", line 52, in invoke
raise ValueError(f"{f}: not a valid pipeline stage")
ValueError: None: not a valid pipeline stage
I'd appreciate it if anyone would take a look into this. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7314 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7314/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7314/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7314/events | https://github.com/NVIDIA/NeMo/pull/7314 | 1,865,142,601 | PR_kwDOC_bI7s5YsYYK | 7,314 | migrated class CSVFieldsMemmapDataset from BioNeMo | {
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] | closed | false | null | [] | null | null | 2023-08-24T13:18:44 | 2023-08-24T21:41:24 | 2023-08-24T21:41:24 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adding class `CSVFieldsMemmapDataset `
**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
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} | null | null | null | **Describe the bug**
The tutorial links in https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html are not working for the TTS stuff like Fastpitch. There are also bugs in the code of some of the jupyternotebooks. The code in the ngc models for the tutorials also does not work if you take it from the overview tab in https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_de_fastpitch_multispeaker_5.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR extends the API for `AudioCodec`, adds comments and small fixes.
**Collection**: TTS
# Changelog
- Added `encode` to convert a time-domain signal into a discrete representation
- Added `decode` to convert a discrete (quantized) representation to audio
- Use `quantize` to convert a continuous encoded representation into a quantized discrete representation
- Use `dequantize` to convert a discrete representation to a continuous encoded representation
- Use batch-first format for quantized discrete representation
- Updated input/output type definitions
- Added docstrings in audio_codec modules
- Added logging in audio_codec modules and callbacks
# Usage
Example of usage:
```python
# instantiate a model
model = AudioCodecModel(…)
# convert `audio_in` to disrete representation
# `tokenized` has shape (batch_size, num_codebooks, num_frames)
tokenized, tokenized_len = model.encode(audio=audio_in, audio_len=audio_in_len)
# convert a discrete representation to time-domain audio
audio_out, audio_out_len = model.decode(tokens=tokenized, tokens_len=tokenized_len)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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When I follow the tutorial "ASR_CTC_Language-Finetuning. ipynb" on Colab(https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb),
I found this tutorial a bit outdated, so I tried to modify this line of code
```
VERSION="cv corpus 6.1-2020-12-11"
LANGUAGE="ja"
```
by
```
VERSION="cv corpus 14.0-2023-06-28"
LANGUAGE="ja"
```
Discovered that it didn't work anymore.
Here is the error message:
```
Resolving voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com (voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com)... 52.92.161.65, 52.218.224.66, 52.92.195.193, ...
Connecting to voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com (voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com)|52.92.161.65|:443... connected.
HTTP request sent, awaiting response... 403 Forbidden
2023-08-24 02:24:22 ERROR 403: Forbidden.
Traceback (most recent call last):
File "/content/scripts/get_commonvoice_data.py", line 214, in <module>
main()
File "/content/scripts/get_commonvoice_data.py", line 181, in main
tar = tarfile.open(target_file)
File "/usr/lib/python3.10/tarfile.py", line 1804, in open
raise ReadError(f"file could not be opened successfully:\n{error_msgs_summary}")
tarfile.ReadError: file could not be opened successfully:
- method gz: ReadError('empty file')
- method bz2: ReadError('not a bzip2 file')
- method xz: ReadError('not an lzma file')
- method tar: ReadError('empty file')
```
So I opened the xx file for review and found the code inside:
```
Parser.add_ Argument（
'-- version',
Default='cv corpus 5.1-2020-06-22 ',
Type=str,
Help='Version of the dataset (observable via https://commonvoice.mozilla.org/en/datasets ',
）
Parser.add_ Argument（
'-- language',
Default='en ',
Type=str,
Help='Which language to download.' (default English, '
Check https://commonvoice.mozilla.org/en/datasets For more language codes',
）
```
I don't know what to fill in for VERSION and LANGUAGE? I am on this website（ https://commonvoice.mozilla.org/en/datasets ）Didn't I find the answer I wanted in?
