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"Hi @MelikaBahmanabadi Thanks for your question.\r\nWe have Voice Activity Detection support ([tutorial](https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/06_Voice_Activiy_Detection.ipynb)) in NeMo. For KWS I think you can start with that and train on data to have a classier to handle `the keyword vs. oth... | 2021-04-24T07:31:02 | 2021-04-28T00:11:09 | 2021-04-28T00:11:09 | NONE | {
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I am very interested in finding out if NEMO supports keyword spotting.
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Melika Bahman-Abadi | {
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} | fixed punctuation for itn in SH to be recognized as word
fixed punctuation for tn in SH to be recognized as word
added tests
simplified deployment route
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- Add two end-to-end models:
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} | fix bug when itn encounters line break.
fixing bug report https://github.com/NVIDIA/NeMo/issues/2094 | {
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"@taufique74 first of all, thanks for catching the bug and PR! Since we would prefer to keep the line breaks which fits more the user's expectations i will replace this PR my this one https://github.com/NVIDIA/NeMo/pull/2101\r\nAnd close this one."
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} | This fixes the empty line issue of `nemo_text_processing/inverse_text_normalization/run_predict.py` discussed in https://github.com/NVIDIA/NeMo/issues/2094
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} | This PR does the following
1. Implements NLL Loss to be used at validation/test time for NMT (without label smoothing)
2. Removes logic that changes the attention mask to not be causal at test time for beam search, which was causing high validation loss | {
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"@yzhang123 ",
"@taufique74 this is true, we do not handle upper cased words at this point, since we assume its the output of the asr model. please lower case your input"
] | 2021-04-22T22:08:40 | 2022-12-19T21:50:26 | 2021-04-22T22:28:51 | NONE | {
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} | null | null | null | **Bug Description**
Inverse Text Normalization text-processing fails to normalize a verbal form if it has a word with an upper case.
While I understand that the transcriptions are usually all-lower cased words, it'd be nice if we lower the text before passing it to `inverse_normalizer`.
**Steps to reproduce bug**
Let's create a text file `input.txt` containing the following information:
```text
one thousand and seventy four
One thousand and seventy four
one Thousand and seventy four
one thousand And seventy four
one thousand and Seventy four
one thousand and seventy Four
```
if we run `python run_predict.py --input=input.txt --output=output.txt`, the generated `output.txt` will contain the following results:
```text
1074
One thousand and 74
one Thousand and 74
1000 And 74
1000 and Seventy four
1070 Four
```
So only the first line that contains all lower-cased words was correctly normalized.
**Environment overview**
- Environment location: Cloud(AWS)
- Method of NeMo install: from source
**Environment details**
- OS version: Ubuntu 18.04.5 LTS
- PyTorch version: 1.8.1+cu102
- Python version: 3.7
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"Hey @yzhang123 \r\nLet me know if you'd be interested in a PR, it's a quick fix.",
"@taufique74 sure go ahead! thanks!",
"closing since https://github.com/NVIDIA/NeMo/pull/2101 is merged. Please re-open if the bug persists"
] | 2021-04-22T21:47:58 | 2022-12-19T21:50:37 | 2021-04-23T21:57:28 | NONE | {
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} | null | null | null | **Bug Description**
While running `python run_predict.py --input=input.txt --output=output.txt`, it breaks saying `pywrapfst.FstOpError: Operation failed`. I noticed there is an empty line in `input.txt`, and after removing that empty line the error was gone.
**Steps to reproduce the bug**
Let's first create a text file `input.txt` with some examples along with an empty line
```bash
$ echo "one hundred and twenty four" >> input.txt
$ echo "" >> input.txt
$ echo "may third nineteen sixty" >>
```
Now if we run `python run_predict.py --input=input.txt --output=output.txt`, it will throw an error.
Here is the complete traceback:
```python-traceback
Traceback (most recent call last):
File "/home/ubuntu/prtc/itn/NeMo/nemo_text_processing/inverse_text_normalization/inverse_normalize.py", line 209, in inverse_normalize_nemo
text = inverse_normalize(input, verbose=verbose)
File "/home/ubuntu/prtc/itn/NeMo/nemo_text_processing/inverse_text_normalization/inverse_normalize.py", line 169, in inverse_normalize
tagged_text = select_tag(tagged_lattice)
File "/home/ubuntu/prtc/itn/NeMo/nemo_text_processing/inverse_text_normalization/inverse_normalize.py", line 125, in select_tag
tagged_text = pynini.shortestpath(lattice, nshortest=1, unique=True).string()
File "extensions/_pynini.pyx", line 464, in _pynini.Fst.string
File "extensions/_pynini.pyx", line 509, in _pynini.Fst.string
_pywrapfst.FstOpError: Operation failed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "run_predict.py", line 77, in <module>
inverse_normalizer_prediction = inverse_normalizer(data, verbose=args.verbose)
File "/home/ubuntu/prtc/itn/NeMo/nemo_text_processing/inverse_text_normalization/inverse_normalize.py", line 211, in inverse_normalize_nemo
raise Exception
Exception
```
**Environment overview**
- Environment location: Cloud(AWS)
- Method of NeMo install: from source
**Environment details**
- OS version: Ubuntu 18.04.5 LTS
- PyTorch version: 1.8.1+cu102
- Python version: 3.7 | {
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"This pull request ... | 2021-04-22T17:15:48 | 2021-04-22T18:01:31 | 2021-04-22T18:01:13 | CONTRIBUTOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2088 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2088/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2088/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2088/events | https://github.com/NVIDIA/NeMo/pull/2088 | 864,513,869 | MDExOlB1bGxSZXF1ZXN0NjIwNzM2OTYx | 2,088 | Distillation Model PT | {
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"This pull request **introduces 24 alerts** when merging fcbfbdd0a2934da73a8898d6688a615c4431cde4 into fbc5c2b4033896b75f643cf169976c1d38505d65 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-719061033791abc13da3e5efcb9ec3fd7f2232e4)\n\n**new alerts:**\n\n* 20 for Unused import\n* 2 for Wrong na... | 2021-04-22T03:56:08 | 2022-05-19T01:22:06 | 2022-05-19T01:22:05 | COLLABORATOR | {
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} | # Changelog
- Prototype generic TeacherStudentModelPT
# Usage
Model extends a mixin class that enables registration of tensors inside of `training_step` for teacher student distillation loss as follows :
```python
# PTL-specific methods
def training_step(self, batch, batch_nb):
signal, signal_len, transcript, transcript_len = batch
if isinstance(batch, DALIOutputs) and batch.has_processed_signal:
log_probs, encoded_len, predictions = self.forward(
processed_signal=signal, processed_signal_length=signal_len
)
else:
log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len)
loss_value = self.loss(
log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len
)
# Distillation support
if self.is_being_distilled():
if self.is_student_model():
loss_key = 'input'
else:
loss_key = 'target'
# Register the tensor for the loss function
self.register_distillation_tensor(loss_key=loss_key, tensor=log_probs)
# No need for further steps, return immediately since later elements are not available on
# both the student and the teacher models
...
```
In any current NeMo model config, attach the following section
```yaml
model:
# DISTILLATION DETAILS
distillation:
model_path: ???
# loss weights
distillation_loss_weight: 1.0
student_train_loss_weight: 1.0
cosine_loss_weight: 1.0
# encoder matching
distill_encoder: False # attempts to distill encoder as well if model shapes are compatible
# logits temperature
temperature: 1.0
# preserve memory for secondary loss computation (true by default)
preserve_distillation_memory: false
# optional - distillation loss config
loss:
primary:
_target_: 'torch.nn.KLDivLoss'
log_target: true
reduction: 'batchmean'
cosine:
_target_: 'nemo.collections.common.losses.cosine_embedding.CosineEmbeddingLossWrapper'
... rest of student model config as usual
```
Then build a model using `DistillationModelPT` with this config and call `trainer.fit()`.
```python
import pytorch_lightning as pl
from omegaconf import OmegaConf
from nemo.core.classes.teacher_student import TeacherStudentModelPT
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="configs/citrinet_bpe", config_name="citrinet_256_same_filters")
def main(cfg):
# logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
teacher_student_model = DistillationModelPT(cfg=cfg.model, trainer=trainer)
trainer.fit(teacher_student_model)
if __name__ == '__main__':
main()
```
# Working
To be expanded once API is finalized.
