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"You may use this example to use the Conformer encoder with CTC-loss:\r\nhttps://github.com/NVIDIA/NeMo/blob/main/examples/asr/experimental/conformer/conformer_linear_decoder_bpe.yaml\r\n\r\nWe have the plan to release some checkpoints trained with Confomer-CTC in our next release in March, maybe even Conformer-RNN... | 2020-11-19T23:01:30 | 2021-05-28T08:45:06 | 2021-05-28T08:45:06 | NONE | {
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"@vladgets and/or @tkornuta-nvidia could you please resolve merge conflicts?",
"Hey @okuchaiev I am not responsible for this PR.\r\n\r\nAll the TLT-related changes to this model that I need were incorporated in #1477 .\r\n\r\n@vladgets it is your shot on what you want to do with this.",
"Since Tomasz did his c... | 2020-11-19T22:10:28 | 2020-12-02T00:07:33 | 2020-12-02T00:07:22 | CONTRIBUTOR | {
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} | # Changelog
- As discussed in https://github.com/NVIDIA/NeMo/issues/1458, greedy decoding can be simplified by reusing `blank_mask` rather than maintaining both `blank_mask` and `time_mask`.
Closes https://github.com/NVIDIA/NeMo/issues/1458 | {
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"@titu1994, please merge when ready\r\n"
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} | # Changelog
- Adds `init_mode=None` option which will preserve the default pytorch initialization for the `ConvASREncoder` and `ConvASRDecoder` modules
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"This pull request **introduces 4 alerts** when merging 17d0dd296bac97e6824925ddabcacee075b7fe8b into cfc84cebd98d92fa06089cdd42da685e60521d26 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d88619b7202d9dc56b09659798de28b51248b54a)\n\n**new alerts:**\n\n* 4 for Unused import",
"This pull requ... | 2020-11-19T19:26:41 | 2020-12-04T06:34:58 | 2020-12-04T06:34:52 | COLLABORATOR | {
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} | This PR will refactor the `TransformerMTModel` so that pre-trained HF and Megatron encoders and decoders can easily be used along with the `TransformerEncoder` and `TransformerDecoder` for NMT.
# Plan for this PR
- rnd -> rnd
# Plan for future PRs
- pretrained encoder -> rnd
- pretrained encoder -> pretrained ... | {
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} | # Bugfix
- Enables sentencepiece tokenizers to capture the entire vocabulary for small vocab languages like English with cases.
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"Thanks, @vadimkantorov for reporting this. Will look into the solution and test it out. ",
"Indeed, when @squire-tomsk investigated stubbing out `.data` in the UserList to our share-safe list holder in https://gist.github.com/vadimkantorov/86c3a46bf25bed3ad45d043ae86fff57, the memory leak goes away. The other so... | 2020-11-18T12:30:42 | 2023-05-17T22:06:01 | 2021-07-13T15:20:39 | CONTRIBUTOR | {
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https://github.com/NVIDIA/NeMo/blob/8d506d5e47367643328375e7e5b0ff6643ddb82b/nemo/collections/asr/parts/collections.py#L90
If this object is then scattered to all DataLoader workers, it leads to leaking Linux shared memory (measured by PSS)... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1466 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1466/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1466/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1466/events | https://github.com/NVIDIA/NeMo/pull/1466 | 745,443,077 | MDExOlB1bGxSZXF1ZXN0NTIzMDA3NTM3 | 1,466 | fix for scripts for audio->manifest in SD/VAD | {
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} | tiny fix write_long_audio_manifest.py
Results are same actually, Just the previous code casue negative offsets in manifests which is not good.
Signed-off-by: fayejf <fayejf07@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1465 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1465/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1465/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1465/events | https://github.com/NVIDIA/NeMo/issues/1465 | 745,341,881 | MDU6SXNzdWU3NDUzNDE4ODE= | 1,465 | [Question] why is _test_dl defiend in setup_training_data() and setup_validation_data() at qa_model.py | {
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**Describe your question**
NeMo version is '1.0.0b2'
This is a question about setup_training_data() and setup_validation_data() nemo/collections/nlp/models/question_answeri... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1464 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1464/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1464/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1464/events | https://github.com/NVIDIA/NeMo/pull/1464 | 745,093,262 | MDExOlB1bGxSZXF1ZXN0NTIyNzExMjk3 | 1,464 | [Bug] Ensure lr scheduler uses limit_train_batches when working out max steps | {
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"Hi, thanks for the bugfix! Could you fix the style issues by running `python setup.py style --scope=nemo/ --fix` ? Oh theres also a style issue in tests so another pass of `python setup.py style --scope=tests/ --fix` ",
"@SeanNaren Thanks for the test fix, youll need to run style fixes as mentioned above to pas... | 2020-11-17T21:15:49 | 2020-11-17T22:27:39 | 2020-11-17T22:27:06 | COLLABORATOR | {
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} | When `limit_train_batches` is different to the default, this is not included in the inferred calculation of max_steps. This leads to the incorrect schedule being used when `limit_train_batches` is used.
