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* fix estimation of num of speakers when embs size < 6
* fix torch linalg import error when version <1.9 | {
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} | null | null | null | You need to call ```.detach()``` before calling ```.numpy()``` in a ```text_to_audio()``` function.
Edited:
```
def text_to_audio(text):
parsed = spectrogram_generator.parse(text)
spectrogram = spectrogram_generator.generate_spectrogram(tokens=parsed)
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
return audio.to('cpu').detach().numpy()
``` | {
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} | null | null | null | I have seen [Nemo-2-jarvis](https://docs.nvidia.com/deeplearning/jarvis/user-guide/docs/model-overview.html#nemo2jarvis-export-for-nemo) conversion approach.
.ejrvs file can be made from .nemo model
But , to build .jmir file for Machine Translation models(pretrained), what would be **pipeline** then,
jarvis-build **pipeline** /servicemaker-dev/<jmir_filename>:<encryption_key> /servicemaker-dev/<ejrvs_filename>:<encryption_key> <optional_args>
Thanks
@okuchaiev | {
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} | null | null | null | **Describe the bug**
When attempting to set the training data information for ASR training I am getting an error from omegaconf
AttributeError: 'dict' object has no attribute '_get_node_flag'
**Steps/Code to reproduce bug**
This is a minimal way to reproduce the bug
import nemo.collections.asr as nemo_asr
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='stt_en_jasper10x5dr')
quartznet.setup_training_data(train_data_config={'manifest_filepath': './training/training_samples.json', 'sample_rate': 16000, 'labels': [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', "'"], 'batch_size': 16, 'trim_silence': True, 'max_duration': 16.7, 'shuffle': True, 'is_tarred': False, 'tarred_audio_filepaths': None})
**Expected behavior**
The location of the training data should be updated
**Environment overview (please complete the following information)**
- AWS SageMaker, Python 3 (Base Python), 4vCPU + 16GiB + 1 GPU.
- Method of NeMo install: %pip install nemo_toolkit['all']
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version - Debian GNU/Linux 10 (buster)
- PyTorch version - 1.9.0
- Python version - 3.6.13
**Additional context**
The full stack trace
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-27fbbcbb2846> in <module>
1 import nemo.collections.asr as nemo_asr
2 quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='stt_en_jasper10x5dr')
----> 3 quartznet.setup_training_data(train_data_config={'manifest_filepath': './training/training_samples.json', 'sample_rate': 16000, 'labels': [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', "'"], 'batch_size': 16, 'trim_silence': True, 'max_duration': 16.7, 'shuffle': True, 'is_tarred': False, 'tarred_audio_filepaths': None})
/usr/local/lib/python3.6/site-packages/nemo/collections/asr/models/ctc_models.py in setup_training_data(self, train_data_config)
421 self._update_dataset_config(dataset_name='train', config=train_data_config)
422
--> 423 self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
424
425 # Need to set this because if using an IterableDataset, the length of the dataloader is the total number
/usr/local/lib/python3.6/site-packages/nemo/collections/asr/models/ctc_models.py in _setup_dataloader_from_config(self, config)
346
347 # Automatically inject args from model config to dataloader config
--> 348 audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate')
349 audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels')
350
/usr/local/lib/python3.6/site-packages/nemo/collections/asr/data/audio_to_text_dataset.py in inject_dataloader_value_from_model_config(model_cfg, dataloader_cfg, key)
53 # Dataloader `key` is None or values match
54 # Propagate from model level `key` (even if they match)
---> 55 with open_dict(dataloader_cfg):
56 dataloader_cfg[key] = model_cfg[key]
57
/usr/local/lib/python3.6/contextlib.py in __enter__(self)
79 def __enter__(self):
80 try:
---> 81 return next(self.gen)
82 except StopIteration:
83 raise RuntimeError("generator didn't yield") from None
/usr/local/lib/python3.6/site-packages/omegaconf/omegaconf.py in open_dict(config)
935 @contextmanager
936 def open_dict(config: Container) -> Generator[Container, None, None]:
--> 937 prev_state = config._get_node_flag("struct")
938 try:
939 OmegaConf.set_struct(config, False)
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**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 - Google Colab
- PyTorch version
- Python version - 3.7
**Additional context**
Add any other context about the problem here.
Example: GPU model
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https://api.github.com/repos/NVIDIA/NeMo/issues/2554 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2554/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2554/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2554/events | https://github.com/NVIDIA/NeMo/issues/2554 | 952,504,280 | MDU6SXNzdWU5NTI1MDQyODA= | 2,554 | Save (.nemo) and restore punctuation and capitalization models | {
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} | null | null | null | Hi,
I'm following this tutorial to train a punctuation and capitalization model: https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb
The model is initialized with bert-base-uncased. I use `model.save_to(save_path="mymodel.nemo")` to export the trained model and use `PunctuationCapitalizationModel.restore_from(restore_path="mymodel.nemo")` to load it in a separate script. I got this error:
```
prospectiveTraceback (most recent call last):
File "punctuation_server.py", line 58, in <module>
server = PunctuationCapitalizationServer()
File "punctuation_server.py", line 23, in __init__
restore_path="/home/boliang/punctuation_and_capitalization/mymodel.nemo"
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/collections/nlp/models/nlp_model.py", line 428, in restore_from
return super().restore_from(restore_path, override_config_path, map_location, strict, return_config)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/core/classes/modelPT.py", line 482, in restore_from
return cls._default_restore_from(restore_path, override_config_path, map_location, strict, return_config)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/core/classes/modelPT.py", line 433, in _default_restore_from
instance = cls.from_config_dict(config=conf)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/core/classes/common.py", line 471, in from_config_dict
instance = cls(cfg=config)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py", line 57, in __init__
self.setup_tokenizer(cfg.tokenizer)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/collections/nlp/models/nlp_model.py", line 110, in setup_tokenizer
vocab_file = self.register_artifact(config_path='tokenizer.vocab_file', src=cfg.vocab_file)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/collections/nlp/models/nlp_model.py", line 61, in register_artifact
return super().register_artifact(config_path, src, verify_src_exists=verify_src_exists)
File "/home/boliang/anaconda3/envs/speech_translation/lib/python3.7/site-packages/nemo/core/classes/modelPT.py", line 238, in register_artifact
assert os.path.exists(return_path)
AssertionError
```
Checked `modelPT.py` and found that it seems this file is missing: `/tmp/tmp5t7keady/e751620140c8488d9f23db68c172a1aa_tokenizer.vocab_file`. Then I extracted the saved model and got: `model_config.yaml` and `model_weights.ckpt`. While I tried to untar the pretrained model `punctuation_en_bert.nemo` and got: `manifest.yaml`, `model_config.yaml`, `model_weights.ckpt`, `tokenizer_vocab_dict.json`, and `tokenizer.vocab_file`. It looks like the model I trained dose not have tokenizer vocab files. Is this the reason I got the error above? How should I fix it?
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} | null | null | null | I would like to use the [tutorial](https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb) to train a model on a VCTK data set. There are wav files and text files with identical descriptors. The text file contains the sentence spoken in the wav file.
