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} | # Add support for prepending data prefix to manifests paths and `audio_paths`
This feature is to add support for automatically adding prefix to manifest paths as well as the `audio_filepath` field in the manifest. The simple trick is by adding a `data_prefix` in the top level of your yaml config file. The program has a default value of `None` for `data_prefix`, so that it's compatible with all previous config files.
# Features
- Adding single prefix to all input manifests. Each train/val/test dataset can have their own data prefix.
- Add different prefix to different input manifests, where the number of specified prefix must match the number of input manifests.
- Fully compatible with previous manifest, since `data_prefix=None` by default.
# Why Data Prefix?
Consider the following dataset folder:
```
/home/steve/libri_speech/
----------------------------->: train_manifest.json
----------------------------->: wav/
------------------------------------->: 11111.wav
------------------------------------->: 22222.wav
```
and the content of ` train_manifest.json` is:
```
{"audio_filepath": "/home/steve/libri_speech/wav/11111.wav", duration: "2.3", "text": "one one one one one"}
```
If say, Kevin, wants to use my dataset on his project by copying my folder : `cp -r /home/steve/libri_speech/ /home/kevin/`, then he must also change all the `audio_filepath` in the manifest `/home/steve/libri_speech/train_manifest.json`, which is certainly a tedious work.
However, if we allow `audio_filepath` to be relative paths to the data folder, then we can have a more flexible `train_manifest.json`:
```
{"audio_filepath": "wav/11111.wav", duration: "2.3", "text": "one one one one one"}
```
Also, when added with a `data_prefix`, we can move the dataset to anywhere without modifying the manifest. For example, all Kevin has to do is simply:
```
python train.py \
--config-path=../conf/conformer --config-name=config \
model.train_ds.data_prefix=/home/kevin/libri_speech \
model.train_ds.manifest_filepath="train_manifest.json"
```
And the program will automatically resolve the path for manifest as `/home/kevin/libri_speech/train_manifest.json`, and all `audio_filepath` in the manifest will also be added with the prefix:
```
{"audio_filepath": "wav/11111.wav", duration: "2.3", "text": "one one one one one"}
becomes
{"audio_filepath": "/home/kevin/libri_speech/wav/11111.wav", duration: "2.3", "text": "one one one one one"}
```
# Example Usage
Take the `examples/asr/conf/config.yaml` as an example. We just need to add `data_prefix` as follows:
```yaml
name: &name "QuartzNet15x5"
sample_rate: &sample_rate 16000
repeat: &repeat 1
dropout: &dropout 0.0
separable: &separable true
labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
data_prefix: null
model:
train_ds:
data_prefix: null
manifest_filepath: ???
.......(other params)
validation_ds:
data_prefix: null
manifest_filepath: ???
......(other params)
```
Then suppose the data folder is `/home/heh/libri_speech`, and the `audio_filepath` are relative paths to the data folder, we can run the program by:
```bash
python speech_to_text_ctc.py \
--config-path=../conf --config-name=config \
model.train_ds.data_prefix="/home/heh/libri_speech" \
model.train_ds.manifest_filepath="train_manifest.json" \
model.validation_ds.data_prefix="/home/heh/libri_speech" \
model.validation_ds.manifest_filepath="train_manifest.json" \
trainer.devices=2 \
trainer.max_epochs=2 \
```
# Change Log
- [5/10/2022] Modified config example: remove global `data_prefix`, and let `model.train_ds`, `model.validation_ds` and `model.test_ds` have their own `data_prefix`.
- [5/10/2022] Prioritize absolute path for manifest over relative path resolution. Now the `item_iter()` first try to locate the original manifest file, and will try to prepend `data_prefix` to the manifest path only if the file is not found at the original manifest path.
# Suggested Future Change
- In data downloading scripts, change all absolute `audio_filepath` to relative paths. | {
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} | # What does this PR do ?
When tarred dataset is created temporary files for text and labels are used. Previously if several scripts were run simultaneously there were risk of race condition because temporary files had hardcoded names.
**Collection**: nlp
# Changelog
- `tempfile.mksfile()` lib is used
- File creation and usage is wrapped into `try: ... finally: ...` clause.
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**Describe the solution you'd like**
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} | # What does this PR do ?
Changes a wrong description in offline_diarization_with_asr.yaml
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
Changed the description about asr_based_vad_threshold in offline_diarization_with_asr.yaml.
Fixed multiple typos and grammatical errors.
# 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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} | Signed-off-by: Jocelyn Huang <[email protected]>
Corrects entry that mistakenly has "YI1" to "IY1":
Line 133963: NVME EH1 N V IY1 EH1 M YI1 | {
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} | # What does this PR do ?
This PR fixes bugs for certain cases and adds few functions. Previously, write_rttm2manifest function had following issues:
(1) Offset is not considered
(2) Occasionally, the function was returning the segments outside the given (offset, offset+duration) range.
The newly written part is compared with the original code, and confirmed to
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
Added following functions in speaker_utils.py.
These functions are needed to extract oracle VAD stamp from manfiest file, considering the offset and duration.
In addition, these functions are designed for the future use for streaming diarization.
get_uniq_id_with_dur()
getSubRangeList()
getMinMaxOfRangeList()
getMergedRanges()
int2fl()
fl2int()
combine_int_overlaps()
combine_float_overlaps()
getOverlapRange()
isOverlap()
and modified following functions:
write_rttm2manifest() - to resolve existing issues
segments_manifest_to_subsegments_manifest() - Now, if include_uniq_id = True, the function adds uniq_id to the created subsegment manifest file. This is for using segmentation files for truncated (chopped) diarization dataset, where uniq_id is overlapped if not specified. (One audio file creates multiple 30~40sec utterances and these short utterances should be identifed too.)
Details can be found in the docstring in each function definition.
# 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
- [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 ?
1. Adds megatron memory-mapped dataloaders to NMT.
2. Inference script/config with a translate() method.
**Collection**: NLP
# Changelog
- Add a new dataset class for megatron memmap dataset.
- Add an inference script with the associated yaml config.
- Change the `use_tarred_dataset` arg to a generic `dataset_type` arg that can take `[text, tarred, bin_memmap, text_memmap]`
# Usage
* Set the following in the yaml config
- `dataset_type: bin_memmap`.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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https://api.github.com/repos/NVIDIA/NeMo/issues/4134 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4134/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4134/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4134/events | https://github.com/NVIDIA/NeMo/pull/4134 | 1,229,677,177 | PR_kwDOC_bI7s43gCXk | 4,134 | fix typo | {
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} | # Fix a typo in output message
Location: Line 42 at `nemo.collections.asr.data.audio_to_text_dataset.inject_dataloader_value_from_model_config` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4131 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4131/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4131/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4131/events | https://github.com/NVIDIA/NeMo/issues/4131 | 1,229,138,456 | I_kwDOC_bI7s5JQyoY | 4,131 | High value for WER using Citrinet1024 | {
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} | null | null | null | Hi, thanks for your great tutorial.
