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https://api.github.com/repos/NVIDIA/NeMo/issues/3814 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3814/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3814/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3814/events | https://github.com/NVIDIA/NeMo/issues/3814 | 1,163,598,227 | I_kwDOC_bI7s5FWxmT | 3,814 | ASR transcription using citrinet or quartznet models sometimes does not give output in proper English words. | {
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} | null | null | null | This issue was just created to understand the internal model of the engines. I understand that the engine uses language and lexicon pronunciation model. Doesnt that mean that the output should be in proper English words at the very least? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3813 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3813/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3813/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3813/events | https://github.com/NVIDIA/NeMo/issues/3813 | 1,163,486,570 | I_kwDOC_bI7s5FWWVq | 3,813 | Problem loading wavfiles using AudioSegment. | {
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"description": "Something isn't working"
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} | null | null | null | **Describe the bug**
I am getting a bug loading wavfiles using AudioSegment.
File /mnt/disks/uberduck-experiments-v0/NeMo/nemo/collections/asr/parts/preprocessing/segment.py:180, in AudioSegment.from_file(cls, audio_file, target_sr, int_values, offset, duration, trim, orig_sr)
178 if duration > 0:
179 seconds = duration * 1000
--> 180 samples = samples[: int(seconds)]
181 samples = np.array(samples.get_array_of_samples())
182 except CouldntDecodeError as err:
File /opt/conda/envs/ub_march/lib/python3.8/site-packages/pydub/audio_segment.py:331, in AudioSegment.__getitem__(self, millisecond)
326 if missing_frames > self.frame_count(ms=2):
327 raise TooManyMissingFrames(
328 "You should never be filling in "
329 " more than 2 ms with silence here, "
330 "missing frames: %s" % missing_frames)
--> 331 silence = audioop.mul(data[:self.frame_width],
332 self.sample_width, 0)
333 data += (silence * missing_frames)
335 return self._spawn(data)
error: Size should be 1, 2, 3 or 4
Here, size refers to "self.sample_width" I believe
**Steps/Code to reproduce bug**
```
from nemo.collections.asr.parts.preprocessing import AudioSegment
file_path= path_to_wav
AudioSegment.from_file(
file_path,
target_sr=22050,
int_values=True,
offset=0,
duration=1000,
trim=0,
orig_sr=22050,
)
```
**Expected behavior**
I would expect this to load the file. scipy.io.wavfile.read is able to load file_path no problem.
**Environment overview (please complete the following information)**
GCP
NeMo from source (download and ./reinstall.sh)
**Environment details**
- Debian GNU/Linux 10
- PyTorch '1.10.2+cu102'
- Python 3.8
**Additional context**
T4 GPU
These files load on a Collab notebook running NeMo 1.0.2 and Pydub 25.1. However, when I install NeMo 1.0.1 and Pydub on this GCP machine, I get the same error.
Any help appeciated. Thanks! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3812 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3812/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3812/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3812/events | https://github.com/NVIDIA/NeMo/pull/3812 | 1,163,455,927 | PR_kwDOC_bI7s40JbE1 | 3,812 | Add readme files describing model execution flow for ASR tasks | {
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"merged_at": "2022-03-10T01:16:21"
} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds high level architecture / model execution flow diagrams to better enable users to understand how to write model scratch training / fine-tuning steps / inference steps
**Collection**: [ASR]
# Changelog
- Add flow diagrams for CTC/RNNT model training
- Add flow diagram for ASR inference
- Add flow diagram for Speech classification
- Add flow diagram for Speech SSL
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/3809 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3809/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3809/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3809/events | https://github.com/NVIDIA/NeMo/pull/3809 | 1,163,174,604 | PR_kwDOC_bI7s40Ien_ | 3,809 | add fr asr ckpt to doc | {
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} | [] | closed | false | null | [] | null | null | 2022-03-08T21:49:01 | 2022-03-11T16:43:27 | 2022-03-11T16:43:23 | CONTRIBUTOR | {
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} | Signed-off-by: Yang Zhang <[email protected]>
# What does this PR do ?
add fr asr model ckpt to doc | {
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"merged_at": "2022-03-08T21:15:58"
} | Signed-off-by: ekmb <[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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https://api.github.com/repos/NVIDIA/NeMo/issues/3806 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3806/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3806/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3806/events | https://github.com/NVIDIA/NeMo/pull/3806 | 1,161,927,430 | PR_kwDOC_bI7s40EXu7 | 3,806 | Raise error if FP16 training is tried with O2 recipe. | {
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} | # What does this PR do ?
Raise an error if fp16 training is tried with O2 recipe. This is not yet supported.
**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/3805 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3805/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3805/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3805/events | https://github.com/NVIDIA/NeMo/pull/3805 | 1,161,724,499 | PR_kwDOC_bI7s40Dsep | 3,805 | Fixed soft prompt eval loading bug | {
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} | Signed-off-by: Virginia Adams <[email protected]>
Soft prompt weights were accidently being over written when loading a prompt tuned model for evaluation. This PR fixes this bug.
**Collection**: BigNLP?
# Changelog
Fixed evaluation bug and updated prompt tuning config with default hyperparams found to lead to better performance.
# Usage
Same as before
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | nemo-toolkit 1.7.0 requires numpy>=1.21
but with import asr models:
Traceback (most recent call last):
File "nemo_check.py", line 45, in <module>
import nemo.collections.asr as nemo_asr
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/losses/__init__.py", line 15, in <module>
from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/losses/angularloss.py", line 18, in <module>
from nemo.core.classes import Loss, Typing, typecheck
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/core/classes/__init__.py", line 18, in <module>
import pytorch_lightning
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/pytorch_lightning/__init__.py", line 20, in <module>
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/pytorch_lightning/callbacks/__init__.py", line 14, in <module>
from pytorch_lightning.callbacks.base import Callback
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/pytorch_lightning/callbacks/base.py", line 26, in <module>
from pytorch_lightning.utilities.types import STEP_OUTPUT
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/pytorch_lightning/utilities/types.py", line 25, in <module>
from torchmetrics import Metric
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/torchmetrics/__init__.py", line 14, in <module>
from torchmetrics import functional # noqa: E402
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/torchmetrics/functional/__init__.py", line 14, in <module>
from torchmetrics.functional.audio.pit import permutation_invariant_training, pit, pit_permutate
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/torchmetrics/functional/audio/__init__.py", line 26, in <module>
from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality # noqa: F401
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/torchmetrics/functional/audio/pesq.py", line 20, in <module>
import pesq as pesq_backend
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/pesq/__init__.py", line 6, in <module>
from .cypesq import cypesq, cypesq_retvals, cypesq_error_message as pesq_error_message
File "pesq/cypesq.pyx", line 1, in init cypesq
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
pip3 install --upgrade numpy
resolves the issue above, but there is new then
Traceback (most recent call last):
File "nemo_check.py", line 45, in <module>
import nemo.collections.asr as nemo_asr
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/__init__.py", line 16, in <module>
from nemo.collections.asr.models.classification_models import EncDecClassificationModel
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/classification_models.py", line 28, in <module>
from nemo.collections.asr.data import audio_to_label_dataset
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_label_dataset.py", line 15, in <module>
from nemo.collections.asr.data import audio_to_label
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_label.py", line 22, in <module>
from nemo.collections.asr.parts.preprocessing.segment import available_formats as valid_sf_formats
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/__init__.py", line 16, in <module>
from nemo.collections.asr.parts.preprocessing.features import (
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 38, in <module>
import librosa
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/librosa/__init__.py", line 209, in <module>
from . import core
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/librosa/core/__init__.py", line 5, in <module>
from .convert import * # pylint: disable=wildcard-import
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/librosa/core/convert.py", line 7, in <module>
from . import notation
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/librosa/core/notation.py", line 8, in <module>
from ..util.exceptions import ParameterError
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/librosa/util/__init__.py", line 77, in <module>
from .utils import * # pylint: disable=wildcard-import
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/librosa/util/utils.py", line 9, in <module>
import numba
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/numba/__init__.py", line 200, in <module>
_ensure_critical_deps()
File "/media/meshkovaki/34EC69CAF782C377/Projects/experiments/text/nemo/lib/python3.8/site-packages/numba/__init__.py", line 140, in _ensure_critical_deps
raise ImportError("Numba needs NumPy 1.21 or less")
ImportError: Numba needs NumPy 1.21 or less
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I see that many TTS models are being deprecated in the upcoming 1.8 version. I am still selecting a model - what models are planned to have support beyond version 1.8? | null | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3802 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3802/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3802/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3802/events | https://github.com/NVIDIA/NeMo/issues/3802 | 1,161,091,003 | I_kwDOC_bI7s5FNNe7 | 3,802 | Missing dependencies in TTS examples | {
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} | null | null | null | **Describe the bug**
Pyini (and probably some other things) are missing when running in a Colob environment.
**Steps/Code to reproduce bug**
Run FastPitch_Finetuning.ipynb on a Colab instance.
