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https://api.github.com/repos/NVIDIA/NeMo/issues/1678 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1678/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1678/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1678/events | https://github.com/NVIDIA/NeMo/pull/1678 | 796,572,495 | MDExOlB1bGxSZXF1ZXN0NTYzNzI5NDgw | 1,678 | add new quantization nodes in QuartzNet | {
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"Could you please sign your commits. See https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md for general guidelines.\r\nTo sign already committed commits, checkout DCO's bot output: https://github.com/NVIDIA/NeMo/pull/1678/checks?check_run_id=1789269686",
"CI failed on style check. \r\nTo check for style do:... | 2021-01-29T04:40:48 | 2021-01-29T18:42:14 | 2021-01-29T18:42:14 | CONTRIBUTOR | {
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"Every NeMo model inherits from ``torch.nn.Module`` at the top of it's inheritance chain. So everything applicable to torch.nn.Module objects, including serving them via Triton Server should apply here as well.\r\n\r\n",
"Great, thanks for the response. "
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"Hi, is there any update on this?\r\n\r\nI would also like to ask if it's possible to add a language model in this notebook :\r\nhttps://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb\r\n\r\nI would like to add my language model to train my data in order to achieve bette... | 2021-01-28T11:24:31 | 2021-06-19T04:41:37 | 2021-06-19T04:41:37 | NONE | {
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} | null | null | null | Hello, I am currently training a QuartzNet15x5 model for speech recognition. As far as I know this uses greedy decoder by default. I want to use a language model to achieve better WER if possible. I have 2 questions:
1) If I want to use a language model instead of greedy decoder, should I restart my training and train language model in parallel with my encoder-decoder, or does language model part act like a separate entity which can be plugged in after training?
2) I am trying to follow tutorials but most of the links give me 404 error because of the structure change in this repository. Do old tutorials not work or can I safely follow them? For example, I couldn't find the tutorial given in this link:
https://nvidia.github.io/NeMo/asr/tutorial.html#evaluation-with-language-model
Thanks. | {
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"Thanks @yzhang123 !"
] | 2021-01-28T08:09:18 | 2021-01-28T18:25:23 | 2021-01-28T18:24:50 | COLLABORATOR | {
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} | # Changelog
- Add support for external tokenizer files for transformer language models
- Add support for serialization of tokenizer vocab + tokenizer file (if present)
- Add support to disable optimizers by passing file_path = None
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"This pull request **introduces 4 alerts** when merging 8804196b70e585ebb8bd2847f4a503ba19358ac2 into cd8f1c5a74e0d0f2c5bf5847951e30d1170909c3 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-33c1b211c8938e3824030f9c8b2b1d9708c3bfdb)\n\n**new alerts:**\n\n* 3 for Unused import\n* 1 for Unused loc... | 2021-01-28T03:23:30 | 2022-08-16T16:15:48 | 2021-01-29T22:35:08 | CONTRIBUTOR | {
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} | This PR adds support for Tarred Datasets when training NMT models on very large datasets. It avoids loading the entire dataset into CPU memory for every DDP process. It uses webdataset (https://github.com/tmbdev/webdataset) similar to ASR and language modeling.
To use this functionality
1. Run `python examples/nlp/machine_translation/create_tarred_dataset.py --clean --bpe_dropout 0.1 --shared_tokenizer --src_fname src_file.txt --tgt_fname tgt_file.txt --out_dir /path/to/out_dir --vocab_size 32000 --tokens_in_batch 16000 --lines_per_dataset_fragment 5000000 --num_batches_per_tarfile 1000` to create a folder that contains many tar files. Each tar file consists of a certain number of batches (specified by `num_batches_per_tarfile`), where each batch is a pickle file. The output folder will also contain `metadata.json` that contains the total number of training batches and YTTM tokenizer model files.
2. Point to the tarred dataset folder when training like so
```
python examples/nlp/machine_translation/enc_dec_nmt.py -c aayn_base \
trainer.gpus=8 \
+trainer.max_steps=300000
...........
model.train_ds.src_file_name=/path/to/out_dir/batches.tokens.16000._OP_1..100_CL_.tar \
model.train_ds.tgt_file_name=/path/to/out_dir/batches.tokens.16000._OP_1..100_CL_.tar \
+model.train_ds.load_from_tarred_dataset=true \
+model.train_ds.metadata_path=/path/to/out_dir/metadata.json \
+model.train_ds.tar_shuffle_n=100 \
.............
``` | {
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"can you please send a PR?",
"fixed"
] | 2021-01-28T00:17:29 | 2021-02-02T04:53:16 | 2021-02-02T04:53:16 | CONTRIBUTOR | {
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} | null | null | null | In the intent/slot tutorial notebook, I think this line:
https://github.com/NVIDIA/NeMo/blob/ad0edf2e6910b7b6de3ae93fba9b728f6479c50e/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb#L508
Should be changed to: `trainer.test(model=eval_model, ckpt_path=None, verbose=False)`
So that the reloaded/best model is the one that gets evaluated. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1671 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1671/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1671/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1671/events | https://github.com/NVIDIA/NeMo/issues/1671 | 795,547,478 | MDU6SXNzdWU3OTU1NDc0Nzg= | 1,671 | Missing ipywidgets package in Docker causes intent/slot tutorial to raise ImportError | {
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} | [] | closed | false | null | [] | null | [] | 2021-01-27T23:58:42 | 2021-07-13T15:22:26 | 2021-07-13T15:22:26 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
When running in the NeMo Docker container, the Intent/slot model tutorial errors out due to missing ipywidgets package.
**Steps/Code to reproduce bug**
Running [tutorial notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb) in the nvcr.io/nvidia/nemo:1.0.0b3 docker container
This line raises an ImportError:
`model = nemo_nlp.models.IntentSlotClassificationModel(config.model, trainer=trainer)`
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py in __init__(self, pretrained_model_name, vocab_file, mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token)
103 else:
--> 104 self.tokenizer = AUTOTOKENIZER.from_pretrained(pretrained_model_name_or_path=pretrained_model_name)
105 except Exception as e:
/opt/conda/lib/python3.6/site-packages/transformers/tokenization_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs)
305 if not isinstance(config, PretrainedConfig):
--> 306 config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
307
/opt/conda/lib/python3.6/site-packages/transformers/configuration_auto.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
332 """
--> 333 config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
334
/opt/conda/lib/python3.6/site-packages/transformers/configuration_utils.py in get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
387 resume_download=resume_download,
--> 388 local_files_only=local_files_only,
389 )
/opt/conda/lib/python3.6/site-packages/transformers/file_utils.py in cached_path(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, local_files_only)
954 user_agent=user_agent,
--> 955 local_files_only=local_files_only,
956 )
/opt/conda/lib/python3.6/site-packages/transformers/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)
1163
-> 1164 http_get(url_to_download, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)
1165
/opt/conda/lib/python3.6/site-packages/transformers/file_utils.py in http_get(url, temp_file, proxies, resume_size, user_agent)
1033 desc="Downloading",
-> 1034 disable=bool(logging.get_verbosity() == logging.NOTSET),
1035 )
/opt/conda/lib/python3.6/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs)
231 self.container = self.status_printer(
--> 232 self.fp, total, self.desc, self.ncols)
233 if display_here:
/opt/conda/lib/python3.6/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols)
96 raise ImportError(
---> 97 "IProgress not found. Please update jupyter and ipywidgets."
98 " See https://ipywidgets.readthedocs.io/en/stable"
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-5-676c262478c3> in <module>
1 # initialize the model
----> 2 model = nemo_nlp.models.IntentSlotClassificationModel(config.model, trainer=trainer)
3
4
5 # train
/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/models/intent_slot_classification/intent_slot_classification_model.py in __init__(self, cfg, trainer)
62 )
63
---> 64 self._setup_tokenizer(cfg.tokenizer)
65 # init superclass
66 super().__init__(cfg=cfg, trainer=trainer)
/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/models/intent_slot_classification/intent_slot_classification_model.py in _setup_tokenizer(self, cfg)
235 tokenizer_model=cfg.tokenizer_model,
236 special_tokens=OmegaConf.to_container(cfg.special_tokens) if cfg.special_tokens else None,
--> 237 vocab_file=cfg.vocab_file,
238 )
239 self.tokenizer = tokenizer
/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/modules/common/tokenizer_utils.py in get_tokenizer(tokenizer_name, tokenizer_model, vocab_file, special_tokens)
71 return CharTokenizer(vocab_file=vocab_file, **special_tokens_dict)
72
---> 73 return AutoTokenizer(pretrained_model_name=tokenizer_name, vocab_file=vocab_file, **special_tokens_dict,)
/opt/conda/lib/python3.6/site-packages/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py in __init__(self, pretrained_model_name, vocab_file, mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token)
104 self.tokenizer = AUTOTOKENIZER.from_pretrained(pretrained_model_name_or_path=pretrained_model_name)
105 except Exception as e:
--> 106 raise ValueError(f'{pretrained_model_name} is not supported by HuggingFace. {e}')
107
108 special_tokens_dict = {}
ValueError: bert-base-uncased is not supported by HuggingFace. IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```
**Environment overview (please complete the following information)**
- Environment location: local machine
- Method of NeMo install: Docker
**Additional context**
Fixed by `pip install ipywidgets` | {
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"@okuchaiev Could you review the move of citrinet configs from experimental to conf/citrinet/ and update to Jenkinsfile? "
] | 2021-01-27T23:22:19 | 2021-02-01T20:12:22 | 2021-02-01T20:12:15 | COLLABORATOR | {
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} | # Changelog
- Adds ASR with subword tokenization notebook
- Move configs from experimental to conf/citrinet/
- Updates change_vocabulary() in CTCBPE and RNNTBPE so that vocabulary updated is actual tokens instead of token indices.
- Update readme with link to colab
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"This pull request **introduces 1 alert** when merging 03dc67e4e06a7fd939ec32ae6a3ae2ed07371759 into ad0edf2e6910b7b6de3ae93fba9b728f6479c50e - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-c00b16affad2c7a4c82e0998d3d0995a058cd2fa)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-01-27T17:58:25 | 2021-02-09T03:18:56 | 2021-02-09T03:18:52 | COLLABORATOR | {
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} | The default for `find_unused_parameters` was changed from `True` to `False` in PTL. We're seeing higher accuracy in NMT with `find_unused_parameters=True`. Overriding this in NMT model until we learn more. | {
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"Currently we do not have wav2letter implementation. Checkout Jasper and QuartzNet networks which were inspired by wav2letter",
"Ok, thank you."
] | 2021-01-26T17:56:37 | 2021-01-30T16:21:00 | 2021-01-29T20:16:24 | NONE | {
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} | null | null | null | **Describe your question**: As Nemo is a Pytorch lightening module (as wav2letter) is there an easy to implement Wav2letter with Nemo ?
A clear and concise description of your question.
Describe what you want to achieve. And/or what NeMo APIs are unclear/confusing.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | null | null | null | **Describe your question**
I don't manage to find wav2letter in Nemo ASR models wheareas it existed in OpenSeq2Seq.
What's the point ?
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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"This pull request **introduces 1 alert** when merging cdb20c1742dca80fd3519991029d356e95573f98 into 9ea071605034f9f56db9b677eb1fe54fdee2b820 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cc6090175340df7c3bed280609bfe623e3253748)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-01-26T03:01:31 | 2021-01-29T01:09:46 | 2021-01-29T01:09:42 | COLLABORATOR | {
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} | # Changelog
- Adds nearly concrete implementations of dataclasses for QuartzNet 15x5, Jasper 10x5-DR and MatchboxNet-3x1x64 and VAD modes.
