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"Can you also update latest TalkNet weights in pretrained link?",
"LGTM\r\ni will update ckpts links in following days, as well as TTS notebook for inference",
"@stasbel @blisc I found additional bug in HiFiGAN tuning. I will fix it and after that we will merge it",
"@blisc let's merge it after this PR: https... | 2021-03-30T17:24:43 | 2021-04-01T16:45:17 | 2021-04-01T15:12:28 | CONTRIBUTOR | {
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} | * Updated HiFiGAN training blocks and config (main version doesn't work)
* Updated TalkNet and config (main version doesn't work)
* Small changes in audio_to_text.py (load_audio flag)
* Small changes in vocabs.Phonemes/vocabs.Chars (add_blank_at flag) | {
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} | Notation:
k - kernel size
p - padding
d - dilation
The old formula is only correct when d <= 3.
This can be verified by choosing d = 4, k = 3 and computing the value of padding with an old formula _p_old = (d * k) // 2 - 1 = 5._ Whereas the correct answer is _(d * (k - 1)) // 2 = 4_.
This result can be verified in [convolution visualizer](https://ezyang.github.io/convolution-visualizer/index.html).
The change **does not affect** a case when **d=1 and k is odd**. In that case, (k - 1) // 2 = k // 2. Also **d=2** and **d=3** are unaffected due to coincidental correct answers that old formula gave.
The behaviour **changes for even kernels** but there's no correct symmetrical padding in that case, so we don't use it in practice.
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1. Applies the pre-LN fix #1880 to the rc1 branch.
2. Disables dataset ID caching by default. | {
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"This pull request **introduces 1 alert** when merging d5f866692e69b78a5565dd635e396521ebecbd67 into 91146cf360415e9656e62c635b48ad639b1b45af - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-a6442172376caa13bb8863b03862186b4e24cc86)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-03-30T00:10:15 | 2022-09-20T23:57:04 | 2021-04-27T17:16:51 | COLLABORATOR | {
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} | PTL significantly changed their API which broke our existing model parallel support.
This PR updates `NLPModel` so that model parallel training and auto-resume works again.
# TODO:
- Add model parallel support to .nemo files. | {
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"> get test is working, export is skipped (not working)\r\n\r\n\r\n- No, locally I get: tests/collections/nlp/test_megatron.py::TestMegatron::test_get_model FAILED \r\nE RuntimeError: The following operation failed in the TorchScript interpreter.\r\nE Traceback of TorchScript (most recent call ... | 2021-03-29T23:07:48 | 2021-03-30T06:15:49 | 2021-03-30T06:15:43 | MEMBER | {
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} | This PR adds "--with_downloads" argument for unittests.
```
# regular, GPU-based unit tests, without downloading pre-trained checkpoints from the clould
pytest
```
```
# regular, GPU-based unit tests including the ones which download pre-trained checkpoints from the clould
pytest --with_downloads
```
I also marked some tests which download checkpoints from the cloud with corresponding ```@pytest.mark.with_downloads()``` fixture.
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">>\"While this works for all restore_from calls, it is not the case for from_pretrained. All domains have their own way of populating the list of pretrained models - and therefore calling from_pretrained from ModelPT will fail since ModelPT has no information of the subclass pretrained models.\"\r\n\r\n- I don't u... | 2021-03-29T22:45:29 | 2021-03-30T02:53:39 | 2021-03-30T02:51:15 | COLLABORATOR | {
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} | # Changelog
- If `target` exists on its own inside a config, will attempt to resolve the class path inside target
- If target classpath can be resolved, attempts to load model by calling the constructor of the target class
- If model is successfully loaded via this target classpath, then return this model
- If model fails to load via the target classpath, log under debug scope and fallback to current `cls` as the target class.
- Adds a test case to cover this functionality
# Example usage
With this, given any .nemo file, it can be loaded as
```python
from nemo.core import ModelPT
model = ModelPT.restore_from("<path/to/any/.nemo/file>") # will be subclass of ModelPT if loads successfully
```
# Restrictions
While this works for all `restore_from` calls, it is not the case for `from_pretrained`. All domains have their own way of populating the list of pretrained models - and therefore calling `from_pretrained` from ModelPT will fail since ModelPT has no information of the subclass pretrained models.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"Just saw @ekmb didn't review yet. Changes should be fine, but revert in a subsequent PR for NLP model test if needed. "
] | 2021-03-29T18:47:54 | 2021-04-01T16:46:43 | 2021-03-29T19:26:24 | MEMBER | {
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"@vsl9 Could you please describe how the next parameters are calculated?\r\n\r\n```\r\n# 20ms is duration of a timestep at output of the model\r\ntime_stride = 0.02\r\n\r\n# calibration offset for timestamps: 180 ms\r\noffset = -0.18\r\n```\r\nand what should be the numbers for `stt_en_citrinet_1024` model?",
"@... | 2021-03-27T18:47:05 | 2021-10-26T23:44:18 | 2021-10-26T23:44:18 | NONE | {
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} | null | null | null | I'm using the code provided [here](https://github.com/NVIDIA/NeMo/blob/73875d874b006b672a8e407687ef414ef7a7b282/tutorials/asr/Offline_ASR.ipynb) to extract timestamps along with the transcription. The audio files are fairly long (30 to 60 mins). Problem is, the timestamps get progressively offset until they are completely misaligned halfway through the document. Any idea how to fix this?