**Expected behavior**
Unless I'm missing something, I should be able to hit run all after I uncomment the first few lines to fetch dependencies, right?
**Environment overview (please complete the following information)**
Colab
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version - colab
- PyTorch version 1.10.0+cu111
- Python version - Python 3.7.12
**Additional context**
I keep hitting this regardless of which notebook I pick. | {
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} | null | null | null | **Describe the bug**
When running multi-GPU / multi-node training of NLP Entity Linking model against full UMLS dataset, a return of None for loss & lr from training_step results in a crash of a worker and deadlock (at 100% GPU / CPU utilization). It appears this is incompatible with multi-GPU, but entity linking notebooks reference multi-gpu training.
**Steps/Code to reproduce bug**
Training fails at around 75% of first epoch, which is ~18 hours in on 4x A100s, or more than double that on single GPU setups.
Error message is "pytorch_lightning.utilities.exceptions.MisconfigurationException: In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present"
Relevant code in training_step: https://github.com/NVIDIA/NeMo/blob/69385daa999907a15f86e461b670a866424f5bb9/nemo/collections/nlp/models/entity_linking/entity_linking_model.py#L83-L103
Note PyTorch Lightning docs on [training_step](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#training-step) return value:
```
None - Training will skip to the next batch. This is only for automatic optimization.
**This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.**
```
Thus this will work on single GPU, but not multi-GPU according to the docs.
**Expected behavior**
Training completes without crashing.
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: Nemo NGC v.1.6.1
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
Pull: docker pull nvcr.io/nvidia/nemo:1.6.1
Run: docker run --gpus all -it -v /opt/nvidia/nemo:/workspace/nemo/local --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 -v /dev/infiniband --uts=host --net=host --ipc=host --cap-add IPC_LOCK --device=/dev/snd --device /dev/infiniband/uverbs0 --device /dev/infiniband/rdma_cm --device /dev/infiniband/umad0 nvcr.io/nvidia/nemo:1.6.1 /bin/bash
**Environment details**
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.
Tested on A100 & Titan RTX - same results
Single vs. Multi-node does not appear to make a difference either.
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} | Signed-off-by: Michael Menefee <[email protected]>
# What does this PR do ?
Corrects config file parsing to use number of workers specified instead of default "2", which generates warnings and slows training.
**Collection**: NLP
# Changelog
- Fix spelling error in "num_workers" parameter to actually set number of dataset workers specified in yaml configs
# Usage
* Validating fix -
```
cd examples/nlp/entity_linking
vim ./conf/umls_medical_entity_linking_config.yaml
## Change num_workers ~= nproc
python data/umls_dataset_processing.py
python self_alignment_pretraining.py project_dir=.
## should no longer see num_workers warnings
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N/A] Did you write any new necessary tests?
- [N/A] Did you add or update any necessary documentation?
- [N/A] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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: Iztok Lebar Bajec <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: nlp
# Changelog
- Support for loading Huggingface Camembert models, e.g. https://huggingface.co/camembert-base
# Usage
* You can potentially add a usage example below
In `/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml` set
```python
pretrained_model_name: camembert-base
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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https://api.github.com/repos/NVIDIA/NeMo/issues/3798 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3798/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3798/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3798/events | https://github.com/NVIDIA/NeMo/pull/3798 | 1,160,357,663 | PR_kwDOC_bI7s4z_b6d | 3,798 | [TTS] Fix: Tacotron2 decoder inference thread-safety bug | {
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} | # What does this PR do?
This PR makes Tacotron2 decoder module inference thread-safe and avoids unintentional side-effects that can occur due to concurrent invocation of the function.
**Collection**: TTS
# Changelog
- Encapsulated the mutable internal decoder states into a dataclass called `DecoderStates`
- Initialize new local `DecoderStates` object at every invocation of Tacotron2 Decoder module inference instead of shared variable states.
- Decode method writes to said local Decoder states instead of overwriting shared variable states.
# Usage
Usage hasn't changed but this PR prevents RuntimeError or prediction quality issues from happening due to crosstalk between threads when making multi-threaded invocations of the Tacotron2 Inference method in eval mode.
# 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? No
- [x] Did you add or update any necessary documentation? No
- [x] Does the PR affect components that are optional to install? No
- [x] Reviewer: Does the PR have correct import guards for all optional libraries? N/A
**PR Type**:
- [x] Bugfix
## 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
* This PR fixes #3797
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https://api.github.com/repos/NVIDIA/NeMo/issues/3797 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3797/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3797/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3797/events | https://github.com/NVIDIA/NeMo/issues/3797 | 1,160,349,318 | I_kwDOC_bI7s5FKYaG | 3,797 | [TTS] Tacotron 2 Inference is not thread-safe | {
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} | null | null | null | **Describe the bug**
Tacotron 2 TTS model inference is not thread-safe despite being in eval mode and results in various runtime errors (shared below) due to shared decoder internal states being written to by multiple threads. Even when a runtime error doesn't occur, the resulting audios are gibberish and noisy and have almost no coherent speech.
```
[2022-03-05 12:38:40,296] ERROR in app: Exception on /api/synthesize/ [POST]
Traceback (most recent call last):
...
[application specific traceback]
...
File "./common/tts.py", line 22, in synthesize_text
y, sr = tts(text, language_code)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/tts_middleware/core.py", line 24, in _tts
y, sr = tts_function(raw_text, language_code)
File "./common/inference.py", line 63, in predict
mel = eval(f"inference_{self.text2mel_type}")(self, text)
File "./common/infer/infer_nemo.py", line 4, in inference_nemo
return obj.model["text2mel"].generate_spectrogram(tokens=parsed)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/core/classes/common.py", line 798, in __call__
outputs = wrapped(*args, **kwargs)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/collections/tts/models/tacotron2.py", line 203, in generate_spectrogram
tensors = self(tokens=tokens, token_len=token_len)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/core/classes/common.py", line 798, in __call__
outputs = wrapped(*args, **kwargs)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/collections/tts/models/tacotron2.py", line 187, in forward
memory=encoder_embedding, memory_lengths=token_len
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/core/classes/common.py", line 798, in __call__
outputs = wrapped(*args, **kwargs)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/collections/tts/modules/tacotron2.py", line 204, in forward
return self.infer(**kwargs)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/collections/tts/modules/tacotron2.py", line 325, in infer
mel_output, gate_output, alignment = self.decode(decoder_input)
File "/root/.cache/pypoetry/virtualenvs/tts-service-Fv16d9lr-py3.7/lib/python3.7/site-packages/nemo/collections/tts/modules/tacotron2.py", line 265, in decode
(self.attention_weights.unsqueeze(1), self.attention_weights_cum.unsqueeze(1)), dim=1,
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 49 but got size 53 for tensor number 1 in the list.
...
[similar errors for most concurrent requests]
...
```
**Steps/Code to reproduce bug**
1. Load Tacotron2 model in any multi-threaded inference application (in my case, a flask server app)
2. Make multiple concurrent requests to the application serving the model with sufficiently different text inputs
**Expected behavior**
Expected thread-safe read-only inference from the Tacotron2 TTS model resulting in sensible audios identical to audios produced by single-threaded inference.
**Environment overview (please complete the following information)**
- Environment location: Base docker image `nvidia/cuda:10.2-base-ubuntu18.04` with Python 3.7
- Method of NeMo install: pip install nemo
**Additional context**
Simple fix: internal decoder states should not be shared Variables that can be written to by concurrent invocations of inference function.
Reference: https://discuss.pytorch.org/t/is-inference-thread-safe/88583 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3796 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3796/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3796/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3796/events | https://github.com/NVIDIA/NeMo/issues/3796 | 1,160,335,762 | I_kwDOC_bI7s5FKVGS | 3,796 | training_batch_wer fast growth from 0,9 to 200 model predict only "⁇" | {
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} | null | null | null | I try perform steps from https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_with_Transducers.ipynb witn my own dataset.
After 10% of first epoch i see in tensorboard fast growth training_batch_wer from from 0,9 to 200.
When it happen i see in console weird predictions:
My data is calls with sample_rate = 8000. My maximum train_ds.batch_size = 8, if I try somthing biger i get OOM error.
