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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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"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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"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 <[email protected]> | {
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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,
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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 <[email protected]> | {
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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 <[email protected]>
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 <[email protected]> | {
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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 <[email protected]> | {
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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 <[email protected]> | {
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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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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 <[email protected]>
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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"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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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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"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."
] | 2021-06-01T06:08:30 | 2021-08-17T01:03:19 | 2021-08-17T01:03:19 | NONE | {
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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 pull request **introduces 1 alert** when merging 3f1390819c64cd3be2bee2a150775db6fab8ef99 into 43add2a37812a4786e6229322f7cf30ac2103313 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-772b7f88024564503202282a9d0d11fb42a5b969)\n\n**new alerts:**\n\n* 1 for NotImplemented is not an Exception... | 2021-05-31T09:20:05 | 2022-10-20T10:52:52 | 2021-06-01T22:47:56 | COLLABORATOR | {
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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 model.
USAGE:
1. Obtain `.tsv` file with beams and their corresponding scores. Scores can be from a regular beam search decoder or
in fusion with an N-gram LM scores. For a given beam size `beam_size` and a number of examples
for evaluation `num_eval_examples`, it should contain (`num_eval_examples` x `beam_size`) lines of
form `beam_candidate_text \t score`. This file can be generated by `scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py`.
2. Rescore the candidates:
python eval_neural_rescorer.py
--lm_model=[path to .nemo file of the LM]
--beams_file=[path to beams .tsv file]
--beam_size=[size of the beams]
--eval_manifest=[path to eval manifest .json file]
--batch_size=[batch size used for inference on the LM model]
--alpha=[the value for the parameter rescorer_alpha]
--beta=[the value for the parameter rescorer_beta]
--scores_output_file=[the optional path to store the rescored candidates]
You may find more info on how to use this script at:
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html
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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['encoder']['jasper']))
for i in range(0,18):
cfg['encoder']['jasper'][i]['dropout'] = 0.2
print(OmegaConf.to_yaml(cfg))
quartznet2 = quartznet.from_config_dict(cfg)
But this seems to just cause loss to explode?
Thanks! | {
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"This pull request **introduces 1 alert** when merging b5663d69d6300672c44fe37a32927240ffeb55ef into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-a4416774459ffc898579744c1c2ba41bbcca79c4)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-05-28T23:24:59 | 2021-06-08T22:08:40 | 2021-06-08T22:08:36 | COLLABORATOR | {
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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://github.com/NVIDIA/NeMo/issues/2266) and partially [this](https://github.com/NVIDIA/NeMo/issues/2267) (`k` -> `kay` is not supported yet)
- more verbalization options added for cardinals and whitelist
- now all possible verbalization options are provided not only the first shortest 100 | {
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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()."
] | 2021-05-28T17:29:50 | 2021-06-21T11:24:10 | 2021-06-21T11:24:10 | NONE | {
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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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} | 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.
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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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} | null | null | null | I'm trying to deliberately worsen the outputs of an existing pre-trained model. Specifically, I was hoping to apply CutOut to the audio data so that the model outputs will be missing some tokens / frequencies.
My implementation thus far is to just change to `.spec_augmentation` attribute of the model.
