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TabularTokenizer and ColumnCode api needs QOL improvements. It currently does not support partial row generation because of [this](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/tokenizers/tabular_tokenizer.py#L132) and [this](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/tokenizers/tabular_tokenizer.py#L164), impacting certain generative applications. because it expects all the columns and the columns to be in order.
**Describe the solution you'd like**
- add 'best' transform into FloatCode, and include new transforms, with some simple stats/heuristics for selecting a 'best' transform
- refactor column_coder.py into datatype_code and column_code to separate out the datatypes that a column has from the column_code.
- refactor Code into a code_spec, and add a NumericCode base class inheriting from Code. Push fill_all and has_nan into NumericCode as those params are only useful for numeric dataytypes
- add a partial row encoder and decoder for the tabular tokenizer
- rename some class methods to be more clear, ex. array_convert_to_int to transform_data_to_int_array to more accurately describe the function
- Fix `bug` where the [FloatCode accuracy ](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/tokenizers/column_coder.py#L231) leads to cases where the decoded output is less accurate than it needed to be.
- remove `column_coder.py` and replace it with `datatype_code`. Uses `code` over `coder` since `code` is for a mapping of the dtype col itself.
- write tests
A clear and concise description of what you want to happen.
Tests will clarify usage. ex.
```
from datatype_code import FloatCode
float_code = FloatCode('name', code_len=3, start_id=0, fill_all=True, base=100, has_nan=True, transform='identity')
float_code.compute_code(data_series)
```
This keeps the new code that users will interface with inline with the current API.
**Describe alternatives you've considered**
putting `compute_code` into the into a `fit_transform`, and encode as `transform` and decode as `inverse_transform` to make the API similar to sklearn.
A clear and concise description of any alternative solutions or features you've considered.
- instead of tokenizing the text token by token. Group the tokens by column and tokenize by column in a vectorizable fashion. This speeds up decoding a lot. Even converting to a DataFrame and doing a `df.replace` or `map` will speed up over existing method. The caveat is this will not allow for partial row generation.
- other vector based methods or parallel lookup methods to speed up encoding and decoding as this consumes quite a bit of time.
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} | # What does this PR do ?
Automatically sets O2=False when running inference with fp16.
**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 ?
Fix for T5 FT model tensor shape
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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This restores support for interleaved pipeline parallelism after https://github.com/NVIDIA/NeMo/pull/6393.
**Collection**: NLP
# Changelog
- Restore custom synchronization logic to GPT training and validation steps
- Fix bug where all model chunks in first pipeline stage were created with embeddings
- Support dynamic attention masks in pipeline parallelism
# Usage
Set `virtual_pipeline_model_parallel_size > 1`:
https://github.com/NVIDIA/NeMo/blob/5468077f5127be1a4c88065de2544f4268b9a6e4/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L51
This requires `pipeline_model_parallel_size > 2` and `virtual_pipeline_model_parallel_size*pipeline_model_parallel_size` divides `num_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)
- [ ] 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.
Pinging @aklife97.
# Additional Information
* Restores functionality broken in https://github.com/NVIDIA/NeMo/pull/6393
* Depends on https://github.com/NVIDIA/Megatron-LM/pull/331 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6527 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6527/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6527/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6527/events | https://github.com/NVIDIA/NeMo/pull/6527 | 1,691,553,347 | PR_kwDOC_bI7s5PipFG | 6,527 | Updating num_workers in lm_encoder_decoder | {
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} | # What does this PR do ?
Updating the num_workers from 0 to the one mentioned in the 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**:
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/6526 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6526/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6526/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6526/events | https://github.com/NVIDIA/NeMo/pull/6526 | 1,691,378,876 | PR_kwDOC_bI7s5PiBxK | 6,526 | Purely GPU-based optimized RNN-T greedy batched decoding | {
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A highly optimized implementation for RNN-T greedy batched decoding with GPUs.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
The method could be invoked by setting rnnt_decoding.cuda_optimized as True, e.g.
```
python examples/asr/transcribe_speech.py \
model_path=$model \
dataset_manifest=$manifest \
rnnt_decoding.strategy=greedy_batch \
+rnnt_decoding.cuda_optimized=True \
batch_size=$batch \
output_filename=$out_file
```
This gives significant inference speed-up for large batch sizes. E.g. Librispeech Transducer-large model with stateless decoders running inference on test-other, with batch=128,
| inference type | WER | decoding time |
| --------------- | ----- | --------------- |
| baseline greedy batch | 4.70 | 119s |
| GPU optimized | 4.64 | 86s |
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6525 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6525/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6525/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6525/events | https://github.com/NVIDIA/NeMo/pull/6525 | 1,691,152,748 | PR_kwDOC_bI7s5PhPeo | 6,525 | Update quartznet_15x5.yaml | {
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} | # What does this PR do ?
tb logging leads to error
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 ?
Removing casts around norms from the set of default pre-export replacements and moving them to a separate function.
Latest TRT should not need that anymore - also the casts may prevent some fusion.
That function may still be used from Nemo2Riva explicitly should it be needed on case-by-case basis.
**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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* Related to # (issue)
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} | # What does this PR do ?
Fix typos
**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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} | null | null | null | **Describe the bug**
When training FastPitch on more than 1 GPU the following error occurs:
`AttributeError: Can't pickle local object 'FilterbankFeatures.__init__.<locals>.<lambda>'`
docker run command:
```
python fastpitch.py --config-name=fastpitch_align_v1.05.yaml train_dataset=/path/to/manifest.json validation_datasets=/path/to/manifest.json sup_data_path=/path/to/wav exp_manager.exp_dir=path/to/dir trainer.max_epochs=10 trainer.check_val_every_n_epoch=1 model.train_ds.dataloader_params.batch_size=70 model.validation_ds.dataloader_params.batch_size=30 model.n_speakers=1 model.pitch_mean=152.3 model.pitch_std=64.0 model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=1e-1 trainer.devices=6 trainer.strategy=null
```
**Environment details**
Docker image: nvcr.io/nvidia/nemo:23.01
GPU model: NVIDIA A100-SXM | {
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} | # What does this PR do ?
Correct a few typographic errors and make a few cosmetic suggestions to markup.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Corrected typographical errors in some of the NLP documentation.
# Usage
Not applicable.
# 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
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/6520 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6520/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6520/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6520/events | https://github.com/NVIDIA/NeMo/issues/6520 | 1,689,565,972 | I_kwDOC_bI7s5ktLsU | 6,520 | ModuleNotFoundError: No module named '_swig_decoders' | {
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} | null | null | null | hello
when i try to install beam search decoders and kenlm to build a language model for asr i face this error:
`
(asr) root@DESKTOP-GD87QE2:/home/azad/decoders# ./setup.sh
Install decoders ...
scorer.h:22: Warning 401: Nothing known about base class 'lm::EnumerateVocab'. Ignored.
running install
/root/anaconda3/envs/asr/lib/python3.9/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
/root/anaconda3/envs/asr/lib/python3.9/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
running bdist_egg
running egg_info
writing ctc_decoders.egg-info/PKG-INFO
writing dependency_links to ctc_decoders.egg-info/dependency_links.txt
writing top-level names to ctc_decoders.egg-info/top_level.txt
reading manifest file 'ctc_decoders.egg-info/SOURCES.txt'
writing manifest file 'ctc_decoders.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
copying swig_decoders.py -> build/lib.linux-x86_64-cpython-39
running build_ext
building '_swig_decoders' extension
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c ctc_beam_search_decoder.cpp -o build/temp.linux-x86_64-cpython-39/ctc_beam_search_decoder.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c ctc_greedy_decoder.cpp -o build/temp.linux-x86_64-cpython-39/ctc_greedy_decoder.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c decoder_utils.cpp -o build/temp.linux-x86_64-cpython-39/decoder_utils.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c decoders_wrap.cxx -o build/temp.linux-x86_64-cpython-39/decoders_wrap.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
In file included from decoder_utils.cpp:1:
decoder_utils.h:5:10: fatal error: fst/log.h: No such file or directory
5 | #include "fst/log.h"
| ^~~~~~~~~~~
compilation terminated.
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c kenlm/lm/bhiksha.cc -o build/temp.linux-x86_64-cpython-39/kenlm/lm/bhiksha.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
In file included from ctc_greedy_decoder.cpp:2:
decoder_utils.h:5:10: fatal error: fst/log.h: No such file or directory
5 | #include "fst/log.h"
| ^~~~~~~~~~~
compilation terminated.
