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Cannot run the notebook
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
Try running this notebook: https://colab.research.google.com/drive/1-QmFrSHsnirtnu6gB0Tx-JQAXWDtm1yE?usp=sharing
A helpful guide on on how to cr... | {
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This is a PR for fixing issue #3094 where the index will be out-of-bound when truncating long text ext to max_seq_length in text_classification_dataset.py
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This update will crash at PyTorch21.10 so should be made sure this is packaged with PyTorch21.11.
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Punctuation datasets cannot be constructed with more than a few million examples (depending on the max length). Even these small datasets can consume a huge amount of memory while preprocessing (100's of GB) and while training (10's of GB per process).
This issue was mentioned in passing in ... | {
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First I read model input from files,
Second, each model input from the standard input.
While I found first case inference time is about 70 ms except the first call, the second case starts to be highly inconsistent, ranging from 72 ms to 483 ms. ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3122 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3122/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3122/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3122/events | https://github.com/NVIDIA/NeMo/issues/3122 | 1,042,035,209 | I_kwDOC_bI7s4-HDIJ | 3,122 | ImportError: cannot import name 'QConfig' from 'torch.ao.quantization' | {
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} | null | null | null | **Bug description**
Cannot import `asr` from `nemo.collections`
**Steps/Code to reproduce bug**
```bash
# Fresh install
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo/
conda create -n nemo python=3.8
conda activate nemo
./reinstall.sh
# Then
python
# in the python terminal:
from nemo.collect... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3119 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3119/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3119/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3119/events | https://github.com/NVIDIA/NeMo/pull/3119 | 1,041,704,370 | PR_kwDOC_bI7s4t84-p | 3,119 | Multiple ASR Fixes to SPE tokenization | {
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} | # Changelog
- Fix issue with transcribe building more workers than samples
- Fix SPE tokenizer vocab being built incorrectly for tokenizers with BOS EOS and PAD
- Update tokenizer script to designate tokenizer dir with BOS EOS and PAD token.
# Breaking change
- SPE tokenizer based models, when requested for thei... | {
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} | Adds `.setup_optimizer_param_groups` method to `ModelPT`. The `.setup_optimization` method will give optimizers `self._optimizer_param_groups` instead of `self.parameters()`.
The `.setup_optimizer_param_groups` method will by default set `self._optimizer_param_groups=self.parameters()`.
This enables NeMo models t... | {
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} | null | null | null | **Describe the bug**
`WER` and `WERBPE` metrics are computed wrongly if method `update` is called more than once before method `compute`.
**Steps/Code to reproduce bug**
```python
import string
from functools import partial
from itertools import chain
from typing import List, Tuple
import torch
from to... | {
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} | Fixes error which occurs if use `trainer.max_epochs` instead of `trainer.max_steps`. Got this error with `enc_dec_nmt.py` script.
```
Traceback (most recent call last):
File "enc_dec_nmt.py", line 133, in main
trainer.fit(mt_model)
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} | Replace `trainer.distributed_backend` with `trainer.accelerator` everywhere in the repo, because `distributed_backend` is [renamed ](https://pytorch-lightning.readthedocs.io/en/latest/generated/CHANGELOG.html?highlight=distributed_backend#id119) into `accelerator` in `pytorch-lightning==1.0.5`.
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Training of machine translation model fails if config parameter `trainer.max_epochs` is used. Method `ModelPT.setup_optimization` tries to get deprecated `pytorch_lightning.Trainer` parameter `distributed_backend`.
```
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} | This PR brings a major revamp to speaker diarization inference, with following are notable changes:
* Input to offline_diarization.py and offline_diarization_with_asr.py expects manifest file ( Now no longer need to create individual files for audio paths, rttms, oracle speakers and uem filepaths)
* With oracle_vad o... | {
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} | Currently `WER` works incorrectly if `batch_dim_index=1`.
The outputs for `batch_dim_index=1` and `batch_dim_index=0` in the following example have to be equal.
Code to reproduce the error:
```python
from string import ascii_lowercase
from typing import List
import torch
from nemo.collections.asr.metrics.w... | {
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Thanks | {
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} | This PR adds convenience scripts to checkpoint average any `.nemo` file (regardless of the class).