`import nemo.collections.asr as nemo_asr
jasper = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_jasper10x5dr")
spectrogram_aug = nemo_asr.modules.SpectrogramAugmentation(rect_masks=1000, rect_time=1200, rect_freq=500) # huge values to see if anything changes
jasper.spec_augmentation = spectrogram_aug
transcripts = jasper.transcribe(paths2audio_files=[
'LibriSpeech_sample.flac', # random audio files
'CHiME5_sample.wav',
])
`
However, I found that the transcripts do not change, suggesting that the SpectrumAugmentation is not being applied to data in evaluation. Even when the`jasper.training` is set to `True`, there is no change in the outputted transcripts. Am I missing something? Thank you! | {
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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)
- User provided flag `use_numba_spec_augment` is set to True (True by default)
- In such a scenario, if the above conditions are met, SpecAugment() will be replaced by SpecAugmentNumba()
- Users can disable this kernel by setting the flag `use_numba_spec_augment` to False. | {
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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 "
] | 2021-05-26T21:35:18 | 2021-07-20T07:26:30 | 2021-07-20T07:26:30 | NONE | {
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} | null | null | null | Hi, I've been trying to use nemo for neural machine translation but i keep hitting a roadblock since the only for of intro notebook on this subject is the text2sparql. Any intro notebook on the matter would be very helpful | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
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 "B fifty two S". The correctly pluralized serial should be discernible with the CER and audio transcript.
These appear often in the financial, military, and other realms (think "401ks" "1099s" "W2s" etc).
**Describe the solution you'd like**
Add a parameter to the Serial FST. If it ends in "s" (or a plural form), offer a plural form of serial[-2] to the verbalizers as an option (and drop serial[-1]).
This may be non-trivial when serials end in a letter eg "401k" --> "four oh one kays" may be more useful than "four oh one ks"
**Describe alternatives you've considered**
The usual regex preprocessing which should be more efficiently implemented in FSTs?
**Additional context**
```python
from nemo_text_processing.text_normalization.normalize_with_audio import NormalizerWithAudio
audio_normalizer = NormalizerWithAudio(input_case='cased')
texts = [
"Twelve B-17s were totally destroyed.",
"8 401ks were emptied.",
]
for text in texts:
normalized_texts = audio_normalizer.normalize_with_audio(text, verbose=False)
print('\n'.join([x for x in normalized_texts]))
assert "eight four oh one kays were emptied." in normalized_texts or "Twelve B seventeens were totally destroyed" in normalized_texts
``` | {
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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_normalization.normalize_with_audio import NormalizerWithAudio
normalizer = Normalizer(input_case='cased')
audio_normalizer = NormalizerWithAudio(input_case='cased')
texts = [
"It had 12-inch guns and a 21-knot cruising speed.",
"Each 550-pound bomb exploded.",
]
for text in texts:
normalized_text = normalizer.normalize(text, verbose=False)
print(normalized_text)
normalized_texts = audio_normalizer.normalize_with_audio(text, verbose=False)
print(normalized_texts)
```
The results from normalized_texts will be something like (notice the spaces between each letter in the word for the units):
"five hundred fifty p o u n d bomb"
and
"twenty one k n o t cruising speed"
It seems like the sparrow/pynini rules don't allow for dashed text input followed by a unit and interpret the unit as an abbreviation. The CER calculation in NormalizerWithAudio doesn't repair this because the results from normalize_with_audio() don't ever contain the expected outputs.
**Expected behavior**
"five hundred fifty pound bomb"
"twenty one knot cruising speed"
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install from source, main branch
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 20.10
- PyTorch version 1.7.1
- Python version 3.8.5
I'm trying to track down where in pynini or the nemo rules this is happening and if I find it I will follow-up. | {
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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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} | null | null | null | Please note the TTS user guide quick start seems to have two issues (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/tts/intro.html):
1) More easily solvable: spec_generator = SpectrogramGenerator.from_pretrained("tts_en_tacotron2") <- the variable should be named spec_gen to match the lines that follow
2) The last line:
sf.write("speech.wav", audio.to('cpu').numpy(), 22050)
This gives the error "RuntimeError: Error opening 'speech.wav': Format not recognised." I believe this is an error with soundfile, see: https://github.com/bastibe/python-soundfile/issues/203
EDIT: For issue 2, to fix this in Colab, simply create audio2 = audio.transpose(), which works. May just be an issue with the version of soundfile imported by Colab.
