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https://api.github.com/repos/NVIDIA/NeMo/issues/4800 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4800/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4800/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4800/events | https://github.com/NVIDIA/NeMo/issues/4800 | 1,349,288,668 | I_kwDOC_bI7s5QbILc | 4,800 | Segmentation fault when importing nemo.collections.asr (protobuf?) | {
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] | closed | true | null | [] | null | null | 2022-08-24T11:27:10 | 2022-11-14T07:00:33 | 2022-08-24T14:50:03 | NONE | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | **Describe the bug**
Importing `nemo.collections.asr` throws a segmentation fault.
**Steps/Code to reproduce bug**
Install NeMo in a fresh conda environment as per the instructions on the README.md: https://github.com/NVIDIA/NeMo#installation
<details>
<summary> Packages </summary>
```
# packages in environment at /opt/conda/envs/hf:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
absl-py 1.2.0 pypi_0 pypi
aiohttp 3.8.1 pypi_0 pypi
aiosignal 1.2.0 pypi_0 pypi
alabaster 0.7.12 pypi_0 pypi
aniso8601 9.0.1 pypi_0 pypi
antlr4-python3-runtime 4.8 pypi_0 pypi
appdirs 1.4.4 pypi_0 pypi
asttokens 2.0.8 pypi_0 pypi
async-timeout 4.0.2 pypi_0 pypi
attrdict 2.0.1 pypi_0 pypi
attrs 22.1.0 pypi_0 pypi
audioread 3.0.0 pypi_0 pypi
babel 2.10.3 pypi_0 pypi
backcall 0.2.0 pypi_0 pypi
beautifulsoup4 4.11.1 pypi_0 pypi
black 19.10b0 pypi_0 pypi
blas 1.0 mkl
boto3 1.24.58 pypi_0 pypi
botocore 1.27.58 pypi_0 pypi
braceexpand 0.1.7 pypi_0 pypi
brotlipy 0.7.0 py38h0a891b7_1004 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
ca-certificates 2022.6.15 ha878542_0 conda-forge
cachetools 5.2.0 pypi_0 pypi
certifi 2022.6.15 py38h578d9bd_0 conda-forge
cffi 1.15.1 py38h4a40e3a_0 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
click 8.1.3 pypi_0 pypi
colorama 0.4.5 pypi_0 pypi
commonmark 0.9.1 pypi_0 pypi
cryptography 37.0.1 py38h9ce1e76_0
cudatoolkit 11.6.0 hecad31d_10 conda-forge
cycler 0.11.0 pypi_0 pypi
cython 0.29.32 pypi_0 pypi
debugpy 1.6.3 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
distance 0.1.3 pypi_0 pypi
docker-pycreds 0.4.0 pypi_0 pypi
docopt 0.6.2 pypi_0 pypi
docutils 0.19 pypi_0 pypi
editdistance 0.6.0 pypi_0 pypi
einops 0.4.1 pypi_0 pypi
entrypoints 0.4 pypi_0 pypi
executing 0.10.0 pypi_0 pypi
faiss-cpu 1.7.2 pypi_0 pypi
fasttext 0.9.2 pypi_0 pypi
ffmpeg 4.3 hf484d3e_0 pytorch
filelock 3.8.0 pypi_0 pypi
flask 2.2.2 pypi_0 pypi
flask-restful 0.3.9 pypi_0 pypi
fonttools 4.37.0 pypi_0 pypi
freetype 2.12.1 hca18f0e_0 conda-forge
frozendict 2.3.4 pypi_0 pypi
frozenlist 1.3.1 pypi_0 pypi
fsspec 2022.7.1 pypi_0 pypi
ftfy 6.1.1 pypi_0 pypi
g2p-en 2.1.0 pypi_0 pypi
gdown 4.5.1 pypi_0 pypi
gitdb 4.0.9 pypi_0 pypi
gitpython 3.1.27 pypi_0 pypi
gmp 6.2.1 h58526e2_0 conda-forge
gnutls 3.6.13 h85f3911_1 conda-forge
google-auth 2.11.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
grpcio 1.47.0 pypi_0 pypi
h5py 3.7.0 pypi_0 pypi
huggingface-hub 0.9.0 pypi_0 pypi
hydra-core 1.1.2 pypi_0 pypi
idna 3.3 pyhd8ed1ab_0 conda-forge
ijson 3.1.4 pypi_0 pypi
imagesize 1.4.1 pypi_0 pypi
importlib-metadata 4.12.0 pypi_0 pypi
importlib-resources 5.2.3 pypi_0 pypi
inflect 6.0.0 pypi_0 pypi
iniconfig 1.1.1 pypi_0 pypi
intel-openmp 2021.4.0 h06a4308_3561
ipadic 1.0.0 pypi_0 pypi
ipykernel 6.15.1 pypi_0 pypi
ipython 8.4.0 pypi_0 pypi
ipywidgets 8.0.1 pypi_0 pypi
isort 4.3.21 pypi_0 pypi
itsdangerous 2.1.2 pypi_0 pypi
jarowinkler 1.2.1 pypi_0 pypi
jedi 0.18.1 pypi_0 pypi
jieba 0.42.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
jmespath 1.0.1 pypi_0 pypi
joblib 1.1.0 pypi_0 pypi
jpeg 9e h166bdaf_2 conda-forge
jupyter-client 7.3.4 pypi_0 pypi
jupyter-core 4.11.1 pypi_0 pypi
jupyterlab-widgets 3.0.2 pypi_0 pypi
kaldi-python-io 1.2.2 pypi_0 pypi
kaldiio 2.17.2 pypi_0 pypi
kiwisolver 1.4.4 pypi_0 pypi
lame 3.100 h7f98852_1001 conda-forge
latexcodec 2.0.1 pypi_0 pypi
lcms2 2.12 hddcbb42_0 conda-forge
ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge
lerc 4.0.0 h27087fc_0 conda-forge
libdeflate 1.13 h166bdaf_0 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc-ng 12.1.0 h8d9b700_16 conda-forge
libgomp 12.1.0 h8d9b700_16 conda-forge
libiconv 1.17 h166bdaf_0 conda-forge
libnsl 2.0.0 h7f98852_0 conda-forge
libpng 1.6.37 h753d276_4 conda-forge
librosa 0.9.2 pypi_0 pypi
libsqlite 3.39.2 h753d276_1 conda-forge
libstdcxx-ng 12.1.0 ha89aaad_16 conda-forge
libtiff 4.4.0 h0e0dad5_3 conda-forge
libuuid 2.32.1 h7f98852_1000 conda-forge
libwebp-base 1.2.4 h166bdaf_0 conda-forge
libxcb 1.13 h7f98852_1004 conda-forge
libzlib 1.2.12 h166bdaf_2 conda-forge
llvmlite 0.39.0 pypi_0 pypi
lxml 4.9.1 pypi_0 pypi
markdown 3.4.1 pypi_0 pypi
markupsafe 2.1.1 pypi_0 pypi
marshmallow 3.17.1 pypi_0 pypi
matplotlib 3.5.3 pypi_0 pypi
matplotlib-inline 0.1.6 pypi_0 pypi
mecab-python3 1.0.5 pypi_0 pypi
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py38h95df7f1_0 conda-forge
mkl_fft 1.3.1 py38h8666266_1 conda-forge
mkl_random 1.2.2 py38h1abd341_0 conda-forge
mpmath 1.2.1 pypi_0 pypi
multidict 6.0.2 pypi_0 pypi
ncurses 6.3 h27087fc_1 conda-forge
nemo-toolkit 1.11.0rc0 dev_0 <develop>
nest-asyncio 1.5.5 pypi_0 pypi
nettle 3.6 he412f7d_0 conda-forge
nltk 3.7 pypi_0 pypi
numba 0.56.0 pypi_0 pypi
numpy 1.22.4 pypi_0 pypi
oauthlib 3.2.0 pypi_0 pypi
omegaconf 2.1.2 pypi_0 pypi
onnx 1.12.0 pypi_0 pypi
opencc 1.1.4 pypi_0 pypi
openh264 2.1.1 h780b84a_0 conda-forge
openjpeg 2.5.0 h7d73246_1 conda-forge
openssl 3.0.5 h166bdaf_1 conda-forge
packaging 21.3 pypi_0 pypi
pandas 1.4.3 pypi_0 pypi
pangu 4.0.6.1 pypi_0 pypi
parameterized 0.8.1 pypi_0 pypi
parso 0.8.3 pypi_0 pypi
pathspec 0.9.0 pypi_0 pypi
pathtools 0.1.2 pypi_0 pypi
pesq 0.0.4 pypi_0 pypi
pexpect 4.8.0 pypi_0 pypi
pickleshare 0.7.5 pypi_0 pypi
pillow 9.2.0 py38ha3b2c9c_2 conda-forge
pip 22.2.2 pyhd8ed1ab_0 conda-forge
pip-api 0.0.30 pypi_0 pypi
pipreqs 0.4.11 pypi_0 pypi
pluggy 1.0.0 pypi_0 pypi
pooch 1.6.0 pypi_0 pypi
portalocker 2.5.1 pypi_0 pypi
promise 2.3 pypi_0 pypi
prompt-toolkit 3.0.30 pypi_0 pypi
protobuf 3.19.4 pypi_0 pypi
psutil 5.9.1 pypi_0 pypi
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
ptyprocess 0.7.0 pypi_0 pypi
pure-eval 0.2.2 pypi_0 pypi
py 1.11.0 pypi_0 pypi
pyannote-core 4.5 pypi_0 pypi
pyannote-database 4.1.3 pypi_0 pypi
pyannote-metrics 3.2.1 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pybind11 2.10.0 pypi_0 pypi
pybtex 0.24.0 pypi_0 pypi
pybtex-docutils 1.0.2 pypi_0 pypi
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pydantic 1.9.2 pypi_0 pypi
pydeprecate 0.3.2 pypi_0 pypi
pydub 0.25.1 pypi_0 pypi
pygments 2.13.0 pypi_0 pypi
pynini 2.1.5 pypi_0 pypi
pyopenssl 22.0.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.9 pypi_0 pypi
pypinyin 0.47.1 pypi_0 pypi
pysocks 1.7.1 py38h578d9bd_5 conda-forge
pystoi 0.3.3 pypi_0 pypi
pytest 7.1.2 pypi_0 pypi
pytest-runner 6.0.0 pypi_0 pypi
python 3.8.13 ha86cf86_0_cpython conda-forge
python-dateutil 2.8.2 pypi_0 pypi
python_abi 3.8 2_cp38 conda-forge
pytorch 1.12.1 py3.8_cuda11.6_cudnn8.3.2_0 pytorch
pytorch-lightning 1.7.2 pypi_0 pypi
pytorch-mutex 1.0 cuda pytorch
pytz 2022.2.1 pypi_0 pypi
pyyaml 5.4.1 pypi_0 pypi
pyzmq 23.2.1 pypi_0 pypi
rapidfuzz 2.6.0 pypi_0 pypi
readline 8.1.2 h0f457ee_0 conda-forge
regex 2022.8.17 pypi_0 pypi
requests 2.28.1 pyhd8ed1ab_0 conda-forge
