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**Collection**: [TTS]
# Changelog
- Add compute_features.py script as an alternative to extract_sup_data.py, that is standalone without depending on TTSDataset.
- Add "parts.preprocessing.features.py" with functions for computing pitch, energy, and mel spectrogram. Functions use numpy arrays for non-batched preprocessing, but could also have Tensor based inputs/outputs if it helps in the future.
- Add parts.preprocessing.features.TTSFeature as a clean and minimalistic alternative to tts_data_types to be used in the next iteration of TTS data loader.
- Add example feature configurations for 22khz and 44khz audio.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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
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} | # What does this PR do ?
Fix some typos in Multitask_Prompt_and_PTuning.ipynb
**Collection**: NLP
# Changelog
- Fixed typos in notebook text
# Usage
n/a
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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
n/a
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Add a knob to control loss broadcast. It's enabled by default.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] 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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**Collection**: Dockerfile
# Changelog
- Update `Dockerfile` to install AIS CLI only if `REQUIRE_AIS_CLI == true`
- Added an installer script for AIS CLI
# Usage
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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?
- [ ] 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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- [ ] 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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* [x] add WER
* [x] refactor script into function
* [ ] add to training script | {
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Hi. Thank you very much for your implementation on ACEVC. I'm interested in this Zero-shot Voice Conversion project. I tried to train the model following the document on the website https://paarthneekhara.github.io/ace/code.html. However, it takes much time because of my poor hardware conditions. Are there any pretrained models for ACEVC,such as the pre-trained Conformer-SSL in step 1 or the HiFiGAN model in step 3. I'm sincerely thankful with your help. | {
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} | # What does this PR do ?
Minor cleaning, followup from #5837
@VahidooX I think I've address you comments. Let me know if I missed something.
**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?
@VahidooX
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix multi-processing bug in data simulator
**Collection**:
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
- Changed silence sampling logic to follow overlap sampling logic
- Changed perturbator config structure to include `add_xxx_aug` in side `xxxxxx_augmentor`
- Some comment changes
- Fixed annotation file writing mechanism to fix multi-processing issue.
- Added more unit-tests for util functions
# Usage
```python
This script creates a synthetic diarization session using the provided audio dataset with ctm files.
Usage:
python <NEMO_ROOT>/tools/speech_data_simulator/multispeaker_simulator.py \
num_workers=10 \
data_simulator.random_seed=42 \
data_simulator.manifest_filepath=manifest_with_alignment_file.json \
data_simulator.outputs.output_dir=./simulated_data \
data_simulator.outputs.output_filename=sim_spk2_sess20 \
data_simulator.session_config.num_sessions=1000 \
data_simulator.session_config.num_speakers=2 \
data_simulator.session_config.session_length=20 \
data_simulator.background_noise.add_bg=False \
data_simulator.background_noise.background_manifest=background_noise.json \
data_simulator.background_noise.snr=40 \
Check out parameters in ./conf/data_simulator.yaml.
```
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
add warning about "The incompatibility of components could lead to failure of running this notebook locally with container, we might deprecate this notebook and provide a better tutorial in soon releases."
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fix the wrong branch name in speaker diarization inference notebook
**Collection**: [Note which collection this PR will affect]
# Changelog
It should be BRANCH="r1.17.0" but it was BRANCH="main" in r1.17.0
# Usage
Run Speaker_Diarization_Inference.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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR
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} | # What does this PR do ?
add warning about "The incompatibility of components could lead to failure of running this notebook locally with container, we might deprecate this notebook and provide a better tutorial in soon releases."
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] 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 ?
Allows to specify multiple datasets with different configs in validation and test
ALL
# Changelog
When processing val / test dataset configs, if a dict is detected instead of a list, overload the entire dataset config, not just the manifest path.
# Usage
Specify multiple, different dataset configs as shown below.
```yaml
validation_ds:
ds_item:
- name: en
manifest_filepath:
- ${d}/en/val_test/fisher/audio_manifest_dev_clean_en.json
- ${d}/en/val_test/europarl/audio_manifest_dev_clean_en.json
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
max_duration: 20.0
min_duration: 0.1
use_start_end_token: false
- name: es
manifest_filepath:
- ${d}/es/nemo_sp_asr_set_3pt0/dev/fisher/dev_fisher_manifest_es.json
- ${d}/es/nemo_sp_asr_set_3pt0/dev/mcv12/dev_mcv12_manifest_es.json
- ${d}/es/nemo_sp_asr_set_3pt0/dev/mls/dev_mls_manifest_es.json
- ${d}/es/nemo_sp_asr_set_3pt0/dev/voxpopuli/dev_voxpopuli_manifest_es.json
sample_rate: ${model.sample_rate}
batch_size: 32
shuffle: false
num_workers: 8
pin_memory: true
max_duration: 10.0
min_duration: 1.0
use_start_end_token: false
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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The PR adds support of recent `distutils` for CTC decoder installation.
**Collection**: ASR (optional CTC beam search decoder)
# Changelog
- Fixed CTC decoder installation to support recent distutils
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [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?
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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- Added RPE for MegatronNMT models
- Fixed an issue with RMSNorm
**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 ?
The PR adds support of recent `distutils` for CTC decoder installation.
**Collection**: ASR (optional CTC beam search decoder)
# Changelog
- Fixed CTC decoder installation to support recent distutils
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [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?
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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- Adds RPE support for NMT export
- Fixed RMSNorm export in Megatron
**Collection**: [Note which collection this PR will affect]
# Changelog
- Adds RPE support for NMT export
- Fixed RMSNorm export in Megatron
# 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 ?
Fix the wrong branch name in speaker diarization inference notebook
**Collection**: [Note which collection this PR will affect]
# Changelog
It should be BRANCH="r1.17.0" but it was BRANCH="main" in r1.17.0
# Usage
Run Speaker_Diarization_Inference.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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR
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https://api.github.com/repos/NVIDIA/NeMo/issues/6299 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6299/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6299/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6299/events | https://github.com/NVIDIA/NeMo/pull/6299 | 1,642,222,633 | PR_kwDOC_bI7s5M-m0r | 6,299 | MixedFusedRMSNorm Export Fix | {
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] | closed | false | null | [] | null | null | 2023-03-27T14:35:42 | 2023-03-31T16:16:55 | 2023-03-31T16:16:54 | CONTRIBUTOR | {
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} | # What does this PR do ?