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"This pull request **introduces 1 alert** and **fixes 2** when merging 0e2ecc61c2b1caa9eac17b42025c9df2b9bad6cf into 4ce74f134ca5c318f421a54e5fbd571879bbb418 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3895299e38b5f30ef6af355511dca3a9d47a19f1)\n\n**new alerts:**\n\n* 1 for Unused import\n\n*... | 2021-04-22T01:18:08 | 2021-04-22T20:47:33 | 2021-04-22T20:47:29 | CONTRIBUTOR | {
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} | * adding configuration to distinguish between lower_cased and cased input, affects whitelist, word classes
* added test
* added pynini sentence predict test to jenkins
* deleted sentence boundary exception
Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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"This pull request **introduces 2 alerts** when merging a4efaa132eae55f2663972c1d8f368b23419aff6 into c2d9559e0cf36dfeb88c1848e8360ae9a6b08776 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-2714799267083096cd0a3b6f4f534df19d9bcabb)\n\n**new alerts:**\n\n* 2 for Unused import",
"is it draft or... | 2021-04-21T23:04:39 | 2021-05-19T06:06:44 | 2021-05-19T06:06:37 | COLLABORATOR | {
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} | Add feature loader to accept external features stored in pickle file.
Add feature to seq_label data loader
Add function to convert seq_label to relative labels.
Add test and test data.
Input manifest file looks like:
```
{"feature_filepath": "tests/.data/asr/feat/AMI-00IB4001-000.p", "seq_label": "zero one two two one two one two two one two two three one two two two zero two one one two two one one two one one one one...."}
{"feature_filepath": "tests/.data/asr/feat/AMI-00IB4001-001.p", "seq_label": "0 1 0 2 1 1 0 1 1 1 1........"}
```
Output looks like:
Tensor(external feature), Tensor(feature length) ,Tensor(label), Tensor(length of seq list)
```
(tensor([[-0.6812, 0.5182, -0.3697, ..., -0.4531, -0.8935, -1.6601],
[-0.2840, 0.8350, 0.9644, ..., 0.0409, 0.0546, -0.6683],
.....), tensor(50), tensor([0., 1., 2., 2., 1., 2., 1., 2., 2., 1., 2
., 2., 3., 1., 2., 2., 2., 0.,
2., 1., 1., 2., 2., 1., 1., 2., 1., 1., 1., 1., 0., 0., 0., 2., 0., 2.,
2., 2., 1., .......]), tensor(50))
``` | {
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"@mschonwe we changed a few things regarding normalization since 1.0.0rc.\r\n please refer to RC tutorial https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/tools/Text_Normalization_Tutorial.ipynb which is tested to work in nvcr.io/nvidia/nemo:1.0.0rc1.",
"Thank you. Got it working following the updated tut... | 2021-04-21T21:08:36 | 2022-12-19T21:50:45 | 2021-04-22T03:46:40 | NONE | {
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} | null | null | null | I'm following the instruction in
`https://github.com/NVIDIA/NeMo/blob/main/tutorials/text_processing/Text_Normalization_Tutorial.ipynb`
Running code on a docker container `nvcr.io/nvidia/nemo:1.0.0rc1`
There are no errors when running the suggested install code:
```
BRANCH = 'main'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]
```
Many lines of `Requirement already satisfied:...`
However, the `import nemo_text_processing` fails:
```
ModuleNotFoundError: No module named 'nemo_text_processing'
```
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"It is normal to get 100% WER for first few epochs. How many epochs did train?",
"Also, there might be a mismatch between the datasets. For example, is the sampling rate in your data the same as it is in the LibriSpeech dataset?",
"around 50 epoch, after 50 epochs its crashing\r\n> It is normal to get 100% WER ... | 2021-04-21T10:50:12 | 2021-04-23T23:49:33 | 2021-04-23T23:49:32 | NONE | {
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} | null | null | null | Hello,
I am finetuning the QuartznetBase-en5x5 model on my own dataset, but I am getting trainingWER 100% and training loss is flatted after few steps although val_WER and val_loss is decreasing gradually
**Dataset Details**
train Dataset consists of 169630 files totaling 410.18 hours
Val_ Dataset consists of 25036 files totaling 84.82 hours
Audio data is of length between( 5-60secs) and made sure that there are no samples without text output
I'm using the file [speech_to_text.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text.py) just made a little changes and added the following line before _'trainer.fit'_
` asr_model.restore_from("QuartzNet15x5Base-En.nemo")`
**other details are:**
trainer.max_epochs=200\
model.train_ds.batch_size=16\
model.validation_ds.batch_size=16 \
model.optim.lr=0.00015 \
+model.validation_ds.num_workers=2 \
+model.train_ds.num_workers=10 \
+trainer.precision=16
**nemo version = 1.0.0rc1**
. Could you please troubleshoot where could the problem be? | {
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"Thanks for your question.\r\nYes. Have a look at **_VAD diarization_** part in diarization [tutorial](https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/speaker_recognition/Speaker_Diarization_Inference.ipynb ) which will generate VAD RTTM-like json file that could suit your request.\r\n",
"Yes, thank you ... | 2021-04-21T06:16:27 | 2021-04-22T10:41:11 | 2021-04-22T10:41:11 | NONE | {
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} | null | null | null | Hi, I want to extract the speech time intervals from the audios using VAD (vad_marbelnet) model. Is there a direct method (function) to do it without modifying the config file? | {
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} | # Changelog
- Add jenkins test to run DDP predict and measure the model internal ranks calculated
- Exit early if these ranks in logs dont correspond to expected global ranks.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | # Bugfix
- Correct DDP initialization (single/multi node) for datasets that depend on self.global_rank, self.local_rank, self.node_rank.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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- Update core jenkins container to 21.04
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"This pull request **introduces 1 alert** when merging 760f3012e9f086af1d1893d0a1c378bd2b2aabab into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-7b0cbe4483c3653568bbc31df8b151ec2086aefc)\n\n**new alerts:**\n\n* 1 for Unused local variable"
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"@titu1994 any thoughts? ",
"Just to text, can you add accelerator=\"ddp\" (even for 1 gpu, just a test). \r\n\r\nAs far as I can parse the error, it's due to a deep copy of a model which can't be pickled, so it is raising a bound function wrapper error. \r\n\r\nYou'll probably have to come up with a toy example... | 2021-04-20T13:48:25 | 2021-04-25T12:43:39 | 2021-04-24T00:07:04 | NONE | {
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} | null | null | null | **Describe the bug**
When adding ModelPruning callback to the Pytorch Lightning trainer in order to train ASR models (in particular, I tried Quartznet15x5) I get the following exception:
TypeError Traceback (most recent call last)
<ipython-input-16-2116e952a23e> in <module>
1 # Start training!!!
----> 2 trainer.fit(first_asr_model)
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
497
498 # dispath `start_training` or `start_testing` or `start_predicting`
--> 499 self.dispatch()
500
501 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
544
545 else:
--> 546 self.accelerator.start_training(self)
547
548 def train_or_test_or_predict(self):
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
71
72 def start_training(self, trainer):
---> 73 self.training_type_plugin.start_training(trainer)
74
75 def start_testing(self, trainer):
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
112 def start_training(self, trainer: 'Trainer') -> None:
113 # double dispatch to initiate the training loop
--> 114 self._results = trainer.run_train()
115
116 def start_testing(self, trainer: 'Trainer') -> None:
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
635 with self.profiler.profile("run_training_epoch"):
636 # run train epoch
--> 637 self.train_loop.run_training_epoch()
638
639 if self.max_steps and self.max_steps <= self.global_step:
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
575
576 if should_check_val:
--> 577 self.trainer.run_evaluation(on_epoch=True)
578
579 # reset stage to train
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, max_batches, on_epoch)
750
751 # hook
--> 752 self.evaluation_loop.on_evaluation_end()
753
754 # log epoch metrics
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py in on_evaluation_end(self, *args, **kwargs)
98 self.trainer.call_hook('on_test_end', *args, **kwargs)
99 else:
--> 100 self.trainer.call_hook('on_validation_end', *args, **kwargs)
101
102 def reload_evaluation_dataloaders(self):
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py in call_hook(self, hook_name, *args, **kwargs)
1093 if hasattr(self, hook_name):
1094 trainer_hook = getattr(self, hook_name)
-> 1095 trainer_hook(*args, **kwargs)
1096
1097 # next call hook in lightningModule
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_hook.py in on_validation_end(self)
183 """Called when the validation loop ends."""