requests-oauthlib 1.3.1 pypi_0 pypi
resampy 0.4.0 pypi_0 pypi
rich 12.5.1 pypi_0 pypi
rsa 4.9 pypi_0 pypi
ruamel-yaml 0.17.21 pypi_0 pypi
ruamel-yaml-clib 0.2.6 pypi_0 pypi
s3transfer 0.6.0 pypi_0 pypi
sacrebleu 2.2.0 pypi_0 pypi
sacremoses 0.0.53 pypi_0 pypi
scikit-learn 1.1.2 pypi_0 pypi
scipy 1.9.0 pypi_0 pypi
sentence-transformers 2.2.2 pypi_0 pypi
sentencepiece 0.1.97 pypi_0 pypi
sentry-sdk 1.9.5 pypi_0 pypi
setproctitle 1.3.2 pypi_0 pypi
setuptools 59.5.0 pypi_0 pypi
shellingham 1.5.0 pypi_0 pypi
shortuuid 1.0.9 pypi_0 pypi
simplejson 3.17.6 pypi_0 pypi
six 1.16.0 pyh6c4a22f_0 conda-forge
smmap 5.0.0 pypi_0 pypi
snowballstemmer 2.2.0 pypi_0 pypi
sortedcontainers 2.4.0 pypi_0 pypi
soundfile 0.10.3.post1 pypi_0 pypi
soupsieve 2.3.2.post1 pypi_0 pypi
sox 1.4.1 pypi_0 pypi
sphinx 5.1.1 pypi_0 pypi
sphinxcontrib-applehelp 1.0.2 pypi_0 pypi
sphinxcontrib-bibtex 2.5.0 pypi_0 pypi
sphinxcontrib-devhelp 1.0.2 pypi_0 pypi
sphinxcontrib-htmlhelp 2.0.0 pypi_0 pypi
sphinxcontrib-jsmath 1.0.1 pypi_0 pypi
sphinxcontrib-qthelp 1.0.3 pypi_0 pypi
sphinxcontrib-serializinghtml 1.1.5 pypi_0 pypi
sqlite 3.39.2 h4ff8645_1 conda-forge
stack-data 0.4.0 pypi_0 pypi
sympy 1.11 pypi_0 pypi
tabulate 0.8.10 pypi_0 pypi
tensorboard 2.10.0 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tk 8.6.12 h27826a3_0 conda-forge
tokenizers 0.12.1 pypi_0 pypi
toml 0.10.2 pypi_0 pypi
tomli 2.0.1 pypi_0 pypi
torchaudio 0.12.1 py38_cu116 pytorch
torchmetrics 0.9.3 pypi_0 pypi
torchvision 0.13.1 py38_cu116 pytorch
tornado 6.2 pypi_0 pypi
tqdm 4.64.0 pypi_0 pypi
traitlets 5.3.0 pypi_0 pypi
transformers 4.21.1 pypi_0 pypi
typed-ast 1.5.4 pypi_0 pypi
typer 0.6.1 pypi_0 pypi
typing_extensions 4.3.0 pyha770c72_0 conda-forge
unidecode 1.3.4 pypi_0 pypi
urllib3 1.26.11 pyhd8ed1ab_0 conda-forge
wandb 0.13.2 pypi_0 pypi
wcwidth 0.2.5 pypi_0 pypi
webdataset 0.1.62 pypi_0 pypi
werkzeug 2.2.2 pypi_0 pypi
wget 3.2 pypi_0 pypi
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
widgetsnbextension 4.0.2 pypi_0 pypi
wrapt 1.14.1 pypi_0 pypi
xorg-libxau 1.0.9 h7f98852_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yarg 0.1.9 pypi_0 pypi
yarl 1.8.1 pypi_0 pypi
youtokentome 1.0.6 pypi_0 pypi
zipp 3.8.1 pypi_0 pypi
zlib 1.2.12 h166bdaf_2 conda-forge
zstd 1.5.2 h6239696_4 conda-forge
```
</details>
Now try and import `nemo.collections.asr`:
```python
import nemo.collections.asr
```
```
Segmentation Fault
```
Debugging with [gdb](https://wiki.python.org/moin/DebuggingWithGdb):
<details>
<summary> Traceback </summary>
```
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
[Detaching after fork from child process 10897]
[New Thread 0x7fff6f483700 (LWP 10898)]
[New Thread 0x7fff6ec82700 (LWP 10899)]
[New Thread 0x7fff6a481700 (LWP 10900)]
[New Thread 0x7fff67c80700 (LWP 10901)]
[New Thread 0x7fff6547f700 (LWP 10902)]
[New Thread 0x7fff64c7e700 (LWP 10903)]
[New Thread 0x7fff6247d700 (LWP 10904)]
[New Thread 0x7fff5fc7c700 (LWP 10905)]
[New Thread 0x7fff5d47b700 (LWP 10906)]
[New Thread 0x7fff5ac7a700 (LWP 10907)]
[New Thread 0x7fff56479700 (LWP 10908)]
[New Thread 0x7fff53c78700 (LWP 10909)]
[New Thread 0x7fff51477700 (LWP 10910)]
[New Thread 0x7fff4ec76700 (LWP 10911)]
[New Thread 0x7fff4c475700 (LWP 10912)]
[New Thread 0x7fff49c74700 (LWP 10913)]
[New Thread 0x7fff47473700 (LWP 10914)]
[New Thread 0x7fff44c72700 (LWP 10915)]
[New Thread 0x7fff42471700 (LWP 10916)]
[New Thread 0x7fff3fc70700 (LWP 10917)]
[New Thread 0x7fff3d46f700 (LWP 10918)]
[New Thread 0x7fff3cc6e700 (LWP 10919)]
[New Thread 0x7fff3a46d700 (LWP 10920)]
[New Thread 0x7fff2cd3c700 (LWP 10921)]
[New Thread 0x7fff2c53b700 (LWP 10922)]
[New Thread 0x7fff29d3a700 (LWP 10923)]
[New Thread 0x7fff27539700 (LWP 10924)]
[New Thread 0x7fff24d38700 (LWP 10925)]
[New Thread 0x7fff22537700 (LWP 10926)]
[New Thread 0x7fff1fd36700 (LWP 10927)]
[New Thread 0x7fff1d535700 (LWP 10928)]
[New Thread 0x7fff1ad34700 (LWP 10929)]
[New Thread 0x7fff18533700 (LWP 10930)]
[New Thread 0x7fff15d32700 (LWP 10931)]
[New Thread 0x7fff13531700 (LWP 10932)]
[New Thread 0x7fff0ed30700 (LWP 10933)]
[New Thread 0x7fff0c52f700 (LWP 10934)]
[New Thread 0x7fff09d2e700 (LWP 10935)]
[New Thread 0x7fff0752d700 (LWP 10936)]
[New Thread 0x7fff04d2c700 (LWP 10937)]
[New Thread 0x7fff0252b700 (LWP 10938)]
[New Thread 0x7ffeffd2a700 (LWP 10939)]
[New Thread 0x7ffefd529700 (LWP 10940)]
[New Thread 0x7ffefcd28700 (LWP 10941)]
[New Thread 0x7ffef8527700 (LWP 10942)]
[New Thread 0x7ffef5d26700 (LWP 10943)]
Thread 1 "python" received signal SIGSEGV, Segmentation fault.
0x00007ffeefe711e1 in google::protobuf::internal::ReflectionOps::FindInitializationErrors(google::protobuf::Message const&, std::string const&, std::vector<std::string, std::allocator<std::string> >*) ()
from /opt/conda/envs/hf/lib/python3.8/site-packages/google/protobuf/pyext/_message.cpython-38-x86_64-linux-gnu.so
(gdb)
```
</details>
Is this possibly related to protobuf? Note: uninstalling and re-installing protobuf gives the same error:
```
pip uninstall protobuf
conda uninstall protobuf
conda install -c anaconda protobuf
python -c "import nemo.collections.asr"
```
```
Segmentation Fault
```
**Expected behavior**
Import of the module!
**Environment overview (please complete the following information)**
- Environment location: GCP. GPU: a2-highgpu-2g. CPU: Intel Cascade Lake
- Method of NeMo install: from source (as per https://github.com/NVIDIA/NeMo#from-source)
**Environment details**
- OS version: Google, Debian 10 based Deep Learning VM with CUDA 11.0, M95, Base CUDA 11.0, Deep Learning VM Image with CUDA 11.0 preinstalled.
- PyTorch version: 1.12.1
- Python version: 3.8 | {
"login": "sanchit-gandhi",
"id": 93869735,
"node_id": "U_kgDOBZhWpw",
"avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/sanchit-gandhi",
"html_url": "https://github.com/sanchit-gandhi",
"followers_url": "https://api.github.com/users/sanchit-gandhi/followers",
"following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}",
"gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}",
"starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions",
"organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs",
"repos_url": "https://api.github.com/users/sanchit-gandhi/repos",
"events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}",
"received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
} | {
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} | # What does this PR do ?
Generalize dist Adam support for GPT-3 to T5 and other Megatron-LM models.
**Collection**: NLP
# Changelog
- When params are BF16, distributed Adam will store 16-bit param remainders instead of FP32 main params
- Decouples distributed Adam support from Megatron O2-level optizations
- Add support for Apex distributed Adam optimizer with Megatron-LM models
# Usage
Set optimizer to `distributed_fused_adam` in config file:
https://github.com/NVIDIA/NeMo/blob/265f7b17876aef7937cf0fedf7a76885bdc63288/examples/nlp/language_modeling/conf/megatron_t5_config.yaml#L137
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
Pinging @ericharper and @yaoyu-33.
# Additional Information
* Option for distributed Adam to store 16-bit remainders with BF16 params was added in https://github.com/NVIDIA/apex/pull/1463
* Closes https://github.com/NVIDIA/NeMo/pull/4796 | {
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} | # What does this PR do ?
