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"No, that flag can accept a list of GPU ids. https://pytorch-lightning.readthedocs.io/en/stable/trainer.html#gpus \r\nSo that placeholder looks right to me."
] | 2020-10-29T14:48:21 | 2020-10-29T16:32:10 | 2020-10-29T16:32:10 | NONE | {
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} | Line 29 in `examples/nlp/glue_benchmark` says `trainer.gpus="[<CHANGE_TO_GPU_YOU_WANT_TO_USE>]` which is not correct since this parameter sets the number of GPUs to use during training.
I have updated the placeholder text accordingly to say `trainer.gpus="[<CHANGE_TO_NUMBER_OF_GPUS_TO_USE>]`. | {
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"No. Currently NeMo ASR models transcribe 55 as \"fifty five\". \r\nIn fact, if En normalization is tuned on (default) in data layer, then 55 will be converted to fifty five.\r\n\r\nKeep in mind that there could be many valid ways to say, for example, 2020 differently - you can say \"two thousands twenty\" or \"twe... | 2020-10-29T13:48:17 | 2022-12-19T21:51:25 | 2021-02-26T18:01:04 | NONE | {
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Are you guys planing to add **Inverse text normalization** models in NeMo. Something [like this](https://docs.aws.amazon.com/transcribe/latest/dg/how-numbers.html).
If anyone have worked on this before can you share some resources where I can study about this.
Thanks :) | {
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"are these fixes to 1.0.0b2? Why is this sent to main?\r\n"
] | 2020-10-28T23:22:10 | 2020-10-29T16:35:03 | 2020-10-29T16:34:21 | COLLABORATOR | {
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} | # Bugfixes
- NeMo Model Primer updated to use PTL 1.x api
- Fix WER calculation for ASR notebook
- Speech commands notebook update to use `-last.ckpt` instead of `-end.ckpt` as `-end.ckpt` no longer is created.
Edit: Colab updated some internal format, hence the large diff. No content has been changed.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | * pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* hard coding nemo version after kernel restart
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* pin to v1.0.0b2
Signed-off-by: ericharper <complex451@gmail.com>
* change --end to --last
Signed-off-by: ericharper <complex451@gmail.com> | {
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} | - [x] EFF dependency in requirements.txt (0.1.0 release)
- [x] Modified `ModelPT.save_to()`
- [x] Modified `ModelPT.restore_from()`
- [x] `ModelPT.__set_eff_save()`/`Model.__use_eff_save()` static methods for manual enabling/disabling saving using EFF (flag set to `True` by default).
- [x] unit test - polished test adding artifact
- [x] unit test - testing checkpoint encryption when using _eff_save_to()/_eff_restore_from() with provided encryption key.
`ModelPT.save_to()` logic:
- if EFF present and __use_eff_save flag set: save .nemo using EFF
- else: save .nemo using the old method
`ModelPT.restore_from()` logic:
- if EFF present: try to restore the model using EFF, if fails: try to restore using the old method
- else: restore .nemo using the old method
In short:
- Restore will be able to load both the existing .nemo files and the new EFF-based .nemo files
- All newly saved .nemo files will have EFF under the hood.
Note that from the content of the archive perspective, the new .nemo archive is a superset of the old format, i.e. the old .nemo file content:
```
+ model_config.yaml
+ model_weights.chkt
+ … (additional files a.k.a. artifacts)
```
The new .nemo file content:
```
+ manifest.yaml (<- this file is the only difference!)
+ model_config.yaml
+ model_weights.chkt
+ … (additional files a.k.a. artifacts)
``` | {
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"This pull request **introduces 1 alert** when merging 9c18170f1ed653b80bdf62dc30c810dbf3e5b87f into 48620bb1369dc72dcac7b9a4e75b113f1cc55d36 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-29b3095947becb5ca10111ca89fa4e93061b71fb)\n\n**new alerts:**\n\n* 1 for Unused import",
"is this a fix? ... | 2020-10-28T15:48:33 | 2020-11-06T21:19:57 | 2020-10-29T16:22:07 | COLLABORATOR | {
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} | 1. set exp_manager to `null` to reduce CI run time for most of the NLP examples - @VahidooX, @vladgets, @yzhang123 please check if it's ok to do for your scipts
2.Reorganized Megatron-LM tests, now we're testing:
- single GPU megatron loading from config and checkpoint
- 2 GPU model parallel
- 2 GPU loading from ngc
Signed-off-by: ekmb <ebakhturina@nvidia.com> | {
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"@SeanNaren when you commit, could you please do \"git commit -s ...\" to \"sign\" your commits.\r\nFor the already committed commits, DCO's bot output tells you https://github.com/NVIDIA/NeMo/pull/1361/checks?check_run_id=1321341664 how to sign already committed commits. \r\n\r\nThe -s flag means: \"certifies that... | 2020-10-28T09:54:15 | 2020-10-28T18:57:19 | 2020-10-28T17:49:18 | COLLABORATOR | {
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} | Ties to #568, it would be nice to have a script here to download common voice (and be language agnostic). The script works in a similar fashion to the librispeech script. Let me know if theres any issues. We have a blog post coming out where I had to format CV for NeMo :) | {
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"Reformatted ipynb using Colab as per Vahid's recommendation"
] | 2020-10-28T07:25:37 | 2020-10-29T16:26:06 | 2020-10-29T16:26:06 | CONTRIBUTOR | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
Pycharm reformats ipynb files, couldn't find how to turn it off. Duh... | {
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"Sadly, I don't know any right-to-left language so I can not comment on specifics.\r\n\r\nHowever, I would imagine that right-to-left could pose a problem for CTC mechanism because it tries to learn alignments left-to-right. Have you tried doing a mirror reflection of your transcripts (e.g. converting them to left... | 2020-10-27T22:11:04 | 2021-05-28T08:30:27 | 2021-05-28T08:30:27 | NONE | {
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} | null | null | null | I have followed all steps outlined in the link:
[](https://developer.nvidia.com/blog/jump-start-training-for-speech-recognition-models-with-nemo/)
I am using Quartznet 15*15 model for pretraining.
As mentioned, I have changed the vocabulary and set normalized transcription to false using the code below.
```
quartznet.change_vocabulary( new_vocabulary=[' ', 'ا', 'ب', 'پ', 'ت', 'ٹ', 'ث', 'ج', 'چ', 'ح', 'خ', 'د', 'ذ', 'ڈ', 'ر',
'ض', 'س', 'ش', 'ص', 'ط', 'ظ', 'ع', 'غ', 'ف', 'ق', 'ک', 'گ', 'ل', 'م','ن','و','ہ' , 'ھ' , 'ي', 'ے', 'ۃ', 'آ', 'ء',
]
)
```
And
```
params['model']['train_ds']['normalize_transcripts'] = False
params['model']['validation_ds']['normalize_transcripts'] = False
```
I have also changed the labels to the Urdu alphabet with:
```
params['model']['train_ds']['labels'] = [' ', 'ا', 'ب', 'پ', 'ت', 'ٹ', 'ث', 'ج', 'چ', 'ح', 'خ', 'د', 'ذ', 'ڈ', 'ر',
'ض', 'س', 'ش', 'ص', 'ط', 'ظ', 'ع', 'غ', 'ف', 'ق', 'ک', 'گ', 'ل', 'م','ن','و','ہ' , 'ھ' , 'ي', 'ے', 'ۃ', 'آ', 'ء',
]
params['model']['validation_ds']['labels'] = [' ', 'ا', 'ب', 'پ', 'ت', 'ٹ', 'ث', 'ج', 'چ', 'ح', 'خ', 'د', 'ذ', 'ڈ', 'ر',
'ض', 'س', 'ش', 'ص', 'ط', 'ظ', 'ع', 'غ', 'ف', 'ق', 'ک', 'گ', 'ل', 'م','ن','و','ہ' , 'ھ' , 'ي', 'ے', 'ۃ', 'آ', 'ء',
]
```
I have used learning rate of 0.000001 as mentioned on link:
[](https://github.com/NVIDIA/NeMo/issues/1107)
I am not sure why my WER is so high. The audio files are sampled at 16k. Please see sample screen shot below...
Is someone who attempted something similar able to help resolve this?
