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`ValueError` when trying to import pretrained model.
**Steps/Code to reproduce bug**
```python
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
[NeMo I 2021-07-06 22:51:41 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo to /root/.cache/torch/NeMo/NeMo_1.1.0/QuartzNet15x5Base-En/2b066be39e9294d7100fb176ec817722/QuartzNet15x5Base-En.nemo
[NeMo I 2021-07-06 22:51:42 cloud:79] Download from cloud failed. Attempt 1 of 3
[NeMo I 2021-07-06 22:51:42 cloud:79] Download from cloud failed. Attempt 2 of 3
[NeMo I 2021-07-06 22:51:42 cloud:79] Download from cloud failed. Attempt 3 of 3
ValueError: Not able to download url right now, please try again.
```
**Expected behavior**
Model is downloaded without error.
**Environment overview (please complete the following information)**
- Environment location: Databricks
- Method of NeMo install: Tried with both pip magic `%pip install nemo_toolkit['all']` and directly in Databricks.
**Environment details**
- Databricks runtime 8.3 ML
- PyTorch==1.8.1
- Python version==3.8.8
**Additional context**
I thought this was similar to [#2555](https://github.com/NVIDIA/NeMo/issues/2255) but I have confirmed that my Databricks cluster has internet access. | {
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- Speed up on NME analysis by subsampling.
- Fixed the diarization evaluation parameter (no score collar: from 0.25 s to 0.5 s) | {
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I am using the02_Online_ASR_Microphone_Demo.ipynb notebook. Followed all the steps but in the end, I am getting ERROR: No audio input device found. Colab has microphone access through the browser. Any fix for this?
Thanks | {
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I'm doing a training with 10_ASR_CTC_Language_Finetuning, I was wondering if you would have a reference of these settings, 
which values would be better for a training in Portuguese?
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} | null | null | null | It would be helpful to add Hyperpeter Optimization with ray tune and pytorch lightning for different nemo models (ASR/TTS)
This will help in finding the best set of paramters when training using Nemo on different sorts of data.
This can make the training more straightforward and can be of an advantage compared to other repos.
Thank you! | {
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I am trying to finetune the Citrinet_1024 nemo model first on 50K chunks of the Indian accent English-language dataset using 1 gpu.
Since [here](https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/citrinet) is no config file for citrinet_1024. So I changed the filters 512 to 1024, set the dropout to 0.01 and changed the model name from "Citrinet-512-8x-Stride" to "Citrinet-1024-8x-Stride".
Is that sufficient which I did above or need to change something else in the config file?
The config file which I modified is attached below in txt format;
[citrinet_1024.txt](https://github.com/NVIDIA/NeMo/files/6759687/citrinet_1024.txt)
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} | * Brought in the needed changes from Vahid's earlier PR: https://github.com/NVIDIA/NeMo/pull/2359/files
* Tested conversion with scripts/export/convasr_to_single_onnx.py
* Tested the converted large, medium and small models with dev-clean and WER is very close (within 0.01%) of correspondiong .nemo models
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} | null | null | null | **Describe the bug**
Hi,
Today after I have installed NeMo, I have tried the ASR both in English and Chinese -- it all works very well!!
Also I did a quick tts test on the English based on following setting -- it works!
# Download and load the pretrained tacotron2 model
spec_generator = SpectrogramGenerator.from_pretrained("tts_en_tacotron2")
# Download and load the pretrained waveglow model
vocoder = Vocoder.from_pretrained("tts_waveglow_88m")
Could you please let me know what is the pre-trained model to be used for the Mandarin TTS? Also can tts_waveglow_88m is also used for that case?
Thanks a lot
Jimmy
**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 : ubuntu 18.04
- PyTorch version 1.9.0+cu102
- Python version: 3.6
**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/2436 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2436/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2436/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2436/events | https://github.com/NVIDIA/NeMo/issues/2436 | 936,003,216 | MDU6SXNzdWU5MzYwMDMyMTY= | 2,436 | Model QuartzNet15x5Base-Zh was not found | {
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} | null | null | null | **Describe the bug**
Hi,
I am new to NeMo. After I have installed the NeMo in my dGPU laptop, I have written a simple test to test the ASR.
1. first I have test the .wav file in English ("Where is the bus station?")
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En").cuda()
print("\nGot the TEXT")
english_text = quartznet.transcribe([Audio_sample])
print("\n")
print(english_text) --- it prints the "where is the bus station" ----Good thing
2. Then: I have test the .wav file in Chinese in same word where is the bus station in Chinese
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-Zh").cuda()
But I have got: FileNotFoundError: Model QuartzNet15x5Base-Zh was not found.
Per NeMo user guide, QuartzNet15x5Base-Zh is supported model.
Can you let me know how to solve the problem?
Thanks
Jimmy
**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: Ubuntu 18.04
- PyTorch version 1.9.0+cu102
- Python version: 3.6
**Additional context**
Add any other context about the problem here.
Example: GPU model: RTX 2080Ti
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https://api.github.com/repos/NVIDIA/NeMo/issues/2434 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2434/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2434/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2434/events | https://github.com/NVIDIA/NeMo/issues/2434 | 935,449,234 | MDU6SXNzdWU5MzU0NDkyMzQ= | 2,434 | Error: Cannot resolve schedulerDictConfig when run VAD training tutorial | {
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} | null | null | null | Hi teams, I tried to run this notebook https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_Detection.ipynb
However, when I ran `trainer.fit(vad_model)`. It returns following errors
`---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-60-6abb1ca1f787> in <module>
----> 1 trainer.fit(vad_model)
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
458 self.call_setup_hook(model)
459 self.call_hook("on_before_accelerator_backend_setup", model)
--> 460 self.accelerator.setup(self, model) # note: this sets up self.lightning_module
461
462 # ----------------------------
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/accelerators/cpu.py in setup(self, trainer, model)
13 raise MisconfigurationException(f"Device should be CPU, got {self.root_device} instead")
14
---> 15 return super().setup(trainer, model)
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in setup(self, trainer, model)
67 """
68 self.connect_training_type_plugin(self.training_type_plugin, model)
---> 69 self.setup_optimizers(trainer)
70 self.connect_precision_plugin(self.precision_plugin)
71
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in setup_optimizers(self, trainer)
314 return
315 optimizers, lr_schedulers, optimizer_frequencies = self.training_type_plugin.init_optimizers(
--> 316 trainer=trainer, model=self.lightning_module
317 )
318 self.optimizers = optimizers
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in init_optimizers(self, trainer, model)
158
159 def init_optimizers(self, trainer: "Trainer", model: LightningModule):
--> 160 return trainer.init_optimizers(model)
161
162 def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs):
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/optimizers.py in init_optimizers(self, model)
32 def init_optimizers(self, model: LightningModule) -> Tuple[List, List, List]:
33 self._lightning_optimizers = None
---> 34 optim_conf = model.configure_optimizers()
35 if optim_conf is None:
36 rank_zero_warn(
/opt/conda/lib/python3.7/site-packages/nemo/core/classes/modelPT.py in configure_optimizers(self)
760
761 def configure_optimizers(self):
--> 762 self.setup_optimization()
763
764 if self._scheduler is None:
/opt/conda/lib/python3.7/site-packages/nemo/core/classes/modelPT.py in setup_optimization(self, optim_config)
752 # Try to instantiate scheduler for optimizer
753 self._scheduler = prepare_lr_scheduler(
--> 754 optimizer=self._optimizer, scheduler_config=scheduler_config, train_dataloader=self._train_dl
755 )
756
/opt/conda/lib/python3.7/site-packages/nemo/core/optim/lr_scheduler.py in prepare_lr_scheduler(optimizer, scheduler_config, train_dataloader)
610
611 # Get the scheduler class from the config
--> 612 scheduler_cls = get_scheduler(scheduler_name, **scheduler_args)
613
614 # Instantiate the LR schedule
/opt/conda/lib/python3.7/site-packages/nemo/core/optim/lr_scheduler.py in get_scheduler(name, **kwargs)
396 if name not in AVAILABLE_SCHEDULERS:
397 raise ValueError(
--> 398 f"Cannot resolve scheduler{name}'. Available optimizers are : " f"{AVAILABLE_SCHEDULERS.keys()}"
399 )
400
ValueError: Cannot resolve schedulerDictConfig'. Available optimizers are : dict_keys(['WarmupPolicy', 'WarmupHoldPolicy', 'SquareAnnealing', 'CosineAnnealing', 'NoamAnnealing', 'WarmupAnnealing', 'InverseSquareRootAnnealing', 'SquareRootAnnealing', 'PolynomialDecayAnnealing', 'PolynomialHoldDecayAnnealing', 'StepLR', 'ExponentialLR', 'ReduceLROnPlateau', 'CyclicLR'])
`
Would you mind helping me solve this bug?