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c kenlm/lm/binary_format.cc -o build/temp.linux-x86_64-cpython-39/kenlm/lm/binary_format.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
In file included from scorer.h:14,
from ctc_beam_search_decoder.h:8,
from ctc_beam_search_decoder.cpp:1:
path_trie.h:10:10: fatal error: fst/fstlib.h: No such file or directory
10 | #include "fst/fstlib.h"
| ^~~~~~~~~~~~~~
compilation terminated.
gcc -pthread -B /root/anaconda3/envs/asr/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -I/root/anaconda3/envs/asr/include -fPIC -O2 -isystem /root/anaconda3/envs/asr/include -fPIC -I. -Ikenlm -Iopenfst-1.6.3/src/include -IThreadPool -I/root/anaconda3/envs/asr/include/python3.9 -c kenlm/lm/config.cc -o build/temp.linux-x86_64-cpython-39/kenlm/lm/config.o -O3 -DNDEBUG -DKENLM_MAX_ORDER=6 -std=c++11 -DHAVE_ZLIB -DHAVE_BZLIB -DHAVE_XZLIB
error: command '/usr/bin/gcc' failed with exit code 1
In file included from scorer.h:14,
from decoders_wrap.cxx:2840:
path_trie.h:10:10: fatal error: fst/fstlib.h: No such file or directory
10 | #include "fst/fstlib.h"
| ^~~~~~~~~~~~~~
compilation terminated.
Testing installation ...
kenlm/lm/binary_format.cc: In member function ‘void lm::ngram::BinaryFormat::FinishFile(const lm::ngram::Config&, lm::ngram::ModelType, unsigned int, const std::vector<long unsigned int>&)’:
kenlm/lm/binary_format.cc:261:9: warning: ‘void* memset(void*, int, size_t)’ clearing an object of type ‘struct lm::ngram::Parameters’ with no trivial copy-assignment; use assignment or value-initialization instead [-Wclass-memaccess]
261 | memset(¶ms, 0, sizeof(Parameters));
| ~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from kenlm/lm/binary_format.cc:1:
kenlm/lm/binary_format.hh:42:8: note: ‘struct lm::ngram::Parameters’ declared here
42 | struct Parameters {
| ^~~~~~~~~~
Traceback (most recent call last):
File "/home/azad/decoders/ctc_decoders_test.py", line 5, in <module>
from ctc_decoders import Scorer, ctc_beam_search_decoder
File "/home/azad/decoders/ctc_decoders.py", line 6, in <module>
import swig_decoders
File "/home/azad/decoders/swig_decoders.py", line 15, in <module>
import _swig_decoders
ModuleNotFoundError: No module named '_swig_decoders'`
any help is really appreciated.
thanks | {
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} | # What does this PR do ?
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} | # What does this PR do ?
forward_torch_softmax gives error difference in apex only when mask is set.
default to normal behavior when mask is None
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# 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)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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## 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
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# Usage
* You can potentially add a usage example below
```python
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# Before your PR is "Ready for review"
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This bug occurs on a specific dataset where the data loading process mistakenly checks for `<` instead of `<=` in an assert statement, causing the "last epoch number of samples exceeded max value" error message to be falsely displayed.
**Steps/Code to reproduce bug**
1. Prepare a dataset and configure the GPT training. The size of the model is not important.
2. Start the training process.
3. During the data loading phase, the issue arises.
This issue does not occur on all datasets. It is difficult to specify which datasets it occurs on.
**Expected behavior**
If the training process excludes the dataset where the issue occurs, it proceeds normally.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal with slurm and enroot
- Method of NeMo install: `docker pull nvcr.io/ea-bignlp/bignlp-training:23.03-py3`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Additional context**
It appears that https://github.com/NVIDIA/NeMo/blob/65db9359bcb1cee96ffe13f36325f66bb8f17fd5/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py#L588-L590 is causing the issue. It should work properly if `<=` is used instead. | {
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} | # What does this PR do ?
forward_torch_softmax gives error difference in apex only when mask is set.
default to normal behavior when mask is None
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Update the energy prediction logic in FastPitch to match the new conditioning logic, similar to pitch. Current code throws a syntax error because `prosody_input `is no longer defined.
**Collection**: [TTS]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Add support for using dynamic length batches for GPT SFT
Significantly improves perf
~2 it/s to ~4 it/s on a local Quadro RTX 6000 GPUs with the same test as on CI
**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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
The existing TTS workflow requires the user to provide speaker as an integer index in the training manifest. This is awkward because
1. User can provide raw ID from datasets like LibriTTS as an integer, but then we end up creating 10,000+ element speaker array for only hundreds of speakers.
2. User can manually create a map from speaker ID string to sequential integer index and then overwrite their training manifest.
3. Training with multiple manifests is awkward because you need to rewrite the speaker field for all manifests.
So the idea is for the new data loading logic, user will provide speaker as a string name/ID in the manifest and then provide a separate JSON file which maps the speaker IDs to integer IDs. The below script is an example of how to create that map.
**Collection**: [TTS]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
This script is similar to `compute_speaker_stats.py` but uses the new experimental featurizer class for saving and loading the features. We can consider deprecating `compute_speaker_stats.py` in the future if we end up deprecating existing TTS data loader and `extract_sup_data.py`.
**Collection**: [TTS]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add an interface and initial implementations to process features at training time, as part of TTS data loading refactoring.
The intention is for the data loader to load and merge feature dictionaries (produced by tts.parts.preprocessing.feature.py) and to use these processors to apply on necessary on the fly calculations, such as feature normalization.
I can provide a diff for the data loader itself if it helps provide context. Otherwise I will post PR for the data loader after this one, as it depends on this change.
**Collection**: [TTS]
# Changelog
- Define interface to processing dataset features
- Provide implementation for feature scaling, log compression, and mean-variance normalization.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do?
Current impulse response (IR) in `ImpulsePerturbation` is not correct.
This PR fixes the issue.
Check below for
1. Audio examples
2. IR examples
3. Scripts to reproduce the issue
**Collection**: ASR
# Changelog
- Fixed normalization to correctly normalize mean and max
- Added a new flag to make IR normalization optional
- Default to not using impulse normalization
1. This doesn't negatively impact the current behavior, since currently IR is incorrectly normalized
2. Normalization is in general not necessary for IRs, especially since output data is normalized to max amplitude 1
3. If necessary, normalization can be turned on to remove the mean from the IR.
- fixed convolution and impulse shifting
- `shift_impulse=False` used `same` convolution, which centered the output with respect to `full`. This resulted in an output which was not aligned with the RIR. This is now done by computing the full convolution and trimming the output. This is a bugfix.
- `shift_impulse=True` used trimmed RIR. This is now done by convolving with the full RIR and shifting the output. This is a minor change.
# Detailed description
Current impulse response (IR) in `ImpulsePerturbation` is not correct.
More specifically, it introduces a DC offset in the normalized IR, resulting in an incorrect signal.
It's obvious from listening to the output audio that it doesn't correspond to a realistic IR.
## Example scripts
* [rir_normalization_issue_example.zip](https://github.com/NVIDIA/NeMo/files/11339425/rir_normalization_issue_example.zip)
To run, unpack the archive and run
```python
python example.py
```
## RIR example
This figure shows an original IR, the normalized IR with the current normalization and the normalized IR with the new normalization (in this PR).
The current normalization introduces a large DC offset.
The new normalization makes the RIR zero mean, and scales the max amplitude to 1.
## Audio examples
### Output audio with the current normalization
* [output_cur.wav.zip](https://github.com/NVIDIA/NeMo/files/11339429/output_cur.wav.zip)
### Output audio with the new normalization
* [output_new.wav.zip](https://github.com/NVIDIA/NeMo/files/11339432/output_new.wav.zip)
## Test with different setups
Scripts
[rir_normalization_issue_example_with_shift.zip](https://github.com/NVIDIA/NeMo/files/11346247/rir_normalization_issue_example_with_shift.zip)
In this example, the RIR introduces a 0.5 second delay.
This was artificially constructed to test the setup with `shift_impulse`.