Replaces MixedFusedRMSNorm with PyT equivalent during export process
**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/6298 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6298/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6298/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6298/events | https://github.com/NVIDIA/NeMo/issues/6298 | 1,641,996,190 | I_kwDOC_bI7s5h3t-e | 6,298 | Nemo Toolkit install fail | {
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I run command:
`pip install "nemo_toolkit[all]"
`
And I get more errors,
the first one is:
`
->nemo_toolkit[all]) (0.9.1)
Building wheels for collected packages: fasttext
Building wheel for fasttext (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> [61 lines of output]
/home/grega/miniconda3/envs/nemo/lib/python3.8/site-packages/setuptools/dist.py:720: UserWarning: Usage of dash-separated 'description-file' will not be supported in future versions. Please use the underscore name 'description_file' instead
`
the second one is:
`
RuntimeError: Unsupported compiler -- at least C++11 support is needed!
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for fasttext
Running setup.py clean for fasttext
Failed to build fasttext
Installing collected packages: fasttext, black, beautifulsoup4, anyio, aiosignal, texterrors, starlette, sphinx, numba, mdit-py-plugins, matplotlib, markdown, hydra-core, httpcore, GitPython, Flask, botocore, aiohttp, wandb, transformers, torchmetrics, s3transfer, pybtex-docutils, pyannote.database, paramiko, librosa, kornia, ipython, httpx, google-auth-oauthlib, gdown, g2p-en, flask-restful, fastapi, tensorboard, sphinxcontrib-bibtex, sentence-transformers, pytorch-lightning, pyannote.metrics, ipywidgets, gradio, boto3, nemo_toolkit
Running setup.py install for fasttext ... error
error: subprocess-exited-with-error
× Running setup.py install for fasttext did not run successfully.
│ exit code: 1
╰─> [64 lines of output]
`
the last one is:
```
RuntimeError: Unsupported compiler -- at least C++11 support is needed!
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: legacy-install-failure
× Encountered error while trying to install package.
╰─> fasttext
note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.
```
Please help me.
Thank you.
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} | # What does this PR do ?
https://github.com/NVIDIA/NeMo/pull/5920 adds support for BF16 grad reductions with distopt, with embedding grad reductions done in FP32. @mikolajblaz found some bugs and it turns out the FP32 reductions were not being done at all. This PR fixes those issues. When I run GPT-3 175B, I confirm the embedding grads are now optimized with the FP32 optimizer. Loss values are the same with FP32 and BF16 grad reductions, up to 50 steps and within numerical accuracy.
This turned out messier than I would have liked. It would have been better to integrate distopt support for multiple grad dtypes into Apex.
**Collection**: NLP
# Changelog
- Debug distopt support for BF16 grad reductions
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/aaa0ccacf5b4d204ed18f6052e55ff6f20575996/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L207
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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Fixes bugs from https://github.com/NVIDIA/NeMo/pull/5920
* Depends on https://github.com/NVIDIA/apex/pull/1575
* Depends on https://github.com/NVIDIA/TransformerEngine/pull/75 | {
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} | # What does this PR do ?
Replaces MixedFusedRMSNorm with PyT equivalent during export process
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hello. I tried using extract_sup_data.py script, where my manifest.json file had only these 4 keys: "audio_filepath", "text", "text_no_preprocessing" and "duration". The script didn't work, but when I removed "text_no_preprocessing" key it worked. I guess it would have initially worked if I had additional "text_normalized" key. So my questions are:
- What is the purpose of "text_no_preprocessing" key?
- How the model recognizes punctuation in input sentence when manifest.json only has "text" keys?
Thank you! | {
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} | # What does this PR do ?
updated ipa-cmudict files for TTS
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?
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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 ?
Fixes issues in nemo/collections/asr/models/hybrid_rnnt_ctc_models.py regarding Augmentor parameters to enable transcribe functionality for hybrid RNNT-CTC models
Collection: [ASR]
# Changelog
- in the function transcribe() of class EncDecHybridRNNTCTCModel, added
* augmentor argument in function call (line 103)
* augmentor argument in the config for calling _setup_transcribe_dataloader() (line 179)
# Usage
* You can potentially add a usage example below
```python
python NeMo/examples/asr/transcribe_speech.py \
model_path="path to hybrid model" \
pretrained_name="name of hybrid model from NGC directory" \
audio_dir="path to folder of audio files" \
dataset_manifest="path to manifest" \
output_filename="output filename" \
batch_size=32 \
compute_timestamps=False \
compute_langs=False \
cuda=0 \
amp=True \
append_pred=False \
pred_name_postfix="another model name for output filename, if required"
```
# 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 ?
Resolves a bug with feature training for radtts
**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 issues in nemo/collections/asr/models/hybrid_rnnt_ctc_models.py regarding Augmentor parameters to enable transcribe functionality for hybrid RNNT-CTC models
Collection: [ASR]
# Changelog
- in the function transcribe() of class EncDecHybridRNNTCTCModel, added
* augmentor argument in function call (line 103)
* augmentor argument in the config for calling _setup_transcribe_dataloader() (line 179)
# Usage
* You can potentially add a usage example below
```python
python NeMo/examples/asr/transcribe_speech.py \
model_path="path to hybrid model" \
pretrained_name="name of hybrid model from NGC directory" \
audio_dir="path to folder of audio files" \
dataset_manifest="path to manifest" \
output_filename="output filename" \
batch_size=32 \
compute_timestamps=False \
compute_langs=False \
cuda=0 \
amp=True \
append_pred=False \
pred_name_postfix="another model name for output filename, if required"
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add support for MPS device (Apple Silicon M-series GPU) for ASR inference
**Collection**: [ASR]
# Changelog
- allow to select MPS device in `transcribe_speech.py`
# Usage
MPS device will be automatically selected in `transcribe_speech.py` or `speech_to_text_eval.py` if `allow_mps=true` flag is explicitly set, and MPS device is available.
Will work only with PyTorch >= 2.0, for now env variable `PYTORCH_ENABLE_MPS_FALLBACK=1` should be set.
Tested with models:
- https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large
- https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_large
```shell
PYTORCH_ENABLE_MPS_FALLBACK=1 python examples/asr/transcribe_speech.py \
allow_mps=true \
<other script params>
PYTORCH_ENABLE_MPS_FALLBACK=1 python examples/asr/speech_to_text_eval.py \
allow_mps=true \
<other script params>
```
# 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.
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} | # What does this PR do ?
Update docs and model for HiFiTTS version
**Collection**: [TTS]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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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} | # 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 ?
Fixes issues in nemo/collections/asr/models/hybrid_rnnt_ctc_models.py regarding Augmentor parameters to enable transcribe functionality for hybrid RNNT-CTC models
**Collection**: [ASR]
# Changelog
- in the function transcribe() of class EncDecHybridRNNTCTCModel, added
* augmentor argument in function call (line 103)
* augmentor argument in the config for calling _setup_transcribe_dataloader() (line 179)
# Usage
* You can potentially add a usage example below
```python
python NeMo/examples/asr/transcribe_speech.py \
model_path="path to hybrid model" \
pretrained_name="name of hybrid model from NGC directory" \
audio_dir="path to folder of audio files" \
dataset_manifest="path to manifest" \
output_filename="output filename" \
batch_size=32 \
compute_timestamps=False \
compute_langs=False \
cuda=0 \
amp=True \
append_pred=False \
pred_name_postfix="another model name for output filename, if required"
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix some bugs and improve usability of TTS preprocessing script.
