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https://api.github.com/repos/NVIDIA/NeMo/issues/2789 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2789/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2789/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2789/events | https://github.com/NVIDIA/NeMo/pull/2789 | 990,407,789 | MDExOlB1bGxSZXF1ZXN0NzI5MDAxMjg0 | 2,789 | Tn fix bugs | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2788 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2788/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2788/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2788/events | https://github.com/NVIDIA/NeMo/pull/2788 | 990,378,644 | MDExOlB1bGxSZXF1ZXN0NzI4OTc2MDUw | 2,788 | Deprecate experimental RNNT flag and make it fully supported | {
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} | # Changelog
- Deprecate `experimental_fuse_loss_wer` and replace with `fuse_loss_wer` flag instead.
- Add missing zh models back
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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The SacreBLEU score in [mt_enc_dec_model](https://github.com/NVIDIA/NeMo/blob/r1.3.0/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py) is computed using detokenized target text:
```
286: ground_truths = [self.decoder_tokenizer.ids_to_text(tgt) for tgt in np_tgt]
28... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2783 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2783/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2783/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2783/events | https://github.com/NVIDIA/NeMo/issues/2783 | 990,113,577 | MDU6SXNzdWU5OTAxMTM1Nzc= | 2,783 | normalize_batch ValueError | {
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} | null | null | null | Hello,
I am fine-tuning Conformer-small using a custom tarred dataset.
During training the following error occurs:
```
Error executing job with overrides:
['+init_from_nemo_model=/home/ael/Scripts/nemo/training/pretrained_models/stt_en_conformer_ctc_small/stt_en_conformer_ctc_small.nemo']
Traceback (most rec... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2782 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2782/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2782/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2782/events | https://github.com/NVIDIA/NeMo/issues/2782 | 989,515,647 | MDU6SXNzdWU5ODk1MTU2NDc= | 2,782 | Tune frame length while training Tacotron? | {
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I'm trying to train Tarcotron using Korean datasets.
While training I just noticed that depending on `max_duration`, frame padding length will be changed, below images show `val_target` Mel spectrogram where `max_duration` is over 3.5sec and below 3.5sec.
![... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2781 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2781/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2781/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2781/events | https://github.com/NVIDIA/NeMo/issues/2781 | 989,203,857 | MDU6SXNzdWU5ODkyMDM4NTc= | 2,781 | ValueError: Error instantiating 'nemo.collections.asr.modules.conv_asr.SpeakerDecoder' : invalid literal for int() with base 10: ',' | {
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Following "NGC Pretrained Checkpoints"
from
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_recognition/results.html
**Steps/Code to reproduce bug**
Running:
nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="speakerrecognition_spe... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2780 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2780/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2780/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2780/events | https://github.com/NVIDIA/NeMo/issues/2780 | 989,117,375 | MDU6SXNzdWU5ODkxMTczNzU= | 2,780 | torch.stft() signature has been updated for PyTorch 1.7+ Please update PyTorch to remain compatible with later versions of NeMo. | {
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[NeMo W 2021-09-06 11:58:47 patch_utils:50] torch.stft() signature has been updated for PyTorch 1.7+
Please update PyTorch to remain compatible with later versions of NeMo.
and followed by
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in _pad(input, pad, mode, value)
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We have a BERT model pretrained using Megatron-LM (v2.2) with the tensor model parallel set to 2.
We use NeMo to fine-tune the model for text classification.
We do not have an issue with fine-tuning and test, but we have an issue with prediction.
We try to apply the classifytext function from t... | {
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* Add asr with diarization script | {
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} | This PR Adds on-the-fly validation of tarred dataset files.
Changes:
1. If file is missing, looking for the file in same path as metadata file.
2. If file cannot be found, raises an exception.
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} | This PR introduces a max-pooling and average-pooling encoders.
Each block halves the steps of the encoder using max pooling / average pooling over steps dimension.
In addition, a control parameter `MTBottleneckModel.deterministic_translate` can be used for deterministic translation for VAE and MIM ( i.w., using the... | {
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I am using the provided [punctuation_capitalization_train.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/punctuation_capitalization_train.py) and [punctuation_capitalization_evaluate.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/pu... | {
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This issue is related to issue #2686.
