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"And if I change `punctuation_en_distilbert.nemo` to `punctuation_en_bert.nemo` than .onnx model has 3 inputs, but the issue with \r\n`onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(float)) , expected: (tensor(int6... | 2021-06-09T15:17:36 | 2021-12-16T13:09:13 | 2021-06-10T10:38:31 | NONE | {
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I was able to do so using this block of code:
```
model = PunctuationCapitalizationModel.restore_from(restore_path="punctuation_en_distilbert.nemo")
model.export("punctuation_en_distilbert.onnx")
```
This works fine and I get the... | {
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"How to do real time streaming using Citrinet asr model? Will it be the same way as of Quartznet?",
"> How to do real time streaming using Citrinet asr model? Will it be the same way as of Quartznet?\r\n\r\ndo you know how to implement quartznet for real time streaming??",
"\r\n\r\n> > How to do real time stre... | 2021-06-09T05:40:41 | 2021-06-09T23:22:50 | 2021-06-09T23:22:49 | NONE | {
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I used nvidia quartznet for asr and it is taking wav files to transcribe. How to do real time streaming asr model
Thanks | {
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"Please hold off merge of this until we make sure neural type information is included in nemo files.",
"> Please hold off merge of this until we make sure neural type information is included in nemo files.\r\n\r\n@ryanleary @junkin Should we try to merge this into NeMo? Or do you still want to block for now?",
... | 2021-06-09T00:12:12 | 2022-09-22T18:46:32 | 2022-09-22T18:46:32 | CONTRIBUTOR | {
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"This pull request **introduces 2 alerts** when merging 265bf1cf644ca545c07361b0c69b139176a04c16 into cfe2548d4db9dcc4ca393ee63604139c9f681cd8 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-daa3ed728ff039b73f402e72c8d2b2aa4efa1369)\n\n**new alerts:**\n\n* 2 for Unused exception object"
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} | - [x ] is backwards compatible
- [x ] fixes loading previously trained .nemo model for testing
Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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"what models are you training?",
"We are using own dataset, with audios (wav) and .txt, in Portuguese. Like a small LibriSpeech",
"Language model is trained separately from the acoustic model such as QuartzNet. Then you can use LM during beam search process to improve the results. Please refer to this doc https... | 2021-06-08T20:37:28 | 2021-06-19T04:34:05 | 2021-06-19T04:34:05 | NONE | {
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} | null | null | null | We are trying to train models with a language model '.arpa', but reading the documentation and trying some scripts we can't get anything but the same result without the language model. I think we are doing something wrong. Do you have any tip for this question, another documentation or something like this to help us? D... | {
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"We don't (yet) have something that sophisticated as an API that emits word level confidence. However, we do have CTC alignments available. You can use the CTC model.transcribe(..., return_hypotheses=True) argument, which will return a hypothesis object with a value in it called `alignment` - that describes what to... | 2021-06-08T19:19:49 | 2021-07-08T13:25:56 | 2021-06-09T21:25:18 | NONE | {
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} | null | null | null | We are doing training/transcription tests using NeMo for the Portuguese language and we are getting great results. But now I want to evolve the transcripts in order to get the confidence rate, timestamp of each word or sentence. Is there any tool inside NeMo that already does this autonomously?
Just for comparison, ot... | {
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} | Apply updates where OmegaConf is breaking with 2.1.0.
See #2316 for the original PR to v1.0.0.
Note, we have to upper bound OmegaConf as Hydra 1.0.6 is not compatible with OmegaConf 2.1.0. When the new version of Hydra is released we should remove the upper bound. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2318 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2318/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2318/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2318/events | https://github.com/NVIDIA/NeMo/pull/2318 | 915,087,463 | MDExOlB1bGxSZXF1ZXN0NjY0OTQ3MDg1 | 2,318 | Exportable fastpitch | {
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"@blisc @borisfom Think we can get this merged?",
"> @blisc @borisfom Think we can get this merged?\r\n\r\nCan you merge with main?",
"@blisc merged in the latest changes from main, so this should have the remaining deltas only now."