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} | null | null | null | **Describe the bug**
I trained SpeakerNet with my own dataset and got checkpoint to test speaker diarization. I tried both configs of SpeakerNet in here.
[Configs](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_recognition/conf)
Because pytorch lightning trainer save checkpoint as .ckpt format so i using model.load_from_checkpoint to load trained model.
**Code to reproduce bug**
```
def _init_speaker_model(self):
model_path = self._cfg.diarizer.speaker_embeddings.model_path
if model_path is not None and model_path.endswith('.nemo'):
self._speaker_model = ExtractSpeakerEmbeddingsModel.restore_from(model_path)
logging.info("Speaker Model restored locally from {}".format(model_path))
elif model_path.endswith('.ckpt'):
self._speaker_model = EncDecClassificationModel.load_from_checkpoint(model_path)
logging.info("Speaker model loaded locally from {}".format(model_path))
else:
logging.info("Speaker model not available")
```
This is error log:
```
File "infer.py", line 42, in <module>
sd_model = ClusteringDiarizer(cfg=config)
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/models/clustering_diarizer.py", line 92, in __init__
self._init_speaker_model()
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/models/clustering_diarizer.py", line 107, in _init_speaker_model
self._speaker_model = EncDecClassificationModel.load_from_checkpoint(model_path)
File "/opt/conda/lib/python3.6/site-packages/nemo/core/classes/modelPT.py", line 445, in load_from_checkpoint
**kwargs,
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/saving.py", line 158, in load_from_checkpoint
model = cls._load_model_state(checkpoint, strict=strict, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/saving.py", line 198, in _load_model_state
model = cls(**_cls_kwargs)
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/models/classification_models.py", line 53, in __init__
self._update_decoder_config(self._cfg.decoder)
File "/opt/conda/lib/python3.6/site-packages/nemo/collections/asr/models/classification_models.py", line 489, in _update_decoder_config
labels = self.cfg.labels
File "/opt/conda/lib/python3.6/site-packages/omegaconf/dictconfig.py", line 305, in __getattr__
self._format_and_raise(key=key, value=None, cause=e)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/base.py", line 101, in _format_and_raise
type_override=type_override,
File "/opt/conda/lib/python3.6/site-packages/omegaconf/_utils.py", line 629, in format_and_raise
_raise(ex, cause)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/_utils.py", line 610, in _raise
raise ex # set end OC_CAUSE=1 for full backtrace
File "/opt/conda/lib/python3.6/site-packages/omegaconf/dictconfig.py", line 303, in __getattr__
return self._get_impl(key=key, default_value=DEFAULT_VALUE_MARKER)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/dictconfig.py", line 361, in _get_impl
node = self._get_node(key=key)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/dictconfig.py", line 383, in _get_node
self._validate_get(key)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/dictconfig.py", line 136, in _validate_get
key=key, value=value, cause=ConfigAttributeError(msg)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/base.py", line 101, in _format_and_raise
type_override=type_override,
File "/opt/conda/lib/python3.6/site-packages/omegaconf/_utils.py", line 694, in format_and_raise
_raise(ex, cause)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/_utils.py", line 610, in _raise
raise ex # set end OC_CAUSE=1 for full backtrace
omegaconf.errors.ConfigAttributeError: Key 'labels' is not in struct
full_key: labels
reference_type=Optional[Dict[Union[str, Enum], Any]]
object_type=dict
```
Any idea about this. Thanks a lot!
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} | null | null | null | **Describe the bug**
I am trying to fine-tune **stt_en_conformer_ctc_large** model using the following:
`asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_large")`
Then I set the training params:
```
# not working:
from omegaconf import OmegaConf, open_dict
params = asr_model.cfg
new_opt = copy.deepcopy(params.optim)
new_opt.lr = 0.0001
params['train_ds']['manifest_filepath'] = train_manifest_path
params['train_ds']['batch_size'] = 8
params['validation_ds']['manifest_filepath'] = dev_manifest_path
params['validation_ds']['batch_size'] = 8
# Use the smaller learning rate we set before
asr_model.setup_optimization(optim_config=new_opt)
# Point to the new training data for fine-tuning
asr_model.setup_training_data(train_data_config=params['train_ds'])
# Point to the new validation data for fine-tuning
asr_model.setup_validation_data(val_data_config=params['validation_ds'])
```
Received error:
```
~/NeMo/nemo/collections/asr/models/ctc_models.py in setup_training_data(self, train_data_config)
423 # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
424 # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
--> 425 if isinstance(self._trainer.limit_train_batches, float):
426 self._trainer.limit_train_batches = int(
427 self._trainer.limit_train_batches
AttributeError: 'NoneType' object has no attribute 'limit_train_batches'
```
However, it **works** when I load the config from the config_bpe.yaml:
```
from omegaconf import OmegaConf, open_dict
# ! wget -P configs/ https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/asr/conf/citrinet/config_bpe.yaml --no-check-certificate
params = OmegaConf.load('configs/config_bpe.yaml')
new_opt = copy.deepcopy(params.model.optim)
new_opt.lr = 0.0001
params['model']['train_ds']['manifest_filepath'] = train_manifest_path
params['model']['train_ds']['batch_size'] = 8
params['model']['validation_ds']['manifest_filepath'] = dev_manifest_path
params['model']['validation_ds']['batch_size'] = 8
# Use the smaller learning rate we set before
asr_model.setup_optimization(optim_config=new_opt)
# Point to the new validation data for fine-tuning
asr_model.setup_validation_data(val_data_config=params['model']['validation_ds'])
# Point to the data we'll use for fine-tuning as the training set
asr_model.setup_training_data(train_data_config=params['model']['train_ds'])
```
What is the correct procedure? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2425 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2425/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2425/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2425/events | https://github.com/NVIDIA/NeMo/issues/2425 | 933,551,648 | MDU6SXNzdWU5MzM1NTE2NDg= | 2,425 | Adding training_step to parameters in ModelPT breaks DP accelerator | {
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] | closed | false | null | [] | null | null | 2021-06-30T10:39:03 | 2021-07-13T15:46:37 | 2021-07-13T15:46:37 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
Similar to https://github.com/pytorch/pytorch/issues/8637, ModelPT class adds training_step to its parameters [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py#L159). Because of that, when training with 'dp' accelerator workers call the same instanced function instead of copies on different devices.
**Steps/Code to reproduce bug**
Create any model inherited from ModelPT.
Fit with torch lightning trainer and 'dp' accelerator.
Check module id [here](https://github.com/PyTorchLightning/pytorch-lightning/blob/4af8eff0a13309b2e030c423db91156b1e869fac/pytorch_lightning/overrides/base.py#L84) and in your training_step - they will differ.
**Expected behavior**
No function should be a parameter in the class.
**Environment overview (please complete the following information)**
- Environment location: My own machine
- Method of NeMo install: pip install nemo v1.0.2
**Environment details**
- OS version Ubuntu 18.04
- PyTorch version 1.9.0
- Python version 3.8.3
**Additional context**
I understand that NeMo doesn't support DP accelerator, but this is a very hard to trace bug that can be easily fixed and can help someone in the future. | {
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} | # Bugfix
- Onnx is at 1.9, onnxruntime is at 1.8
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | # Changelog
- ONNX version updated for notebooks
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- Parallelize CPU and GPU unit tests
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | Typo for Voice Activi**t**y Detection notebooks/links fix
Update links in docs for branches to **stable**
Update links in notebooks for branches to **stable**
Will submit a PR to update links in **main** branch to main once this PR has been merged to r1.1.0 and main
Please make sure to update **stable** before release | {
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} | Added code for training/evaluating a text normalization system which consists of:
(1) A tagger - for identifying semiotic classes in the input
(2) A decoder - for decoding the spans of semiotic classes into spoken form.