`
joepareti54@MSI /cygdrive/f/x/finance-2020/AI/Listen_attend_spell/VCTK-Corpus
$ ls -l txt/p225 | head -5
total 231
-rw-r--r--+ 1 joepareti54 None 20 Aug 22 2012 p225_001.txt
-rw-r--r--+ 1 joepareti54 None 55 Aug 22 2012 p225_002.txt
-rw-r--r--+ 1 joepareti54 None 103 Aug 22 2012 p225_003.txt
-rw-r--r--+ 1 joepareti54 None 68 Aug 22 2012 p225_004.txt
joepareti54@MSI /cygdrive/f/x/finance-2020/AI/Listen_attend_spell/VCTK-Corpus
$ ls -l wav48/p225 | head -5
total 93464
-rw-r--r--+ 1 joepareti54 None 196990 Aug 23 2012 p225_001.wav
-rw-r--r--+ 1 joepareti54 None 389676 Aug 23 2012 p225_002.wav
-rw-r--r--+ 1 joepareti54 None 749754 Aug 23 2012 p225_003.wav
-rw-r--r--+ 1 joepareti54 None 423528 Aug 23 2012 p225_004.wav`
`
Is this feasible? Any guidelines to implement? | {
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} | null | null | null | This [notebook ](https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb)under Ubuntu fails to `import nemo.collections.asr as nemo_asr`
Here is the exception report:
`ImportError: cannot import name 'SCMode' from 'omegaconf' (/home/joepareti54/anaconda3/lib/python3.8/site-packages/omegaconf/__init__.py)` | {
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"merged_at": null
} | # Changelog
- Add Github Actions support to run CPU tests
- Add Github Actions support to run ITN tests for branches/PRs with name `itn` in them
- Run ITN tests nightly otherwise.
- Update Numba CPU tests to perform CPU tests even if no GPU is available | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2550 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2550/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2550/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2550/events | https://github.com/NVIDIA/NeMo/pull/2550 | 951,897,164 | MDExOlB1bGxSZXF1ZXN0Njk2MjEwOTM2 | 2,550 | add seconds to time for english tn | {
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"merged_at": "2021-07-27T14:34:11"
} | Signed-off-by: Yang Zhang <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2549 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2549/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2549/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2549/events | https://github.com/NVIDIA/NeMo/pull/2549 | 951,810,640 | MDExOlB1bGxSZXF1ZXN0Njk2MTM4MjM4 | 2,549 | Fix branch for ASR notebooks | {
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} | Signed-off-by: smajumdar <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2548 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2548/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2548/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2548/events | https://github.com/NVIDIA/NeMo/pull/2548 | 951,786,688 | MDExOlB1bGxSZXF1ZXN0Njk2MTE3ODE5 | 2,548 | Update branch for ASR finetuning notebook | {
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} | # Changelog
- Update notebook version | {
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} | null | null | null | Hi
With a supposed Colab update yesterday, I have been having the following issue during training.
I say supposed update because I noticed changes in the Colab interface, but I didn't find any version talking about updating
I didn't make changes to the training process, the processes I used worked and didn't give this error before
As a way to try to solve this problem, I changed the torch versions from 1.9.0 to 1.8.0 and torchvision versions from 0.10.0 to 0.9.0 and torchtext from 0.10.0 to 0.9.0
I use nemo version "r1.1.0" but the same error occurred in versions "r1.0.2" and "r1.0.1"
The problem I had since yesterday when running the training is this:
```
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
456 )
457
--> 458 self._run(model)
459
460 assert self.state.stopped
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)
754
755 # dispatch `start_training` or `start_evaluating` or `start_predicting`
--> 756 self.dispatch()
757
758 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
795 self.accelerator.start_predicting(self)
796 else:
--> 797 self.accelerator.start_training(self)
798
799 def run_stage(self):
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
94
95 def start_training(self, trainer: 'pl.Trainer') -> None:
---> 96 self.training_type_plugin.start_training(trainer)
97
98 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
142 def start_training(self, trainer: 'pl.Trainer') -> None:
143 # double dispatch to initiate the training loop
--> 144 self._results = trainer.run_stage()
145
146 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)
805 if self.predicting:
806 return self.run_predict()
--> 807 return self.run_train()
808
809 def _pre_training_routine(self):
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
867 with self.profiler.profile("run_training_epoch"):
868 # run train epoch
--> 869 self.train_loop.run_training_epoch()
870
871 if self.max_steps and self.max_steps <= self.global_step:
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
497 # ------------------------------------
498 with self.trainer.profiler.profile("run_training_batch"):
--> 499 batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
500
501 # when returning -1 from train_step, we end epoch early
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py in run_training_batch(self, batch, batch_idx, dataloader_idx)
736
737 # optimizer step
--> 738 self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
739 if len(self.trainer.optimizers) > 1:
740 # revert back to previous state
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py in optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
440 on_tpu=self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE,
441 using_native_amp=using_native_amp,
--> 442 using_lbfgs=is_lbfgs,
443 )
444
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/lightning.py in optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs)
1401
1402 """
-> 1403 optimizer.step(closure=optimizer_closure)
1404
1405 def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/optimizer.py in step(self, closure, *args, **kwargs)
212 profiler_name = f"optimizer_step_and_closure_{self._optimizer_idx}"
213
--> 214 self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
215 self._total_optimizer_step_calls += 1
216
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/optimizer.py in __optimizer_step(self, closure, profiler_name, **kwargs)
132
133 with trainer.profiler.profile(profiler_name):
--> 134 trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
135
136 def step(self, *args, closure: Optional[Callable] = None, **kwargs):
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py in optimizer_step(self, optimizer, opt_idx, lambda_closure, **kwargs)
327 )
328 if make_optimizer_step:
--> 329 self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
330 self.precision_plugin.post_optimizer_step(optimizer, opt_idx)
331 self.training_type_plugin.post_optimizer_step(optimizer, opt_idx, **kwargs)
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py in run_optimizer_step(self, optimizer, optimizer_idx, lambda_closure, **kwargs)
334 self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any
335 ) -> None:
--> 336 self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)
337
338 def optimizer_zero_grad(self, current_epoch: int, batch_idx: int, optimizer: Optimizer, opt_idx: int) -> None:
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in optimizer_step(self, optimizer, lambda_closure, **kwargs)
191
192 def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs):
--> 193 optimizer.step(closure=lambda_closure, **kwargs)
194
195 @property
/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py in wrapper(*args, **kwargs)
63 instance._step_count += 1
64 wrapped = func.__get__(instance, cls)
---> 65 return wrapped(*args, **kwargs)
66
67 # Note that the returned function here is no longer a bound method,
/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py in wrapper(*args, **kwargs)
87 profile_name = "Optimizer.step#{}.step".format(obj.__class__.__name__)
88 with torch.autograd.profiler.record_function(profile_name):
---> 89 return func(*args, **kwargs)
90 return wrapper
91
