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} | null | null | null | **Describe the bug**
I have been trying to get wav2vec ASR training up and running. I am mainly trying to use the model defined in
```
examples/asr/experimental/wav2vec/configs/wav2vecCTC.yaml
```
I also tried to get a model with wav2vec_pretrain.yaml running, but I get the same error as outlined below.
The call I use is:
```
python /workspace/nemo/examples/asr/asr_ctc/speech_to_text_ctc.py \
--config-path=/workspace/nemo/examples/asr/experimental/wav2vec/configs \
--config-name=wav2vecCTC \
exp_manager.name=wav2vec \
+exp_manager.exp_dir=/experiments \
+exp_manager.version=20220613-1105 \
exp_manager.resume_if_exists=true \
exp_manager.resume_ignore_no_checkpoint=true \
+exp_manager.checkpoint_callback_params.save_best_model=true \
model.train_ds.manifest_filepath=/data/train.nemo \
model.train_ds.batch_size=16 \
model.train_ds.max_duration=17 \
+model.train_ds.min_duration=1 \
model.validation_ds.manifest_filepath=/data/validation.nemo \
model.validation_ds.batch_size=16 \
model.test_ds.manifest_filepath=/data/test_old.nemo \
model.test_ds.batch_size=16 \
model.tokenizer.dir=/data/out/tokenizer_spe_bpe_v128 \
model.tokenizer.type=bpe
```
The error is get is:
```
KeyValidationError('ListConfig indices must be integers or slices, not str\n full_key: [0][emb_dim]\n object_type=list')
```
It seems like something is wrong with `emd_dim`. I leave this at its default in the `embedding_dim` parameter in the YAML file. Even if I set this explicitly I get the same error. Attached is also the full output of the call: [output_20220613-1105.txt](https://github.com/NVIDIA/NeMo/files/8890509/output_20220613-1105.txt).
Another minor thing I also noticed is that the command call crashes even earlier if I do not provide a test set. Based on the YAML file test set should not be mandatory as it is set to null and not ??.
I am aware that this is an experimental functionality, so any help will be greatly appreciated.
**Steps/Code to reproduce bug**
Full command call can be found in the previous section.
**Expected behavior**
I expect the wav2vec model to start the training process.
**Environment overview (please complete the following information)**
I am using the official NVIDIA pytorch 22.01 container, the used NeMo version is the current version of the master branch.
**Environment details**
I am using the official NVIDIA pytorch 22.01 container, the used NeMo version is the current version of the master branch.
**Additional context**
Will provided any additional info if required.
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I am trying to convert normalized asr model outputs into written form using _**inverse_normalizer.inverse_normalize**_.
Whereas some intractable problems arose when dealing a specific expression about time like **"* to *"**.
When I convert this verbal form **"ten to eleven"**.
The ouput is **"ten to eleven"** which is inconsistent with the expected output **"10:50"**.
If there is a sentence containing such expression about time, it will cause a more erroneous result.
For example:
The ITN output of **"set an alarm at ten to eleven pm"** is **''set an alarm at ten to 11:00 p.m."**
This is a general problem in my dataset.
I'd like to know how to solve this problem.
Should I add some specific custom rules?
Thanks a lot for any answer!
**Environment overview**
- Environment location: Docker
- Method of NeMo install: from source
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I was wondering if it's possible to add first class support for W&B artifacts to the `modelPT.restore_from()` method?
Currently, I run something like the following if I want to download a NeMo model saved on W&B:
```py
from urllib.parse import urlparse
model_path = "wandb://{TEAM}/{PROJECT}/{MODEL_NAME}:{LABEL}"
url_path = urlparse(model_path)
if url_path.scheme == "wandb":
artifact_name = f"{url_path.netloc}{url_path.path}"
model_path = download_model_from_wandb(artifact_name, "EncDecCTCModel")
```
and `download_model_from_wandb` would look something like
```py
from pathlib import Path
import wandb
from nemo.utils import logging, model_utils
ARTIFACT_DIR = "/tmp"
def download_model_from_wandb(artifact_name: str, model_class: str) -> Path:
api = wandb.Api()
artifact = api.artifact(artifact_name)
model_name = artifact.name.split(":")[0] # Remove the tag/label
model_name = Path(model_name).stem
root = ARTIFACT_DIR / model_name
nemo_path = root / f"{model_name}.nemo"
artifact.download(root=root)
return nemo_path
```
Maybe we could use some logic similar to the above with `restore_from()` to load the downloaded model?