**My train data**
[NeMo I 2022-03-05 16:36:45 mixins:146] Tokenizer SentencePieceTokenizer initialized with 1000 tokens
[NeMo I 2022-03-05 16:36:53 collections:173] Dataset loaded with 361193 files totalling 610.54 hours
[NeMo I 2022-03-05 16:36:53 collections:174] 38078 files were filtered totalling 208.93 hours
[NeMo I 2022-03-05 16:36:54 collections:173] Dataset loaded with 26211 files totalling 63.04 hours
[NeMo I 2022-03-05 16:36:54 collections:174] 0 files were filtered totalling 0.00 hours
[NeMo I 2022-03-05 16:36:54 collections:173] Dataset loaded with 435 files totalling 1.00 hours
[NeMo I 2022-03-05 16:36:54 collections:174] 0 files were filtered totalling 0.00 hours
**My config is:**
name: &name "ContextNet-8x-Stride-RNNT"
model:
sample_rate: &sample_rate 8000
compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
train_ds:
manifest_filepath: 'demo_kz/manifest_train.json'
sample_rate: 8000
batch_size: 8 # Can be increased if memory allows or when using smaller model
trim_silence: false
max_duration: 16.7
shuffle: true
use_start_end_token: false
num_workers: 38
pin_memory: true
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: "scatter"
shuffle_n: 2048
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: 'demo_kz/manifest_dev.json'
sample_rate: 8000
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 38
pin_memory: true
test_ds:
manifest_filepath: 'demo_kz/manifest_test.json'
sample_rate: 8000
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 38
pin_memory: true
model_defaults:
filters: 1024
repeat: 5
dropout: 0.1
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
# encoder / decoder / joint values
enc_hidden: 640
pred_hidden: 640
joint_hidden: 640
tokenizer:
dir: "demo_kz/tokenizer/dev/tokenizer_spe_unigram_v1000/"
type: bpe
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: 8000
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: &n_mels 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 16
stft_conv: false
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 0 # should be kept at 2
time_masks: 0 # can be 5 for small-med models, 10 for larger models.
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: *n_mels
activation: swish
conv_mask: true
init_mode: "tds_uniform"
jasper:
- filters: 1024
repeat: 1
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: true
se: true
se_context_size: -1
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [2]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
stride_last: true
residual_mode: "stride_add"
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [2] # *stride
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
stride_last: true
residual_mode: "stride_add"
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [2] # stride
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
stride_last: true
residual_mode: "stride_add"
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 1024
repeat: 5
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
- filters: 640
repeat: 1
kernel: [5]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: 640
pred_rnn_layers: 1 # only 1 layer LSTM networks are exportable.
t_max: null # Maximum possible target seq length used for Chrono Initialization - https://arxiv.org/abs/1804.11188. Disabled by default.
dropout: 0.1
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # sets it according to cpu/gpu device
preserve_memory: false # dramatically slows down training, but might preserve some memory
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: 640
activation: "relu"
dropout: 0.1
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
greedy:
max_symbols: 10
beam:
beam_size: 4
score_norm: true
return_best_hypothesis: False
softmax_temperature: 1.0 # scale the logits by some temperature prior to softmax
tsd_max_sym_exp: 10 # for Time Synchronous Decoding, int > 0
alsd_max_target_len: 5.0 # for Alignment-Length Synchronous Decoding, float > 1.0
maes_num_steps: 2 # for modified Adaptive Expansion Search, int > 0
maes_prefix_alpha: 1 # for modified Adaptive Expansion Search, int > 0
maes_expansion_beta: 2 # for modified Adaptive Expansion Search, int >= 0
maes_expansion_gamma: 2.3 # for modified Adaptive Expansion Search, float >= 0
loss:
loss_name: "default"
warprnnt_numba_kwargs:
fastemit_lambda: 0.001 # Values can be in range [1e-4, 1e-2]. Generally, 0.001 is good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
optim:
name: novograd
lr: 0.05
betas: [0.9, 0.0]
weight_decay: 0.001
sched:
name: CosineAnnealing
warmup_steps: null
warmup_ratio: null
min_lr: 1e-6
last_epoch: -1
trainer:
devices: 4 # number of gpus
max_steps: null # computed at runtime if not set
num_nodes: 1 # Should be set via SLURM variable `SLURM_JOB_NUM_NODES`
accelerator: gpu
strategy: ddp
accumulate_grad_batches: 1
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
log_every_n_steps: 100 # Interval of logging.
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
check_val_every_n_epoch: 1 # RNNT decoding is slower than CTC, so eval takes longer. Increase value to speed up training slightly.
precision: 32 # RNNT requires a lot of memory, so precision 16 is very important. Use very small batch size for precision 32.
gradient_clip_val: 1.0 # Gradient norm clip value
sync_batchnorm: true
benchmark: false
exp_manager:
exp_dir: "/nemo/demo_kz/nemo_checkpoint/exp"
name: *name
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: "val_wer"
mode: "min"
save_top_k: 3
always_save_nemo: true
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
entity: null
resume_if_exists: false
resume_ignore_no_checkpoint: false
**My python code:**
import nemo.collections.asr as nemo_asr
from ruamel.yaml import YAML
import gc
import torch
config_path = 'demo_kz/contextnet_rnnt2.yaml'
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
import pytorch_lightning as pl
from pytorch_lightning import loggers as pl_loggers
tb_logger = pl_loggers.TensorBoardLogger("/nemo/demo_kz/TensorBoard/", name = 'BPE_1000', version = 'ContextNet_1epoch2')
trainer = pl.Trainer(strategy='ddp', gpus=[0, 1, 2, 3], max_epochs=1, logger=tb_logger, precision=16)
from omegaconf import DictConfig
first_asr_model = nemo_asr.models.EncDecRNNTBPEModel(cfg=DictConfig(params['model']), trainer=trainer)
gc.collect()
torch.cuda.empty_cache()
trainer.fit(first_asr_model)
first_asr_model.save_to('demo_kz/checkpoint/ContextNet_1epoch_BPE_10002.nemo')
**I train my model on 4 Titan X server:**
- OS version: Ubuntu
- PyTorch version: torch==1.11.0a0+b6df043
- Python version: Python 3.8.12
- Nemo: nemo-toolkit==1.5.1
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https://api.github.com/repos/NVIDIA/NeMo/issues/3795 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3795/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3795/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3795/events | https://github.com/NVIDIA/NeMo/issues/3795 | 1,159,586,496 | I_kwDOC_bI7s5FHeLA | 3,795 | NeMo no longer supports transcripts with diacritics | {
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] | closed | false | null | [] | null | null | 2022-03-04T12:18:48 | 2022-03-10T00:45:31 | 2022-03-08T22:26:26 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I am training an Arabic model with [diacritics](https://en.wikipedia.org/wiki/Arabic_diacritics).
Digitally, each diacritic is represented as a separate (unicode) character from the actual letter.
[Here's](https://github.com/Tarteel-io/quran-assets/blob/main/text/vocab-DI.txt) an example of the vocabulary/text that we are using.
There are 10,878 unique words in the vocabulary (so large enough for a SPE tokenizer with a `vocab_size` of 1024).
I am able to generate a SPE-based tokenizer using our diacritized vocabulary.
I can confirm that the generated `document.txt` and `vocab.txt` for the tokenizer have Arabic text represented correctly.
However, somewhere during training the tokenizer fails to decode the text properly.
This is an example output from the `WER` metric:
```
[NeMo I 2022-03-04 09:48:36 wer_bpe:204] reference:غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇
[NeMo I 2022-03-04 09:48:36 wer_bpe:205] predicted:جعلنا غبعوثون الض تجري غ ض غ استكبروا غ استكبرواࣲ غ وللكافرين إ وإليه الض قلتم استكبروابعوثون غ وإلى أظلم استكبروا س استكبروا غ عنهم أحسن استكبروا جعلنا الض الجاه ]
تجري الهمد للذين غ استكبروا القرآن غر جعلنا غ رؤوس أصببعوثون ضآيات شديد شديد وإلى غࣲ استكبروا غ استكبروا غ جعلنا غ أحسن الض ألفافا أنفسكم والسماء آتينا لله غ ض آتينا غ جعلنا الض غ قلتم آتينا الجاه تجري غ وأخ جعلنا ]
⁇ ألفافا جعلنا غإ غ استكبروا وأخ وإلى وأخ غ إ غ جعلنا تجري وإلى استكبروابعوثون
```
Notice two things here:
1. The `reference` text is only two characters: `⁇` and `غ`.
2. The predicted text does not have any diacritics (despite the tokenizer and vocab having diacritics).
After a few epochs, the model "converges" and predicts only those two characters. Basically the loss goes to 0 in ~1k steps.
```
[NeMo I 2022-03-04 09:49:46 wer_bpe:204] reference:غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇
[NeMo I 2022-03-04 09:49:46 wer_bpe:205] predicted:غ ⁇ غ ⁇ جعلنا جعلنا ⁇ وإلى غ ⁇ ⁇ غ ⁇ غ ⁇ ⁇ غ ⁇ جعلنا عليها ⁇ ألفافا جعلنا ⁇ غ ⁇ غ ⁇ غ ⁇ غ ⁇ لله بأفواه
```
If I train a model without diacritics, the same behavior occurs. The last working NeMo version was `1.4.0` (will test with `1.5.0`).
Previously, the model would converge at a reasonable rate and achieves good results (~4% WER).
**Steps/Code to reproduce bug**
There's nothing really special about my training script, it's taken from the examples.
The model is finetuned from the `stt_en_citrinet_1024` model.
