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} | # What does this PR do ?
Add an option to disable distributed parameters in the distributed Adam optimizer. This is similar to standard distributed data-parallelism (i.e. with gradient all-reduces), but it takes advantage of the distributed optimizer's support for overlapping communication with backward compute.
**Collection**: NLP
# Changelog
- Add option to disabled distributed parameters in the distributed Adam optimizer
# Usage
Set the optimizer to `distributed_fused_adam` with `disable_distributed_parameters: True` in the config file:
https://github.com/NVIDIA/NeMo/blob/80cd1c135f4cf01aa04e62aa4c60fb130c889831/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L193
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Orthogonal to https://github.com/NVIDIA/NeMo/pull/5560 and https://github.com/NVIDIA/NeMo/pull/5684.
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} | # What does this PR do ?
This PR adds experimental support in GPT for the distributed Adam optimizer to overlap its parameter all-gather with the forward compute. It integrates with features added in https://github.com/NVIDIA/apex/pull/1559.
This is done with a hacky monkey-patching approach, so long-term it may be better to transition toward PyTorch's FSDP. That said, I have seen some modest speedups (~2%) training GPT-3 175B on Selene with no difference in loss values.
**Collection**: NLP
# Changelog
- Configure dist Adam so all model parameters are views into a large contiguous buffer.
- Add GPT support for overlapping forward compute and param all-gathers
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/80cd1c135f4cf01aa04e62aa4c60fb130c889831/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L193
The overlapped param all-gathers are enabled by default. To disable, configure the optimizer with `overlap_param_sync: False`.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Depends on https://github.com/NVIDIA/NeMo/pull/5560
* Depends on https://github.com/NVIDIA/apex/pull/1559, which in turn depends on https://github.com/NVIDIA/apex/pull/1559.
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
- added a doc on TN/ITN customization + resources
- moved G2P to TTS
- removed text_processing folder from the docs
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Also, is there any way I can create a trial file for my dataset?
Thanks! | {
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} | # What does this PR do ?
Closes #5660
Expose the device so users can set this via the config.
**Collection**: ASR
# Changelog
- Expose clustering diarizer device so user can set device.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add cluster validation tests for cloud service providers to NeMo Megatron directory.
**Collection**: nemo_megatron_launcher
# Changelog
- For all CSPs
- DCGM diagnostics test job
- NCCL tests build job
- NCCL `all_reduce_perf` test job
- NCCL topology file
- All-in-one run script (cluster_validation.sh)
- AWS, additionally
- Dockerfile on top of NeMo Megatron container with AWS EFA drivers
# Usage
See NeMo Megatron README (Cluster Validation section)
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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# What does this PR do ?
Adds tpmlp to T5-based models
**Collection**: [Note which collection this PR will affect]
# Changelog
- modifying "megatron_base_prompt_learning_model.py"
# Usage
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# What does this PR do ?
TN/ITN moved to a new repo https://github.com/NVIDIA/NeMo-text-processing
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
```
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I wonder if we should start moving away from named tuples, switching to data classes instead. NamedTuple somehow interfere with pickling which might become necessary soon with PT 2.0 (maybe)
_Originally posted by @titu1994 in https://github.com/NVIDIA/NeMo/pull/5356#discussion_r1049069660_
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} | null | null | null | this is very close to
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/audio_preprocessing.py#L95. it would just be adding one or two args to return intermediate STFT from AudioToMelSpectrogramPreprocessor or sub-classing it.
_Originally posted by @krishnacpuvvada in https://github.com/NVIDIA/NeMo/pull/5356#discussion_r1018418772_
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_Originally posted by @titu1994 in https://github.com/NVIDIA/NeMo/pull/5356#discussion_r1049079041_
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I tried to convert the vad_multilingual_marblenet.nemo model to ONNX format and perform the vad inference stage in the clustering_diar_infer.py script from the diarization example.
The following is the code I implemented:
```
self._vad_model = EncDecClassificationModel.restore_from(model_path)
self._vad_model.eval()
self._vad_model = self._vad_model.to(self._device)
self._vad_model.export('vad_onnx.onnx')
ort_session = onnxruntime.InferenceSession('vad_onnx.onnx')
time_unit = int(self._vad_window_length_in_sec / self._vad_shift_length_in_sec)
trunc = int(time_unit / 2)
trunc_l = time_unit - trunc
all_len = 0
data = []
for line in open(manifest_file, 'r', encoding='utf-8'):
file = json.loads(line)['audio_filepath']
data.append(get_uniqname_from_filepath(file))
status = get_vad_stream_status(data)
for i, test_batch in enumerate(tqdm(self._vad_model.test_dataloader(), desc='vad', leave=True)):
test_batch = [x.to(self._device) for x in test_batch]
with autocast():
processed_signal, processed_signal_len = self._vad_model.preprocessor(input_signal=test_batch[0].to(self._vad_model.device),length=test_batch[1].to(self._vad_model.device))
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(processed_signal)}
log_probs = torch.Tensor(ort_session.run([],ort_inputs))
print(log_probs)
probs = torch.softmax(log_probs, dim=-1)
```
The output of log_probs when running the original _run_vad() function from /nemo/collections/asr/models/clustering_diarizer.py#190 is the following:
```
tensor([[ 0.7704, -0.7704],
[ 0.7360, -0.7361],
[ 0.7352, -0.7353],
...,
[ 1.3244, -1.3244],
[ 1.3309, -1.3310],
[ 1.2513, -1.2513]], grad_fn=<AddmmBackward0>)
vad: 20%|██████████▌ | 1/5 [00:03<00:12, 3.15s/it]tensor([[ 0.8877, -0.8878],
[ 0.8913, -0.8913],
[ 0.9222, -0.9222],
...,
[ 0.5388, -0.5389],
[ 0.5645, -0.5645],
[ 0.4665, -0.4665]], grad_fn=<AddmmBackward0>)
vad: 40%|█████████████████████▏ | 2/5 [00:06<00:09, 3.04s/it]tensor([[ 0.0150, -0.0150],
[ 0.0219, -0.0220],
[ 0.0102, -0.0102],
...,
[-0.0134, 0.0133],
[ 0.0912, -0.0912],
[ 0.0313, -0.0313]], grad_fn=<AddmmBackward0>)
vad: 60%|███████████████████████████████▊ | 3/5 [00:09<00:06, 3.04s/it]tensor([[-2.9656, 2.9656],
[-3.0097, 3.0097],
[-3.0016, 3.0015],
...,
[ 0.1544, -0.1544],
[ 0.2534, -0.2535],
[ 0.2242, -0.2242]], grad_fn=<AddmmBackward0>)
```
When running the modfied function with onnx runtime, the output is:
```
tensor([[[ 0.7704, -0.7704],
[ 0.7360, -0.7361],
[ 0.7352, -0.7352],
...,
[ 1.3244, -1.3244],
[ 1.3309, -1.3310],
[ 1.2513, -1.2513]]])
vad: 20%|██████████▌ | 1/5 [00:03<00:13, 3.31s/it]tensor([[[ 0.8877, -0.8878],
[ 0.8913, -0.8913],
[ 0.9222, -0.9222],
...,
[ 0.5388, -0.5389],
[ 0.5644, -0.5645],
[ 0.4665, -0.4665]]])
vad: 40%|█████████████████████▏ | 2/5 [00:06<00:08, 3.00s/it]tensor([[[ 0.0150, -0.0150],
[ 0.0219, -0.0220],
[ 0.0102, -0.0102],
...,
[-0.0134, 0.0134],
[ 0.0912, -0.0912],
[ 0.0313, -0.0313]]])
vad: 60%|███████████████████████████████▊ | 3/5 [00:09<00:05, 2.99s/it]tensor([[[-2.9656, 2.9656],
[-3.0097, 3.0097],
[-3.0016, 3.0015],
...,
[ 0.1544, -0.1544],
[ 0.2534, -0.2535],
[ 0.2242, -0.2242]]])
```
The tensors look almost the same, except that when running my onnx implementation, there is no grad_fn=<AddmmBackward0>. Also, when running ONNX runtime, the script returns an error that says "There is silence detected in the entire audio file".
