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} | # What does this PR do ?
Adds new feature to SDE, and transcribe speech
**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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<div type='discussions-op-text'>
<sup>Originally posted by **rkrohit14092** September 27, 2022</sup>
How to add additional words in the dictionary of fastspeech2. I want to customize the dictionary as per my use case.
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} | null | null | null | /opt/miniconda3/envs/nemo/bin/python /home/henry/Projects/NeMo/nemo_text_processing/text_normalization/normalize.py --lang zh --text 最高气温38°C
[NeMo W 2022-09-27 17:45:38 optimizers:55] Apex was not found. Using the lamb or fused_adam optimizer will error out.
Traceback (most recent call last):
File "/home/henry/Projects/NeMo/nemo_text_processing/text_normalization/normalize.py", line 38, in <module>
from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor
File "/home/henry/Projects/NeMo/nemo/collections/common/__init__.py", line 16, in <module>
from nemo.collections.common import data, losses, parts, tokenizers
File "/home/henry/Projects/NeMo/nemo/collections/common/losses/__init__.py", line 15, in <module>
from nemo.collections.common.losses.aggregator import AggregatorLoss
File "/home/henry/Projects/NeMo/nemo/collections/common/losses/aggregator.py", line 19, in <module>
from nemo.core.classes import Loss, typecheck
File "/home/henry/Projects/NeMo/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/home/henry/Projects/NeMo/nemo/core/classes/__init__.py", line 33, in <module>
from nemo.core.classes.modelPT import ModelPT
File "/home/henry/Projects/NeMo/nemo/core/classes/modelPT.py", line 43, in <module>
class ModelPT(LightningModule, Model):
File "/home/henry/Projects/NeMo/nemo/core/classes/modelPT.py", line 1374, in ModelPT
@LightningModule.trainer.getter
AttributeError: type object 'LightningModule' has no attribute 'trainer' | {
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} | # What does this PR do ?
Fixes invalid link in notebook.
**Collection**: ASR
# Changelog
- Fix invalid ASR inference link.
# 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 ?
Add a GitHub action to automatically stale and close issues and PRs. The grace periods and actions are shown below,
* Add a label "Stale" on issues and pull requests after 60 days of inactivity and comment on them.
* Close the stale issues after 14 days of inactivity.
* Don't close stale pull requests.
**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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fixes a bug in squeezeformer preventing loading of checkpoints.
**Collection**: [ASR]
# Changelog
- Fix a calculation of groups that prevents loading of checkpoints
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Updates some older libraries
**Collection**: [Core]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix some bugs in export script.
**PR Type**:
- [ ] New Feature
- [x ] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
Following the instructions here, speech data explorer doesn't work out of the box because some dependencies aren't installed: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/tools/speech_data_explorer.html#getting-started
**Steps/Code to reproduce bug**
Start a new conda environment (I used python 3.8), then:
```
git clone --recursive https://github.com/NVIDIA/NeMo.git
cd NeMo
pip install -r tools/speech_data_explorer/requirements.txt
python tools/speech_data_explorer/data_explorer.py
```
Gives:
```
Traceback (most recent call last):
File "tools/speech_data_explorer/data_explorer.py", line 42, in <module>
from plotly import express as px
File "/home/dgalvez/scratch/miniconda3/envs/nemo/lib/python3.8/site-packages/plotly/express/__init__.py", line 10, in <module>
raise ImportError(
ImportError: Plotly express requires pandas to be installed.
```
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
Clearly some of the requirements of speech data explorer are missing. It works after I do `pip install pandas` manually.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
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Otherwise, please provide:
Ubuntu 18.04
python 3.8
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As said in [page](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_large), the 1.6.0 version of the Conformer-Transducer-Large model has much better word error rate performance on Librispeech than the version 1.4.0 (3.6/1.7 vs 4.3/1.9).
Could you please tell me the key to the better word error rate? Is it the training dataset, the model config or the training recipe?
Many Thanks! | {
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} | null | null | null | Hi, thanks for the great work!
I saw the recently added speaker interpolation tutorial and decided to have a try.
Sometime ago I followed the `FastPitch_Finetuning.ipynb` tutorial and mixed a small dataset with a big dataset and did the finetuning with `n_speakers=2`. This model can be used to do speaker interpolation, is that right?
The speaker interpolation tutorial says `Choose new_speaker ID such that it is not already assigned to an existing speaker` and in [fastpitch.py](https://github.com/NVIDIA/NeMo/blob/a74f262dbd39aff9d7ec3c12c9322c3a4036a97c/nemo/collections/tts/models/fastpitch.py#L687-691) it says `the new_speaker_id should be less than the total number of speakers`.
So in my case, speaker 0, 1 have been assigned, and `n_speakers=2`. How should I set the `new_speaker` ID?
Thank you! | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds a test for Nested ASR model with detached state dict management.
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | Can you please tell me how to properly train a model with mixed precision?
I set to train with a model with such a config, but on validation I get NaN all the time.
Config:
```
name: "Conformer-CTC-BPE"
model:
sample_rate: &sample_rate 8000
log_prediction: false # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
train_ds:
manifest_filepath: "../data/processed/manifests/train.json"
sample_rate: *sample_rate
batch_size: 2 # you may increase batch_size if your memory allows
shuffle: true
num_workers: &num_workers 8
# pin_memory: false
trim_silence: true
max_duration: 32.9 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: "../data/processed/manifests/val.json"
sample_rate: *sample_rate
batch_size: 1
shuffle: false
num_workers: 2
pin_memory: false
test_ds:
manifest_filepath: "../data/processed/manifests/test.json"
sample_rate: *sample_rate
batch_size: 1 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 1
pin_memory: false
tokenizer:
dir: "../models/tokenizers/tokenizer_spe_bpe_v128_max_2"
type: bpe
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
# you may use lower time_masks for smaller models to have a faster convergence
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1
n_layers: 18
d_model: 256
# Sub-sampling params
subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory
subsampling_factor: 4 # must be power of 2
subsampling_conv_channels: -1 # 176 set to -1 to make it equal to the d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 4 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
conv_norm_type: 'batch_norm' # batch_norm or layer_norm
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: null
optim:
name: adamw
lr: 5.0
# optimizer arguments
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 0.0
# scheduler setup NoamAnnealing
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 10000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 100
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: gpu
strategy: dp
accumulate_grad_batches: 16
gradient_clip_val: 1.0
precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP.