**Describe the solution you'd like**
I would think that this could live in the [`restore_from()`](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py#L258) method itself, but it seems there's a new [`SaveRestoreConnector`](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/connectors/save_restore_connector.py) so not sure if it's more logical to put something like this there.
**Additional context**
Can open a PR for this if it is welcome. | {
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} | [] | closed | false | null | [] | null | null | 2022-06-09T18:58:35 | 2022-06-09T19:04:20 | 2022-06-09T19:04:20 | NONE | {
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} | null | null | null | ### Discussed in https://github.com/NVIDIA/NeMo/discussions/4217
**Original post:**
<div type='discussions-op-text'>
<sup>Originally posted by **riqiang-dp** May 20, 2022</sup>
I'm trying to train ASR models on a pretty unbalanced data set (lengthwise, a lot short utterances but also long utterances (up to 1 min)). I have been able to train with smaller batch size and also with some pre-segmentation to shorten the max duration. I'm now just trying bucketing, but it seems like it only works with tarred data sets.
After tarring the original set and using some bucketing, I'm seeing that the estimated time for one epoch goes up to ~7h instead of 2h for training with the raw form audio files. If I don't do bucketing and still use the tarred version, I get ~10h estimated time for one epoch.
I'm setting something like 4 buckets, 128 shards. Using A100 GPUs on a Google Cloud VM, using NeMo 1.7.0
I'm just wondering, are these numbers normal? If so when does it make more sense to use tarred + bucketing strategy versus just train using the wav files?
</div>
**Updated info**
The info below doesn't 100% align with the description above but the gist is the same. For example I have used just shorter audio for further testing, instead of using an unbalanced dataset. But the slow down is still happening. For something that's usually 20-30 mins / epoch with wav files, it's hours for tar files. I've tested a lot of combinations still don't know why this is happening. Everything's stored in a disk attached to the GCP VM. Please see details below. Otherwise, I did notice in the logs that the train_step_timing looks normal (apart from some very high spikes) but when I observe the progress bar it seems to pause every `batch_size` steps.
Below are the stats of the tarred dataset and the training steps/speed
> bucket 1 with min_duration=2.5 and max_duration=6.875
> 172416 files
> bucket 2 with min_duration=6.875 and max_duration=11.25
> 107136 files
> bucket 3 with min_duration=11.25 and max_duration=15.625
> 105600 files
> bucket 4 with min_duration=15.625 and max_duration=20.00001
> 136192 files
> total: 521344
>
> eval files: 2406
> eval batch size: 8 (these are long form audio)
>
> num gpus: 8 (for all the cases)
> batch size: 1
> bucketing batch size: 30
> bucketing strategy: synced_randomized
> step per epoch (bucketing): 8966
> training time per epoch: 1h15m
>
> batch size: 30
> step per epoch (non bucketing): 17417
> training time per epoch: 1h45m
However, if I just train the original wav data set (same eval set)
> total files: 521543
> train batch size: 30
> step per epoch: 2212
> training time per epoch: 20m
code to create the tarred / bucketed dataset
```
python <scripts path>/convert_to_tarred_audio_dataset.py \
--manifest_path=data/train.json \
--target_dir=data/train \
--num_shards=128 \
--max_duration=20 \
--min_duration=2.5 \
--shuffle \
--shuffle_seed=1 \
--sort_in_shards \
--workers=32 \
--buckets_num=4
```
Extra args to make my normal training script work with tarred dataset
```
model.train_ds.manifest_filepath=[[data/train/bucket1/tarred_audio_manifest.json],[data/train/bucket2/tarred_audio_manifest.json],[data/train/bucket3/tarred_audio_manifest.json],[data/train/bucket4/tarred_audio_manifest.json]] \
+model.train_ds.tarred_audio_filepaths=[[data/train/bucket1/audio__OP_0..127_CL_.tar],[data/train/bucket2/audio__OP_0..127_CL_.tar],[data/train/bucket3/audio__OP_0..127_CL_.tar],[data/train/bucket4/audio__OP_0..127_CL_.tar]] \
