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https://api.github.com/repos/NVIDIA/NeMo/issues/3021 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3021/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3021/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3021/events | https://github.com/NVIDIA/NeMo/issues/3021 | 1,029,054,374 | I_kwDOC_bI7s49Vh-m | 3,021 | Dropout value in QuartzNet 15*5 | {
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I fine-tuned the QuartzNet 15*5 model with 235 hours dataset of Persian Mozilla Common Voice. After that I restored the ckpt file and fine-tuned it with 25 hours of another persian dataset. Now Totally I have 50 epochs. From 18th epoch the loss start to increase. While before the 18th epoch the WER and loss both we... | {
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} | 1. Two methods in `PunctuationCapitalizationModel` were insecure because of possible implicit array change.
2. Add `tqdm`
3. Fixed my English a little | {
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} | +Added transcribe_speech_parallel.py which supports multi-gpu/multi-node inference for ASR models
+Supports both tarred and non-tarred datasets
+works for both CTC and Transducer models
+Added extra sample_id output to the samples returned by the ASR datasets. It enables us to track down the samples. Would be usefu... | {
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} | 1. Fix assertions in `pl_utils.py`. `assert (condition, message)` is incorrect use of assertion.
2. Fix function `reference_loss_func` in file `pl_utils.py`. Previously the function implicitly modified its argument `loss_sum_or_avg`.
3. Fix method `update` in class `GlobalAverageLossMetric`. Previously it implicitly ... | {
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- Add transducer documentation
- Fix few issues in rst files
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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- Add num_workers and pin_memory to data loaders
- Add missing flag for use start end token in transcribe data loaders for BPE models
# Bugfix
- Remove trim_silence = True as default is now False
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I ran the script "Voice_Activity_Detection.ipynb"
in colab, and it kills the execution when it starts the training, thus giving the message "Session failure, restarting automatically", I couldn't identify more information about this problem. I leave the error prints.
script is at version 1.4.0
... | {
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```
<ipython-input-24-56e2b5b7ea8a> in perform_clustering_ori(self, embeddings, time_stamps, speakers, audio_rttm_map, out_rttm_dir, max_num_speakers)
570
571 cluster_labels = COSclustering(
--> 572 uniq_key, emb, oracle_num_speakers=NUM_speakers, max_num... | {
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The transcribe dataloader is then created with that number of workers to avoid 0 GPU usage.
#3009 | {
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2. Add default timer setting to the config | {
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} | This is PR for newly added feature (VAD based word-timestamp compensation, ConformerCTC support).
This PR reflects @fayejf 's contribution on asr_with_diarization.py (VAD model input etc)
Also, tutorial file and README file are also revised to reflect the modified parts.
Still lots of issues are remaining, but for t... | {
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} | This PR adds three features:
1. A utility to collect stats of tokenization of raw text given a tokenizer (i.e., regarding sentences length)
2. NMT inference script supports detailed timing of encoder/decoder/sampler (i.e., timing per sentence length).
3. A plotting script to visualize the detailed timing.
# Summa... | {
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2. Add GPU-synced step time to progressbar | {
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} | - Added support for ConformerCTC model
The script can extract word timestamps from both QuartzNet and ConformerCTC.
- Added VAD based word-timestamp compensation feature
If a pre-trained VAD model is provided to asr_with_diarization.py script, the script compensates the word-timestamp using the frame level VAD o... | {
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} | Cleaned & signed version of #2839 (avoiding messy rebase over ~1 month's worth of commits)
Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com> | {
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+ Added the support of pretrained models to the ASR LM scripts. | {
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} | null | null | null | @haythemtellili couldn't reproduce your error, but could you try:
`! pip install transformers==2.6 # due to HuggingFace transformer library updates`
`! pip install nemo_toolkit[all]`
`import nemo.collections.nlp as nemo_nlp`
_Originally posted by @ekmb in https://github.com/NVIDIA/NeMo/issues/830#issuecomment-656... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2993 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2993/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2993/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2993/events | https://github.com/NVIDIA/NeMo/issues/2993 | 1,025,039,799 | I_kwDOC_bI7s49GN23 | 2,993 | Transcribing with existing Conformer RNNT (tokenizer missing) | {
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When trying to transcribe (or even import) Conformer transducer model, the following error comes:
```bash
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "~/NeMo/nemo/core/classes/common.py", line 705, in from_pretrained
instance = class_.restore_from(
... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2992 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2992/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2992/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2992/events | https://github.com/NVIDIA/NeMo/issues/2992 | 1,024,991,159 | I_kwDOC_bI7s49GB-3 | 2,992 | PunctuationCapitalizationModel fails on a sentence with specific length | {
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`nemo.collections.nlp.models.PunctuationCapitalizationModel.add_punctuation_capitalization` fails when inferencing on a sentence with specific length
**Steps/Code to reproduce bug**
```
import nemo.collections.nlp
punct = nemo.collections.nlp.models.PunctuationCapitalizationModel.from_pr... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2990 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2990/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2990/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2990/events | https://github.com/NVIDIA/NeMo/pull/2990 | 1,023,223,185 | PR_kwDOC_bI7s4tC0AN | 2,990 | Adding bucketing for ASR models with tarred datasets | {
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} | Adding bucketing support for training ASR models with tarred datasets.