This PR adds `limit_train_batches` to the calculation to ensure the schedule is correct! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1463 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1463/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1463/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1463/events | https://github.com/NVIDIA/NeMo/issues/1463 | 745,032,728 | MDU6SXNzdWU3NDUwMzI3Mjg= | 1,463 | Confidence measures for Quartznet predictions | {
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"I would also be interested in this",
"I am interested in this as well. \r\nFor now, I'm using a simple approach by averaging the confidence score of each letter per word, resulting in a confidence score per word.\r\nYou can use [parlance](https://github.com/parlance/ctcdecode) to get a timestamp per letter with ... | 2020-11-17T19:39:36 | 2021-08-17T00:56:58 | 2021-08-17T00:56:58 | NONE | {
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I am interested in getting the word level confidences from Quartznet model. What would be the best approach to obtain some proxy values to word level confidences? Taking the logit sequences and normalizing them by length seems like the most straight forward approach but I am not sure how reliable this would ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1462 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1462/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1462/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1462/events | https://github.com/NVIDIA/NeMo/issues/1462 | 744,770,813 | MDU6SXNzdWU3NDQ3NzA4MTM= | 1,462 | Possible problems with weight initialization in NeMo ASR[Question] | {
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"This is a good observation. I'll run some full scale experiments regarding this and respond in a day or two, if you don't mind. For clarification, when you say \"pytorch default\" - you mean you do not use the the weight initialization method at all and just use the default initialized weights that pytorch provide... | 2020-11-17T14:10:36 | 2021-06-19T04:58:44 | 2021-06-19T04:58:44 | CONTRIBUTOR | {
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} | null | null | null | ## Describe your question
1. Weight initialization in the `ConvASREncoder` and `ConvASRDecoder` defaults to `xavier_uniform` but the architectures use ReLU which does best with kaiming initialization. Why was xavier initialization chosen?
2. `ConvASREncoder` and `ConvASRDecoder` have an `init_mode` argument that d... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1459 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1459/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1459/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1459/events | https://github.com/NVIDIA/NeMo/issues/1459 | 744,274,661 | MDU6SXNzdWU3NDQyNzQ2NjE= | 1,459 | [Question] Jasper/Quartznet ASR - Recommended length per file is between 5 and 25 seconds. Why? | {
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"Not an expert, but I'll try to help (assuming you're talking about ASR). I believe the recommendation is due to most of the training data being in that range. Since the ASR models, Quartznet and Jasper, are fully convolutional, there's no RNN and I believe this means longer than 25s shouldn't cause too much of a p... | 2020-11-16T23:10:45 | 2021-06-19T04:58:05 | 2021-06-19T04:58:05 | CONTRIBUTOR | {
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Recommended length per file is between 5 and 25 seconds for most models. I'm curious where/how we arrive at this limitation, and what this means for results I get from running model.transcribe() on a audio longer than 25s. Some insight into where/why we have this limitation would be apprec... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1458 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1458/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1458/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1458/events | https://github.com/NVIDIA/NeMo/issues/1458 | 743,941,672 | MDU6SXNzdWU3NDM5NDE2NzI= | 1,458 | RNN-T decoding: question about greedy batch inference | {
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"Yes, this is correct. I didn't think about reusing blank_mask for the label decoding step. Would you like to submit a PR to improve this for both greedy decoding implementations?"
] | 2020-11-16T15:44:18 | 2020-11-21T09:11:01 | 2020-11-21T09:11:01 | CONTRIBUTOR | {
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https://github.com/NVIDIA/NeMo/blob/5b6dd3850129898be47cf0d65587897ec45a5b59/nemo/collections/asr/parts/rnnt_greedy_decoding.py#L451-L454
with the following code (without ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1457 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1457/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1457/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1457/events | https://github.com/NVIDIA/NeMo/issues/1457 | 743,711,829 | MDU6SXNzdWU3NDM3MTE4Mjk= | 1,457 | [Question] How to solve Exception while using another wav file: RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension, but got: padding (256, 256) at dimension 2 of input [1, 1, 2] ? | {
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"@sankulka few questions:\r\n\r\n1) Are you able to successfully execute this notebook https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/NeMo_voice_swap_app.ipynb \r\n2) If you replace your file with this one https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav , does th... | 2020-11-16T10:21:17 | 2022-01-02T10:30:42 | 2020-11-19T08:09:39 | NONE | {
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I just started off learning Nemo for ASR activities and getting exception if I send a different wav file to convert into text. Could you please share what pre-processing has to be performed for any other different wav file/format than an4 dataset
**A clear and concise description of your ... | {
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"You may use the .pt file as any other Pytorch checkpoint or you can save your model as a .nemo file. The .nemo file will have everything needed (except datasets) to use your model. Please see the NeMo Primer tutorial for more information: https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials... | 2020-11-14T15:29:07 | 2020-11-23T05:08:53 | 2020-11-23T05:08:53 | NONE | {
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Do I need to (2. Define and initialize the neural network) as mentioned in https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html
How may I do it?