`cfg.model_target` is `nemo.collections.asr.models.EncDecCTCModelBPE`
```py
from omegaconf import OmegaConf
import pytorch_lightning as pl
import wandb
from nemo.collections.asr.models import ASRModel
from nemo.core.config import hydra_runner
from nemo.utils import logging, model_utils
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="conf/citrinet/", config_name="config")
def main(cfg):
# Setup trainer and exp. manager
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
# Setup Model
model_class = model_utils.import_class_by_path(cfg.model_target) # type: ASRModel
asr_model = model_class.from_pretrained(model_name=cfg.init_from_pretrained_model)
asr_model.cfg = cfg.model
asr_model.set_trainer(trainer)
asr_model.setup_training_data(cfg.model.train_ds)
asr_model.setup_multiple_validation_data(cfg.model.validation_ds)
asr_model.setup_optimization(cfg.model.optim)
# Setup Augmentation
asr_model.spec_augmentation = asr_model.from_config_dict(cfg.model.spec_augment)
# Change vocab
asr_model.change_vocabulary(
new_tokenizer_dir=cfg.model.tokenizer.dir,
new_tokenizer_type=cfg.model.tokenizer.type
)
trainer.fit(asr_model)
if __name__ == '__main__':
main()
```
The tokenizer is generated using [process_asr_text_tokenizer.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py):
```sh
python process_asr_text_tokenizer.py --manifest=<path to train manifest files, seperated by commas> \
--data_root=tokenizers \
--vocab_size=1024 \
--tokenizer=spe \
--log
```
**Expected behavior**
The model should be able to converge and predict Arabic text accurately.
**Environment overview (please complete the following information)**
- Environment location: Docker (`nvcr.io/nvidia/pytorch:21.10-py3`)
- Method of NeMo install: `pip install nemo_toolkit[all]==1.7.0`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
Dockerfile:
```
FROM nvcr.io/nvidia/pytorch:21.10-py3
ARG DEBIAN_FRONTEND=noninteractive
COPY ./requirements.txt requirements.txt
RUN apt update && \
apt install -y ffmpeg libsndfile1 && \
python3 -m pip install --upgrade pip && \
python3 -m pip install -r requirements.txt
```
```sh
docker run --rm -it --gpus all --shm-size 64G --ipc=host --env-file .env -v /home/$USER:/home/$USER train
```
**Additional context**
GPU: 8xV100 (AWS p3dn.24xlarge)
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https://api.github.com/repos/NVIDIA/NeMo/issues/3794 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3794/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3794/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3794/events | https://github.com/NVIDIA/NeMo/issues/3794 | 1,159,571,733 | I_kwDOC_bI7s5FHakV | 3,794 | size mismatch for fastpitch encoder | {
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} | [] | closed | false | null | [] | null | null | 2022-03-04T12:01:55 | 2022-03-04T12:04:30 | 2022-03-04T12:04:30 | NONE | {
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} | null | null | null | I am training my model on (Hi-Fi TTS) dataset and when I am calling model. fit, it is giving me the following error.
`Traceback (most recent call last):
File "fastpitch_finetune.py", line 33, in main
model.maybe_init_from_pretrained_checkpoint(cfg=cfg)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py", line 50, in wrapped_fn
return fn(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 998, in maybe_init_from_pretrained_checkpoint
self.load_state_dict(restored_model.state_dict(), strict=False)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1370, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for FastPitchModel:
size mismatch for fastpitch.encoder.word_emb.weight: copying a param with shape torch.Size([115, 384]) from checkpoint, the shape in current model is torch.Size([114, 384]).`
I am using Nemo branch 1.7.1 for this | null | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3792 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3792/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3792/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3792/events | https://github.com/NVIDIA/NeMo/issues/3792 | 1,158,381,959 | I_kwDOC_bI7s5FC4GH | 3,792 | Some Questions about SSL Pre-training | {
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} | null | null | null | Hi,
The SSL pre-training code & tutorial are very great; however, I have some questions about configurations of models in SSL pre-training (there are some ambiguities for me to set parameters correctly). I have prepared a `citrinet_ssl_512.yaml` config file for SSL pre-training of Citrinet512 model as below: (I have implement it based on the [Nemo citrinet_ssl_1024.yaml](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ssl/citrinet/citrinet_ssl_1024.yaml) file. But, as Citrinet1024 is a little huge model I decided to switch to Citrinet512 model for my research experiments).
[citrinet_ssl_512.zip](https://github.com/NVIDIA/NeMo/files/8178032/citrinet_ssl_512.zip)
My questions are as follows:
1- In the above uploaded file, I have written `doubt` comment beside some of parameters. Can you please kindly check these parameters and let me know if they are set correctly or not? I also suggest that add this yaml file (and also yaml config files for other ASR models in the [SSL conf folder](https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/ssl) of NeMo (_e.g._, yaml config file for QuartzNet, Citrinet256, contextnet256, etc.)).
2- If we set `precision: 16` is it adequate for training the model with fp16? or we need apply extra changes?
3- I have set `min_duration: 2.0`. But, in my data set there are audio utterances with duration less than 2 seconds. So, it means that all of these utterances will be removed form the training process? Is there any way to handle this issue and keep them? (I have realized that there is a relation between `spec_augment` parameters and `min_duration`. As I have set `patch_size: 16` & `mask_patches: 10`, so 16*10=160, and it means that the minimum acceptable duration of an utterance must be 1.6 seconds. As a result, I have set `min_duration: 2.0`. Am I correct?)
4- I have set the `max_duration: 60.0`. Is this parameter important and related to `spec_augment` category parameters?
5- I have set `trim_silence: false` & `pin_memory: true` & add `num_workers: 16`. Do the values of these parameters have a special effect on the training process?
6- I have set `num_negatives: 40` & `sample_from_same_utterance_only: true` & `sample_from_non_masked: false`. Do you think the values of these parameters are set correctly? I have set these values based on your [tutorial file](https://nbviewer.org/github/NVIDIA/NeMo/blob/main/tutorials/asr/Self_Supervised_Pre_Training.ipynb), and other `spec_augment` category parameters' values.
7- About loading `init_checkpoint` (_i.e._, `+init_from_pretrained_model`) I have previously send an [issue](https://github.com/NVIDIA/NeMo/issues/3728). What I learned from that is that to do this, I must first download the model manually from NGC and then enter the .nemo file address of the model to code. Did I get it right?
In fact, I want to evaluate the impact of loading another model's weight at the beginning of SSL process for my self, and I know that it is not a formal acceptable task.
8- After the process of SSL training has been finished, can one extract the log probability of the SSL model for evaluation? I mean that, can we extract log probability of model for each word (or token) to for example evaluate (analyze) which words the model transcribe with more skepticism and which words it is more confident about transcribing. Can you help me with this too?
I apologize to you that my number of questions has increased and I thank you in advance if you can guide me.
Best
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
Diarization with ASR JSON output now also includes sentences.
# Usage
You can use the `examples/speaker_tasks/diarization/offline_diarization.py` script and check the output JSON file (`.../pred_rttms/uniq_name.json`).
# 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? **No.**
- [x] Did you add or update any necessary documentation? **No.**
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)? **No.**
- [x] Reviewer: Does the PR have correct import guards for all optional libraries? **N/A.**
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@titu1994, @redoctopus, @jbalam-nv, or @okuchaiev
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 #3708 (https://github.com/NVIDIA/NeMo/issues/3708)
When implementing I used an approach that changes the original codebase as little as possible. In other words, I tried to be non-invasive. After reviewing the existing code for sentences (`string_out`) preparation in `make_json_output()` of `/nemo/collections/asr/parts/utils/diarization_utils.py` I feel like the whole process could be done much more efficiently and elegantly. Currently, the sentence preparation is appending words to a string where the beginning of the string are the time stamps. When the ending time stamp needs to be corrected the code then uses string splitting to extract and replace/correct the end time value. This is quite clunky and inefficient. It would be much more elegant if time stamps and the sentences were stored and built within a dictionary and then printed out at the end.
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https://github.com/NVIDIA/NeMo/blob/256236f684cd08abd27008d6a9c41d77993be138/nemo/collections/asr/parts/submodules/jasper.py#L506
It seems that no matter what timesteps and context_window are, same formula is applied.
By the way, suggesting to use `.to` instead of `.type`. [Reference](https://discuss.pytorch.org/t/why-does-pytorch-needs-the-three-functions-to-type-and-type-as/20164/2) | {
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
With those changes, exported Conformer with autocast can be converted to TRT with TRT 8.2 (e.g. current nemo2riva).
Also, rolled back previous change of expand->repeat as expand yields better performance.
**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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A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
Provide a code snippet on how new APIs/changes would be used by others.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Automatically assigns prompt ids to prompt tags if no prompt ids were specified in the config. This fixes a backward compatibility bug with models that were prompt tuned before this NeMo release.
