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https://api.github.com/repos/NVIDIA/NeMo/issues/4036 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4036/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4036/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4036/events | https://github.com/NVIDIA/NeMo/pull/4036 | 1,210,004,019 | PR_kwDOC_bI7s42gfOV | 4,036 | Merge r1.8.0 main | {
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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
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# 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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**PR Type**:
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## Who can review?
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} | null | null | null | When I try to run `experiments/tts/fastpitch_finetune.py` with `fastpitch_align_v1.05.yml` with `ddp` on multiple GPUs the training immediately crashes with the following trace
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1234, in _run
results = self._run_stage()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1321, in _run_stage
return self._run_train()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1351, in _run_train
self.fit_loop.run()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 231, in advance
self.trainer._call_callback_hooks("on_train_batch_end", batch_end_outputs, batch, batch_idx, **extra_kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1628, in _call_callback_hooks
self._on_train_batch_end(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1660, in _on_train_batch_end
callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx)
File "/workspace/nemo/nemo/utils/exp_manager.py", line 144, in on_train_batch_end
self._on_batch_end("train_step_timing", pl_module)
File "/workspace/nemo/nemo/utils/exp_manager.py", line 138, in _on_batch_end
pl_module.log(name, self.timer[name], on_step=True, on_epoch=False)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 381, in log
value = apply_to_collection(value, numbers.Number, self.__to_tensor)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 99, in apply_to_collection
return function(data, *args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 515, in __to_tensor
return torch.tensor(value, device=self.device)
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "examples/tts/fastpitch_finetune.py", line 41, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/workspace/nemo/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "examples/tts/fastpitch_finetune.py", line 37, in main
trainer.fit(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 768, in fit
self._call_and_handle_interrupt(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 736, in _call_and_handle_interrupt
self._teardown()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1298, in _teardown
self.strategy.teardown()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 447, in teardown
super().teardown()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/parallel.py", line 134, in teardown
super().teardown()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 444, in teardown
optimizers_to_device(self.optimizers, torch.device("cpu"))
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/optimizer.py", line 27, in optimizers_to_device
optimizer_to_device(opt, device)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/optimizer.py", line 33, in optimizer_to_device
optimizer.state[p] = apply_to_collection(v, torch.Tensor, move_data_to_device, device)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 107, in apply_to_collection
v = apply_to_collection(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 99, in apply_to_collection
return function(data, *args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 354, in move_data_to_device
return apply_to_collection(batch, dtype=dtype, function=batch_to)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 99, in apply_to_collection
return function(data, *args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 347, in batch_to
data_output = data.to(device, **kwargs)
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```
This does not happen if I run on a single GPU. | {
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} | null | null | null | While analysing datasets via [SpeechDataExplorer](https://github.com/NVIDIA/NeMo/tree/main/tools/speech_data_explorer) I discovered that transcripts (in our case generated with Conformer CTC BPE) occasionally contain multiple consecutive spaces. This influences the computation of CER, but is also very confusing because the `text diff` section in SDE does not show any visual differences. The `text diff` also fails to show differences for UTF-8 symbols that appear in different versions in text and pred_text (composed and decomposed form), but the difference is correctly taken into account when computing WER and CER, hence this becomes confusing because there is no visual difference between the two transcripts.
Tracking down the source for multiple consecutive spaces I see that this can occur if a `blank_id` token separates consecutive space characters: https://github.com/NVIDIA/NeMo/blob/91e6c2552a9ca8d00bf8ce758adea68d9c49f803/nemo/collections/asr/metrics/wer.py#L162-L165
Perhaps following Huggingface `tokenizer.decode` a flag `clean_up_tokenization_spaces` with the default value of `True` could be added to `decode_tokens_to_str` and `decode_ids_to_tokens`. The sole purpose of this flag would be to clean up the resulting data of multiple consecutive space symbols. If you agree I can make a PR. | {
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} | # What does this PR do ?
1. If in DDP mode number of batches is not evenly divisible by number of processes, some batches may be processed several times. This lead to distorted metrics. This PR adjusts number of batches to number of processes.
2. In DDP mode batches in different processes are shuffled independently. This PR sets random seed for this shuffling. Before this fix, shuffling lead to some batches being processed several times and some batches not processed at all.
**Collection**: nlp
# Changelog
- [adjusting number of batches](https://github.com/NVIDIA/NeMo/blob/45e9deca8c29b6f3142294352c7350f385ba6b28/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L1354)
- [setting random seed](https://github.com/NVIDIA/NeMo/blob/45e9deca8c29b6f3142294352c7350f385ba6b28/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py#L787)
# Usage
To notice changes support numbers you can use this [stage](https://github.com/NVIDIA/NeMo/blob/91e6c2552a9ca8d00bf8ce758adea68d9c49f803/Jenkinsfile#L1219) in Jenkins.
**PR Type**:
- [x] Bugfix
Closes bug https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3610560&cmtNo=
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} | # What does this PR do ?
Adds the ability to fine-tune to T5 models on generic and small text-to-text datasets provided a two separate text files (one for source and one for target).
**Collection**: NLP
# Changelog
- Create a new MegatronT5FinetuneModel class that inherits from MegatonT5Model. MegatronGLUEModel now inherits from MegatronT5FinetuneModel and only overrides `build_train_valid_test_datasets()`.
# Usage
```python
python megatron_t5_seq2seq_finetune.py \
-cn megatron_t5_finetuning \
model.data.train_ds.src_file_name=/raid/Data/KLUE/klue-nli-v1.1/train_knli_src.txt \
model.data.train_ds.tgt_file_name=/raid/Data/KLUE/klue-nli-v1.1/train_knli_tgt.txt \
model.data.validation_ds.src_file_name=/raid/Data/KLUE/klue-nli-v1.1/validation_knli_src.txt \
model.data.validation_ds.tgt_file_name=/raid/Data/KLUE/klue-nli-v1.1/validation_knli_tgt.txt \
model.restore_from_path=/path/to/your.nemo \
trainer.devices=2 \
trainer.val_check_interval=100 \
trainer.max_epochs=10
```
# 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 ?
This adds a tutorial notebook on how to use p-tuning and prompt-tuning within NeMo. It also updates the NeMo Documentation page and adds some example data preprocessing scripts.
**Collection**: BigNLP
# Changelog
- Add Multitask prompt and p-tuning notebook
- Add squad, intent and slot, and financial sentiment analysis dataset preprocessing example scripts
- Updates documentation site
# Usage
```python
python squad_preprocessing.py
python financial_phrase_bank_preprocessing.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?
- [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
- [ ] 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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds the ability to use Huggingface Hub to load pretrained models. **This includes publically published models from any user that uploads their model to the hub - so it enables public contributions to NeMo from the community**.
**Collection**: [Core, Documentation]
# Changelog
- Refactor NGC + HF Hub handling
# Note
As of right now, there are no uploaded checkpoints on the HF Hub, so the below call will fail, but we hope to have some checkpoints there soon.
# Usage
## Uploading a new checkpoint
When uploading a new checkpoint to HF Hub, make sure the name of the .nemo model matches the name of the model card itself
Example : If user ABC has model card `stt_en_citrinet_1024.nemo` then the model card should have the name `ABC/stt_en_citrinet_1024` when loading it in nemo.
## Use after uploading
```python
# Assuming you have nemo_toolkit[asr] installed ...
import nemo.collections.asr as nemo_asr
# replace `nvidia` with any username / public checkpoint repo on the hub for community checkpoints
model = nemo_asr.models.ASRModel.from_pretrained("nvidia/stt_en_conformer_ctc_medium")
```
# 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?
@okuchaiev @ericharper | {
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} | # What does this PR do ?
fix itn vi tests, contributed by @binh234 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4028 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4028/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4028/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4028/events | https://github.com/NVIDIA/NeMo/pull/4028 | 1,208,936,536 | PR_kwDOC_bI7s42dHzT | 4,028 | refactor tn data folder, and update of measure | {
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} | Signed-off-by: Yang Zhang <[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.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hello everyone, in the diarization process I need to receive the exact number of speakers within an audio without having to pass this number in the manifest.
Exemplifying better:
My problem is that I can only guarantee the maximum number of speakers within these audios, I have no way of knowing if the audio will come with 2, 3, 5 speakers. When I pass the 10 speakers manifest it tries to "find" 10 speakers within an audio that only has 3 speakers.
Is there any alternative in which I receive the exact number of speakers in the rttm output without having to go through the manifest first? | {
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} | # What does this PR do ?
Adds log_local_rank_0_only and log_global_rank_0_only args to exp_manager.
**Collection**: All
# Changelog
- Makes creation of log files based on world size configurable instead of hard-coded.
# Usage
* exp_manager.log_rank_0_only: True
* exp_manager.log_global_rank_0_only: False
# Before your PR is "Ready for review"
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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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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
```
# Before your PR is "Ready for review"
**Pre checks**:
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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.
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
This PR fixes the type casting issue in nmesc_clustering.py. With torch.jit.script decorator, torch.tensor should always strictly match the type when assigining/changing a variable. Without this fix, in some cases torch.jit compiler returns an error at nmesc_clustering.py.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- changed variable name from random_trial to n_random_trials
- changed variable assignment with a proper type conversion
# Usage
Please refer to [speaker diarization part of the NeMo docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/datasets.html).
```python
python examples/speaker_tasks/diarization/offline_diarization.py \
diarizer.manifest_filepath="/path/to/ch109_input_manifest.json" \
diarizer.oracle_num_speakers=2 \ # Since there are exactly 2 speakers per each CH109 session
diarizer.oracle_vad=True \ # Use oracle VAD extracted from RTTM files.
diarizer.collar=0.25 \
diarizer.ignore_overlap=True \
diarizer.speaker_embeddings.model_path="titanet_large" \
diarizer.speaker_embeddings.window_length_in_sec=[1.5,1.0,0.5] # Multiscale setting \
diarizer.speaker_embeddings.shift_length_in_sec=[0.75,0.5,0.25] # Multiscale setting \
diarizer.speaker_embeddings.parameters.multiscale_weights=[0.33,0.33,0.33] # Equal weights
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] 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
- [x] Bugfix
- [ ] Documentation
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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?
