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} | # What does this PR do ?
Task 2: re-organize nemo.collection.tts.parts
- utils
- tts_dataset_utils.py # move torch/helpers.py here; merge tts/data/data_utils.py here.
- g2p_utils.py # move g2p/data/data_utils.py here * Evelina may comment.
- helpers.py # move tts/helpers/helpers.py here. Note that copyright header mixed with BSD 3-Clause. So not sure how to merge to ones with only Nvidia copyright.
- splines.py # move tts/helpers/splines.py here. Same as above, it mixed Nvidia and MIT copyrights.
- preprocessing
- audio_trimmer.py # move data/audio_trimmer.py here
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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https://api.github.com/repos/NVIDIA/NeMo/issues/6104 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6104/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6104/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6104/events | https://github.com/NVIDIA/NeMo/issues/6104 | 1,597,862,804 | I_kwDOC_bI7s5fPXOU | 6,104 | Cuda OutOfMemory issue when following Colab tutorial | {
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I am trying to follow the tutorial at: [Speaker_Identification_Verification.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb)
I create a new Colab environment with 15GB GPU attached and run all of the cells in that sheet. It runs until cell 21, which is the command:
`trainer.fit(speaker_model)`
That is where I get this error:
`OutOfMemoryError: CUDA out of memory. Tried to allocate 468.00 MiB (GPU 0; 14.76 GiB total capacity; 13.17 GiB already allocated; 47.88 MiB free; 13.59 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF` | {
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} | # What does this PR do ?
Task 6: remove nemo/collection/tts/torch/README.md and tts_dataset.yaml.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6099 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6099/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6099/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6099/events | https://github.com/NVIDIA/NeMo/pull/6099 | 1,597,760,599 | PR_kwDOC_bI7s5Kp10O | 6,099 | [TTS][refactor] Part 1 - nemo.collections.tts.data | {
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} | # What does this PR do ?
This is a series of refactoring tasks forth.
Task 1: re-organize nemo.collections.tts.data
```
tts_dataset.py # move torch/data.py here
g2p_dataset.py # combine g2p/data/ctc_g2p.py, g2p/data/t5_g2p.py, and g2p/data/heteronym_classification_data.py
# 2. update import paths.
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Task 7:
1. remove duplicate import of `torch`.
2. `GreedyCTCDecoder` is actually a module and should move to nemo/collection/tts/modules.
3. Lack of exposure from import (see below graph). For example, `fastpitch_ssl` and `ssl_tts` should be added into `__init__.py`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Make the spectrogram enhancer work on arbitrary length spectrograms irrespective of lengths during training. This a convolutional model (=locally-connected network), really no reason why it should not support arbitrary lengths
Original StyleGAN 2 architecture starts from a fixed-size noise-initialized tensor and generate images. To support arbitrary length spectrograms, the architecture was modified to start from a rather long tensor and chop off part of necessary length early on. This was not a good decision.
This PR makes the move from a long starting tensor to a tensor with a fixed length of 1.
**Collection**: TTS
# Changelog
- Change length of the starting ("initial") tensor in the enhancer to 1
- Remove `max_spectrogram_length` from the model and the config
# 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
* Fixes #6007 | {
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} | # What does this PR do ?
Add prototype implementation of TTS pipeline that is intended to be used for inference before and after model training.
These are far from finalized. The intent is for this to be experimental and to slowly incorporate them into our inference workflows (eg. for preprocessing, evaluation, analysis, deployment) and expand and iterate on the interfaces and design as we figure out what needs to be changed.
**Collection**: [TTS]
# Changelog
- Add TTS pipeline and module definitions and documentation.
- Add module implementations using existing backends.
- Add unit and functional tests for all modules.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
This PR adds support in external sampler function to Megatron encoder-decoder.
**Collection**: [Note which collection this PR will affect]
# Changelog
1. Added support in a custom token sampling function to `MegatronLMEncoderDecoderModel.decode`.
2. Added greedy and top k / top p token sampling methods.
# 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 build arguments `REQUIRE_TORCHAUDIO` and `REQUIRE_K2` for `torchaudio` and `k2` in Dockerfile
- `false` (by default): library will be installed if possible, errors ignored
- `true`: if library not installed correctly, end build with error
- fix `k2` installation script on macOS
- fix `k2` installation in Docker (`k2` depends on `torchaudio`)
- fix `torchaudio` installation for latest PyTorch versions
- also fixes bug https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3940510&cmtNo= (`apt update && apt install -y libavdevice-dev` should be also used before executing install script)
NB: This PR doesn't fix `k2` + `Cuda 12.0` compilation errors.
**Collection**: [CI]
# Changelog
- `k2` installation script is compatible with macOS
- `k2` installation script fails if error occurred during installation
- `Dockerfile`: add build arguments (see description above)
- `torchaudio` installation script - fixes for latest PyTorch versions (ffmpeg, match release branch, correct version)
# Usage
```shell
# build container, ignore k2/torchaudio installation error
docker build --tag "nemo:latest" .
# ignore k2 installation error, fail on torchaudio installation error
docker build --tag "nemo:latest" --build-arg REQUIRE_TORCHAUDIO=true --build-arg REQUIRE_K2=false .
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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. Improved testing of text memmap generated index files, and improved error message when files are missing.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds batch size to generate function in prompt_learning class
**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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Adds batch size to generate function in prompt_learning class
**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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* Related to # (issue)
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Fix enhancer usage in ASR-TTS models
**Collection**: [ASR, TTS]
# Changelog
- suppress type checking for enhancer 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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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The pre-refactor code saved inference ready `.nemo` files. This PR ensures that the new `.nemo` has a key that mirrors the old inference ready `.nemo` file.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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# Usage
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# Before your PR is "Ready for review"
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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**:
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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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[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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Removed "mv" line.
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# 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)
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- [ ] Documentation
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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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**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
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
- [ ] Documentation
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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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} | null | null | null | **Describe the bug**
Using tarred buckets with ASR data exhibits cyclical behavior that makes it difficult for ASR models to converge in some situations.