```
[NeMo I 2020-10-27 22:07:37 wer:160] reference:مران بگٹ شاد امرتا سنگھ کا بھس بدل کر منڈھے پر چھ آا
[NeMo I 2020-10-27 22:07:37 wer:161] decoded : ابڈب گبگب بذب ب خء ابف بمٹشفگبءبارتخروتبءخب مبمتاف بم اخءخڈابخبڈبمبڈذ ب بابٹگۃءمٹحڈاءمءمپخفابڈفبامافاڈ
[NeMo I 2020-10-27 22:07:38 wer:159]
[NeMo I 2020-10-27 22:07:38 wer:160] reference:عمر چور اور اغوا کار پراچہ کو رشم باھ سے باندھ کر ٹھڈے لگاتا موضع کے باہر مقبوضہ تھانہ تک لاا
[NeMo I 2020-10-27 22:07:38 wer:161] decoded :ت سبڈب متمتڈخ طابابمبمگبلابف بچابرببمبڈراتخبمبمبخ پتمخڈخذ رخباخاباخ ت اب مبتءتءبء اب مب تبمرامڈ ءما ب خبب خبڈ بمبم بمربڈ ءطڈبخب سباڈف مبڈابمبٹڈف تذم مذءمبخگبمرمڈمبثبخگبٹمبگبءبخءٹبمگ
[NeMo I 2020-10-27 22:07:40 wer:159]
[NeMo I 2020-10-27 22:07:40 wer:160] reference:ورلڈکپ نے اقوام کا افسوسناک تنا اور انگخت ہچ کا اور دنا م شگفتہ کلچر بدار کا
[NeMo I 2020-10-27 22:07:40 wer:161] decoded :ت اڈباڈ ذگذافڈچ بٹمتواخڈا مخابخ بمبو پ ءتطءطشخ گمتمب بءبخگبمبڈخا ب ابباتبء خ فتف بءبم ڈب بب مءٹمب ا خ ب راذخف ءا رلءستڈبببابڈا ابلرخبخفگ
```
**Environment overview (please complete the following information)**
- Environment location: Kaggle
- Method of NeMo install: [pip install or from source]. !python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
**Environment details**
- OS version: Kaggle
- PyTorch version: 1.0.3
- Python version: 3.7
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"Just To let you know that I trained Tacotron2 without wandb logging and it worked successfully ",
"That makes sense, since I run TTS experiments with the tensorboard logger only and add_image() is tensorboard's API. I'll add a check so it only logs to tensorboard. If you want to log images to WandB, feel free to... | 2020-10-27T15:48:07 | 2020-11-25T16:51:27 | 2020-11-25T16:51:27 | NONE | {
"total": 0,
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} | null | null | null | Nemo version: 1.0.0b1
PTL version: 0.9.0
**when running Tacotron2 with wandb logging I got this error**
`Traceback (most recent call last):
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/hydra/_internal/utils.py", line 203, in run_and_report
return func()
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/hydra/_internal/utils.py", line 355, in <lambda>
overrides=args.overrides,
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/hydra/_internal/hydra.py", line 109, in run
job_subdir_key=None,
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/hydra/core/utils.py", line 123, in run_job
ret.return_value = task_function(task_cfg)
File "aaj/tts/tacotron2_v1.py", line 32, in main
trainer.fit(tocatron2)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn
result = fn(self, *args, **kwargs)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1058, in fit
results = self.accelerator_backend.spawn_ddp_children(model)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/accelerators/ddp_backend.py", line 123, in spawn_ddp_children
results = self.ddp_train(local_rank, mp_queue=None, model=model, is_master=True)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/accelerators/ddp_backend.py", line 224, in ddp_train
results = self.trainer.run_pretrain_routine(model)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1224, in run_pretrain_routine
self._run_sanity_check(ref_model, model)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1257, in _run_sanity_check
eval_results = self._evaluate(model, self.val_dataloaders, max_batches, False)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 396, in _evaluate
eval_results = self.__run_eval_epoch_end(test_mode, outputs, dataloaders, using_eval_result)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 490, in __run_eval_epoch_end
eval_results = model.validation_epoch_end(eval_results)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/nemo/collections/tts/models/tacotron2.py", line 266, in validation_epoch_end
add_audio=False,
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py", line 27, in wrapped_fn
return fn(*args, **kwargs)
File "/home/khalil/anaconda3/envs/nemo_1.0.0b1/lib/python3.7/site-packages/nemo/collections/tts/helpers/helpers.py", line 89, in tacotron2_log_to_tb_func
swriter.add_image(
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"Changing [here](https://github.com/NVIDIA/NeMo/blob/6796faa62e9d9c42ee86f860f1a79d8db15561ae/nemo/collections/asr/modules/beam_search_decoder.py#L57) `return {\"predictions\": NeuralType(('B', 'T'), PredictionsType())}` to `return {\"predictions\": [NeuralType(('B', 'T'), PredictionsType())]}` (note the square bra... | 2020-10-27T15:26:28 | 2021-10-07T11:30:58 | 2021-06-10T18:57:37 | NONE | {
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} | null | null | null | **Describe the bug**
Traceback (most recent call last):
File "speech_to_text_beamsearch.py", line 141, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "speech_to_text_beamsearch.py", line 122, in main
res = beam_search_lm.forward(log_probs=log_probs, log_probs_length=log_probs_len)
File "/opt/conda/lib/python3.6/site-packages/nemo_toolkit-1.0.0b2-py3.6.egg/nemo/core/classes/common.py", line 513, in __call__
instance._attach_and_validate_output_types(output_types=output_types, out_objects=outputs)
File "/opt/conda/lib/python3.6/site-packages/nemo_toolkit-1.0.0b2-py3.6.egg/nemo/core/classes/common.py", line 161, in _attach_and_validate_output_types
len(out_container), len(output_types)
TypeError: Number of output arguments provided (4) is not as expected (1)
**Steps/Code to reproduce bug**
My batch size is 4, I'm trying to use beam search decoder

**Expected behavior**
When I remove typecheck [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/beam_search_decoder.py#L87), it works fine.
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"Thank you!"
] | 2020-10-27T14:52:09 | 2020-10-27T21:22:35 | 2020-10-27T21:22:35 | CONTRIBUTOR | {
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} | The `attach_onnx_to_onnx` method merges two onnx graphs. It uses
`onnx.helper.make_model`. That has an `opset_import` optional argument.
If this is not set, it will use whatever the default for your onnx
version is.
I found this playing around with exporting `quartznet` in the `main` branch on the `nvcr.io/nvidia/pytorch:20.09-py3` container. While the `encoder` and `decoder` `.onnx` models were opset version 12 (as specified in the `export` method), the single model `.onnx` was opset version 13. This change causes it to use the same opset version as model1.
For example: https://github.com/NVIDIA/NeMo/blob/d061dfd7970b95790d713b24f7874511129833e5/nemo/collections/asr/models/ctc_models.py#L503
I think, given the context, it is safe to assume that `model1` and `model2` will have the same opset version? | {
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"You can use .save_to/.restore_from methods to save to and restore from \".nemo\" file. \r\n\".nemo\" file is a simply \".tar.gz\" file (you can rename it to tar.gz and unpack) with checkpoint weights, configuration yaml which was used and (for some models) additional artifacts such as tokenizer's vocabulary, etc. ... | 2020-10-27T13:10:27 | 2020-10-27T16:04:59 | 2020-10-27T16:04:58 | NONE | {
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I am trying to save the model after fine tuning by using: torch.save(quartznet,'fine_tuned2.pt')
but I get this error:
AttributeError: Can't pickle local object 'FilterbankFeatures.__init__.<locals>.<lambda>'
how can I save the model and load it back again?
thanks
Niloo
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} | Added `BRANCH` variable to first cell of tutorial notebooks, so that the NeMo version can be easily updated for releases.
Updated README to link to the latest release so that the linked Tutorial Notebooks will be pinned to a tested version and also installing the correct version of NeMo when using the notebook on Google Colab. | {
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"This pull request **introduces 5 alerts** when merging 8c0e7e3d211117ebadddc45c3491357adb7fc085 into 94b64d32c0c54fd680b34c3b7dfb664b1035481b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-590b9ded82eb997004669a408fbab57b8f746582)\n\n**new alerts:**\n\n* 3 for Unused import\n* 1 for Inconsiste... | 2020-10-26T20:47:17 | 2021-01-12T22:24:14 | 2021-01-12T21:59:37 | COLLABORATOR | {
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} | Closes #1342. I think the code has been ported over, but I'm sure it can be cleaned up and contains a few bugs.
Haven't tested the wav2vec_asr.py model at all and it's probably broken, but I managed to get pre-training working on AN4, but still need to investigate further!
Any assistance will be appreciated :) cc @VahidooX | {
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"Let's merge #1345 into your PR"
] | 2020-10-26T18:03:38 | 2020-10-26T20:48:49 | 2020-10-26T20:25:42 | CONTRIBUTOR | {
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} | Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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"@Fackor I had this error while working with `pytorch_lightning==0.9.0`. Upgrading it to the `pytorch_lightning==1.0.3` solved the issue for me.\r\n\r\nIf you still want to work with `pytorch_lightning==0.9.0`, you should change `accelerator: ddp` to `distributed_backend: ddp` in [the config file](https://github.... | 2020-10-26T14:54:10 | 2021-05-28T08:29:47 | 2021-05-28T08:29:47 | NONE | {
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} | null | null | null | **Description**
The following error is reported when trying to run the GLUE benchmark notebook in Google Collab.
`TypeError: __init__() got an unexpected keyword argument 'accelerator'`
**Steps/Code to reproduce bug**
1. Open https://github.com/NVIDIA/NeMo.
2. Open the [GLUE benchmark notebook](https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/GLUE_Benchmark.ipynb) in Google Collab.
4. Run the required cells until the following code block is reached.
```# lets modify some trainer configs
# checks if we have GPU available and uses it
cuda = 1 if torch.cuda.is_available() else 0
config.trainer.gpus = cuda
config.trainer.precision = 16 if torch.cuda.is_available() else 32
# for mixed precision training, uncomment the line below (precision should be set to 16 and amp_level to O1):
# config.trainer.amp_level = O1
# remove distributed training flags
config.trainer.accelerator = None
# setup max number of steps to reduce training time for demonstration purposes of this tutorial
config.trainer.max_steps = 128
trainer = pl.Trainer(**config.trainer)
```
**Expected behavior**
The GLUE benchmark notebook should run without any errors.
**Environment overview (please complete the following information)**
- Environment location: Google Collab
- Method of NeMo install: `pip install nemo-toolkit[nlp]==1.0.0b1`
**Environment details**
- OS version: MacOS Catalina 10.15.7
- PyTorch version: 1.6.0+cu101
- Python version: Python 3.6.9 | {
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"Thanks for starting this! Yes, wave2vec 2.0 looks very intriguing and is something we wanted to try but did not have a chance yet.\r\n\r\nTo answer your questions:\r\n\r\n1. Yes! We will be happy to see your wave2vec 2.0 PR. Please have a look at https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md and reach o... | 2020-10-26T14:01:45 | 2022-01-18T18:39:54 | 2022-01-18T18:39:54 | COLLABORATOR | {
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} | null | null | null | Hey guys! Well done on the repo and congrats on the PL 1.0 integration :)
Something I've been working on for a bit is migrating [fairseq's Wav2Vec 2.0](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec) implementation to NeMo, primarily because FairSeq is a pain to install, has far too much boilerplate for my liking and doesn't offer the flexibility/integration like NeMo (backed by PL).
Based on results from the [paper](https://arxiv.org/pdf/2006.11477.pdf) this model has extreme promise. If it's made more accessible to users and easier to scale, we could continue to see impressive results!
You can see my progress [here](https://github.com/SeanNaren/NeMo/tree/feature/wav2vec/nemo/collections/asr/models/wav2vec) which has stagnated a bit (planning to get back to it soon).
