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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(int64))` for `\"attention_mask\"` still exist",
"In forward DistilBERT takes only input_ids and attention_mask (no token_type_ids as in BERT).\r\n@borisfom could you help with the ONNX part?",
"Well, finally I ended up with the next code:\r\n\r\nExport .onnx and config yaml:\r\n\r\n```\r\nmodel = PunctuationCapitalizationModel.restore_from(restore_path=\"punctuation_en_distilbert.nemo\")\r\nmodel.export(\"punctuation_en_distilbert.onnx\")\r\n\r\nmodel = PunctuationCapitalizationModel.restore_from(restore_path=\"punctuation_en_distilbert.nemo\", return_config = True)\r\ntextfile = open(\"punctuation_en_distilbert.yaml\", \"w\")\r\ntextfile.write(str(OmegaConf.to_yaml(model)))\r\ntextfile.close()\r\n```\r\n\r\nExport tokenizer:\r\n```\r\nfrom transformers import BertTokenizer\r\ntokenizer = BertTokenizer.from_pretrained('distilbert-base-uncased')\r\ntokenizer.save_pretrained(\"./my_model_directory/\")\r\n```\r\nand rename `tokenizer_config.json` to `distilbert-base-uncased.json`\r\nAlso need to download https://huggingface.co/distilbert-base-uncased/blob/main/tokenizer.json to the my_model_directory folder and change `distilbert-base-uncased.json` content a bit, set `\"tokenizer_file\"` to `\"tokenizer.json\"`\r\nNow make processing with onnx:\r\n\r\n```\r\nimport numpy as np\r\nimport torch\r\nimport yaml\r\nfrom nemo.collections.common.tokenizers import AutoTokenizer\r\nfrom nemo.collections.nlp.data.token_classification.punctuation_capitalization_dataset import \\\r\n BertPunctuationCapitalizationInferDataset\r\nimport onnxruntime\r\n\r\nimport logging\r\nlogging.getLogger('nemo_logger').setLevel(logging.ERROR)\r\n\r\ndef create_infer_dataloader(tokenizer, queries):\r\n batch_size = len(queries)\r\n\r\n dataset = BertPunctuationCapitalizationInferDataset(\r\n tokenizer=tokenizer, queries=queries, max_seq_length=512\r\n )\r\n return torch.utils.data.DataLoader(\r\n dataset=dataset,\r\n collate_fn=dataset.collate_fn,\r\n batch_size=batch_size,\r\n shuffle=False,\r\n num_workers=1,\r\n drop_last=False,\r\n )\r\n\r\ndef to_numpy(tensor):\r\n return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()\r\n\r\nqueries = [\"by the end of no such thing the audience like beatrice has a watchful affection for the monster\", \\\r\n \"let me guess you're the kind of guy that ignores the rules cause it makes you feel in control am i right \\\r\n you're not wrong you think that's cute do you think it's cute\"]\r\n\r\ntokenizer = AutoTokenizer(pretrained_model_name=\"./my_model_directory/distilbert-base-uncased.json\", \\\r\n vocab_file=\"./my_model_directory/vocab.txt\")\r\n\r\ndata_loader = create_infer_dataloader(tokenizer, queries)\r\n\r\nwith open(\"punctuation_en_distilbert.yaml\") as f:\r\n params = yaml.safe_load(f)\r\n\r\nall_punct_preds = []\r\nall_capit_preds = []\r\n\r\noptions = onnxruntime.SessionOptions()\r\noptions.intra_op_num_threads = 1\r\n\r\nsess_punct = onnxruntime.InferenceSession(\"punctuation_en_distilbert.onnx\", options, providers=[\"CPUExecutionProvider\"]) \r\n\r\n# most of processing was taken from \r\n# https://github.com/NVIDIA/NeMo/blob/5839aee402f314aa413b28e9042b1e1cac10a114/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py#L403\r\nfor batch in data_loader:\r\n input_ids, input_type_ids, input_mask, subtokens_mask = batch\r\n input_mask = input_mask.type(torch.int64) # why?\r\n ort_inputs = {\"attention_mask\": to_numpy(input_mask), \"input_ids\": to_numpy(input_ids)}\r\n\r\n punct_logits, capit_logits = sess_punct.run(None, input_feed = ort_inputs)\r\n\r\n subtokens_mask = subtokens_mask > 0.5\r\n\r\n punct_preds = [\r\n list(p_l[subtokens_mask[i]]) for i, p_l in enumerate(np.argmax(punct_logits, axis=-1))\r\n ]\r\n capit_preds = [\r\n list(c_l[subtokens_mask[i]]) for i, c_l in enumerate(np.argmax(capit_logits, axis=-1))\r\n ]\r\n\r\n all_punct_preds.extend(punct_preds)\r\n all_capit_preds.extend(capit_preds)\r\n\r\npunct_ids_to_labels = {v: k for k, v in params['punct_label_ids'].items()}\r\ncapit_ids_to_labels = {v: k for k, v in params['capit_label_ids'].items()}\r\n\r\nqueries = [q.strip().split() for q in queries]\r\n\r\nresult = []\r\n\r\nfor i, query in enumerate(queries):\r\n punct_preds = all_punct_preds[i]\r\n capit_preds = all_capit_preds[i]\r\n if len(query) != len(punct_preds):\r\n # removing the end of phrase punctuation of the truncated segment\r\n punct_preds[-1] = 0\r\n max_len = len(punct_preds)\r\n query = query[:max_len]\r\n\r\n query_with_punct_and_capit = ''\r\n for j, word in enumerate(query):\r\n punct_label = punct_ids_to_labels[punct_preds[j]]\r\n capit_label = capit_ids_to_labels[capit_preds[j]]\r\n\r\n if capit_label != params['dataset']['pad_label']:\r\n word = word.capitalize()\r\n query_with_punct_and_capit += word\r\n if punct_label != params['dataset']['pad_label']:\r\n query_with_punct_and_capit += punct_label\r\n query_with_punct_and_capit += ' '\r\n\r\n result.append(query_with_punct_and_capit.strip())\r\n```\r\n\r\nnot sure if I did everything correct with tokenizer export and import, but it works ",
"I am still facing the same issue after following the steps. ValueError: Model requires 3 inputs. Input Feed contains 2 \r\nDid I miss something?"