/usr/local/lib/python3.7/dist-packages/nemo/core/optim/novograd.py in step(self, closure)
81 loss = None
82 if closure is not None:
---> 83 loss = closure()
84
85 for group in self.param_groups:
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py in train_step_and_backward_closure()
731 def train_step_and_backward_closure():
732 result = self.training_step_and_backward(
--> 733 split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
734 )
735 return None if result is None else result.loss
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py in training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
821 with self.trainer.profiler.profile("training_step_and_backward"):
822 # lightning module hook
--> 823 result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
824 self._curr_step_result = result
825
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py in training_step(self, split_batch, batch_idx, opt_idx, hiddens)
288 model_ref._results = Result()
289 with self.trainer.profiler.profile("training_step"):
--> 290 training_step_output = self.trainer.accelerator.training_step(args)
291 self.trainer.accelerator.post_training_step()
292
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py in training_step(self, args)
202
203 with self.precision_plugin.train_step_context(), self.training_type_plugin.train_step_context():
--> 204 return self.training_type_plugin.training_step(*args)
205
206 def post_training_step(self) -> None:
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in training_step(self, *args, **kwargs)
153
154 def training_step(self, *args, **kwargs):
--> 155 return self.lightning_module.training_step(*args, **kwargs)
156
157 def post_training_step(self):
/usr/local/lib/python3.7/dist-packages/nemo/utils/model_utils.py in wrap_training_step(wrapped, instance, args, kwargs)
337 @wrapt.decorator
338 def wrap_training_step(wrapped, instance: pl.LightningModule, args, kwargs):
--> 339 output_dict = wrapped(*args, **kwargs)
340
341 if isinstance(output_dict, dict) and output_dict is not None and 'log' in output_dict:
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/models/ctc_models.py in training_step(self, batch, batch_nb)
557 )
558 else:
--> 559 log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len)
560
561 loss_value = self.loss(
/usr/local/lib/python3.7/dist-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
790
791 # Call the method - this can be forward, or any other callable method
--> 792 outputs = wrapped(*args, **kwargs)
793
794 instance._attach_and_validate_output_types(
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/models/ctc_models.py in forward(self, input_signal, input_signal_length, processed_signal, processed_signal_length)
541
542 if self.spec_augmentation is not None and self.training:
--> 543 processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length)
544
545 encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length)
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/usr/local/lib/python3.7/dist-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
790
791 # Call the method - this can be forward, or any other callable method
--> 792 outputs = wrapped(*args, **kwargs)
793
794 instance._attach_and_validate_output_types(
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/modules/audio_preprocessing.py in forward(self, input_spec, length)
492 @typecheck()
493 def forward(self, input_spec, length):
--> 494 augmented_spec = self.spec_cutout(input_spec=input_spec)
495
496 # To run the Numba kernel, correct numba version is required as well as
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/usr/local/lib/python3.7/dist-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
790
791 # Call the method - this can be forward, or any other callable method
--> 792 outputs = wrapped(*args, **kwargs)
793
794 instance._attach_and_validate_output_types(
/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
25 def decorate_context(*args, **kwargs):
26 with self.__class__():
---> 27 return func(*args, **kwargs)
28 return cast(F, decorate_context)
29
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/spectr_augment.py in forward(self, input_spec)
146 for i in range(self.rect_masks):
147 rect_x = self._rng.randint(0, sh[1] - self.rect_freq)
--> 148 rect_y = self._rng.randint(0, sh[2] - self.rect_time)
149
150 w_x = self._rng.randint(0, self.rect_freq)
/usr/lib/python3.7/random.py in randint(self, a, b)
220 """
221
--> 222 return self.randrange(a, b+1)
223
224 def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type,
/usr/lib/python3.7/random.py in randrange(self, start, stop, step, _int)
198 return istart + self._randbelow(width)
199 if step == 1:
--> 200 raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width))
201
202 # Non-unit step argument supplied.
ValueError: empty range for randrange() (0,-7, -7)
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Closes #2545 | {
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} | null | null | null | **Describe the bug**
`NeMo/examples/asr/vad_infer.py` script fails with `IndexError` if option `--dont_auto_split` is not set and at least 1 `.wav` file is long enough so that `nemo.collections.asr.parts.utils.vad_utils.prepare_manifest()` function split the `.wav` file.
**Steps/Code to reproduce bug**
```
mkdir -p ~/debug_data
wget http://i13pc106.ira.uka.de/~jniehues/IWSLT-SLT/data/eval/en-de/IWSLT-SLT.tst2019.en-de.tgz -O ~/debug_data/IWSLT-SLT.tst2019.en-de.tgz
tar xzf ~/debug_data/IWSLT-SLT.tst2019.en-de.tgz -C ~/debug_data/
cd ~/NeMo/examples/asr/
wget https://raw.githubusercontent.com/NVIDIA/NeMo/feat/asr/iwslt_audio_to_nemo_format/scripts/dataset_processing/prepare_iwslt_audio_data.py
python prepare_iwslt_audio_data.py -a ~/debug_data/IWSLT.tst2019/wavs/ -t ~/debug_data/IWSLT.tst2019/IWSLT.TED.tst2019.en-de.en.xml -o ~/debug_data/IWSLT.tst2019/manifest.json
python vad_infer.py --dataset ~/debug_data/IWSLT.tst2019/manifest.json --out_dir ~/debug_data/IWSLT.tst2019/vad --vad_model vad_marblenet
```
**Expected behavior**
No errors
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install nemo_toolkit[all]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.8.1
- Python version: 3.8.10
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} | null | null | null | **Bug description**
When trying to prepare data for ASR neural LM training, using the script `scripts/asr_language_modeling/neural_rescorer/create_tarred_transformer_lm_dataset.py` getting the following error (and a few preceding log lines):
```
[Parallel(n_jobs=-2)]: Done 68 out of 128 | elapsed: 10.6s remaining: 9.3s
[Parallel(n_jobs=-2)]: Done 81 out of 128 | elapsed: 11.4s remaining: 6.6s
[Parallel(n_jobs=-2)]: Done 94 out of 128 | elapsed: 12.0s remaining: 4.3s
[Parallel(n_jobs=-2)]: Done 107 out of 128 | elapsed: 12.6s remaining: 2.5s
[Parallel(n_jobs=-2)]: Done 120 out of 128 | elapsed: 13.5s remaining: 0.9s
[Parallel(n_jobs=-2)]: Done 128 out of 128 | elapsed: 14.1s finished
Traceback (most recent call last):
File "./scripts/asr_language_modeling/neural_rescorer/create_tarred_transformer_lm_dataset.py", line 318, in <module>
main()
File "./scripts/asr_language_modeling/neural_rescorer/create_tarred_transformer_lm_dataset.py", line 299, in main
chunk_paths, chunk_lens = __tokenize_text(
File "./scripts/asr_language_modeling/neural_rescorer/create_tarred_transformer_lm_dataset.py", line 174, in __tokenize_text
chunk = np.asarray(chunk, dtype=np.int64)
File "/home/pe.honnet/Tools/miniconda3/envs/nemo/lib/python3.8/site-packages/numpy/core/_asarray.py", line 102, in asarray
return array(a, dtype, copy=False, order=order)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
Read 1000000 lines: : 1048576 lines [00:18, 57331.13 lines/s]
```
**Steps/Code to reproduce bug**
Command run as follows
```bash
python ./scripts/asr_language_modeling/neural_rescorer/create_tarred_transformer_lm_dataset.py \
--text_path=training_data.txt \
--data_root=./data/prep_nn_lm \
--tokenizer_model=conformer_ctc_large/tokenizer/tokenizer.model \
--tokenizer_vocab_file=bert_uncased_vocab.txt \
--tokenizer_name='bert-base-uncased'
```
I have tried using the conformer ctc tokenizer vocab as well with the same error.