Gives the user the ability to specify separate train,test and validation datasets as a dictionary in data_prefix for gpt model
**Collection**: nlp/language_modelling
# Changelog
- Supports dictionary format in data_prefix for megatron gpt config file.
- Uses the existing data loader to load each individual dataset appropriately by changing split string
# Usage
* data_prefix: {train:/path/to/train, test:/path/to/test, validation:/path/to/validation}
# 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.
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 ?
Gives the user the ability to specify separate train,test and validation datasets as a dictionary in data_prefix for gpt model
**Collection**: nlp/language_modelling
# Changelog
- Supports dictionary format in data_prefix for megatron gpt config file.
- Uses the existing data loader to load each individual dataset appropriately by changing split string
# Usage
* data_prefix: {train:/path/to/train, test:/path/to/test, validation:/path/to/validation}
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
https://github.com/NVIDIA/apex/pull/1463 adds an option to the distributed Adam optimizer that reduces memory usage for BF16 models. Instead of storing FP32 main params in the optimizer, we can just store 16-bit param remainders and reconstruct the FP32 main params from the BF16 model params.
**Collection**: NLP
# Changelog
- Store 16-bit param remainders in dist Adam instead of full FP32 main params
- Update Dockerfile to build Apex with dist Adam support
# Usage
Set optimizer to `distributed_fused_adam` in config file:
https://github.com/NVIDIA/NeMo/blob/c0bfa6f07f766a3abd1804f5b666474887e0a1e4/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L150
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
Pinging @ericharper and @yaoyu-33.
# Additional Information
* Depends on https://github.com/NVIDIA/apex/pull/1463
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} | null | null | null | **Describe the bug**
Hi, I'm new to nemo system.
I have no idea why my program stops at "generating predictions with overlapping input segments".
```
[NeMo I 2022-08-23 15:08:22 features:200] PADDING: 16
[NeMo I 2022-08-23 15:08:23 label_models:100] loss is Angular Softmax
[NeMo I 2022-08-23 15:08:23 save_restore_connector:243] Model EncDecSpeakerLabelModel was successfully restored from /home/ec5017b/.cache/torch/NeMo/NeMo_1.10.0/titanet-l/492c0ab8416139171dc18c21879a9e45/titanet-l.nemo.
[NeMo I 2022-08-23 15:08:23 speaker_utils:82] Number of files to diarize: 1
[NeMo I 2022-08-23 15:08:23 clustering_diarizer:293] Split long audio file to avoid CUDA memory issue
100%|████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.32it/s]
[NeMo I 2022-08-23 15:08:24 vad_utils:89] The prepared manifest file exists. Overwriting!
[NeMo I 2022-08-23 15:08:24 classification_models:244] Perform streaming frame-level VAD
[NeMo I 2022-08-23 15:08:24 collections:289] Filtered duration for loading collection is 0.000000.
[NeMo I 2022-08-23 15:08:24 collections:293] # 4 files loaded accounting to # 1 labels
100%|████████████████████████████████████████████████████████████████████| 4/4 [00:03<00:00, 1.09it/s]
[NeMo I 2022-08-23 15:08:28 clustering_diarizer:242] Generating predictions with overlapping input segments
0%| | 0/1 [00:00<?, ?it/s]
```
**Steps/Code to reproduce bug**
`python offline_diarization.py`
my manifest.json
`{"audio_filepath": "/home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/diarization/data/voxconverse/voxconverse_test_wav/voxconverse_test_wav/aepyx.wav", "offset": 0, "duration": null, "label": "infer", "text": "-"}`
offline_diarization.yaml
```
name: &name "ClusterDiarizer"
num_workers: 4
sample_rate: 16000
batch_size: 64
diarizer:
manifest_filepath: /home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/diarization/manifest.json
out_dir: /home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/diarization/output
oracle_vad: False # If True, uses RTTM files provided in manifest file to get speech activity (VAD) timestamps
collar: 0.25 # Collar value for scoring
ignore_overlap: True # Consider or ignore overlap segments while scoring
vad:
model_path: vad_marblenet # .nemo local model path or pretrained model name or none
external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set)
window_length_in_sec: 0.15 # Window length in sec for VAD context input
shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction
smoothing: "median" # False or type of smoothing method (eg: median)
overlap: 0.875 # Overlap ratio for overlapped mean/median smoothing filter
onset: 0.4 # Onset threshold for detecting the beginning and end of a speech
offset: 0.7 # Offset threshold for detecting the end of a speech
pad_onset: 0.05 # Adding durations before each speech segment
pad_offset: -0.1 # Adding durations after each speech segment
min_duration_on: 0.2 # Threshold for small non_speech deletion
min_duration_off: 0.2 # Threshold for short speech segment deletion
filter_speech_first: True
speaker_embeddings:
model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
parameters:
window_length_in_sec: 1.5 # Window length(s) in sec (floating-point number). Either a number or a list. Ex) 1.5 or [1.5,1.0,0.5]
shift_length_in_sec: 0.75 # Shift length(s) in sec (floating-point number). Either a number or a list. Ex) 0.75 or [0.75,0.5,0.25]
multiscale_weights: null # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. Ex) [0.33,0.33,0.33]
save_embeddings: False # Save embeddings as pickle file for each audio input.
clustering:
parameters:
oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
max_num_speakers: 20 # Max number of speakers for each recording. If oracle num speakers is passed, this value is ignored.
enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
sparse_search_volume: 30 # The higher the number, the more values will be examined with more time.
maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers.
# json manifest line example
# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/filepath"}
```
**Expected behavior**
I hope to finish the inference.
**Environment overview**
- Environment location: Bare-metal
- Method of NeMo install:
```
conda create --name nemo python==3.8
conda activate nemo
pip install Cython
pip install nemo_toolkit[all]
git clone https://github.com/NVIDIA/NeMo
```
**Environment details**
- OS version: 16.04
- PyTorch version: 1.12.1
- Python version: 3.8
**Additional context**
I have not prepared Megatron GPT & numba
Moreover, I only use the aepyx.wav in the Voxconverse test.
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} | # What does this PR do ?
Update Megatron LM Encoder Decoder to maintain compatibility with python 3.7 to enable notebooks to run on Colab
**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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} | [] | closed | false | null | [] | null | null | 2022-08-22T22:26:04 | 2022-09-02T16:19:52 | 2022-09-02T16:19:48 | CONTRIBUTOR | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Sets the number of data workers to 0 for validation and test sets for all encoder-decoder models to sidestep a strange CUDA error.
NOTE: This is not a permanent solution and just a temporary fix.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | [] | closed | false | null | [] | null | null | 2022-08-22T19:28:26 | 2022-09-01T17:28:49 | 2022-09-01T17:28:41 | COLLABORATOR | {
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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Introduce a experimental implementation of a generic IPA tokenizer using phonemizer/eSpeak for text normalization & G2P.
You can still optionally use NeMo text normalizer as well by providing it in the config or putting the text through it prior to calling this G2P class, similar to existing tokenizers.
**Collection**: [TTS]
# Changelog
- Added PhonemizerTokenizer class implementation.
- Added phonemizer as a TTS dependency (though eSpeak backend cannot be installed with pip or conda).
- Added an empty pronounce dictionary which we could extend with additional language-agnostic vocabulary, or could replace with language specific pronunciations of certain words.
- Added unit tests
- Install eSpeak on docker & Jenkins
# Usage
```python
import os
from nemo.collections.tts.torch.tts_tokenizers import PhonemizerTokenizer
nemo_root_dir = "..."
ipa_dict_path = os.path.join(nemo_root_dir , "scripts", "tts_dataset_files", "ipa_dict-default.txt")
tokenizer = PhonemizerTokenizer(
language="es",
phoneme_dict=ipa_dict_path,
pad_with_space=True
)
text = "la ciudad es bellísima y tan alegre"
tokens = tokenizer(text)
phonemes = tokenizer.text_to_phonemes(text)
# phonemes -> " lˈa θjuðˈad ˈes beʎˈisima i tˈan alˈeɣɾe "
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] 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.
# Additional Information
Here are examples of the output for a few Spanish sentences from openSLR. Please review and let me know how consistent or inconsistent the character-set/rules/semantics are with the other IPA systems currently being developed.
```
Input: alex, ¿podrás buscar algún restaurante para hoy en la noche?
Output: alˈeks, ¿poðɾˈas buskˈaɾ alɣˈun ɾɾˌestaʊɾˈante pˈaɾa ˈoɪ ˈen lˈa nˈotʃe?
Input: es muy importante el apoyo nasal en la pronunciación
Output: ˈes mˈujj ˌimpoɾtˈante ˈel apˈojjo nasˈal ˈen lˈa pɾˌonunθjaθjˈon
Input: la ciudad es bellísima y tan alegre
Output: lˈa θjuðˈad ˈes beʎˈisima i tˈan alˈeɣɾe
```
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} | Signed-off-by: ericharper <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[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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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 ?
Closes #4772
**Collection**: NLP
# Changelog
- Fix logger reference in `text_memmap_dataset.py`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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I'm new in ASR and want to know the best model architecture or recipe to meet my requirement.
I'm looking a model that can servers:
1. Streaming ASR with minimum latency.
2. SOTA accuracy in generic domain with decent amount of data (1-4K hours).
3. Can handle good throughput in streaming. | {
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} | # What does this PR do ?
* Adds scripts for synthetic code-switched data generation for ASR
* Adds the En-Es code-switched ASR models (stt_enes_conformer_transducer_large_codesw and stt_enes_conformer_ctc_large_codesw)
**Collection**: ASR
# Changelog
- Lines 537 - 543 in nemo/collections/asr/models/ctc_bpe_models.py
- Lines 200 - 206 in nemo/collections/asr/models/rnnt_bpe_models.py
- File code_switching_audio_data_creation.py in scripts/speech_recognition/code_switching/
- File code_switching_manifest_creation.py in scripts/speech_recognition/code_switching/
- File README.md in scripts/speech_recognition/code_switching/
# 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 syntheic 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 snythesized audios> --manifest_save_path <absolute path to save the created manifest> --audio_normalized_amplitude <sclaed normalized amplitude desired> --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> `
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
This PR amends the start time for the last token in RNNT decoding because the code aggregates (inclusively) since the previous_token_index all tokens in the current word. This has tripped me up in my own implementations of similar code external to NeMo in the past. My apologies for not catching it in #4779. Without these changes, some transcription invocations fail.
