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} | # What does this PR do ?
Attempts to fix all issues after many deprecations/lightning lite becoming the backbone for a lot of the underlying pytorch lightning code.
cc @titu1994
# 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
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} | null | null | null | **Describe the bug**
Unable to deploy a fine-tuned Punctuation and Capitalization model to Riva pipeline
**Steps/Code to reproduce bug**
1. Fine tune a nemo punctuation and capitalization model.
2. Convert it to rive using this guide: https://docs.nvidia.com/deeplearning/riva/user-guide/docs/model-overview.html
3. Deploy it using nvcr.io/nvidia/riva/riva-speech:2.7.0 and it is giving an empty response.
Deploy command
NV_GPU=1 nvidia-docker run -ti --name riva_source -p 8008:8008 -p 50051:50051 -p 5800:8000 -p 58001:8001 -v <localtion_models>:/data/models -e MODEL_REPOSITORY="--model-repository /data/models" -e INTERIM_RESULTS="--interim-results" -e CUSTOM_ITN="--custom-itn" --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/riva/riva-speech:2.4.0-server tritonserver --log-verbose=0 --strict-model-config=true --model-repository /data/models --cuda-memory-pool-byte-size=0:1000000000 --exit-on-error=false
This was working while using riva 1.10-beta server
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
My ourput logs have the following. Please note the output contents are empty
model_name: "riva-punctuation-en-US"
id: "1"
inputs {
name: "PIPELINE_INPUT"
datatype: "BYTES"
shape: 1
shape: 1
}
outputs {
name: "PIPELINE_OUTPUT"
}
raw_input_contents: "\372\000\000\000in a new password i can take it from here alrighty is there anything else i can help you with them thank you very much ms rostow it was my pleasure to assist you thank you for using optum r x and if you chose our survey it will be next okay thank you"
model_name: "riva-punctuation-en-US"
model_version: "1"
id: "1"
outputs {
name: "PIPELINE_OUTPUT"
datatype: "BYTES"
shape: 1
shape: 1
}
raw_output_contents: "\000\000\000\000"
[[b'']]
**Expected behavior**
Deploy the model correctly and output content is a punctuated input string
**Environment overview (please complete the following information)**
- Environment location: nvcr.io/nvidia/riva/riva-speech:2.4.0-server
- Method of NeMo install: install from source
- 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:
nvcr.io/nvidia/riva/riva-speech:2.4.0-server
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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Environment details
OS version : Ubuntu 20.04 LTS
PyTorch version: 1.13.0a0+08820cb
Python version: 3.8.0
Nemo Version : 1.12.0
Additional context
I ran my model on 2 GPU's :
RTX 3090 and
24267MiB
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} | # What does this PR do ?
Fix #5349
**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)
- [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**:
- [ ] 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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```python
cfg = copy.deepcopy(char_model.cfg)
"""
read cleaned manifest
"""
train_manifest_data = read_manifest(train_manifest_cleaned)
train_charset = get_charset(train_manifest_data)
dev_manifest_data = read_manifest(dev_manifest_cleaned)
dev_charset = get_charset(dev_manifest_data)
train_dev_set = set.union(set(train_charset.keys()), set(dev_charset.keys()))
# Setup train, validation, test configs
with open_dict(cfg):
# Train dataset (Concatenate train manifest cleaned and dev manifest cleaned)
cfg.train_ds.manifest_filepath = f'{train_manifest_cleaned},{dev_manifest_cleaned}'
cfg.train_ds.labels = list(train_dev_set)
cfg.train_ds.normalize_transcripts = False
cg.train_ds.batch_size = 32
cfg.train_ds.use_dali = True
cfg.train_ds.num_workers = 8
cfg.train_ds.pin_memory = True
cfg.train_ds.trim_silence = True
char_model.setup_training_data(cfg.train_ds)
```

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} | # What does this PR do ?
fixes bug in computation of volume loss
Add a one line overview of what this PR aims to accomplish.
fixes bug in computation of volume loss
**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
```
# 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 ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Yang Zhang <[email protected]>
# What does this PR do ?
provides both python and c++ alignment
**Collection**: tools | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
This is to fix the doc build error below,
```
Could not import extension IPython.sphinxext.ipython_console_highlighting (exception: No module named 'IPython')
```
[root-cause] Previously, TTS doc added a block `.. ipython::` to show the list of models (https://github.com/NVIDIA/NeMo/blob/main/docs/source/tts/checkpoints.rst), and added extension (https://github.com/NVIDIA/NeMo/blob/main/docs/source/conf.py#L113-L114).
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# What does this PR do ?
Some YAML config may not use `use_beta_binomial_interpolator` explicitly so that the script would raise errors like below,
``` bash
pyxis: importing docker image ...
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
Traceback (most recent call last):
File "scripts/dataset_processing/tts/generate_mels_for_finetune_hifigan.py", line 177, in <module>
main()
File "scripts/dataset_processing/tts/generate_mels_for_finetune_hifigan.py", line 149, in main
use_beta_binomial_interpolator = spec_model.cfg.train_ds.dataset.use_beta_binomial_interpolator
File "/opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 357, in __getattr__
self._format_and_raise(key=key, value=None, cause=e)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/base.py", line 190, in _format_and_raise
format_and_raise(
File "/opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py", line 738, in format_and_raise
_raise(ex, cause)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py", line 716, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set end OC_CAUSE=1 for full backtrace
File "/opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 351, in __getattr__
return self._get_impl(key=key, default_value=_DEFAULT_MARKER_)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 438, in _get_impl
node = self._get_node(key=key, throw_on_missing_key=True)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 465, in _get_node
self._validate_get(key)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/dictconfig.py", line 166, in _validate_get
self._format_and_raise(
File "/opt/conda/lib/python3.8/site-packages/omegaconf/base.py", line 190, in _format_and_raise
format_and_raise(
File "/opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py", line 818, in format_and_raise
_raise(ex, cause)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/_utils.py", line 716, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set end OC_CAUSE=1 for full backtrace
omegaconf.errors.ConfigAttributeError: Key 'use_beta_binomial_interpolator' is not in struct
full_key: train_ds.dataset.use_beta_binomial_interpolator
object_type=dict
```
**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)
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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: David Mosallanezhad <[email protected]>
# What does this PR do ?
Updates export process for Megatron-based models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Changed ORT optimization due to a bug in onnxruntime (https://github.com/microsoft/onnxruntime/issues/12579)
# Usage
```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
- [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 ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
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} | Signed-off-by: Sasha Meister <[email protected]>
# What does this PR do ?
[STT] Add Ru ASR Conformer-CTC and Conformer-Transducer
**Collection**: [Note which collection this PR will affect]
# Changelog
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} | Signed-off-by: Sasha Meister <[email protected]>
# What does this PR do ?
[STT] Add Ru ASR Conformer-CTC and Conformer-Transducer
**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 ?
Adds support for the following:
1. To be able to do fine-tuning and fine-tuning eval with `.ckpt` files as well as `.nemo files.
2. Refactoring fine-tuning to allow for `check_val_every_n_epochs` > 1 and no more additional checks needed for `trainer.val_check_interval`. This also fixes the progress bar when grad acc > 1.