**Collection**: [TTS]
# Changelog
- Replace hydra runner based script with regular python script that uses OmegaConf to parse .yaml config. The hydra script used AudioPreprocessingConfig as input, but in some situations this might not work because classes in the "scripts" directory are typically not installed or importable.
- Update the audio trimmer class paths because they were moved to "tts.parts.preprocessing", and moved them from "scripts" to "examples".
- Support relative paths in manifest.
- Ignore min_duration and max_duration filters when they are not provided.
# 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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} | null | null | null | **Describe the bug**
Diarization continues until clustering, upon which I get this error.
"linalg.eigh: The algorithm failed to converge because the input matrix is ill-conditioned or has too many repeated eigenvalues (error code: 529)"
**Steps/Code to reproduce bug**
I'm loosely following the notebook at https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb except i turned oracle vad off.
Here's the diarization part of my notebook
https://colab.research.google.com/drive/1zN9LZ5-hPEI15K1NWl2obvEapJbTWd9P?usp=sharing
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Resuming pre-training sometimes fails immediately complaining of empty validation outputs tensor. This PR provides a fix for this issue.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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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} | # What does this PR do ?
Fixes a bug that makes T5 decode use bos_id even when asked to replace with pad.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
After exporting an asr model to onnx "stt_es_citrinet_1024_gamma_0_25", I try to make an inference with onnxruntime and I get the following error:
2023-03-23 01:39:32.514839309 [E:onnxruntime:Default, cuda_call.cc:119 CudaCall] CUDNN failure 3: CUDNN_STATUS_BAD_PARAM ; GPU=0 ; hostname=w1 ; expr=cudnnAddTensor(cudnnHandle, &alpha, Base::s_.z_tensor, Base::s_.z_data, &alpha, Base::s_.y_tensor, Base::s_.y_data);
2023-03-23 01:39:32.514864313 [E:onnxruntime:, sequential_executor.cc:494 ExecuteKernel] Non-zero status code returned while running FusedConv node. Name:'/encoder/encoder/encoder.1/res.0.0/conv/Conv' Status Message: CUDNN failure 3: CUDNN_STATUS_BAD_PARAM ; GPU=0 ; hostname=w1 ; expr=cudnnAddTensor(cudnnHandle, &alpha, Base::s_.z_tensor, Base::s_.z_data, &alpha, Base::s_.y_tensor, Base::s_.y_data);
**Steps/Code to reproduce bug**
Export model:
```
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_es_citrinet_1024_gamma_0_25", map_location='cuda')
model.export('/models/model.onnx', onnx_opset_version=18)
```
For legal reasons I cannot show the complete code, but I will try to put as much as I can:
```
import numpy as np
import soundfile as sf
import onnxruntime as ort
def get_samples(audio_file, target_sr=16000):
with sf.SoundFile(audio_file, 'r') as f:
dtype = 'int16'
sample_rate = f.samplerate
samples = f.read(dtype=dtype)
if sample_rate != target_sr:
samples = librosa.core.resample(samples, sample_rate, target_sr)
samples = samples.astype('float32')/32768
samples = samples.transpose()
return samples
class AudioChunkIterator():
def __init__(self, samples, frame_len, sample_rate, chunk_len_in_secs):
self._samples = samples
self._chunk_len = chunk_len_in_secs*sample_rate
self._start = 0
self.output = True
def __iter__(self):
return self
def __next__(self):
if not self.output:
raise StopIteration
last = int(self._start + self._chunk_len)
if last <= len(self._samples):
chunk = self._samples[self._start: last]
self._start = last
else:
chunk = np.zeros([int(self._chunk_len)], dtype='float32')
samp_len = len(self._samples) - self._start
chunk[0:samp_len] = self._samples[self._start:len(self._samples)]
self.output = False
return chunk
ort_session = ort.InferenceSession('/models/model.onnx', providers=["CUDAExecutionProvider"])
**You can put the inference code by chunks for ctc models**
def _get_batch_preds(self, audio_signal, audio_signal_len, merge=True):
processed_signal, processed_signal_len = self.preprocessor(
input_signal=audio_signal, length=audio_signal_len
)
binding = ort_session.io_binding()
processed_signal_tensor = processed_signal.contiguous()
binding.bind_input(
name=ort_session.get_inputs()[0].name, # audio_signal
device_type='cuda',
device_id=0,
element_type=np.float32,
shape=tuple(processed_signal_tensor.shape),
buffer_ptr=processed_signal_tensor.data_ptr(),
)
# print(processed_signal_len, 'processed_signal_len')
processed_signal_len_tensor = processed_signal_len.contiguous()
binding.bind_input(
name=ort_session.get_inputs()[1].name, #length
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(processed_signal_len_tensor.shape),
buffer_ptr=processed_signal_len_tensor.data_ptr(),
)
binding.bind_output('logprobs', 'cuda')
ort_session.run_with_iobinding(binding)
logprobs = binding.copy_outputs_to_cpu()
print(logprobs)
```
**Expected behavior**
The expected behavior is to print the probabilities
**Environment overview (please complete the following information)**
- Environment location: [Local]
- Method of NeMo install: [pip install]. Please specify exact commands you used to install. : pip3 install nemo-toolkit[all]==1.16.0
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04
- pytorch version: 2.0.0
- Python version: 3.8
**Additional context**
GPU model: Tesla T4
onnxruntime version: onnxruntime-gpu==1.14.1
onnx version: 1.13.1
Cuda version: I tried with different versions of cuda included
11.4, 11.6, 11.8, 12.0
cudNN version: I tried with different versions of cudNN included
8.2.4, 8.4.0, 8.8.0
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} | # What does this PR do ?
This PR fixes the args passing problems with Float16Module
**Collection**: [Note which collection this PR will affect]
nlp
# Changelog
Changing how Float16Module uses share_token_embeddings, if it detects it in the module it should use that and not overwrite it
# Problem fixed
Float16Module ignores the share_token_embeddings flag set by the module because it calls [init](https://github.com/NVIDIA/NeMo/blob/14effd79731ec76a047aefcb3ac4d15c0a7330dd/nemo/collections/nlp/modules/common/megatron/module.py#L262) with no arguments which causes the flag to default to true because MegatronModule sets the default to [True](https://github.com/NVIDIA/NeMo/blob/14effd79731ec76a047aefcb3ac4d15c0a7330dd/nemo/collections/nlp/modules/common/megatron/module.py#L46) which subsequently causes checks like [this](https://github.com/NVIDIA/NeMo/blob/14effd79731ec76a047aefcb3ac4d15c0a7330dd/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L255) to pass despite the user specifying False in the module. Instead Float16Module should inherit this flag from the underlying module whenever possible.