] | 2021-06-08T14:01:38 | 2021-06-14T14:00:55 | 2021-06-14T14:00:55 | CONTRIBUTOR | {
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"I'm not very familiar with ONNX, @borisfom could you take a look at this ?",
"@borisfom would comment on the onnx export and why length is ignored. But AFAIK with QuartzNet you do not need to pass the lengths as they are not used at all in the forward pass (unless you enable the masked convolutions). You just n... | 2021-06-08T06:14:51 | 2021-10-26T23:45:33 | 2021-10-26T23:45:33 | CONTRIBUTOR | {
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I'm trying to export Quartznet model to onnx by using asr_model.export() function. However, the generated onnx model doesn't have the input length as an input and it only has 'audio_signal'. Therefore after converting the onnx model to trt engine, I find the inference results have a WER much wors... | {
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} | # Bugfix
- Update OmageConf signature to correctly save and restore models in OmegaConf 2.1.0 release
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | Shallow fusion with LM during NMT beam search generation.
1. Implements `BeamSearchSequenceGeneratorWithLanguageModel` for shallow fusion with external LM based on the same tokenizer as NMT model.
2. Updates `examples/nlp/machine_translation/nmt_transformer_infer.py` script to support LM shallow fusion. | {
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"This pull request **introduces 2 alerts** when merging f139082ed38998ea2474a09dc2f8759b68625969 into 29b6dd8890d5c196ded96351607ff6642406e75a - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-90934cd23206306438231b8b7dab39a60d08f70b)\n\n**new alerts:**\n\n* 2 for Unused import",
"@titu1994 Coul... | 2021-06-07T14:47:59 | 2021-06-08T02:53:32 | 2021-06-07T20:19:09 | CONTRIBUTOR | {
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} | Hi @titu1994
I've further tested the quantization part code and I get the below results:
First, calibrate the model:
```bash
python speech_to_text_calibrate.py --asr_model=./QuartzNet15x5-Zh-NR.nemo --dataset=/raid/data/data_aishell2/dev_manifest.json --dont_normalize_text
```
Then, test the quant model:
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"BERT is a masked LM and different from regular Transformer LM. Using a maksed LM model to score a sentence is not as efficient as Transformer. You need to call it X times and X is equal to the number of tokens in a candidate while Transformer just needs one pass. For instance, if average of your candidates have 30... | 2021-06-07T08:24:35 | 2021-07-13T15:27:32 | 2021-07-13T15:27:32 | NONE | {
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I use QuartzNet15-5 for fine-tuning. Based on this [paper](https://arxiv.org/pdf/1910.10261.pdf) I understand that QuartzNet15-5 uses 6-gram and T-XL language models. How can I change the language model in QuartNet15-5? For instance, using Bert.
**Environment overview**
Environment location: Google Colab
M... | {
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"We dont support Windows 10 in any capacity at the moment. ",
"I face the same problem in Colab, I installed all prerequisite."
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`scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh `
I also installed [swig for windows](http://www.swig.org/download.html), [wget for windows](https://eternallybored.org/misc/wget/) & [ma... | {
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} | Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
Usage:
`speaker_model = ExtractSpeakerEmbeddingsModel.from_pretrained(model_name="speakerverification_speakernet")`
` embs = speaker_model.get_embedding('audio_path')` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2309 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2309/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2309/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2309/events | https://github.com/NVIDIA/NeMo/pull/2309 | 911,761,719 | MDExOlB1bGxSZXF1ZXN0NjYxOTg5Mjc1 | 2,309 | Update container version to 21.05 | {
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} | # Changelog
- Update container version to 21.05 for CI and Dockerfile
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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Hi,
I have an error about tutorial of relation_extraction-Biomegatron.
In Colab, there was no problem to install and to test.
Step/Code : from nemo.collections import nlp as nemo_nlp
During import process, import error was happened like this.