Questions I have to the team are:
- Is there the all clear to integrate Wav2Vec into NeMo via porting code from FairSeq into the repo? Currently it seems most support ASR architectures are NVIDIA based research, but I'm hoping for flexibility here :) The alternative would be to live on it's own fork or branch but I'd rather see it make its way into NeMo
- Is there enough popularity for the model for any assistance in the integration portion? I think I can manage on my own, but would be nice to gain additional eyes
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"This works to me on the latest master:\r\n```\r\n model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En')\r\n```\r\nI think we had a fix for strict=False merged recently. Could you please try again (without strict=False) and re-open the bug if the issue still persists",
"Just do... | 2020-10-26T13:52:37 | 2020-11-09T17:04:36 | 2020-11-09T17:04:35 | NONE | {
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Getting below error while loading ASR model:-
Traceback (most recent call last):
File "offline_asr.py", line 3, in <module>
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En', strict=False)
File "C:\Users\e5602894\Desktop\NeMo-main\nemo\core\classes\common.py", line 407, in from_pretrained
strict=strict,
File "C:\Users\e5602894\Desktop\NeMo-main\nemo\core\classes\modelPT.py", line 244, in restore_from
instance = cls.from_config_dict(config=conf)
File "C:\Users\e5602894\Desktop\NeMo-main\nemo\core\classes\common.py", line 259, in from_config_dict
instance = cls(cfg=config)
File "C:\Users\e5602894\Desktop\NeMo-main\nemo\collections\asr\models\ctc_models.py", line 112, in __init__
dist_sync_on_step=True,
TypeError: Can't instantiate abstract class WER with abstract methods forward
I was not getting this error till last week even while using 1.0.0b1 version. But after #1319 I re-downloaded source code to use CTC Beam Search Decoder with LM and getting this error. | {
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"Hey @catskillsresearch, I don't have a solution to your problem but I rather have a question for you. \r\nI am also working on Unicode range characters, like yours. When I am training on a pretrained model, the reference string I am getting while training is empty. Did you face the same issue, how did you resolve ... | 2020-10-24T15:01:24 | 2021-04-23T23:59:55 | 2021-04-23T23:59:53 | NONE | {
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Can you think of some workarounds to improve this other than just grinding on with the training? For example:
```
[NeMo I 2020-10-24 10:51:06 wer:149] reference:chứ sao cũng gần mà chắc là cố gắng cưới lúc mùa hè để cho bé Dung về
[NeMo I 2020-10-24 10:51:06 wer:150] decoded :chứ sao cũng gần mà chắc là cố gắng cướii lúc mùa hè để cho bé Dung vềé
Epoch 171: 21%|███████████████████████████████████████████▋ | 99/464 [00:58<03:35, 1.69it/s, loss=8.938, v_num=4][NeMo I 2020-10-24 10:51:35 wer:148]
[NeMo I 2020-10-24 10:51:35 wer:149] reference:chửi gì chút xíu nữa đi chút xíu nữa à
[NeMo I 2020-10-24 10:51:35 wer:150] decoded :chứi vì chút xíu nữa đâi chút xíu nữa àa
Epoch 171: 32%|█████████████████████████████████████████████████████████████████▌ | 149/464 [01:28<03:07, 1.68it/s, loss=9.278, v_num=4][NeMo I 2020-10-24 10:52:05 wer:148]
[NeMo I 2020-10-24 10:52:05 wer:149] reference:chắc là không ơi cuối tháng sáu mới thi xong mà
[NeMo I 2020-10-24 10:52:05 wer:150] decoded :chắc là không ơi cuối tháng sáu mới thi xongàé
Epoch 171: 43%|███████████████████████████████████████████████████████████████████████████████████████▍ | 199/464 [01:55<02:34, 1.72it/s, loss=8.951, v_num=4][NeMo I 2020-10-24 10:52:32 wer:148]
[NeMo I 2020-10-24 10:52:32 wer:149] reference:tự nhiên ở nhà với Tư rồi vòng về cho tụi nó ăn bữa còn có một ngày à
[NeMo I 2020-10-24 10:52:32 wer:150] decoded :tự nhên ở nhà với Tư rồi vòng về cho tụi ng ăn bữa còn có một ngày àé
``` | {
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"Thanks Tom,\r\nat this moment it's unavoidable, but let's talk to Vlad - the owner",
"What are the questions to me? ",
"How can we instantiate the model without providing so much data?",
"To make inference with a trained model basically we need only Dictionary of Intent and Slots, to know how to interpret th... | 2020-10-24T05:00:39 | 2020-10-27T22:08:00 | 2020-10-27T22:08:00 | CONTRIBUTOR | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com> | {
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} | # Changelog
- Patch multidataloader to add all of the keys from the validation / test steps.
- Add the default `val/test` log results, where default is designated by `val_dl_idx` or `test_dl_idx` (optional keys in the *_ds configs which default to the 0th dataloader).
- Patch ASR wer computation.
- It was computing wer two times, which impacts execution time and causes logs to be printed twice.
- Patch all subclasses of `training_step` to automatically remove all log dicts and simply use the new logging api in place.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | PR introduces:
- configen files to generate PT DataLoader and PTL Trainer Structured Configs
- generated (and manually curated) Structured Configs for PT DataLoader and PTL Trainer
Just wanting to draw your attention to the hydra `configen` tool. | {
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] | 2020-10-22T21:17:03 | 2020-12-28T19:51:45 | 2020-10-23T22:42:57 | COLLABORATOR | {
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} | Hooks that we were previously using are now gone.
We were able to initialize model parallel and configure ddp from `setup` hook. | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
Now we can do
```
$ python convasr_to_single_onnx.py --nemo_file SpeakerNet.nemo --onnx_file SpeakerNet.onnx --model_type speaker
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1330 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1330/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1330/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1330/events | https://github.com/NVIDIA/NeMo/issues/1330 | 727,303,010 | MDU6SXNzdWU3MjczMDMwMTA= | 1,330 | Problem faced during Training on Different Language. | {
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"I am facing similar issues using unicode, can you share some example lines from your manifest? One possibility is that you need to use `ensure_ascii=False` as an option to json.dump when generating the manifests: `json.dump(metadata, f, ensure_ascii=False)`",
"@gopesh97 have you got any success on Hindi. I'm wor... | 2020-10-22T11:30:06 | 2021-04-23T23:59:08 | 2021-04-23T23:59:08 | NONE | {
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} | null | null | null | I am using pre-trained quartznet 15x5, for transfer learning for the Hindi language, with a different set of vocab (Devanagari characters.)
While training I am facing mainly 2 issues:
1. The reference string that appears during training is blank.
2. While saving the model using `quartznet.save_to('path/to/save')` , I am getting `'ascii' codec can't encode character '\u091b' in position 4407: ordinal not in range(128)`
Please provide a solution to overcome the abovementioned issues.
**Environment overview**
- Environment location: PyTorch GPU docker
- Method of NeMo install: Installed from source
**Additional context**
GPU model - 4 x RTX 2080Ti , 12 GB vRAM. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1329 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1329/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1329/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1329/events | https://github.com/NVIDIA/NeMo/issues/1329 | 727,124,207 | MDU6SXNzdWU3MjcxMjQyMDc= | 1,329 | [Question] How can I get character timestamps / word timestamps in Nemo ASR | {
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"I think you need to wait for this PR to merge first https://github.com/NVIDIA/NeMo/pull/1319\r\n\r\nBased on what I saw in this notebook https://github.com/NVIDIA/NeMo/blob/73875d874b006b672a8e407687ef414ef7a7b282/tutorials/asr/Offline_ASR.ipynb"
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Thanks,
S
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https://api.github.com/repos/NVIDIA/NeMo/issues/1328 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1328/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1328/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1328/events | https://github.com/NVIDIA/NeMo/pull/1328 | 726,981,776 | MDExOlB1bGxSZXF1ZXN0NTA3OTYyMDU3 | 1,328 | Add support for model level dataclass/config class | {
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} | # Changelog
- Adds ModelPT level support for Dataclass and infrastructure to merge dataclasses with OmegaConf/Hydra overrides
- **[Prototype phase]**
# PR overview
- At the model level, enable potential signature `ModelPT(cfg=Union[OmegaConf, ModelPTConfig], ...).
- Support hybrid override approach - dataclass for base config, hydra/omegaconf for user provided overrides.
- Explore potential drawbacks of usage
- Explore interop with Hydra.
# Examples
- A test case which creates a Model config and then updates with existing model.cfg attributes.
- Two scripts which explore hybrid Dataclass + yaml overrides and full structured config workflow
- For hybrid workflow, notice reduced number of config parameters (some more can potentially be removed if we dont want to allow users to override easily using Hydra).
- For full structured config workflow, shows the severe overhead in code to describe model architecture and supporting components etc.
# Limitations
- This PR does not explore / operate on Hydra's ConfigStore. It uses base dataclass and interops at OmegaConf level only. This is by design.
- ConfigStore does not interop with the hybrid approach, and it is not practical to provide entire config of model etc from command line.
- A middle ground might eventually be possible with a signficantly concretized version of `EncDecCTCModelConfig` - say as a `Quartznet15x5CTCConfig`, which described explicitly the entire model architecture and most of the supporting components, but all in code.
- This is very high cost to develop for all variants of the Model. Imagine Jasper10x5, Jasper5x5, QN15x5, QN5x5 etc and all that code.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1327 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1327/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1327/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1327/events | https://github.com/NVIDIA/NeMo/pull/1327 | 726,918,327 | MDExOlB1bGxSZXF1ZXN0NTA3OTA5NTI5 | 1,327 | Adding Conformer model | {
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} | Here are some of the main changes:
+ Added the modules needed for Conformer
+ Added use_start_end_tokens to the data layers to support dropping these tokens
+ Updated our CTC loss to support different reduction methods including 'mean_batch'. User may select other reduction approaches from Pytorch. User may set it via ctc_reduction param added to the model config.
+ Added log_prediction parameter to model's config to control if we want to see prediction samples in output or not
+ Added LSTM decoder and Swish activation
+ Added NoamScheduler
+ Added subsampling module which supports VGGNet and striding approach for subsampling
+ Added multi-head attention, relative multi-head attention along with positional embedding and relative positional embedding
+ Fixed the bug in the data layer which added some paddings after normalization (fixing this bug would fail the tests! going to investigate it) | {
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"@okuchaiev that won't work sadly with multiple data loaders. Each will be tagged with a specific name prepended so exp manager cannot infer it. At least not unless we also attach a \"default\" validation wer to our logs \r\n\r\nWe actually do attach a default \"val_loss\", with the default as the first dataloader ... | 2020-10-21T21:35:30 | 2020-10-26T15:15:23 | 2020-10-23T20:08:08 | COLLABORATOR | {
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"I don't think this command\r\n```\r\npython -m pip install --upgrade git+https://github.com/NVIDIA/NeMo.git@main\r\n``` \r\nis installing NeMo properly. The error you are getting is from using an old version of NeMo with a new version of Megatron-LM. When we upgrade Megatron-LM, we fixed that error. This has all h... | 2020-10-21T17:23:03 | 2021-02-25T23:53:28 | 2021-02-25T23:53:28 | COLLABORATOR | {
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} | null | null | null | **Describe the bug**
Megatron-LM checkpoints appear to not work properly with the recent changes in NLP module.
**Steps/Code to reproduce bug**
1. implement minimal change to the [Token Classification yaml file](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/conf/token_classification_config.yaml) to use Meagtron-LM checkpoint.
1.1. Change [`bert-base-uncased`](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/conf/token_classification_config.yaml#L77) to `megatron-bert-345m-cased`
1.2. Download and unpack the `gmb_v-2.2.0_clean` dataset for NER as instructed in the [tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb), specify the [location of the dataset in the yaml file](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/conf/token_classification_config.yaml#L44).
2. Run token classification example `python token_classification.py`
**Expected behavior**
Instead of training, error occurs and the program exits:
`TypeError: bert_extended_attention_mask() missing 1 required positional argument: 'dtype`
**Environment overview (please complete the following information)**
- Environment location: Based on [NeMo NGC container](nvcr.io/nvidia/nemo:v1.0.0b1), upgrade NeMo with pip install: `python -m pip install --upgrade git+https://github.com/NVIDIA/NeMo.git@main`
Because without the additional pip install, different error occurs (another bug - mismatch between NGC container and pip package).