**PR Type**:
- [x] Bugfix
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} | # What does this PR do ?
updating Conformer CTC and RNNT BPE config files docstring to include xlarge model details.
**Collection**: ASR
# Changelog
- updating docstring for conformer BPE config files with xlarge models.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Pr allows you to pretrain a prompt-encoder.
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} | null | null | null | Thanks for your great repository. I have a question about speaker diarization.
I do the inference steps according to this tutorial: https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb
According to this tutorial, audio will be written to manifest file, output will be saved in .rttm format in out_dir ( manifest file and out_dir are defined in _**diar_infer_telephonic.yaml**_ . Is there a direct way to pass the input as audio metadata (path, duration, ...) and give the results without writing the manifest and the .rttm files? | {
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} | null | null | null | **Describe the bug**
Hello. I'm trying to fine-tune the HiFi-GAN model the same way as in Finetuning HiFi-GAN section of FastPitch_Finetuning.ipynb tutorial (https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb), but I get the following error when I try to run the script: "In 'hifigan.yaml': Could not find 'model/validation_ds/val_ds'".
I used the following script:
```
!(python hifigan_finetune.py \
--config-name=hifigan.yaml \
model.train_ds.dataloader_params.batch_size=16 \
model.max_steps=1000 \
model.optim.lr=0.00001 \
~model.optim.sched \
train_dataset=./hifigan_train_ft.json \
validation_datasets=./hifigan_val_ft.json \
exp_manager.exp_dir=hifigan_ft \
+init_from_pretrained_model=tts_en_hifigan \
trainer.check_val_every_n_epoch=10 \
model/train_ds=train_ds_finetune \
model/validation_ds=val_ds_finetune \
)
```
**Steps/Code to reproduce bug**
Before running it, I: created melspectograms and manifests for both training and validation sets (just used the code from the tutorial and for validation files updated the path); downloaded hifigan_finetune.py and hifigan.yaml by running the following code:
```
# BRANCH = 'main'
!wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/hifigan_finetune.py
!cd conf && mkdir -p hifigan && cd hifigan \
&& wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/conf/hifigan/hifigan.yaml \
&& wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/conf/hifigan/hifigan_44100.yaml \
&& cd .. && cd ..
```
Here is the snippet of my folder structure:
hifigan_val_ft.json config points to these file paths:
```
..., "mel_filepath": "9017_manifest_val_dur_5_mins_local_mels/mel_0.npy"}
..., "mel_filepath": "9017_manifest_val_dur_5_mins_local_mels/mel_1.npy"}
```
When I run the hifigan_finetune.py script, I get the following error (full):
```
[NeMo W 2023-03-22 05:09:39 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-03-22 05:09:39 experimental:27] Module <class 'nemo.collections.tts.models.fastpitch_ssl.FastPitchModel_SSL'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-03-22 05:09:39 experimental:27] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-03-22 05:09:39 experimental:27] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-03-22 05:09:40 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-03-22 05:09:41 experimental:27] Module <class 'nemo.collections.tts.models.ssl_tts.SSLDisentangler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-03-22 05:09:41 experimental:27] Module <class 'nemo.collections.tts.models.vits.VitsModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-03-22 05:09:41 nemo_logging:349] /usr/local/lib/python3.9/dist-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'hifigan.yaml': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information
warnings.warn(msg, UserWarning)
In 'hifigan.yaml': Could not find 'model/validation_ds/val_ds'
Config search path:
provider=hydra, path=pkg://hydra.conf
provider=main, path=file:///content/conf/hifigan
provider=schema, path=structured://
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Expected behavior**
Script should run without any errors.
**Environment overview**
- Environment location: Collab
- Method of NeMo install: as in Collab (pip)
**Environment details**
- PyTorch version: 1.13.1+cu116
- Python version: Python 3.9.16
**Additional comments**
I feel like the solution is extremely easy. I'm not sure why the error only points to 'val_ds' but not 'train_ds'. Perhaps the problem is in this line in the script: "+init_from_pretrained_model=tts_en_hifigan" ? | {
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Are there any instructions or documentation about this? | {
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} | # What does this PR do ?
I've been experiencing a hang in the first validation step when running GPT with interleaved pipeline parallelism. This first shows up in https://github.com/NVIDIA/NeMo/pull/6049, so it seems that accessing the data loader in middle pipeline stages is the root cause.
**Collection**: NLP
# Changelog
- Avoid unnecessarily accessing data loader with pipeline parallelism
# Usage
No change in usage
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/pull/6049
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} | # What does this PR do ?
- Overlap P2P communication between pipeline stages
- Overlap individual send and receive ops
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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**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 ?
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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Fix batch_size of VAD to 1, since current VAD pipeline only supports one sample at a time during inference.
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} | # What does this PR do ?
Fix typo
**Collection**: [Core]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix typo
**Collection**: [Core]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add HAT model as a new joint network type (HATJoint) for RNNT model. The difference is only in decoding time -- HAT.joint.joint returns two outputs: hat_logprobs and internal_lm_logprobs (for internal lm subtraction in case of Shallow Fusion with external n-gram LM).
**Collection**: [ASR]
# Usage
* For HAT model training you need replace `_target_: nemo.collections.asr.modules.RNNTJoint` with `_target_: nemo.collections.asr.modules.HATJoint` in `joint` part of standard transducer config.
* For Shallow Fusion with external n-gram LM use RNNT `maes` decoding algorithm which is able to work with HATJoint model.
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6259 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6259/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6259/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6259/events | https://github.com/NVIDIA/NeMo/issues/6259 | 1,632,170,458 | I_kwDOC_bI7s5hSPHa | 6,259 | NER task failing with CUDA errors with text blocks > 300 tokens | {
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} | null | null | null | We are using NeMo to do named entity recognition on blocks of text. The NER task works correctly when the text size we apply is under around 250 or 300 tokens. Once the text size goes above that, the task will start to fail. Between 250 tokens and around 600 tokens, the task will randomly either succeed or fail. Above 500-600 tokens or so, the task fails consistently.
The errors given are somewhat varied, depending on circumstance. They are one of the following:
```
RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)`
RuntimeError: CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`
RuntimeError: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```
Following the advice in that last message, the error changes to:
```
RuntimeError: CUDA error: device-side assert triggered
```
The model we are using is a dataset trained off of the medmentions data set, and the .nemo file is 1.2 GB and is being run on a 20GB GPU RAM slice of an A100 card which is shared on a kubernetes cluster. Based on our reading of other error reports, we suspected that running out of GPU RAM might be the ultimate issue. However, with 20 GB of GPU RAM we were surprised that only 300 tokens would be too much for it.