Python script is heavily based on code by @MaximumEntropy and @titu1994.
# Usage
The script `checkpoint_averaging.py` builds a `.nemo` file with average weights over multiple `.ckpt` files (assumes `.ckpt` files in... | {
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```python
if max_seq_length > 0 and len(sent_subtokens) + 1 > max_seq_length:
sent_subtokens = sent_subtokens[:max_seq_length]
too_long_count += 1
sent_subtokens.append(tokenizer.sep_token)
sent_lengths.append(len(sent_subtokens))
```
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- Update container version
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I'm having this problem with some audios when I try to generate your graphs using the plot in the script "Speaker_Diarization_Inference"
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-i... | {
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eval_beamsearch_ngram.py not working because logprobs argument is not defined in rnnt_model.transcribe
**Steps/Code to reproduce bug**
run eval_beamsearch_ngram.py as described in https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html#evaluate-by-be... | {
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I am using biomegatron-bert-345m-uncased for training my nemo model, after setting the config.model.language_model.pretrained_model_name ="biomegatron-bert-345m-uncased" in the config file.
When I am trying to restore the model .nemo model using the command
model = TextClassificationModel.restore_from(r... | {
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This is error log:
```
Traceback (most recent call last):
File "speaker_veri_training.py", line 61, in <module>
... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3062 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3062/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3062/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3062/events | https://github.com/NVIDIA/NeMo/pull/3062 | 1,036,838,331 | PR_kwDOC_bI7s4ttmDI | 3,062 | Fix quantization bug in Asr | {
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- Fix ASR quantization bug
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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* distributed_backened is removed and replaced with strategy inside accelerator connector
* accelerator now only accepts the type of device input | {
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} | # Bugfix
- Fix issues with config saving `TypeError: __init__() missing 1 required positional argument: 'cfg'` when trying to do model.load_from_checkpoint(XYZ)
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3057 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3057/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3057/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3057/events | https://github.com/NVIDIA/NeMo/issues/3057 | 1,035,958,453 | I_kwDOC_bI7s49v3i1 | 3,057 | vocab_size in tokenizer for Citrinet | {
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I used **stt_en_citrinet_1024** as a based model for fine-tuning with the Persian audio dataset. In your idea what is the best tokenizer for this? (**wpe** or **spe**). if **spe** is good what is the better type? (**bpe**, **unigram**, **char**, **word**). Also, I want to know about the vocab_size. What is the max... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3055 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3055/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3055/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3055/events | https://github.com/NVIDIA/NeMo/pull/3055 | 1,035,831,618 | PR_kwDOC_bI7s4tqSfQ | 3,055 | (1) reduce the validation loss within a epoch, (2) convert global-bat… | {
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} | Images built with nltk==3.6.2 fails contamer. Updated NLTK version with a CVE fix(3.6.4). https://github.com/advisories/GHSA-2ww3-fxvq-293j
Please review and merge @okuchaiev @ericharper @MaximumEntropy | {
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} | null | null | null | Hey guys
I've been tinkering with the "Voice_Activity_Detection.ipynb" script, and I've had some questions about mounting the data set.
The first, is it really necessary to use human voices speaking a specific word?
And would I have to keep feeding this base by adding new voices speaking other words?
Would I ... | {
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} | null | null | null | Testing in your [colab notebooks](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb) seem to indicate your requirements files don't support pytorch lighting's rc versions so it fails installation. May need to fix that since the install fails.
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} | # Changelog
- Adds publications.md to include research articles that utilize NeMo.
# Format
* Years are sorted in descending order, articles within the same year are sorted by month in ascending order.
* Articles may contain Arxiv submissions, but reviewed articles are preferred.