+model.train_ds.bucketing_strategy=synced_randomized \
+model.train_ds.bucketing_batch_size=30 \
model.train_ds.batch_size=1 \
+model.train_ds.tarred_shard_strategy="scatter" \
model.train_ds.shuffle_n=2048 \
model.train_ds.is_tarred=True \
model.train_ds.max_duration=20 \
model.train_ds.min_duration=2.5 \
model.train_ds.num_workers=32 \
model.train_ds.pin_memory=True \
```
Environment: Using 8 A100 GPUs on a single Google Cloud VM, using NeMo 1.7.0
I have also tried setting num_workers to 8 as you guys suggested, doesn't seem to affect the speed much. | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
1. Makes position embedding expansion specific to a batch to avoid checkpoint size mismatches.
2. Logs a warning when position embeddings are being expanded.
**Collection**: NLP
# Changelog
See "What does PR do" above.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Raises an error if a trainer is not passed to `MegatronBaseModel`
**Collection**: NLP
# Changelog
- Raises an error if a trainer is not passed to `MegatronBaseModel`.
# Usage
N/A
# 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**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Shantanu Acharya <[email protected]>
# What does this PR do ?
Fixed the issue where the auto-switching functionality of the `ConformerEncoderAdapter` input feature dimension was missing.
**Collection**: ASR
# Changelog
File changed: `nemo/collections/asr/modules/conformer_encoder.py`
Function added: `_update_adapter_cfg_input_dim`
Updates the `in_features` value in the adapter config with the correct value i.e. `self.d_model`
Function modified: `add_adapter`
Calls the `self._update_adapter_cfg_input_dim` function to update the adapter input feature dimensions.
# 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 NeMo ASR.
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Tags are propagated to the output text during normalization.
**Steps/Code to reproduce bug**
```python
from nemo_text_processing.text_normalization import normalize
tn = normalize.Normalizer("lower_cased")
text = "google.com is related to apple.com"
print(tn.normalize(example))
"""
google dot com " right brace right brace TOKENS left brace NAME: "IS" right brace TOKENS left brace NAME: "RELATED" right brace TOKENS left brace NAME: "TO" right brace TOKENS left brace ELECTRONIC left brace DOMAIN: "APPLE dot COM
"""
example = "google.com is related to com.apple"
print(tn.normalize(example))
"""
google dot com is related to com.apple
"""
```
**Expected behavior**
The first example should return `google dot com is related to apple dot com`, the second one is ok as I believe one would need to add rules to handle such a "strange" case.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: `git clone https://github.com/NVIDIA/NeMo`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version: 1.12.0
- Python version: 3.8.12
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | Signed-off-by: stevehuang52 <[email protected]>
This PR fixes the RNNT tutorial by updating the manifest processing code and contextnet config path. | {
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} | [] | closed | true | null | [] | null | null | 2022-06-08T09:02:24 | 2022-06-22T16:38:16 | 2022-06-22T16:38:16 | NONE | {
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} | null | null | null | I am training a conformer-ctc model using the encoder weights of a pre-trained English conformer-ctc model.
For the test, I trained on a validation sample of GOLOS dataset and on my own data. By the time 14 hours. For validation, I used our other data and a test GOLOS dataset.
At first, the loss and ver on the train dataset and on the validation one converged.
After a 100k step, the loss began to diverge, and the ver slowly but continued to converge.
Could you help me understand what this means?
After all, if the predictions began to differ, then the words should be predicted incorrectly and WER should increase in this case...