We have written code to chop the text into smaller windows and process it successively. However, both performance and accuracy suffer the smaller the window is. We would very much like to be able to run this on much larger blocks of text if possible. | {
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} | null | null | null | I have tried to fine-tune my .nemo model for question answering task in my own dataset, but the process have many bugs that I can fix my self.
Here is my code
```
CUDA_VISIBLE_DEVICES=0,1 python ~/dat/NeMo/examples/nlp/question_answering/question_answering.py \
--config-path=conf \
--config-name=qa_conf \
trainer.devices=1 \
trainer.num_nodes=1 \
trainer.max_epochs=20 \
trainer.max_steps=-1 \
trainer.accumulate_grad_batches=1 \
trainer.precision=16 \
model.library=megatron \
model.language_model.pretrained_model_name=gpt2 \
model.nemo_path=./nemo_experiments/megatron_gpt/checkpoints/megatron_gpt.nemo \
model.dataset.use_cache=False \
model.dataset.version_2_with_negative=True \
model.train_ds.file=./data/MRC_VLSP/v2_train_ViQuAD_new.json \
model.validation_ds.file=./data/MRC_VLSP/v2_dev_ViQuAD.json \
model.test_ds.file=./data/MRC_VLSP/v2_test_ViQuAD.json \
model.train_ds.batch_size=128 \
model.validation_ds.batch_size=128 \
model.test_ds.batch_size=128 \
model.tokenizer.tokenizer_name=sentencepiece \
model.tokenizer.tokenizer_model=./data/tokenizer/spm_100mb_wiki.model \
model.optim.lr=1e-4 \
exp_manager.exp_dir=./qna-experiments \
exp_manager.name=QnA-VLSP \
exp_manager.create_wandb_logger=False
```
And this is response of terminal.
```
[NeMo I 2023-03-20 09:00:16 question_answering:51] Restoring model from ./nemo_experiments/megatron_gpt/checkpoints/megatron_gpt.nemo
[NeMo E 2023-03-20 09:00:16 common:503] Model instantiation failed!
Target class: nemo.collections.nlp.models.language_modeling.megatron_gpt_model.MegatronGPTModel
Error(s): Trainer cannot be None for Megatron-based models. Please provide a PTL trainer object.
Traceback (most recent call last):
File "/home/jovyan/dat/NeMo/nemo/core/classes/common.py", line 482, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/home/jovyan/dat/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 78, in __init__
super().__init__(cfg, trainer=trainer, no_lm_init=True)
File "/home/jovyan/dat/NeMo/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 65, in __init__
raise ValueError(f"Trainer cannot be None for Megatron-based models. Please provide a PTL trainer object.")
ValueError: Trainer cannot be None for Megatron-based models. Please provide a PTL trainer object.
Error executing job with overrides: ['trainer.devices=1', 'trainer.num_nodes=1', 'trainer.max_epochs=20', 'trainer.max_steps=-1', 'trainer.accumulate_grad_batches=1', 'trainer.precision=16', 'model.library=megatron', 'model.language_model.pretrained_model_name=gpt2', 'model.nemo_path=./nemo_experiments/megatron_gpt/checkpoints/megatron_gpt.nemo', 'model.dataset.use_cache=False', 'model.dataset.version_2_with_negative=True', 'model.train_ds.file=./data/MRC_VLSP/v2_train_ViQuAD_new.json', 'model.validation_ds.file=./data/MRC_VLSP/v2_dev_ViQuAD.json', 'model.test_ds.file=./data/MRC_VLSP/v2_test_ViQuAD.json', 'model.train_ds.batch_size=128', 'model.validation_ds.batch_size=128', 'model.test_ds.batch_size=128', 'model.tokenizer.tokenizer_name=sentencepiece', 'model.tokenizer.tokenizer_model=./data/tokenizer/spm_100mb_wiki.model', 'model.optim.lr=1e-4', 'exp_manager.exp_dir=./qna-experiments', 'exp_manager.name=QnA-VLSP', 'exp_manager.create_wandb_logger=False']
Traceback (most recent call last):
File "/home/jovyan/dat/NeMo/examples/nlp/question_answering/question_answering.py", line 52, in main
model = model_class.restore_from(cfg.model.nemo_path)
File "/home/jovyan/dat/NeMo/nemo/core/classes/modelPT.py", line 311, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/jovyan/dat/NeMo/nemo/core/connectors/save_restore_connector.py", line 235, in restore_from
loaded_params = self.load_config_and_state_dict(
File "/home/jovyan/dat/NeMo/nemo/core/connectors/save_restore_connector.py", line 158, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/home/jovyan/dat/NeMo/nemo/core/classes/common.py", line 504, in from_config_dict
raise e
File "/home/jovyan/dat/NeMo/nemo/core/classes/common.py", line 496, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
File "/home/jovyan/dat/NeMo/nemo/collections/nlp/models/question_answering/qa_gpt_model.py", line 35, in __init__
self.setup_tokenizer(cfg.tokenizer)
File "/home/jovyan/dat/NeMo/nemo/collections/nlp/models/nlp_model.py", line 200, in setup_tokenizer
if cfg.tokenizer_name or vocab_file or cfg.tokenizer_model:
omegaconf.errors.ConfigAttributeError: Key 'tokenizer_name' is not in struct
full_key: tokenizer.tokenizer_name
object_type=dict
```
This is the whole error, and now I will go in to description. Firstly, to my knowledge, method restore_from() will extract all config from my `megatron_gpt.nemo` file, and build tokenizer and also model weight to training. But I have try to printed my config and it show me that
```
{'micro_batch_size': 128, 'global_batch_size': 512, 'tensor_model_parallel_size': 1, 'pipeline_model_parallel_size': 1, 'resume_from_checkpoint': None, 'encoder_seq_length': 1024, 'max_position_embeddings': 1024, 'num_layers': 12, 'hidden_size': 768, 'ffn_hidden_size': 3072, 'num_attention_heads': 12, 'init_method_std': 0.021, 'hidden_dropout': 0.1, 'kv_channels': None, 'apply_query_key_layer_scaling': True, 'layernorm_epsilon': 1e-05, 'make_vocab_size_divisible_by': 128, 'pre_process': True, 'post_process': True, 'persist_layer_norm': True, 'tokenizer': {'library': 'sentencepiece', 'type': 'GPT2BPETokenizer', 'model': 'nemo:a9c1fd3579454343baabf2926c114ac5_spm_100mb_wiki.model', 'vocab_file': None, 'merge_file': None, 'delimiter': None, 'sentencepiece_legacy': False}, 'native_amp_init_scale': 4294967296, 'native_amp_growth_interval': 1000, 'hysteresis': 2, 'fp32_residual_connection': False, 'fp16_lm_cross_entropy': False, 'megatron_amp_O2': False, 'grad_allreduce_chunk_size_mb': 125, 'grad_div_ar_fusion': True, 'seed': 1234, 'use_cpu_initialization': False, 'onnx_safe': False, 'apex_transformer_log_level': 30, 'gradient_as_bucket_view': True, 'gradient_accumulation_fusion': False, 'activations_checkpoint_granularity': None, 'activations_checkpoint_method': None, 'activations_checkpoint_num_layers': None, 'sequence_parallel': False, 'data': {'data_prefix': [1.0, './data/gpt_training_data_passage_content_document'], 'index_mapping_dir': None, 'data_impl': 'mmap', 'splits_string': '980,10,10', 'seq_length': 256, 'skip_warmup': True, 'num_workers': 2, 'dataloader_type': 'single', 'reset_position_ids': False, 'reset_attention_mask': False, 'eod_mask_loss': False}, 'nsys_profile': {'enabled': False, 'start_step': 10, 'end_step': 10, 'ranks': [0], 'gen_shape': False}, 'optim': {'name': 'fused_adam', 'lr': 0.0006, 'weight_decay': 0.1, 'betas': [0.9, 0.95], 'sched': {'name': 'CosineAnnealing', 'warmup_steps': 750, 'constant_steps': 80000, 'min_lr': 6e-05}}, 'precision': 16, 'target': 'nemo.collections.nlp.models.language_modeling.megatron_gpt_model.MegatronGPTModel', 'nemo_version': '1.11.0'}
```
This config then will be go in to self.setup_tokenizer(cfg.tokenizer), but it misses many argument eg: tokenizer_name, tokenizer_model, vocab_file,... I do not know why my nemo file missing the config but I have trained it completely and successfully inference.