- Adds *ConfigBuilder class supporting the construction of arbitrarily complex interdependencies between sub-configs
- Adds various dataclasses for ASR datasets + ASR Classification datasets
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} | null | null | null | **Describe the bug**
I am trying to run the Relation Extraction - BioMegatron notebook where once the model is trained, I am trying to load the best checkpoint.
**Steps/Code to reproduce bug**
- Train the model as usual
`checkpoint_path = trainer.checkpoint_callback.best_model_path`
`eval_model = nemo_nlp.models.TextClassificationModel.load_from_checkpoint(checkpoint_path=checkpoint_path)`
**Expected behavior**
<img width="1048" alt="Screen Shot 2021-01-25 at 8 01 09 PM" src="https://user-images.githubusercontent.com/7240751/105785078-13d5e100-5f48-11eb-991d-3560fa24a1e3.png">
**Environment overview (please complete the following information)**
- Environment Location: Collab
- Method of NeMo install: pip
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https://api.github.com/repos/NVIDIA/NeMo/issues/1662 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1662/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1662/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1662/events | https://github.com/NVIDIA/NeMo/issues/1662 | 793,650,751 | MDU6SXNzdWU3OTM2NTA3NTE= | 1,662 | Handle shape mismatch when restoring pretrained weights | {
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"This is not a good approach imo, because it allows the chance that model weights are skipped with a warning only and training progresses as is. When developing on scheduled dispatchers like Slurm, we rarely every take constant look at slurm logs so this can silently introduce bugs.\r\n\r\nThe official way to chang... | 2021-01-25T19:10:51 | 2021-02-24T17:53:03 | 2021-02-24T17:53:03 | CONTRIBUTOR | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Loading pretrained weights onto a model with a slightly different architecture results in a shape mismatch error. This makes it unwieldy to use pretrained weights for fine-tuning a model with a different decoder shape (for example, ASR with a different vocab size). modelPT.restore_from has a strict=False option but will error if a layer has a similar name but different shape.
**Describe the solution you'd like**
My preferred approach is for restore_from to warn of shape mismatch and ignore loading weights in that circumstance. Something like this perhaps:
```
instance = cls.from_config_dict(config=conf)
instance = instance.to(map_location)
instance_state_dict = instance.state_dict()
state_dict = torch.load(model_weights, map_location=map_location)
if not strict:
mismatched_layers = set()
for k, v in state_dict.items():
if k in instance_state_dict and instance_state_dict[k].shape != v.shape:
mismatched_layers.add(k)
logging.warning(f"Size mismatch for {k}: ignoring weights")
[state_dict.pop(name) for name in mismatched_layers]
instance.load_state_dict(state_dict=state_dict, strict=strict)
logging.info(f'Model {cls.__name__} was successfully restored from {restore_path}.')
``` | {
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"1. NeMo software is released under [Apache 2.0 license](https://github.com/NVIDIA/NeMo/blob/main/LICENSE)\r\n2. Pre-trained checkpoints which you can find on NVIDIA NGC are under [NGC TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified.\r\n\r\nSpecificall... | 2021-01-25T16:12:35 | 2021-01-29T20:15:15 | 2021-01-29T20:15:14 | NONE | {
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} | null | null | null | Hi, do the pretrained models from https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels have the same Apache2 licence as this repository? | {
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"@lodm94 You are right, there seems to be an issue with the default value of noise_tar_paths. Until we push a fix, as a workaround you could change your dict to the following:\r\n\r\naudio_augmentations = dict(\r\n rir_noise_aug = dict(\r\n prob=0.5,\r\n rir_manifest_path = 'rir_manifest.json',... | 2021-01-25T10:02:05 | 2022-11-09T09:16:05 | 2021-10-26T23:43:31 | NONE | {
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} | null | null | null | Hi all,
I'm still having trouble implementing RIR in my ASR model after i solved [this](https://github.com/NVIDIA/NeMo/issues/1648) problem.
Let's assume i have alredy created manifest file for both noise and rir aug. Let's assume also that i want to implement augmentation with implicit definition, as suggested in [tutorial n.5](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/05_Online_Noise_Augmentation.ipynb).
With this in mind, i created the nested dictionary:
```
audio_augmentations = dict(
rir_noise_aug = dict(
prob=0.5,
rir_manifest_path = 'rir_manifest.json',
rir_prob = 0.5,
noise_manifest_paths = 'noise_manifest.json',
min_snr_db = [0,0],
max_snr_db = [30,30],
bg_noise_manifest_paths = 'noise_manifest.json',
bg_min_snr_db = [10,10],
bg_max_snr_db = [40,40]
)
)
```
And supplied the 'augmentor' to the model.train_ds config:
`config.model.train_ds.augmentor = audio_augmentations`
But when i go for:
`asr_model.setup_training_data(train_data_config=config.model.train_ds)`
It returns an error:
```
TypeError Traceback (most recent call last)
<ipython-input-29-e364e6754a27> in <module>
----> 1 asr_model.setup_training_data(train_data_config=config.model.train_ds)
2 asr_model.setup_validation_data(val_data_config=config.model.validation_ds)
3 asr_model.set_trainer(trainer)
4 asr_model.setup_optimization(optim_config=config.model.optim)
/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/models/ctc_models.py in setup_training_data(self, train_data_config)
326 self._update_dataset_config(dataset_name='train', config=train_data_config)
327
--> 328 self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
329
330 # Need to set this because if using an IterableDataset, the length of the dataloader is the total number
/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/models/ctc_models.py in _setup_dataloader_from_config(self, config)
224 def _setup_dataloader_from_config(self, config: Optional[Dict]):
225 if 'augmentor' in config:
--> 226 augmentor = process_augmentations(config['augmentor'])
227 else:
228 augmentor = None
/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/parts/perturb.py in process_augmentations(augmenter)
753
754 try:
--> 755 augmentation = perturbation_types[augment_name](**augment_kwargs)
756 augmentations.append([prob, augmentation])
757 except KeyError:
/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/parts/perturb.py in __init__(self, rir_manifest_path, rir_prob, noise_manifest_paths, min_snr_db, max_snr_db, rir_tar_filepaths, rir_shuffle_n, noise_tar_filepaths, apply_noise_rir, orig_sample_rate, max_additions, max_duration, bg_noise_manifest_paths, bg_min_snr_db, bg_max_snr_db, bg_noise_tar_filepaths, bg_orig_sample_rate)
508 min_snr_db=min_snr_db[i],
509 max_snr_db=max_snr_db[i],
--> 510 audio_tar_filepaths=noise_tar_filepaths[i],
511 orig_sr=orig_sr,
512 )
TypeError: 'NoneType' object is not subscriptable
```
Actually i can't figure it out where is the problem. I tried to search in perturb.py but i guess i'am too naive to find the solution. It seems the problem relies in noise_tar_filepath. But my rir and noise are not tarred!!
Thanks in advance | {
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"Hmm wait, the function exists in main but the error exists in b3? If the error exists only in main, then for now comment out these patches with a note that it needs to be uncommented. Or use a try catch. \r\n\r\nAlso, tests are failing on a notebook only pr?",
"> Hmm wait, the function exists in main but the err... | 2021-01-23T02:40:57 | 2021-01-29T02:18:23 | 2021-01-29T02:18:10 | COLLABORATOR | {
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} | Update and fix the **outside of the model constructor configs** in ASR tutorials. Converting outside Hydra 0.x configs to Hydra 1.x configs.
Tiny bug fix for notebook 03 speech command
Related bug: [1649](https://github.com/NVIDIA/NeMo/issues/1649) | {
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">>\"Can I integrate it with my college website\"\r\n- Yes.\r\n\r\n>> \"could you please guide me?\"\r\n- Have a look at this notebook for example. \r\nhttps://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/NeMo_voice_swap_app.ipynb"
] | 2021-01-22T19:25:35 | 2021-05-28T08:35:08 | 2021-05-28T08:35:08 | NONE | {
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} | null | null | null | I am new to ASR (Automatic Speech Recognition), I am working on a college website where professors can upload their lectures. I want to integrate an automatic speech transcript generator so that users can turn on the captions while streaming. While searching for open source options, I came across Nemo. Could anyone please tell me whether it is possible to do that and if Yes, could you please guide me? | {
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"currently we don't support such feature. Also, it would put high requirements on available RAM"
] | 2021-01-21T18:13:34 | 2021-05-28T08:35:55 | 2021-05-28T08:35:55 | CONTRIBUTOR | {
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} | null | null | null | **Caching datasets for ASR training**
The current data loaders for ASR seem to load the file from disk for every iteration with AudioSegment. This becomes a bottleneck when the disk is not very fast (i.e. is not a SSD). Is there a way to cache the audio files in RAM memory? Should we add the option to cache the datasets? We think that it may accelerate training when it fits.
Kind regards
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"Could you try by replacing gpus=[1] with gpus=1? This seems to be trying to index GPU [1] which would be the second gpu, but colab has just 1 gpu",
"Hi Good Sir,\n\nYes, it seems that this is the case. It runs smoothly now once I have\nchanged to \"gpus=1\".\n\nAlthough would just like to inform you that looks l... | 2021-01-21T03:29:24 | 2021-01-21T16:46:10 | 2021-01-21T16:46:10 | NONE | {
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} | null | null | null | Hi, I am a beginner in ASR technology and I am currently studying more about this field using your tutorial from this link:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb
I was running the codes in your notebook, everything was fine until it reaches the "**Transfer Learning**" section, on the part where it asks to fine-tune the model for 2 epochs on AN4 dataset, I have stumbled on an error from this part of the code(bolded):
_#And now we can create a PyTorch Lightning trainer and call `fit` again.
**trainer = pl.Trainer(gpus=[1], max_epochs=2)
trainer.fit(quartznet)**_
The error was:
MisconfigurationException:
You requested GPUs: [1]
But your machine only has: [0]
I am running the notebook on Colab and not locally, and there were no changes made on any part of the code. It was just me running cells to study. The runtime type has already been changed to GPU and it was a success on the "**Training with PyTorch Lightning**" section but not for **Transfer Learning**.
I would like to know what may be the reason for this error?
For your information, I am using Chrome to run the notebook
If there is any inquires regarding my side, kindly do comment and reply, I will do my best to provide them.
Thank you in advance.
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"This pull request **introduces 2 alerts** when merging 1548c44f5dc90b95d7fc3d8fd1f1e83347753c74 into eb927fe6aa69f8d73705dcb785a2afb95e4c279d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-2b1f14de5396282d23465f680fcf532753f62246)\n\n**new alerts:**\n\n* 1 for Non\\-callable called\n* 1 for Un... | 2021-01-21T01:59:59 | 2021-01-22T15:26:50 | 2021-01-22T15:26:46 | CONTRIBUTOR | {
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} | This PR fixes hydra runner, enabling to use schemas with manual passing of configs files using command line.
- [x] Modified hydra_runner so it injects schema into the right "place" in ConfigStore.
- [x] Covers runner with integration tests
Usage: please take a look at tests and the used "example" (`tests/hydra/my_app.py`)
Additionally cleans up some hydra settings in NeMo:
- [x] Sets hydra logging outputs only to stdout (no empty log file in . dir!!)
- [x] Disables the creation of .hydra subdir
- [x] Removes the old set_config wrapper
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} | # Changelog
- Create SPE vocabulary as a key=subword to value=id mapping to be consistent with WPE
- Use keys() as the actual vocabulary to inject into the decoder
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | # Changelog
- Address Neural Type comments from https://github.com/NVIDIA/NeMo/issues/1634 regarding LSTM
- Address clarification of "GPT Encoder neural module" vs "Transformer Decoder network" issue raised in https://github.com/NVIDIA/NeMo/issues/1638
Closes https://github.com/NVIDIA/NeMo/issues/1634 and https://github.com/NVIDIA/NeMo/issues/1638
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"Thanks for reporting this. We will fix it asap and let you know.",
"@shfaizan [Fixed](https://github.com/NVIDIA/NeMo/pull/1659)! Please pull and update. Thanks!"