184 for callback in self.callbacks:
--> 185 callback.on_validation_end(self, self.lightning_module)
186
187 def on_test_start(self):
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in on_validation_end(self, trainer, pl_module)
210 checkpoints can be saved at the end of the val loop
211 """
--> 212 self.save_checkpoint(trainer, pl_module)
213
214 def on_save_checkpoint(self, trainer, pl_module, checkpoint: Dict[str, Any]) -> Dict[str, Any]:
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in save_checkpoint(self, trainer, pl_module)
257 # Mode 1: save all checkpoints OR only the top k
258 if self.save_top_k:
--> 259 self._save_top_k_checkpoints(trainer, pl_module, monitor_candidates)
260
261 # Mode 2: save the last checkpoint
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in _save_top_k_checkpoints(self, trainer, pl_module, metrics)
561
562 if self.check_monitor_top_k(trainer, current):
--> 563 self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)
564 elif self.verbose:
565 rank_zero_info(f"Epoch {epoch:d}, step {step:d}: {self.monitor} was not in top {self.save_top_k}")
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in _update_best_and_save(self, current, epoch, step, trainer, pl_module, ckpt_name_metrics)
603 f' (best {self.best_model_score:0.5f}), saving model to "{filepath}" as top {k}'
604 )
--> 605 self._save_model(filepath, trainer, pl_module)
606
607 if del_filepath is not None and filepath != del_filepath:
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in _save_model(self, filepath, trainer, pl_module)
333 # delegate the saving to the trainer
334 if self.save_function is not None:
--> 335 self.save_function(filepath, self.save_weights_only)
336 else:
337 raise ValueError(".save_function() not set")
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/properties.py in save_checkpoint(self, filepath, weights_only)
325
326 def save_checkpoint(self, filepath, weights_only: bool = False) -> None:
--> 327 self.checkpoint_connector.save_checkpoint(filepath, weights_only)
328
329 @property
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py in save_checkpoint(self, filepath, weights_only)
395 """
396 # dump states as a checkpoint dictionary object
--> 397 checkpoint = self.dump_checkpoint(weights_only)
398 if self.trainer.is_global_zero:
399 # write the checkpoint dictionary on the file
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py in dump_checkpoint(self, weights_only)
282 if not weights_only:
283 # dump callbacks
--> 284 checkpoint['callbacks'] = self.trainer.on_save_checkpoint(checkpoint)
285
286 optimizer_states = []
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_hook.py in on_save_checkpoint(self, checkpoint)
220 state = callback.on_save_checkpoint(self, self.lightning_module) # noqa: parameter-unfilled
221 else:
--> 222 state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
223 if state:
224 callback_states[type(callback)] = state
/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/pruning.py in on_save_checkpoint(self, trainer, pl_module, checkpoint)
398 prev_device = pl_module.device
399 # prune a copy so training can continue with the same buffers
--> 400 copy = deepcopy(pl_module.to("cpu"))
401 self.make_pruning_permanent(copy)
402 checkpoint["state_dict"] = copy.state_dict()
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/opt/conda/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/opt/conda/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/opt/conda/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/opt/conda/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/opt/conda/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/opt/conda/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/opt/conda/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/opt/conda/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/opt/conda/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
167 reductor = getattr(x, "__reduce_ex__", None)
168 if reductor:
--> 169 rv = reductor(4)
170 else:
171 reductor = getattr(x, "__reduce__", None)
TypeError: can't pickle BoundFunctionWrapper objects
**Steps/Code to reproduce bug**
The easiest way I could think of to replicate this problem is to take the ASR with NeMo tutorial (No. 1), and replace
```python
trainer = pl.Trainer(gpus=1, max_epochs=50)
```
with
```python
mp = pl.callbacks.ModelPruning("l1_unstructured", amount=0.5)
trainer = pl.Trainer(gpus=1, callbacks=[mp], max_epochs=50)
```
and run until the first fit. It will work with any simple training process, since I met this bug when using NeMo/examples/asr/speech_to_text.py on my own data.
**Expected behavior**
I expected the training to proceed as usual, and the model to be pruned during training
**Environment overview (please complete the following information)**
- Environment location: Docker (clean installation of Nvidia's Pytorch 20.11)
- Method of NeMo install: I installed from source using
```shell
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
pip install ipywidgets #otherwise the Jupyter Notebook crashes when trying to show progress bar
```
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
```shell
sudo docker pull nvcr.io/nvidia/pytorch:20.11-py3
sudo docker run --gpus all -it --rm -v ./nemo:/NeMo --shm-size=8g \
-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:21.03-py3
```
**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**
I don't think additional context is needed. My GPU is GTX 1080-ti, but that's probably irrelevant
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https://api.github.com/repos/NVIDIA/NeMo/issues/2071 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2071/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2071/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2071/events | https://github.com/NVIDIA/NeMo/issues/2071 | 861,559,958 | MDU6SXNzdWU4NjE1NTk5NTg= | 2,071 | How can I drop the last layer to get output as hidden states in terms of feature extraction? | {
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"This question is a little bit vague. Would you mind expanding on what you're trying to do? For example, what NeMo model are you working with and what would you like to do with the hidden states?"
] | 2021-04-19T17:19:57 | 2021-04-20T05:15:38 | 2021-04-20T05:15:37 | NONE | {
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} | null | null | null | **Describe your question**
Hi, I'm working on the Megatron model how can I drop the last layer to get output as hidden states in terms of feature extraction?
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https://api.github.com/repos/NVIDIA/NeMo/issues/2070 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2070/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2070/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2070/events | https://github.com/NVIDIA/NeMo/issues/2070 | 861,195,643 | MDU6SXNzdWU4NjExOTU2NDM= | 2,070 | how to convert .pt models into .nemo format[Question] | {
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"We have completely changed NeMo since .11. We highly recommend to start using our latest stable version. Please see our User Guide: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/ and our Tutorials: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/starthere/tutorials.html for ho... | 2021-04-19T11:12:16 | 2021-04-20T05:21:01 | 2021-04-20T05:21:00 | NONE | {
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I have following blog [Jump-start Training for Speech Recognition Models in Different Languages with NVIDIA NeMo](https://developer.nvidia.com/blog/jump-start-training-for-speech-recognition-models-with-nemo/#respond) and successfully finetune the model on my custom data. Now I have to test the models but in checkpoint directory I have 3 .pt files as output (JasperDecoderForCTC-STEP-80000.pt JasperEncoder-STEP-80000.pt trainer-STEP-80000.pt)
I am confuse how would I convert them into .nemo format or is there any other way to use those models for inference.
code : [quartznet.py](https://nemo-public.s3.us-east-2.amazonaws.com/transfer-learning-04-2020/quartznet.py)
nemo version = 0.11
Also it will be helpful if anyone can share tutorial for finetuning on latest release
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https://api.github.com/repos/NVIDIA/NeMo/issues/2069 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2069/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2069/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2069/events | https://github.com/NVIDIA/NeMo/pull/2069 | 860,882,399 | MDExOlB1bGxSZXF1ZXN0NjE3NjQ0NzY0 | 2,069 | [docs] reviewed/edited ASR and NLP sections/topics | {
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} | Updated intro.rst for [Text to speech](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/intro.html)
Changed spec_gen -> spec_generation
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"This pull request **introduces 2 alerts** when merging a5a058a6d13568253e1a2e49d996cdc6cda8c522 into 21a17b267fac68d4cdd20f3969a580a0a40dbdb4 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d8d2c21d7f0c2b2b08d057ea93e8c9524bb8826e)\n\n**new alerts:**\n\n* 1 for Unused local variable\n* 1 for Va... | 2021-04-16T21:51:45 | 2021-04-22T21:55:02 | 2021-04-22T21:55:02 | COLLABORATOR | {
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} | This PR adds the support for training and evaluating N-gram LM for ASR models.
The N-gram language model is trained with KenLM library (https://github.com/kpu/kenlm) which can be used with the beam search decoders on top of the ASR models. It supports both character level and BPE level encodings and models which is detected automatically from the type of the model.
The KenLM library and the beam search decoders need to get installed to use this feature. Please refer to 'scripts/ngram_lm/install_beamsearch_decoders.sh' on how to install them.