Appreciate any help T.T Tha... | {
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} | Also reinstated a script for processing hub5 data from NeMo 0.11 | {
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"> looks good to me. sorry for slow review\r\n\r\n@okuchaiev No worries! Thanks Oleksii!"
] | 2020-11-14T00:16:59 | 2021-01-22T15:29:24 | 2020-11-20T21:30:59 | COLLABORATOR | {
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} | I've tested on my local machine (macOS). Could you please help testing on your machine with microphone? @okuchaiev
Signed-off-by: fayejf <fayejf07@gmail.com> | {
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I follow [this link](https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/asr/01_ASR_with_NeMo.ipynb) to try to achieve quartznet inference optimization through tensorrt. I have installed onnxruntime-gpu-tensorrt and use tensorrt provider in onnxruntime. I did obser... | {
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Long audio files and corresponding transcripts are segmented with the algorithm described in [CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition](https://arxiv.org/pdf/2007.09127.pdf).
Provided functionality:
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"You should not be overriding your decoder as such. You should use a custom neural module. \r\n\r\n1) Make your custom decoder extend neural module class\r\n2) Override the nemo.collections.asr.modules.ConvASRDecoder classpath with the classpath of your new decoder. \r\n3) Create the model with this config and trai... | 2020-11-12T15:40:11 | 2021-07-13T15:20:02 | 2021-07-13T15:20:02 | CONTRIBUTOR | {
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I would like to implement my own decoder for transfer learning to experiment with multiple linear layers. I did so by overriding quartznet.decoder.decoder_layers with my own `nn.Sequential` using the code below
```
quartznet.decoder.decoder_layers = nn.Sequential(nn.Conv1d(1024, 256, k... | {
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"@anujsuchchal Did you come across a solution for this?",
"@ShantanuNair Yes. For this I am using another CTC Beam Search Decoder package by [Parlance](https://github.com/parlance/ctcdecode). \r\nThis returns timesteps of characters as well in addition to Beam results which you can use to align Audio with Transc... | 2020-11-12T15:25:15 | 2023-12-16T17:05:20 | 2020-12-12T17:13:30 | NONE | {
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Since the word timestamps logic given in Offline_ASR.ipynb works on Greedy CTC decoder. Number of words in Greedy decoder and Beam Search decoder are usually different so we can't use this logic with Beam Search Decoder.
Is there any way to... | {
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"Can you share more info about your training process?\r\n\r\n-How many hours of training data do you have?\r\n-What is the size of the Persian vocab? Did you change the decoder to use the new vocab? \r\n-What is the loss doing? Is it falling to a certain level and stalling? What's the lowest loss you get and can yo... | 2020-11-12T11:09:53 | 2021-05-28T08:45:35 | 2021-05-28T08:45:35 | NONE | {
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First of all, thank you guys specially @okuchaiev for this magnificent project on Conversational A.I. Good job Guys.
I am using NeMo **ASR** for **Persian** language mainly because of being able to use **transfer learning** due to lack of labeled data. I got 40% WER on my validation data after working on N... | {
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"This pull request **introduces 2 alerts** when merging 918f97f81514c0f243321e81eb73adcd49c18cc3 into a711ca1ad2987797b1e80ac9b0ffd7479c0a4730 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-a817d9e013ff7a00f492166aeb5bf1830ad2cba6)\n\n**new alerts:**\n\n* 2 for Except block handles 'BaseExc... | 2020-11-12T08:22:30 | 2020-11-13T21:53:11 | 2020-11-13T21:53:07 | COLLABORATOR | {
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} | Added new features to Speech Data Explorer:
- backend paging/sorting/filtering (allows to work with large datasets)
- support for assigning a port
- minor tweaks to UI
Signed-off-by: Vitaly Lavrukhin <vlavrukhin@nvidia.com> | {
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"awesome!!",
"This pull request **introduces 2 alerts** when merging 12122b266f59f46f76414748c73da675f830ec03 into a711ca1ad2987797b1e80ac9b0ffd7479c0a4730 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cf2265463b3732f561da98b7d2b7b43d39332d52)\n\n**new alerts:**\n\n* 2 for Unused import",
... | 2020-11-11T21:59:04 | 2020-11-19T04:44:25 | 2020-11-19T04:44:20 | COLLABORATOR | {
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} | This PR adds the `.register_bert_model` and `.register_tokenizer` methods to `NLPModel` and lifts the `_setup_tokenizer` method to `NLPModel`.