- Method of NeMo install: pip install (`python -m pip install --upgrade git+https://github.com/NVIDIA/NeMo.git@main`)
- If method of install is [Docker], provide `docker pull` & `docker run` commands used: `docker pull nvcr.io/nvidia/nemo:v1.0.0b1`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: `nvcr.io/nvidia/nemo:v1.0.0b1` docker container
- PyTorch version: `nvcr.io/nvidia/nemo:v1.0.0b1` docker container
- Python version: `nvcr.io/nvidia/nemo:v1.0.0b1` docker container
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"Reviewers, please replace `main` branch with `ctc-decoders` in the notebook for testing in Colab.",
"This pull request **introduces 2 alerts** when merging fd2333bc64a3bb281a02e93fc5bd4d0319972e8a into 87f3ef66fdf01eb61ea603024f5d9140fa9055de - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-90... | 2020-10-21T05:38:42 | 2021-03-11T17:29:32 | 2020-10-22T22:54:20 | COLLABORATOR | {
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} | - Added CTC beam search decoder (module, package installation)
- Added Offline ASR notebook for greedy decoding, words splitting, decoding with N-gram re-scoring | {
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"Can you try\r\n```\r\n# This line will download pre-trained QuartzNet15x5 model from NVIDIA's NGC cloud and instantiate it for you\r\nquartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"QuartzNet15x5Base-En\", strict=False)\r\n```\r\nIt's related to this issue: https://github.com/NVIDIA/NeMo/is... | 2020-10-20T23:24:07 | 2020-10-22T03:25:40 | 2020-10-22T03:25:40 | NONE | {
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} | null | null | null | **Describe your question**
I am trying to run the first tutorial for NeMo, from https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb
The intent is to use the transfer learning portion of this tutorial and see how it works for Urdu. I want to use the "Out-of-the-box model" and then fine tune in Urdu (local language).
I am getting an error on the line:
`quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")`
```
RuntimeError: Error(s) in loading state_dict for EncDecCTCModel:
Missing key(s) in state_dict: "_wer.scores", "_wer.words".
```
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install: !python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
- 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: Colab
- PyTorch version: Default version on the notebook
- Python version: Default version on the notebook
**Additional context**
Nothing to add here. It is a basic tutorial that isn't working, maybe the tutorial is using the wrong NeMo branch but I would not know enough to troubleshoot. | {
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"This pull request **introduces 1 alert** when merging c31c5e1e995742c1b70b20089e0b9085fa8b6d6b into fcb5fcec71d7b633fde6cfaaba88857ae79b656e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-8d5493e35b1d8b30a706ef41354a307fc8c234b9)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2020-10-20T22:59:42 | 2020-10-23T23:42:53 | 2020-10-23T23:42:49 | COLLABORATOR | {
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"Could you try to load the state_dict using nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"QuartzNet15x5Base-En\", strict=False)? \r\nThe current work-around is to pass strict=False to either #1297",
"Can you try `from nemo.collections.asr.models import EncDecCTCModel`?",
"please try suggestion fro... | 2020-10-20T21:48:51 | 2021-09-22T14:51:06 | 2020-10-26T17:37:57 | NONE | {
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quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
it is giving error
AttributeError: module 'nemo.collections.asr' has no attribute 'models'
[Question] | {
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} | null | null | null | **Describe your question**
A clear and concise description of your question.
Describe what you want to achieve. And/or what NeMo APIs are unclear/confusing.
**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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"Or, a better solution would be to disable T5 as a choice from https://github.com/NVIDIA/NeMo/blob/693c556f7bcffaf78e698f69b36331d872abf533/nemo/collections/nlp/modules/common/lm_utils.py#L44\r\n\r\nAnd in this CI test config use \"bert-base-uncased\"? @ekmb what do you think?",
"> Why would we disable the test?... | 2020-10-20T20:00:30 | 2020-10-20T20:33:40 | 2020-10-20T20:32:53 | COLLABORATOR | {
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Is it possible to use a customized language model with the current implementation of the ASR?
How to produce a list of candidate transcriptions? | {
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} | Updates all TTS to new `_target_` hydra config.
Improve tutorial.ipynb to use parent classes
Make a better OperationMode setter for tts models
Update TTS docs | {
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Now uses the latest release as opposed to RC versions. | {
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"Yes, increasing depth will increase your effective receptive field. \r\nBut, yes, then you'll have to train from scratch.\r\n",
"So 5x30 or 10x15? Can you say intuitively what the 5 and 15 do in 5x15? If it was 5x30 could I start with weights for 5x15 and randomize the extra 5x15 to begin with?\r\n\r\nNOTE: I ... | 2020-10-18T23:25:39 | 2021-04-23T23:56:19 | 2021-04-23T23:56:18 | NONE | {
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} | null | null | null | I have training data where some clips are up to 33 seconds long. I've tried splitting them on silence and allocating the text proportional to size but that is very ad hoc. So I prefer to keep them.
I've noticed during training that the QuartzNet 15x5 tends to more errors at the end of a phrase than at the beginning. I feel like it may be losing long-term dependencies or that those are harder to learn.
On the other hand, what I hear you saying about the parameter space is that it is independent of the `max_duration` parameter of the config. So same number of parameters to parse 1 second phrases as 33 second phrases.
Is that really true? Or can I get better long-term dependency by adding layers? If so, can you recommend a config which will work better for training against longer phrases?
If that would improve matters, I assume that means I have to train from scratch? I.e. if I add layers, I can't use the pretrained weights for the base Q15x5? Or if I can, can you give me a snippet of code which would show me how to add the extra layers to the pretrained model?
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"yes, if not noted otherwise, all files in the repo are under Apache 2.0 "
] | 2020-10-18T19:06:56 | 2020-10-19T17:42:41 | 2020-10-19T17:42:41 | NONE | {
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I would like to know if I need to document the changes if I take [this example](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/02_Online_ASR_Microphone_Demo.ipynb) as the starting point.
Thanks | {
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"The .ckpt files are saved by Pytorch Lightning, you can load them like so:\r\n```\r\nmodel = model.load_from_checkpoint('foo.ckpt')\r\n```\r\nEvery NeMo model is a LightningModule which is a torch.nn.Module. Thus for inference, you can use it either with PTL or \"pure\" Pytorch.",
"@okuchaiev what about when yo... | 2020-10-17T08:06:35 | 2021-04-23T23:55:47 | 2021-04-23T23:55:47 | NONE | {
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} | null | null | null | I have trained a model using speech_to_text.py and got some files in my checkpoint directory. The files are:
`QuartzNet15x5--end.ckptQuartzNet15x5--last.ckpt`
`QuartzNet15x5--val_loss=0.00-epoch=77.ckpt`
`QuartzNet15x5--val_loss=0.00-epoch=85.ckpt`
However, there is no .nemo file and I can't apply these file to speech_to_text_infer.py to perform inference with the command "--asr_model= QuartzNet15x5--end.ckpt"
So how should I apply inference with the files in the checkpoint directory? The version I use is 1.0.0b1. | {
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"We don't have (yet) speaker diarization models yet.\r\n\r\nThis task: \"returns a list of start and end indices in the audio array for each period of detected speech\" sounds like voice activity detection for which we have model and tutorial: https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_... | 2020-10-17T04:37:23 | 2020-12-06T06:32:35 | 2020-11-11T21:41:47 | NONE | {
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} | null | null | null | **Describe your question**
I would like to write and train a model in Nemo which, given a ground truth labelling of where I think speech or silence is, returns a list of start and end indices in the audio array for each period of detected speech.
Is this possible?
Also it would be great if I could detect how many people speak on the recording, and label each speech segment with the ID (1,2,3) of the speaker.
Something similar to this: https://github.com/pyannote/pyannote-audio | {
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} | null | null | null | **Describe the bug**
After #1278 is merged, any model that uses Metrics (WERBPE, WER, TopKClassificationAccuracy, ClassificationReport, Perplexity) might throw an error like:
```
RuntimeError: Error(s) in loading state_dict for PunctuationCapitalizationModel:
Missing key(s) in state_dict: "punct_class_report.tp", "punct_class_report.fn", "punct_class_report.fp", "capit_class_report.tp", "capit_class_report.fn", "capit_class_report.fp".
```
**Steps/Code to reproduce bug**
N/A
**Expected behavior**
N/A
**Environment overview (please complete the following information)**
N/A
**Environment details**
N/A
**Additional context**
The current work-around is to pass strict=False to either
NeMo's Model.from_pretrained(..., strict=False)
NeMo's ModelPT.load_from_checkpoint(..., strict=False)
Lightnings's LightningModule.load_from_checkpoint(..., strict=False)
Torch's Module.load_state_dict(..., strict=False)
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"hi, we recently completed a major re-design on NeMo and this (lazy execution and NMtensor) is no longer the applicable.\r\n\r\nStarting from 1.0.0b1 NeMo is built on top of Pytorch Lightning. It still has Neural Types though in slightly better form. I would recommend you refer to this blogpost for high-level overv... | 2020-10-16T02:07:00 | 2020-10-16T05:29:05 | 2020-10-16T05:29:04 | NONE | {
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} | null | null | null | Hi, thanks for such an awesome project, but I am confusing **why nemo make the eager execution of pytorch to be lazy execution** and also some other data structure such as **NMtensor**. I have search all the documentation but did not find some more explicit explanation. Really to hope to get some answers!
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"Actually I think I figured it out, it goes in the YAML under `train_ds` and `validation_ds` per `NeMo/examples/tts/conf/glow_tts.yaml`."
] | 2020-10-16T01:34:41 | 2020-10-16T02:01:48 | 2020-10-16T02:01:48 | NONE | {
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} | null | null | null | **Describe your question**
I am starting up training for an ASR model like this:
```
from ruamel.yaml import YAML
from omegaconf import DictConfig
config_path = 'amharic_16000.yaml'
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
train_manifest=f'{C.build_dir}/train_manifest.json'
test_manifest=f'{C.build_dir}/test_manifest.json'
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['validation_ds']['manifest_filepath'] = test_manifest
model.set_trainer(trainer)
model.setup_training_data(train_data_config=params['model']['train_ds'])
model.setup_validation_data(val_data_config=params['model']['validation_ds'])
model.setup_optimization(optim_config=DictConfig(params['model']['optim']))
trainer.fit(model)
```
Then `trainer.fit` gives me the message
```
[NeMo W 2020-10-15 21:33:23 nemo_logging:349] /home/catskills/anaconda3/envs/nemo/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:37:
UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck.
Consider increasing the value of the `num_workers` argument`
(try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
```
Where/how do I set `num_workers`?
**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 overview (please complete the following information)**
- Environment location: Ubuntu 20.04, Anaconda Python 3.7
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04LTS
- PyTorch version: 1.6.0
- Python version: 3.7.4
- GPU model: NVidia GeForce RTX 2080TI
- CUDA: 10.1.243
- cuDNN: 7.6.5
Add any other context about the problem here.