**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**:
- [ ] 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: ssh-meister <[email protected]>
Add Ru ASR Conformer-CTC Large and Conformer-Transducer Large to NeMo models collections
# 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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} | # What does this PR do ?
Add Ru ASR Conformer-CTC Large and Conformer-Transducer Large to NeMo models collections
**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 #5314
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} | Fixing training/script issues for radtts, also removing grad from constant/cache buffers
1. A view to the bias was cached as a buffer - that led to cpu/gpu mismatch on backward pass when training - also the caching was wrong - removed. That led to script() breaking for export - had to change script routine to do trace on PartialConv first.
2. Training had NaNs when using partial convolutions in mixed precision mode - had to increase epsilon for mask ratio to fit in float16 range.3.
3. While we are there, removed gradients from cache buffers in some other modules.
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.
# Additional Information
* Related to # (issue)
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177 start = torch.zeros(append_len_start)
178 end = torch.zeros(append_len_end)
--> 179 sig = torch.cat((start, sig, end))
180 sig_len += slice_length
182 if has_audio:
RuntimeError: Tensors must have same number of dimensions: got 1 and 2
Is anyone else seeing this error?
I am using the Pytorch version = 1.13.0 | {
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178 end = torch.zeros(append_len_end)
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
the files changes after running below,
``` bash
$ pre-commit run --all-files
```
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/5332 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5332/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5332/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5332/events | https://github.com/NVIDIA/NeMo/pull/5332 | 1,437,321,491 | PR_kwDOC_bI7s5CRiAx | 5,332 | removed uninstall nemo_cv and nemo_simple_gan and relax numba version… | {
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* removed nemo_cv and nemo_simple_gan in reinstall.sh.
* relaxed numba version limits.
* added tensorboard requirement to avoid any incpmpatible issue.
not quite sure why we should enforce `numba==0.55`. When I run `./reinstall.sh`, I got incompatible env issue below for `tensorboard` and `numba`.
``` bash
(nemo) xueyang@LOCAL:~/workspace/NeMo:(main)$ ./reinstall.sh
Uninstalling stuff
Found existing installation: nemo-toolkit 1.12.0rc0
Uninstalling nemo-toolkit-1.12.0rc0:
Successfully uninstalled nemo-toolkit-1.12.0rc0
Found existing installation: sacrebleu 2.2.1
Uninstalling sacrebleu-2.2.1:
Successfully uninstalled sacrebleu-2.2.1
WARNING: Skipping nemo_asr as it is not installed.
WARNING: Skipping nemo_nlp as it is not installed.
WARNING: Skipping nemo_tts as it is not installed.
WARNING: Skipping nemo_simple_gan as it is not installed.
WARNING: Skipping nemo_cv as it is not installed.
Requirement already satisfied: setuptools in /Users/xueyang/miniconda3/envs/nemo/lib/python3.8/site-packages (65.5.0)
Collecting setuptools
Downloading setuptools-65.5.1-py3-none-any.whl (1.2 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 12.1 MB/s eta 0:00:00
Installing collected packages: setuptools
Attempting uninstall: setuptools
Found existing installation: setuptools 65.5.0
Uninstalling setuptools-65.5.0:
Successfully uninstalled setuptools-65.5.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorboard 2.10.0 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.1 which is incompatible.
numba 0.55.0 requires numpy<1.22,>=1.18, but you have numpy 1.23.3 which is incompatible.
```
# 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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] | closed | false | null | [] | null | null | 2022-11-05T05:35:06 | 2022-11-09T09:22:56 | 2022-11-09T09:22:56 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to do Post training static quantization on ASR model, following example here: (https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#post-training-static-quantization) but keeps on getting following error.
Two questions:
1) Is there a way to bypass this error and get this working?
2) How to know what is the input shape for "stt_en_conformer_ctc_xlarge" model? Is there any API through which I know the shape?
```
File "asr_ctc.py", line 20, in <module>
print(model.transcribe([audio_filepath], batch_size=1)[0])
---
----
---
NotImplementedError: Could not run 'quantized::conv2d.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d.new' is only available for these backends: [Negative, UNKNOWN_TENSOR_TYPE_ID, QuantizedXPU, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, SparseCPU, SparseCUDA, SparseHIP, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, SparseVE, UNKNOWN_TENSOR_TYPE_ID, NestedTensorCUDA, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID].
```
**Steps/Code to reproduce bug**
```
# Streaming with CTC based models
import torch
import nemo.collections.asr as nemo_asr
device = 'cpu'
model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("stt_en_conformer_ctc_xlarge", map_location=device)
model = model.to(device)
model.eval()
audio_filepath = './datasets/mini-dev-clean/LibriSpeech/dev-clean-2-processed/2428-83699-0042.wav'
model.qconfig = torch.quantization.get_default_qconfig()
torch.quantization.prepare(model, inplace=True)
torch.quantization.convert(model, inplace=True)
print("The transcribed speech is: ")
print(model.transcribe([audio_filepath], batch_size=1)[0])
```
**Expected behavior**
Expected transcribed output
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal]
- Method of NeMo install: [pip install Nemo].
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 22.04
- PyTorch = 1.12.1
- Python version = 3.8
**Additional context**
Add any other context about the problem here.
Example: CPU model
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [ASR]
# Changelog
- Add `num_proc` argument to HF to speed up processing
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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# Changelog
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} | Signed-off-by: arendu <[email protected]>
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https://api.github.com/repos/NVIDIA/NeMo/issues/5327 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5327/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5327/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5327/events | https://github.com/NVIDIA/NeMo/pull/5327 | 1,436,679,868 | PR_kwDOC_bI7s5CPfLJ | 5,327 | [TTS] update organization of model checkpoints and their pointers. | {
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} | * added a column pointing correct checkpoint url.
* revised `#Spk` to real numbers.
Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
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[screenshot](https://drive.google.com/file/d/1ZbRCAZbf7vzuNsjfR5hHnZHHO8Eo373K/view?usp=sharing)
**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
# Add a code snippet demonstrating how to use this
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} | Signed-off-by: Xuesong Yang <[email protected]>
# 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"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/5325 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5325/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5325/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5325/events | https://github.com/NVIDIA/NeMo/pull/5325 | 1,436,386,518 | PR_kwDOC_bI7s5COgvV | 5,325 | small bugfix for r1.13.0 | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: ASR
# Changelog
- typo fix
- transcribe_partial_audio. https://github.com/NVIDIA/NeMo/pull/5253
# 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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} | null | null | null | Learning starts and then stops at a certain point.

Termination of training without explanation, without logs.

Termination of training without explanation, without logs.