For HuggingFace and Megatron BERT models the BERT config will be written to `pretrained_model_name_encoder_config.json` and added to the `.nemo` archive.
For tokenizers, ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1439 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1439/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1439/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1439/events | https://github.com/NVIDIA/NeMo/pull/1439 | 740,513,184 | MDExOlB1bGxSZXF1ZXN0NTE4OTg5MTM4 | 1,439 | vad outputs to manifest script for speaker diarization | {
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} | Switching to EFF NeMoCookbook. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1437 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1437/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1437/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1437/events | https://github.com/NVIDIA/NeMo/pull/1437 | 740,236,195 | MDExOlB1bGxSZXF1ZXN0NTE4NzU4ODE5 | 1,437 | Update NeMo core for melgan | {
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} | Split off updates to nemo core that are required for #1418
Changes:
- Remove empty *_step() methods from ModelPT
- Treat None inside of ElementType's parameters as a Void that matches anything
- Use case being removing the default 16kHz sample_rate from AudioSignal and replacing it with None. This allows a mod... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1436 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1436/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1436/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1436/events | https://github.com/NVIDIA/NeMo/issues/1436 | 739,965,249 | MDU6SXNzdWU3Mzk5NjUyNDk= | 1,436 | [Question] ASR Correction output | {
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"The best bet it to train on more data.\r\nIf you do not have more data, then try fine-tuning from pretrained model (even from another language) by re-using acoustic encoder.\r\nAnd, of course, you will need to play/experiment with hyperparameters"
] | 2020-11-10T14:37:39 | 2020-11-11T00:19:47 | 2020-11-11T00:19:47 | NONE | {
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I'm trying to train ASR models for different languages. The problem is that the output contains many mistakes. Do you have any idea to correct them.
Thanks in advance,
Sincerely. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1435 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1435/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1435/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1435/events | https://github.com/NVIDIA/NeMo/issues/1435 | 739,773,675 | MDU6SXNzdWU3Mzk3NzM2NzU= | 1,435 | How to export model from the checkpoint and then load the same model for prediction? | {
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"You should be able to:\r\n```\r\nmymodel.save_to(save_path=\"mymodel.nemo\")\r\n```\r\nthen (possibly in another python script)\r\n```\r\nmymodel=IntentSlotClassificationModel.restore_from(restore_path=\"mymodel.nemo\")\r\n```\r\nNote that ```restore_from``` is a class method which returns class instance. Are you ... | 2020-11-10T10:13:24 | 2021-08-17T00:56:20 | 2021-08-17T00:56:20 | NONE | {
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} | null | null | null | Hi,
I have trained my own dataset using NLP Joint Intent & slot classification NeMo models. I am able to train & predict the data. However i am not getting how can i save the model from that checkpoint and then reload the same model & predict on example test queries. I can understand using experiment manager, path ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1434 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1434/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1434/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1434/events | https://github.com/NVIDIA/NeMo/pull/1434 | 739,701,884 | MDExOlB1bGxSZXF1ZXN0NTE4MzE2ODAx | 1,434 | Add support for Tarred dataset for Transformer LM | {
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"This pull request **introduces 2 alerts** when merging b7801269d9ee2ca213f5bd43e4302fd148ed66bc into 38c854da3f553b6577d6f8b08490e026b53b7ea3 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fa359a48d5f95a10eb6e93d11f38e27ea636a1e6)\n\n**new alerts:**\n\n* 1 for Unused local variable\n* 1 for Un... | 2020-11-10T08:33:37 | 2020-11-18T21:08:15 | 2020-11-18T21:08:12 | COLLABORATOR | {
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} | # Changelog
- Adds tarred dataset support for transformer based Language models
- Pre-tokenizes and chunks the dataset for more efficient external dataset handling
- Adds utility script to construct a tarred tokenized LM dataset
- Adds `cache_ids` flag support for transformer based language models | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1433 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1433/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1433/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1433/events | https://github.com/NVIDIA/NeMo/issues/1433 | 739,684,641 | MDU6SXNzdWU3Mzk2ODQ2NDE= | 1,433 | How can i the change the provided classes on the mapping into my own [Token_Classification_Named_Entity_Recognition] | {
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"If your labels are not a subset of the provided labels, it's recommended to use [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/token_classification.py). Note, you `shouldn't` use a pre-trained TokenClassificationModel, instead start fine-tuning with the original BERT model... | 2020-11-10T08:06:29 | 2020-11-11T04:09:57 | 2020-11-11T04:09:57 | NONE | {
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} | null | null | null | Hi, I am using the Token_Classification_Named_Entity_Recognition tutorial for a project.
