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"can you please sign your commit? See DCO's bot output on how to sign already committed commits: https://github.com/NVIDIA/NeMo/pull/1575/checks?check_run_id=1569064051 \r\n\r\nThanks!",
"> can you please sign your commit? See DCO's bot output on how to sign already committed commits: https://github.com/NVIDIA/Ne... | 2020-12-17T08:26:53 | 2020-12-18T08:10:11 | 2020-12-18T08:10:11 | CONTRIBUTOR | {
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"Could you please try with `BRANCH='main'` in the first cell of the notebook.",
"@ekmb Tried `BRANCH='main'` but still getting the same error",
"I forgot to restart the kernel when trying `BRANCH='main'`. So I restarted the kernel and tried `from nemo.collections import nlp as nemo_nlp` and the original `Module... | 2020-12-16T22:26:25 | 2020-12-16T23:12:45 | 2020-12-16T23:12:45 | NONE | {
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"> can you please replace all references to 1.0.0b2 with 1.0.0b3 in README.md ?\r\n\r\n@okuchaiev 1.0.0b3 won't work, the notebooks were not updated from `1.0.0b2` to `1.0.0b3` when 1.0.0b3 was released. for example, this [one with `/blob/v1.0.0b3/`](https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b... | 2020-12-16T18:58:14 | 2021-01-22T15:28:47 | 2020-12-18T04:57:17 | COLLABORATOR | {
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"I am also getting this error",
"It looks like the latest version of transformers (4.0.1) is being installed. I reverted to 3.5, and the code works.\r\nBefore importing nemo.collections, run the following and it should work:\r\n```\r\n!pip uninstall transformers\r\n!pip install transformers==3.5\r\n```\r\n",
"T... | 2020-12-16T11:17:09 | 2022-04-05T20:40:57 | 2020-12-18T04:57:17 | NONE | {
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} | null | null | null | Hello. I am getting an error.
I was using the[ Sentiment analysis notebook](https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb#scrollTo=dzqD2WDFOIN-) on colab by NVIDIA NeMo
I followed the instructions as it said(run the first cell if you are on colab and vice versa).
In the second cell, it is said that restart runtime after running it. After restarting kernal, (or even if I don't) I am getting this error:
`ModuleNotFoundError: No module named 'transformers.tokenization_bert'`. It is from the first import of the 3rd cell,
`from nemo.collections import nlp as nemo_nlp`
The full traceback is:
```---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-6-9dea72b2f46b> in <module>()
----> 1 from nemo.collections import nlp as nemo_nlp
2 from nemo.utils.exp_manager import exp_manager
3
4 import os
5 import wget
3 frames
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/__init__.py in <module>()
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp import data, models, modules
16 from nemo.package_info import __version__
17
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/data/__init__.py in <module>()
25 NeuralMachineTranslationDataset,
26 )
---> 27 from nemo.collections.nlp.data.question_answering_squad.qa_dataset import SquadDataset
28 from nemo.collections.nlp.data.token_classification.token_classification_dataset import (
29 BertTokenClassificationDataset,
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/data/question_answering_squad/qa_dataset.py in <module>()
26
27 from nemo.collections.common.parts.utils import _compute_softmax
---> 28 from nemo.collections.nlp.data.question_answering_squad.qa_squad_processing import (
29 EVALUATION_MODE,
30 INFERENCE_MODE,
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/data/question_answering_squad/qa_squad_processing.py in <module>()
20
21 from tqdm import tqdm
---> 22 from transformers.tokenization_bert import BasicTokenizer
23
24 from nemo.collections.nlp.data.data_utils import DataProcessor, is_whitespace, normalize_answer
ModuleNotFoundError: No module named 'transformers.tokenization_bert'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
```
I tried searching stack overflow, but it didn't helped. And I don't want to tinker the main NeMo module myself cause I don't know what might go wrong if I change something.
What shall I do? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1565 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1565/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1565/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1565/events | https://github.com/NVIDIA/NeMo/pull/1565 | 768,337,144 | MDExOlB1bGxSZXF1ZXN0NTQwODA4ODEw | 1,565 | Correct ASR issues + Patch for Pytorch 1.8 | {
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"@blisc Could you review the TTS side changes to `stft` and `torch.ifft`?",
"trim_silence default in the model.transcribe method is still True!\r\nThis issue can be seen here:\r\ncollections/asr/models/ctc_models.py: _setup_transcribe_dataloader"
] | 2020-12-16T00:31:07 | 2021-07-06T07:22:02 | 2020-12-17T22:15:00 | COLLABORATOR | {
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} | # Changelog
- `trim_silence` will now be False by default for all dataloaders in ASR and only active for train_ds (set by flag in config).
- Update ASR stft, ifft ops to be compatible with Pytorch 1.8 deprecation warnings
Note:
- Due to change in `trim_silence`, scores obtained via trainer.test(), or model.transcribe() or any other evaluation process will change slightly. We expect only minor changes to WER due to this bugfix.
Closes https://github.com/NVIDIA/NeMo/issues/1561 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1564 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1564/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1564/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1564/events | https://github.com/NVIDIA/NeMo/issues/1564 | 768,303,079 | MDU6SXNzdWU3NjgzMDMwNzk= | 1,564 | Token_Classification_Named_Entity_Recognition.ipynb specifies specific gpu instead of a single gpu | {
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} | null | null | null | **Describe the bug**
In the notebook [Token_Classification_Named_Entity_Recognition.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb), the last cell specifies a specific gpu, which happens to be the second gpu, when it most likely was meant to specify a single gpu. This causes an error if only one gpu is present on the system.<br>
The error is:
```
MisconfigurationException:
You requested GPUs: [1]
But your machine only has: [0]
```
**Steps/Code to reproduce bug**
Set up a single-GPU box with the v1.0.0b3 version of NeMo running in a container
Upload the [notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb)
Run all the cells.
**Expected behavior**
no error when running a single GPU
**Proposed fix for the bug**
In the final cell of the [notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb), we see the following:<br>
`trainer = pl.Trainer(gpus=[1], fast_dev_run=fast_dev_run)`<br>
I believe the intent was to run on a only one GPU, but instead we are saying GPU No. 1, which is generally the second GPU in a system where the first GPU is GPU No. 0. We get an error when there is only one GPU. To run on a single GPU, the line should be:<br>
`trainer = pl.Trainer(gpus=1, fast_dev_run=fast_dev_run)`<br>
This is explained in the [PyTorch Lighnting gpus documentation](https://pytorch-lightning.readthedocs.io/en/latest/trainer.html#gpus).
**Environment overview (please complete the following information)**
- Environment location: Docker on AWS V100
- Method of NeMo install: Docker
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
pulled from `nvcr.io/nvidian/nemo/nemo:1.0.0b3`
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https://api.github.com/repos/NVIDIA/NeMo/issues/1563 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1563/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1563/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1563/events | https://github.com/NVIDIA/NeMo/pull/1563 | 768,261,262 | MDExOlB1bGxSZXF1ZXN0NTQwNzYwMjY2 | 1,563 | Adding the feature of variational noise to the RNNT-based models | {
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} | It is going to add Gaussian noise to the decoder's weights of rnnt models after each step to avoid overfitting.