> import nemo
import nemo.collections.asr as nemo_asr
import pytorch_lightning as pl
from nemo.utils.exp_manager import exp_manager
from omegaconf import DictConfig, OmegaConf
> params = OmegaConf.load(model_conf_path)
> min_duration = 0.5
epochs = 15
gpus = 1
lr_optim = 0.001
> params.trainer.devices = gpus
params.trainer.max_epochs = epochs
params.model.optim.lr = lr_optim
params.model.train_ds.manifest_filepath = train_manifest_path
params.model.train_ds.min_duration = min_duration
params.model.validation_ds.manifest_filepath = validation_manifest_path
params.model.test_ds.manifest_filepath = test_manifest_path
params.exp_manager.exp_dir = "/path/to/save_tr_nemo/"
> trainer = pl.Trainer(**params.trainer)
model = nemo_asr.models.EncDecCTCModel(cfg=params.model, trainer=trainer)
exp_manager(trainer=trainer, cfg=params.exp_manager)
> trainer.fit(model)
trainer.test(model)
There is yaml config:
https://github.com/Project-fil/Project-fil/blob/ee79825609cca9e8b01a9c4eb43b564bb5c2b35d/quartznet_ru.yaml
Cuda version: 11.6
Python version: 3.9.12
Torch version: 1.13.0a0+git35563f4
Nemo version: 1.11.0rc0
OS - Ubuntu 20.04.5 LTS
Graphic card - Nvidia A40
What could be wrong?
https://github.com/NVIDIA/NeMo/issues/5324#tasklist-block-cccbe04f-c2c1-4b01-a878-bd03a164bbc4
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Update the manifest filepath name of the audio_filepath
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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I'm trying to follow [Information Retrieval document](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.12.0/nlp/information_retrieval.html), however, the link for its notebook cannot be found. | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix librosa issue in script
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Removes onnx graph surgery from Dockerfile
**Collection**: [Core]
**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?
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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?
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} | * expand to support flexible dictionary entry formats in IPAG2P.
* removed unused imports in test.collections.tts
* removed unused imports in nemo.collections.tts.modules
* removed unused imports in nemo_text_processing.text_normalization.zh
* updated unit tests with new cases
* renamed test function names because we only test IPAG2P rather than all classes in the `modules.py`.
Signed-off-by: Xuesong Yang <[email protected]>
```bash
$ pytest --cpu tests/nemo_text_processing/g2p/test_modules.py
A valid `test_data.tar.gz` test archive (10445891B) found in the `/Users/xueyang/workspace/NeMo/tests/.data` folder.
/Users/xueyang/miniconda3/envs/nemo/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
if not hasattr(tensorboard, "__version__") or LooseVersion(
/Users/xueyang/miniconda3/envs/nemo/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
) < LooseVersion("1.15"):
[NeMo W 2022-11-03 19:03:03 optimizers:55] Apex was not found. Using the lamb or fused_adam optimizer will error out.
Setting numba compat : True
==================================================================== test session starts =====================================================================
platform darwin -- Python 3.8.13, pytest-7.1.2, pluggy-1.0.0 -- /Users/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /Users/xueyang/workspace/NeMo, configfile: setup.cfg
plugins: anyio-3.5.0, hydra-core-1.1.2
collected 13 items
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_normalize_dict_with_phonemes PASSED [ 7%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_normalize_dict_with_graphemes_and_phonemes PASSED [ 15%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call PASSED [ 23%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_with_file_or_object_dict_type PASSED [ 30%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_with_oov_word PASSED [ 38%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_with_oov_func PASSED [ 46%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_with_graphemes_uppercase PASSED [ 53%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_with_graphemes_lowercase PASSED [ 61%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_with_escaped_characters PASSED [ 69%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_instantiate_unsupported_locale PASSED [ 76%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_de_de PASSED [ 84%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_en_us PASSED [ 92%]
tests/nemo_text_processing/g2p/test_modules.py::TestIPAG2P::test_forward_call_es_es PASSED [100%]
===================================================================== slowest durations ======================================================================
(39 durations < 0.005s hidden. Use -vv to show these durations.)
===================================================================== 13 passed in 0.43s =====================================================================
```
# 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
# 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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python ./normalize_with_audio.py --text=" Hold on one second. 429-020" --language=en --n_tagged=10 --cache_dir="cache_dir" --audio_data="./0001_00002.wav" --model="QuartzNet15x5Base-En" --verbose
I always get the error:
Traceback (most recent call last):
File "./normalize_with_audio.py", in <module>
raise ValueError("NeMo ASR collection is not installed.")
ValueError: NeMo ASR collection is not installed.
I have install nemo all
pip install nemo_toolkit[all]
**Environment details**
- OS version CentOS 7
- PyTorch version 1.10.2+cu113
- Python version Python 3.8.0
Thanks
Mark | {
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} | Signed-off-by: Igor Gitman <[email protected]>
# What does this PR do ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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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?
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/5315 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5315/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5315/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5315/events | https://github.com/NVIDIA/NeMo/pull/5315 | 1,435,049,727 | PR_kwDOC_bI7s5CKEg7 | 5,315 | Add italian model checkpoints | {
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} | Signed-off-by: Igor Gitman <[email protected]>
# What does this PR do ?
Add italian model checkpoints to be available for download 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"
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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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/5314 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5314/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5314/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5314/events | https://github.com/NVIDIA/NeMo/pull/5314 | 1,434,580,401 | PR_kwDOC_bI7s5CIfNW | 5,314 | [STT] add Ru ASR conformers to models collections | {
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} | Signed-off-by: Aleksandr Meister [[email protected]](mailto:[email protected])
# What does this PR do ?
Add Ru ASR Conformer-CTC Large and Conformer-Transducer Large to NeMo models collections
**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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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add support for RNN-T loss implemented with PyTorch only that relies on PyTorch auto-grad for gradient computation.
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# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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?
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- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fix path to get_data.py since the directory was renamed
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: ASR
# Changelog
- typo fix
- transcribe_partial_audio. https://github.com/NVIDIA/NeMo/pull/5253
# 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.
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https://api.github.com/repos/NVIDIA/NeMo/issues/5309 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5309/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5309/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5309/events | https://github.com/NVIDIA/NeMo/pull/5309 | 1,433,562,478 | PR_kwDOC_bI7s5CFE0y | 5,309 | Fixes for Conformer-xl export | {
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} | # What does this PR do ?
Fixes for Conformer-xl model export:
1) Fixes ONNXRT error: Type parameter (Tind) of Optype (Slice) bound to different types (tensor(int32) and tensor(int64) in node (Slice_63).
2) Fixes 'runtime-check' crash on ONNX > 2GB
**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 | **Describe the bug**
01. Running this notebook in colab:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb
02. encountered apex error. Follow the steps in readme file to install apex, successful.
Additional context:
user = False
home = None
root = None
prefix = None
Successfully installed apex-0.1
Removed build tracker: '/tmp/pip-req-tracker-k560_7qd'
03. continue running the codes and it fail at model training
import apex (this line no error)
model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
error:
[NeMo W 2022-11-02 09:07:02 lm_utils:81] biomegatron345m_biovocab_30k_cased is not in get_pretrained_lm_models_list(include_external=False), will be using AutoModel from HuggingFace.