I fine-tuned Citrinet 1024 using a Persian audio data set. The WER starts from 4, and the loss value is about 500. But I did the same with Citrinet 256, the WER starts from 0.99 and after each epoch decreases. Why in Citrinet 1024 I faced a large value for WER?
**Environment overview**
- Environment location: Google Colab
- Method of NeMo install: !pip install nemo_toolkit[asr]
- NeMo version: main
**Environment details**
- OS version : "Ubuntu20.04.3 LTS" | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4129 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4129/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4129/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4129/events | https://github.com/NVIDIA/NeMo/issues/4129 | 1,228,784,964 | I_kwDOC_bI7s5JPcVE | 4,129 | Fine-tuning Conformer with Persian language | {
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} | null | null | null | Hi, would you please share with me a collab notebook file for fine-tuning conformer_ctc_bpe? I want to use it for the Persian language. when I use the` ASR_CTC_Language_Finetuning.ipynb` notebook I got the desired result with all Citrinet models, but I can not use this notebook file for any of the CTC Conformer models. For example when I use `stt_en_conformer_ctc_large` model in the following part `results = analyse_ctc_failures_in_model(model)` I have this error:
`
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 226, in map
for sample in data:
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/data/audio_to_text.py", line 671, in __next__
audio_bytes, audio_filename = next(self.iterator)
File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 282, in to_tuple
for sample in data:
File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 240, in rename
for sample in data:
File "/usr/local/lib/python3.7/dist-packages/webdataset/iterators.py", line 187, in shuffle
for sample in data:
File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 153, in group_by_keys
for fname, value in data:
File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 131, in tar_file_expander
for source in data:
File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 86, in url_opener
if handler(exn):
File "/usr/local/lib/python3.7/dist-packages/webdataset/utils.py", line 12, in reraise_exception
raise exn
File "/usr/local/lib/python3.7/dist-packages/webdataset/tariterators.py", line 82, in url_opener
stream = gopen.gopen(sample["url"], **kw)
File "/usr/local/lib/python3.7/dist-packages/webdataset/gopen.py", line 192, in gopen
return open(url, mode, buffering=bufsize)
FileNotFoundError: [Errno 2] No such file or directory: '/data/NeMo_ASR_SET/English/v2.0/train/audio_2568.tar'
`
How should I solve this error?
Thanks | {
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} | # What does this PR do ?
unifying to extract mel spectrogram using `nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor`.
**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/4127 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4127/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4127/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4127/events | https://github.com/NVIDIA/NeMo/pull/4127 | 1,228,499,554 | PR_kwDOC_bI7s43cX3p | 4,127 | Multiprocess improvements | {
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} | # What does this PR do ?
Improve multiprocessing speed by reading audio file only once, and also remove type_check decorator for forward_to_export function in label_models.py
**Collection**: ASR
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Adds the ability to
1. Randomly sample audio segments of specified length from input during run-time
2. Fraction based mask count specification in MaskedPatchAugmentation
3. Enable flac file support
**Collection**: ASR
# Changelog
- Updated `MaskedPatchAugmentation` in `audio_preprocessing.py`
- Added `RandomSegmentPertubation` in `perturn.py`
- Enabled `flac` file flag in `audio_to_text.py`
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fixes a bug where doing implicit grad acc on your validation data would not check on GLUE/XNLI dataset building. This would only happen in generic finetuning.
**Collection**: NLP
# Changelog
- Add implicit gradacc checks in MegatronT5GLUEModel
# 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 the ability to
1. Specify the validation metric you want to use while fine-tuning GLUE/XNLI or custom datasets
2. Write model predictions on the validation and test sets to a file even with data parallel > 0.
**Collection**: NLP
# Changelog
- Modify setup_metric in MegatronT5FinetuneModel to configure metrics based on what the user provides in their config
- Update inference step and inference epoch ends to log with this metric accoridngly.
- Update yaml files to reflect these changes.
# Usage
model.data.validation_ds.metric_name='spearman_corr_coeff'
model.data.validation_ds.write_predictions_to_file=True
model.data.validation_ds.output_file_path_prefix='/path/to/model_predictions'
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Added RETRO model, pretraining script, Mock Dataset.
Added CI test pretraining with Mock Dataset. | {
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} | 1. Add tutorial on ITN with Thutmose tagger. 2. Fix multiple spaces in the output
Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # 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.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Ramanathan Arunachalam <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds significant functionality to adapters, allowing multiple module-specific adapters, save and restore of just the adapters themselves rather than the full model (saving several hundred megabytes-to-gigabytes), and a suite of tests for the new functionality.
**Collection**: [Core, ASR]
# Changelog
- Adds support for both "global" and "module" specific adapters. Module specific adapters allow for targetted adapters to certain modules, independent of the others.
- Adds support for saving and restoring just the adapter modules, independent of the original model weights. This allows savings of significant disk storage, since just the adapters are usually a tiny fraction of the param count of the models.
- Adds a battery of tests for the new capabilities.
- Add support for dropout to adapter modules
- Add support for stochastic dropout to all adapters via ResidualAddStrategy.
# Usage
Subclass implementations can chose between global adapters, module adapters or both. A basic implementation of Adapters for a toy model is provided in the `tests/core/mixins/adapters/test_adapter_model_mixin.py`
```python
# Global adapters (adds "abc" adapter to all supported modules - encoder+decoder+others1)
model.add_adapter(name="abc", cfg=cfg)
# Module adapters
model.add_adapter(name="decoder:abc", cfg=cfg)
# Save all of the adapters (only the adapters themselves)
model.save_adapters("adapters.pt", name=None)
OR
model.save_adapters("adapters.pt", name={global or module adapter name})
# Restore one-or-all adapters (only the adapters themselves)
new_model.load_adapters("adapters.pt", name=None)
OR
new_model.load_adapters("adapters.pt", name={exact module name from state dict},
map_location='cpu', strict=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
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Add spec augmentation to label models
**Collection**: ASR
# Changelog
- edit label_models.py to include spec augmentation
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4112 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4112/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4112/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4112/events | https://github.com/NVIDIA/NeMo/pull/4112 | 1,226,056,771 | PR_kwDOC_bI7s43UsFn | 4,112 | Dialogue tasks unit test | {
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"merged_at": "2022-05-05T23:07:02"
} | # What does this PR do ?
Add unit test to dialogue tasks and some refactor to facilitate unit test
**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**:
- [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
- [ ] 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/4111 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4111/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4111/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4111/events | https://github.com/NVIDIA/NeMo/issues/4111 | 1,225,980,552 | I_kwDOC_bI7s5JEvqI | 4,111 | Cannot select speaker while inferencing on models trained on latest FastPitch_Finetuning.ipynb version | {
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} | null | null | null | Hi all.
I'm trying to generate audio with Inference_DurationPitchControl.ipynb. I've trained the FastPitch model with both LJSpeech and a custom dataset on the latest release (r1.8.2) with FastPitch_Finetuning (https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb). Also, I've models previously trained on a previous version of this script (r1.6.2).