amp_level: O1
amp_backend: apex
log_every_n_steps: 50 # Interval of logging.
progress_bar_refresh_rate: 10
resume_from_checkpoint: null
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: true # Provided by exp_manager
# logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
# you need to set these two to True to continue the training
resume_if_exists: false
resume_ignore_no_checkpoint: false
# You may use this section to create a W&B logger
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
Output:
```
[NeMo W 2022-09-24 19:03:30 nemo_logging:349] /home/user/projects/conformer/venv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:611: UserWarning: Checkpoint directory /home/maxikon/projects/conformer/models/nemo/conformer/trained exists and is not empty.
rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo I 2022-09-24 19:03:32 modelPT:597] Optimizer config = AdamW (
Parameter Group 0
amsgrad: False
betas: [0.8, 0.98]
capturable: False
eps: 1e-08
foreach: None
lr: 5.0
maximize: False
weight_decay: 0.0
)
[NeMo I 2022-09-24 19:03:32 lr_scheduler:910] Scheduler "<nemo.core.optim.lr_scheduler.NoamAnnealing object at 0x7f98981dc3a0>"
will be used during training (effective maximum steps = 38900) -
Parameters :
(d_model: 256
warmup_steps: 10000
warmup_ratio: null
min_lr: 1.0e-06
max_steps: 38900
)
| Name | Type | Params
------------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | encoder | ConformerEncoder | 30.5 M
2 | decoder | ConvASRDecoder | 33.2 K
3 | loss | CTCLoss | 0
4 | spec_augmentation | SpectrogramAugmentation | 0
5 | _wer | WERBPE | 0
------------------------------------------------------------------------
30.5 M Trainable params
0 Non-trainable params
30.5 M Total params
122.154 Total estimated model params size (MB)
[NeMo W 2022-09-24 19:03:32 nemo_logging:349] /home/user/projects/conformer/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the num_workers argument` (try 12 which is the number of cpus on this machine) in the DataLoader init to improve performance.
rank_zero_warn(
Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods.
Defaults for this optimization level are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Epoch 0: 19%
1470/7790 [04:32<19:32, 5.39it/s, loss=565, v_num=29]
[NeMo W 2022-09-24 19:03:38 nemo_logging:349] /home/user/projects/conformer/venv/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:124: UserWarning: Seems like optimizer.step() has been overridden after learning rate scheduler initialization. Please, make sure to call optimizer.step() before lr_scheduler.step(). See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Seems like optimizer.step() has been overridden after learning rate scheduler "
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1024.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 512.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 256.0
```
**Environment details**
- Linux Ubuntu 20.04
- PyTorch 11.6
- Python 3.8
- NeMo 1.11.0 (or 1.8.2)
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Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
fix the bug of tokenizer below,
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In [16], line 13
10 print(pron2_g2p)
12 # Tokenize!
---> 13 pron1_tokens = aligner.tokenizer.encode_from_g2p(pron1_g2p)
14 pron2_tokens = aligner.tokenizer.encode_from_g2p(pron2_g2p)
15 print("\n=== Tokens===")
AttributeError: 'EnglishPhonemesTokenizer' object has no attribute 'encode_from_g2p'
```
**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.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Ondřej Vacek <[email protected]>
# What does this PR do ?
- Fix filename extraction in vad_utils.py
- Old behavior makes vad_infer script unfunctional on windows since it assumed path separator is always "/"
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
- asr.parts.utils.vad_utils.py
---> load_tensor_from_file(filepath: str) -> Tuple[torch.Tensor, str]:
# 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 filename extraction os independent in vad_utils.py
- Old behavior makes vad_infer script unfunctional on windows since it assumed path separator is always "/"
**Collection**: [Note which collection this PR will affect]
- nemo.collections.asr.parts.utils.vad_utils
# 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] Bugfix
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 support for FastPitch speaker interpolation
**Collection**: [Note which collection this PR will affect]
TTS
# 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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Adds speaker interpolation support to FastPitch
**Collection**: [Note which collection this PR will affect]
TTS
# 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
- [ ] 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 ?
When optimising MSDD parameters, using `diarize` to obtain outputs made the most sense. Modified the code to return this.
**Collection**: ASR
# Changelog
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} | null | null | null | The example at the top of megatron_lm_ckpt_to_nemo.py sugests that you can convert the megatron-lm checkpoint without hparams.yaml file. I did not spend huge amount of time trying to understand this code but its seems not possible and the only reason why hparams is not a manadatory argument seems to be because one can optionally pass cnf via kwargs.
Could we add literally one sentence describing that in the top of the file preferably also providing an example of a yaml file - happy to send out working 5B example for GPT. | {
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Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
fix the bug of tokenizer below,
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In [16], line 13
10 print(pron2_g2p)
12 # Tokenize!
---> 13 pron1_tokens = aligner.tokenizer.encode_from_g2p(pron1_g2p)
14 pron2_tokens = aligner.tokenizer.encode_from_g2p(pron2_g2p)
15 print("\n=== Tokens===")
AttributeError: 'EnglishPhonemesTokenizer' object has no attribute 'encode_from_g2p'
```
**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.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Bug fixes
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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# What does this PR do ?
Removing Phonemizer/eSpeak dependency.
**Collection**: [TTS]
# Changelog
- Remove PhonemizerTokenizer and associated code
# 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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- [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
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a bug that causes decoding for enc-dec models with O2 to crash
**Collection**: [Note which collection this PR will affect]
# Changelog
- Change the module being inspected with O2 to determine forward args.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a bug that causes decoding for enc-dec models with O2 to crash.
**Collection**: NLP
# Changelog
- Change the module being inspected to determine forward args.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Adds support for speaker interpolation in FastPitch
**Collection**: [Note which collection this PR will affect]
tts
# 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Signed-off-by: subhankar-ghosh <[email protected]>
Adds support for conditioning all components of fastpitch on speaker embedding.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
tts
# 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
- [ ] 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 ?
Refactor the text generation code to be model independent by using different inference strategies.
Added the GPT inference strategy and prompt learning inference strategy and verify they are working properly.
Implement the RETRIVAL service that can provides retrieval results on the fly.