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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] | null | null | 2022-04-17T18:16:50 | 2022-06-09T05:42:07 | 2022-05-24T22:25:49 | NONE | {
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} | null | null | null | Hello,
I am training a model in TalkNet. Before start training the spectrogram I created the durations.pt and f0s.pt
After that I started training the spectrogram with talknet_spect.py from examples folder.
When talknet_spect.py process N epochs it works well.
The training crashes when start a validation and it throws the next exception:
_RuntimeError: The size of tensor a (648) must match the size of tensor b (649) at non-singleton dimension 2_
I tried to train from branches main, r1.8.0 and tag v1.7.2, but I got the same error.
It is not the first time that I train a model with talknet but I never had this kind of issue.
**I share with you the complete stacktrace:**
python3 talknet_spect.py sample_rate=22050 \
train_dataset=/home/pc/Downloads/dataset.json \
validation_datasets=/home/pc/Downloads/dataset.json \
durs_file=/home/pc/Downloads/talknet/durations.pt \
f0_file=/home/pc/Downloads/talknet/f0s.pt \
trainer.max_epochs=100 \
trainer.check_val_every_n_epoch=1 \
model.train_ds.dataloader_params.batch_size=10 \
model.train_ds.dataloader_params.num_workers=8 \
model.validation_ds.dataloader_params.num_workers=8
/home/pc/.local/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
[NeMo W 2022-04-17 18:25:22 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-04-17 18:25:23 nemo_logging:349] /home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:287: LightningDeprecationWarning: Passing `Trainer(accelerator='ddp')` has been deprecated in v1.5 and will be removed in v1.7. Use `Trainer(strategy='ddp')` instead.
rank_zero_deprecation(
[NeMo W 2022-04-17 18:25:23 nemo_logging:349] /home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:151: LightningDeprecationWarning: Setting `Trainer(checkpoint_callback=False)` is deprecated in v1.5 and will be removed in v1.7. Please consider using `Trainer(enable_checkpointing=False)`.
rank_zero_deprecation(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo W 2022-04-17 18:25:23 nemo_logging:349] /home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:61: LightningDeprecationWarning: Setting `Trainer(flush_logs_every_n_steps=1000)` is deprecated in v1.5 and will be removed in v1.7. Please configure flushing in the logger instead.
rank_zero_deprecation(
[NeMo I 2022-04-17 18:25:23 exp_manager:281] Experiments will be logged at /home/pc/Downloads/talknet/trainers/nemo_experiments/TalkNetSpect/2022-04-17_18-25-23
[NeMo I 2022-04-17 18:25:23 exp_manager:647] TensorboardLogger has been set up
[NeMo W 2022-04-17 18:25:23 nemo_logging:349] /home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:2313: LightningDeprecationWarning: `Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.
rank_zero_deprecation("`Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.")
[NeMo W 2022-04-17 18:25:23 exp_manager:881] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo W 2022-04-17 18:25:23 deprecated:63] Class ``TalkNetSpectModel`` is deprecated. It is going to be removed in the 1.9 version. TalkNetSpectModel will be removed. Please, use MixerTTSModel instead.
Created a temporary directory at /tmp/tmp663jyb35
Writing /tmp/tmp663jyb35/_remote_module_non_sriptable.py
[NeMo W 2022-04-17 18:25:23 deprecated:63] Class ``AudioToCharWithDursF0Dataset`` is deprecated. It is going to be removed in the 1.8 version. Please, use ``nemo.tts.collections.torch.data.TTSDataset`` instead.
[NeMo E 2022-04-17 18:25:23 vocabs:324] Torch distributed needs to be initialized before you initialized <nemo.collections.common.data.vocabs.Phonemes object at 0x7f7f41882b80>. This class is prone to data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this before rank 0, errors might result.
[NeMo I 2022-04-17 18:25:25 collections:186] Dataset loaded with 85959 files totalling 120.93 hours
[NeMo I 2022-04-17 18:25:25 collections:187] 0 files were filtered totalling 0.00 hours
[NeMo E 2022-04-17 18:33:42 vocabs:324] Torch distributed needs to be initialized before you initialized <nemo.collections.common.data.vocabs.Phonemes object at 0x7f7f18548910>. This class is prone to data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this before rank 0, errors might result.
[NeMo I 2022-04-17 18:33:44 collections:186] Dataset loaded with 85959 files totalling 120.93 hours
[NeMo I 2022-04-17 18:33:44 collections:187] 0 files were filtered totalling 0.00 hours
[NeMo E 2022-04-17 18:41:43 vocabs:324] Torch distributed needs to be initialized before you initialized <nemo.collections.common.data.vocabs.Phonemes object at 0x7f7efc733d30>. This class is prone to data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this before rank 0, errors might result.
[NeMo I 2022-04-17 18:41:43 features:259] PADDING: 1
[NeMo I 2022-04-17 18:41:43 features:276] STFT using torch
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
Added key: store_based_barrier_key:1 to store for rank: 0
Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo W 2022-04-17 18:41:46 modelPT:496] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f7f4796fa00>. We recommend to use 'ddp' instead.
[NeMo I 2022-04-17 18:41:46 modelPT:587] Optimizer config = Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 0.001
maximize: False
weight_decay: 1e-06
)
[NeMo I 2022-04-17 18:41:46 lr_scheduler:833] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f7f073e6f70>"
will be used during training (effective maximum steps = 859600) -
Parameters :
(min_lr: 1.0e-05
warmup_ratio: 0.02
max_steps: 859600
)
| Name | Type | Params
-------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | embed | GaussianEmbedding | 7.6 K
2 | norm_f0 | MaskedInstanceNorm1d | 0
3 | res_f0 | StyleResidual | 512
4 | encoder | ConvASREncoder | 8.7 M
5 | proj | Conv1d | 82.0 K
-------------------------------------------------------------------
8.7 M Trainable params
0 Non-trainable params
8.7 M Total params
34.986 Total estimated model params size (MB)
Epoch 0: 0%| | 0/9940 [00:00<?, ?it/s][W reducer.cpp:1289] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
Epoch 0: 11%|██████████▎ | 1093/9940 [17:53<2:24:50, 1.02it/s, loss=3.37, v_num=5-2Epoch 0: 11%|██████████▏ | 1094/9940 [17:53<2:24:42, 1.02it/s, loss=3.37, v_num=5-23]Epoch 0: 11%|██████████▏ | 1095/9940 [17:53<2:24:34, 1.02it/s, loss=3.37, v_num=5-Epoch 0: 11%|██████████ | 1096/9940 [17:54<2:24:27, 1.02it/s, loss=3.37, v_num=5-23]Epoch 0: 11%|██████████ | 1096/9940 [17:54<2:24:27, 1.02it/s, loss=3.39, v_num=Epoch 0: 11%|█████████▉ | 1097/9940 [17:59<2:24:59, 1.02it/s, loss=3.39, v_num=5-23]Epoch 0: 15%|██████████Epoch 0: 33%|██████████████████████████████▌ | 3231/9940 [51:30<1:46:56, 1.05Epoch 0: 33%|█████████████████████████████▎ | 3232/9940 [51:34<1:47:02, 1.04it/s, loss=2.66, v_num=5-23]Epoch 0: 45%|██████████████████Epoch 0: 49%|█████████████████████████████████████████████ | 4870/9940 [1:17:48<1:21:00, 1.04it/s, loss=2.54, v_num=5-23]Error executing job with overrides: ['sample_rate=22050', 'train_dataset=/home/pc/Downloads/dataset.json', 'validation_datasets=/home/pc/Downloads/dataset.json', 'durs_file=/home/pc/Downloads/talknet/durations.pt', 'f0_file=/home/pc/Downloads/talknet/f0s.pt', 'trainer.max_epochs=100', 'trainer.check_val_every_n_epoch=1', 'model.train_ds.dataloader_params.batch_size=10', 'model.train_ds.dataloader_params.num_workers=8', 'model.validation_ds.dataloader_params.num_workers=8']
Traceback (most recent call last):
File "talknet_spect.py", line 29, in main
trainer.fit(model)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit
self._call_and_handle_interrupt(
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 722, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 812, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1237, in _run
results = self._run_stage()
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1324, in _run_stage
return self._run_train()
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1354, in _run_train
self.fit_loop.run()
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 203, in advance
result = self._run_optimization(
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 256, in _run_optimization
self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 369, in _optimizer_step
self.trainer._call_lightning_module_hook(
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1596, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1625, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py", line 168, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 278, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 193, in optimizer_step
return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 155, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 65, in wrapper
return wrapped(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper
return func(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/torch/optim/adam.py", line 100, in step
loss = closure()
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 140, in _wrap_closure
closure_result = closure()
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in __call__
self._result = self.closure(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 134, in closure
step_output = self._step_fn()
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 427, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *step_kwargs.values())
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1766, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 344, in training_step
return self.model(*args, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/home/pc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/pc/.local/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 82, in forward
output = self.module.training_step(*inputs, **kwargs)
File "/home/pc/Downloads/NeMo/nemo/utils/model_utils.py", line 364, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/home/pc/Downloads/NeMo/nemo/collections/tts/models/talknet.py", line 301, in training_step
pred_mel = self(text=text, text_len=text_len, durs=durs, f0=f0)
File "/home/pc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/pc/Downloads/NeMo/nemo/core/classes/common.py", line 835, in __call__
outputs = wrapped(*args, **kwargs)
File "/home/pc/Downloads/NeMo/nemo/collections/tts/models/talknet.py", line 285, in forward
x = self.res_f0(x, f0)
File "/home/pc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/pc/Downloads/NeMo/nemo/collections/tts/modules/talknet.py", line 136, in forward
return x + self.rs(s)
RuntimeError: The size of tensor a (648) must match the size of tensor b (649) at non-singleton dimension 2
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Do you know why is it raising that error and how to solve it?