**Environment overview**
- Environment location: Colab
- Method of NeMo install: !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$v1.0.0b3#egg=nemo_toolkit[all]
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"This pull request **introduces 1 alert** and **fixes 1** when merging 7bc984d05a1cb0684428995fdc4135ef3b9a8c36 into 8f7c18973bf92f30679c0f4416412d629bfc7bff - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-0ebacc9e5c231176025a17986eb8897d98fa1097)\n\n**new alerts:**\n\n* 1 for Unnecessary delete... | 2021-03-27T01:59:51 | 2021-12-11T00:26:54 | 2021-07-20T00:15:53 | COLLABORATOR | {
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} | It enables user to start training by loading weights from another checkpoint ('.ckpt') or '.nemo' file.
It can be done by setting the 'model.init_weights_from_model' parameter in the config or by:
model.load_weights_from_checkpoint(init_model_path='/path/to/model.nemo', strict=True)
It is a feature which enables fine-tuning out of the box for all nemo model/scripts/examples by just using the config. | {
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"Can I revert this? Our main branch is failing due to these DALI tests",
"@blisc looking at it on main\r\n"
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"looks good to me. Please remove \"Draft\" status when you are done.\r\n"
] | 2021-03-26T06:56:00 | 2021-03-30T00:33:05 | 2021-03-29T23:20:16 | CONTRIBUTOR | {
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"What resource manager are you using? So far we tested multi-node in a SLURM environment only",
"I am using google cloud vm without any resource manager.",
"Doesn't GCP have at maximum just 8 GPUs? I'm not familiar with any one click solution to launching a 32 gpu (8 nodes, 4 GPUs per node) cluster on GCP - ple... | 2021-03-26T05:59:15 | 2021-07-16T17:38:20 | 2021-07-16T17:38:20 | NONE | {
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} | null | null | null | I am doing the nemo asr training on multi gpus and workers and it hangs on the step:
`trainer = pl.Trainer(gpus=4, num_nodes=8, accelerator='ddp',max_epochs=200,amp_level='O1', precision=16,`
At the console
The same code works fine when using a single GPU and 1 core.
Is there any fix for it, my dataset is very large and thus training will take very long if using single core and GPU.
THanks | {
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"Doesn't have to be this PR, but I think the contents of the README would be a great addition to the NeMo NLP Docs.\r\n"
] | 2021-03-26T02:12:20 | 2021-04-01T16:46:27 | 2021-03-29T22:46:38 | COLLABORATOR | {
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} | ### Summary of the PR ###
Broadly, this PR does the following:
* Changes `SentencePiece` wrapper and `create_spt_model` function to handle special tokens inside SentencePiece model (instead of inside the wrapper that happens currently).
* Makes all TokenizerSpec tokenizer wrappers (AutoTokenizer, CharTokenizer, WordTokenizer, YTTM, SentecePiece) APIs consistent - except for a few differences as discussed later.
* Adds tests for the current SentencePiece and YTTM tokenizers.
### SentencePiece Changes ###
Current implementation of the SentencePiece wrapper handles special tokens inside the wrapper using a special token dictionary - rather than inside the tokenizer model. This makes the special tokens created non-persistent and volatile to change during different runs.
To better handle this situation, the `create_spt_model` function now supports the addition of `bos`, `eos`, and `pad` tokens directly and special tokens can only be added during train time. More custom special tokens can be passed as `Control symbols` or `User defined symbols` as defined by sentencepiece. `bos`, `eos`, and `pad` are by default `custom symbols`. The major difference between setting a special token as `control` vs `user defined` is that `user defined symbols` can encoded and decoded directly from the text, whereas `control symbols` can only be added manually. For more information on the differnce, refer [SentencePiece](https://github.com/google/sentencepiece/blob/master/doc/special_symbols.md). Despite these changes, any previously coded calls to `create_spt_model` would work in the **_same fashion and do not need to be changed_**.
The SentencePiece wrapper has also been changed to accommodate the above. While the default behavior of the SentencePiece wrapper has been changed with this, older behavior for previously trained models can be brought back by setting `legacy = True` while instantiating the wrapper.