**Collection**: [Note which collection this PR will affect]
# Changelog
Changed the init function for the Megatron GPT Class
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Automatically assigns prompt ids to prompt tags if no prompt ids were specified in the config. This fixes a backward compatibility bug with models that were prompt tuned before this NeMo release.
**Collection**: [Note which collection this PR will affect]
# Changelog
Changed the init function for the Megatron GPT Class
# Usage
Same as before
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Updates prompt tuning documentation
**Collection**: [Note which collection this PR will affect]
# Changelog
- Only changed the megatron.rst file
# Usage
* Documentation
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
This PR makes two fixes to T5 before r1.7.0 (a) Sets async grad allreduce to false for bf16 O2 (b) undoes whole word masking logic for sentencepiece
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* N/A
```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**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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] | null | null | 2022-02-28T20:53:08 | 2022-07-03T18:01:50 | 2022-07-03T18:01:50 | NONE | {
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method `def load_LM_for_CTC_decoder` of the `ASR_TIMESTAMPS` class in the `decoder_timestamps_utils.py` has a bug. When checking the type of the ASR model, 'EncDecCTCModelBPE' should be checked first and then 'EncDecCTCModel'. Otherwise 'EncDecCTCModelBPE' models will always be treated as `EncDecCTCModel` models.
https://github.com/NVIDIA/NeMo/blob/431234ae08db3fe241a387f527d3b1ce26af4c10/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py#L390 | {
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} | # What does this PR do ?
This PR updates NeMo to use the NVIDIA NGC PyTorch 22.02 container.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Jason <[email protected]>
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- [ ] Did you write any new necessary tests?
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} | # What does this PR do ?
Added the support of P-tuning T5 model. It supports multiple NLP tasks.
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} | null | null | null | Hi guys,
I'm trying to train conformer rnnt model with accumulating several batches for gradient. For that, I specified it in the trainer as follow :
```
trainer = pl.Trainer(gpus=gpus, max_epochs=nb_epochs, distributed_backend="ddp", precision=16, accumulate_grad_batches=32)
```
but it seems like nothing changes. and the training evolution is the same as without adding accumulate_grad_batches parameter.
Do you have any idea how to solve it ?
With kind regards,
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} | null | null | null | Here's a few feature requests + bugs related to the punctuation and capitalization model.
### Punctuation issues
#### Inverted punctuation
For languages like Spanish, we need two predictions per token to account for the possibility of inverted punctuation tokens preceding a word.
#### Subword masking
By always applying subtoken masks, continuous-script languages (e.g., Chinese) cannot be punctuated without applying some sort of pre-processing. It would be useful if the model processed text in its native script.
#### Arbitrary punctuation tokens
The text-based data set does not allow to punctuate languages such as Thai, where a space character is a punctuation token. These languages could work by having a token-based data set and removing subword masks (essentially, resolving the other issues resolves this one).
### Capitilization issues
#### Acronyms
The capitalization prediction is simply whether a word starts with a capital letter. So acronyms like 'amc' will not be correctly capitalized.
#### Names that begin with a particle
Similar to the acronym issue, words that begin with a particle, e.g., 'mcdonald', cannot be properly capitalized to 'McDonald'.
#### Capitalization is independent of punctuation
Currently, the two heads are conditioned only on the encoder's output and independent of each other. But capitalization is dependent on punctuation in many cases.
An example of what might go wrong is we may end up with "Hello, world, What's up?" because the capitalization model might expect a period after 'world'. Essentially the capitalization head is predicting what the punctuation head will do.
In practice I have found this problem to be an uncommon manifestation, but to be correct, capitalization should take into account the punctuator's output. Implicitly, we are forcing the capitalization head to learn punctuation (and predict the punctuation head's output).
### Data set issues
#### Dataset as text
First (this may be a personal preference), it is unnatural to have a preprocessed data set in text format rather than token IDs.
More importantly, text data sets are incompatible with other issues mentioned in this thread (subwords, space as punctuation)
#### Supported data set classes
Dataset is fixed to one class, but it would be more convenient to simply expect an abstract base class, and let the user specify a `_target_` in the config and use hydra.utils.instantiate, as in some other models. E.g.,
https://github.com/NVIDIA/NeMo/blob/bc6215f166e69502fd7784fc73a5c2c39b465819/nemo/collections/tts/models/melgan.py#L298
For example, a user may wish to implement a different dataset that generates examples on-the-fly, or use a ConcatDataset with multiple languages and temperature sampling, etc.
### Paragraph segmentation
A primary benefit of this model is to improve NMT results on unpunctuated and uncased ASR output. However, running MT on an arbitrarily-long inputs will inevitably end poorly.
For this model to be complete, I would argue it needs to implement a third token classification analytic: paragraph segmentation (splitting a paragraph into its constituent sentences). Translating sentences as separate units would improve results in many cases. Furthermore, a Transformer's runtime complexity is N^2 in the sequence length. | null | {
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} | # What does this PR do ?
Add language modelling support for transducer models. It enables using N-gram LM with Transducer models. It also enables the neural rescoring feature for Transducer models.
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | …s. Also making fixes to measure so sounds more fluent.
Signed-off-by: Bonham79 <[email protected]>
# What does this PR do ?
Updates tn_es to make gender randomized.
Add a one line overview of what this PR aims to accomplish.
This will update some minor aspects of tn_es to lower error rate.
**Collection**: [Nemo Text Processing]
# Changelog
- Cardinal and Decimal have randomized gender assignment
- Fraction has randomized gender assignment for integer portion
- Measure now has more fluent normalization of fractional amounts. (Note, due to Sparrowhawk architecture it can sound slightly awkward: 1 1/2 l-> "uno y medio litros" as opposed to more common "uno litro y medio"). Change would require rewriting of how measure verbalizer manages fraction tags.
- Non-deterministic strings now permit apocope for "una". This arises when the number precedes a noun with stressed "a" sound. Since this is hard to determine which FST setup, this is left to normalize_with_audio to manage.
# Usage
# Before your PR is "Ready for review"
**Pre checks**:
- [y ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [n ] Did you write any new necessary tests?
- [ n] Did you add or update any necessary documentation?
- [ y] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ y] New Feature
- [ y] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix directories in ssl notebook for running off-colab
**Collection**: ASR
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | null | null | null | **Describe the bug**
It is very strange , only one audio file has this error when calling speaker diariation function, Error Message as below:
Error:cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR, when calling `cusolverDnSsyevd(handle, jobz, uplo, n, A, lda, W, work, lwork, info)`
**Steps/Code to reproduce bug**
I tracked to the root of error, which is in the function (nmesc_clustering.py), when call eigh(laplacian) with cuda (cuda device =1).
def eigDecompose(laplacian, cuda, device=None):
print(len(laplacian))
if TORCH_EIGN:
if cuda:
if device is None:
device = torch.cuda.current_device()
laplacian = torch.from_numpy(laplacian).float().to(f"cuda:{device}")
else:
laplacian = torch.from_numpy(laplacian).float()
**lambdas, diffusion_map = eigh(laplacian)**
lambdas = lambdas.cpu().numpy()
diffusion_map = diffusion_map.cpu().numpy()
else:
lambdas, diffusion_map = eigh(laplacian)
return lambdas, diffusion_map
**Environment overview (please complete the following information)**
Linux environment, 2 gpus, using gpu 1
**Additional context**
the error happens when laplacian shape is 592x592, while other audio files (no error) with laplacian shape of odd dimensions, not sure if it is the reason.
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} | null | null | null | **Describe the bug**
I have a small dataset consisting of ~1 hrs of audio. I mixed the dataset and the dataset of "old speaker" (the [Nancy corpus](http://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/) in my case) according to `FastPitch_Finetuning.ipynb`.
I trained it using config `fastpitch_align.yaml` and had finetuned a Hifigan, it was successful.
Since `fastpitch_align_v1.05.yaml` comes with the text normalizer, I decided to retrain it with the same mixed manifest.
But the result model does not always produce speech by the new speaker. Most of the speeches produced are still by the old speaker, only occasionally the new speaker will come up.
I tried finetuning with another small dataset using `fastpitch_align_v1.05.yaml`, and have fine-tuned Hifigan with the spectrogram generated by the result Fastpitch model. The speech produced also does not sound right.