] | 2021-01-19T18:09:28 | 2021-01-29T02:19:58 | 2021-01-29T02:19:58 | NONE | {
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} | null | null | null | I am trying to run the[ VAD notebook](https://github.com/NVIDIA/NeMo/blob/9aa4ada92b2e6ddd418704dc392e8576d44e5f41/tutorials/asr/07_Online_Offline_Microphone_VAD_Demo.ipynb).
I followed every step but got this error:
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"@lodm94 The issue here is that the script get_openslr_rir.py only works with absolute paths. \r\nPlease change the first line in the cell to:\r\n`rir_data_path = os.getcwd()`\r\nI will fix this in the main branch too. Thanks for letting us know of this issue.",
"Holy moly it finally works! Cheers man! Thank you\... | 2021-01-19T11:32:42 | 2021-01-20T09:39:20 | 2021-01-20T09:39:20 | NONE | {
"total": 0,
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} | null | null | null | Hello,
im tryin to implement RIR aug, just like suggested in the [tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/05_Online_Noise_Augmentation.ipynb
)
But when i run the cell:
```
rir_data_path = '.'
!python ../../scripts/get_openslr_rir.py --data_root {rir_data_path}
rir_manifest_path = os.path.join(rir_data_path, 'processed', 'rir.json')
!head -n 3 {rir_manifest_path}
```
It returns:
```
python3: can't open file '../../scripts/get_openslr_rir.py': [Errno 2] No such file or directory
head: cannot open './processed/rir.json' for reading: No such file or directory
```
So, i downloaded get_openslr_rir.py script manually from [here](https://raw.githubusercontent.com/NVIDIA/NeMo/main/scripts/get_openslr_rir.py)
And the cell to run became:
```
rir_data_path = '.'
!python get_openslr_rir.py --data_root {rir_data_path}
rir_manifest_path = os.path.join(rir_data_path, 'processed', 'rir.json')
!head -n 3 {rir_manifest_path}
```
But still get an error after downloading and extracting the file:
```
INFO:root:
Working on: slr28
INFO:root:Getting slr28
INFO:root:./slr28.zip does not exist. Downloading ...
INFO:root:Downloaded ./slr28.zip.
INFO:root:Extracting slr28
INFO:root:Processing slr28
Traceback (most recent call last):
File "get_openslr_rir.py", line 139, in <module>
main()
File "get_openslr_rir.py", line 133, in main
os.path.join(os.path.join(data_root, "processed", "rir.json")),
File "get_openslr_rir.py", line 105, in __process_data
with open(manifest_file, 'w') as man_f:
FileNotFoundError: [Errno 2] No such file or directory: './processed/rir.json'
head: cannot open './processed/rir.json' for reading: No such file or directory
```
Is this a known issue? Or i'm just bad af and i don't know what to do? I can't even look at how rir manifest are structured. And this is realy important for me to add RiRAug in my ASR pipeline.
Appreciate some help
Thanks in advance
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"The type error mentions that \"input_signal_length\" has no corresponding input types - and the two valid input names are input_signal and length. \r\n\r\nSo if you replace the call function argument of empty_model.preprocessor() from input_signal_length to just length, it should work ",
"Thank you, I was able t... | 2021-01-18T17:21:26 | 2021-01-29T20:17:15 | 2021-01-29T20:17:15 | CONTRIBUTOR | {
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} | null | null | null | **Describe your question**
I would like to be able to display the spectrograms that are being generated by Quartznet and SpecAugment but I'm having trouble being able to do so.
Things I tried:
- If I apply the preprocessor directly to a tensor (loaded with torchaudio) I get a TypeError because the input type doesn't match (included below).
- There's no way to hook in and log the spectrogram (that I'm aware of) because it is only available from the middle of the forward method.
- I could copy the entire `EncDecCTCModel` and paste in code but I was hoping there was a better way to be able to view spectrograms as they are very important.
- I checked the ASR_with_nemo tutorial but the only spectrograms displayed are manually generated using librosa.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-198-408d2f6bb357> in <module>()
----> 1 empty_model.preprocessor(input_signal=y_resampled, input_signal_length=y_resampled.size(-1))
2 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/usr/local/lib/python3.6/dist-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
506
507 # Perform rudimentary input checks here
--> 508 instance._validate_input_types(input_types=input_types, **kwargs)
509
510 # Call the method - this can be forward, or any other callable method
/usr/local/lib/python3.6/dist-packages/nemo/core/classes/common.py in _validate_input_types(self, input_types, **kwargs)
96 if key not in input_types:
97 raise TypeError(
---> 98 f"Input argument {key} has no corresponding input_type match. "
99 f"Existing input_types = {input_types.keys()}"
100 )
TypeError: Input argument input_signal_length has no corresponding input_type match. Existing input_types = dict_keys(['input_signal', 'length'])
```
**Environment overview (please complete the following information)**
- Colab w Nemo 1.0.0b2
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"Hi! Thanks for the PR. Could you sign your commit using the instructions here - https://github.com/NVIDIA/NeMo/pull/1644/checks?check_run_id=1721045606",
"@titu1994 I cannot read the details of check, please check it. Thanks"
] | 2021-01-18T11:15:36 | 2021-02-04T01:21:50 | 2021-02-03T21:25:51 | CONTRIBUTOR | {
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"You don't need the decoder.params.vocabulary anymore. Directly use decoder.vocabulary",
"Yes this solves the issue thanks"
] | 2021-01-18T10:21:39 | 2021-01-19T17:38:23 | 2021-01-19T17:38:23 | NONE | {
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} | null | null | null | I try to run the [offline colab notebook](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/asr/Offline_ASR.ipynb#scrollTo=8qgXHl78WneK).
Without changing anything I run cells and get error while running this line `labels = list(asr_model.cfg.decoder.params.vocabulary) + ['blank']:`
Error:
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https://api.github.com/repos/NVIDIA/NeMo/issues/1641 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1641/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1641/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1641/events | https://github.com/NVIDIA/NeMo/issues/1641 | 787,937,241 | MDU6SXNzdWU3ODc5MzcyNDE= | 1,641 | Error while processing Punctuation module | {
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"I am also encountering this problem. Tried setting num_workers=0 in _setup_infer_dataloader method of nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py, but it did not solve problem.",
"We don’t support Windows at this point, please try running in Colab `https://colab.research.... | 2021-01-18T05:42:30 | 2021-03-02T21:34:22 | 2021-02-26T18:38:52 | NONE | {
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} | null | null | null | **Environment overview (please complete the following information)**
Installed Nemo using pip
**Environment details**
- OS version Windows 10
- PyTorch version 1.7.0+cu101
- Python version 3.6
This is the error I am getting while running the voice_swap demo in my machine.
> NotImplementedError Traceback (most recent call last)
>
> <ipython-input-4-51c966b428c7> in <module>
> 7
> 8 # Add capitalization and punctuation
> ----> 9 res = punctuation.add_punctuation_capitalization(queries=[raw_text])
> 10 text = res[0]
> 11 print(f'\nRaw recognized text: {raw_text}. \nText with capitalization and punctuation: {text}')
>
> E:\FaceFirst_Projects\NeMo-main\nemo\collections\nlp\models\token_classification\punctuation_capitalization_model.py in add_punctuation_capitalization(self, queries, batch_size)
> 390 all_capit_preds = []
> 391
> --> 392 for batch in infer_datalayer:
> 393 input_ids, input_type_ids, input_mask, subtokens_mask = batch
> 394
>
> c:\python36\lib\site-packages\torch\utils\data\dataloader.py in __iter__(self)
> 350 return self._iterator
> 351 else:
> --> 352 return self._get_iterator()
> 353
> 354 @property
>
> c:\python36\lib\site-packages\torch\utils\data\dataloader.py in _get_iterator(self)
> 292 return _SingleProcessDataLoaderIter(self)
> 293 else:
> --> 294 return _MultiProcessingDataLoaderIter(self)
> 295
> 296 @property
>
> c:\python36\lib\site-packages\torch\utils\data\dataloader.py in __init__(self, loader)
> 799 # before it starts, and __del__ tries to join but will get:
> 800 # AssertionError: can only join a started process.
> --> 801 w.start()
> 802 self._index_queues.append(index_queue)
> 803 self._workers.append(w)
>
> c:\python36\lib\multiprocessing\process.py in start(self)
> 103 'daemonic processes are not allowed to have children'
> 104 _cleanup()
> --> 105 self._popen = self._Popen(self)
> 106 self._sentinel = self._popen.sentinel
> 107 # Avoid a refcycle if the target function holds an indirect
>
> c:\python36\lib\multiprocessing\context.py in _Popen(process_obj)
> 221 @staticmethod
> 222 def _Popen(process_obj):
> --> 223 return _default_context.get_context().Process._Popen(process_obj)
> 224
> 225 class DefaultContext(BaseContext):
>
> c:\python36\lib\multiprocessing\context.py in _Popen(process_obj)
> 320 def _Popen(process_obj):
> 321 from .popen_spawn_win32 import Popen
> --> 322 return Popen(process_obj)
> 323
> 324 class SpawnContext(BaseContext):
>
> c:\python36\lib\multiprocessing\popen_spawn_win32.py in __init__(self, process_obj)
> 63 try:
> 64 reduction.dump(prep_data, to_child)
> ---> 65 reduction.dump(process_obj, to_child)
> 66 finally:
> 67 set_spawning_popen(None)
>
> c:\python36\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
> 58 def dump(obj, file, protocol=None):
> 59 '''Replacement for pickle.dump() using ForkingPickler.'''
> ---> 60 ForkingPickler(file, protocol).dump(obj)
> 61
> 62 #
>
> NotImplementedError: object proxy must define __reduce_ex__()
Any idea why this error?? | {
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"Adding a `me too` comment here. I'm actually interested in loading features archives computed by kaldi (like fbank). This is a feature very useful we find in other toolkits like ESPnet for example. Is there some documentation or some guidelines how one would implement that?",
"@jprobichaud I already fixed her... | 2021-01-18T04:05:49 | 2021-08-17T01:00:51 | 2021-08-17T01:00:51 | CONTRIBUTOR | {
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} | null | null | null | Hi, guys. I am new to NeMo.
And I still reusing the data augment in Kaldi, since there have a lot data prepare script.
is it possible to read pipeline data in NeMo, I already wrote a script generate Manifest .json file from wav.scp and text file in kaldi stype dataset. But it only work for .wav not pipeline data.
And I found (https://github.com/NVIDIA/NeMo/issues/743) KaldiFeatureDataLayer already removed. Do you guys have any plan for these? | {
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} | Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>
Fixing possible incorrect default overrides.
Adding building onnx-surgeon from source to Dockerfile.
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"While not consistent with the paper, this is a tutorial to explain how to refactor a monolithic concrete model into an abstract NeMo model - comprising of neural modules that represent the abstraction with neural types, and then instantiate them with actual concrete modules (which can be anything that fits the neu... | 2021-01-16T01:21:15 | 2021-01-20T04:28:32 | 2021-01-20T04:28:32 | NONE | {
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} | null | null | null | In the NeMo Models tutorial, in the refactored minGPT we have `GPTEmbedding`, `GPTTransformerEncoder`, `GPTDecoder`. I think `GPTTransformerEncoder` and `GPTDecoder` are confusing names both with respect to the original GPT architecture and the Transformer architecture. In the original Tranformer paper, namely the left stack of identical layers is called encoder and the right stack of identical layers is called decoder and that's why in general both Transformer encoder and decoder are used to describe two different and specific sub-architectures in the NLP community.
.