Not sure if I'm using it wrong, or if this is be design.
I believe the problem comes from here:
https://github.com/NVIDIA/NeMo/blob/d06d353ae3ce1224d95dc3cdeffb987f66d911ab/nemo/collections/asr/data/audio_to_text.py#L1286-L1287
For large ASR training sets, I use tarred buckets with the typical 8 buckets up to ~18 seconds max. This issue happens across many languages with a similar setup.
<details>
<summary>Example config</summary>
```yaml
train_ds:
sample_rate: 16000
batch_size: 1 # 1 because we're using tarred datasets
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20.0
min_duration: 1.0
shuffle_n: 512
is_tarred: true
manifest_filepath:
- - /path/to/bucket1/tarred_audio_manifest.json
- - /path/to/bucket2/tarred_audio_manifest.json
- - /path/to/bucket3/tarred_audio_manifest.json
- - /path/to/bucket4/tarred_audio_manifest.json
- - /path/to/bucket5/tarred_audio_manifest.json
- - /path/to/bucket6/tarred_audio_manifest.json
- - /path/to/bucket7/tarred_audio_manifest.json
- - /path/to/bucket8/tarred_audio_manifest.json
tarred_audio_filepaths:
- - /path/to/bucket1/audio_{0..511}.tar
- - /path/to/bucket2/audio_{0..511}.tar
- - /path/to/bucket3/audio_{0..511}.tar
- - /path/to/bucket4/audio_{0..511}.tar
- - /path/to/bucket5/audio_{0..511}.tar
- - /path/to/bucket6/audio_{0..511}.tar
- - /path/to/bucket7/audio_{0..511}.tar
- - /path/to/bucket8/audio_{0..511}.tar
bucketing_batch_size: [44, 26, 18, 14, 11, 9, 8, 7]
```
</details>
Fine-tuning a large Conformer, this is what I see when evaluating every 1/4 epoch (domain fine-tuning, expect a couple point drop in WER):
Since this is a fine-tuning, so a low LR is used (effective LR on the order of 1e-5), and LR is unlikely the problem w.r.t. convergence.
Re-scaling shows that the two datasets are negatively correlated, as the `RandomizedChainDataset` tunes the model to different buckets:
I suspect this is because `RandomizedChainDataset` exhausts each bucket in turn (as linked above) and does not re-shuffle at each epoch, and the training set duration are clustered as shown here (four common, large, publicly-available datasets):
So different buckets are dominated by different datasets, and exhausting a bucket without training from the others fine-tunes the model to a particular dataset.
**Expected behavior**
* Sample from a random bucket each step, rather than exhausting each bucket in turn.
* Use different order each epoch.
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} | # What does this PR do ?
Fix error when padding batches.
**Collection**: NLP
# Changelog
- Fix batches padding in MegatronPretrainingSampler
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adding important main PRs to 1.16 branch
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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 ?
**Collection**: asr
# Changelog
Make KenLM with punctuation and capitalization (PC) for AggregateTokenizer
Merge ARPA models with OpenGRM
## For scripts/asr_language_modeling/ngram_lm/train_kenlm.py
- allow to use multilanguage NeMo model with AggregateTokenizer (nemo_model_file)
- allow to make ngram model from folder with many files (train_paths)
- prun kenlm model (ngram_prune)
```python
python3 /base_path/NeMo/scripts/asr_language_modeling/ngram_lm/train_kenlm.py \
nemo_model_file=/path/to/aggregate_model.nemo \
train_paths=[/path/train_clean_100.json,/path/train_clean_360.json,/path/folder_with_other_files] \
kenlm_bin_path=/workspace/nemo/decoders/kenlm/build/bin \
kenlm_model_file=/path/lms/7gram_0001.kenlm \
preserve_arpa=true \
ngram_length=7 \
ngram_prune=[0,0,1]
```
## Use opengrm to merge ngrams
scripts/asr_language_modeling/ngram_lm/opengrm.py
- merge two ngram arpa models
```python
python3 scripts/asr_language_modeling/ngram_lm/ngram_merge.py \
--kenlm_bin_path /workspace/nemo/decoders/kenlm/build/bin \
--ngram_bin_path /workspace/nemo/decoders/ngram-1.3.14/src/bin \
--arpa_a /path/ngram_a.kenlm.tmp.arpa \
--alpha 2 \
--arpa_b /path/ngram_b.kenlm.tmp.arpa \
--beta 1 \
--out_path /path/out \
--test_file /path/test_manifest.json \
--symbols /path/isyms.txt \
--nemo_model_file /path/model.nemo
```
##Install scripts for kenlm, decoders and opengrn
- scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh
- scripts/installers/setup_os2s_decoders.py
- scripts/installers/install_opengrm.sh
- scripts/installers/Dockerfile.ngramtools
**PR Type**:
- [ V] New Feature
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} | # What does this PR do ?
Split the g2p data_utils into 2 util classes, one containing tokenization functions and the other g2p specific ones.
This fixes a circular import between the the text tokenizers and G2p.
**Collection**: [TTS]
# Changelog
- Move relevant helper methods to a new tokenizer utility to avoid tts_tokenizers importing tts g2p.
- Update unit 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)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Offline it was mentioned that API docs were failing due to some syntax available in Python 3.8. This can be seen here: https://readthedocs.com/projects/nvidia-nemo/builds/1305908/
Setting the build python version to 3.8 for readthedocs.
**Collection**: Common
# Changelog
- Increment readthedocs build python version to 3.8.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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it looks like there's a mistake in the [cache-aware streaming example.](https://github.com/NVIDIA/NeMo/blob/786a8501db2bd3854ed6f629a51fe2792b58bb00/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py)
[This argument ](https://github.com/NVIDIA/NeMo/blob/0322b324e815c155f90e75dde2eff78d3b9d3dd4/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py#L238) doesn't have any impact, because by design it applies input features normalization right before return from [this function](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/utils/streaming_utils.py#L1499), but output of this function is never used (because [self.buffer](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/utils/streaming_utils.py#L1311) is the actual place where NN inputs are stored).