Thank you!
OS version: Ubuntu 21.10
Python3.8
GPU: RTX 3080
pytorch-lightning 1.6.0
torch 1.11.0+cu113
torch-stft 0.1.4
torchaudio 0.11.0+cu113
torchmetrics 0.7.3
torchvision 0.12.0+cu113
David Martin Rius | {
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} | # What does this PR do ?
Update grammar and fix errors for Vietnamese ITN
**Collection**: Vietnamese ITN
# Changelog
- Update Vietnamese grammar
- Fix errors in Cardinal FST, Decimal FST, and Time FST
- Change Date and Time verbalizers
- Update test cases
# 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?
- [x] Does the PR affect components that are optional to install? Pynini
- [ ] 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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix the shape mismatch when doing broadcast, which causes logprob returns bad numbers.
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} | null | null | null | Hey guys i am training a tacotron2 model but im not sure how to plot the attention graph and loss as well, any help on that would be appreciated. | {
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After pull new git changes something is not working properly.
The way to reproduce the error is the next one:
from nemo.collections.asr.models import EncDecCTCModel
asr_model = EncDecCTCModel.from_pretrained(model_name="asr_talknet_aligner").cpu().eval()
After executing this code it will crash with the next error: (dataset_durations.py line 29 is the above code)
[NeMo I 2022-04-16 10:40:01 cloud:56] Found existing object /home/pc/.cache/torch/NeMo/NeMo_1.9.0rc0/qn5x5_libri_tts_phonemes/656c7439dd3a0d614978529371be498b/qn5x5_libri_tts_phonemes.nemo.
[NeMo I 2022-04-16 10:40:01 cloud:62] Re-using file from: /home/pc/.cache/torch/NeMo/NeMo_1.9.0rc0/qn5x5_libri_tts_phonemes/656c7439dd3a0d614978529371be498b/qn5x5_libri_tts_phonemes.nemo
[NeMo I 2022-04-16 10:40:01 common:705] Instantiating model from pre-trained checkpoint
[NeMo W 2022-04-16 10:40:01 features:233] Using torch_stft is deprecated and has been removed. The values have been forcibly set to False for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True as needed.
[NeMo I 2022-04-16 10:40:01 features:259] PADDING: 1
[NeMo I 2022-04-16 10:40:01 features:276] STFT using torch
Traceback (most recent call last):
File "dataset_durations.py", line 29, in <module>
asr_model = EncDecCTCModel.from_pretrained(model_name="asr_talknet_aligner").cpu().eval()
File "/home/pc/Downloads/NeMo/nemo/core/classes/common.py", line 708, in from_pretrained
instance = class_.restore_from(
File "/home/pc/Downloads/NeMo/nemo/core/classes/modelPT.py", line 305, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/pc/Downloads/NeMo/nemo/core/connectors/save_restore_connector.py", line 156, in restore_from
instance.load_state_dict(state_dict, strict=strict)
File "/home/pc/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for EncDecCTCModel:
Missing key(s) in state_dict: "preprocessor.featurizer.window".
Unexpected key(s) in state_dict: "preprocessor.featurizer.stft.forward_basis", "preprocessor.featurizer.stft.inverse_basis".
I checkout to tag v1.7.2 and it is working well. But I would prefer to use the latest code if possible.
Could you help me to solve it?
- OS version: Ubuntu 21.10
- Python3.8
- GPU: RTX 3080
pytorch-lightning 1.6.0
torch 1.11.0+cu113
torch-stft 0.1.4
torchaudio 0.11.0+cu113
torchmetrics 0.7.3
torchvision 0.12.0+cu113
Thank you!
David Martin Rius | {
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} | Signed-off-by: Anurag Dixit <[email protected]>
# What does this PR do ?
Adds a QAT finetuning workflow for ASR models.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
ASR
# 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
TRAIN_MANIFEST=<path to manifest file for training dataset>
EVAL_MANIFEST=<path to manifest file for evaluation dataset>
CONF_PATH=<path to model configuration>
NUM_GPU=<number of gpus>
NUM_NODES=<number of nodes>
BATCH_SIZE=<batch size>
LEARNING_RATE=0.00015
NUM_WORKERS=32
QUANT_MODEL=<Path to quantized model>
SAVE_MODEL=<Path to save model>
TOKENIZER_PATH=<Path to tokenizer>
python speech_to_text_qat.py \
--config-path=${CONF_PATH} \
--config-name=<config file name for model> \
model.train_ds.manifest_filepath=${TRAIN_MANIFEST} \
model.train_ds.batch_size=${BATCH_SIZE} \
model.validation_ds.manifest_filepath=${EVAL_MANIFEST} \
+model.train_ds.normalize=false \
++model.train_ds.num_workers=${NUM_WORKERS} \
++model.validation_ds.num_workers=${NUM_WORKERS} \
++model.train_ds.pin_memory=True \
++model.validation_ds.pin_memory=True \
+use_cer=false \
+trainer.devices=${NUM_GPU} \
trainer.num_nodes=${NUM_NODES} \
hydra.run.dir=${PWD} \
trainer.log_every_n_steps=50 \
+save_model=${SAVE_MODEL} \
+quantized_model=${QUANT_MODEL} \
++model.optim.lr=${LEARNING_RATE} \
++model.tokenizer.dir=${TOKENIZER_PATH} \
++model.tokenizer.type='bpe'
```
# 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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} | null | null | null | @borisfom As per comment https://github.com/NVIDIA/NeMo/issues/3619#issuecomment-1089551439, I'm re-opening the bug as the issue persists with `nemo:22.01` (1.7.1), `nemo:1.7.2` and `nemo:1.9.0rc0` (e4ee26b601a8e6f907f926019367d3d545c62f11) combined with `Riva:2.0.0`. I tried both by starting off with `nemo:22.01` and upgrading to more recent versions of nemo, as well as building new containers from the specific GitHub repo tag. I tested with T4 (16GB) and A100 (40GB) GPUs. On all occasions the conversion of [tts_en_fastpitch_align.nemo](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_fastpitch) fails with `RuntimeError: CUDA out of memory`. | {
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} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
A new tagger-based model for inverse text normalization
**Collection**: [NLP]
# 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?
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 ?
Critical: Fix single GPU training issue
Also, change deprecated Lightning args
**Collection**: Core + most others
# Changelog
The PR changes:
`num_process` --> `devices`/`num_devices`
`num_gpus` --> `num_devices`
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Adds a method to NMT to apply ITN/TN before/after translating text.
**Collection**: NLP
# Changelog
- Adds an itn_translate_tn method to `mt_enc_dec_model`.
# Usage
```python
inverse_normalizer = InverseNormalizer(lang="en")
normalizer = Normalizer(input_case="cased", lang="de")
translation_model.itn_translate_tn(["this is a sentence"], "en", "de", inverse_normalizer=inverse_normalizer, normalizer=normalizer)
```
# 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 ?
AudioToCharWithDursF0Dataset class will be deprecated so this PR adds code and config to use old FastPitch checkpoint (checkpoint that was trained using AudioToCharWithDursF0Dataset class which is now deprecated) with new code (TTSDataset) to support inference.
**Collection**: tts.models.fastpitch
# Changelog
- Adds Warning when using old checkpoint
- Adds config file to be used with old checkpoint
# Usage
* Fastpitch will only support inference using the old fastpitch checkpoint
# Before your PR is "Ready for review"
**Pre checks**:
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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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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
update changed function name in notebook
**Collection**: ASR
# 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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- [x] Bugfix
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## Who can review?
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Add a new model for inverse text normalization.
**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
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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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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
* You can potentially add a usage example below
```python
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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.
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Add a one line overview of what this PR aims to accomplish.
Fix broken links and maintain backward compatibility for MegatronBert pertained models from NGC
**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
- [ 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 ?
Adds support for fine-tuning BART models.
**Collection**: NLP
# Changelog
- Checkpoint to nemo converter addition for BART.
# Usage
# 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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Fix the broken P-tune T5 model after the PP T5 feature is merged. | {
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} | null | null | null | Is there a pretrained model with character encoding using conformer-CTC for English? I could only find the onw with BPE encoding.
As per https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc
> This model supports both the sub-word level and character level encodings. You can find more details on the config files for the Conformer-CTC models at Conformer-CTC <./configs.html#conformer-ctc>. The variant with sub-word encoding is a BPE-based model which can be instantiated using the EncDecCTCModelBPE class, while the character-based variant is based on EncDecCTCModel.
Does this mean, that it is there to be trained, but there is no pretrained version?
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} | # What does this PR do ?
compatibility with MCV versions 2 through8
**Collection**: ASR
# Changelog
- If tsv file does not include .mp3 extension for files, it is added
- If an mp3 file is empty, it is correctly ignored
- if the archive file is not in the {language}.tar.gz format, it is downloaded in that format anyways for correct downstream processing
- if the archive does not have layered directories (e.g. /version/language/files.tsv), it is correctly processed anyways
# Usage
```python
python get_commonvoice_data.py --data_root /data/temp --log --num_workers=16 --language es -- version cv-corpus-6.1-2020-12-11
python get_commonvoice_data.py --data_root /data/temp --log --num_workers=16 --language es --version cv-corpus-4-2019-12-10
python get_commonvoice_data.py --data_root /data/temp --log --num_workers=16 --language es --version cv-corpus-3
python get_commonvoice_data.py --data_root /data/temp --log --num_workers=16 --language es --version cv-corpus-2
```
# 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 ?
**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 ?
Nemo megatron models were not properly registering artifacts for the tokenizer.