ImportError: libhdf5.so.101: cannot open sha... | {
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} | Extended `transcribe_speech.py`
- added support for JSON dataset manifest as input (to transcribe datasets for analysis in SDE)
- modified output format to JSON manifest (to store filenames with ASR transcripts)
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Signed-off-by: Oktai Tatanov <oktai.tatanov@gmail.com> | {
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"Please check your pytorch lightning version - RC1 required 1.1.5, main requires 1.3+",
"I have nemo-toolkit-1.0.0rc2 .. with pytorch lightning version 1.1.5..getting error as \r\n from pytorch_lightning.plugins import DDPPlugin\r\nImportError: cannot import name 'DDPPlugin'\r\n\r\n\r\nAnd with 1.3+ same erro... | 2021-06-02T12:04:03 | 2021-07-08T01:04:40 | 2021-07-08T01:04:40 | NONE | {
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I am trying to run BERT Pre- training with sample data with reference to https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/zh/latest/nlp/bert_pretraining.html
Steps followed :
1. examples/nlp/language_modeling/get_wkt2.sh download_dir.
2. Changed the config file NeMo/examples/nlp/language_modeling/... | {
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} | Signed-off-by: slyned <slyned@nvidia.com>
Add CER support
But the results of speech_to_text_quant_infer_trt.py and speech_to_text_quant_infer.py are a little different.
I've also tried to use speech_to_text_quant_infer_trt.py to do inference with NeMo models downloaded from ngc without adding --qat, but the r... | {
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} | Adding the following models:
https://ngc.nvidia.com/catalog/models/nvidia:nemo:asrlm_en_transformer_large_ls
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small_ls
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium_ls
https://ngc.nvidia.com/catalog/models/nvidia... | {
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"@okuchaiev fyi - updating the signature for config update to remove deprecated API call for OmegaConf in ModelPT."
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} | # Changelog
- Update Citrinet configs.
- Improved error message when numba is not installed.
- Add tarred dataset support for all supported filetypes.
- Ignore extra parameters for layer-normed LSTM.
- Correct deprecated call signature for OmegaConf. | {
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"Per feature normalization will not be sufficient to perform normalization over small buffer sizes, that's why a fixed mean and std was used for QuartzNet. You can try it, but I would not expect great results. \r\n\r\nThere are scripts for LM usage with both those types of models - https://docs.nvidia.com/deeplearn... | 2021-06-01T10:35:39 | 2021-09-24T21:14:06 | 2021-06-09T21:26:09 | NONE | {
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Thank you for great toolkit, tutorials and models.
I have some questions:
1. I want to use pretrained Citrinet in Online_ASR_Microphone_Demo instead of QuartzNet. I changed normalization to 'per_feature' and initialized EncDecCTCModelBPE. What else do I need to change for the model to work correctly?
2.... | {
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"Do you wish to train Citrinet 1024 from scratch ? Or simply fine tune?\r\n\r\nDuring training, it is not possible to have samples longer than 16-20 seconds per sample, no matter the model. It is only during inference that you would be able to pass much longer audio samples. \r\n\r\nThe config for 1024 is not share... | 2021-06-01T08:17:39 | 2021-06-09T21:27:51 | 2021-06-09T21:27:51 | NONE | {
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Thanks a lot for the powerful ASR toolkit.
I am new to ASR. I recently started working with QuartzNet15*5 from this [link](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb):
but I have long duration acoustic dataset so I saw Citrinet paper at this [link](https://arxiv.org/abs/210... | {
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"EntityLinkingModel is a new model and was not a part of 1.0.0rc1. You may use the main branch or wait for the release of 1.0.0 which is going to happen very soon."
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As per Entity linking tutorial, release = **1.0.0rc1**, does not have **EntityLinkingModel** available.
We are getting error -
AttributeError: module 'nemo.collections.nlp.models' has no attribute 'EntityLinkingModel'.
Could you please help us with the issue? | {
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} | This PR would add a script to evaluate a neural language model (Transformer) trained with
`examples/nlp/language_modeling/transformer_lm.py' as a rescorer for ASR systems.