@yzhang123 @ekmb | {
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I'm testing the Offline_ASR in Colab, at the beamsearch part I getting some results, as expected. But I just need the first
hypothesis, so I won't need to process the other hypothesis. Have any way to do this? A way to just process one hypothesis. | {
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I'm using the Offline_ASR notebook testing some large and small values for width, alpha and beta in 'beamsearch', all is correct and returning good results, but needs so much time to return those results. This script is running in Colab.
Have some way to run this notebook or only the beamsearch part with a GPU, or just accelerate in general?
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} | null | null | null | Hello nemo team,
I have a queue of audio files waiting to be transcribed. when I run the citrinet model on them I see there's still free memory on the gpu hence my question - is there a way and how, to run multiple nemo models (can be all citrinet for example, but also others) concurrently on a single gpu?
btw unrelated: is there a compatible pretrained LM model for bpe models (citrinet)? also is downsampling needed/important for audio files higher than 16kHz ? (22, 41..) so far it seems to handle them well.
Thank you 😊 | {
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} | null | null | null | **Describe the bug**
The data preparation script `run_sample.sh` errors out pretty frequently, caused by opening too many files at the same time and hitting the process file limit. This seems to arise from the 'prepare_data.py' script which is called
**Steps/Code to reproduce bug**
(This seems to stem from the use of multiprocessing, so this may not be consistently reproducible)
1. Create a data directory with ~1000 files
2. Run the script
3. Script fails before completion of step 1 (`prepare_data.py`), with an OS Error
**Expected behavior**
Script should complete successfully
**Environment overview (please complete the following information)**
- Environment location: Local dev system
- Method of NeMo install: install from source (as described in readme, in a Conda environment)
**Environment details**
- OS version: Linux 5.12.9-arch1-1 x86_64 GNU/Linux
- PyTorch version: 1.9.0
- Python version: 3.9.5
**Additional context**
I was able to solve the issue by switching the use of `multiprocessing.Process` with `multiprocessing.Pool` (and other changes required to use it). Not sure if it is a robust solution, or if it can be solved in other ways
| {
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} | null | null | null | How to fine-tune **nemo:asrlm_en_transformer_large_ls LM** [here](https://ngc.nvidia.com/catalog/models/nvidia:nemo:asrlm_en_transformer_large_ls)
I found this training script here (https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/transformer_lm.py), but I did not know how to use it? can you give more instruction. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2407 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2407/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2407/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2407/events | https://github.com/NVIDIA/NeMo/pull/2407 | 930,318,367 | MDExOlB1bGxSZXF1ZXN0Njc4MDMzNzgx | 2,407 | Merge r1.1 bugfixes into main | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2406 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2406/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2406/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2406/events | https://github.com/NVIDIA/NeMo/issues/2406 | 930,213,486 | MDU6SXNzdWU5MzAyMTM0ODY= | 2,406 | Wav2Vec2 Fine-tuning Tutorial | {
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} | null | null | null | Hi,
As you currently don't have a tutorial for training wav2vec2 #2342, I tried to create a new yaml file to train wav2vec2.
However, I didn't succeed and I need your guidance and help.
I have created a Colab example that replicate my experiment here:
https://colab.research.google.com/drive/1kJQA0wm7EVLGsDlZmhCv-2siXXJvTpUt?usp=sharing
This is the current error:
```
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
[NeMo I 2021-06-25 14:10:52 exp_manager:216] Experiments will be logged at /content/nemo_experiments/Wav2vec_Pretrain/2021-06-25_14-10-52
[NeMo I 2021-06-25 14:10:52 exp_manager:563] TensorboardLogger has been set up
Traceback (most recent call last):
File "wav2vec_pretrain.py", line 97, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 103, in wrapper
strict=None,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 347, in _run_hydra
lambda: hydra.run(
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 201, in run_and_report
raise ex
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 198, in run_and_report
return func()
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 350, in <lambda>
overrides=args.overrides,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 112, in run
configure_logging=with_log_configuration,
File "/usr/local/lib/python3.7/dist-packages/hydra/core/utils.py", line 127, in run_job
ret.return_value = task_function(task_cfg)
File "wav2vec_pretrain.py", line 92, in main
wav2vec_encoder_model = Wav2VecEncoderModel(cfg=cfg.model, trainer=trainer)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/models/wav2vec/wav2vec_model.py", line 54, in __init__
self.global_rank = 0
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1178, in __setattr__
object.__setattr__(self, name, value)
AttributeError: can't set attribute
```
Your feedback is highly appreciated. | {
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} | null | null | null | I am following tutorial "08_ASR_with_Subword_Tokenization.ipynb" to fine-tune Citrinet.
My question is: which configuration file should I modify for fine-tuning:
option 1: the original config file of the model obtained from **asr_model.cfg**
option 2: download **config_bpe** [here ](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/config_bpe.yaml)
Note, I want to use "**stt_en_conformer_ctc_large**" model.
Thanks in advance! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2404 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2404/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2404/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2404/events | https://github.com/NVIDIA/NeMo/issues/2404 | 929,873,366 | MDU6SXNzdWU5Mjk4NzMzNjY= | 2,404 | No pad_token,eos_token,sep_token,cls_token in SentencePieceTokenizer | {
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Hi I find actually no pad_token,eos_token,sep_token,cls_token in SentencePieceTokenizer's property. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2403 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2403/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2403/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2403/events | https://github.com/NVIDIA/NeMo/pull/2403 | 929,873,264 | MDExOlB1bGxSZXF1ZXN0Njc3NjYwNTY0 | 2,403 | remove accelerator=DDP in tutorial notebooks to avoid errors. | {
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} | Signed-off-by: Hoo Chang Shin <hshin@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2402 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2402/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2402/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2402/events | https://github.com/NVIDIA/NeMo/pull/2402 | 929,675,711 | MDExOlB1bGxSZXF1ZXN0Njc3NDkxMDk2 | 2,402 | Correct typos and issues with notebooks | {
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} | # Bugfix
- Correct OmegaConf issue with primer notebook
- Correct typo in ASR notebook | {
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} | Adding blossom workflow for testing blossom CI. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2398 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2398/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2398/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2398/events | https://github.com/NVIDIA/NeMo/pull/2398 | 928,724,775 | MDExOlB1bGxSZXF1ZXN0Njc2Njg4OTc0 | 2,398 | Bugfix adaptive spec augment time masking | {
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} | # Bugfix
- Fix a bug in adaptive time masking where the mask start index could begin in a padded region, therefore making the mask useless (ASR losses dont access masked sections when computing loss).
- In effect, augmentation was reduced. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2397 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2397/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2397/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2397/events | https://github.com/NVIDIA/NeMo/pull/2397 | 928,713,482 | MDExOlB1bGxSZXF1ZXN0Njc2Njc5MTQz | 2,397 | Mean normalization | {
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- Correct spelling mistakes
- Correct branch of notebook
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} | This PR adds a bottleneck architecture to NMT models, along with support for training VAE and MIM latent variable models.
# Summary
The bottleneck class (MTBottleneckModel) supports two architectures:
* No bottleneck: model_type=seq2seq, the usual NMT mdoel.
* Fixed-size bottleneck: model_type in [seq2seq-br, seq2seq-mim, seq2seq-vae], where the output of the encoder is projected to a fixed number of steps.
The projection to a fixed number of steps is based on the paper <https://arxiv.org/pdf/1703.03130.pdf>.
The idea is to use K attention heads to compute K weighted-average hidden states, projecting a variable number of steps into K steps.
The bottleneck variants offer different losses:
* The loss of seq2seq-br is reconstruction only (like seq2seq).
* The loss of seq2seq-mim is reconstruction + latent entropy minimization (See MIM, <https://arxiv.org/pdf/2003.02645.pdf>).
* The loss of seq2seq-vae is reconstruction + latent entropy regularization (See <https://arxiv.org/pdf/1312.6114.pdf>) correspondingly.
# YAML Configuration
The following configurations were added to the YAML config:
```
model:
model_type: 'seq2seq-br' # supports seq2seq, seq2seq-br, seq2seq-mim, seq2seq-vae (see description above)
min_logv: -8 # minimal allowed logv for seq2seq-mim
ortho_loss_coef: 0.0 # orthogonality coefficient for attention bridge
att_bridge_size: 512 # dimension of a step in attention bridge
att_bridge_k: 16 # fixed number of steps in attention bridge
att_bridge_inner_size: 1024 # feedforward size in attention bridge
non_recon_warmup_batches: 200000 # warm-up steps for seq2seq-mim, seq2seq-vae
recon_per_token: true # when false reconstruction is computed per sample, not per token
```
* ortho_loss_coef - encou
* non_recon_warmup_batches - anneals the KL divergence term (for VAE) or latent entropy term (for MIM)
* recon_per_token - if false, loss is computed per sample (i.e., summed over all tokens), otherwise loss is averaged per token (the default behaviour).