Implement the RETRO model text generation and fix a default bias problem.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Bug fixes
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/4983",
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/4983.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/4983.patch",
"merged_at": "2022-09-22T20:16:24"
} | # What does this PR do ?
Related #4972
We add a file to stop the failing CI run, but do not include Black until we find a way to resolve the difference in formatting.
# Changelog
- Add pre-commit ci file.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
"CUDA error: out of memory" was reported when training.
```bash
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
File <timed eval>:1, in <module>
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:770, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
751 r"""
752 Runs the full optimization routine.
753
(...)
767 datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
768 """
769 self.strategy.model = model
--> 770 self._call_and_handle_interrupt(
771 self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
772 )
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:723, in Trainer._call_and_handle_interrupt(self, trainer_fn, *args, **kwargs)
721 return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
722 else:
--> 723 return trainer_fn(*args, **kwargs)
724 # TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7
725 except KeyboardInterrupt as exception:
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:811, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
807 ckpt_path = ckpt_path or self.resume_from_checkpoint
808 self._ckpt_path = self.__set_ckpt_path(
809 ckpt_path, model_provided=True, model_connected=self.lightning_module is not None
810 )
--> 811 results = self._run(model, ckpt_path=self.ckpt_path)
813 assert self.state.stopped
814 self.training = False
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1217, in Trainer._run(self, model, ckpt_path)
1214 self._logger_connector.reset_metrics()
1216 # strategy will configure model and move it to the device
-> 1217 self.strategy.setup(self)
1219 # hook
1220 if self.state.fn == TrainerFn.FITTING:
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/strategies/single_device.py:71, in SingleDeviceStrategy.setup(self, trainer)
70 def setup(self, trainer: pl.Trainer) -> None:
---> 71 self.model_to_device()
72 super().setup(trainer)
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/strategies/single_device.py:68, in SingleDeviceStrategy.model_to_device(self)
67 def model_to_device(self) -> None:
---> 68 self.model.to(self.root_device)
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/core/mixins/device_dtype_mixin.py:121, in DeviceDtypeModuleMixin.to(self, *args, **kwargs)
119 out = torch._C._nn._parse_to(*args, **kwargs)
120 self.__update_properties(device=out[0], dtype=out[1])
--> 121 return super().to(*args, **kwargs)
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/nn/modules/module.py:927, in Module.to(self, *args, **kwargs)
923 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,
924 non_blocking, memory_format=convert_to_format)
925 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
--> 927 return self._apply(convert)
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/nn/modules/module.py:579, in Module._apply(self, fn)
577 def _apply(self, fn):
578 for module in self.children():
--> 579 module._apply(fn)
581 def compute_should_use_set_data(tensor, tensor_applied):
582 if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):
583 # If the new tensor has compatible tensor type as the existing tensor,
584 # the current behavior is to change the tensor in-place using `.data =`,
(...)
589 # global flag to let the user control whether they want the future
590 # behavior of overwriting the existing tensor or not.
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/nn/modules/module.py:579, in Module._apply(self, fn)
577 def _apply(self, fn):
578 for module in self.children():
--> 579 module._apply(fn)
581 def compute_should_use_set_data(tensor, tensor_applied):
582 if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):
583 # If the new tensor has compatible tensor type as the existing tensor,
584 # the current behavior is to change the tensor in-place using `.data =`,
(...)
589 # global flag to let the user control whether they want the future
590 # behavior of overwriting the existing tensor or not.
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/nn/modules/module.py:625, in Module._apply(self, fn)
623 for key, buf in self._buffers.items():
624 if buf is not None:
--> 625 self._buffers[key] = fn(buf)
627 return self
File ~/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/nn/modules/module.py:925, in Module.to.<locals>.convert(t)
922 if convert_to_format is not None and t.dim() in (4, 5):
923 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,
924 non_blocking, memory_format=convert_to_format)
--> 925 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```
**Steps/Code to reproduce bug**
1. Copy the ipynb to a server with a GPU and install CUDA 11.6, CuDNN 8.4.1 , PyTorch 1.12.1
2. Install Nvidia Apex and run all the command listed in the first block
3. Run every block till `%%time
trainer.fit(char_model)`
**Expected behavior**
Start training as what I've seen when running in colab.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal **✓**, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install **✓** or from source]. Please specify exact commands you used to install.
~~If method of install is [Docker], provide `docker pull` & `docker run` commands used~~
**Environment details**
~~If NVIDIA docker image is used you don't need to specify these.~~
Otherwise, please provide:
- OS version 5.4.0-100-generic Ubuntu SMP(Ubuntu 18.04.6 LTS) x86_64 x86_64 x86_64 GNU/Linux
- PyTorch version 1.12.1
- Python version 3.9.13
**Additional context**
GPU model: 3090
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} | null | null | null | **Describe the bug**
Inference with the medium model specifically causes an error;
Works totally fine on the same audio file with the following models;
- stt_en_conformer_ctc_small_ls
- stt_en_conformer_ctc_large
- stt_en_conformer_transducer_large
I've so far only encountered the problem in python 3.8.10. In Google Colab and in Python 3.10.4 it works fine.
**Steps/Code to reproduce bug**
In python 3.8.10,
installed with;
`nemo_toolkit[all] @ git+https://github.com/NVIDIA/NeMo.git@a4ff215ecc9fafd7127223bc21fd50cf023de592"`
Loaded from local pretrained checkpoint (previously downloaded from NGC)
`model = nemo_asr.models.ASRModel.restore_from(restore_path=model_path)`
`model.eval()`
`model.freeze()`
`model.preprocessor.featurizer.dither = 0.0`
`model.preprocessor.featurizer.pad_to = 0`
`decoding_cfg = model.cfg.decoding`
`decoding_cfg.preserve_alignments = True`
`decoding_cfg.compute_timestamps = True`
`model.change_decoding_strategy(decoding_cfg)`
`hypothesis = model.transcribe(paths2audio_files=[audio_path_or_url], return_hypotheses=True)[0]`
**Expected behavior**
Inference should finish without "list index out of range"
**Environment details**
- OS version Ubuntu 20.04
- PyTorch version: 1.12.1
- Python version: 3.8.10
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} | null | null | null | **Describe the bug**
I try to train fastpitch with multispeaker dataset and got error like this:
```
[NeMo W 2022-09-22 14:36:06 optimizers:55] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2022-09-22 14:36:07 experimental:27] Module <class 'nemo.collections.tts.torch.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-09-22 14:36:07 experimental:27] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
Using 16bit native Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo I 2022-09-22 14:36:08 exp_manager:286] Experiments will be logged at /media/DATA-2/TTS/NEMO_FASTPITCH/NeMo/nemo_text_processing/g2p/nemo_experiments/FastPitch/2022-09-22_14-36-08
[NeMo I 2022-09-22 14:36:08 exp_manager:660] TensorboardLogger has been set up
[NeMo W 2022-09-22 14:36:08 nemo_logging:349] /media/DATA-2/TTS/NEMO_FASTPITCH/fastpitch_env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:2319: LightningDeprecationWarning: `Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.
rank_zero_deprecation("`Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.")