I see that code related to this example was moved in December from one place to another, as well as some refactoring was done, so I expect there may be that some changes were done in these 2 files I'm referring to, and docstrings were not updated.
Summarizing all the above, this is my question:
is there a mistake in the docs [here](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py#L54) and we just don't need to do online normalization, or is it mistake in the [CacheAwareStreamingAudioBuffer](https://github.com/NVIDIA/NeMo/blob/2a6101421f4366ec28bac1165f26fec8eeedea59/nemo/collections/asr/parts/utils/streaming_utils.py#L1298) and normalized features should be stored in the buffer?
Thank you for response! | {
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} | # What does this PR do ?
Adds CITATION.eff file
**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 ?
fixed broken procedures in the tutorial and minor related code chagnes.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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 ?
fixed tutorial errors and update the instructions.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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 ?
Makes DataLoader for Spectrogram enhancer (#5565) use correct dimension to determine length of the spectrogram.
Currently it uses number of mel bands as length, so in practice enhancer trains on 80x80 patches, which is obviously suboptimal and a **bug**.
**Collection**: TTS
# Changelog
- Fixes just one single dimension index
# 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 #6007 but there's more work to do | {
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} | # What does this PR do ?
* Update hydra config on published NGC titanet-model and release as v1 version
* Improve speaker label tests with latest hydra config
* Add a feature to write model labels `labels.txt` to exp directory
* Update batch inference function to return gt labels and trained model labels (if available)
**Collection**: ASR
# Changelog
- Update label models to include latest v1 version of titanet-large
- Added titanet-small config file (model will be published soon)
- Now instead of returning index of gt labels from AudioSpeechLabelDataset, return the speaker label names directly as seen from 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
- [x] Bugfix
- [x] Documentation | {
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} | # What does this PR do ?
This PR fixes the deprecated configuration in speaker diarization training notebook.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Now, create alignment script for libri speech should follow the following commands
```python
!python NeMo/scripts/speaker_tasks/create_alignment_manifest.py \
--input_manifest_filepath LibriSpeech/dev_clean.json \
--base_alignment_path LibriSpeech_Alignments \
--output_manifest_filepath ./dev-clean-align.json \
--ctm_output_directory ./ctm_out \
--libri_dataset_split dev-clean
```
# 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 ?
Fixes two small bugs in the flashlight decoder, one due to superfluous import
**Collection**: ASR
# Changelog
- Two changes to nemo/collections/asr/modules/flashlight_decoder.py
# Usage
* No changes, just continue to use flashlight decoding as normal
# 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.
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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 ?
Moves esperanto into a notebook instead of a documentation page.
**Collection**: ASR
# Changelog
- Move esperanto example to a notebook.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | null | null | null | Hi👋 I've been trying to use [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_vad/speech_to_text_with_vad.py) for ASR without VAD, but I keep getting strange results. I've tried this script with 2 models: `stt_en_conformer_ctc_large` and `stt_ru_conformer_ctc_large`.
____________________________________________________________________________________________________________________________________________
For the first model `stt_en_conformer_ctc_large` I used this command:
```
python speech_to_text_with_vad.py \
manifest_filepath=input_manifest.json \
asr_model=stt_en_conformer_ctc_large \
use_rttm=False \
vad_config=vad_inference_postprocessing.yaml
```
`input_manifest.json`:
```
{"audio_filepath": "an4_diarize_test.wav", "offset": 0, "duration": 5.2, "label": "infer", "text": "eleven twenty seven fifty seven october twenty four nineteen seventy", "num_speakers": 2, "rttm_filepath": "an4_diarize_test.rttm", "uem_filepath": null}
```
This is what I get as a result:
```
{"audio_filepath": "an4_diarize_test.wav", "offset": 0, "duration": 5.2, "label": "infer", "text": "eleven twenty seven fifty seven october twenty four nineteen seventy", "num_speakers": 2, "rttm_filepath": "an4_diarize_test.rttm", "uem_filepath": null, "pred_text": "it's a cretyve new complex na bit very strany you onur", "feature_file": "outputs/features/c45bc24490f3e2395bdd4649bc16f165.pt"}
```
I use the audio `an4_diarize_test.wav` from [this tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb).
___________________________________________________________________________________________________________________________________________
As for the second model `stt_ru_conformer_ctc_large`, I used this command:
```
python speech_to_text_with_vad.py \
manifest_filepath=input_manifest.json \
asr_model=stt_ru_conformer_ctc_large \
use_rttm=False \
vad_config=vad_inference_postprocessing.yaml
```
`input_manifest.json`:
```
{"audio_filepath": "/nas/Data/Audio/Processed/RU/Golos/test/crowd/files/c45bc24490f3e2395bdd4649bc16f165.wav", "offset": 0, "duration": 4.84, "label": "infer", "num_speakers": 1, "text": "закажите мне комплексный обед в ресторане в номер"}
```
I use the audio file from the dataset Golos https://github.com/sberdevices/golos , tried it with multiple files from the dataset and got the same result as below:
```
{"audio_filepath": "/nas/Data/Audio/Processed/RU/Golos/test/crowd/files/c45bc24490f3e2395bdd4649bc16f165.wav", "offset": 0, "duration": 4.84, "label": "infer", "num_speakers": 1, "text": "закажите мне комплексный обед в ресторане в номер", "pred_text": " ⁇ ", "feature_file": "/nas/PipelineService/From1Server/speaker_identification/outputs/features/c45bc24490f3e2395bdd4649bc16f165.pt"}
```
I keep getting these results regardless of using VAD or not, I checked it for multiple VAD models (`vad_multilingual_marblenet` and `vad_marblenet`). Also, I tried the same command as above with `stt_ru_conformer_transducer_large` model and it worked just fine:
```
{"audio_filepath": "/nas/Data/Audio/Processed/RU/Golos/test/crowd/files/c45bc24490f3e2395bdd4649bc16f165.wav", "offset": 0, "duration": 4.84, "label": "infer", "num_speakers": 1, "text": "закажите мне комплексный обед в ресторане в номер", "pred_text": "закажите мне комплексный обед в ресторане в номер", "feature_file": "/nas/PipelineService/From1Server/speaker_identification/outputs/features/c45bc24490f3e2395bdd4649bc16f165.pt"}
```
So, it seems to be just conformer_ctc type of models problem.