[NeMo I 2022-11-02 09:07:02 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.13.0rc0/BioMegatron345m-biovocab-30k-cased/5885010653185bba59bf489ff757bf09/BioMegatron345m-biovocab-30k-cased.nemo.
[NeMo I 2022-11-02 09:07:02 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.13.0rc0/BioMegatron345m-biovocab-30k-cased/5885010653185bba59bf489ff757bf09/BioMegatron345m-biovocab-30k-cased.nemo
[NeMo I 2022-11-02 09:07:02 common:911] Instantiating model from pre-trained checkpoint
[NeMo E 2022-11-02 09:07:07 common:504] Model instantiation failed!
Target class: nemo.collections.nlp.models.language_modeling.megatron_bert_model.MegatronBertModel
Error(s): Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/nemo/core/classes/common.py", line 483, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py", line 56, in __init__
"Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
ImportError: Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
[<ipython-input-29-1f0d02a2ff61>](https://localhost:8080/#) in <module>
----> 1 model_ner = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)
2
3 #ImportError: Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.
4 #Successfully installed apex-0.1
9 frames
[/usr/local/lib/python3.7/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py](https://localhost:8080/#) in __init__(self, cfg, trainer)
54 if not HAVE_APEX:
55 raise ImportError(
---> 56 "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
57 )
58 super().__init__(cfg, trainer=trainer, no_lm_init=False)
ImportError: Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.
**Expected behavior**
Model training should be successful.
**Environment overview (please complete the following information)**
running tutorial notebook in colab.
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Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
* we should remove the legacy unit test marker `torch_tts` and use the common `run_only_on('CPU')` marker. `torch_tts` marker was only used for testing `TTSDataset` on CPU.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
Move speaker clustering arguments to forward function
Also added scripting option.
Fixed type issues for torch scripting.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Moved tunable parameters to forward function
- removed unnecessary class varialbes
- Moved clustering instance creation to the outside of for loop in speaker_utils.py
# Usage
If you want to export clustering script module, add the following line
`+diarizer.clustering.parameters.export_script_module=True`
```python
python clustering_diarizer/offline_diar_infer.py \
+diarizer.clustering.parameters.export_script_module=True \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_output' \
diarizer.speaker_embeddings.parameters.save_embeddings=False \
diarizer.vad.model_path=<pretrained model name or path to .nemo> \
diarizer.speaker_embeddings.model_path=<pretrained speaker embedding model
'name or path to .nemo>
```
# 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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add support for the distributed Adam optimizer to BERT.
**Collection**: NLP
# Changelog
- Add support for the Apex distributed Adam optimizer to BERT
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/79a22eaee0813d5a0e4dc82ebd4a43071399c899/examples/nlp/language_modeling/conf/megatron_bert_config.yaml#L118
# 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.
Pinging @shanmugamr1992.
# Additional Information
* Depends on https://github.com/NVIDIA/NeMo/pull/5293.
* BERT distopt support in this PR is basically identical to GPT and T5 distopt support in https://github.com/NVIDIA/NeMo/pull/5236.
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https://api.github.com/repos/NVIDIA/NeMo/issues/5304 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5304/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5304/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5304/events | https://github.com/NVIDIA/NeMo/pull/5304 | 1,432,243,749 | PR_kwDOC_bI7s5CApQC | 5,304 | [TTS] bugfix IPAG2P and refactor to remove duplicate process. | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
1. fixed a bug for IPAG2P: the returned self.phoneme_dict mismatches between the one obtained from [self.parse_as_cmu_dict](https://github.com/NVIDIA/NeMo/blob/44de3950f0bf2f3e85d7df1a962eb24674f4fa4e/nemo_text_processing/g2p/modules.py#L320) and the one directly updated from a [dict obj](https://github.com/NVIDIA/NeMo/blob/44de3950f0bf2f3e85d7df1a962eb24674f4fa4e/nemo_text_processing/g2p/modules.py#L351). The former will return something like `{..., "WIRE": [["ˈ", "w", "a", "ɪ", "ɚ"], ["ˈ", "w", "a", "ɪ", "ɹ"]], ...}`, while the latter will return like `{..., "WIRE": ["ˈ", "w", "a", "ɪ", "ɚ"], ...}`.
2. refactored to remove duplicate processes for the case when the phoneme dict input is a dict object and for the case when the phoneme dict is a file.
3. added type hints and docstrings for the whole class `IPAG2P`.
**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**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/5303 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5303/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5303/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5303/events | https://github.com/NVIDIA/NeMo/pull/5303 | 1,432,173,708 | PR_kwDOC_bI7s5CAaEw | 5,303 | Fix dialogue tutorial bug | {
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} | # What does this PR do ?
Set default value of num_prompt_tokens to 0 for huggingface GPT model (where virtual prompts do not exist).
**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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
UB PTL until we upgrade to it.
**Collection**: [Core]
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# What does this PR do ?
Starts port of ASR tests to Blossom
**Collection**: [ASR]
# Changelog
- Add asr test
- Add some checks to blossom ci main tests
# Usage
- `/blossom-ci`
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Currently you cannot configure the IPATokenizer in .yaml config because there is no way to modify the lambda arguments. This PR replaces the lambda arguments with a locale string that maps to the correct behavior, which now just maps to English vs Non-English.
An alternative approach would be to replace the lambda arguments with a new class/interface, but we thought that was too complicated for now. And that having default configurations available that someone can control with just a language/locale string would be nice to have regardless.
**Collection**: [TTS]
# Changelog
- Replace IPA tokenizer lambda arguments with locale string that maps to English vs non-English IPA functions.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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} | # What does this PR do ?
Set default value of num_prompt_tokens to 0 for huggingface GPT model (where virtual prompts do not exist).
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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: Viraj Karandikar <[email protected]>
Changes some of the clustering parameters from init time to run time.
**asr**
# Changelog
- Moves parameters like max_num_speaker, multiscale_weights, sparse_search_volume from init-time to run-time
# Usage
* Used when clustering is exported as torchscript model and some parameters need to be changed runtime.
# 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? - NA
- [*] Did you add or update any necessary documentation? - NA
- [*] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - No
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [*] New Feature (minor enhancement actually)
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
Fix minor bugs in multispeaker simulation tutorial notebook
https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb
Bugfix for
https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3848353
- rm throws out an error if there is no such file
**Collection**: [Note which collection this PR will affect]
# Changelog
added rm -f (try deleting only if file exists)
added wget -nc (no duplication)
# Usage
Please run the tutorial notebook
# 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?
any one in ASR
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} | Signed-off-by: Matvei Novikov <[email protected]>
# What does this PR do ?
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# Changelog
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```python
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If you haven't finished some of the above items you can still open "Draft" PR.