The problem is using the new models, the voice is always the voice of the LJSpeech woman, regardless the speaker selection I choose. Nevertheless, there is still a subtle difference in the generated audio if I choose one speaker or another.
If I load one of the 'old' models, I can select the speaker without a problem.
Here I paste the code that I've used for training the new models on the latest FastPitch_Finetuning script (I've changed batch size to 16):
```
!(python fastpitch_finetune.py --config-name=fastpitch_align_v1.05.yaml \
train_dataset=./train.json \
validation_datasets=./val.json \
sup_data_path=./fastpitch_sup_data \
phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.01 \
heteronyms_path=tts_dataset_files/heteronyms-030921 \
whitelist_path=tts_dataset_files/lj_speech.tsv \
exp_manager.exp_dir=$OUTPUT_CHEKPOINTS \
+init_from_nemo_model=./tts_en_fastpitch_align.nemo \
+trainer.max_steps=$max_steps ~trainer.max_epochs \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=16 model.validation_ds.dataloader_params.batch_size=16 \
model.n_speakers=2 model.pitch_mean=121.9 model.pitch_std=20 \
model.pitch_fmin=65 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
+model.text_tokenizer.add_blank_at=true \
)
```
Here is the code of the old models training:
```
!(python fastpitch_finetune.py --config-name=fastpitch_align.yaml \
train_dataset=./train.json \
validation_datasets=./val.json \
+init_from_nemo_model=./tts_en_fastpitch_align.nemo \
+trainer.max_steps=$max_steps ~trainer.max_epochs trainer.check_val_every_n_epoch=25 \
prior_folder=./PriorsCustom model.train_ds.dataloader_params.batch_size=24 \
model.validation_ds.dataloader_params.batch_size=24 \
exp_manager.exp_dir=$OUTPUT_CHEKPOINTS \
model.n_speakers=2 model.pitch_avg=90 model.pitch_std=20 \
model.pitch_fmin=65 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
)
```
Thank you very much. | {
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Add tutorial for Thutmose Tagger
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
**Pre checks**:
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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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# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
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# Before your PR is "Ready for review"
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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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# Usage
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**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
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: Iztok Lebar Bajec <[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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**PR Type**:
- [ ] New Feature
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## Who can review?
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# Additional Information
* Related to # (issue)
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} | null | null | null | Regardless of the model being exported the script fails with
```python3
File "scripts/export.py", line 160, in nemo_export
raise e
File "scripts/export.py", line 139, in nemo_export
output_example = forward_method(model)(*input_list, **input_dict)
File "/data/nemo_main/nemo/collections/tts/models/hifigan.py", line 425, in forward_for_export
return self.generator(x=spec)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1111, in _call_impl
return forward_call(*input, **kwargs)
File "/data/nemo_main/nemo/collections/tts/modules/hifigan_modules.py", line 245, in forward
x = self.conv_pre(x)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1118, in _call_impl
result = hook(self, input)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py", line 64, in __call__
setattr(module, self.name, self.compute_weight(module))
File "/opt/conda/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py", line 24, in compute_weight
return _weight_norm(v, g, self.dim)
RuntimeError: Inference tensors do not track version counter.
```
Tested with `nemo:1.9.0rc0` based on `pytorch:22.04-py3`. Related to #4100 | {
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} | null | null | null | **Describe the bug**
In [asr_with_diarization config](https://github.com/NVIDIA/NeMo/blob/2aa68822bbfb8e7e8c221817b66de28928d6443b/examples/speaker_tasks/diarization/conf/offline_diarization_with_asr.yaml) the threshold is described as follows:
```yml
parameters:
asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
asr_based_vad_threshold: 50 # threshold (multiple of 10ms) for ignoring the gap between two words when generating VAD timestamps using ASR based VAD.
```
However in [ASR_DIAR_OFFLINE.get_speech_labels_from_decoded_prediction](https://github.com/NVIDIA/NeMo/blob/2aa68822bbfb8e7e8c221817b66de28928d6443b/nemo/collections/asr/parts/utils/diarization_utils.py#L185) it is used as a value in seconds:
```python
if word_ts[count][0] - word_ts[count - 1][1] <= self.nonspeech_threshold:
```
So, the default config defines minimum distance between words as 50 seconds, which usually results in the vad manifest being just one line with start and end time of the audio file.
**Steps/Code to reproduce bug**
```python
from nemo.collections.asr.parts.utils.diarization_utils import ASR_DIAR_OFFLINE
import json
with open("test_manifest.json",'w') as f:
f.write(json.dumps({'audio_filepath':"test.wav"}))
cfg_diarizer = {
"manifest_filepath":"test_manifest.json",
"asr":{"parameters":{"asr_based_vad_threshold":50,"fix_word_ts_with_VAD":False},"ctc_decoder_parameters":{},
"realigning_lm_parameters":{}},
"out_dir":"."
}
asr_diar_offline = ASR_DIAR_OFFLINE(**cfg_diarizer)
fake_ts = {'test.wav': [[0.0, 20.], [40., 45.]]}
asr_diar_offline.save_VAD_labels_list(fake_ts)
!cat {asr_diar_offline.VAD_RTTM_MAP['test.wav']['rttm_filepath']}
```
**Expected behavior**
Two time ranges 0-20 and 40-45 are split in the vad rttm
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} | Refactoring Tacotron2 to use new TTSDataset class
**Collection**: [Note which collection this PR will affect]
# Changelog
nemo.collections.asr.data.audio_to_text.AudioToCharDataset -> nemo.collections.tts.torch.data.TTSDataset
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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} | # What does this PR do ?
Add path_prefix to concat to the begining of audio_file.
**Collection**: ASR collection
# Changelog
- Add path_prefix to concat to the begining of audio_file.
# Usage
* You can use path_prefix on the cloud platforms like AWS Sagemaker, where you don't know the full path of the data before the training process is started.
* Ones training is started you get path of data from environment variables and provide to path_prefix script parameter
```python
# python3 examples/asr/asr_ctc/speech_to_text_ctc.py +model.train_ds.path_prefix=your/data/path +model.validation_ds.path_prefix=your/data/path
```
**PR Type**:
- New Feature
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} | Signed-off-by: Карпов Николай Вячеславович <[email protected]>
# What does this PR do ?
Add RUCharParser to parse Cyrillic letters
**Collection**: ASR collection
# Changelog
- Add RUCharParser to parse Cyrillic letters
- Add config with cyrillic letters examples/asr/conf/quartznet/quartznet_15x5_ru.yaml
- Use RUCharParser as "parser: ru" in train_ds, validation_ds, test_ds
# Usage
* With quartznet_15x5_ru.yaml you can train acoustic model using Golos dataset
```python
# python3 examples/asr/asr_ctc/speech_to_text_ctc.py -cn quartznet/quartznet_15x5_ru.yaml
```
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} | null | null | null | I tried to export a FastPitch model I trained to ONNX with [export.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/export.py).