I expect that if I pre-train an IntentSlotClassificationModel and export the checkpoint to a .nemo file that I can restore that checkpoint elsewhere. Further, I expect that, once exported, I can delete the data directory used for training and later be able ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2767 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2767/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2767/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2767/events | https://github.com/NVIDIA/NeMo/pull/2767 | 986,945,122 | MDExOlB1bGxSZXF1ZXN0NzI2MDk0NzUy | 2,767 | Fix classification_models.py transcribe bug for MFCC (#2762) | {
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(cherry picked from commit 4430fb1a2d41ca73edde9671d1ab0d9bda54123e) | {
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I'm building a communication API for transcription with NeMo and I ran into a critical infinite memory allocation problem.
When using a lib that monitors memory allocation, I noticed that the "transcribe" method used on top of a restored model is allocating memory every time when transcribing a new file.
My API... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2763 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2763/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2763/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2763/events | https://github.com/NVIDIA/NeMo/pull/2763 | 985,846,847 | MDExOlB1bGxSZXF1ZXN0NzI1MTA3MTQz | 2,763 | segmentation tutorial minor update | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2762 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2762/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2762/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2762/events | https://github.com/NVIDIA/NeMo/pull/2762 | 985,765,406 | MDExOlB1bGxSZXF1ZXN0NzI1MDMxMDc0 | 2,762 | Fix classification_models.py transcribe bug for MFCC | {
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} | # Bugfix
- Fix an issue when MFCC model is used and transcribe is attempted due to attempted patch to `dither` and `pad_to`
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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I get the following error:
```
[NeMo W 2021-09-01 16:08:13 experimental:27] Module <class 'nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset'> is experimental, not ready for production and is no... | {
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} | null | null | null | **Describe the bug**
According to documentation on page https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.3.0/nlp/machine_translation.html#tarred-datasets-for-large-corpora the second way to use tarred dataset is
**Quote**
2. Using a separate script without Hydra.
Tarred datasets for parallel co... | {
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Parameter `--coverage` in script `examples/nlp/machine_translation/create_tarred_parallel_dataset.py` is unused.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2756 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2756/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2756/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2756/events | https://github.com/NVIDIA/NeMo/issues/2756 | 984,349,986 | MDU6SXNzdWU5ODQzNDk5ODY= | 2,756 | Training performance in GPU Tesla vs GPU RTX | {
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I've been working on a dataset for a long time and slowly I see that my simple GPU isn't playing such a good role.
When searching I see several people using RTX and Tesla. Does anyone know what is the advantage of a Tesla GPU over an RTX GPU? Going into the question of price, I see that an RTX it's much more af... | {
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https://github.com/NVIDIA/NeMo/blob/1d262c725df150ba837146e7899d8167adf524f6/requirements/requirements_lightning.txt#L5-L7 | {
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} | null | null | null | The conformer network sometimes predicts "⁇" symbols. After greedy decoding, all the outputs have zero index, which corresponds to <"unk"> token. And finally, after applying sentencepiece tokenization I am getting ⁇ symbols.
Btw, all my transcripts successfully tokenized without producing any <"unk"> symbols.
... | {
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AN4 Dataset not resolving can't download data for nemo asr tutorial
**Steps/Code to reproduce bug**
```python
import glob
import os
import subprocess
import tarfile
import wget
# Download the dataset. This will take a few moments...
print("******")
if not os.path.exists(data_dir ... | {
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NeMo ASR developer/architectures, what would you recommend in terms of model setup so that I can process training samples of this size? | {
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When I attempt to convert a nemo file (fine tuned from the conformer model), I get the following error
```
nemo2riva --out moneypenny-confirmer-v1.riva /data/models/moneypenny-confirmer-v1.nemo
[NeMo W 2021-08-28 08:49:32 optimizers:47] Apex was not found. Using the lamb optimizer will erro... | {
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I want to create ITN grammar for the Ukrainian language. As we already have one for Russian I think it would be easy to create a new one for Ukrainian (as it has almost the same rules a Russian).