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prev_state = config._get_node_flag("struct")
AttributeError: 'dict' object has no attribute '_get_node_flag'
Thanks. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5289 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5289/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5289/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5289/events | https://github.com/NVIDIA/NeMo/issues/5289 | 1,430,139,285 | I_kwDOC_bI7s5VPjGV | 5,289 | transcribe() outputting different results than validate() | {
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} | null | null | null | Hello,
I have fine-tuned the German Conformer Transducer on a subset of Mozilla Common Voice. The WER on the validation set decreases throughout the 20 training epochs. However, when I load the checkpoint and use the `transcribe` method, the model outputs weird predictions containing `??`. For example:
```
' ⁇ un müssen meines ⁇ rachtens konkrete und erkennbare ⁇ aßnahmen ergriffen werden ⁇ '
```
This does not occur when I use the pre-trained model offered by Nvidia. Only when I use the one I have fine-tuned myself. I have run another validation loop with the same checkpoint, and the WER is lower than for the model that is not fine-tuned.
I assume that there must be a difference between these two methods (`validate` and `transcribe`) that makes one work but not the other. Do you have any clue as to why this is? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5288 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5288/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5288/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5288/events | https://github.com/NVIDIA/NeMo/pull/5288 | 1,429,947,740 | PR_kwDOC_bI7s5B47iY | 5,288 | PCLA tutorial typo fix | {
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} | Signed-off-by: Matvei Novikov <[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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https://api.github.com/repos/NVIDIA/NeMo/issues/5285 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5285/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5285/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5285/events | https://github.com/NVIDIA/NeMo/issues/5285 | 1,428,844,171 | I_kwDOC_bI7s5VKm6L | 5,285 | How to get asr result of audio with offset and duration | {
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} | null | null | null | Hello!
I want to test an audio file that has a specific offset and duraion, how can I do this?
For example, I want to test from 20s to 40s. The below scripts can only give me the result of the whole audio.
```
import nemo
import nemo.collections.asr as nemo_asr
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
files = [os.path.join(data_dir, 'an4/wav/an4_clstk/mgah/cen2-mgah-b.wav')]
for fname, transcription in zip(files, quartznet.transcribe(paths2audio_files=files)):
print(f"Audio in {fname} was recognized as: {transcription}")
``` | {
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but it always show me
command: /anaconda/envs/azureml_py38/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"/NeMo/setup.py'"'"'; __file__='"'"'/NeMo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps
cwd: /mnt/batch/tasks/shared/LS_root/mounts/clusters/k80gpu/code/Users/lokinfey/NeMo/
Complete output (44 lines):
running develop
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/command/easy_install.py:156: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/mnt/batch/tasks/shared/LS_root/mounts/clusters/k80gpu/code/Users/lokinfey/NeMo/setup.py", line 209, in <module>
setuptools.setup(
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/__init__.py", line 153, in setup
return distutils.core.setup(**attrs)
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/core.py", line 148, in setup
dist.run_commands()
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/dist.py", line 984, in run_command
cmd_obj.ensure_finalized()
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/cmd.py", line 107, in ensure_finalized
self.finalize_options()
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/command/develop.py", line 52, in finalize_options
easy_install.finalize_options(self)
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 268, in finalize_options
self._expand(
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 1344, in _expand
config_vars = self.get_finalized_command('install').config_vars
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/cmd.py", line 299, in get_finalized_command
cmd_obj.ensure_finalized()
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/cmd.py", line 107, in ensure_finalized
self.finalize_options()
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/command/install.py", line 45, in finalize_options
orig.install.finalize_options(self)
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/command/install.py", line 366, in finalize_options
self.set_undefined_options('build',
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/cmd.py", line 286, in set_undefined_options
src_cmd_obj = self.distribution.get_command_obj(src_cmd)
File "/anaconda/envs/azureml_py38/lib/python3.8/distutils/dist.py", line 857, in get_command_obj
klass = self.get_command_class(command)
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/setuptools/dist.py", line 890, in get_command_class
self.cmdclass[command] = cmdclass = ep.load()
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pkg_resources/__init__.py", line 2465, in load
return self.resolve()
File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pkg_resources/__init__.py", line 2471, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
ModuleNotFoundError: No module named 'setuptools.command.build'
----------------------------------------
ERROR: Command errored out with exit status 1: /anaconda/envs/azureml_py38/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/mnt/batch/tasks/shared/LS_root/mounts/clusters/k80gpu/code/Users/lokinfey/NeMo/setup.py'"'"'; __file__='"'"'/mnt/batch/tasks/shared/LS_root/mounts/clusters/k80gpu/code/Users/lokinfey/NeMo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
can you help me | {
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Forgive me for the stupid question but:
Is expected the RAM consumption to increase or have high usage values?
I've been using a `stt_en_conformer_ctc_large.nemo` in GPU without any problem, to be more specific I'm running two models in two child processes. After every transcription, `torch.cuda.empty_cache()` solve my problems with the GPU cache and so on.
But I noticed when these processes start, the RAM had a significantly increased consumption (basically starting from model size + other things) and increase transcription after transcription.
That is the way I'm loading the models:
```python
asr_model = nemo_asr.models.EncDecCTCModel.restore_from(_model_path)
asr_model.eval()
asr_model = asr_model.to(asr_model.device)
```
Is this normal behavior? Is there anything that I can do to solve it?
Thank you! | {
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] | closed | false | null | [] | null | null | 2022-10-29T01:26:25 | 2022-10-29T01:47:56 | 2022-10-29T01:47:53 | CONTRIBUTOR | {
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} | # What does this PR do ?
set find_unused_parameters=True to overcome RuntimeError of unused pooler layer for BERT model.
**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**:
- [ ] 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 ?
This PR adds `cpWER` calculation feature to diarization-ASR pipeline.
`WDER` features are removed from `diarization_utils.py` to keep the code simple.
Additionally, docstrings are added to the functions in `diarization_utils.py`
From this PR on, NeMo multispeaker ASR or ASR-with-diarization will be evaluated by cpWER.
To evaluate cpWER value, corresponding CTM files are needed.
All diarization related evaluation (DER, cpWER) is now included in collections/utils/metric/der.py.
This is for reusability of the diarization evaluation code
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added functions regarding `cpWER` calculation
- Added docstings in `diarization_utils.py`
- Changed variable names:
`total_riva_dict` -> `trans_info_dict`,
`riva_dict` -> `session_trans_dict`,
- Removed functions and imports regarding `WDER` and word-timestamp calculation.
- DER, cpWER are now included in collections/utils/metric/der.py.
# Usage
* You can potentially add a usage example below
```python
python offline_diar_with_asr_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.model_path=<pretrained model name or path to .nemo> \
diarizer.asr.model_path=<pretrained model name or path to .nemo> \
diarizer.speaker_embeddings.parameters.save_embeddings=False \
diarizer.asr.parameters.asr_based_vad=True \
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Add label inference support for EncDecSpeakerLabel models
**Collection**: ASR
# Changelog
- Add new `infer_file` function which returns both logits and embeddings
- update existing `get_embedding` function to use `infer_file`
- add new `get_label` function that returns label automatically (based on trained model labels)
- update existing speaker inference scripts to use these functions
# Usage
* For getting label (for eg: langid)
```python
from nemo.collections.asr.models import EncDecSpeakerLabelModel
model = EncDecSpeakerLabelModel.from_pretrained('langid_ambernet')
lang = model.get_label(<path_to_audio_file>)
```
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
set find_unused_parameters=True to overcome RuntimeError of unused pooler layer for BERT model.
