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VAD tutorial missed cell added
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2020-10-29T21:44:08
2020-11-03T00:26:31
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Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
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Add Checks to TTS Models for Tensorboard Logger [ReRoute to v1.0.0b2]
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2020-10-29T16:52:16
2020-10-29T17:33:13
2020-10-29T17:32:52
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Fixes #1356
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2020-10-29T16:36:54
2020-10-31T05:07:05
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2020-10-29T14:58:29
2020-11-25T16:50:40
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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
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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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Inverse text normalization in NeMo
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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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**Describe your question** 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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731,993,111
MDExOlB1bGxSZXF1ZXN0NTEyMDA3ODY5
1,369
Megatron notebook fixes v1.0.0b2
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2020-10-29T04:41:36
2020-10-29T04:50:44
2020-10-29T04:50:37
COLLABORATOR
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Trainer config updated. Setting trainer.max_steps caused bug because ModelCheckpoint needed val_loss metric.
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731,890,498
MDExOlB1bGxSZXF1ZXN0NTExOTI0NDE0
1,368
Fixed ORT installer
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2020-10-28T23:57:35
2020-10-29T16:18:58
2020-10-29T16:18:58
CONTRIBUTOR
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Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
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731,861,226
MDExOlB1bGxSZXF1ZXN0NTExODk3NjE4
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Add patches for Notebooks (Model Primer, ASR)
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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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v1.0.0b2 to main
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2020-10-28T20:35:32
2020-10-28T20:57:27
2020-10-28T20:57:27
MEMBER
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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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731,714,719
MDExOlB1bGxSZXF1ZXN0NTExNzc0MTEy
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Model saving/restoring using EFF
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2020-10-28T19:06:04
2020-10-30T22:48:51
2020-10-30T22:48:47
CONTRIBUTOR
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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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Remove experimental automodel
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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
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731,491,684
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Add Checks to TTS Models for Tensorboard Logger
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2020-10-28T14:24:32
2020-11-25T16:51:37
2020-10-29T16:51:40
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Fixes #1356
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Reduce CI time NLP
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2020-10-28T14:15:15
2020-10-29T04:09:47
2020-10-29T04:09:43
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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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Script to download commonvoice dataset
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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
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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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Token Classification aka Named Entity Recognition exportable
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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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[Question]What are the correct steps to follow to fine-tune a pre-trained ASR model on right-to-left language not using latin letters? #1267
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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
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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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2020-10-27T19:50:32
2020-10-27T22:11:08
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2020-10-27T19:48:55
2020-10-27T23:34:35
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AttributeError: 'list' object has no attribute 'add_image' in 'swriter.add_image()', when training Tacotron2 whith wandb logging
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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
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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( AttributeError: 'list' object has no attribute 'add_image'`
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swriter.add_image()AttributeError: 'list' object has no attribute 'add_image'
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2020-10-27T15:47:58
2020-10-27T15:55:22
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BeamSearchDecoderWithLM, TypeError: Number of output arguments
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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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**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 ![image](https://user-images.githubusercontent.com/33082061/97324583-a946d380-187a-11eb-844f-299b8df402a4.png) **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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Ensure `onnx_opset_version` consistency.
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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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how to save a fine tuned model and load it back?[Question]
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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
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hi 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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Fixed Dockerfile
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2020-10-27T06:19:47
2021-01-22T15:29:51
2020-10-27T16:14:05
COLLABORATOR
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Signed-off-by: Vitaly Lavrukhin <vlavrukhin@nvidia.com>
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Pin tutorials for v1.0.0b2 release
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2020-10-27T00:08:10
2020-10-27T04:46:20
2020-10-27T04:46:15
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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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729,929,933
MDExOlB1bGxSZXF1ZXN0NTEwMzI1Njk2
1,349
Save_to Fix
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2020-10-26T21:44:03
2020-10-26T21:59:37
2020-10-26T21:54:14
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Make save_to rank_zero_only
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729,910,250
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Patch to test_asr_metrics
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2020-10-26T21:10:05
2020-10-26T22:05:36
2020-10-26T21:54:52
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Skip assert when reference is empty since empty reference does not make sense for WER.
