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} | This PR makes `find_unused_parameters` user configurable in `NLPDDPPlugin` by setting a default dictionary with the current value (`False`) and updating that dictionary with user provided kwargs. This ensures that the value is always `False`, even if it changes from its current default (also `False`) in PyTorch's `DistributedDataParallel`.
An alternative option would be to remove it from the initialization call to `DistributedDataParallel` since it is already `False` by default in PyTorch.
The reason for this request is that `find_unused_parameters` must be `True` for MegaMolBART.
I have read the contribution guidelines. All tests that are currently passing in `main` pass with this commit. (Note that two tests fail in NeMo `main`.) The code has been formatted with black and the commit is signed. This change was discussed with @ericharper several months ago.
Signed-off-by: Michelle Gill <[email protected]> | {
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} | - Adds phoneme probability to our current g2p module.
- Updates FastPitch yaml to use this functionality. | {
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} | null | null | null | I have a prebuilt Punctuation_and_Capitalization model (NeMo:1.4.0). Running nemo2riva with NeMo:1.5.1 performs the conversion as it should, however, running nemo2riva with master branch of NeMo (1.6.0rc0) crashes with the following error
```
INFO: Logging level set to 20
INFO: Restoring NeMo model from 'data/nemo/Punctuation_and_Capitalization.nemo'
[NeMo I 2022-01-20 15:59:39 common:489] Model instantiation from target class nemo.collections.nlp.models.token_classification.punctuation_capitalization_model.PunctuationCapitalizationModel failed with following error.
Falling back to `cls`.
Traceback (most recent call last):
File "/data/NeMo/nemo/core/classes/common.py", line 463, in from_config_dict
imported_cls = import_class_by_path(target_cls)
File "/data/NeMo/nemo/utils/model_utils.py", line 462, in import_class_by_path
mod = __import__(path, fromlist=[class_name])
File "/data/NeMo/nemo/collections/nlp/__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "/data/NeMo/nemo/collections/nlp/models/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
File "/data/NeMo/nemo/collections/nlp/models/dialogue_state_tracking/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
File "/data/NeMo/nemo/collections/nlp/models/dialogue_state_tracking/sgdqa_model.py", line 33, in <module>
from nemo.collections.nlp.models.nlp_model import NLPModel
File "/data/NeMo/nemo/collections/nlp/models/nlp_model.py", line 29, in <module>
from nemo.collections.nlp.modules import BertModule
File "/data/NeMo/nemo/collections/nlp/modules/__init__.py", line 16, in <module>
from nemo.collections.nlp.modules.common import (
File "/data/NeMo/nemo/collections/nlp/modules/common/__init__.py", line 29, in <module>
from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer, get_tokenizer_list
File "/data/NeMo/nemo/collections/nlp/modules/common/tokenizer_utils.py", line 27, in <module>
from nemo.collections.nlp.parts.nlp_overrides import HAVE_APEX
File "/data/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 33, in <module>
from nemo.collections.nlp.modules.common.megatron.module import Float16Module
File "/data/NeMo/nemo/collections/nlp/modules/common/megatron/__init__.py", line 15, in <module>
from nemo.collections.nlp.modules.common.megatron.megatron_bert import MegatronBertEncoder
File "/data/NeMo/nemo/collections/nlp/modules/common/megatron/megatron_bert.py", line 30, in <module>
from nemo.collections.nlp.modules.common.megatron.language_model import get_language_model
File "/data/NeMo/nemo/collections/nlp/modules/common/megatron/language_model.py", line 26, in <module>
from nemo.collections.nlp.modules.common.megatron.transformer import ParallelTransformer
File "/data/NeMo/nemo/collections/nlp/modules/common/megatron/transformer.py", line 24, in <module>
from apex.transformer.utils import divide as safe_divide
ModuleNotFoundError: No module named 'apex.transformer.utils'
```
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} | 1. If `BertPunctuationCapitalizationDataset.__init__` arguments `punct_label_ids` and `capit_label_ids` are `DictConfig`s, then transform them to dictionaries.
2. Make sure that `PunctuationCapitalizationModel` attributes `punct_label_ids` and `capit_label_ids` are always dictionaries and not `DictConfig`s. Previously, type of these attributes varied depending on where the attributes are set.
3. Add more typing and improve error messages. | {
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} | # Changelog
- Revert previous code that constructs a trainer on one GPU to evaluate results. The code is misleading because the torch environment is still distributed, and all processes run the code
- From now on, test only if the model was trained on a single GPU with no ddp.
Signed-off-by: smajumdar <[email protected]> | {
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* Patch fix for the multiple last checkpoints issue. Reference: https://github.com/PyTorchLightning/pytorch-lightning/issues/11451 | {
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} | `Joint_Intent_and_Slot_Classification.ipynb`
`Text_Classification_Sentiment_Analysis.ipynb`
problem are caused by extra `cfg` key in the checkpoint file
`Relation_Extraction-BioMegatron.ipynb`
problem is caused by forgetting to set the correct `model_config` variable.
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} | null | null | null | **Describe the bug**
Error when converting Nemo model to Riva
**Steps/Code to reproduce bug**
Shell command:
`nemo2riva --out /mnt/devdata/zzh/tao/results/Citrinet/stt_zh_citrinet_512.riva /mnt/devdata/zzh/tao/results/Citrinet/stt_zh_citrinet_512.nemo
`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: ubuntu18.04
- Python version: 3.8
- nemo2riva: Downloaded from riva_quickstart_v1.8.0-beta/nemo2riva-1.8.0b0-py3-none-any.whl
- stt_zh_citrinet_512.nemo: curl -LO https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemo
**Additional context**
Error message:
```
(riva) $ nemo2riva --out ./tao/results/Citrinet/stt_zh_citrinet_512.riva ./tao/results/Citrinet/stt_zh_citrinet_512.nemo
INFO: Logging level set to 20
INFO: Restoring NeMo model from '/mnt/devdata/zzh/tao/results/Citrinet/stt_zh_citrinet_512.nemo'
ERROR: Failed to restore model from NeMo file : /mnt/devdata/zzh/tao/results/Citrinet/stt_zh_citrinet_512.nemo. Please make sure you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/home/user/miniconda3/envs/riva/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/home/user/miniconda3/envs/riva/lib/python3.8/site-packages/nemo2riva/cli/nemo2riva.py", line 50, in nemo2riva
Nemo2Riva(args)
File "/home/user/miniconda3/envs/riva/lib/python3.8/site-packages/nemo2riva/convert.py", line 41, in Nemo2Riva
raise e
File "/home/user/miniconda3/envs/riva/lib/python3.8/site-packages/nemo2riva/convert.py", line 34, in Nemo2Riva
model = ModelPT.restore_from(restore_path=nemo_in)
AttributeError: type object 'ModelPT' has no attribute 'restore_from'
```
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This PR is for the following:
- Fix the errors in ASR with diarization tutorial
- Fix the errors in diarization_utils.py and decoder_timestamp_utils.py
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} | - Removed local machine path inserted in ASR with diarization tutorial.