+ User need to enable --sort_in_shard and also use --buckets_num=NUM_OF_BUCKETS with scripts/speech_recognition/convert_to_tarred_audio_dataset.py script. The script would create multiple tarred dataset, one per bucket, based on the audio durati... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2989 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2989/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2989/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2989/events | https://github.com/NVIDIA/NeMo/pull/2989 | 1,023,089,214 | PR_kwDOC_bI7s4tCZQo | 2,989 | typos | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2988 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2988/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2988/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2988/events | https://github.com/NVIDIA/NeMo/issues/2988 | 1,023,035,639 | I_kwDOC_bI7s48-kj3 | 2,988 | Batchsize decreasing with each new GPU | {
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With every new GPU I add I find I'm forced to lower batchsize because of memory overflows. This is normal?
For example with 1 GPU I can use 68 BatchSize, with 2 GPUs I can use 67 and with 5 I can use 62.
OS:
Ubuntu 18.04
NeMo 1.3.0
Python 3.8 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2987 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2987/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2987/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2987/events | https://github.com/NVIDIA/NeMo/issues/2987 | 1,023,026,805 | I_kwDOC_bI7s48-iZ1 | 2,987 | Training never finished | {
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} | null | null | null | Hi,
My last workouts never end correctly, he gets stuck at this stage:
If I force a ctrl+c it ends, but I'm afraid to kill some processing. I leave it for hours and this stage is never changed. Any sugge... | {
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Is it efficient to set up a dataset to recognize songs? I notice that when there is a song in an audio it tries to somehow transcribe, if I train these songs saying "music" in the manefesto would it be efficient? Or are there alternatives that can help me in this scenario? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2985 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2985/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2985/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2985/events | https://github.com/NVIDIA/NeMo/issues/2985 | 1,023,011,891 | I_kwDOC_bI7s48-ewz | 2,985 | Memory usage during training | {
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I would like to know if there is any documentation on memory usage during training. I have a base of approximately 1000 hours and there are times when memory usage reaches 160GB (which is my current limit). I'm afraid this will interfere with training, hence my doubt. I also notice that this increase is constant... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2982 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2982/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2982/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2982/events | https://github.com/NVIDIA/NeMo/issues/2982 | 1,022,288,844 | I_kwDOC_bI7s487uPM | 2,982 | Using .nemo model for prediction | {
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Can you please guide me how can I use .nemo model produced after training on our data set so it is not completely using pretrained model, it is into transfer learning and now i want to use it for predicting on new dataset, the way other models work using .predict method
Your response is highly awaited | {
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
nemo2riva --out /servicemaker-dev/stt_en_contextnet_1024.riva /servicemaker-dev/stt_en_contextnet_1024.nemo
nemo-toolkit 1.4.0
nemo2riva 1.6.0b0
Please list... | {
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} | null | null | null | **Describe the bug**
AttributeError: type object 'EncDecRNNTModel' has no attribute 'from_pretrained'
code:
import nemo.collections.asr as nemo_asr
conformer_rnnt = nemo_asr.models.EncDecRNNTModel.from_pretrained()
console:
'''
Traceback (most recent call last):
File "exp1.py", line 19, in <module>
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https://api.github.com/repos/NVIDIA/NeMo/issues/2977 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2977/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2977/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2977/events | https://github.com/NVIDIA/NeMo/pull/2977 | 1,021,424,513 | PR_kwDOC_bI7s4s9cPp | 2,977 | update zero shot intent model | {
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- Update tutorial notebook with link to BERT base pretrained model in NGC
- Change name of parameter from `multi_class` to `multi_label` in `predict` method
- Change `multi_label` default value from False to True | {
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} | This PR is being used to merge #2735 so that the `megatron_gpt` branch can remain stable.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2971 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2971/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2971/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2971/events | https://github.com/NVIDIA/NeMo/issues/2971 | 1,020,917,452 | I_kwDOC_bI7s482fbM | 2,971 | MTEncDecModel has no attribute device | {
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I called
MTEncDecModel.restore_from('zh_en_24x6.nemo').cuda('cuda:0')
if I assign the parameter to .cuda() as above, it will raise an error
'no attribute device'
**Steps/Code to reproduce bug**
as above
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/wo... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2966 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2966/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2966/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2966/events | https://github.com/NVIDIA/NeMo/issues/2966 | 1,020,746,557 | I_kwDOC_bI7s4811s9 | 2,966 | AttributeError: module 'nemo.collections' has no attribute 'nlp' | {
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`AttributeError: module 'nemo.collections' has no attribute 'nlp'
`
I am on the latest version of nemo, and also, `nemo.collections.asr` or `nemo.collections.cv` works perfectly
| {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2965 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2965/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2965/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2965/events | https://github.com/NVIDIA/NeMo/pull/2965 | 1,020,566,115 | PR_kwDOC_bI7s4s61EP | 2,965 | Fixing BERT export and ORT check | {
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} | This is major ONNX export bug fix - most NLP networks were affected.