I use this config file `vad_inference_postprocessing.yaml`:
```
name: &name "vad_inference_postprocessing"
dataset: null # Path of json file of evaluation data. Audio files should have unique names
num_workers: 4
sample_rate: 16000
# functionality
gen_seg_table: True # whether to converting frame level prediction to speech/no-speech segment in start and end times format
write_to_manifest: True # whether to writing above segments to a single manifest json file.
prepare_manifest:
auto_split: True # whether to automatically split manifest entry by split_duration to avoid potential CUDA out of memory issue.
split_duration: 400 # try smaller number if you still have CUDA memory issue
vad:
model_path: null #.nemo local model path or pretrained model name or none
parameters: # Parameters were tuned on 0~20db SNR noisy and clean multilingual ASR data.
normalize_audio: False
window_length_in_sec: 0.63 # window length in sec for VAD context input
shift_length_in_sec: 0.08 # shift length in sec for generate frame level VAD prediction, Here we use 0.08 for faster inferene
smoothing: False # false or type of smoothing method (eg: median, mean)
overlap: 0.875 # overlap ratio for overlapped mean/median smoothing filter. If smoothing=False, ignore this value.
postprocessing:
onset: 0.5 # onset threshold for detecting the beginning and end of a speech
offset: 0.3 # offset threshold for detecting the end of a speech.
pad_onset: 0.2 # adding durations before each speech segment
pad_offset: 0.2 # adding durations after each speech segment
min_duration_on: 0.5 # threshold for small non_speech deletion
min_duration_off: 0.5 # threshold for short speech segment deletion
filter_speech_first: True
prepared_manifest_vad_input: null # if not specify, it will automatically generated be "manifest_vad_input.json"
frame_out_dir: "vad_frame"
smoothing_out_dir: null # if not specify, it will automatically generated be frame_out_dir + "/overlap_smoothing_output" + "_" + smoothing_method + "_" + str(overlap)
table_out_dir: null # if not specify, it will automatically be frame_out_dir + "/table_output_tmp_" + key and value in postprocessing params
out_manifest_filepath: null # if not specify it will automatically be "vad_out.json"
# json manifest line example
# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-"}
```
I use nemo ver. 1.15.0.
What could be the issue here? Maybe I'm missing something important in config files?
Thanks!
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In the recently added flashlight module, https://github.com/NVIDIA/NeMo/blob/0e70135ccf24d882dcdc549321e747199a232bdf/nemo/collections/asr/modules/flashlight_decoder.py#L102 has a never-used import `from flashlight.lib.sequence.criterion import get_data_ptr_as_bytes.` This import appears to be from a different part of flashlight, not flashlight text, so it introduces a new (huge) dependency.
Edit: The sequence bindings are in https://github.com/flashlight/sequence/. | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
So while training a conformer model in an unsupervised fashion. I saw usage of two loss, i.e. contrastive loss and mlm loss.
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
So if i want to train the conformer model for ASR as well as language detection at the same time provided lang_ID/labels. and using to two losses. Multitasking
**Describe the solution you'd like**
to thave the data like this
{audio_filepath:"English/audio/file1/path", "duration": 2.0, text: "Transcritpion in english ", "lang_id": "English"}
{audio_filepath:"English/audio/file2/path", "duration": 2.0, text: "Transcritpion in english ", "lang_id": "English"}
{audio_filepath:"English/audio/file3/path", "duration": 2.0, text: "Transcritpion in english ", "lang_id": "English"}
{audio_filepath:"Hindi/audio/file/path", "duration": 2.0, text: "Transcritpion in Hindi ", "lang_id": "Hindi"}
{audio_filepath:"Hindi/audio/file/path", "duration": 2.0, text: "Transcritpion in Hindi ", "lang_id": "Hindi"}
{audio_filepath:"Hindi/audio/file/path", "duration": 2.0, text: "Transcritpion in Hindi ", "lang_id": "Hindi"}
A clear and concise description of what you want to happen.
Similarly here also two losses will be taken into account. one can be ctc and other can be cross entropy. can in decoding, if a file is given first 30sec is chunked on which it will first detect the language and then decode the transcription in the aforementioned language
@titu1994 @meghmak13
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"merged_at": "2023-03-20T22:30:03"
} | # What does this PR do ?
Add a Chinese bert recipe for polyphone disambiguation.
**Collection**: [Note which collection this PR will affect]
G2P
# Usage
```sh
# prepare manifest
mkdir -p /mnt/samsung-t7/yuekai/tts/cpp_manifest
git clone https://github.com/kakaobrain/g2pM.git
python3 export_zh_cpp_data_to_manifest.py --data_folder g2pM/data/ --output_folder /mnt/samsung-t7/yuekai/tts/cpp_manifest
```
```sh
# model training
python3 heteronym_classification_train_and_evaluate.py \
--config-name "heteronym_classification_zh.yaml" \
train_manifest="/mnt/samsung-t7/yuekai/tts/cpp_manifest/train.json" \
validation_manifest="/mnt/samsung-t7/yuekai/tts/cpp_manifest/dev.json" \
model.test_ds.dataset.manifest="/mnt/samsung-t7/yuekai/tts/cpp_manifest/test.json" \
model.wordids="/mnt/samsung-t7/yuekai/tts/cpp_manifest/wordid.tsv" \
do_training=False \
do_testing=True
```
# 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/6064 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6064/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6064/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6064/events | https://github.com/NVIDIA/NeMo/pull/6064 | 1,592,405,205 | PR_kwDOC_bI7s5KX61D | 6,064 | Change ExpManager's save _best_model and always_save_nemo behavior | {
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} | # What does this PR do ?
`save_best_model` and `always_save_nemo` will work with tp/pp > 1 with this PR.