- Fixed some of the outdated docstrings in diarization_utils.py and decode_timestamp_utils. | {
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} | null | null | null | I am using a pre-trained stt_en_conformer_transducer and it is doing well. However, since my dataset has some special terms I want to use KenLM with Beam Search.
In one tutorial, I saw that we can change the decoding strategy to Beam using:
```
import copy
decoding_config = copy.deepcopy(asr_model.cfg.decoding)
# Update the config for the decoding strategy
decoding_config.strategy = "beam" # Options are `greedy`, `greedy_batch`, `beam`, `tsd` and `alsd`
decoding_config.beam.beam_size = 8 # Increase beam size for better scores, but it will take much longer for transcription !
asr_model.change_decoding_strategy(decoding_config)
asr_model.transcribe(['/home/wesam/wavs/X1.wav'], )
```
It works well, but I did not know how to feed it with my own Language Model? | {
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} | null | null | null | I'm trying to fine-tine stt_en_conformer_transducer_large model on a custom dataset of 300H. I have a problem with setting up the training configuration.
In [this ](https://github.com/NVIDIA/NeMo/blob/8c912f515a77efb0e16b1433030da23e488960d5/tutorials/asr/ASR_with_Transducers.ipynb) example you reported how to train a model from scratch, but I understood from the model card (https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_small) that we can fine-tune it.
I tried to load the model, extract the config file, modify it and re-assign it to the mode, but did not work.
Here is what I did:
# Loading the model
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="stt_en_conformer_transducer_large", map_location='cpu')
```
# Loading the model configurations:
```
import copy
from omegaconf import DictConfig
modelConfig = DictConfig(asr_model.cfg)
modelConfig
```
Set the training data:
```
modelConfig['train_ds']['manifest_filepath'] = TRAIN_MANIFEST
modelConfig['validation_ds']['manifest_filepath'] = TEST_MANIFEST
```
Assigned it to the model:
`asr_model.setup_training_data(modelConfig['train_ds'])`
Here is the error I get:
```
[NeMo I 2022-01-18 18:44:40 collections:173] Dataset loaded with 1503 files totalling 2.81 hours
[NeMo I 2022-01-18 18:44:40 collections:174] 0 files were filtered totalling 0.00 hours
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_3295870/3465286351.py in <module>
----> 1 asr_model.setup_training_data(modelConfig['train_ds'])
~/anaconda3/envs/nemo/lib/python3.7/site-packages/nemo/collections/asr/models/rnnt_models.py in setup_training_data(self, train_data_config)
499 # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
500 # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
--> 501 if isinstance(self._trainer.limit_train_batches, float):
502 self._trainer.limit_train_batches = int(
503 self._trainer.limit_train_batches
AttributeError: 'NoneType' object has no attribute 'limit_train_batches'
```
Thanks! | {
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} | null | null | null | **Describe the bug**
I have a problem when deloy Text Nemo Text Normarlzation on Triton Inference Server. The Normalizer can not load the default "whitelist.tsv" file. Whenever I run ```tritonserver --model-repository models/```, the bug will appear.
```python
from nemo_text_processing.text_normalization.normalize import Normalizer
normalizer = Normalizer(input_case='cased', lang='en')
```
Results:
```bash
Internal: UnicodeDecodeError: 'ascii' codec can't decode byte 0xce in position 150: ordinal not in range(128)
At:
/usr/lib/python3.8/encodings/ascii.py(26): decode
/usr/local/lib/python3.8/dist-packages/nemo_text_processing/text_normalization/en/utils.py(41): load_labels
/usr/local/lib/python3.8/dist-packages/nemo_text_processing/text_normalization/en/taggers/whitelist.py(49): _get_whitelist_graph
/usr/local/lib/python3.8/dist-packages/nemo_text_processing/text_normalization/en/taggers/whitelist.py(64): __init__
/usr/local/lib/python3.8/dist-packages/nemo_text_processing/text_normalization/en/taggers/tokenize_and_classify.py(107): __init__
/usr/local/lib/python3.8/dist-packages/nemo_text_processing/text_normalization/normalize.py(76): __init__
```
**Environment overview (please complete the following information)**
- Environment: Triton Inference Server 21.10
- Method of NeMo install: pip install nemo_toolkit['all']
**Additional context**
**There is no problem when I test this code without Triton Inference Server!**. | {
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} | The PR reduces number of permutations processed in [this](https://github.com/NVIDIA/NeMo/blob/a8154fd825c34de0a59309536e11ba7d5d1f94e1/nemo_text_processing/text_normalization/normalize.py#L149) loop.
1. Count number of permutations that each token gives.
2. Split sequence of tokens into smaller parts so that number of permutations from one part would not exceed certain number.
3. Verbalize every part.
4. Unite verbalizations of parts.
Closes #3450 | {
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} | null | null | null | **Describe the bug**
When I apply text normalization to a string that contains a lot of dates, the normalization takes too much time. For example, the string
```
Hotel built in 2002 , Last complete renovation 2002 , Last partial renovation 2011 , Number of floors 4 , Total number of rooms 35 , Number of single rooms 2 , Number of double rooms 33 , of which are suitable as three-bed rooms 15 , of which are suitable as four-bed rooms 3 , Number of junior suites 3 , Hotel open all year round , Reception manned 24 hours a day , Earliest check-in 15 : 00 , Latest check-out 11 : 00 , English-speaking staff available during the day , Hotel , City hotel , Boutique hotel , Trade fair / conference hotel , child-friendly hotel , Hiking hotel , No-smoking room 35 , Family rooms available 3 , Children 's beds / cots 2 , Renovation work planned 2018-01-08 , to 2018-12-31 , in area room , Renovation work planned 2018-01-08 , to 2018-12-31 , in area restaurant , Renovation work planned 2018-01-08 , to 2018-12-31 , in area lobby , Renovation work planned 2018-01-08 , to 2018-12-31 , in area other
```
was processed for 30 min before I interrupted the normalization.