Based on the original Transformer architecture we have all the subsequent Transformer architecture descriptions using the encoder and decoder terminology. For example, authors of [BERT](https://arxiv.org/pdf/1810.04805.pdf) describe it as
> BERT’s model architecture is a multi-layer **bidirectional Transformer encoder** based on the original implementation described in Vaswani et al. (2017).
Similarly, the original [GPT paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) describes the architecture of GPT as a
> **12-layer decoder-only transformer**
So there is no "encoder" in GPT's architecture and that's why to a beginner the tutorial's `GPTTransformerEncoder` would come across as confusing. Especially because the work it is doing is that of the decoder as defined in the GPT paper. Similarly, the tutorial calls the final layer (LayerNorm + Linear) in the architecture `GPTDecoder`. Again, with the respect to the GPT paper this is a confusing name because the work this module is doing is not actually that of the GPT decoder. Including the GPT architecture from the original paper for reference:
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} | # Changelog
- Add optional directory path to reduce checkpoint_paths to checkpoint_names
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"Hmmm, I think the line that was breaking NeMo installation on my MacBook was in `setup.py`:\r\nhttps://github.com/NVIDIA/NeMo/blob/45f60f03e5d15a4b6e7db145b5d731d73f3a891d/setup.py#L235\r\n\r\n@borisfom ideas?",
"> Hmmm, I think the line that was breaking NeMo installation on my MacBook was in `setup.py`:\r\n> h... | 2021-01-15T05:46:11 | 2021-01-16T03:00:11 | 2021-01-15T22:15:23 | COLLABORATOR | {
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} | # Changelog
- onnx-graphsurgery is not available on all platforms, and breaks pip install (currently tested on darwin and some versions of ubuntu).
- Make it an optional import that performs runtime check instead. | {
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- Add support for `trainer.test()` on Transformer based LM | {
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"This is a good find, I keep forgetting TF and PT have different semantics for their RNNs. \r\n\r\nNeMo currently ignores nested structures (h, c) (though we plan to add this), so that will be a todo for a subsequent patch. In the diagram above, \"h\" corresponds to \"y_t\" and \"c\" corresponds to both (h_n, c_n),... | 2021-01-15T00:14:16 | 2021-01-20T04:28:32 | 2021-01-20T04:28:32 | NONE | {
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} | null | null | null | Under the Neural Types section, for Case 2 LSTM [ERROR CELL], the tutorial gives the wrong reason for the `TypeError: Number of output arguments provided (2) is not as expected (1)`. Specifically, it states:
> What exactly is going on here? Well, inside our `LSTMModule` class, we declare the output types to be a single NeuralType - an `EncodedRepresentation` of shape [B, T, C].
> But the output of an LSTM layer is a tuple of two state values - the hidden state `h` and the cell state `c`!
But actually from the nn.LSTM [docs](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html) we see that  it outputs a tuple `output, (h_n, c_n)` whose first element `output` contains the output features from the last layer of the LSTM for all timesteps. With `batch_first=True` this should be of the shape `B x T x C`. The second element of the tuple is again itself a tuple of the final hidden state (`h_n`) and final cell state (`c_n`). So the axes in `CorrectLSTMModule` is also wrong. Particularly, even with `batch_first=True` in the LSTM `h_n` and `c_n` are of shape `(num_layers * num_directions, batch, hidden_size)`. Since `h_n` and `c_n` are the final states there should be no Time (T) or seq_len dim in their shape. Here we are using unidirectional LSTM and representing batch with axis B and hidden_size with axis C so shape becomes `(num_layers, B, C)` not `(B, T, C)` as implemented in `CorrectLSTMModule`
.
Also wondering, is there an AxisKind to represent `num_layers` or `num_directions`? Couldn't find a suitable AxisKind in https://github.com/NVIDIA/NeMo/blob/main/nemo/core/neural_types/axes.py. Maybe "Any" should be used?
To give a more concrete example of the LSTM output. Let `(h_n, c_n)` be the tuple of final hidden and cell state and `y_t` be the outputs for all timestamps then
```
lstm = torch.nn.LSTM(1, 30, batch_first=True)
x3 = torch.rand(2, 5, 1)
# x3 shape is [batch_size x time stamps x dimension]
y_t, (h_n, c_n) = lstm(x3)
```
`y_t` is of shape ([2, 5, 30]) i.e `batch_size x time stamps x dimension`
`h_n` is of shape ([1, 2, 30]) i.e. `num_layers x batch_size x dimension`
`c_n` is of shape ([1, 2, 30]) i.e. `num_layers x batch_size x dimension`
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https://api.github.com/repos/NVIDIA/NeMo/issues/1633 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1633/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1633/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1633/events | https://github.com/NVIDIA/NeMo/issues/1633 | 786,235,226 | MDU6SXNzdWU3ODYyMzUyMjY= | 1,633 | [Question] MatchboxNet and MarbleNet | {
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"> After training the Voice Activity Detection model, How to deploy it for inferencing on arm based devices such as Raspberry Pi?\r\n\r\nWe dont have deployment scripts for Raspberry Pi. \r\n\r\n> How much memory is required to inference MatchboxNet\r\n\r\nDepends on deployment framework, but it should be small con... | 2021-01-14T18:58:28 | 2021-01-21T18:27:21 | 2021-01-21T18:27:21 | NONE | {
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} | null | null | null | Hi there,
@fayejf @titu1994 @borisgin
In the task of Voice Activity Detection, the following questions arise:
- After training the Voice Activity Detection model, How to deploy it for inferencing on arm based devices such as Raspberry Pi?
- How much memory is required to inference MatchboxNet
- How does MatchboxNet perform against MarbleNet | {
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"Hi @skubicius Thanks for your question 😃 . This is the expected plot for this demo wav. There are a few reasons for a not ideal plot for the pre-trained models, such as data and sample rate mismatch, diverse recording (microphone) environment, language, etc. And please note that metrics would be different for dif... | 2021-01-13T22:30:10 | 2021-06-15T21:56:07 | 2021-01-14T21:58:00 | NONE | {
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} | null | null | null | I ran the [VAD demo](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/07_Online_Offline_Microphone_VAD_Demo.ipynb) and noticed that the prediction accuracy on the [example audio](https://dldata-public.s3.us-east-2.amazonaws.com/VAD_demo.wav) seems quite low (see chart below).
Do you see a similar output when you run the notebook?
I changed the script in one place - I commented out these lines from function `offline_inference`, because my host doesn't have an audio device:
```
stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
channels=CHANNELS,
rate=RATE,
output = True)
```
**Environment overview**
- Environment location: AWS EC2
- Method of NeMo install: `!python3 -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]`
**Environment details**
- OS version Ubuntu 20.04.1 LTS (Focal Fossa)
- PyTorch version 1.6.0
- Python version 3.8.5
**Additional context**
GPU model: Tesla T4 | {
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"This pull request **introduces 1 alert** when merging 657e3e50296a8181164b830a812b40858d5917a6 into bc5f03459c7273b71c50af7c2378d5e950b408c4 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-a3b2ef6ca144959b67e47fc21de10d8f7c6251b2)\n\n**new alerts:**\n\n* 1 for 'import \\*' may pollute n... | 2021-01-13T08:51:53 | 2021-02-03T22:50:24 | 2021-02-03T22:50:24 | CONTRIBUTOR | {
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} | Extend the current speech classification models (e.g. Speaker Recognition) to accommodate the prediction of a numeric target (regression).
This can be applied to use cases like [Speech Quality Assessment ](https://arxiv.org/pdf/1903.06908.pdf) | {
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} | # Changelog
- Add some forward pass tests for ASR to check effect of batch size on evaluation mode
# Bugfix
- Add brace expansion for transformer tarred datasets | {
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} | fix sgdqa imports with release installation
change nemo_toolkit installation to release from development | {
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"Overall, the PR looks good to merge. Please fix the tests and final review from @MaximumEntropy please. "
] | 2021-01-12T19:13:48 | 2021-01-22T15:27:46 | 2021-01-20T00:00:45 | CONTRIBUTOR | {
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} | Add a PTL metric for averaging loss across processes in ddp mode.
Motivation.
If ddp is used, the validation loss for only one gpu is logged. This behavior impairs "Reduce on plateau" and "Early stopping" scheduling strategies. If many GPUs are used and the validation dataset is small, logged validation loss can differ from real validation loss a lot. | {
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} | Signed-off-by: Vlad Getselevich <vgetselevich@nvidia.com> | {
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"It is implemented for a WIP project now, It could be done using collate func to slice the batches and combine back them by backtracking the values. \r\n\r\nFor reference see here:\r\nhttps://github.com/NVIDIA/NeMo/blob/df360cd3c856c645d23505305ee8b689636b2dd1/nemo/collections/asr/data/audio_to_label.py#L161\r\n\r\... | 2021-01-12T17:46:49 | 2021-02-10T23:26:24 | 2021-02-10T23:26:24 | NONE | {
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} | null | null | null | **How to extract embeddings from a specific portion of audio?**
I have audio files which contain speech from two different individuals, I want to use the pre-trained SpeakerNet_verification model to extract speaker embeddings of each audio file in a sliding window fashion (an embedding for every 1 second with 0.1 seconds overlap). Since the manifest file only accepts the absolute path to the audio is there a way to specify which chunk of audio the model should embed?
What would be the easiest way to implement this? Alternatively, if I wish to add such a functionality myself, where can I start?
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https://api.github.com/repos/NVIDIA/NeMo/issues/1623 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1623/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1623/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1623/events | https://github.com/NVIDIA/NeMo/issues/1623 | 783,986,195 | MDU6SXNzdWU3ODM5ODYxOTU= | 1,623 | error while importing nlp from nemo | {
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"@rahul-art should be fixed now"
] | 2021-01-12T07:23:58 | 2021-05-28T08:35:17 | 2021-05-28T08:35:17 | NONE | {
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} | null | null | null | nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /root/nltk_data...
[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.
[nltk_data] Downloading package cmudict to /root/nltk_data...
[nltk_data] Unzipping corpora/cmudict.zip.
[NeMo W 2021-01-12 07:16:18 experimental:28] Module nemo.collections.asr.data.audio_to_text.AudioToCharDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:18 experimental:28] Module nemo.collections.asr.data.audio_to_text.AudioToBPEDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:18 experimental:28] Module nemo.collections.asr.data.audio_to_text.AudioLabelDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:18 experimental:28] Module nemo.collections.asr.data.audio_to_text.TarredAudioToTextDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:18 experimental:28] Module nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:18 experimental:28] Module nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:23 experimental:28] Module <class 'nemo.collections.asr.losses.ctc.CTCLoss'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-01-12 07:16:23 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
[NeMo W 2021-01-12 07:16:23 nemo_logging:349] /usr/local/lib/python3.6/dist-packages/torchaudio/backend/utils.py:54: UserWarning: "sox" backend is being deprecated. The default backend will be changed to "sox_io" backend in 0.8.0 and "sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.