How to train an N-gram LM:
```
python train_kenlm.py --nemo_model_file <path to the .nemo file of the model> \
--train_file <path to the training text or json manifest file \
--kenlm_bin_path <path to the bin folder of KenLM library> \
--kenlm_model_file <path to store the binary KenLM model> \
--ngram_length <order of N-gram model>
```
After training is done, the binary LM model is stored at the path specified by '--kenlm_model_file'. Then you may use 'scripts/ngram_lm/eval_beamsearch_ngram.py' to evaluate it on an ASR model. This script would evaluate an N-gram language in fusion with beam search decoders on top of a trained ASR model. NeMo's beam search decoders are capable of using the KenLM's N-gram models to find the best candidates.
You may evaluate a model with N-gram LM and beam search decoding as the following:
```
python eval_beamsearch_ngram.py --nemo_model_file <path to the .nemo file of the model> \
--input_manifest <path to the evaluation Json manifest file \
--kenlm_model_file <path to the binary KenLM model> \
--beam_width <list of the beam widths> \
--beam_alpha <list of the beam alphas> \
--beam_width <list of the beam betas> \
--preds_output_folder <optional folder to store the predictions> \
--decoding_mode beam_search_ngram
```
The beam search decoder would calculated the scores as:
```
final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length
beam_alpha: specifies the The amount of importance to place on the N-gram language model.
beam_beta: a penalty term to consider the sequence length in the scores. Negative values for beta will give penalty to longer sequences and make the decoder to prefer shorter predictions, while positive values would result in longer candidates.
```
The results would be reported in Word Error Rate (WER) and Character Error Rate (CER). The results if the best candidate is selected from the candidates is also reported as the best WER/CER. It can show how good the predicted candidates are.
Hyperparameter Grid Search:
Beam search decoding with N-gram LM has three main hyperparameters: beam_width, beam_alpha, and beam_beta. The accuracy of the model is dependent to the values of these parameters, specially beam_alpha and beam_beta. You may specify a single or list of values for each of these parameters to perform grid search. It would perform the beam search decoding on all the combinations of the these three hyperparameters. For instance, the following set of parameters would results in 2*1*2=4 beam search decodings:
```
python eval_beamsearch_ngram.py ... \
--beam_width 64 128 \
--beam_alpha 1.0 \
--beam_beta 1.0 0.5 \
```
The user can select the decoding strategy from the following list:
--decoding_mode: ["greedy", "beamsearch", "beamsearch_ngram"]
The "beamsearch" mode would ignore the LM and calculates the performs regular beam search decoding. | {
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"Duplicate of https://github.com/NVIDIA/NeMo/discussions/2072"
] | 2021-04-16T08:56:47 | 2021-04-20T05:23:07 | 2021-04-20T05:22:54 | NONE | {
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} | null | null | null | How to get the embedding from the BioMegatron checkpoint file?
Will there be any checkpoint converter for the BioMegatron in order to be loaded into Transformers? | {
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"I think @ericharper may have had this in place on purpose to make sure both metadata and tarfiles were provided and not just the metadata. I'm fine with providing just the metadata JSON file (even better would be to allow specifying preproc_out_dir to avoid specifying tokenizers as well). \r\n\r\nCan you also hand... | 2021-04-16T05:33:11 | 2021-04-20T03:51:06 | 2021-04-20T03:51:06 | CONTRIBUTOR | {
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} | If just a metadata_file is passed in through the config, it won't enter the correct elif statement | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2061 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2061/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2061/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2061/events | https://github.com/NVIDIA/NeMo/pull/2061 | 858,227,167 | MDExOlB1bGxSZXF1ZXN0NjE1NTE2MDQz | 2,061 | Update documentation to latest nemo container | {
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} | # Changelog
- Update nemo container to nemo:1.0.0rc1 throughout docs and build scripts
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2057 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2057/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2057/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2057/events | https://github.com/NVIDIA/NeMo/issues/2057 | 857,529,662 | MDU6SXNzdWU4NTc1Mjk2NjI= | 2,057 | Is there multi-lingual support for NeMo? I am looking particularly for a Hindi model. | {
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"yes, we support other languages https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html \r\nBut at the moment, we don't have Hindi ASR model."
] | 2021-04-14T05:18:45 | 2021-04-20T05:23:40 | 2021-04-20T05:23:40 | NONE | {
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} | null | null | null | Hello,
Does NeMo ASR support Hindi and other languages? Will there be any tutorial/doc released any time soon on how to use NeMo ASR for multi lingual training? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2056 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2056/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2056/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2056/events | https://github.com/NVIDIA/NeMo/pull/2056 | 857,234,245 | MDExOlB1bGxSZXF1ZXN0NjE0Njg4MDk2 | 2,056 | Add Aliases to Model Cards | {
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} | - Adds new `aliases` property to PretrainedModelInfo
- Allow models to use `from_pretrained()` with aliases
Example:
```python
PretrainedModelInfo(
pretrained_model_name="tts_en_tacotron2",
location="https://api.ngc.nvidia.com/...nemo",
description="model description",
class_=cls,
aliases=["Tacotron2-22050Hz"],
)
model.from_pretrained("tts_en_tacotron2")
model.from_pretrained("Tacotron2-22050Hz") # Both will load the same model
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2054 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2054/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2054/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2054/events | https://github.com/NVIDIA/NeMo/pull/2054 | 856,470,959 | MDExOlB1bGxSZXF1ZXN0NjE0MDQzNzE5 | 2,054 | Cleanup of NeMo tests and tools | {
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} | # Changelog
- Import guard all of inverse text normalization (pynini) and related tests
- Run all Export tests only on GPU
- Fix docker build + remove redundant RNNT loss function build
- Patch Numba test for cuda compatibility when on CPU | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2052 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2052/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2052/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2052/events | https://github.com/NVIDIA/NeMo/pull/2052 | 856,354,064 | MDExOlB1bGxSZXF1ZXN0NjEzOTQ0NzE1 | 2,052 | Text processing docs | {
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"@yzhang123 there is merge conflict\r\n",
"> @yzhang123 there is merge conflict\r\n\r\n@okuchaiev fixed"
] | 2021-04-12T20:55:51 | 2021-04-14T05:35:01 | 2021-04-14T05:34:55 | CONTRIBUTOR | {
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"Hi, Ulucsahin\r\n\r\nThe `speech_to_text_quant_infer.py` script is meant for evaluating numerical property and accuracy of the quantized network, not speed/throughput. It is expected to be slower because it runs emulated quantization.\r\n\r\nThe script can generate an ONNX graph with quantization nodes. To deploy ... | 2021-04-12T13:21:02 | 2021-04-23T23:50:39 | 2021-04-23T23:50:38 | NONE | {
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} | null | null | null | **Describe your question**
I am quantizing QuartzNet5x5 model that I use for ASR. I followed the following example: `/examples/asr/quantization/speech_to_text_quant_infer.py`
My code is as follows:
```
quant_modules.initialize()
params['model']['encoder']['quantize'] = True
self.first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']))
self.first_asr_model = self.first_asr_model.load_from_checkpoint(self.model_to_load)
self.first_asr_model.cpu()
self.first_asr_model.eval()
```
Code runs without errors. I am getting lots of line saying:
```
Input is fake quantized to 8 bits in QuantConv1d with axis None!
Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
Creating max calibrator
Creating max calibrator
```
Then I get the result from model. Result is correct, however, inference is a slowed to 0.33 seconds average from 0.29 second average.
Am I missing something here?
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: Tested installing from source, also tested pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04 and Windows 10
- PyTorch version: 1.7.1 and 1.9
- Python version: 3.6 and 3.8
**Additional context**
Tested on CPU. Tried setting num_threads to 1 and 2.
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"This pull request **introduces 18 alerts** when merging ed2b68b7bc10a626a2ae82fb0cbcc0e3f83a456c into 52121810b428e011666e656d313b42c954def26b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-8fbe4d26b21589a2bf3447334aae25b590af73e0)\n\n**new alerts:**\n\n* 16 for Unused import\n* 1 for Redundan... | 2021-04-12T06:39:59 | 2021-05-05T20:32:43 | 2021-05-05T20:32:43 | CONTRIBUTOR | {
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} | This pull request adds an entity linking model, dataset, loss, example training code, and demo notebook based on the paper "Self-Alignment Pretraining for Biomedical Entity Representations"
You can run through the notebook tutorial or run everything on the command line.
To run everything on the command line follow these steps
1) Download the UMLS datset file `MRCONSO.RRF` from the NIH website and place it in the `examples/nlp/entity_linking/data` directory.