And if the tokenizer bulit-in use the .nemo config, this means the all model.tokenizer configs in the qa_conf.yaml file not to be used for the "megatron" type.
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I've seen that you are using custom implementation of MHA block - https://github.com/NVIDIA/NeMo/blob/da6d825b8c2fec0a3b8e0d2c33a7600f242db4c1/nemo/collections/asr/parts/submodules/multi_head_attention.py#L108
I think it would be better to use `torch.nn.MultiheadAttention` which is meant to improve the performance of this block (there is FlashAttention now) and torch continues to maintain and update this block while you're doing basic implementation of it.
**Describe the solution you'd like**
I'd like to see an update of models with torch implementation of MHA
https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html
What do you think about it?
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} | # What does this PR do ?
Adds the following:
1. `MegatronUGPTModel` which subclasses `MegatronGPTModel` and overrides dataset building, attention mask changes etc.
2. `UGPTDataset` which subclasses UL2Dataset and implements GPT-specific data processing.
3. Megatron U-GPT pre-training/fine-tuning script and YAML config.
**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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# Changelog
- Add the rank of the data worker to the bucketing dataset to keep the randomness for fully_randomzied mode of buceking.
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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- [X] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Adds Longformer-style attention for Conformer encoder (limited context + few tokens with full context attention). Unlike with just limited context attention, when using global tokens model needs to be fine-tuned with this approach to get good results. After fine-tuning results are improved particularly on long audio (even if it's much longer than training data). When using the default Fast Conformer configs with this attention, inference on audio above 1 hour is supported.
**Collection**: ASR
# Changelog
- Add option to use global tokens
- Adds corresponding test
- Configs for training Conformer with this attention and mention in docs
# Usage
Add the following parameters to the Conformer encoder in config:
```
self_attention_model: rel_pos_local_attn # longformer-style attention (sliding window + global tokens)
global_tokens: 1 # number of tokens that attend and are attended to by all tokens
global_attn_separate: false # whether global tokens should use separate q,k,v layers
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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**
It's a similar observation to #6084, but since that one is closed and it's slightly different (plus I have some additional information) I'd like to ask again if this is a bug.
I looked at my training loss / batch WER when using bucketing, and found that the loss and WER follow the same pattern for every epoch, even though I specified `fully_randomized` as the bucketing strategy. Later I discovered that because used `pl.seed_everything()`, all the random generators were initialized the same and therefore all the GPUs use the same buckets. I changed this (not seeding now), and printed the `shuffled order` and `dataset_idx` in `RandomizedChainDataset`'s iter method. I can see that now there's some randomization happening when the datasets are loaded, but the train_loss graph still looks exactly the same as before. If I track the loss in the terminal progress bar, I can also see that for each epoch the loss is about in the same range at a certain point, e.g. 25% into the epoch, the loss is always around 100 whereas 75% it's about 50.
My questions are:
- is this desired behavior? I would expect that for example for 2 buckets, instead of following a pattern every epoch, it would have cycle through a pattern every half epoch if it's fully randomized. And even if it's synced randomized, it should be different every epoch.
- does it have to do with the `global_rank` and how the datasets are loaded at each GPU? I'm not familiar with this and it would be good if someone can explain how this works. Because now I see that when I print the `shuffled_order` at the start of every epoch, and I have 8 GPUs, it is printed 8x8 = 64 times. Is this `num_gpus` x `num_workers`?
- I would have expected the randomization of bucketing to still work despite using a random seed for my experiments. Is that doable?
Some graphs:
For training with 2 buckets, data size about 1000 hrs.
Training loss of the same training, orange is when I used random seed, blue is when I stopped the first run, removed random seed and continued from checkpoint. I see random dataset orders printed out, but the the train loss is exactly the same patter every epoch despite the randomization.
<img width="827" alt="image" src="https://user-images.githubusercontent.com/43883260/226186308-3826aee6-e91c-45bf-bf55-6c7d91d651f5.png">
Training batch WER for the same run. It looks slightly `smoothed out` after I removed the random seed, but I would have expected 2 peaks per epoch for example.
<img width="709" alt="image" src="https://user-images.githubusercontent.com/43883260/226186561-56244d11-5b06-488e-8c98-662d36ecd59f.png">
For training with 4 buckets
<img width="428" alt="image" src="https://user-images.githubusercontent.com/43883260/226186732-45ac67fe-ccd6-4faa-b95e-ee1171cc6503.png">
Here I started the training run again without the random seed (blue line) and with a higher batch size. But still it seems like I can clearly see where one bucket ends and another bucket starts because they have different lengths and therefore different average loss. Even though in the terminal during one epoch I see different `dataset_idx` printed out at different times. This training also doesn't converge, I'm wondering if it only has to do with the longer audio or because of the lack of randomization as well.
**Steps/Code to reproduce bug**
used `[speech_to_text_ctc_bpe.py](https://github.com/NVIDIA/NeMo/blob/a0f584a4480165ff144601336280a2276b43d4bd/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py)` to run the training,
Cfg is conformer_ctc_bpe_streaming.yaml.