Could you please explain why there is only silence detected, despite the log_probs having the same output tensors in both cases, but the grad_fn missing? What are the correct steps for running inference using onnx? | {
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} | # What does this PR do ?
The goal of this PR is to add a supervised fine-tuning functionality for GPT.
**Collection**: NLP - language modeling
# Changelog
- Created a new class similar to GPT pretraining: MegatronGPTSFTModel
- Create a new dataset class for SFT, allowing answer-only loss and the inclusion of EOS and SEP tokens
- Create a new usage example
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [X] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds support for nested NeMo models (with resources).
It's possible to instantiate and save models with NeMo submodules. Artifacts are correctly saved and restored.
**Collection**: [core]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Example usage:
- 3 ways to instantiate child models:
- use subconfig directly
- use `child_model_path` with `.nemo` checkpoint path to load the model
- use pretrained model
- child model can contain artifacts
- child model config can be changed after parent model instantiation. Config will be saved when saving parent model
```python
from nemo.core.classes import ModelPT
class ChildModel(ModelPT):
... # implement necessary methods
class ParentModel(ModelPT):
def __init__(self, cfg, trainer=None):
super().__init__(cfg=cfg, trainer=trainer)
# optionally annotate type for IDE autocompletion and type checking
self.child_model: Optional[ChildModel]
if cfg.get("child_model") is not None:
# load directly from config
# either if config provided initially, or automatically
# after model restoration
self.register_nemo_submodule(
"child_model",
config_field="child_model",
model=ChildModel(self.cfg.child_model),
)
elif cfg.get('child_model_path') is not None:
# load from .nemo model checkpoint
# while saving, config will be automatically assigned/updated
# in cfg.child_model
self.register_nemo_submodule(
"child_model",
config_field="child_model",
model=ChildModel.restore_from(self.cfg.child_model_path),
)
elif cfg.get('child_model_name') is not None:
# load from pretrained model
# while saving, config will be automatically assigned/updated
# in cfg.child_model
self.register_nemo_submodule(
"child_model",
config_field="child_model",
model=ChildModel.from_pretrained(self.cfg.child_model_name),
)
else:
self.child_model = None
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Allow changing `attention_dropout` from a config file level in the GPT model
**Collection**: language_model
# Changelog
- Allow changing `attention_dropout` from a config file level in GPT
# Usage
Set `model.attention_dropout` in GPT config file
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Fixes a bug when restarting from a save checkpoint with multiple GPUs. We shouldn't override the device (or even store the device at all), relying on the LightningModule to provide the device.
**Collection**: Common
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hi All,
I would like to add some special symbols in to transcription like lang. id of each utterance "\<english\>" and retrain the model. Is there a way to do that? Can I do it by passing some parameters to process_asr_text_tokenizer.py.
Currently, I have tried adding some unused symbols like "<" for English, and ">" for Hindi etc. Pls let me know if this is correct? Or let me know if there is any better/efficient way to do that. Thanks
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
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# Changelog
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} | null | null | null | **Describe the bug**
common\losses\cross_entropy.py doesn't work on some gpus if mask tensor has integer type.
Given that mask is a tensor of all 1 with dtype=torch.int64
in lines 71-72
if loss_mask.dtype is not torch.bool:
loss_mask = loss_mask > 0.5
loss_mask should become all True, if run on cpu or on some (newer?) gpus.
But I see two cases when loss_mask becomes all False (GTX780, Tesla K40m )
It seems that comparison between int and float works differently.
This leads to labels_flatten be empty and torch original cross entropy function is called with not-flattened tensors resulting in frustrating error like: RuntimeError: Expected target size [4, 11], got [4, 256]
**Steps/Code to reproduce bug**
import torch
import numpy as np
from nemo.collections.common.losses import CrossEntropyLoss
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
loss_fn = CrossEntropyLoss(logits_ndim=3)
logits = torch.tensor(np.ones((4, 256, 11), dtype=np.float), device=device)
labels = torch.tensor(np.ones((4, 256), dtype=np.int64), device=device)
labels_mask = torch.tensor(np.ones((4, 256), dtype=np.int64), device=device)
val_loss = loss_fn(logits=logits, labels=labels, loss_mask=labels_mask)
print("val_loss=", val_loss)
**Expected behavior**
This works on cpu and in colab's gpu, but gives error in GTX780 and Tesla K40m
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
add ngc cli installation following what TAO toolkit used.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Virginia Adams <[email protected]>
Dataset sample truncation sometimes errors in NeMo. This is because the text segments from a users dataset can have different tokenization depending on the prompt template text that is present around that text segment. This makes it difficult to find the truncated field's subsequence of token ids and thus truncate just that portion of the training example. This PR is a different approach to truncation where we:
1. After getting the truncated field's text id's, I convert these token ids back into a string using the tokenizer
2. re-build the prompt using this truncated text string instead of the full text string
3. re-tokenize the entire input at the same time.
**Collection**: NLP
# Changelog
- GPT Prompt Learning Dataset Class
# Usage
Same as before
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://nvidia.slack.com/archives/C02A7LYGHK8/p1670592730656789 and https://nvidia.slack.com/archives/C03AQCUKQ5T/p1671029551818229
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Adds AutoConfigurator to NeMo Megatron directory.
**Collection**: nemo_megatron_launcher
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@okuchaiev @yaoyu-33 @ericharper @Davood-M
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds support for hybrid ASR-TTS models.