This section need to be added to the model section of the config:
```
model:
variational_noise:
std: 0.075
start_step: 2000
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/1562 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1562/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1562/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1562/events | https://github.com/NVIDIA/NeMo/pull/1562 | 767,727,422 | MDExOlB1bGxSZXF1ZXN0NTQwMzQ0ODg0 | 1,562 | TalkNet 1.x | {
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"ping @stasbel ",
"This pull request **introduces 2 alerts** when merging bb66ad0cb949eab426b3fd510525e4edcf0fdace into fc4017a08db4cf2aaad2288dd158e9c287f7107a - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-01fe481edaf7d6ac977150b56a2b758a568a4873)\n\n**new alerts:**\n\n* 1 for Unused impor... | 2020-12-15T15:43:36 | 2021-04-01T16:45:35 | 2021-03-09T22:47:19 | CONTRIBUTOR | {
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"merged_at": "2021-03-09T22:47:19"
} | TODOs:
- [x] Pitch predictor (with available models)
- [x] Spect prediction new things (with available models)
- [x] Durs predictor (with available models)
- [x] LJSpeech with durs and f0
- [x] 3x pretrained models (no need)
- [x] Documentation
- [x] Update TTS Notebook inference
Signed-off-by: Stanislav Beliaev <stasbelyaev96@gmail.com> | {
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"Hey @rbracco, thanks for pointing this out! I submitted a PR to patch this bug.",
"Thank you for all that you do for the repo! "
] | 2020-12-15T14:11:34 | 2020-12-16T01:04:17 | 2020-12-16T01:04:17 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
My assumption would be that `trim_silence` would be False for valid/test unless explicitly turned on. It appears that that is the intention [here in the CTC Models Config](https://github.com/NVIDIA/NeMo/blob/f0378b10cdadc82752d70032bd80219e531519c0/nemo/collections/asr/models/configs/ctc_models_config.py#L66) is to have `trim_silence=True` for training but not validation. Also all the ASR configs for the tutorials have an explicit `trim_silence=True` for train and no entry for validation, implying that the default is False.
I believe the default is set to True [here in audio_to_text_dataset.py](https://github.com/NVIDIA/NeMo/blob/c0fedd91e316a4ba95ac6b14330ff92844c56097/nemo/collections/asr/data/audio_to_text_dataset.py#L46).
**Steps/Code to reproduce bug**
Print out the duration of the audio in the validation loop for some dataset, then set `params["model"]["validation_ds"]["trim_silence"] = False` and try it again and some of the audios will be of different length because they are no longer being trimmed.
**Expected behavior**
Validation silence trimming defaults to False, or the documentation changes to make it more explicit that silence is being trimmed during validation/inference.
**Environment overview (please complete the following information)**
-Colab + Nemo 1.0.0b2 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1560 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1560/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1560/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1560/events | https://github.com/NVIDIA/NeMo/issues/1560 | 767,439,096 | MDU6SXNzdWU3Njc0MzkwOTY= | 1,560 | Docker image "v1.0.0b3 not found" | {
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"Do you mean v1.0.0b2? I haven't seen a b3 version yet, but there is a branch called r1.0.0b3.",
"Well, I assumed since there is [v1.0.0b3](https://github.com/NVIDIA/NeMo/releases/tag/v1.0.0b3) release, then is should be docker image for that version.",
"Sorry, I missed the 1.0.0b3 release, you are correct! In ... | 2020-12-15T10:32:08 | 2020-12-16T17:49:50 | 2020-12-16T17:49:50 | NONE | {
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} | null | null | null | **Steps/Code to reproduce bug**
```bash
docker pull nvcr.io/nvidia/nemo:v1.0.0b3
Error response from daemon: manifest for nvcr.io/nvidia/nemo:v1.0.0b3 not found: manifest unknown: manifest unknown
```
Similar to #1498 | {
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"This pull request **fixes 9 alerts** when merging 4f9ad400d51c3fe596d79169394c70ad936cb80b into d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-80363198ef854198c67fd4639ab1230057dd89b0)\n\n**fixed alerts:**\n\n* 9 for Unused import",
"This pull request... | 2020-12-14T19:33:39 | 2020-12-15T17:30:28 | 2020-12-15T17:30:24 | COLLABORATOR | {
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2. Fixed Bio-megatron download issue | {
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"Thanks for catching this bug. It should be fixed now"
] | 2020-12-14T16:26:23 | 2020-12-14T19:50:56 | 2020-12-14T19:50:37 | NONE | {
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} | null | null | null | **Describe the bug**
Training (or just passing input with ground-truth spectrogram) Tacotron2 with a batch dimension of 1 does not work due to a dimension problem when aggregating the gate outputs.
The line here
https://github.com/NVIDIA/NeMo/blob/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b/nemo/collections/tts/modules/tacotron2.py#L256
should have ```unsqueeze``` instead of ```squeeze``` when the batch dimension is 1.
(I didn't want to submit a pull request as I'm not familiar with the conventions of the project)
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"Hi @diego-fustes Thanks for reporting this. \r\nYou don't need to include the parameter batch_size in the dict passed. [It's been set here.](https://github.com/NVIDIA/NeMo/blob/9da1d27ddd7f1d5d9b6cd75f7308787976680dd8/nemo/collections/asr/models/classification_models.py#L167) \r\nThere isn't a vad_infer.py in v1... | 2020-12-14T16:10:02 | 2020-12-15T18:10:15 | 2020-12-15T18:06:22 | CONTRIBUTOR | {
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} | null | null | null | I'm trying to perform Voice Activity Detection with NeMo using the script _vad_infer.py_ that is included in the examples. However, the following error comes at first:
```
Traceback (most recent call last):
File "/home/diego/Sutherland/speechanalytics/speechanalyticsapi/speechanalytics/test.py", line 159, in <module>
main()
File "/home/diego/Sutherland/speechanalytics/speechanalyticsapi/speechanalytics/test.py", line 89, in main
'trim_silence': False,
File "/home/diego/miniconda3/envs/speechanalytics/lib/python3.6/site-packages/nemo/collections/asr/models/classification_models.py", line 110, in setup_test_data
self._test_dl = self._setup_dataloader_from_config(config=test_data_config)
File "/home/diego/miniconda3/envs/speechanalytics/lib/python3.6/site-packages/nemo/collections/asr/models/classification_models.py", line 89, in _setup_dataloader_from_config
batch_size=config['batch_size'],
KeyError: 'batch_size'
```
I've been able to fix it by including the parameter _batch_size_ in the dict passed to the _vad_model.setup_data_ method. After that, the script runs til the end, but it behaves in an unexpected way as it generates only one prediction value (one float) per audio file, while it should be generating one prediction per frame in every audio file... shouldn't it?
**Environment**
Nemo Toolkit version 1.0.0b2
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"@avikpal00 thank you for reporting the issue. PR #1558 should fix it, no need to change the transformers version.",
"@ekmb I am still getting this error for the tutorial notebook [Token_Classification-BioMegatron.ipynb](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/nlp/Token_Classi... | 2020-12-14T13:09:42 | 2021-01-06T00:10:23 | 2020-12-15T17:30:24 | NONE | {
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} | null | null | null | **Describe the bug**
Not able to download and initialize the pretrained biomegatron bert model.
config.model.language_model.pretrained_model_name = "biomegatron-bert-345m-cased"
Error Line: nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
Error: 'OSError: Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility.'
But, I don't get any error if I replace the pre-trained model to a Megatron model ("megatron-bert-345m-cased").
Since the architecture of both models are same I would only assume there is some sort of error in packaging of the biomegatron model.
**Expected behavior**
The notebook to run without any error.