**Steps/Code to reproduce bug**
```python
from nemo_text_processing.text_normalization.normalize import Normalizer
s = (
"Hotel built in 2002 , Last complete renovation 2002 , Last partial renovation 2011 , Number of floors 4 , "
"Total number of rooms 35 , Number of single rooms 2 , Number of double rooms 33 , of which are "
"suitable as three-bed rooms 15 , of which are suitable as four-bed rooms 3 , Number of junior suites 3 , "
"Hotel open all year round , Reception manned 24 hours a day , Earliest check-in 15 : 00 , Latest check-out "
"11 : 00 , English-speaking staff available during the day , Hotel , City hotel , Boutique hotel , "
"Trade fair / conference hotel , child-friendly hotel , Hiking hotel , No-smoking room 35 , "
"Family rooms available 3 , Children 's beds / cots 2 , Renovation work planned 2018-01-08 , "
"to 2018-12-31 , in area room , Renovation work planned 2018-01-08 , to 2018-12-31 , in area "
"restaurant , Renovation work planned 2018-01-08 , to 2018-12-31 , in area lobby , Renovation "
"work planned 2018-01-08 , to 2018-12-31 , in area other"
)
normalizer = Normalizer(input_case='cased', lang='en')
normalized_string = normalizer.normalize(s)
```
**Expected behavior**
Normalization is expected to finish much faster. For example, in a second.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: from source (`git clone https://github.com/NVIDIA/NeMo.git && cd NeMo && bash reinstall.sh`)
**Environment details**
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.10.0
- Python version: 3.8.12
**Additional context**
The problem is in [this](https://github.com/NVIDIA/NeMo/blob/a8154fd825c34de0a59309536e11ba7d5d1f94e1/nemo_text_processing/text_normalization/normalize.py#L148) line. A method `generate_permutations()` yields all possible permutations of date components and the number of permutations is at least `3 ** number_of_dates_in_a_string`.
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@yzhang123 | {
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} | Added and modified pronunciations can be found at the end of the file under the headings "Words added by NVIDIA" and "Pronunciation changes from 0.7b" respectively.
Signed-off-by: Jocelyn Huang <[email protected]> | {
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} | + This PR Adds different bucketing strategies to improve the performance of ASR bucketing training accuracy-wise. A new parameter added to the data loaders (train_ds.bucketing_strategy) to specify one of the following modes:
1-fixed_order: the same order of buckets are used for all epochs
2-sycned_randomized: each epoch would have a different order of buckets
3-fully_randomized: similar to synced_randomized but each GPU has its own random order. So GPUs would not be synced.
+Also added the support of layer norm for Conformer layers instead of batch norm. | {
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- Make transcribe() method use 0 workers for compatibility
- Change the way eval mode limited context SE works | {
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} | null | null | null | **Describe the bug**
On attempting inference of a recording, I received the following error:
PicklingError: Can't pickle <class 'nemo.collections.common.parts.preprocessing.collections.AudioTextEntity'>: attribute lookup AudioTextEntity on nemo.collections.common.parts.preprocessing.collections failed
**Steps/Code to reproduce bug**
```
import nemo
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En", strict=False)
files = ['./soundsample.wav']
for fname, transcription in zip(files, asr_model.transcribe(paths2audio_files=files)):
print(f"Audio in {fname} was recognized as: {transcription}")
```
**Environment overview**
- Conda Virtual Environment
- Method of NeMo install: from source
**Environment details**
- Mac OS Catalina 10.15.7
- PyTorch version: 1.10.1
- Python version: 3.8.5
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} | # Changelog
- Update ContextNet checkpoints trained on 11.5 K hours of data for English
Signed-off-by: smajumdar <[email protected]> | {
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} | Added loss mask configurability to include or exclude the text portion of the input in the prompt tuning loss calculation.
```
[soft` prompt][Input Text][Answer][EOD Padding]
[000000000000011111111111111111111000000000000]
```
vs
```
[soft prompt][Input Text][Answer][EOD Padding]
[00000000000000000000000011111111000000000000]
```
I also added prompt tuning CI tests and prompt tuning dataset unit tests that get called during CI testing.
Calculating the loss on the answer only helped. The 2nd score is from calculating the loss on the full input text and the 3rd is from calculating the loss on the answer only. These scores are on Winogrande formulated as fill in the blank instead of sentence completion. (The GPT eval scores in the spread sheet are for Winogrande as sentence completion. We couldn't find a Winogrande sentence completion training set split to use for prompt tuning, so I added Winogrande fill in the blank to the model eval code on gitlab.)
Allen AI Winogrande
Baseline 1.3B Megatron GPT: 0.489345
PromptTuned 1.3B Megatron GPT Full Text Loss: 0.486977
PromptTuned 1.3B Megatron GPT Answer Only Loss: 0.528019 | {
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} | Bumps [numpy](https://github.com/numpy/numpy) from 1.18.5 to 1.21.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/numpy/numpy/releases">numpy's releases</a>.</em></p>
<blockquote>
<h2>v1.21.0</h2>
<h1>NumPy 1.21.0 Release Notes</h1>
<p>The NumPy 1.21.0 release highlights are</p>
<ul>
<li>continued SIMD work covering more functions and platforms,</li>
<li>initial work on the new dtype infrastructure and casting,</li>
<li>universal2 wheels for Python 3.8 and Python 3.9 on Mac,</li>
<li>improved documentation,</li>
<li>improved annotations,</li>
<li>new <code>PCG64DXSM</code> bitgenerator for random numbers.</li>
</ul>
<p>In addition there are the usual large number of bug fixes and other
improvements.</p>
<p>The Python versions supported for this release are 3.7-3.9. Official
support for Python 3.10 will be added when it is released.</p>
<p>:warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .</p>
<ul>
<li>Optimization level <code>-O3</code> results in many wrong warnings when running the tests.</li>
<li>On some hardware NumPy will hang in an infinite loop.</li>
</ul>
<h2>New functions</h2>
<h3>Add PCG64DXSM BitGenerator</h3>
<p>Uses of the PCG64 BitGenerator in a massively-parallel context have
been shown to have statistical weaknesses that were not apparent at the
first release in numpy 1.17. Most users will never observe this weakness
and are safe to continue to use PCG64. We have introduced a new
PCG64DXSM BitGenerator that will eventually become the new default
BitGenerator implementation used by <code>default_rng</code> in future releases.