'"sox" backend is being deprecated. '
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-3-424875f6207b> in <module>()
1 import nemo.collections.asr as nemo_asr
----> 2 import nemo.collections.nlp as nemo_nlp
3 import nemo.collections.tts as nemo_tts
3 frames
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/data/question_answering_squad/qa_squad_processing.py in <module>()
20
21 from tqdm import tqdm
---> 22 from transformers.tokenization_bert import BasicTokenizer
23
24 from nemo.collections.nlp.data.data_utils import DataProcessor, is_whitespace, normalize_answer
ModuleNotFoundError: No module named 'transformers.tokenization_bert'
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https://api.github.com/repos/NVIDIA/NeMo/issues/1622 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1622/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1622/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1622/events | https://github.com/NVIDIA/NeMo/pull/1622 | 783,740,487 | MDExOlB1bGxSZXF1ZXN0NTUzMDQ5Mzc4 | 1,622 | Add nemo checkpoint averaging script | {
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} | # Changelog
- Implements checkpoint averaging for all NeMo models
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"This pull request **introduces 1 alert** when merging 73e9a8a210002ac77a8166a176651abbbea1a390 into d8150b41bbba0c2ee2e291b309651cef2ed248bf - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-81dddf30b77cce1659e91a2d505dce40fca10a67)\n\n**new alerts:**\n\n* 1 for Unused import"
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https://api.github.com/repos/NVIDIA/NeMo/issues/1620 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1620/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1620/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1620/events | https://github.com/NVIDIA/NeMo/pull/1620 | 783,694,534 | MDExOlB1bGxSZXF1ZXN0NTUzMDExNDMx | 1,620 | Correct onesided for PT < 1.7 compatibility | {
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- PT 1.6 expects explicit bool type for `onesided` (defaults to true)
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"This pull request **introduces 5 alerts** when merging f2b24d2a402f18b2856dc4da6bb769c04ff5ab9d into c6b90b3dd1321b3e95df8f91b89f55326d52a81c - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-b24b016b8363544667997fab2a9e26552d659897)\n\n**new alerts:**\n\n* 5 for Unused import",
"This pull requ... | 2021-01-11T05:43:51 | 2021-01-30T01:43:53 | 2021-01-30T01:43:32 | COLLABORATOR | {
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} | Cleanup and continued work on previous PR #1578
- [x] Refactor classification models and label models in audio_to_label
- [x] Add support for tarred dataset for audio_to_label
- [x] Update classification and label models
- [x] Update VAD inference (should belong to Speaker Diarization pipelines PR but need to be used here)
- [x] Update training script, config, and instructions to use tarred dataset and improve consistency (classification and speaker model)
- [x] Update converting_to_tarred_dataset script to support label models.
- [x] Upload test data
- [x] Add test scripts
- [x] Ok a new one. enable using the duplicate name for tarred dataloader. (webdataset doesn't allow it)
Will try some hacky ways and discuss with people about a cleaner / long-term way after break.
- [x] Add instruction/warning that audio_filepath **should not contain "-sub"** cause we will need it to handle duplicate filename (different offset) samples.
Signed-off-by: fayejf <fayejf07@gmail.com> | {
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"This MR, make training so much slower. Pydub takes too much of CPU to convert media files. Need to make sure pydub is only used for unsupported media formats, right now we using FLAC, and it goes through pydub, but libsnd supports flac.\r\n\r\n![image](https://user-images.githubusercontent.com/3962002/104477831-9f... | 2021-01-09T11:44:57 | 2021-01-28T15:56:42 | 2021-01-11T23:12:21 | CONTRIBUTOR | {
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} | Might solve #1549
Currently I let pydub handle file that is not .wav format because there is something I'm not understand with current implementation.
1. "samples.transpose()" before init with cls
2. User also need to install [ffmpeg](https://github.com/jiaaro/pydub#getting-ffmpeg-set-up) to be able to load non wav file format, I don't know where to put the warning or write a script when user pip install
3. I'm not sure if NeMo dataloader will cache the loaded samples because it might convert using ffmpeg everytime it try to load non wav file.
So far I only tested inference with mp3 audio from Common Voice with model trained on wav file format, there is no problem with the prediction result. | {
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"Thanks! Could you run the style fix please?",
"Thanks for the PR fixes!"
] | 2021-01-09T04:48:22 | 2021-01-11T18:14:18 | 2021-01-11T18:03:35 | CONTRIBUTOR | {
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} | fix for strategy:
greedy and greedy_batch
beam of beam_size 1 | {
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"I updated pytorch lightning and that seems to have fixed it. Must have been a specific combination of PTL + NeMo that didn't work. Closing,"
] | 2021-01-08T20:51:32 | 2021-01-11T14:48:50 | 2021-01-11T14:48:49 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
With multiple optimizers in the `training_step`, only the final optimizer's logs get printed/logged on the progress bar and in tfevents file. No logged values from the previous optimizers appear on the progress bar or in the tfevents.
**Steps/Code to reproduce bug**
Running examples/tts/melgan.py will show the issue. Only `loss_generator` is logged, despite in the first optimizer's code `self.log("loss_discriminator", sum_loss_dis, prog_bar=True, sync_dist=True)`
**Expected behavior**
All values logged in the earlier optimizers should persist and be logged.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: source, current head (1.0.0b4)
**Environment details**
- PyTorch version 1.7
- Python version 3.7
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"Hi @TobiasPuetzKenbun ,\r\n\r\nDid you check out [this script ](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_checkpoint_port.py)to convert your 0.11 checkpoints to 1.0? \r\n\r\nAnd to your second question yes we did that on purpose, initially when we started out speaker recognition it was first trained end... | 2021-01-08T15:06:13 | 2021-01-25T07:28:46 | 2021-01-25T07:28:46 | NONE | {
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} | null | null | null | Hi, thanks for the great library!
I am currently porting a 0.11 speaker recognition checkpoint to 1.0.0b, I ran into some difficulties reproducing the outputs of the 0.11 checkpoint in 1.0.0b. In the 0.11 model, the decoder is `JasperDecoderForSpkrClass`, to my understanding, the corresponding class in 1.0.0b is `SpeakerDecoder`.
In [JasperDecoderForSpkrClass.forward](https://github.com/NVIDIA/NeMo/blob/ac09a4cc2ae3fdfedaf6f6832bd2dfca94de19ec/nemo/collections/asr/jasper.py#L459), the returned embedding is the first transformation of the pooled encoder output, in the current release, [SpeakerDecoder.forward](https://github.com/NVIDIA/NeMo/blob/ede204df0d5adc54714981511b83cf6d2fa905b2/nemo/collections/asr/modules/conv_asr.py#L421) returns the last transformation of the pool. Is there a reason for this change?
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"This pull request **introduces 9 alerts** when merging 5a92be0f55a1185bf37c846b9367ab1f897fda2c into 7cb30843427fdf77a55a0cdd67cbbde3c067c850 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-07bdc86a1172f21038bff622c9c4381f3d7e1e83)\n\n**new alerts:**\n\n* 9 for Unused import",
"This pull requ... | 2021-01-07T22:26:50 | 2021-01-12T02:44:48 | 2021-01-12T01:56:07 | COLLABORATOR | {
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} | This PR create encoder and decoder neural modules for `TransformerEmbedding` and `TransfomerEncoder` / `TransformerDecoder`.
This is making it so that the neural types will be the same across NeMo Transformer, HF BERT, and Megatron BERT encoders.
The idea is to have `EncoderModule` which accepts `input_ids`, ` encoder_mask`, and `num_tokentypes` and outputs `last_hidden_states`.
Then `DecoderModule` will accept `input_ids`, `decoder_mask`, `encoder_mask` and `encoder_states` and outputs `last_hidden_states`.
This PR also cleans up the relevant configs as the configuration needed to be updated to accept the new model structure. | {
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} | # Bugfix
- Resolution for dataclass somehow interferes with key interpolation
- Patch via check for type explicitly
Resolves https://github.com/NVIDIA/NeMo/issues/1606
@ekmb for review | {
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"can you please switch to the NeMo version 1.0.0b3 or above ?",
"Looks like this is still an issue with 1.0.0b3, but the fix @ https://github.com/NVIDIA/NeMo/issues/1581#issuecomment-748399696\r\nworked for me."
] | 2021-01-07T15:10:43 | 2021-05-28T08:42:03 | 2021-05-28T08:42:03 | NONE | {
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} | null | null | null | **Describe the bug**
Using environment - Docker image nvcr.io/nvidia/nemo:v0.10 with additionally installed CTC DECODERS (https://github.com/NVIDIA/OpenSeq2Seq).
By this tutorial:
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v0.10.1/asr/tutorial.html
running jasper_eval.py leads to the error:
File "/workspace/nemo/examples/asr/jasper_eval.py", line 158, in main
logprob.append(batch[j][: evaluated_tensors[4][i][j], :].cpu().numpy())
TypeError: only integer tensors of a single element can be converted to an index
What am i doing wrong?
Is there a Docker image with fully preinstalled libs (CTC DECODERS, etc.) to run the examples?
Full Traceback:
root@a44a85ffe228:/workspace# python /workspace/nemo/examples/asr/jasper_eval.py --model_config=/workspace/model/quartznet15x5_ru.yaml --eval_datasets=/workspace/dataset/test_quartznetru.json --load_dir=/workspace/model --lm_path=/workspace/model/lm.binary
/opt/conda/lib/python3.6/site-packages/torchaudio/backend/utils.py:54: UserWarning: "sox" backend is being deprecated. The default backend will be changed to "sox_io" backend in 0.8.0 and "sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.
'"sox" backend is being deprecated. '
[NeMo I 2021-01-07 14:40:21 collections:138] Dataset loaded with 15 files totalling 0.13 hours
[NeMo I 2021-01-07 14:40:21 collections:139] 0 files were filtered totalling 0.00 hours
[NeMo I 2021-01-07 14:40:21 jasper_eval:117] Evaluating 15 examples
[NeMo I 2021-01-07 14:40:21 features:144] PADDING: 16
[NeMo I 2021-01-07 14:40:21 features:152] STFT using conv
[NeMo I 2021-01-07 14:40:25 jasper_eval:130] ================================
[NeMo I 2021-01-07 14:40:25 jasper_eval:131] Number of parameters in encoder: 18894656
[NeMo I 2021-01-07 14:40:25 jasper_eval:132] Number of parameters in decoder: 36900
[NeMo I 2021-01-07 14:40:25 jasper_eval:133] Total number of parameters in model: 18931556
[NeMo I 2021-01-07 14:40:25 jasper_eval:134] ================================
[NeMo I 2021-01-07 14:40:25 actions:1454] Restoring JasperEncoder from /workspace/model/JasperEncoder-STEP-10150.pt
[NeMo I 2021-01-07 14:40:25 actions:1454] Restoring JasperDecoderForCTC from /workspace/model/JasperDecoderForCTC-STEP-10150.pt
Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods.
Defaults for this optimization level are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ImportError('/opt/conda/lib/python3.6/site-packages/amp_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN3c10lsERSoNS_12TensorTypeIdE',)
[NeMo I 2021-01-07 14:40:25 actions:728] Evaluating batch 0 out of 1
[NeMo I 2021-01-07 14:40:25 jasper_eval:152] Greedy WER inf%
Traceback (most recent call last):
File "/workspace/nemo/examples/asr/jasper_eval.py", line 207, in <module>
main()
File "/workspace/nemo/examples/asr/jasper_eval.py", line 158, in main
logprob.append(batch[j][: evaluated_tensors[4][i][j], :].cpu().numpy())
TypeError: only integer tensors of a single element can be converted to an index
**Steps/Code to reproduce bug**
Ubuntu 20.04
Install Docker
Pull nvcr.io/nvidia/nemo:v0.10
Install SWIG
apt-get update
apt-get install swig -y
Install torchaudio
pip install torchaudio
Install CTC DECODERS
git clone https://github.com/NVIDIA/OpenSeq2Seq -b ctc-decoders
cd /workspace/OpenSeq2Seq/decoders
./setup.sh
PREPARE DATASET
copy data and model inside container
**Environment overview (please complete the following information)**
nvcr.io/nvidia/nemo:v0.10
**Additional context**
NVIDIA GTX 980Ti
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"@borisfom please fix formatting for CI to start:\r\n```\r\npython setup.py style --fix\r\n```",
"This pull request **introduces 1 alert** when merging f261f1a3a5ceaa203dfe605be371dcdc20e4d95f into 4f46bd84d162da80a310c69f72980de935c1e99a - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-abaf66c... | 2021-01-07T01:59:35 | 2021-01-15T01:52:23 | 2021-01-15T01:08:06 | COLLABORATOR | {
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"You can try this script https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_checkpoint_port.py ",
"When i run the command:\r\npython ./asr_checkpoint_port.py --config_path='/workspace/nemo/quartznet_ru/quartznet15x5_ru.yaml' --encoder_ckpt='/workspace/nemo/quartznet_ru/JasperEncoder-STEP-520000.pt' --decoder_ck... | 2021-01-06T21:22:43 | 2021-05-28T08:42:16 | 2021-05-28T08:42:16 | NONE | {
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} | null | null | null | Greetings!
