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https://api.github.com/repos/NVIDIA/NeMo/issues/2327 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2327/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2327/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2327/events | https://github.com/NVIDIA/NeMo/issues/2327 | 916,328,884 | MDU6SXNzdWU5MTYzMjg4ODQ= | 2,327 | Exporting PunctuationCapitalizationModel model to onnx | {
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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 onnx file and I am able to load it using onnx.load and check it using onnx.checker.check_model.
The issues are that exported onnx model has only 2 inputs:
1.
```
[name: "input_ids"
type {
tensor_type {
elem_type: 7
shape {
dim {
dim_param: "input_ids_dynamic_axes_1"
}
dim {
dim_param: "input_ids_dynamic_axes_2"
}
}
}
}
, name: "attention_mask"
type {
tensor_type {
elem_type: 7
shape {
dim {
dim_param: "attention_mask_dynamic_axes_1"
}
dim {
dim_param: "attention_mask_dynamic_axes_2"
}
}
}
}
]
```
and
2. if I call it like:
```
options = onnxruntime.SessionOptions()
options.intra_op_num_threads = 1
sess_punct = onnxruntime.InferenceSession("punctuation_en_distilbert.onnx", options, providers=["CPUExecutionProvider"])
modelPunct = PunctuationCapitalizationModel.restore_from("punctuation_en_distilbert.nemo")
data_loader = create_infer_dataloader(modelPunct.tokenizer, queries)
for batch in data_loader:
input_ids, input_type_ids, input_mask, subtokens_mask = batch
ort_inputs = {"input_ids": to_numpy(input_ids),"attention_mask":to_numpy(input_mask)}
punct_logits, capit_logits = sess_punct.run(None, input_feed = ort_inputs)
```
where `create_infer_dataloader()` is
```
def create_infer_dataloader(tokenizer, queries):
batch_size = len(queries)
dataset = BertPunctuationCapitalizationInferDataset(
tokenizer=tokenizer, queries=queries, max_seq_length=512
)
return torch.utils.data.DataLoader(
dataset=dataset,
collate_fn=dataset.collate_fn,
batch_size=batch_size,
shuffle=False,
num_workers=1,
drop_last=False,
)
```
it gives me an error: `onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(float)) , expected: (tensor(int64))`
So the question is what is wrong with the exported model?
Why the first question is why there are only 2 input parameters instead of 3, that we can see in different tutorials or source code of the PunctuationCapitalizationModel model and why data types are messed | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2325 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2325/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2325/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2325/events | https://github.com/NVIDIA/NeMo/issues/2325 | 915,798,666 | MDU6SXNzdWU5MTU3OTg2NjY= | 2,325 | How to use asr model for streaming | {
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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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} | null | null | null | Hello folks
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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https://api.github.com/repos/NVIDIA/NeMo/issues/2323 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2323/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2323/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2323/events | https://github.com/NVIDIA/NeMo/pull/2323 | 915,610,283 | MDExOlB1bGxSZXF1ZXN0NjY1NDA5Mzcw | 2,323 | sgdqa update data directories for testing | {
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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"
] | 2021-06-08T22:48:30 | 2021-06-16T18:23:59 | 2021-06-16T18:23:55 | CONTRIBUTOR | {
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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? Do we just use at the inference or not?
Regards | {
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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, other transcribers return Json similar to this:
"words": [
{
"end_time": 0.39077207,
"score": 66,
"start_time": 0.272892,
"text": "a"
},
{
"end_time": 1.1700554,
"score": 94,
"start_time": 0.40797436,
"text": "internet"
}
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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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"@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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} | null | null | null | **Describe the bug**
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 worse than inferencing using NeMo directly and the trt engine is vulnerable to different batch sizes. Could you help confirm whether this is a bug ?
I find that in conformer encoder, [the input_example](https://github.com/NVIDIA/NeMo/blob/7ef1782b94386629fbfafece72f618096c33a9f3/nemo/collections/asr/modules/conformer_encoder.py#L75) used to generate onnx model has input example length. However, in [conv asr](https://github.com/NVIDIA/NeMo/blob/7ef1782b94386629fbfafece72f618096c33a9f3/nemo/collections/asr/modules/conv_asr.py#L250), there's only input example.