Example: GPU model
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https://api.github.com/repos/NVIDIA/NeMo/issues/1293 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1293/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1293/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1293/events | https://github.com/NVIDIA/NeMo/issues/1293 | 722,775,101 | MDU6SXNzdWU3MjI3NzUxMDE= | 1,293 | Strange # of hours | {
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"@okuchaiev FYI",
"@catskillsresearch not sure what is going on here. Could you please help by answering these questions:\r\n1) Is the number of files correct (e.g. 9341)\r\n2) In .json manifest files, is field \"duration\" populated correctly?\r\n3) which code produced 259472.22 hours? Did you use setup_training... | 2020-10-16T00:34:43 | 2021-10-18T16:49:02 | 2021-04-23T23:55:47 | NONE | {
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} | null | null | null | **Describe the bug**
I put in 10 hours of clips and I get this:
```
[NeMo I 2020-10-15 20:26:54 collections:173] Dataset loaded with 9341 files totalling 259472.22 hours
[NeMo I 2020-10-15 20:26:54 collections:174] 0 files were filtered totalling 0.00 hours
```
**Steps/Code to reproduce bug**
```
import nemo.collections.asr as nemo_asr
from glob import glob
model = nemo_asr.models.EncDecCTCModel.load_from_checkpoint(model_fn)
files=list(sorted(glob(f'audio/*.wav')))
translations=model.transcribe(paths2audio_files=files, batch_size=16)
```
**Expected behavior**
When I run this:
```
model.setup_training_data(train_data_config=params['model']['train_ds'])
model.setup_validation_data(val_data_config=params['model']['validation_ds'])
```
I get this, which is a more realistic count of # hours:
```
[NeMo I 2020-10-15 20:19:29 collections:173] Dataset loaded with 8367 files totalling 8.43 hours
[NeMo I 2020-10-15 20:19:29 collections:174] 30 files were filtered totalling 0.17 hours
```
**Environment overview (please complete the following information)**
- Environment location: Ubuntu 20.04, Anaconda Python 3.7
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04LTS
- PyTorch version: 1.6.0
- Python version: 3.7.4
- GPU model: NVidia GeForce RTX 2080TI
- CUDA: 10.1.243
- cuDNN: 7.6.5
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"If max steps is not provided to the trainer, setup optimization will estimate effective max steps based on the training data loader. \r\n\r\nTherefore please call setup training data before calling setup optimization to allow estimation of maximum steps. ",
"I do call setup training data first:\r\n\r\n```\r\nfro... | 2020-10-15T22:46:04 | 2021-04-23T23:55:47 | 2021-04-23T23:55:47 | NONE | {
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} | null | null | null | **Describe your question**
I am getting this message:
```
[NeMo W 2020-10-15 18:42:14 lr_scheduler:526] Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !
Scheduler will not be instantiated !
```
when I do
`model.setup_optimization(optim_config=DictConfig(params['model']['optim']))`
The stock YAML file has `max_steps` as null and no setting for `iters_per_batch`:
```
trainer:
gpus: 0 # number of gpus
max_epochs: 5
max_steps: null # computed at runtime if not set
num_nodes: 1
distributed_backend: ddp
accumulate_grad_batches: 1
checkpoint_callback: False # Provided by exp_manager
logger: False # Provided by exp_manager
row_log_interval: 100 # Interval of logging.
val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch
```
How do I set this up to get a learning rate scheduler?
**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 overview (please complete the following information)**
- Environment location: Ubuntu 20.04, Anaconda Python 3.7
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04LTS
- PyTorch version: 1.6.0
- Python version: 3.7.4
- GPU model: NVidia GeForce RTX 2080TI
- CUDA: 10.1.243
- cuDNN: 7.6.5
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https://api.github.com/repos/NVIDIA/NeMo/issues/1291 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1291/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1291/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1291/events | https://github.com/NVIDIA/NeMo/issues/1291 | 722,362,313 | MDU6SXNzdWU3MjIzNjIzMTM= | 1,291 | Model restored from checkpoint does not behave like model still in core | {
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"[This question ](https://github.com/NVIDIA/NeMo/issues/1202) has the answer. Loading the pretrained model and then changing the vocabulary and reloading the weights doesn't work. There is apparently a lot more state there that doesn't get recovered that way. It turns out there is a much simpler way to reload, th... | 2020-10-15T13:57:43 | 2020-10-15T16:48:24 | 2020-10-15T14:13:20 | NONE | {
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} | null | null | null | **Describe the bug**
Model restored from checkpoint does not behave like model still in core.
**Steps/Code to reproduce bug**
My model is checkpointed with the following callback:
```
model_save_dir='save/nemo_amharic'
class ModelCheckpointAtEpochEnd(pl.callbacks.ModelCheckpoint):
def on_epoch_end(self, trainer, pl_module):
metrics = trainer.callback_metrics
metrics['epoch'] = trainer.current_epoch
trainer.checkpoint_callback.on_validation_end(trainer, pl_module)
pid=os.getpid()
dt=datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
checkpoint_callback = ModelCheckpointAtEpochEnd(
filepath=model_save_dir+'/amharic_'+f'{dt}_{pid}'+'_{epoch:02d}',
verbose=True,
save_top_k=-1,
save_weights_only=False,
period=1)
trainer = pl.Trainer(gpus=[0], max_epochs=200, amp_level='O1', precision=16, checkpoint_callback=checkpoint_callback)
```
I train a model for a long time and then I run this check:
`pred=model.transcribe(paths2audio_files=audio_files, batch_size=1)`
I get back values that reflect training in particular:
```
gold pred
0 አዎ እኔ አጋጣሚ አጭር ቀጠሮ ነበረችኝ እ አዎ እኔ አጋጣሚ አጭር ቀጠሮ ነበረችኝ እ
1 አዎ ኑሯቸው ነዋ እንደ እኛ አቧራ እያቦነኑ አላደጉ አዎ ኑሯቸው ነዋ እንደ እኛ አቧራ እያቦነኑ አላደጉ
2 ታድያ ምን ላደርግ ትምህርቱ እንደት ነው የማታው ታድያ ምን ላደርግ ትምህርቱ እንደት ነው የማታው
3 ከሥራው ጋር ሄደልህ ግን ሥራው ጋር ሄደልግህን
4 ከሥራው ጋር ሄደልህ ከሥራው ጋር ሄደልህ
5 አይ አጣጥሞ መያዝ ነው በኋላ እሱ የሚያዋጣው ሲያልቅ አይ አጣጥመ እኔው የያዝ ነው በኋላ እሱንን ያዋጣቴያልክ
6 አዲስ ሥራ ነው የገባኸው እንደ አዲስ ሥራ ነው የገባኸው እንደ
7 የት ነው የት ነው
8 እ እ
9 ምን ጊዜያዊ ነው ኤርፖርት ወይስ ቋሚ ምን ጊዜያዊ ነው ኤርፖርት ወይስ ቋሚ
10 ምን ሥራው አዎ አየር መንገድ እኮ ክፍያው ጥሩ እኮ ነው ምን ሥራው አዎ አየር መንገድ እኮ ክፍያው ጥሩ እኮ ነው
11 አዎ ግን የፖኬት መኒ ምናምን የኪስ ገንዘብ ያስብሏቸዋል አዎ ግን ያዎው ፐኬት መን ምናምን ኦኪከዛ አስቡላችኋም
```
I then reload the model from the training checkpoint (saved every epoch, and previous model stopped at end of epoch) as follows, and rerun the transcription on the same files:
```
yaml2 = YAML(typ='safe')
with open('amharic_16000.yaml') as f:
params2 = yaml2.load(f)
model2 = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
model2.change_vocabulary(new_vocabulary=params2['labels'])
model2.load_from_checkpoint('save/nemo_amharic/amharic_20201015_005720_353924_epoch=166.ckpt')
model2.cuda(0)
pred2=model2.transcribe(paths2audio_files=audio_files, batch_size=1)
pd.DataFrame(zip(transcripts,pred2), columns=['gold','pred'])
```
The transcription fails, it doesn't match the first transcription at all and looks like a totally untrained model:
```
gold pred
0 አዎ እኔ አጋጣሚ አጭር ቀጠሮ ነበረችኝ እ ኘፔኘፔሥኝጁኔጲኙሱድታጁገፌውሴፌሴፌሠኘኸዟሱእታገሪሱዌሡገኋዟቺጮዎገኸጴዟኳጆኳ...
1 አዎ ኑሯቸው ነዋ እንደ እኛ አቧራ እያቦነኑ አላደጉ ፔኘኢኝሱኘገቺሱፌሴጁገፌሄሡጉጩገዙትቡገምጵሜኘቸሞሴጎሪፌፉሯቤሣትዙውዛዌገሯኳብ...
2 ታድያ ምን ላደርግ ትምህርቱ እንደት ነው የማታው ኘግሲኘጁኘዛብኝጃዋጁዋገሉውድኋጁዟንትዟሞሴጁኝፎጋኤጴጲጿወዟለኘንዟዴሩኘሯኳጁኩ...
3 ከሥራው ጋር ሄደልህ ግን ኘሥኘዟዋቺጃቺኘዋጴፌቺጴጆጲፌሴዳኘጫፌቤቺጽጲዉኳጫንቤሱዘኘገፌቤሱእኘጇፌቺሥኘሿ...
4 ከሥራው ጋር ሄደልህ ኘሥሺዋጴሒሴማኸሿኘጴኒውጎሪጉገቱኘዛፌሉሱጆሠሿዋጎፌዙቺጉጮዮሠዋፌዟቭቸሻዋኸዟሴ...
5 አይ አጣጥሞ መያዝ ነው በኋላ እሱ የሚያዋጣው ሲያልቅ ኘፔሥቺኝሁጁዛዟለፌቸፌገሪጵፌጫገዟሴሙዮጵጠጴፌውሴዲቸኝሯኽጮፌዙሱሩጆኘጴዟሴዟሣ...
6 አዲስ ሥራ ነው የገባኸው እንደ ኘዟጁኘፔሥሿዣቺሥኘቺፔሥገዛቺውሱሿጫሸሉሱዉቤሥቾዟሴሏሣገኘዛቤለሷሸኘቤሯሥንዟሱ...
7 የት ነው ኘሴሔሱጃጫሪዟሴታዌጢኸቤቺቄረዊኋሷጆሰቭኽኤኸሞቺሄጬለኘ
8 እ ኘፔኘፔሴፔሴኘሥቺሥዋሁቺሱሿጁኸጁተጁሴኸታፌጴሥኢኘ
9 ምን ጊዜያዊ ነው ኤርፖርት ወይስ ቋሚ ኘፔሥዟቺጓኘገዛቺውዚኘኸፌዟሌቺኘሁሱጴሌቺሱጴቹሱጫዋሁቺሱሁዋጫዋሔቤጴሱሌምሏኘጴ...
10 ምን ሥራው አዎ አየር መንገድ እኮ ክፍያው ጥሩ እኮ ነው ኘዟኘዛዎሺዋሥፌዟውሴእኘታቤሥረጮቤኘትዟዋጮጆኤትዟቺጴፌጃገቾዟኘጴዟኘጴቱጁፌጃፌ...
11 አዎ ግን የፖኬት መኒ ምናምን የኪስ ገንዘብ ያስብሏቸዋል ጴኘጓሥሺፔፌጼሥሱዉሥሴሥቺፔሥቺኘጁሥቺጃኬጁጃጁጧጁኘጲቺኘቢጁኘንዟዳጃዳጁዳሊዳጃ...