# Usage
See usage example below, training a seq2seq-br with 32 steps.
NOTE: max_generation_delta must be big enough to allow the generation of the longest sequence given the chosen bottleneck size.
```
python -- examples/nlp/machine_translation/enc_dec_nmt-bottleneck.py \
--config-name=aayn_bottleneck \
...
model.max_generation_delta=256 \
...
model.model_type=seq2seq-br \
model.att_bridge_size=1024 \
model.ortho_loss_coef=0.0 \
model.att_bridge_k=32 \
model.att_bridge_inner_size=1024 \
model.recon_per_token=true \
model.non_recon_warmup_batches=150000 \
...
```
# Additional Info
* The attention bridge class (AttentionBridge) was added to ```nemo/collections/nlp/modules/common/transformer/transformer_modules.py```.
* The bottleneck class (MTBottleneckModel) supports logging of various loss terms. | {
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} | fix 'DDP' to 'ddp'. | {
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} | null | null | null | Hi, I have tried the tts inference notebook with the different tts models and vocoders. I tried adding question mark and exclamations to the end of the sentence but they have no effect on the intonation of the synthesized speech. | {
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} | null | null | null | **Describe the bug**
I'm receiving these warnings and errors when I'm training Quartznet using spanish dataset at A100:
***Warnings:***
- Could not import torchaudio. Some features might not work. warnings.warn('Could not import torchaudio. Some features might not work.')libibverbs:
- Warning: no userspace device-specific driver found for /sys/class/infiniband_verbs/uverbs0
***Error***:
- Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.0
**Environment overview (please complete the following information)**
- Docker image: https://ngc.nvidia.com/catalog/containers/nvidia:nemo
- Using Enroot to start the instance
- Following the steps of: Spanish__Cross_Language.ipynb
- NeMo Parameters:
- nproc_per_node=8
- batch_size=32
- amp_opt_level = 'O1'
- Dataset: Mozilla Common Voice - Spanish Version 6.1
- Running at a DGX A100 80G
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} | null | null | null | I want to finetune pre-trained citrinet-1024 for my **8Khz** data set. What steps should I follow?
I did these steps
1) tokenizer traininig with my train data
But then
2) asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_citrinet_1024") does not see my config file and gives error
3) then i created the citrinet model from scratch using config_bpe.yaml file,it gives very high WERs.
What should i do for this task ?
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] | closed | false | null | [] | null | null | 2021-06-22T06:27:43 | 2021-06-23T13:50:44 | 2021-06-23T13:49:45 | NONE | {
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} | null | null | null | **Describe the bug**
Given the default configuration of fastspeech2 and HifiGAN, their implemented learning rates deviate severely from what's reported in their respective original papers.
**Steps/Code to reproduce bug**
Preprocess LJSpeech as specified by the official NeMo example,
python fastspeech2.py (or hifigan.py)
**Expected behavior**
**What is observed**
FastSpeech2
HiFiGAN
We believe the error comes from the missing scaler, which needs to be 1000. given the default model dimension 256.
mult = self._normalize * min(step ** (-0.5), step * (self.warmup_steps ** (-1.5)))
mult = 1000. * self._normalize * min(step ** (-0.5), step * (self.warmup_steps ** (-1.5)))
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} | # Changelog
- Update docker container to nemo:1.0.1
- Add Citrinet 1024 Gamma 0.25 model card for Mandarin to CTC char models
- Update tokenizer scripts to support adding bos, eos and pad tokens to SentencePiece tokenizers via `--spe_bos`, `--spe_eos` and `--spe_pad` flags.
- Update dataset building script to always provide a max length when building tarred datasets.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | We need to instantiate the `NLPDDPPlugin` with the `num_nodes` gotten from the trainer config. | {
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} | This PR implements the following features
1. ListConfig for training NMT datasets that aren't restricted to multilingual training. Implication - users can specify multiple training text/tar files and sample from different sources with probabilities.
For example,
```
python examples/nlp/machine_translation/enc_dec_nmt.py -cn aayn_base \
...
model.train_ds.tar_files=["/path/to/dataset1_tarfiles_OP_1..990_CL_.tar, /path/to/dataset2_tarfiles_OP_1..200_CL_.tar"] \
model.train_ds.concat_sampling_technique=random \
model.train_ds.concat_sampling_probabilities=["0.6, 0.4"] \
...
```
2. Right to left factorized models. Implication - users can train NMT models that generate sequences from right to left (YTTM only). This is typically used in offline knowledge distillation.
For example, right to left training can be enabled with the following flags.
```
python examples/nlp/machine_translation/enc_dec_nmt.py -cn aayn_base \
...
model.decoder_tokenizer.r2l=true \
model.encoder_tokenizer.r2l=false \
...
```
It is also possible to create a tarred dataset that is right to left using:
```
python examples/nlp/machine_translation/create_tarred_parallel_dataset.py \
...
--src_fname /path/to/source.txt \
--tgt_fname /path/to/target.txt \
--decoder_tokenizer_r2l \
...
```
3. Implements noisy channel re-ranking. Implication - given a reverse direction translation model and a target side language model, this re-ranks beam search outputs based on (a) forward direction translation model scores, (b) reverse direction scores (c) language model scores. This typically improves translation quality. Requirement: All models must be trained with the same tokenizer.
Noisy channel re-ranking involves two steps (a) Generating translations from the forward model (typically with large beam sizes) (b) Re-ranking beam candidates
```
python examples/nlp/machine_translation/nmt_transformer_infer.py \
--model /path/to/forward_model.nemo \
--srctext /path/to/source_test.txt \
--tgtout /path/to/translations.txt \
--write_scores \
--beam_size 15 \
...
```
This will generate two files (a) /path/to/translations.txt and (b) /path/to/translations.txt.scores. The second is a TSV file structured as source \t target \t score for every beam hypothesis. We will use this to re-rank beam candidates
```
python examples/nlp/machine_translation/noisy_channel_reranking.py \
--reverse_model /path/to/reverse_model.nemo \
--beam_size 15 \
--tgtout /path/to/reranked_translations.txt \
--srctext /path/to/translations.txt.scores \
--lm_model /path/to/target_lm.nemo \
--forward_model_coef 1.0 \
--reverse_model_coef 0.5 \
--target_lm_coef 0.07 \
--len_pen 0.7 \
...
```
4. Ensemble decoding. Implication - given a list of translation models (trained with the same tokenizer), ensemble decoding computes the probability over the next token in the sequence as the average of the probabilities given by each individual model. The averaged probabilities are used to do beam search. This again improves translation quality.
```
python examples/nlp/machine_translation/nmt_transformer_infer.py \
--model /path/to/forward_model_1.nemo,/path/to/forward_model_2.nemo,/path/to/forward_model_3.nemo \
--srctext /path/to/source_test.txt \
--tgtout /path/to/translations_ensemble.txt \
--beam_size 4 \
...
```
Note: Noisy channel re-ranking also supports an ensemble of reverse NMT models and can also be provided as a comma-separated string. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2377 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2377/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2377/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2377/events | https://github.com/NVIDIA/NeMo/issues/2377 | 926,155,193 | MDU6SXNzdWU5MjYxNTUxOTM= | 2,377 | examples/asr/quantization/speech_to_text_quant_infer.py run error | {
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nvcr.io/nvidia/nemo:1.0.0rc1
command:
python speech_to_text_quant_infer.py --asr_model ./QuartzNet15x5Base-En-max-16.nemo --dataset ./test.json --sensitivity --onnx
error:
W0621 12:00:53.572198 140041427298112 tensor_quantizer.py:280] Use Pytorch's native experimental fake quantization.