**Describe the solution you'd like**
I'm looking... | {
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} | This pull request provides intial support for incorporating retrieval models by
- creating a new `RetrievalTranslationDataset`
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- adding a new... | {
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} | null | null | null | **Issue in saving the checkpoint after epoch**
After the epoch runs successfully it tries to save the model checkpoint file in storage and throws exception
**MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['lr', 'train_loss']**
**Steps/Code to reproduce bug**
... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2737 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2737/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2737/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2737/events | https://github.com/NVIDIA/NeMo/pull/2737 | 980,769,315 | MDExOlB1bGxSZXF1ZXN0NzIwOTQ2MjIy | 2,737 | 1. Updated perceiver architecture. | {
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} | This PR updates the architecture of Perceiver-encoder.
The upgraded architecture:
1. Matches performance of non-bottleneck NMT models.
2. More stable to train.
3. All available model names are defined in a single place. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2736 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2736/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2736/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2736/events | https://github.com/NVIDIA/NeMo/pull/2736 | 980,665,661 | MDExOlB1bGxSZXF1ZXN0NzIwODYxNjQ2 | 2,736 | Nmt perceiver arch update | {
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The upgraded architecture:
1. Matches performance of non-bottleneck NMT models.
2. More stable to train. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2735 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2735/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2735/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2735/events | https://github.com/NVIDIA/NeMo/pull/2735 | 980,450,194 | MDExOlB1bGxSZXF1ZXN0NzIwNjg1NTY3 | 2,735 | [NLP] Megatron GPT pretraining | {
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} | ## This PR is highly under construction with many things subject to change.
This PR depends on libraries that have not been released yet.
## Overview
In this PR we are adding Megatron GPT pretraining to NeMo. This includes adding the model architecture to NeMo (instead of importing from megatron-lm). Currently, we... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2734 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2734/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2734/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2734/events | https://github.com/NVIDIA/NeMo/issues/2734 | 980,244,958 | MDU6SXNzdWU5ODAyNDQ5NTg= | 2,734 | View NVidia/Riva advertised pipeline in nemo | {
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} | null | null | null | The configuration of nvidia/Riva advertised benchmarks, **pipeline tab** is unclear,
https://developer.nvidia.com/deep-learning-performance-training-inference
Goal: replicate the models in Nemo and see the config.
I.e, have a clear config file per Nemo model.
Examples:
- which model is used for TTS?
- what... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2733 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2733/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2733/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2733/events | https://github.com/NVIDIA/NeMo/issues/2733 | 980,120,134 | MDU6SXNzdWU5ODAxMjAxMzQ= | 2,733 | ConformerEncoder object has no attribute output_dim | {
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} | null | null | null | Hi 👋 As I was trying to train a Conformer model from scratch, I got these exceptions:
> Traceback (most recent call last):
> File "/home/ubuntu/.local/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
> return target(*args, **kwargs)
> File "/home/ubuntu/... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2729 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2729/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2729/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2729/events | https://github.com/NVIDIA/NeMo/pull/2729 | 979,679,728 | MDExOlB1bGxSZXF1ZXN0NzIwMDU2MDIw | 2,729 | Enhanced speaker counting for short audio recordings | {
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} | This pull request is for adding the function getEnhancedSpeakerCount() that performs an enhanced speaker counting for speaker diarization module.
It improves speaker counting accuracy from 50% to 80% especially for short (less than 1 min) recordings.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2728 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2728/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2728/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2728/events | https://github.com/NVIDIA/NeMo/pull/2728 | 979,667,785 | MDExOlB1bGxSZXF1ZXN0NzIwMDQ2MDIy | 2,728 | add punct to tn inference and test | {
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1. Output reference is not correct on chinese, could it affect the model?

I follow the link below:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/... | {
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I trained VAD model and Speaker Net model to get timestamp of who is speaker in audio.
After speaker diarize, i want to pass the signal of speech to ASR model to get both who is speaker and text of speech.
Some thing like that:
.
During the training epoch, RAM is constantly increasing until no free space is left. Is there anything i can do to handle this?
Params:
RAM = 256G
Batch_size = 32
Num_workers = 8
Also when I experimented with 21000 hours, at t... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2717 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2717/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2717/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2717/events | https://github.com/NVIDIA/NeMo/pull/2717 | 977,898,093 | MDExOlB1bGxSZXF1ZXN0NzE4NTc0MTU1 | 2,717 | Adding conformer-transducer models. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2714 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2714/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2714/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2714/events | https://github.com/NVIDIA/NeMo/pull/2714 | 977,654,756 | MDExOlB1bGxSZXF1ZXN0NzE4MzY3OTc0 | 2,714 | Fix the feat_out param. | {
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} | null | null | null | Hello guys, I have two question.