[NeMo I 2022-09-22 14:36:10 tokenize_and_classify:87] Creating ClassifyFst grammars.
Error executing job with overrides: ['train_dataset=/media/DATA-2/TTS/NEMO_FASTPITCH/train_manifest.json', 'validation_datasets=/media/DATA-2/TTS/NEMO_FASTPITCH/validation_manifest.json', '+init_from_nemo_model=/media/DATA-2/TTS/NEMO_FASTPITCH/NeMo/tts_id_fastpitch_align.nemo', '+trainer.max_steps=1000', '~trainer.max_epochs', 'trainer.check_val_every_n_epoch=25', 'model.train_ds.dataloader_params.batch_size=24', 'model.validation_ds.dataloader_params.batch_size=24', 'model.n_speakers=4', 'model.pitch_mean=121.9', 'model.pitch_std=23.1', 'model.pitch_fmin=30', 'model.pitch_fmax=512', 'model.optim.lr=2e-4', '~model.optim.sched', 'model.optim.name=adam', 'trainer.devices=1', 'trainer.strategy=null', '+model.text_tokenizer.add_blank_at=false']
Error locating target 'modules.EnglishG2p', see chained exception above.
full_key: text_tokenizer.g2p
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
**Steps/Code to reproduce bug**
```
BRANCH = 'main'
import os
import json
import torch
import IPython.display as ipd
from matplotlib.pyplot import imshow
from matplotlib import pyplot as plt
from nemo.collections.tts.models import FastPitchModel
from pathlib import Path
# TODO(oktai15): remove +model.text_tokenizer.add_blank_at=true when we update FastPitch checkpoint
!(python /media/DATA-2/TTS/NEMO_FASTPITCH/NeMo/tutorials/tts/fastpitch_finetune.py --config-name=fastpitch_align_v1.05.yaml \
train_dataset=/media/DATA-2/TTS/NEMO_FASTPITCH/train_manifest.json \
validation_datasets=/media/DATA-2/TTS/NEMO_FASTPITCH/validation_manifest.json \
+init_from_nemo_model=/media/DATA-2/TTS/NEMO_FASTPITCH/NeMo/tts_id_fastpitch_align.nemo \
+trainer.max_steps=1000 ~trainer.max_epochs \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 \
model.n_speakers=4 model.pitch_mean=121.9 model.pitch_std=23.1 \
model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
+model.text_tokenizer.add_blank_at=true \
)
```
**Expected behavior**
Training without errors
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
Ubuntu
Pytorch 1.12.1+cu113
Pytorch-lightning 1.6.5
Python 3.8.10
NeMo-toolkit 1.11.0
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} | null | null | null | I wanted to know if it's possible to get the indices for text normalization for semiotic classes. For example if I have a text
```
I was born in 3/7/1989.
```
which normalized to:
```
I was born in March seventh nineteen eighty nine.
```
The index of begin and end of the text part "3/7/1989" have the correspondence in "March seventh nineteen eighty nine" in the second one.
I want to have a function that give me the indices (start, end) of the input text aligned with the (start, end) in the normalized text.
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
torch creates state dict of weight in weighted cross entropy and while restoring if weight is None, then it throws error.
Fixes by introducing weight with all ones.
**Collection**: ASR
# Changelog
- Fixes by introducing weight with all ones.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
- Sync AMI evaluation dataset annotations with https://github.com/BUTSpeechFIT/AMI-diarization-setup. This follows the standardization by pyannote
- Add training dataset
**Collection**:
- ASR
# Changelog
- AMI download script improvements
# 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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Currently style checking is done within the `setup.py` and enforced via jenkins.
This is problematic for users who do not have jenkins access and builds fail due to simple style fixes. By using pre-commit CI, we can automate style fixes in PRs meaning less work for users (and have a dedicate CI job for this).
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- Multiple argument functions are expanded based on length. this is primarily for readability (and hence why the number of lines go up):
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# old
{'label': NeuralType(tuple('B'), LabelsType()), 'label_length': NeuralType(tuple('B'), LengthsType()),}
# new
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'label_length': NeuralType(tuple('B'), LengthsType()),
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```
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- Introduce pre-commit, update Black version.
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**Collection**: Core
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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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} | # What does this PR do ?
Check if conda environment is active rather than binary check
**Collection**: Core
# Changelog
- check using CONDA_PREFIX variable (which gets assigned to active conda directory)
# Before your PR is "Ready for review"
**Pre checks**:
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# What does this PR do ?
This PR adds Speaker Interpolation tutorial to tutorials/tts
**Collection**: [Note which collection this PR will affect]
None
# 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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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 ?
torch creates state dict of weight in weighted cross entropy and while restoring if weight is None, then it throws error.
Fixes by introducing weight with all ones.
**Collection**: ASR
# Changelog
- Fixes by introducing weight with all ones.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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# Usage
* You can potentially add a usage example below
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# Add a code snippet demonstrating how to use this
```
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] 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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Cherry-pick of PR 4962.
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**Collection**: [bug]
# Changelog
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} | Signed-off-by: SeanNaren <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Automatically cherry-pick release commits to main when PR is merged.