[NeMo W 2021-06-21 12:01:03 nemo_logging:349] /opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py:1112: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input audio_signal
'Automatically generated names will be applied to each dynamic axes of input {}'.format(key))
[NeMo W 2021-06-21 12:01:03 nemo_logging:349] /opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py:1112: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input logprobs
'Automatically generated names will be applied to each dynamic axes of input {}'.format(key))
[NeMo W 2021-06-21 12:01:03 nemo_logging:349] /opt/conda/lib/python3.6/site-packages/pytorch_quantization-2.1.0-py3.6-linux-x86_64.egg/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
inputs, amax.item() / bound, 0,
[NeMo W 2021-06-21 12:01:03 nemo_logging:349] /opt/conda/lib/python3.6/site-packages/pytorch_quantization-2.1.0-py3.6-linux-x86_64.egg/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])
Warning: Constant folding supported for only opsets 9, 10, and 11. Constant folding not applied.
Traceback (most recent call last):
File "speech_to_text_quant_infer.py", line 202, in <module>
asr_model.export('./quant.onnx', onnx_opset_version=13)
File "/opt/conda/lib/python3.6/site-packages/nemo/core/classes/exportable.py", line 222, in export
example_outputs=output_example,
File "/opt/conda/lib/python3.6/site-packages/torch/onnx/__init__.py", line 230, in export
custom_opsets, enable_onnx_checker, use_external_data_format)
File "/opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py", line 91, in export
use_external_data_format=use_external_data_format)
File "/opt/conda/lib/python3.6/site-packages/torch/onnx/utils.py", line 662, in _export
_check_onnx_proto(proto)
RuntimeError: Unrecognized attribute: axis for operator QuantizeLinear
==> Context: Bad node spec: input: "encoder.encoder.0.mconv.0.conv.weight" input: "1001" input: "1003" output: "1004" name: "QuantizeLinear_9" op_type: "QuantizeLinear" attribute { name: "axis" i: 0 type: INT }
Any thoughts? | {
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} | null | null | null | Hello, I tried to run a BERT pre-training in a distributed fashion with [Ray](https://github.com/ray-project), more specifically using [ray_lightning](https://github.com/ray-project/ray_lightning) as a plugin. Apparently, the BERTLMModel cannot be serialized (stack trace below)
```
Traceback (most recent call last):
File "models/bert-nvidia/bert_pretraining.py", line 34, in main
trainer.fit(bert_model)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 499, in fit
self.dispatch()
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 546, in dispatch
self.accelerator.start_training(self)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 73, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray_lightning/ray_ddp.py", line 213, in start_training
results = self.execution_loop(trainer, tune_enabled=False)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray_lightning/ray_ddp.py", line 180, in execution_loop
model_ref = ray.put(model)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/_private/client_mode_hook.py", line 62, in wrapper
return func(*args, **kwargs)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/worker.py", line 1527, in put
object_ref = worker.put_object(value)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/worker.py", line 280, in put_object
serialized_value = self.get_serialization_context().serialize(value)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/serialization.py", line 326, in serialize
return self._serialize_to_msgpack(value)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/serialization.py", line 306, in _serialize_to_msgpack
self._serialize_to_pickle5(metadata, python_objects)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/serialization.py", line 266, in _serialize_to_pickle5
raise e
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/serialization.py", line 262, in _serialize_to_pickle5
inband = pickle.dumps(
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle_fast.py", line 73, in dumps
cp.dump(obj)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle_fast.py", line 580, in dump
return Pickler.dump(self, obj)
NotImplementedError: object proxy must define __reduce_ex__()
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
I was wondering if there was a quick work-around about this and if it's possible for me to fix it and still use the [BERT pre-training script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/bert_pretraining.py). Any help will be much appreciated, thanks in advance!
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} | null | null | null | Hi,
Can I use Persian Multiple speaker audio datasets for fine-tuning QuartzNet15*5? Will be the WER low as I fine-tune it with a single speaker dataset?
Thanks
**Environment overview**
- Environment location: Google Colab
- Method of NeMo install: !pip install nemo_toolkit[asr]
- NeMo version: 1.0.0
- Learning Rate: 1e-3
**Environment details**
- OS version : "Ubuntu20.04.3 LTS"
- PyTorch version : "1.7.1" | {
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} | # Changelog
- Adds Numpy + CPU + Numba CUDA support for FastEmit regularization from the paper [FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization](https://arxiv.org/abs/2010.11148)
# Usage
```yaml
model: # RNNT Model config
...
loss:
loss_name: "warprnnt_numba"
warprnnt_numba_kwargs:
fastemit_lambda: 0.0
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2373 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2373/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2373/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2373/events | https://github.com/NVIDIA/NeMo/pull/2373 | 925,252,101 | MDExOlB1bGxSZXF1ZXN0NjczNzcwMjM1 | 2,373 | Script for ASR inference on long files | {
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} | * speech_to_text_buffered_infer.py supports inference on long audio files by running inference on smaller chunks of audio and them merging the tokens for final transcription.
* This version only supports EncDecCTCModelBPE with AudioMelSpectrogramProcessor as the preprocessor
* Feature normalization is done on each buffer using "per_feature" normalization, future versions will have other methods for experimentation | {
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} | null | null | null | Hello.
I have successfully trained VAD according to your tutorial with my background data set. However, when I start training VAD with a background sample length of 0.1s, I get an error. Please tell me which parameter needs to be changed to avoid this.
`trainer.fit(vad_model)`
```
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
[NeMo I 2021-06-18 12:37:26 modelPT:751] Optimizer config = SGD (
Parameter Group 0
dampening: 0
lr: 0.01
momentum: 0.9
nesterov: False
weight_decay: 0.001
)
[NeMo I 2021-06-18 12:37:26 lr_scheduler:621] Scheduler "<nemo.core.optim.lr_scheduler.PolynomialHoldDecayAnnealing object at 0x7fcb9347f2e0>"
will be used during training (effective maximum steps = 2370) -
Parameters :
(power: 2.0
warmup_ratio: 0.05
hold_ratio: 0.45
min_lr: 0.001
last_epoch: -1
max_steps: 2370
)
| Name | Type | Params
-------------------------------------------------------------------
0 | spec_augmentation | SpectrogramAugmentation | 0
1 | preprocessor | AudioToMFCCPreprocessor | 0
2 | encoder | ConvASREncoder | 88.9 K
3 | decoder | ConvASRDecoderClassification | 258
4 | loss | CrossEntropyLoss | 0
5 | _accuracy | TopKClassificationAccuracy | 0
-------------------------------------------------------------------
89.2 K Trainable params
0 Non-trainable params
89.2 K Total params
0.357 Total estimated model params size (MB)
| Name | Type | Params
-------------------------------------------------------------------
0 | spec_augmentation | SpectrogramAugmentation | 0
1 | preprocessor | AudioToMFCCPreprocessor | 0
2 | encoder | ConvASREncoder | 88.9 K
3 | decoder | ConvASRDecoderClassification | 258
4 | loss | CrossEntropyLoss | 0
5 | _accuracy | TopKClassificationAccuracy | 0
-------------------------------------------------------------------
89.2 K Trainable params
0 Non-trainable params
89.2 K Total params
0.357 Total estimated model params size (MB)
[NeMo W 2021-06-18 12:37:27 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 24 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
warnings.warn(*args, **kwargs)
[NeMo W 2021-06-18 12:37:27 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 24 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
warnings.warn(*args, **kwargs)
ValueError Traceback (most recent call last)
<ipython-input-185-6abb1ca1f787> in <module>
----> 1 trainer.fit(vad_model)
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-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)
/opt/conda/lib/python3.8/site-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):
/opt/conda/lib/python3.8/site-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:
/opt/conda/lib/python3.8/site-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:
/opt/conda/lib/python3.8/site-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):
/opt/conda/lib/python3.8/site-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:
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
432
433 # model hook
--> 434 model_ref.optimizer_step(
435 self.trainer.current_epoch,
436 batch_idx,
/opt/conda/lib/python3.8/site-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):