* In cases where Arxiv arti... | {
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+ Adding the support of bucketing tarred datasets | {
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```
terminate called after throwing an instance of 'lm::FormatLoadException'
what(): kenlm/lm/vocab.cc:40 in void lm::ngram::{anonymous}::ReadWords(int, lm::EnumerateVocab*, lm::WordIndex, uint64_t) threw Form... | {
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When setting `use_dali: true` in the dataset config, the following error occurs:
<details>
<summary>Error</summary>
<br>
```sh
Traceback (most recent call last): ... | {
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python NeMo/examples/asr/vad_infer.py --vad_model=vad_marblenet.nemo --dataset=/fullpath/to/manifest/ --out_dir='frame/demo' --time_length=0.63
but i don't know what is /fullpath/to/manifest/ , it is json file ?
i construct one json file, The contents are as f... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3037 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3037/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3037/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3037/events | https://github.com/NVIDIA/NeMo/pull/3037 | 1,033,036,541 | PR_kwDOC_bI7s4thcRg | 3,037 | Enable stateful decoding of RNNT over multiple transcribe calls | {
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} | # Changelog
- Enable transcribe method to store hypothesis and reuse them for stateful multi step predictions
- RNNT `transcribe` now takes `partial_hypotheses` as optional input, provided in returned hypothesis from previous transcribe call
- Add abstract `batch_concat_states` and `batch_copy_states` support for R... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3036 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3036/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3036/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3036/events | https://github.com/NVIDIA/NeMo/pull/3036 | 1,032,986,359 | PR_kwDOC_bI7s4thR2V | 3,036 | T5 Pre-training in NeMo using Megatron | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3035 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3035/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3035/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3035/events | https://github.com/NVIDIA/NeMo/issues/3035 | 1,032,983,573 | I_kwDOC_bI7s49khQV | 3,035 | Replicate quartznet results | {
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I am training Quartznet 5x5 to do some experiments of my own, after I get the baseline.
I want to first replicate results with WSJ. What are some hyperparameters you recommend for training with a small number of GPUs. Now I am training with 3 GPUs, with a learning rate of 0.05, batch 32 with DDP in PyTor... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3034 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3034/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3034/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3034/events | https://github.com/NVIDIA/NeMo/pull/3034 | 1,032,966,843 | PR_kwDOC_bI7s4thNwJ | 3,034 | Remove STFT checks due to min PT version of 1.10 | {
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} | # Changelog
- Remove old stft checks and defer to latest PT signatures
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3032 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3032/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3032/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3032/events | https://github.com/NVIDIA/NeMo/pull/3032 | 1,032,705,970 | PR_kwDOC_bI7s4tgWqx | 3,032 | Update the webapp for ASR | {
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- Update ASR webapp to support RNNT models properly
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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Yesterday I ran the ipynb and it could work. But today, I'm not sure why it just couldn't work
**Steps/Code to reproduce bug**
I literally just ran the same codes from here https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Question_Answering_Squad.ipynb#scrollTo=... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3028 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3028/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3028/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3028/events | https://github.com/NVIDIA/NeMo/pull/3028 | 1,031,782,717 | PR_kwDOC_bI7s4tdW3y | 3,028 | delete nltk download for TN | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3027 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3027/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3027/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3027/events | https://github.com/NVIDIA/NeMo/pull/3027 | 1,031,672,233 | PR_kwDOC_bI7s4tc_vh | 3,027 | Minor notebook update | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3025 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3025/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3025/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3025/events | https://github.com/NVIDIA/NeMo/issues/3025 | 1,031,100,156 | I_kwDOC_bI7s49dVb8 | 3,025 | Decoding hangs with pretrained models (conformer transducer, contextnet) | {
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} | null | null | null | **Describe the bug**
The decoding hangs with some pretrained models, no error, it just freezes at some point. Killing the process, re-running the same command gets the same behavior.
I have observed it with different models on different data: `stt_en_conformer_tranducer_large.nemo`, `stt_en_contextnet_256.nemo`.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3024 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3024/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3024/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3024/events | https://github.com/NVIDIA/NeMo/pull/3024 | 1,030,862,993 | PR_kwDOC_bI7s4taYP6 | 3,024 | Tn clean upsample | {
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} | * added cleaning script
* added upsampling scrpit
* modified input processing , e.g. to also add space between alpha and numerical characters
* added electronic wfst as part of preprocessing to inference script | {
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