`save_best_model` and `always_save_nemo` will work independently i.e
- to track best .nemo just set `save_best_model=True` (the other flag can be false)
- to track last .nemo just set `always_save_nemo=True` (the other flag can be false)
- to distinguish the two .nemo files the suffix is now `.best.nemo` and `.last.nemo`
Training metadata is inserted into the .yaml file in these .nemo files.
**Collection**: [ASR,NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
Add these flags to your yaml file.
```
exp_manager.checkpoint_callback_params.always_save_nemo=True
exp_manager.checkpoint_callback_params.save_best_model=True
```
```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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: ASR
# Changelog
- Add preprocess() and separate_punctuation() functions for texts with punctuation and capitalization (PC)
- Run examples/asr/transcribe_speech.py with beamsearch decoder (beam_strategy=beam)
- Save N-best hypothesis into the "beams" field of manifest file
Flashlight decoder is not supported yet
# Usage
* speech_to_text_eval.py
```python
python3 ./examples/asr/speech_to_text_eval.py \
model_path=/some/path/ctc_conformer_bpe.nemo \
dataset_manifest=some/path/test_pc.json \
output_filename=some/path/test_pc_ctc1.json \
ctc_decoding.strategy=beam \
ctc_decoding.beam.kenlm_path=/some/path/a1.kenlm \
ctc_decoding.beam.return_best_hypothesis=false \
do_lowercase=true \
rm_punctuation=true
```
* eval_beamsearch_ngram.py
```python
python3 ./scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py \
nemo_model_file=/some/path/ctc_conformer_bpe.nemo \
input_manifest=some/path/test_pc.json \
kenlm_model_file=/some/path/a1.kenlm \
decoding_mode=beamsearch_ngram \
decoding_strategy=beam \
do_lowercase=true \
rm_punctuation=true
```
**PR Type**:
- New Feature
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} | # What does this PR do ?
Currently loss masking for GPT (used when `model.data.pad_samples_to_global_batch_size=True`) issues a warning for every masked sample. In typical workflows there might be hundreds of masked samples in 1 batch, which spawns hundreds of warnings every validation. This PR fixes that
**Collection**: NLP
# Changelog
- Turn loss mask warning into a debug log
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Enables using LayerNorm1P implementation with TransformerEngine.
So far, only non-TE path allowed using LayerNorm1P.
NOTE: this PR requires TE version >=0.6!
**Collection**: NLP
# Changelog
- enable LayerNorm1P for TransformerEngine
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] 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 ?
Make the spectrogram enhancer work on arbitrary length spectrograms irrespective of lengths during training. This a convolutional model (=locally-connected network), really no reason why it should not support arbitrary lengths
Original StyleGAN 2 architecture starts from a fixed-size noise-initialized tensor and generate images. To support arbitrary length spectrograms, the architecture was modified to start from a rather long tensor and chop off part of necessary length early on. This was not a good decision.
This PR makes the move from a long starting tensor to a tensor with a fixed length of 1.
**Collection**: TTS
# Changelog
- Change length of the starting ("initial") tensor in the enhancer to 1
- Remove `max_spectrogram_length` from the model and the config
# 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
* Fixes #6007 | {
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} | # What does this PR do ?
Fix enhancer usage in ASR-TTS models
**Collection**: [ASR, TTS]
# Changelog
- suppress type checking for enhancer usage
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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 fast versions of geglu, swiglu and reglu. By having a single large matmul and chunking the outputs.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Adds Sampled softmax to Speech Translation preliminary support
**Collection**: [ASR, ST]
# Changelog
- Update config for adding ctc and transformer sampled softmax
- Make text augmenttion optional
- Update code in transformer model to support ctc and transformer sampled softmax
- Add `SampledConvASRDecoder` and `SampledMultiLayerPerceptron` for CTC and transformer sampled softmax support
- Add `SampledTokenClassifier` to wrap `SampledMultiLayerPerceptron` to support both normal MLP and sampled MLP.
- Auto style fixes
# Usage
```shell
python speech_to_text_transf.py \
--config-path="<Path to config>" \
--config-name="speech_translation_with_tts.yaml" \
...
model.ctc_coef=0.3 \
model.sampled_softmax=True \
model.n_samples=256 \ # <any value > 1>
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Follow up of #6054
Refactor of GPT Prompt Tuning to use the same code structure as P-Tuning.
**Collection**: [NLP]
# Changelog
- Adds an option to train a PromptEncoder with just the embeddings which is functionally same as prompt-tuning.
- Removes references to prompt_tables in gpt prompt learning model.
- streamlines the training and inference time code
# 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 ?
fixed broken procedures in the tutorial and minor related code chagnes.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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 ?
The `PromptEncoder` class has variations like `PromptEncoderMLP` and `BIGLSTMPromptEncoder`. The PR removes the `BIGLSTMPromptEncoder` (it was not used has has straight up compile errors) and moves `PromptEncoderMLP` inside the `PromptEncoder` class as a member var.
Essentially the PR creates a single `PromptEncoder` which houses all the required variations.
# 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 ?
fix typo in asr evaluator readme
**Collection**: ASR
# Changelog
- fix typo in asr evaluator readme
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Add cache aware streaming model in nemo force aligner
Should be easy for ctc segmentation tool to call and get the log probs as well.
Most of the codes are copied from ```speech_to_text_cache_aware_streaming_infer.py```.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
Added two functions in ASRModuleMixin (mixin.py) (Not sure if this is the right place to add. Please comment and suggest.)
# Usage
* You can potentially add a usage example below
```python
nemo_model="nemo_models/cache_aware_ctc.nemo"
cd NeMo/tools/nemo_forced_aligner/
python3 align.py \
batch_size=2 \
model_path=$nemo_model \
model_downsample_factor=4 \
manifest_filepath=$manifest \
output_dir=./streaming_ctm_with_pred \
align_using_pred_text=true \
simulate_cache_aware_streaming=true
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
@VahidooX @erastorgueva-nv
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fixed tutorial errors and update the instructions.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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The tutorial is invalid after removal of TN: https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Tacotron2_Training.ipynb
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} | # What does this PR do ?