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-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):
/opt/conda/lib/python3.8/site-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)
/opt/conda/lib/python3.8/site-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:
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-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,
/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py in wrapper(*args, **kwargs)
86 profile_name = "Optimizer.step#{}.step".format(obj.__class__.__name__)
87 with torch.autograd.profiler.record_function(profile_name):
---> 88 return func(*args, **kwargs)
89 return wrapper
90
/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
26 def decorate_context(*args, **kwargs):
27 with self.__class__():
---> 28 return func(*args, **kwargs)
29 return cast(F, decorate_context)
30
/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py in step(self, closure)
85 if closure is not None:
86 with torch.enable_grad():
---> 87 loss = closure()
88
89 for group in self.param_groups:
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in train_step_and_backward_closure()
730
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 )
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-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
/opt/conda/lib/python3.8/site-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:
/opt/conda/lib/python3.8/site-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):
/workspace/nistrian/NeMo/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:
/workspace/nistrian/NeMo/nemo/collections/asr/models/classification_models.py in training_step(self, batch, batch_nb)
470 self.training_step_end()
471 audio_signal, audio_signal_len, labels, labels_len = batch
--> 472 logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len)
473 loss_value = self.loss(logits=logits, labels=labels)
474
/workspace/nistrian/NeMo/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(
/workspace/nistrian/NeMo/nemo/collections/asr/models/classification_models.py in forward(self, input_signal, input_signal_length)
547 @typecheck()
548 def forward(self, input_signal, input_signal_length):
--> 549 logits = super().forward(input_signal=input_signal, input_signal_length=input_signal_length)
550 return logits
551
/workspace/nistrian/NeMo/nemo/collections/asr/models/classification_models.py in forward(self, input_signal, input_signal_length)
145 # Spec augment is not applied during evaluation/testing
146 if self.spec_augmentation is not None and self.training:
--> 147 processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_len)
148 encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_len)
149 logits = self.decoder(encoder_output=encoded)
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/workspace/nistrian/NeMo/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(
/workspace/nistrian/NeMo/nemo/collections/asr/modules/audio_preprocessing.py in forward(self, input_spec, length)
491 @typecheck()
492 def forward(self, input_spec, length=None):
--> 493 augmented_spec = self.spec_cutout(input_spec=input_spec)
494
495 # To run the Numba kernel, correct numba version is required as well as
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/workspace/nistrian/NeMo/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(
/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
26 def decorate_context(*args, **kwargs):
27 with self.__class__():
---> 28 return func(*args, **kwargs)
29 return cast(F, decorate_context)
30
/workspace/nistrian/NeMo/nemo/collections/asr/parts/submodules/spectr_augment.py in forward(self, input_spec)
143 for i in range(self.rect_masks):
144 rect_x = self._rng.randint(0, sh[1] - self.rect_freq)
--> 145 rect_y = self._rng.randint(0, sh[2] - self.rect_time)
146
147 w_x = self._rng.randint(0, self.rect_freq)
/opt/conda/lib/python3.8/random.py in randint(self, a, b)
246 """
247
--> 248 return self.randrange(a, b+1)
249
250 def _randbelow_with_getrandbits(self, n):
/opt/conda/lib/python3.8/random.py in randrange(self, start, stop, step, _int)
224 return istart + self._randbelow(width)
225 if step == 1:
--> 226 raise ValueError("empty range for randrange() (%d, %d, %d)" % (istart, istop, width))
227
228 # Non-unit step argument supplied.
ValueError: empty range for randrange() (0, -13, -13)
```
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} | null | null | null | Hi,
I was able to run the https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/10_ASR_CTC_Language_Finetuning.ipynb script in Colab with no problems. However, due to disk and GPU limitations, I took this script and ran it in my environment using JupyterLab.
When starting the training, exactly in the part `trainer.fit(char_model)` It crashes in the message:
**`"initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/2"`**
The only change made to the code is in this part, where I set that I want to use 2 GPUs and the ddp accelerator:
```
if torch.cuda.is_available():
gpus = 2
else:
gpus = 0
EPOCHS = 50
trainer = ptl.Trainer(gpus=gpus,
max_epochs=EPOCHS,
accumulate_grad_batches=1,
checkpoint_callback=False,
logger=False,
log_every_n_steps=5,
check_val_every_n_epoch=1,
accelerator="ddp")
```
**lightning_logs.txt**
```
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/2
```
**nemo_log_globalrank-0_localrank-0.txt**
```
[NeMo W 2021-06-17 18:46:12 nemo_logging:349] /opt/conda/lib/python3.6/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work
warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)
[NeMo W 2021-06-17 18:46:15 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.
[NeMo I 2021-06-17 18:47:06 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.0.2/stt_en_quartznet15x5/16661021d16e679bdfd97a2a03944c49/stt_en_quartznet15x5.nemo.
[NeMo I 2021-06-17 18:47:06 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.0.2/stt_en_quartznet15x5/16661021d16e679bdfd97a2a03944c49/stt_en_quartznet15x5.nemo
[NeMo I 2021-06-17 18:47:06 common:675] Instantiating model from pre-trained checkpoint
[NeMo W 2021-06-17 18:47:08 modelPT:138] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /data2/voices/train_1k.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: 32
trim_silence: true
max_duration: 16.7
shuffle: true
is_tarred: false
tarred_audio_filepaths: /asr_set_1.2/train/train_{0..1023}.tar
num_workers: 20
[NeMo W 2021-06-17 18:47:08 modelPT:145] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: /data2/voices/train_1k_samp.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: 32
shuffle: false
[NeMo I 2021-06-17 18:47:08 features:252] PADDING: 16
[NeMo I 2021-06-17 18:47:08 features:269] STFT using torch
[NeMo I 2021-06-17 18:47:09 modelPT:434] Model EncDecCTCModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.0.2/stt_en_quartznet15x5/16661021d16e679bdfd97a2a03944c49/stt_en_quartznet15x5.nemo.
[NeMo I 2021-06-17 18:47:09 ctc_models:333] Changed decoder to output to ['i', 'e', 'c', 'a', 'b', 'y', 'v', ' ', 'f', 'j', '5', 'â', 'ú', 'á', 'h', 'ç', "'", '9', 'é', 'p', 'n', '\x00', 'ö', 'ü', '3', 'ê', 'z', 'ó', 'õ', 'x', 'q', 'd', 'å', 'm', 'l', 'í', 's', 'o', 'u', 'à', 'g', 'k', 'è', 'r', 'ã', 'w', 'ñ', 'ò', 'ô', 't', '\ufeff', '2'] vocabulary.
[NeMo I 2021-06-17 18:47:09 <ipython-input-37-2dee6ca3573c>:7] Model encoder has been un-frozen
[NeMo I 2021-06-17 18:47:51 collections:173] Dataset loaded with 935459 files totalling 1020.24 hours
[NeMo I 2021-06-17 18:47:51 collections:174] 16725 files were filtered totalling 90.65 hours
[NeMo I 2021-06-17 18:47:52 collections:173] Dataset loaded with 2338 files totalling 10.50 hours
[NeMo I 2021-06-17 18:47:52 collections:174] 0 files were filtered totalling 0.00 hours
[NeMo I 2021-06-17 18:47:52 exp_manager:216] Experiments will be logged at /home/f2it/NeMo/dataSetMuitasHorasCompleta/experiments/lang-pt/ASR-Char-Model-Language-pt/2021-06-17_18-47-52
[NeMo I 2021-06-17 18:47:52 exp_manager:563] TensorboardLogger has been set up
[NeMo I 2021-06-17 18:47:54 modelPT:748] Optimizer config = Novograd (
Parameter Group 0
amsgrad: False
betas: [0.95, 0.5]
eps: 1e-08
grad_averaging: False
lr: 0.01
weight_decay: 0.001
)
[NeMo I 2021-06-17 18:47:54 lr_scheduler:621] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f7498d04f98>"
will be used during training (effective maximum steps = 2923350) -
Parameters :
(warmup_steps: null
warmup_ratio: 0.05
min_lr: 1.0e-05
last_epoch: -1
max_steps: 2923350
)
```
Any solution for this problem?
**Environment:**
pytorch-lightning 1.2.10
torch 1.8.0+cu111
nemo-toolkit 1.0.2
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} | # Changelog
- Add Citrinet Gamm 0.25 model card info
- Remove older models that are deprecated due to better checkpoints being availabls
- stt_zh_quartznet_15x5
- Cleanup duplicated documentation for ASR notes | {
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} | null | null | null | **Hi, how are you? will you be able to help me with this question...**
About the "Speaker Recognition Verification", after all the training process you give us the option to extract the Embeddings from the speakers, but we would like to know if we can extract the speaker ID, we also look at your other scripts the ones from "Speaker Diarization Inference" and "ASR with Speaker Diarization" which also don't reach the point we want, would you have an example of how I can get a speaker's biometrics where I can calculate the probability rate of that speaker who is speaking in the audio really be himself?