**Collection**: Core
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Signed-off-by: subhankar-ghosh <[email protected]>
# What does this PR do ?
Adds JoC optimization in binarize_attention_parallel, Aligner loss or ForwardSumLoss
**Collection**: [Note which collection this PR will affect]
TTS
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- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Virginia Adams <[email protected]>
Adds pipeline parallel T5 prompt learning
**Collection**: NLP
# Changelog
- Updated T5 prompt learning model class
- Updated base prompt learning model class
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
We're seeing failures on main due to the the latest transformers version and our reliance on TRANSFORMERS_OFFLINE in the CI. Running the failed test locally reproduces the error:
```
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 pytest tests/collections/nlp/test_prompt_learning.py::TestMegatronGPTPromptLearningDataset::test_init_prompt_learning_dataset
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | Training or Fine-Tuning an Acoustic Model：
Model fine-tuning is a set of techniques that makes fine adjustments to a pre-existing model using new data, so as to make it adapt to new situations while also retaining its original capabilities.
so I download this model:speechtotext_zh_cn_conformer.tlt and modidy the evaluate.yaml on test_ds.labels to the Mandarin then use this api:
!tao speech_to_text_citrinet evaluate \
-e $SPECS_DIR/speech_to_text_citrinet/evaluate.yaml \
-g 1 \
-k $KEY \
-m $RESULTS_DIR/speechtotext_zh_cn_conformer.tlt \
-r $RESULTS_DIR/citrinet/evaluate \
test_ds.manifest_filepath=$DATA_DIR/train.json
but i got this error
raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !")
ValueError: `cfg` must have `tokenizer` config to create a tokenizer !
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"merged_at": "2022-09-26T18:07:51"
} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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if 'EncDecCTCModel' in str(type(asr_model)):
labels = asr_model.decoder.vocabulary
elif 'EncDecCTCModelBPE' in str(type(asr_model)):
vocab = asr_model.tokenizer.tokenizer.get_vocab()
labels = list(vocab.keys())
labels[0] = "<unk>"
I found that
'EncDecCTCModel' is in 'EncDecCTCModelBPE'
so I think the order should be changed.
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} | …ion environment
Signed-off-by: Jin Li <[email protected]>
# What does this PR do ?
Import pycuda.autoprimaryctx or pycuda.autoinit to init pycuda execution environment to fix "invalid device context - no currently active context?" issue
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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# Changelog
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**PR Type**:
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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# Usage
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If you haven't finished some of the above items you can still open "Draft" PR.
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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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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} | # What does this PR do ?
This PR adds flag `--class_path` to checkpoint averaging script to allow explicit class path (e..g, `module.submodule.class`).
Example:
```
NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py \
--class_path nemo.collections.nlp.models.language_modeling.megatron_bert_model.MegatronBertModel \
my_model.nemo
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hello everyone,
I'm looking to Text Normalization, Inverse Text Normalization to be more specific, and I saw something "weird".
For simples numbers, like "Um" (equivalent "one") returns "Um" instead "1", should this be normal?
The ITN normalize with some prefix or suffix, or can inverse normalize numbers in general?
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} | null | null | null | **Describe the bug**
I'm building an ASR with Conformer Transducer with "beam" decoding strategy beam_size of 2 but when I inference some audio contains a repeat number (telephone, ID..) the ASR model is often missing the number.
For example:
one one one two two three three => one one two three
nine nine nine nine => nine nine
...
I thought it was because of the decoding strategy so I tried to change decoding strategy to "greedy" but so many repeated words are generated (eg: one one two two => one one oneo one one two two two)
I also try ASR with Adapters by training only audio with repeated number only but I doesn't solve problem well
**Expected behavior**
ASR model should generate enough number even the number or words are repeated
**Environment details**
-OS version - Ubuntu 18.04.5 LTS
-PyTorch version - 1.10.1
-Python version - 3.8.11
**Others**
-Decoding strategy: beam decoding with beam_size: 2
How can I solve this problem?
Thanks!
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when I set lr = 0.025
the max_lr I see after warmup is 1 e -6, its not 0.025
so should we only use scheduler while training from scratch and not fine-tuning ?
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} | # What does this PR do ?
This PR does the following:
1) Adds capability to condition decoder, pitch and duration predictor of FastPitch and Aligner on speaker embeddings.
2) Adds capability to condition aligner with any kind of embedding.
**Collection**: [Note which collection this PR will affect]
nemo/tts/collection
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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I noticed that the ASR models have been trained on 16k examples and expect a test example to be at the same sampling rate.
1. I have a higher sampling rate example eg. 24kHz and I downsample it to 16kHz and want to get the encoder outputs. Is there a way to verify the encoder outputs are correct?
One high-level sanity check I can think of is if I could get the frame-level transcriptions on the downsampled 16kHz signal and compare with what I hear in the audio approximately at that point of time.
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https://api.github.com/repos/NVIDIA/NeMo/issues/4937 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4937/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4937/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4937/events | https://github.com/NVIDIA/NeMo/pull/4937 | 1,373,799,777 | PR_kwDOC_bI7s4-_N3V | 4,937 | Adding minor fixes | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
it changes training config files structure to not depend on training or validation config setting
Revert back Radam instead of using pytorch implementation
unsorted lens in RADTTS Encoder
**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 examples/tts/radtts.py trainer.max_epochs=400 export_dir="/dir" model.optim.lr=0.00005 model.train_ds.dataloader_params.batch_size=16 model.validation_ds.dataloader_params.batch_size=16 train_dataset="/Training_data.json" validation_datasets="Validation_data.json" pitch_mean=202.67527982176898 pitch_std=54.12737646048864 sample_rate=44100 n_window_size=2048 n_window_stride=512 highfreq=null n_fft=2048 sup_data_path="dir_for sup_data"
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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. Increase speed of rank candidates
2. Include support for p tuning for Megatron GPT
3. Include support for TP> 1 model (i.e. 5B Megatron GPT )
4. Update generate_candidate interface for Megatron GPT based on unified text generation method
5. Included custom collate_fn to remove excess padding in batch for Dialogue GPT Classification Dataset
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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 ?
- Makes GPT Prompt Learning Model class inherit from the base prompt learning model class, as a result:
1. Memory optimizations made for GPT prompt learning with large LMs are now also applied to T5
2. T5 pipeline parallel prompt learning is enabled
3. Future improvements made on the base prompt learning model class will effect both T5 and GPT without duplication of effort
- Adds ability to optionally save an inference ready .nemo file after each validation run if the val loss is lower than all previous val losses
- Reorganizes GPT and T5 prompt learning model classes so methods are listed in the same order, for easier class navigation and understandability.