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Wav2Vec 2.0 Integration
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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
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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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reduce squad ci
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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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Fix CI
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2020-10-26T17:42:50
2020-10-26T21:59:58
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Correct max_steps calculation
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2020-10-26T17:10:15
2020-10-26T21:34:56
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TypeError while trying to run the GLUE Benchmark notebook in Google Colabs.
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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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**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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Integrate Wav2Vec 2.0
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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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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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Error in instantiating pretrained ASR model
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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
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Hi, 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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Would a QuartzNet 15x5 pretrained on English have a notably hard time hearing nuances in Vietnamese?
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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
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Would a QuartzNet 15x5 pretrained on English have a notably hard time hearing nuances in Vietnamese? Will training gradually improve the pretrained Encoder layer to fix this, or is the Encoder layer too far down, gradient-wise, to notice? I'm just asking because I've been retraining QuartzNet 15x5 English for a number of days on Vietnamese and it still seems to be missing what seems to be isolated phonemic subtleties quite frequently, and my intuitive sense is that it is listening with an English "ear" that just can't distinguish the Vietnamese phonemes. 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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IntentSlotClassificationModel exportable
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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
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Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
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Adding inference from examples for Intent and Slot model
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2020-10-24T03:42:08
2020-10-26T18:36:34
2020-10-26T18:36:34
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Signed-off-by: Vlad Getselevich <vgetselevich@nvidia.com>
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Fix multi dataloader and ASR logging + patch asr wer calculation
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2020-10-23T20:31:18
2020-10-24T06:35:26
2020-10-23T22:24:25
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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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Update max_step calculation in LR Scheduler
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2020-10-23T16:41:51
2020-10-26T22:00:02
2020-10-23T19:24:58
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Talknet tutorial
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[ "This pull request **introduces 1 alert** when merging eaf6cf589e86f83acb2f48c1e6dc831e1533af89 into cd55594e0f60a4bcbaf6d0e4196c1404591e0076 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9ee197eb87b278c5e13fc11f091b00114da5f04b)\n\n**new alerts:**\n\n* 1 for Unused import" ]
2020-10-23T15:25:56
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Configen example
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2020-10-23T04:00:44
2023-02-22T18:35:31
2021-07-19T22:58:39
CONTRIBUTOR
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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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Patch to Model Parallelism after PTL 1.0.3 Upgrade
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[ "This pull request **introduces 1 alert** when merging 77f3867cb8ed29de19137c14b7606acbd6ed58fd into 53b31f7b902f96e3b964214373c328b3fa2d3606 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fdbaf1bef5bfb2c8b4b97e7eda74c8c0cb5d0a77)\n\n**new alerts:**\n\n* 1 for Unused import" ]
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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.nemo to single .onnx CLI converter script for ASR models
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2020-10-22T20:51:49
2020-10-23T17:52:13
2020-10-23T17:52:13
CONTRIBUTOR
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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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Update tlt integration branch
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2020-10-22T19:29:00
2020-10-22T19:48:23
2020-10-22T19:47:44
CONTRIBUTOR
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merge main into tlt-integration
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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
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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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[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" ]
2020-10-22T07:24:04
2021-04-23T23:58:42
2021-04-23T23:58:42
NONE
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How can I get relative character level timestamps or word level timestamps in nemo asr. Thanks, S
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Add support for model level dataclass/config class
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[ "This pull request **introduces 1 alert** when merging 02d1de2a5df5480faf148a0623fcfa60089a3cff into c09012b6018243861a59a51401393bc8d64c163b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-7e2ade19bb3ce1e9ccec72842028c33c8fea56c0)\n\n**new alerts:**\n\n* 1 for Unused import", "@blisc `_targe...
2020-10-22T02:01:03
2020-11-24T00:13:07
2020-11-24T00:13:01
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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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726,918,327
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Adding Conformer model
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[ "This pull request **introduces 3 alerts** when merging 428a72d3463ecaa1d11dee86742ade414cdf67b5 into 4e4597a85d80c77603102603e35fb033ba852c64 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-7a79f4903246158dc860d19779ae73ee01878006)\n\n**new alerts:**\n\n* 2 for Signature mismatch in overriding ...