**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] Bugfix
@titu1994 I tested this code against an ASR corpus of 163,708 words, and it's looking stable with no more errors thrown. My tests were in WER rather than time stamps. There's a jitter of about 0.1%pt WER on clean English and 0.5%pts on noisy English, which is similar to what I've seen before on repeated invocations of custom models, though perhaps a bit larger for noisy audio. I don't know where to enumerate more test cases in this codebase, but this logic is a bit difficult to exhaustively test. I've seen 200-300 millisecond drifts in word timestamps across different models, so I don't know how to formalize a test for these outputs more specifically. | {
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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Fixes two bugs recently introduced into the TTS data loader initialization.
1. When pynini is not installed, then Normalizer = None. This causes `isinstance(self.text_normalizer, Normalizer)` to throw an exception.
2. The statement `("text_normalized" not in item) or ("normalized_text" not in item)` should be an `and `rather than an `or`. But rather than make that specific change, I rewrote the condition checks to be less confusing/error prone.
**Collection**: [TTS]
# Changelog
- Avoid TypeError when user does not use a text normalizer and pynini is not installed.
- Avoid applying text normalization when "text_normalized" is provided as a field
# 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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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes another observed edge case for the final word in RNNT word timestamps
**Collection**: ASR
# Changelog
- the last word in rnnt decoding for a segment may contain multiple tokens, but the data model for how these were aggregated
# 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)
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
# Additional Information
* Related to # (issue)
Example object from real use case
```python
from torch import tensor
offsets = [{'char': [tensor(456, dtype=torch.int32), tensor(1024, dtype=torch.int32)],
'start_offset': 0,
'end_offset': 1},
{'char': [tensor(28, dtype=torch.int32),
tensor(291, dtype=torch.int32),
tensor(1024, dtype=torch.int32)],
'start_offset': 2,
'end_offset': 3}]
built_token = [456, 28, 291]
```
If you attempt to index offsets via `offsets[-len(built_token)]]` you get an error because `len(offsets)==2` and `len(built_token)==3` | {
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes formatting error introduced in previous PR
**Collection**: TTS
# Changelog
- Missing comma led to failure to parse the notebook
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Fix restoring issue with PTL 1.7
**Collection**: Core
# Changelog
Move all non-zero rank value checkpoints to rank 0 checkpoints before restoring.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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"merged_at": "2022-08-19T14:07:01"
} | # What does this PR do ?
Adds conditional logic to rnnt_wer to check if arrays exist and have elements before accessing them. This error occasionally blocks transcription.
**Collection**: ASR
# Changelog
Bug fix for transcription with rnnt sentencepiece tokens
# 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?~ Unable to share specific test file that found this error
- [X] Did you add or update any necessary documentation?
~- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)~
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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Hi,
I have just found a broken link to a notebook in this page: https://docs.nvidia.com/deeplearning/nemo/best-practices/index.html
In the "Table 1. Tutorials", the "Question answering with SQuAD" notebook link is broken.
Instead of:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Question_Answering_Squad.ipynb
it should be:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Question_Answering.ipynb
Hope it helps! | {
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} | null | null | null | **Describe the bug**
I am using a pre-trained Conformer Transducer and want to initialize an optimizer and an LR scheduler using its method `setup_optimization`, which returns the optimizer and the LR scheduler.
The problem I am having is that the ASR model's method `setup_optimization` method requires `max_steps` to be part of the scheduler config. Otherwise, it doesn't initialize the scheduler. But given that `StepLR` does not accept `max_steps` as an argument, it raises an error.
**Steps/Code to reproduce bug**
```python
from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel
config = {
"name": "adamw",
"lr": 0.1,
"betas": [0.9, 0.98],
"weight_decay": 0.001,
"sched": {"name": "StepLR", "step_size": 1, "last_epoch": 1},
}
model = EncDecRNNTBPEModel.from_pretrained("stt_de_conformer_transducer_large")
model.setup_optimization(config)
```
Running this script outputs the following log:
```
[NeMo W 2022-08-19 10:30:46 lr_scheduler:816] Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !
Scheduler will not be instantiated !
```
When adding `max_steps` with an integer value (e.g. `"max_steps". 1`) to `config["sched"]`, the following exception occurs:
```
Exception has occurred: TypeError
__init__() got an unexpected keyword argument 'max_steps'
File "/home/carlos/repos/accsr/lr_issue.py", line 12, in <module>
model.setup_optimization(config)
```
**Expected behavior**
StepLR does not require a `max_steps` argument and should be initialized without it.
**Environment overview (please complete the following information)**
- Environment location: Local machine, conda environment
- Method of NeMo install: pip install (version 1.10.0)
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04
- PyTorch version: 1.12.0
- Python version: 3.8.0
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} | # What does this PR do ?
Creates online data simulation in NeMo dataloader for training diarization models
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add SyntheticDataloader class
- Add AudioToSpeechMSDDSyntheticTrainDataset class
- Modified msdd_models.py for online simulated data generation
# 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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https://github.com/NVIDIA/NeMo/blob/6abfbbfda654f44313068b950edb0f70b01449b1/nemo/collections/nlp/data/language_modeling/text_memmap_dataset.py#L157
I believe this should be switched to `logging`, which is used throughout the rest of the module. | {
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} | # What does this PR do ?
Fixes the following things for T5 prompt-learning:
1. T5 models with RPE.
2. Models trained with asymmetric enc-dec configurations where hidden size is present in cfg.encoder.hidden_size.
3. Aligns config variables names with the GPT prompt learning config.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] 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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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes the use of `self.log()` inside `configure_gradient_clipping` in `megatron_base_model.py`.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Merges the existing NeMo CMUdict with a version of 0.7b with added entries.
**Collection**: TTS
# Changelog
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} | null | null | null | Hi, I'm getting an error trying to execute the colab example: https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_MixerTTS_Training.ipynb with Mixer-TTS-X.
Execution cell:
```
assert isinstance(spec_gen, SpectrogramGenerator)
if isinstance(spec_gen, FastPitchModel):
tokens = spec_gen.parse(str_input="Hey, this produces speech!")
else:
tokens = spec_gen.parse(text="Hey, this produces speech!")
spectrogram = spec_gen.generate_spectrogram(tokens=tokens)
# Now we can visualize the generated spectrogram
# If we want to generate speech, we have to use a vocoder in conjunction to a spectrogram generator.
# Refer to the Inference_ModelSelect notebook on how to convert spectrograms to speech.
imshow(spectrogram.cpu().detach().numpy()[0,...], origin="lower")
plt.show()
```
The same error occurs if I try with MixerTTSModel:
```
assert isinstance(spec_gen, SpectrogramGenerator)
if isinstance(spec_gen, MixerTTSModel):
tokens = spec_gen.parse("Hey, this produces speech!")
else:
tokens = spec_gen.parse(text="Hey, this produces speech!")
spectrogram = spec_gen.generate_spectrogram(tokens=tokens)
# Now we can visualize the generated spectrogram
# If we want to generate speech, we have to use a vocoder in conjunction to a spectrogram generator.
# Refer to the Inference_ModelSelect notebook on how to convert spectrograms to speech.
imshow(spectrogram.cpu().detach().numpy()[0,...], origin="lower")
plt.show()
```
Just in case, the MixerTTSModel loaded is Mixer-TTS-X:
```
# In the same way, we can load the pre-trained Mixer-TTS model as follows
pretrained_model = "tts_en_lj_mixerttsx"
spec_gen = MixerTTSModel.from_pretrained(pretrained_model)
spec_gen.eval();
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-6-e1795f3aeeee>](https://localhost:8080/#) in <module>
6 tokens = spec_gen.parse(text="Hey, this produces speech!")
7
----> 8 spectrogram = spec_gen.generate_spectrogram(tokens=tokens)
9
10 # Now we can visualize the generated spectrogram
1 frames
[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/models/mixer_tts.py](https://localhost:8080/#) in generate_spectrogram(self, tokens, tokens_len, lm_tokens, raw_texts, norm_text_for_lm_model, lm_model)
619 if self.cond_on_lm_embeddings and lm_tokens is None:
620 if raw_texts is None:
--> 621 raise ValueError("raw_texts must be specified if lm_tokens is None")
622
623 lm_model_tokenizer = self._get_lm_model_tokenizer(lm_model)
ValueError: raw_texts must be specified if lm_tokens is None
```
Thanks, | {
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
There are issues with PTL 1.7.2 on megatron scripts, meanwhile, upper bound to 1.7.1
**Collection**: Core
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} | # What does this PR do ?
Adds Exponential Moving Average (EMA) support. We've seen promising results in reducing training times to reach convergence.
Things to do:
- [x] Capture speed degradation using the EMA callback.
There is practically none when the EMA weights are already on CUDA. Below are convergence results using LibriSpeech (SP) and the Conformer RNNT config for ASR:
**Precision 32**
WER vs Global Step
WER vs Process Time
**Precision 16**
WER vs Global Step
WER vs Process Time
- [x] Explore keeping EMA weights on the CPU (transfer to GPU just for the multi-tensor apply, then bring it back to CPU)
- [x] Test using Quartznet/Conformer to ensure we see expected improvements using the EMA callback
- [ ] Add multi-node/gpu correctness test (EMA weights should be the same across processes) to jenkinsfile
**Collection**:
- Common
# Changelog
- Adds Exponential Moving Average (EMA) support.
# Usage
The callback can be added to pure lightning, however it requires Apex to be installed (for the multi-tensor apply).
```python
...
lenet5 = MNISTLeNet5(MNISTLeNet5Config())
lenet5.setup_training_data()
lenet5.setup_optimization()
trainer = Trainer(
accelerator='gpu',
devices=1,
callbacks=EMA(ema=0.999)
)
trainer.fit(model=lenet5)
```
Currently I've exposed this through the exp manager (questionable if this is the right approach or not) to use through the hydra configs.
```bash
python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=... \
model.validation_ds.manifest_filepath=... \
trainer.devices=1 \
+exp_manager.enable_ema=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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
cc @titu1994
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/4763.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/4763.patch",
"merged_at": "2022-08-18T16:47:38"
} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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/4762 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4762/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4762/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4762/events | https://github.com/NVIDIA/NeMo/pull/4762 | 1,342,371,851 | PR_kwDOC_bI7s49Wvbg | 4,762 | fix bug relating to ddp strategy in joint intent slot classification … | {
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"patch_url": "https://github.com/NVIDIA/NeMo/pull/4762.patch",
"merged_at": "2022-08-17T23:09:40"
} | # What does this PR do ?
Fix joint intent slot classification tutorial
**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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} | null | null | null | We are trying to convert a .pt model to .nemo to use for prompt learning, following this example: **examples/nlp/language_modeling/megatron_t5_prompt_learning.py**.