2) Run the following commands from the `examples/nlp/entity_linking` directory
```
python data/umls_dataset_processing.py
python self_alignment_pretraining.py project_dir=.
python data/umls_dataset_processing.py --index
python build_index.py --restore
python query_index.py --restore
```
The model will take ~24hrs to train on two GPUs and ~48hrs to train on one GPU. By default the project directory will be "." but can be changed by adding the flag `--project_dir=<PATH>` after each of the above commands and changing `project_dir=<PATH>` in the `self_alignment_pretraining.py` command. | {
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"this is not a bug. The container version looks like 1.8.0\"something\" - where \"something\" is commit id pre 1.8.0 release but a little after 1.7.1"
] | 2021-04-10T18:24:17 | 2021-04-12T16:59:47 | 2021-04-12T16:59:47 | NONE | {
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torch 1.8
but "version currently does not support Pytorch 1.8.0"
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"Thanks for your question. \r\nOf course! You can change the `diarizer.vad.model_path` and `diarizer.speaker_embeddings.model_path` in [config file](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/conf/speaker_diarization.yaml) to use your own models."
] | 2021-04-10T04:34:41 | 2021-04-20T05:24:28 | 2021-04-20T05:24:27 | NONE | {
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} | null | null | null | **Describe your question**
Is it possible to use our own VAD or Speaker Embedding models in speaker diarization? If so, how one can do it properly?
Many thanks.
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* renamed denormalization throughout to inverse_text_normalization (tutorial, docs, readme, folder, test folder)
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"Using the last DockerFile still get same problem \r\n\r\nUsing /opt/conda/lib/python3.8/site-packages\r\nFinished processing dependencies for pytorch-quantization==2.1.0\r\nRemoving intermediate container 46d355fa5012\r\n ---> 481e54a4d4dd\r\nStep 12/29 : WORKDIR /tmp/... | 2021-04-09T12:58:02 | 2021-11-19T22:04:39 | 2021-04-23T23:52:18 | NONE | {
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} | null | null | null | Trying to build Nemo Docker image fails when using the last Dockerfile
(Also when trying do use also other docker file in the zip at https://github.com/NVIDIA/NeMo/releases/tag/v1.0.0rc1)
Step 12/29 : WORKDIR /tmp/nemo
---> Running in 9b5b7d241aee
Removing intermediate container 9b5b7d241aee
---> e5fe9b0fabe1
Step 13/29 : COPY requirements .
COPY failed: file not found in build context or excluded by .dockerignore: stat requirements: file does not exist
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"You could finetune Citrinet using the same Tokenizer on the specific domain (if there is sufficient data). \r\n\r\nIf you have some noise files, noise robust training, the same method as QuartzNet can be applied to Citrinet. \r\n\r\nFor preprocessing, there should be monochannel 16Khz wav files. We find that attem... | 2021-04-09T12:52:10 | 2021-04-23T21:56:03 | 2021-04-23T21:56:02 | NONE | {
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} | null | null | null | Hi
I am using stt_en_citrinet_1024 model and able to get good transcript , I am using the recorded audios with microphone and WER is varying from 3.5% to 15%. This has names of person and place, how to include the words in the model.
any suggestions with following aspects
1. Preprocessing the audio
2. using LM model for decoding ( for citrinet is there any implementation in nemo)
3. Post processing steps say spell correction
looking for inputs .
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"I am not sure what kind of precision is used for computation by your Arm processor. \r\nIs it IEEE float32? Then it should work. However, if it is something else, then precision-related issue would be likely. "
] | 2021-04-08T13:23:32 | 2021-04-24T00:07:24 | 2021-04-24T00:07:24 | NONE | {
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Hello, I trained an ASR model for speech to text conversion. My model has decent accuracy and I want to use this model on a device with ARM Cortex-A72 processor.
I am doing inference using EncDecCTCModel.transcribe. Everything is fine on Windows and Ubuntu with x86 architecture. However, when doing inference on device with Arm processor, I am getting either a single character transcription or an empty transciption. I checked the input to see if audio file was loaded correctly and it was correct.
I set the seed to 0 on all my devices to check array values and I tracked down the issue to following line in nemo/collections/asr/models/ctc_models.py:
`encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length)`
encoded values are same on arm and x86 devices with very small differences, however, I am getting lots of "nan" values in log_tensor values on arm device in the next line:
`log_probs = self.decoder(encoder_output=encoded)`
I am guessing my result gets messed up due to these values. I tried with different environments but the problem persists.
Is there a way to make this work on Arm processors? I don't have gpu on my device with arm processor so I have to do the computation on Arm processor itself (raspberry pi 4).
I am fine with alternate solutions such as exporting the model (if that is going to solve it).
**Environment overview (please complete the following information)**
- Environment location: Tested on Raspberry Pi 4, Windows and Ubuntu devices. Tested both with and without docker.
- Method of NeMo install: Tested pip installation and building from source.
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04, Windows 10
- PyTorch version: Tested both 1.7.1 and 1.9
- Python version: 3.8
**Additional context**
Add any other context about the problem here.
CPU Model: Arm Cortex-A72, Intel i5
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"~~Can you open a new PR?~~ nvm.",
"I'm looking forward for this! I'm hoping to train a citrinet model from scratch soon on A100 GPUs which could leverage really well mix-precision training. Is there an ETA for this patch to land in the main trunk?",
"As discussed with Jason and Som, we're leaving the workaro... | 2021-04-07T14:40:32 | 2021-04-19T12:52:38 | 2021-04-16T14:59:24 | CONTRIBUTOR | {
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} | Squeeze-and-Excitation block introduced instabilities to Citrinet when training with mixed precision.
This change avoids computing SE in float16 all together.
As SE block does not take much of computational time, the time and memory benefits of other computations done in float16 can allow to train model faster.
The runs on NGC and multinode cluster confirm that there is no performance degradation or instabilities when using fp16 with current patch. The speedup is 15% and memory savings are around 3Gb for 21M parameter model. | {
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"Seems you're encountering a neural type error. Are you certain the audio is a 16 KHz monochannel wav file ? As a temporary measure to check if the file is not the issue you can disable type checking globally by following https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.0.0rc1/core/core.html#neural-t... | 2021-04-07T13:21:50 | 2021-07-08T07:04:48 | 2021-04-08T12:47:50 | NONE | {
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I am trying to run NeMo ASR inside Ubuntu 20.04 docker container on Raspberry pi 4. I have compiled NeMo from source.