Currently GPT gets the attention_mask from the fused softmax kernel.
If, the kernel isn't used, then the fallback to pytorch softmax breaks.
This PR makes it configurable to get attention_mask from the kernel.
**Collection**: NLP
# Changelog
- Update forward and loss func
# 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 ?
Makes DataLoader for Spectrogram enhancer (#5565) use correct dimension to determine length of the spectrogram.
Currently it uses number of mel bands as length, so in practice enhancer trains on 80x80 patches, which is obviously suboptimal and a **bug**.
**Collection**: TTS
# Changelog
- Fixes just one single dimension index
# 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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #6007 but there's more work to do | {
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} | null | null | null | I am trying to train MSDD on audios containg 3-5 speakers. After preparing the manifest file that points to the pairwise rttms and setting max_speakers to 5, I get the following error:
```
, line 3086, in binary_cross_entropy
raise ValueError(
ValueError: Using a target size (torch.Size([102, 2])) that is different to the input size (torch.Size([102, 5])) is deprecated. Please ensure they have the same size.
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
I've also seen this comment in
```max_num_of_spks: 2 # Number of speakers per model. This is currently fixed at 2.```
Does this mean that msdd can be used only on audios with 2 speakers? Or does it mean that only 2 speakers overlaps are considered? | {
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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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} | Add model.eval() to ensure the accuracy.
# What does this PR do ?
fix a small bug
**Collection**: [Note which collection this PR will affect]
no collection would be affected
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@erastorgueva-nv
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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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 ?
fix a small bug
**Collection**: [Note which collection this PR will affect]
no collection would be affected
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@erastorgueva-nv
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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Change perturb rng for reproducing results easily
**Collection**: ASR
# Changelog
- Remove Random number generator and use int rng and seed.
- update tutorial.
# Usage
You can use rng (int) for Perturbations.
```python
augmentor:
silence:
prob: 0.8
min_start_silence_secs: 0
max_start_silence_secs: 5
min_end_silence_secs: 0
max_end_silence_secs: 5
rng: 42 # default is None**
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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] | closed | false | null | [] | null | null | 2023-02-16T17:48:24 | 2023-02-17T10:29:27 | 2023-02-17T10:29:27 | NONE | {
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} | null | null | null | **Describe the bug**
Hi! I'm having an issue while fine-tuning `stt_en_conformer_transducer_large`. After a while I get an error stating:
`Output length mismatch! Given U: 35, Expected max U from target lengths: 35 + 1` triggered [here](https://github.com/NVIDIA/NeMo/blob/3e7499599a13869e772612d286d660b574715a5e/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py#L405).
In my mind this is strange because I was already 4 epochs in and I'd imagine the loss has already been calculated for all samples. My understanding of the RNNT loss itself is limited though so I might be missing something.
The maximum number of tokens in the dataset labels is 39 (using the sp tokenizer). Training was not resumed from a previous checkpoint. The architecture of the model has stayed the same during training.
**Steps/Code to reproduce bug**
Normal training with own dataset and custom tokenizer. Max token length in the dataset: 39 (a clue?).
Train config:
```yaml
loss:
loss_name: default
warprnnt_numba_kwargs:
fastemit_lambda: 0.0
clamp: -1.0
variational_noise:
start_step: 0
std: 0.0
optim:
name: adamw
lr: 5.0
betas:
- 0.9
- 0.98
weight_decay: 0.001
sched:
name: NoamAnnealing
d_model: 512
warmup_steps: 1000
warmup_ratio: null
min_lr: 1.0e-06
```
**Expected behavior**
Train loss should be calculated normally.
**Environment overview (please complete the following information)**
- Environment location: GCP VM
- Method of NeMo install: `pip install nemo-toolkit` (v1.14.0)
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS: Ubuntu 20.04
- PyTorch version: 1.12.1+cu113
- Python version: 3.7.12
**Additional context**
Add any other context about the problem here.
GPU: 1xV100
EDIT: Added NeMo version
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https://api.github.com/repos/NVIDIA/NeMo/issues/6039 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6039/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6039/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6039/events | https://github.com/NVIDIA/NeMo/pull/6039 | 1,586,842,361 | PR_kwDOC_bI7s5KFgqr | 6,039 | Update data_prep.py | {
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} | # What does this PR do ?
A bug fix
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
nemo tools
coolections will not be affected
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
python NeMo/tools/nemo_forced_aligner/align.py \
pretrained_name="stt_en_citrinet_1024_gamma_0_25" \
model_downsample_factor=8 \
batch_size=2
manifest_filepath=./test_manifest.json \
output_dir=./ctm_test_manifest
```
# 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
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/6038 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6038/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6038/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6038/events | https://github.com/NVIDIA/NeMo/pull/6038 | 1,586,819,043 | PR_kwDOC_bI7s5KFbp1 | 6,038 | Update data_prep.py | {
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} | When batch_size >= 2, the align.py doesn't work on the batch_size examples. This function should return [start, end] (inclusive) examples.
# What does this PR do ?
Fix a function logic: https://github.com/NVIDIA/NeMo/blob/main/tools/nemo_forced_aligner/utils/data_prep.py#L78
**Collection**: [Note which collection this PR will affect]
nemo neural aligner
no collections will be affected
# Changelog
-
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
python NeMo/tools/nemo_forced_aligner/align.py \
pretrained_name="stt_en_citrinet_1024_gamma_0_25" \
model_downsample_factor=8 \
manifest_filepath=./test_manifest.json \
output_dir=./ctm_test_manifest
batch_size=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)
- [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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Add Spanish pronunciation dictionaries following the same format and naming convention as the German dictionary.
**Collection**: [TTS]
# Changelog
- Add dictionary and heteronym files for es_ES (Spain Spanish) and es_LA (Latin American Spanish).