I've got 4 files of a Quartznet 15x5 model:
JasperDecoderForCTC-STEP-520000.pt
JasperEncoder-STEP-520000.pt
lm.binary
quartznet15x5_ru.yaml
The model was trained using NeMo 0.10.1.
I want to fine tune this model using NeMo 1.0.0b3.
Is there any script to convert a model from old versions to the new format?
I've compared .yaml files format of 0.10.1 and 1.0.0b3 and it seemed to be completely different.
**Environment overview (please complete the following information)**
Docker container nvcr.io/nvidia/nemo:1.0.0b3
**Additional context**
Add any other context about the problem here.
GPU: Nvidia GTX 980ti
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} | null | null | null | I'm trying to reproduce [Named Entity Recognition - BioMegatron colab](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/nlp/Token_Classification-BioMegatron.ipynb), everything is fine until the following cell:
`model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)`
Output:
```
[NeMo I 2021-01-06 10:36:00 megatron_utils:235] Downloading from https://api.ngc.nvidia.com/v2/models/nvidia/biomegatron345mcased/versions/0/files/vocab.txt
Downloading: 100%
625/625 [00:00<00:00, 1.31kB/s]
Downloading: 100%
213k/213k [00:00<00:00, 3.31MB/s]
Using bos_token, but it is not set yet.
Using eos_token, but it is not set yet.
---------------------------------------------------------------------------
ConfigKeyError Traceback (most recent call last)
<ipython-input-25-4645443f78bc> in <module>()
----> 1 model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
8 frames
/usr/local/lib/python3.6/dist-packages/omegaconf/base.py in _resolve_simple_interpolation(self, key, inter_type, inter_key, throw_on_missing, throw_on_resolution_failure)
326 if throw_on_resolution_failure:
327 raise ConfigKeyError(
--> 328 f"{inter_type} interpolation key '{inter_key}' not found"
329 )
330 else:
ConfigKeyError: str interpolation key 'model.language_model.pretrained_model_name' not found
```
I believe that `config.model.language_model.pretrained_model_name = 'biomegatron-bert-345m-cased'`. In addition, I'm getting an error in the installation phase though I'm not sure it would be related to this issue:
```
# install NeMo
BRANCH = 'main'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]
```
Output:
```
Collecting nemo_toolkit[nlp]
Cloning https://github.com/NVIDIA/NeMo.git (to revision main) to /tmp/pip-install-hz__8v6m/nemo-toolkit
Running command git clone -q https://github.com/NVIDIA/NeMo.git /tmp/pip-install-hz__8v6m/nemo-toolkit
Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.6/dist-packages (from nemo_toolkit[nlp]) (1.19.4)
...
Successfully built nemo-toolkit wget future PyYAML antlr4-python3-runtime sacremoses idna-ssl
ERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.
ERROR: botocore 1.19.49 has requirement urllib3<1.27,>=1.25.4; python_version != "3.4", but you'll have urllib3 1.24.3 which is incompatible.
Installing collected packages: onnx, multidict, yarl, async-timeout, idna-ssl, aiohttp, fsspec, future, PyYAML, pytorch-lightning, wget, ruamel.yaml.clib, ruamel.yaml, omegaconf, antlr4-python3-runtime, hydra-core, sacremoses, tokenizers, transformers, sentencepiece, typer, braceexpand, simplejson, objectio, webdataset, jmespath, botocore, s3transfer, boto3, matplotlib, unidecode, youtokentome, rapidfuzz, pybind11, megatron-lm, nemo-toolkit
Found existing installation: future 0.16.0
Uninstalling future-0.16.0:
Successfully uninstalled future-0.16.0
Found existing installation: PyYAML 3.13
Uninstalling PyYAML-3.13:
Successfully uninstalled PyYAML-3.13
Found existing installation: matplotlib 3.2.2
Uninstalling matplotlib-3.2.2:
Successfully uninstalled matplotlib-3.2.2
Successfully installed PyYAML-5.3.1 aiohttp-3.7.3 antlr4-python3-runtime-4.8 async-timeout-3.0.1 boto3-1.16.49 botocore-1.19.49 braceexpand-0.1.6 fsspec-0.8.5 future-0.18.2 hydra-core-1.0.4 idna-ssl-1.1.0 jmespath-0.10.0 matplotlib-3.3.3 megatron-lm-1.1.5 multidict-5.1.0 nemo-toolkit-1.0.0b4 objectio-0.2.29 omegaconf-2.0.5 onnx-1.8.0 pybind11-2.6.1 pytorch-lightning-1.1.3 rapidfuzz-0.14.2 ruamel.yaml-0.16.12 ruamel.yaml.clib-0.2.2 s3transfer-0.3.3 sacremoses-0.0.43 sentencepiece-0.1.94 simplejson-3.17.2 tokenizers-0.9.4 transformers-4.1.1 typer-0.3.2 unidecode-1.1.2 webdataset-0.1.40 wget-3.2 yarl-1.6.3 youtokentome-1.0.6
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/1605 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1605/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1605/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1605/events | https://github.com/NVIDIA/NeMo/pull/1605 | 779,874,869 | MDExOlB1bGxSZXF1ZXN0NTQ5ODEwNzI3 | 1,605 | Add timestep and length in Hypothesis | {
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"Thank you for the awesome PR! Some minor fixes required here and there but overall this PR is good to go.\r\n\r\nPlease run the style fix script by using `python setup.py style --scope=nemo/ --fix` at the root of the nemo repository",
"Thanks for the review, I learned a lot\r\nI have made the changes",
"Also, ... | 2021-01-06T02:06:16 | 2021-01-09T04:44:59 | 2021-01-08T19:10:40 | CONTRIBUTOR | {
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"merged_at": "2021-01-08T19:10:40"
} | Add timestep and length for calculating timestamp
Example code for calculating timestamp:
```
@torch.no_grad()
def predict(filename):
if type(filename) != list:
filename = [filename]
res = {}
hypotheses,_ = asr_model.transcribe([str(i) for i in filename],len(filename),return_hypotheses=True)
timesteps = []
# start timesteps
for idx,h in enumerate(hypotheses):
timestep = h.timestep
tokens = []
time = []
# this implementation is for bpe tokenizer since it always better than character based model. Look at Idea 3 https://thegradient.pub/towards-an-imagenet-moment-for-speech-to-text/
for t,token in zip(timestep,h.tokens): # for each timestep and token
if "[" in token: # token such as [CLS] [SEP] etc
pass
elif "##" in token: # token that is continuation of previous token
tokens[-1] += token.replace("##","")
time[-1] = (time[-1] + t) // 2
else:
tokens.append(token)
time.append(int(t))
duration = librosa.core.get_duration(filename=filename[idx],sr=None) # get duration in second
mult = duration/float(h.length) # get duration for each timestep
ttt = [{"time":round(t[1]*mult,2), "word":t[0]} for t in zip(tokens,time)]
timesteps.append(ttt)
return timesteps
out = predict("filename.wav")
print(json.dumps(out,indent=4))
```
Example output
```
[
[
{
"time": 0.0,
"word": "pada"
},
{
"time": 0.16,
"word": "peringkat"
},
{
"time": 0.44,
"word": "ini"
},
{
"time": 1.52,
"word": "pembukaan"
},
{
"time": 2.08,
"word": "sekolah"
},
{
"time": 2.44,
"word": "hanya"
},
{
"time": 4.0,
"word": "melibatkan"
}
]
]
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https://api.github.com/repos/NVIDIA/NeMo/issues/1604 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1604/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1604/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1604/events | https://github.com/NVIDIA/NeMo/issues/1604 | 779,839,034 | MDU6SXNzdWU3Nzk4MzkwMzQ= | 1,604 | For Megatron models, local vocab files are not supported. | {
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"I was able to run \r\n```\r\nvocab = get_megatron_vocab_file('biomegatron-bert-345m-uncased')\r\n```\r\nwithout any issues.\r\n\r\nI was wondering if you could help clarify what you're trying to do.\r\n\r\nAre you looking to use 'biomegatron-bert-345m-uncased', or are you pretraining a megatron language model on y... | 2021-01-06T01:25:45 | 2021-02-25T23:52:59 | 2021-02-25T23:52:21 | NONE | {
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} | null | null | null | https://github.com/NVIDIA/NeMo/blob/51092ef7a9810ab096148cdf2d44ddfe6c72d028/nemo/collections/nlp/modules/common/megatron/megatron_utils.py#L135
It seems from the above code snippet that for Megatron models, it always tries to download the vocab files from the cloud. However, currently for bio-Megatron models the [link](https://api.ngc.nvidia.com/v2/models/nvidia/biomegatron345mcased/versions/0/files/vocab.txt) doesn't work. This has been already raised as a separate [issue](https://github.com/NVIDIA/NeMo/issues/1582).
It should accept the vocab file passed to `config.model.tokenizer.vocab_file` and should only check for vocab file online, if the user doesn't provide a vocab file.
ERROR while running Token Classification - BioMegatron notebook
<img width="1080" alt="Screen Shot 2021-01-05 at 8 24 51 PM" src="https://user-images.githubusercontent.com/7240751/103717828-14580900-4f94-11eb-925b-25ce3650b002.png">
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https://api.github.com/repos/NVIDIA/NeMo/issues/1603 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1603/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1603/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1603/events | https://github.com/NVIDIA/NeMo/pull/1603 | 779,822,118 | MDExOlB1bGxSZXF1ZXN0NTQ5NzYxOTcw | 1,603 | 1579 changes plus fix attempt | {
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"DO NOT MERGE.\r\nFor reference only.",
"Should we close this PR?",
"Yes it can be closed now that we are on 21.02"
] | 2021-01-06T01:07:11 | 2021-03-09T19:53:52 | 2021-03-09T19:53:52 | CONTRIBUTOR | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1602 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1602/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1602/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1602/events | https://github.com/NVIDIA/NeMo/issues/1602 | 778,969,375 | MDU6SXNzdWU3Nzg5NjkzNzU= | 1,602 | docker container cannot launch tensorboard | {
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"You managed to solve this?\r\nI'm having the same issue",
"not yet",
"Hi, i think i solved the problem. \r\nAfter you pull the container and run it, send a pip list command and search for something called tensorboard-plugine-dlproof.\r\nUninstall it.\r\nThen:\r\n\r\npip uninstall -y tensorboard\r\npip install ... | 2021-01-05T12:38:00 | 2021-01-18T06:06:19 | 2021-01-18T06:06:19 | NONE | {
"total": 0,
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} | null | null | null | Hi all,
I was running the docker container using this command
```shell
docker run --gpus all -it --rm -v /mnt/NeMo:/NeMo --shm-size=8g \
-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:1.0.0b3
```
and the NeMo repository was on branch 1.0.0b3.
In this container, I have successfully trained my model but when I was trying to monitor my training using tensorboard using the following command,
```shell
tensorboard --bind_all --logdir .
```
the error appeared.
```
TensorFlow installation not found - running with reduced feature set.