The command I used was:
`python scripts/export.py "/home/xxx/TTSs/Nemo-models/nancy_fastpitch-44k-new-v3.nemo" "nancy_fastpitch-44k-new-v3.onnx" --runtime-check --device="cpu" --autocast`
But it produced an error:
```
Traceback (most recent call last):
File "scripts/export.py", line 169, in <module>
nemo_export(sys.argv[1:])
File "scripts/export.py", line 158, in nemo_export
raise e
File "scripts/export.py", line 139, in nemo_export
output_example = forward_method(model)(*input_list, **input_dict)
File "/mnt/d/TTSs/NeMo/nemo/collections/tts/models/fastpitch.py", line 576, in forward_for_export
return self.fastpitch.infer(text=text, pitch=pitch, pace=pace, speaker=speaker)
File "/mnt/d/TTSs/NeMo/nemo/collections/tts/modules/fastpitch.py", line 288, in infer
enc_out, enc_mask = self.encoder(input=text, conditioning=spk_emb)
File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/d/TTSs/NeMo/nemo/collections/tts/modules/transformer.py", line 262, in forward
return self._forward(self.word_emb(input), (input != self.padding_idx).unsqueeze(2), conditioning) # (B, L, 1)
File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/modules/sparse.py", line 158, in forward
return F.embedding(
File "/home/tony/Envs/nemo/lib/python3.8/site-packages/torch/nn/functional.py", line 2044, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Inference tensors do not track version counter.
```
when it was `Getting output example`.
What am I missing? Thank you! | {
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} | # What does this PR do ?
Adding unit tests for GPT
_Need to add merge and vocab files to the test tarfile_
**Collection**: NLP
# Changelog
- Test tokenizer
- Test constructor
- Test forward
# Usage
```bash
TRANSFORMERS_OFFLINE=1 pytest tests/collections/nlp/test_gpt_model.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**:
- [ ] 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: sam1373 <[email protected]>
# What does this PR do ?
Adds mixin for accessing intermediate layers of a model.
**Collection**: [core, asr]
# Changelog
# Usage
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Allow to use relative paths in manifest file.
**Collection**: ASR or any other collection which use manifest file with field "audio_file"
# Changelog
- Allow to use relative paths in the ASR manifest file
# Usage
* Allow to use relative paths in the manifest file. Field audio_file may be relative to the folder of manifest file
# 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
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
This PR fixes the bug reported in Issue #4059.
Before this fix, QuartzNet ASR Model could not generate word timestamps for an utterance with a single word.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Function named get_word_ts_from_spaces() has been changed to handle the cases where there is only one word in the decoded output.
- Few changes in docstrings.
# Usage
Please refer to [README](https://github.com/NVIDIA/NeMo/blob/d4bf4ed37d9465baa5a83dbf824864b0081c6098/examples/speaker_tasks/diarization/README.md)
```python
python offline_diarization_with_asr.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.model_path=<pretrained modelname or path to .nemo> \
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)
- [] 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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} | # What does this PR do ?
Fix link to tutorial in dialogue docs
**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**:
- [ x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix for an issue where during tarred dataset, max_steps computation fails in label models
**Collection**: ASR
# Changelog
- Adds it during setting up of training data
- Deleted duplication code for mapping tarred audio paths
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Generalize the way adapters are merged with their input, so as to allow more complicated adapters for the future, such as SimAdapter or MetaAdapter
**Collection**: [Core, ASR]
# Changelog
- Add `AbstractAdapterStrategy` as a base strategy that is used for other adapter strategies
- Add `ResidualAddAdapterStrategy` for simple residual addition between input and adapter output
- Add tests
# 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
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**PR Type**:
- [x ] Documentation
* cleaned up nemo docs on wfst-based TN/ITN, removed redundant docs
* removed run_predict.py and added functionality to normalize.py
* cleaned up WFST_tutorial and Text_normalization tutorials, merged inverse text normalization into text normalization tutorial | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Update script to use plugin necessary for BCP
# 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: smajumdar <[email protected]>
# Changelog
- Correct link to PTL
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | null | null | null | **Describe the bug**
Punctuation and capitalization model is run on 1 GPU even if `strategy=ddp`, `accelerator=gpu`, `devices=2`. For running on several GPUs a user has to set parameter `num_processes`.
**Steps/Code to reproduce bug**
Run script `examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py` with `trainer.accelerator=gpu`, `trainer.devices > 1`, `trainer.strategy=ddp` and check GPU utilization.
**Expected behavior**
Several GPUs used.
**Environment overview (please complete the following information)**
- Environment location: bare-metal
- Method of NeMo install: from source
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.10.1
- Python version: 3.8
**Additional context**
The reason for the problem is that in this [line](https://github.com/NVIDIA/NeMo/blob/f6987f4fb9e6c45afe0559e82d5d09e7d28a365d/examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py#L101) default values of trainer config from this [file](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/pytorch_lightning.py) are added. Default value of `num_processes=1` blocks the model from running on multiple GPUs.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: Core
# 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 async grad allreduce, work for T5 and GPT-3.
Increase allreduce granularity by grouping params into multiple chunks. Every time a chunk is finished, we start an allreduce for the chunk (sending large chunk of data can better utilize bandwidth).
Current implementation works for BF16 O2 and PP=1.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Update train_step of GPT-3 and T5 for async grad allreduce
- Add chunk optimization in MainParamsOptimizerWrapper
# Usage
add a knob "grad_allreduce_chunk_size_mb", default config is 125MB. You can change it for different models and/or model sizes.
# 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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} | null | null | null | **Describe the bug**
I recently updated from NeMo 1.7.2 to 1.8.2
When running a training session, the speed is now `4.04s/it` whereas it used to be `2.97it/s`.
Note it's taking ~4 **seconds per iteration** whereas it used to be ~3 **iterations per second**!
Here's my trainer config since it's the only things that's different from the NeMo examples config for CitriNet-1024 with SPE tokenizer:
```yaml
trainer:
devices: 8
accelerator: gpu
max_epochs: 100
max_steps: -1
num_nodes: 1
strategy: ddp
accumulate_grad_batches: 4
enable_checkpointing: false # Provided by exp_manager
logger: false # Provided by exp_manager
log_every_n_steps: 100
val_check_interval: 1.0
precision: 16
```
**Steps/Code to reproduce bug**
Run any training example script.
**Expected behavior**
Speed should not be significantly affected.
**Environment overview (please complete the following information)**
- Environment location: Docker (tried `nvcr.io/nvidia/pytorch:22.03-py3` and `nvcr.io/nvidia/pytorch:22.02-py3`)
- Method of NeMo install: `pip install nemo_toolkit[all]==1.8.2`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
```
docker run --rm -it --gpus all --ipc=host --env-file .env train
```
**Additional context**
Running this on an AWS p3dn.24xlarge instance (8xV100)
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} | # What does this PR do ?
Remove TTS models that were deprecated and marked for deletetion.