In addition, I would like to know if there is a relatively new tutorial that can help me train a new model. For example, if I want to train a Cantonese model, what should I do?
I downloaded the dataset from CommonVoice and it no longer has the **dev/train/test.tsv** file. Instead, it has **other/validated/validated/reported.tsv,** times.txt, and a clip folder with audio. Does this mean I need to categorize the files in the clip into three parts of dev/train/test myself?
I hope to receive your reply as soon as possible!!! | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
**Helps to speed up ASR models training without quality degradation.**
The experiments below showed a **19-12% training speed up with the same quality** for the some models.
**Collection**: ASR, core
# Changelog
- `nemo/collections/asr/parts/utils/asr_batching.py`
Add a semi sorted batch sampler and its preparation function to a new file (in the future it might be some other samplers or new techniques like dynamic batching in the file).
- `nemo/collections/asr/models/ctc_models.py`
`nemo/collections/asr/models/ctc_bpe_models.py`
`nemo/collections/asr/models/rnnt_models.py`
`nemo/collections/asr/models/rnnt_bpe_models.py`
`nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py`
Add changes to the data loader preparation function in the models class.
- `nemo/core/optim/lr_scheduler.py`
Add changes to the scheduler preparation function because the batch sampler is passed to the dataloader as a sampler and we need to parse the batch size from it.
- `nemo/collections/asr/data/audio_to_text.py`
Add a change to the `__getitem__` dataset method, as the batch sampler becomes an iterator and returns a list of indices instead of a single index.
# Usage
The sampler is currently added to the CTC, Transducer and Hybrid models.
**Need just to add one line to the config.**
```bash
++config.model.train_ds.use_semi_sorted_batching=true
```
and two line if we want to specify randomization factor (see more in additional information).
```bash
++config.model.train_ds.randomization_factor=0.1
```
Paper default value for randomization_factor is `0.2`.
# 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?
- [ ] 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?
@titu1994, @redoctopus, @jbalam-nv, @okuchaiev
# Additional Information
* Semi Sorted Batching, was proposed in "Speed up training with variable length inputs by efficient batching strategies.", Zhenhao Ge et al. (2021). ([link](https://www.isca-speech.org/archive/pdfs/interspeech_2021/ge21_interspeech.pdf) to the paper)
* **Idea**: Get the duration of the samples, add pseudo noise to them and sort them. Sample the batches from the sorted list. This way we avoid strict sorting and retain some level of randomness with a significant reduction in padding in batches.
* Experiment for Conformer char CTC:
* train script: `examples/asr/asr_ctc/speech_to_text_ctc.py`
* train config: `asr/examples/asr/conf/conformer/conformer_ctc_char.yaml`
* train data: libri-speech `train-clean-100` `train-clean-360`
* test sata: libri-speech `test-clean`
* overrided parameters:
* `model.validation_ds.batch_size=32`
* `trainer.devices=2`
* `trainer.max_epochs=50`
* `trainer.accumulate_grad_batches=10`
* `trainer.gradient_clip_val=1.0 `
* `++model.train_ds.use_semi_sorted_batching=true` (only experiment with ssb)
* `++model.train_ds.randomization_factor=0.1` (only experiment with ssb)
* devices: Nvidia Tesla V100 x2
* tensorboard logs:
* time
<img src="https://github.com/fedorovgv/buf/blob/main/char_ctc_time.png?raw=true" alt="alt text" width="400" height="200">
* wer
<img src="https://github.com/fedorovgv/buf/blob/main/char_ctc_wer.png?raw=true" alt="alt text" width="400" height="200">
* average, time per epoch / total time / final wer metric
* `ssb`: **22 min 28 sec** / **18 hours 44 min 14 sec** / 9,371 wer
* `without ssb`: 27 min 44 sec / 23 hours 7 min 31 sec / **9,08** wer
**Model training speed up by 18,9% with 3% loss of quality.** | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | https://api.github.com/repos/NVIDIA/NeMo/issues/7306/timeline | null | null | true |
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