*Train*
*Val*
`config.yaml` for Conformer-ctc:
```bash
name: "Conformer-CTC-Char"
model:
sample_rate: 8000
# timesteps: 128
labels: &labels [ " ", "а", "б", "в", "г", "д", "е", "ж", "з", "и", "й", "к", "л", "м", "н", "о", "п",
"р", "с", "т", "у", "ф", "х", "ц", "ч", "ш", "щ", "ъ", "ы", "ь", "э", "ю", "я" ]
log_prediction: true # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
skip_nan_grad: false
train_ds:
manifest_filepath: "../data/processed/manifests/etalon_golos_train.json"
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: &batch_size 4
shuffle: true
num_workers: 8
pin_memory: true
trim_silence: false # true
max_duration: 55
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
# augmentor:
# # shift:
# # prob: 1.0
# # min_shift_ms: -5.0
# # max_shift_ms: 5.0
# white_noise:
# prob: 0.5
# min_level: -90
# max_level: -46
# speed:
# prob: 0.5
# sr: ${model.sample_rate}
# resample_type: 'kaiser_fast'
# min_speed_rate: 0.95
# max_speed_rate: 1.05
validation_ds:
manifest_filepath: "../data/processed/manifests/etalon_val.json"
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: *batch_size
shuffle: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: "../data/processed/manifests/test.json"
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: *batch_size
shuffle: false
num_workers: 8
pin_memory: true
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
freq_masks: 2
time_masks: 2
freq_width: 15
time_width: 25
rect_masks: 5
rect_time: 25
rect_freq: 15
# crop_or_pad_augment:
# _target_: nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation
# audio_length: ${model.timesteps}
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 16
d_model: 176
# 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: 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: 33
vocabulary: ${model.labels}
optim:
name: adamw
lr: 2.0
# 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: 1e-3
# 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: 200
max_steps: null # 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: auto
strategy: dp
accumulate_grad_batches: 1
gradient_clip_val: 0.0
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
log_every_n_steps: 100 # Interval of logging.
progress_bar_refresh_rate: 10
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
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: 1
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
resume_if_exists: false
resume_ignore_no_checkpoint: false
```
Thanks a lot for any answer!
| null | {
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} | Fix a bug about how to do post-ln in the transformer layer.
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} | [] | closed | false | null | [] | null | null | 2022-06-07T21:54:03 | 2022-06-08T02:51:47 | 2022-06-08T02:51:43 | CONTRIBUTOR | {
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} | Signed-off-by: Yang Zhang <[email protected]>
sacremoses is used and imported in [common/tokenizers](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/tokenizers/moses_tokenizers.py#L17)
so need to go into common requirements | {
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} | Signed-off-by: stevehuang52 <[email protected]>
# What does this PR do ?
This PR fixes the problem of the program hanging during training with fully randomized bucketing.
# Why hanging?
When training RNNT models with fully randomized bucketing, and enabling fused batch, the program will hang when calculating training WER: [https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/rnnt.py#L931](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/rnnt.py#L931)
The bug happens when the GPU with largest batch size is still calculating the forward fused-batch while the other GPUs with smaller batch are already finished:
[https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/rnnt.py#L853-L933](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/rnnt.py#L853-L933)
This is due to the fact that, when `compute()` is called, PTL will perform synchronization across all devices, which eventually calls `distributed.barrier()`:
[https://github.com/PyTorchLightning/metrics/blob/master/torchmetrics/metric.py#L113](https://github.com/PyTorchLightning/metrics/blob/master/torchmetrics/metric.py#L113), [https://github.com/PyTorchLightning/metrics/blob/master/torchmetrics/metric.py#L485-L513](https://github.com/PyTorchLightning/metrics/blob/master/torchmetrics/metric.py#L485-L513), [https://github.com/PyTorchLightning/metrics/blob/master/torchmetrics/utilities/distributed.py#L122
](https://github.com/PyTorchLightning/metrics/blob/master/torchmetrics/utilities/distributed.py#L122
)
In fully randomized bucketing, different GPUs tend to have different batch sizes, and when some GPUs have finished their batch earlier than others, the unfinished ones will keep hanging at `distributed.barrier()` to wait for the synchronization that will never happen.