] | 2021-06-09T15:17:36 | 2021-12-16T13:09:13 | 2021-06-10T10:38:31 | NONE | {
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} | null | null | null | I am trying to export the nemo_nlp PunctuationCapitalizationModel to onnx format.
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 streaming using Citrinet asr model? Will it be the same way as of Quartznet?\r\n> \r\n> do you know how to implement quartznet for real time streaming??\r\n\r\nNo, I didn't try yet by myself.",
"For streaming you need FrameAsr like from this notebook https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/02_Online_ASR_Microphone_Demo.ipynb or use Nvidia Jarvis",
"> For streaming you need FrameAsr like from this notebook https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/02_Online_ASR_Microphone_Demo.ipynb or use Nvidia Jarvis\r\n\r\nYes, I tried this earlier but it didn't work for CitriNet model.",
"What you mean by didn't work? Did you use correct stats? https://github.com/NVIDIA/NeMo/issues/1203\r\nAnd from what I know CitriNet use different stride,you might need to take that into account, I don't have experience with CitriNet yet",
"> What you mean by didn't work? Did you use correct stats? #1203\r\n> And from what I know CitriNet use different stride,you might need to take that into account, I don't have experience with CitriNet yet\r\n\r\nCorrect, It is working for Quartznet model, but no output was coming for Citrinet model. I wanted to know what could be the reason of it. @khursani8 thanx for your response, I'll be checking for different stride."
] | 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?",
"@blisc @junkin and @ryanleary what's the status of this? Do we need this PR? If not, someone, please close"
] | 2021-06-09T00:12:12 | 2022-09-22T18:46:32 | 2022-09-22T18:46:32 | CONTRIBUTOR | {
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"This pull request **introduces 2 alerts** when merging 265bf1cf644ca545c07361b0c69b139176a04c16 into cfe2548d4db9dcc4ca393ee63604139c9f681cd8 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-daa3ed728ff039b73f402e72c8d2b2aa4efa1369)\n\n**new alerts:**\n\n* 2 for Unused exception object"
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} | - [x ] is backwards compatible
- [x ] fixes loading previously trained .nemo model for testing
Signed-off-by: Yang Zhang <[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://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html ",
"Thanks @okuchaiev, this help us a lot and confirm something that we was thinking. \r\nNow we tried to use the same example in this doc and we got that:\r\n![image](https://user-images.githubusercontent.com/47003942/121403939-d17ad280-c931-11eb-8cb5-7b4af8f08098.png)\r\n\r\nWe use beam search with lm:\r\nbeam_search_with_lm = nemo_asr.BeamSearchDecoderWithLM(\r\n vocab=labels,\r\n beam_width=beam_width,\r\n alpha=alpha,\r\n beta=beta,\r\n lm_path=lm_path,\r\n num_cpus=max(os.cpu_count(),1))\r\n \r\nI don't know if have anything wrong",
"The documentation above links to a utility script that will perform beam search for you - please try with that. The KenLM binary built for character models via that script should be evaluated using that script only. ",
"Thank you all for help, we got something news and got some results.\r\nNow, I just want one more help. About the parameters, beam_width, alpha and beta. Do have a documentation about that, blog or something that I can study about, to understand more how it work?",
"Beam width = beam size. Larger beam size will take longer for marginally better results. \r\nAlpha = LM weight. \r\nBeta = Length penalty. \r\nWe perform a sparse grid search over alpha and beta, keeping beam width fixed. "
] | 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 token the model predicted at each timestep of the Acoustic sequence. Note that this will include the CTC blank token (which is by conversion the last token in the decoding vocabulary).\r\n\r\nYou also get the log probabilities, so I suppose you could perform softmax at each timestep to compute the actual confidence per token predicted. \r\n\r\nStart and end times can be estimated as well depending on token boundaries and reversing the timesteps count by number of stride steps the model performs. Note that QuartzNet is a 2x stride model where as Citrinet is an 8x stride model. ",
"@titu1994 \r\nIs there any way to calculate the word timestep after applying beamsearch decoding on top of EncDecCTCModelBPE citrinet_1024.\r\n\r\nI tried [parlance ](https://github.com/parlance/ctcdecode) solution but took a very long time to execute, maybe because of the large vocabulary size."
] | 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 need to mask-out the outputs in the decoding procedure. For Conformer, lengths are necessary as we need to mask the paddings in self-attention.",
"@VahidooX I checked the [config file](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/quartznet/quartznet_15x5.yaml) for Quartznet and it uses `conv_mask: True`. It would be better to give users an option to decide whether to have input length or not.",
"@borisfom Any update ?",
"@titu1994 Do you mind I add seq length to \r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/7ef1782b94386629fbfafece72f618096c33a9f3/nemo/collections/asr/modules/conv_asr.py#L259",
"I don't know the impact this will have on downstream tools that require the onnx file in current format.\r\n\r\n@borisfom please advise here ",
" @Slyne : the change needs to be more extensive QuartzNet removes masked convolutions for inference, and you would need to keep them, too : \r\n def _prepare_for_export(self, **kwargs):\r\n m_count = 0\r\n for m in self.modules():\r\n if isinstance(m, MaskedConv1d):\r\n m.use_mask = False\r\n m_count += 1\r\n Exportable._prepare_for_export(self, **kwargs)\r\n logging.warning(f\"Turned off {m_count} masked convolutions\")\r\n\r\nThis has to be controlled by some other flag, not the main conv_mask - because there are currently many nets that do have this flag as True for training but do not need that for inference. \r\nFor your local experiments, you can try hacking it out - please also note we originally had to use removal of masked convolutions to get the code exported in ONNX at all - that may have been fixed, but I did not check.",
"@borisfom \r\n\"This has to be controlled by some other flag, not the main conv_mask - because there are currently many nets that do have this flag as True for training but do not need that for inference.\"\r\n\r\nDo you mean the sequence length will not affect the inference procedure for quartznet ? Or we can just trim the quartznet output by sequence length?"