### Making TokenizerSpec tokenizer APIs consistent ###
This PR also tries to make all the tokenizer wrappers that inherit from TokenizerSpec consistent. It changes the YTTM tokenizer's `ids_to_tokens` function to make it consistent with others. Again, while this has been set as the default behavior, previous behavior can be restored by setting `legacy = True`.
While the tokenizers are now mostly similar, there are some exceptions:
* AutoTokenizer differs from all other tokenizers in the fact that the AutoTokenizer's `tokens_to_text` function _**does not**_ remove special tokens. This is probably a bug, and should ideally be addressed when it's certain that it won't break anything.
* YTTM and SentecePiece tokenizers do not encode special tokens (control symbols for SentencePiece) when `text_to_ids` or `text_to_tokens` is called. While this should ideally be consistent across all tokenizers, as this inhibits user text to change the programmatic meaning of the text, other tokenizers (char, word, and auto) encode the special tokens. An example:
When trying to tokenize (`text_to_tokens`)
`text: a b [UNK] c`
yttm and sentencepice: `['a', 'b', '[', 'U', 'N', 'K', ']', 'c']`
others: `['a', 'b', '[UNK]', 'c']`
#### Behavior of TokenizerSpec API ####
* `[tokens|ids]_to_[ids|tokens]`: Converts tokens to ids and vice versa while preserving all special token infomation, i.e. `len(ids) == len(tokens)`
* `text_to_[tokens|ids]`: Encodes text as tokens/ids. Exception: Auto/word/char encode special tokens in text as is.
* `[tokens|id]_to_text`: Converts tokens/ids to text and removes special tokens (only control symbols for SentencePiece). Exception: `tokens_to_text` of `AutoTokenizer`.
### SentencePiece and YTTM tests ###
Added tests for testing YTTM and SentencePiece.
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"Thanks for this issue @Omarnabk! We moved the Label Studio ML backend to a new repo, so the import path needs to be updated, and you'll also need to install the ML backend in addition to Label Studio.\r\n\r\na PR to fix this in the Jupyter notebook is coming soon, but in the meantime you can do the following:\r\n1... | 2021-03-25T23:03:55 | 2021-03-30T23:02:46 | 2021-03-30T23:02:46 | NONE | {
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---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-2-ddf1c99aa95c> in <module>
1 import nemo
2 import nemo.collections.asr as nemo_asr
----> 3 from label_studio.ml import LabelStudioMLBase
4
5
ImportError: cannot import name 'LabelStudioMLBase' from 'label_studio.ml' (/home/SAM/anaconda3/envs/nemo/lib/python3.7/site-packages/label_studio/ml/__init__.py)
what could be the problem? | {
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I try to run demo asr using asr_webapp but it's show error:
`ImportError: cannot import name 'UnencryptedCookieSessionFactoryConfig' from 'pyramid.session' (unknown location)`
and then i check pyramid version: 2.0 and in pyramid github, they say UnencryptedCookieSessionFactoryConfig has remove. I try to another version pyramid for can running demo but it's failed.
So, Can anyone check this demo again?
Thanks. | {
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"Please start using rc1 version, we are going to release it soon\r\n```\r\n python -m pip install git+https://github.com/NVIDIA/NeMo.git@r1.0.0rc1#egg=nemo_toolkit[all]\r\n```"
] | 2021-03-25T07:11:57 | 2021-03-31T17:21:28 | 2021-03-31T17:21:28 | NONE | {
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} | null | null | null | **Describe the bug**
I created a fresh conda environment and installed PyTorch==1.7.1 and nemo_toolkit[all]==1.0.0b3.
Although it says that NeMo is not supporting PyTorch 1.8, installing NeMo uninstalled PyTorch 1.7.1 and installed 1.8 version. I am guessing this is a bug. I even got the following message:
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchaudio 0.7.2 requires torch==1.7.1, but you have torch 1.8.0 which is incompatible.
```
**Steps/Code to reproduce bug**
- Create a new conda environment with Python 3.8.2
- Install PyTorch 1.7.1 with pip
- Install NeMo 1.0.0b3 with pip
**Expected behavior**
Should install PyTorch 1.7.1 instead of 1.8
**Environment overview (please complete the following information)**
- Environment location: Windows 10
- Method of NeMo install: pip install nemo_toolkit[all]==1.0.0b3
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Windows 10
- PyTorch version: 1.7.1 - 1.8
- Python version: 3.8.2
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} | # Changelog
- Update ASR tarred dataset preprocessing script to enable more than 1 workers via joblib
- Maintaining backward compatibility with exact same results even when `workers` > 1 as compared to `workers` == 1.
