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"Hi @MelikaBahmanabadi Thanks for your question.\r\nWe have Voice Activity Detection support ([tutorial](https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/06_Voice_Activiy_Detection.ipynb)) in NeMo. For KWS I think you can start with that and train on data to have a classier to handle `the keyword vs. oth... | 2021-04-24T07:31:02 | 2021-04-28T00:11:09 | 2021-04-28T00:11:09 | NONE | {
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I am very interested in finding out if NEMO supports keyword spotting.
Regards,
Melika Bahman-Abadi | {
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} | fixed punctuation for itn in SH to be recognized as word
fixed punctuation for tn in SH to be recognized as word
added tests
simplified deployment route
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- Add two end-to-end models:
- FastPitchHifiGanE2EModel: An end-to-end model combining FastPitch and HiFiGan
- FastSpeech2HifiGanE2EModel: : An end-to-end model combining FastSpeech2 and HiFiGan | {
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fixing bug report https://github.com/NVIDIA/NeMo/issues/2094 | {
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"@taufique74 first of all, thanks for catching the bug and PR! Since we would prefer to keep the line breaks which fits more the user's expectations i will replace this PR my this one https://github.com/NVIDIA/NeMo/pull/2101\r\nAnd close this one."
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1. Implements NLL Loss to be used at validation/test time for NMT (without label smoothing)
2. Removes logic that changes the attention mask to not be causal at test time for beam search, which was causing high validation loss | {
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"@taufique74 this is true, we do not handle upper cased words at this point, since we assume its the output of the asr model. please lower case your input"
] | 2021-04-22T22:08:40 | 2022-12-19T21:50:26 | 2021-04-22T22:28:51 | NONE | {
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Inverse Text Normalization text-processing fails to normalize a verbal form if it has a word with an upper case.
While I understand that the transcriptions are usually all-lower cased words, it'd be nice if we lower the text before passing it to `inverse_normalizer`.
**Steps to reproduce bug*... | {
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"Hey @yzhang123 \r\nLet me know if you'd be interested in a PR, it's a quick fix.",
"@taufique74 sure go ahead! thanks!",
"closing since https://github.com/NVIDIA/NeMo/pull/2101 is merged. Please re-open if the bug persists"
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} | null | null | null | **Bug Description**
While running `python run_predict.py --input=input.txt --output=output.txt`, it breaks saying `pywrapfst.FstOpError: Operation failed`. I noticed there is an empty line in `input.txt`, and after removing that empty line the error was gone.
**Steps to reproduce the bug**
Let's first create... | {
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} | # Changelog
- Prototype generic TeacherStudentModelPT
# Usage
Model extends a mixin class that enables registration of tensors inside of `training_step` for teacher student distillation loss as follows :
```python
# PTL-specific methods
def training_step(self, batch, batch_nb):
signal, signal... | {
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} | * adding configuration to distinguish between lower_cased and cased input, affects whitelist, word classes
* added test
* added pynini sentence predict test to jenkins
* deleted sentence boundary exception
Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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"is it draft or... | 2021-04-21T23:04:39 | 2021-05-19T06:06:44 | 2021-05-19T06:06:37 | COLLABORATOR | {
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} | Add feature loader to accept external features stored in pickle file.
Add feature to seq_label data loader
Add function to convert seq_label to relative labels.
Add test and test data.