**Collection**: nlp
# 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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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
* rename openslr directory to openslr_95 for disambiguation.
* fixed wrong type of filepath.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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```
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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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Elena Rastorgueva <[email protected]>
# What does this PR do ?
Adds documentation to Speech Dataset Processor
**Collection**: [Note which collection this PR will affect]
tools/speech_dataset_processor
# 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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**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)
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**PR Type**:
- [ ] New Feature
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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: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes some minor typos in the TTS intro tutorial. No code changes.
**Collection**: TTS | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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: smajumdar <[email protected]>
# What does this PR do ?
Adds a Gradio demo app embedded in the ASR Docs page
**Collection**: [ASR]
# Usage
Visit https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/intro.html after PR merge on main branch
Example -

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | Hi, I used pretrain titanet-large on one audio of Alimeeting_Eval_far (Chinese), and its Confusion Error Rate (CER) is 0.0283
So I think if fine-tune titanet-large with Chinese data (CN-celeb2) it will have a better result.
However, the first result of CER is 0.1473 (learning rate: 1-e4), and the second one is 0.2120 (learning rate: 1-e6)
Do you have some suggestions?
**titanet-finetune.yaml**
```
name: &name "TitaNet-Finetune-cn2-300-30ep-lr1-e5"
sample_rate: &sample_rate 16000
init_from_pretrained_model:
speaker_tasks:
name: 'titanet_large'
include: ["preprocessor","encoder"]
exclude: ["decoder.final"] # Add specific layer names here to exlude or just ["decoder"] if to exclude all of decoder pretrained weights
model:
train_ds:
manifest_filepath: /home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/recognition/data/train.json
sample_rate: 16000
labels: null
batch_size: 64
shuffle: True
is_tarred: False
tarred_audio_filepaths: null
tarred_shard_strategy: "scatter"
augmentor:
speed:
prob: 0.3
sr: *sample_rate
resample_type: 'kaiser_fast'
min_speed_rate: 0.95
max_speed_rate: 1.05
validation_ds:
manifest_filepath: /home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/recognition/data/dev.json
sample_rate: 16000
labels: null
batch_size: 128
shuffle: False
model_defaults:
filters: 1024
repeat: 3
dropout: 0.1
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
normalize: "per_feature"
window_size: 0.025
sample_rate: *sample_rate
window_stride: 0.01
window: "hann"
features: &n_mels 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: *n_mels
activation: relu
conv_mask: true
jasper:
- filters: ${model.model_defaults.filters}
repeat: 1
kernel: [3]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: ${model.model_defaults.filters}
repeat: ${model.model_defaults.repeat}
kernel: [7]
stride: [1]
dilation: [1]
dropout: ${model.model_defaults.dropout}
residual: true
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: ${model.model_defaults.filters}
repeat: ${model.model_defaults.repeat}
kernel: [11]
stride: [1]
dilation: [1]
dropout: ${model.model_defaults.dropout}
residual: true
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: ${model.model_defaults.filters}
repeat: ${model.model_defaults.repeat}
kernel: [15]
stride: [1]
dilation: [1]
dropout: ${model.model_defaults.dropout}
residual: true
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: &enc_feat_out 3072
repeat: 1
kernel: [1]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
decoder:
_target_: nemo.collections.asr.modules.SpeakerDecoder
feat_in: *enc_feat_out
num_classes: 299
pool_mode: 'attention'
emb_sizes: 192
angular: True
loss:
scale: 30
margin: 0.2
optim:
name: adamw
lr: 0.0001 #(original titanet-large was trained with 0.08 lr)
weight_decay: 0.0002
# scheduler setup
sched:
name: CosineAnnealing
warmup_ratio: 0.1
min_lr: 0.0
trainer:
devices: 1 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each)
max_epochs: 30
max_steps: -1 # computed at runtime if not set
num_nodes: 1
accelerator: gpu
strategy: ddp
deterministic: True
enable_checkpointing: False
logger: False
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
exp_manager:
exp_dir: null
name: *name
create_tensorboard_logger: True
create_checkpoint_callback: True
```
Add any other context about the problem here.
GV100GL [Tesla V100 PCIe 16GB] (rev a1)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Adds the volume loss to CTC models
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
Added the new reduction to the ctc loss class
# Usage
In the config file, set
ctc_reduction: 'mean_volume'
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
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## 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 ?
From the previous PR (https://github.com/NVIDIA/NeMo/pull/5191/files),
Speaker clustering code is now fully jit-scriptable, but speaker counting algorithm for
short audio sample (less than 40s) was dropped.
In this PR, we are adding `getEnhancedSpeakerCount` function back and now tutorial notebooks are working correctly.
**Collection**: [Note which collection this PR will affect]
# Changelog
- added `getEnhancedSpeakerCount` function back that handles very short audio samples
- checked the python and torch.jit.script modes
# Usage
* You can potentially add a usage example below
```python
python neural_diarizer/multiscale_diar_decoder_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_output' \
diarizer.speaker_embeddings.parameters.save_embeddings=False \
diarizer.vad.model_path=<pretrained model name or path to .nemo> \
diarizer.speaker_embeddings.model_path=<pretrained speaker embedding model name or path to .nemo> \
diarizer.msdd_model.model_path=<pretrained MSDD model name or path .nemo> \
```
# 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.
# Additional Information
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes some minor typos in the TTS intro tutorial. No code changes.
**Collection**: TTS | {
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} | # What does this PR do ?
1. Fixed text-memmap issue when boundary (e.g., new-line) is missing from end of file.
2. Added support in custom index building method (determines how to split int data into samples) using `build_index_fn(fn, newline_int)` argument. The method can override default indexing of the file `fn`.
3. Added support in partial sample loading and alternative decoding by overriding `_fetch_sample_from_memmap(self, mdata, i, j)`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds the option to specify the audio signal dropout separately for a conformed encoder.
**Collection**: asr
# Changelog
- Updated the configs using conformed encoder
- Add new dropout_audio parameter in conformer_encoder.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)
- [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
## Who can review?
Anyone in the NeMo ASR team.
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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Add a script for doing audio preprocessing for TTS training.
**Collection**: [TTS]
# Changelog
- Created a "data" directory under nemo.collections.tts following the same convention as the ASR & NLP code.
- Added data_util class for generic operations like processing training manifest. Not sure of volume normalization should be there, but currently have no better place to put it.
- Added silence trimming implementations using energy and VAD based approaches.
- Added audio_preprocessing script for running the data processing logic over a training manifest, with example config files.
# Usage
```
HYDRA_FULL_ERROR=1 python /home/rlangman/Code/NeMo/scripts/dataset_processing/tts/audio_processing/preprocess_audio.py \
--config-path=/home/rlangman/Code/NeMo/scripts/dataset_processing/tts/audio_processing/config \
--config-name=preprocessing.yaml \
data_base_dir="/home/rlangman/Data/nemo_processing" \
config.num_workers=2 \
config.max_entries=100 \
trim=vad \
trim.device=cuda
```
# 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
# Additional Information
I manually tested the silence trimming using the openSLR Spanish data. For the 2 modes the dataset size decreases from:
Energy: 35 hours -> 28.8 hours
VAD: 35 hours -> 22.6 hours
This is because lots of the audio has noise in the long silences at the start and end of files. The VAD can differentiate the noise from speech while energy cannot.