# Caveats
- The new manifest itself will temporarily require double the memory in ram now until all of the tarfiles are processed as we need to flatten the intermediate representation of each create tarfile output manifest | {
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"This pull request **introduces 4 alerts** when merging 146952ca71c98f2274c49f2472a080f786157e45 into d79d5d3ed06d86d24b82214363755174313d6d9d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-f5bf2e88dcb9955a61864b2cba2d1fc2e6b13b3b)\n\n**new alerts:**\n\n* 2 for Variable defined multiple times\n... | 2021-03-25T03:42:37 | 2021-03-26T21:40:54 | 2021-03-26T21:40:34 | COLLABORATOR | {
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} | Tokenizing data and saving it to a tarred dataset is an embarrassingly parallel task.
We parallelize the data processing by partioning the src and tgt text lines into chunks.
Each chunk is then written to a tarred dataset in parallel using the joblib library.
Also note that tarred datasets are to be used when the dataset is too large to be handled within system memory so
we made sure that our approach was also memory friendly. The memory consumed while multiprocessing depends
only on the number of jobs and the chunk size (n_jobs * chunk_size).
# Results
We tested the end-to-end time of automatic data processing for NMT (do_training=False) with multiprocessing for two language pairs. We used a chunk size of 1M lines.
En-De used a parallel training set with 4524868 lines (~4.5M):
```
n_preproc_jobs: 4, elapsed seconds: 122
n_preproc_jobs: 2, elapsed seconds: 207
n_preproc_jobs: 1, elapsed seconds: 373
```
En-Es used a parallel training set with 65630789 lines (~65M):
```
n_preproc_jobs: 64, elapsed seconds: 514
n_preproc_jobs: 32, elapsed seconds: 602
n_preproc_jobs: 16, elapsed seconds: 837
n_preproc_jobs: 8, elapsed seconds: 1326
n_preproc_jobs: 1, elapsed seconds: 8168
```
As we can see, for larger datasets the speedup can save a lot of time! ~15x speed up or 2.2 hours vs 8.5 minutes.
Since we have to preprocess our dataset for different tokenizers, vocabulary sizes, and batch sizes, it's process that we end up doing quite a lot when experimenting and it can be very useful to save a couple of hours per experiment.
# Usage
Multi-processing will now happen automatically. By default NMT will attempt to use every process available (except for 2):
```
train_ds.n_preproc_jobs=-2
```
Not every process will necessarily be used as there has to be at least one chunk per process. To configure multi-processing manually simply set:
```
train_ds.n_preproc_jobs=4
```
# TODO
- [x] Testing for backwards compatibility and convergence.
- [x] General clean up
- [x] Add jenkins tests
- [x] Update script version | {
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- Preserve updated logs inside parameter used by wandb and tensorboard. | {
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"@okuchaiev , small enhancement to the quantization script.\r\n\r\nI'll file another PR to add README later"
] | 2021-03-24T20:36:42 | 2021-03-24T22:57:59 | 2021-03-24T22:57:59 | CONTRIBUTOR | {
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"Can you try installing from main branch the most recent version?",
"Thank you for your prompt response! \r\nI've tried to installed it like this: \r\napt-get update && apt-get install -y libsndfile1 ffmpeg\r\npip install Cython\r\npip install nemo_toolkit[all]==1.0.0b3\r\n\r\nand I got another error related to w... | 2021-03-24T14:15:37 | 2021-03-29T10:58:07 | 2021-03-29T10:58:07 | NONE | {
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} | null | null | null | **Describe your question**
How to convert .nemo ckpt to onnx?
I've tried to convert a .nemo model trained on 1.0.0b1 version of nemo to onnx.
I want to use the onnx for deployment using Triton later on.
**Environment overview **
- Environment location: Conda
- Method of NeMo install: pip install== 1.0.0b3
**Environment details**
- OS version: ubuntu 18.04
- Torch 1.7.1
- Python 3.6.13
**Additional context**
The model was trained on Arabic langauge.
I've tried to use this file: https://github.com/NVIDIA/NeMo/blob/v1.0.0b3/scripts/convasr_to_single_onnx.py
and got this error:
Thank you for your time! | {
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"This pull request **introduces 2 alerts** and **fixes 2** when merging b07c408dc655861e9fbfee59e04050c384b155ae into cd194c88d7581e684a5ad6a0d2031a6b68300969 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-693d8acbfaccfaceca6d6d47d3c4d90daddf0595)\n\n**new alerts:**\n\n* 2 for Unused import\n\n... | 2021-03-23T21:15:58 | 2021-03-30T14:54:31 | 2021-03-30T14:54:28 | COLLABORATOR | {
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} | - Deprecates use of torch_stft and associated parameters: stft_conv, stft_exact_pad for the FilterbankFeatures and AudioToMelSpectrogramPreprocessor classes
- stft_exact_pad has been replaced with exact_pad
- Update AudioToMelSpectrogramPreprocessor's calculation of seq_len
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- Remove add_misc and load_audio from AudioTextDataset and child classes
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"@titu1994 Thanks! Updated. Will merge after the checks. "
] | 2021-03-23T06:22:19 | 2021-03-23T21:47:24 | 2021-03-23T20:38:42 | COLLABORATOR | {
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"@skyw any thoughts?",
"It should be fixed in 21.03.\r\nWhat failed is the checker when calling `torch.onnx.export`. It needs opset13 support which comes with Pytorch 1.8 release. 21.02 was based on a dev version of 1.8.",
"thanks! Close this question."