4 buckets: batch size = 24, 2 buckets batch size = 60 (bucketing batch size)
8 gpus
8 workers
lr: 2
warmup 10000 / 20000
**Expected behavior**
- Training loss should not be uniformly cyclical per epoch training using multiple GPUs and fully randomized bucketing.
**Environment overview (please complete the following information)**
- Environment location: GCP
- Method of NeMo install: followed the instruction in the readme to install in a conda env. First using pip install, then pip install -e for printing out the dataset_idx.
- 8 GPUs
**Environment details**
python: 3.8.0
pytorch: 1.24.1
pytorch-lightning: 1.8.6
nemo-toolkit: 1.15.0
**Additional context**
GPU: A100 | {
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} | # What does this PR do ?
Correct a bug in waveglow model where you used predefined values for the n_fft and hop_length. The commit aims at retrieving the correct values from the preprocessor
**Collection**: tts
# Changelog
- Retrieve the correct n_fft and hop_length values for the waveglow model
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Further modifications in other modules called used by waveglow may be needed to properly allow to change the preprocessor feature banks values changed and the waveglow model accordingly
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I am trying to train a conformer_ctc_bpe (small) model with librispeech960 using four NVIDIA 2080 GPUs (11GB memory).
But, when training model from scratch, I can't reproduce the reported performance with my experimental settings.
Looking at your training details, it seems that a batch size of 2K (maybe 2,048?) and a learning rate of 5.0 are used.
However, with my GPUs of 11GB, I can't use such a large batch size; the maximum batch size I can use is 12.
Could you please share some guidelines and tips about appropriate training details for my experimental condition..?
(i.e., **batch size**, **learning rate**, **warmup step**, **weight decay**, **accumulate_grad_batches**, etc. or another optimizer / scheduler ..?)
Some configurations I set are as follows
```
model:
sample_rate: 16000
log_prediction: true
ctc_reduction: 'mean_batch'
skip_nan_grad: false
train_ds:
manifest_filepath: "/home/shmun/NeMo/data/train_all_960.json"
sample_rate: 16000
batch_size: 12 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 16.7
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
tokenizer:
dir: "/home/shmun/NeMo/data/tokenizer_ctc_conformer_large_128/"
type: "bpe"
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2
time_masks: 5 #10
freq_width: 27
time_width: 0.05
optim:
name: adamw
lr: 5.0 #2.0
betas: [0.9, 0.98]
weight_decay: 1e-3
sched:
name: NoamAnnealing
d_model: 176
warmup_steps: 10000
warmup_ratio: null
min_lr: 1e-6
``` | {
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} | # What does this PR do ?
Update docstrings for asr beam search computation
**Collection**: [ASR]
# Changelog
- Update train kenlm arg
- Update args for beam search eval
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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Fixes a bug in the ASR bucketing which made the training to hang at the end of first epoch.
**Collection**: ASR
# Changelog
- Fixed the bucketing bug by making the bucketing dataloaders to loop over the samples indefinitely.
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Update docstrings for asr beam search computation
**Collection**: [ASR]
# Changelog
- Update train kenlm arg
- Update args for beam search eval
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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Flashlight improvements and fixes, also fixed potential VRAM leak in ctc_models.transcribe
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# Changelog
- Set logits to CPU in ctc_models.transcribe
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- Removed the unit_lm boolean requirement in flashlight_cfg. It's not necessary, the decoder knows that if you pass a lexicon it should use it, otherwise do lexicon-free mode. There's no need for a boolean too, it's superfluous, the presence of the lexicon file is the only indicator the class needs
# Usage
No changes to how anything is used, except don't pass `unit_lm` anymore to `flashlight_cfg`
# Before your PR is "Ready for review"
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Fix typos in notebooks
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# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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Fix typos in notebooks
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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This PR fixes a small issue when running ALSD beam-search with stateless decoder transducers.
**Collection**: ASR
# Changelog
Add additional checks in alsd beam-search code to handle different decoders differently.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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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?
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- [ ] New Feature
- [x] 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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small bugfixes for rng in augmentor and eval_metrics for asr evaluator
add asr evaluator to doc.
[TODO] add asr evaluator to main page once we merge 1.17.0 to main so we had link to doc.
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# Usage
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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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Bug fixes from https://github.com/NVIDIA/NeMo/pull/6215/
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# Usage
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Fix diarization result path errors in tutorial notebook for `r1.17.0`
Merging the change happened for main in https://github.com/NVIDIA/NeMo/pull/6217 to release branch
**Collection**:
ASR
# Changelog
- Fixed diarization result path errors and tested notebook again.
# Usage
- Run [Speaker_Diarization_Inference.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb) tutorial 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?
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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
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https://api.github.com/repos/NVIDIA/NeMo/issues/6233 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6233/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6233/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6233/events | https://github.com/NVIDIA/NeMo/pull/6233 | 1,628,580,230 | PR_kwDOC_bI7s5MRLdW | 6,233 | [ASR] Re-organizing data simulator with faster audio loading speed for data-simulator | {
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} | # What does this PR do ?
Added a feature to data-simulator that loads audio files faster by limiting the target audio duration.
**Collection**: ASR
# Changelog
- Audio reading functions in `build_sentence` are updated
- Added fully reproducible seeding mechanism for all the randomness.
- Changed all the wav reading functions to `AudioSegment.from_file()`.
- Moved util functions to `data_simulation_utils.py`
- Added unit tests in `test_data_simul_utils.py`
# Usage
For relatively longer audio file sources (e.g. Fisher, Swbd etc) set the following
`data_simulator.session_params.max_audio_read_sec`
to 2.5~3.5 seconds (min is 2.5 sec).
The shorter the faster the simulation is, but it will concatenate lots of words to form a sentence (speaker turn)
For datasets that are already segmented (e.g VoxCeleb, LibriSpeech etc), `max_audio_read_sec` does not need to be short (no speed gains). For already-segmented datasets, I recommend 10~20 seconds.
When we choose 2.5 secs for `data_simulator.session_params.max_audio_read_sec` in case of unsegmented datasets (Fisher, SWBD, TEDtalks, AMI, etc), we can reach 80~90% speed of the segmented datasets (Libri, Vox, etc)
```python
python <NEMO_ROOT>/tools/speech_data_simulator/multispeaker_simulator.py \
num_workers=10 \
data_simulator.random_seed=42 \
data_simulator.manifest_filepath=manifest_with_alignment_file.json \
data_simulator.session_params.max_audio_read_sec=3.0 \
data_simulator.outputs.output_dir=./simulated_data \
data_simulator.outputs.output_filename=sim_spk2_sess20 \
data_simulator.session_config.num_sessions=1000 \
data_simulator.session_config.num_speakers=2 \
data_simulator.session_config.session_length=20 \
data_simulator.background_noise.add_bg=False \
data_simulator.background_noise.background_manifest=background_noise.json \
data_simulator.background_noise.snr=40 \
```
# 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 ASR
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix issue with latest container interaction with Numba RNNT loss
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
fixes contiguous labels exception in RNNT model training
**Collection**: ASR
# Changelog
in losses/rnnt.py:
if not targets.is_contiguous():
targets = targets.contiguous()
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/6229 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6229/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6229/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6229/events | https://github.com/NVIDIA/NeMo/pull/6229 | 1,628,293,317 | PR_kwDOC_bI7s5MQN4W | 6,229 | small bugfix and add asr evaluator to doc | {
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} | # What does this PR do ?
small bugfixes for rng in augmentor and eval_metrics for asr evaluator
add asr evaluator to doc.