PCG64DXSM solves the statistical weakness while preserving the
performance and the features of PCG64.</p>
<p>See <code>upgrading-pcg64</code> for more details.</p>
<p>(<a href="https://github-redirect.dependabot.com/numpy/numpy/pull/18906">gh-18906</a>)</p>
<h2>Expired deprecations</h2>
<ul>
<li>The <code>shape</code> argument <code>numpy.unravel_index</code> cannot be
passed as <code>dims</code> keyword argument anymore. (Was deprecated in NumPy
1.16.)</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/numpy/numpy/commit/b235f9e701e14ed6f6f6dcba885f7986a833743f"><code>b235f9e</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/numpy/numpy/issues/19283">#19283</a> from charris/prepare-1.21.0-release</li>
<li><a href="https://github.com/numpy/numpy/commit/34aebc2824cf8c2bdbe19040b82f98f18557c8ba"><code>34aebc2</code></a> MAINT: Update 1.21.0-notes.rst</li>
<li><a href="https://github.com/numpy/numpy/commit/493b64bfe9c5396498325b87e5e80e1917555c41"><code>493b64b</code></a> MAINT: Update 1.21.0-changelog.rst</li>
<li><a href="https://github.com/numpy/numpy/commit/07d7e72ab6880c05b5fdd98482cf88982e778393"><code>07d7e72</code></a> MAINT: Remove accidentally created directory.</li>
<li><a href="https://github.com/numpy/numpy/commit/032fca5e2e9749b152ec56153f476e05efdff287"><code>032fca5</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/numpy/numpy/issues/19280">#19280</a> from charris/backport-19277</li>
<li><a href="https://github.com/numpy/numpy/commit/7d25b81025a50cc0368f5727c65e875ca769469a"><code>7d25b81</code></a> BUG: Fix refcount leak in ResultType</li>
<li><a href="https://github.com/numpy/numpy/commit/fa5754e8c159a37fcd9345df261cf82821088ea0"><code>fa5754e</code></a> BUG: Add missing DECREF in new path</li>
<li><a href="https://github.com/numpy/numpy/commit/61127bb4d46d523b699da1b63abaa5035670da27"><code>61127bb</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/numpy/numpy/issues/19268">#19268</a> from charris/backport-19264</li>
<li><a href="https://github.com/numpy/numpy/commit/143d45fff3ed9e051bdeef7bdb4df38025ea7d1c"><code>143d45f</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/numpy/numpy/issues/19269">#19269</a> from charris/backport-19228</li>
<li><a href="https://github.com/numpy/numpy/commit/d80e4738f781a1d206bbc04a2e863299e5f2e104"><code>d80e473</code></a> BUG: Removed typing for == and != in dtypes</li>
<li>Additional commits viewable in <a href="https://github.com/numpy/numpy/compare/v1.18.5...v1.21.0">compare view</a></li>
</ul>
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Currently it fails to reproduce the same accuracy. Need to investigate the bug. | {
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} | null | null | null | There are two issues I am having with exporting [MTEncDecModel](https://github.com/NVIDIA/NeMo/blob/r1.2.0/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py) to ONNX, they might be related in some way so going to post them together.
## 1. ONNX error in embedding in nemo version > 1.2:
If I try to run below example in nemo 1.5 I get the following error:
```
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION :
Non-zero status code returned while running Reshape node. Name:'Reshape_67' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:41
onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&, bool)
gsl::narrow_cast<int64_t>(input_shape.Size()) == size was false.
The input tensor cannot be reshaped to the requested shape. Input shape:{1,44,1024}, requested shape:{2,16,16,64}
```
However, it seems like this error is not present if using an older version of nemo.
## 2. Big performance discrepancy when using ONNX version of the model:
I notice a big performance drop when exporting the MTEncDecModel to ONNX. I am trying a minimal setup with greedy search and get about half the BLEU score in comparison to using MTEncDecModel directly with `TopKSequenceGenerator(top_k=1)`.
Here is a full script to get the translation with ONNX, you would need to additionally install `onnxruntime-gpu`:
```python
import torch
import numpy as np
import onnxruntime
from nemo.collections.nlp.models import MTEncDecModel
# Load the model form pretrained:
model = MTEncDecModel.from_pretrained('nmt_en_de_transformer12x2')
# Export all model components to onnx:
model.encoder.export('encoder.onnx')
model.decoder.export('decoder.onnx')
model.log_softmax.export('classifier.onnx')
# Initialise all the onnx sessions:
encoder_session = onnxruntime.InferenceSession('encoder.onnx', providers=['CUDAExecutionProvider'])
decoder_session = onnxruntime.InferenceSession('decoder.onnx', providers=['CUDAExecutionProvider'])
classifier_session = onnxruntime.InferenceSession('classifier.onnx', providers=['CUDAExecutionProvider'])
# Preprocess the data using the original nemo model for simplicity:
TEXT = ['They are not even 100 metres apart: On Tuesday, the new B 33 pedestrian lights in Dorfparkplatz in Gutach became operational - within view of the existing Town Hall traffic lights.']
src_ids, src_mask = model.prepare_inference_batch(TEXT)
src_ids = src_ids.cpu().numpy() # Convert to numpy for use with onnx
src_mask = src_mask.cpu().numpy().astype(int)
# Compute encoder hidden state:
encoder_input = {'input_ids': src_ids, 'encoder_mask': src_mask}
encoder_hidden_state = encoder_session.run(['last_hidden_states'], encoder_input)[0]
# Simple greedy search:
MAX_GENERATION_DELTA = 5
BOS = model.encoder_tokenizer.bos_id
EOS = model.encoder_tokenizer.eos_id
PAD = model.encoder_tokenizer.pad_id
def decode(tgt: np.array, embeding: np.array, src_mask: np.array) -> np.array:
decoder_input = {
'input_ids': tgt,
'decoder_mask': (tgt != PAD).astype(np.int64),
'encoder_mask': embeding,
'encoder_embeddings': src_mask
}
decoder_hidden_state = decoder_session.run(['last_hidden_states'], decoder_input)[0]
log_probs = classifier_session.run(['log_probs'], {'hidden_states': decoder_hidden_state})[0]
return log_probs
max_out_len = encoder_hidden_state.shape[1] + MAX_GENERATION_DELTA
tgt=np.full(shape=encoder_hidden_state.shape[:-1], fill_value=0)
tgt[:, 0] = BOS
for i in range(1, max_out_len):
log_probs = decode(tgt[:, :i], encoder_hidden_state, src_mask)
next_tokens = log_probs[:, -1].argmax(axis=1) # NOTE: ONNX decoder returns multiple outputs which is different to pytorch version, so I get the last one (this could be where error is?)
tgt[:, i] = next_tokens
if ((tgt == EOS).sum(axis=1) > 0).all():
break
tgt_torch = torch.from_numpy(tgt).to('cuda:0')
onnx_translation = model.ids_to_postprocessed_text(tgt_torch, model.decoder_tokenizer, model.target_processor)
```
I have run above against newstest2014 set and got a BLUE score of 13 vs 29 if I use `MTEncDecModel.translate` method. It seems like onnx model works well for shorter sentences, but for the longer ones it cuts off too soon. Could be because torch model uses decoder_mems, and onnx doesn't?