**Features:**
- [x] hybrid ASR-TTS model (`ASRWithTTSModel`) from CTC-BPE and RNNT-BPE for finetuning
- [x] hybrid ASR-TTS model (`ASRWithTTSModel`) from ASR model config for training from scratch
- [x] fusing BatchNorm to improve the quality of the finetuned models
- [x] text-to-text datasets
- [x] mixing ASR and text-to-text datasets
- [x] enhancer support: will be added after https://github.com/NVIDIA/NeMo/pull/5565
Dependent on:
- https://github.com/NVIDIA/NeMo/pull/5671
- https://github.com/NVIDIA/NeMo/pull/5774
**Collection**: [ASR]
# Changelog
- **new hybrid ASR-TTS model** `ASRWithTTSModel` in `nemo/collections/asr/models/hybrid_asr_tts_models.py`
- **text-to-text datasets**: `TextToTextDataset` (map-style) + `TextToTextIterableDataset` (iterable) in `nemo/collections/asr/data/text_to_text.py`
- **fused BatchNorm**: `nemo/collections/asr/parts/submodules/batchnorm.py` + support for fused BatchNorm in ConformerEncoder in `nemo/collections/asr/parts/submodules/conformer_modules.py`
- **default config** for hybrid model: `examples/asr/conf/asr_tts/hybrid_asr_tts.yaml`
- **example scripts** for finetuning existing models and training from scratch: `examples/asr/asr_with_tts/speech_to_text_bpe_with_text_finetune.py` + `examples/asr/asr_with_tts/speech_to_text_bpe_with_text.py`
# Usage
- `examples/asr/asr_with_tts/speech_to_text_bpe_with_text_finetune.py` for finetuning pretrained ASR model with TTS
- `examples/asr/asr_with_tts/speech_to_text_bpe_with_text.py` for training ASR with TTS from scratch
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Adds an option to log with DLLogger in the experiment manager.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Changed exp_manager.py to add a new logger
- Updated the exp_manager documentation
# Usage
In the exp_manager config:
```yaml
enable_dllogger_logger: True
dllogger_logger_kwargs:
stdout: False
verbose: False
json_file: "./dllogger.json"
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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**Collection**: [Note which collection this PR will affect]
# Changelog
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- Add nemo megatron Dockerfile in side `nemo_megatron_launcher/`
- Add nemo megatron README in side `nemo_megatron_launcher/`
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Combines adapters with retrieval augmented language model retro with different configurations.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | UL2 objectives should be sampled with uniform probability, including subtasks. Instead, currently, first the overarching denoiser is uniformly sampled and then the denoising subtask is again sampled uniformly. This is only the case [here for X-denoisers](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/ul2_dataset.py#L174-L178) currently, because one R- and one X-denoising objective from the paper are missing.
Compare:
```python
import itertools
import random
# The integers indicate the different task types in the paper.
tasks = {'R': [0, 1], 'S': [2], 'X': [3, 4, 5, 6]}
# How it's currently done in NeMo:
denoiser_subtasks = random.choice(tasks)
subtask = random.choice(denoiser_subtasks)
# Each of 0, 1 has 1/3 * 1/2 chance.
# 2 has 1/3 chance.
# Each of 3, 4, 5, 6 has 1/3 * 1/4 chance.
# How it's done in the code snippet from the UL2 paper:
subtask = random.choice(list(itertools.chain.from_iterable(tasks.values())))
# Each of 0, 1, 2, 3, 4, 5, 6 has 1/7 chance.
``` | {
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} | null | null | null | **Describe the bug**
not able to complete the training process using pretrained model [biomegatron345m_biovocab_50k_cased]
**Steps/Code to reproduce bug**
NVIDIA NeMo Introduction Notebook
Setup
using the NGC nvcr.io/nvidia/pytorch:22.04-py3 PyTorch container.
Install [NeMo](https://github.com/NVIDIA/NeMo) using the following steps:
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
pip install nemo_toolkit['all']
# ERROR at this step
# load gatortron checkpoint into biomegatron model
# (the architecture is the same, just the weights are different)
config.model.language_model.lm_checkpoint = "gatortron_og/MegatronBERT.nemo"
config.model.language_model.pretrained_model_name = 'biomegatron345m_biovocab_50k_cased'
config.model.tokenizer.tokenizer_name=None
model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)
############ ERROR ############
[NeMo W 2022-12-16 07:59:06 lm_utils:80] biomegatron345m_biovocab_50k_cased is not in get_pretrained_lm_models_list(include_external=False), will be using AutoModel from HuggingFace.
---------------------------------------------------------------------------
ConfigAttributeError Traceback (most recent call last)
Input In [18], in <cell line: 8>()
5 config.model.language_model.pretrained_model_name = 'biomegatron345m_biovocab_50k_cased'
6 config.model.tokenizer.tokenizer_name=None
----> 8 model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py:43, in TextClassificationModel.__init__(self, cfg, trainer)
40 self.dataset_cfg = cfg.dataset
41 self.class_weights = None
---> 43 super().__init__(cfg=cfg, trainer=trainer)
45 self.classifier = SequenceClassifier(
46 hidden_size=self.hidden_size,
47 num_classes=cfg.dataset.num_classes,
(...)
53 idx_conditioned_on=0,
54 )
56 self.create_loss_module()
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py:81, in NLPModel.__init__(self, cfg, trainer, no_lm_init)
79 elif pretrain_model_name in all_pretrained_megatron_bert_models:
80 copy_cfg = copy.deepcopy(cfg)
---> 81 bert_model = get_lm_model(
82 config_file=config_file,
83 config_dict=config_dict,
84 vocab_file=vocab_file,
85 trainer=trainer,
86 cfg=copy_cfg,
87 )
88 # set the tokenizer if it is not initialized explicitly
89 if (
90 (hasattr(self, 'tokenizer') and self.tokenizer is None) or not hasattr(self, 'tokenizer')
91 ) and hasattr(bert_model, 'tokenizer'):
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/lm_utils.py:112, in get_lm_model(config_dict, config_file, vocab_file, trainer, cfg)
109 return x
111 if cfg.language_model.get("lm_checkpoint"):
--> 112 model = MegatronBertModel.restore_from(restore_path=cfg.language_model.lm_checkpoint, trainer=trainer)
113 else:
114 model = MegatronBertModel.from_pretrained(cfg.language_model.get('pretrained_model_name'), trainer=trainer)
File /opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py:316, in ModelPT.restore_from(cls, restore_path, override_config_path, map_location, strict, return_config, save_restore_connector, trainer)
313 app_state.model_restore_path = restore_path
315 cls.update_save_restore_connector(save_restore_connector)
--> 316 instance = cls._save_restore_connector.restore_from(
317 cls, restore_path, override_config_path, map_location, strict, return_config, trainer
318 )
319 if isinstance(instance, ModelPT):
320 instance._save_restore_connector = save_restore_connector
File /opt/conda/lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py:235, in SaveRestoreConnector.restore_from(self, calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer)
210 """
211 Restores model instance (weights and configuration) into .nemo file
212
(...)