By default, the dataset provided these labels:
LOC = Geographical Entity
ORG = Organization
PER = Person
GPE = Geopolitical Entity
TIME = Time indicator
ART = Artifact
EVE = Event
NAT = Natural Phenomenon
How can I c... | {
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"> This looks fine to me. But I am wondering where it will be used?\r\n\r\nIn EFF: ONNXCookbook :)"
] | 2020-11-10T07:18:12 | 2020-11-13T19:47:41 | 2020-11-13T19:28:04 | CONTRIBUTOR | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com> | {
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} | Bugfix for when max duration is set to None
Add support for min duration filtering | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1430 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1430/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1430/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1430/events | https://github.com/NVIDIA/NeMo/pull/1430 | 739,456,064 | MDExOlB1bGxSZXF1ZXN0NTE4MTE2Mzc4 | 1,430 | Update tarred dataset conversion script | {
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"This pull request **introduces 6 alerts** when merging 57bbdd1984e6529cb18f138488bad775e10a19b1 into c0fedd91e316a4ba95ac6b14330ff92844c56097 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fc89a4569b49ec7de84b75ce25de8500ede41b9d)\n\n**new alerts:**\n\n* 4 for Unused import\n* 2 for Syntax err... | 2020-11-09T23:56:54 | 2020-11-10T00:10:14 | 2020-11-09T23:57:07 | COLLABORATOR | {
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Added minimum duration filter | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1429 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1429/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1429/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1429/events | https://github.com/NVIDIA/NeMo/pull/1429 | 738,840,234 | MDExOlB1bGxSZXF1ZXN0NTE3NjA2OTkw | 1,429 | Add RIR perturbation example. | {
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} | Add RIR(Room impulse Reverbation) perturbation example in the tutorial. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1428 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1428/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1428/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1428/events | https://github.com/NVIDIA/NeMo/issues/1428 | 738,412,606 | MDU6SXNzdWU3Mzg0MTI2MDY= | 1,428 | [Question] Difference between `blank` and `space` character in ASR quartznet? | {
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"`space` character is a regular delimiter between words [` `]. Usually, it is also returned for silent segments. \r\n`blank` symbol is a special extra character in CTC modeling that allows representing double characters, for example. It is removed from final decoded transcript. More details could be found in the fo... | 2020-11-08T07:41:05 | 2020-11-10T19:24:15 | 2020-11-10T19:24:15 | NONE | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1427 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1427/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1427/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1427/events | https://github.com/NVIDIA/NeMo/issues/1427 | 738,174,179 | MDU6SXNzdWU3MzgxNzQxNzk= | 1,427 | [Question] How may i save the model to my own file in colab? | {
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"You can use google.colab library to download files off of google colab - https://stackoverflow.com/questions/48774285/how-to-download-file-created-in-colaboratory-workspace.\r\n\r\nInstead of `example.txt` in that snippet, you can provide the full path to the checkpoint instead and it should (hopefully) download t... | 2020-11-07T05:08:06 | 2020-11-10T06:11:27 | 2020-11-10T06:11:27 | NONE | {
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" Epoch 0: val_loss reached 0.38735 (best 0.38735), saving model to /content/nemo_experiments/token_classification_model/2020-11-07_03-05-01/checkpoints/token_classification_model---val_loss=0.39-epoch=0.ckpt as top 3"
How ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1426 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1426/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1426/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1426/events | https://github.com/NVIDIA/NeMo/pull/1426 | 738,113,841 | MDExOlB1bGxSZXF1ZXN0NTE3MDM4MDcy | 1,426 | Add support for replicated shard strategy for tarred datasets | {
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"As in remove the configs shard_strategy = scatter portion? We could drop them, but it reduces visibility. Practically, it should be fine, since the shard strategy is not used unless is_tarred is set \r\n\r\nEdit: I do agree that the configs are going quite big though. ",
"I was worried about it potentially causi... | 2020-11-07T00:16:58 | 2020-11-09T23:43:12 | 2020-11-09T23:42:55 | COLLABORATOR | {
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} | # Changelog
- Support `replicate` strategy for sharding of tarfiles under ddp for Tarred WebDataset.