**Collection**: NLP
# Changelog
- Update register_artifact calls
-
# 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)
- [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
- [ ] 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 ?
Update NeMo docs
**Collection**: [Docs]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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] | closed | true | null | [] | null | null | 2022-04-14T09:06:24 | 2022-04-18T21:03:44 | 2022-04-18T21:03:44 | CONTRIBUTOR | {
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If not; having two manifests, one small and one big will give an unfairly large influence to the small manifest in case of averaging, or exclude one if based on manifest with index 0. I believe the correct approach would be to do a division between the sum of numerators and sum of denominators over individual manifests (hence the same result if the two manifests were merged into one) as mentioned in https://github.com/NVIDIA/NeMo/blob/2aa68822bbfb8e7e8c221817b66de28928d6443b/nemo/collections/asr/metrics/wer.py#L75, but I do not see this happening.
Browsing through code I can see https://github.com/NVIDIA/NeMo/blob/2aa68822bbfb8e7e8c221817b66de28928d6443b/nemo/collections/asr/models/asr_model.py#L40-L45, but my Tensorboard apart from `val_wer` contains also `<manifestfile>_val_wer` and `val_wer==<manifestfile#0>_val_wer`. This value is also used for checkpoints. I am unable to find where in the code the individual `<manifestfile>_val_wer` are appended. | null | {
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} | # What does this PR do ?
Moves the override to `load_state_dict()` from TalkNet (which doesn't need it) to EncDecCTCModel for compatibility with the TalkNet Aligner checkpoint. Related ISSUE: https://github.com/NVIDIA/NeMo/issues/4015
This should be removed in 1.9.0 when the TalkNet Aligner model is deprecated.
**Collection**: TTS, ASR
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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# Usage
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```python
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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.
## Who can review?
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} | # What does this PR do ?
Adding missing script from docs. Changed docs to point to the right scripts directory.
**Collection**: ASR
# Changelog
- Restored missing fisher_audio_to_wav script
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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} | # What does this PR do ?
Adds a configurable parameter to apply log softmax on output logits for RIVA support
**Collection**: [Note which collection this PR will affect]
nlp/models/token_classification/punctuation_capitalization_model.py
# 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**:
- [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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} | Signed-off-by: Virginia Adams <[email protected]>
Notebook bug fixes for nv-bugs:
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3596793 [ML-NeMo][A30] Text_Classification_Sentiment_Analysis.ipynb failed with ConfigAttributeError: Key 'model' is not in struct
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3596634 [ML-NeMo][A30] Joint_Intent_and_Slot_Classification.ipynb failed with ConfigAttributeError: Key 'model' is not in struct
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3596616 [ML-NeMo][A30] Entity_Linking_Medical.ipynb failed with MisconfigurationException: `Trainer(strategy='ddp')`
**Collection**: N/A
# Changelog
Changed `nlp_model.py` and 3 tutorial notebooks
# 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
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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 ?
On-going TN refactoring for TTS
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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} | 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?
- [ ] 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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} | Signed-off-by: Vladimir Bataev <[email protected]>
# What does this PR do ?
Fix inner type checking system for outputs to be compatible with named tuples.
This adds the ability to use named tuples (`typing.NamedTuple` or `collections.namedtuple`) for outputs. This is especially useful when the number of output elements is large.
Named tuples are compatible with simple tuples, there should be no problems with mixing them.
# Usage
```python
class OutputType(NamedTuple):
y: torch.Tensor
z: torch.Tensor
class MultipleOutputTypesWithNamedTuple(Typing):
@property
def output_types(self):
return {"y": NeuralType(('B',), ElementType()), "z": NeuralType(('B',), ElementType())}
@typecheck()
def __call__(self, x):
y = x + 1
z = x + 2
return OutputType(y=y, z=z)
obj = MultipleOutputTypesWithNamedTuple()
result = obj(x=torch.zeros(10))
result_y, result_z = result # can unpack or use `result.y`, `result.z`
```
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
@titu1994, @okuchaiev, @ericharper
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} | # What does this PR do ?
Fixes a minor NMT variable passing bug.
**Collection**: NLP
# 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
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"merged_at": "2022-04-15T18:09:58"
} | # What does this PR do ?
Make log prob calculation faster and handles longer sequences. | {
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} | Signed-off-by: rvygon <[email protected]>
# What does this PR do ?
Fixes #3982, where get_samples passes int16 to librosa.core.resample instead of float32.
**Collection**: ASR
# Usage
```python
from pydub import AudioSegment
from nemo.collections.asr.parts.utils.decoder_timestamps_utils import get_samples
segment = AudioSegment.silent(duration=10000).set_frame_rate(22050).set_channels(1)
segment.export('test.wav', format='wav')
arr = get_samples('test.wav')
```
# 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.
# Additional Information
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} | null | null | null | **Describe the bug**
nemo.collections.asr.parts.utils.decoder_timestamps_utils.get_samples fails if the audio file's original sample rate differs from the target sample rate.
`ParameterError: Audio data must be floating-point`
**Steps/Code to reproduce bug**
```python
from pydub import AudioSegment
from nemo.collections.asr.parts.utils.decoder_timestamps_utils import get_samples
segment = AudioSegment.silent(duration=1000).set_frame_rate(22050)
segment.export('test.wav', format='wav')
get_samples('test.wav')
```
**Expected behavior**
File is resampled successfully.
**Environment overview (please complete the following information)**
- Method of NeMo install: pip
- NeMo version: 1.6.2
- librosa version: 0.8.1 | {
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} | Signed-off-by: Micha Livne <[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)
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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.
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} | Signed-off-by: Micha Livne <[email protected]>
# What does this PR do ?
This PR creates a 2-hierarchy YAML configuration.
The goal is to make sure that updates to config that are shared between all megatron models are affecting all relevant models.
**Collection**: [Note which collection this PR will affect]
language nlp.language_modelling (megatron)
# 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: nithinraok <[email protected]>
# What does this PR do ?
Fix path to creation of tarred dataset script in docs
**Collection**: docs
# Changelog
- Path location
# 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**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Overrides `load_state_dict` for four TTS models (to be deprecated in 1.9.0) such that they work with old NGC checkpoints, plus minor tutorial fixes/updates.
**Collection**: TTS
# Changelog
- Removes `forward_basis` and `inverse_basis` from the `state_dict` of existing checkpoints, which were trained before NeMo moved away from using torch_stft
- Adds `window` to `state_dict` for the preprocessor
- Semicolon added to one line in the TalkNet training tutorial to avoid printing the whole model
- Added `trainer.gpu` command line argument to Tacotron2 notebook
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3977 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3977/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3977/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3977/events | https://github.com/NVIDIA/NeMo/pull/3977 | 1,202,375,825 | PR_kwDOC_bI7s42ITSz | 3,977 | Megatron NMT Restore from T5/BART and finetune | {
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} | # What does this PR do ?
Adds the ability to restore from a T5/BART model and fine-tune it for NMT. With tarred datasets only.
**Collection**: NLP
# Changelog
- Fixes issues with rebasing to the Encoder-Decoder PP changes.
- Overrides the pre-training config with NMT-related things.
- Reconfigures the microbatch calculator on every training/eval step.
# Usage
Edit the aayn_base_megatron.yaml file with
- pretrained_model_path: /path/to/pretrained/t5.nemo
- pretrained_model_type: t5/bart
# 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 | **Describe the bug**
Running pip install nemo_toolkit['all'] fails at installing mecab-python3==1.0.3 for python version 3.10.
As per mecab documentation 1.0.5 is python 3.10 compatible
**Steps/Code to reproduce bug**
On a system with python 3.10 installed do:
python3 -m venv venv
source ven/bin/activate
pip install Cython
pip install nemo_toolkit['all']
**Expected behavior**
It should install fine
**Environment overview (please complete the following information)**
- Ubuntu 22.04 LTS
- Method of NeMo install: tried with both pip and using git repo
**Environment details**
- Ubuntu 22.04 LTS
- Python version: 3.10.4
**Additional context**
Collecting mecab-python3==1.0.3
Using cached mecab-python3-1.0.3.tar.gz (77 kB)
Preparing metadata (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [44 lines of output]
/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/installer.py:27: SetuptoolsDeprecationWarning: setuptools.installer is deprecated. Requirements should be satisfied by a PEP 517 installer.
warnings.warn(
WARNING: The wheel package is not available.
WARNING: The wheel package is not available.
running egg_info
creating /tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info
writing /tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info/PKG-INFO
writing dependency_links to /tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info/dependency_links.txt
writing entry points to /tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info/entry_points.txt
writing requirements to /tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info/requires.txt
writing top-level names to /tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info/top_level.txt
writing manifest file '/tmp/pip-pip-egg-info-_e2t6_9n/mecab_python3.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "<pip-setuptools-caller>", line 34, in <module>
File "/tmp/pip-install-vrffzbza/mecab-python3_8c966bababd04383857aeb72d4c98956/setup.py", line 201, in <module>
setup(name = "mecab-python3",
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/__init__.py", line 153, in setup
return distutils.core.setup(**attrs)
File "/usr/lib/python3.10/distutils/core.py", line 148, in setup
dist.run_commands()
File "/usr/lib/python3.10/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/usr/lib/python3.10/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 299, in run
self.find_sources()
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 306, in find_sources
mm.run()
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 541, in run
self.add_defaults()
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 578, in add_defaults
sdist.add_defaults(self)
File "/usr/lib/python3.10/distutils/command/sdist.py", line 226, in add_defaults
self._add_defaults_python()
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/sdist.py", line 113, in _add_defaults_python
self._add_data_files(self._safe_data_files(build_py))
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/sdist.py", line 132, in _add_data_files
self.filelist.extend(
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 489, in extend
self.files.extend(filter(self._safe_path, paths))
File "/hdd/nemo/nemo-env/lib/python3.10/site-packages/setuptools/command/sdist.py", line 134, in <genexpr>
for _, src_dir, _, filenames in data_files
ValueError: not enough values to unpack (expected 4, got 1)
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
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} | # What does this PR do ?