**Collection**: TTS, ASR
# Changelog
- Removes the following tts models and associated modules
- DegliModel
- EDMel2SpecModel
- FastPitchHifiGanE2EModel
- FastSpeech2Model
- FastSpeech2HifiGanE2EModel
- UniGlowModel
- TalkNet models: TalkNetDursModel, TalkNetPitchModel, TalkNetSpectModel\
- SqueezeWaveModel
- MelGanModel
- GlowTTSModel
- Removed `torch_stft` dependency from ASR and TTS
- Removed `stft_exact_pad`, and `stft_conv` parameters
- Removed the following datasets from the ASR and TTS collections
- ASR: AudioToCharWithDursF0Dataset, AudioToCharWithPriorDataset, AudioToCharWithPriorAndPitchDataset, FastPitchDataset
- TTS: AudioDataset, MelAudioDataset, SplicedAudioDataset, NoisySpecsDataset, FastSpeech2Dataset
# TODO
- [ ] Update documentation to remove old models
- [ ] Test tutorials
- [ ] Update NGC model cards to mark them as deprecated | {
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} | # What does this PR do ?
After #4046 fix the link in the README to point to the correct page!
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds documentation for `@typecheck()` decorator for neural typechecking.
**Collection**: [Docs]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Updates various files with the new path for the LJSpeech whitelist file
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #4076
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https://api.github.com/repos/NVIDIA/NeMo/issues/4077 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4077/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4077/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4077/events | https://github.com/NVIDIA/NeMo/pull/4077 | 1,217,822,523 | PR_kwDOC_bI7s425jA3 | 4,077 | Added exception handling for audio player in SDE | {
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"merged_at": "2022-04-28T02:03:59"
} | # What does this PR do ?
The PR adds logger message for exceptions during audio processing in SDE player.
**Collection**: Tools/SDE
# Changelog
- Added exception handling for audio player in SDE
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #4067
Signed-off-by: Vitaly Lavrukhin <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4076 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4076/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4076/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4076/events | https://github.com/NVIDIA/NeMo/issues/4076 | 1,217,784,075 | I_kwDOC_bI7s5IlekL | 4,076 | Unable to run Fastpitch_finetuning.ipynb version main from scratch | {
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```
# TODO(oktai15): remove +model.text_tokenizer.add_blank_at=true when we update FastPitch checkpoint
!(python fastpitch_finetune.py --config-name=fastpitch_align_v1.05.yaml \
train_dataset=./6097_manifest_train_dur_5_mins_local.json \
validation_datasets=./6097_manifest_dev_ns_all_local.json \
sup_data_path=./fastpitch_sup_data \
phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.01 \
heteronyms_path=tts_dataset_files/heteronyms-030921 \
whitelist_path=tts_dataset_files/whitelist_lj_speech.tsv \
exp_manager.exp_dir=./ljspeech_to_6097_no_mixing_5_mins \
+init_from_nemo_model=./tts_en_fastpitch_align.nemo \
+trainer.max_steps=1000 ~trainer.max_epochs \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 \
model.n_speakers=1 model.pitch_mean=121.9 model.pitch_std=23.1 \
model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
+model.text_tokenizer.add_blank_at=true \
)
```
And the error:
```
Error executing job with overrides: ['train_dataset=./6097_manifest_train_dur_5_mins_local.json', 'validation_datasets=./6097_manifest_dev_ns_all_local.json', 'sup_data_path=./fastpitch_sup_data', 'phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.01', 'heteronyms_path=tts_dataset_files/heteronyms-030921', 'whitelist_path=tts_dataset_files/whitelist_lj_speech.tsv', 'exp_manager.exp_dir=./ljspeech_to_6097_no_mixing_5_mins', '+init_from_nemo_model=./tts_en_fastpitch_align.nemo', '+trainer.max_steps=1000', '~trainer.max_epochs', 'trainer.check_val_every_n_epoch=25', 'model.train_ds.dataloader_params.batch_size=24', 'model.validation_ds.dataloader_params.batch_size=24', 'model.n_speakers=1', 'model.pitch_mean=121.9', 'model.pitch_std=23.1', 'model.pitch_fmin=30', 'model.pitch_fmax=512', 'model.optim.lr=2e-4', '~model.optim.sched', 'model.optim.name=adam', 'trainer.devices=1', 'trainer.strategy=null', '+model.text_tokenizer.add_blank_at=true']
Traceback (most recent call last):
File "fastpitch_finetune.py", line 32, in main
model = FastPitchModel(cfg=cfg.model, trainer=trainer)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/models/fastpitch.py", line 84, in __init__
self._setup_normalizer(cfg)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/models/fastpitch.py", line 166, in _setup_normalizer
'text_normalizer.whitelist', cfg.text_normalizer.whitelist
File "/usr/local/lib/python3.7/dist-packages/nemo/core/classes/modelPT.py", line 220, in register_artifact
return self._save_restore_connector.register_artifact(self, config_path, src, verify_src_exists)
File "/usr/local/lib/python3.7/dist-packages/nemo/core/connectors/save_restore_connector.py", line 345, in register_artifact
f"src path does not exist or it is not a path in nemo file. src value I got was: {src}. Absolute: {os.path.abspath(src)}"
FileNotFoundError: src path does not exist or it is not a path in nemo file. src value I got was: tts_dataset_files/whitelist_lj_speech.tsv. Absolute: /content/tts_dataset_files/whitelist_lj_speech.tsv
```
Thanks in advance | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4075 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4075/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4075/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4075/events | https://github.com/NVIDIA/NeMo/issues/4075 | 1,217,515,442 | I_kwDOC_bI7s5Ikc-y | 4,075 | `bias_dropout_add_fused_train` drops `grad_fn` in the output tensor | {
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} | null | null | null | **Describe the bug**
when running p-tuning of T5 model in branch1.8.0, freezing the LM model weights, it raises
```
Exception has occurred: RuntimeError (note: full exception trace is shown but execution is paused at: _run_module_as_main)
element 0 of tensors does not require grad and does not have a grad_fn
```
It turns out to be a problem with `bias_dropout_add_fused_train` method.
The `attention_output` has `grad_fun` as arguments for the following code in line717 of `transformer.py`:
```python
layernorm_input = bias_dropout_add_func(
attention_output, attention_bias.expand_as(residual), residual, self.hidden_dropout
)
```
However, the result doesn't have the `grad_fn` and `requires_grad=False`
If use the none-fused version, it works fine.
It looks like to be a `pytorch.script` problem. Following is how it is implemented
```python
def bias_dropout_add(x, bias, residual, prob, training):
# type: (Tensor, Tensor, Tensor, float, bool) -> Tensor
out = torch.nn.functional.dropout(x + bias, p=prob, training=training)
out = residual + out
return out
@torch.jit.script
def bias_dropout_add_fused_train_(
x: torch.Tensor, bias: torch.Tensor, residual: torch.Tensor, prob: float
) -> torch.Tensor:
# type: (Tensor, Tensor, Tensor, float) -> Tensor
return bias_dropout_add(x, bias, residual, prob, True)
```
The work around is to set `bias_dropout_fusion=False` | {
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"merged_at": "2022-04-28T22:55:30"
} | # What does this PR do ?