2020-10-21T22:57:47
2020-11-03T23:49:11
2020-11-03T23:49:11
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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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MDExOlB1bGxSZXF1ZXN0NTA3OTAyODcw
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modelPT constructor bugfix
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2020-10-21T22:37:43
2020-10-22T00:36:37
2020-10-22T00:36:32
MEMBER
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Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com>
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QA squad update tutorial and example script with usage examples
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2020-10-21T22:03:48
2020-10-23T00:50:26
2020-10-23T00:50:22
CONTRIBUTOR
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QA squad update tutorial and example script with usage examples after model changes
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Update exp_manager for Lightning 1.0
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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
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Update to Lightning 1.0.3
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2020-10-21T20:01:26
2020-10-21T21:24:15
2020-10-21T21:24:01
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Fixes #1297
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Pin NeMo Version for Colab
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2020-10-21T18:01:37
2020-10-21T19:02:53
2020-10-21T19:02:48
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Jupyter Notebooks were installing NeMo main branch for Colab. Changed this so that they will install the latest release where notebooks have been tested by QA.
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Megatron-LM checkpoints don't work properly
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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
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**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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EncDecSpeakerLabelModel exportable
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2020-10-21T06:17:34
2020-10-22T18:47:13
2020-10-22T18:47:13
CONTRIBUTOR
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Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
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CTC decoders
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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
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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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upgrade to PTL 1.0.2
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2020-10-21T05:14:35
2020-10-21T17:18:06
2020-10-21T05:40:45
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Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
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moved all tensorboard logs to self.log
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Patch QA training_step
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Fixed QA training_step.
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[Question] ASR_with_NeMo: Missing keys in state_dict
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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
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**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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Punctuation model with TLT
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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...
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cant load pretrained model
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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
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when i try to upload a pretrained model 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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minor changes to PTL 1.0 support
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2020-10-20T21:46:49
2020-10-21T17:20:00
2020-10-20T22:57:40
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Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
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when i try to load a pretrained model like thisquartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En") [Question]
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2020-10-20T21:44:12
2020-10-20T21:52:16
2020-10-20T21:44:50
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**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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1,310
Disable T5 Test
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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
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TLT integration
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[ "This pull request **introduces 3 alerts** when merging f88d17c8c9e18e5ab54c92daa1ad4507bc2b9db6 into cd55594e0f60a4bcbaf6d0e4196c1404591e0076 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cd7113f9f6c4a6d00463f645cd84fd574adb297f)\n\n**new alerts:**\n\n* 2 for Unused import\n* 1 for Unused loc...
2020-10-20T18:15:07
2020-10-27T23:34:02
2020-10-27T23:33:56
CONTRIBUTOR
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The branch contains up to date changes required for models ported to TLT to work. - changes to Token Classification Model (NLP) - changes related to Question Answering (NLP) - changes related to Punctuation Model (NLP)
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Close opened file to prevent IOError
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2020-10-20T08:37:16
2020-10-21T08:15:33
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Close an opened text file when done, in order to prevent reaching the number of opened files.
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[Question]
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[ "we are working on it (external LM support) - should be available soon." ]
2020-10-19T22:51:17
2020-10-23T23:06:38
2020-10-23T23:06:38
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**ASR produce probabilities** 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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Question answering tlt
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[ "This pull request **introduces 1 alert** when merging a16eecc6c2fa090ee3d06d4ae9063366ecec5402 into fcb5fcec71d7b633fde6cfaaba88857ae79b656e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3cc06276c5c635785dda317471c3755d1528e61d)\n\n**new alerts:**\n\n* 1 for Unused import", "> @VahidooX lef...
2020-10-19T21:35:10
2020-10-22T17:33:16
2020-10-21T01:58:58
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Changed QAModel to be compatible with tlt: * particularly added additional inference function
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TTS Collection Cleanup
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[ "This pull request **introduces 10 alerts** when merging 14140663268bf215e0b77adf3219ae2e9aefa45f into 4561fbfc91c2ffe80be55fdbe9edc9611bd0b2c0 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-ef9bb12c9428d747aca58bd86b42637053f34682)\n\n**new alerts:**\n\n* 10 for Unused import", "This pull re...