**Steps/Code to reproduce bug**
```bash
# generate calibrated model
python3 speech_to_text_calibrate.py --dataset=/raid/data/LibriSpeech/manifest.dev-other --asr_model=QuartzNet15x5Base-En
# generate onnx model
python3 speech_to_text_quant_infer.py --dataset=/raid/data/LibriSpeech/manifest.dev-other --asr_model=QuartzNet15x5Base-En-max-256.nemo --onnx
# Got WER 10.7x%
python3 speech_to_text_quant_infer_trt.py --dataset=/raid/data/LibriSpeech/manifest.dev-other --asr_model=QuartzNet15x5Base-En-max-256.nemo --asr_onnx=./QuartzNet15x5Base-En-max-256.onnx --qat
# Got WER 11.x%
```
**Expected behavior**
Expect the `speech_to_text_quant_infer.py` to have the same result as `speech_to_text_quant_infer_trt.py`
**Environment overview (please complete the following information)**
nvcr.io/nvidia/pytorch:21.05-py3
+ Tensorrt 8.0.0.3
+ NeMo main
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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:
```bash
python3 speech_to_text_quant_infer.py --asr_model=./QuartzNet15x5-Zh-NR-max-256.nemo --dont_normalize_text --use_cer --onnx --dataset=/raid/data/data_aishell2/dev_manifest.json
```
I get a CER of 7.34%.
Finally, to test with trt with the generated onnx model:
```bash
python3 speech_to_text_quant_infer_trt.py --asr_model=./QuartzNet15x5-Zh-NR.nemo --asr_onnx=./QuartzNet15x5-Zh-NR-max-256.onnx --dont_normalize_text --use_cer --qat --dataset=/raid/data/data_aishell2/dev_manifest.json --batch_size=256
```
I get a CER of 9.08% with batch_size=256, 8.06% with batch_size=256 & sort dev set by audio length, 7.95% with batch_size=1.
I know it is normal to have 0.01%~0.05% error tolerance due to the padding of Quartznet. But the above result shows that there might be some issues with the script or the generated trt engine file.
(The size of the generated engine file is 24MB, which seems reasonable and the original model trt engine file is 95MB.)
I build my environment by starting from `nvcr.io/nvidia/pytorch:21.05-py3` + tensorrt 8 EA + nemo_toolkit[all].
Not sure if I made any mistakes in the above steps. Please correct me.
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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
Method of NeMo install: !pip install nemo_toolkit[asr]
NeMo version: 1.0.0
Learning Rate: 1e-3
**Environment details**
OS version : "Ubuntu20.04.3 LTS"
PyTorch version : "1.7.1"
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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."
] | 2021-06-05T06:40:36 | 2021-09-12T11:24:22 | 2021-07-13T15:27:22 | NONE | {
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} | null | null | null | I tried using the above feature in my Windows system, then I found that I need to execute the bash file in
`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/) & [make for windows](https://sourceforge.net/projects/gnuwin32/files/make/3.81/)
additionally for running the wget & make commands in the .sh files.
After installing all these, I am getting an error while I am trying to run the above `install_beamsearch_decoders.sh ` inside the nemo folder.
`ModuleNotFoundError: No module named '_swig_decoders'`
Earlier, I was also getting error `ModuleNotFoundError: No module named 'swig_decoders'` but I resolved that by installing make & wget, so the command is able to generate swig_decoder.py file, but now I am getting `ModuleNotFoundError: No module named '_swig_decoders'` which is another python module file.
I have attached the log file with this. As per my understanding, this is happening mainly because in the log there comes an error
```
cl : Command line warning D9002 : ignoring unknown option '-O3'
cl : Command line warning D9002 : ignoring unknown option '-std=c++11'
```
[swig-error.txt](https://github.com/NVIDIA/NeMo/files/6602117/swig-error.txt)
However, I would appreciate if you can check the log file and see what is the problem if in case I am wrong. Also let me know if this is impossible to use in Windows system.
Thanks for the help! | {
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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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} | # Changelog
- Update container version to 21.05 for CI and Dockerfile
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | null | null | null | **Describe the bug**
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 shared object file: No such file or directory
- Environment location: Linux centOS, Jupyter notebook.
- OS version: Linux CentOS
- PyTorch version: 1.7.1 (from nemo toolkit)
- Python version: 3.6.10
- GPU model (V100 on server)
Best Regards.
[ImportError.docx](https://github.com/NVIDIA/NeMo/files/6606953/ImportError.docx)
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"important fix - to export not normalizer.verbalizer.fst (which is VerbalizeFinalFst), but VerbalizeFst. There are a lot of other inconsistencies, but they seem to be related to serialization indeed:\r\n\r\nfailed to verbalize: \"time { hours: \"three\" minutes: \"thirty\" }\", because Unable to convert string to i... | 2021-06-03T22:47:44 | 2021-06-16T00:57:54 | 2021-06-16T00:57:54 | NONE | {
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Paper for (inverse) text normalization describes that its easy to deploy developed text (de)normalization rules into prod using sparrowhawk. I havent found any documentation for that though. So far I figured how to export grammars as far files consumed by sparrowhawk:
```
normalizer = Normalizer(input_case="cased")
tag_exp = grm.Exporter(args.tagger)
tag_exp['TOKENIZE_AND_CLASSIFY'] = normalizer.tagger.fst
tag_exp.close()
verb_exp = grm.Exporter(args.verbalizer)
verb_exp['ALL'] = normalizer.verbalizer.fst
verb_exp.close()
```
But when I try to apply those rules, verbalization fails.