When I try to run EncDecCTCModel.transcribe, I am getting the following error:
> Traceback (most recent call last):
> File "main.py", line 43, in <module>
> quartznet_inferencer.debugging("./debugging/infer_2.wav")
> File "/workspace/codes/uluc/intellioven-ml/wake-up-word/quartznet_inferencer.py", line 54, in debugging
> prediction = self.first_asr_model.transcribe([file_path])
> File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
> return func(*args, **kwargs)
> File "/workspace/install_stuff/NeMo/nemo/collections/asr/models/ctc_models.py", line 238, in transcribe
> logits, logits_len, greedy_predictions = self.forward(
> File "/workspace/install_stuff/NeMo/nemo/core/classes/common.py", line 772, in __call__
> instance._validate_input_types(input_types=input_types, ignore_collections=self.ignore_collections, **kwargs)
> File "/workspace/install_stuff/NeMo/nemo/core/classes/common.py", line 210, in _validate_input_types
> raise TypeError("\n".join(error_msg))
> TypeError: NeuralTypeComparisonResult.INCOMPATIBLE :
> Input type expected : axes: (batch, time); elements_type: AudioSignal
> input param_0 : freq: 16000
> Input type found : axes: (batch, time); elements_type: VoidType
My pip freeze is as follows:
> absl-py==0.12.0
> aiohttp==3.7.4.post0
> alabaster==0.7.12
> antlr4-python3-runtime==4.8
> appdirs==1.4.4
> async-timeout==3.0.1
> atomicwrites==1.1.5
> attrdict==2.0.1
> attrs==20.3.0
> audioread==2.1.9
> Babel==2.9.0
> black==19.10b0
> boto3==1.17.45
> botocore==1.20.45
> braceexpand==0.1.6
> cachetools==4.2.1
> certifi==2019.11.28
> cffi==1.14.5
> chardet==3.0.4
> click==7.1.2
> colorama==0.4.4
> configparser==5.0.2
> cycler==0.10.0
> Cython==0.29.22
> dbus-python==1.2.16
> decorator==5.0.5
> Distance==0.1.3
> distro-info===0.23ubuntu1
> docker-pycreds==0.4.0
> docopt==0.6.2
> docutils==0.17
> editdistance==0.5.3
> filelock==3.0.12
> frozendict==1.2
> fsspec==0.9.0
> future==0.18.2
> g2p-en==2.1.0
> gdown==3.12.2
> gitdb==4.0.7
> GitPython==3.1.14
> google-auth==1.28.0
> google-auth-oauthlib==0.4.4
> grpcio==1.36.1
> h5py==3.2.1
> hydra-core==1.0.6
> idna==2.8
> imagesize==1.2.0
> importlib-metadata==1.5.0
> importlib-resources==5.1.2
> inflect==5.3.0
> iniconfig==1.1.1
> ipadic==1.0.0
> isort==4.3.21
> jieba==0.42.1
> Jinja2==2.11.3
> jmespath==0.10.0
> joblib==1.0.1
> kaldi-io==0.9.4
> kaldi-python-io==1.2.2
> kaldiio==2.17.2
> kiwisolver==1.3.1
> latexcodec==2.0.1
> librosa==0.8.0
> llvmlite==0.36.0
> Markdown==3.3.4
> MarkupSafe==1.1.1
> marshmallow==3.11.1
> matplotlib==3.4.1
> mecab-python3==1.0.3
> megatron-lm==2.2.0
> more-itertools==4.2.0
> mpmath==1.2.1
> multidict==5.1.0
> -e git+https://github.com/NVIDIA/NeMo@347cbeaa3695312385ec3c408910ccb6aa4f29e4#egg=nemo_toolkit
> nltk==3.5
> nose==1.3.7
> numba==0.53.1
> numpy==1.20.2
> oauthlib==3.1.0
> olefile==0.46
> omegaconf==2.0.6
> onnx==1.8.1
> OpenCC==0.2
> packaging==20.9
> pandas==1.2.3
> pangu==4.0.6.1
> parameterized==0.8.1
> pathspec==0.8.1
> pathtools==0.1.2
> pesq==0.0.2
> Pillow==8.2.0
> pip-api==0.0.20
> pipreqs==0.4.10
> playsound==1.2.2
> pluggy==0.13.1
> pooch==1.3.0
> portalocker==2.0.0
> promise==2.3
> protobuf==4.0.0rc2
> psutil==5.8.0
> py==1.10.0
> pyannote.core==4.1
> pyannote.database==4.0.4
> pyannote.metrics==3.0.1
> pyasn1==0.4.8
> pyasn1-modules==0.2.8
> PyAudio==0.2.11
> pybind11==2.6.2
> pybtex==0.24.0
> pybtex-docutils==1.0.0
> pycparser==2.20
> pydub==0.25.1
> Pygments==2.3.1
> PyGObject==3.36.0
> pyparsing==2.4.7
> pypinyin==0.41.0
> PySocks==1.7.1
> pystoi==0.3.3
> pytest==6.2.3
> pytest-runner==5.3.0
> python-apt==2.0.0+ubuntu0.20.4.4
> python-dateutil==2.8.1
> pytorch-lightning==1.2.6
> pytz==2021.1
> PyYAML==5.3.1
> rapidfuzz==1.4.1
> regex==2021.4.4
> requests==2.22.0
> requests-oauthlib==1.3.0
> requests-unixsocket==0.2.0
> resampy==0.2.2
> rsa==4.7.2
> ruamel.yaml==0.17.2
> ruamel.yaml.clib==0.2.2
> s3transfer==0.3.6
> sacrebleu==1.5.1
> sacremoses==0.0.44
> scikit-learn==0.22.2.post1
> scipy==1.6.2
> semantic-version==2.8.5
> sentencepiece==0.1.95
> sentry-sdk==1.0.0
> setuptools-rust==0.12.1
> shellingham==1.4.0
> shortuuid==1.0.1
> simplejson==3.17.2
> six==1.14.0
> smmap==4.0.0
> snowballstemmer==2.1.0
> sortedcontainers==2.3.0
> SoundFile==0.10.3.post1
> sox==1.4.1
> spectralcluster==0.1.0
> Sphinx==3.5.3
> sphinxcontrib-applehelp==1.0.2
> sphinxcontrib-bibtex==2.2.0
> sphinxcontrib-devhelp==1.0.2
> sphinxcontrib-htmlhelp==1.0.3
> sphinxcontrib-jsmath==1.0.1
> sphinxcontrib-qthelp==1.0.3
> sphinxcontrib-serializinghtml==1.1.4
> subprocess32==3.5.4
> sympy==1.7.1
> tabulate==0.8.9
> tensorboard==2.4.1
> tensorboard-plugin-wit==1.8.0
> threadpoolctl==2.1.0
> tokenizers==0.10.2
> toml==0.10.2
> torch @ https://github.com/KumaTea/pytorch-aarch64/releases/download/v1.7.1/torch-1.7.1-cp38-cp38-linux_aarch64.whl
> torch-stft==0.1.4
> torchaudio @ https://github.com/KumaTea/pytorch-aarch64/releases/download/v1.7.1/torchaudio-0.7.2-cp38-cp38-linux_aarch64.whl
> torchmetrics==0.2.0
> torchvision @ https://github.com/KumaTea/pytorch-aarch64/releases/download/v1.7.1/torchvision-0.8.2-cp38-cp38-linux_aarch64.whl
> tqdm==4.60.0
> transformers==4.4.2
> typed-ast==1.4.2
> typer==0.3.2
> typing-extensions==3.7.4.3
> unattended-upgrades==0.1
> Unidecode==1.2.0
> urllib3==1.25.8
> wandb==0.10.25
> wcwidth==0.1.8
> webdataset==0.1.54
> Werkzeug==1.0.1
> wget==3.2
> wrapt==1.12.1
> yarg==0.1.9
> yarl==1.6.3
> youtokentome==1.0.6
> zipp==1.0.0
Anyone knows how can I fix this? Thanks.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Docker with Ubuntu 20.04
- PyTorch version: 1.7.1
- Python version: 3.8.5
**Additional context**
Add any other context about the problem here.
On aarch64 cpu
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"@titu1994 do you see any problems with us making `cache_dir` into an argument?",
"This seems like a good plan, let's check for existence of global override in os, use that otherwise default to current default. ",
"This is now supported. ",
"> This is now supported.\r\n\r\nHello! I understand that we can now ... | 2021-04-07T05:07:02 | 2022-12-24T18:44:19 | 2021-11-30T23:05:38 | CONTRIBUTOR | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Directory where pretrained model is downloaded is hardcoded to
`cache_dir = Path.joinpath(Path.home(), f'.cache/torch/NeMo/NeMo_{nemo.__version__}/{filename[:-5]}')` in https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/common.py#L652.
So model download fails in environments where $HOME is not writeable.
**Describe the solution you'd like**
Provide option to specify path to download pretrained model. May be via environment variable or a parameter?
**Describe alternatives you've considered**
To unblock myself, I have currently created a change in my fork to specify download path via environment variable.
https://github.com/jarivk/NeMo/commit/f2e1c57e400b71ddcb0c76499c0cd7f29893ba9c
**Additional context**
None
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"This pull request **fixes 4 alerts** when merging 592b26b4f30a57a3fec5afa470e1e56ffdf51d04 into e70231dd920468e2d8562aefd43d01d7572e135e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9d3db6c91cb004379050ced8d60b6fc51287ad21)\n\n**fixed alerts:**\n\n* 4 for Explicit export is not defined",
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} | # Changelog
- Adds `future_context` parameter to JasperBlock to enable asymmetric padding for the convolution layers.
- Adds several tests for JasperBlock components
- Adds docstring to explain how to use asymmetric padding + potential pitfalls. | {
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} | We need to use the trainer object, no the config. It forces the user to have those in the config file.
Also fixed the check_slurm method to detect the slurm. | {
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} | # Changelog
- After class resolution of `target`, if calling `cls` is subclass of resolved `imported_cls`, then defer to subclass `cls` instantiation instead of the imported target class.