] | 2021-03-23T03:27:28 | 2021-03-26T03:50:24 | 2021-03-26T03:50:24 | CONTRIBUTOR | {
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```python
python examples/asr/quantization/speech_to_text_quant_infer.py --asr_model=QuartzNet15x5Base-En-max-256.nemo --dataset=/raid/data/data_librispeech/manifest.sample --onnx
```
The error:
```
W0323 03:12:57.813599 140599427417920 tensor_quantizer.py:280] Use Pytorch's native experimental fake quantization.
[NeMo W 2021-03-23 03:12:57 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py:1169: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input audio_signal
warnings.warn('No names were found for specified dynamic axes of provided input.'
[NeMo W 2021-03-23 03:12:57 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py:1169: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input logprobs
warnings.warn('No names were found for specified dynamic axes of provided input.'
[NeMo W 2021-03-23 03:12:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_quantization-2.1.0-py3.8-linux-x86_64.egg/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
inputs, amax.item() / bound, 0,
[NeMo W 2021-03-23 03:12:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_quantization-2.1.0-py3.8-linux-x86_64.egg/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])
Warning: Constant folding supported for only opsets 9, 10, and 11. Constant folding not applied.
[W] Inference failed. You may want to try enabling partitioning to see better results. Note: Error was:
[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (3317) of operator (DequantizeLinear) in node (DequantizeLinear_2326) is invalid.
```
QuartzNet15x5Base-En-max-256.nemo is from:
```python
python speech_to_text_calibrate.py --asr_model=QuartzNet15x5Base-En --dataset=/raid/data/data_librispeech/manifest.sample
```
Is this the right way to quantize asr model ? If it is, how can I fix this error ?
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)] Docker
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install. ./reinstall.sh
- If method of install is [Docker], provide `docker pull` & `docker run` commands used.
```
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo
DOCKER_BUILDKIT=1 docker build -f ./Dockerfile .
```
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | Reorg notebook
Include diagram to explain overlapped prediction
Add/update instruction on
1. offline streaming inference on audio files (offline VAD);
2. finetuning and use posterior;
3. threshold tuning;
4. online streaming inference;
5. online streaming inference from a microphone's stream.
Signed-off-by: fayejf <fayejf07@gmail.com> | {
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- Fix link to nemo docs in ASR BPE Notebook | {
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"Hi, unfortunately, at this moment we only support Linux (Ubuntu). "
] | 2021-03-22T18:50:02 | 2021-03-25T04:17:37 | 2021-03-25T04:17:37 | NONE | {
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} | null | null | null | **Describe your question**
Hi i'm new to NeMo and I wanted to get started with it to use it for some ProjectIdeas.
I want to use NeMo offline so I tried to get it going on Pycharm.
Now I installed everything from Cuda to Kaldi and it's still not working.
I'm getting a freeze.Support() Runtime Error from multiprocessing/spawn.py when I only try to import
nemo.collections.asr only on Pycharm.
When I tryed it on JupiterNotebook this Error was gone but if I try to run a model torch/nn/modules/module.py l.826
throws "AttributeError: can't set attribute"
I wanna know now if there is a easy way to get such Problems fixed or a way to uninstall everything and get one clean installation for windows that works on pycharm aswell.
Or is it the best Choice to get Linux in a virtual machine and run it there?
I would very much appreciate any help.
Greetings from Germany.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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"Hi thanks for your question! The offset in manifest (for all audio related manifest not limited to VAD) means the **starting point (second) of the audio snippet**. \r\nFor example` \"audio_a.wav, offset:0, duarion:10\"` means \"audio snippet of audio_a from 0 to 10 seconds\"; SImilarly \r\n`\"audio_a.wav, offset:... | 2021-03-22T17:10:27 | 2021-03-23T05:32:55 | 2021-03-23T05:32:55 | NONE | {
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How can I decide it and what is it exactly?
Thanks in advance. | {
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Fixing those here.