**Environment overview**
- nemo installed with `reinstall.sh` script, on CentOS 8.0
**Environment details**
commit used: 034941566 (HEAD -> main, origin/main, origin/HEAD) Merge bugfixes and doc updates from 1.2.0 to main (#2533)
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} | null | null | null | **Describe the bug**
On the Speech Data Explorer, on the "Samples" page, using an `int` or a `float` value to filter text by results in the Flask app throwing a TypeError such as:
```
10.2.48.115 - - [23/Jul/2021 14:40:13] "POST /_dash-update-component HTTP/1.1" 500 -
[2021-07-23 14:44:01,300] ERROR in app: Exception on /_dash-update-component [POST]
Traceback (most recent call last):
File "/home/kathyreid/.local/lib/python3.8/site-packages/flask/app.py", line 2070, in wsgi_app
response = self.full_dispatch_request()
File "/home/kathyreid/.local/lib/python3.8/site-packages/flask/app.py", line 1515, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/home/kathyreid/.local/lib/python3.8/site-packages/flask/app.py", line 1513, in full_dispatch_request
rv = self.dispatch_request()
File "/home/kathyreid/.local/lib/python3.8/site-packages/flask/app.py", line 1499, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**req.view_args)
File "/home/kathyreid/.local/lib/python3.8/site-packages/dash/dash.py", line 1096, in dispatch
response.set_data(func(*args, outputs_list=outputs_list))
File "/home/kathyreid/.local/lib/python3.8/site-packages/dash/dash.py", line 1017, in add_context
output_value = func(*args, **kwargs) # %% callback invoked %%
File "data_explorer.py", line 565, in update_datatable
data_view = [x for x in data_view if filter_value in str(x[col_name])]
File "data_explorer.py", line 565, in <listcomp>
data_view = [x for x in data_view if filter_value in str(x[col_name])]
TypeError: 'in <string>' requires string as left operand, not float
```
Here is a visual:
**Steps/Code to reproduce bug**
* Go to [IP address that SDE is running through on Flask/samples]
* In the Filter row of the table, enter an `int` or `float` value in the `text` column
* The terminal in which Flask is running will throw the error
**Expected behavior**
I expect SDE to filter for numbers - so that we can inspect a dataset to see if it has been properly normalized.
**Environment overview (please complete the following information)**
* Environment, bare metal localhost running Ubuntu Linux 20.04 LTS, running Python 3.8.10
* Install method - git clone from GitHub, SDE is spawned with
* `$ python3 data_explorer.py ../../scripts/dataset_processing/commonvoice_train_manifest.json `
Add any other context about the problem here.
* I am evaluating datasets using SDE
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1. Fixes typos
2. Fixes MeCab import issues by uninstalling and reinstalling sacrebleu[ja] | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2540 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2540/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2540/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2540/events | https://github.com/NVIDIA/NeMo/issues/2540 | 950,859,088 | MDU6SXNzdWU5NTA4NTkwODg= | 2,540 | Transcript test during training | {
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Do you still support version 0.10.1? I have a question about. I have been training a dataset in Portuguese in this version and in the results folder I see several checkpoints (decoder and encoder) being generated during training, specifically every 1000 steps.
Can I count on them to take my transcription tests? As an eventual evolution test, for example, or should I just wait for all epochs to complete and get the last decoder and encoder?
I have this question because during training I do transcription tests, but the result varies a lot. There are times that are great, there are times that are terrible. | {
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After fine tuning, if I want to supplement the dataset with a little more audio, should I restart training or can I restore the epoch where I left off? | {
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|
https://api.github.com/repos/NVIDIA/NeMo/issues/2537 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2537/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2537/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2537/events | https://github.com/NVIDIA/NeMo/pull/2537 | 950,642,006 | MDExOlB1bGxSZXF1ZXN0Njk1MTU4MjA5 | 2,537 | Update requirements.txt to include tqdm and pandas as deps for SDE and plotly | {
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} | This PR adds `tqdm` as a dependency for the Speech Dataset Explorer; it fails as below without it.
```
✔ ~/NeMo/tools/speech_data_explorer [main|✔]
23:02 $ python3 data_explorer.py
Traceback (most recent call last):
File "data_explorer.py", line 37, in <module>
import tqdm
ModuleNotFoundError: No module named 'tqdm'
✘-1 ~/NeMo/tools/speech_data_explorer [main|✔]
23:02 $ pip install tqdm
Defaulting to user installation because normal site-packages is not writeable
Collecting tqdm
Downloading tqdm-4.61.2-py2.py3-none-any.whl (76 kB)
|████████████████████████████████| 76 kB 325 kB/s
Installing collected packages: tqdm
Successfully installed tqdm-4.61.2
``` | {
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} | null | null | null | I am finetuning existing Citrinet model (stt_en_citrinet_1024). We have around 130h of transcribed T-mobile data.
The test and evaluation sets are fixed: test(3.53h) and evaluation(5.45h). I can't use all the train data because of out of memory error. This is the type of error that I'm getting:
`RuntimeError: CUDA out of memory. Tried to allocate 434.00 MiB (GPU 0; 39.59 GiB total capacity; 37.22 GiB already allocated; 356.19 MiB free; 37.44 GiB reserved in total by PyTorch)`
The most amount of data that I could use was (90.29h) of data (max_duration = 65s).
I am using 4gpus (40g each). I reduced the batch_size to 16 and precision to 16.
I also, used the ctc-segmentation to split long audio files and the corresponding transcripts into shorter fragments. So, instead of 90h of data I can use 66h of data most with max_duration = 66. (not that much improvement on increasing the train size)
Any suggestion about how I can increase the train data? ( we are still not covering 1/3 of data) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2526 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2526/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2526/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2526/events | https://github.com/NVIDIA/NeMo/issues/2526 | 949,823,162 | MDU6SXNzdWU5NDk4MjMxNjI= | 2,526 | Error when running eval_beamsearch_ngram.py | {
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} | null | null | null | Hi,
I started looking into LM with Nemo. I was able to create a kenlm model using train_kenlm.py script.
Then I tried to run eval_beamsearch_ngram.py using a fine-tuned model we trained, kenlm model, and test manifest, but got error.
Could you tell us what to do to fix this? By the way there is no log file created, and we didn’t modify those NVIDIA scripts.
Thank you so much!