**Steps/Code to reproduce bug**
Training with `fastpitch_align_v1.05.yaml`:
`examples/tts/fastpitch_finetune.py --config-name=fastpitch_align_v1.05.yaml train_dataset=/root/autodl-tmp/syb-male1--with--nancy_train.normed.json validation_datasets=/root/autodl-tmp/syb-male1--with--nancy_val.normed.json +init_from_nemo_model=/root/autodl-tmp/nemo-training/nancy_22k/nancy_fastpitch-22k_new.nemo sup_data_path=/root/autodl-tmp/nemo-training/syb-male1/sup_syb-male1 model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 exp_manager.exp_dir=/root/autodl-tmp/nemo-training/syb-male1 model.n_speakers=2 pitch_mean=117.298095703125 pitch_std=54.89501953125 model.optim.lr=2e-4 ~model.optim.sched model.optim.name=adam`
Training with `fastpitch_align.yaml`:
`examples/tts/fastpitch_finetune.py --config-name=fastpitch_align.yaml train_dataset=/root/autodl-tmp/syb-male1--with--nancy_train.json validation_datasets=/root/autodl-tmp/syb-male1--with--nancy_val.json +init_from_nemo_model=/root/autodl-tmp/nemo-training/nancy_fastpitch-22k.nemo prior_folder=/root/autodl-tmp/nemo-training/multi-males/prior_syb-male1 model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 exp_manager.exp_dir=/root/autodl-tmp/nemo-training/syb-male1 model.n_speakers=2 model.pitch_avg=121.9 model.pitch_std=37.1 model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=2e-4 ~model.optim.sched model.optim.name=adam`
(Since the Fastpitch model I trained from scratch on Nancy corpus using `fastpitch_align.yaml` (the `nancy_fastpitch-22k.nemo` in the above code) seemed unable to be used to finetune using `fastpitch_align_v1.05.yaml`, I trained a new Fastpitch model on Nancy corpus using `fastpitch_align_v1.05.yaml` (the `nancy_fastpitch-22k_new.nemo` in the above code).)
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: Cloud
- Method of NeMo install: from source
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**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: 1.10.2
- Python version: 3.8
**Additional context**
Add any other context about the problem here.
Example: GPU model
RTX 3090 | {
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} | # What does this PR do ?
Update docs for RNNT decoding with overridden fused batch size
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Bug fixes for Duplex inference and WFST non-ascii error
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ X] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 ?
Specify gpus in SSL notebook.
**Collection**: ASR
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | If I do a Details search recursive if it takes long to search my Memory and swap use Max out, and that's leads to a crash of Nemo.
I test it out in a Virtualbox and a Second PC and there is works fine.
The other's System doesn't have NVIDIA so I guess it's has something to do with that.
Journalctl give me:
```
Feb 24 20:38:17 xps kernel: [ 429320] 1000 429320 8718113 5245311 43945984 189482 0 nemo
Feb 24 20:38:17 xps kernel: oom-kill:constraint=CONSTRAINT_NONE,nodemask=(null),cpuset=auto-cpufreq.service,mems_allowed=0,global_oom,task_memcg=/user.slice/user-1000.slice/session-2.scope,task=nemo,pid=429320,uid=1000
Feb 24 20:38:17 xps kernel: Out of memory: Killed process 429320 (nemo) total-vm:34872452kB, anon-rss:20977252kB, file-rss:0kB, shmem-rss:3992kB, UID:1000 pgtables:42916kB oom_score_adj:0
Feb 24 20:38:17 xps kernel: oom_reaper: reaped process 429320 (nemo), now anon-rss:0kB, file-rss:0kB, shmem-rss:3992kB
```
and Data to my System is:
```
Kernel: 5.16.7-1-MANJARO x86_64 bits: 64 compiler: gcc v: 11.1.0
parameters: BOOT_IMAGE=/boot/vmlinuz-5.16-x86_64
i915.enable_dpcd_backlight=0
Desktop: Xfce 4.16.0 tk: Gtk 3.24.29 info: xfce4-panel wm: xfwm 4.16.1
vt: 7 dm: LightDM 1.30.0 Distro: Manjaro Linux base: Arch Linux
Machine:
Type: Laptop System: Dell product: XPS 15 9570 v: N/A
serial: <superuser required> Chassis: type: 10 serial: <superuser required>
Mobo: Dell model: 0D0T05 v: A00 serial: <superuser required> UEFI: Dell
v: 1.15.0 date: 12/25/2019
Battery:
ID-1: BAT0 charge: 65.3 Wh (100.0%) condition: 65.3/97.0 Wh (67.3%)
volts: 12.6 min: 11.4 model: SMP DELL GPM0365 type: Li-ion serial: <filter>
status: Full
CPU:
Info: model: Intel Core i7-8750H bits: 64 type: MT MCP arch: Coffee Lake
family: 6 model-id: 0x9E (158) stepping: 0xA (10) microcode: 0xEA
Topology: cpus: 1x cores: 6 tpc: 2 threads: 12 smt: enabled cache:
L1: 384 KiB desc: d-6x32 KiB; i-6x32 KiB L2: 1.5 MiB desc: 6x256 KiB
L3: 9 MiB desc: 1x9 MiB
Speed (MHz): avg: 3918 high: 3964 min/max: 800/4100 scaling:
driver: intel_pstate governor: performance cores: 1: 3914 2: 3931 3: 3915
4: 3907 5: 3934 6: 3761 7: 3948 8: 3954 9: 3927 10: 3964 11: 3940 12: 3927
bogomips: 52815
Flags: avx avx2 ht lm nx pae sse sse2 sse3 sse4_1 sse4_2 ssse3 vmx
Vulnerabilities:
Type: itlb_multihit status: KVM: Split huge pages
Type: l1tf
mitigation: PTE Inversion; VMX: conditional cache flushes, SMT vulnerable
Type: mds mitigation: Clear CPU buffers; SMT vulnerable
Type: meltdown mitigation: PTI
Type: spec_store_bypass
mitigation: Speculative Store Bypass disabled via prctl
Type: spectre_v1
mitigation: usercopy/swapgs barriers and __user pointer sanitization
Type: spectre_v2 mitigation: Full generic retpoline, IBPB: conditional,
IBRS_FW, STIBP: conditional, RSB filling
Type: srbds mitigation: Microcode
Type: tsx_async_abort status: Not affected
Graphics:
Device-1: Intel CoffeeLake-H GT2 [UHD Graphics 630] vendor: Dell
driver: i915 v: kernel bus-ID: 00:02.0 chip-ID: 8086:3e9b class-ID: 0300
Device-2: NVIDIA GP107M [GeForce GTX 1050 Ti Mobile] vendor: Dell
driver: nvidia v: 510.47.03 alternate: nouveau,nvidia_drm bus-ID: 01:00.0
chip-ID: 10de:1c8c class-ID: 0302
Device-3: Microdia Integrated_Webcam_HD type: USB driver: uvcvideo
bus-ID: 1-12:7 chip-ID: 0c45:671d class-ID: 0e02
Display: x11 server: X.Org 1.21.1.3 compositor: xfwm4 v: 4.16.1 driver:
loaded: modesetting,nvidia alternate: fbdev,nouveau,nv,vesa display-ID: :0.0
screens: 1
Screen-1: 0 s-res: 3840x1080 s-dpi: 96 s-size: 1016x285mm (40.0x11.2")
s-diag: 1055mm (41.5")
Monitor-1: eDP-1 res: 1920x1080 hz: 60 dpi: 142
size: 344x194mm (13.5x7.6") diag: 395mm (15.5")
Monitor-2: DP-3 res: 1920x1080 hz: 60 dpi: 82 size: 598x336mm (23.5x13.2")
diag: 686mm (27")
OpenGL: renderer: NVIDIA GeForce GTX 1050 Ti with Max-Q Design/PCIe/SSE2
v: 4.6.0 NVIDIA 510.47.03 direct render: Yes
Audio:
Device-1: Intel Cannon Lake PCH cAVS vendor: Dell driver: snd_hda_intel
v: kernel alternate: snd_soc_skl,snd_sof_pci_intel_cnl bus-ID: 00:1f.3
chip-ID: 8086:a348 class-ID: 0403
Sound Server-1: ALSA v: k5.16.7-1-MANJARO running: yes
Sound Server-2: sndio v: N/A running: no
Sound Server-3: JACK v: 1.9.20 running: no
Sound Server-4: PulseAudio v: 15.0 running: no
Sound Server-5: PipeWire v: 0.3.45 running: yes
Network:
Device-1: Intel Wireless-AC 9260 driver: iwlwifi v: kernel modules: wl
bus-ID: 3b:00.0 chip-ID: 8086:2526 class-ID: 0280
IF: wlp59s0 state: up mac: <filter>
IF-ID-1: docker0 state: down mac: <filter>
Bluetooth:
Device-1: Intel Wireless-AC 9260 Bluetooth Adapter type: USB driver: btusb
v: 0.8 bus-ID: 1-4:3 chip-ID: 8087:0025 class-ID: e001
Report: bt-adapter ID: hci0 rfk-id: 1 state: up address: <filter>
Drives:
Local Storage: total: 1.82 TiB used: 1.49 TiB (81.7%)
SMART Message: Required tool smartctl not installed. Check --recommends
ID-1: /dev/nvme0n1 maj-min: 259:0 vendor: Samsung
model: SSD 970 EVO Plus 2TB size: 1.82 TiB block-size: physical: 512 B
logical: 512 B speed: 31.6 Gb/s lanes: 4 type: SSD serial: <filter>
rev: 2B2QEXM7 temp: 38.9 C scheme: GPT
Partition:
ID-1: / raw-size: 1.82 TiB size: 1.79 TiB (98.38%) used: 1.49 TiB (83.1%)
fs: ext4 block-size: 4096 B dev: /dev/dm-0 maj-min: 254:0 mapped: cryptroot
ID-2: /boot/efi raw-size: 260 MiB size: 256 MiB (98.46%)
used: 452 KiB (0.2%) fs: vfat block-size: 512 B dev: /dev/nvme0n1p1
maj-min: 259:1
Swap:
Kernel: swappiness: 10 (default 60) cache-pressure: 100 (default)
ID-1: swap-1 type: file size: 5 GiB used: 2.57 GiB (51.5%) priority: -2
file: /swapfile
Sensors:
System Temperatures: cpu: 57.0 C pch: 51.0 C mobo: N/A
Fan Speeds (RPM): cpu: 3186 fan-2: 3173
Info:
Processes: 441 Uptime: 5h 13m wakeups: 15755 Memory: 30.98 GiB
used: 14.92 GiB (48.2%) Init: systemd v: 250 tool: systemctl Compilers:
gcc: 11.1.0 clang: 13.0.1 Packages: 1888 pacman: 1882 lib: 506 flatpak: 0
snap: 6 Shell: Zsh v: 5.8 running-in: xfce4-terminal inxi: 3.3.12
```
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} | Signed-off-by: Yu Yao <[email protected]>