We expect the following
- `shift_impulse=False`: The output should be delayed by 0.5s due to delay in the RIR
- `shift_impulse=True`: The output should not include the 0.5s delay
### Input signal
### Output signals
| | `shift_impulse=False` | `shift_impulse=True` |
| ---------------------------- | ---------------------- | --------------------- |
| `normalize_impulse=False` |  |  |
| `normalize_impulse=True` |  |  |
# 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
- [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 #6429
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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The notebook in https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Offline_ASR.ipynb cannot be visualized as it says "The Notebook Does Not Appear to Be Valid JSON"
**Steps/Code to reproduce bug**
Open https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Offline_ASR.ipynb
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} | When using tar dataset this error appears due to undefined reference:
datastore_path_to_webdataset_url(p) if is_datastore_path(p) and is_tarred_path(p) else p NameError: name 'is_tarred_path' is not defined
# What does this PR do ?
fixes error mentioned above
Add a one line overview of what this PR aims to accomplish.
**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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | …stamps generators
# What does this PR do ?
The argument of `_get_batch_preds` in `FrameBatchASRLogits` class has not been updated when `FrameBatchASR` is updated.
Now, this PR force `_get_batch_preds()` to keep logits in decoder timestamps generators.
This is for r1.18.0 of PR #6499.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added `force_keep_logits` for `_get_batch_preds` function in class `FrameBatchASRLogits` in `decoder_timestamp_utils.py`
- Changed class name `FrameBatchASR_Logits` to `FrameBatchASRLogits`
# Usage
* You can potentially add a usage example below
```python
python offline_diar_with_asr_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.speaker_embeddings.parameters.save_embeddings=False
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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?
ASR contributors
# Additional Information
This is for main, and there will be another PR for r1.18
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} | # What does this PR do ?
The argument of `_get_batch_preds` in `FrameBatchASRLogits` class has not been updated when `FrameBatchASR` is updated.
Now, this PR force `_get_batch_preds()` to keep logits in decoder timestamps generators.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added `force_keep_logits` for `_get_batch_preds` function in class `FrameBatchASRLogits` in `decoder_timestamp_utils.py`
- Changed class name `FrameBatchASR_Logits` to `FrameBatchASRLogits`
# Usage
* You can potentially add a usage example below
```python
python offline_diar_with_asr_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.speaker_embeddings.parameters.save_embeddings=False
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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?
ASR contributors
# Additional Information
This is for main, and there will be another PR for r1.18
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} | # What does this PR do ?
Add support for virtual pipeline parallelism conversion of Megatron GPT models
**Collection**: [NLP]
# Changelog
-
# Usage
```python
python megatron_change_num_partitions.py \
--model_extracted_dir="<Directory of Pytorch Lightning ckpt folders>" \
--target_file="<Name of the target NeMo file>" \
--ckpt_name="<Name of a single Pytorch Lightning ckpt file inside the extracted dir>" \
--tensor_model_parallel_size=<TP SIZE> \
--target_tensor_model_parallel_size=<TARGET TP SIZE> \
--pipeline_model_parallel_size=<PP SIZE> \
--target_pipeline_model_parallel_size=<TARGET PP SIZE> \
--virtual_pipeline_model_parallel_size=<VP SIZE> \
--hparams_file="<Path to HPARAMS.yaml file>" \
--precision=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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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How to create pickle files, which include out, output_sizes, conv, rnn_0, rnn_1, rnn_2, rnn_3, rnn_4 layers using the STT De Conformer-Transducer Large for an german audio files samples?
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} | # What does this PR do ?
**Collection**: [ASR]
# Changelog
- Add disclaimer to all ASR tutorials regarding dataset 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
- [ ] Bugfix
- [x] Documentation
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**Collection**: [Doc]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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This PR adds a quickstart tutorial for training a simple speech enhancement model with NeMo.
**Collection**: ASR, Tutorials
# Changelog
- added a simple tutorial for speech enhancement
- moved examples from `asr` to `audio_tasks`
- added an option to save noise in `add_noise.py`
- minor fixes in `audio_models`
# Usage
Check `Speech_Enhancement_with_NeMo.ipynb`.
Tested locally and on Google Colab with GPU runtime.
# 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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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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- [ ] 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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[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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- [ ] 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/6488 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6488/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6488/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6488/events | https://github.com/NVIDIA/NeMo/pull/6488 | 1,683,609,018 | PR_kwDOC_bI7s5PIGjl | 6,488 | Update README to add core installation | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/6488",
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"merged_at": "2023-04-25T18:09:43"
} | # What does this PR do ?
Update README to add core installation
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/6487 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6487/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6487/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6487/events | https://github.com/NVIDIA/NeMo/issues/6487 | 1,682,805,226 | I_kwDOC_bI7s5kTZHq | 6,487 | WFST for inverse text normalization (ITN) | {
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} | null | null | null | **Describe the bug**
when i declare some states with pynini.string_map it cant find the shortest pass of its inverse for no reason
**Steps/Code to reproduce bug**
```
import pynini
from pynini.lib import pynutil
def apply_fst(text, fst):
""" Given a string input, returns the output string
produced by traversing the path with lowest weight.
If no valid path accepts input string, returns an
error.
"""
try:
print(pynini.shortestpath(text @ fst).string())
return pynini.shortestpath(text @ fst).string()
except pynini.FstOpError:
print(f"Error: No valid output with given input: '{text}'")
teens = pynini.string_map([
("ده", "10"),
("یازده", "11"),
("دوازده", "12"),
("سیزده", "13"),
("چهارده", "14"),
("چاهارده", "14"),
("پونزده", "15"),
("1پا نزده", "5"),
("1پانزده", "5"),
("شانزده", "16"),
("1شونزده", "6"),
("1هفده", "7"),
("1هیفده", "7"),
("هجده", "18"),
("هیجده", "18"),
("19", "نوزده")
])
graph_tens = teens
apply_fst("یازده", graph_tens)
apply_fst("دوازده", graph_tens)
apply_fst("سیزده", graph_tens)
apply_fst("چهارده", graph_tens)
apply_fst("چاهارده", graph_tens)
apply_fst("پونزده", graph_tens)
apply_fst("پانزده", graph_tens)
apply_fst("شانزده", graph_tens)
apply_fst("شونزده", graph_tens)
apply_fst("هفده", graph_tens)
apply_fst("هیفده", graph_tens)
apply_fst("هیجده", graph_tens)
apply_fst("هجده", graph_tens)
apply_fst("نوزده", graph_tens)
```
**Expected behavior**
11
12
13
14
14
15
15
16
16
17
17
18
18
19
**produced output**
11
12
13
14
14
15
Error: No valid output with given input: 'پانزده'
16
Error: No valid output with given input: 'شونزده'
Error: No valid output with given input: 'هفده'
Error: No valid output with given input: 'هیفده'
18
18
Error: No valid output with given input: 'نوزده'
Error: No valid output with given input: 'یک'
ERROR: StringFstToOutputLabels: Invalid start state
ERROR: StringFstToOutputLabels: Invalid start state
ERROR: StringFstToOutputLabels: Invalid start state
ERROR: StringFstToOutputLabels: Invalid start state
ERROR: StringFstToOutputLabels: Invalid start state
ERROR: StringFstToOutputLabels: Invalid start state
Process finished with exit code 0
**Environment overview (please complete the following information)**
- Environment location: my pc
- Method of NeMo install: conda install pynini
-
**Environment details**
- OS version : ubuntu 20
- PyTorch version
- Python version : 3.8
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} | null | null | null | **Describe the bug**
I'm following the tutorial to finetune the Fastpitch and hifigan models through the NeMo 23.02 container. After updating paths, following the tutorial and fine tuning both models, I couldn't make inferences from the models or export nemo models to riva.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
Download docker image by: docker pull nvcr.io/nvidia/nemo:23.02
Running the NeMo 23.02 docker container by
sudo docker run --runtime=nvidia -it --name nemo -v /dev/shm:/dev/shm --shm-size=16g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 - -ulimit stack=67108864 nvcr.io/nvidia/nemo:23.02
Download the NeMo-main folder from github inside the container
Download the fine-tuning notebook tutorial
**Expected behavior**
when executing this cell:
hifigan_nemo_file_path = '/workspace/nemo/finetuning/NeMo/results_dir/HifiGan/2023-04-25_03-04-02/checkpoints/HifiGan.nemo'
hifigan_riva_file_path = hifigan_nemo_file_path[:-5]+".riva"
fastpitch_nemo_file_path = '/workspace/nemo/finetuning/NeMo/results_dir/FastPitch/2023-04-25_01-27-33/checkpoints/FastPitch.nemo'
fastpitch_riva_file_path = fastpitch_nemo_file_path[:-5]+".riva"
!nemo2riva --out {fastpitch_riva_file_path} --key=nemotoriva {fastpitch_nemo_file_path}
!nemo2riva --out {hifigan_riva_file_path} --key=nemotoriva {hifigan_nemo_file_path}
However i got this error:
INFO: Created a temporary directory at /tmp/tmp04gd3x52
INFO: Writing /tmp/tmp04gd3x52/_remote_module_non_scriptable.py
[NeMo W 2023-04-25 03:39:33 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 2023-04-25 03:39:33 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 2023-04-25 03:39:33 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 2023-04-25 03:39:33 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 2023-04-25 03:39:33 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 2023-04-25 03:39:33 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 I 2023-04-25 03:39:33 nemo2riva:44] Logging level set to 20
[NeMo I 2023-04-25 03:39:33 convert:41] Restoring NeMo model from '/workspace/nemo/finetuning/NeMo/results_dir/FastPitch/2023-04-25_01-27-33/checkpoints/FastPitch.nemo'
WARNING: Logging before flag parsing goes to stderr.