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2964 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2964/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2964/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2964/events | https://github.com/NVIDIA/NeMo/issues/2964 | 1,020,372,540 | I_kwDOC_bI7s480aY8 | 2,964 | Nemo engine that extracts speaker details | {
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I would like to know if NeMo has any mechanism that extracts details from the speakers, for example, age, gender or accent.
Thanks in advance | {
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Closes #2829 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2961 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2961/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2961/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2961/events | https://github.com/NVIDIA/NeMo/pull/2961 | 1,020,260,351 | PR_kwDOC_bI7s4s530Q | 2,961 | Reading max_sequence_len parameter from config fixed | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2960 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2960/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2960/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2960/events | https://github.com/NVIDIA/NeMo/pull/2960 | 1,020,255,283 | PR_kwDOC_bI7s4s52z5 | 2,960 | [BigNLP] Fix wrong argument passed into ParallelTransformerLayer | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2957 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2957/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2957/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2957/events | https://github.com/NVIDIA/NeMo/issues/2957 | 1,019,681,911 | I_kwDOC_bI7s48xxx3 | 2,957 | Getting error when making inference using nemo ASR pretrained model converted to pytroch mobile model: RuntimeError: Given groups=64, weight of size [64, 1, 33], expected input[1, 1, 63840] to have 64 channels, but got 1 channels instead | {
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my_code
import torch
from torch import Tensor
from torch.utils.mobile_optimizer import optimize_for_mobile
import torchaudio
import nemo.colle... | {
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} | This PR extends the timing measurement for NMT models, in addition to bug and performance fixes.
# Summary
* Added timing measurement of encoder, decoder, and sampling stages in eval_step.
* Added support in sliding mean - useful in cases when post processing of log is not available, and smoother timing is requi... | {
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```
[NeMo I 2021-10-05 08:44:52 speaker_utils:337] Performing Clustering
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* Added Ogg-vorbis codec augmentation at lower rates to cover wideband codec augmentation | {
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import nemo.collections.asr as nemo_asr
import torch.nn as nn
model1 = model.extract_state_dict_from('/root/.cache/torch/NeMo/NeMo_1.3.0/QuartzNet15x5Base-En/2b066be39e9294d7100fb176ec817722/QuartzNet15x5Base-En.nemo', save_dir='./pt_ckpt/QuartzNet15x5Base-En.pt')
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I have used the script at https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/prepare_dataset.py to create the durations and pitch .pt files. On starting the training, I get the assertion error.... | {
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Hi teams, may I know whether Speaker_Diarization models can separate speaker utterances from overlap speech? If not, will model directly ignore this overlap part?
https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb
Thanks in advance | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2937 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2937/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2937/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2937/events | https://github.com/NVIDIA/NeMo/pull/2937 | 1,012,781,091 | PR_kwDOC_bI7s4siPVj | 2,937 | Manually migrating timing of step into branch | {
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} | This PR adds the ability to log the timing of a step for train/validation/test modes during training.
This feature is added to all NeMo models.
Time is measured in seconds.
# Configuration
The following config allow to control logging of step timing:
```
exp_manager:
log_step_timing: True # logging is en... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2935 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2935/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2935/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2935/events | https://github.com/NVIDIA/NeMo/pull/2935 | 1,012,729,678 | PR_kwDOC_bI7s4siFwg | 2,935 | Adding complete method to MegatronGPTModel | {
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} | This PR adds the following method ```response=MegatronGPTModel.complete(request)``` and example how to use it.