Cherry picks fastpitch bugfix.
**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 ?
minor fix to the signature `eval_epoch_end`
# Changelog
add `global_rank` to `eval_epoch_end`
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Virginia Adams <[email protected]>
Made saving prompt encoder embeddings to prompt table non-configurable
Add a one line overview of what this PR aims to accomplish.
# Changelog
- updated MegatronGPTPromptLearning class
- removed line from example config
# Usage
removed `save_tuned_prompts_to_prompt_table`
```python
p_tuning:
dropout: 0.0
num_layers: 2
```
# 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? (Updated in separate PR)
- [ ] 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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Replace sampled tokens with EOD after EOD has been sampled once
**Collection**: BigNLP
# Changelog
- Updated
# Usage
Same as before
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Virginia Adams <[email protected]>
Adds ability to calculate loss on answer portion of the prompt only for p-tuning/prompt-tuning.
**Collection**: BigNLP
Updated
- GPTPromptLearningDataset class
- MegatronGPTPromptLearningModel class
- megatron_gpt_prompt_learning_config
# Usage
Same as before, but task templates in the config now have ``answer_only_loss`` and ``answer_field`` added to them.
They are added to the config like:
```python
- taskname: 'rte'
prompt_template: '<|VIRTUAL_PROMPT_0|>{text}{answer}'
total_virtual_tokens: 100
virtual_token_splits: [100]
truncate_field: null
answer_only_loss: True
answer_field: 'answer'
```
# 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? **(In separate PR)**
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Micha Livne <[email protected]>
# What does this PR do ?
Has mechanism to retire older ind files by updating internal idx version.
Indexing speed of 1443990774 samples in 147 files using 6 workers
```
```
Loading speed
```
[NeMo I 2022-04-29 00:23:22 text_memmap_dataset:85] Time loading 147 mem-mapped files: 0:00:04.395558
```
```
In [9]: len(ds)
Out[9]: 1443990774
# Timing without tokenizer
In [10]: %timeit -n 1000 ds[np.random.randint(len(ds))]
555 µs ± 16.9 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
# TIming with 'byte-level' tokenizer
In [20]: %timeit -n 1000 ds[np.random.randint(len(ds))]
724 µs ± 19.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Added `TextMemMapDataset`
- Added `CSVMemMapDataset`
- Retired `MegatronDataset`
- Added `nemo/collections/nlp/data/language_modeling/text_memmap_dataset.py` to preprocess indices (else happs on the fly at first run)
- Added `nemo/collections/nlp/data/machine_translation/sequence_to_sequence_dataset.py`
- Added `scripts/nlp_language_modeling/build_index_memmap_data.py`
# Usage
Example for caching index files:
```bash
NeMo/scripts/nlp_language_modeling/build_index_memmap_data.py *.txt
```
Index files will be created when instantiating a memory mapped dataset if missing.
```python
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | I am trying to explore ASR training data through the speech explorer tool and prepared the required manifest file in json format. It has three fields, audio_filepath, duration, and text.
**Steps/Code to reproduce bug**
python tools/speech_data_explorer/data_explorer.py manifest.json
**Expected behavior**
It is loading the data in an expected manner. I am able to see text, duration, and file name. However, I am not able to play the audio. The audio player does not seem to be working.
**Environment overview (please complete the following information)**
pip install -r tools/speech_data_explorer/requirements.txt
- Using on an M1 mac machine
**Environment details**
- OS version - macOS Monterry M1
- Python version - 3.8.11
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https://api.github.com/repos/NVIDIA/NeMo/issues/4066 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4066/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4066/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4066/events | https://github.com/NVIDIA/NeMo/issues/4066 | 1,215,807,009 | I_kwDOC_bI7s5Id74h | 4,066 | Audio sample rate for transcribing | {
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} | null | null | null | Hi, thanks for your priceless tutorial,
I know the audio sample rate for fine-tuning the Nemo models should be 16khz. Is the sample rate should be 16khz for the audios to transcribe with Nemo models? | {
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} | null | null | null | Hi thanks for your great tutorial,
I have an error `Interpolation key 'model.labels' not found` when I set up the training params model. I do the following steps:
1) I used `stt_en_conformer_ctc_small` as pre-trained model.
2) I load `conformer_ctc_char.yaml ` file.
3) I change the model.labels in this file manually to new language characters.
4) I change the train test monifest path and lr.
5) fit the model.
I attach my code. so what is the problem? How can I fine-tune the conformer for another language in CTC char-based?
```
import nemo
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_small")
from omegaconf import OmegaConf, open_dict
params = OmegaConf.load("conformer_ctc_char.yaml")
print(OmegaConf.to_yaml(params))
train_manifest = "./train.json"
test_manifest = "./test.json"
params.model.train_ds.manifest_filepath = train_manifest
params.model.validation_ds.manifest_filepath = test_manifest
params.model.spec_augment.rect_masks = 0
import copy
new_opt = copy.deepcopy(params.model.optim)
new_opt.lr = 0.001
print(params.model.optim)
print(asr_model.decoder.vocabulary)
asr_model.setup_optimization(optim_config=new_opt)
asr_model.setup_training_data(train_data_config=params['model']['train_ds'])
asr_model.setup_validation_data(val_data_config=params['model']['validation_ds'])
asr_model.encoder.freeze()
import pytorch_lightning as pl
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
early_stop_callback = EarlyStopping(
monitor= 'val_wer',
min_delta=0.05,
patience=3,
verbose=True,
mode='min'
)
trainer = pl.Trainer(max_epochs=2,
accumulate_grad_batches=1,
checkpoint_callback=False,
logger=False,
log_every_n_steps=5,
check_val_every_n_epoch=1,
callbacks=[early_stop_callback],
gpus=0,
# accelerator='ddp',
# plugins='ddp_sharded'
)
asr_model.set_trainer(trainer)
trainer.fit(asr_model)
```
**Environment overview**
Environment location: Google Colab
Method of NeMo install: !pip install nemo_toolkit[asr]
NeMo branch version: main
Learning Rate: 1e-3
**Environment details**
OS version : "Ubuntu20.04.3 LTS"
PyTorch version : "1.10.0"
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds support to SaveRestoreConnector to employ a pre-extracted nemo directory path as a restoration directory.
This is required for extremely large language model restoration, which cannot be extracted partially during multinode inference.
**Collection**: [Core, Megatron]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Note
- During restore_from, the path of the nemo file is ignored in its entirety since the actual pre-extracted directory is passed.
- The filepath passed to restore_from in such a case is not preserved, it is overwritten by the value of `connector.model_extracted_dir` inside AppState.