# How we fix it?
The simple fix is to move the `compute()` function call outside of the loop of sub-batches. Note that, theoretically this modification have the same output as the original one, and here's the logic.
Suppose that the there are 3 GPUs and each has 2 sub-batches, where the score produced by each GPU is fixed as its rank:
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 1 | 2 | 3 |
|sub-batch 2| 1 | 2 | 3 |
In the original code, where `compute()` is called inside the for loop of sub-batches, it's actually syncing across all GPUs in each step, then aggregate across all sub-batches. In other words, it first perform all_reduce() to sync each column for each row, then sum over the rows:
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 1 | 2 | 3 |
Call `compute()` on each GPU and all_reduce ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 6 | 6 | 6 |
Run sub-batch 2 ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 6 | 6 | 6 |
|sub-batch 2| 1 | 2 | 3 |
Call `compute()` on each GPU and all_reduce ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 6 | 6 | 6 |
|sub-batch 2| 6 | 6 | 6 |
Sum for each GPU ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|batch total | 12 | 12 | 12 |
On the other hand, this PR first aggregate the scores per GPU, then sync across all GPUs after the for loop of sub-batches. In other words, we sum the rows for each column, and then perform all_reduce to sync across all rows:
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 1 | 2 | 3 |
update on each GPU ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|batch total| 1 | 2 | 3 |
run sub-batch 2 ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|sub-batch 1| 1 | 2 | 3 |
|sub-batch 2| 1 | 2 | 3 |
update on each GPU ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
batch total | 2 | 4 | 6 |
Call `compute()` and and all_reduce ↓
| | GPU 1 | GPU 2 | GPU 3 |
|-------------|--------|--------|---------|
|batch total | 12 | 12 | 12 |
We can see that, two approaches actually have the same results, and it doesn't matter whether we perform the synchronization in or after the for loop of sub-batches. By moving `compute()` outside the for loop, we can guarantee that there is no hanging even when GPUs have different batch sizes.
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} | Signed-off-by: Shantanu Acharya <[email protected]>
# What does this PR do ?
Improves the speed of the google speech commands dataset processing script by adding multiprocessing support.
**Collection**: dataset processing scripts
# Changelog
File changed: `scripts/dataset_processing/process_speech_commands_data.py`
Functions added:
- `__construct_filepaths`: Refactored the filepath construction of the audio samples into a separate function
- `__construct_silence_set`: Used for multiprocessing - Constructs the silence set
- `__rebalance_files`: Used for multiprocessing - rebalances the number of files in each class
- `__prepare_metadata`: Used for multiprocessing - create the manifest entry for a particular audio file
Functions modified: `__process_data`
- Accepts a new argument `num_processes` which determines the number of processes to use for multiprocessing
- Refactored the filepath construction, silence set construction, rebalancing and metadata construction logic to separate functions as described above
For writing the silence files, changed `librosa.output` to `soundfile.write` since in newer versions of librosa, the output module has been removed.
# Usage
```python
python process_speech_commands_data.py \
--data_root=<absolute path to where the data should be stored> \
--data_version=<either 1 or 2, indicating version of the dataset> \
--class_split=<either "all" or "sub", indicates whether all 30/35 classes should be used, or the 10+2 split should be used> \
--num_processes=<number of processes to use for data preprocessing> \
--rebalance \
--log
```
# 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
## Who can review?
Anyone in the NeMo.
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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 | I'm currently using nemo only for text normalization - I only import nemo_text_processing.text_normalization.normalize.Normalizer.
I install nemo toolkit using nemo-toolkit[all]==1.8.2. Is there any way to reduce the number of dependencies, for example by not installing [all]? I haven't found anything in the docs.
Thanks for any help in advance.