] | 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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https://api.github.com/repos/NVIDIA/NeMo/issues/2314 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2314/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2314/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2314/events | https://github.com/NVIDIA/NeMo/pull/2314 | 913,632,108 | MDExOlB1bGxSZXF1ZXN0NjYzNjM0MDIw | 2,314 | patch quantization | {
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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 Could you help check the above issue ?",
"I'm not particularly familiar with the quantization aware training methods. Will request someone from team to help with this. "
] | 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 tokens, BERT is 30 times slower than Transformer. It makes BERT very expensive for rescoring while the improvement may not be that much significant. There are some papers which have tried to address this issue like this one:\r\n\r\nhttps://assets.amazon.science/cc/54/980cb7d74f93849b49b9d5c42466/masked-language-model-scoring.pdf\r\n\r\n"
] | 2021-06-07T08:24:35 | 2021-07-13T15:27:32 | 2021-07-13T15:27:32 | NONE | {
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} | null | null | null | Hi
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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`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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} | [] | closed | false | null | [] | null | [] | 2021-06-04T21:15:00 | 2021-06-05T20:05:41 | 2021-06-05T20:05:38 | COLLABORATOR | {
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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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} | # 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 int32. I guess this is typing in proto.\r\n\r\nand another:\r\n\r\n[ERROR:protobuf_parser.cc:183] Unknown field: [integer_part]\r\n[ERROR:protobuf_parser.cc:342] Full input: [tokens { name: \"mister\" } tokens { name: \"Snookums\" } tokens { name: \"was\" } tokens { name: \"on\" } tokens { name: \"the\" } tokens { name: \"train\" } tokens { name: \"carrying\" } tokens { money { currency: \"dollars\" integer_part: \"forty\" fractional_part: \"two five\" } } tokens { name: \"(\" } tokens { money { currency: \"pounds\" integer_part: \"thirty\" fractional_part: \"six o\" } } tokens { name: \")\" } tokens { name: \"of\" } tokens { name: \"Belgian\" } tokens { name: \"chocolate\" } tokens { name: \"in\" } tokens { name: \"a\" } tokens { measure { cardinal { integer: \"three\" } units: \"kilograms\" } } tokens { name: \"box\" } tokens { name: \"that\" } tokens { name: \"was\" } tokens { measure { cardinal { integer: \"twenty\" } units: \"centimeters\" } } tokens { name: \"wide.\" }]\r\n[ERROR:normalizer.cc:131] Failed to parse tokens from FST for \"Mr. Snookums was on the train carrying $40.25 (£30.60) of Belgian chocolate in a 3kg box that was 20cm wide.\"",
"The documentation could be found [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.0.0/tools/text_processing_deployment.html) and tutorials: [Text normalization](https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/text_processing/Text_Normalization.ipynb) and [ITN](https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/text_processing/Inverse_Text_Normalization.ipynb)",
"@balacoon Did the instructions @ekmb helped you solve your problem?",
"yes, perfect. thanks!"
] | 2021-06-03T22:47:44 | 2021-06-16T00:57:54 | 2021-06-16T00:57:54 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
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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Signed-off-by: Oktai Tatanov <[email protected]> | {
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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**fixed alerts:**\n\n* 1 for Unused import"
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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 error as above",
"@kruthikakr I wasn't able to reproduce it. Could you share full stack trace that shows where the DDPPlugin occurs? It looks like an environment setup issue. As an alternative option you could run inside NeMo container. ",
"python bert_pretraining.py\r\nTraceback (most recent call last):\r\n File \"bert_pretraining.py\", line 18, in <module>\r\n from pytorch_lightning.plugins import DDPPlugin\r\nImportError: cannot import name 'DDPPlugin'\r\n\r\nNemo Installation fro Source\r\ngit clone https://github.com/NVIDIA/NeMo\r\ncd NeMo\r\n./reinstall.sh\r\npytorch lightning version 1.1.5.\r\n",
"This looks like an environmental problem. After running ./reinstall.sh on the main branch, you should have PyTorch Lightning >= 1.3.\r\n\r\nMake sure that source code that you're running matches the version that you installed.",