2020-10-19T18:46:25
2020-10-20T20:58:24
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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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Unpins OmegaConf and Hydra. Now uses the latest release as opposed to RC versions.
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Should I modify the Quartznet 5x15 architecture for longer sentences?
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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
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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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[Question] Are the examples under the same Apache License 2.0
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[ "yes, if not noted otherwise, all files in the repo are under Apache 2.0 " ]
2020-10-18T19:06:56
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Are the examples under the same Apache License 2.0 as the rest of the source? 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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How to apply inference with trained model?
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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
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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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Speaker diarization in NeMO?
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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
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**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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Visual Reasoning Collection
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[ "This pull request **introduces 46 alerts** and **fixes 4** when merging 1b3226b4d2d13c5dcd73421c85509894a57f63f7 into 910caf6aeb1685303d8b7388c771ac9e02ed815b - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-50135da9bf71683b2ea5021871f188f04cae8c41)\n\n**new alerts:**\n\n* 38 for Unused import\n...
2020-10-16T22:30:05
2022-09-22T17:04:22
2021-07-19T22:56:46
CONTRIBUTOR
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For now - a draft only, just to check CI
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Loading NLP and ASR models might result in `Missing key(s) in state_dict` error
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2020-10-16T18:04:58
2020-10-21T21:24:01
2020-10-21T21:24:01
COLLABORATOR
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**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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EncDecClassificationModel exportable - format
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2020-10-16T05:51:24
2020-10-16T21:02:16
2020-10-16T21:02:16
CONTRIBUTOR
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Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com>
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[Question] what is benefit of lazy execution and also other nemo datatype such NMtensor
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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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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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How do I set num_workers in DataLoader for an ASR model?
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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
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**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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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
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**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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How do I get the scheduler going?
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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
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**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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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
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**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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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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- Added support for custom fields in manifest - Improved interactive plots
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Perplexity is computed incorrectly
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2020-10-14T16:02:35
2020-11-11T07:56:38
2020-11-11T07:56:38
CONTRIBUTOR
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**Describe the bug** 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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Saved model named QuartNet15x5--val_loss-0.00-epoch-xxx.ckpt but val_loss on tensorboard is non-zero
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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
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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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RNN-Transducer Support (Beta)
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[ "This pull request **introduces 21 alerts** when merging e66d1e17c5d326bd82cd24399827a5eb7865f242 into fd98a89adf80012987851a2cd3c3f4dc63bb8db6 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-374ed711bd66f7baf09c4deb09b2dacc56617b95)\n\n**new alerts:**\n\n* 15 for Unused import\n* 3 for Unused l...
2020-10-14T00:20:32
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2020-11-06T17:23:35
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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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Fix Typo
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[ "This pull request **introduces 4 alerts** and **fixes 2** when merging c3eaff2836c80fa495410d35975128e81a5783f1 into 5dc7942ddaa7390c10888dcc5b5b24625d4447eb - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-ff9b11205d6444349936f5cd9ea2c5a196ef4695)\n\n**new alerts:**\n\n* 2 for Unused local vari...
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Update Lightning
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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
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2020-10-13T21:17:16
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Fixes: - row_log_interval -> log_every_n_steps - log_save_interval -> flush_logs_every_n_steps - LearningRateLogger -> LearningRateMonitor
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Signed-off-by: smajumdar <titu1994@gmail.com>
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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
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**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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[Question] Why using Learning Rate = 0.001 instead of 0.01 during fine-tuning ASR NeMo on non-English?
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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![image](https://user-images.githubusercontent.com/22035961/95894741-5e877080-0d3e-11eb-8c41-1304bb84a3a1.png)\r\nNotice that at the end of the trainin...
2020-10-13T13:50:26
2020-11-12T11:12:08
2020-11-12T11:12:08
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**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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Add Frequency re-sampling
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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...
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2020-10-20T14:30:19
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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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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...
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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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Incompatible SAMPLE_RATE
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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
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**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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Upgrade PTL to 1.0.2
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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
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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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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
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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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Download Permission Denied
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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
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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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