The notebook is complaining about Trainer `misconfiguration` error because it tries to spawn processes in the python interactive session. Fixed it by using torch elastic environment. | {
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I am trying to convert marblnet to onnx format. Compared to torch outputs, onnx outputs are slightly different.
Did you check that onnx and torch outputs are exactly the same? | {
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} | # What does this PR do ?
Adds to option to remove exclude terms from megatron transformer weight matrices.
**Collection**: NLP
# Changelog
# Usage
* Change yaml config to
```
bias: False
bias_gelu_fusion: False
bias_dropout_add_fusion: False
```
# 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 ?
Fix tensor parallel variable name and move models to CPU in Change partition
Sidenote: Looks like Megatron models disregard the accelerator mentioned in the trainer. To work things for now, this script manually moves model to CPU
**Collection**: NLP
# 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
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} | # What does this PR do ?
Fix tensor parallel variable name and move models to CPU in Change partition
**Collection**: NLP
# 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
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} | # What does this PR do ?
add special text support | {
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} | # What does this PR do ?
1. Fixes the condition for verifying if val check interval is too small.
2. Does not load train data when running trainer.validate or trainer.test
3. Fixes an error in glue_eval
**Collection**: NLP
# Changelog
# Usage
N/A
# 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: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes some execution errors for the training notebook (in Colab) by adding & pinning installs.
**Collection**: TTS
# Changelog
- Pynini pinned to version 2.1.4 (needed for training)
- Scipy pinned to version 1.7.3 (fixes comparison issue from #3961)
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #3961
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} | null | null | null | Start off with `nemo:22.01` container, install `riva_quickstart_v2.0.0` (`riva_api` and `nemo2riva`), download `stt_en_conformer_ctc_large_1.6.0` from ngc and unzip it.
Running `nemo2riva --out stt_en_conformer_ctc_large.riva stt_en_conformer_ctc_large.nemo` gives the following output
```
INFO: Logging level set to 20
INFO: Restoring NeMo model from 'stt_en_conformer_ctc_large.nemo'
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
[NeMo I 2022-04-11 20:32:55 mixins:165] Tokenizer SentencePieceTokenizer initialized with 128 tokens
[NeMo W 2022-04-11 20:32:56 modelPT:148] 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: /data/NeMo_ASR_SET/English/v2.0/train/tarred_audio_manifest.json
sample_rate: 16000
batch_size: 32
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20.0
min_duration: 0.1
shuffle_n: 2048
is_tarred: true
tarred_audio_filepaths: /data/NeMo_ASR_SET/English/v2.0/train/audio__OP_0..4095_CL_.tar
[NeMo W 2022-04-11 20:32:56 modelPT:155] 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:
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-clean.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-clean.json
sample_rate: 16000
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: na
[NeMo W 2022-04-11 20:32:56 modelPT:161] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath:
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-clean.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-clean.json
sample_rate: 16000
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: na
[NeMo I 2022-04-11 20:32:56 features:259] PADDING: 0
[NeMo I 2022-04-11 20:32:56 features:276] STFT using torch
[NeMo I 2022-04-11 20:32:59 save_restore_connector:158] Model EncDecCTCModelBPE was successfully restored from /data/stt_en_conformer_ctc_large.nemo.
[NeMo I 2022-04-11 20:33:03 export_utils:261] Swapped 108 modules
[NeMo W 2022-04-11 20:33:04 nemo_logging:349] /workspace/nemo/nemo/collections/asr/modules/conformer_encoder.py:240: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if max_audio_length > self.max_audio_length:
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
...
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
[W] 'Shape tensor cast elision' routine failed with: None
```
Running `nemo2riva --validate stt_en_conformer_ctc_large.nemo` gives the same output, but at the end appends the following lines
```
WARNING: Logging before flag parsing goes to stderr.
E0411 20:34:31.553215 140467938121536 <frozen eff.validator.validator>:59] Condition for key 'min_nemo_version' (1.3 <built-in function eq> 1.1) is not fulfilled
E0411 20:34:31.553421 140467938121536 schema.py:198] Exported model at stt_en_conformer_ctc_large.riva failed Riva compliance, using schema at /opt/conda/lib/python3.8/site-packages/nemo2riva/validation_schemas/asr-stt-exported-encdectcmodelbpe.yaml !
```
Using `reinstall.sh` to install `nemo:1.9.0rc0` makes no difference.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3965 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3965/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3965/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3965/events | https://github.com/NVIDIA/NeMo/pull/3965 | 1,200,457,033 | PR_kwDOC_bI7s42Br7O | 3,965 | Reapplying FastPitch FT notebook changes from #3954 | {
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} | Signed-off-by: Jocelyn Huang <[email protected]>
Reapplies the changes made in #3954 that were overwritten by 1.8.0 merge.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3962 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3962/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3962/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3962/events | https://github.com/NVIDIA/NeMo/pull/3962 | 1,199,039,412 | PR_kwDOC_bI7s419KjP | 3,962 | [ASR] k2: Epsilon-Transducer and code imprevements | {
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} | # What does this PR do ?
Introduces Epsilon-Transducer and major k2 code refactoring.
**Collection**: ASR
# Note
This PR is an interim one, meant to keep the k2 core code up to date and preserve some useful prototypes like the Pruned Epsilon-Transducer. The k2-based RNNT code design is subject to upcoming refactoring following the merger of this PR and https://github.com/NVIDIA/NeMo/pull/6168.
# Changelog
- k2 losses and decoders refactored
- Epsilon-Transducer RNNT variant added
- RNNT greedy and forced algnment added for Epsilon-Transducer
- Pruned Epsilon-Transducer proposed
- CTC and RNNT tests added
# Usage
* RNNT forced alignment: `python ./experimental/k2/align_speech_parallel.py model=stt_en_conformer_transducer_large predict_ds.manifest_filepath=./data/Librispeech/dev_clean.json predict_ds.batch_size=8 output_path=./ali_rnnt model_stride=4 aligner_args.decode_batch_size=2 aligner_args.rnnt_cfg.predictor_window_size=40 aligner_args.rnnt_cfg.predictor_step_size=4`
# 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)
- [x] 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?
@artbataev
[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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https://api.github.com/repos/NVIDIA/NeMo/issues/3961 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3961/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3961/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3961/events | https://github.com/NVIDIA/NeMo/issues/3961 | 1,198,979,374 | I_kwDOC_bI7s5Hdvku | 3,961 | FastPitch_MixerTTS_Training tutorial not working on Colab | {
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} | null | null | null | **Describe the bug**
The function `pre_calculate_supplementary_data` fails when running _FastPitch_MixerTTS_Training.ipynb_ on Colab.
**Steps/Code to reproduce bug**
Open https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_MixerTTS_Training.ipynb on Colab, then run all the cells. It should fail here:
```
fastpitch_sup_data_path = "fastpitch_sup_data_folder"
sup_data_types = ["align_prior_matrix", "pitch"]
pitch_mean, pitch_std, pitch_min, pitch_max = pre_calculate_supplementary_data(
fastpitch_sup_data_path, sup_data_types, text_tokenizer, text_normalizer, text_normalizer_call_kwargs
)
```
This is the output
```
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py", line 447, in __getitem__
align_prior_matrix = beta_binomial_prior_distribution(text_length, mel_len)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/helpers.py", line 57, in beta_binomial_prior_distribution
mel_i_prob = betabinom(phoneme_count, a, b).pmf(x)
File "/usr/local/lib/python3.7/dist-packages/scipy/stats/_distn_infrastructure.py", line 495, in pmf
return self.dist.pmf(k, *self.args, **self.kwds)
File "/usr/local/lib/python3.7/dist-packages/scipy/stats/_distn_infrastructure.py", line 2997, in pmf
cond1 = (k >= _a) & (k <= _b) & self._nonzero(k, *args)
TypeError: '<=' not supported between instances of 'numpy.ndarray' and 'Tensor'
```
**Expected behavior**
I expect it to work
**Environment overview (please complete the following information)**
- Environment location: Google Colab
**Environment details**
- PyTorch version 1.10.0+cu111
- Python version 3.7.13 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3960 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3960/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3960/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3960/events | https://github.com/NVIDIA/NeMo/issues/3960 | 1,198,532,262 | I_kwDOC_bI7s5HcCam | 3,960 | Assign a language model to Citrinet1024 | {
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} | null | null | null | Hi, Thanks for your excellent tutorial. Refer to [Citrinet paper](https://arxiv.org/abs/2104.01721), you got WER in 3 modes: without language model, with language model(6-gram and Transf). How can I assign a language model to Citrinet1024. I started fine-tuning Citrinet1024 with the Persian audio dataset. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3959 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3959/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3959/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3959/events | https://github.com/NVIDIA/NeMo/pull/3959 | 1,197,913,548 | PR_kwDOC_bI7s415i-N | 3,959 | Merge r1.8 main | {
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} | # What does this PR do ?
Many megatron configs had `trainer.max_epochs: null`. This resulted in PTL setting `trainer.max_epochs=-1` which means an infinite dataset but our datasets are not infinite.
This was causing problems when resuming training with GPT.
**Collection**: NLP
# Changelog
- Update configs that have max_epochs set to null
-
# 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.
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} | # What does this PR do ?
Fixed fast pitch for 22.03
**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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fixes a segfault in nemo ASR models due to hydra / omegaconf access during DDP.