Given a trained TransformerLMModel `.nemo` file, this script can be used to re-score the beams obtained from a beam
search decoder of an ASR mode... | {
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"You should not apply dropout to the first or final Encoder layer. Also 0.2 is quite high - it's only required for training for an elongated period. For short finetuning runs, a small value of 0.1 is sufficient. Also weight decay should be 0.001 or lower when using dropout to prevent over regularization. ",
"Than... | 2021-05-30T21:22:05 | 2021-05-31T00:31:40 | 2021-05-31T00:31:40 | NONE | {
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} | null | null | null | Hello! Thank you again for the amazing work, I really appreciate it. I am fine-tuning a pre-trained QuartzNet model and wanted to ask what you'd recommend for regularization. I have updated the dropout from the pre-trained QuartzNet default 0.0 to 0.2 using:
cfg = copy.deepcopy(quartznet.cfg)
print(len(cfg['encode... | {
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} | - added jenkins tests for the normalization_with_audio script (text only, text + audio, manifest)
- all default normalization unit tests will be executed for normalization with audio as well
- added serial class to the default normalization (deterministic): using single digits by default
- addressed [this](https://g... | {
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"> LGTM. Just wondering why typecheck is being disabled there.\r\n\r\nFor some reason, doing `super().forward()` uses it's own input_types but not the super() module's input_types",
"> For some reason, doing `super().forward()` uses it's own input_types but not the super() module's input_types\r\n\r\nThat's expec... | 2021-05-28T18:12:37 | 2021-05-28T21:06:24 | 2021-05-28T21:06:21 | COLLABORATOR | {
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"The model after instantiation has the config file within it. You can access it via model.cfg, and export it as always using OmegaConf.save()."
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Signed-off-by: Stanislav Beliaev <stasbelyaev96@gmail.com> | {
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"Hi @sciai-ai , thanks for the question. We are aware of this limitation with new NMSE Clustering schema we introduced. It has parameters that limits its performance for smaller samples, and works wonderfully for larger audio samples. \r\n\r\nIn notebook, we used small audio sample hence the incorrect labels. While... | 2021-05-28T04:59:13 | 2021-07-27T08:06:44 | 2021-07-27T08:06:44 | NONE | {
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"This pull request **introduces 7 alerts** when merging d95d5d521e8ce34b24793e8587a66c1eaeb1d925 into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-37ade276ee5798ce168ea17564133c5501e5e918)\n\n**new alerts:**\n\n* 4 for Unused import\n* 2 for First para... | 2021-05-27T16:23:35 | 2021-07-19T23:11:36 | 2021-07-19T23:11:35 | COLLABORATOR | {
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} | Adds an option `use_dali` to `AudioToMelSpectrogramPreprocessor` to choose between the native torch implementation of the audio preprocessing pipeline or a DALI based implementation.
Adds unit tests to verify that the results produced by both implementations are equivalent.
Removes code related with previous atte... | {
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"> SpectrumAugmentation is not being applied to data in evaluation\r\n\r\nAll spec augment is disabled by default in evaluation steps (validation and testing). https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_models.py#L542-L543\r\n\r\nmodel.transcribe() forcibly uses model.eval() - which ag... | 2021-05-27T09:16:41 | 2021-05-28T01:51:10 | 2021-05-28T01:51:10 | NONE | {
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My implementation thus far is to just change to `.spec_augmentation` attribute of the model.
`import nem... | {
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} | # Changelog
- Adds support for numba cuda kernel to perform SpecAugment when following criterion are met:
- Numba version is >= 0.53.1
- Input tensor device = CUDA
- Lengths of the input tensors are provided (handled by NeMo, user's responsibility if called separately)
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"please have a look at the NMT docs: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/r1.0.0rc1/nlp/machine_translation.html "
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Pluralized verbalizers of Serial objects are not available in the results from NormalizerWithAudio.normalize_with_audio(), but a large number of plural serials exist in English eg "B-52s" should allow for a verbalizer of "B fifty twos" in addition to... | {
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"The taggers pick these examples up as Serials and not as Measures. The options with the spaces between the letters make sense if it's a Serial.\r\n\r\nI think a modification to the tagger for Measure that allows the dash between Cardinal and Unit may do the trick. If I can figure out the syntax...",
"Yes, this i... | 2021-05-26T16:05:50 | 2022-12-19T21:50:11 | 2021-06-08T22:08:35 | NONE | {
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} | null | null | null | **Describe the bug**
Example sentence fragments for text normalizer:
"It had 12-inch guns and a 21-knot cruising speed."
"Each 550-pound bomb exploded."