231 An instance of type cls or its underlying config (if return_config is set).
232 """
233 # Get path where the command is executed - the artifacts will be "retrieved" there
234 # (original .nemo behavior)
--> 235 loaded_params = self.load_config_and_state_dict(
236 calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer,
237 )
238 if not isinstance(loaded_params, tuple):
239 return loaded_params
File /opt/conda/lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py:158, in SaveRestoreConnector.load_config_and_state_dict(self, calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer)
156 # get the class
157 calling_cls._set_model_restore_state(is_being_restored=True, folder=tmpdir)
--> 158 instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
159 instance = instance.to(map_location)
160 # add load_state_dict override
File /opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py:505, in Serialization.from_config_dict(cls, config, trainer)
503 if prev_error:
504 logging.error(prev_error)
--> 505 raise e
507 if not hasattr(instance, '_cfg'):
508 instance._cfg = config
File /opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py:497, in Serialization.from_config_dict(cls, config, trainer)
495 accepts_trainer = Serialization._inspect_signature_for_trainer(cls)
496 if accepts_trainer:
--> 497 instance = cls(cfg=config, trainer=trainer)
498 else:
499 instance = cls(cfg=config)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py:56, in MegatronBertModel.__init__(self, cfg, trainer)
52 if not HAVE_APEX:
53 raise ImportError(
54 "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
55 )
---> 56 super().__init__(cfg, trainer=trainer, no_lm_init=False)
57 self.cfg = cfg
59 # used in NVIDIA NGC PyTorch containers
60 # buffer used during train_step for logging average loss over gradient accumulation steps
File /opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_base_model.py:78, in MegatronBaseModel.__init__(self, cfg, trainer, no_lm_init)
74 self.tokenizer = None
76 super().__init__(cfg, trainer=trainer, no_lm_init=no_lm_init)
---> 78 self.with_distributed_adam = cfg.optim.get('name') == 'distributed_fused_adam'
80 # used in NVIDIA NGC PyTorch containers
81 self._enable_nvidia_optimizations()
File /opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py:357, in DictConfig.__getattr__(self, key)
353 self._format_and_raise(
354 key=key, value=None, cause=e, type_override=ConfigAttributeError
355 )
356 except Exception as e:
--> 357 self._format_and_raise(key=key, value=None, cause=e)
File /opt/conda/lib/python3.8/site-packages/omegaconf/base.py:190, in Node._format_and_raise(self, key, value, cause, type_override)
187 def _format_and_raise(
188 self, key: Any, value: Any, cause: Exception, type_override: Any = None
189 ) -> None:
--> 190 format_and_raise(
191 node=self,
192 key=key,
193 value=value,
194 msg=str(cause),
195 cause=cause,
196 type_override=type_override,
197 )
198 assert False
File /opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py:738, in format_and_raise(node, key, value, msg, cause, type_override)
736 ex = type_override(str(cause))
737 ex.__dict__ = copy.deepcopy(cause.__dict__)
--> 738 _raise(ex, cause)
740 object_type: Optional[Type[Any]]
741 object_type_str: Optional[str] = None
File /opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py:716, in _raise(ex, cause)
714 else:
715 ex.__cause__ = None
--> 716 raise ex.with_traceback(sys.exc_info()[2])
File /opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py:351, in DictConfig.__getattr__(self, key)
348 raise AttributeError()
350 try:
--> 351 return self._get_impl(key=key, default_value=_DEFAULT_MARKER_)
352 except ConfigKeyError as e:
353 self._format_and_raise(
354 key=key, value=None, cause=e, type_override=ConfigAttributeError
355 )
File /opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py:438, in DictConfig._get_impl(self, key, default_value)
436 def _get_impl(self, key: DictKeyType, default_value: Any) -> Any:
437 try:
--> 438 node = self._get_node(key=key, throw_on_missing_key=True)
439 except (ConfigAttributeError, ConfigKeyError):
440 if default_value is not _DEFAULT_MARKER_:
File /opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py:465, in DictConfig._get_node(self, key, validate_access, throw_on_missing_value, throw_on_missing_key)
462 return None
464 if validate_access:
--> 465 self._validate_get(key)
467 value: Optional[Node] = self.__dict__["_content"].get(key)
468 if value is None:
File /opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py:166, in DictConfig._validate_get(self, key, value)
164 else:
165 msg = f"Key '{key}' is not in struct"
--> 166 self._format_and_raise(
167 key=key, value=value, cause=ConfigAttributeError(msg)
168 )
File /opt/conda/lib/python3.8/site-packages/omegaconf/base.py:190, in Node._format_and_raise(self, key, value, cause, type_override)
187 def _format_and_raise(
188 self, key: Any, value: Any, cause: Exception, type_override: Any = None
189 ) -> None:
--> 190 format_and_raise(
191 node=self,
192 key=key,
193 value=value,
194 msg=str(cause),
195 cause=cause,
196 type_override=type_override,
197 )
198 assert False
File /opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py:818, in format_and_raise(node, key, value, msg, cause, type_override)
815 ex.ref_type = ref_type
816 ex.ref_type_str = ref_type_str
--> 818 _raise(ex, cause)
File /opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py:716, in _raise(ex, cause)
714 else:
715 ex.__cause__ = None
--> 716 raise ex.with_traceback(sys.exc_info()[2])
ConfigAttributeError: Key 'optim' is not in struct
full_key: optim
object_type=dict
**Expected behavior**
model training is successful
**Environment overview (please complete the following information)**
docker run --gpus all -it --rm --shm-size=8g -p 8888:8888 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:22.04-py3
**Environment details**
using the NGC nvcr.io/nvidia/pytorch:22.04-py3 PyTorch container.
**Additional context**
Add any other context about the problem here.
Model: RTX 3090 windows 11
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# What does this PR do ?
Changed unfold to reshape for inference to be able to export in ONNX.
Also using extra hx parameter to LSTM call to work around Pytorch issues with tracing the hx=None.
Now Torchscript export works without any scripting - so I was also able to get rid of unnecessary autocast=False block.
ONNX export still fails due to internal bugs, even though each part of the network is now ONNX-exportable.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
add ngc cli installation following what TAO toolkit used.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
During inference, radtts++ duration predictions tend to be close to zero when given random inputs. This can be an issue as a sequence with length less than the grouping size in the radtts flow operation will result in a crash.
The solution is to set the last input token to correspond to a blank space, and stretch out the duration for said token until the the total duration of the sequence reaches the next multiple of group_size.
**Collection**: [Note which collection this PR will affect]
TTS
# Changelog
- sets the last element of the generated text token sequences in RadTTSModule.input_example() to zero (represents blank space). Note that this still needs to be done separately during inference with raw text, otherwise a non-space token may be replicated.