**Collection**: [ASR]
# Changelog
- Add new flag and method to pre-allocate the value of the check
- Update skip_nan_grad code to support global DDP access.
# 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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- [ ] 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
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/3954",
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/3954.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/3954.patch",
"merged_at": "2022-04-08T19:27:12"
} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Clarifications to the FastPitch fine-tuning notebook regarding how to improve speech quality by generating synthesized mels; added missing instructions for adding more data/multiple speakers for FastPitch.
**Collection**: TTS
# Changelog
- Moved synthesized mel code out of text block and into a separate code block, fixed typos s.t. it runs
- Added missing info for fine-tuning FastPitch on multiple speakers (need to update `sup_data_types`)
- Additional info about mixing speakers
- Other minor text updates to the notebook
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
# Additional Information
* Related to #3678 and #3895
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} | null | null | null | I'm running Tacotron2 on custom dataset and this error pops on, any idea what to do:
`[NeMo W 2022-04-07 13:27:18 optimizers:55] Apex was not found. Using the lamb or fused_adam optimizer will error out.
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
[NeMo W 2022-04-07 13:27:19 __init__:23] `pynini` is not installed !
Please run the `nemo_text_processing/setup.sh` script prior to usage of this toolkit.
[NeMo W 2022-04-07 13:27:19 experimental:28] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
Traceback (most recent call last):
File "tacotron2.py", line 44, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 108, in wrapper
config_name=config_name,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 378, in _run_hydra
lambda: hydra.run(
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 381, in <lambda>
overrides=args.overrides,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 92, in run
run_mode=RunMode.RUN,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 563, in compose_config
from_shell=from_shell,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/config_loader_impl.py", line 145, in load_configuration
from_shell=from_shell,
File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/config_loader_impl.py", line 231, in _load_configuration_impl
parsed_overrides = parser.parse_overrides(overrides=overrides)
File "/usr/local/lib/python3.7/dist-packages/hydra/core/override_parser/overrides_parser.py", line 100, in parse_overrides
) from e.__cause__
hydra.errors.OverrideParseException: Error parsing override 'Year'
missing EQUAL at '<EOF>'
See https://hydra.cc/docs/next/advanced/override_grammar/basic for details` | {
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} | null | null | null | **Describe the bug**
I am following the ASR with speaker diarization on some wav files. It fails on some files giving the following error.
_LinAlgError
----> 1 diar_hyp, diar_score = asr_diar_offline.run_diarization(cfg, word_ts_hyp)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/diarization_utils.py:238, in ASR_DIAR_OFFLINE.run_diarization(self, diar_model_config, word_timestamps)
235 diar_model_config.diarizer.vad.external_vad_manifest = oracle_manifest
237 diar_model = ClusteringDiarizer(cfg=diar_model_config)
--> 238 score = diar_model.diarize()
239 if diar_model_config.diarizer.vad.model_path is not None and not diar_model_config.diarizer.oracle_vad:
240 self.get_frame_level_VAD(vad_processing_dir=diar_model.vad_pred_dir)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py:424, in ClusteringDiarizer.diarize(self, paths2audio_files, batch_size)
419 embs_and_timestamps = get_embs_and_timestamps(
420 self.multiscale_embeddings_and_timestamps, self.multiscale_args_dict
421 )
423 # Clustering
--> 424 all_reference, all_hypothesis = perform_clustering(
425 embs_and_timestamps=embs_and_timestamps,
426 AUDIO_RTTM_MAP=self.AUDIO_RTTM_MAP,
427 out_rttm_dir=out_rttm_dir,
428 clustering_params=self._cluster_params,
429 )
431 # TODO Resegmentation -> Coming Soon
432
433 # Scoring
434 score = score_labels(
435 self.AUDIO_RTTM_MAP,
436 all_reference,
(...)
439 ignore_overlap=self._diarizer_params.ignore_overlap,
440 )
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/speaker_utils.py:346, in perform_clustering(embs_and_timestamps, AUDIO_RTTM_MAP, out_rttm_dir, clustering_params)
343 else:
344 num_speakers = None
--> 346 cluster_labels = COSclustering(
347 uniq_embs_and_timestamps=embs_and_timestamps[uniq_id],
348 oracle_num_speakers=num_speakers,
349 max_num_speaker=max_num_speakers,
350 enhanced_count_thres=clustering_params.enhanced_count_thres,
351 max_rp_threshold=clustering_params.max_rp_threshold,
352 sparse_search_volume=clustering_params.sparse_search_volume,
353 cuda=cuda,
354 )
356 base_scale_idx = max(embs_and_timestamps[uniq_id]['scale_dict'].keys())
357 lines = embs_and_timestamps[uniq_id]['scale_dict'][base_scale_idx]['time_stamps']
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/nmesc_clustering.py:663, in COSclustering(uniq_embs_and_timestamps, oracle_num_speakers, max_num_speaker, min_samples_for_NMESC, enhanced_count_thres, max_rp_threshold, sparse_search_volume, fixed_thres, cuda)
651 nmesc = NMESC(
652 mat,
653 max_num_speaker=max_num_speaker,
(...)
659 cuda=cuda,
660 )
662 if emb.shape[0] > min_samples_for_NMESC:
--> 663 est_num_of_spk, p_hat_value = nmesc.NMEanalysis()
664 affinity_mat = getAffinityGraphMat(mat, p_hat_value)
665 else:
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/nmesc_clustering.py:486, in NMESC.NMEanalysis(self)
484 self.p_value_list = self.getPvalueList()
485 for p_value in self.p_value_list:
--> 486 est_num_of_spk, g_p = self.getEigRatio(p_value)
487 est_spk_n_dict[p_value] = est_num_of_spk
488 eig_ratio_list.append(g_p)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/nmesc_clustering.py:550, in NMESC.getEigRatio(self, p_neighbors)
530 """
531 For a given p_neighbors value,
532 calculates g_p, which is a ratio
(...)
546 The ratio between p_neighbors value and the maximum eigen gap value.
547 """
549 affinity_mat = getAffinityGraphMat(self.mat, p_neighbors)
--> 550 est_num_of_spk, lambdas, lambda_gap_list = estimateNumofSpeakers(affinity_mat, self.max_num_speaker, self.cuda)
551 arg_sorted_idx = np.argsort(lambda_gap_list[: self.max_num_speaker])[::-1]
552 max_key = arg_sorted_idx[0]
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/nmesc_clustering.py:329, in estimateNumofSpeakers(affinity_mat, max_num_speaker, is_cuda)
319 """
320 Estimate the number of speakers using eigen decompose on laplacian Matrix.
321 affinity_mat: (array)
(...)
326 if cuda availble eigh decomposition would be computed on GPUs
327 """
328 laplacian = getLaplacian(affinity_mat)
--> 329 lambdas, _ = eigDecompose(laplacian, is_cuda)
330 lambdas = np.sort(lambdas)
331 lambda_gap_list = getLamdaGaplist(lambdas)
File /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/utils/nmesc_clustering.py:304, in eigDecompose(laplacian, cuda, device)
302 else:
303 laplacian = torch.from_numpy(laplacian).float()
--> 304 lambdas, diffusion_map = eigh(laplacian)
305 lambdas = lambdas.cpu().numpy()
306 diffusion_map = diffusion_map.cpu().numpy()
_LinAlgError: torch.linalg.eigh: The algorithm failed to converge because the input matrix is ill-conditioned or has too many repeated eigenvalues (error code: 566).
**Steps/Code to reproduce bug**
Following this notebook exactly : https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/ASR_with_SpeakerDiarization.ipynb
**Expected behavior**
Expected to run diarization with ASR successfully
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: RUN pip install nemo_toolkit[asr]
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
Using nvidia Image : FROM nvcr.io/nvidia/pytorch:21.04-py3
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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} | # What does this PR do ?
Implements the ReGLU and SwiGLU activations for Megatron-based transformers from https://arxiv.org/abs/2002.05202
**Collection**: NLP
# Changelog
- Changes `ParallelMLP` to add new activation functions.
# Usage
* Edit the `activation` in `megatron_t5_config.yaml` to `swiglu` or `reglu`.
* Or pass the activation function appropriately to `ParallelMLP`
# 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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix librosa deprecations
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
Fix 22.03 fastpitch export (WAR around PyTorch export bug).
Also, works around new export issues brought from updated Apex (will be submitting an issue report there, but the WAR is need in the meantime).
**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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} | # What does this PR do ?
Fixed the biomegatron model NGC link error.
`get_lm_model` can get biomegatron model through model names.
Fix the biomegatron notebooks to directly load the biomegatron from NGC.
Fix the MegatronLM ckpt conversion error.
Fix the typos in synthetic data generation notebook.
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} | # What does this PR do ?
Adds rampup batch size to NeMo Megatron GPT. Scales the batch size linearly from starting batch size to global batch size by a batch size increment over a number of consumed samples.
There was a bug in the apex rampup batch size implementation that has been fixed here: https://github.com/NVIDIA/apex/commit/7d9038782b7a39c7dd05201e17586fd38b7a7c94.
Temporarily moved the needed functions over to NeMo until the above commit is in the NGC PyTorch container.
**Collection**: NLP
# Changelog
- Add rampup batch size to the config
- Input rampup batch size to the apex num microbatch calculator
- Update the num microbatch calculator after each training step
-
# Usage
Can be set in the config:
```bash
# For example:
global_batch_size: 1024
rampup_batch_size:
- 16
- 8
- 300000
# will start with global batch size 16 and over
# (1024 - 16) / 8 = 126 intervals will increase
# the batch size linearly to 1024. In each interval
# we will use approximately 300000 / 126 = 2380 samples
```
Or by the CLI:
```bash
model.rampup_batch_size=[16,8,300000]
```
# 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**:
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/3946 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3946/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3946/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3946/events | https://github.com/NVIDIA/NeMo/pull/3946 | 1,194,985,469 | PR_kwDOC_bI7s41wO1L | 3,946 | remove old doc | {
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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?