**Collection**: NLP
# Changelog
- Updates GPT Prompt Learning Model class
- Updates T5 Prompt Learning Model class
- Updates Base Prompt Learning Model class
- Adds `save_intermediate_nemo_file` option to GPT and T5 prompt learning configs
# Usage
```python
python examples/nlp/language_modeling/megatron_gpt_prompt_learning.py \
--config-name=story_generation.yaml \
trainer.devices=2 \
trainer.num_nodes=1 \
trainer.max_epochs=${EPOCHS} \
trainer.max_steps=-1 \
exp_manager.exp_dir=/results \
exp_manager.create_wandb_logger=True \
exp_manager.wandb_logger_kwargs.project=${PROJECT} \
exp_manager.wandb_logger_kwargs.name=${EXPNAME} \
exp_manager.resume_ignore_no_checkpoint=True \
model.language_model_path=/5b_fp16_tp2-O2.nemo.unpacked \
model.nemo_path=/prompt-learning/rocstories_mlp_encoder_${SIZE}_tokens${TOKENS}_epochs${EPOCHS}_${LR}.nemo \
model.save_intermediate_nemo_file=True \
model.existing_tasks=[] \
model.new_tasks=['rocstories'] \
model.tensor_model_parallel_size=2 \
model.pipeline_model_parallel_size=1 \
model.global_batch_size=128 \
model.micro_batch_size=2 \
model.optim.lr=${LR} \
model.data.train_ds=[${DATA}/${TRAIN_FILE}] \
model.data.validation_ds=[${DATA}/${VAL_FILE}] \
model.virtual_prompt_style=p-tuning
```
# 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 ?
Update docs link for `prompt_learning` tutorial
**Collection**: NLP
# 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.
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} | # What does this PR do ?
Add a primer document to our TTS tutorials covering general introductory material related on models and running them in NeMo.
**Collection**: TTS
# Changelog
- Add TTS Primer notebook
- Add images directory (similar to ASR tutorials)
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation | {
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The "Inference_DurationPitchControl" tutorial covers changing the pace of the entire clip by some fixed percentage, but what I actually want is to change the duration of the individual words/phonemes (and the gaps between them) to match their durations in the reference audio.
If this isn't currently available, is it feasible as a future feature? | {
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} | # What does this PR do ?
Confidence estimation based on the maximum probability and entropy methods for CTC and RNNT models.
**Collection**: ASR
# Changelog
- Hypothesis now has frame_confidence, token_confidence, and word_confidence attributes
# Usage
* You can potentially add a usage example below
```python
python scripts/speech_recognition/benchmark_asr_confidence.py pretrained_name=stt_en_conformer_transducer_large_ls dataset_manifest=<librispeech>/test_other.json output_dir=<your_output_dir> 'grid_params="{\"aggregation\": [\"mean\", \"min\"], \"temperature\": [0.33, 0.5]}"'
```
# 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?
@vsl9 @hainan-xv @titu1994
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} | # What does this PR do ?
This PR adds tutorials and documentations for the newly published model "Multi-scale diarization decoder".
Not only tutorial and docs, we need to ha
**Collection**: ASR
# Changelog
Major updated files:
`tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb`
`tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb`
`docs/source/asr/speaker_diarization/*.rst` files
Minor updates:
Some changes are made to use a model published on NGC.
`nemo/collections/asr/models/msdd_models.py`
`nemo/collections/asr/models/clustering_diarizer.py`
# Usage
* You can potentially add a usage example below
These are tutorials and documents.
# 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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
NeMo ASR contributors
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: Core
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | 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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} | # What does this PR do ?
This PR aims to add a tool for generating a set of multi-channel RIRs.
**Collection**: ASR
# Changelog
- Added `RoomCorpusGenerator` in `asr/data/data_simulation.py`
- Added unit tests in `tests/collections/asr/test_asr_data_simulation.py`
- Added an example config and a readme file in `tools/rir_corpus_generator`
# Usage
The tool can be used as
```bash
python rir_corpus_generator.py output_dir=OUTPUT_DIR
```
where `OUTPUT_DIR` is a path to the output directory. This will use the default configuration in `conf/rir_corpus_v1.yaml`.
The output will be structured as
```bash
OUTPUT_DIR
+--{train, dev, test}
| +--*.h5
+--config.yaml
+--{train, dev, test}_manifest.yaml
+--{train, dev, test}_info.png
```
Each directoy, e.g, `{train, dev, test}`, corresponds to a subset of data and contain the `*.h5` files with RIRs. Corresponding `*_manifest.yaml` files contain metadata for each subset.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* n/a
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} | null | null | null | **Describe the bug**
While exporting grammar we are facing error while creating the docker image.
**Steps/Code to reproduce bug**
``` bash
cd tools/text_processing_deployment
bash export_grammars.sh --GRAMMARS=tn_grammars --LANGUAGE=en --INPUT_CASE=cased --FORCE_REBUILD=True --OVERWRITE_CACHE=True
```
Above command is stopping with the following error:
**Expected behavior**
To export the grammar the sparrowhawk image has to be built completely.
**Environment details**
``` text
Client: Docker Engine - Community
Version: 20.10.17
API version: 1.41
Go version: go1.17.11
Git commit: 100c701
Built: Mon Jun 6 23:02:57 2022
OS/Arch: linux/amd64
Context: default
Experimental: true
Server: Docker Engine - Community
Engine:
Version: 20.10.17
API version: 1.41 (minimum version 1.12)
Go version: go1.17.11
Git commit: a89b842
Built: Mon Jun 6 23:01:03 2022
OS/Arch: linux/amd64
Experimental: false
containerd:
Version: 1.6.8
GitCommit: 9cd3357b7fd7218e4aec3eae239db1f68a5a6ec6
runc:
Version: 1.1.4
GitCommit: v1.1.4-0-g5fd4c4d
docker-init:
Version: 0.19.0
GitCommit: de40ad0
```
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04
- Python version: Python 3.8.10 (on the host machine) | {
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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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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I'd like to be able to evaluate the performance of speech/audio classification models (in particular, VAD/SAD models) with different collars, using metrics such as are available in `md-eval.pl` in SCTK (used by Kaldi's SAD recipes) or PyAnnote metrics (see https://github.com/pyannote/pyannote-metrics/blob/develop/pyannote/metrics/detection.py).