"@kruthikakr did it work? can this issue be closed?",
"I am facing similar kind of issue here.\r\nWhile running the bert pretraining file\r\n\r\n[NeMo I 2021-07-05 05:26:45 bert_pretraining:28] Config:\r\n name: PretrainingBERTFromText\r\n trainer:\r\n gpus: 1\r\n num_nodes: 1\r\n max_epochs: 5\r\n max_steps: null\r\n accumulate_grad_batches: 1\r\n precision: 16\r\n amp_level: O1\r\n accelerator: ddp\r\n gradient_clip_val: 0.0\r\n log_every_n_steps: 1\r\n val_check_interval: 1.0\r\n checkpoint_callback: false\r\n logger: false\r\n model:\r\n nemo_path: null\r\n only_mlm_loss: false\r\n num_tok_classification_layers: 1\r\n num_seq_classification_layers: 2\r\n max_seq_length: 128\r\n mask_prob: 0.15\r\n short_seq_prob: 0.1\r\n language_model:\r\n pretrained_model_name: bert-base-uncased\r\n lm_checkpoint: null\r\n config:\r\n attention_probs_dropout_prob: 0.1\r\n hidden_act: gelu\r\n hidden_dropout_prob: 0.1\r\n hidden_size: 768\r\n initializer_range: 0.02\r\n intermediate_size: 3072\r\n max_position_embeddings: 512\r\n num_attention_heads: 12\r\n num_hidden_layers: 12\r\n type_vocab_size: 2\r\n vocab_size: 30522\r\n config_file: null\r\n tokenizer:\r\n tokenizer_name: ${model.language_model.pretrained_model_name}\r\n vocab_file: null\r\n tokenizer_model: null\r\n special_tokens:\r\n unk_token: '[UNK]'\r\n sep_token: '[SEP]'\r\n pad_token: '[PAD]'\r\n bos_token: '[CLS]'\r\n mask_token: '[MASK]'\r\n eos_token: '[SEP]'\r\n cls_token: '[CLS]'\r\n train_ds:\r\n data_file: /media/nlp_drive/syed/BERT-NeMo/DeepLearningExamples/PyTorch/LanguageModeling/BERT/NeMo/examples/nlp/language_modeling/download_dir/wikitext-2/train.txt\r\n max_seq_length: ${model.max_seq_length}\r\n mask_prob: ${model.mask_prob}\r\n short_seq_prob: ${model.short_seq_prob}\r\n batch_size: 16\r\n shuffle: true\r\n num_samples: -1\r\n num_workers: 2\r\n drop_last: false\r\n pin_memory: false\r\n validation_ds:\r\n data_file: null\r\n max_seq_length: ${model.max_seq_length}\r\n mask_prob: ${model.mask_prob}\r\n short_seq_prob: ${model.short_seq_prob}\r\n batch_size: 16\r\n shuffle: false\r\n num_samples: -1\r\n num_workers: 2\r\n drop_last: false\r\n pin_memory: false\r\n optim:\r\n name: adamw\r\n lr: 3.0e-05\r\n weight_decay: 0.0\r\n sched:\r\n name: CosineAnnealing\r\n warmup_steps: null\r\n warmup_ratio: 0.1\r\n min_lr: 0.0\r\n last_epoch: -1\r\n exp_manager:\r\n exp_dir: null\r\n name: PretrainingBERTFromText\r\n create_tensorboard_logger: true\r\n create_checkpoint_callback: true\r\n \r\nGPU available: True, used: True\r\nTPU available: False, using: 0 TPU cores\r\nUsing native 16bit precision.\r\n[NeMo I 2021-07-05 05:26:45 exp_manager:216] Experiments will be logged at /media/nlp_drive/syed/BERT-NeMo/DeepLearningExamples/PyTorch/LanguageModeling/BERT/NeMo/examples/nlp/language_modeling/nemo_experiments/PretrainingBERTFromText/2021-07-05_05-26-45\r\n[NeMo I 2021-07-05 05:26:45 exp_manager:563] TensorboardLogger has been set up\r\nTraceback (most recent call last):\r\n File \"bert_pretraining.py\", line 31, in main\r\n bert_model = BERTLMModel(cfg.model, trainer=trainer)\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/bert_lm_model.py\", line 63, in __init__\r\n super().__init__(cfg=cfg, trainer=trainer)\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/core/classes/modelPT.py\", line 130, in __init__\r\n self.setup_multiple_validation_data(val_data_config=None)\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/core/classes/modelPT.py\", line 564, in setup_multiple_validation_data\r\n model_utils.resolve_validation_dataloaders(model=self)\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/utils/model_utils.py\", line 242, in resolve_validation_dataloaders\r\n model.setup_validation_data(cfg.validation_ds)\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/bert_lm_model.py\", line 199, in setup_validation_data\r\n else self._setup_dataloader(val_data_config)\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/bert_lm_model.py\", line 230, in _setup_dataloader\r\n dataset = BertPretrainingDataset(\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/collections/nlp/data/language_modeling/lm_bert_dataset.py\", line 70, in __init__\r\n sentence_indices, sentence_idx_file, data_dir = load_data_indices(\r\n File \"/anaconda/envs/py38_default/lib/python3.8/site-packages/nemo/collections/nlp/data/data_utils/data_preprocessing.py\", line 468, in load_data_indices\r\n data_dir = data_file[: data_file.rfind('/')]\r\nAttributeError: 'NoneType' object has no attribute 'rfind'\r\n",
"Can you suggest some small dataset which is similar to wikicorpus, so that I can run the bert_pretraining.py file",
"@BakingBrains you need to specify validation file too, changes those arguments to mandatory args \r\nhttps://github.com/NVIDIA/NeMo/pull/2449"
] | 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."