Traceback (most recent call last):
File "/opt/conda/bin/tensorboard", line 8, in <module>
sys.exit(run_main())
File "/opt/conda/lib/python3.6/site-packages/tensorboard/main.py", line 65, in run_main
default.get_plugins(),
File "/opt/conda/lib/python3.6/site-packages/tensorboard/default.py", line 108, in get_plugins
return get_static_plugins() + get_dynamic_plugins()
File "/opt/conda/lib/python3.6/site-packages/tensorboard/default.py", line 146, in get_dynamic_plugins
"tensorboard_plugins"
File "/opt/conda/lib/python3.6/site-packages/tensorboard/default.py", line 145, in <listcomp>
for entry_point in pkg_resources.iter_entry_points(
File "/opt/conda/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2472, in load
return self.resolve()
File "/opt/conda/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2478, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
File "/opt/conda/lib/python3.6/site-packages/tensorboard_plugin_dlprof/plugin.py", line 31, in <module>
from tensorboard.plugins.graph import dlprof_pb2
ImportError: cannot import name 'dlprof_pb2'
```
Any help is appreciated. Thanks. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1601 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1601/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1601/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1601/events | https://github.com/NVIDIA/NeMo/pull/1601 | 778,590,177 | MDExOlB1bGxSZXF1ZXN0NTQ4NjQ5NjI1 | 1,601 | Add entire dataset caching via a preproc script, bpe dropout and deto… | {
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"This pull request **introduces 2 alerts** when merging 9ccc0dbd61d9c6b0e034e406f90b3a5795fac574 into 8ed6b966a843286c715484b0fadc266253f2f272 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-162bbcfbe9077b4f7d1d3dca2ab7d0f7aec26a49)\n\n**new alerts:**\n\n* 2 for Unused import",
"This pull requ... | 2021-01-05T05:11:22 | 2021-01-06T01:53:54 | 2021-01-06T01:53:54 | CONTRIBUTOR | {
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} | Adding the following:
1. A way to cache the entire translation dataset object rather than just the IDs, to avoid recomputing the bucketing and padding steps.
2. BPE dropout to YTTM.
3. Sacremoses for detokenization. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1599 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1599/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1599/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1599/events | https://github.com/NVIDIA/NeMo/pull/1599 | 778,378,929 | MDExOlB1bGxSZXF1ZXN0NTQ4NDc2MjU5 | 1,599 | Patch ASR to support < PT 1.7 | {
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"Can we add the following?\r\n1) A reminder to remove this patch once we require pytorch >= 1.7\r\n2) A unittest that fails or gives a warning if a PR contains \"torch.stft\" while we still allow pytorch < 1.7"
] | 2021-01-04T21:28:00 | 2021-01-05T00:47:58 | 2021-01-05T00:47:55 | COLLABORATOR | {
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} | # Changelog
- Wrap torch.stft to switch signature between PT< 1.7 and >= 1.7 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1598 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1598/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1598/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1598/events | https://github.com/NVIDIA/NeMo/issues/1598 | 778,139,516 | MDU6SXNzdWU3NzgxMzk1MTY= | 1,598 | [Question] NeMo public roadmap | {
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"Hi @adiIspas,\r\n\r\nThis is a good question. First, let me mention that:\r\n* NeMo is being developed in the open. E.g. what you see in the \"main\" branch on GH is what our team members use in their daily work. In this sense, things which are now WIP in the main branch will be appearing as features in our next r... | 2021-01-04T14:37:28 | 2021-01-29T20:19:05 | 2021-01-29T20:19:05 | NONE | {
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} | null | null | null | Hello, awesome people!
First of all, thank you very much for the job that you do with this toolkit.
Secondly, do you have a public roadmap for NeMo toolkit? I think will be useful for everyone that uses NeMo to know when new version/features will come.
In my case, I'm interested to know when will be available support for words timestamps and confidence with language model.
Regards,
Adrian | {
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} | null | null | null | I use 4 GPU setup and trying to make DALI work as a backend for DataLoader (making slight corrections in existing NeMo code to at least make it without errors till pipeline compilation) but training still fails (fails PTL sanity check)
```
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
self.padding, self.dilation, self.groups)
**RuntimeError: Expected tensor for argument #1 'input' to have the same device as tensor for argument #2 'weight'; but device 0 does not equal 2 (while checking arguments for cudnn_convolution)**
result = self.forward(*input, **kwargs)
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 257, in forward
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 257, in forward
self.padding, self.dilation, self.groups)
self.padding, self.dilation, self.groups)
```
**Expected tensor for argument #1 'input' to have the same device as tensor for argument #2 'weight'; but device 0 does not equal 2 ** is the main part here and it repeats for devices {0,1,3}.
Seems like some of the operations in pipeline are done on cpu, and the others on different gpu or something in the like. Is there a way, that I can work through this problem?
**Environment details**
Environment location: DGX-1
Torch '1.6.0'
Python 3.76
Ubuntu 18.04.4 LTS"
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} | SGD QA model
* added ci test
* added documentation | {
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"Currently, quite a few classes in ASR collection cannot be pickled easily. DDP is suggested for any distributed training. "
] | 2020-12-29T15:11:02 | 2021-01-29T20:18:23 | 2021-01-29T20:18:23 | NONE | {
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} | null | null | null | **Describe the bug**
EncDecCTCModel can't be pickled due to `training_step` wrappted with [wrapt](https://github.com/GrahamDumpleton/wrapt)
**Steps/Code to reproduce bug**
```
model_names = ["Jasper10x5Dr-En", "QuartzNet15x5NR-En"]
models = [EncDecCTCModel.from_pretrained(model_name=model_name) for model_name in model_names]
```
then try to pickle `models`.
wrapt's FunctionWrapper throws `NotImplementedError: object proxy must define __reduce_ex__()`
see:
https://github.com/GrahamDumpleton/wrapt/blob/develop/src/wrapt/_wrappers.c#L1241
https://github.com/GrahamDumpleton/wrapt/issues/102#issuecomment-456528633
**Expected behavior**
EncDecCTCModel should be pickled and unpickled.
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"This pull request **introduces 3 alerts** when merging 3dd3fe5c85e29181cf3e7d49bc36e456105d42c1 into 297a8bdbb0370a51cb74a41288025befd9cd2392 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-98ed3610935c7e6e69dbd68cfdbb8d9904f289b7)\n\n**new alerts:**\n\n* 2 for Unused import\n* 1 for Except blo... | 2020-12-28T20:10:21 | 2021-01-29T23:31:16 | 2021-01-29T23:31:04 | COLLABORATOR | {
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} | Adds training auto-resume for NLP models that use model parallel megatron-lm. The existing autoresume logic depends on there being only one checkpoint, this is not the case for model parallel models. There is a checkpoint for each model parallel rank.
We were able to add this feature with minimal changes to exp_manager. The only functional change is to log a warning rather than throw an error when exp_manager detects multiple `last` checkpoints. We also use the exp_manager to set some properties in `AppState` that we need in `NLPModel`.
# Usage
```
python examples/nlp/text_classification/text_classification_with_bert.py \
--config-path=${CONFIG_PATH} \
--config-name=${CONFIG_NAME} \
...
exp_manager.exp_dir=${EXP_DIR}/nemo_experiments \
exp_manager.create_checkpoint_callback=true \
+exp_manager.create_wandb_logger=true \
+exp_manager.wandb_logger_kwargs.name=${EXP_NAME} \
+exp_manager.wandb_logger_kwargs.project=${PROJECT_NAME} \
exp_manager.name=${EXP_NAME} \
+exp_manager.resume_if_exists=true \
+exp_manager.resume_ignore_no_checkpoint=true \
+exp_manager.version=0 \
+exp_manager.use_datetime_version=false \
```
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"Good catch. I was trying our a few things with decoupled lstms and forgot to update signature. "
] | 2020-12-26T20:58:52 | 2021-05-28T08:35:30 | 2021-05-28T08:35:30 | CONTRIBUTOR | {
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@titu1994, why does `h` have this type? I suppose it should be `Optional[[Tuple[torch.Tensor, torch.Tensor]]`, just like returnable `h` | {
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"https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/features.py#L269",
"if you change to torch>=1.7.0 this should no longer be an issue",
"yes",
"I had the issue when using Anaconda Python, but error after switching over to Linux installed python."
] | 2020-12-25T05:31:12 | 2024-08-28T12:24:19 | 2021-01-11T19:01:13 | NONE | {
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} | null | null | null | Seems like a problem in a feature extractror module. Training doesn't start, until I comment out line containing that keyword
**Steps/Code to reproduce bug**
start the basic ASR training
- Environment location: DGX-1
- Method of NeMo install: pip install -e
Ubuntu
Torch '1.6.0'
Python 3.76
Ubuntu 18.04.4 LTS"
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"This is discussed in #1486, there is no built in way to do it but instead you need to write your own code to convert the mic input into a tensor that you pass directly to the model (numpy.frombuffer might be helpful). You then need to pass that tensor as input to your saved nemo model (you wont be able to use the ... | 2020-12-24T11:05:07 | 2022-11-13T20:25:37 | 2021-01-04T09:42:13 | NONE | {
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} | null | null | null | **Describe your question**
I want to transcribe audio input from a microphone without saving it as an audio file
**Environment overview (please complete the following information)**
- Environment location: Local
- Method of NeMo install: pip install nemo_toolkit[all]==1.0.0b2
**Environment details**
OS Version: Ubuntu 18.04
Python version: 3.7
Pytorch Version: 1.7.1
If NVIDIA docker image is used you don't need to specify these.
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"It's somewhat misleadingly named.\r\n```\r\nstft_conv (bool): If True, uses pytorch_stft and convolutions. If False, uses torch.stft. Defaults to False.\r\n```"
] | 2020-12-24T10:48:19 | 2021-01-04T19:50:59 | 2021-01-04T19:50:59 | NONE | {
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} | null | null | null | Hello,
I have a question about one parameter used in the config file of quartznet model. Can someone tell me what is the purpose of stft_conv parameter? in the config file it's set to False.
With kind regards. | {
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"what is the manifest file you instantiated with? "
] | 2020-12-23T19:04:35 | 2021-08-17T01:00:39 | 2021-08-17T01:00:39 | NONE | {
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} | null | null | null | Hi all,
I wish to instantiate a brand new ASR Quartznet 15x5 from a .yaml file and train it from scratch.
Actually, what i'm tryin to do is what is shown in 01_ASR_with_Nemo tutorial:
`first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']), trainer=trainer)`
Even just running the tutorial cell by cell, the following ValueError is returned:
`num_samples should be a positive integer value, but got num_samples=0`
What's wrong? I appreciate your help. Sorry if the question may sound silly.
Thanks
- Environment location: Google Colab
- Method of NeMo install: pip install
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"A maximum of 16-20 seconds is recommended for ASR training. Anything more than that will reduce batch size and reduce training efficiency too much"
] | 2020-12-22T08:51:28 | 2021-06-19T04:42:31 | 2021-06-19T04:42:31 | NONE | {
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} | null | null | null | I'm working on finetuning the ASR to adapt to a specific domain. I am using the CTC segmentation tool ([here](https://github.com/NVIDIA/NeMo/tree/main/tools/ctc_segmentation)) to produce the training data. The wave lengths vary between 2 minutes to 2 seconds. I'm wondering what is the best wave length to use (or range of length to use).
Thanks! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1584 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1584/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1584/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1584/events | https://github.com/NVIDIA/NeMo/issues/1584 | 772,487,094 | MDU6SXNzdWU3NzI0ODcwOTQ= | 1,584 | Tacotron2: tokens of pad value confuse decoder at inference | {
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"Closing. re-open if issue is still relevant."
] | 2020-12-21T21:53:55 | 2022-04-05T23:34:05 | 2022-04-05T23:34:05 | NONE | {
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} | null | null | null | **Describe your question**
When running inference on a batch with a Tacotron2 model, the decoder correctly ends sequences when none of the sequences in the batch have too many tokens with the ```pad``` (which is ```0``` here) value at the end - so inferencing is fine when the batch size is 1 as shown in the examples or the sequences in the batch happen to have very similar length.