**Environment overview (please complete the following information)**
- Environment location: Google Colab
- Tutorial Notebook - https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb
- Changed transformers version from 4.0.0 to 3.5.0. (I downgraded the version as it seems that the notebook was built on an older version of transformers and I kept getting ModuleNotFoundError because of module name changes in the newer version)
**Environment details**
- Python version = 3.6.9
- Transformers version = 3.5.0
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"Can you share your training command? As well as the python file that you're using?",
"Sorry, It is my fault. I found that I was not pass parameter correctly to trainer, so it use default configuration. And solved this problem. I saw the example in \"speech_to_text.py\" and found I should pass parameters separate... | 2020-12-14T03:57:40 | 2020-12-16T02:14:49 | 2020-12-15T15:35:06 | NONE | {
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} | null | null | null | Question:
I setup the config.yaml and run the script. It thorw this message
"The pytorch lightning trainer that was passed to exp_manager contained a logger, and either "
nemo.utils.exp_manager.LoggerMisconfigurationError: The pytorch lightning trainer that was passed to exp_manager contained a logger, and either create_tensorboard_logger: True or create_wandb_logger: False was set to True. These can only be used if trainer does not already have a logger. You can disable lightning's trainer from creating a logger by passing logger=False to its constructor. "
I have disabled the logger in trainer. but it still has this message. and I saw the code in "nemo/utils/exp_manager.py"
it will throw above following message if trainer is not None . However, I have set the logger to be False in config.yaml . Then, print the
"logger" in trainer. It shows the object "<pytorch_lightning.loggers.base.LoggerCollection object at 0x7f41b5538550>"
How to disable logger and use exp_manager.py?
**My yaml configuration (in attached file)**
**trainer:**
gpus: 1
max_epochs: 150
max_steps: null
num_nodes: 1
accelerator: ddp
accumulate_grad_batches: 1
checkpoint_callback: **False**
logger: null
row_log_interval: 1
val_check_interval: 0.25
**exp_manager:**
exp_dir:
name:
use_datetime_version: **True**
create_tensorboard_logger: **True**
create_checkpoint_callback: **True**
checkpoint_callback_params:
monitor: "val_wer"
mode: "min"
create_wandb_logger: **False**
wandb_logger_kwargs:
name:
project:
**Environment overview (please complete the following information)**
- Environment location: Ubuntu 16.04 + python virtual env (python 3.7)
- Method of NeMo install: pip installation [1.0.0.b2]
**Environment details**
python == 3.7.0
torch == 1.7.0
[quartznet_5x3_cer_single.zip](https://github.com/NVIDIA/NeMo/files/5686333/quartznet_5x3_cer_single.zip)
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"P.S.: I do not want to use docker since I have to implement modified training and inference into an app running locally on the Xavier...\r\n",
"\"pip install nemo_toolkit[all]\" will install an OLD version without PTL (0.11).\r\n\r\nTry: \"pip install nemo_toolkit[all]=1.0.0b3\"\r\n\r\nAt the moment we do not do... | 2020-12-13T14:12:51 | 2021-02-10T23:27:40 | 2021-02-10T23:27:40 | NONE | {
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NeMo installs flawessly with pip3 install nemo_toolkit[all]. Also reinstall.sh does not throw an error and ends with success message although it comments that nemo asr, nlp and tts will not be removed since they are not installed (?!).
Again nemo_toolkit[asr], nemo_toolkit[nlp], nemo_toolkit[tts] can all be installed with pip3 install. Onnxruntime_gpu, Apex and sentencepiece also installed without error - anyhow when I try any library of nemo asr I get a segmentation fault:
python3
Python 3.6.9 (default, Oct 8 2020, 12:12:24)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pytorch_lightning as pl
>>> from omegaconf import OmegaConf
>>> from nemo.collections.asr.models import EncDecCTCModel
[NeMo W 2020-12-13 14:51:09 experimental:28] Module <class 'nemo.collections.asr.losses.ctc.CTCLoss'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-13 14:51:12 experimental:28] Module nemo.collections.asr.data.audio_to_text.AudioToCharDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-13 14:51:12 experimental:28] Module nemo.collections.asr.data.audio_to_text.AudioToBPEDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-13 14:51:12 experimental:28] Module nemo.collections.asr.data.audio_to_text.AudioLabelDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-13 14:51:12 experimental:28] Module nemo.collections.asr.data.audio_to_text.TarredAudioToTextDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-13 14:51:12 experimental:28] Module nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-13 14:51:12 experimental:28] Module nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
Segmentation fault (core dumped)
When I do unittests for the tests directory - the same.
Any Idea, why? Meanwhile I am lost in the posts stating how NeMo can run on a Jetson Xavier.
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"This pull request **introduces 1 alert** when merging 260e2af8a0d4ea45c2f7ce8cfec9f17fc52a3912 into d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-838abc09fd547a8fab60739d22febc15820ebfe7)\n\n**new alerts:**\n\n* 1 for Unused import",
"@vladgets can ... | 2020-12-12T02:52:50 | 2020-12-14T22:49:51 | 2020-12-14T22:49:42 | CONTRIBUTOR | {
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"what is the task or example you were trying to run? Did you first run with internet data and then later without internet? Some examples where we fetch model from NGC needs to be run with the internet, probably your example got stuck there. "
] | 2020-12-11T20:47:39 | 2021-05-28T08:31:48 | 2021-05-28T08:31:48 | CONTRIBUTOR | {
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If this is the case, do you know where it happens? I can't think of any other reason why it takes so long without an internet connection.
Using the Nemo v1.0.0b2 container. | {
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"Any word on this @titu1994? I'm curious if theres a specific reason. I'm starting to use CommonVoice English and it's 56GB when stored as MP3 so it will be unusable in wav format. Thank you!",
"@vsl9 Any thoughts about this ? ",
"Hi\r\n\r\ntorchaudio developer here. Recently [I am adding support for file-like ... | 2020-12-11T17:25:49 | 2021-06-19T04:43:34 | 2021-06-19T04:43:34 | CONTRIBUTOR | {
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It appears that NeMo only works with wav files, but they are 10x larger than equivalent MP3s. Many popular datasets like Mozilla Commonvoice are stored as MP3 and have to be converted before being used. This is time consuming and also makes it harder for those using limited resources like Google Colab.
I was wondering if there's a specific reason that only WAV is used.
**Describe the solution you'd like**
For NeMo to work with non wav files.
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"This pull request **introduces 9 alerts** when merging 1f4320a27481466e8223cf8c37375a6f529d7221 into 08d1ccc503e35796e2874b24eca677e9eebad71c - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-95caec7dea0dda6c9d810101bc940f1b7fcbe7f0)\n\n**new alerts:**\n\n* 3 for Except block handles 'BaseExc... | 2020-12-11T16:19:08 | 2020-12-16T22:48:31 | 2020-12-16T22:48:31 | CONTRIBUTOR | {
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"Some details can be found here - https://arxiv.org/abs/2010.12715"
] | 2020-12-11T10:33:15 | 2021-07-13T15:21:48 | 2021-07-13T15:21:48 | NONE | {
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} | null | null | null | Thank you for sharing the valuable [ASR augmentation code](https://github.com/NVIDIA/NeMo/blob/08d1ccc503e35796e2874b24eca677e9eebad71c/nemo/collections/asr/parts/perturb.py#L596) recently. I would like to ask what is the result of your experiment with these different perturbations. Have you ever done an ablation study on all of the perturbations? Can you share the results with us?