```
It seems like the weights are not getting loaded at all. So I try a different save/restore route, outside of the checkpoint:
```
model.save_to('ihopethisworks.pt')
model2 = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
model2.change_vocabulary(new_vocabulary=params2['labels'])
model2.restore_from('ihopethisworks.pt')
model2.cuda(0)
pred2=model2.transcribe(paths2audio_files=audio_files, batch_size=1)
pd.DataFrame(zip(transcripts,pred2), columns=['gold','pred'])
```
This model also fails to reproduce the results of the model still in core, in the same way:
```
gold pred
0 አዎ እኔ አጋጣሚ አጭር ቀጠሮ ነበረችኝ እ ሀፋሀሡቁሀዮፋማቹሀዮፋቢዮሾለቅጭፋማቪዶሬሀሡፋጾጹደጭፋቁዮሀሡፋኑጉፈቷማፋዮቷሀ...
1 አዎ ኑሯቸው ነዋ እንደ እኛ አቧራ እያቦነኑ አላደጉ ሀቢቅቢቤሀፐቅቢማሀቅሀፐዮፋዦፈዓዮፋዮጴዥሀፈእፓጭፋሀዮፏፋጨቹዕሞለፋሕሙልሒቢጉ...
2 ታድያ ምን ላደርግ ትምህርቱ እንደት ነው የማታው ሀፋፎፋፏሡኩቷኑቅሀቷፋቷቱቷዮጨኪጨፏታሀዮቷጪዎቷዮኙዮቷሬሀቷልፋቷጎሃፈጉሀሡቷሀ...
3 ከሥራው ጋር ሄደልህ ግን ሀሡሀጥጾቱደቱሷሀደፋኔቱፋቤሀዮፋቷዮቱፋዥሀኩፋቷቱሷሀፋቷዌልሷቷፐልፋቷዥፁፑሀቷ...
4 ከሥራው ጋር ሄደልህ ሀሡቤሬሀዮፋፓዮኖሀቷሙፋስፏሹፏፈቷሆቷሀቷልቢቷሆሀልዊጀዮሀቾልፋዮልፋዮልቷ ልፋ...
5 አይ አጣጥሞ መያዝ ነው በኋላ እሱ የሚያዋጣው ሲያልቅ ሀሡቅቤሀቅሀዩለፏልሀጉቢልዮዥፐልፋማልሀሡኽሡጅሡፋሀልቢግኑሀቷፋዮሀፋሒኦሬሀ ዎ...
6 አዲስ ሥራ ነው የገባኸው እንደ ሀእሡቷቱልቷሀዮልፋቷቤቅሀቷልፋቷዥሬሀዌልፋዮለፎዶሀፎሙፋፌልፋዥሀዮልፋዮቱፑሀዮ...
7 የት ነው ሀፋሀሡቢቤልፋፏዌፑቷፋቢዮኧጤሀጤልፋቤባሀልፋሡማጉፀግ
8 እ ሀፋሀዮፋቱሡቁቤሀዮፋቤሬሀሡፋፈጾቹፐግሣፋልማፀግ
9 ምን ጊዜያዊ ነው ኤርፖርት ወይስ ቋሚ ሀፋሀፋሀሡፋኔቷቤሥፋቷጬሀፋቱደቱደሀቷሀፋቷታልፋቷሀልፋቷዮልሀዮፋማሀልሀቷሡፋቱ...
10 ምን ሥራው አዎ አየር መንገድ እኮ ክፍያው ጥሩ እኮ ነው ሀሡቤሀዮፋኢዮጎቷልፋዮዌቾዥቷልዎቢዮጌፋቷዮጴፋሡፋቤሏሙቢቤሆሏዎቷሏፐዎቷሆሏቷፋ...
11 አዎ ግን የፖኬት መኒ ምናምን የኪስ ገንዘብ ያስብሏቸዋል ሀፋእፋኔሷኔቷሷቷግቷሡግቷዮግፋጥሡቷቤጬፋኔፋኔቷኔፋቤቃግቷኔፋቷግቷፋቷፋቷፋቷል...
```
**Expected behavior**
Transcription of model in core and model recreated from in core model using both checkpoint/restore from checkpoint and save to/restore from methods should result in the same transcription.
**Environment overview (please complete the following information)**
- Environment location: Ubuntu 20.04, Anaconda Python 3.7
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04LTS
- PyTorch version: 1.6.0
- Python version: 3.7.4
- GPU model: NVidia GeForce RTX 2080TI
- CUDA: 10.1.243
- cuDNN: 7.6.5
** YAML file**
The YAML file `amharic_16000.yaml` loaded above is as follows:
```
name: &name "QuartzNet15x5"
sample_rate: &sample_rate 16000
repeat: &repeat 1
dropout: &dropout 0.0
separable: &separable true
labels: &labels [' ', ሀ, ሁ, ሂ, ሃ, ሄ, ህ, ሆ, ለ, ሉ,
ሊ, ላ, ሌ, ል, ሎ, ሏ, ሐ, ሑ, ሒ, ሔ, ሕ, መ, ሙ,
ሚ, ማ, ሜ, ም, ሞ, ሟ, ሠ, ሡ, ሣ, ሥ, ሦ, ረ, ሩ,
ሪ, ራ, ሬ, ር, ሮ, ሯ, ሰ, ሱ, ሲ, ሳ, ሴ, ስ, ሶ,
ሷ, ሸ, ሹ, ሺ, ሻ, ሼ, ሽ, ሾ, ሿ, ቀ, ቁ, ቂ, ቃ,
ቄ, ቅ, ቆ, ቋ, በ, ቡ, ቢ, ባ, ቤ, ብ, ቦ, ቧ, ቨ,
ቪ, ቫ, ቬ, ቭ, ተ, ቱ, ቲ, ታ, ቴ, ት, ቶ, ቷ, ቸ,
ቹ, ቺ, ቻ, ቼ, ች, ቾ, ቿ, ኋ, ነ, ኑ, ኒ, ና, ኔ,
ን, ኖ, ኗ, ኘ, ኙ, ኚ, ኛ, ኜ, ኝ, ኞ, አ, ኡ, ኢ,
ኤ, እ, ኦ, ኧ, ከ, ኩ, ኪ, ካ, ኬ, ክ, ኮ, ኳ, ኸ,
ኻ, ኼ, ኽ, ወ, ዉ, ዊ, ዋ, ዌ, ው, ዎ, ዐ, ዑ, ዓ,
ዕ, ዘ, ዙ, ዚ, ዛ, ዜ, ዝ, ዞ, ዟ, ዠ, ዢ, ዣ, ዤ,
ዥ, ዦ, የ, ዩ, ዪ, ያ, ዬ, ይ, ዮ, ደ, ዱ, ዲ, ዳ,
ዴ, ድ, ዶ, ዷ, ጀ, ጁ, ጂ, ጃ, ጄ, ጅ, ጆ, ጇ, ገ,
ጉ, ጊ, ጋ, ጌ, ግ, ጎ, ጓ, ጠ, ጡ, ጢ, ጣ, ጤ, ጥ,
ጦ, ጧ, ጨ, ጩ, ጪ, ጫ, ጬ, ጭ, ጮ, ጱ, ጲ, ጳ, ጴ,
ጵ, ጸ, ጹ, ጺ, ጻ, ጼ, ጽ, ጾ, ጿ, ፀ, ፁ, ፃ, ፄ,
ፅ, ፆ, ፈ, ፉ, ፊ, ፋ, ፌ, ፍ, ፎ, ፏ, ፐ, ፑ, ፒ,
ፓ, ፔ, ፕ, ፖ]
model:
train_ds:
manifest_filepath: ???
sample_rate: 16000
labels: *labels
batch_size: 16
trim_silence: True
max_duration: 33
shuffle: True
is_tarred: False
tarred_audio_filepaths: null
validation_ds:
manifest_filepath: ???
sample_rate: 16000
labels: *labels
batch_size: 16
shuffle: False
preprocessor:
cls: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
params:
normalize: "per_feature"
window_size: 0.02
sample_rate: *sample_rate
window_stride: 0.01
window: "hann"
features: &n_mels 64
n_fft: 512
frame_splicing: 1
dither: 0.00001
stft_conv: false
spec_augment:
cls: nemo.collections.asr.modules.SpectrogramAugmentation
params:
rect_freq: 50
rect_masks: 5
rect_time: 120
encoder:
cls: nemo.collections.asr.modules.ConvASREncoder
params:
feat_in: *n_mels
activation: relu
conv_mask: true
jasper:
- filters: 128
repeat: 1
kernel: [11]
stride: [1]
dilation: [1]
dropout: *dropout
residual: true
separable: *separable
se: true
se_context_size: -1
- filters: 256
repeat: *repeat
kernel: [13]
stride: [1]
dilation: [1]
dropout: *dropout
residual: true
separable: *separable
se: true
se_context_size: -1
- filters: 256
repeat: *repeat
kernel: [15]
stride: [1]
dilation: [1]
dropout: *dropout
residual: true
separable: *separable
se: true
se_context_size: -1
- filters: 256
repeat: *repeat
kernel: [17]
stride: [1]
dilation: [1]
dropout: *dropout
residual: true
separable: *separable
se: true
se_context_size: -1
- filters: 256
repeat: *repeat
kernel: [19]
stride: [1]
dilation: [1]
dropout: *dropout
residual: true
separable: *separable
se: true
se_context_size: -1
- filters: 256
repeat: 1
kernel: [21]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: *separable
se: true
se_context_size: -1
- filters: &enc_feat_out 1024
repeat: 1
kernel: [1]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: *separable
se: true
se_context_size: -1
decoder:
cls: nemo.collections.asr.modules.ConvASRDecoder
params:
feat_in: 1024
num_classes: 28
vocabulary: *labels
optim:
name: novograd
# cls: nemo.core.optim.optimizers.Novograd
lr: .01
# optimizer arguments
betas: [0.8, 0.5]
weight_decay: 0.001
# scheduler setup
sched:
name: CosineAnnealing
# pytorch lightning args
monitor: val_loss
reduce_on_plateau: false
# Scheduler params
warmup_steps: null
warmup_ratio: null
min_lr: 0.0
last_epoch: -1
trainer:
gpus: 0 # number of gpus
max_epochs: 5
max_steps: null # computed at runtime if not set
num_nodes: 1
distributed_backend: ddp
accumulate_grad_batches: 1
checkpoint_callback: False # Provided by exp_manager
logger: False # Provided by exp_manager
row_log_interval: 1 # Interval of logging.
val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch
exp_manager:
exp_dir: null
name: *name
create_tensorboard_logger: True
create_checkpoint_callback: True
hydra:
run:
dir: .
job_logging:
root:
handlers: null
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https://api.github.com/repos/NVIDIA/NeMo/issues/1290 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1290/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1290/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1290/events | https://github.com/NVIDIA/NeMo/pull/1290 | 721,612,023 | MDExOlB1bGxSZXF1ZXN0NTAzNTAxMTcw | 1,290 | Speech Data Explorer Improvements | {
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"@vsl9 thanks a lot, this is very useful! \r\nJust suggestions: could you please add port to the args (or I can do it my PR) and maybe decrease the font of the detailed output below the main table? "
] | 2020-10-14T16:37:39 | 2021-01-22T15:30:19 | 2020-10-14T17:40:52 | COLLABORATOR | {
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- Improved interactive plots | {
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Perplexity is computed incorrectly [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/metrics/perplexity.py) and in [TransformerLMModel](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/transformer_lm_model.py). Perplexity is an exponent of entropy whereas in these scripts it is computed as an exponent of cross-entropy.