- [ ] 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
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 | Hello! 👋🏻
I'm trying to implement a script which allows me to train models from scratch or to use pre-trained ones (.nemo files or from the cloud) accordingly to the given **config file**.
The goal here is to either train models for the European Portuguese language from scratch, to transfer from the pre-trained English to European Portuguese, or to transfer other Portuguese models (trained from scratch), e.g. trained with Brazilian Portuguese, to other European Portuguese.
I've seen [this](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc.py) example and it is the one that I'm trying to replicate, but since my Portuguese labels (41) differ in size from the english ones (29) it gives me the following error:
```
RuntimeError: Error(s) in loading state_dict for EncDecCTCModel:
size mismatch for decoder.decoder_layers.0.weight: copying a param with shape torch.Size([29, 1024, 1]) from checkpoint, the shape in current model is torch.Size([42, 1024, 1]).
size mismatch for decoder.decoder_layers.0.bias: copying a param with shape torch.Size([29]) from checkpoint, the shape in current model is torch.Size([42]).
```
I'm setting up the field `init_from_pretrained_model` field in the config file (can see it below) to `"QuartzNet15x5Base-En"` and I intend to change it to `init_from_nemo_model: "MyModel.nemo"` in the other mentioned cases.
Is there any way I can make this without having to reccour to the `.change_vocabulary()`, `.setup_training_data()`, `.setup_validation_data()` and `.setup_test_data()` functions?
Code:
```Python
import nemo
import torch
import hydra
import nemo.collections.asr as nemo_asr
import pytorch_lightning as pl
from omegaconf import DictConfig, OmegaConf
@hydra.main(config_path="./configs/")
def main(cfg: DictConfig):
trainer = pl.Trainer(**cfg.trainer)
model = nemo_asr.models.EncDecCTCModel(cfg=cfg.model, trainer=trainer)
model.maybe_init_from_pretrained_checkpoint(cfg)
# Train the model
trainer.fit(model)
model.save_to(f"./models/{cfg.name}_test.nemo")
if __name__ == "__main__":
main()
```
Config:
```yaml
name: &name "TRANSFER_ENG_TO_PT"
model:
sample_rate: &sample_rate 16000
repeat: &repeat 5
dropout: &dropout 0.0
separable: &separable true
batch_size: &batch_size 32
num_workers: &num_workers 256
labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n",
"o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "ç", "à", "á",
"â", "ã", "é", "ê", "í", "ó", "ô", "õ", "ú", "-", "'"]
train_ds:
manifest_filepath: "censored"
sample_rate: *sample_rate
labels: *labels
batch_size: *batch_size
trim_silence: True
max_duration: 16.7
shuffle: True
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
num_workers: *num_workers
validation_ds:
manifest_filepath: "censored"
sample_rate: *sample_rate
labels: *labels
batch_size: *batch_size
shuffle: False
num_workers: *num_workers
test_ds:
manifest_filepath: "censored"
sample_rate: *sample_rate
labels: *labels
batch_size: *batch_size
shuffle: False
num_workers: *num_workers
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
normalize: "per_feature"
window_size: 0.02
sample_rate: *sample_rate
window_stride: 0.01
window: "hann"
features: &n_mels 64
n_fft: 512
frame_splicing: 1
dither: 0.00001
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
rect_freq: 50
rect_masks: 5
rect_time: 120
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: *n_mels
activation: relu
conv_mask: true
jasper:
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: 1
residual: false
separable: *separable
stride: [2]
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [33]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [39]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [39]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 256
kernel: [39]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [51]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [51]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [51]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [63]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [63]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [63]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [75]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [75]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: 512
kernel: [75]
repeat: *repeat
residual: true
separable: *separable
stride: [1]
- dilation: [2]
dropout: *dropout
filters: 512
kernel: [87]
repeat: 1
residual: false
separable: *separable
stride: [1]
- dilation: [1]
dropout: *dropout
filters: &enc_filters 1024
kernel: [1]
repeat: 1
residual: false
stride: [1]
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: *enc_filters
num_classes: 0
vocabulary: *labels
optim:
name: novograd
# _target_: nemo.core.optim.optimizers.Novograd
lr: .01
# optimizer arguments
betas: [0.8, 0.5]
weight_decay: 0.001
# scheduler setup
sched:
name: CosineAnnealing
# pytorch lightning args
# monitor: val_loss
# reduce_on_plateau: false
# Scheduler params
warmup_steps: null
warmup_ratio: null
min_lr: 0.0
last_epoch: -1
trainer:
gpus: -1 # number of gpus
max_epochs: 100
max_steps: -1 # computed at runtime if not set
num_nodes: 1
strategy: ddp
accumulate_grad_batches: 1
enable_checkpointing: False # Provided by exp_manager
logger: False # Provided by exp_manager
log_every_n_steps: 1 # Interval of logging.
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
init_from_pretrained_model: "QuartzNet15x5Base-En"
exp_manager:
exp_dir: null
name: *name
create_tensorboard_logger: False
create_checkpoint_callback: False
checkpoint_callback_params:
monitor: "val_wer"
mode: "min"
create_wandb_logger: False
wandb_logger_kwargs:
name: null
project: null
hydra:
run:
dir: .
job_logging:
root:
handlers: null
``` | {
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} | null | null | null | **Describe the bug**
running BERT_large = BERTLMModel.from_pretrained("bertlargeuncased") with error:
FileNotFoundError: src path does not exist or it is not a path in nemo file. src value I got was: /home/yzhang/data/nlp/bert/pretrained_models_google/uncased_L-24_H-1024_A-16/bert_config.json. Absolute: /home/yzhang/data/nlp/bert/pretrained_models_google/uncased_L-24_H-1024_A-16/bert_config.json
**Steps/Code to reproduce bug**
# Import BERT
from nemo.collections.nlp.models import BERTLMModel
model_large = BERTLMModel.from_pretrained("bertlargeuncased")
**Expected behavior**
like running BERT_base = BERTLMModel.from_pretrained("bertbaseuncased") successfully, should return downloaded Bert large pre-train model
**Environment overview (please complete the following information)**
NeMo_1.7.2
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} | # What does this PR do ?
Adds support for post-trained Adapter modules to any model that supports it.
**Collection**: [ASR]
# Changelog
- Adds `AdapterModuleMixin` that is the core framework on which the layered Adapter framework works
- Adds 3 level API for propagating and storing config information of adapters added after model is loaded
- ModelPT level - EncoderAdapterModelMixin
- Encoder level - AdapterModuleMixin
- Layer level - AdapterModuleMixin
- Adds support for adapters to CTC/RNNT models - Jasper/QuartzNet/Citrinets/ContextNets/Conformers.
- Support `LinearAdapter`
TODO :
- [x] Add tests
- [x] Add scripts for CTC BPE / RNNT BPE
- [ ] Add Tutorial (separate from PR)
- [x] Add Jenkins test
- [ ] Documentation
- [x] Core `AdapterModuleMixin`
- [x] Subclasses / Mixin instances in ASR
- [ ] Doc page for Adapters in RTD
# Usage
```python
from nemo.collections.asr.models import ASRModel
from nemo.core import adapter_mixins
# Load the original config of a pretrained model
cfg = ASRModel.from_pretrained('stt_en_citrinet_256', map_location='cpu', return_config=True)
# Override its encoder `_target_`, append the target class name with `Adapter` (conversion for now)
adapter_metadata = adapter_mixins.get_registered_adapter(cfg.encoder._target_)
if adapter_metadata is not None:
cfg.encoder._target_ = adapter_metadata.adapter_class_path
# Load the model with an encoder that supports Adapters
model = ASRModel.from_pretrained('stt_en_citrinet_256', override_config_path=cfg)
# Add an adapter with some config
def get_adapter_cfg(in_features=50, dim=100, norm_pos='post'):
cfg = {
'_target_': 'nemo.collections.common.parts.adapter_modules.LinearAdapter',
'in_features': in_features,
'dim': dim,
'norm_position': norm_pos,
}
return cfg
model.add_adapter('adapter_0', cfg=get_adapter_cfg(in_features=cfg.encoder.jasper[0].filters, dim=5))
assert model.is_adapter_available()
# Enable/Disable adapters before/after training / inference / model building
model.set_enabled_adapters(enabled=False) # disable all adapters prior to training
model.set_enabled_adapters('adapter_0', enabled=True) # enable just one adapter by name
model.set_enabled_adapters(enabled=True) # or enable all adapters for training
# First, Freeze all the weights of the model (not just encoder, everything)
model.freeze()
# Then, Unfreeze just the adapter weights that were enabled above (no part of encoder/decoder/joint/etc)
model.unfreeze_enabled_adapters()
# Adapter module(s) are now ready to train and adapt to new domain.
assert model.num_weights < 1e5
```
# 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?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fixes the length of TarredSentenceDataset with shard strategy as scatter.
**Collection**: NLP
# Changelog
- Divide num batches by world size for the total length.
# Usage
N/A
# 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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https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/tts/Inference_DurationPitchControl.ipynb
an error has been made.
instead of:
# Load the models from NGC
fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch").eval().cuda()
hifigan = HifiGanModel.from_pretrained("tts_hifigan").eval().cuda()
Should be:
# Load the models from NGC
fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch")
hifigan = HifiGanModel.from_pretrained(model_name="tts_hifigan")
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} | null | null | null | We have successfully trained a Conformer CTC BPE L model (non-English) with a very good WER in the lower teens mark. There is one peculiar thing that is happening though. The model is dropping numbers, interestingly not always and mostly dates. Especially if more are spoken in succession as part of a sentence. I have found a similar issue/questionon regarding the Spanish language on Riva Forums https://forums.developer.nvidia.com/t/riva-with-asr-doesnt-recognize-numbers-on-my-audio/203244, but no explanation.