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} | null | null | null | Hi,
I'm using the container https://ngc.nvidia.com/catalog/containers/nvidia:nemo_asr_app_img and running the step by step from the notebook 'English to Spanish Cross Language Finetuning'. I did the training and everything works perfectly.
At the end of training as a result we have checkpoints that can be used for decoding in Jasper.
Is there a possibility to export these checkpoints in model .NeMo?
Since most of the tutorials I find in the documentation are for .NeMo models, I was left with this question | {
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} | # Bugfix
- Change colab link to new notebook.
- TODO: Change link to next release version from `main`
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2365 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2365/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2365/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2365/events | https://github.com/NVIDIA/NeMo/pull/2365 | 921,702,854 | MDExOlB1bGxSZXF1ZXN0NjcwNjY1ODY2 | 2,365 | byt5 unicode implementation | {
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} | This PR adds:
* a straightforward implementation of unicode encoding & decoding from [ByT5](https://arxiv.org/abs/2105.13626)
* a very bare-bones text processor for use with this tokenizer that can support any language.
Usage:
To enable this, add the following lines to your nemo launch script:
```
model.encoder_tokenizer.library='byte-level'
model.decoder_tokenizer.library='byte-level'
```
Things work as-is with tarred datasets, but you will need to re-create your datasets using the `byte-level` tokenizer class.
Currently, the following model, which is 260M params, gets within 1 BLEU (18.9) of my transformer-large run on wmt20 ja-en (19.8):
```
model.encoder_tokenizer.library='byte-level' \
model.decoder_tokenizer.library='byte-level' \
model.encoder.max_sequence_length=1024 \
model.encoder.hidden_size=1536 \
model.encoder.inner_size=4096 \
model.encoder.num_attention_heads=16 \
model.encoder.attn_layer_dropout=0.1 \
model.encoder.ffn_dropout=0.1 \
model.encoder.num_layers=10 \
model.encoder.pre_ln=true \
model.decoder.max_sequence_length=1024 \
model.decoder.hidden_size=1536 \
model.decoder.inner_size=4096 \
model.decoder.num_attention_heads=16 \
model.decoder.attn_layer_dropout=0.1 \
model.decoder.ffn_dropout=0.1 \
model.decoder.num_layers=2 \
model.decoder.pre_ln=true \
model.src_language=ja \
model.tgt_language=en \
model.beam_size=3 \
model.max_generation_delta=3 \
model.label_smoothing=0.1 \
model.optim.lr=4e-4 \
+model.optim.sched.warmup_steps=35000 \
~model.optim.sched.warmup_ratio \
```
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} | null | null | null | A few hours ago the release of Torch 1.9.0 and Torchaudio 0.9.0 was released and since then the notebooks are not working on Colab. As the scripts don't specify the exact version number, pip is installing the latest one.
An example of this error is in the notebook "Speaker Diarization Inference":
I used Torch versions 1.8.0 and torchaudio versions 0.8.0 and the problems were fixed.
I found it important to report this bug to help new users. | {
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I get this error when the duration number is set to a bigger number: ValueError: num_samples should be a positive integer value, but got num_samples=0
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} | null | null | null | I trained VAD model using my own dataset follow instructions in [tutorial 6](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_Detection.ipynb)
My dataset is Vietnamese telephony conversation, 15 hours with 8 hours of speech and 7 hours of noise. I got accuracy 95% on my test set.
The format of dataset quite similar with google speech dataset. Each sample of speech is one statement and have duration 2s to 5s.
When i follow instruction in [tutorial 7](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/07_Online_Offline_Microphone_VAD_Demo.ipynb) to inference sample in test set what contain only one statement, the prediction is good. The problem is when i inference long audio has duration 20s of speech and noise, the prediction is really bad.
This is predict sample of speech has only one statement
And long audio, 0 to 5s of audio is noise.
1. Any idea to deal with this problem
2. I want to tune threshold but it required RTTM ground truth, what is the format of RTTM file and how to generate this file from dataset like google speech.
3. How to tune best window size with my own dataset
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} | null | null | null | I'd like to train my own TTS model in Italian, using the portion of the MAILABS dataset (~18 hours). If I train the Tacotron 2 model with this dataset (16 kHz) can I fine tune from a pre-existing English model? I think that would increase performance but I didn't find any tutorial or example that performs it. Another question is whether vocoder training would be necessary or whether the English/Chinese pre-trained vocoders give acceptable performance even in a different language such as Italian. Last question is if the two end-to-end models, FastPitch_HifiGan_E2E and FastSpeech2_HifiGan_E2E, can be trained or fine-tuned from pre-existing checkpoints avoiding the need to train both Spectrogram Generator and Vocoder.
Thanks in advance for your answers | {
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} | null | null | null | **Describe the bug**
I am trying to transcribe an audio file with following ground truth:
**in the late 1980s switzerland witnessed a huge increase**
and getting the following prediction by Citrinet:
**in the late one thousand nine hundred eighty seconds switzerland witnessed a huge increase**
**Steps/Code to reproduce bug**
NeMo version : 1.0.0rc1
Enviroment: Nvidia-docker
Inference script: https://github.com/NVIDIA/NeMo/blob/v1.0.0rc1/examples/asr/transcribe_speech.py
**Expected behavior**
Seems like a bug in text-normalization that wrongly normalizes 1980s to one thousand nine hundred eighty seconds
**Environment overview (please complete the following information)**
- Environment location: Nvidia-Docker
- Docker run command: docker run -it nvcr.io/nvidia/nemo:1.0.0b1, installed nemo-1.0.0rc1 using reinstall.sh
**Additional context**
GPU model: RTX 2080 | {
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} | null | null | null | I changed the diarizer.vad.model_path in [Config file ](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/conf/speaker_diarization.yaml) to use my own trained models. But it required .nemo
Is posible to use .ckpt model in Config file.
Many thanks. | {
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} | Hydra-core version 1.1.0 was released on 11th June 2021, and it requires OmegaConf == 2.1.\*, while Nemo requires OmegaConf >=2.0.5 & <2.1.*. Therefore, the existing tutorial notebooks( can be found [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/starthere/tutorials.html), on importing the models, reported a configuration error. This pull request changed the specified version of hydra-core, specified in the requirements, from >=1.0.4 to ==1.0.6, which supports OmegaConf <2.1.0, and the tutorial notebooks run correctly, without any error | {
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} | # Changelog
- Adds tutorial to fine-tune an ASR CTC model onto another language (MCV Japanese for demo)
- Adds documentation to point user to finetuning via ASR tutorials
- Update link color for visited links in NeMo Docs | {
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} | null | null | null | If I run following code:
`punctuation_model = PunctuationCapitalizationModel.from_pretrained("punctuation_en_bert")`
I get this error:
```
ConfigAttributeError Traceback (most recent call last)
<ipython-input-2-e68f34283879> in <module>()
15 import time
16 translate_model = MTEncDecModel.from_pretrained("nmt_de_en_transformer12x2")
---> 17 punctuation_model = PunctuationCapitalizationModel.from_pretrained("punctuation_en_bert")
18 qa_model = QAModel.from_pretrained("qa_squadv1.1_bertbase")
19 #nmt_model = nemo_nlp.models.MTEncDecModel.from_pretrained(model_name='nmt_ru_en_transformer6x6').cuda()
14 frames
/usr/local/lib/python3.7/dist-packages/omegaconf/dictconfig.py in _get_node(self, key, validate_access, throw_on_missing_value, throw_on_missing_key)
468 if value is None:
469 if throw_on_missing_key:
--> 470 raise ConfigKeyError(f"Missing key {key}")
471 elif throw_on_missing_value and value._is_missing():
472 raise MissingMandatoryValue("Missing mandatory value")
ConfigAttributeError: Missing key update_node
full_key: update_node
object_type=dict
```
It happens since today. Yesterday it worked fine that way
A clear and concise description of what the bug is.
Normally it should just import the models so I am able to use them. Here is my insall code
```
BRANCH = 'v1.0.0'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
```
and my imports:
```
import nemo
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
from nemo.collections.nlp.models import MTEncDecModel
import numpy as np
import IPython.display as ipd
from IPython.display import Audio
from IPython.core.display import display
from nemo.collections.nlp.models import PunctuationCapitalizationModel
from nemo.collections.nlp.models import QAModel
from nemo.collections.nlp.models import TokenClassificationModel
```
It is executed on a Google Collab with 14 GB of VRAM
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I was able only to find the pre-training script.