I0425 03:39:33.679497 140719000115008 setup.py:163] GPU available: True (cuda), used: False
I0425 03:39:33.679834 140719000115008 setup.py:166] TPU available: False, using: 0 TPU cores
I0425 03:39:33.679970 140719000115008 setup.py:169] IPU available: False, using: 0 IPUs
I0425 03:39:33.680056 140719000115008 setup.py:172] HPU available: False, using: 0 HPUs
[NeMo W 2023-04-25 03:39:33 nemo_logging:349] /usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.
rank_zero_warn(
[NeMo W 2023-04-25 03:40:05 fastpitch:209] This checkpoint support will be dropped after r1.18.0.
[NeMo E 2023-04-25 03:40:05 common:506] Model instantiation failed!
Target class: nemo.collections.tts.models.fastpitch.FastPitchModel
Error(s): Error locating target 'nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p', see chained exception above.
full_key: text_tokenizer.g2p
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 639, in _locate
obj = getattr(obj, part)
AttributeError: module 'nemo.collections.tts.g2p' has no attribute 'models'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 645, in _locate
obj = import_module(mod)
File "/usr/lib/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 973, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nemo.collections.tts.g2p.models'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 134, in _resolve_target
target = _locate(target)
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 648, in _locate
raise ImportError(
ImportError: Error loading 'nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p':
ModuleNotFoundError("No module named 'nemo.collections.tts.g2p.models'")
Are you sure that 'models' is importable from module 'nemo.collections.tts.g2p'?
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/common.py", line 485, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/tts/models/fastpitch.py", line 100, in __init__
self._setup_tokenizer(cfg)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/tts/models/fastpitch.py", line 224, in _setup_tokenizer
text_tokenizer_kwargs["g2p"] = instantiate(cfg.text_tokenizer.g2p, **g2p_kwargs)
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
return instantiate_node(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 325, in instantiate_node
_target_ = _resolve_target(node.get(_Keys.TARGET), full_key)
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 139, in _resolve_target
raise InstantiationException(msg) from e
hydra.errors.InstantiationException: Error locating target 'nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p', see chained exception above.
full_key: text_tokenizer.g2p
[NeMo E 2023-04-25 03:40:05 convert:56] Failed to restore model from NeMo file : /workspace/nemo/finetuning/NeMo/results_dir/FastPitch/2023-04-25_01-27-33/checkpoints/FastPitch.nemo. Please make sure you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/usr/local/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/cli/nemo2riva.py", line 55, in nemo2riva
Nemo2Riva(args)
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/convert.py", line 61, in Nemo2Riva
raise e
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/convert.py", line 54, in Nemo2Riva
model = ModelPT.restore_from(restore_path=nemo_in, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/modelPT.py", line 436, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/usr/local/lib/python3.8/dist-packages/nemo/core/connectors/save_restore_connector.py", line 239, in restore_from
loaded_params = self.load_config_and_state_dict(
File "/usr/local/lib/python3.8/dist-packages/nemo/core/connectors/save_restore_connector.py", line 162, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/common.py", line 507, in from_config_dict
raise e
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/common.py", line 499, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data
INFO: Created a temporary directory at /tmp/tmprpbstve7
INFO: Writing /tmp/tmprpbstve7/_remote_module_non_scriptable.py
[NeMo W 2023-04-25 03:40:12 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 2023-04-25 03:40:12 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 2023-04-25 03:40:12 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 2023-04-25 03:40:13 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 2023-04-25 03:40:13 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 2023-04-25 03:40:13 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 I 2023-04-25 03:40:13 nemo2riva:44] Logging level set to 20
[NeMo I 2023-04-25 03:40:13 convert:41] Restoring NeMo model from '/workspace/nemo/finetuning/NeMo/results_dir/HifiGan/2023-04-25_03-04-02/checkpoints/HifiGan.nemo'
WARNING: Logging before flag parsing goes to stderr.
I0425 03:40:14.087827 140341964519232 setup.py:163] GPU available: True (cuda), used: False
I0425 03:40:14.088244 140341964519232 setup.py:166] TPU available: False, using: 0 TPU cores
I0425 03:40:14.088342 140341964519232 setup.py:169] IPU available: False, using: 0 IPUs
I0425 03:40:14.088403 140341964519232 setup.py:172] HPU available: False, using: 0 HPUs
[NeMo W 2023-04-25 03:40:14 nemo_logging:349] /usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.
rank_zero_warn(
[NeMo W 2023-04-25 03:40:14 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
dataset:
_target_: nemo.collections.tts.data.tts_dataset.VocoderDataset
manifest_filepath: data_dir/es_cl_female/mels/merged_train_mel.json
sample_rate: 22050
n_segments: 8192
max_duration: null
min_duration: 0.75
load_precomputed_mel: true
hop_length: 256
dataloader_params:
drop_last: false
shuffle: true
batch_size: 32
num_workers: 4
pin_memory: true
[NeMo W 2023-04-25 03:40:14 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
dataset:
_target_: nemo.collections.tts.data.tts_dataset.VocoderDataset
manifest_filepath: data_dir/es_cl_female/mels/merged_val_mel.json
sample_rate: 22050
n_segments: 66048
max_duration: null
min_duration: 3
load_precomputed_mel: true
hop_length: 256
dataloader_params:
drop_last: false
shuffle: false
batch_size: 16
num_workers: 4
pin_memory: true
[NeMo I 2023-04-25 03:40:14 features:287] PADDING: 0
[NeMo I 2023-04-25 03:40:14 features:295] STFT using exact pad
[NeMo I 2023-04-25 03:40:14 features:287] PADDING: 0
[NeMo I 2023-04-25 03:40:14 features:295] STFT using exact pad
[NeMo I 2023-04-25 03:40:16 save_restore_connector:247] Model HifiGanModel was successfully restored from /workspace/nemo/finetuning/NeMo/results_dir/HifiGan/2023-04-25_03-04-02/checkpoints/HifiGan.nemo.
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-tkc-exported-bert.yaml for nemo.collections.nlp.models.TokenClassificationModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-pc-exported-bert.yaml for nemo.collections.nlp.models.PunctuationCapitalizationModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdecctcmodel.yaml for nemo.collections.asr.models.EncDecCTCModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-hifiganmodel.yaml for nemo.collections.tts.models.HifiGanModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-isc-exported-bert.yaml for nemo.collections.nlp.models.IntentSlotClassificationModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-fastpitchmodel.yaml for nemo.collections.tts.models.FastPitchModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-mt-exported-encdecmtmodel.yaml for nemo.collections.nlp.models.MTEncDecModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-qa-exported-bert.yaml for nemo.collections.nlp.models.QAModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-tc-exported-bert.yaml for nemo.collections.nlp.models.TextClassificationModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-scr-exported-encdecclsmodel.yaml for nemo.collections.asr.models.classification_models.EncDecClassificationModel
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdectcmodelbpe.yaml for nemo.collections.asr.models.EncDecCTCModelBPE
[NeMo I 2023-04-25 03:40:16 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-radttsmodel.yaml for nemo.collections.tts.models.RadTTSModel
[NeMo I 2023-04-25 03:40:16 schema:193] Found validation schema for nemo.collections.tts.models.HifiGanModel at /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-hifiganmodel.yaml
[NeMo I 2023-04-25 03:40:16 schema:222] Checking installed NeMo version ... 1.17.0 OK (>=1.1)
[NeMo I 2023-04-25 03:40:16 artifacts:65] Found model at ./model_weights.ckpt
[NeMo E 2023-04-25 03:40:16 artifacts:112] nemo2riva's get_artifacts() was called but was unable to continue due to missing modules. Please ensure that nemo_toolkit and it's dependencies are all installed before re-running nemo2riva.