It is true that the train dataset does not cover such specific cases, but we were under the impression that in general the model learns to recognise BPE tokens from the waveform, and no "internal LM" or context dependency between BPE tokens (or at max a short one). In any case we'd expect the model to output at least something, not miss entire sections with multiple consecutive words. An example of the worst case we are seeing (translated in English) would be:
- reference: "accepted on twelfth fifth twenty twenty and modified on seventh seventh twenty twenty-one"
- transcription: "accepted and modified"
We also get different results depending if we run transcription with `transcribe_speech.py`, `transcribe_speech_parallel.py` or buffered inference.
Any thoughts to what might be the source of the problem, or what can we do to resolve it?
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"merged_at": "2022-04-08T22:42:47"
} | # What does this PR do ?
Fixes the global batch data fetcher for T5 Glue.
**Collection**: NLP
# Changelog
- Remove FitLoop abstraction
- Update data fetcher in T5 Glue LightningModule hooks.
# 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 | **Describe the bug**
This Nemo model:
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_citrinet_1024
When build and deploy with Riva 1.10.0-beta (offline config) fails with:
```
2022-04-05 05:08:47,705 [INFO] Writing Riva model repository to '/data/models'...
2022-04-05 05:08:47,705 [INFO] The riva model repo target directory is /data/models
2022-04-05 05:09:02,391 [INFO] Using tensorrt
2022-04-05 05:09:02,396 [INFO] Extract_binaries for nn -> /data/models/riva-trt-my_speech_service-offline-2-am-streaming-offline/1
2022-04-05 05:09:02,396 [INFO] extracting {'onnx': ('nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE', 'model_graph.onnx')} -> /data/models/riva-trt-my_speech_service-offline-2-am-streaming-offline/1
2022-04-05 05:09:16,155 [INFO] Printing copied artifacts:
2022-04-05 05:09:16,155 [INFO] {'onnx': '/data/models/riva-trt-my_speech_service-offline-2-am-streaming-offline/1/model_graph.onnx'}
2022-04-05 05:09:16,155 [INFO] Building TRT engine from ONNX file
[W] colored module is not installed, will not use colors when logging. To enable colors, please install the colored module: python3 -m pip install colored
[W] 'Shape tensor cast elision' routine failed with: None
[libprotobuf WARNING /home/jenkins/agent/workspace/OSS/OSS_L0_MergeRequest/oss/build/third_party.protobuf/src/third_party.protobuf/src/google/protobuf/io/coded_stream.cc:604] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING /home/jenkins/agent/workspace/OSS/OSS_L0_MergeRequest/oss/build/third_party.protobuf/src/third_party.protobuf/src/google/protobuf/io/coded_stream.cc:81] The total number of bytes read was 572097855
[04/05/2022-05:09:19] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:364: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:02] [TRT] [W] Output type must be INT32 for shape outputs
[04/05/2022-05:10:06] [TRT] [E] 4: [shapeCompiler.cpp::evaluateShapeChecks::832] Error Code 4: Internal Error (kOPT values for profile 0 violate shape constraints: condition '==' violated. Where_35: dimensions not compatible for select)
2022-04-05 05:10:06,610 [ERROR] Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/servicemaker/cli/deploy.py", line 100, in deploy_from_rmir
generator.serialize_to_disk(
File "/usr/local/lib/python3.8/dist-packages/servicemaker/triton/triton.py", line 398, in serialize_to_disk
module.serialize_to_disk(repo_dir, rmir, config_only, verbose, overwrite)
File "/usr/local/lib/python3.8/dist-packages/servicemaker/triton/triton.py", line 282, in serialize_to_disk
self.update_binary(version_dir, rmir, verbose)
File "/usr/local/lib/python3.8/dist-packages/servicemaker/triton/asr.py", line 133, in update_binary
RivaTRTConfigGenerator.update_binary_from_copied(self, version_dir, rmir, copied, verbose)
File "/usr/local/lib/python3.8/dist-packages/servicemaker/triton/triton.py", line 688, in update_binary_from_copied
with self.build_trt_engine_from_onnx(model_weights) as engine, open(
AttributeError: __enter__
```
**Steps/Code to reproduce bug**
1. Download the above model from NGC. Convert to Riva with:
`nemo2riva --out my_speech_service-2.riva stt_en_citrinet_1024.nemo`
Using docker container: nvcr.io/nvidia/nemo:22.01
2. Build for offline deployment using docker container: nvcr.io/nvidia/riva/riva-speech:1.10.0-beta-servicemaker
```
riva-build speech_recognition -f \
/servicemaker-dev/stt_en_citrinet_1024_v1.0.0rc1/my_speech_service-offline-2.rmir /servicemaker-dev/stt_en_citrinet_1024_v1.0.0rc1/my_speech_service-2.riva \
--offline \
--name=my_speech_service-offline-2 \
--ms_per_timestep=80 \
--featurizer.use_utterance_norm_params=False \
--featurizer.precalc_norm_time_steps=0 \
--featurizer.precalc_norm_params=False \
--chunk_size=900 \
--left_padding_size=0. \
--right_padding_size=0. \
--decoder_type=flashlight \
--flashlight_decoder.asr_model_delay=-1 \
--decoding_language_model_binary=/myworkspace/speechtotext_en_us_lm_vdeployable_v1.1/riva_asr_train_datasets_3gram.binary \
--decoding_vocab=/myworkspace/speechtotext_en_us_lm_vdeployable_v1.1/flashlight_decoder_vocab.txt \
--flashlight_decoder.lm_weight=0.2 \
--flashlight_decoder.word_insertion_score=0.2 \
--flashlight_decoder.beam_threshold=20. \
--language_code=en-US
```
3. Riva deploy using docker container: nvcr.io/nvidia/riva/riva-speech:1.10.0-beta-servicemaker:
```
riva-deploy -f /servicemaker-dev/stt_en_citrinet_1024_v1.0.0rc1/my_speech_service-offline-2.rmir /data/models
```
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
GPU: V100 32GB
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} | # What does this PR do ?
[NLP] bug fixes for duplex model
**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
- [ 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
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} | # What does this PR do ?
Tutorial for inference and training of multilingual ASR models
**Collection**: ASR
# Changelog
- Adding the jupyter notebook and a link to it in the readme
# 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: Taejin Park <[email protected]>
# What does this PR do ?
This PR replaces the numpy based functions with torch based functions in VAD-Diarization pipeline. This conversion is motivated for easier conversion from NeMo system to Riva production system. Most of the functions in vad_utils.py and nmesc_clustering.py are compatible with torch.jit.script decorator.
**Collection**: ASR
# Changelog
- Converted all the numpy based functions in nmesc_clustering.py, vad_utils.py
- Converted numpy based signal chain in speaker_utils.py
- Changed numpy or python list initialization to torch in clustering_diarizer.py
# Usage
Please refer to [speaker diarization part of the NeMo docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/datasets.html).
```python
python examples/speaker_tasks/diarization/offline_diarization.py \
diarizer.manifest_filepath="/path/to/ch109_input_manifest.json" \
diarizer.oracle_num_speakers=2 \ # Since there are exactly 2 speakers per each CH109 session
diarizer.oracle_vad=True \ # Use oracle VAD extracted from RTTM files.
diarizer.collar=0.25 \
diarizer.ignore_overlap=True \
diarizer.speaker_embeddings.model_path="titanet_large" \
diarizer.speaker_embeddings.window_length_in_sec=[1.5,1.0,0.5] # Multiscale setting \
diarizer.speaker_embeddings.shift_length_in_sec=[0.75,0.5,0.25] # Multiscale setting \
diarizer.speaker_embeddings.parameters.multiscale_weights=[0.33,0.33,0.33] # Equal weights
```
# Before your PR is "Ready for review"
**Pre checks**:
- [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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
ASR collection contributors.
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} | # What does this PR do ?
Fixes the bug preventing the user from being able to turn off log_prediction in RNNT models
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
- one line change checking to see whether wer.log_predictions = True before turning them on
# Usage
model.wer.log_predictions = False
```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 ?
AudioToCharWithDursF0Dataset class will be deprecated so this PR adds code and config to use old FastPitch checkpoint (checkpoint that was trained using AudioToCharWithDursF0Dataset class which is now deprecated) with new code (TTSDataset) to support inference.
**Collection**: tts.models.fastpitch
# Changelog
- Adds Warning when using old checkpoint
- Adds config file to be used with old checkpoint
# Usage
* In FastPitchModel.from_pretrained use https://github.com/NVIDIA/NeMo/tree/main/examples/tts/conf/fastpitch_align_old.yaml \
as config_file to avoid using deprecated AudioToCharWithDursF0Dataset class and run into errors. \
```python
# FastPitchModel.from_pretrained("tts_en_fastpitch", override_config_path="/path/to/fastpitch_align_old.yaml")
```
# 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
- [ ] 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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https://api.github.com/repos/NVIDIA/NeMo/issues/3927 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3927/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3927/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3927/events | https://github.com/NVIDIA/NeMo/pull/3927 | 1,192,338,556 | PR_kwDOC_bI7s41nWka | 3,927 | Refactor restorefrom | {
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} | # What does this PR do ?
Reduce code duplications in restore_from overridden in NLP
Reorganize export files in script/export/
**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**:
- [ 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
- [ ] 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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https://api.github.com/repos/NVIDIA/NeMo/issues/3926 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3926/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3926/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3926/events | https://github.com/NVIDIA/NeMo/pull/3926 | 1,192,282,431 | PR_kwDOC_bI7s41nKda | 3,926 | Update package info | {
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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.
# Additional Information
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