I followed:
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html
python eval_beamsearch_ngram.py --nemo_model_file ../../models/model_2021-07-19_12-01-41_4GPU.nemo \
--input_manifest ../../manifests/test.jsonl \
--kenlm_model_file kenlm_model_2021-07-19_12-01-41 \
--acoustic_batch_size 32 \
--preds_output_folder output \
--decoding_mode beamsearch_ngram \
--beam_width 64 128 \
--beam_alpha 1.0 \
--beam_beta 1.0 0.5
Traceback (most recent call last):
File "eval_beamsearch_ngram.py", line 345, in <module>
main()
File "eval_beamsearch_ngram.py", line 217, in main
asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from(
File "/NeMo/nemo/core/classes/modelPT.py", line 479, in restore_from
return cls._default_restore_from(restore_path, override_config_path, map_location, strict, return_config)
File "/NeMo/nemo/core/classes/modelPT.py", line 430, in _default_restore_from
instance = cls.from_config_dict(config=conf)
File "/NeMo/nemo/core/classes/common.py", line 471, in from_config_dict
instance = cls(cfg=config)
File "/NeMo/nemo/collections/asr/models/ctc_bpe_models.py", line 98, in __init__
raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !")
ValueError: `cfg` must have `tokenizer` config to create a tokenizer ! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2525 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2525/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2525/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2525/events | https://github.com/NVIDIA/NeMo/issues/2525 | 949,380,398 | MDU6SXNzdWU5NDkzODAzOTg= | 2,525 | Reproducing stt_en_conformer_ctc_medium_ls training details | {
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} | null | null | null | Hello,
I'm trying to train conformer_ctc_medium model from scratch. I use the config file from the pretrained model stt_en_conformer_ctc_medium_ls and only change the manifest_filepath and batch_size configuraion (I changed the batch_size to 16 to fit my GPU).
After training 42 epoches, the WER on librispeech validate data (about 15%) is still much higher than the pretrained stt_en_conformer_ctc_medium_ls model.
May I know the trained epoch of the pretrained model stt_en_conformer_ctc_medium_ls ? Or is there any other configuration or training process I should pay attention to acheive similar results as the pretrained model ?
Thank you very much! | {
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} | null | null | null | Getting this error
ERROR: Command errored out with exit status 1:
command: /usr/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-J5oGxi/nemo-toolkit/setup.py'"'"'; __file__='"'"'/tmp/pip-install-J5oGxi/nemo-toolkit/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-weXhSc
cwd: /tmp/pip-install-J5oGxi/nemo-toolkit/
Complete output (6 lines):
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-install-J5oGxi/nemo-toolkit/setup.py", line 106
f'--use-parentheses --line-width={__LINE_WIDTH} -rc -ws'
^
SyntaxError: invalid syntax
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
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1. Fixes typos in the NMT data processing notebook.
2. Fixes an issue where MeCab could not be imported because pip install nemo[all] does not install sacrebleu[ja]. | {
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* introduce segmentation module
* support multi-batch inference for speaker embedding extraction | {
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} | -added support for addresses
-added support for telephone numbers with words
-added support for url
-cardinal numbers that consist of 5 and more digits long will now be normalized digit by digit
-SH tests and docker setup updated to work for different languages
-fix for fractions in SH | {
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} | null | null | null | I tried following ways to install Nemo:
1. `pip3 install nemo_toolkit`
2. `pip3 install git+https://github.com/NVIDIA/NeMo.git `
3. Running `git clone git+https://github.com/NVIDIA/NeMo.git ` and then `cd NeMo` and finally `pip3 install .`
The above mentioned commands are successfully executed without any errors.
After running these commands on terminal, when I run python `import nemo` it shows me `ModuleNotFoundError: No module named 'nemo'`
OS version - macOS Big Sur 11.4
Python version - 3.9.6 | {
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} | # Changelog
- Enable RNNT Export to ONNX (No torchscript yet)
- Add inference script for onnx exported rnnt models
- Update neural types for export
- Refactor Exportable to be less monolithic
- Add `ExportableEncDecJointModel` that is subclass of Exportable | {
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I've trained a Neural Machine Translation model using NeMo. Is it possible to use Multiple GPU's for model inference?
Thanks | {
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} | This PR updates the `ctc-segmentation` Python package used for the CTC segmentation tool from version 1.1.0 to 1.4.0. The update provides a bugfix, as well as sane defaults, which results in better alignments.
I would also recommend the latest version, however, the interface changed at two points:
1. As not every ASR architecture uses subsampling, I replaced the `subsampling` and `frame_duration` parameters with a new parameter `index_duration`. This parameter can simply be calculated as `frame_duration_ms * subsampling_factor / 1000`.
2. The text can now be given as a token ID sequence obtained from the tokenizer (which is already integrated into Nemo). The text input was chosen for our initial implementation, because our ASR model had long tokens and it made sense to scan for partial tokens. As the pre-trained Nemo models are already character-based, scanning for partial tokens is not necessary; using this method will improve alignment speed and probably also generate better alignments (because the pre-processing of the text may be better with a tokenizer).
@ekmb | {
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Does NeMo provide model check pointing for Machine Translation Models? That way, a saved checkpoint can be restored and be used for training.
Thanks! | {
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} | ERROR: type should be string, got "https://github.com/NVIDIA/NeMo/pull/2415 was mainly targetting English. This PR extends it to support other languages.\r\n\r\n- [x] Testing the new code on Russian\r\n- [x] Testing the new code on German\r\n\r\nResults on Google Russian dataset and internal German dataset:\r\n- Russian Duplex System. TN accuracy: 96.20%. ITN accuracy: 85.57%\r\n- German Duplex System. TN accuracy: 94.38%. ITN accuracy: 79.89%" | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2495 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2495/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2495/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2495/events | https://github.com/NVIDIA/NeMo/issues/2495 | 946,345,904 | MDU6SXNzdWU5NDYzNDU5MDQ= | 2,495 | Version comparison 1.0.2 x 0.10.1 | {
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I've been doing transcription training for the Portuguese language for a few months and so far I'm getting great results. But I came across a question when performing a comparison of results from version 0.10.1 and 1.0.2 with the same dataset.
For this comparison I respectively used the scripts:
**(Version 0.10.1)**
Container https://ngc.nvidia.com/catalog/containers/nvidia:nemo_asr_app_img and notebook 'English to Spanish Cross Language Finetuning'
**(Version 1.0.2)**
Notebook https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/10_ASR_CTC_Language_Finetuning.ipynb
In their respective parameters I used the recommended values:
**(Version 0.10.1)**
```
# training parameters
project.set_am_num_gpus(1)
project.set_am_batch_size(42)
project.set_am_num_epochs(50) # as an example we use 1 epoch but in reality we finetuned the model for 256 epochs
project.set_am_learning_rate(0.0015) # lr in finetuning an order of magnitude smaller than training from scratch
project.manifest.am.train_params.amp_opt_level = 'O1'
# optimizer novograd
project.manifest.am.train_params.beta1 = 0.95
project.manifest.am.train_params.beta2 = 0.25
project.manifest.am.train_params.lr_policy = "CosineAnnealing"
project.manifest.am.train_params.weight_decay = 0.002
project.manifest.am.train_params.warmup_ratio = 0.12
```
**(Version 1.0.2)**
```
# Setup train, validation, test configs
with open_dict(cfg):
# Train dataset (Concatenate train manifest cleaned and dev manifest cleaned)
cfg.train_ds.manifest_filepath = train_manifest_cleaned
cfg.train_ds.labels = list(train_dev_set)
cfg.train_ds.normalize_transcripts = False
cfg.train_ds.batch_size = 28
cfg.train_ds.num_workers = 8
cfg.train_ds.pin_memory = True
cfg.train_ds.trim_silence = True
# Validation dataset (Use test dataset as validation, since we train using train + dev)
cfg.validation_ds.manifest_filepath = test_manifest_cleaned
cfg.validation_ds.labels = list(train_dev_set)
cfg.validation_ds.normalize_transcripts = False
cfg.validation_ds.batch_size = 8
cfg.validation_ds.num_workers = 1
cfg.validation_ds.pin_memory = True
cfg.validation_ds.trim_silence = True
with open_dict(char_model.cfg.optim):
char_model.cfg.optim.lr = 0.01
char_model.cfg.optim.betas = [0.95, 0.5] # from paper
char_model.cfg.optim.weight_decay = 0.001 # Original weight decay
char_model.cfg.optim.sched.warmup_steps = None # Remove default number of steps of warmup
char_model.cfg.optim.sched.warmup_ratio = 0.05 # 5 % warmup
char_model.cfg.optim.sched.min_lr = 1e-5
```
For all values I'm following the recommendations of the scripts themselves. The only change was that BatchSize could only use 28 in the most recent script and 42 in the oldest script. (That's really a question, does the latest version of NeMo use more VRam? For these tests I'm using an Nvidia T4 - 16GB).