] | 2021-06-01T23:37:37 | 2021-06-02T20:02:43 | 2021-06-02T20:02:40 | COLLABORATOR | {
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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 local variable\n* 2 for 'import \\*' may pollute namespace\n* 1 for Illegal raise\n* 1 for Variable defined multiple times"
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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/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html\r\n\r\nThere will also be addition of neural rescoring in the recent future. ",
"@titu1994 Thank you. I think that I can't use original std and mean constants because of different shape ([1,64] in notebook, Citrinet requires [1,80]). Can you share fixed mean and std constants for Citrinet models (in case you have it)? \r\n\r\nAlso, I met a problem with notebook modification for Citrinet. After changing\r\n`asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained('QuartzNet15x5Base-En')` (works ok with per feature normalization)\r\nto\r\n`asr_model = nemo_asr.models.ctc_bpe_models.EncDecCTCModelBPE.from_pretrained('stt_en_citrinet_256')` (still using per feature)\r\noutput text is always empty (and `logits.shape[0]` is 0). \r\nCan you please give me some advice? What could I have missed?",
"We haven't precomputed these values for the larger datasets that we currently train on.\r\n\r\nI don't know the timeline, but @jbalam-nv is working on streaming asr notebook, which mostly doesn't require this pre calculated normalization tensor. \r\n \r\nLogits.shape[0] corresponds to batch dimension, how is batch dim 0? ",
"@titu1994 Ok, thank you.\r\nI think I found the problem - after loading Citrinet instead of QuartzNet `timestep_duration` is very large (327.68, this is strange..) and `n_timesteps_overlap = int(frame_overlap / timestep_duration) - 2` equals to -2. \r\nAfter that `logits[self.n_timesteps_overlap:-self.n_timesteps_overlap]` is empty when I call _greedy_decoder. \r\n\r\n`n_timesteps_overlap` calculation takes values from Citrinet config. I will try to fix that problem.\r\nEverything works fine when I switch back to QuartzNet.",
"Citrinet performs 8x downsampling, vs QuartzNet which does 2x downsampling. That would be the source of the problem. The notebook was designed to work with 2x and would need modifications to work with 8x. ",
"@ValeryNikiforov did you succeed in running Citrinet over streaming?",
"@aayush6897 \r\nYes, I decode small chunks with new audio data (+ left side context: 2-4 seconds) and use beamsearch_ngram after that. \r\nFor me, text merging is the main problem. I switched to ctcdecode decoder and it's output token's timestamps helped me a lot.",
"Streaming tutorial that should work with Both Conformer-CTC and Citrinet are now in NeMo: https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Streaming_ASR.ipynb\r\n\r\nThis is also available as a script for offline long form audio decoding: https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_buffered_infer.py\r\n"
] | 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 shared since it's describes in the paper, the only thing which would need to be changed is the number of filters in the 512 config provided and change dropout to 0.1. I can share one in this thread just for clarity if needed. \r\n\r\nTo answer your questions -\r\n\r\n1) Filters in CNN scales the width of the model - effectively increasing the capacity of the model due to increase in number of parameters. We see significantly improved wer (roughly 4-6%) when moving from 256 filters to 1024. \r\n\r\n2) This tutorial explains how tokenizarion helps ASR models - https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0/tutorials/asr/08_ASR_with_Subword_Tokenization.ipynb\r\n\r\n3) The model config is provided inside the instantiated model - once you initialize the model, you'll be able to do `cfg = model.cfg` to extract the final model config after training. You can print it out, modify it and then create a new model with the modified config if needed. \r\n\r\nThe training and other config info are available in the paper and the configs provided in the examples/ASR/conf/Citrinet path. \r\n\r\n\r\n",
"> Do you wish to train Citrinet 1024 from scratch ? Or simply fine tune?\r\n> \r\n> During 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\n> The config for 1024 is not shared since it's describes in the paper, the only thing which would need to be changed is the number of filters in the 512 config provided and change dropout to 0.1. I can share one in this thread just for clarity if needed.\r\n> \r\n> To answer your questions -\r\n> \r\n> 1. Filters in CNN scales the width of the model - effectively increasing the capacity of the model due to increase in number of parameters. We see significantly improved wer (roughly 4-6%) when moving from 256 filters to 1024.\r\n> 2. This tutorial explains how tokenizarion helps ASR models - https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0/tutorials/asr/08_ASR_with_Subword_Tokenization.ipynb\r\n> 3. The model config is provided inside the instantiated model - once you initialize the model, you'll be able to do `cfg = model.cfg` to extract the final model config after training. You can print it out, modify it and then create a new model with the modified config if needed.\r\n> \r\n> The training and other config info are available in the paper and the configs provided in the examples/ASR/conf/Citrinet path.\r\n\r\nThanks a lot.\r\nwhat should I put in tokenizer at config file in below:\r\n`tokenizer:`\r\n` dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)`\r\n ` type: ??? # Can be either bpe or wpe`\r\nIn other words, I do not know what should be as tokens here.",
"Please review the ASR with Subword tokenizarion tutorial on how to build your own Tokenizer - https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0/tutorials/asr/08_ASR_with_Subword_Tokenization.ipynb",
"Hello, Thanks for this amazing citrinet model.\r\nI want to finetune Citrinet model for my data.How can i do the speech recognition fine-tuning ? Is there any end to end instruction set or example ? \r\nThanks in advance\r\n@titu1994 ",
"I'm preparing a tutorial for finetuning Char and subword models on other languages, it should be released in the coming week or two. ",
"Both the ASR tutorials also have* segments for finetuning on same language (eng) in them. ",
"> Both the ASR tutorials also hand segments for finetuning on same language (eng) in them.\r\n\r\nSo you mean current tutorials are enough to finetune the model for my data? Thanks a lot for this quick answer.",
"Yes, the Tutorial 1 and tutorial 8 (char and subword respectively) show how to take a pretrained model and finetuned on AN4 (same language - EN). \r\n\r\nMost of the steps remain the same for finetuning on other languages too. "
] | 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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"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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} | null | null | null | Hi Team,
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",
"what is the `eval_manifest.json` file supposed to contain?\r\nI want to rescore sentences with a gpt2 language model, any guide?"
] | 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. ",
"Thanks! In that case, probably makes sense for me to just leave dropout at 0 and just use an increased weight decay from the .0001 default? Thanks!",
"You could refer to the QuartNet paper, generally we recommend 0.001 to 0.0001 range of weights decay, tending to the higher side. ",
"Thank you!"
] | 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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} | - 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 expected. super().forward() is just calling super()'s method instead of overridden one, but your object is still of derived class and resolution of all method calls from within forward() will not change.\r\n"
] | 2021-05-28T18:12:37 | 2021-05-28T21:06:24 | 2021-05-28T21:06:21 | COLLABORATOR | {
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"The model after instantiation has the config file within it. You can access it via model.cfg, and export it as always using OmegaConf.save()."