Traceback (most recent call last):
File "/usr/local/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/cli/nemo2riva.py", line 55, in nemo2riva
Nemo2Riva(args)
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/convert.py", line 85, in Nemo2Riva
artifacts, manifest = get_artifacts(restore_path=nemo_in, model=model, passphrase=key, **patch_kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/artifacts.py", line 117, in get_artifacts
if isinstance(_HAVE_PATCHES_ERROR_MSG, exception):
TypeError: isinstance() arg 2 must be a type or tuple of types
**Additional context**
I'm using a T4 in AWS
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} | # What does this PR do ?
Add more detail to SDP section in NeMo docs, and add link to that page in the NeMo README.rst.
**Collection**: docs
# Changelog
- update SDP section in NeMo docs
- update NeMo README
# 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)
- [N/A] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes some bugs to support Hybrid and FastConformer variants of cache-aware streaming models.
# Changelog
- Fixed a bug which prevented switching between decoders for Hybrid models
- Added support of Hybrid models to the cache-aware streaming inference scripts
- Added the feature to switch between decoders to the cache-aware streaming simulation script
- Added support of causal downsamling for dw_striding approach.
# Usage
Users may use the --set_decoder "rnnt" or "ctc" to change the decoder with speech_to_text_cache_aware_streaming_infer.py.
They may use asr_model.change_decoding_strategy(decoder_type='ctc' or 'rnnt') to change the decoding for a Hybrid model in general.
**PR Type**:
- [X] New Feature
- [X] Bugfix
- [ ] Documentation
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} | This PR adds support in external sample mapping (to be able to compute on the fly).
# What does this PR do ?
The added support does not add any new functionality, but allows providing the sample mapping externally to be able to avoid loading dataset samples.
**Collection**: Megatron `dataset_utils`.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] 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 ?
Set garbage collection interval manually. This helps align garbage collection execution across GPUs, which mitigates CPU overhead dramatically.
Using an integer value for `gc_interval` set the garbage collection interval in global-batch steps.
# 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 ?
Removes the no longer unnecessary avoid_bfloat16_autocast_context in conv2d subsampling
**Collection**: ASR
# Changelog
In `nemo/collections/asr/parts/submodules/subsampling.py`, no longer wrapping conv2d calls in the avoid... context
# Usage
Use bf16 precision as usual, performance should improve a few % because the conv2d subsampling is no longer wrapped in fp32.
Performance in fp16 and fp32 is not affected.
```python
n/a
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds support to KERPLE (https://arxiv.org/abs/2205.09921) positional embeddings for Megatron encoder-decoder classes.
**Collection**: nlp megatron
# Changelog
- Added implementation of KERPLE
- Added Jenkins test
- Added comment to document feature in YAML file (`examples/nlp/language_modeling/conf/megatron_model_base_config.yaml`)
# Usage
Set config
```
model.encoder.position_embedding_type: 'kerple'
model.decoder.position_embedding_type: 'kerple'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 post attention for conformer and off pos_emb computation
**Collection**: ASR
# Changelog
- Using prefix `post_` for all `self_attention_model` options will move attention after conv
- New `non_pos` option for `self_attention_model` for off computation of positional emb
# Usage
* You can potentially add a usage example below
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
This PR adds support to KERPLE (https://arxiv.org/abs/2205.09921) positional embeddings for Megatron encoder-decoder classes.
**Collection**: nlp megatron
# Changelog
- Added implementation of KERPLE
- Added Jenkins test
- Added comment to document feature in YAML file (`examples/nlp/language_modeling/conf/megatron_model_base_config.yaml`)
# Usage
Set config
```
model.encoder.position_embedding_type: 'kerple'
model.decoder.position_embedding_type: 'kerple'
```
# 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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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | **How to train MSDD on multiple GPUs?**
When training MSDD model for speaker diarization on multiple GPU units, an error is thrown in the manifest generation step.
FileNotFoundError: [Errno 2] No such file or directory: `<temp_dir>/speaker_outputs/oracle_vad_manifest.json`
where `<temp_dir>` refers to what we set up in the config for `model.train_ds.emb_dir` or `model.valid_ds.emb_dir`.
This happens in the init of the `EncDecDiarLabelModel` class. Below is the complete error trace.
If training is initiated on a single GPU, the `EncDecDiarLabelModel` gets initialised without any problems. Is this a known limitation of the MSDD model training code or there is something else I need to do to initi the training on multiple GPUs?
Thanks
```
Error executing job with overrides: ['num_workers=20', 'batch_size=15', 'model.train_ds.manifest_filepath=/data/multispeaker-dataset/msdd_manifest_train_for_trainer.json', 'model.validation_ds.manifest_filepath=/data/multispeaker-dataset/msdd_manifest_valid_for_trainer.json', 'model.train_ds.emb_dir=/data/multispeaker-dataset/temp_dir_train', 'model.validation_ds.emb_dir=/data/multispeaker-dataset/temp_dir_valid', 'model.diarizer.speaker_embeddings.model_path=titanet_large', 'model.emb_batch_size=100', 'exp_manager.name=msdd_v1', '+exp_manager.exp_dir=/experiments', '+exp_manager.version=20230422-0827', '+exp_manager.resume_if_exists=true', '+exp_manager.resume_ignore_no_checkpoint=true', '+exp_manager.checkpoint_callback_params.always_save_nemo=true', '+exp_manager.checkpoint_callback_params.save_best_model=false', 'trainer.devices=-1', 'trainer.num_nodes=1', 'trainer.strategy=ddp', 'trainer.accelerator=gpu', '+trainer.check_val_every_n_epoch=1', 'trainer.max_epochs=1000']
Traceback (most recent call last):
File "/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py", line 51, in <module>
main()
File "/usr/local/lib/python3.8/dist-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper
_run_hydra(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
return func()
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py", line 46, in main
msdd_model = EncDecDiarLabelModel(cfg=cfg.model, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/models/msdd_models.py", line 123, in __init__
super().__init__(cfg=self.cfg_msdd_model, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/modelPT.py", line 143, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/models/msdd_models.py", line 278, in setup_training_data
self._train_dl = self.__setup_dataloader_from_config(config=train_data_config,)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/models/msdd_models.py", line 217, in __setup_dataloader_from_config
dataset = AudioToSpeechMSDDTrainDataset(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/data/audio_to_diar_label.py", line 774, in __init__
super().__init__(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/data/audio_to_diar_label.py", line 226, in __init__
self.multiscale_timestamp_dict = prepare_split_data(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/utils/speaker_utils.py", line 1469, in prepare_split_data
write_rttm2manifest(split_audio_rttm_map, _speaker_manifest_path, include_uniq_id=True)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/utils/speaker_utils.py", line 854, in write_rttm2manifest
with open(manifest_file, 'w') as outfile:
FileNotFoundError: [Errno 2] No such file or directory: '/data/multispeaker-dataset/temp_dir_train/speaker_outputs/oracle_vad_manifest.json'
```
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} | # What does this PR do ?
Adding the conv_pointwise_type parameter that to ConformerEncoder enabling the use of nn.Linear() for a 10% acceleration
**Collection**: ASR
# Changelog
Adding the conv_pointwise_type parameter to
- nemo/collections/asr/modules/conformer_encoder.py : adding the conv_pointwise_type parameter and propagating it to ConformerLayer
- nemo/collections/asr/parts/submodules/conformer_modules.py: adding conv_pointwise_type parameter to ConformerLayer and propagating it to ConformerConvolution.
- in ConformerConvolution, if conv_pointwise_type is 'linear', use nn.Linear() instead of nn.Conv1d()
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
+model.encoder.conv_pointwise_type='linear'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Move core lib to NVIDIA/Megatron-LM from custom fork
**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
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} | # What does this PR do ?