# TODO
~~We need to cut a new pip package from Megatron and update NeMo requirements.~~
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"This pull request **introduces 2 alerts** when merging 82fa2be0844042c845af3cf43e7e5e2cf989eab0 into da48c997177f89a9e305e508ab27b15954044814 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-87eabc38d29520a4b3fc1bcfa066c69c4774062f)\n\n**new alerts:**\n\n* 1 for Unnecessary pass\n* 1 for Unused ... | 2021-03-19T22:48:41 | 2021-03-23T21:47:14 | 2021-03-23T05:22:56 | COLLABORATOR | {
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} | # Changelog
- Adds Flask based ASR Service with support for gunicorn and docker container
- Enables inference via pretrained checkpoint to evaluate models on audio data that is formatted to 16 KHz and mono-channel wav files.
- Enables upload of trained checkpoints for evaluation of custom models (can be disabled inside `templates/main.html`)
# Usage
## Flask Server
```bash
$ python asr_service.py
```
## Gunicorn Server
By default, the gunicorn server config is loaded from `gunicorn.conf.py`. Command line values will override these arguments.
**NOTE**: Limit the number of workers since each worker will maintain the cache of models in memory.
```bash
# wsgi.py is a thin wrapper over `asr_service.py` to enable the service without debugging mode
$ gunicorn wsgi:app
```
## Docker Server
The app can be built and run easily using docker.
Build the docker container using -
```bash
$ bash docker_container_build.sh
```
Run the docker container (in detached mode with all available GPUs) using -
```bash
$ bash docker_container_run.sh
``` | {
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"To save checkpoints at every epoch pass `+exp_manager.checkpoint_callback_params.save_top_k=num_epochs` while calling script or add through yaml file ",
"nemo saves the last checkpoint, not the ones at the end of each epoch. It can also saves the top K best ones. You need to set K along with what metric to moni... | 2021-03-19T11:20:02 | 2021-04-23T21:58:15 | 2021-04-23T21:58:15 | NONE | {
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} | null | null | null | **Describe the bug**
when i try to train the NeMo ASR model with the help of pre-train model quartznet(QuartzNet15x5Base-En) . model not saving checkpoint at the end of every epoch.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
model save chechpoint at end of every epoch.
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 18.04
- PyTorch 1.1.4
- Python 3.6
**Additional context**
Add any other context about the problem here.
Example: GPU model
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"At the moment we do not test/support Arm architectures. Do you know which module need MKL? Are you able to install Pytorch on Arm without MKL?"
] | 2021-03-18T12:38:06 | 2021-04-28T00:12:41 | 2021-04-28T00:12:41 | NONE | {
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} | null | null | null | **Describe your question**
I am getting the error on title on arm processor when trying to do inference with QuartzNet model. This is expected since MKL is not supported on arm. How can do inference on arm devices? Are there any alternatives?
I am using PyTorch 1.9 but I think this is not the reason for this error.
**Environment overview (please complete the following information)**
- Arm emulated Docker Container, running on Windows 10
- From source. Followed exact instructions explained on readme of 1.0.0b3:
```
apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
```
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 20.04 version
- PyTorch version 1.9
- Python version 3.8.5 | {
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"This pull request **introduces 1 alert** when merging c2593f8fe561b87c84cc95e892d8995a09a40592 into 29b1cbb91a96d97c4935739ea53bd3aa57214563 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d327932951605936d384c4f20833f2afebe27fb5)\n\n**new alerts:**\n\n* 1 for Implicit string concatenation in a... | 2021-03-18T08:43:54 | 2021-03-23T21:47:05 | 2021-03-23T17:34:15 | COLLABORATOR | {
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- Update MCV dataset preparation script to utilize v6.1 dataset | {
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"Hi, you can remove validation by either removing it from the YAML file or from CLI you can use the `~` operator.\r\n\r\n```\r\n~model.validation_ds\r\n```\r\n\r\nCould you give me some more information on your environment? Are you using an NVIDIA container?\r\n\r\nAlso could you clarify `Nemo branch - main (1.0.0r... | 2021-03-17T08:34:39 | 2021-04-20T05:31:31 | 2021-04-20T05:31:14 | NONE | {
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Hello,
This error report might seem little rough around the edges mainly because I've been trying out different language models in different nemo branches and within different environments too.
Starting with multi-gpu training of QAModel with _"megatron-bert-345m-uncased"_ lm on squadv2 dataset, gives me this error while its doing evaluation.