We have trained a T5 model from scratch with Megatron LM codebase, the version we used is 3.0; NeMo code comes with the docker image we used that has nemo-toolkit 1.10.0
The file megatron_t5_prompt_learning.py mentions to use **megatron_ckpt_to_nemo.py** or **megatron_lm_ckpt_to_nemo.py** to convert a model from .pt to .nemo, but no one of the above methods work.
With this command:
```
python -m torch.distributed.launch --nproc_per_node=1 megatron_lm_ckpt_to_nemo.py \
--checkpoint_folder /workspace/datadrive/t5_checkpoint/iter_0896000 \
--checkpoint_name model_optim_rng.pt \
--nemo_file_path /workspace/datadrive/t5_checkpoint/ \
--model_type t5 \
--tensor_model_parallel_size 1 \
--pipeline_model_parallel_size 1 \
--gpus_per_node 1
```
I get this error:
```
root@693cc90da271:/workspace/nemo/examples/nlp/language_modeling# python -m torch.distributed.launch --nproc_per_node=1 megatron_lm_ckpt_to_nemo.py \
> --checkpoint_folder /workspace/datadrive/t5_checkpoint/iter_0896000 \
> --checkpoint_name model_optim_rng.pt \
> --nemo_file_path /workspace/datadrive/t5_checkpoint/ \
> --model_type t5 \
> --tensor_model_parallel_size 1 \
> --pipeline_model_parallel_size 1 \
> --gpus_per_node 1
/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
/opt/conda/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
[NeMo W 2022-08-17 20:49:04 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 2022-08-17 20:49:04 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 2022-08-17 20:49:04 distributed:31] Initializing torch.distributed with local_rank: 0, rank: 0, world_size: 1
[NeMo W 2022-08-17 20:49:06 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
rank_zero_warn(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo I 2022-08-17 20:49:06 megatron_lm_ckpt_to_nemo:387] loading checkpoint /workspace/datadrive/t5_checkpoint/iter_0896000/model_optim_rng.pt
Traceback (most recent call last):
File "megatron_lm_ckpt_to_nemo.py", line 478, in <module>
convert(local_rank, rank, world_size, args)
File "megatron_lm_ckpt_to_nemo.py", line 420, in convert
raise NotImplemented("{} is not supported".format(args.model_type))
TypeError: 'NotImplementedType' object is not callable
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 911) of binary: /opt/conda/bin/python
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
main()
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
launch(args)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
run(args)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run
elastic_launch(
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
megatron_lm_ckpt_to_nemo.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2022-08-17_20:49:09
host : 693cc90da271
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 911)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
```
With this other command:
```
export PYTHONPATH=$PYTHONPATH:/workspace/datadrive/Megatron-LM
python -m torch.distributed.launch --nproc_per_node=1 \
megatron_ckpt_to_nemo.py \
--gpus_per_node 1 \
--model_type t5 \
--checkpoint_folder /workspace/datadrive/t5_checkpoint/iter_0896000 \
--checkpoint_name model_optim_rng.pt \
--nemo_file_path /workspace/datadrive/t5_nemo \
--tensor_model_parallel_size 1 \
--pipeline_model_parallel_size 1
```
I get this error:
```
root@693cc90da271:/workspace/nemo/examples/nlp/language_modeling# export PYTHONPATH=$PYTHONPATH:/workspace/datadrive/Megatron-LM
root@693cc90da271:/workspace/nemo/examples/nlp/language_modeling# python -m torch.distributed.launch --nproc_per_node=1 megatron_ckpt_to_nemo.py --gpus_per_node 1 --model_type t5 --checkpoint_folder /workspace/datadrive/t5_checkpoint/iter_0896000 --checkpoint_name model_optim_rng.pt --nemo_file_path /workspace/datadrive/t5_nemo --tensor_model_parallel_size 1 --pipeline_model_parallel_size 1
/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
/opt/conda/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
[NeMo W 2022-08-17 20:47:57 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 2022-08-17 20:47:57 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 2022-08-17 20:47:57 distributed:31] Initializing torch.distributed with local_rank: 0, rank: 0, world_size: 1
[NeMo W 2022-08-17 20:47:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
rank_zero_warn(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo I 2022-08-17 20:47:58 megatron_ckpt_to_nemo:111] rank: 0, local_rank: 0, is loading checkpoint: /workspace/datadrive/t5_checkpoint/iter_0896000/model_optim_rng.pt for tp_rank: 0 and pp_rank: 0
Traceback (most recent call last):
File "megatron_ckpt_to_nemo.py", line 144, in <module>
convert(local_rank, rank, world_size, args)
File "megatron_ckpt_to_nemo.py", line 122, in convert
model = MegatronT5Model.load_from_checkpoint(checkpoint_path, hparams_file=args.hparams_file, trainer=trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 354, in load_from_checkpoint
model = cls._load_model_state(checkpoint, strict=strict, cfg=cfg, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 203, in _load_model_state
model = cls(**_cls_kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_t5_model.py", line 35, in __init__
super().__init__(cfg, trainer=trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py", line 72, in __init__
super().__init__(cfg, trainer=trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 61, in __init__
super().__init__(cfg, trainer=trainer, no_lm_init=no_lm_init)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 98, in __init__
super().__init__(cfg, trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 97, in __init__
cfg = model_utils.convert_model_config_to_dict_config(cfg)
File "/opt/conda/lib/python3.8/site-packages/nemo/utils/model_utils.py", line 393, in convert_model_config_to_dict_config
raise ValueError(f"cfg constructor argument must be of type DictConfig/dict but got {type(cfg)} instead.")
ValueError: cfg constructor argument must be of type DictConfig/dict but got <class 'dict'> instead.
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 690) of binary: /opt/conda/bin/python
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
main()
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
launch(args)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
run(args)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run
elastic_launch(
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
megatron_ckpt_to_nemo.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2022-08-17_20:48:03
host : 693cc90da271
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 690)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
```
**Environment overview**
- I’m running the commands on a VM in GCP with A100 GPUs
- I’m using NeMo from docker image
- sudo docker run --shm-size=256g --gpus all -it --rm -v ~/datadrive:/workspace/datadrive nvcr.io/nvidia/nemo:22.05
- the code snippet are taken directly from the corresponding .py file
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Pin min version for PTL 1.7.2 and remove patch due to .to(cuda) issue with earlier versions.
**Collection**: Core
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation | {
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} | …model
Signed-off-by: Ameya Mahabaleshwarkar <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: nlp
# Changelog
- Added a data processor for a custom dataset for the DialogueS2SGenerationModel.
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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https://api.github.com/repos/NVIDIA/NeMo/issues/4756 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4756/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4756/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4756/events | https://github.com/NVIDIA/NeMo/pull/4756 | 1,342,170,663 | PR_kwDOC_bI7s49WEqG | 4,756 | [TTS] updated config with a German IPA phoneme tokenizer | {
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} | * disabled spline interpolation for beta-binomial distribution by default. Let it generate align prior and save to disks.
* updated config using a phoneme tokenizer because the final model release was built upon the phoneme tokenizer instead of a mixer of grapheme/phoneme.
* note that phoneme tokenizer's `encode()` class method was extended to support `c == "\u0303"` for German, but not supported in the previous single speaker German model release.
* use consistent spline interpolation with fastpitch checkpoint when generating mel-spectrograms for hifigan fine-tune.
* note: this was already merged in main (https://github.com/NVIDIA/NeMo/pull/4679), but is missing in r1.11.0. Affected file is `scripts/dataset_processing/tts/generate_mels_for_finetune_hifigan.py` (you could ignore this file during your reviewing.)
Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes NLPModel's `load_from_checkpoint` because PTL changed `_load_model_state` in 1.7.1.
**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 ?
Updates prompt learning documentation paths and updates some parts of the prompt learning documentation. Addressing https://nvbugs/3748879 [ML-NeMo][A40] Multitask_Prompt_and_PTuning.ipynb contains a broken link and wrong branch
**Collection**: NLP
# Changelog
- Prompt learning docs
- Prompt learning tutorial
- README.md
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [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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Replace functools.cached_property with functools.lru_cache for compatibility with py37 (which Colab uses).
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] 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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Replace functools.cached_property with functools.lru_cache for compatibility with py37 (which Colab uses).
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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# What does this PR do ?
Fix jenkins CI issue with data simulator section config file
**Collection**: Tools
# Changelog
- remove single quotes
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | How to calculate `models_stride_per_sec` for stt_es_conformer_ctc_large. model. I am trying to transcribe audio of 1 hr using the
stt_en_conformer_ctc_large its 0.04
`from nemo.collections.asr.parts.utils.decoder_timestamps_utils import FrameBatchASR_Logits`
is there a better way to stream log audios on the conformer models.
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# What does this PR do ?
Revert to colab Python 3.7friendly version of cached_property
**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
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
Add a pretrained model for English itn t5 on NGC
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# 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**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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
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# Usage
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```python
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```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?
- [ ] 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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Move over to new heteronyms list based on Wikipedia's [heteronyms short list](https://en.wikipedia.org/wiki/Heteronym_(linguistics)#English) and the [Wikipedia Homograph Data](https://github.com/google-research-datasets/WikipediaHomographData/blob/master/data/wordids.tsv).
**Collection**: TTS
# Changelog
- Update existing TTS configs to use the new heteronyms list
- Remove outdated heteronyms list
- Add missing pronunciation of "merchandise" to CMUdict | {
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} | # What does this PR do ?
Move over to new heteronyms list based on Wikipedia's [heteronyms short list](https://en.wikipedia.org/wiki/Heteronym_(linguistics)#English) and the [Wikipedia Homograph Data](https://github.com/google-research-datasets/WikipediaHomographData/blob/master/data/wordids.tsv).
**Collection**: TTS
# Changelog
- Update existing TTS configs to use the new heteronyms list
- Remove outdated heteronyms list
- Add missing pronunciation of "merchandise" to CMUdict | {
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} | # What does this PR do ?
Port Megatron features to match GPT3 convergence
**Collection**: NLP/Megatron
# Changelog
- Add LayerNorm1P class implementing numerically stable LayerNorm with formula: `(self.weight + 1) * x + self.bias` instead of `self.weight * x + self.bias`
- add option to use weight decay also on LayerNorm weights
- add option to use unscaled residuals initialization
All 3 options are exposed with new hydra flags. With default flag values, the **model behaviour is unchanged**.