[TODO] add asr evaluator to main page once we merge 1.17.0 to main so we had link to doc.
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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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Bug fixes from https://github.com/NVIDIA/NeMo/pull/6215/
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# Usage
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```python
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# 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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- [ ] 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**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
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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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Only update the logits if the current token passes the context length.
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} | # What does this PR do ?
Allow pre-commit to run.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds megatron core support for our T5 model. This only works for pre-training, due to dynamic max lengths in the GLUE/XLNI datasets fine-tuning seems to be broken.
The fix going forward I think will be to pad to the maximum length to ensure the size of sequences are always the same. This is a requirement of the iterator object now used in the `training_step`.
I also need to confirm that the weights of the model are the same between main and this branch.
- [ ] Fix support for GLUE/XLNI Fine-tuning
- [x] Ensure convergence is the same
- [x] Fix support for encode/decode functions
**Collection**: NLP
# 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 | Trying to load AmberNet with the next command:
vad_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="langid_ambernet")
and get download error messages:
[NeMo I 2023-03-16 11:50:19 cloud:81] Download from cloud failed. Attempt 1 of 3
[NeMo I 2023-03-16 11:50:20 cloud:81] Download from cloud failed. Attempt 2 of 3
[NeMo I 2023-03-16 11:50:20 cloud:81] Download from cloud failed. Attempt 3 of 3
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} | # What does this PR do ?
fixed code alert https://github.com/NVIDIA/NeMo/security/code-scanning/871
* fixed alert
* removed unused p_voiced argument.
* remove unsued out_lens argument in func get_first-order-features
* remove unused sigma values for infer function
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/5938
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} | # What does this PR do ?
remove whitelist path.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds support for changing pipeline parallel version post construction for GPT
**Collection**: [Core, NLP]
# Changelog
- Add new script for pp conversion to avoid breaking old script (should probably be deprecated eventually when new script fully supports all functionality)
- Add some modifications to allow partially loading model parallel models into memory to extract parameters
- Add PP TP conversion support for both Megatron GPT and Megatron T5 (only when shared embeddings are used between encoder and decoder)
- Add support for forcing CPU construction of T5 and GPT models
- Add support for loading model based on AppState parameters instead of Trainer parameters.
# Usage
```python
Usage:
# Megatron GPT
python megatron_change_num_partitions.py \
--model_file=PATH_TO_SRC_FILE \
--target_file=PATH_TO_TGT_FILE \
--tensor_model_parallel_size=1 \
--target_tensor_model_parallel_size=1 \
--pipeline_model_parallel_size=1 \
--target_pipeline_model_parallel_size=1 \
--precision=bf16
# Megatron T5
python megatron_change_num_partitions.py \
--model_file=PATH_TO_SRC_FILE \
--target_file=PATH_TO_TGT_FILE \
--model_class="nemo.collections.nlp.models.language_modeling.megatron_t5_model.MegatronT5Model" \
--tensor_model_parallel_size=1 \
--target_tensor_model_parallel_size=1 \
--pipeline_model_parallel_size=1 \
--target_pipeline_model_parallel_size=1 \
--target_pipeline_model_parallel_split_rank=0 \
--precision=bf16
# NOTE: When converting large models, always ensure that you pre-extract the nemo model and then only perform conversion
$ mkdir "unpacked_nemo_file"
$ tar -xvf "<path to nemo file>" -C "<absolute path to pwd>/unpacked_nemo_file/"
python megatron_change_num_partitions.py \
...
--model_extracted_dir="<Absolute path to pwd>/unpacked_nemo_file/"
# NOTE: Conversion of other model types.
# Default model type is MegatronGPTModel, if you want another model you need to pass classpath of the model
# For example - MegatronT5Model -
python megatron_change_num_partitions.py \
...
--model_class="nemo.collections.nlp.models.language_modeling.megatron_t5_model.MegatronT5Model"
# Additional arguments:
--num_gpu_per_node: Number of GPUs per node. Default is 8.
--megatron_legacy: Whether the model is a legacy Megatron model or not. Default is False. May be unsuported for
Pipeline Parallelism change.
--tokenizer_model_path: Path to tokenizer model. Default is None. When not None, overrides the tokenizer model path
in the model config.
--tokenizer_vocab_file: Path to tokenizer vocab file. Default is None. When not None, overrides the tokenizer vocab
file in the model config.
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix diarization result path errors in tutorial notebook
**Collection**:
ASR
# Changelog
- Fixed diarization result path errors and tested notebook again.
# Usage
- Run [Speaker_Diarization_Inference.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb) tutorial 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?
ASR contributors
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} | # What does this PR do ?
remove whitelist path.
**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 ?
Adding interCTC loss to hybrid models. To solve issue that ctc and hybrid models use different names for things that we need (e.g., wer vs ctc_wer), I'm now directly passing all necessary "main" class variables to the interctc_setup method (except for self.cfg). I'm not sure if there is much sense in still keeping interctc as mixin, given that it does not access anything from the main class anymore, but not making any changes for now.
Additionally, there are 2 slight bugs in the original code. First, inter_ctc_loss was incorrectly calculated in the logs (main loss was used, not inter_ctc there). Note that this is purely a logging thing and does not affect training. Second, main loss was not multiplied by the correct coefficient, which does affect training to some extent. So, the calculation should have been something like `loss = final_loss * 0.7 + inter_loss * 0.3`, while it was just `loss = final_loss + inter_loss * 0.3`
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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.
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**Collection**: TTS
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Doc fixes and typos for tutorials
**Collection**: [ASR]
# Changelog
- Fix incorrect line end of ASR adapters
- Fix typos
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
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} | # What does this PR do ?
Doc fixes and typos for tutorials
**Collection**: [ASR]
# Changelog
- Fix incorrect line end of ASR adapters
- Fix typos
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
When using spawn, the dataloader must have persistent_workers=True, otherwise validation will be very slow (all workers will be reinitialized every validation)
**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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} | # What does this PR do ?
Adds the ability to fine-tune Megatron GPT Models.
**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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