_Environment location - GCP instance (with 2 x Nvidia Tesla T4)
Nemo branch - main (1.0.0rc1)_
I am also getting this error when using DGX machine
_Environment location - NVIDIA DGX Station Version 4.7.0 (using 2 x Tesla V100)
Nemo branch - main (1.0.0rc1)_
Changing language model from megatron to "bert-base-uncased" does not give keyerror but gets stuck after running evaluation:
_Environment location - NVIDIA DGX Station Version 4.7.0 (using 2 x Tesla V100)
Nemo branch - main (1.0.0rc1)_
However I don't get any issue training on single gpu in this branch and also when training on multi-gpu in nemo's r1.0.0b3 branch, but I am not able to use megatron there. I found a similar bug report in huggingface transformers gitrepo ([#1472](https://github.com/huggingface/transformers/issues/1472)) where they mention workaround to not do evaluation while training, can this be done for QAModel too by its config file?
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"This pull request **introduces 5 alerts** when merging 9e7ed5e0720eb7b5f9498f3bd09ad4470488b8da into 3517e5686e2178e14dc4066e391784ccb9a8aba4 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-c0ff736e4f47dcf8fd899a21d2ce471404b7cbef)\n\n**new alerts:**\n\n* 5 for Explicit export is not defined",
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- Remove redundant subclasses for ASR which resolve to same base class | {
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"@yzhang123 is this the same qa PR but with DCO resolved?"
] | 2021-03-16T21:49:50 | 2021-03-16T22:29:30 | 2021-03-16T22:29:27 | CONTRIBUTOR | {
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} | Speech classification (Speech Command +VAD) docs
TODO:
make long table pretty
move speech command and vad inference once asr transcribe instruction is ready to have consistency.
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"@ekmb also updated PretrainedModelInfo for BERT and QAModel, also added quick start guide with available models for both to docs"
] | 2021-03-16T19:06:41 | 2021-03-18T03:14:13 | 2021-03-18T03:14:01 | CONTRIBUTOR | {
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* Adds speaker recognition and verification docs | {
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"This pull request **introduces 2 alerts** when merging 8d829d152d8410a49d235910aef490433478d3a9 into 39e2e4a86ec76fb96ac79e47b523e89b6bd3e7a0 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-b4d06bfdcdc04bba5c53e0dd570d5e155687daa4)\n\n**new alerts:**\n\n* 2 for Testing equality to None",
"Als... | 2021-03-16T03:50:37 | 2021-04-01T16:46:19 | 2021-03-30T21:02:42 | COLLABORATOR | {
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} | This PR adds support for training (as well as providing pre-trained) SentencePiece tokenizer to the **MTEncDecModel**.
~~Minor changes in SentencePiece wrapper to retrieve _unk_id_ and remove _special_tokens_ at the time of "ID to Text" conversion.~~ Changes to the SentencePiece wrapper have been moved to a new, more generic PR #1973.
### SentencePiece for NMT Usage ###
To use sentencepiece with MTEncDecModel, just set:
`model.encoder_tokenizer.library=sentencepiece` and
`model.decoder_tokenizer.library=sentencepiece `
A pre-trained SentencePiece model can be provided using the:
`model.encoder_tokenizer.tokenizer_model` and `model.decoder_tokenizer.tokenizer_model` tags.
In case no pre-trained SentencePiece model is provided, it will be trained based on the configurations provided.
The SentencePiece tokenizer can also be used for only the encoder while using a different tokenizer for the decoder, and vice versa. Similar mix-n-matches can also be done for training a tokenizer for encoder/decoder only (while providing a pre-trained version of the other) etc. | {
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- Update offline asr notebook to properly import and install RC branch NeMo for swig tools
- Patch RC1 dependency installation to force pin all torch dependencies | {
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} | These changes make our docs style similar to TLT docs. | {
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"Technically no, but it is always good to provide maximum number of speakers for higher accuracy. Support for dynamic computation for number of speakers is not yet fully tested. If you would like to try change \r\nmin_clusters=1 and max_clusters=max_num_speakers (which you are sure it won't exceed that number) \r\n... | 2021-03-14T21:55:22 | 2021-04-01T21:59:33 | 2021-04-01T21:59:33 | NONE | {
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} | null | null | null | From the[ speaker_diarization.yaml](https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/speaker_recognition/conf/speaker_diarization.yaml) file
There is `num_speakers: 2 # for each recording` value added in the yaml file.
Hence, do we need to know the number of speakers present in a audio recording to perform inference of speaker_diarization or can it be done unsupervised ? | {
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} | # Changelog
- Add equality check for set comparison
- Add total ordering and lesser-than impl for sorting
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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2. Update VAD model name in SD notebook | {
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} | The second part of https://github.com/NVIDIA/NeMo/pull/1777
1. Change self.log(tensorboard_logs) and remove the 'log' part in the return requested for the regression model
2. Fix tarred dataloader for speech regression support | {
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"@DEKHTIARJonathan The code here is installed via reinstall.sh - which does pip install .[all]. \n\nIt's still following all wheel semantics. The issue is that the pytorch container 21.02 is masquerading as a Pytorch 1.8.0a* - but it is in fact NOT pytorch 1.8. \n\nThis confuses Pytorch Lightning which has code swi... | 2021-03-12T18:44:11 | 2021-03-13T01:02:17 | 2021-03-13T00:14:53 | COLLABORATOR | {
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"should this also go to rc1 as well?"