Is there maybe a better way to set up onnx inference?
**Environment overview (please complete the following information)**
- Environment Location: Docker on Kubernetes
- Method of NeMo install: using helm chart using nvcr.io/nvidia/nemo:1.2.0 / 1.5.1
- Additionally install `pip install onnxruntime-gpu==1.10.0`
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} | null | null | null | Upon call to retrieve a pre-trained model, the following line fails:
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
Additionally, list_available_models() also fails with the same above error.
I have installed NeMo via:
python -m pip install git+https://github.com/NVIDIA/NeMo.git
OS: Mac OS Catalina 10.15.7
Pytorch: 1.10.0
Python: 3.8.5
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} | small update due to https://github.com/NVIDIA/NeMo/pull/3419
Signed-off-by: fayejf <[email protected]> | {
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} | This is to implement https://jirasw.nvidia.com/browse/JARS-1682.
It is important to provide user ability to generate input example of particular dimensions - as certain networks would reconfigure themselves according to input dimensions - thus export needs to be performed with real life size example.
Signed-off-by: Boris Fomitchev <[email protected]> | {
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} | WAR changes to prevent issues described in https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3429305
"ONNX model exported with PyTorch container > 21.06 fails when data of different size is provided to dynamic axes"
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} | null | null | null | Dear colleagues, after install Nemo r1.5.0 I have error in method oracle_model.diarize() in
[Speaker_Diarization_Inference] https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb)
```
from nemo.collections.asr.models import ClusteringDiarizer
oracle_model = ClusteringDiarizer(cfg=config)
oracle_model.diarize()
PicklingError: Can't pickle <class 'nemo.collections.common.parts.preprocessing.collections.SpeechLabelEntity'>: attribute lookup SpeechLabelEntity on nemo.collections.common.parts.preprocessing.collections failed
```
Environment:
- Anaconda 3
- Python3.8
- NeMo r1.5.0 | {
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This PR updates speaker diarization documents that is compatible with the current main.
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In the following code snippet:
`from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder`
`def load_spectrogram_model():`
`pretrained_model = "tts_en_fastpitch"`
`model = SpectrogramGenerator.from_pretrained(pretrained_model, override_config_path=None)`
`return model`
`spec_gen = load_spectrogram_model().eval()`
I get the following error on the above line:
`AttributeError: type object 'SpectrogramGenerator' has no attribute 'from_pretrained'`
Moreover, `print(SpectrogramGenerator.list_available_models())` returns an empty list. However, none of this is an issue in Colab.
Is it something environment related or am I missing something?
Any help would be appreciated. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3415 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3415/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3415/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3415/events | https://github.com/NVIDIA/NeMo/issues/3415 | 1,098,060,873 | I_kwDOC_bI7s5BcxRJ | 3,415 | Export trained MarbleNet VAD model into onnx | {
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is there a way to export the trained MarbleNet VAD model into onnx | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3414 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3414/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3414/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3414/events | https://github.com/NVIDIA/NeMo/issues/3414 | 1,098,036,574 | I_kwDOC_bI7s5BcrVe | 3,414 | ASR transcribe_speech with Language Model | {
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} | null | null | null | Hi,
I have 2 questions about ASR Language Modeling:
1- Is there any implemented code in Nemo, for generating the json file of the predictions of an ASR model with a N-gram LM? Something like the [transcribe_speech_parallel.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/transcribe_speech_parallel.py) code. I know that, I can easily implement this code via [Offline_ASR.ipynb Tutorial](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/Offline_ASR.ipynb#scrollTo=nV1CAy0Dit-g). But I want to know if such a code is already available in the framework?
2- Would you please kindly describe what is the `Oracle WER/CER` mean when using the [eval_beamsearch_ngram.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py) code?
Best | {
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He seemed to be there
Thanks in advance | {
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] | null | null | 2022-01-07T08:27:26 | 2022-04-05T23:47:40 | 2022-04-05T23:47:40 | NONE | {
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} | null | null | null | hello, thanks for your great job.
I tried to download pre-trained model of Fastspeech2 Hifigan E2E.
but, browser download doesn't working and wget too.
I attach the wget log below
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_e2e_fastspeech2hifigan/versions/1.0.0/zip -O tts_en_e2e_fastspeech2hifigan_1.0.0.zip
--2022-01-07 17:17:54-- https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_e2e_fastspeech2hifigan/versions/1.0.0/zip
Resolving api.ngc.nvidia.com (api.ngc.nvidia.com)... 52.38.144.30, 35.165.139.54
Connecting to api.ngc.nvidia.com (api.ngc.nvidia.com)|52.38.144.30|:443... connected.
HTTP request sent, awaiting response... 404
2022-01-07 17:18:00 ERROR 404: (no description).
Thank you. | {
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} | null | null | null | I was trying the single liner diarization as mentioned in this comment
https://github.com/NVIDIA/NeMo/issues/2722#issuecomment-905306973
But I'm getting this error: AttributeError: module 'posixpath' has no attribute 'json' in this line:
https://github.com/NVIDIA/NeMo/blob/b4a28b4e29a4eb03ac2e0003dfe08fb7ddedea63/nemo/collections/asr/models/clustering_diarizer.py#L395
I was wondering if it should be join instead of json?
I'm using python 3.7.12 and nemo-toolkit 1.6.0rc0 | {
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} | null | null | null | Hi,
I fine-tuned a pre-trained French ASR model "stt_fr_conformer_ctc_large" with my own data. And I replaced the tokenizer with my own by `model.change_vocabulary(new_tokenizer_dir=my_own_tokenizer, new_tokenizer_type="bpe")`.
The training was smooth and I got 17% valid WER which is rather good for our dataset, but when I did `transcription=asr_model.transcribe(["example.wav"])` for inference, I got weird transcriptions as below :
It seems that something changed the alphabet, and here is another example:
I looked back into the training log and I found the same thing :
How can I get good transcriptions please ? And is my 17% WER correct ?
Thanks in advance.
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} | null | null | null | Hi,
The Speech Data Explorer (SDE) tool is really a great tool for error analysis. I have some questions about it:
1- What is `Vocabulary size` exactly mean in SDE? Is it unique words separated by space in the manifest? (because when I have tested SDE with QuartzNet & Citrinet (with bpe tokenizer) models its vocabulary size have been changed!!)