- Add tests to check these scenarios
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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* Docker for sparrowhawk/ production backend
* pynini grammar export script pynini_export.py
* main script export_grammars.sh
* readme | {
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"can you please try with the latest main branch and re-open if the issue still exists"
] | 2021-04-03T13:09:47 | 2021-04-24T00:01:17 | 2021-04-24T00:01:17 | NONE | {
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} | null | null | null | Hi,
I am trying to fine-tuning my model, but after a successful restore of my checkpoint, i keep on stumbling on this error: TypeError: 'NoneType' object is not iterable
I am a beginner and is using Nemo on Google Colab. I am following the tutorial provided in one of your notebooks. Below is the code on how i restored my checkpoint and how i started the training:
----------------------------------------------------------------------------------------------
import pytorch_lightning as pl
trainer = pl.Trainer(gpus=1, max_epochs=50)
from omegaconf import DictConfig
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['validation_ds']['manifest_filepath'] = test_manifest
my_model = nemo_asr.models.EncDecCTCModel.restore_from('my_checkpoint.nemo')
trainer.fit(my_model)
----------------------------------------------------------------------------------------------
How i saved my checkpoint:
.
.
.
checkpoint_path = "my_checkpoint.nemo"
my_model.save_to(checkpoint_path)
----------------------------------------------------------------------------------------------
This was the error produced:
.
.
.
.
TypeError Traceback (most recent call last)
<ipython-input-11-2116e952a23e> in <module>()
1 # Start training!!!
----> 2 trainer.fit(my_model)
6 frames
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py in _with_is_last(self, iterable)
43 """Pass through values from the given iterable with an added boolean indicating if this is the last item.
44 See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_"""
---> 45 it = iter(iterable)
46 last = next(it)
47 for val in it:
TypeError: 'NoneType' object is not iterable
----------------------------------------------------------------------------------------------
I was able to load my checkpoint and get the original WER, but i could not proceed to further train it.
I have tried extracting my .nemo checkpoint file and find that it holds another my_checkpoint.nemo zip file that contains two other files which is model_config.yaml and model_weights.ckpt.
Am I loading the correct checkpoint file type? It says NoneType object is not iterable, I am guessing there is a problem with the format of my checkpoint?
Thank you in advance.
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install: Pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- PyTorch version: 1.7.1
- Python version: Python 3
**Additional context**
- none | {
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"> \r\n> \r\n> Please update the docstring of exp_manger so that it matches the new behaviour.\r\n> Please add a note that the behaviour of this argument changed as of version 1.0.0.\r\n\r\nAdded to the docstring."
] | 2021-04-02T19:08:20 | 2021-05-03T21:55:25 | 2021-04-06T21:35:03 | COLLABORATOR | {
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"This pull request **introduces 5 alerts** when merging 0cd9b47b003516235ee980d6f536408b4660227e into 6962f75547e7ac2bb576f4e5a862b38d815ed79d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-655a61c578559798dab12e49c55fba35de8fde65)\n\n**new alerts:**\n\n* 3 for Unused import\n* 1 for Except blo... | 2021-04-02T18:34:11 | 2021-04-21T19:26:45 | 2021-04-21T19:26:40 | COLLABORATOR | {
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} | Updating model parallel to work with new PTL DDP API and adding .nemo support.
# Usage
We support 4 main workflows:
- Training from megatron-lm checkpoint
- Auto-resume training from experiment manager
- Training from model parallel .nemo file
- Evaluation from model parallel .nemo file
To enable these workflows in a NeMo NLP example training script, the PyTorch Lightning `Trainer` must be instantiated with the `NLPDDPPlugin`. This plugin contains the necessary logic to instantiate model parallel Megatron-LM models and train them.
```python
trainer = Trainer(plugins=[NLPDDPPlugin()], **cfg.trainer)
```
Since model parallel models always require multiple GPUs, we must always have a `Trainer` object.
This means that when we restore a model parallel .nemo file, we must also pass in the `Trainer`.
```python
model = TextClassificationModel.restore_from(cfg.model.nemo_path, trainer=trainer)
```
## Training from Megatron-LM checkpoint
Megatron-LM checkpoints have the following specific format:
```
iter_0080000/
├── mp_rank_00
│ └── model_optim_rng.pt
└── mp_rank_01
└── model_optim_rng.pt
```
In order to start training from these checkpoints, pass in the parent directory and model configuration file:
```
model.language_model.lm_checkpoint=/raid/megatron/bert/iter_0080000 \
model.language_model.config_file=/raid/data/megatron/bert/config.json \
```
The model configuration can be specified in YAML:
```yaml
model:
language_model:
config:
hidden_size: 1024
num_attention_heads: 16
num_layers: 24
max_position_embeddings: 512
```
Additionally, the vocab file must be specified for megatron-lm models:
```
model.tokenizer.vocab_file=/path/to/vocab.txt
```
or if using the Megatron-LM default tokenizer for training BERT:
```
model.tokenizer.tokenizer_name=megatron-bert-uncased
```
## Auto-resume from experiment manager
Auto resume works exactly the same as non-model parallel models.
While training with PTL, model parallel checkpoint will be saved and loaded properly.
```
checkpoints/
├── mp_rank_00
│ ├── mp_autoresume-last.ckpt
│ ├── mp_autoresume---val_loss=0.35-epoch=0.ckpt
│ ├── mp_autoresume---val_loss=0.38-epoch=1.ckpt
│ └── mp_autoresume---val_loss=0.39-epoch=2.ckpt
└── mp_rank_01
├── mp_autoresume-last.ckpt
├── mp_autoresume---val_loss=0.35-epoch=0.ckpt
├── mp_autoresume---val_loss=0.38-epoch=1.ckpt
└── mp_autoresume---val_loss=0.39-epoch=2.ckpt
```
## Training from model parallel .nemo
Model parallel checkpoints are saved in the correct format:
```
text_class_350m
├── megatron-bert-uncased_encoder_config.json
├── megatron_checkpoint_version.json
├── model_config.yaml
├── mp_rank_00
│ └── model_weights.ckpt
├── mp_rank_01
│ └── model_weights.ckpt
├── tokenizer_vocab_dict.json
└── tokenizer.vocab_file
```
Training from the .nemo is the same as other NeMo NLP example scripts:
```python
trainer = pl.Trainer(plugins=[NLPDDPPlugin()], **cfg.trainer)
model = TokenClassificationModel.restore_from(cfg.pretrained_model, trainer=trainer)
trainer.fit(model)
```
## Evaluation from model parallel .nemo
Current NeMo NLP example scripts often assume using a single GPU for evaluation, which will not work.
However, evaluation with model parallel .nemo is very simple:
```python
trainer = pl.Trainer(plugins=[NLPDDPPlugin()], **cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = TextClassificationModel.restore_from(cfg.model.nemo_path, trainer=trainer)
model.setup_test_data(test_data_config=cfg.model.test_ds)
trainer.test(model=model, ckpt_path=None)
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/2010 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2010/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2010/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2010/events | https://github.com/NVIDIA/NeMo/issues/2010 | 849,056,696 | MDU6SXNzdWU4NDkwNTY2OTY= | 2,010 | [Question] A couple of questions about MarbleNet VAD | {
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"Hi @OmarHory Thanks for your questions. \r\n\r\n> My dataset totals to 7 hours of speech and 7 hours of background. Is that enough for training the VAD?\r\n\r\nIn [MarbleNet](https://arxiv.org/pdf/2010.13886.pdf) we have intotal 160,994 segments (160,994 * 0.63s /60/60=28.2h) for training. I'm not sure if 14 hou... | 2021-04-02T08:02:49 | 2021-04-07T05:17:30 | 2021-04-07T05:17:29 | NONE | {
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} | null | null | null | Hello!
I have been training a VAD but the results I am getting are not optimal as I have done prior data-preprocessing like trimming and Noise Injection. My dataset totals to 7 hours of speech and 7 hours of background. Is that enough for training the VAD?
Also, I would like to know how to be sure that Noise Augmentation is activated, because when I start training, I see the following:

Spectrogram Augmentation has 0 params, does that mean it is not activated?