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"PTAL. I addressed the ordering issue.",
"LGTM will merge after tests pass"
] | 2021-03-12T14:50:23 | 2021-08-17T14:50:44 | 2021-03-15T21:36:33 | CONTRIBUTOR | {
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} | It is 2021, we should use parallel computations for slow scripts when possible.
As each directory contains independent data, we can easily add more processes to get_librispeech_data script to decrease waiting time.
On my machine the speedup when using 4 processes was 7-8 fold.
The whole download process went from >2h waiting time to 15 minutes.
The downloaded data is identical, so there should not be any problems introduced by parallelizing the script. | {
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+ Speaker recognition fine-tuning script | {
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"Thanks @VahidooX ! Let's merge once @redoctopus gives go ahead."
] | 2021-03-12T05:30:05 | 2021-03-15T21:23:52 | 2021-03-15T21:23:49 | COLLABORATOR | {
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} | # Changelog
- Update documentation with new links and new models
- Add documentation for newer models
- Add docstrings for EncDecCTCModel and EncDecRNNTModel for training, validation and test set data loader setup
- Add documentation with links for datasets themselves
- Add documentation for useful misc classes | {
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"@borisfom or @drnikolaev would you please checkout the asr_model.py? \r\nI needed to make some changes to the export part. We did not pass the length to the encoder and it was fine for convolutional modules which do not use that input. But Conformer would use that input and export would not work on this model with... | 2021-03-11T23:19:10 | 2022-01-12T01:38:02 | 2021-03-18T23:57:26 | COLLABORATOR | {
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} | + Made positional embedding more efficient.
+ Updated the rel_shift() method to avoid flipping which needs significant memory.
+ Fixed the onnx issue.
+ Added att_context_size feature to limit the context of attention with masking. | {
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"> \r\n> \r\n> Can you remove it from the unittest test too.\r\n\r\nDone."
] | 2021-03-11T22:52:28 | 2021-05-03T21:54:37 | 2021-03-12T19:09:44 | COLLABORATOR | {
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"It's up to you if you want to merge it or not. My concern is that we are lacking good docstrings and are trying to make up for it by adding it in the documentation",
"LGTM merge when ready ",
"Ideally the API documentation for this module should incorporate the above information. So just adding it to the docst... | 2021-03-11T21:21:43 | 2021-03-11T23:14:18 | 2021-03-11T23:14:15 | COLLABORATOR | {
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} | Removed redundant parameter table
Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com> | {
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"Hi @fnsrc Thanks for reporting this.\r\nDid you run the first cell about the installation? Are you using the docker container? Would you mind share the version of the packages you are using? Thanks!",
"Looks like it's related to this[ PyTorch-lightning version issue](https://github.com/PyTorchLightning/pytorch-... | 2021-03-11T19:19:52 | 2021-04-24T14:29:26 | 2021-04-24T00:09:58 | NONE | {
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} | null | null | null | I run the code in https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_Detection.ipynb, however I got an error while runing below cell:
1 # NeMo's "core" package
----> 2 import nemo
3 # NeMo's ASR collection - this collections contains complete ASR models and
4 # building blocks (modules) for ASR
5 import nemo.collections.asr as nemo_asr
11 frames
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py in <module>()
23 TORCHTEXT_AVAILABLE = importlib.util.find_spec("torchtext") is not None
24 if TORCHTEXT_AVAILABLE:
---> 25 from torchtext.data import Batch
26 else:
27 Batch = type(None)
ImportError: cannot import name 'Batch' from 'torchtext.data' (/usr/local/lib/python3.7/dist-packages/torchtext/data/__init__.py)
and I got another error when I run below cell:
AttributeError Traceback (most recent call last)
<ipython-input-40-2e00f7ee79ea> in <module>()
----> 1 exp_dir = exp_manager(trainer, config.get("exp_manager", None))
1 frames
/content/MyVADNeMo/nemo/utils/exp_manager.py in get_log_dir(trainer, exp_dir, name, version, explicit_log_dir, use_datetime_version)
428 if trainer.logger.save_dir:
429 if exp_dir:
--> 430 raise LoggerMisconfigurationError(
431 "The pytorch lightning trainer that was passed to exp_manager contained a logger, the logger's "
432 f"save_dir was not None, and exp_dir ({exp_dir}) was not None. If trainer.logger.save_dir "
AttributeError: 'Trainer' object has no attribute 'is_slurm_managing_tasks'.
Thanks in advance! | {
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