- Add config line to all ASR models
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1425 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1425/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1425/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1425/events | https://github.com/NVIDIA/NeMo/issues/1425 | 737,770,909 | MDU6SXNzdWU3Mzc3NzA5MDk= | 1,425 | [Question]Quantized QuartzNet | {
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"I don't think they can help with this\r\nPytorch need to support Conv1d and BatchNorm1d in order for you to quantize it.\r\n\r\nThe only way you can do it is by converting Conv1d to Conv2d(same for BatchNorm) using [this](https://github.com/NVIDIA/NeMo/blob/c90e3aaee67a73dc1c2b0c0614766246e403e1c1/nemo/utils/expor... | 2020-11-06T13:49:15 | 2021-06-19T04:54:39 | 2021-06-19T04:54:39 | NONE | {
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```python
jasper_encoder = jasper_encoder.cpu()
jasper_encoder.eval()
jasper_encoder.qconfig = torch.quantization.get_default_qconfig('qnnpack')
torch.quantization.propagate_qconfig_(jasper_encoder)
jasper_encoder = torch.quantization.prepare(jasper_enco... | {
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"Hello @4nton-P \r\n\r\n- I just want to feed a track into the model and get a predicted time. To do this, I'll just rewrite the callback function, but how to get around the problem with the lack of devices?\r\nIt is designed for the demonstration in the tutorial you don't have to use it to do inference for VAD.\r\... | 2020-11-06T11:19:57 | 2020-11-11T21:44:08 | 2020-11-11T21:44:08 | NONE | {
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} | null | null | null | Hello!
I ran into the VAD Inference problem. I cannot use offline inference if I do not have an output or input of a device, because the pyaudio requires a device. Are there any solutions?
I just want to feed a track into the model and get a predicted time. To do this, I'll just rewrite the callback function, but how... | {
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} | * Add perplexity draft
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Add perplexity metric
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Add docstrings and copyright, fix comments
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Fix code style
Signed-off-by: PeganovAnton <peganoff2@mail.ru... | {
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} | - Adds the fullband MelGAN model as described in https://arxiv.org/abs/2005.05106
- Remove empty *_step() from ModelPT
- Split UniGlowLoss into glow portion and STFTLoss portion
- Simply stft_exact_pad option in FilterBankFeatures
ToDos
- [x] ~Enable FP16~. No currently a priority.
- [x] Add NeMo Loss subclas... | {
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"sorry this feature have not been ported yet.",
"@okuchaiev is it wip or will take some time?",
"This is available in Nemo. Please refer to Nemo docs for ASR Language Modelling. "
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} | null | null | null | How can we deploy ASR post processing on the new Nemo Version 1.0.0b1 ?
I found this [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v0.11.0/nlp/asr-improvement.html#asr-improvement) and this [page](https://github.com/NVIDIA/NeMo/tree/fa68d336cfefbbbb1849ff1b6ef454149f45234d/examples/nlp/asr_po... | {
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"Thanks for the issue. Please upgrade your version of NeMo to 1.0.0b*.\r\n\r\nSee https://github.com/NVIDIA/NeMo/issues/1091 for a discussion on what has changed."
] | 2020-11-05T12:58:42 | 2020-12-04T00:46:20 | 2020-12-04T00:46:20 | NONE | {
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I have following problem when I run speech2text file:
**Setup NeuralModuleFactory to control training nf = nemo.core.NeuralModuleFactory( create_tb_writer=True, ...)*
The system will raise an error:
################################################################################
### WARNING, path does... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1412 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1412/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1412/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1412/events | https://github.com/NVIDIA/NeMo/issues/1412 | 736,257,913 | MDU6SXNzdWU3MzYyNTc5MTM= | 1,412 | [Question] Does TTS modules (for example Tacotron2) support fine-tuning like ASR ? | {
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"Hello, any update on this issue?\r\n\r\nI have the same problem, I would like to perform a fine-tuning of Tacotron 2 model with a different set of labels (characters/phonemes in input to the model).\r\n\r\nI'm also interested in simple fine-tuning without changing the set of labels, is this feature already availab... | 2020-11-04T16:36:47 | 2022-01-18T18:40:42 | 2022-01-18T18:40:06 | NONE | {
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Am working on transfer learning with TTS models From English to the Arabic language. I saw an example of fine-tuning using ASR Quartznet model mentioned [here](https://github.com/NVIDIA/NeMo/issues/1107#issuecomment-686005952).
I am using this pre-trained model:
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https://api.github.com/repos/NVIDIA/NeMo/issues/1411 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1411/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1411/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1411/events | https://github.com/NVIDIA/NeMo/issues/1411 | 735,978,617 | MDU6SXNzdWU3MzU5Nzg2MTc= | 1,411 | When i use GPU to infer, everything is ok. But when i choose CPU, it turn out runtime error: code is too big. I have no idea what is it mean. Did anybody know how can i fix it. | {
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"Please upgrade to 1.0.0b* version of NeMo. See https://github.com/NVIDIA/NeMo/issues/1091 for a discussion on the latest changes."