Input manifest file looks like:
```
{"feature_filepath": "tests/.data/asr/feat/AMI-00IB4001-000.p", "seq_label": "zero one t... | {
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"@mschonwe we changed a few things regarding normalization since 1.0.0rc.\r\n please refer to RC tutorial https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/tools/Text_Normalization_Tutorial.ipynb which is tested to work in nvcr.io/nvidia/nemo:1.0.0rc1.",
"Thank you. Got it working following the updated tut... | 2021-04-21T21:08:36 | 2022-12-19T21:50:45 | 2021-04-22T03:46:40 | NONE | {
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} | null | null | null | I'm following the instruction in
`https://github.com/NVIDIA/NeMo/blob/main/tutorials/text_processing/Text_Normalization_Tutorial.ipynb`
Running code on a docker container `nvcr.io/nvidia/nemo:1.0.0rc1`
There are no errors when running the suggested install code:
```
BRANCH = 'main'
!python -m pip install git+... | {
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"It is normal to get 100% WER for first few epochs. How many epochs did train?",
"Also, there might be a mismatch between the datasets. For example, is the sampling rate in your data the same as it is in the LibriSpeech dataset?",
"around 50 epoch, after 50 epochs its crashing\r\n> It is normal to get 100% WER ... | 2021-04-21T10:50:12 | 2021-04-23T23:49:33 | 2021-04-23T23:49:32 | NONE | {
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} | null | null | null | Hello,
I am finetuning the QuartznetBase-en5x5 model on my own dataset, but I am getting trainingWER 100% and training loss is flatted after few steps although val_WER and val_loss is decreasing gradually
**Dataset Details**
train Dataset consists of 169630 files totaling 410.18 hours
Val_ Dataset consists o... | {
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"Thanks for your question.\r\nYes. Have a look at **_VAD diarization_** part in diarization [tutorial](https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/speaker_recognition/Speaker_Diarization_Inference.ipynb ) which will generate VAD RTTM-like json file that could suit your request.\r\n",
"Yes, thank you ... | 2021-04-21T06:16:27 | 2021-04-22T10:41:11 | 2021-04-22T10:41:11 | NONE | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2080 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2080/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2080/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2080/events | https://github.com/NVIDIA/NeMo/pull/2080 | 863,321,369 | MDExOlB1bGxSZXF1ZXN0NjE5NzUyOTA4 | 2,080 | Add Multi GPU Rank tests | {
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} | # Changelog
- Add jenkins test to run DDP predict and measure the model internal ranks calculated
- Exit early if these ranks in logs dont correspond to expected global ranks.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2079 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2079/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2079/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2079/events | https://github.com/NVIDIA/NeMo/pull/2079 | 863,289,352 | MDExOlB1bGxSZXF1ZXN0NjE5NzI2MTI1 | 2,079 | Correct DDP initialization for model | {
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} | # Bugfix
- Correct DDP initialization (single/multi node) for datasets that depend on self.global_rank, self.local_rank, self.node_rank.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | # Changelog
- Update core jenkins container to 21.04
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2077 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2077/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2077/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2077/events | https://github.com/NVIDIA/NeMo/pull/2077 | 863,023,555 | MDExOlB1bGxSZXF1ZXN0NjE5NDk3NDcz | 2,077 | Fix and enable DALI tests | {
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"This pull request **introduces 1 alert** when merging 760f3012e9f086af1d1893d0a1c378bd2b2aabab into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-7b0cbe4483c3653568bbc31df8b151ec2086aefc)\n\n**new alerts:**\n\n* 1 for Unused local variable"
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https://api.github.com/repos/NVIDIA/NeMo/issues/2076 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2076/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2076/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2076/events | https://github.com/NVIDIA/NeMo/issues/2076 | 862,838,380 | MDU6SXNzdWU4NjI4MzgzODA= | 2,076 | Unable to integrate ModelPruning callback in ASR training | {
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"@titu1994 any thoughts? ",
"Just to text, can you add accelerator=\"ddp\" (even for 1 gpu, just a test). \r\n\r\nAs far as I can parse the error, it's due to a deep copy of a model which can't be pickled, so it is raising a bound function wrapper error. \r\n\r\nYou'll probably have to come up with a toy example... | 2021-04-20T13:48:25 | 2021-04-25T12:43:39 | 2021-04-24T00:07:04 | NONE | {
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} | null | null | null | **Describe the bug**
When adding ModelPruning callback to the Pytorch Lightning trainer in order to train ASR models (in particular, I tried Quartznet15x5) I get the following exception:
TypeError Traceback (most recent call last)
<ipython-input-16-2116e952a23e> in <module>
... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2071 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2071/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2071/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2071/events | https://github.com/NVIDIA/NeMo/issues/2071 | 861,559,958 | MDU6SXNzdWU4NjE1NTk5NTg= | 2,071 | How can I drop the last layer to get output as hidden states in terms of feature extraction? | {
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"This question is a little bit vague. Would you mind expanding on what you're trying to do? For example, what NeMo model are you working with and what would you like to do with the hidden states?"
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Hi, I'm working on the Megatron model how can I drop the last layer to get output as hidden states in terms of feature extraction?