After doing the training on both scripts, I noticed that the older version has 30% better accuracy. Is there any explanation for this? Could BatchSize be the reason? | {
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https://github.com/NVIDIA/NeMo/blob/main/tutorials/AudioTranslationSample.ipynb
It has 3 modules speech to text, translation and text to speech. But have seen it uses the IPython audio function for converting the text to audio. I was wondering is there any native module for converting text to speech. | {
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I have a Quartz model trained on NeMO and I am bit confused on some for the things-
1. When nemo model is converted to enemo, it first converts to [onnx](https://github.com/NVIDIA/NeMo/blob/main/scripts/export/convasr_to_enemo.py#L60). But there are 3 onnx model generated. why?
2. While enemo to jmir conversion, quartz.onnx(1 onnx model) is used only. why?
The conversion is happened successfully but when I ran test, it gives output gibberish. Output is going continued. It did not stop.
I can not understand the reason behind.
https://forums.developer.nvidia.com/t/jarvis-hindi-model-giving-gibberish-output/183813
@okuchaiev
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} | null | null | null | In order to get the timestamps of each word, I am using Offline asr ( https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/asr/Offline_ASR.ipynb#scrollTo=I9eIxAyKHREB) for my experiment but I am unable to find the install_ctc_decoder.sh in scripts.
Can you suggest any other alternative so that I can use this tutorial for my experimentation purpose?
Thanks in Advance.
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SH tests all passing | {
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} | thanks to Tyler Zhu
Signed-off-by: Yang Zhang <[email protected]> | {
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} | null | null | null | using bert-case-chinese as pre-trained model, but has this issue:
[NeMo I 2021-07-14 13:41:17 bert_module:54] Restoring weights from /data/cmrc2018/bert-base-chinese/pytorch_model.bin
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 198, in run_and_report
return func()
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 347, in <lambda>
lambda: hydra.run(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 107, in run
return run_job(
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 127, in run_job
ret.return_value = task_function(task_cfg)
File "/tlt-nemo/nlp/question_answering/scripts/train.py", line 118, in main
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/question_answering/qa_model.py", line 61, in __init__
self.bert_model = get_lm_model(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/lm_utils.py", line 92, in get_lm_model
model.restore_weights(restore_path=checkpoint_file)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/bert_module.py", line 79, in restore_weights
assert len(pretrained_dict) == len(model_dict)
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/tlt-nemo/nlp/question_answering/scripts/train.py", line 140, in <module>
File "/opt/conda/lib/python3.8/site-packages/nemo/core/config/hydra_runner.py", line 98, in wrapper
_run_hydra(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 346, in _run_hydra
run_and_report(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 237, in run_and_report
assert mdl is not None
AssertionError
2021-07-14 06:41:18,254 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
Training with TLT, backend is Nemo bugs
git clone https://huggingface.co/bert-base-chinese
tlt question_answering train \
-e /specs/question_answering/train_bert.txt \
-g 1 \
-k 'tlt_encode' \
-r /results/question_answering/train \
training_ds.file=/data/cmrc2018/squad-style-data/cmrc2018_train.json \
validation_ds.file=/data/cmrc2018/squad-style-data/cmrc2018_dev.json \
trainer.max_epochs=2 \
trainer.val_check_interval=1.0 \
training_ds.num_workers=4 \
validation_ds.num_workers=4
[train_bert.txt](https://github.com/NVIDIA/NeMo/files/6816666/train_bert.txt)
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} | When loading segments, specified with offset, files that are not supported by SoundFile (i.e. filetypes that are non-seekable such as GSM encoded wavs) the script resorts to using [pydub](https://github.com/jiaaro/pydub).
Pydub uses millisecond indexing. Fix to remove incorrect multiplication with sampling rate at offset. | {
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} | `imp` module has been deprecated since 3.4. Since we do not support older versions of python, we should use supported functions.
Unfortunately, there is no direct equivalent to `load_source()` function. While `exec_module` loads necessary objects into `package_info` object, it's not possible to use `package_info` in the import statement directly.
I could not find any way to use this module in the import statements without hacks, but I must admit that I am not very well versed in the python import system.
The resulting code is not clean, but I want to use this PR a starting point for further discussion.
Signed-off-by: Aleksei Kalinov <[email protected]> | {
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I have converted the Nemo-based Citrinet(for ASR) Model to ONNX then to TRT, but I am stuck at writing an inference code for the TRT model.
Having said that, while I am doing an Inference in Nemo, there is a single line of code for Inference. So, I am not sure about the TRT inference for the Converted TRT model. it will really help me if you can share some materials on TRT inference for Citrinet models(converted from Nemo).
Thanks and Regards,
Darshan C G | {
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} | refactored text processing library to extend to other languages
updated docs and tutorial
Signed-off-by: Yang Zhang <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2473 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2473/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2473/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2473/events | https://github.com/NVIDIA/NeMo/issues/2473 | 943,421,421 | MDU6SXNzdWU5NDM0MjE0MjE= | 2,473 | import nemo.collections.asr as nemo_asr generates an error? | {
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} | null | null | null | I tried to execute the tutorial: [](https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb)
Unfortunately, the line `import nemo.collections.asr as nemo_asr` is not working and generates an error like:
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-18-7cc0b6be15f9> in <module>()
3 # NeMo's ASR collection - this collections contains complete ASR models and
4 # building blocks (modules) for ASR
----> 5 import nemo.collections.asr as nemo_asr
6 from omegaconf import OmegaConf
8 frames
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/__init__.py in <module>()
13 # limitations under the License.
14
---> 15 from nemo.collections.asr import data, losses, models, modules
16 from nemo.package_info import __version__
17
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/losses/__init__.py in <module>()
13 # limitations under the License.