] | 2020-11-04T10:02:02 | 2020-12-04T02:39:11 | 2020-12-04T02:38:33 | NONE | {
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} | null | null | null | I set batch_size = 1, it still shows runtime error.
But when my test_set only have one wav, it can run successfully.
part of my code:
neural_factory = nemo.core.NeuralModuleFactory(
backend=nemo.core.Backend.PyTorch,
#local_rank=args.local_rank,
#optimization_level=args.amp_opt_level... | {
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} | Rebased branch, reopened the PR | {
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} | Moved from previous PR due to wrong base branch.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1408 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1408/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1408/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1408/events | https://github.com/NVIDIA/NeMo/issues/1408 | 735,896,038 | MDU6SXNzdWU3MzU4OTYwMzg= | 1,408 | [Question] Conformer pre-trained models | {
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"I would also be very interested!",
"Hi, first thanks for developing this repo. It is quite useful. I am also interested in the pretrained conformer model. It would be helpful if this could also be added to ngc\r\n\r\nThanks,",
"We have the plan to release publicly some checkpoints with Conformer-CTC, and maybe... | 2020-11-04T07:56:35 | 2021-10-25T17:40:40 | 2021-06-10T21:59:48 | NONE | {
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} | null | null | null | Thanks authors for publishing ASR sources. I know that QuartzNet & Jasper are comparable to some SOTA model for S2T tasks. Recently, Google published "Conformer" encoder network and achieved very good performance on LibriSpeech. I saw that, that network source is released on new Nemo version. Could you please release p... | {
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"> @vladgets do you still want to merge this?\r\n\r\nYes, I do",
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"code format check fails - see CI output"
] | 2020-11-02T20:14:13 | 2020-11-05T01:49:41 | 2020-11-05T01:49:41 | CONTRIBUTOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1395 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1395/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1395/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1395/events | https://github.com/NVIDIA/NeMo/issues/1395 | 734,311,552 | MDU6SXNzdWU3MzQzMTE1NTI= | 1,395 | [Question] How to train a ASR model finetuned with RIR and noise augmentation, just like `QuartzNet15x5NR-En`? | {
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"Here are some scripts that can help you create datasets for RIR and noise augmentation:\r\nhttps://github.com/NVIDIA/NeMo/blob/main/scripts/get_openslr_rir.py (downloads and creates a json manifest for RIRs from openslr)\r\n\r\nSome scripts to download noise samples from freesound are here: https://github.com/NVID... | 2020-11-02T08:44:46 | 2021-06-19T04:47:18 | 2021-06-19T04:47:18 | CONTRIBUTOR | {
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} | null | null | null | **Describe your question**
How to train a ASR model finetuned with RIR and noise augmentation, just like `QuartzNet15x5NR-En`?
I find that there is a pretrained `QuartzNet15x5NR-En` model on NGC, and a config file for augmentation `/home/jack/a/PycharmProjects/NeMo/examples/asr/experimental/configs/quartznet_15x5... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1394 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1394/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1394/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1394/events | https://github.com/NVIDIA/NeMo/issues/1394 | 734,213,887 | MDU6SXNzdWU3MzQyMTM4ODc= | 1,394 | How to use/port quartzNet model on mobile? [Question] | {
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"> Can I deploy NeMo trained ASR on mobile platform (Android/iOS)? \r\n- Yes!\r\n\r\n> If yes then what are the steps?\r\n- Every NeMo model is inherited from ```torch.nn.Module``` , therefore you should checkout Pytorch tutorials about deploying models on mobile. For example, you can export QuartzNet ASR models in... | 2020-11-02T06:08:42 | 2021-02-10T23:29:38 | 2021-02-10T23:29:38 | NONE | {
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"@uditdixit11, \r\nIf you have finetuned or trained all together with a new speaker recognition model, you could test or infer by two steps as shown similarly in [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_infer.py) . \r\n1. set up test data and \r\n2. get log_prob by calling ... | 2020-11-02T05:48:02 | 2021-06-08T08:15:28 | 2021-01-25T07:29:23 | NONE | {
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Please suggest how do I perform the next steps i.e then predict the speaker on unseen data using trained model... | {
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"@AlexGrinch can you please fix the PR so that CI passes ",
"@AlexGrinch code style",
"please fix code formatting to unblock CI:\r\n```\r\npython setup.py style --fix\r\n```"
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"Yes, NeMo is not English-only. You should be able to train good Japanese models if you have Japanese training data. Of course, knowledge of Japanese helps too :) ",
"> Yes, NeMo is not English-only. You should be able to train good Japanese models if you have Japanese training data. Of course, knowledge of Japan... | 2020-11-01T09:56:01 | 2020-11-03T09:07:55 | 2020-11-03T09:07:55 | NONE | {