- helpers.regulate_len is extended to support last-valid-token replication until the next nearest multiple of group_size
- RadTTSModule.infer() now calls regulate_len with additional parameters for the replication behavior
# Usage
* Radtts should now pass the tests in:
```python
pytest tests/collections/tts/test_tts_exportables.py
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
This tutorial requires users to download datasets from NGC, but we did not provide any guidance about how to install NGC registry CLI. Verified to running through without errors.
Add a one line overview of what this PR aims to accomplish.
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# Changelog
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```python
# Add a code snippet demonstrating how to use this
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} | null | null | null | **Describe the bug**
Not sure if this is intended to be supported or not, but I don't seem to be able to run the entire Conformer CTC Large in fp16 format in CUDA. The problem seems to occur because of a missing op in the preprocessor. This seem error is mentioned here: https://github.com/pytorch/pytorch/issues/71680
Perhaps nemo can enable the preprocessor to run in fp32 while the rest runs in fp16 as a work around?
**Steps/Code to reproduce bug**
```
import nemo.collections.asr as nemo_asr
import torch
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="stt_en_conformer_ctc_small", map_location=torch.device("cuda"))
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
asr_model.half()
asr_model.eval()
asr_model.encoder.freeze()
asr_model.decoder.freeze()
length = 16_000 * 2
input_signal = torch.randn((1, length), dtype=torch.float16)
input_signal_length = torch.tensor([length], dtype=torch.int64)
# This will crash
_ = asr_model.forward(input_signal=input_signal, input_signal_length=input_signal_length)
```
Error message is this:
```
Traceback (most recent call last):
File "/home/dgalvez/scratch/code/asr/riva-asrlib-decoder/src/riva/asrlib/decoder/reproducer.py", line 16, in <module>
_ = asr_model.forward(input_signal=input_signal, input_signal_length=input_signal_length)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/core/classes/common.py", line 1084, in __call__
outputs = wrapped(*args, **kwargs)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/collections/asr/models/ctc_models.py", line 540, in forward
processed_signal, processed_signal_length = self.preprocessor(
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/core/classes/common.py", line 1084, in __call__
outputs = wrapped(*args, **kwargs)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 85, in forward
processed_signal, processed_length = self.get_features(input_signal, length)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 268, in get_features
return self.featurizer(input_signal, length)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 352, in forward
x = self.stft(x)
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 244, in <lambda>
self.stft = lambda x: torch.stft(
File "/home/dgalvez/scratch/miniconda3/envs/wfst/lib/python3.9/site-packages/torch/functional.py", line 630, in stft
input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
RuntimeError: "reflection_pad1d" not implemented for 'Half'
```
**Expected behavior**
It would be great if float16 worked out of the box for NeMo
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install nemo_toolkit[asr]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 18.04
- PyTorch version: 1.13.0+cu117
- Python version: Python 3.9.13
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} | …e changelog.
Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
During inference, radtts++ duration predictions tend to be close to zero when given random inputs. This can be an issue as a sequence with length less than the grouping size in the radtts flow operation will result in a crash.
The solution is to set the last input token to correspond to a blank space, and stretch out the duration for said token until the the total duration of the sequence reaches the next multiple of group_size.
**Collection**: [Note which collection this PR will affect]
TTS
# Changelog
- sets the last element of the generated text token sequences in RadTTSModule.input_example() to zero (represents blank space). Note that this still needs to be done separately during inference with raw text, otherwise a non-space token may be replicated.
- helpers.regulate_len is extended to support last-valid-token replication until the next nearest multiple of group_size
- RadTTSModule.infer() now calls regulate_len with additional parameters for the replication behavior
# Usage
* Radtts should now pass the tests in:
```python
pytest tests/collections/tts/test_tts_exportables.py
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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# What does this PR do ?
This tutorial requires users to download datasets from NGC, but we did not provide any guidance about how to install NGC registry CLI. Verified to running through without errors.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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[STT] Add Esperanto (Eo) ASR Conformer-CTC and Conformer-Transducer models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
patch to allow using tokenizers without additional_special_tokens_ids…
**Collection**: [NLP]
# Changelog
- checks for `additional_special_tokens_ids` attribute in tokenizer.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
[STT] Add Esperanto (Eo) ASR Conformer-CTC and Conformer-Transducer models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds AWS SageMaker example notebooks for ASR!
**Collection**: ASR
# Changelog
- Add AWS SageMaker examples to NeMo repo.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [x] New Feature
- [ ] Bugfix
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I've been testing the NeMo diarization example, using clustering inference on many different datasets and inputs using CPU only. On files up to 90 minutes in length, the diarizer reached around 25Gb RAM consumed in the clusterization stage. When I tried to diarize a 4 hour file that has 2 speakers using CPU only, the process was killed in the clusterization stage, no matter the configuration file used.
The configuration I used:
- clustering_diarizer/offline_diar_infer
- vad_multilingual_marblenet
- embeddings extraction: titanet_l
- configurations: either meeting or general ( both crashed)
The way I solved the process crashing problem was by splitting the 4 hour audio in 4 parts, and then merge the predicted rttms at the end by taking in consideration the offset between parts and checking if the speaker labels match. The issue is that the cluster labels on each file may differ from the other files, even if it is the same original audio split into 4. With only 2 speakers, it may be easy to check and switch the speakers labels so they match between all corresponding parts, but when there are more than 2 speakers, it's impossible.
My questions are: is there a way to avoid the process being killed when using long audio files(above 2 hours for example) on CPU(I can't used GPU)? If there isn't, is there a way to match the speakers from each split from the original audio? (example: speaker 1 in part 1 may be corresponding to speaker 4 in part 2) I was thinking about extracting an mean affinity matrix for each speaker in each part and then calculating the cosine distance between all speakers.
Any solution, idea or feature that is already implemented would be greatly appreciated! | {
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In the Setup I get this:
`ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.9.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.
tensorflow 2.9.2 requires tensorboard<2.10,>=2.9, but you have tensorboard 2.11.0 which is incompatible.`
And in the TN Example, this:
```
AttributeError Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py](https://localhost:8080/#) in _dep_map(self)
3015 PKG_INFO = 'METADATA'
-> 3016 EQEQ = re.compile(r"([\(,])\s*(\d.*?)\s*([,\)])")
3017
24 frames
AttributeError: _DistInfoDistribution__dep_map
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
AttributeError: _pkg_info
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py](https://localhost:8080/#) in _get(self, path)
1609 def _listdir(self, path):
1610 return os.listdir(path)
-> 1611
1612 def get_resource_stream(self, manager, resource_name):
1613 return open(self._fn(self.module_path, resource_name), 'rb')
FileNotFoundError: [Errno 2] No such file or directory: '/usr/local/lib/python3.8/dist-packages/Pygments-2.6.1.dist-info/METADATA'
```
I haven't changed anything, just run the cells you provide in the tutorial. The environment is set with GPU. | {
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} | # What does this PR do ?