**Describe the solution you'd like**
I'd like to have a script that takes a manifest (formatted the same way as for train/validation in speech classification; not necessarily preprocessed to have only short sections) as input and produces error/accuracy metrics. As PyAnnote Metrics is already used elsewhere in NeMo, it could probably easily used for this purpose. Or perhaps there already exist internal/unpublished scripts for that purpose that could be added to NeMo.
**Describe alternatives you've considered**
Creating RTTM output from VAD predictions and evaluating them using SCTK/md-eval.pl.
Also tagging @fayejf
Thanks! | {
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} | Add type checking before calling .split(). The old code will break if the input is a list of strings, which happens when using multiple manifests.
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} | # What does this PR do ?
When restarting training from a saved checkpoint during validation, validation is re-run as a first step (as the checkpoint was saved just before validation). This PR introduces a new loop injected via the `exp_manager` that skips validation if we're restarting.
There was also a mistake with the test definition from #4905 and I extended the test further to check @yaoyu-33s' case.
cc @MaximumEntropy @ericharper @titu1994
# Changelog
- Skip initial validation by default when resuming from checkpoint which saved after a validation pass has occurred.
# 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
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
I am using google colab for using nemo, nemo supports python 3.8 but colab has python 3.7, I can succesfully install python 3.8 in colab and pip but when I try to install nemo, it gives me an error in the end that nemo got installed in legacy version becaue pip failed to build a wheel. After using the `!pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all] --verbose --log ./pip.log` and reading the logs, I find this
> Traceback (most recent call last):
> File "<string>", line 2, in <module>
> File "<pip-setuptools-caller>", line 34, in <module>
> File "/tmp/pip-install-0anzil8f/nemo-toolkit_8b9af28d0a0a4f5d861548d9508e4c38/setup.py", line 209, in <module>
> setuptools.setup(
> File "/usr/local/lib/python3.8/dist-packages/setuptools/__init__.py", line 153, in setup
> return distutils.core.setup(**attrs)
> File "/usr/lib/python3.8/distutils/core.py", line 148, in setup
> dist.run_commands()
> File "/usr/lib/python3.8/distutils/dist.py", line 966, in run_commands
> self.run_command(cmd)
> File "/usr/lib/python3.8/distutils/dist.py", line 985, in run_command
> cmd_obj.run()
> File "/usr/lib/python3/dist-packages/wheel/bdist_wheel.py", line 256, in run
> self.egg2dist(self.egginfo_dir,
> File "/usr/lib/python3/dist-packages/wheel/bdist_wheel.py", line 426, in egg2dist
> pymeta = pkginfo_to_dict(metadata_path,
> File "/usr/lib/python3/dist-packages/wheel/metadata.py", line 177, in pkginfo_to_dict
> new_requirements = sorted(convert_requirements(requirements))
> File "/usr/lib/python3/dist-packages/wheel/metadata.py", line 234, in convert_requirements
> parsed_requirement = pkg_resources.Requirement.parse(req)
> File "/usr/local/lib/python3.8/dist-packages/pkg_resources/__init__.py", line 3154, in parse
> req, = parse_requirements(s)
> ValueError: too many values to unpack (expected 1)
**Steps/Code to reproduce bug**
Install python 3.8 and then run this
**Expected behavior**
Nemo should be installed correctly and its logs should show any problem.
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install: [pip install or from source]. `!pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all] --verbose --log ./pip.log`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu (colab)
- pytorch 1.12+cu116
- Python 3.8
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"merged_at": "2022-09-13T00:24:37"
} | # What does this PR do ?
This PR is to merge `r1.11.0` branch to the `main` branch. There were merge conflicts according to several updates in all domains. I fixed all conflicts. For some conflicts, I chose the `main` changes, while for other conflicts, I made a decision after reading detailed changes in both `r1.11.0` and `main`.
**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?
Owners of PRs in every domain, please help to review the changes. Thanks.
# Additional Information
* Related to # (issue)
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} | null | null | null | On my test set when I run trainer.test() I am getting a wer of 0.081.
global_step │ 0.0 │
│ test_loss │ 7.94022274017334 │
│ test_wer │ 0.08155332505702972
but when I try running
jiwer.wer or
from nemo.collections.asr.metrics.wer import word_error_rate
I am getting wer == 0.139 why is there such a big difference.
have also tried :
WER = WERBPE(decoding=ctc_bpe_decoding, use_cer = False,log_prediction=False, fold_consecutive=False,dist_sync_on_step=True)
Even this gives me a wer of 0.139
I am running this function for every sample and then taking mean
```
from nemo.collections.asr.metrics.wer_bpe import CTCBPEDecoding, WERBPE
from nemo.collections.asr.metrics.wer_bpe import CTCDecodingConfig
ctc_bpe_decoding = CTCBPEDecoding(decoding_cfg = CTCDecodingConfig(), tokenizer = asr_model.tokenizer)
asr_model._wer.log_predictions = False
asr_model._wer.use_cer = False
WER = WERBPE(decoding=ctc_bpe_decoding, use_cer = False,log_prediction=False, fold_consecutive=False,dist_sync_on_step=True)
@torch.no_grad()
def cal_wer(gt, pred):
global WER
# print(asr_model.tokenizer.text_to_tokens(gt))
gt = asr_model.tokenizer.text_to_ids(gt)
pred = asr_model.tokenizer.text_to_ids(pred)
len_gt = torch.tensor([len(gt)])
len_pred = torch.tensor([len(pred)])
gt = torch.tensor([gt])
pred = torch.tensor([pred])
WER.update(targets = gt, predictions = pred, target_lengths = len_gt, predictions_lengths=len_pred)
return WER.compute()[0].item()
``` | {
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} | # What does this PR do ?
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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
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https://api.github.com/repos/NVIDIA/NeMo/issues/4916 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4916/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4916/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4916/events | https://github.com/NVIDIA/NeMo/issues/4916 | 1,368,541,804 | I_kwDOC_bI7s5Rkkps | 4,916 | [TTS][German][Single Speaker][Fastpitch + HifiGAN] Bad Interference RTF on CPU | {
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} | null | null | null | **Describe the bug**
The RTF for interference is greather than 2 on Intel CPUs.