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"Yes, this is a misconfiguration bug that will raise from failing silently to an application crash after #1278.\r\nWe will work to fix this on all models soon. For now, your solution is a good work around if you are sticking to the NeMo ASR models."
] | 2020-10-14T08:28:55 | 2021-04-23T23:55:47 | 2021-04-23T23:55:47 | NONE | {
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} | null | null | null | Hi,
I used NeMo-v1.0.0b1 and trained the ASR model by modifying examples/asr/speech_to_text.py.
After training I got 3 models named QuartzNet15x5--val_loss-0.00-epoch-xxx.ckpt.
The val_loss in model name is 0.00 but is non-zero on tensorboard.
<img width="353" alt="picture" src="https://user-images.githubusercontent.com/19429441/95961171-48111000-0e37-11eb-9f8c-c356b9e07a52.png">
My solution:
I modified line 550 in nemo/utils/exp_manager.py :
- original
```
checkpoint_callback = NeMoModelCheckpoint(
filepath=Path(log_dir / 'checkpoints' / '{val_loss:.2f}-{epoch}'), ...
)
```
- my solution
```
checkpoint_callback = NeMoModelCheckpoint(
filepath=Path(log_dir / 'checkpoints' / '{validation_loss:.2f}-{epoch}'), ...
)
```
The val_loss in model name is correct after modifying the code.
Is this a bug?
Thanks for your reply. | {
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} | # Changelog
- Adds support for RNN-T based models
- Adds support for RNN-T BPE based models
- Adds basic configs (prototypes) for RNN-T based models.
- Adds examples/asr/speech_to_text_rnnt.py and examples/asr/speech_to_text_rnnt_bpe.py training scripts.
- Adds support for RNN-T loss based on https://github.com/HawkAaron/warp-transducer
- Add support for [Greedy] - `greedy`, `greedy_batch` and [Beam] - `beam`, `tsd` and `alsd` decoding support for RNNT models
- Detach *Decoding from WER computation so it should be easier to update to PTL 1.x
- Add experimental support for fused joint-loss-wer step in order to save memory by trading off compute speed.
# Todo
- [x] Model infrastructure
- [x] Basic config prototypes
- [x] Unittests
- [x] Save restore RNNT and RNNTBPE models
- [x] Change vocab for RNNT and RNNTBPE models
- [x] Basic functionality test for RNNTJoint and RNNTDecoder
- [x] Cleanup commented code
- [x] Detach `RNNTDecoding` and `RNNTBPEDecoding` from PTL 1.x TensorMetric replacement.
- [x] Wrap the output of *Decoding to PTL 1.x TensorMetric replacement
- [x] Update configs to support PTL 1.x
- [ ] ~(Maybe) Factorize common elements of CTC and RNNT out into ASRCTCModel and ASRRNNTModel.~
- [ ] ~(Maybe) Refactor `rnn.py`~
- [ ] CI pipeline
- [x] Documentation
- [x] RNNT Models
- [x] RNNT BPE Models
- [x] RNNT Loss
- [x] RNNT Decoding + RNNT WER
- [x] RNNT WPE Decoding + RNNT BPE WER
- [x] RNNT Modules
- [x] `rnn.py`
- [x] GreedyRNNTInfer
- [x] BeamRNNTInfer
- [x] `rnnt_utils.py`
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"This pull request **introduces 2 alerts** and **fixes 2** when merging 7c84696134f25a2a0db4deeb0956490025789cb0 into b147370de5a05ef29e96901c383a0ac81d9e352e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-549dc7ace7021ee28a131c1ef4e5f669078256bb)\n\n**new alerts:**\n\n* 2 for Syntax error\n\n*... | 2020-10-13T21:11:57 | 2020-10-13T21:22:39 | 2020-10-13T21:17:16 | COLLABORATOR | {
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- row_log_interval -> log_every_n_steps
- log_save_interval -> flush_logs_every_n_steps
- LearningRateLogger -> LearningRateMonitor | {
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"Empty references typically mean that training/validation_ds was not setup correctly - e.g. vocabulary is not passed to it as \"labels\" parameter.\r\n\r\nLooking at your code:\r\n```\r\nimport logging, json, copy, nemo\r\nfrom Cfg import Cfg\r\nfrom ruamel.yaml import YAML\r\nfrom omegaconf import DictConfig\r\nim... | 2020-10-13T14:58:38 | 2021-04-23T23:55:46 | 2021-04-23T23:55:46 | NONE | {
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} | null | null | null | **Describe the bug**
I have changed the vocabulary on pretrained QuartzNet:
```
vocabulary=list(sorted(set(''.join([x.target.value for x in samples]))))
quartznet.change_vocabulary(new_vocabulary=vocabulary)
```
giving
```
NeMo I 2020-10-13 10:39:05 ctc_models:189] Changed decoder to output to [' ', 'ሀ', 'ሁ', 'ሂ', 'ሃ', 'ሄ', 'ህ', 'ሆ', 'ለ', 'ሉ', 'ሊ', 'ላ', 'ሌ', 'ል', 'ሎ', 'ሏ', 'ሐ', 'ሑ', 'ሒ', 'ሔ', 'ሕ', 'መ', 'ሙ', 'ሚ', 'ማ', 'ሜ', 'ም', 'ሞ', 'ሟ', 'ሠ', 'ሡ', 'ሣ', 'ሥ', 'ሦ', 'ረ', 'ሩ', 'ሪ', 'ራ', 'ሬ', 'ር', 'ሮ', 'ሯ', 'ሰ', 'ሱ', 'ሲ', 'ሳ', 'ሴ', 'ስ', 'ሶ', 'ሷ', 'ሸ', 'ሹ', 'ሺ', 'ሻ', 'ሼ', 'ሽ', 'ሾ', 'ሿ', 'ቀ', 'ቁ', 'ቂ', 'ቃ', 'ቄ', 'ቅ', 'ቆ', 'ቋ', 'በ', 'ቡ', 'ቢ', 'ባ', 'ቤ', 'ብ', 'ቦ', 'ቧ', 'ቨ', 'ቪ', 'ቫ', 'ቬ', 'ቭ', 'ተ', 'ቱ', 'ቲ', 'ታ', 'ቴ', 'ት', 'ቶ', 'ቷ', 'ቸ', 'ቹ', 'ቺ', 'ቻ', 'ቼ', 'ች', 'ቾ', 'ቿ', 'ኋ', 'ነ', 'ኑ', 'ኒ', 'ና', 'ኔ', 'ን', 'ኖ', 'ኗ', 'ኘ', 'ኙ', 'ኚ', 'ኛ', 'ኜ', 'ኝ', 'ኞ', 'አ', 'ኡ', 'ኢ', 'ኤ', 'እ', 'ኦ', 'ኧ', 'ከ', 'ኩ', 'ኪ', 'ካ', 'ኬ', 'ክ', 'ኮ', 'ኳ', 'ኸ', 'ኻ', 'ኼ', 'ኽ', 'ወ', 'ዉ', 'ዊ', 'ዋ', 'ዌ', 'ው', 'ዎ', 'ዐ', 'ዑ', 'ዓ', 'ዕ', 'ዘ', 'ዙ', 'ዚ', 'ዛ', 'ዜ', 'ዝ', 'ዞ', 'ዟ', 'ዠ', 'ዢ', 'ዣ', 'ዤ', 'ዥ', 'ዦ', 'የ', 'ዩ', 'ዪ', 'ያ', 'ዬ', 'ይ', 'ዮ', 'ደ', 'ዱ', 'ዲ', 'ዳ', 'ዴ', 'ድ', 'ዶ', 'ዷ', 'ጀ', 'ጁ', 'ጂ', 'ጃ', 'ጄ', 'ጅ', 'ጆ', 'ጇ', 'ገ', 'ጉ', 'ጊ', 'ጋ', 'ጌ', 'ግ', 'ጎ', 'ጓ', 'ጠ', 'ጡ', 'ጢ', 'ጣ', 'ጤ', 'ጥ', 'ጦ', 'ጧ', 'ጨ', 'ጩ', 'ጪ', 'ጫ', 'ጬ', 'ጭ', 'ጮ', 'ጱ', 'ጲ', 'ጳ', 'ጴ', 'ጵ', 'ጸ', 'ጹ', 'ጺ', 'ጻ', 'ጼ', 'ጽ', 'ጾ', 'ጿ', 'ፀ', 'ፁ', 'ፃ', 'ፄ', 'ፅ', 'ፆ', 'ፈ', 'ፉ', 'ፊ', 'ፋ', 'ፌ', 'ፍ', 'ፎ', 'ፏ', 'ፐ', 'ፑ', 'ፒ', 'ፓ', 'ፔ', 'ፕ', 'ፖ'] vocabulary.