**Steps/Code to reproduce bug**
```python
from nemo_text_processing.text_normalization.normalize import Normalizer
from nemo_text_processing.text_normaliz... | {
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"how about we remove links to non-ready checkpoints and merge this?",
"Agreed. Everything else seems ready to merge ",
"> \r\n> \r\n> how about we remove links to non-ready checkpoints and merge this?\r\n\r\n\r\n\r\n> \r\n> \r\n> how about we remove links to non-ready checkpoints and merge this?\r\n\r\nDropped ... | 2021-05-26T06:02:01 | 2021-08-06T08:00:39 | 2021-06-01T22:26:51 | COLLABORATOR | {
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"1) will be fixed in our upcoming release\r\nThanks for finding 2. I'll push a fix.",
"Thanks! On #2, this may just be an issue with what version Colab imports (so users not using Colab might not get the error with the current NeMo code, not sure?)",
"Should be fixed now. If not, please open another issue."
] | 2021-05-25T20:43:01 | 2021-06-08T17:14:24 | 2021-06-08T17:14:23 | NONE | {
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1) More easily solvable: spec_generator = SpectrogramGenerator.from_pretrained("tts_en_tacotron2") <- the variable should be named spec_gen to match the lines that... | {
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- Update NeMo primer notebook to use Citrinet | {
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"Hi @cjameyson , \r\nYou are probably using a notebook from main branch but codebase version `1.0.0rc1`, \r\nwe changed `num_speakers` to `oracle_num_speakers` which takes number of speakers per input session else None, also included `max_num_speakers` option. \r\nRefer to this section for more info:\r\nhttps://doc... | 2021-05-25T00:01:59 | 2021-05-26T00:34:27 | 2021-05-26T00:34:27 | NONE | {
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Which causes the tutorial for ASR_with_SpeakerDiarization.ipynb to break on this code cell:
```
from nemo.collections.asr.models import ClusteringDiarizer
oracle_model = ClusteringDiarizer(cfg=config);
oracle_model... | {
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"Was able to download just now. Could you retry - maybe some issue with the server in the last attempt?",
"I am getting the same error again and again. I am actually downloading it on a cluster. That could be the reason?",
"If the cluster does not have access to the internet (other than the local intranet), the... | 2021-05-23T22:02:31 | 2021-05-30T14:51:39 | 2021-05-30T14:51:39 | NONE | {
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| {
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"You can change the batch size for training by following same steps you perform to update the training manifest - you can use train_ds.batch_size and validation_ds.batch_size "
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"Does the loss reduce to earlier levels when you combine datasets? How many finetuning steps are performed?",
"> Does the loss reduce to earlier levels when you combine datasets? How many finetuning steps are performed?\r\n\r\nYes. The loss reduces. I test different epochs such as 5,10,20,50,100, and 200 but the ... | 2021-05-22T08:17:16 | 2021-06-19T04:36:49 | 2021-06-19T04:36:49 | NONE | {
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} | null | null | null | **Fine-tuning on restored checkpoint has WER 100% with Persian audio**
Hi
I fine-tuned the Quartz Net 15*5 model with 235 hours dataset of Persian Mozilla Common Voice. When I tested the fine-tuned model with Persian Common Voice It worked well with WER 35%. So I saved the checkpoint of it.
Now I restore that chec... | {
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} | null | null | null | [This recent paper](https://arxiv.org/abs/2104.08189) is titled "TalkNet 2". But the model is referred to as "TalkNet" in the actual text (no 2), and recent pull requests here mention "TalkNet 1.0".
Is there a difference between the TalkNet discussed in the paper, and the TalkNet that's implemented here? | {
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"This pull request **introduces 1 alert** when merging e64d265f4be1ff2f93a15bec7937e84e96091137 into 9c75c348bc8137717ca79a34a93c50028ffdfd38 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-815154e263c9e74ca0cef21d3ca85e9d209c2060)\n\n**new alerts:**\n\n* 1 for Unused import",
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https://api.github.com/repos/NVIDIA/NeMo/issues/2246 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2246/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2246/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2246/events | https://github.com/NVIDIA/NeMo/issues/2246 | 897,828,497 | MDU6SXNzdWU4OTc4Mjg0OTc= | 2,246 | loss equals to nan when I use rir and noise augmentor | {
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"I have the same issue. When I am trying to train with Background Noises, the ctc-loss becomes nan. Also i checked spectrogram, it contains nans. So, I guess, the bug comes from preprocessor or comes from augmentation script.\r\n@amejri Do you find the problem? ",
"I found the bug. In my case, It comes from the d... | 2021-05-21T08:44:39 | 2021-10-26T23:45:07 | 2021-10-26T23:45:07 | NONE | {
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} | null | null | null | Hi guys,
I am trying to finetune a quartznet model using rir noise augmentor. During the training step, the loss become nan and at the end when I save the model, I have a file with clearly nothing on it ( Size: 132K instead of the usual 69 Mo size).