However, when there are more than a few such tokens at the end, the decoder does not stop and reaches ```max_decoder_steps``` even for very large values of ```max_decoder_steps```, even on sequences of in-domain length, even for very low values of ```gate_threshold```.
Intuitively, I'd expect the model to have learned that any amount of ```pad``` tokens signal the end, especially since they're preceded by the ```eos``` token. The sequences are not longer than ones encountered during training, so there shouldn't be attention alignment problems, at least not every time as to guarantee this issue.
What could be going wrong? I suspect it may have something to do with either masking of loss during training and/or one of the dropouts being used, but I'm not sure how to tackle this.
Note that this issue is also replicated in the TTS example colab notebook - add as few as 5 zeroes to the end of your parsed input and see what happens. Note that in the example the pad value is equivalent to the token assigned to the whitespace character, which isn't the case in my application, although I'm not sure if that's of any significance given that the issue arises in both cases.
This issue is, in my view, fairly significant because it makes batch inferencing essentially impossible.
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https://api.github.com/repos/NVIDIA/NeMo/issues/1583 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1583/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1583/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1583/events | https://github.com/NVIDIA/NeMo/issues/1583 | 772,364,854 | MDU6SXNzdWU3NzIzNjQ4NTQ= | 1,583 | Error running Speaker Recognition notebook: arguments are located on different GPUs | {
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"I am also getting the same issue.\r\nhttps://github.com/NVIDIA/NeMo/issues/1502 this might help.",
"please use `ddp` instead of `dp` as accelerator. Feel free to open the issue again incase you encounter it again. "
] | 2020-12-21T18:07:49 | 2021-01-05T05:36:03 | 2021-01-05T05:35:16 | NONE | {
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} | null | null | null | **Describe the bug**
When running a speaker recognition notebook on multi GPUs machine I get the following error:
RuntimeError: arguments are located on different GPUs at /tmp/pip-req-build-xlj_h8ax/aten/src/THC/generic/THCTensorMathBlas.cu:25
**Steps/Code to reproduce bug**
1) 8-V100 GPUs machine
2) using the latests NeMo docker image 1.0.0b3.
3) run the Speaker_Recognition_Verification.ipynb notebook
4) modified the trainer sets up cell to
```
# Let us modify some trainer configs for this demo
# Checks if we have GPU available and uses it
cuda = 1 if torch.cuda.is_available() else 0
config.trainer.gpus = 4
# Reduces maximum number of epochs to 5 for quick demonstration
config.trainer.max_epochs = 5
# Remove distributed training flags
config.trainer.accelerator = 'dp'
```
**Expected behavior**
running the trainer.fit(speaker_model) I get:
```
...
357 # dot with filterbank energies
--> 358 x = torch.matmul(self.fb.to(x.dtype), x)
359
360 # log features if required
RuntimeError: arguments are located on different GPUs at /tmp/pip-req-build-xlj_h8ax/aten/src/THC/generic/THCTensorMathBlas.cu:25
```
**Environment overview (please complete the following information)**
docker run command used:
```
docker run --gpus all -it --rm -d -v `pwd`:/NeMo -v /home/nshaposhnikov/datasets:/datasets -v /home/nshaposhnikov/work:/work --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:1.0.0b3
Unable to find image 'nvcr.io/nvidia/nemo:1.0.0b3' locally
1.0.0b3: Pulling from nvidia/nemo
f08d8e2a3ba1: Already exists
3baa9cb2483b: Already exists
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/1582 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1582/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1582/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1582/events | https://github.com/NVIDIA/NeMo/issues/1582 | 771,816,689 | MDU6SXNzdWU3NzE4MTY2ODk= | 1,582 | Missing vocab files at url in Biomegatron tutorial notebooks | {
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"I am also running into the same issue while running the tutorial notebooks.\r\n\r\n<img width=\"1075\" alt=\"Screen Shot 2021-01-02 at 1 05 30 PM\" src=\"https://user-images.githubusercontent.com/7240751/103463568-5b779d00-4cfb-11eb-8626-bb36d32e1373.png\">\r\n",
"I was able to get this working by adding these t... | 2020-12-21T04:01:54 | 2021-02-25T23:53:51 | 2021-02-25T23:53:51 | NONE | {
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} | null | null | null | **Describe the bug**
Following the biomegatron fix in [#1558](https://github.com/NVIDIA/NeMo/pull/1558).
Getting the following new error while running the Biomegatron tutorial notebooks.
Error line : model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
Output Error:
[NeMo I 2020-12-21 03:39:23 megatron_utils:235] Downloading from https://api.ngc.nvidia.com/v2/models/nvidia/biomegatron345mcased/versions/0/files/vocab.txt
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
<ipython-input-25-4645443f78bc> in <module>()
----> 1 model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
12 frames
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/models/token_classification/token_classification_model.py in __init__(self, cfg, trainer)
66 raise ValueError(f'{cfg.label_ids} not found.')
67
---> 68 self.setup_tokenizer(cfg.tokenizer)
69 self.class_weights = None
70 super().__init__(cfg=cfg, trainer=trainer)
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/models/nlp_model.py in setup_tokenizer(self, cfg)
112 vocab_file=vocab_file,
113 special_tokens=OmegaConf.to_container(cfg.special_tokens) if cfg.special_tokens else None,
--> 114 tokenizer_model=self.register_artifact(config_path='tokenizer.tokenizer_model', src=cfg.tokenizer_model),
115 )
116 self.tokenizer = tokenizer
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/modules/common/tokenizer_utils.py in get_tokenizer(tokenizer_name, tokenizer_model, vocab_file, special_tokens, use_fast)
60 if vocab_file is None:
61 vocab_file = nemo.collections.nlp.modules.common.megatron.megatron_utils.get_megatron_vocab_file(
---> 62 tokenizer_name
63 )
64 tokenizer_name = get_megatron_tokenizer(tokenizer_name)
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/modules/common/megatron/megatron_utils.py in get_megatron_vocab_file(pretrained_model_name)
197
198 path = os.path.join(MEGATRON_CACHE, pretrained_model_name + "_vocab")
**--> 199 path = _download(path, url)**
200 return path
201
/usr/local/lib/python3.6/dist-packages/nemo/collections/nlp/modules/common/megatron/megatron_utils.py in _download(path, url)
234 os.makedirs(MEGATRON_CACHE, exist_ok=True)
235 logging.info(f"Downloading from {url}")
--> 236 wget.download(url, path)
237 # wait until the master process downloads the file and writes it to the cache dir
238 if torch.distributed.is_initialized():
/usr/local/lib/python3.6/dist-packages/wget.py in download(url, out, bar)
524 else:
525 binurl = url
--> 526 (tmpfile, headers) = ulib.urlretrieve(binurl, tmpfile, callback)
527 filename = detect_filename(url, out, headers)
528 if outdir:
/usr/lib/python3.6/urllib/request.py in urlretrieve(url, filename, reporthook, data)
246 url_type, path = splittype(url)
247
--> 248 with contextlib.closing(urlopen(url, data)) as fp:
249 headers = fp.info()
250
/usr/lib/python3.6/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
221 else:
222 opener = _opener
--> 223 return opener.open(url, data, timeout)
224
225 def install_opener(opener):
/usr/lib/python3.6/urllib/request.py in open(self, fullurl, data, timeout)
530 for processor in self.process_response.get(protocol, []):
531 meth = getattr(processor, meth_name)
--> 532 response = meth(req, response)
533
534 return response
/usr/lib/python3.6/urllib/request.py in http_response(self, request, response)
640 if not (200 <= code < 300):
641 response = self.parent.error(
--> 642 'http', request, response, code, msg, hdrs)
643
644 return response
/usr/lib/python3.6/urllib/request.py in error(self, proto, *args)
568 if http_err:
569 args = (dict, 'default', 'http_error_default') + orig_args
--> 570 return self._call_chain(*args)
571
572 # XXX probably also want an abstract factory that knows when it makes
/usr/lib/python3.6/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
502 for handler in handlers:
503 func = getattr(handler, meth_name)
--> 504 result = func(*args)
505 if result is not None:
506 return result
/usr/lib/python3.6/urllib/request.py in http_error_default(self, req, fp, code, msg, hdrs)
648 class HTTPDefaultErrorHandler(BaseHandler):
649 def http_error_default(self, req, fp, code, msg, hdrs):
--> 650 raise HTTPError(req.full_url, code, msg, hdrs, fp)
651
652 class HTTPRedirectHandler(BaseHandler):
HTTPError: HTTP Error 500:
Vocab file missing at the specified url of megatron_utils.py.
**Steps/Code to reproduce bug**
I am running the Token_Classification-BioMegatron.ipynb notebook on Google Colab using gpu without any change in the code.
**Expected behavior**
The code cell to successfully pull the vocab file and the notebook to run without error.
**Environment overview (please complete the following information)**
- Environment location: Google Colab (GPU enabled)
**Environment details**
- Python version - 3.6.9 | {
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"This is an issue due to Pytorch 1.7.1 changes regarding how float tensors are cast inside torch.tensor(). There is currently a PR to update NeMo to 1.7.1 here - https://github.com/NVIDIA/NeMo/pull/1579\r\n\r\nHowever, there are other pressing issues before that PR can be merged, and it is unlikely to be merged in ... | 2020-12-19T01:26:07 | 2021-02-17T22:57:17 | 2021-02-17T22:57:17 | NONE | {
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} | null | null | null | **Describe the bug**
I tried to fine-tune EncDecClassificationModel with my labels and encountered `TypeError: only integer tensors of a single element can be converted to an index`. The error came from `NeMo/nemo/collections/common/metrics/classification_accuracy.py`
**Steps/Code to reproduce bug**
```python3
vad_model = EncDecClassificationModel.from_pretrained(model_name="MatchboxNet-VAD-3x2")
my_labels = ['a', 'b', 'c']
vad_model.change_labels(my_labels)
vad_model.setup_training_data(
train_data_config={
'vad_stream': True,
'sample_rate': 16000,
'manifest_filepath': my_train_path,
'labels': my_labels,
'num_workers': 10,
'shuffle': True,
'time_length': 0.67,
'shift_length': 0.01,
'trim_silence': False,
}
)
trainer = pl.Trainer(gpus=[0])
trainer.fit(vad_model)
```
**Environment overview (please complete the following information)**
- pip install using `reinstall.sh` in the NeMo repository
**Environment details**
- OS version Debian GNU/Linux 10
- PyTorch version 1.7.1
- Python version 3.8.6
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"@drnikolaev the unit tests crash for all onnx related exports.",
"closing as duplicate of https://github.com/NVIDIA/NeMo/pull/1603 "
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"This pull request **introduces 4 alerts** when merging d616be61a367dbac439f7a42e93a8e2638d23806 into 8c6152787e1eae5c6b23677df1be47041efe2d61 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-7804a833bde0dad8ce222e13d982fae64a3c4981)\n\n**new alerts:**\n\n* 4 for Unused import",
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} | - [x] Refactor classification models and label models in audio_to_label
- [x] Add support for tarred dataset for audio_to_label
- [x] Update classification models
- [x] Update VAD inference (should belong to Speaker Diarization pipelines PR but need to be used here)
- [x] Update training script, config, and instructions to use tarred dataset and improve consistency
- [x] Update converting_to_tarred_dataset script to support label models
- [x] Upload test data
- [x] Add test script
- [x] Ok a new one. enable using the duplicate name for tarred dataloader. (webdataset doesn't allow it)
Will try some hacky ways and discuss with people about a cleaner / long-term way after break.
Updates for label_models will be in another PR 🚢 basically add the tarred option to the configs files.
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