**Steps/Code to reproduce bug**
The below code produces a 7 second audio (speech2.wav) after properly loading models and takes more than 14 seconds. For me its unclear how many cores are utilized and where to configure this.
git clone https://github.com/NVIDIA/NeMo/
cd NeMo
python3.8 -m venv venv
source venv/bin/activate
pip3 install a-lot-of-missing-requirements when executing
python3 tts_german.py where inference code is as follows tts_german.py
```
import soundfile as sf
from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder
import time
# Download and load the pretrained fastpitch model
spec_generator = SpectrogramGenerator.from_pretrained(model_name="tts_de_fastpitch_singlespeaker")#.cuda()
# Download and load the pretrained hifigan model
vocoder = Vocoder.from_pretrained(model_name="tts_de_slr_hifigan_ft_fastpitch_singlespeaker")#.cuda()
# All spectrogram generators start by parsing raw strings to a tokenized version of the string
parsed = spec_generator.parse("Was schreibst Du da gerade Papa?")
# They then take the tokenized string and produce a spectrogram
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
# Finally, a vocoder converts the spectrogram to audio
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
# Save the audio to disk in a file called speech.wav
# Note vocoder return a batch of audio. In this example, we just take the first and only sample.
sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 22050)
starttime=time.time()
parsed = spec_generator.parse("In den alten Zeiten, wo das Wünschen noch geholfen hat, lebte ein König, dessen Töchter waren alle schön.")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
sf.write("speech2.wav", audio.to('cpu').detach().numpy()[0], 22050)
endtime=time.time()
print(endtime-starttime)
```
**Expected behavior**
Faster interference time, RTF < 1.
**Environment overview (please complete the following information)**
See above
**Environment details**
See above
**Additional context**
--
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
- Adds support to train and test models with adoptive lookahead
**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
Need to add/modify following flags to train streaming conformer. `ATT_CONTEXT_STYLE` can be set to `regular`/`chunked_limited`. If `CONV_DUAL_MODE` is set to `true`, then `CONV_CONTEXT_SIZE` should be set to `null`, otherwise `CONV_CONTEXT_SIZE` can be set to `causal` to train streaming models. `ATT_CONTEXT_SIZE` can be set something like `[102,[16,33,50,0],full]` to train chunk-based streaming (middle four value) and non-streaming (last) modes together. Probability or fraction of updates for non-streaming mode can be controlled by setting `NON_STREAMING_PROB` in [0, 1] range. If both modes are trained together, separate layer norm can be instantiated by setting `STREAMING_LAYER_NORM` to `true`.
```python
model.preprocessor.normalize=NA \
model.encoder.att_context_size=$ATT_CONTEXT_SIZE \
model.encoder.att_context_style=$ATT_CONTEXT_STYLE \
model.encoder.causal_downsampling=true \
model.encoder.conv_context_size=$CONV_CONTEXT_SIZE \
model.encoder.conv_norm_type=layer_norm \
++model.encoder.non_streaming_prob=$NON_STREAMING_PROB \
++model.encoder.conv_dual_mode=$CONV_DUAL_MODE \
++model.streaming_layer_norm=$STREAMING_LAYER_NORM
```
To compute wer for different modes, use the following command
```
python ./examples/asr/transcribe_speech.py model_path=$MODEL_PATH \
+val_random_params_enc.att_context_size_right=$ATT_CONTEXT_SIZE_RIGHT
```
where `ATT_CONTEXT_SIZE_RIGHT` can be set to any desired value from`{0, 16, 33, 50, 'full'}` if the model was trained with `ATT_CONTEXT_SIZE=[102,[16,33,50,0],full]`
```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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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes `ptl_load_state` calls not providing the class attribute.
**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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} | # What does this PR do ?
Fix and refactor label models
**Collection**: ASR
# Changelog
- fix hanging issue happens in test after train in` speech_to_label.py `, `speaker_reco.py` and `speaker_reco_finetune`.py
- fix weighted cross entropy by disabling cal_label_occurences for val and test
- refactor loss instantiation part in label_models
- updated yamls file for above refactory.
# Usage
Specify loss as below in config files for label_models
```python
loss:
_target_: nemo.collections.common.losses.cross_entropy.CrossEntropyLoss
weight: 'auto' # could be 'auto' or 1D tensor or null
# or
loss:
_target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss # you could also use cross-entrophy loss
scale: 30
margin: 0.2
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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] | closed | false | null | [] | null | null | 2022-09-09T13:44:01 | 2022-10-23T02:14:27 | 2022-10-23T02:14:27 | NONE | {
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} | null | null | null | Getting the following error : Early stopping conditioned on metric `val_loss` which is not available. Pass in or modify your `EarlyStopping` callback to use any of the following: ``
using nemo-toolkit['asr'] == 1.10.0
pytorch-lightning 1.7.3
torch 1.12.1
torchmetrics 0.9.3
looks like this is a Pytorch-lightning help any work around this?
trainer = ptl.Trainer(
val_check_interval= FLAGS.val_check_interval,
devices=-1,
max_epochs=EPOCHS,
accumulate_grad_batches=FLAGS.accumulate_grad_batches,
enable_checkpointing=FLAGS.enable_checkpointing,
logger=FLAGS.logger,
log_every_n_steps=FLAGS.log_every_n_steps, ##value should be high to save memory.
callbacks = [early_stop],
sync_batchnorm = FLAGS.sync_batchnorm,
gradient_clip_val=FLAGS.gradient_clip_val,
num_sanity_val_steps=0,
)
@titu1994 | {
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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
- Adds adapter tuning support for t5 models
- Adds Infused Adapter (IA3) support for gpt and t5 models.
- Ensures that no test example is skipped due to max-length issues (a wrong answer is better than skipping)
**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 NeMo/examples/nlp/language_modeling/tuning/megatron_t5_adapter_tuning.py
python NeMo/examples/nlp/language_modeling/tuning/megatron_t5_ia3_tuning.py
python NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_ia3_tuning.py
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/4907 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4907/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4907/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4907/events | https://github.com/NVIDIA/NeMo/pull/4907 | 1,367,124,332 | PR_kwDOC_bI7s4-pHAW | 4,907 | Remove unused cv collection | {
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} | Signed-off-by: Oleksii Kuchaiev <[email protected]>
# What does this PR do ?
Removes not used CV collection
**Collection**: cv
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation | {
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