# Usage
```python
# Extract a nemo file apriori to restoration
# Here we will use the SaveRestoreConnector's method to extract the checkpoint to a given directory
connector = MySaveRestoreConnector()
MySaveRestoreConnector._unpack_nemo_file(nemo_filepath, extracted_directory)
# Then update SaveRestoreConnector with path of the extracted directory
connector.model_extracted_dir = extracted_directory
# Call restore as always
# note, we pass in the "old" nemo_filepath, stored somewhere other than the extracted directory
restored_model = ModelPT.restore_from(nemo_filepath, save_restore_connector=connector)
```
# 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?
@okuchaiev @ericharper | {
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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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I'm using the script https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/ASR_with_SpeakerDiarization.ipynb in Colab and I came across a problem. When trying to transcribe an audio that has the transcription of only one word, it gives the following error in the "Run ASR and get word timestamps" block:
The audio only contains transcription of the word "Eleven". In case you need it, here's the audio:
[audio.zip](https://github.com/NVIDIA/NeMo/files/8558127/audio.zip)
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} | remove conda pynini dependency, add pip pynini to requirements_text_processing.txt and as extra req for requirements_common.txt | {
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Removes conditionals in favor of simple calculation of num workers due to changes in PTL flags, as per conversation with @nithinraok and prior discussion in #3589
# Changelog
- Update calculation of num workers in model class (optimizer setup)
- Update calculation of num workers in TTS helpers
- Bump PTL version requirement from 1.6.0 -> 1.6.1
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | remove pynini guard, since pynini dependency is no longer depending on conda.
moved punct_post_process function from nlp collection to text_processing collection to avoid circular dependency | {
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} | Signed-off-by: Yi Dong <[email protected]>
# What does this PR do ?
Fix the `broadcast` inconsistent type issue which cause the model parallel gpt eval failure. | {
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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**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | Good morning,
Thank you very much for your excellent repository.
I am having problems when I want to use Nemo for speaker diarization with "long" audios, could you please help me to make it work correctly?.
The error is the following:
```
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
return self.collate_fn(data)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/data/audio_to_label.py", line 420, in vad_frame_seq_collate_fn
return _vad_frame_seq_collate_fn(self, batch)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/data/audio_to_label.py", line 194, in _vad_frame_seq_collate_fn
audio_signal = torch.stack(audio_signal)
RuntimeError: stack expects a non-empty TensorList
```
I leave an example of the code to reproduce the error:
https://colab.research.google.com/drive/1DnrIBrqaBiIZX-nPcoEMg3jrEGFR1zWF?usp=sharing
And one of the audios I am testing with:
https://drive.google.com/file/d/1eyzfzLh7u_xo3pzkGJ_GqYx38nukolrR/view?usp=sharing
Thank you very much for your time
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"merged_at": "2022-04-25T17:17:58"
} | # What does this PR do ?
1. If in DDP mode number of batches is not evenly divisible by number of processes, some batches may be processed several times. This lead to distorted metrics. This PR adjusts number of batches to number of processes.
2. In DDP mode batches in different processes are shuffled independently. This PR sets random seed for this shuffling. Before this fix, shuffling lead to some batches being processed several times and some batches not processed at all.
**Collection**: nlp
# Changelog
- [adjusting number of batches](https://github.com/NVIDIA/NeMo/blob/45e9deca8c29b6f3142294352c7350f385ba6b28/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L1354)
- [setting random seed](https://github.com/NVIDIA/NeMo/blob/45e9deca8c29b6f3142294352c7350f385ba6b28/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py#L787)
# Usage
To notice changes support numbers you can use this [stage](https://github.com/NVIDIA/NeMo/blob/91e6c2552a9ca8d00bf8ce758adea68d9c49f803/Jenkinsfile#L1219) in Jenkins.
**PR Type**:
- [x] Bugfix
Closes bug https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3610560&cmtNo=
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} | null | null | null | **Describe the bug**
I am looking for text normalization tools and select nemo text normalization tools.
In order to use it in c++, I follow the tutorial in [https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_normalization/wfst/wfst_text_processing_deployment.html](url)
and run test tn grammar code based on sparrowhawk and get result like:
<img width="828" alt="image" src="https://user-images.githubusercontent.com/29497466/164977748-1d21a641-7398-4bde-b8d0-11eeba3c1255.png">
It seems that numbers in time pattern is removed. Is this right?
Besides, many "Unknown field" error occurred like:
<img width="1269" alt="image" src="https://user-images.githubusercontent.com/29497466/164977824-fac1188c-c0ca-46bb-9ce9-ffc13695f5e8.png">
Really need your help about WFST grammar export and sparrowhawk errors.
Thanks a lot !
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} | # What does this PR do ?
Implements the following
1. RMSNorm normalization, used in T5 when not using bias terms.
2. Normformer that adds extra normalization layers after self-attention and between the MLP feedforward layers. Also adds the option of configuring the additional MHA scale parameter.
3. Fixed some issues with merging code from 1.8.0 to main that removed code relevant to removing bias terms.
**Collection**: NLP
# Changelog
- Mostly changes to `transformer.py`
# Usage
* You can potentially add a usage example below
Configure the following args in your T5/BART yaml file
encoder_arch: "normformer"
decoder_arch: "normformer"
headscale: True
normalization: rmsnorm
bias: False
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Running the following script
```bash
# train
python examples/tts/hifigan_finetune.py \
--config-path=conf/hifigan \
--config-name=hifigan \
exp_manager.name=hifigan \
+exp_manager.exp_dir=/experiments \
+exp_manager.version=20220423-1956 \
exp_manager.resume_if_exists=true \
exp_manager.resume_ignore_no_checkpoint=true \
+exp_manager.checkpoint_callback_params.always_save_nemo=true \
train_dataset=/data/manifests/manifest_v4_train.json \
validation_datasets=/data/manifests/manifest_v4_valid.json \
trainer.devices=-1 \
trainer.num_nodes=1 \
trainer.strategy=ddp \
trainer.accelerator=gpu \
trainer.precision=bf16
```
on one A100 GPU results in the following stack trace
```python3
[NeMo W 2022-04-23 19:56:25 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work
warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)
[NeMo W 2022-04-23 19:56:26 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-04-23 19:56:27 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'hifigan': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information
warnings.warn(msg, UserWarning)
Multiprocessing is handled by SLURM.
Using bfloat16 Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo W 2022-04-23 19:56:27 exp_manager:409] There was no checkpoint folder at checkpoint_dir :/experiments/hifigan/20220423-1956/checkpoints. Training from scratch.
[NeMo I 2022-04-23 19:56:27 exp_manager:281] Experiments will be logged at /experiments/hifigan/20220423-1956
[NeMo I 2022-04-23 19:56:27 exp_manager:647] TensorboardLogger has been set up
[NeMo W 2022-04-23 19:56:27 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:2302: LightningDeprecationWarning: `Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.
rank_zero_deprecation("`Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.")
[NeMo W 2022-04-23 19:56:27 exp_manager:881] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 25000000. Please ensure that max_steps will run for at least 10 epochs to ensure that checkpointing will not error out.