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"Retrying building pytorch etc from source:\r\nUsing transformers==4.0.0:\r\npython\r\nimport nemo\r\nimport nemo.collections.asr as nemo_asr\r\nERROR .......\r\nsite-packages/transformers/trainer_pt_utils.py\", line 40, in <module>\r\n from torch.optim.lr_scheduler import SAVE_STATE_WARNING\r\nI believe SAVE_ST... | 2020-12-11T08:43:24 | 2020-12-11T13:45:44 | 2020-12-11T13:45:44 | NONE | {
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cuda 11.1
Latest built from source: torch, torchvision, torchaudio, torchtext, pytorch_lightning, apex, horovod, tf-nightly
To reproduce the error (usinng nemo_asr in the example below):
python
Python 3.7.9 (default, Aug 31 2020, 12:42:55)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import faulthandler
>>> faulthandler.enable()
>>> import nemo
>>> import nemo.collections.asr as nemo_asr
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.losses.ctc.CTCLoss'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioToCharDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioToBPEDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioLabelDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text._TarredAudioToTextDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-12-11 19:26:29 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
2020-12-11 19:26:30.074971: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
Fatal Python error: Segmentation fault
Current thread 0x00007f19db597740 (most recent call first):
File "/home/daniel/my37/lib/python3.7/site-packages/torch/jit/_script.py", line 940 in script
File "/home/daniel/my37/lib/python3.7/site-packages/transformers/models/deberta/modeling_deberta.py", line 461 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/transformers/models/deberta/__init__.py", line 11 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 953 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/transformers/models/auto/configuration_auto.py", line 28 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/transformers/models/auto/__init__.py", line 6 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 953 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/transformers/modelcard.py", line 31 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/transformers/__init__.py", line 85 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py", line 18 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/common/tokenizers/huggingface/__init__.py", line 15 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 953 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/common/tokenizers/__init__.py", line 16 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1035 in _handle_fromlist
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/common/__init__.py", line 16 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 953 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/asr/models/classification_models.py", line 31 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/asr/models/__init__.py", line 16 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1035 in _handle_fromlist
File "/home/daniel/my37/lib/python3.7/site-packages/nemo/collections/asr/__init__.py", line 15 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 728 in exec_module
File "<frozen importlib._bootstrap>", line 677 in _load_unlocked
File "<frozen importlib._bootstrap>", line 967 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 983 in _find_and_load
File "<stdin>", line 1 in <module>
Segmentation fault (core dumped)
pip show transformers
shows version 4.0.1 is installed
To fix the issue:
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"Workaround for https://github.com/PyTorchLightning/pytorch-lightning/issues/5063"
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"This pull request **introduces 1 alert** when merging 340f7482b74ee14f376e15450762888c0536e201 into 16055d86b0be550b87ee5d327e351a1562ce7eb2 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-2be8828844b45e4c518c59e6cf4c908a99fc228e)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2020-12-10T03:32:15 | 2021-01-22T15:28:40 | 2020-12-17T21:18:09 | CONTRIBUTOR | {
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} | Modifications enabling TLT text classification train to use ExpManager
- [x] added postfix to ckpt callback structured config + its handling
- [x] minimally polished logic for explicit_log_dir
- [x] minor polish of names
- [x] Bumped versions in requirements: omegaconf, hydra to make Optional[List]] work
- [x] Bumped version of PTL to 1.1 to enable CheckpointConnector overload in TLT
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"@vinchg could you rebase this PR with main? There was a fix this morning that we need to pass CI.",
"@ericharper Yeah, PR has been rebased!"
] | 2020-12-10T02:03:44 | 2021-01-11T20:08:31 | 2020-12-10T23:01:45 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
With the update of the requirements to transformers >= 4.0.0 on the main branch, calling a model now passes `return_dict=True` by default rather than a tuple previously: https://github.com/huggingface/transformers/releases/tag/v4.0.0
The fix is simple - any transformers model call should explicitly state `return_dict=False` to maintain old functionality.
**Steps/Code to reproduce bug**
I've only looked at the text2sparql model myself - training and evaluating a model will error. I expect some of the other transformer models to do so as well.
**Expected behavior**
Model trains and evaluates properly.
**Environment overview (please complete the following information)**
- Environment location: Local
- Method of NeMo install: Installing from main branch:
```
pip install Cython
pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]
./reinstall.sh
```
**Environment details**
- OS version: Ubuntu 20.04.1
- PyTorch version: 1.8.0.dev20201207+cu110 (I'm using a RTX 3090)
- Python version: 3.8.3
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} | # Changelog
- ASR BPE now will explicitly set Hugging Face flag `TOKENIZERS_PARALLELISM` if not provided by user
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- Fixed issue in timestamp calculation for boundaries of contiguous of space (" ") characters. It did not account for blank tokens that could appear after a space, increasing the boundary. | {
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"Fixed in main branch, thanks for pointing it out !"
] | 2020-12-08T14:41:48 | 2021-06-19T04:44:10 | 2021-06-19T04:44:09 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
The inference code in the ASR tutorial 01_ASR_with_NeMo doesn't put the model in training mode before inference. If copied and used in someone's code, this would cause augmentation like SpecAugment to be inadvertantly be applied to the test set resulting in higher WER than the model actually produces using `transcribe`.
**Expected behavior**
The inference loop should
1. Store the current mode (as transcribe does) with `mode=first_asr_model.training`
2. Call `first_asr_model.eval()` before running the pytorch inference loop
3. Reset the mode with `first_asr_model.train(mode=mode)` upon completion
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"@purn3ndu please check the default names for the class names in the 3 configs"
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3. updates for the latest asr.model config changes
4. added command to upgrade jupyter for LM models tutorial | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1526 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1526/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1526/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1526/events | https://github.com/NVIDIA/NeMo/pull/1526 | 758,727,919 | MDExOlB1bGxSZXF1ZXN0NTMzODUyMDgy | 1,526 | Megatron-lm checkpoints need to have version set. | {
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} | Checking for version in restore and then setting it.
Need to save checkpoint version when saving NeMo Megatron-lm models. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1525 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1525/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1525/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1525/events | https://github.com/NVIDIA/NeMo/pull/1525 | 758,245,437 | MDExOlB1bGxSZXF1ZXN0NTMzNDQ4OTQy | 1,525 | Bug fixes to EncDec MT model | {
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"This pull request **introduces 1 alert** when merging 6b8bc699ec29a04c8be043eacebdfd0974c576e7 into 601b6789bcbfcd6721b9d27aee6008c9e44ba42f - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-c5dcb90b11d16b32e076d0ab25205640cc2cda01)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2020-12-07T07:28:35 | 2020-12-09T19:16:52 | 2020-12-09T19:16:47 | MEMBER | {
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} | Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1524 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1524/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1524/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1524/events | https://github.com/NVIDIA/NeMo/pull/1524 | 757,972,600 | MDExOlB1bGxSZXF1ZXN0NTMzMjMwNjg5 | 1,524 | fix for modelpt config.optim | {
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"Thanks!"