2- What does `Score` means in SDE?
3- In the last plot of SDE (_e.g._, the last plot in `statistics` section of http://18.223.252.228:8050/) what does `sometimes recognized` exactly means? It means these words recognition accuracy is greater than zero and less than 100%?
4- How one can save or export tables in SDE for example as a csv file (excel file)? Also if one can save them after applying a filter it would be great.
5- After running the SDE for a manifest according to the predicted results of an ASR model, is there any systematic approach for analyzing and detecting the common errors of the ASR model and address them? In other words, how one can improve the accuracy of an ASR model by using the SDE tool? Is there any paper/tutorial in this area?
Best | {
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} | 1. Organize and move parameters used in `vad_infer.py` to _yaml_ config file` conf/vad_inference_postprocessing.yaml`.
2. Add gen_overlap_seq, gen_seg_table and write_to_manifest choice to `vad_infer.py` so the script can cover the whole pipeline from
**frame -> overlapped smoothing filter -> write to rttm-like table -> write to manfiest json file (for possible next step)** instead of using multiple scripts as in previous.
3. Remove duplicate code in `vad_overlap_posterior.py` and `write_long_audio_manifest.py` in scripts/voice_activity_detection.
4. Reflect updates in `tutorials/asr/Online_Offline_Microphone_VAD_Demo.ipynb ` and `tutorials/asr/Voice_Activity_Detection.ipynb`
5. bugfix for diarization yaml file about input data example.
6. Unify and rename _window_length_in_sec_ and _shift_length_in_sec_ across files to reduce confusion. | {
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} | # Changelog
- Refactor ASR directory to modularize against - type of task (ASR/Speech Pretraining/Speech Classification) and type of ASR training (CTC/RNNT).
- Keep all inference scripts in base dir for easy access to infer/eval ASR models without tracking down which model to evaluate.
- Move VAD/Speech Classification to its own subdomain
- Move Speech Pretraining to its own subdomain (anticipating more advanced SSL capabilities)
- Separate CTC / RNNT training scripts to inform which type of model is being trained.
Signed-off-by: smajumdar <[email protected]> | {
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} | - reduce the number of non-deterministic options
- weights update for common cases | {
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@fayejf @titu1994 @borisgin
i am interested in knowing your thoughts and comparison between MarbleNet and silero-vad on cpu-only devices. | {
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} | PR to add pipeline parallelism to GPT using fwd/bwd functions from Apex.
FP32, FP16, and BF16 are all working now.
When using pipeline parallel it is recommended to use BF16 + Megatron amp O2.
```
model.megatron_amp_O2=True
trainer.precision='bf16'
```
### TODOs
- under review
### Known issues
- complete method will be supported in a subsequent PR
- prompt tuning temporarily disabled, use NeMo 1.6 if needed
- when using tensor parallel only, we're still using sync grad all-reduce which reduces perf. Will be fixed in NeMo 1.8.
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} | null | null | null | **Describe the bug**
When (inverse) normalizing text using the InverseNormalizer's `normalize` function, a recursion error is raised when the number words in the input surpasses a threshold.
**Steps/Code to reproduce bug**
Running the following script produces the error when `len(t)=994`.
```
from nemo_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer
from pynini.lib import pynutil
import pynini
if __name__ == '__main__':
itn = InverseNormalizer(lang='en')
for length in range(990, 999):
t = " ".join(['a' for i in range(0, length)])
print(f"Testing input of length {len(t.split())} words.")
itn.inverse_normalize(t, verbose=False)
```
A recursion error is raised once `length=994`:
```
>> python recursion_error.py
[NeMo I 2022-01-03 12:35:00 tokenize_and_classify:68] ClassifyFst.fst was restored from ./cache/_en_itn.far.
Testing input of length 990 words.
Testing input of length 991 words.
Testing input of length 992 words.
Testing input of length 993 words.
Testing input of length 994 words.
Traceback (most recent call last):
File "recursion_error.py", line 12, in <module>
itn.inverse_normalize(t, verbose=False)
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/inverse_text_normalization/inverse_normalize.py", line 91, in inverse_normalize
return self.normalize(text=text, verbose=verbose)
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 143, in normalize
for tagged_text in tags_reordered:
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 213, in _helper
yield from _helper(prefix + token_option, tokens, idx + 1)
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 213, in _helper
yield from _helper(prefix + token_option, tokens, idx + 1)
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 213, in _helper
yield from _helper(prefix + token_option, tokens, idx + 1)
[Previous line repeated 990 more times]
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 211, in _helper
token_options = self._permute(tokens[idx])
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 178, in _permute
rec = self._permute(v)
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 176, in _permute
subl = ["".join(x) for x in itertools.product(subl, [f"{k}: \"{v}\" "])]
File "/home/lhr/.conda/envs/nemo-text-process/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize.py", line 176, in <listcomp>
subl = ["".join(x) for x in itertools.product(subl, [f"{k}: \"{v}\" "])]
RecursionError: maximum recursion depth exceeded while calling a Python object
```
**Expected behavior**
Normalization supported for sequences of arbitrary length.
**Environment overview (please complete the following information)**
- Environment location: venv on local machine
- Method of NeMo install: pip install nemo-toolkit (v. 1.5.1)
**Environment details**
- OS version - Ubuntu 18.04.5 LTS
- PyTorch version - 1.10.1
- Python version - 3.8.11
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In addition, the default value of encoder future masking is set to be false (i.e., joint prediction instead of auto-regressive). | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3382 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3382/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3382/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3382/events | https://github.com/NVIDIA/NeMo/issues/3382 | 1,091,784,800 | I_kwDOC_bI7s5BE1Bg | 3,382 | Add Word Level Laguage Model to ASR Models | {
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} | null | null | null | Hi,
I have a question. How one can add a Word Level Pre-Trained Language Model (_e.g.,_ BERT or DistilBERT from HuggingFace) to an ASR model (_e.g._, character based like QuartzNet or Token based like Citrinet) for inference time?
Best | {
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} | Signed-off-by: nithinraok <[email protected]>
This PR
* removes time-length and shift-length concept for training speaker label models.
> This assumes all the samples present in training manifest must adhere to max of 8 secs. (3 sec is ideal)
* removes sliced_seq_collate_fn : Since we introduced segmentation for creating manifest files hence this collate func is no longer required now, and existing seq_collate_fn can be used for getting speaker embeddings for diarization as well.