First, I had a "small" dataset, around 20 hours for some tests. I successfully trained this dataset but I had some doubts about this train, if I train correctly or not. So, do you give an advice about the train in general? Like how much hours I need to train, an order to do the train,... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2712 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2712/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2712/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2712/events | https://github.com/NVIDIA/NeMo/issues/2712 | 977,220,919 | MDU6SXNzdWU5NzcyMjA5MTk= | 2,712 | RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! | {
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```
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
456 )
4... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2711 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2711/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2711/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2711/events | https://github.com/NVIDIA/NeMo/pull/2711 | 977,120,287 | MDExOlB1bGxSZXF1ZXN0NzE3OTA5MDU1 | 2,711 | Add Small Test | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2710 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2710/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2710/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2710/events | https://github.com/NVIDIA/NeMo/pull/2710 | 977,113,975 | MDExOlB1bGxSZXF1ZXN0NzE3OTAzNzcz | 2,710 | Update Notebook img src | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2709 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2709/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2709/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2709/events | https://github.com/NVIDIA/NeMo/issues/2709 | 977,067,673 | MDU6SXNzdWU5NzcwNjc2NzM= | 2,709 | Minimim requirments to run a training on hi-mia dataset? | {
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Indeed, did you have any time expectation to finish the training on hi-mia? one day, two days, etc | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2707 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2707/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2707/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2707/events | https://github.com/NVIDIA/NeMo/issues/2707 | 976,375,647 | MDU6SXNzdWU5NzYzNzU2NDc= | 2,707 | module 'nemo.collections' has no attribute 'nlp' | {
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} | null | null | null | **Describe the bug**
When I import nemo.collections.nlp module in my code, I got "AttributeError: module 'nemo.collections' has no attribute 'nlp'".
/Users/chendan/.virtualenvs/nemo/bin/python3.6 /Users/chendan/PycharmProjects/nemo_learn/tts_test.py
[NeMo W 2021-08-22 21:52:34 optimizers:47] Apex was not found. ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2706 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2706/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2706/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2706/events | https://github.com/NVIDIA/NeMo/issues/2706 | 976,238,961 | MDU6SXNzdWU5NzYyMzg5NjE= | 2,706 | Packages not installed after setup.py install, error importing mpu from megatron | {
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} | null | null | null | **Describe the bug**
After installing using setup.py inside NVIDIA Pytorch container the following were not installed and had to be manually installed:
pip install braceexpand
pip install webdataset
pip install transformers
pip install pytorch_lightning
pip install rapidfuzz
pip install megatron
**Steps/... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2705 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2705/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2705/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2705/events | https://github.com/NVIDIA/NeMo/pull/2705 | 975,987,444 | MDExOlB1bGxSZXF1ZXN0NzE3MDUzMjg1 | 2,705 | Add mAES beam search for Transducer models | {
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} | # Changelog
- Add mAES Transducer algorithm for further improved WER at the cost of speed of execution.
- Update configs with new decoding scheme
- Begin adding hooks for external LM rescoring
# References
- [Accelerating RNN Transducer Inference via Adaptive Expansion Search](https://ieeexplore.ieee.org/docume... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2704 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2704/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2704/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2704/events | https://github.com/NVIDIA/NeMo/pull/2704 | 975,890,078 | MDExOlB1bGxSZXF1ZXN0NzE2OTcyNjE5 | 2,704 | TN Duplex model update | {
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} | The PR adds class evaluation during training and eval on multiple datasets. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2703 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2703/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2703/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2703/events | https://github.com/NVIDIA/NeMo/pull/2703 | 975,757,512 | MDExOlB1bGxSZXF1ZXN0NzE2ODYyMjA1 | 2,703 | Remove asr with transducers notebook time macro | {
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} | # Changelog
- Remove time macro from notebook | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2702 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2702/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2702/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2702/events | https://github.com/NVIDIA/NeMo/pull/2702 | 975,432,733 | MDExOlB1bGxSZXF1ZXN0NzE2NTgzMDE0 | 2,702 | vad_infer.py. Change the type of an argument "num_workers" from float to int. | {
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} | num_workers' type must be int, not float.