Created a temporary directory at /tmp/tmpw14tzmbq
Writing /tmp/tmpw14tzmbq/_remote_module_non_sriptable.py
[NeMo I 2022-04-23 19:56:27 data:775] Loading dataset from /data/manifests/manifest_v4_train.json.
[NeMo I 2022-04-23 19:56:27 data:799] Loaded dataset with 10322 files.
[NeMo I 2022-04-23 19:56:27 data:801] Dataset contains 16.66 hours.
[NeMo I 2022-04-23 19:56:27 data:297] Pruned 0 files. Final dataset contains 10322 files
[NeMo I 2022-04-23 19:56:27 data:299] Pruned 0.00 hours. Final dataset contains 16.66 hours.
[NeMo I 2022-04-23 19:56:27 data:775] Loading dataset from /data/manifests/manifest_v4_valid.json.
0it [00:00, ?it/s]
10322it [00:00, 181425.66it/s]
[NeMo I 2022-04-23 19:56:27 data:799] Loaded dataset with 543 files.
[NeMo I 2022-04-23 19:56:27 data:801] Dataset contains 0.84 hours.
[NeMo I 2022-04-23 19:56:27 data:297] Pruned 87 files. Final dataset contains 456 files
[NeMo I 2022-04-23 19:56:27 data:299] Pruned 0.05 hours. Final dataset contains 0.79 hours.
[NeMo I 2022-04-23 19:56:27 features:259] PADDING: 0
[NeMo I 2022-04-23 19:56:27 features:276] STFT using torch
[NeMo I 2022-04-23 19:56:27 features:278] STFT using exact pad
[NeMo I 2022-04-23 19:56:28 features:259] PADDING: 0
[NeMo I 2022-04-23 19:56:28 features:276] STFT using torch
[NeMo I 2022-04-23 19:56:28 features:278] STFT using exact pad
0it [00:00, ?it/s]
543it [00:00, 170791.68it/s]
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
Added key: store_based_barrier_key:1 to store for rank: 0
Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo W 2022-04-23 19:56:32 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py:327: RuntimeWarning: The lr scheduler dict contains the key(s) ['interval'], but the keys will be ignored. You need to call `lr_scheduler.step()` manually in manual optimization.
rank_zero_warn(
| Name | Type | Params
------------------------------------------------------------------------
0 | audio_to_melspec_precessor | FilterbankFeatures | 0
1 | trg_melspec_fn | FilterbankFeatures | 0
2 | generator | Generator | 13.9 M
3 | mpd | MultiPeriodDiscriminator | 41.1 M
4 | msd | MultiScaleDiscriminator | 29.6 M
5 | feature_loss | FeatureMatchingLoss | 0
6 | discriminator_loss | DiscriminatorLoss | 0
7 | generator_loss | GeneratorLoss | 0
------------------------------------------------------------------------
84.7 M Trainable params
0 Non-trainable params
84.7 M Total params
338.643 Total estimated model params size (MB)
SLURM auto-requeueing enabled. Setting signal handlers.
[NeMo W 2022-04-23 19:56:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 256 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
Error executing job with overrides: ['exp_manager.name=hifigan', '+exp_manager.exp_dir=/experiments', '+exp_manager.version=20220423-1956', 'exp_manager.resume_if_exists=true', 'exp_manager.resume_ignore_no_checkpoint=true', '+exp_manager.checkpoint_callback_params.always_save_nemo=true', 'train_dataset=/data/manifests/manifest_v4_train.json', 'validation_datasets=/data/manifests/manifest_v4_valid.json', 'trainer.devices=-1', 'trainer.num_nodes=1', 'trainer.strategy=ddp', 'trainer.accelerator=gpu', 'trainer.precision=bf16']
Traceback (most recent call last):
File "examples/tts/hifigan_finetune.py", line 32, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/workspace/nemo/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "examples/tts/hifigan_finetune.py", line 28, in main
trainer.fit(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 768, in fit
self._call_and_handle_interrupt(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1234, in _run
results = self._run_stage()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1321, in _run_stage
return self._run_train()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1343, in _run_train
self._run_sanity_check()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1411, in _run_sanity_check
val_loop.run()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 153, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 127, in advance
output = self._evaluation_step(**kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 222, in _evaluation_step
output = self.trainer._call_strategy_hook("validation_step", *kwargs.values())
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1763, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 347, in validation_step
return self.model(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1111, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 968, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1111, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 93, in forward
return self.module.validation_step(*inputs, **kwargs)
File "/workspace/nemo/nemo/collections/tts/models/hifigan.py", line 234, in validation_step
pred_denoised = self._bias_denoise(audio_pred, audio_mel).squeeze(1)
File "/workspace/nemo/nemo/collections/tts/models/hifigan.py", line 306, in _bias_denoise
self.stft_bias, _ = stft(audio_bias)
File "/workspace/nemo/nemo/collections/tts/models/hifigan.py", line 292, in stft
comp = torch.stft(x.squeeze(1), n_fft=1024, hop_length=256, win_length=1024)
File "/opt/conda/lib/python3.8/site-packages/torch/functional.py", line 693, in stft
input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/functional.py", line 4371, in _pad
return torch._C._nn.reflection_pad1d(input, pad)
RuntimeError: "reflection_pad1d_out_template" not implemented for 'BFloat16'
Sanity Checking: 0it [00:00, ?it/s]
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]
```
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} | # What does this PR do ?
Adds a prompt learning page to the NeMo docs.
**Collection**: BigNLP
# Changelog
- Added prompt_learning.rst
- Edited megatron.rst
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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 ?
Adds support of stacking to conformer which was missing.
# Changelog
Adding support of stacking downsampling to conformer models.
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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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
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```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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**PR Type**:
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- [ ] 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: Virginia Adams <[email protected]>
Checks if micro batch calculator has already been initialized before calling apex micro batch setup function.
**Collection**: BigNLP
# Changelog
- Changed megation_init.py
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Remove all support for kaldi-io.
Reason - very old library that has poor logging format and is very rarely used.
**Collection**: [ASR]
# Changelog
- Remove any support for kaldi-io
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | Signed-off-by: Subhankar Ghosh <[email protected]>
# What does this PR do ?
Removes the requirement of 'max_token_duration', 'min_token_duration' and 'zero_emb' to be restored from the state_dict. This PR sets persistent=False in register_buffer for the above variables.
This PR also sets the correct URL to download the latest Fastpitch checkpoint.
**Collection**: [Note which collection this PR will affect]
1) nemo.collection.tts.models
2) nemo.collection.tts.modules
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] 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: smajumdar <[email protected]>
# What does this PR do ?
Add back Citrinet ZH to pretrained models.
**Collection**: [ASR]
**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**: Core
# 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 Rotary positional embedding to parallel attention
Add Chunked Cross attention module
Add RETRO encoder module
Add RETRO decoder module
Add unit-test for individual modules.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Tries to force the CI to perform import tests
**Collection**: [Core]
# 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.
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