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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# 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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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] Bugfix
- [ ] Documentation
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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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} | null | null | null | **W0215 06:48:41.808522 140192834852672 x2num.py:14] NaN or Inf found in input tensor**
**Pytorch Lighting Parameters**
omit amp_level and amp_backend,only used precision=16 to enable AMP
#amp_level="O1",
precision=16,
#amp_backend='apex'
though it enables AMP [Using 16bit native Automatic Mixed Precision (AMP)],during training it shows training loss nan with a warning message **W0215 06:48:41.808522 140192834852672 x2num.py:14] NaN or Inf found in input tensor**
**Learning Rate =2**
**Additional context**
- Nemo Docker version nemo:22.12
- Multi GPU
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} | # What does this PR do ?
Integrates the microbatch loader for bert
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Allows users to read data into prompt-and-completion format .jsonl as expected by the Customization service/NeMo LLM P tuning service
Signed-off-by: Zhilin Wang [email protected]
# What does this PR do ?
Allows users to read data into prompt-and-completion format .jsonl as expected by the Customization service/NeMo LLM P tuning service
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
See tutorial.ipynb
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds a early stopping callback to the ptl trainer object for prompt and p-tuning for gpt and t5 models.
**Collection**: [NLP]
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Adds missing dependency for using `build` to build the nemo pip wheel
**Collection**: [Core]
# Changelog
- Add missing dependency `pytest-runner`
# 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
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} | # What does this PR do ?
Adds PyCTCDecode to high level beam search API
**Collection**: [ASR]
# Changelog
- Adds pyctcdecode support.
- Approximates the tokenized text output since the engine does not return token indices
# Usage
```python
import nemo.collections.asr as nemo_asr
filepath = "<Path to audio file>"
kenlm_path = "<Path to ARPA or BIN KenLM File built in CHAR / WORD mode; not SUBWORD mode !>"
model = nemo_asr.models.ASRModel.from_pretrained('stt_en_conformer_ctc_large') # type: nemo_asr.models.EncDecCTCModelBPE
# Create default decoding config
model.change_decoding_strategy(None)
decoding_cfg = model.cfg.decoding
decoding_cfg.strategy = "pyctcdecode"
decoding_cfg.beam.beam_size = 128
decoding_cfg.beam.return_best_hypothesis = False
decoding_cfg.beam.beam_alpha = 1.0
decoding_cfg.beam.beam_beta = 0.0
decoding_cfg.beam.kenlm_path = kenlm_path
# pyctcdecode config modifications
decoding_cfg.beam.pyctcdecode_cfg.beam_prune_logp = -10.
decoding_cfg.beam.pyctcdecode_cfg.token_min_logp = -5.
# Prepare beam search decoding strategy
model.change_decoding_strategy(decoding_cfg)
hypothesis = model.transcribe([filepath], return_hypotheses=True)
print("Num hypothesis per sample :", len(hypothesis[0]))
print("Result type :", hypothesis[0][0].__class__.__name__)
for candidate in range(len(hypothesis[0])):
print(f"Beam Search {candidate + 1} :", hypothesis[0][candidate].text, hypothesis[0][candidate].score)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
replace `! -z` -> `-n`.
https://github.com/koalaman/shellcheck/wiki/SC2236
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds experimental support in BERT for the distributed Adam optimizer to overlap its parameter all-gather with the forward compute.
**Collection**: NLP
# Changelog
- Add BERT support for overlapping forward compute with distopt communication
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/a7c2a04db4375ea8669c2c8c33cb4c4c30ffabe9/examples/nlp/language_modeling/conf/megatron_bert_config.yaml#L144
Configure the optimizer with `overlap_param_sync: 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?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
Pinging @shanmugamr1992.
# Additional Information
* Follow-up to https://github.com/NVIDIA/NeMo/pull/5684. | {
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} | # What does this PR do ?
* Update hydra config on published NGC titanet-model and release as v1 version
* Improve speaker label tests with latest hydra config
* Add a feature to write model labels `labels.txt` to exp directory
* Update batch inference function to return gt labels and trained model labels (if available)
**Collection**: ASR
# Changelog
- Update label models to include latest v1 version of titanet-large
- Added titanet-small config file (model will be published soon)
- Now instead of returning index of gt labels from AudioSpeechLabelDataset, return the speaker label names directly as seen from 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
- [x] Bugfix
- [x] Documentation | {
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} | # What does this PR do ?
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- This enables overlapping AMAX reduction and other DP-communications e.g., gradient reduction and parameter gathering
# Changelog
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I have following three issues:
1. I tried to finetune the titanet-large model using the German dataset with 274 speakers and the total data size is small. THe dataset is around 30h - 40h in total. I tried with few different configurations like - adamw optimizer with 0.0001 learning rate and angularsoftmax loss, adamw optimizer with 0.0001 learning rate and crossentropy loss, novograd optimizer with 0.0001 learning rate and crossentropy loss, sgd optimizer with 0.0001 learning rate and crossentropy loss, adamw optimizer with 0.008 learning rate and crossentropy loss, and so on.
Issue: I have a small test set with 14 speakers who are common with training set, the total test size is 14minutes. The test accuracy is 0 with high loss (around 30 - 60). Please do provide some inputs.
2. I had earlier tried to finetune the TitaNet-large model with VoxCeleb German subset data. The test accuracy was around 96%. I updated the nemo-toolkit with newer version or from the main branch like two days ago and used the model from earlier to finetune the model with another German dataset with 274 speakers. I am having the following issue:
'''
Error executing job with overrides: []
Error in call to target 'nemo.collections.common.losses.cross_entropy.CrossEntropyLoss': TypeError("__init__() got an unexpected keyword argument 'scale'")
full_key: loss
'''
3. After updating the nemo-toolkit to a newer version, I am not able to load the models that were finetuned using the earlier versions of the nemo-toolkit. Receiving the following erroe message. please provide inputs:
'''
RuntimeError: Error(s) in loading state_dict for EncDecSpeakerLabelModel:
Unexpected key(s) in state_dict: "decoder.final.bias".