It induces TypeError even when using additional input as `--num_workers=20`
```
>>> python vad_infer.py --dataset=manifest.json --num_workers=20
Traceback (most recent call last):
File "vad_infer.py", line 171, in <module>
main()
File "vad_infer.py", line 10... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2701 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2701/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2701/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2701/events | https://github.com/NVIDIA/NeMo/pull/2701 | 975,148,025 | MDExOlB1bGxSZXF1ZXN0NzE2MzQ2Mjkx | 2,701 | Rnnt greedy_batch decoding fix | {
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} | # Bugfix
- Fixes a bug with an extreme edge case thereby bringing 1:1 parity with sample wise greedy decoding.
# Case
- Timestep T = 0, Label ID U = 0
- Batch size > 1 and greedy_batch decoding is used
- Some samples of the first batch emit a label but other samples do not. Therefore for some samples T=0, U=1 an... | {
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Add new notebook to showcase how duration and pitch control work in fastpitch | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2699 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2699/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2699/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2699/events | https://github.com/NVIDIA/NeMo/pull/2699 | 974,957,583 | MDExOlB1bGxSZXF1ZXN0NzE2MTg0MzQz | 2,699 | Allow non divisible split_size | {
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} | # Bugfix
- Text fixes for ASR and Core notebooks
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | Fixes a bug in FastPitchDataset
Fixes a bug when training FastPitch without aligner
Updates TTS CI to be catch more bugs | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2696 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2696/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2696/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2696/events | https://github.com/NVIDIA/NeMo/issues/2696 | 974,327,716 | MDU6SXNzdWU5NzQzMjc3MTY= | 2,696 | ASR model to TensorRT | {
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> [TensorRT] WARNING: Int8 calibration in TensorRT won't be used on networks containing boolean operators or loops. Please use quantization-aware traini... | {
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} | Signed-off-by: Ghasem Pasandi <gpasandi@nvidia.com> | {
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} | null | null | null | I see in the catalog for Conformer that there were experiments performed using an N-Gram LM for Librispeech evaluation (https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large). Can these pretrained LM's be made available? | {
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} | Fixes #2686
When a trained IntentSlotClassificationModel is exported via `save_to` the resulting archive includes BERT configuration artifacts. During `IntentSlotClassificationModel.restore_from` using the saved archived the BERT model should be initialized using these configuration artifacts. However, because the ... | {
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} | 1. Added support for SentencePiece and HF tokenizers when training LMs to fix the bug (https://github.com/NVIDIA/NeMo/issues/2544#issuecomment-899922327)
2. Removed obsolete text post-processing in AutoTokenizer | {
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Installing `requirements_nlp.txt` is not enough to import `nemo.collections.nlp`.
**Steps/Code to reproduce bug**
1. Create new `conda` `Python 3.8` environment
2. Install PyTorch 1.8.2
3. Run `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]`
4. R... | {
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} | null | null | null | **Overview**
I am unable to restore an IntentAndSlotClassificationModel from a `.nemo` file using the static `IntentAndSlotClassificationModel.restore_from(model_path)`.
**Steps to reproduce**
These steps rely heavily upon the existing [Joint_Intent_and_Slot_Classification](https://github.com/NVIDIA/NeMo/blob/main... | {
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} | # Bugfix
- Fix a gradient issue with Voice App demo
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | The config is the final part of the addition of [CarneliNet](https://arxiv.org/abs/2107.10708) model to NeMo repository.
The base model is chosen to be 386, providing a good balance between size and quality. The description on what has to be changed to obtain models of different sizes should be enough to reproduce ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2680 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2680/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2680/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2680/events | https://github.com/NVIDIA/NeMo/issues/2680 | 972,613,225 | MDU6SXNzdWU5NzI2MTMyMjU= | 2,680 | Cut long dataset | {
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Assuming I have a large volume of data, around 10,000 hours, doing this on a relatively weak gpu would take a long time, more than months...
If I want, can I split my base into several parts and do a workout for each of them, always restoring the model from the last workout I did? Or is it better if I do the full... | {
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} | null | null | null | Installation:
!sudo apt-get update
!sudo apt-get install -y libsndfile1 ffmpeg
!pip install Cython
!pip install nemo_toolkit['all']
Successfully installed
However, using from nemo.collections import nlp as nemo_nlp will cause problems
ttributeError Traceback (most recent cal... | {
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