# Usage
Override these flags to enable respective features described above:
- model.normalization=layernorm1p
- model.do_layer_norm_weight_decay=1
- model.use_scaled_init_method=0
Each of the flags work independently from each other
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Adds checks for trainer.limit_val/test_batches and trainer.val_check_interval
**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 ?
Add a one line overview of what this PR aims to accomplish.
**Collection**:
NLP
# Changelog
- Fix issues with strategy and plugin mix (with PTL 1.7 upgrade)
# 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 ?
Enable sync gradient reduction only when DP size is larger than 1.
This fixes the bug that NCCL kernels for grad-AR were issued asynchronously even when DP=1 slowing down compute kernel execution.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix typos in ASR notebooks.
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix typos in ASR tutorials
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Fix a typo in a parameter name. Instead of 'save_on_train_end', the correct parameter name is 'save_nemo_on_train_end'.
Signed-off-by: Miguel Martínez <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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 a typo in a parameter name. Instead of 'save_on_train_end', the correct parameter name is 'save_nemo_on_train_end'.
**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
- [ 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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* Related to # (issue)
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/4734.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/4734.patch",
"merged_at": "2022-08-15T21:23:29"
} | # What does this PR do ?
Release new multilingual VAD model that has been trained on 6 languages.
Find more details and evaluation results in [NGC model card](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet).
**Collection**: ASR
# Changelog
- Link to new model.
- VAD+ASR: Tuned postprocessing parameters on 0~20db SNR noisy and clean multilingual ASR data in `examples/asr/conf/vad/vad_inference_postprocessing.yaml`
- VAD in Speaker Diarization: Tuned postprocessing parameters on 11 moved session from CH109 in `examples/speaker_tasks/diarization/conf/offline_diarization.yaml` and `offline_diarization_with_asr.yaml`
- Tutorials and Docs.
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4733 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4733/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4733/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4733/events | https://github.com/NVIDIA/NeMo/pull/4733 | 1,337,243,510 | PR_kwDOC_bI7s49F_Zj | 4,733 | Add pynini to Docker container | {
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} | # What does this PR do ?
Install `pynini` library in Docker container.
Currently pynini is not installed in Docker container, which causes TTS models to crash
**Collection**: [Note which collection this PR will affect]
* text processing
* tts
# Changelog
- install pynini (`Dockerfile`)
# Problem
Run the following code in container built from `r1.11.0` branch:
```python
from nemo.collections.tts.models import FastPitchModel
model = FastPitchModel.from_pretrained("tts_en_fastpitch_multispeaker")
```
*Expected results:* model is loaded correctly
*Actual result:*
```
AttributeError Traceback (most recent call last)
File /opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py:583, in _locate(path)
582 try:
--> 583 obj = getattr(obj, part)
584 except AttributeError as exc_attr:
AttributeError: module 'nemo_text_processing.text_normalization' has no attribute 'normalize'
During handling of the above exception, another exception occurred:
ModuleNotFoundError Traceback (most recent call last)
File /opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py:589, in _locate(path)
588 try:
--> 589 obj = import_module(mod)
590 continue
File /opt/conda/lib/python3.8/importlib/__init__.py:127, in import_module(name, package)
126 level += 1
--> 127 return _bootstrap._gcd_import(name[level:], package, level)
File <frozen importlib._bootstrap>:1014, in _gcd_import(name, package, level)
File <frozen importlib._bootstrap>:991, in _find_and_load(name, import_)
File <frozen importlib._bootstrap>:975, in _find_and_load_unlocked(name, import_)
File <frozen importlib._bootstrap>:671, in _load_unlocked(spec)
File <frozen importlib._bootstrap_external>:843, in exec_module(self, module)
File <frozen importlib._bootstrap>:219, in _call_with_frames_removed(f, *args, **kwds)
File /opt/conda/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py:24, in <module>
22 from typing import Dict, List, Union
---> 24 import pynini
25 import regex
ModuleNotFoundError: No module named 'pynini'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #4674
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My code:
```
import nemo
import nemo.collections.asr as nemo_asr
import pytorch_lightning as pl
from nemo.utils.exp_manager import exp_manager
from omegaconf import DictConfig, OmegaConf
params = OmegaConf.load(model_conf_path)
params.trainer.gpus = 1
params.model.train_ds.manifest_filepath = train_manifest_path
params.model.validation_ds.manifest_filepath = validation_manifest_path
params.model.test_ds.manifest_filepath = test_manifest_path
params.exp_manager.exp_dir = 'path/to/save'
trainer = pl.Trainer(**params.trainer)
model = nemo_asr.models.EncDecCTCModel(cfg=params.model, trainer=trainer)
exp_manager(trainer=trainer, cfg=params.exp_manager)
trainer.fit(model)
trainer.test(model)
```
Train manifest file looks like this:
{"audio_filepath": ""/path/to/wav", "duration": 4.8, "text": "у вас просроченных дней две тысячи сто шестьдесят один дней"}
{"audio_filepath": "/path/to/wav", "duration": 9.1, "text": "выключай выключай давайте следующую свечу других нет сейчас расскажу один анекдот когда"}
Total files: 34786
Duration - 601 hours.
There is yaml config:
https://github.com/Project-fil/Project-fil/blob/main/quartznet_ru.yaml
The training takes place in 4 epoch.
Test_loss - 65.60244750976562
Test_wer - 0.928061842918396
I get five files in dir 'checkpoints':
```
'QuartzNet15x5_ru--val_wer=0.9281-epoch=4-last.ckpt'
'QuartzNet15x5_ru--val_wer=0.9765-epoch=2.ckpt'
'QuartzNet15x5_ru--val_wer=0.9594-epoch=3.ckpt'
'QuartzNet15x5_ru--val_wer=0.9281-epoch=4.ckpt'
QuartzNet15x5_ru.nemo
```
And when i try to decode wav file, with this code:
```
first_path = path/to/QuartzNet15x5_ru--val_wer=0.9281-epoch=4-last.ckpt # or another *.ckpt
restored_model = nemo_asr.models.EncDecCTCModel.load_from_checkpoint(first_path)
rt = restored_model.transcribe([aud_ru])
print(rt)
```
I get this:
['просротеяне две тстистоиеяьдесят тоде нде']
and
['аич д брдие еча скожеден ниду']
instead:
['у вас просроченных дней две тысячи сто шестьдесят один дней']
and
['выключай выключай давайте следующую свечу других нет сейчас расскажу один анекдот когда']
All files that I decode are decrypted incorrectly
Am I doing something wrong or not enough hours to learn? | {
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} | # What does this PR do ?
Intent slot model onnx export test
**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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} | # What does this PR do ?
Update Intent Slot model ONNX Export Test
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Update TTS tutorials to install pynini with the new script.
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #4674
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} | Signed-off-by: ericharper <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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try test .py
test .py code content only
import nemo.collections.asr as nemo_asr
had a error about
```
Traceback (most recent call last):
File "test.py", line 1, in <module>
import nemo.collections.asr as nemo_asr
File "/code/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/code/nemo/collections/asr/models/__init__.py", line 16, in <module>
from nemo.collections.asr.models.classification_models import EncDecClassificationModel
File "/code/nemo/collections/asr/models/classification_models.py", line 27, in <module>
from pytorch_lightning.metrics.regression import MeanAbsoluteError, MeanSquaredError
ModuleNotFoundError: No module named 'pytorch_lightning.metrics'
```
i try install pip install pytorch-lightning==1.2.2
had another error about
```
Traceback (most recent call last):
File "test.py", line 1, in <module>
import nemo.collections.asr as nemo_asr
File "/code/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/code/nemo/collections/asr/models/__init__.py", line 16, in <module>
from nemo.collections.asr.models.classification_models import EncDecClassificationModel
File "/code/nemo/collections/asr/models/classification_models.py", line 31, in <module>
from nemo.collections.asr.parts.features import WaveformFeaturizer
File "/code/nemo/collections/asr/parts/features.py", line 44, in <module>
from torch_stft import STFT
ModuleNotFoundError: No module named 'torch_stft'
```
i try install pip install torch-stft
now is work, but I don't know if there will be the next problem | {
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} | # What does this PR do ?
Add the interface to enable TransformerEngine/eMHA at NeMo
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
**training.model.use_emha=True**
Should be used with **training.model.transformer_engine=True**
```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/4725 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4725/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4725/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4725/events | https://github.com/NVIDIA/NeMo/pull/4725 | 1,335,342,958 | PR_kwDOC_bI7s48_r0F | 4,725 | [TTS] bugfix for missing configs. | {
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# What does this PR do ?
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# What does this PR do ?
Making small adjustments to exported methods to match Riva expectations.
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.
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```python
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
adding decoder memory for faster inference - MegatronNMT
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fix setting up of `ReduceLROnPlateau` learning rate scheduler.
**Collection**: core
Steps to reproduce
```bash
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo
git checkout fix/reduce_on_plateau_r1.11.0
git checkout r1.11.0 -- nemo/core/optim/lr_scheduler.py nemo/core/config/schedulers.py
pytest tests/core/test_optimizers_schedulers.py
```
# Before your PR is "Ready for review"
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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https://api.github.com/repos/NVIDIA/NeMo/issues/4719 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4719/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4719/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4719/events | https://github.com/NVIDIA/NeMo/pull/4719 | 1,335,002,032 | PR_kwDOC_bI7s48-j0Y | 4,719 | Change Num Partitions size expansion fix | {
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} | # What does this PR do ?
Fixes model disk size expansion in Change Num Partitions
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/4718 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4718/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4718/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4718/events | https://github.com/NVIDIA/NeMo/pull/4718 | 1,334,844,216 | PR_kwDOC_bI7s48-Cjs | 4,718 | [TTS] FastPitch training: speed up align_prior_matrix calculation | {
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} | # What does this PR do ?
Speed up align_prior_matrix calculation and don't save it to disk. They take lots of space (tens of gigabytes per experiment) and it is perfectly fine to compute them on-the-fly. Matrix calculation speed-up is significant.
```python
%timeit beta_binomial_prior_distribution_numpy_old(80, 300)
226 ms ± 5.86 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit beta_binomial_prior_distribution_new_torch(80, 300)
586 µs ± 22 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```
**Collection**: TTS
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
# 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/4717 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4717/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4717/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4717/events | https://github.com/NVIDIA/NeMo/pull/4717 | 1,334,093,162 | PR_kwDOC_bI7s487jQJ | 4,717 | Adapter tuning for Megatron GPT models | {
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} | # What does this PR do ?