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} | null | null | null | HI Team,
I need to draw learning curve for the trained Biomegatron model. I have trained the model as per the tutorial https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb#scrollTo=DQhsamclRtxJ
Now when I am trying to get the learning-curve which is the plot for each training loss and validation loss as per the number of epochs as sklearn library providing attribute as from sklearn.model_selection import learning_curve
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} | null | null | null | **Describe the bug**
The validation loss is very high compared to training loss even though the data used for training and validation is the same. Training loss for my dataset is at 0.328 after 70 epochs whereas validation loss is ~16.
**Steps/Code to reproduce bug**
This is the config used -
```yaml
name: Tacotron2-Exp
sample_rate: 22050
# <PAD>, <BOS>, <EOS> will be added by the tacotron2.py script
labels: ['ट', 'ॉ', 'प', ' ', 'न', '्', 'य', 'ू', 'ज', ':', 'द', 'स', 'ब', 'ड़', 'ी', 'ख', 'र', 'े', 'ं', ',', 'आ', 'ि', 'ह', 'ु', 'ई', 'ै', 'इ', 'ध', 'ा', 'व', 'ो', 'म', 'ग', 'क', 'भ', 'ल', 'फ', '।', 'घ', 'त', 'ओ', 'च', 'ड', 'ौ', 'ष', 'ठ', 'ए', '.', 'उ', 'श', 'थ', '़', 'अ', 'ढ', 'ऐ', 'औ', 'छ', '-', "'", '!', 'ण', '(', ')', 'झ', 'ञ', 'ऊ', 'ृ', '[', ']', '?', 'ऑ', 'ः', 'ढ़', ';', 'ऍ', 'ज़', 'E', 'V', 'M', 'X', 'C', 'L', 'U', 'S', 'I', 'ङ', 'क़', 'O', 'A', 'B', 'ँ', 'ॅ', '*', 'J', 'N', 'D', 'H', 'G', '2', '/', '5', 'T', 'P', 'o', 'K', '0', 'ऋ', '8', '7', '–', '3', '6', 'n', 'e', 't', 'h', 'r', 'f', 'i', 'v', 's', 'ऩ', '9', 'फ़', 'R', '4', 'य़', 'W', 'b', '¨', 'a', 'l', 'c', 'k', 'y', '@', 'F', 'Y', '1']
n_fft: 1024
n_mels: 80
fmax: 8000
n_stride: 256
pad_value: -11.52
train_dataset: ???
validation_datasets: ???
model:
labels: ${labels}
train_ds:
dataset:
_target_: "nemo.collections.asr.data.audio_to_text.AudioToCharDataset"
manifest_filepath: ${train_dataset}
max_duration: null
min_duration: 0.1
trim: false
int_values: false
normalize: true
sample_rate: ${sample_rate}
# bos_id: 66
# eos_id: 67
# pad_id: 68 These parameters are added automatically in Tacotron2
dataloader_params:
drop_last: false
shuffle: true
batch_size: 32
num_workers: 4
validation_ds:
dataset:
_target_: "nemo.collections.asr.data.audio_to_text.AudioToCharDataset"
manifest_filepath: ${validation_datasets}
max_duration: null
min_duration: 0.1
int_values: false
normalize: true
sample_rate: ${sample_rate}
trim: false
# bos_id: 66
# eos_id: 67
# pad_id: 68 These parameters are added automatically in Tacotron2
dataloader_params:
drop_last: false
shuffle: false
batch_size: 32
num_workers: 4
preprocessor:
_target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures
dither: 0.0
nfilt: ${n_mels}
frame_splicing: 1
highfreq: ${fmax}
log: true
log_zero_guard_type: clamp
log_zero_guard_value: 1e-05
lowfreq: 0
mag_power: 1.0
n_fft: ${n_fft}
n_window_size: 1024
n_window_stride: ${n_stride}
normalize: null
pad_to: 16
pad_value: ${pad_value}
preemph: null
sample_rate: ${sample_rate}
window: hann
encoder:
_target_: nemo.collections.tts.modules.tacotron2.Encoder
encoder_kernel_size: 5
encoder_n_convolutions: 3
encoder_embedding_dim: 512
decoder:
_target_: nemo.collections.tts.modules.tacotron2.Decoder
decoder_rnn_dim: 1024
encoder_embedding_dim: ${model.encoder.encoder_embedding_dim}
gate_threshold: 0.5
max_decoder_steps: 1000
n_frames_per_step: 1 # currently only 1 is supported
n_mel_channels: ${n_mels}
p_attention_dropout: 0.1
p_decoder_dropout: 0.1
prenet_dim: 256
prenet_p_dropout: 0.5
# Attention parameters
attention_dim: 128
attention_rnn_dim: 1024
# AttentionLocation Layer parameters
attention_location_kernel_size: 31
attention_location_n_filters: 32
early_stopping: true
postnet:
_target_: nemo.collections.tts.modules.tacotron2.Postnet
n_mel_channels: ${n_mels}
p_dropout: 0.5
postnet_embedding_dim: 512
postnet_kernel_size: 5
postnet_n_convolutions: 5
optim:
name: adam
lr: 1e-3
weight_decay: 1e-6
# scheduler setup
sched:
name: CosineAnnealing
min_lr: 1e-5
trainer:
gpus: 1 # number of gpus
max_epochs: ???
num_nodes: 1
accelerator: ddp
accumulate_grad_batches: 1
checkpoint_callback: False # Provided by exp_manager
logger: False # Provided by exp_manager
gradient_clip_val: 1.0
flush_logs_every_n_steps: 1000
log_every_n_steps: 200
check_val_every_n_epoch: 5
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: True
create_checkpoint_callback: True
```
And this is the command used to train -
```sh
python3 tacotron2.py train_dataset=data.json validation_datasets=data.json trainer.gpus=-1 trainer.max_epochs=200
```
**Expected behavior**
I am not sure why there is such a huge difference in loss values.
**Environment overview**
- Environment location: Bare-metal
- Method of NeMo install: From source using `./reinstall.sh`
**Environment details**
- OS version: Ubuntu 20.04
- PyTorch version: 1.10.1
- Python version: 3.8.10
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} | 1. Fix creating tarred dataset for cases when tokenizer name contains `/` character, e.g. `'facebook/mbart-large-50'`.
2. Fix subclass check [here](https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/collections/nlp/data/token_classification/punctuation_capitalization_tarred_dataset.py#L383). It did not work if `error_class_or_function` is not a class
3. Fix return typing in a [method](https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L436) `TokenizeCreateMasksClipWorker.__call__()`.
4. Make comment [here](https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L67) more comprehensible. Before fix it was identical to a name of a model config section `common_dataset_parameters`, whereas it was not related to the section in any way.