Reduce data workers on NMT CI for speedup
**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"
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
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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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: ASR
# Changelog
- Adds FC models to the catalog
# Usage
When loading pre-trained models, we can now specify:
stt_en_fastconformer_transducer_large
stt_en_fastconformer_ctc_large
stt_en_fastconformer_transducer_large_ls
stt_en_fastconformer_ctc_large_ls
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [X ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Reduce data workers on NMT CI for speedup
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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the code I run with:
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} | # What does this PR do ?
Set garbage collection interval manually. This helps align garbage collection execution across GPUs, which mitigates CPU overhead dramatically.
Using an integer value for `gc_interval` set the garbage collection interval in global-batch steps.
# 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 ?
Commeting out `mp.set_start_method("spawn", force=True)` from scripts and just using persistent_workers
**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 ?
Added some new configurations and updated some of the older configuration files for Conformer models.
# Changelog
- Added configs for cache-aware fastconformer and hybrid ASR.
- Added more info on batch size and precision suggestions in the configs.
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Fixes some bugs to support Hybrid and FastConformer variants of cache-aware streaming models.
# Changelog
- Fixed a bug which prevented switching between decoders for Hybrid models
- Added support of Hybrid models to the cache-aware streaming inference scripts
- Added the feature to switch between decoders to the cache-aware streaming simulation script
- Added support of causal downsamling for dw_striding approach.
# Usage
Users may use the --set_decoder "rnnt" or "ctc" to change the decoder with speech_to_text_cache_aware_streaming_infer.py.
They may use asr_model.change_decoding_strategy(decoder_type='ctc' or 'rnnt') to change the decoding for a Hybrid model in general.
**PR Type**:
- [X] New Feature
- [X] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Move core lib to NVIDIA/Megatron-LM from custom fork
**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 ?
This is the third PR for Frame-VAD. Please merge the previous two before this: https://github.com/NVIDIA/NeMo/pull/6441, https://github.com/NVIDIA/NeMo/pull/6463
This PR adds Frame-VAD to ASR+VAD pipeline, and also adds the drop-frame mode to ASR+VAD, which previously only supports masking mode.
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] 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 ?
The second PR of Frame-VAD. Please merge the first PR first: https://github.com/NVIDIA/NeMo/pull/6441
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# 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)
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} | This PR adds support in external sample mapping (to be able to compute on the fly).
# What does this PR do ?
The added support does not add any new functionality, but allows providing the sample mapping externally to be able to avoid loading dataset samples.
**Collection**: Megatron `dataset_utils`.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR fixes a bug where `MegatronLMEncoderDecoderModel.validation_epoch_end` might get empty outputs.
**Collection**: nlp
# Changelog
- nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py:validation_epoch_end
# 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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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This PR fixes a bug where `MegatronLMEncoderDecoderModel.validation_epoch_end` might get empty outputs.
**Collection**: nlp
# Changelog
- nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py:validation_epoch_end
# 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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/6458",
"html_url": "https://github.com/NVIDIA/NeMo/pull/6458",
"diff_url": "https://github.com/NVIDIA/NeMo/pull/6458.diff",
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} | # What does this PR do ?
Adds export functionality for GPT model trained with FP8 enabled
**Collection**: [NLP]
# Changelog
- Add export script
- Add export config
- Update export_utils to enable support for this model
- Bugfix: Typecast precision check for 16 and 32
# Usage
```bash
python3 examples/nlp/language_modeling/megatron_gpt_export.py gpt_model_file=5b_fp8_tp1.nemo onnx_model_file=5b_fp8_tp1.onnx
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you add or update any necessary documentation?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [X] Documentation
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] | closed | false | null | [] | null | null | 2023-04-19T21:49:34 | 2023-05-27T01:51:41 | 2023-05-27T01:51:41 | NONE | {
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} | null | null | null | **Describe the bug**
Following the example from here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html
I downloaded and processed the data to run GPT pretraining. But I encountered the following error.
I am using the latest nemo docker container (docker pull nvcr.io/nvidia/nemo:23.01) and did everything from within the container.
```
File "/usr/local/lib/python3.8/dist-packages/apex/transformer/pipeline_parallel/schedules/fwd_bwd_no_pipelining.py", line 80, in forward_backward_no_pipelining
cur_micro_batch = get_kth_microbatch(batch, i)
File "/usr/local/lib/python3.8/dist-packages/apex/transformer/pipeline_parallel/utils.py", line 135, in get_kth_microbatch
size = x.size(0)
AttributeError: 'dict' object has no attribute 'size'
```
**Steps/Code to reproduce bug**
I followed the steps outlined in the above link, trying both GPT2 or SPM options.
**Expected behavior**
training on a single GPU
**Environment overview (please complete the following information)**
docker pull nvcr.io/nvidia/nemo:23.01
**Additional context**
* Files:
root@bd75e03a74c9:/workspace/data# ls
enwiki-latest-pages-articles.xml.bz2 hfbpe_gpt_training_data_text_document_test_indexmap_9648ns_1024sl_1234s_doc_idx.npy
gpt2-merges.txt hfbpe_gpt_training_data_text_document_test_indexmap_9648ns_1024sl_1234s_sample_idx.npy
gpt2-vocab.json hfbpe_gpt_training_data_text_document_test_indexmap_9648ns_1024sl_1234s_shuffle_idx.npy
gpt_training_data_text_document.bin hfbpe_gpt_training_data_text_document_train_indexmap_57888000ns_1024sl_1234s_doc_idx.npy
gpt_training_data_text_document.idx hfbpe_gpt_training_data_text_document_train_indexmap_57888000ns_1024sl_1234s_sample_idx.npy
gpt_training_data_text_document_test_indexmap_9648ns_1024sl_1234s_doc_idx.npy hfbpe_gpt_training_data_text_document_train_indexmap_57888000ns_1024sl_1234s_shuffle_idx.npy
gpt_training_data_text_document_test_indexmap_9648ns_1024sl_1234s_sample_idx.npy hfbpe_gpt_training_data_text_document_valid_indexmap_0ns_1024sl_1234s_doc_idx.npy
gpt_training_data_text_document_test_indexmap_9648ns_1024sl_1234s_shuffle_idx.npy hfbpe_gpt_training_data_text_document_valid_indexmap_0ns_1024sl_1234s_sample_idx.npy
gpt_training_data_text_document_train_indexmap_57888000ns_1024sl_1234s_doc_idx.npy hfbpe_gpt_training_data_text_document_valid_indexmap_0ns_1024sl_1234s_shuffle_idx.npy
gpt_training_data_text_document_train_indexmap_57888000ns_1024sl_1234s_sample_idx.npy hfbpe_gpt_training_data_text_document_valid_indexmap_9657648ns_1024sl_1234s_doc_idx.npy
gpt_training_data_text_document_train_indexmap_57888000ns_1024sl_1234s_shuffle_idx.npy hfbpe_gpt_training_data_text_document_valid_indexmap_9657648ns_1024sl_1234s_sample_idx.npy
gpt_training_data_text_document_valid_indexmap_9657648ns_1024sl_1234s_doc_idx.npy hfbpe_gpt_training_data_text_document_valid_indexmap_9657648ns_1024sl_1234s_shuffle_idx.npy
gpt_training_data_text_document_valid_indexmap_9657648ns_1024sl_1234s_sample_idx.npy nemo_experiments
gpt_training_data_text_document_valid_indexmap_9657648ns_1024sl_1234s_shuffle_idx.npy sentencepiece
hfbpe_gpt_training_data_text_document.bin spm_32k_wiki.model
hfbpe_gpt_training_data_text_document.idx spm_32k_wiki.vocab
hfbpe_gpt_training_data_text_document_test_indexmap_193ns_1024sl_1234s_doc_idx.npy text
hfbpe_gpt_training_data_text_document_test_indexmap_193ns_1024sl_1234s_sample_idx.npy text_for_tokenizer.txt
hfbpe_gpt_training_data_text_document_test_indexmap_193ns_1024sl_1234s_shuffle_idx.npy train_data.jsonl
* Detailed command line and outputlog:
Epoch 0: 0%| | 0/351750 [00:00<?, ?it/s][NeMo W 2023-04-19 21:40:25 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 2023-04-19 21:40:27 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-04-19 21:40:27 experimental:27] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2023-04-19 21:40:27 indexed_dataset:454] reading sizes...
[NeMo I 2023-04-19 21:40:27 indexed_dataset:456] reading pointers...
[NeMo I 2023-04-19 21:40:27 indexed_dataset:460] reading document index...