This PR provides support to train and evaluate Adapter Infused GPT Models. The adapters supported are based off Houlsby et al. https://arxiv.org/pdf/1902.00751.pdf for NLP tasks. Some minor modifications were made to support Pipeline parallel training and inference.
**Collection**: [NLP]
# Changelog
- Added a training script for Adapter Infused training of a GPT Model.
- Added an evaluation script that loads a base GPT Model and it's corresponding adapter weights and runs inference.
- Added an Adapter Wrapper model which handles insertion of Adapters, loading and saving of Adapters for a base GPT Model.
# Usage
* Training Usage:
```
python megatron_gpt_adapter_tuning.py \
model.data.train_ds=["PATH TO TRAINING .jsonl FILE"] \
model.data.validation_ds=["PATH TO VALIDATION .jsonl FILE"] \
model.language_model_path="PATH TO BASE GPT MODEL .nemo FILE" \
name="NAME OF TRAINING RUN" \
exp_manager.exp_dir="DIR TO SAVE CHECKPOINTS and ADAPTER .nemo FILE" \
trainer.max_epochs=2
```
* Inference Usage:
```
python megatron_gpt_adapter_eval.py \
gpt_model_file="PATH TO GPT MODEL .nemo FILE" \
adapter_model_file="PATH TO ADAPTER MODEL .nemo FILE (generated by training script: ./megatron_gpt_adapter_tuning.py)" \
data_paths=["PATH TO A .jsonl FILE CONTAINING PROMPTS"] \
output_file="PATH TO OUTPUT FILE TO DUMP PREDICTIONS"
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
Jenkins Tests to train and evaluation Adapter Support for tp=2 and pp=2 GPT Models.
- [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)
# Future Work
* Refactor class hierarchy between Adapters, P-Tuning and Prompt Tuning.
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} | # What does this PR do ?
Fix bug intent slot classification tokenizer
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Adding decoder memory to increase inference performance
**Collection**: [Note which collection this PR will affect]
# Changelog
- changes in Megatron decoder and related files
- added use_memory for translate function in megatron_nmt
# 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**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Adds NGC model loading for the RAD-TTS Aligner.
**Collection**: TTS
# Changelog
- Add newly published NGC checkpoint to list of pretrained Aligner models
- Update tutorial to load this checkpoint instead of a local one
# Usage
```python
from nemo.collections.tts.models import AlignerModel
aligner = AlignerModel.from_pretrained("tts_en_radtts_aligner")
```
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation | {
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} | # What does this PR do ?
To handle very large dataset, e.g. hundreds of gigabyte to terrabyte compressed raw data, we need multiple nodes to create sharding index and combine them together.
It enhance the KNN index data structure to handling sharding index.
In this PR, it creates GPU Faiss index in 3 stages.
1. stage 0, train Faiss index structure.
2. stage 1, create Faiss sharding index
3. stage 2, merge sharding index into one.
It creates KNN index in 2 stages.
1. stage 1, create KNN sharding index
2. stage 2, merge KNN sharding index into one.
It includes some unit tests to cover the KNN sharding index, dedup funcitons etc.
I have been used it to successfuly create Faiss and KNN index for 350G dataset in a slurm cluster enviroment. | {
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} | Signed-off-by: David Mosallanezhad <[email protected]>
# What does this PR do ?
Fixed Megatron export for updated Megatron models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Changed tokens_head and row parallel layers to linear during the export process
# 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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} | Added comparation tool to SDE, work in progress
# What does this PR do ?
Adds new page to SDE
**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
```python data_explorer.py FTAll_books_train.json -c1 Conf_test.json -c2 Context_test.json
```
# 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**:
- [yes] 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 ?
This PR adds stateless decoder support for RNN-T ASR models.
**Collection**: ASR
# Changelog
- adds a stateless decoder class for RNN-T ASR models.
- adds another set of asr model tests for partial decoding.
- small bus fixes for asr greedy decode during partial decoding
# Usage
To launch an RNN-T model with a stateless decoder, change the training script by adding the following lines with recommended settings,
```
model.decoder._target_=nemo.collections.asr.modules.StatelessTransducerDecoder \
+model.decoder.context_size=2 \
model.decoder.normalization_mode=layer \
~model.decoder.random_state_sampling \
~model.decoder.blank_as_pad \
```
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
**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 used NeMo/scripts/dataset_processing/ljspeech/README,
and i ran NeMo/scripts/dataset_processing/ljspeech/extract_ljspeech_phonemes_and_durs.sh .
However alignments directory is empty.
My logs;
`Skipping environment setup. Assuming env name aligner exists.
Starting MFA with dictionary at: /home/mesut/Documents/MFA/pretrained_models/dictionary/english_us_arpa.dict
INFO - Setting up corpus information...
INFO - Loading corpus from source files...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13100/13100 [00:15<00:00, 872.68it/s]
INFO - Found 1 speaker across 13100 files, average number of utterances per speaker: 13100.0
INFO - Initializing multiprocessing jobs...
WARNING - Number of jobs was specified as 3, but due to only having 1 speakers, MFA will only use 1 jobs.
INFO - Creating corpus split for feature generation...
INFO - Generating base features (mfcc)...
INFO - Generating MFCCs...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13100/13100 [06:02<00:00, 36.14it/s]
INFO - Calculating CMVN...
INFO - Creating corpus split with features...
INFO - Compiling training graphs...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13100/13100 [00:03<00:00, 3984.20it/s]
INFO - Performing first-pass alignment...
INFO - Generating alignments...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13100/13100 [01:43<00:00, 126.89it/s]
INFO - Calculating fMLLR for speaker adaptation...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00, 7.93s/it]
INFO - Performing second-pass alignment...
INFO - Generating alignments...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13100/13100 [01:41<00:00, 129.40it/s]
INFO - Exporting TextGrids to /media/mesut/kdtmDisk1/data/LJSpeech-1.1/alignments...
INFO - Collecting phone and word alignments from alignment lattices...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13100/13100 [00:06<00:00, 2021.65it/s]
0%| | 0/13100 [00:00<?, ?it/s]
INFO - Finished exporting TextGrids to /media/mesut/kdtmDisk1/data/LJSpeech-1.1/alignments!
INFO - Done! Everything took 619.2278959751129 seconds
Creating word->phone and phone->idx mappings at /media/mesut/kdtmDisk1/data/LJSpeech-1.1/mappings.json...
Total number of phone indices: 508
Calculating phoneme durations...
Calculating phoneme durations, files will be in: /media/mesut/kdtmDisk1/data/LJSpeech-1.1/phoneme_durations/
0it [00:00, ?it/s]
Getting rid of 0 samples with OOV words.
List of OOV samples written to: /media/mesut/kdtmDisk1/data/LJSpeech-1.1/wavs_to_ignore.pkl
Phoneme durations and tokens written.
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} | null | null | null | **Describe the bug**
Problem with fitting model step-by-step in [this](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Megatron_Synthetic_Tabular_Data_Generation.ipynb) notebook.
**Steps/Code to reproduce bug**
On step
```
!python megatron_gpt_pretraining.py \
trainer.devices={NUM_GPUS} \
trainer.accelerator=gpu \
trainer.log_every_n_steps=100 \
trainer.val_check_interval=500 \
trainer.accumulate_grad_batches=1 \
trainer.max_steps=10000 \
trainer.precision=16 \
trainer.gradient_clip_val=1.0 \
exp_manager.exp_dir=gpt_creditcard_results \
model.tensor_model_parallel_size={TENSOR_MP_SIZE} \
model.pipeline_model_parallel_size={PIPELINE_MP_SIZE} \
model.optim.name=fused_adam \
model.optim.lr=2e-4 \
model.optim.sched.warmup_steps=2 \
model.optim.sched.constant_steps=2 \
model.optim.sched.min_lr=8e-5 \
model.max_position_embeddings={SEQ_LENGTH} \
model.encoder_seq_length={SEQ_LENGTH} \
model.data.seq_length={SEQ_LENGTH} \
model.tokenizer.type=Tabular \
model.tokenizer.library=tabular \
model.tokenizer.vocab_file={CC_OUTPUT_P} \
model.tokenizer.delimiter=\',\' \
model.data.eod_mask_loss=True \
model.data.splits_string=\'3800,198,2\' \
model.num_layers={NUM_LAYERS} \
model.hidden_size={HIDDEN_SIZE} \
model.num_attention_heads={NUM_ATTENTION_HEADS} \
model.activations_checkpoint_method=\'block\' \
model.activations_checkpoint_num_layers=1 \
model.data.data_prefix=[tabular_data_text_document]
```
I've got such traceback
```
Traceback (most recent call last):
File "megatron_gpt_pretraining.py", line 88, in main
trainer.fit(model)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in fit
self._call_and_handle_interrupt(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run
results = self._run_stage()
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage
return self._run_train()
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1345, in _run_train
self._run_sanity_check()
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1413, in _run_sanity_check
val_loop.run()
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 154, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 128, in advance
output = self._evaluation_step(**kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 226, in _evaluation_step
output = self.trainer._call_strategy_hook("validation_step", *kwargs.values())
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1765, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 355, in validation_step
return self.model(*args, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 888, in forward
output = self.module(*inputs, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 93, in forward
return self.module.validation_step(*inputs, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 397, in validation_step
losses_reduced_per_micro_batch = forward_backward_no_pipelining(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/apex/transformer/pipeline_parallel/schedules/fwd_bwd_no_pipelining.py", line 90, in forward_backward_no_pipelining
output_tensor = forward_step(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/apex/transformer/pipeline_parallel/schedules/common.py", line 299, in forward_step
output_tensor, loss_func = forward_step_func(batch, model)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 330, in fwd_output_and_loss_func
output_tensor = model(tokens, position_ids, attention_mask, labels)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron/gpt_model.py", line 173, in forward
lm_output = self.language_model(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/language_model.py", line 505, in forward
encoder_output = self.encoder(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/transformer.py", line 1757, in forward
hidden_states = self._checkpointed_forward(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/transformer.py", line 1675, in _checkpointed_forward
hidden_states = tensor_parallel.checkpoint(
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/apex/transformer/tensor_parallel/random.py", line 311, in checkpoint
return CheckpointFunction.apply(function, distribute_saved_activations, *args)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/apex/transformer/tensor_parallel/random.py", line 256, in forward
outputs = run_function(*args)
File "/home/user/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/transformer.py", line 1626, in custom_forward
encoder_decoder_position_bias = inputs[6]
IndexError: tuple index out of range
```
All previous cells worked fine.