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"You may have N-gram LM with beam search decoder or just beam search decoder without N-gram to produce some top candidates, then use the Transformer LM as a Neural Rescorer to rescore the candidates. Then the scores from Neural Rescorer can get weighted average with scores from beam search decoder to produce the fi... | 2021-05-25T19:40:41 | 2021-06-04T08:24:41 | 2021-06-04T08:24:41 | NONE | {
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} | # Changelog
- 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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} | null | null | null | `num_speakers` is missing from `NeMo/examples/speaker_recognition/conf/speaker_diarization.yaml`
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.diarize();
```
For the following error:
```
ConfigAttributeError: Key 'num_speakers' is not in struct
full_key: diarizer.num_speakers
reference_type=Any
object_type=dict
```
nemo.__version__ = '1.0.0rc1'
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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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"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 checkpoint. I start fine-tune it with another Persian dataset in new domain. The audios of new dataset are recorded telephony audio. I changed the sample rate of new dataset from 8KHZ to 16KHZ and convert each telephony audio to different chunks due to long duration and lack of RAM problem in Colab tarring process.
After I fine-tuned the checkpoint with new dataset I test it but the test result in transcription is not good any more. The model can not transcribe any audio even one correct word. In fact it transcribe some alphabet not a complete word or sentence.
What`s your suggestion for new fine-tuning with telephony Persian audio dataset on the mentioned checkpoint?
**Environment overview**
- Environment location: Google Colab
- Method of NeMo install: !pip install nemo_toolkit[asr]
- NeMo version: 1.0.0
- Learning Rate: 1e-5
**Environment details**
- OS version : "Ubuntu20.04.3 LTS"
- PyTorch version : "1.7.1"
- Dataset used for first Fine-Tuning: "Persian Common Voice" (Mozilla) and duration: 235 hours
- Dataset used for first Fine-Tuning: "Persian Telephony Audio" and duration: 22 minutes | {
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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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"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 regards. | {
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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 used, its restoration path is None).
- During registration of artifacts, each model registers its own copy of artifacts (via self)
- During save to, the restoration path is retrieved from AppState and artifacts are copied from the restoration file. If restoration path is None, assume local path artifacts
- During restore from, the restoration path is first registered in an ever growing list. Then the model is constructed (which allocates a GUID and registers this GUID with the restoration path).
# Bugfix
- Fixes a bug which prevents a model constructed via constructor to call save_to multiple times.
Signed-off-by: smajumdar <[email protected]> | {
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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",
"With the main branch, transcribe() m... | 2021-05-20T22:32:34 | 2021-05-26T22:22:41 | 2021-05-20T23:10:09 | COLLABORATOR | {
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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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"This pull request **fixes 4 alerts** when merging 4bb2fba8802557742830e81b58da9109482c32f8 into b8ed0839bcc7626efaa302d0b9d47baa0f47f580 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5bc8f85be7fd6c8ba3037e3e9a41c5f43034eb3a)\n\n**fixed alerts:**\n\n* 4 for Unused import",
"1 question, is th... | 2021-05-19T23:34:19 | 2021-05-26T22:07:05 | 2021-05-26T22:07:02 | COLLABORATOR | {
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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 point to new paths for parts
- Cherry pick PR https://github.com/NVIDIA/NeMo/pull/2219 into this so that audio files that could not be loaded log a warning but do not immediately crash.