[NeMo I 2023-04-19 21:40:27 indexed_dataset:513] creating numpy buffer of mmap...
[NeMo I 2023-04-19 21:40:27 indexed_dataset:515] creating memory view of numpy buffer...
[NeMo W 2023-04-19 21:40:33 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 2023-04-19 21:40:35 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-04-19 21:40:35 experimental:27] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2023-04-19 21:40:35 indexed_dataset:454] reading sizes...
[NeMo I 2023-04-19 21:40:35 indexed_dataset:456] reading pointers...
[NeMo I 2023-04-19 21:40:35 indexed_dataset:460] reading document index...
[NeMo I 2023-04-19 21:40:35 indexed_dataset:513] creating numpy buffer of mmap...
[NeMo I 2023-04-19 21:40:35 indexed_dataset:515] creating memory view of numpy buffer...
Error executing job with overrides: ['trainer.devices=1', 'trainer.num_nodes=1', 'trainer.max_epochs=null', 'trainer.max_steps=300000', 'trainer.val_check_interval=300', 'trainer.log_every_n_steps=50', 'trainer.limit_val_batches=50', 'trainer.limit_test_batches=50', 'trainer.accumulate_grad_batches=1', 'trainer.precision=16', 'model.micro_batch_size=6', 'model.global_batch_size=192', 'model.tensor_model_parallel_size=1', 'model.pipeline_model_parallel_size=1', 'model.max_position_embeddings=1024', 'model.encoder_seq_length=1024', 'model.hidden_size=768', 'model.ffn_hidden_size=3072', 'model.num_layers=12', 'model.num_attention_heads=12', 'model.init_method_std=0.021', 'model.hidden_dropout=0.1', 'model.layernorm_epsilon=1e-5', 'model.tokenizer.vocab_file=gpt2-vocab.json', 'model.tokenizer.merge_file=gpt2-merges.txt', 'model.data.data_prefix=[1.0,hfbpe_gpt_training_data_text_document]', 'model.data.num_workers=2', 'model.data.seq_length=1024', "model.data.splits_string='980,10,10'", 'model.optim.name=fused_adam', 'model.optim.lr=6e-4', 'model.optim.betas=[0.9,0.95]', 'model.optim.weight_decay=0.1', 'model.optim.sched.name=CosineAnnealing', 'model.optim.sched.warmup_steps=750', 'model.optim.sched.constant_steps=80000', 'model.optim.sched.min_lr=6e-5', 'exp_manager.resume_if_exists=True', 'exp_manager.resume_ignore_no_checkpoint=True', 'exp_manager.create_checkpoint_callback=True', 'exp_manager.checkpoint_callback_params.monitor=val_loss', 'exp_manager.checkpoint_callback_params.save_top_k=3', 'exp_manager.checkpoint_callback_params.mode=min', 'exp_manager.checkpoint_callback_params.always_save_nemo=False']
Traceback (most recent call last):
File "/workspace/nemo/examples/nlp/language_modeling/megatron_gpt_pretraining.py", line 86, in main
trainer.fit(model)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 88, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1103, in _run
results = self._run_stage()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1182, in _run_stage
self._run_train()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1205, in _run_train
self.fit_loop.run()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 213, in advance
batch_output = self.batch_loop.run(kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(optimizers, kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 202, in advance
result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position])
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 249, in _run_optimization
self._optimizer_step(optimizer, opt_idx, kwargs.get("batch_idx", 0), closure)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 370, in _optimizer_step
self.trainer._call_lightning_module_hook(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1347, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/module.py", line 1744, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/optimizer.py", line 169, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/ddp.py", line 280, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/strategy.py", line 234, in optimizer_step
return self.precision_plugin.optimizer_step(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/plugins/precision/native_amp.py", line 75, in optimizer_step
closure_result = closure()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 149, in __call__
self._result = self.closure(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 135, in closure
step_output = self._step_fn()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 419, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1485, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/ddp.py", line 351, in training_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1480, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/parallel/distributed.py", line 1098, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/parallel/distributed.py", line 1054, in _run_ddp_forward
return module_to_run(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1480, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/overrides/base.py", line 98, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/utils/model_utils.py", line 362, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 367, in training_step
losses_reduced_per_micro_batch = fwd_bwd_function(
File "/usr/local/lib/python3.8/dist-packages/apex/transformer/pipeline_parallel/schedules/fwd_bwd_no_pipelining.py", line 80, in forward_backward_no_pipelining
cur_micro_batch = get_kth_microbatch(batch, i)
File "/usr/local/lib/python3.8/dist-packages/apex/transformer/pipeline_parallel/utils.py", line 135, in get_kth_microbatch
size = x.size(0)
AttributeError: 'dict' object has no attribute 'size'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
* GPU is H100.
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} | # What does this PR do ?
Uses spawn for mp for BERT pre-training
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# 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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Cache inputs (attention mask, loss mask, and position ids) and reuse.
This mitigates CPU-side overhead.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add support for swiglu in weight conversion script
**Collection**: [NLP]
# Changelog
- Update TP PP weight conversion logic with swiglu config
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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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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} | # What does this PR do ?
minor fix for replace_bos_with_pad not found error in CI
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | …lining
# 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 ?
Added functionality to create synthetic code-switching (CS) audio data and manifest in the multi-sample lid format. Added flag (--is_lid_manifest) to choose between standard flat text format and the multi-sample lid format, to ensure backward compatibility.
**Collection**: ASR
# Changelog
- in scripts/speech_recognition/code_switching/code_switching_audio_data_creation.py,
- added flag '--is_lid_manifest' which default to True
- modified the 'create_cs_data' function to now create the text field in the format “text”: [{“str”:“esta muestra ” “lang”:”es”},{“str”:“was generated synthetically”: “lang”:”en”}]
- updated the README to add the new functionality
# Usage
1. Create the (intermediate) manifest file using `code_switching_manifest_creation.py`. It's usage is as follows:
`python code_switching_manifest_creation.py --manifest_language1 <absolute path of Language 1's manifest file> --manifest_language2 <absolute path of Language 2's manifest file> --manifest_save_path <absolute path to save the created manifest> --id_language1 <language code for language 1 (e.g. en)> --id_language2 <language code for language 2 (e.g. es)> --max_sample_duration_sec <maximum duration of generated sample in seconds> --min_sample_duration_sec <maximum duration of generated sample in seconds> --dataset_size_required_hrs <size of generated synthetic dataset required in hrs>`
2. Create the synthetic audio data and the corresponding manifest file using `code_switching_audio_data_creation.py` It's usage is as follows:
`python code_switching_audio_data_creation.py --manifest_path <absolute path to intermediate CS manifest generated in step 1> --audio_save_folder_path <absolute path to directory where you want to save the synthesized audios> --manifest_save_path <absolute path to save the created manifest> --audio_normalized_amplitude <scaled normalized amplitude desired> --cs_data_sampling_rate <desired sampling rate for generated audios> --sample_beginning_pause_msec <pause to be added to the beginning of the generated sample in milli seconds> --sample_joining_pause_msec <pause to be added between segments while joining, in milli seconds> --sample_end_pause_msec <pause to be added to the end of the generated sample in milli seconds> --is_lid_manifest <boolean to create manifest in the multi-sample lid format for the text field, true by default> --workers <number of worker processes>`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fixes an issue with scheduler param registration.
The only way to override the scheduler args is via Hydra / OmegaConf at the moment, all defaults are picked directly from the constructor of the scheduler instead of the defaults of the dataclass.
In effect, it sidesteps the validation and defaults of the dataclass of the scheduler entirely. There is no significant effect as it can still be fully overridden from the yaml config.
**Collection**: [Core]
# Changelog
- Fix issue with scheduler dataclass registration
- Fix issue with scheduler dataclass validation with config overrides
# 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
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} | # What does this PR do ?
Fixes an issue with scheduler param registration.
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In effect, it sidesteps the validation and defaults of the dataclass of the scheduler entirely. There is no significant effect as it can still be fully overridden from the yaml config.
**Collection**: [Core]
# Changelog
- Fix issue with scheduler dataclass registration
- Fix issue with scheduler dataclass validation with config overrides
# 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
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} | # What does this PR do ?