'''
Regards,
Sreeni... | {
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
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"merged_at": "2023-02-15T01:41:44"
} | 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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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | [multilang_output_6.txt](https://github.com/NVIDIA/NeMo/files/10732583/multilang_output_6.txt)
[multilang_error_6.txt](https://github.com/NVIDIA/NeMo/files/10732586/multilang_error_6.txt)
**Describe the bug**
using 22.07 image from NGC cloud
unable to resume my experiment even after setting ### resume_if_exist=true
AssertionError: If capturable=False, state_steps should not be CUDA tensors.
i have attache my log file your perusal
@titu1994 @meghmak13 | {
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} | # What does this PR do ?
Adding ragged batch input option for RadTTS - with some refactoring
Fixing 2 bugs from previous PR:
1. clamp(1,..)
2. argsort() for unsort_tensor() was in wrong direction
**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 ?
Adding [ClearML ](https://github.com/allegroai/clearml) integration for logging of training process:
- Enable PyTorch auto logging (cfg parameter)
- TensorBoard auto logging (on default)
- Logging .nemo checkpoint files
**Collection**: [Note which collection this PR will affect]
utils
# Changelog
- Add new `loggers` module into `nemo.utils`
- Add new `clearml_logger.py` into `nemo.utils.loggers`
- Add new `ClearMLParams` and `ClearMLLogger` classes into `clearml_logger.py`
- Move `dllogger.py` to `nemo.utils.loggers`
- Integrate ClearMLLogger to PL loggers
# Usage
In model config.yml file:
```yml
exp_manager:
# You may use this section to create a ClearML logger
create_clearml_logger: true
clearml_logger_kwargs:
task: "Test Integration ClearML" # Task name for this experiment
project: "Speech/ASR/RUS_OS/Train_NeMo" # Project name for this experiment
tags: ["Test1", "2"] # Additional tags (default always include "NeMo" tag)
model_name: "Some name" # Model name in ClearML (default from exp_manager:name)
log_model: true # If true - logging model .nemo file ClearML
log_cfg: true # If true - logging model config to model ClearML instance
log_metrics: true # If true - logging lightning metrics to model ClearML instance
connect_pytorch: false # If true - enable automatic pytorch logging (all saved checkpoints)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adding intermediate CTC loss and stochastic depth as described in https://arxiv.org/abs/2102.03216.
The current implementation is only for conformer encoder, but I'm not really sure how to write a generic code for this case. Please let me know if you have some ideas here.
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
To use, specify parameters in the config. E.g., for stochastic depth:
```
model.encoder.stochastic_depth_mode=linear
model.encoder.stochastic_depth_drop_prob=0.3
model.encoder.stochastic_depth_start_layer=0
```
For intermediate CTC loss:
```
model.encoder.capture_output_at_layers=[9]
model.intermediate_loss_weights=[0.3]
```
I've added the docs to ConformerEncoder, but not sure how to add the docs to the new `intermediate_loss_weights` parameter of the `EncDecCTCModel`. Please let me know what's the right place to put those docs in.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add two popular read-only properties for tensor and pipeline parallelism values.
Change some of the self._cfg to self.cfg and cfg to self.cfg
Adds from_pretrained for NM Models on HF Hub
**Collection**: [Note which collection this PR will affect]
NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
# Add a code snippet demonstrating how to use this
print(model.tensor_model_parallel_size)
rint(model.pipeline_model_parallel_size)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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# What does this PR do ?
Defaults the targets of RNNT to int64 to match CTC loss. Should mostly be a cosmetic change, since it is near impossible to train an RNNT with target vocabulary of > 2B tokens, or with seq length > 2B.
**Collection**: [ASR]
# Changelog
- Update internal casts to int64
- Update tests to force int64 targets
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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# What does this PR do ?
Just a minor change to main docs site to separate docs navigation by domain from top level. | {
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
This PR adds a section in ASR/Datasets to describe how to use a dataset from AIStore.
**Collection**: ASR, Documentation
# Changelog
- Added a section in ASR/Datasets with a description of using a dataset from AIStore
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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If one trains TTS spectrogram enhancer (#5565) on short spectrograms, at inference time it doesn't generalize to longer ones. A patch in the beginning gets enhanced but further frames do not.
Example (before, after):
**Steps/Code to reproduce bug**
1. Train a spectrogram enhancer
2. Apply it to a spectrogram that's longer than anything from the training set
3. Only a patch in the beginning gets enhanced
**Expected behavior**
The whole spectrogram should have got additional details, not just the first patch | {
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} | Signed-off-by: Vladimir Bataev <[email protected]>
# What does this PR do ?
Simplify `k2` library import guard: use module-based approach.
**Advantages:**
- less code is required for using guard
- no need to suppress linters (isort, etc.)
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
from nemo.core.utils.k2_guard import k2 # import k2 with performing all required checks
```
# 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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} | null | null | null | ** P tuning with Megatron GPT2 is generating repetitive sentence**
I am P tuning with Megatron GPT2 model with 100 train and 100 test dataset for a particular activity. I am getting repetitive sentence during inference for most of the samples.
I have used below things to resolve this issue.
1. Used instruction sentence with sentence(tried without instruction as well)
2. Used a end token after each sentence(tried without end token as well)
3. Used a end token after each label(tried without end token as well)
4. Changed "repetition_penalty" to 0, 0.5, 1000, 10000 etc, however, the issue is still there.
I am trying to replicate https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb for generative AI.
Please let me know the solution for this issue. | {
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} | # What does this PR do ?
Fix transribe function in EncDecHybridRNNTCTCModel.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add variables for transcribe invocation if use_rnnt_decoder.
- Change name of variable (logits_len to encoded_len).
- Change ErrorValue to ValueError in change_decoding_strategy of EncDecHybridRNNTCTCBPEModel.
# 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)
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} | # What does this PR do ?
Fix transribe function in EncDecHybridRNNTCTCModel.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add variables for transcribe invocation if use_rnnt_decoder.
- Change name of variable (logits_len to encoded_len).
- Change ErrorValue to ValueError in change_decoding_strategy of EncDecHybridRNNTCTCBPEModel.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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