```
I defined training samples such as
```
{'audio_filepath': 'NIST/openasr20_amharic/build/audio_split/BABEL_OP3_307_15902_20140422_235151_outLine_2606600_2615480.wav',
'duration': 1.11,
'text': 'ጉርድ ሾላ ምነው'},
{'audio_filepath': 'NIST/openasr20_amharic/build/audio_split/BABEL_OP3_307_25767_20140403_234644_outLine_3567640_3637240.wav',
'duration': 8.7,
'text': 'እንትን እንትኑን አይቼዋለሁ ሳምፕሉን አይቼዋለሁ ግን ሳምፕሉን አይቼዋለሁ በሳምፕሉ ጥራት ደረጃ ከሆነ ሌላውም ቲ ቲሸርት የሚመጣው ትንሽ እንትን አለበት'},
```
When I run the trainer as follows:
```
quartznet.setup_training_data(train_data_config=params['model']['train_ds'])
quartznet.setup_validation_data(val_data_config=params['model']['validation_ds'])
trainer = pl.Trainer(gpus=[0], max_epochs=2, amp_level='O1', precision=16)
```
I see this output, where the decoded shows in Amharic but the reference is blank:
```
[NeMo I 2020-10-13 10:39:14 wer:149] reference:
[NeMo I 2020-10-13 10:39:14 wer:150] decoded :ቸሡፐሸዞሡፖገሡቸፖሦጡሦዮገሡቸጴዞቸቦችሡፉዞፁሾሦሡቸሡቸሡሁዞቸቼሡቸሡቋአቸቿሾኢፐሡ
[NeMo I 2020-10-13 10:39:14 wer:148]
[NeMo I 2020-10-13 10:39:14 wer:149] reference:
[NeMo I 2020-10-13 10:39:14 wer:150] decoded :ቸሡቸፖጤንቸሲጴሡቸክቃሣፉሡኢሀዞሸዞፖጴዞጎጂሦዉቻዷዞቸጓንዷቸኗናፐሁዞንቸዉፐጴዞሡቃሦሡጄቋፁማንዉቫሸፁኼጊዉቺዎዞፁፉንቸትዊዞፁቃቸሦዉፐጤሡጮሸዞኗፐጤሁቸኗሡሸሡጿሦሀፖፐሟቸሡቸንፑፖቪሀዞቸፑቸሡኢዞፐቼጹሡ
[NeMo I 2020-10-13 10:39:15 wer:148]
```
**Steps/Code to reproduce bug**
Here is my whole training script:
```
import logging, json, copy, nemo
from Cfg import Cfg
from ruamel.yaml import YAML
from omegaconf import DictConfig
import nemo.collections.asr as nemo_asr
import pytorch_lightning as pl
from amharic_vocabulary import amharic_vocabulary
logger = logging.getLogger()
logger.setLevel(logging.INFO)
C = Cfg('NIST', 8000, 'amharic')
config_path = './NeMo/examples/asr/conf/config.yaml'
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
train_manifest=f'{C.build_dir}/train_manifest.json'
test_manifest=f'{C.build_dir}/test_manifest.json'
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['validation_ds']['manifest_filepath'] = test_manifest
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
quartznet.change_vocabulary(new_vocabulary=amharic_vocabulary)
quartznet.setup_training_data(train_data_config=params['model']['train_ds'])
quartznet.setup_validation_data(val_data_config=params['model']['validation_ds'])
trainer = pl.Trainer(gpus=[0], max_epochs=1, amp_level='O1', precision=16)
trainer.fit(quartznet)
```
**Expected behavior**
The "reference" string should match the manifest.
**Environment overview (please complete the following information)**
- Environment location: Ubuntu 20.04, Anaconda Python 3.7
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04LTS
- PyTorch version: 1.6.0
- Python version: 3.7.4
- GPU model: NVidia GeForce RTX 2080TI
- CUDA: 10.1.243
- cuDNN: 7.6.5
**Additional context**
Trying to follow notebook ASR_with_Nemo.ipynb to transfer learn a new language.
**NOTE**
I fixed this problem by starting from scratch from a customized YAML file and not by trying to change the vocabulary on the fly, which seems iffy.
Of course that means I got a whole lotta training to do, but at least I've flipped the problem around a little bit:
```
[NeMo I 2020-10-13 12:51:10 wer:149] reference:እ ሀብቱን በደምብ ሰላም በይልኝ ኮራ ኩራት ቀልዶብሻል ስልክ ስደውልልሽ እማታነሺው
[NeMo I 2020-10-13 12:51:10 wer:150] decoded :
[NeMo I 2020-10-13 12:51:27 wer:148]
```
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"When we train base model (e.g. from which you fine-tune), we typically decay learning rate using cosine policy. The full lr plot thus looks something like this:\r\n\r\nNotice that at the end of the trainin... | 2020-10-13T13:50:26 | 2020-11-12T11:12:08 | 2020-11-12T11:12:08 | NONE | {
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} | null | null | null | **Question**
According to #1107 which @okuchaiev suggest "**Try 0.001 (or even smaller). your learning rate is certainly too high. **"
I want to know the reason why we should do that?
I would appreciate if anyone can help me with that.
**Environment overview:**
-Environment location: Google Colab
-Method of NeMo install: !python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
**Environment details**
-OS version : Ubuntu 18.04
-PyTorch version : 1.6.0+cu101
-Python version : Python 3.6.9
-GPU model: Tesla T4 | {
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"Thank you very much for this PR! Could you please sign your committed commits. DCO bot's output tells you how to do that https://github.com/NVIDIA/NeMo/pull/1281/checks?check_run_id=1244379368 ",
"Thanks for the PR! It is a nice example of very fast signal resampling.\r\n\r\nBut I wouldn't recommend to use it du... | 2020-10-12T21:08:03 | 2020-10-20T14:30:19 | 2020-10-20T14:30:19 | NONE | {
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} | Add code to change the frequency to the desired (16 kHz) from any pre-specified microphone sample rate.
Solves #1279
**Approach:**
Calculating the LCM (Least Common Multiple) of the Microphone sample rate and the expected sample rate(16 kHz)
**Upscaling** the Microphone chunk array by duplicating adjacent values.
**Downscaling** the upscaled array to the desired frequency by calculating mean to approximate closely without loss of generality
**Example**
Consider a very common microphone frequency 44.1 kHz or 44100
The LCM of 44100 and 16000 is 7056000
In simple terms:
44100 * 160 = 7056000: Every element in the input array is repeated 160 times in the upscaled array,
16000 * 441 = 7056000: The mean of every 441 elements comprise of a single element in the downscaled array
This ensures that regardless of the input sampling rate, the re-sampled array always has a length of 16000 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1280 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1280/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1280/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1280/events | https://github.com/NVIDIA/NeMo/pull/1280 | 719,650,919 | MDExOlB1bGxSZXF1ZXN0NTAxODA0MDM1 | 1,280 | Add numba_utils to beta | {
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"where this will be used?",
"It's checked and imported by perterb.py, but I guess git didn't add this file during the updates to perturb.py. ",
"This pull request **introduces 6 alerts** and **fixes 9** when merging 47dac024901a77d0cea2aab0e7367c872d26b519 into 661d4321bc5f3db601b6fa9faaf0a3d5dacaaeee - [view o... | 2020-10-12T20:32:59 | 2020-10-13T18:44:40 | 2020-10-13T18:39:20 | COLLABORATOR | {
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} | # Changelog
- Add back the numba_utils for time stretch augmentation on CPU at sample level perturbation
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"this notebook was written for models which we distributed and those are in 16Khz.\r\nYou are right that different sampling rate won't work there and re-sampling would be needed. But this looks more like a feature request to add to the notebook rather than bug to me. Feel free to send a PR with it. \r\nClosing this... | 2020-10-12T20:22:32 | 2020-10-13T04:18:04 | 2020-10-13T04:17:50 | NONE | {
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} | null | null | null | **Describe the bug**
Arises when a microphone of a different sample frequency is used to run `/tutorials/02_Online_ASR_Microphone_Demo.ipynb`
Working on a fix as well!
**Steps/Code to reproduce bug**
run the ipython notebook found at `/tutorials/02_Online_ASR_Microphone_Demo.ipynb`
at the last cell. a SAMPLE_RATE of 16000 or 16khz is expected. This cannot be the case always. A simple approach might be to transform the input frequency to the expected frequency using LCM -> Upscaling -> Downscaling. Working on a PR for the same
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"let's hold on with this until 1.0.0 is available.",
"This pull request **introduces 1 alert** when merging 390d2eb622de95294958add106f3226e965958fb into fd98a89adf80012987851a2cd3c3f4dc63bb8db6 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-396aed525cd9b16926c60913e9476e3a472326cc)\n\n**new ... | 2020-10-12T16:39:25 | 2020-10-19T17:43:33 | 2020-10-19T17:36:13 | COLLABORATOR | {
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} | Lightning Changes:
- Trainer parameters
- `row_log_interval` -> `log_every_n_steps`: https://pytorch-lightning.readthedocs.io/en/latest/trainer.html#log-every-n-steps
- `distributed_backend` -> `accelerator`: https://pytorch-lightning.readthedocs.io/en/latest/trainer.html#accelerator
- `log_save_interval` -> `flush_logs_every_n_steps`
- `pl.callbacks.LearningRateLogger` -> `pl.callbacks.LearningRateMonitor`
- `TensorMetric` -> `Metric`: https://pytorch-lightning.readthedocs.io/en/latest/metrics.html#
- Please note, this involved an API change. All TensorMetrics have been ported but are not guaranteed to work 100% correctly
- Ported Metrics include [`WERBPE`, `WER`, `TopKClassificationAccuracy`, `ClassificationReport`, `Perplexity`]
- The checkpoint callback has changed and is partially supported in this PR.
- The recommended logging API has changed. Please switch to `self.log` for logging scalars instead of return dictionaries from `training_step`, `training_epoch_end`, `validation...`, `test...`. The logger object should be used for logging anything other than scalars.
NeMo Changes
- Added `strict` to most NeMo restoring functions to handle #1297
Under the hood changes:
- Getting error `AttributeError: 'Trainer' object has no attribute 'configure_logger'`, now `Trainer.logger_connector.configure_logger`
- `configure_checkpoint_callback` -> `callback_connector.init_default_checkpoint_callback`
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https://api.github.com/repos/NVIDIA/NeMo/issues/1277 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1277/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1277/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1277/events | https://github.com/NVIDIA/NeMo/pull/1277 | 719,416,162 | MDExOlB1bGxSZXF1ZXN0NTAxNjA3NjE2 | 1,277 | Add initial integration with NVIDIA DALI | {
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"This pull request **introduces 6 alerts** when merging ea3f41cc628b363163f58e9130577d655c2bed2c into ccab141b54fab6fd7f6370e1bd3e18ce0c529364 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-42dbc1969064c1fd79da7e6da14818775c8b4584)\n\n**new alerts:**\n\n* 4 for Unused import\n* 2 for Asserting ... | 2020-10-12T14:15:57 | 2020-10-26T18:00:58 | 2020-10-26T17:23:26 | COLLABORATOR | {
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} | Enables the possibility to use NVIDIA DALI for data loading and preprocessing, instead of Pytorch's data loader and torchaudio, respectively.
General idea:
A user can easily switch to a DALI based preprocessing pipeline by simply switching a flag ``use_dali`` in the configuration file.
A new new module `AudioToCharDALIDataset` is introduced, which is able to also handle preprocessing (`AudioToMelSpectrogramPreprocessor` and `AudioToMFCCPreprocessor`) as requested. Because the DALI implementation does things a little bit differently (bypass Pytorch dataloader, combine loading and processing into a single module, etc) it requires special treatment in ctc_models.py.
I was able to obtain significant speedups. I am observing ~3x speed improvement when training Quartznet with a batch size of 128.
Signed-off-by: Joaquin Anton <janton@nvidia.com> | {
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"Solved, I had to use Sudo",
"You can also add your user to the Docker group via these instructions: https://docs.docker.com/engine/install/linux-postinstall/\r\nThen you don't have to use sudo when using Docker commands."
] | 2020-10-12T13:31:10 | 2020-10-12T16:24:46 | 2020-10-12T16:24:46 | NONE | {
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} | null | null | null | I am getting "permission denied" when trying to pull v1.0.0.b1 using the tag created.
`docker pull nvcr.io/nvidia/nemo:v1.0.0b1`
`Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Post http://%2Fvar%2Frun%2Fdocker.sock/v1.40/images/create?fromImage=nvcr.io%2Fnvidia%2Fnemo&tag=v1.0.0b1: dial unix /var/run/docker.sock: connect: permission denied` | {
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