here is normalization using default sparrowhawk grammars shipped as part of documentation:
```
# cat test.txt
Hello 13 world
# normalizer_main --config=sparrowhawk_configuration.ascii_proto --multi_line_text < test.txt
[DEBUG:normalizer.cc:162] Verbalizing: ["13(r
13]
[DEBUG:normalizer.cc:195] Verbalize output: Words
Hello thirteen world
```
This is how it fails using grammars from NeMo:
```
[DEBUG:normalizer.cc:162] Verbalizing: ["13(r
thirteen]
[ERROR:rule_system.cc:121] Application of rule "ALL" failed[ERROR:normalizer.cc:213] Failed to verbalize "rthirteen"[WARNING:normalizer.cc:167] First-pass verbalization FAILED for ["13(rthirteen][ERROR:rule_system.cc:121] Application of rule "ALL" failed[ERROR:normalizer.cc:213] Failed to verbalize "�13"[ERROR:normalizer.cc:180] Verbalization FAILED for ["13(rthirteen][DEBUG:normalizer.cc:195] Verbalize output: Words
Hello world
```
I think this might be due to fact that parsing of tokenize_classify output is somehow incompatible. Is it the case? is there any tutorial?
**Describe the solution you'd like**
Documentation on how to export NeMo grammars to sparrowhawk as paper suggests.
**Describe alternatives you've considered**
Any pointers to debug the issue, maybe some simple grammar that actually works in export.
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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)
Signed-off-by: Vitaly Lavrukhin <vlavrukhin@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2302 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2302/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2302/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2302/events | https://github.com/NVIDIA/NeMo/pull/2302 | 910,554,624 | MDExOlB1bGxSZXF1ZXN0NjYwOTU4MDQ1 | 2,302 | Fix SqueezeExcite forward for fp16 | {
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} | We need to have ability of run inference model with SE blocks in fp16
Signed-off-by: Oktai Tatanov <oktai.tatanov@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2301 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2301/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2301/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2301/events | https://github.com/NVIDIA/NeMo/pull/2301 | 910,201,974 | MDExOlB1bGxSZXF1ZXN0NjYwNjYwMDU1 | 2,301 | fixed paths in tutorials | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2300 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2300/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2300/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2300/events | https://github.com/NVIDIA/NeMo/pull/2300 | 910,029,875 | MDExOlB1bGxSZXF1ZXN0NjYwNTE1MjIz | 2,300 | Fastpitch export | {
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"This pull request **introduces 3 alerts** and **fixes 1** when merging e99c2fbea67b64a856bd9d59420ad32249fde875 into d23ee3e3d5ed2fd17608b736ebae2b415a258a9e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-f7d8f6f310df97f17db8897a9e541b9001de6c60)\n\n**new alerts:**\n\n* 3 for Unused import\n\n... | 2021-06-03T02:20:25 | 2021-06-08T21:02:19 | 2021-06-08T21:02:19 | COLLABORATOR | {
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} | Dependant on https://github.com/NVIDIA/NeMo/pull/2278
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https://api.github.com/repos/NVIDIA/NeMo/issues/2299 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2299/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2299/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2299/events | https://github.com/NVIDIA/NeMo/issues/2299 | 909,427,538 | MDU6SXNzdWU5MDk0Mjc1Mzg= | 2,299 | BERT Pre-training | {
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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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} | null | null | null | Hi
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/conf/bert_pretraining_from_text_config.yaml.
3. after running the python bert_pretraining.py
Getting this error
[NeMo W 2021-06-02 17:24:59 optimizers:46] Apex was not found. Using the lamb optimizer will error out.
[NeMo W 2021-06-02 17:25:01 nemo_logging:349] /home/kruthika/NeMo/venv/lib/python3.6/site-packages/omegaconf/basecontainer.py:232: UserWarning: cfg.pretty() is deprecated and will be removed in a future version.