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Signed-off-by: Stanislav Beliaev <[email protected]> | {
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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 we push a fix for it, If you would like to use previous clustering algorithm you may just replace the following line \r\nhttps://github.com/NVIDIA/NeMo/blob/bee43e840837d7e30e17a65b972e9fe89ecb9ca5/nemo/collections/asr/parts/utils/speaker_utils.py#L259 with\r\n\r\n`cluster_method = SpectralClusterer(min_clusters=2, max_clusters=NUM_speakers) \r\n`\r\n`cluster_labels = cluster_method.predict(emb)`"
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} | null | null | null | Hi, I was trying the notebook for diarization from the main branch with max and orcle speakers. I noticed that for the default an4.wav file the confusion rate has increased to 0.4 whereas in the previous releases .1.r.c it used be zero meaning perfect diarization. May I know what has changed and how can we get better accuracy on the example wav file? | {
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"This pull request **introduces 7 alerts** when merging d95d5d521e8ce34b24793e8587a66c1eaeb1d925 into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-37ade276ee5798ce168ea17564133c5501e5e918)\n\n**new alerts:**\n\n* 4 for Unused import\n* 2 for First parameter of a method is not named 'self'\n* 1 for Unused local variable",
"This pull request **introduces 1 alert** when merging 9002b4daff2956fda9e65035b2cb145626887f76 into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-0767ff2f37952d52429a366b751eee2a01f544b0)\n\n**new alerts:**\n\n* 1 for Unreachable code",
"This pull request **introduces 1 alert** when merging ff4ac0444bc4feaf108dab29e65cd22fe28db159 into bee43e840837d7e30e17a65b972e9fe89ecb9ca5 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fd490f27420186d5c834ebc7dc7115a88adb9302)\n\n**new alerts:**\n\n* 1 for Unreachable code",
"Closed for the time being while other approaches are tested"
] | 2021-05-27T16:23:35 | 2021-07-19T23:11:36 | 2021-07-19T23:11:35 | COLLABORATOR | {
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} | Adds an option `use_dali` to `AudioToMelSpectrogramPreprocessor` to choose between the native torch implementation of the audio preprocessing pipeline or a DALI based implementation.
Adds unit tests to verify that the results produced by both implementations are equivalent.
Removes code related with previous attempts to integrate with NVIDIA DALI. | {
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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 again disables model.training to False. This is by design - we do not allow spec augment to be used during inference.\r\n\r\nIf its a requirement, you can copy paste the transcribe() method and simply force the model.training = True before calling the data loaders - but note that batch norm will be active and your results will be incorrect as compared to the original model.transcribe() (ofc the spec augment will also affect outputs).\r\n\r\nAnother approach is subclass EncDecCTCModel and override the forward method to always apply spec augment instead of looking at the model.training property. ",
"That makes sense! I had to re-bind the forward method instead of subclassing since from_pretrained returns the model directly, but same idea. Thank you for the prompt help!"
] | 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 | **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 is a known issue, the fix is a part of the upcoming update. \r\nI'll update this bug when the PR is ready.",
"I'll be following the progress closely and am happy to help if I can get more familiar with pynini -- is this repo the best place to file / discuss similar issues?"
] | 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 the links for new models and reverted to older checkpoints. "
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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 final scores.",
"Please take a look here:\r\nhttps://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.0.0/asr/asr_language_modeling.html",
"Thank you!"
] | 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://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/configs.html \r\nhttps://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/conf/speaker_diarization.yaml ",
"I see, thanks @nithinraok!\r\n"
] | 2021-05-25T00:01:59 | 2021-05-26T00:34:27 | 2021-05-26T00:34:27 | NONE | {
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Which causes the tutorial for ASR_with_SpeakerDiarization.ipynb to break on this code cell:
```
from nemo.collections.asr.models import ClusteringDiarizer
oracle_model = ClusteringDiarizer(cfg=config);
oracle_model.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), then yes. "
] | 2021-05-23T22:02:31 | 2021-05-30T14:51:39 | 2021-05-30T14:51:39 | NONE | {
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![error11](https://user-images.githubusercontent.com/50978534/119278039-31c7ff80-bc23-11eb-99ba-059a106d2f99.PNG)
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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 results are the same.",
"It is hard to debug without the dataset. The fact that loss reduces properly means the model is training - however it cannot be known why predictions are not correct at test time. I would suggest training with just the smaller dataset first to see if it learns at all - long clips might simply be causing wer to jump since model is unable to learn both short and long audio.\r\n\r\nAlso can you try combining the dataset and training from scratch if that works "
] | 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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"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 data. When the voice energy is high and the noise energy is very small. The multiplication gain becomes very large which raises nan's in mel spectrogram.",
"Did you manually inspect the spectrograms? Or a pass through the data post spectrogram generation to read the min max ?",
"Yes, I manually set NaN checker.",
"@naymaraq could you share with us how you fix the problem please ? I didn't solve it yet."
] | 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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"This pull request **introduces 1 alert** when merging a788ea5a7364e462abf1caf5c09bdcdd1751ee41 into 9c75c348bc8137717ca79a34a93c50028ffdfd38 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-43861f4a74f04ebba08b42ab74d68064cf672f88)\n\n**new alerts:**\n\n* 1 for Unused local variable",
"This pull request **introduces 1 alert** when merging b6570a95e471971781d3e6def93f87762983de80 into 9c75c348bc8137717ca79a34a93c50028ffdfd38 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-18a5ed1dd2475d6de64d6fc685c0f5614d9b5c05)\n\n**new alerts:**\n\n* 1 for Unused local variable"
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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() method will return numpy arrays of logits instead of torch tensors to preserve gpu memory after transcription has occurred. \r\n\r\nCould you paste the error trace here ?",
"I added detach().cpu().numpy() to logits and it ran without errors.\r\nWould you like me to rerun it without converting and show the error?",
"Yes, please. Which version/branch are you using? ",
"I am using current main, not sure which is the latest version. I modified the script a bit (cut the analysis with librosa and plots from it, as the server where I am running the script does not support it).\r\nWithout correcting logits, trace is as follows:\r\n\r\n```\r\nTranscribing: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 9.30it/s]\r\nTranscript: \"i cannot follow you she said\"\r\nTranscribing: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 10.81it/s]\r\nTraceback (most recent call last):\r\n File \"transcribe_audio_with_timestamps.py\", line 110, in <module>\r\n probs = softmax(logits)\r\n File \"transcribe_audio_with_timestamps.py\", line 98, in softmax\r\n e = np.exp(logits - np.max(logits))\r\n File \"<__array_function__ internals>\", line 6, in amax\r\n File \"/usr/local/lib/python3.6/site-packages/numpy/core/fromnumeric.py\", line 2706, in amax\r\n keepdims=keepdims, initial=initial, where=where)\r\n File \"/usr/local/lib/python3.6/site-packages/numpy/core/fromnumeric.py\", line 85, in _wrapreduction\r\n return reduction(axis=axis, out=out, **passkwargs)\r\nTypeError: max() received an invalid combination of arguments - got (out=NoneType, axis=NoneType, ), but expected one of:\r\n * ()\r\n * (name dim, bool keepdim)\r\n didn't match because some of the keywords were incorrect: out, axis\r\n * (Tensor other)\r\n * (int dim, bool keepdim)\r\n didn't match because some of the keywords were incorrect: out, axis\r\n```\r\n",
"This should be working with main branch as well.\r\n\r\nCould you run\r\n\r\n```\r\ncd NeMo\r\n./reinstall.sh\r\n```\r\nand try again? ",
"Ok, I fetched the main branch, recompiled and it worked. So I guess I didn't have some new changes, not sure when they appeared. Thanks for pointing it out!"