# What does this PR do ?
Add megatron t5 glue eval-only script
**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**:
- [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/3750 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3750/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3750/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3750/events | https://github.com/NVIDIA/NeMo/pull/3750 | 1,149,638,934 | PR_kwDOC_bI7s4zbG8z | 3,750 | T5 pipeline parallel | {
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} | # What does this PR do ?
Adds pipeline parallel training support to T5.
**Collection**: NLP
# Changelog
- TBD
# Usage
Set `pipeline_model_parallel_size=2/4` in `megatron_t5_config.yaml`.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Allows import test to be run from forked branches.
**Collection**: [all]
# Changelog
- Should allow ci import check to always be run
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} | # What does this PR do ?
Fix SSL configs for 1.7
**Collection**: ASR
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | Signed-off-by: ekmb <[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.
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* Related to # (issue)
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} | # What does this PR do ?
Update ASR SSL including tutorial
**Collection**: ASR
# Changelog
- transfer ssl updates from main
- fix an4 dataset loading
- fix some typos
- add more explanations
- change some config values
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} | Signed-off-by: sam1373 <[email protected]>
# What does this PR do ?
Update ASR SSL tutorial
**Collection**: ASR
# Changelog
- fix an4 dataset loading
- fix some typos
- add more explanations
- change some config values
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} | # What does this PR do ?
We fix the case where batch_size > 1 and also fix a typo.
**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)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add mirror to AN4 dataset while CMU servers are down.
**Collection**: [ASR, Speaker Tasks]
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} | # What does this PR do ?
Multi-Label Intent Model for Joint Intent Slot Classification Task.
**Collection**: [Note which collection this PR will affect]
This PR will involves changes in collections/nlp within NeMo.
# Changelog
- New loss function added to collections/common/losses (BCEWithLogitsLoss)
- Multi-Label Classification Report added to collections/nlp/metrics/classification_report
- Multi-Label Classification Model, Descriptor, Dataset files added in similar folders to intent_slot_classification
- Data Conversion and Augmentation scripts added to example/nlp/multi_label_intent_slot_classification Multi-Label Training Tutorial added to https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb
- Added Weight Calculation Function for BCEWithLogitsLoss (lines 512-537) and Multi Label Stats function (lines 201-242) in nlp/data/data_utils/data_preprocessing.py
# Usage
Refer to https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb for training. To run the tutorial you will need to change github string link name from NVIDIA to chenrichard10 if the PR has not been approved.
A simple script that can be used to train the model locally can be with
```python
from nemo.collections import nlp as nemo_nlp
from nemo.utils.exp_manager import exp_manager
from nemo.utils import logging
from nemo.collections.nlp.parts.utils_funcs import tensor2list
import os
import time
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
HOME_DIR=""
# directory with data converted to nemo format
data_dir = os.path.join(HOME_DIR, "new_multiatis")
run_name = "test"
config_file = ""
config = OmegaConf.load(config_file)
config.model.data_dir = data_dir
config.model.validation_ds.prefix = "dev"
config.model.test_ds.prefix = "test"
config.model.class_balancing = "weighted_loss"
config.model.head.num_output_layers = 2
config.trainer.max_epochs = 40
# checks if we have GPU available and uses it
cuda = 1 if torch.cuda.is_available() else 0
config.trainer.gpus = cuda
# config.trainer.precision = 16 if torch.cuda.is_available() else 32
config.trainer.precision = 32
# for mixed precision training, uncomment the line below (precision should be set to 16 and amp_level to O1):
# config.trainer.amp_level = 'O1'
# remove distributed training flags
config.trainer.accelerator = None
# early_stop_callback = EarlyStopping(monitor='intent_f1', min_delta=1e-1, patience=10, verbose=True, mode='max')
trainer = pl.Trainer(**config.trainer)
config.exp_manager.exp_dir = os.path.join(HOME_DIR, "output/" + run_name)
config.exp_manager.create_checkpoint_callback = True
config.exp_manager.version = time.strftime('%Y-%m-%d_%H-%M-%S')
exp_dir = exp_manager(trainer, config.get("exp_manager", None))
model = nemo_nlp.models.MultiLabelIntentSlotClassificationModel(config.model, trainer=trainer)
trainer.fit(model)
```
**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
Available model has yet to be trained and uploaded on NGC. Section will be updated once a model has been trained. | {
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} | # What does this PR do ?
Update the paths/version of some of the conformer models trained with nemo asrset 2.0.
# Changelog
- Updated the versions.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [TTS]
# Changelog
- Fix bugs in E2E TTS, Mixer-TTS and FastPitch
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Yang Zhang <[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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} | This PR will load the initial consumed samples from the checkpoint file names. The consumed samples will be calculated based on the relative training steps and global batch size.
The T5 doesn't seem to calculate the consumed samples correctly if it supports pipeline parallel.
```python
def compute_consumed_samples(self, global_relative_step=0):
app_state = AppState()
consumed_samples = (
self.init_consumed_samples
+ global_relative_step
* app_state.data_parallel_size
* self._cfg.micro_batch_size
* self.trainer.accumulate_grad_batches
)
return int(consumed_samples)
```
Need to confirm we will replace `self.trainer.accumulate_grad_batches` with `get_num_microbatches()` | {
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} | # What does this PR do ?
Text normalization docs improvements:
- added quick start
- merged all docs related to TN/ITN
- fixed references issues
- added docs for audio-based TN
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?
- [ X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Yang Zhang <[email protected]>
add cython requirement | {
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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
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} | # What does this PR do ?
Fix device placement on notebook
**Collection**: []
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds a test to check if grad accumulation works with Numba CUDA/CPU RNNT Loss.
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
pytest tests/collections/asr/numba --relax_numba_compat
```
# 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?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | Hi,
I have used the [SSL pre-training script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py) as follows:
```sh
python speech_pre_training.py \
--config-path="../conf/ssl/citrinet/" \
--config-name="citrinet_ssl_1024" \
model.train_ds.manifest_filepath="./train_LS_manifest.json" \
model.validation_ds.manifest_filepath="./test_LS_manifest.json" \
trainer.gpus=1 \
trainer.accelerator="ddp" \
trainer.max_epochs=100 \
trainer.log_every_n_steps=100 \
trainer.accumulate_grad_batches=1 \
trainer.check_val_every_n_epoch=1 \
trainer.precision=32 \
model.train_ds.batch_size=12 \
model.validation_ds.batch_size=10 \
model.spec_augment.freq_masks=4 \
model.spec_augment.time_masks=10 \
model.spec_augment.freq_width=27 \
model.spec_augment.time_width=0.05 \
model.optim.name="novograd" \
model.optim.lr=0.05 \
model.optim.betas=[0.8,0.25] \
model.optim.weight_decay=0.001 \
model.optim.sched.warmup_steps=5000 \
model.optim.sched.min_lr=1e-9 \
exp_manager.exp_dir="ssl_experiments/" \
exp_manager.name="SSL-Citrinet1024" \
exp_manager.create_tensorboard_logger=True \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_wandb_logger=False \
+init_from_pretrained_model="stt_en_citrinet_1024"
```
However, I have faced an error when the PyTorch lightening attempts to load the pre-trained `stt_en_citrinet_1024` weights (_i.e.,_ `+init_from_pretrained_model="stt_en_citrinet_1024"`). The error refers to the [line 61](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py#L61) of code (`maybe_init_from_pretrained_checkpoint` method). I have investigated and I realized that the `maybe_init_from_pretrained_checkpoint` method has the internal attribute of `strict=False` [ref](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/core/core.html?highlight=maybe_init_from_pretrained_checkpoint#nemo.core.ModelPT.maybe_init_from_pretrained_checkpoint). So, would you please help me that can I load the pre-trained wights? If yes, How I can do that?