Per-micro-batch input loader and sampler for Megatron-based language models
# Changelog
- `Use dataloader_iter` based step iterator API
- Update batch sampler to accommodate changes for global-batch-based sampling
- Update batch H2D copier
- Update training step hooks to reflect micro-batch to global-batch index difference
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | …tes.
Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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# Changelog
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```python
# Add a code snippet demonstrating how to use this
```
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Found a few tests that are taking ~ 10 minutes to complete. Disabling them until they can be fixed.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | **Describe the bug**
EMA saves a separate EMA based ckpt when normally saving. The problem is when saving a set amount of checkpoints (i.e k=5) the older EMA ckpts are not being deleted. This means there is an excess of EMA based ckpts.
What is expected is that older EMA ckpts are deleted.
**Proposed Solution**
Overriding the code is tricky, as the call to remove ckpts is found within functions, like such:
https://github.com/Lightning-AI/lightning/blob/master/src/pytorch_lightning/callbacks/model_checkpoint.py#L652
https://github.com/Lightning-AI/lightning/blob/master/src/pytorch_lightning/callbacks/model_checkpoint.py#L671
https://github.com/Lightning-AI/lightning/blob/master/src/pytorch_lightning/callbacks/model_checkpoint.py#L713
My suggestion would be to move this code into a separate function that can be overridden in the [NeMoModelCheckpoint](https://github.com/NVIDIA/NeMo/blob/4d3e99f986fd9354b5528a3e9c82fe01ec2df916/nemo/utils/exp_manager.py#L745) class by introducing a `delete_checkpoint` function. In this function we will also be able to delete the EMA weights.
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} | # What does this PR do ?
Remove SDP code from tools/ folder and add docs page briefly describing SDP and its new location
**Collection**: tools
# Changelog
- Remove `tools/speech_data_processor` dir
- Update docs with new SDP location
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to [PR #5574](https://github.com/NVIDIA/NeMo/pull/5574)
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
Removes an unused archive, downloaded when preparing the AIS binary.
**Collection**: General
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Adds ConcatDataset features to ASR configs and fixes bugs
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- All parameters are now optional
- One needs to pass train_ds.is_concat=True to activate
- The datasets become a list over the possible current list
- if the concat_sampling_technique = random, one needs to specify concat_sampling_probabilities, one per dataset
# Usage
In the config file
model.train_ds.is_concat=True \
model.train_ds.concat_sampling_technique=random \. # Default: temperature
# model.train_ds.concat_sampling_temperature=5 \. # Default: 5
model.train_ds.concat_sampling_probabilities=[0.5,0.5] \. # Default: none
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x ] New Feature
- [ x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
1. Added missing import for gather_objects.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
This PR fixes a missing import.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | …tes.
Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Reports the model size of enc-dec models across model parallel groups similar to BERT/GPT
**Collection**: NLP
# Changelog
- Moves param aggregation to the base model
- Implements param aggregation for enc-dec models.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Suggestion in slack to reduce confusion. The current message made it seem like the EMA weights are loaded into the main weights which is incorrect.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Makes training and validation batch sizes separately configurable for T5 prompt learning. Related to https://jirasw.nvidia.com/projects/NLP/issues/NLP-393?filter=allopenissues
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
data:
add_eos: true
add_bos: false
decoder_starts_with_pad: False
add_eos_to_decoder_output: True
add_sentinel_to_input: True
ul2_prompt_token: null # <extra_id_s>, <extra_id_r>, <extra_id_x>
train_ds:
path: ["data/squad_train.jsonl"]
global_batch_size: 8
micro_batch_size: 8 # micro batch size should equal global batch size when pipeline parallel = 1
shuffle: true
num_workers: 4
pin_memory: true
validation_ds:
path: ["data/squad_val.jsonl"]
global_batch_size: 8
micro_batch_size: 8 # micro batch size should equal global batch size when pipeline parallel = 1
shuffle: true
num_workers: 4
pin_memory: true
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Signed-off-by: He Huang (Steve) <[email protected]>
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} | Temp workaround: Disable test with cache_audio=True since it is failing in CI (#5607)
Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
Running `test_cache_datastore_manifests` with `cache_audio=True` started to fail in CI.
Here we disable it temporarily to unblock CI.
**Collection**: ASR, tests
# Changelog
- Removed test with `cache_audio=True`
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #5607
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Signed-off-by: He Huang (Steve) <[email protected]>
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} | null | null | null | **Describe the bug**
The address graph is too permissive and always transduces "OK" to "Oklahoma" even in cases when it is not a part of an address.
Could be nice to add some test cases that evaluate the performance of the graph in cases where OK is/is not a part of an address or not, maybe also extending the address graph if possible.
**Steps/Code to reproduce bug**
```
from nemo_text_processing.text_normalization.normalize import Normalizer
print(normalizer.normalize("Ah, OK.", verbose=True, punct_post_process=True))
print(normalizer.normalize("That's OK.", verbose=True, punct_post_process=True))
```
Output:
```
tokens { name: "Ah, Oklahoma" } tokens { name: "." }
Ah, Oklahoma. # incorrectly transduced by the address graph
tokens { name: "That's" } tokens { name: "OK." }
That's OK. # expected
```
**Expected behavior**
The grammar only transforms `OK -> Oklahoma` when in the context of an address.
**Environment overview (please complete the following information)**
- Jupyter notebook in venv on local machine
- Method of NeMo install: pip install nemo_toolkit['all']
**Environment details**
- OS version 5.13.0-40-generic
- PyTorch version 1.13.0
- Python version 3.9.7
**Additional context**
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I trained a pre-trained model Bert on the Squad dataset for question_answer but to get the answer for a single question there is nothing that **model.inference** provides me as it only accepts a file.
```
model = BERTQAModel.restore_from(config.model.nemo_path)
eval_device = [config.trainer.devices[0]] if isinstance(config.trainer.devices, list) else 1
model.trainer = pl.Trainer(
devices=eval_device,
accelerator=config.trainer.accelerator,
precision=16,
logger=False,
)
all_preds, all_nbest = model.inference(
config.model.test_ds.file, # file_name
num_samples=10, # setting to -1 will use all samples for inference
)
```
**Describe the solution you'd like**
So, if we can pass the question string and model infer on the question string to return us the answer, it would be great.
```
answer = model.inference("question")
# here question can be a string on which the model can infer to give us the result or it also can be a JSON obj.
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} | # What does this PR do ?
Update documentations and tutorials for adapters
**Collection**: [ASR, Core]
# Changelog
- Update tutorial to include info about adapter changes and type checking
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [x] Documentation
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} | Signed-off-by: Taejin Park <[email protected]>
[[NeMo Online Speaker Diarization VIDEO DEMO LINK](
https://drive.google.com/file/d/15ro8HKmzevXh_wx9oTtfRUw9wNtj6LuJ/view?usp=share_link)]
# What does this PR do ?
Part 2 for adding core classes and functions for online clustering diarizer.