Doing trimming on my workstation with the ~24k utterance dataset takes about 5 minutes with energy and 30 minutes with VAD.
More automated testing might be possible if we add CI tests that use actual audio, but I would leave that for a future PR as we do not yet have guidelines for TTS CI tests. | {
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Fixes #5259
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Fixes #5258
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# Changelog
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# Usage
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https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Recognition_Verification.ipynb
link appears in NeMo/examples/speaker_tasks/recognition/speaker_reco.py nad in readmes | {
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} | Signed-off-by: Vasyl Spachynskyi <[email protected]>
# What does this PR do ?
Extend ITN rules for English (cardinals) to support hundreds, like:
- eleven hundred -> 1100
- twenty one hundred -> 2100
- ninety nine hundred and ninety nine -> 9999
**Collection**: [Text Processing]
# 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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
The english WFST inverse text normalizer fails to correctly tag "per cent".
**Steps/Code to reproduce bug**
```
from nemo_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer
inverse_normalizer = InverseNormalizer(lang='en')
spoken = "The share price of BHP increased by eleven per cent"
un_normalized = inverse_normalizer.inverse_normalize(spoken, verbose=False)
print(un_normalized)
```
**Expected behavior**
I expect "The share price of BHP increased by 11 %".
The actual output behavior is an unchanged input - "The share price of BHP increased by eleven per cent"
NB: If I change the input to "... per cents", then I do get the expected output. There's an issue with how the plural / singular forms are handled in this case
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} | # What does this PR do ?
Updates new models, results and configs
**Collection**: [ASR]
# 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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} | # What does this PR do ?
Updates new models, results and configs
**Collection**: [ASR]
# 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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# What does this PR do ?
Fix issue with transcriptions type
**Collection**: ASR
# Changelog
- Add return_hypothesis to stay consistent with rest of asr models for partial audio transcribe
# 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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Check if zarr library has any contraindications with other libraries
**Collection**: [Core]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes the Inference_ModelSelect notebook link in the NeMo TTS Primer notebook.
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | null | null | null | Hello,
I installed all necessary dependencies and Nemo, but I got an issue when I tried to transcribe or diarize using Nemo
The exception is: **Floating point exception**, without any stacktrace, just this message
This exception occurred when the Nemo model was initialized
Dependencies:
> Cuda version: 11.2
> Python version: 3.8.10
> Torch version: 1.12.0+cu113
> Nemo version: 1.12.0 (I followed the steps for PIP in the readme)
> OS - Ubuntu 20.04.5 LTS
> Graphic card - Nvidia A40
I tried to use lower versions of PyTorch, it didn't help though.
Usage of PyTorch 1.9.0+cu111 and lower caused another exception: **OSError: libtorch_hip.so: cannot open shared object file**
I found a few topics where developers faced this issue and I even tried to fix it, but without success
If anyone knows how to handle this exception, I will appreciate any help in advance.
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} | null | null | null | **Describe the bug**
I've fine-tuned TitaNet-Large model 10 Epoch for Korean with 1,000,000 datas and 60 speaker dataset because NGC TitaNet(for English) only predict one token. I've checked loss has decreased not only in training step, but also in validation step(min loss was 0.0079).
However, the fine-tuned model only predict one token, <speaker 0>:
```
{'sample': ['1.8 2.16 speaker_0',
'2.32 2.52 speaker_0',
'2.92 3.4 speaker_0',
'4.16 5.0 speaker_0',
'7.44 8.08 speaker_0',
'8.88 9.16 speaker_0',
'9.48 9.84 speaker_0',
'12.76 13.08 speaker_0',
'13.48 13.68 speaker_0',
'14.76 15.24 speaker_0',
'15.8 16.400000000000002 speaker_0',
'16.88 17.04 speaker_0',
'17.2 18.24 speaker_0',
'20.4 20.72 speaker_0',
'20.88 21.24 speaker_0',
'25.2 25.68 speaker_0',
...]
```
Since label in the manifest file is integer, it seems it is a label-related issue. But there is a few reasons i can assume.
1. As I expected in the above, it is a label-related issue.
2. 1,000,000 data and 60 speakers are too small for fine-tuning TitaNet
3. There are problems in the Configuration yaml file.
4. The model is overfitted to training data
5. pre-trained Model weight is not initialized
** + Additional Information **
Diarization score is None in the below code(in the ASR_with_SpeakerDiarization.ipynb)
```python
diar_hyp, diar_score = asr_diar_offline.run_diarization(cfg, word_ts_hyp)
```
**Steps/Code to reproduce bug**
### Example of manifest file for training:
`{"audio_filepath":"\/home\/me\/datas\/speaker_tasks\/datas\/1.Training\/original\/call\/2021-12-16\/3544\/A0210-3544M2010-11020010-06897809.wav","label":3544,"offset":0,"duration":1.38}
`
### YAML Configuration for training
```python
cfg:
train_ds:
manifest_filepath: train_speakers.json
sample_rate: 16000
labels:
- 8
- 191
- 236
- 366
- 423
- 432
- 575
- 624
- 776
- 922
- 1053
- 1057
- 1123
- 1136
- 1221
- 1330
- 1347
- 1379
- 1462
- 1553
- 1625
- 1667
- 1718
- 1964
- 1989
- 2062
- 2104
- 2122
- 2131
- 2177
- 2254
- 2357
- 2556
- 2615
- 2662
- 2674
- 2704
- 2758
- 2805
- 2987
- 3007
- 3070
- 3093
- 3159
- 3499
- 3544
- 3548
- 3562
- 3573
- 3580
- 3604
- 3664
- 3715
- 3740
- 3775
- 3791
- 3858
- 3988
- 4055
- 9036
batch_size: 32
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: scatter
augmentor:
speed:
prob: 0.3
sr: 16000
resample_type: kaiser_fast
min_speed_rate: 0.95
max_speed_rate: 1.05
validation_ds:
manifest_filepath: eval_speakers.json
sample_rate: 16000
labels: null
batch_size: 32
shuffle: false
model_defaults:
filters: 1024
repeat: 3
dropout: 0.1
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
normalize: per_feature
window_size: 0.025
sample_rate: 16000
window_stride: 0.01
window: hann
features: 80
n_fft: 512
frame_splicing: 1
dither: 1.0e-05
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 0
freq_width: 4
time_masks: 0
time_width: 0.03
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: 80
activation: relu
conv_mask: true
jasper:
- filters: 1024
repeat: 1
kernel:
- 3
stride:
- 1
dilation:
- 1
dropout: 0.0
residual: false
separable: true
se: true
se_context_size: -1
- filters: 1024
repeat: 3
kernel:
- 7
stride:
- 1
dilation:
- 1
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
- filters: 1024
repeat: 3
kernel:
- 11
stride:
- 1
dilation:
- 1
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
- filters: 1024
repeat: 3
kernel:
- 15
stride:
- 1
dilation:
- 1
dropout: 0.1
residual: true
separable: true
se: true
se_context_size: -1
- filters: 3072
repeat: 1
kernel:
- 1
stride:
- 1
dilation:
- 1
dropout: 0.0
residual: false
separable: true
se: true
se_context_size: -1
decoder:
_target_: nemo.collections.asr.modules.SpeakerDecoder
feat_in: 3072
num_classes: 60
pool_mode: attention
emb_sizes: 192
angular: false
loss:
scale: 30
margin: 0.2
optim:
name: sgd
lr: 0.006
weight_decay: 0.001
sched:
name: CosineAnnealing
warmup_ratio: 0.1
min_lr: 0.0
target: nemo.collections.asr.models.label_models.EncDecSpeakerLabelModel
nemo_version: 1.12.0
```
### YAML Configuration for prediction
```yaml
# This YAML file is created for all types of offline speaker diarization inference tasks in `<NeMo git root>/example/speaker_tasks/diarization` folder.
# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file.
# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used.
# The configurations in this YAML file is suitable for telephone recordings involving 2~8 speakers in a session and may not show the best performance on the other types of acoustic conditions or dialogues.
# An example line in an input manifest file (`.json` format):
# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"}
name: &name "ClusterDiarizer"
num_workers: 4
sample_rate: 16000
batch_size: 64
diarizer:
manifest_filepath: '/home/me/codes/NeMo/input_manifest.json'
out_dir: ???
oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps
collar: 0.25 # Collar value for scoring
ignore_overlap: True # Consider or ignore overlap segments while scoring
vad:
model_path: ??? # .nemo local model path or pretrained VAD model name
external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set)
window_length_in_sec: 0.15 # Window length in sec for VAD context input
shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction
smoothing: "median" # False or type of smoothing method (eg: median)
overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter
onset: 0.1 # Onset threshold for detecting the beginning and end of a speech
offset: 0.1 # Offset threshold for detecting the end of a speech
pad_onset: 0.1 # Adding durations before each speech segment
pad_offset: 0 # Adding durations after each speech segment
min_duration_on: 0 # Threshold for small non_speech deletion
min_duration_off: 0.2 # Threshold for short speech segment deletion
filter_speech_first: True
speaker_embeddings:
model_path: /'home/insutil/codes/NeMo/nemo_experiments/TitaNet/2022-10-17_19-48-25/checkpoints/TitaNet.nemo' # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
parameters:
window_length_in_sec: [1.5,1.25,1.0,0.75,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5]
shift_length_in_sec: [0.75,0.625,0.5,0.375,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25]
multiscale_weights: [1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33]
save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`.
clustering:
parameters:
oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
max_num_speakers: 2 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored.
enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
sparse_search_volume: 30 # The higher the number, the more values will be examined with more time.
maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers.
msdd_model:
model_path: ??? # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD)
parameters:
use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used.
infer_batch_size: 25 # Batch size for MSDD inference.
sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps.
seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False.
split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference.
diar_window_length: 50 # The length of split short sequence when split_infer is True.
overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated.
asr:
model_path: /home/me/codes/NeMo/checkpoints/conformer/Conformer-CTC-BPE.nemo # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
parameters:
asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
asr_based_vad_threshold: 0.05 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD.
asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
lenient_overlap_WDER: True # If true, when a word falls into speaker-overlappedregions, consider the word as a correctly diarized word.
decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2].
word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript.
break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
beam_width: 32
alpha: 0.5
beta: 2.5
realigning_lm_parameters: # Experimental feature
arpa_language_model: null # Provide a KenLM language model in .arpa format.
min_number_of_words: 3 # Min number of words for the left context.
max_number_of_words: 10 # Max number of words for the right context.
logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses.
### Training code
```python
from omegaconf import OmegaConf
import pytorch_lightning as pl
try:
from ruamel.yaml import YAML
except ModuleNotFoundError:
from ruamel_yaml import YAML
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
import nemo.collections.asr as nemo_asr
@hydra_runner(config_path="../../model_cards/", config_name='config')
def __main__(cfg):
"""
Train a Speaker embedding model.
Args:
cfg: DictConfig contains model configurations.
"""
logging.info(f"hydra config: {OmegaConf.to_yaml(cfg)}")
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get('exp_manager', None))
model = nemo_asr.models.label_models.EncDecSpeakerLabelModel(cfg=cfg)
model.setup_training_data(cfg.model.train_ds)
model.setup_validation_data(cfg.model.validation_ds)
model.setup_test_data(cfg.model.test_ds)
model.maybe_init_from_pretrained_checkpoint(cfg.init_from_pretrained_model)
trainer.fit(model)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if model.prepare_test(trainer):
trainer.test(model)
```
prediction: Uses ASR_with_SpeakerDiarization.ipynb notebook in tutorials/speaker_tasks
**Expected behavior**
- Predict 2 speaker, <speaker 0> and <speaker 1>
**Environment overview (please complete the following information)**
- location: Bare-metal
- installation method: downloaded source code from repository.
- model ckpt method: downloaded from NGC, fine-tuned in the machine.
**Environment details**
- OS: Ubuntu 20.24
- GPU: Nvidia A40 * 2EA for training, A40 1EA for prediction
- pytorch version: 1.11.0
- CUDA Toolkit version: 11.3
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} | # What does this PR do ?
Improve the script for onnx rnnt export and add support for torchscript export of rnnt
**Collection**: [ASR]
# Changelog
- Add support for pretrained checkpoint in onnx export
- Add GPU inference support with `onnxruntime-gpu` installed for RNNT models
- Add support for Torchscript exported RNNT model
- Add support for eval of torchscripted RNNT model on GPU
# 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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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes the Inference_ModelSelect notebook link in the NeMo TTS Primer notebook.
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
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} | 1. Fixes a precision bug for ptuning which improves perf by ~25%
- If model is in bf16, Pytorch autocasting currently unnecessarily casts operations to be in fp16 from bf16, and casts the output back again to bf16
- This fix keeps the precision in bf16, removing many unnecessary casts
2. Implements amp O2 which improves convergence times
- O2 accumulates prompt encoder gradients in fp32 master weights, rather than in bf16 or fp16 currently

(validation loss of GPT3 530b on 7 nodes, O2 (orange) vs baseline (pink))
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} | Option to pad the last validation input sequence if its smaller than the encoder sequence length for MegatronGPT
# What does this PR do ?
If validation_drop_last is set to False, the last input sample from the validation dataset will not be dropped if its smaller than the encode sequence length for MegatronGPT
**Collection**: [Note which collection this PR will affect]
nlp/language_modeling/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: Vahid <[email protected]>
# What does this PR do ?
Fixes a bug in exporting to ONNX for non-streaming conformer models.
**PR Type**:
- [ ] New Feature
- [x ] Bugfix
- [ ] Documentation
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