] | 2020-12-06T17:01:45 | 2020-12-07T19:46:02 | 2020-12-06T18:01:43 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1523 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1523/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1523/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1523/events | https://github.com/NVIDIA/NeMo/pull/1523 | 757,526,202 | MDExOlB1bGxSZXF1ZXN0NTMyODg2ODg5 | 1,523 | changes to NLP config for ejrvs | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1522 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1522/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1522/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1522/events | https://github.com/NVIDIA/NeMo/pull/1522 | 757,447,994 | MDExOlB1bGxSZXF1ZXN0NTMyODIzOTQ5 | 1,522 | Add WaveGlow Denoiser | {
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} | - Adds new GlowVocoder
- Removed the SqueezeWaveDenoiser module
- Denoisers are now moved into the GlowVocoder class with support for WaveGlow and SqueezeWave
- WaveGlow and SqueezeWave's denoisers are now enabled by default in their convert_spectrogram_to_audio() functions | {
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} | # Changelog
- Patch the abstract definition of RNNT decoding
- Update configs to use 8x stride to reduce memory and execution time
Fixes https://github.com/NVIDIA/NeMo/issues/1520
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1520 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1520/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1520/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1520/events | https://github.com/NVIDIA/NeMo/issues/1520 | 757,281,302 | MDU6SXNzdWU3NTcyODEzMDI= | 1,520 | Broken tokenizer ref in AbstractRNNTDecoding in rnnt_wer.py | {
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"Yup that's a bug during the refactor, I'll send a patch in sometime. ",
"Added a PR https://github.com/NVIDIA/NeMo/pull/1521, it might take some time to get merged so you could update the code locally in the mean time (its a one liner at the end of the PR). \r\n\r\nBtw, just a heads up. The configs for RNNT are ... | 2020-12-04T17:40:22 | 2020-12-04T18:21:10 | 2020-12-04T18:21:10 | NONE | {
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} | null | null | null | **Describe the bug**
On [line 209 of rnnt_wer.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/metrics/rnnt_wer.py#L209) there is a reference to `self.tokenizer.ids_to_text(prediction)` which errors out because there is no tokenizer. This blocks the example script in speech_to_text_rnnt.py below at the validation/sanity check stage. Possibly an artifact of refactoring for BPE? What is the intended NeMo way of incorporating the tokenizer?
I changed self.tokenizer to an explicitly defined CharTokenizer and it fixed the error.
**Steps/Code to reproduce bug**
```
python examples/asr/speech_to_text_rnnt.py \
model.train_ds.manifest_filepath="<path to train dataset>" \
model.validation_ds.manifest_filepath="<path to validation dataset>" \
trainer.gpus=0 \
trainer.max_epochs=50
```
**Expected behavior**
Sanity check run of RNN-T.
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https://api.github.com/repos/NVIDIA/NeMo/issues/1518 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1518/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1518/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1518/events | https://github.com/NVIDIA/NeMo/issues/1518 | 757,128,967 | MDU6SXNzdWU3NTcxMjg5Njc= | 1,518 | log_every_n_steps only applies to training, not validation/test | {
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"This is still an issue but note you can turn off the validation output entirely with \r\n`quartznet._wer.log_prediction=False` where quartznet is your model instance.",
"Validation frequency is handled by PTL and can be configured in NeMo via YAML or command line. You can use either `val_check_interval`: https:/... | 2020-12-04T14:07:34 | 2021-05-28T08:32:45 | 2021-05-28T08:32:45 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
Forgive me if this is a [pytorch lightning issue](https://github.com/PyTorchLightning/pytorch-lightning/issues/4527), and not a NeMo one. If that's the case let me know and I'll close here and continue there. Setting log_every_n_steps on the trainer limits the output of decoded examples in ASR, but not during validation. This causes massive output which in turn causes issues in Colab. As a result I have set `check_val_every_n_epochs` to prevent validation, but ideally I would like to validate and log the metrics to tensorboard, just without all the printed output.
**Steps/Code to reproduce bug**
Run ASR notebook with `log_every_n_steps=250` and note that the validation outputs logs to terminal for every batch.
**Expected behavior**
Both validation and training only log output every `log_every_n_steps` steps.
**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**
Colab using NeMo 1.0.0b2
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"I support this change. Can you share code for how you validate using CER? I'm using the validate code from the ASR with NeMo tutorial and it does something like \r\n\r\n```\r\nfor test_batch in pb(model.test_dataloader()):\r\n test_batch = [x.cuda() for x in test_batch]\r\n xs, x_lens, ys, y_lens... | 2020-12-04T13:19:33 | 2021-08-17T01:00:01 | 2021-08-17T01:00:01 | CONTRIBUTOR | {
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} | null | null | null | Currently one has to choose whether `use_cer` or not, and the choice is exclusive. Both are useful as the target metric, CER is more smooth. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1516 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1516/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1516/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1516/events | https://github.com/NVIDIA/NeMo/pull/1516 | 756,674,406 | MDExOlB1bGxSZXF1ZXN0NTMyMTg2NDMx | 1,516 | Update every ASR config to new Hydra format | {
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"Heh let's hope this is the last time we ever need such a heavy update to configs",
"🤞 still, I expect one more change related to this project:\r\nhttps://github.com/pytorch/hydra-torch\r\nonce it will be mature enough...",
"@tkornuta-nvidia I miss you!\r\n\r\nMy goal is to have the initial versioned release ... | 2020-12-03T23:34:39 | 2021-01-20T02:27:00 | 2020-12-04T06:33:52 | COLLABORATOR | {
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} | # Changelog
- Update every ASR config to new hydra 1.x format | {
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"This pull request **introduces 2 alerts** when merging 51ac7df55e15ff6e742b6761ef5ee336208f4c94 into 97b1c96b64a64ebd45ca75409347bcf5514c0fb9 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-832bc76fc446076c1c275776064deb558da6fc7e)\n\n**new alerts:**\n\n* 2 for Unused import"
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https://api.github.com/repos/NVIDIA/NeMo/issues/1514 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1514/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1514/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1514/events | https://github.com/NVIDIA/NeMo/pull/1514 | 756,353,361 | MDExOlB1bGxSZXF1ZXN0NTMxOTExOTA5 | 1,514 | Fix quantization dependency | {
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} | There is only one python version of prebuild wheel of TensorRT's pytorch-quantization on nvidia pyindex, which causes install with all other version of python to fail with a confusion error message.
Removed requirement_quantization.txt. Instead, install from source in Dockerfile.
Also added error message if running quantization scripts without pytorch-quantization.
@okuchaiev | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1513 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1513/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1513/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1513/events | https://github.com/NVIDIA/NeMo/issues/1513 | 756,302,696 | MDU6SXNzdWU3NTYzMDI2OTY= | 1,513 | [Question] how to sold Jasper ASR Loss: inf or Gradient overflow issue? | {
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"try very small learning rate first. And / or learning rate policy with a longer warmup period. Then see if you can increase them."
] | 2020-12-03T15:29:25 | 2020-12-04T02:00:57 | 2020-12-04T02:00:57 | NONE | {
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} | null | null | null | Hello,
I train Jasper ASR but the loss is inf and Gradient overflow. How should I solve it.
I already refer to the issue #637 and see that it is already fixed as suggested for the current version but my model still has the same issue.