Could you please share an example for fine-tuning wav2vec2?
https://github.com/NVIDIA/NeMo/blob/ee480c1a7905a6488ede29c9d892b6be3038759f/examples/asr/experimental/wav2vec/wav2vec_pretrain.py
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Assuming I have a dataset that consists of different sampling rage "32k, 16k, and 8k", Can Nemo automatically resample them during training into the model-specific sample rate defined in the YAML file? | {
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} | # Changelog
- Update all notebooks and docs to refer to r1.0.2 release
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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- Updates ranges of hydra and omegaconf to support latest release
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | null | null | null | Hello!
I try FineTune ASR with freeze encoder on Russian language.
I am taking the parameters of the optimizer, learning rate from the this paper
https://arxiv.org/pdf/2005.04290.pdf and i set warmup_steps the value to 2000, but when training the model, the loss value is "buried", and WER is in one.
What actions to take to solve this problem?
model_weights.ckpt is unpack stt_en_jasper10x5dr.nemo model
code for training:
```
@hydra_runner(config_path="./", config_name="jasper_10x5dr.yaml")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer, amp_level='O1')
exp_manager(trainer, cfg.get("exp_manager", None))
asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
# Initialize the weights of the model from another model, if provided via config
state_dict = torch.load('model_weights.ckpt' , map_location='cpu')
encoder_state_dict = {key[8:] : value for key,value in state_dict.items() if key.split('.')[0] == 'encoder'}
asr_model.encoder.load_state_dict(encoder_state_dict)
del state_dict
del encoder_state_dict
torch.cuda.empty_cache()
asr_model.encoder.freeze()
trainer.fit(asr_model)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
gpu = 1 if cfg.trainer.gpus != 0 else 0
test_trainer = pl.Trainer(
gpus=gpu,
precision=trainer.precision,
amp_level=trainer.accelerator_connector.amp_level,
amp_backend=cfg.trainer.get("amp_backend", "native"),
)
if asr_model.prepare_test(test_trainer):
test_trainer.test(asr_model)
if __name__ == '__main__':
main()
``` | {
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} | Added a quick list of main features with links to docs.
Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2330 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2330/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2330/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2330/events | https://github.com/NVIDIA/NeMo/pull/2330 | 916,719,173 | MDExOlB1bGxSZXF1ZXN0NjY2MzUzODE3 | 2,330 | Added documentation for export() | {
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} | Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2329 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2329/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2329/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2329/events | https://github.com/NVIDIA/NeMo/issues/2329 | 916,677,391 | MDU6SXNzdWU5MTY2NzczOTE= | 2,329 | speech_to_text.py doesn't work | {
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"description": "Something isn't working"
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} | null | null | null | **Describe the bug**
I am trying to run the "speech_to_text.py " but it fails and gives the following error:
```
File "speech_to_text.py", line 88, in main
asr_model.maybe_init_from_pretrained_checkpoint(cfg)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 948, in __getattr__
type(self).__name__, name))
AttributeError: 'EncDecCTCModel' object has no attribute 'maybe_init_from_pretrained_checkpoint'
```
**Steps/Code to reproduce bug**
The whole code is accessible through this Colab notebook:
https://colab.research.google.com/drive/1Z5d7KL_LOWQthozKAPnBpubN8Ub3ZuZ3?usp=sharing
**Expected behavior**
I am expecting the model to work and load a pretrained model.
- Environment location: Google Colab
- 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
- PyTorch version: 1.8
- Python version: 3.7
**Additional context**
I am trying to finetune a pretrained QuartzNet15x5.
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} | null | null | null | ## Issue :
I'm trying to train a NMT model using Nemo on my machine. I followed the instructions from the [documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/machine_translation.html) using the example script and the default configuration file. This runs perfectly on colab, but when I'm trying it on my local machine, I get the following error :
( I'm using branch r1.0.0rc1 and have tried using the Nemo docker image and installing from a repository on a PyTorch Image.)
## The error :
Traceback (most recent call last):
File "enc_dec_nmt.py", line 105, in main
cfg = update_model_config(default_cfg, cfg)
File "/opt/conda/lib/python3.6/site-packages/nemo/utils/config_utils.py", line 79, in update_model_config
model_cls = _add_subconfig_keys(model_cls, update_cfg, subconfig_key='optim')
File "/opt/conda/lib/python3.6/site-packages/nemo/utils/config_utils.py", line 169, in _add_subconfig_keys
subconfig = OmegaConf.merge(update_subconfig, subconfig)
ValueError: Cannot merge with a None config
full_key: model.optim
reference_type=Any
object_type=dict
## Here's the code I'm running :
!python enc_dec_nmt.py \
-cn aayn_base \
do_training=true \
trainer.gpus=1 \
~trainer.max_epochs \
+trainer.max_steps=100000 \
+trainer.val_check_interval=1000 \
+exp_manager.exp_dir=/workspace/dirname/exp \
+exp_manager.create_checkpoint_callback=True \
+exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU \
+exp_manager.checkpoint_callback_params.mode=max \
+exp_manager.checkpoint_callback_params.save_top_k=5 \
model.preproc_out_dir=/workspace/dirname/preproc \
model.train_ds.use_tarred_dataset=true \
model.train_ds.lines_per_dataset_fragment=1000000 \
model.train_ds.num_batches_per_tarfile=200 \
model.train_ds.src_file_name=/workspace/dirname/train_in \
model.train_ds.tgt_file_name=/workspace/dirname/train_out \
model.validation_ds.src_file_name=/workspace/dirname/splits/val_in \
model.validation_ds.tgt_file_name=/workspace/dirname/val_out \
model.encoder_tokenizer.vocab_size=32000 \
model.decoder_tokenizer.vocab_size=32000 \
~model.test_ds
(I have put the aayn_base.yaml file inside a folder _conf_ in the same directory)
## Here's the full stack trace
Traceback (most recent call last):
File "enc_dec_nmt.py", line 136, in <module>
main()
File "/opt/conda/lib/python3.6/site-packages/nemo/core/config/hydra_runner.py", line 103, in wrapper
strict=None,
File "/opt/conda/lib/python3.6/site-packages/hydra/_internal/utils.py", line 347, in _run_hydra
lambda: hydra.run(
File "/opt/conda/lib/python3.6/site-packages/hydra/_internal/utils.py", line 201, in run_and_report
raise ex
File "/opt/conda/lib/python3.6/site-packages/hydra/_internal/utils.py", line 198, in run_and_report
return func()
File "/opt/conda/lib/python3.6/site-packages/hydra/_internal/utils.py", line 350, in <lambda>
overrides=args.overrides,
File "/opt/conda/lib/python3.6/site-packages/hydra/_internal/hydra.py", line 112, in run
configure_logging=with_log_configuration,
File "/opt/conda/lib/python3.6/site-packages/hydra/core/utils.py", line 127, in run_job
ret.return_value = task_function(task_cfg)
File "enc_dec_nmt.py", line 105, in main
cfg = update_model_config(default_cfg, cfg)
File "/opt/conda/lib/python3.6/site-packages/nemo/utils/config_utils.py", line 79, in update_model_config
model_cls = _add_subconfig_keys(model_cls, update_cfg, subconfig_key='optim')
File "/opt/conda/lib/python3.6/site-packages/nemo/utils/config_utils.py", line 169, in _add_subconfig_keys
subconfig = OmegaConf.merge(update_subconfig, subconfig)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/omegaconf.py", line 321, in merge
target.merge_with(*others[1:])
File "/opt/conda/lib/python3.6/site-packages/omegaconf/basecontainer.py", line 331, in merge_with
self._format_and_raise(key=None, value=None, cause=e)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/base.py", line 101, in _format_and_raise
type_override=type_override,
File "/opt/conda/lib/python3.6/site-packages/omegaconf/_utils.py", line 694, in format_and_raise
_raise(ex, cause)
File "/opt/conda/lib/python3.6/site-packages/omegaconf/_utils.py", line 610, in _raise
raise ex # set end OC_CAUSE=1 for full backtrace
ValueError: Cannot merge with a None config
full_key: model.optim
reference_type=Any
object_type=dict
Any help is greatly appreciated. Thank you! | {
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