14
---> 15 from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
16 from nemo.collections.asr.losses.ctc import CTCLoss
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/losses/angularloss.py in <module>()
16 import torch
17
---> 18 from nemo.core.classes import Loss, Typing, typecheck
19 from nemo.core.neural_types import LabelsType, LogitsType, LossType, NeuralType
20
/usr/local/lib/python3.7/dist-packages/nemo/core/__init__.py in <module>()
14
15 import nemo.core.neural_types
---> 16 from nemo.core.classes import *
/usr/local/lib/python3.7/dist-packages/nemo/core/classes/__init__.py in <module>()
14
15
---> 16 from nemo.core.classes.common import (
17 FileIO,
18 Model,
/usr/local/lib/python3.7/dist-packages/nemo/core/classes/common.py in <module>()
25 from typing import Dict, List, Optional, Union
26
---> 27 import hydra
28 import wrapt
29 from omegaconf import DictConfig, OmegaConf
/usr/local/lib/python3.7/dist-packages/hydra/__init__.py in <module>()
3 # Source of truth for Hydra's version
4 __version__ = "1.1.0"
----> 5 from hydra import utils
6 from hydra.errors import MissingConfigException
7 from hydra.main import main
/usr/local/lib/python3.7/dist-packages/hydra/utils.py in <module>()
6 from typing import Any, Callable
7
----> 8 import hydra._internal.instantiate._instantiate2
9 import hydra.types
10 from hydra._internal.utils import _locate
/usr/local/lib/python3.7/dist-packages/hydra/_internal/instantiate/_instantiate2.py in <module>()
6 from typing import Any, Callable, Sequence, Tuple, Union
7
----> 8 from omegaconf import OmegaConf, SCMode
9 from omegaconf._utils import is_structured_config
10
ImportError: cannot import name 'SCMode' from 'omegaconf' (/usr/local/lib/python3.7/dist-packages/omegaconf/__init__.py)
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
```
and in the code there is a red line below `import nemo.collections.asr as nemo_asr`.
I am executing the tutorial on colab. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2470 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2470/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2470/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2470/events | https://github.com/NVIDIA/NeMo/issues/2470 | 943,088,312 | MDU6SXNzdWU5NDMwODgzMTI= | 2,470 | Integration of corcua dataset library | {
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} | null | null | null | Hi,
I wanted to notify you about my speech dataset handling tool [corcua](https://gitlab.com/Jaco-Assistant/corcua). The task it solves is loading datasets from different formats and converting them to any framework-customized format we can use for training STT networks. It was built with the goal of making the addition of new datasets as easy as possible. Currently it supports 34 datasets, many in German, but 12 of them are multilingual containing 3 or more languages.
NeMo's `.jsonl` format is already supported, so the integration should be very easy.
I hope this is interesting for your project.
Greetings
Daniel | {
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] | null | null | 2021-07-13T06:52:07 | 2021-07-22T16:50:58 | 2021-07-22T16:50:58 | NONE | {
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import torchaudio
from nemo.collections.tts.models.base import Vocoder
audio, sample_rate = torchaudio.load(audio_path) # sample_rate = 22050
vocoder = Vocoder.from_pretrained('tts_melgan')
audio_len = torch.tensor(audio.size(1)/22050).view(1,1)
gen_mel, _ = vocoder.audio_to_melspec_precessor(audio.cuda(), audio_len.cuda())
plt.imshow(gen_mel.cpu().detach().numpy().squeeze())
plt.show()
```
This code returns a blank(constant) spectrogram.
How do I get the Mel spectrogram for vocoder training/inference? | {
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} | * Adds new modified ecapa ecoder for better speaker embeddings
* adds attention pooling decoder
* adds TAP pooling
* tests | {
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} | Tutotrial notebook for data cleaning and pre-processing of NMT datasets. | {
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} | - [x] Add `pad_id` for `SmoothedCrossEntropyLoss` to ignore label=pad_id in labels. In most cases pad_id is max_label+1.
Handel general cases, and special case that has padding label e.g. 5, but the model output of shape pad_id, e.g. [0,1,2,3,4]
- [x] Add `ignore_id` for `TopKClassificationAccuracy` to Ignore label=ignore_id in labels and corresponding indices in predictions. Typically used for excluding padding_id.
- [x] Add tests for `TopKClassificationAccuracy`
- [x] Add tests for `SmoothedCrossEntropyLoss`
Signed-off-by: fayejf <[email protected]> | {
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} | - Added fasttext to the NLP requirements
- A quick fix for the fasttext warning messages:
`Warning : 'load_model' does not return WordVectorModel or SupervisedModel any more, but a 'FastText' object which is very similar.` | {
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`import nemo.collections.asr` fails, producing the following error:
```$ python import_test.py
[NeMo W 2021-07-12 13:38:25 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
[NeMo W 2021-07-12 13:38:36 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
prepare(preparation_data)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/runpy.py", line 268, in run_path
return _run_module_code(code, init_globals, run_name,
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/Users/Nicholas.Moran/NeMo/import_test.py", line 1, in <module>
import nemo.collections.asr
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/models/__init__.py", line 17, in <module>
from nemo.collections.asr.models.clustering_diarizer import ClusteringDiarizer
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/models/clustering_diarizer.py", line 31, in <module>
from nemo.collections.asr.models.label_models import ExtractSpeakerEmbeddingsModel
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/models/label_models.py", line 32, in <module>
from nemo.collections.asr.parts.utils.speaker_utils import embedding_normalize
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/parts/utils/speaker_utils.py", line 24, in <module>
from pyannote.metrics.diarization import DiarizationErrorRate
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/site-packages/pyannote/metrics/__init__.py", line 40, in <module>
manager_ = Manager()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/context.py", line 57, in Manager
m.start()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/managers.py", line 553, in start
self._process.start()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 42, in _launch
prep_data = spawn.get_preparation_data(process_obj._name)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File "/Users/Nicholas.Moran/NeMo/import_test.py", line 1, in <module>
import nemo.collections.asr
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/models/__init__.py", line 17, in <module>
from nemo.collections.asr.models.clustering_diarizer import ClusteringDiarizer
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/models/clustering_diarizer.py", line 31, in <module>
from nemo.collections.asr.models.label_models import ExtractSpeakerEmbeddingsModel
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/models/label_models.py", line 32, in <module>
from nemo.collections.asr.parts.utils.speaker_utils import embedding_normalize
File "/Users/Nicholas.Moran/NeMo/nemo/collections/asr/parts/utils/speaker_utils.py", line 24, in <module>
from pyannote.metrics.diarization import DiarizationErrorRate
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/site-packages/pyannote/metrics/__init__.py", line 40, in <module>
manager_ = Manager()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/context.py", line 57, in Manager
m.start()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/managers.py", line 557, in start
self._address = reader.recv()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/connection.py", line 255, in recv
buf = self._recv_bytes()
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/Users/Nicholas.Moran/miniconda3/envs/nemo/lib/python3.9/multiprocessing/connection.py", line 388, in _recv
raise EOFError
EOFError
```
Here import_test.py is a file which contains a single line: `import nemo.collections.asr`.