Thank you
| {
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"This pull request **fixes 1 alert** when merging 8ad7e9a62e964dcab1adc45a20f76b4bf09347d3 into e54a3b030e11bcc64327e10bed7ea70724651260 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-656a0fc1adcb486ec3bbd4aec5fbac97896b62f7)\n\n**fixed alerts:**\n\n* 1 for Unused import",
"This pull request ... | 2020-12-03T06:12:26 | 2020-12-03T18:17:50 | 2020-12-03T18:17:47 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1511 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1511/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1511/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1511/events | https://github.com/NVIDIA/NeMo/pull/1511 | 755,679,248 | MDExOlB1bGxSZXF1ZXN0NTMxMzQ2NjE0 | 1,511 | Update model config automatically inside ModelPT | {
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"Hey @titu1994 I am trying to deal with some circular/lazy imports in EFF and noticed in my NeMo Cookbook unit tests I have \r\n`asr_model()` fixture with old structure - and I took this fixture from NeMo.\r\n\r\nSo this is a reminder - please update the structure (params etc.) also down there (i.e. in NeMo unit te... | 2020-12-02T23:10:15 | 2020-12-03T22:36:54 | 2020-12-03T22:36:51 | COLLABORATOR | {
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} | # Changelog
- Immediately convert all Hydra 0.x configs to Hydra 1.x configs inside constructor of ModelPT
- Add tests to ensure that no backward incompatible changes exist
- Update every ASR side config at once - other domains can update as per convenience. | {
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"can you please share: (1) your python script, (2) yaml config and (3) command line args? I suspect smaller LR is not actually set.\r\nAlso, 1.0.0b2 is out - so I'd recommend you use it, but it shouldn't be the root cause.",
"@gtcooke94 which Quartznet15x5 are you using to finetune? Some of our models have been t... | 2020-12-02T17:09:55 | 2021-04-19T20:02:38 | 2020-12-07T15:19:10 | CONTRIBUTOR | {
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} | null | null | null | I'm trying to do some fine-tuning on Quartznet15-5. Specifically, I'm trying to see if I do fine-tuning on Switchboard how much I can increase the performance on Switchboard and how that affects the other datasets it was trained with.
I've simply split swb into an 80/10/10 train/eval/test split.
I read through the paper on fine-tuning and based hyperparameters for the optimizer on that. What I'm running into is that, universally, it becomes substantially worse as training begins, then trends back downwards towards where it started. The un-tuned quartznet is getting around ~12% WER on val/test, and the fine-tuning is typically ending worse than that. Image and optimizer below. Training for more epochs ended up stretching out the curve rather than achieving a lower validation WER.
In addition to it getting substantially worse at first, I'm also surprised that I can't seem to fine-tune it to perform better on switchboard specifically - since it was trained on several other datasets, I figured it would be possible to fine-tune to the sorts of speech we get for switchboard.
Does anyone have advice on what might be happening here, and other things to try to accomplish what I want to do?
Here's an example of an optimizer I tried
new_opt = {
"betas": [0.95, 0.25],
"lr": 0.001, # 0.001 caused increase in error, decrease LR suggested
"name": "novograd",
"sched": {
"last_epoch": -1,
"min_lr": 0.0,
"monitor": "val_loss",
"name": "CosineAnnealing",
"reduce_on_plateau": False,
"warmup_ratio": 0.12,
"warmup_steps": None,
},
"weight_decay": 0.001,
}
Using Docker v1.0.0b1 right now.
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} | # Changelog
- Preserve absolute path of provided path in order to correctly check whether its a tarfile - when cwd() is inside of another tarfile.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | # Changelog
- Huggingface tokenizers 4.0+ drops the explicit dependency on sentencepiece
- Therefore add it as a dependency explicitly
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"Both EncDecCTCModel and it's bpe counterpart are pytorch modules - so you will need to write your own function that is based off of the validation_step method inside these two models. "
] | 2020-12-01T20:33:44 | 2021-06-19T04:46:07 | 2021-06-19T04:46:07 | NONE | {
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} | null | null | null | Hi all,
How can I get the encoder part output in NeMo? Basically, I'm assuming that this part extracts audio features from the raw sample. I'm trying to do some sentiment analysis with those audio features. However, the examples provided use the whole encoder-decoder pipeline. Thanks in advance. | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
Cherry picked fix for fuzzy onnx modles' sizes | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1504 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1504/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1504/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1504/events | https://github.com/NVIDIA/NeMo/pull/1504 | 754,507,008 | MDExOlB1bGxSZXF1ZXN0NTMwMzk1NTg1 | 1,504 | Add tutorial entry for external language model | {
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"@okuchaiev Is there going to be any more work on external LMs? I'm asking as this seems like that notebook is not very direct to productionize, especially since we're moving some compute to the CPU (is this not an area where we could use the GPU effectively?). \r\n\r\nThere's also the issue where the timestamps ge... | 2020-12-01T15:44:13 | 2020-12-03T10:42:43 | 2020-12-01T17:19:52 | CONTRIBUTOR | {
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} | I asked this before I found the tutorial notebook: https://github.com/NVIDIA/NeMo/issues/1503
This question of how to add external language models is very common, so now that it is supported I think it would be worthwhile to add it to the chart of the main README - I'd guess you will all get fewer questions that way as well.
Figured I'd save others some time and go on and submit this small PR. | {
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"For anyone looking at this, I dug into the PRs and found this notebook added:\r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/asr/Offline_ASR.ipynb"
] | 2020-12-01T15:26:59 | 2020-12-01T19:45:03 | 2020-12-01T19:45:03 | CONTRIBUTOR | {
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Is there a notebook on how to do this, or for now is it easiest to dig around in the code?
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https://api.github.com/repos/NVIDIA/NeMo/issues/1502 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1502/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1502/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1502/events | https://github.com/NVIDIA/NeMo/issues/1502 | 754,382,943 | MDU6SXNzdWU3NTQzODI5NDM= | 1,502 | RuntimeError: arguments are located on different GPUs | {
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"I have the same issue.\r\n**Same Environment and GPUs**\r\n\r\n- python 3.6.10\r\n- Pytorch: 1.7.0a0+8deb4fe\r\n- pytorch-lightning==1.0.8\r\n- pytorch-lightning-bolts==0.2.5\r\n- pytorch-transformers==1.1.0",
"> I have the same issue.\r\n> **Same Environment and GPUs**\r\n> \r\n> * python 3.6.10\r\n> * Pytorch:... | 2020-12-01T13:12:52 | 2020-12-08T18:18:59 | 2020-12-08T18:18:59 | NONE | {
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} | null | null | null | **Describe the bug**
During speech_to_text.py on my own data set training while using FilterbankFeatures,
at training step.
I get the error: RuntimeError: arguments are located on different GPUs
at line 358 in /nemo/collections/ast/parts/features.py
x = torch.matmul(self.fb.to(x.dtype), x)
**Steps/Code to reproduce bug**
python speeh_to_text.py
model.train_ds.manifest_filepath=<my dataset>
model.validation_ds.manifest_filepath=<my dataset>
trainer.gpus=3
dataset.num_workers=10
**Environment overview (please complete the following information)**
- Environment location: Nemo Docker container
- Method of NeMo install: cloning NeMo main branch
**Environment details**
- PyTorch 1.6.0
- Python 3.6.7
**Additional context**
Nvidia V100
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"This pull request **introduces 1 alert** when merging 9d1ffa4eb969bae0402026edecde7b854df92928 into d15c09333d009f82ad6fb5996480c18fe7fc4d2b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-562341bd8982145a858e9b5512c28f2b95117bd1)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2020-11-30T21:31:30 | 2020-12-03T01:07:47 | 2020-12-02T21:19:43 | CONTRIBUTOR | {
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} | Uses nvidia's quantization toolkit https://gitlab-master.nvidia.com/TensorRT/Tools/pytorch-quantization.