] | 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 **introduces 1 alert** when merging 833a085c8b95e54bf7d759348a16155301265237 into 41e42f892772586d7ff7bba72562b2075418e442 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cc228ac717f94501424640dde1bd7a5eab44f95a)\n\n**new alerts:**\n\n* 1 for Wrong number of arguments in a call"
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https://api.github.com/repos/NVIDIA/NeMo/issues/2240 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2240/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2240/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2240/events | https://github.com/NVIDIA/NeMo/pull/2240 | 896,046,273 | MDExOlB1bGxSZXF1ZXN0NjQ4MTkzMDcz | 2,240 | ASR Refactoring | {
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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 there a reason you don't want training to fail if the audio file could not be loaded?\r\nThis might hide underlying issues in the dataset instead of forcing the user to fix the issue with the audio files.",
"@piraka9011 Main reason would be the case where more than one file fails - it would be problematic to await several minutes for data loaders to start up and then immediately crash after the first issue. If we log it (and hopefully it doesnt crash and it continues) there will be a continuous stream of error logs that will notify the user of which files are consistently problematic. Of course, this is under the assumption there is no fatal flaw and the data loader doesnt hard crash anyway.",
"I would agree on the utils thing @redoctopus - i pushed it into utils cause I saw like 6 files with _utils.py and I went - why not just move them under a utils folder then. Lets see, if theres a strong opinion on this from others we can just move them out again.",
"This pull request **fixes 4 alerts** when merging 9f2ec2a9912ac3ababc49cb45819b3adf550eaa6 into b8ed0839bcc7626efaa302d0b9d47baa0f47f580 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-19c56da26cfdc189ec56781474801b6e8671dc3d)\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 1 alert** and **fixes 4** when merging 0cf38b08ed5f81029aae14090380af865002fbd5 into b8ed0839bcc7626efaa302d0b9d47baa0f47f580 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-ad6e83921bcc95a0fa54ba0ec9ea62e2b93cf107)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 5 alerts** and **fixes 4** when merging db47f139adee5733ac7bed3d02cbc9e85aedf2e6 into 6c60797a30ab640fcafbd6c647e32ff7ef93d6ec - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d00b2658e3a349dd5a1442a33e48ae2f313ab615)\n\n**new alerts:**\n\n* 4 for Unnecessary delete statement in function\n* 1 for 'import \\*' may pollute namespace\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 5 alerts** and **fixes 4** when merging 2e37bc8595fdaa17886e5b5a41546e220c953945 into ea5cd10cdf86d82db17293e86e84e714be65c268 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3b792dbb882e49d65072c0bf4739ad43e99576c9)\n\n**new alerts:**\n\n* 4 for Unnecessary delete statement in function\n* 1 for 'import \\*' may pollute namespace\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 3 alerts** and **fixes 4** when merging 7b691a5a03d341548d2d9c496a8f5b68b48ef01d into b6c15c3c72da3c408ccba7a05119cf2a9b08c6aa - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-41bbbc594c9f278ea54a02560844a1151e59cff8)\n\n**new alerts:**\n\n* 1 for Unused import\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 2 alerts** and **fixes 4** when merging 9df37a9e9bb812bc8a71a2943365dc4c59889861 into b6c15c3c72da3c408ccba7a05119cf2a9b08c6aa - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-475acb6090706f05d37dde9644f0776ad32f651d)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 2 alerts** and **fixes 4** when merging 24398a8613205e9abe640acb07c57019ee8fb431 into 9c75c348bc8137717ca79a34a93c50028ffdfd38 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5b9703b9cc976dad60cb9537d7bc0d9ad3638acd)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 2 alerts** and **fixes 4** when merging fb78e24712ca56f1a51d28f860abac4ef8bfc080 into 9c75c348bc8137717ca79a34a93c50028ffdfd38 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-2dcd3261e0a3aabbef22457a4cb26eb9a75850cf)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 2 alerts** and **fixes 4** when merging 926f305b702fa3d51b8278bae0073e7c1e9d1fc6 into 9c75c348bc8137717ca79a34a93c50028ffdfd38 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-19e69014bf2ceacc4e2c3ef8963ff035722d4e4e)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import",
"Looks like a better place for the preprocessing directory would be in asr/data. I always found it difficult to find preprocessing code :), I think it would be much easier to find if it is in asr/data.",
"asr/data is meant to be only for **datasets**. The two ***_dataset.py** files are wrappers around said datasets so it is still valid. Parts is where everything else goes that doesnt belong to one of 5 main things - dataset/dataloader, models (which inherit ModelPT), neural modules, losses and metrics.\r\n\r\nFurthermore that preprocessing is shared between asr and tts so best it goes into commons ",
"Also, hmm we need an updated high level architecture diagram. Will put that in a todo list.",
"This pull request **introduces 2 alerts** and **fixes 4** when merging ddd7f4bad5fd83cddad870229f4996f67193bf4f into b94c7a17accf40e4235b9f746caaf8d9ea5d20af - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-585563836962a28d62b98d6ef39247ef1df14f04)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import",
"This pull request **introduces 2 alerts** and **fixes 4** when merging aaec4c77d3e02cfb961b30771bcd99f7a4e181e9 into b94c7a17accf40e4235b9f746caaf8d9ea5d20af - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cdf9bd3bd3bd789eaa36f43801ce9dfd12923129)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute namespace\n* 1 for Unnecessary delete statement in function\n\n**fixed alerts:**\n\n* 4 for Unused import"