However, when run from ipython, the same import works fine:
```$ python -i
Python 3.9.5 (default, May 18 2021, 12:31:01)
[Clang 10.0.0 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import nemo.collections.asr
[NeMo W 2021-07-12 13:37:26 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
[NeMo W 2021-07-12 13:37:43 experimental:27] 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.
```
Note that in the previous output, the Apex warning was printed twice - this makes me suspect that some portion of the import code is being incorrectly run twice, ultimately resulting in the error from the multiprocessing library. I haven't been able to further narrow down the problem, however.
**Steps/Code to reproduce bug**
`import nemo.collections.asr`
**Expected behavior**
The import works fine in ipython, so I would have expected it to work the same from a script file.
**Environment overview (please complete the following information)**
Mac OS Catalina 10.15.7
Installed by cloning the git repo and running `./reinstall.sh`. I have also tried installing via `pip install nemo-toolkit`, and `pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]` , with both 'main' and 'r1.1.0' for BRANCH. These all produced the same result.
**Environment details**
Python 3.9.5
PyTorch 1.9.0
**Additional context**
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It's important for my experiments, thanks in advance. | {
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I could not access the files in https://nemo-public.s3.us-east-2.amazonaws.com
Link for Russian text is
Get example from: https://nemo-public.s3.us-east-2.amazonaws.com/mcv-samples-ru/common_voice_de_19034087.wav | {
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Signed-off-by: roman-vygon <[email protected]> | {
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} | null | null | null | **Describe the bug**
Using [speaker_diarize.py](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/speaker_diarize.py ) results in a negative offset for a file in a manifest, which leads to an error in dataloader:
```
File "speaker_diarize.py", line 45, in main
sd_model.diarize()
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py", line 341, in diarize
self._run_vad(manifest_vad_input)
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py", line 193, in _run_vad
for i, test_batch in enumerate(tqdm(self._vad_model.test_dataloader())):
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/tqdm/std.py", line 1185, in __iter__
for obj in iterable:
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
data = self._next_data()
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/torch/_utils.py", line 425, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_label.py", line 476, in vad_frame_seq_collate_fn
return _vad_frame_seq_collate_fn(self, batch)
File "/home/dev/anaconda3/envs/oMeN/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_label.py", line 245, in _vad_frame_seq_collate_fn
audio_signal = torch.stack(audio_signal)
RuntimeError: stack expects a non-empty TensorList
```
Resulting manifest:
```
{"audio_filepath": "test.wav", "duration": 2.2756875, "label": "infer", "text": "_", "offset": -0.15}
```
**Steps/Code to reproduce bug**
Command:
python speaker_diarize.py diarizer.paths2audio_files=files.txt diarizer.vad.model_path="vad_telephony_marblenet" diarizer.vad.threshold=0.7 diarizer.speaker_embeddings.model_path="speakerverification_speakernet" diarizer.out_dir="demo"
Audio file with manifest:
[reproduce.zip](https://github.com/NVIDIA/NeMo/files/6799603/reproduce.zip)
**Expected behavior**
No negative offsets in manifests.
**Environment overview (please complete the following information)**
Nemo version 1.1.0
Pytorch version 1.9.0
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} | null | null | null | **Describe the bug**
"speech_to_text_bpe.py"(ASR example) with ddp (8-gpus) failed to run.
It produced a "RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable"
**Steps/Code to reproduce bug**
After generating the manifest for LibriSpeech dataset and tokenization, I ran the following script in a command line
(Below codes are from the comments in examples/asr directory, actually)
python speech_to_text_bpe.py \
model.train_ds.manifest_filepath=librispeech/train_all.json \
model.validation_ds.manifest_filepath=librispeech/dev_all.json \
model.tokenizer.dir=librispeech/tokenizer_spe_bpe_v5000 \
model.tokenizer.type=bpe \
trainer.gpus=-1 \
trainer.accelerator="ddp" \
trainer.max_epochs=100 \
model.optim.name="adamw" \
model.optim.lr=0.001 \
model.optim.betas=[0.9,0.999] \
model.optim.weight_decay=0.0001 \
model.optim.sched.warmup_steps=2000
exp_manager.create_wandb_logger=True \
exp_manager.wandb_logger_kwargs.name="exp.tst" \
exp_manager.wandb_logger_kwargs.project="prj.tst"
**Expected behavior**
It should run without errors
**Environment overview (please complete the following information)**
- Environment location: NVIDIA V100 x8, docker
- Method of NeMo install: from source (git-cloned on 10 July, installed with reinstall.sh)
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 18.04.5 LTS
- PyTorch version: 1.7.1+cu110
- Python version: 3.8.5
**Additional context**
Add any other context about the problem here.
Example: GPU model
---
No progress after installing Nemo. :(
Any help would be appreciated.
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} | Update QuartzNet with newer better speech recognition model _Citrinet Mandarin_.
Update TTS models to Fastpitch and HiFiGan
Signed-off-by: fayejf <[email protected]> | {
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} | The change introduces an encoder that can stack and execute several similar Jasper blocks side by side.
This implementation can be used to define and train CarneliNet.
Underlying ParallelBlock supports tower dropout to train models that can be reconfigured during inference time.
# Usage
## Config
Define proper target for encoder. Use multiple values of kernel sizes to define the towers. The rest of the parameters will be shared across all of them.
```
...
encoder:
_target_: nemo.collections.asr.modules.ParallelConvASREncoder
...
jasper:
- filters: 384
kernel: [11, 13, 15, 17]
aggregation_mode: "dropout"
block_dropout: 0.1
...
```
## Code
Just call necessary model with correct config, as usual.
```
cfg = OmegaConf.load(cfg_path)
asr_model = nemo_asr.models.EncDecCTCModelBPE(cfg=cgf, trainer=trainer)
```
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] | closed | false | null | [] | null | null | 2021-07-08T18:28:11 | 2021-07-08T18:44:56 | 2021-07-08T18:44:56 | NONE | {
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docker run --gpus all -it --rm -v abc:/NeMo --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit
memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:21.05-py3
Getting error message:
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].
checking if nvidia driver is installed
nvidia-smi
Thu Jul 8 14:23:48 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 465.27 Driver Version: 465.27 CUDA Version: 11.3 |
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} | The change introduces an encoder that can stack and execute several similar Jasper blocks side by side.
This implementation can be used to define and train CarneliNet.
Underlying ParallelBlock supports tower dropout to train models that can be reconfigured during inference time.
# Usage
## Config
Define proper target for encoder. Use multiple values of kernel sizes to define the towers. The rest of the parameters will be shared across all of them.
```
...
encoder:
_target_: nemo.collections.asr.modules.ParallelConvASREncoder
...
jasper:
- filters: 384
kernel: [11, 13, 15, 17]
aggregation_mode: "dropout"
block_dropout: 0.1
...
```
## Code
Just call necessary model with correct config, as usual.
```
cfg = OmegaConf.load(cfg_path)
asr_model = nemo_asr.models.EncDecCTCModelBPE(cfg=cgf, trainer=trainer)
```
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} | Sometimes I have ckpt files that I want to use/test while my model is still training.
This script simply converts those ckpt's to nemo files to enable various use cases with Jarvix/Onnx/etc. | {
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We are controlling Checkpoints and ".nemo" directly through Drive | {
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