Added two scripts for ASR model, one for calibration, one for post training quantization.
usage
```shell
python examples/asr/speech_to_text_calibrate.py --asr_model QuartzNet15x5Base-En --dataset data/librispeech/eval/librispeech-dev-other.json --batch_size 32
```
```shell
python examples/asr/speech_to_text_infer.py --asr_model QuartzNet15x5Base-En-max-32.nemo --dataset data/librispeech/eval/librispeech-dev-other.json --wer_tolerance 0.1012 --batch_size 64
```
ONNX export will fail because opset13 is not supported in Pytorch yet.
@okuchaiev | {
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} | The following line was passing a `.nemo` file as an argument for PTL Trainer's `resume_from_checkpoint` option.
This would lead to PTL trying to unpickle a tar file and raise an exception. | {
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"The docker image has been published and should be available now."
] | 2020-11-27T09:27:07 | 2020-12-02T17:51:01 | 2020-12-02T17:51:01 | NONE | {
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Error message:
docker pull nvcr.io/nvidia/nemo:v1.0.0b2
Error response from daemon: manifest for nvcr.io/nvidia/nemo:v1.0.0b2 not found: manifest unknown: manifest unknown | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1497 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1497/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1497/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1497/events | https://github.com/NVIDIA/NeMo/pull/1497 | 751,128,203 | MDExOlB1bGxSZXF1ZXN0NTI3NzAwOTk0 | 1,497 | Add transcription functionality to ASR Classification models | {
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"For transcription, duration is a dummy value that's not precalculated. It would cost too much compute to precompute the durations of all the samples simply for logging.",
"Hmm thinking about the vad case, it doesn't work with this dataloader at all. So feel free to push changes to this branch (or an addendum PR ... | 2020-11-25T21:29:16 | 2020-11-30T08:04:01 | 2020-11-30T08:00:25 | COLLABORATOR | {
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} | # Changelog
- Refactor TopKClassification accuracy to make `top_k` a property + perform top_k operation seperately
- Add tests for transcription scenarios
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1496 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1496/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1496/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1496/events | https://github.com/NVIDIA/NeMo/pull/1496 | 751,009,875 | MDExOlB1bGxSZXF1ZXN0NTI3NjA0Njg5 | 1,496 | Add torchscript export for IntentSlotClassificationModel. | {
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"Is there a way to check what jenkins is failing with? It seems the details link points to a non-public IP."
] | 2020-11-25T17:43:09 | 2020-12-12T01:00:52 | 2020-12-12T01:00:52 | CONTRIBUTOR | {
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} | A bit of a roundabout way of allowing `IntentSlotClassificationModel`s to be exported to torchscript. Not immediately sure how to do this in a cleaner way without adding/changing too much. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1495 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1495/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1495/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1495/events | https://github.com/NVIDIA/NeMo/issues/1495 | 750,724,582 | MDU6SXNzdWU3NTA3MjQ1ODI= | 1,495 | [Question] : Docker image nvcr.io/nvidia/nemo:v1.0.0b2 not found | {
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"on the same machine, it works for \"nvcr.io/nvidia/nemo:v1.0.0b1 \"",
"closing as duplicate of https://github.com/NVIDIA/NeMo/issues/1498 "
] | 2020-11-25T11:10:05 | 2020-11-30T18:07:46 | 2020-11-30T18:07:46 | NONE | {
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Docker image "v1.0.0b2 not found"
Error message:
docker pull nvcr.io/nvidia/nemo:v1.0.0b2
Error response from daemon: manifest for nvcr.io/nvidia/nemo:v1.0.0b2 not found: manifest unknown: manifest unknown
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"shouldn't tokenizer_model be handled similarly?",
"> shouldn't tokenizer_model be handled similarly?\r\n\r\nok, got it."
] | 2020-11-25T05:35:33 | 2020-11-25T17:24:05 | 2020-11-25T17:24:01 | COLLABORATOR | {
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} | Changed setup_tokenizer logic so that if the model is being restored it will try to use the vocab file "tokenizer.vocab_file" first.
If "tokenizer.vocab_file" doesn't exist, then it will fall back to cfg.vocab_file.
If no `vocab_file` is given, we will try to get the vocab from the tokenizer and then register the `vocab_dict` and the `vocab_file`.
When writing the `vocab_dict` and `vocab_file` that we get from the tokenizer we write them to a cache directory based on a hash of the `vocab_dict`.
TODO: create docstring to clarify the flow | {
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"this isn't something supported out-of-the box. But you can probably write your own PTL callback for that."
] | 2020-11-24T17:57:32 | 2020-11-30T18:08:43 | 2020-11-30T18:08:42 | NONE | {
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} | # Changelog
- Revert a temporary patch for TLT
- Make ModelPT save_to and restore_from aware of restoration state
- Formalize artifact management
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"closing as this code is in https://github.com/NVIDIA/NeMo/pull/1491 "
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"Only optimizers/schedulers that exist within the AVAILABLE_OPTIMIZERS and AVAILABLE_SCHEDULERS can be resolved via config/classpath. \r\n\r\nYou can register your own scheduler via the following method - before your code calls the hydra main function, in order to add external optimizers and schedulers support wit... | 2020-11-23T16:44:11 | 2021-08-17T00:58:49 | 2021-08-17T00:58:49 | CONTRIBUTOR | {
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} | null | null | null | I promise this is the last issue of the day haha. I would like to use OneCycleLearning (torch.optim.lr_scheduler.OneCycleLR) for my scheduler, but it is not available under the list of NeMo optimizers as far as I can tell. I have included the full list below so that someone can point out if it is available under a different name.
I tried setting sched.cls to be `torch.optim.lr_scheduler.OneCycleLR` but I get a message that the scheduler isn't being used:
```
[NeMo W 2020-11-23 16:34:28 lr_scheduler:463] Could not resolve classpath for Scheduler Config, and `name` was not provided either.
Scheduler cannot be instantiated !
```
Why can't I pass in the cls through the config? Do I need to implement my own subclass of WarmupPolicy as is done in `nemo.core.optim.lr_scheduler`
**Additional context**
I got the class path for OneCycleLR using the following function:
```
def get_class_path(cls):
return f'{cls.__module__}.{cls.__name__}'
```
Full list of schedulers:
```
AVAILABLE_SCHEDULERS = {
'WarmupPolicy': WarmupPolicy,
'WarmupHoldPolicy': WarmupHoldPolicy,
'SquareAnnealing': SquareAnnealing,
'CosineAnnealing': CosineAnnealing,
'NoamAnnealing': NoamAnnealing,
'WarmupAnnealing': WarmupAnnealing,
'InverseSquareRootAnnealing': InverseSquareRootAnnealing,
'SquareRootAnnealing': SquareRootAnnealing,
'PolynomialDecayAnnealing': PolynomialDecayAnnealing,
'PolynomialHoldDecayAnnealing': PolynomialHoldDecayAnnealing,
'StepLR': pt_scheduler.StepLR,
'ExponentialLR': pt_scheduler.ExponentialLR,
'ReduceLROnPlateau': pt_scheduler.ReduceLROnPlateau,
'CyclicLR': pt_scheduler.CyclicLR,
}
``` | {
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"All NeMo models are pytorch modules, so it should be easy enough to look at their validation/test step code and replicate for tensors. \r\n\r\nThe easiest possible way might be to simply copy paste the validation step code and modify it use individual tensors instead of a batch from a data loader. Make sure to use... | 2020-11-23T15:38:03 | 2020-11-23T17:43:38 | 2020-11-23T17:43:38 | CONTRIBUTOR | {
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} | null | null | null | I would like to do inference on a tensor of audio samples instead of a file as I am inferring on audio passed via microphone and saving/loading adds extra latency. The only inference method I've been able to find for quartznet is `transcribe` which takes a list of files. Is there an easy way to infer on tensors? Or is my best bet to look at `nemo.collections.asr.models.ctc_models.transcribe` and replicate for tensors instead of files?