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"We have completely changed NeMo since .11. We highly recommend to start using our latest stable version. Please see our User Guide: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/ and our Tutorials: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/starthere/tutorials.html for ho... | 2021-04-19T11:12:16 | 2021-04-20T05:21:01 | 2021-04-20T05:21:00 | NONE | {
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I have following blog [Jump-start Training for Speech Recognition Models in Different Languages with NVIDIA NeMo](https://developer.nvidia.com/blog/jump-start-training-for-speech-recognition-models-with-nemo/#respond) and successfully finetune the model on my custom data. Now I have to test the models but in checkp... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2068 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2068/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2068/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2068/events | https://github.com/NVIDIA/NeMo/pull/2068 | 860,628,033 | MDExOlB1bGxSZXF1ZXN0NjE3NDU5NTM4 | 2,068 | updated intro.rst | {
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} | Updated intro.rst for [Text to speech](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/intro.html)
Changed spec_gen -> spec_generation
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https://api.github.com/repos/NVIDIA/NeMo/issues/2067 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2067/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2067/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2067/events | https://github.com/NVIDIA/NeMo/pull/2067 | 860,506,087 | MDExOlB1bGxSZXF1ZXN0NjE3Mzc0Nzg3 | 2,067 | Udated docs | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2066 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2066/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2066/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2066/events | https://github.com/NVIDIA/NeMo/pull/2066 | 860,194,018 | MDExOlB1bGxSZXF1ZXN0NjE3MTQyOTYx | 2,066 | Adding N-gram LM for ASR Models | {
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"This pull request **introduces 2 alerts** when merging a5a058a6d13568253e1a2e49d996cdc6cda8c522 into 21a17b267fac68d4cdd20f3969a580a0a40dbdb4 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d8d2c21d7f0c2b2b08d057ea93e8c9524bb8826e)\n\n**new alerts:**\n\n* 1 for Unused local variable\n* 1 for Va... | 2021-04-16T21:51:45 | 2021-04-22T21:55:02 | 2021-04-22T21:55:02 | COLLABORATOR | {
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} | This PR adds the support for training and evaluating N-gram LM for ASR models.
The N-gram language model is trained with KenLM library (https://github.com/kpu/kenlm) which can be used with the beam search decoders on top of the ASR models. It supports both character level and BPE level encodings and models which is... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2065 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2065/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2065/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2065/events | https://github.com/NVIDIA/NeMo/issues/2065 | 859,615,956 | MDU6SXNzdWU4NTk2MTU5NTY= | 2,065 | How to get the embedding from biomegatron? | {
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"Duplicate of https://github.com/NVIDIA/NeMo/discussions/2072"
] | 2021-04-16T08:56:47 | 2021-04-20T05:23:07 | 2021-04-20T05:22:54 | NONE | {
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Will there be any checkpoint converter for the BioMegatron in order to be loaded into Transformers? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2064 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2064/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2064/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2064/events | https://github.com/NVIDIA/NeMo/pull/2064 | 859,471,040 | MDExOlB1bGxSZXF1ZXN0NjE2NTQ2NzI4 | 2,064 | Update preproc_mt_data.py tarred dataset | {
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"I think @ericharper may have had this in place on purpose to make sure both metadata and tarfiles were provided and not just the metadata. I'm fine with providing just the metadata JSON file (even better would be to allow specifying preproc_out_dir to avoid specifying tokenizers as well). \r\n\r\nCan you also hand... | 2021-04-16T05:33:11 | 2021-04-20T03:51:06 | 2021-04-20T03:51:06 | CONTRIBUTOR | {
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} | If just a metadata_file is passed in through the config, it won't enter the correct elif statement | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2061 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2061/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2061/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2061/events | https://github.com/NVIDIA/NeMo/pull/2061 | 858,227,167 | MDExOlB1bGxSZXF1ZXN0NjE1NTE2MDQz | 2,061 | Update documentation to latest nemo container | {
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} | # Changelog
- Update nemo container to nemo:1.0.0rc1 throughout docs and build scripts
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2057 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2057/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2057/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2057/events | https://github.com/NVIDIA/NeMo/issues/2057 | 857,529,662 | MDU6SXNzdWU4NTc1Mjk2NjI= | 2,057 | Is there multi-lingual support for NeMo? I am looking particularly for a Hindi model. | {
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"yes, we support other languages https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html \r\nBut at the moment, we don't have Hindi ASR model."