Use OmegaConf.to_yaml(cfg)
category=UserWarning,
[NeMo I 2021-06-02 17:25:01 bert_pretraining:28] Config:
name: PretrainingBERTFromText
trainer:
gpus: 1
num_nodes: 1
max_epochs: 2
max_steps: null
accumulate_grad_batches: 1
precision: 16
amp_level: O1
accelerator: ddp
gradient_clip_val: 0.0
log_every_n_steps: 1
val_check_interval: 1.0
checkpoint_callback: false
logger: false
model:
nemo_path: null
only_mlm_loss: false
num_tok_classification_layers: 1
num_seq_classification_layers: 2
max_seq_length: 128
mask_prob: 0.15
short_seq_prob: 0.1
language_model:
pretrained_model_name: bert-base-uncased
lm_checkpoint: null
config:
attention_probs_dropout_prob: 0.1
hidden_act: gelu
hidden_dropout_prob: 0.1
hidden_size: 768
initializer_range: 0.02
intermediate_size: 3072
max_position_embeddings: 512
num_attention_heads: 12
num_hidden_layers: 12
type_vocab_size: 2
vocab_size: 30522
config_file: null
tokenizer:
tokenizer_name: ${model.language_model.pretrained_model_name}
vocab_file: null
tokenizer_model: null
special_tokens:
unk_token: '[UNK]'
sep_token: '[SEP]'
pad_token: '[PAD]'
bos_token: '[CLS]'
mask_token: '[MASK]'
eos_token: '[SEP]'
cls_token: '[CLS]'
train_ds:
data_file: /home/kruthika/NeMo/download_dir/wikitext-2/train.txt
max_seq_length: ${model.max_seq_length}
mask_prob: ${model.mask_prob}
short_seq_prob: ${model.short_seq_prob}
batch_size: 16
shuffle: true
num_samples: -1
num_workers: 2
drop_last: false
pin_memory: false
validation_ds:
data_file: /home/kruthika/NeMo/download_dir/wikitext-2/valid.txt
max_seq_length: ${model.max_seq_length}
mask_prob: ${model.mask_prob}
short_seq_prob: ${model.short_seq_prob}
batch_size: 16
shuffle: false
num_samples: -1
num_workers: 2
drop_last: false
pin_memory: false
optim:
name: adamw
lr: 3.0e-05
weight_decay: 0.0
sched:
name: CosineAnnealing
warmup_steps: null
warmup_ratio: 0.1
min_lr: 0.0
last_epoch: -1
exp_manager:
exp_dir: null
name: PretrainingBERTFromText
create_tensorboard_logger: true
create_checkpoint_callback: true
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
Using native 16bit precision.
[NeMo I 2021-06-02 17:25:01 exp_manager:210] Experiments will be logged at /home/kruthika/NeMo/examples/nlp/language_modeling/nemo_experiments/PretrainingBERTFromText/2021-06-02_17-25-01
[NeMo I 2021-06-02 17:25:01 exp_manager:550] TensorboardLogger has been set up
Traceback (most recent call last):
File "bert_pretraining.py", line 30, in main
exp_manager(trainer, cfg.get("exp_manager", None))
File "/home/kruthika/NeMo/nemo/utils/exp_manager.py", line 232, in exp_manager
configure_checkpointing(trainer, log_dir, checkpoint_name, cfg.checkpoint_callback_params)
File "/home/kruthika/NeMo/nemo/utils/exp_manager.py", line 679, in configure_checkpointing
checkpoint_callback = NeMoModelCheckpoint(**params)
File "/home/kruthika/NeMo/nemo/utils/exp_manager.py", line 580, in __init__
super().__init__(**kwargs)
TypeError: __init__() got an unexpected keyword argument 'prefix'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
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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 results also are not consistent with speech_to_text_infer.py.
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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:nemo:stt_en_conformer_ctc_large_ls
Added a new section for language models for ASR in the checkpoint section.
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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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"This pull request **introduces 26 alerts** when merging 426bfeebea7528ea52a4ab130076546f2a37c7e6 into 4781d1744765fafdcd1238d8d4a9c3f94057aa23 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5739c24c7c6ab00b4184aecc25110c9ddffdeb5a)\n\n**new alerts:**\n\n* 14 for Unused import\n* 8 for Unused l... | 2021-06-01T22:16:03 | 2023-02-22T18:45:17 | 2021-06-01T22:23:22 | COLLABORATOR | {
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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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} | null | null | null | Hello,
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. Do you plan to release a tutorial for online ASR with LM for Citrinet or QuartzNet? | {
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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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} | null | null | null | Hello,
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/2104.01721) and try to use Citrinet for speech recognition. I call Citrinet1024 model insted of quartznet like below:
`citrinet_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_citrinet_1024")
`
but I could not find the config file of stt_en_citrinet_1024 from NEMO GitHub. It has just for 348 and 512 due to this [link](https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/citrinet).
Would you please explain me:
1) What do the filters do in each block of convolution layer in Citrinet? for instance Citrinet1024. what is the benefit of more filters?
2) How does Citrinet work and why it uses from tokenization?
2) How should I use citrinet1024 (implementation) and how to set parameters in the config file of Citrinet1024?
**Environment overview**
Environment location: Google Colab
Method of NeMo install: !pip install nemo_toolkit[asr]
NeMo version: 1.0.0
Learning Rate: 1e-3
**Environment details**
OS version : "Ubuntu20.04.3 LTS"
PyTorch version : "1.7.1" | {
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