] | 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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} | # 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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] | 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 seem to have any blank spaces.\r\n\r\n[test_index.zip](https://github.com/NVIDIA/NeMo/files/6525850/test_index.zip)\r\n",
"It looks like you have two extra empty lines at the end of your manifests. Please remove them and try again.",
"Good catch. I tried parsing it with python and didn't notice the extra line at the end. Removing it so there's just 1 blank line fixed it locally. That would be the fix @varunsingh3000 ",
"Worked like a charm. Thanks @VahidooX and @titu1994 "
] | 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
![an4 manifest](https://user-images.githubusercontent.com/64498789/118785567-9a734d00-b8ae-11eb-8e8a-434e39498061.JPG)
voices dataset manifest screenshot
![voices manifest](https://user-images.githubusercontent.com/64498789/118786675-b0cdd880-b8af-11eb-9258-1381b48ba1ce.png)
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 loops with same variable",
"This pull request **introduces 5 alerts** when merging a9fc90e15755844988cbcd6c7a2cb9630424c79d into 202ccc902937b10bd9a0f37d49f8b8dc29e3ca4d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5240fe671cd93a037439119ede238dafcdb30dfe)\n\n**new alerts:**\n\n* 4 for Unused import\n* 1 for Wrong number of arguments in a class instantiation"
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} | Text Normalization conditioned on audio: multiple normalization options considered, the best match selected based on WER.
Python support only (no SH). | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2230 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2230/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2230/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2230/events | https://github.com/NVIDIA/NeMo/pull/2230 | 894,513,330 | MDExOlB1bGxSZXF1ZXN0NjQ2ODY2NTQ5 | 2,230 | Add Russian language | {
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"Could you sign the DCO commits via the instruction here - \r\nhttps://github.com/NVIDIA/NeMo/pull/2230/checks?check_run_id=2612023734",
"Also seems test failed due to style formatting. Could you run `python setup.py style --scope nemo --fix`",
"Could you rebase with the main branch? Seems incorrect branch was selected?",
"The asr parts was refactored in order to be modular, could you apply those changes in the conflicting files? Most of them just had path changes- but manifest and parsers now belong to nemo/collections/commons/parts instead of asr.",
"Closed due to inactivity. Please reach out in order to resume work on it."
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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 above to perform FP16 inference on GPU",
"Does not work for https://huggingface.co/nvidia/parakeet-rnnt-0.6b\r\nI trained in FP32 on RTX titan, infer on fp16 give all `??` and is taking 5 times longer. FP32 works fine.\r\n\r\nNoteworthy is that training with fp16 on this model also does not work.",
"@nithinraok, have we tried this model with fp16?\n\nThere are cases where a model trained in fp32 does not work with fp16 as some weights are out of the range or activations get too large in some layers.",
"Yeah these models are trained with bf16 and haven't tested or updated for fp16. Works out of the box with either fp32 or bf16. "
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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 Just switched the pretrained model.\r\n pretrained_model_name=\"qa_squadv1.1_bertbase\"\r\n\r\n2nd Trail: Occurred Error.\r\n Instead of bert-base-uncased, I used 'biomegatron-bert-345m-uncased.\"\r\n Please refer to the attached file. \r\n\r\nCould you give me any advice on using biomegatron for the QnA of biomedical document? \r\n[Megatron_Trial.pptx](https://github.com/NVIDIA/NeMo/files/6498847/Megatron_Trial.pptx)\r\n\r\nBest Regards.",
"@blackbirt-5 please use for tokenizer for your biomegatron checkpoint bert-large-uncased, you should be also able to set it to null"
] | 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?
![Error_Message](https://user-images.githubusercontent.com/82014275/118435026-07ed7500-b719-11eb-8bf0-dab6b8b0e3a2.jpg)
![Pretrained_model](https://user-images.githubusercontent.com/82014275/118435029-091ea200-b719-11eb-8855-b06d9788d2c6.jpg)
![Pretrained_model2](https://user-images.githubusercontent.com/82014275/118435030-09b73880-b719-11eb-9626-3d908cc140cd.jpg)
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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"+ 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/blob/ner_0.11/examples/nlp/dialogue_state_tracking/dialogue_state_tracking_sgd.py\r\nAnd you can find the documentation here:\r\nhttp://nemo-master-docs.s3-website.us-east-2.amazonaws.com/nlp/dialogue_state_tracking_sgd.html\r\n\r\n+ Where can I find some documentation regarding SGD-QA?\r\n I think here is the only place you may find some detail about SGD-QA:\r\n https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/dialogue_state_tracking/sgd_qa.py\r\n @yzhang123 may be able to provide more info on SGD-QA.\r\n",
"Thank you for the fast reply! I noticed I linked the wrong paper, my apologies, wanted to link (https://arxiv.org/pdf/2008.12335.pdf).",
"@KadarTibor I have uploaded the SGD-QA paper to Arxiv. Will be ready in 1-2 days.",
"That's amazing! Thank you 🙏 ",
"https://arxiv.org/abs/2105.08049"
] | 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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