Does anyone have an idea about it?
Thanks in advance.
With kind re... | {
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} | # Changelog
- Model's are now attached with a one off global unique id (guid) which is registered into the global AppState for maintanence.
- AppState tracks whether the model was restored via a nemo file or via constructor and monitors an ever growing list of model restoration paths (If the model constructor was use... | {
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"@fayejf Hi! I have just tried to run the script and it failed at e = np.exp(logits - np.max(logits)) for the wrong argument type.\r\nWhy did you remove converting torch.tensor into numpy array? \r\ndetach().cpu().numpy() is still needed for the method to run properly.\r\n",
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} | - [x] Remove .cpu().numpy()
- [x] Add -y when apt-get install swig
- [x] Check and install ipywidgets for tdqm/notebook | {
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} | # Changelog
- Significant refactoring of `parts` in ASR to be more modular and systematic
- Move preprocessing steps of ASR text cleaning into `commons` collection.
- Add typing information to SpecAugment and SpecCutout
- Remove NLTK downloading on import and move it to class instantiation time
- Update docs to po... | {
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"This pull request **introduces 1 alert** when merging 7b5bebeebaca9a722f2ab6b3b1ea8947aa99fa8b into f7d3a72e1b5da664e5e1c5ac0855a9e29d32601d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-29052614e94cdee0d929259ae93e0a7126cc6347)\n\n**new alerts:**\n\n* 1 for Unused import"
] | 2021-05-19T22:11:29 | 2021-05-20T21:40:43 | 2021-05-20T14:20:20 | COLLABORATOR | {
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} | # Changelog
- Alias `Swish` activation to `nn.SiLU` which is mathematically identical
- Update activations to inplace execution if possible for `ConvASREncoder`
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2238 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2238/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2238/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2238/events | https://github.com/NVIDIA/NeMo/pull/2238 | 895,846,472 | MDExOlB1bGxSZXF1ZXN0NjQ4MDE1OTY5 | 2,238 | [NMT] Model Parallel Megatron Encoders | {
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"This pull requ... | 2021-05-19T20:18:45 | 2021-06-17T02:32:37 | 2021-06-17T02:32:33 | COLLABORATOR | {
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} | This PR adds Megatron encoders to NeMo NMT.
# Usage
Below is the configuration needed to use Megatron BERT 3.9B (model parallel size 4).
The `checkpoint_file` should be the path to the directory containing the standard megatron checkpoint format:
``` bash
3.9b_bert_no_rng
├── mp_rank_00
│ └── model_optim... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2237 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2237/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2237/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2237/events | https://github.com/NVIDIA/NeMo/pull/2237 | 895,778,434 | MDExOlB1bGxSZXF1ZXN0NjQ3OTU3MDI3 | 2,237 | Move ConcatDataset to common | {
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} | Move `ConcatDataset` (earlier `ConcatTranslationDataset`) to common.