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"This pull request **introduces 2 alerts** when merging 9c213ff48acc513dfd3b02654fa92fdd54b93341 into 75774cf24755bddb393e3b1a4cbcc1921daf8898 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-29dea0dd8c242cb81d2215a8c6323dfb9f2b3347)\n\n**new alerts:**\n\n* 2 for Unused local variable"
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} | Update VAD inference to make it easy to combine with Speaker Diarization and more user friendly (user would like txt output)
Add/update:
1. Truncate long audio files to separate json line to avoid CUDA memory issue
2. Frame level inference similar to speech_to_text_infer.py because the frame level streaming is ki... | {
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Update notebook to use fstrings for branch. | {
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} | Dropped the evaluation with another trainer in the same script. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1381 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1381/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1381/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1381/events | https://github.com/NVIDIA/NeMo/issues/1381 | 733,435,003 | MDU6SXNzdWU3MzM0MzUwMDM= | 1,381 | Is there a tutorial for data augmentation using transfer learning on pretrained QuartzNet? | {
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"Here is a tutorial on data augmentation https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/05_Online_Noise_Augmentation.ipynb "
] | 2020-10-30T18:51:18 | 2021-05-28T08:31:22 | 2021-05-28T08:31:22 | NONE | {
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} | null | null | null | This doc: https://developer.nvidia.com/blog/develop-smaller-speech-recognition-models-with-nvidias-nemo-framework/ mentions that
> The QuartzNet-15×5 model was trained on the LibriSpeech training dataset with two types of data augmentation: speed perturbation and Cutout
If I transfer-learn the English QuartzNet ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1380 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1380/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1380/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1380/events | https://github.com/NVIDIA/NeMo/issues/1380 | 733,414,734 | MDU6SXNzdWU3MzM0MTQ3MzQ= | 1,380 | Is there a Jupyter tutorial notebook for BERT ASR postprocessing? | {
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"Actually never mind, I can just train using other text-to-text notebooks as a new language pair, the pair being ASR output to ground truth.",
"Hey, they've explained in other issues - https://github.com/NVIDIA/NeMo/issues/1156, https://github.com/NVIDIA/NeMo/issues/1126 that it hasn't been ported yet, but looks ... | 2020-10-30T18:17:27 | 2020-11-11T13:39:20 | 2020-10-30T21:41:44 | NONE | {
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} | null | null | null | BERT ASR postprocessing is described here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v0.10.1/nlp/asr-improvement.html
Is there a notebook in the GitHub to demo this in combination with transfer learning of QuartzNet15x5 for a new language?
It would be very helpful to show how to transfer-learn ... | {
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Now I am using your intent_slot_classification.py. The model could be trained, however, the FileNotFoundError was shown. I just discovered you add the inference part.
Jonathan Sung | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1378 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1378/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1378/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1378/events | https://github.com/NVIDIA/NeMo/pull/1378 | 732,908,862 | MDExOlB1bGxSZXF1ZXN0NTEyNzc5MTgy | 1,378 | PunctuationCapitalizationModel exportable | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1377 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1377/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1377/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1377/events | https://github.com/NVIDIA/NeMo/pull/1377 | 732,876,195 | MDExOlB1bGxSZXF1ZXN0NTEyNzUyMjc0 | 1,377 | Fix http 500 ngc v1.0.0b2 | {
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"This pull request **introduces 1 alert** when merging 4f0baa600e628c3bc964be157a258d7dea590259 into 0c575303f49dc0060ad1ff13f3ec9f6e2dcbc381 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d58640ed1f19ca480d854b6e6914fa40c21d5cc6)\n\n**new alerts:**\n\n* 1 for Except block handles 'BaseExce... | 2020-10-30T04:38:14 | 2020-10-30T14:14:17 | 2020-10-30T14:14:11 | COLLABORATOR | {
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} | Try and wait in case NGC download fails. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1376 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1376/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1376/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1376/events | https://github.com/NVIDIA/NeMo/issues/1376 | 732,870,252 | MDU6SXNzdWU3MzI4NzAyNTI= | 1,376 | How do I turn off logging? | {
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"This is my question too.",
"Try this after importing nemo modules:\r\n```python\r\nimport logging\r\nlogging.getLogger('nemo_logger').setLevel(logging.ERROR)\r\n```\r\nyou can choose the logging level from https://docs.python.org/3/library/logging.html#levels",
"[This](https://stackoverflow.com/questions/27647... | 2020-10-30T04:21:34 | 2023-10-24T13:10:22 | 2021-05-22T08:41:58 | NONE | {
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```
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioToCharDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-10-30 0... | {
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