Example usage:
```
python examples/nlp/language_modeling/megatron_gpt_eval.py --model_file megatron_gpt.nemo \
--prompt "How to fix gpu memory? A: " --tokens_to_generate 256
```
With a somewhat good conv... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2934 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2934/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2934/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2934/events | https://github.com/NVIDIA/NeMo/issues/2934 | 1,012,457,550 | I_kwDOC_bI7s48WOBO | 2,934 | How to train end2end tts | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2932 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2932/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2932/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2932/events | https://github.com/NVIDIA/NeMo/issues/2932 | 1,012,105,512 | I_kwDOC_bI7s48U4Eo | 2,932 | I am getting undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE error. This I am getting when I am trying to "import torch from nemo.collections import nlp". I am trying to use pytorch ngc container 21.05. I tried to import torch before the nemo extension. Please s... | {
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} | null | null | null | **Describe the bug**
I am getting this issue when trying to import nlp models from nemo.
**Steps/Code to reproduce bug**
I did the following installation of packages.
!pip install wget
!apt-get install sox libsndfile1 ffmpeg
!pip install unidecode
!pip install braceexpand
!pip install webdataset
BRANCH =... | {
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} | - fixes inference for utterances longer than max_seq_len
- adds additional post-processing to match punct/spaces of the original input
- adds `whitelist` arg to WFST normalize.py
@Oktai15 for viz | {
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} | # Bugfix
- Fix an issue where numba spec augment will throw cryptic CUDA error if batch size > MAX_THREAD_BUFFER
Closes https://github.com/NVIDIA/NeMo/issues/2916
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | This PR adds the following method ```response=MegatronGPTModel.complete(request)``` and example how to use it.
Example usage:
```
python examples/nlp/language_modeling/megatron_gpt_eval.py --model_file megatron_gpt.nemo \
--prompt "How to fix gpu memory? A: " --tokens_to_generate 256
```
With a somewhat good conv... | {
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First of all, thanks for developing this incredibly useful toolkit.
I am experimenting with the wav2vec code in NeMo, and would like to ask a question regarding the computation of the masked indices for the context encoder network. More precisely, the `compute_mask_indices` function in `collections/asr/module... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2917 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2917/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2917/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2917/events | https://github.com/NVIDIA/NeMo/issues/2917 | 1,009,892,319 | I_kwDOC_bI7s48Mbvf | 2,917 | Swapped inputs in decoder onnx file | {
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```python
encoder_mask : ['encoder_mask_dynamic_axes_1', 'encoder_mask_dynamic_axes_2', 1024]
encoder_embeddings : ['encoder_embeddings_dynamic_axes_1', 'encoder_embeddings_dynamic_axes_2']
```
You... | {
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} | null | null | null | Hello!
I train MatchboxNet model with custom labels and standard config file.
If I use batch_size (train, test, validation) = 512 I consume 3Gb of GPU memory (Tesla V100) and everything works fine.
If I increase batch_size to 1024 I get ```RuntimeError: CUDA error: an illegal memory access was encountered``` at the... | {
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} | null | null | null | I am used nemo_toolkit==r1.4.0
However, I have load model error model if I loaded it with
`import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_transducer_medium")`.
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} | without this change, the following error occurs :
max() received an invalid combination of arguments - got (out=NoneType, axis=NoneType, ), but expected one of:
* ()
* (name dim, bool keepdim)
didn't match because some of the keywords were incorrect: out, axis
* (Tensor other)
* (int dim, bool keepdim)
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https://api.github.com/repos/NVIDIA/NeMo/issues/2910 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2910/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2910/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2910/events | https://github.com/NVIDIA/NeMo/pull/2910 | 1,008,807,560 | PR_kwDOC_bI7s4sWL94 | 2,910 | [BigNLP] Remove megatron-lm dependency. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2908 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2908/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2908/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2908/events | https://github.com/NVIDIA/NeMo/issues/2908 | 1,008,525,550 | I_kwDOC_bI7s48HODu | 2,908 | Error when running example training TTS on new data | {
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} | null | null | null | **Bug description**
I want to train tacotron2 on new data and at first I just try to run [2_TTS_Tacotron2_Training.ipynb](https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/tts/2_TTS_Tacotron2_Training.ipynb) in colab without any changes! But when I run following code
```
!wget https://github.com/NVIDIA/NeMo/... | {
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