- Make character based ASR models propagate their sample_rate and labels to dataloaders
- Update ASR training scripts to support easy finetuning via pretrained model name or .nemo file
# TODO
- [ ] Update all ASR configs in next PR to latest format | {
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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"
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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 <[email protected]> | {
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"This pull request **introduces 4 alerts** when merging 79fb48ed35cec7f7a6c38ddac61577ec05e621f3 into 202ccc902937b10bd9a0f37d49f8b8dc29e3ca4d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-884ab3ddf6d76bf3417f94c478f3398a5fba104d)\n\n**new alerts:**\n\n* 4 for Unused import",
"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_rng.pt
├── mp_rank_01
│ └── model_optim_rng.pt
├── mp_rank_02
│ └── model_optim_rng.pt
└── mp_rank_03
└── model_optim_rng.pt
```
``` bash
CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/nlp/machine_translation/enc_dec_nmt.py \
--config-path=conf \
--config-name=megatron \
trainer.gpus=4 \
model.preproc_out_dir=/raid/data/megatron_en_de_preproc \
model.encoder.checkpoint_file=/path/to/megatron/checkpoint/3.9b_bert_no_rng \
model.encoder.hidden_size=2560 \
model.encoder.num_attention_heads=40 \
model.encoder.num_layers=48 \
model.encoder.max_position_embeddings=512 \
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} | Move `ConcatDataset` (earlier `ConcatTranslationDataset`) to common.
The dataset was introduced in #2160
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} | Signed-off-by: Yang Zhang <[email protected]> | {
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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 think I only need to supply the manifest files in the proper format for training and testing. I am running this on colab with the `QuartzNet15x5Base-En`
I have formatted the manifest files in which they specify but while running
`from omegaconf import DictConfig`
`params['model']['train_ds']['manifest_filepath'] = train_manifest`
`params['model']['validation_ds']['manifest_filepath'] = test_manifest`
`first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']), trainer=trainer)`
I keep getting the JSONDecodeError: Expecting value: line 2 column 1 (char 1).
Below I have attached my manifest file screenshot and the manifest file screenshot generated in the tutorial, they both appear to be identical in format so I'm wondering what seems to be the problem. I am guessung specifying "text" or "duration" in different orders shouldn't matter.
an4 dataset manifest screenshot
voices dataset manifest screenshot
Is there something else I need to change to fine tune with my dataset?
Any help would be appreciated.
Thanks.
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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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Python support only (no SH). | {
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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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} | null | null | null | Hi,
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?
Best Regards,
From Simon
Blow I showed the code changed model.
1.Changed Pretrained_Bert_Model
# This is the model configuration file that we will download, do not change this
MODEL_CONFIG = "question_answering_squad_config.yaml"
# model parameters, play with these
BATCH_SIZE = 12
MAX_SEQ_LENGTH = 384
# specify BERT-like model, you want to use
PRETRAINED_BERT_MODEL = "**biomegatron-bert-345m-uncased**" -> changed
TOKENIZER_NAME = "**biomegatron-bert-345m-uncased**" -> changed
2.Changed Pretrained_model
# load pretained model
pretrained_model_name="**qa_squadv1.1_megatron_uncase**d" -> changed
model = nemo_nlp.models.QAModel.from_pretrained(model_name=pretrained_model_name)
3.Error Message
I got the these error message.
[NeMo I 2021-05-13 07:44:44 megatron_utils:269] Downloading from https://api.ngc.nvidia.com/v2/models/nvidia/biomegatron345muncased/versions/0/files/MegatronBERT.pt
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-18-762ee4f9b73a> in <module>()
1 # initialize the model
2 # dataset we'll be prepared for training and evaluation during
----> 3 model = nemo_nlp.models.QAModel(cfg=config.model, trainer=trainer)
7 frames
/usr/local/lib/python3.7/dist-packages/megatron/global_vars.py in _ensure_var_is_not_initialized(var, name)
150 def _ensure_var_is_not_initialized(var, name):
151 """Make sure the input variable is not None."""
--> 152 assert var is None, '{} is already initialized.'.format(name)
153
154
AssertionError: args is already initialized.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2221 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2221/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2221/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2221/events | https://github.com/NVIDIA/NeMo/issues/2221 | 892,749,266 | MDU6SXNzdWU4OTI3NDkyNjY= | 2,221 | FastSGT vs SGD-QA | {
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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 multi-pass type model as opposed to FastSGD.
My questions are the following:
- Why was FastSGT removed?
- Where can I find some documentation regarding SGD-QA? | {
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