The dataset was introduced in #2160
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https://api.github.com/repos/NVIDIA/NeMo/issues/2236 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2236/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2236/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2236/events | https://github.com/NVIDIA/NeMo/pull/2236 | 895,704,661 | MDExOlB1bGxSZXF1ZXN0NjQ3ODkyNjYz | 2,236 | fix comments | {
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} | Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2234 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2234/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2234/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2234/events | https://github.com/NVIDIA/NeMo/issues/2234 | 895,190,710 | MDU6SXNzdWU4OTUxOTA3MTA= | 2,234 | Manifest files error with custom data for finetuning ASR models | {
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"Could you post the full error log. Hard to tell where exactly it fails to decode the json - beginning or end. Could you make sure your voices manifest does not have any additional space or new lines at end end ?",
"This is the zip file for the VOiCES dataset. I have checked the end and beginning and they don't s... | 2021-05-19T09:09:18 | 2021-05-28T08:14:12 | 2021-05-28T08:14:12 | NONE | {
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} | null | null | null | Hi, I am using the ASR with NeMo tutorial notebook on colab ([https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb](url). It works fine for the an4 dataset. But I am trying to do this with the VOiCES dataset (https://iqtlabs.github.io/voices/). As per the tutorial I t... | {
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} | Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2231 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2231/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2231/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2231/events | https://github.com/NVIDIA/NeMo/pull/2231 | 894,747,316 | MDExOlB1bGxSZXF1ZXN0NjQ3MDY0Nzg3 | 2,231 | audio based normalization | {
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"This pull request **introduces 5 alerts** when merging 1c377a54d1e6839552177764d8cda01625ee4d02 into f0b3624dc1aee9670d85b82251ad4e488a622bc7 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9e99806aca1eb1e8466bb5af4cc9ae1424e9a890)\n\n**new alerts:**\n\n* 4 for Unused import\n* 1 for Nested loo... | 2021-05-18T20:21:01 | 2021-05-20T03:05:01 | 2021-05-20T03:04:57 | COLLABORATOR | {
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} | Text Normalization conditioned on audio: multiple normalization options considered, the best match selected based on WER.
Python support only (no SH). | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2230 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2230/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2230/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2230/events | https://github.com/NVIDIA/NeMo/pull/2230 | 894,513,330 | MDExOlB1bGxSZXF1ZXN0NjQ2ODY2NTQ5 | 2,230 | Add Russian language | {
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"Could you sign the DCO commits via the instruction here - \r\nhttps://github.com/NVIDIA/NeMo/pull/2230/checks?check_run_id=2612023734",
"Also seems test failed due to style formatting. Could you run `python setup.py style --scope nemo --fix`",
"Could you rebase with the main branch? Seems incorrect branch was ... | 2021-05-18T15:35:06 | 2021-07-19T23:11:08 | 2021-07-19T23:11:08 | COLLABORATOR | {
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Add "manifest_line_parser" argument and one more parser: __parse_open_stt() | {
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"You can do it like this with autocast of PT:\r\n\r\n```\r\n with torch.cuda.amp.autocast():\r\n with torch.no_grad():\r\n transcriptions = asr_model.transcribe(filepaths, batch_size=cfg.batch_size)\r\n\r\n```",
"@VahidooX this answer is correct. You can use the pytorch cuda decorator for AMP as ab... | 2021-05-18T07:04:53 | 2024-05-25T22:59:23 | 2021-05-28T08:37:30 | NONE | {
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- Make text normalization installation step optional (if in a non-conda environment and user attempts reinstall.sh).
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"you can not initialize two models within same notebook for Megatron, i.e. initialize the model , change the type and initialize again. Could you reset your notebook before you initialize the model?",
"Thanks for your recommend.\r\nAccording to your advice, I tried two times.\r\n\r\n1st Trail: No Problem\r\n I J... | 2021-05-17T05:06:51 | 2021-06-02T19:56:41 | 2021-06-02T19:56:41 | NONE | {
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I tested your tutorial for QA_squad and changed the tutorial code to apply Biomegatron.
But I got an error message during apply biomegatron.
So, would you help to solve this problem?

![Pret... | {
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"+ Why was FastSGD removed?\r\nWhen we moved to PTL, it got removed as we did not have enough time to import it into the new version of NeMo. We may be able to import that model in the near future if time permits.\r\nHowever you can still use it with older version of NeMo here: \r\nhttps://github.com/NVIDIA/NeMo/bl... | 2021-05-16T20:07:46 | 2021-05-23T16:08:50 | 2021-05-18T05:30:52 | NONE | {
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} | null | null | null | I am doing some research related to dialogue state tracking and I have found this paper (https://arxiv.org/pdf/1805.01555.pdf).
While checking out the code I see that it has been removed from this repository as of version v1.0.0.b1
I see that for dialogue state tracking there is a new model called SGD-QA which is a... | {
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