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"It's not supported out of the box, but you can easily add support for it by subclassing EncDecCTCModel and making the required modifications. ",
"So I finally got to work on this and I may need some help as I don't understand all the stuff under the hood. Here's what my subclass of `EncDecCTCModel` looks like. C... | 2020-11-23T15:21:37 | 2021-05-28T08:44:34 | 2021-05-28T08:44:34 | CONTRIBUTOR | {
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} | null | null | null | Is it possible to use discriminative learning rates with NeMo, i.e. a different lr for different layer groups like the encoder and decoder? This is a technique that can be very helpful for transfer learning and I would like to experiment with it if possible. I tried passing a list of LRs to the optimizer config but it seems to be hardcoded for a single LR.
**Additional context**
I'm using quartznet with nemo-toolkit[all]==1.0.0b2
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"The intended usage of the `evaluate_from_file()` is to run the final evaluation. The predictions for all examples are stored in a single line.\r\n\r\nIt's recommended to use `get_predictions()`. Note, each word in the input sequence could be split into one or more tokens. In our implementation, the prediction for... | 2020-11-23T09:08:34 | 2020-11-24T15:53:48 | 2020-11-24T15:53:48 | NONE | {
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} | null | null | null | How do I let `evaluate_from_file` read into my entire text file and produce the output?
`model_from_scratch.evaluate_from_file(
text_file= 'Imported_data.txt',
output_dir='.',
)`
Apparently, it's only reading the first line of my text file as shown below.
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} | I was benchmarking Megatron-LM and ran into this issue when recording memory values. This means on the main process there is a lot of unnecessary CUDA memory allocated when loading the state dictionary. Using the proposed fix remedied the issue! | {
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"PR https://github.com/NVIDIA/NeMo/pull/1497 addresses this. I'll update here once it's merged. ",
"This is merged now, closing issue. Please let us know if the solution is insufficient for your requirements"
] | 2020-11-22T08:58:35 | 2020-11-30T18:59:02 | 2020-11-30T18:59:02 | NONE | {
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The current implementation seems to only taken into batch of wav files and its quite confusing to follow along. | {
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} | null | null | null | I did the Token_Classification_Named_Entity_Recognition tutorial, saved the model as .nemo and now I'm trying to load up the model on another notebook.
[](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb)
I saved it with pytorch's suggestion,
` torch.save(model.state_dict(), PATH)`
and trying to load with:
`model = TheModelClass(*args, **kwargs) model.load_state_dict(torch.load(PATH)) model.eval()`
But I don't have the model class, how else may I do it?
(edit)
Do I have to instantiate with,
`model_from_scratch = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)`
to do
`model = model_from_scratch`
`model.load_state_dict(torch.load(model_file)`
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} | Please don't merge - just checking another solution to Vlad's model - need to test whether the associated fix to ModelPT artifact doesn't break the tests". | {
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"The tutorial `01_ASR_with_NeMo.ipynb` does a good job explaining how transfer learning can be done with Nemo. Thanks "
] | 2020-11-20T18:14:54 | 2020-11-21T17:21:50 | 2020-11-21T17:21:50 | NONE | {
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} | null | null | null | I saw a new script that ports old checkpoints, but it is for 0.9+ versions
What if my Jasper is taught on a version lower than 0.9 and there is no way to retrain it?
P.S. the script does not work with this config
>>
model: "Jasper"
sample_rate: 16000
AudioToTextDataLayer:
max_duration: 16.7
trim_silence: true
normalize_transcripts: true
train:
shuffle: true
eval:
shuffle: false
max_duration: null
AudioToMelSpectrogramPreprocessor:
window_size: 0.02
window_stride: 0.01
window: "hann"
normalize: "per_feature"
n_fft: 512
features: 64
dither: 0.00001
pad_to: 16
stft_conv: true
JasperEncoder:
activation: "relu"
conv_mask: true
jasper:
- filters: 256
repeat: 1
kernel: [11]
stride: [2]
dilation: [1]
dropout: 0.2
residual: false
- filters: 256
repeat: 5
kernel: [11]
stride: [1]
dilation: [1]
dropout: 0.2
residual: true
residual_dense: true
- filters: 256
repeat: 5
kernel: [11]
stride: [1]
dilation: [1]
dropout: 0.2
residual: true
residual_dense: true
- filters: 384
repeat: 5
kernel: [13]
stride: [1]
dilation: [1]
dropout: 0.2
residual: true
residual_dense: true
- filters: 384
repeat: 5
kernel: [13]
stride: [1]
dilation: [1]
dropout: 0.2
residual: true
residual_dense: true
- filters: 512
repeat: 5
kernel: [17]
stride: [1]
dilation: [1]
dropout: 0.2
residual: true
residual_dense: true
- filters: 512
repeat: 5
kernel: [17]
stride: [1]
dilation: [1]
dropout: 0.2
residual: true
residual_dense: true
- filters: 640
repeat: 5
kernel: [21]
stride: [1]
dilation: [1]
dropout: 0.3
residual: true
residual_dense: true
- filters: 640
repeat: 5
kernel: [21]
stride: [1]
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"@titu1994 The fix comes from me doing the cycles (save - restore - save), which is not a _typical_ NeMo use-case, but is a typical TLT workflow. Thanks for the comment though.\r\n",
"Save restore save is the general cycle of NeMo too - save when we train a model, convert to .Nemo and upload, use from_pretrained(... | 2020-11-20T06:49:12 | 2020-11-24T01:13:22 | 2020-11-24T01:13:19 | CONTRIBUTOR | {
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} | This is still work in progress - but at least:
- [x] CI passes
- [x] whole TLT workflow (train, finetune, evaluate, inference, export, infer ONNX scripts) work
- [x] setting datadir also "reconfigures" the model (e.g. slots/intents change => heads change too)
- [x] **!! change in the way ModelPT is handing artifacts!!** => saving original path despite whether artifact was restored from that path or from "./"
Still, the whole logic of the model is just horrible. The major problem I see here is improper `separation of concerns`:
1) there is improper separation of parameters - plenty of parameters are shared between model and dataset/dataloader related sections => each `setup_*_ds` should receive all the params required to create ds&dl
2) there is DataDescriptor that does magical operations on slots and values from both training and validation splits => those values should be gathered together during the data preparation phase, and `setup_train_ds` should load them, store in config and other `setup_*_ds` should simply use them
3) DataDescriptor is needed to be created before calling parent class constructor (which calls `setup_*_ds`) and it requires slots and values from both splits to be loaded first. This creates an chicken and egg problem - get rid of DataDescriptor and follow logic from 2) to get rid of that cycle.
4) model reconfiguration should change only last layers of classifier - now it is (re)instantiating the whole classifier object, which IMO is not the best design...
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"still some tests are not passing"
] | 2020-11-20T00:38:59 | 2021-01-22T15:27:21 | 2020-12-02T23:03:23 | MEMBER | {
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