This adds a new normalisation-based noise perturbation to Nemo ASR based on experiments done by Riva
**Collection**: ASR
# Changelog
- nemo/collections/asr/data/audio_to_text.py
Added new logic to check for infinite datasets in RandomizedChainDataset
- nemo/collections/asr/data/audio_to_text_dataset.py
Amended the process_augmentations API to pass global_rank and world_size
- nemo/collections/asr/parts/preprocessing/perturb.py
Added new NoiseNormPerturbation class
Fixed up the AugmentationDataset to make it an Iterable instead of Iterator and bring it into line with all other IterableDataset classes in Nemo
Added passing of global_rank and world_size to main perturbation constructor for those perturbations who might need this info (the new one uses it)
- nemo/utils/model_utils.py
Added a new bypass mechanism for multi-validation dataloaders which allows you to flatten all validation datasets into one single one, tested and working for both manifest.jsons and tarred audio sets. I know this one will be controversional, but it's something we very much need on Riva.
# Usage
* You can use the new perturbation class as follows:
```yaml
++model.train_ds.augmentor.noise_norm.prob=0.3
++model.train_ds.augmentor.noise_norm.snr_samples=[5,10,15]
++model.train_ds.augmentor.noise_norm.norm_to_db=-25.0
++model.train_ds.augmentor.noise_norm.rng=123
++model.train_ds.augmentor.noise_norm.manifest_path=['/path/to/tars1/tarred_audio_manifest.json','/path/to/tars2/tarred_audio_manifest.json']
++model.train_ds.augmentor.noise_norm.audio_tar_filepaths=['/path/to/tars1/audio__OP_0..255_CL_.tar','/path/to/tars2/audio__OP_0..127_CL_.tar']
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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} | # What does this PR do ?
Add (1) interfaces to TE and initialized (2) process group setting to support tensor-parallel communication overlap with userbuffer backend.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Set `ub_tp_comm_overlap` to `True`
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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minor fix for replace_bos_with_pad not found error in CI
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Usage
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# Before your PR is "Ready for review"
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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
- [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**: [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 | Hi, I wanna fine-tune the multilingual model. For this purpose, I downloaded a pre-trained english model.
Now according to the tutorials in your repository, first of all we should train the tokenizer. I want to train spe bpe.
I made manifest files like the manifest exists in tutorial and run the code like you said.
But when I run the code, I encounter the below error:
'''
trainer_interface.cc(350) LOG(INFO) SentenceIterator is not specified. Using MultiFileSentenceIterator.
trainer_interface.cc(181) LOG(INFO) Loading corpus: /home/multilingual/dataset/deepmine_voices/text_corpus/document.txt
trainer_interface.cc(406) LOG(INFO) Loaded all 0 sentences
trainer_interface.cc(422) LOG(INFO) Adding meta_piece: <unk>
trainer_interface.cc(427) LOG(INFO) Normalizing sentences...
Traceback (most recent call last):
File "NeMo/scripts/tokenizers/process_asr_text_tokenizer.py", line 323, in <module>
main()
File "NeMo/scripts/tokenizers/process_asr_text_tokenizer.py", line 301, in main
tokenizer_path = __process_data(
File "NeMo/scripts/tokenizers/process_asr_text_tokenizer.py", line 246, in __process_data
tokenizer_path, vocab_path = create_spt_model(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py", line 372, in create_spt_model
sentencepiece.SentencePieceTrainer.Train(cmd)
File "/usr/local/lib/python3.8/dist-packages/sentencepiece/__init__.py", line 989, in Train
SentencePieceTrainer._Train(arg=arg, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/sentencepiece/__init__.py", line 945, in _Train
return SentencePieceTrainer._TrainFromString(arg)
File "/usr/local/lib/python3.8/dist-packages/sentencepiece/__init__.py", line 923, in _TrainFromString
return _sentencepiece.SentencePieceTrainer__TrainFromString(arg)
RuntimeError: Internal: src/trainer_interface.cc(428) [!sentences_.empty()]
'''
I want to fine-tune it on a persian dataset.
Could you please help me to solve this problem? | {
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} | # What does this PR do ?
Add config and char tokenizer for russian tts. It contains russian alphabet + ipa alphabet. Each unicode character is regarded as separate token, including stress.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add support for swiglu in weight conversion script
**Collection**: [NLP]
# Changelog
- Update TP PP weight conversion logic with swiglu config
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix a mistake in line 782. FFT calculates the magnitude and phase of each frequency bin not frequency band. Moreover, frequency bins in FFT are not related to pitch. So, we should change _**band (ie. pitch)**_ to _**bin**_.
**Collection**: [TTS]
# Changelog
tutorials/tts/NeMo_TTS_Primer.ipynb
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
## Who can review?
@blisc & @okuchaiev
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} | # What does this PR do ?
Fix FastPitch training appear `nan` loss in aligner loss when precision=32.
**Collection**: [TTS]
# Changelog
- `nemo/collections/tts/losses/aligner_loss`
# Usage
* To reproduce the bug, we can run `FastPitch and Mixer-TTS Training` tutorial by adding `trainer.precision=32` in training command.
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/6434 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6434/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6434/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6434/events | https://github.com/NVIDIA/NeMo/issues/6434 | 1,670,631,671 | I_kwDOC_bI7s5jk9D3 | 6,434 | How to reproduce the same result as in the paper, EER=0.66? | {
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`#!/bin/sh
CUDA_VISIBLE_DEVICES=3 python extract_speaker_embeddings.py --manifest=voxceleb1_test_manifest.json --model_path='./speakerverification_en_titanet_large/model' --embedding_dir='./speakerverification_en_titanet_large/embedding'
CUDA_VISIBLE_DEVICES=3 python voxceleb_eval.py --trial_file='./speech_database/voxceleb/vox_trial_pairs/veri_test.txt' --emb='./speakerverification_en_titanet_large/embedding/embeddings/voxceleb1_test_manifest_embeddings.pkl' `
and i found the model is:
Found existing object .cache/torch/NeMo/NeMo_1.17.0/titanet-l/11ba0924fdf87c049e339adbf6899d48/titanet-l.nemo.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 37720/37720 [00:00<00:00, 110401.53it/s]
0.80
So I want to know what went wrong? thanks very much!!
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} | # What does this PR do ?
This PR upgrades NeMo to use lightning 2.0. Majority of the changes are done based on the migration guide provided by lightning: [https://lightning.ai/docs/pytorch/stable/upgrade/from_1_9.html](url)
**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/6431 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6431/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6431/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6431/events | https://github.com/NVIDIA/NeMo/pull/6431 | 1,669,051,274 | PR_kwDOC_bI7s5OXo2h | 6,431 | [TTS] Add tutorials for FastPitch TTS speaker adaptation with adapters | {
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} | # What does this PR do ?
Add two tutorials for FastPitch TTS speaker adaptation with adapters.
One is FastPitch multi-speaker pretraining, the other is FastPitch adapter finetuning.
**Collection**: [TTS]
# Changelog
- `tutorials/tts/FastPitch_MultiSpeaker_Pretraining.ipynb`
- `tutorials/tts/FastPitch_Adapter_Finetuning.ipynb.`
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #6416 #6417
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | As this is my first issue, I MUST thank anyone contributing to this great repository, so I do :).
Without any context, [this](https://github.com/NVIDIA/NeMo/blob/50f16ba4f445e312b38b75a93881b9db2a10791c/nemo/collections/asr/parts/preprocessing/perturb.py#L377) normalization line seems wrong (first `min` should be `np.mean`).
`impulse_norm = (impulse.samples - min(impulse.samples)) / (max(impulse.samples) - min(impulse.samples))`
This totally changes the RIR signal and adds an overlapping echo to the output. Seems like nobody is using this augmentation :) or they didn't manually check the outputs.
An example RIR file from [BUT reverb dataset](https://speech.fit.vutbr.cz/software/but-speech-fit-reverb-database) is attached for anyone like myself who seeks a RIR example to check the augmentation.
[rir_1sec.zip](https://github.com/NVIDIA/NeMo/files/11230838/rir_1sec.zip) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6428 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6428/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6428/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6428/events | https://github.com/NVIDIA/NeMo/pull/6428 | 1,667,542,624 | PR_kwDOC_bI7s5OSlri | 6,428 | check if grad is none before calling all_reduce | {
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} | # What does this PR do ?
To enable PeFT training via adapter mixins, we should ensure all_reduce is not called on params' grads when the param.requires_grad is False. This does not happen for normal (full fine-tuning) but will happen for PeFT.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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