Best | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3727 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3727/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3727/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3727/events | https://github.com/NVIDIA/NeMo/pull/3727 | 1,147,277,916 | PR_kwDOC_bI7s4zTfCf | 3,727 | Add cython | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3726 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3726/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3726/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3726/events | https://github.com/NVIDIA/NeMo/pull/3726 | 1,147,273,757 | PR_kwDOC_bI7s4zTeKi | 3,726 | Pynini pip | {
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} | # What does this PR do ?
change pynini installation to pip on non-mac unix systems, otherwise use conda | {
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} | null | null | null | Hi guys,
I am trying to finetune STT En Conformer RNNT for spanish. the problem is the following : when I try to change the tokenizer, the model is just such as training from scratch and it takes too much time for converging ( in reality it doesn't converge :p ). Does any one have an explanation, an idea or a trick to bypass this problem.
With best regards,
| null | {
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} | Signed-off-by: George Zelenfroynd [email protected]
# What does this PR do ?
added lines to asr models that have no num_worker param, its absence caused slow training
**Collection**: ASR
# Changelog
- Adds lines in files of asr models
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Added feature for ignoring samples from speakers with very few utterances
**Collection**: [Note which collection this PR will affect]
# Changelog
- Added a helper function for ignoring samples from speakers with very few utterances
- Made code run on multiple processes
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] 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 the ability to aggregate multiple tokenizers for multi-lingual ASR training and inference
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Add the 'lang' field to each entry in your manifest file -- e.g. to add the same language (es) to all entries in a file, you could do cat tarred_audio_manifest.json | jq -c ‘. += {“lang”:“es”}’
Change the tokenizer config to the agg type, specifying the tokenizer for each language, e.g.
tokenizer:
type: agg
langs:
en:
dir: ???
type: bpe
es:
dir: ???
type: bpe
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ X] Did you write any new necessary tests?
- [ X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
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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Ive managed to download the models under quartznet however am having issues regarding citrinet models.
Please see error output below.
Thanks
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} | # What does this PR do ?
- Adds the ability to aggregate multiple tokenizers for multi-lingual ASR training and inference
**Collection**: [ASR]
# Changelog
# Usage
* Add the 'lang' field to each entry in your manifest file -- e.g. to add the same language (`es`) to all entries in a file, you could do `cat tarred_audio_manifest.json | jq -c ‘. += {“lang”:“es”}’`
* Change the tokenizer config to the `agg` type, specifying the tokenizer for each language, e.g.
```
tokenizer:
type: agg
tokenizers:
en:
dir: ???
type: bpe
es:
dir: ???
type: bpe
```
```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 ?
Update contrastive ssl for asr
**Collection**: ASR
# Changelog
- add options to contrastive loss implementation
- add slightly different spec augmentation for ssl
- update corresponding configs for conformer, citrinet, contextnet ssl
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | **Collection**: [TTS]
# Changelog
- Fix bug with random sampling and `numpy` to `torch` converting in `__getitem__`
- Fix bug with `phoneme_probability` field for `EnglishCharsTokenizer`
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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# What does this PR do ?
Minor issue with checking indexes when adding special tokens to SPM.
Figure out what is going on with sentencepiece and whole word masking.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Revert `is_start_piece()`.
- Check if id is >=0 while adding special tokens and not just > 0.
# Usage
* You can potentially add a usage example below
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Yang Zhang <[email protected]>
# What does this PR do ?
include pynini as default pip dependency, remove PYNINI_AVAILBLE guard and conda installation
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} | # What does this PR do ?
Adds support of recent versions of Dash and librosa. Adds new features (metrics, export, UI).
**Collection**: Tools
# Changelog
- Ported SDE to Plotly Dash 2.1, librosa 0.9
- Added new metrics (insertions, deletions, diff)
- Added support for downloading sorted/filtered vocabulary
- Updated UI
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #3395
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} | Signed-off-by: Dmytro Pykhtar <[email protected]>
# What does this PR do ?
Removed log_probs fp16 converting in complete method.
**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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I am developing an API for a speaker diarization task with ASR (`/examples/speaker_tasks/diarization/offline_diarization_with_asr.py`). For my use case the script generates two useful outputs a `.json` that looks something like this:
```
{
"status": "Success",
"session_id": "example",
"transcription": "thank you sunny day",
"speaker_count": 4,
"words": [
{
"word": "thank",
"start_time": 0.0,
"end_time": 0.6,
"speaker_label": "speaker_1"
},
{
"word": "you",
"start_time": 0.7,
"end_time": 1.1,
"speaker_label": "speaker_1"
},
{
"word": "sunny",
"start_time": 1.5,
"end_time": 2.1,
"speaker_label": "speaker_2"
},
{
"word": "day",
"start_time": 2.2,
"end_time": 2.3,
"speaker_label": "speaker_2"
}
]
}
```
So we have the whole transcription which is very useful along with speaker labels for each of the spoken words. For API purposes this JSON is very handy however its contents are not very useful for practical applications. For diarization purposes and practical applications the script's `.txt` output is much more convenient:
```
[00:00.00 - 00:01.17] speaker_1: thank you
[00:01.54 - 00:02.33] speaker_2: sunny day
```
**Describe the solution you'd like**
Would it be possible to add the information from the `.txt` output to the JSON? E.g., something like:
```
"diraization": [
{
"transcription": "thank you",
"start_time": 0.0,
"end_time": 1.17,
"speaker_label": "speaker_1"
},
{
"transcription": "sunny day",
"start_time": 1.54,
"end_time": 2.33,
"speaker_label": "speaker_2"
}
]
```
**Describe alternatives you've considered**
I took a look at the code and I believe I would could code this myself, would you be interested in a pull request that modifies the output JSON?
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
For speaker diarization tasks tutorials and examples currently only offer scripts for offline diarization. I successfully got those running in combination with custom ASR models for Slovenian language. For ASR tasks the repository also contains scripts for buffered (streamed/live) inference, would such inference be possible for diarization?
**Describe the solution you'd like**
A tutorial or example on how to do buffered speaker diarization (if that is possible in the current framework).
**Describe alternatives you've considered**
Some guidance about how I can implement this myself.
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} | # What does this PR do ?
**Collection**: [TTS]
# Changelog
- Change default speaker in FastPitch from `0` to `None` (i.e no speaker embedding is default now)
- Change call of `functools.lru_cache` (for python 3.7 support) and add `cache_size` to BetaBinomialInterpolator
- Add `pynini` install for TTS tutorials
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
Closes #3627 | {
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} | null | null | null | **Describe the bug**
I try to export fast pitch models, but it took a lot of RAM then process is killed. In my case, I use Mac OS to export and swap increase from 3 GB to 54 GB. I also used to try with google colab, but the kernel died after run export because memory issue.
**Steps/Code to reproduce bug**
```
from nemo.collections.tts.models import FastPitchModel
fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch")
fastpitch.export("fastpitch.onnx")
```
**Expected behavior**
The models can be exported to onnx
**Environment overview (please complete the following information)**
- Environment location: Intel(R) Core(TM) i5-8279U CPU @ 2.40GHz
- Method of NeMo install: pip install nemo_toolkit==1.6.2
**Environment details**
- OS version: MacOS 12.0.1
- PyTorch version: 1.10.1
- Python version: 3.8.9
Thank you | {
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} | # What does this PR do ?
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**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"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Speeds. up audio based normalization (from manifest)
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
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```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ X] Bugfix
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## Who can review?
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} | # What does this PR do ?
Enforce that all top level domain imports must be safe - and no optional dependency should break the base import.
**Collection**: [ASR, NLP, TTS]
# Changelog
- Add an explicit import test to the three domains, independently of the others.
- Follow the normal user style of installing nemo - but with individual domains.
- Error out if any domain breaks it, preventing merge of the PR.
# Usage
* Part of CI test run on Github Actions
# 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?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
fix json -> join in clustering diarizer
**Collection**: speaker_tasks
# Changelog
- change json -> join
# 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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} | 1. Added ApexGuardDefaults class that provides None attributes when an Apex class is missing.
2. Fixed Apex guard.
Signed-off-by: Micha Livne <[email protected]>
# What does this PR do ?
This PR fixes an Apex guard that leaves constructors with undefined constructor values.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Added `ApexGuardDefaults` class to `nemo/collections/nlp/modules/common/megatron/utils.py`
- Used `ApexGuardDefaults` to replace missing classes in `nemo/collections/nlp/modules/common/megatron/transformer.py` | {
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In [diarization class (ClusteringDiarizer)](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/clustering_diarizer.py#L395), there is `os.path.json()` function, but I think it is actually `os.path.join()`.
Nemo version: 1.6.2 | {
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} | Signed-off-by: Oktai Tatanov <[email protected]>
**Collection**: [TTS]
# Changelog
- Clean notebook
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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