**Expected behavior**
Model fitting process
**Environment overview (please complete the following information)**
- Environment location: [Cloud provider]
- Method of NeMo install: [pip install nemo_toolkit['all']].
**Environment details**
- OS version Ubuntu 18.04.6 LTS
- PyTorch 1.10.2+cu111
- Python 3.8
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} | null | null | null | Hi!
I trained the model using:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
```
import nemo.collections.asr as nemo_asr
from omegaconf import OmegaConf
import torch
import pytorch_lightning as pl
import os
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.load_from_checkpoint('/home/denis/ttt/result/TitaNet/2022-08-05_08-47-00/checkpoints/TitaNet--val_loss=2.3548-epoch=9-last.ckpt')
#decision = speaker_model.verify_speakers('/home/denis/ttt/data/an4/wav/an4_clstk/mjes/cen7-mjes-b.wav','/home/denis/ttt/data/an4/wav/an4_clstk/mjes/cen2-mjes-b.wav')
decision = speaker_model.verify_speakers('/home/denis/ttt/data/an4/wav/an4_clstk/mjes/cen7-mjes-b.wav','data/an4/wav/an4test_clstk/fcaw/an406-fcaw-b.wav')
print(decision)
```
It works!
I can compare two files. Is there a method to compare all my speakers (many files) with another single file?
Do I need to use a loop or another method?
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https://api.github.com/repos/NVIDIA/NeMo/issues/4705 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4705/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4705/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4705/events | https://github.com/NVIDIA/NeMo/issues/4705 | 1,332,722,215 | I_kwDOC_bI7s5Pb7on | 4,705 | Train LM with a large file | {
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Using this below command:
python /workspace/data/NeMo/examples/nlp/language_modeling/bert_pretraining.py
--config-name=/workspace/data/NeMo/examples/nlp/language_modeling/conf/bert_pretraining_from_text_config.yaml
model.train_ds.data_file="/workspace/data/NeMo/lm/data/public-data/train.txt"
model.validation_ds.data_file="/workspace/data/NeMo/lm/data/public-data/val.txt"
model.train_ds.batch_size=128
model.optim.lr=5e-5
trainer.max_epochs=1
trainer.gpus=1
Then it show this error
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
Using native 16bit precision.
[NeMo I 2022-08-09 02:50:20 exp_manager:216] Experiments will be logged at /workspace/data/NeMo/public-data-lm/nemo_experiments/PretrainingBERTFromText/2022-08-09_02-50-20
[NeMo I 2022-08-09 02:50:20 exp_manager:563] TensorboardLogger has been set up
0%| | 0/1 [00:48<?, ?it/s]
Traceback (most recent call last):
File "/workspace/data/bew/NeMo/examples/nlp/language_modeling/bert_pretraining.py", line 31, in main
bert_model = BERTLMModel(cfg.model, trainer=trainer)
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/models/language_modeling/bert_lm_model.py", line 63, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/opt/conda/lib/python3.6/site-packages/nemo/core/classes/modelPT.py", line 127, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/models/language_modeling/bert_lm_model.py", line 192, in setup_training_data
else self._setup_dataloader(train_data_config)
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/models/language_modeling/bert_lm_model.py", line 235, in _setup_dataloader
short_seq_prob=cfg.short_seq_prob,
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/data/language_modeling/lm_bert_dataset.py", line 90, in __init__
sentence_indices[filename] = array.array("I", newline_indices)
OverflowError: unsigned int is greater than maximum
I can train with the small size data, training is fine but the large file I got this error
**Environment details**
- Ubuntu
- Pytorch 1.8.0a0+17f8c32
- Pytorch-lightning 1.2.10
- Python 3.6.10
- DGX-V100 32G
- NeMO 1.0.0rc1
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} | null | null | null | 純粹好奇詢問...
為什麼NeMo沒有做繁體中文相關的
還是有我只是沒發現? | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Bugfix for ASR buffered inference tutorial
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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# What does this PR do ?
Add introductory post to nvidia.github.io/NeMo/
**Collection**: [Docs]
# Changelog
- Add introductory post
- Add support for MathJax
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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"merged_at": null
} | # What does this PR do ?
Adds decoder memory for faster inference
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds a conversion script and related compatibility args to port Huggingface T5v1_1 weights to NeMo-Megatron.
**Collection**: NLP
# Changelog
- Adds a state dict conversation script.
- Adds necessary args to yaml configs to make sure the forward passes in HF and NeMo result in identical results.
# Usage
* You can potentially add a usage example below
```bash
python scripts/nlp_language_modeling/hf_t5_to_nemo_coverter.py \
--hf_model_name google/ul2 \
--nemo_state_dict /path/to/nemo_ul2_state_dict.pt
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Fix to get the titanet-finetune.conf file before running it.
**Collection**: Speaker Tasks
# Changelog
Previous code line was commented, now uncommented to get the required config file
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
Hi,
I am trying to convert Biomegatron Model finetuned for token classification tasks using nemo to onnx format. I am getting the following error:
ARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of prim::PythonOp type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
[NeMo E 2022-08-08 14:55:29 export:164] Export failed. Please make sure your NeMo model class (<class 'nemo.collections.nlp.models.token_classification.token_classification_model.TokenClassificationModel'>) has working export() and that you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "export.py", line 173, in <module>
nemo_export(sys.argv[1:])
File "export.py", line 169, in nemo_export
raise e
File "export.py", line 155, in nemo_export
_, descriptions = model.export(
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/exportable.py", line 68, in export
out, descr, out_example = model._export(
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/exportable.py", line 164, in _export
torch.onnx.export(
File "/opt/conda/lib/python3.8/site-packages/torch/onnx/__init__.py", line 332, in export
return utils.export(model, args, f, export_params, verbose, training,
File "/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py", line 125, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py", line 824, in _export
proto, export_map, val_use_external_data_format, node_names = graph._export_onnx(
RuntimeError: ONNX export failed: Couldn't export Python operator ScaledMaskedSoftmax
**Steps/Code to reproduce bug**
I'm using the script export.py used in the below tutorial:
[(https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/MegatronBert_export.ipynb)]
Please, download export.py from here:
[(https://raw.githubusercontent.com/NVIDIA/NeMo/{BRANCH}/scripts/export.py)]
Also, tried model.export('model.onnx') separately and getting the same error.
**Environment overview (please complete the following information)**
- Environment location: Docker, GCP
- docker pull nvcr.io/nvidia/nemo:22.05
**Environment details**
NeMo container: nvcr.io/nvidia/nemo:22.05
NeMo version: 1.10.0
Branch: 'Main'
Biomegatron Model: "biomegatron345m_biovocab_50k_cased"
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} | null | null | null | Can I get more data on the dataset cleaning process?
I am a non spanish speaker 😅.
Like voxpoplui datasets says 120hrs after cleaning. I downloaded the dataset it has 150 hrs. Need to know which 30 hrs to clean please.
also in model description it says trained the confirmer large ctc on 7000 hrs of pretrained model. But it points to model trained on 16k hrs.
Thanks for the help
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} | null | null | null | Is it possible to add new tokens to the tokeniser of a pretrained model. say I want to fine tune on some new data, which contains few new tokens eg. punct or nums can I add these to the decoder vocabulary?
Here I don't want to change the entire vocabulary so the initial decoder weights will remain the same hence I can reuse them, but for the new tokens the decoder weights can be randomly initialised and learned during fine-tuning
The only other way I knew to add new tokens was to change the entire tokeniser. but then I am unable to use the decoder. | {
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] | null | null | 2022-08-08T09:24:16 | 2022-10-09T05:59:53 | 2022-10-09T05:59:53 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I'm trying to convert .nemo to .riva, but I'm getting message `AttributeError: 'EncDecRNNTBPEModel' object has no attribute 'input_example`. Converting the Conformer-CTC model is OK but the Conformer-Transducer model cannot.
run command:
```bash
nemo2riva --out model_conformer_transducer.riva Conformer-Transducer-BPE.nemo
```
got following error:
```bash
[NeMo W 2022-08-08 17:31:20 schema:118] Validation schema not found for nemo.collections.asr.models.EncDecRNNTBPEModel.
That means Riva does not yet support a pipeline for this network and likely will not work with it.
[NeMo I 2022-08-08 17:31:20 artifacts:65] Found model at ./model_weights.ckpt
[NeMo I 2022-08-08 17:31:20 artifacts:143] Retrieved artifacts: dict_keys(['08c8ba770083451b8aad07ed7310d7ef_tokenizer.model', '596cc7598fe44de9a3f4db5f5f5b0a3d_tokenizer.vocab', 'e9aec1bd43144fd3a60e65bee8ae73f2_vocab.txt', 'model_config.yaml'])
[NeMo I 2022-08-08 17:31:20 convert:78] Successfully exported model to model_graph.onnx, saving to model_conformer_transducer.riva ...
[NeMo I 2022-08-08 17:31:20 cookbook:93] Exporting model with autocast=False
[NeMo E 2022-08-08 17:31:20 cookbook:122] ERROR: Export failed. Please make sure your NeMo model class (nemo.collections.asr.models.EncDecRNNTBPEModel) has working export() and that you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/opt/conda/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cli/nemo2riva.py", line 56, in nemo2riva
Nemo2Riva(args)
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/convert.py", line 79, in Nemo2Riva
save_archive(model=model, save_path=riva_out, cfg=cfg, artifacts=artifacts, metadata=manifest['metadata'])
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cookbook.py", line 123, in save_archive
raise e
File "/opt/conda/lib/python3.8/site-packages/nemo2riva/cookbook.py", line 96, in save_archive
input_example = model.input_module.input_example(**in_args)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1204, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'EncDecRNNTBPEModel' object has no attribute 'input_example'
```
**Steps/Code to reproduce bug**
1. I trained using the config file from:
- `https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml`
1. When training, .nemo file is saved every epoch.
1. I use .nemo and run it like below:
- `nemo2riva --out model_conformer_transducer.riva Conformer-Transducer-BPE.nemo`
**Expected behavior**
Convert .nemo to .riva file using `nemo2riva`
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: already installed in Docker image
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
- `docker pull nvcr.io/nvidia/nemo:22.05`
docker run command:
```
docker run --runtime=nvidia -it -d --rm \
--gpus '"device=0"' \
-v /home/workspace/NeMo:/root/NeMo \
--shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
--name=nemo_22.05 nvcr.io/nvidia/nemo:22.05
```
**Environment details**
- Docker image : nvcr.io/nvidia/nemo:22.05
I installed additional Python package:
- nemo2riva 2.3.0
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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