** As soon as [PR 5526](https://github.com/NVIDIA/NeMo/pull/5526) gets merged, `Files changed` will be reduced to 2` **
Dividing the origin huge PR into two:
(part 1: online clustering (nmesc_clustering.py) + unit tests: [PR 5526](https://github.com/NVIDIA/NeMo/pull/5526))
part 2: Segmentation + online diarizer class + unit tests
**Collection**:
ASR
# Changelog
the following will be included in this PR
#### `OnlineClusteringDiarizer` in online_diarizer.py (inherits ClusteringDiarizer) (will be included in part2)
online version of `ClusteringDiarizer` class in clustering_diarizer.py
#### `OnlineSegmentor` class in speaker_utils.py: (will be included in part2)
online version of speech segmentations
<img width="966" alt="image" src="https://user-images.githubusercontent.com/13211483/204661137-78d0272b-1594-4509-a486-a8720e835bc4.png">
Checkout [Help Slides Link](https://docs.google.com/presentation/d/1LDUTIGRNVuLJsy81V3GaHZlFDUpxZ9rux-fNPfgcLmw/edit?usp=sharing) for better understanding on why online diarizer needs
(1) linear sum assignment (Hungarian alg)
(2) Interval merging
(3) merge_vector function
and data flow of class and functions.
# Usage
This is core classes and functions update.
Example script will be pushed in the second round of PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
[Online Neural Diarization of Unlimited Numbers of Speakers](https://arxiv.org/pdf/2206.02432.pdf)
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds a yaml file for the conformer transducer model with a stateless decoder.
**Collection**:
ASR
# Changelog
# Usage
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Previously, the char set pattern `[À-ÿ]` includes math operators, such as `÷` and `×`. Revise the pattern to exclude these.
```
ord("À")
Out[5]: 192
ord("ÿ")
Out[6]: 255
chr(247)
Out[8]: '÷'
chr(215)
Out[9]: '×'
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Removes `eval_tts_scores()`, which was unused and had `pesq` (unmaintained) and `pystoi` dependencies.
**Collection**: TTS
# Changelog
- Removed `pesq` and `pystoi` install requirements for TTS
- Removed unused function from `helpers.py`
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# What does this PR do ?
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**Collection**: TTS
# Changelog
- Removed `pesq` and `pystoi` install requirements for TTS
- Removed unused function from `helpers.py`
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} | currently the model is doing essentially nothing.
Next todos:
* adjust the loss to only account for the generated window
* get spectrogram generation in validation runs working
* come up with a reasonable architecture which includes the text embeddings
* move to VM | {
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} | # What does this PR do ?
Port Music Megatron implementation onto NeMo
**Collection**: [Note which collection this PR will affect]
adding one more music tokenizer to tokenizer for megatron_gpt_pretraining.py
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# modify the data location in the https://github.com/NVIDIA/NeMo/tree/main/examples/nlp/language_modeling/conf/megatron_music_gpt_config.yaml file line 167-169
python examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-name=megatron_music_gpt_config.yaml
trainer.devices=1 \
trainer.log_every_n_steps=1 \
trainer.val_check_interval=100 \
trainer.limit_val_batches=2 \
trainer.accumulate_grad_batches=1 \
trainer.max_steps=3 \
trainer.precision=16 \
trainer.gradient_clip_val=1.0 \
exp_manager.exp_dir=examples/nlp/language_modeling/gpt_pretrain_results \
model.pipeline_model_parallel_size=1 \
model.tensor_model_parallel_size=1 \
model.optim.name=fused_adam \
model.optim.lr=2e-4 \
model.optim.sched.warmup_steps=1 \
model.optim.sched.constant_steps=1 \
model.optim.sched.min_lr=8e-5 \
model.max_position_embeddings=128 \
model.encoder_seq_length=128 \
model.data.seq_length=128 \
model.tokenizer.tokenizer_name=music \
model.tokenizer.tokenizer_library=music \
model.tokenizer.vocab_file=/workspace/music_vocab.json \
model.tokenizer.merge_file=None \
model.num_layers=8 \
model.hidden_size=256 \
model.num_attention_heads=8 \
model.data.index_mapping_dir=examples/nlp/language_modeling/gpt_index_mappings
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@okuchaiev @yidong72
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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**Collection**: [TTS]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
Unable to load pre-trained speaker label model
**Steps/Code to reproduce bug**
```
import nemo.collections.asr as nemo_asr
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')
```
**Expected behavior**
Just loading a pertained model should not throw an error
**Environment overview (please complete the following information)**
- Environment location: AWS g5.xlarge
- Method of NeMo install: pip install with the steps in the guide on Python3.9, not from source
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version ubuntu 20
- PyTorch version: '1.13.0'
- Python version: 3.9
**Additional context**
```
[NeMo W 2022-12-11 06:24:03 nemo_logging:349] /opt/conda/envs/classify-audio/lib/python3.9/site-packages/torch/jit/annotations.py:299: UserWarning: TorchScript will treat type annotations of Tensor dtype-specific subtypes as if they are normal Tensors. dtype constraints are not enforced in compilation either.
warnings.warn("TorchScript will treat type annotations of Tensor "
[NeMo I 2022-12-11 06:24:03 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.14.0rc0/titanet-l/492c0ab8416139171dc18c21879a9e45/titanet-l.nemo.
[NeMo I 2022-12-11 06:24:03 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.14.0rc0/titanet-l/492c0ab8416139171dc18c21879a9e45/titanet-l.nemo
[NeMo I 2022-12-11 06:24:03 common:912] Instantiating model from pre-trained checkpoint
[NeMo W 2022-12-11 06:24:04 modelPT:156] 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: /manifests/combined_fisher_swbd_voxceleb12_librispeech/train.json
sample_rate: 16000
labels: null
batch_size: 64
shuffle: true
time_length: 3
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: scatter
augmentor:
noise:
manifest_path: /manifests/noise/rir_noise_manifest.json
prob: 0.5
min_snr_db: 0
max_snr_db: 15
speed:
prob: 0.5
sr: 16000
resample_type: kaiser_fast
min_speed_rate: 0.95
max_speed_rate: 1.05
num_workers: 15
pin_memory: true
[NeMo W 2022-12-11 06:24:04 modelPT:163] 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: /manifests/combined_fisher_swbd_voxceleb12_librispeech/dev.json
sample_rate: 16000
labels: null
batch_size: 128
shuffle: false
time_length: 3
num_workers: 15
pin_memory: true
[NeMo I 2022-12-11 06:24:04 label_models:126] Setting angular: true/false in decoder is deprecated and will be removed in 1.13 version, use specific loss with _target_
[NeMo I 2022-12-11 06:24:04 features:267] PADDING: 16
[NeMo W 2022-12-11 06:24:04 modelPT:156] 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: /manifests/combined_fisher_swbd_voxceleb12_librispeech/train.json
sample_rate: 16000
labels: null
batch_size: 64
shuffle: true
time_length: 3
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: scatter
augmentor:
noise:
manifest_path: /manifests/noise/rir_noise_manifest.json
prob: 0.5
min_snr_db: 0
max_snr_db: 15
speed:
prob: 0.5
sr: 16000
resample_type: kaiser_fast
min_speed_rate: 0.95
max_speed_rate: 1.05
num_workers: 15
pin_memory: true
[NeMo W 2022-12-11 06:24:04 modelPT:163] 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: /manifests/combined_fisher_swbd_voxceleb12_librispeech/dev.json
sample_rate: 16000
labels: null
batch_size: 128
shuffle: false
time_length: 3
num_workers: 15
pin_memory: true
[NeMo I 2022-12-11 06:24:04 features:267] PADDING: 16
[NeMo E 2022-12-11 06:24:05 common:505] Model instantiation failed!