] | 2021-04-14T05:18:45 | 2021-04-20T05:23:40 | 2021-04-20T05:23:40 | NONE | {
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Does NeMo ASR support Hindi and other languages? Will there be any tutorial/doc released any time soon on how to use NeMo ASR for multi lingual training? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2056 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2056/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2056/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2056/events | https://github.com/NVIDIA/NeMo/pull/2056 | 857,234,245 | MDExOlB1bGxSZXF1ZXN0NjE0Njg4MDk2 | 2,056 | Add Aliases to Model Cards | {
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} | - Adds new `aliases` property to PretrainedModelInfo
- Allow models to use `from_pretrained()` with aliases
Example:
```python
PretrainedModelInfo(
pretrained_model_name="tts_en_tacotron2",
location="https://api.ngc.nvidia.com/...nemo",
description="model description",
class_=cls,
alias... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2054 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2054/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2054/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2054/events | https://github.com/NVIDIA/NeMo/pull/2054 | 856,470,959 | MDExOlB1bGxSZXF1ZXN0NjE0MDQzNzE5 | 2,054 | Cleanup of NeMo tests and tools | {
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} | # Changelog
- Import guard all of inverse text normalization (pynini) and related tests
- Run all Export tests only on GPU
- Fix docker build + remove redundant RNNT loss function build
- Patch Numba test for cuda compatibility when on CPU | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2052 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2052/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2052/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2052/events | https://github.com/NVIDIA/NeMo/pull/2052 | 856,354,064 | MDExOlB1bGxSZXF1ZXN0NjEzOTQ0NzE1 | 2,052 | Text processing docs | {
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"@yzhang123 there is merge conflict\r\n",
"> @yzhang123 there is merge conflict\r\n\r\n@okuchaiev fixed"
] | 2021-04-12T20:55:51 | 2021-04-14T05:35:01 | 2021-04-14T05:34:55 | CONTRIBUTOR | {
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"Hi, Ulucsahin\r\n\r\nThe `speech_to_text_quant_infer.py` script is meant for evaluating numerical property and accuracy of the quantized network, not speed/throughput. It is expected to be slower because it runs emulated quantization.\r\n\r\nThe script can generate an ONNX graph with quantization nodes. To deploy ... | 2021-04-12T13:21:02 | 2021-04-23T23:50:39 | 2021-04-23T23:50:38 | NONE | {
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I am quantizing QuartzNet5x5 model that I use for ASR. I followed the following example: `/examples/asr/quantization/speech_to_text_quant_infer.py`
My code is as follows:
```
quant_modules.initialize()
params['model']['encoder']['quantize'] = True
self.first_asr_model = nemo_asr.... | {
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"This pull request **introduces 18 alerts** when merging ed2b68b7bc10a626a2ae82fb0cbcc0e3f83a456c into 52121810b428e011666e656d313b42c954def26b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-8fbe4d26b21589a2bf3447334aae25b590af73e0)\n\n**new alerts:**\n\n* 16 for Unused import\n* 1 for Redundan... | 2021-04-12T06:39:59 | 2021-05-05T20:32:43 | 2021-05-05T20:32:43 | CONTRIBUTOR | {
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} | This pull request adds an entity linking model, dataset, loss, example training code, and demo notebook based on the paper "Self-Alignment Pretraining for Biomedical Entity Representations"
You can run through the notebook tutorial or run everything on the command line.
To run everything on the command line follo... | {
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"this is not a bug. The container version looks like 1.8.0\"something\" - where \"something\" is commit id pre 1.8.0 release but a little after 1.7.1"
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torch 1.8
but "version currently does not support Pytorch 1.8.0"
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"Thanks for your question. \r\nOf course! You can change the `diarizer.vad.model_path` and `diarizer.speaker_embeddings.model_path` in [config file](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/conf/speaker_diarization.yaml) to use your own models."
] | 2021-04-10T04:34:41 | 2021-04-20T05:24:28 | 2021-04-20T05:24:27 | NONE | {
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} | null | null | null | **Describe your question**
Is it possible to use our own VAD or Speaker Embedding models in speaker diarization? If so, how one can do it properly?