5. In a spirit of fix 1 add checking of characters in `tar_file_prefix` parameter in functions [`create_tarred_dataset()`](https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/collections/nlp/data/token_classification/punctuation_capitalization_tarred_dataset.py#L633) and [`get_args()`](https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py#L64). | {
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} | null | null | null | Hello,
I am working with the common voice hindi dataset on collab.
I created a seprate python file as mentioned and passed the train tsv file , but it displayed below error
`SOMETHING WENT WRONG - IGNORING ENTRY`
Kindly provide the fix | {
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} | On SLURM resuming training in a multi-node multi-gpu settings fails, as when `LOCAL_RANK` is undefined `is_globa_rank_zero()` returns true on all processes that run on node 0. In this case `exp_manager.py` https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/utils/exp_manager.py#L446, creates multiple `run_*` folders, and eventually leads to failure (missing files because other processes have moved them already).
Checking also for `SLURM_PROCID` solves this issue, as the environment variable contains the global rank id. | {
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1- Would you please help me how I can save the config (`cfg`) of an ASR model as a yaml file? (_i.e.,_ load a trained asr `.nemo` model file and save its cfg as a yaml file)
2- Another question is that, when I follow your [`ASR_CTC_Language_Finetuning`](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb) tutorial, then the `hparams.yaml` file which is saved in the experiments folder, seems related to the previous checkpoint of the model (I mean it is not updated according to my cfg modifications). So, I want to save its modified `hparams.yaml` file according to my new changes. How can I do that?
Best | {
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] | null | null | 2021-12-23T18:50:04 | 2022-01-28T20:03:43 | 2022-01-28T20:03:43 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
When fine tuning from a NeMo model (ex. stt_en_cn1024), the exp_manager's cfg is not updated properly.
I can see that in my run the model uses one config, but WandB reports another.
This issue did not occur in v1.4.0 and happened after I upgraded to v1.5.0.
Maybe it has to do with order of operations? See below.
**Steps/Code to reproduce bug**
```py
import pytorch_lightning as pl
from nemo.collections.asr.models import EncDecCTCModelBPE
from nemo.core.config import hydra_runner
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="conf/citrinet/", config_name="config")
def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
asr_model = EncDecCTCModelBPE.from_pretrained(model_name=cfg.init_from_pretrained_model)
asr_model.encoder.unfreeze()
asr_model.change_vocabulary(
new_tokenizer_dir=cfg.model.tokenizer.dir,
new_tokenizer_type=cfg.model.tokenizer.type
)
asr_model.setup_optimization(cfg.model.optim)
asr_model.setup_training_data(cfg.model.train_ds)
asr_model.setup_multiple_validation_data(cfg.model.validation_ds)
asr_model.spec_augmentation = asr_model.from_config_dict(cfg.model.spec_augment)
asr_model.set_trainer(trainer)
trainer.fit(asr_model)
```
**Expected behavior**
WandB cfg should display the proper config ([Pastebin of the WandB config](https://pastebin.com/Bc29P9FL))
**Environment overview (please complete the following information)**
- Environment location: Docker (nvcr.io/nvidia/pytorch:21.10-py3) on AWS EC2 using `docker run -it bash <image>`
- Method of NeMo install: `pip install nemo_toolkit[asr]==1.5.1`
**Additional context**
GPU model: V100
Nvidia driver: 460
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} | Tested with 345M Bert and 5B GPT checkpoints. Works fine. | {
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} | null | null | null | Hi,
I trained a corformer CTC BPE model with 8k wav data, but when I tried to load the model for decoding with
`asr_model = nemo_asr.models.ASRModel.restore_from(nemo_model_file)` I got this shape mismatch error
I don't want to train another model with 16k data, is there a way that I load this pretrained model please ? | {
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} | null | null | null | I am trying the tutorial ASR_with_NeMo.ipynb. It's giving me following error. I checked the code for EncDecCTCModel, it doesn't contain the function "from_pretrained". How to load the pre-trained models:
I even tried to use restore_from after downloading the respective *.nemo file. But getting same issue there as well (_'EncDecCTCModel' has no attribute 'restore_from'_)
**# This line will download pre-trained QuartzNet15x5 model from NVIDIA's NGC cloud and instantiate it for you
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")**
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_349758/1493780281.py in <module>
1 # This line will download pre-trained QuartzNet15x5 model from NVIDIA's NGC cloud and instantiate it for you
----> 2 quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
**AttributeError: type object 'EncDecCTCModel' has no attribute 'from_pretrained'** | {
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} | Add support for Byte-level multilingual NMT + Byte-level preproc fixes.
Other change to note:
* `train_tokenizers::preproc_mt_data` Instead of throwing an error if the tokenizer is not trainable, train_tokenizer now returns None. This is done to keep the pipeline simple and same for tokenizers like byte-level which do not require any explicit training. | {
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Please tell me how can I export the ConformerCTC model to ONNX format to run from C ++? If I understand correctly, the spectrograms required for the model can only be obtained using the classes built into NeMo. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3366 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3366/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3366/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3366/events | https://github.com/NVIDIA/NeMo/pull/3366 | 1,085,942,965 | PR_kwDOC_bI7s4wI4H0 | 3,366 | Update Mixer-TTS, FastPitch and TTSDataset | {
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} | - [x] Update nemo.collections.tts.torch.data.TTSDataset and nemo.collections.tts.torch.data.MixerTTSDataset
- [x] Rename "duration_prior" to "align_prior_matrix"
- [x] Add `normalized_text` field
- [x] Fix minor bugs
- [x] Update FastPitch
- [x] Comment unnecessary code and suggest to remove it (+ need to discuss with @blisc)
- [x] Refactor config
- [x] Add correct setup for text normalization and text tokenization to support new config
- [x] Fix minor bugs
- [x] Update Mixer-TTS
- [x] Refactor config
- [x] Add correct setup for text normalization and text tokenization to support new config
- [x] Fix minor bugs
- [x] Add test to Jenkinsfile for Mixer-TTS
- [x] Update documentation for Mixer-TTS and Mixer-TTS-X
- [x] Add checkpoints from NGC Catalog to code
- [x] Update HiFi-GAN
- [x] Add checkpoints from NGC Catalog to code (+ need to discuss with @blisc)
- [x] Add & update notebooks
- [x] Add `MixerTTS_Training.ipynb` notebook
- [x] Update `Inference_ModelSelect.ipynb` notebook
- [x] Fix bugs in `FastPitch_Finetuning.ipynb` notebook
Signed-off-by: Oktai Tatanov [email protected] | {
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