Target class: nemo.collections.asr.models.label_models.EncDecSpeakerLabelModel
Error(s):
Traceback (most recent call last):
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/classes/common.py", line 484, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/collections/asr/models/label_models.py", line 168, in __init__
self._macro_accuracy = Accuracy(num_classes=num_classes, average='macro', task='multiclass')
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/torchmetrics/classification/accuracy.py", line 490, in __new__
assert isinstance(top_k, int)
AssertionError
Traceback (most recent call last):
File "/home/ubuntu/classify-audio/test.py", line 2, in <module>
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/classes/common.py", line 851, in from_pretrained
instance = class_.restore_from(
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/classes/modelPT.py", line 330, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/connectors/save_restore_connector.py", line 235, in restore_from
loaded_params = self.load_config_and_state_dict(
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/connectors/save_restore_connector.py", line 158, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/classes/common.py", line 506, in from_config_dict
raise e
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/core/classes/common.py", line 498, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/nemo/collections/asr/models/label_models.py", line 168, in __init__
self._macro_accuracy = Accuracy(num_classes=num_classes, average='macro', task='multiclass')
File "/opt/conda/envs/classify-audio/lib/python3.9/site-packages/torchmetrics/classification/accuracy.py", line 490, in __new__
assert isinstance(top_k, int)
AssertionError
``` | {
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Hi,
When we use [`eval_beamsearch_ngram.py`](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py) code for large data sets (e.g., with 500,000 utterances), the code crashes after some hours of computation (i.e., the OS kill automatically the process!!). According to my observation, the code runs correctly when the ASR acoustic model runs and the raw transcriptions of data sets extracted correctly, but when the LM wants to apply in a huge amount of raw transcriptions then it crashes. I googled this issue, and I found below link (which I think the issue related to the shortage of memory of the server):
- [Unable to allocate array with shape and data type](https://stackoverflow.com/questions/57507832/unable-to-allocate-array-with-shape-and-data-type)
According to the above-mentioned link, I have changed the `overcommit_memory` mode of my server. however, unfortunately the issue didn't resolved. Would you please help me to address the issue?
Thanks a lot | {
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} | null | null | null | PL soon to be released will work around this problem: https://github.com/Lightning-AI/lightning/issues/9900#issuecomment-1345390290
Might be useful to update the minimum version requirements of PL for Nemo
| {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fixes an issue with delayed process finalization, and race condition with the data thread
**Collection**: [Core]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] New Feature
- [x] Bugfix
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} | null | null | null | I'm using fastpich & univnet from Nemo to generate audio from text (TTS). I want to have the time information of each word in such a way that it shows in what period of time each word was uttered. There are some ways to predict such information, but all of them are applied after running the encoder and vocoder models. Is there any way to get such information from TTS models (any of them) without having to post-process our data?
If there is not, I would be grateful if you tell me what is the most efficient way to get such information.
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Re-add threading of the stderror and drain threads
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# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [ ] Documentation
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Re-add threading of the stderror and drain threads
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Removed unnecessary busy waiting in loop
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- Remove busy waiting in tight loop
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- [ ] Did you add or update any necessary documentation?
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Removed unnecessary busy waiting in loop
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# Changelog
- Remove busy waiting in tight loop
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [x] Bugfix
- [ ] Documentation
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Removed unnecessary busy waiting in loop
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- Remove busy waiting in tight loop
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Removed unnecessary busy waiting in loop
**Collection**: [Core]
# Changelog
- Remove busy waiting in tight loop
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
We missed a c/p mistake in just-merged PR #5569.
This bug does not affect default functionality, just the new param added in #5569.
Fix:
When setting up validation and test data, we need to use `setup_multiple_*`.
**Collection**: Core
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
n/a
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #5437 5569
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Allows configuring a stateless timer via `exp_manager.max_time=03:55:00`.
**Collection**: All
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/5583",
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} | # What does this PR do ?
The goal of this PR is to add a supervised fine-tuning functionality for GPT.
**Collection**: NLP - language modeling
# Changelog
- Created a new class similar to GPT pretraining: MegatronGPTSFT
- Create a new dataset class for SFT, allowing answer-only loss and the inclusion of EOS and SEP tokens
- Create a new usage example
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Suffixes to numbers like double and triple are currently missed/unhandeled. So for a transcript like double 357 should actually return 3357.
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} | null | null | null | Hi, I am trying to use pretrained conformer-ctc-medium Hindi model as encoder, and I would like to add conformer-transducer as decoder initialized with random weights.
I tried following things, but none of them is working:
1. I have used **conformer_transducer_bpe.yaml** thinking that the decoder config parameters would be loaded from the config & "RNNTDecoder" would be added.
2. I have tried initializing in the python code, but that is also not working:
conf_ctc_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_hi_conformer_ctc_medium")
conf_ctc_model.cfg.decoder._target_='nemo.collections.asr.modules.RNNTDecoder'
3. I have tried adding decoder in python code as
RNN_decoder = dict(
_target_ = 'nemo.collections.asr.modules.RNNTDecoder',
normalization_mode = 'null',
random_state_sampling = 'false',
blank_as_pad = 'true',
prednet = dict(
pred_hidden = 640,
pred_rnn_layers = 1,
t_max= 'null',
dropout= 0.2
)
)
params['model']['decoder'] = RNN_decoder
4. In addition to RNN_decoder, tried initializing joint, decoding, loss, variational_noise config parameters. But did not help.
5. Tried change_vocabulary before & after config parameters loading
But, In all cases decoder was being displayed as ConvASRDecoder, loss = CTCloss, encoder= ConformerEncoder, both during training & in hparams.yaml file.
Please suggest how to use pretrained conformer-ctc with a custom decoder. This decoder can be a conformer/transformer.
or
Can I also try using encoder decoder based "Quartznet" hindi pretrained model to accomplish the same.
Or Can I use pyCTCdecoder for decoding?
Thanks
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