Many thanks.
| {
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} | * renamed nemo_tools -> nemo_text_processing
* renamed denormalization throughout to inverse_text_normalization (tutorial, docs, readme, folder, test folder)
* added tools/inverse_text_normalization_deployment.rst
* fixed paths referencing nemo_tools-> nemo_text_processing in ctc_segmentation | {
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} | Resets the state of GlobalAverageLossMetric on validation_epoch_end. This used to be done by PTL earlier but is not anymore after an API change. | {
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(Also when trying do use also other docker file in the zip at https://github.com/NVIDIA/NeMo/releases/tag/v1.0.0rc1)
Step 12/29 : WORKDIR /tmp/nemo
---> Running in 9b5b7d241aee
Removing intermediate container 9b5b7d241aee
---> e5fe9b0fab... | {
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"You could finetune Citrinet using the same Tokenizer on the specific domain (if there is sufficient data). \r\n\r\nIf you have some noise files, noise robust training, the same method as QuartzNet can be applied to Citrinet. \r\n\r\nFor preprocessing, there should be monochannel 16Khz wav files. We find that attem... | 2021-04-09T12:52:10 | 2021-04-23T21:56:03 | 2021-04-23T21:56:02 | NONE | {
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I am using stt_en_citrinet_1024 model and able to get good transcript , I am using the recorded audios with microphone and WER is varying from 3.5% to 15%. This has names of person and place, how to include the words in the model.
any suggestions with following aspects
1. Preprocessing the audio
2. using ... | {
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} | This PR sets the number of token types to 0 by default for NMT since it wasn't being used. Parameters, however, were still being created for it in the model, making it impossible to set`find_unused_parameters=False` in DDP. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2035 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2035/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2035/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2035/events | https://github.com/NVIDIA/NeMo/issues/2035 | 853,469,976 | MDU6SXNzdWU4NTM0Njk5NzY= | 2,035 | NaN values on Arm Processor - QuartzNet [Question] | {
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"I am not sure what kind of precision is used for computation by your Arm processor. \r\nIs it IEEE float32? Then it should work. However, if it is something else, then precision-related issue would be likely. "
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Hello, I trained an ASR model for speech to text conversion. My model has decent accuracy and I want to use this model on a device with ARM Cortex-A72 processor.
I am doing inference using EncDecCTCModel.transcribe. Everything is fine on Windows and Ubuntu with x86 architecture. However... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2030 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2030/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2030/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2030/events | https://github.com/NVIDIA/NeMo/pull/2030 | 852,482,461 | MDExOlB1bGxSZXF1ZXN0NjEwNzI3MjU4 | 2,030 | Compute SE in float32 for stability in AMP training. | {
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"~~Can you open a new PR?~~ nvm.",
"I'm looking forward for this! I'm hoping to train a citrinet model from scratch soon on A100 GPUs which could leverage really well mix-precision training. Is there an ETA for this patch to land in the main trunk?",
"As discussed with Jason and Som, we're leaving the workaro... | 2021-04-07T14:40:32 | 2021-04-19T12:52:38 | 2021-04-16T14:59:24 | CONTRIBUTOR | {
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} | Squeeze-and-Excitation block introduced instabilities to Citrinet when training with mixed precision.
This change avoids computing SE in float16 all together.
As SE block does not take much of computational time, the time and memory benefits of other computations done in float16 can allow to train model faster.
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"Seems you're encountering a neural type error. Are you certain the audio is a 16 KHz monochannel wav file ? As a temporary measure to check if the file is not the issue you can disable type checking globally by following https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.0.0rc1/core/core.html#neural-t... | 2021-04-07T13:21:50 | 2021-07-08T07:04:48 | 2021-04-08T12:47:50 | NONE | {
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} | null | null | null | **Describe your question**
I am trying to run NeMo ASR inside Ubuntu 20.04 docker container on Raspberry pi 4. I have compiled NeMo from source.
When I try to run EncDecCTCModel.transcribe, I am getting the following error:
> Traceback (most recent call last):
> File "main.py", line 43, in <module>
> ... | {
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"@titu1994 do you see any problems with us making `cache_dir` into an argument?",
"This seems like a good plan, let's check for existence of global override in os, use that otherwise default to current default. ",
"This is now supported. ",
"> This is now supported.\r\n\r\nHello! I understand that we can now ... | 2021-04-07T05:07:02 | 2022-12-24T18:44:19 | 2021-11-30T23:05:38 | CONTRIBUTOR | {
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Directory where pretrained model is downloaded is hardcoded to
`cache_dir = Path.joinpath(Path.home(), f'.cache/torch/NeMo/NeMo_{nemo.__version__}/{filename[:-5]}')` in https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/common.py#L652.