Here's my configuration **yaml** model file:
```yml
name: &name "MarbleNet-3x2x64"
model:
sample_rate: 16000
repeat: 2
dropout: 0.3
kernel_size_factor: 1.0
labels: ['background', 'speech']
train_ds:
manifest_filepath: ???
sample_rate: 16000
labels: ['background', 'speech']
batch_size: 16
shuffle: True
is_tarred: False
tarred_audio_filepaths: null
tarred_shard_strategy: "scatter"
augmentor:
shift:
prob: 0.5
min_shift_ms: -5.0
max_shift_ms: 5.0
white_noise:
prob: 0.5
min_level: -90
max_level: -46
validation_ds:
manifest_filepath: ???
sample_rate: 16000
labels: ['background', 'speech']
batch_size: 16
shuffle: False
val_loss_idx: 0
test_ds:
manifest_filepath: null
sample_rate: 16000
labels: ['background', 'speech']
batch_size: 128
shuffle: False
test_loss_idx: 0
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor
window_size: 0.025
window_stride: 0.01
window: "hann"
n_mels: &n_mels 64
n_mfcc: *n_mels
n_fft: 512
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2
time_masks: 2
freq_width: 15
time_width: 25
rect_masks: 5
rect_time: 25
rect_freq: 15
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: *n_mels
activation: relu
conv_mask: true
jasper:
- filters: 128
repeat: 1
kernel: [11]
stride: [1]
dilation: [1]
dropout: 0.3
residual: false
separable: true
kernel_size_factor: 1.0
- filters: 64
repeat: 2
kernel: [13]
stride: [1]
dilation: [1]
dropout: 0.3
residual: true
separable: true
kernel_size_factor: 1.0
- filters: 64
repeat: 2
kernel: [15]
stride: [1]
dilation: [1]
dropout: 0.3
residual: true
separable: true
kernel_size_factor: 1.0
- filters: 64
repeat: 2
kernel: [17]
stride: [1]
dilation: [1]
dropout: 0.3
residual: true
separable: true
kernel_size_factor: 1.0
- filters: 128
repeat: 1
kernel: [29]
stride: [1]
dilation: [2]
dropout: 0.3
residual: false
separable: true
kernel_size_factor: 1.0
- filters: &enc_final_filters 128
repeat: 1
kernel: [1]
stride: [1]
dilation: [1]
dropout: 0.3
residual: false
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoderClassification
feat_in: *enc_final_filters
return_logits: true
pooling_type: 'avg'
optim:
name: sgd
lr: 0.01
# optimizer arguments
weight_decay: 0.001
momentum: 0.9
# scheduler setup
sched:
name: PolynomialHoldDecayAnnealing
# Scheduler params
power: 2.0
warmup_ratio: 0.05
hold_ratio: 0.45
min_lr: 0.001
last_epoch: -1
trainer:
gpus: 0 # number of gpus
max_epochs: 150
max_steps: null # computed at runtime if not set
num_nodes: 1
accelerator: ddp
accumulate_grad_batches: 1
checkpoint_callback: False # Provided by exp_manager
logger: False # Provided by exp_manager
log_every_n_steps: 1 # Interval of logging.
val_check_interval: 1.0 # Set to 0.45 to check 4 times per epoch, or an int for number of iterations
exp_manager:
exp_dir: null
name: *name
create_tensorboard_logger: True
create_checkpoint_callback: True
create_wandb_logger: False
wandb_logger_kwargs:
name: null
project: null
hydra:
run:
dir: .
job_logging:
root:
handlers: null
```
Would appreciate your help.
Omar. | {
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} | * Add has_audio parameter to speech_collate
* @blisc I wouldn't like to write new `speech_collate` for TalkNet dataset, let's reuse old version with `has_audio` parameter?
* Log warning in `setup_optimizaton` when Trainer is not specified
* Update TalkNet models that were without trainer in constructor
* TalkNet Training tutorial notebook
* This version of notebook has 6 TODOs. It is related to branch name, link to new version of paper and pretrained models that are currently not in NGC (@stasbel, please, add them and we can finish it). Without fix, we **shouldn't** merge it.
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Removed setup.py version logic. | {
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"This pull request **introduces 1 alert** when merging af670381c65ac34248df3ca92a7decefe00e40f8 into 00c5429902c5bf8a1ff3efbdff16aa8935ace8d9 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-85e49b514fdccac71e3d647f3e3a3c42454826a7)\n\n**new alerts:**\n\n* 1 for Unused import",
"Closing in favo... | 2021-04-01T16:04:42 | 2021-04-20T17:24:00 | 2021-04-20T17:24:00 | COLLABORATOR | {
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} | When building docker container locally:
```
#17 [nemo-deps 10/12] RUN for f in $(ls requirements/*.txt); do pip install ...
#17 0.515 ls: cannot access 'requirements/*.txt': No such file or directory
#17 DONE 0.6s
```
The files are copied not into a separate `/tmp/nemo/requirements/` folder but directly into `/tmp/nemo/`, so we either have to create new directory first or install directly from where we copied.
Signed-off-by: Aleksei Kalinov <alekseia@nvidia.com> | {
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"I am still waiting for the full run to complete in order to confirm that there is no degradation in quality. For now, keeping in draft state. ",
"Thanks a lot everybody for the review! "
] | 2021-04-01T14:36:01 | 2021-04-19T12:54:45 | 2021-04-06T18:06:28 | CONTRIBUTOR | {
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} | The proposed change includes three optimization steps that I found were beneficial for model training efficiency.
### 1. Mask caching
Currently `arange` for mask is computed at each step from scratch. While it might be cached by GPU, there is no guarantee as it is not needed after forward function execution.
By caching it dynamically, it will be easier to strike balance between numerous short sequences that will benefit from caching and arbitrarily long sequences during inference.
### 2. Multiplication by mask instead of `masked_fill`
`masked_fill `supports arbitrary values to fill into the tensor as, thus, implemented as an iterator over Tensor memory.
In our models masks are binary, so we can leverage vectorized multiplication operation to speedup mask application.
### 3. Shortcutting new sequence length computation
Given that Citrinet uses same padding everywhere and that striding only happens at certain levels. We can avoid doing arithmetic with sequence lengths when not needed.
Additionally, NeMo DatasetLoaders guarantee that lengths are represented as longs, so we can remove redundant `to` conversion.
## Benchmarks
### NGC
Time per epoch, 384 filters model, 8 GPUs, average across 10 epochs:
| baseline | +Mask caching | +Multiplication | +Shortcuts |
|---|---|---|---|
|50 m | 46.5 m | 45 m | 44.5m |
Time per epoch, 384 filters model, 4 GPUs, aggregate grads from 2 batches, average across 34 epochs:
| baseline | +Mask caching | +Multiplication | +Shortcuts |
|---|---|---|---|
|78 m | 71 m | 69 m | 70m |
**Accuracy** for all improvement runs **is in bounds of statistical noise** compared to the baseline. | {
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"Actually EncDecCTCModel and EncDecCTCBPEModel are LightningModule, and you can call the forward like the example you provided. They just have some extra methods to support more capabilities. You should be able to use the same trick in that example for these two models. If you follow the approach used in the examp... | 2021-04-01T03:17:38 | 2021-04-09T18:15:52 | 2021-04-09T18:15:51 | NONE | {
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} | null | null | null | Thanks for this great toolkit!
I'm seriously looking at implementing some knowledge distillation techniques for the ASR classes. I found this very simple and elegant pytorch-lightning approach here: https://github.com/vrvlive/knowlege-distillation/blob/master/training_module.py and I wonder how I could adapt it to `EncDecCTCModel` or `EncDecCTCBPEModel` classes (as well as for the rnn-t or conformer cases).
I would like a pointer or two, if possible, since the NeMo classes aren't trivial `pl.LightningModule`. What "worries" me the most is how the audio preprocessing pipeline would impact how we can simply call "forward()" for example.
Where should I start? | {
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"This pull request **fixes 2 alerts** when merging 4848e4c0e5059d4a31262dcd7c5ba54c077f2691 into 094b58bfcefd069aad930a375f7a93fd26af45a2 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5012310c004b9db9ea2d601621c420f3c4619363)\n\n**fixed alerts:**\n\n* 2 for Unused import",
"This pull request... | 2021-03-31T23:52:47 | 2021-04-02T01:43:19 | 2021-04-02T01:43:16 | COLLABORATOR | {
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} | # Changelog
- Add rnnt loss switching capability.
- Enable passing arbitrary kwargs to the inner loss function.
- Add jenkins tests (commented out until 21.04 container is released which supports minimum numba version 0.53 for CUDA 11.2)
- Enable all RNNT model tests using the numba default loss
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} | # Changelog
- Remove hard numba version requirements
- Dynamically check for version at runtime when it is required
- Update tests to dynamically switch off for cuda if version is incompatible (either due to numba version itself, or due to mismatch between supported CUDA version and actual CUDA version installed).
- Update docker file to forcibly update numba temporarily (to be removed at 20.04 release)
- Update docker file to preemptively import the domains to download whatever is necessary.
- Patch a bug in dynamic resolution of numba losses
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