So ... | {
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"This pull request **fixes 4 alerts** when merging 592b26b4f30a57a3fec5afa470e1e56ffdf51d04 into e70231dd920468e2d8562aefd43d01d7572e135e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9d3db6c91cb004379050ced8d60b6fc51287ad21)\n\n**fixed alerts:**\n\n* 4 for Explicit export is not defined",
"... | 2021-04-07T02:06:47 | 2021-04-08T19:00:31 | 2021-04-08T06:25:22 | COLLABORATOR | {
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} | # Changelog
- Adds `future_context` parameter to JasperBlock to enable asymmetric padding for the convolution layers.
- Adds several tests for JasperBlock components
- Adds docstring to explain how to use asymmetric padding + potential pitfalls. | {
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} | We need to use the trainer object, no the config. It forces the user to have those in the config file.
Also fixed the check_slurm method to detect the slurm. | {
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} | # Changelog
- After class resolution of `target`, if calling `cls` is subclass of resolved `imported_cls`, then defer to subclass `cls` instantiation instead of the imported target class.
- Add tests to check these scenarios
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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* Docker for sparrowhawk/ production backend
* pynini grammar export script pynini_export.py
* main script export_grammars.sh
* readme | {
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} | This PR adds the notebook with an example of ASR training using NeMo and [DefinedCrowd](https://www.definedcrowd.com/) platform for data acquisition. | {
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"can you please try with the latest main branch and re-open if the issue still exists"
] | 2021-04-03T13:09:47 | 2021-04-24T00:01:17 | 2021-04-24T00:01:17 | NONE | {
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I am trying to fine-tuning my model, but after a successful restore of my checkpoint, i keep on stumbling on this error: TypeError: 'NoneType' object is not iterable
I am a beginner and is using Nemo on Google Colab. I am following the tutorial provided in one of your notebooks. Below is the code on how i res... | {
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} | Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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"> \r\n> \r\n> Please update the docstring of exp_manger so that it matches the new behaviour.\r\n> Please add a note that the behaviour of this argument changed as of version 1.0.0.\r\n\r\nAdded to the docstring."
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} | Updating model parallel to work with new PTL DDP API and adding .nemo support.
# Usage
We support 4 main workflows:
- Training from megatron-lm checkpoint
- Auto-resume training from experiment manager
- Training from model parallel .nemo file
- Evaluation from model parallel .nemo file
To enable these work... | {
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I have been training a VAD but the results I am getting are not optimal as I have done prior data-preprocessing like trimming and Noise Injection. My dataset totals to 7 hours of speech and 7 hours of background. Is that enough for training the VAD?
Also, I would like to know how to be sure that Noise Augm... | {
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} | * Add has_audio parameter to speech_collate
* @blisc I wouldn't like to write new `speech_collate` for TalkNet dataset, let's reuse old version with `has_audio` parameter?
* Log warning in `setup_optimizaton` when Trainer is not specified
* Update TalkNet models that were without trainer in constructor
* Talk... | {
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Removed setup.py version logic. | {
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```
#17 [nemo-deps 10/12] RUN for f in $(ls requirements/*.txt); do pip install ...
#17 0.515 ls: cannot access 'requirements/*.txt': No such file or directory
#17 DONE 0.6s
```
The files are copied not into a separate `/tmp/nemo/requirements/` folder but directly into... | {
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"I am still waiting for the full run to complete in order to confirm that there is no degradation in quality. For now, keeping in draft state. ",
"Thanks a lot everybody for the review! "
] | 2021-04-01T14:36:01 | 2021-04-19T12:54:45 | 2021-04-06T18:06:28 | CONTRIBUTOR | {
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} | The proposed change includes three optimization steps that I found were beneficial for model training efficiency.
### 1. Mask caching
Currently `arange` for mask is computed at each step from scratch. While it might be cached by GPU, there is no guarantee as it is not needed after forward function execution.
By c... | {
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I'm seriously looking at implementing some knowledge distillation techniques for the ASR classes. I found this very simple and elegant pytorch-lightning approach here: https://github.com/vrvlive/knowlege-distillation/blob/master/training_module.py and I wonder how I could adapt it to... | {
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} | # Changelog
- Add rnnt loss switching capability.
- Enable passing arbitrary kwargs to the inner loss function.
- Add jenkins tests (commented out until 21.04 container is released which supports minimum numba version 0.53 for CUDA 11.2)
- Enable all RNNT model tests using the numba default loss
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- Remove hard numba version requirements
- Dynamically check for version at runtime when it is required
- Update tests to dynamically switch off for cuda if version is incompatible (either due to numba version itself, or due to mismatch between supported CUDA version and actual CUDA version installed).
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