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} | null | null | null | Hi, I use `examples/asr/speech_pretraining/speech_pre_training.py` code to train a self-supervised using conformer on my custom dataset. I just changed the manifest files address in conf file. When I run the code, I encounter the below issue.
I use NeMo version 1.8.0. Also I test it on version 1.9.0 but the same problem occured.
**Environment details:**
torch 1.9.0a0+c3d40fd
pytorch-lightning 1.6.4
Python 3.8.10
NeMo 1.8.0
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Those are modifications needed to support RadTTS net - which, for now, cannot be integrated into Nemo.
Adding those support bits to Nemo will make it easier to make internal RADTTS implementation a Nemo extension, rather than a fork.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add CTC Decoding modules to any ASR CTC model, equivalent to RNNT decoding
**Collection**: [ASR]
# Changelog
- Add CTC Decoding code for all Char and Subword CTC models
- Add support for hypothesis update with logprobs, alignments, timestamps
- Compute char/subword and word boundaries from CTC models
- Prepare for high level beam search API.
- Add the change_decoding_strategy() API to CTC models
# Usage
* CTC models will now have a `model.decoding` layer
```python
asr_model = EncDecCTCModelBPE.from_pretrained("<NAME>", map_location='cpu') # type: EncDecCTCModel
asr_model.eval()
asr_model.freeze()
decoding_cfg = OmegaConf.create({})
with open_dict(decoding_cfg):
decoding_cfg.strategy = "greedy"
decoding_cfg.compute_timestamps = True
asr_model.change_decoding_strategy(decoding_cfg)
hyp = asr_model.transcribe(['<SOME AUDIO FILEPATH>'],
batch_size=1, return_hypotheses=True)
print("Evaluating -")
print()
print("Transcription: ", hyp[0].text)
print()
print("Total number of timesteps :", hyp[0].length)
print()
print("Character/Subword Time-Stamps :", hyp[0].timestep['char'])
print()
print("Word Time-Stamps :", hyp[0].timestep['word'])
print()
################
'''
Transcription: now we have some pilot things that do this at the sixty to eighty percent level but getting up to that full percentage that will be very tricky and agreeing on where these co two quantities should be put
Total number of timesteps : tensor(337, dtype=torch.int32)
Character/Subword Time-Stamps : [{'char': 'n', 'start_offset': 10, 'end_offset': 11}, {'char': 'o', 'start_offset': 11, 'end_offset': 12}, {'char': 'w', 'start_offset': 12, 'end_offset': 14}, {'char': 'we', 'start_offset': 14, 'end_offset': 16}, {'char': 'have', 'start_offset': 16, 'end_offset': 21}, {'char': 'so', 'start_offset': 21, 'end_offset': 25}, {'char': 'm', 'start_offset': 25, 'end_offset': 27}, {'char': 'e', 'start_offset': 27, 'end_offset': 28}, {'char': 'p', 'start_offset': 28, 'end_offset': 31}, {'char': 'i', 'start_offset': 31, 'end_offset': 34}, {'char': 'l', 'start_offset': 34, 'end_offset': 36}, {'char': 'o', 'start_offset': 36, 'end_offset': 38}, {'char': 't', 'start_offset': 38, 'end_offset': 40}, {'char': '', 'start_offset': 40, 'end_offset': 43}, {'char': 'th', 'start_offset': 43, 'end_offset': 44}, {'char': 'ing', 'start_offset': 44, 'end_offset': 46}, {'char': 's', 'start_offset': 46, 'end_offset': 48}, {'char': 'that', 'start_offset': 48, 'end_offset': 50}, {'char': 'do', 'start_offset': 50, 'end_offset': 54}, {'char': 'this', 'start_offset': 54, 'end_offset': 59}, {'char': 'a', 'start_offset': 59, 'end_offset': 65}, {'char': 't', 'start_offset': 65, 'end_offset': 66}, {'char': 'the', 'start_offset': 66, 'end_offset': 68}, {'char': 's', 'start_offset': 68, 'end_offset': 73}, {'char': 'i', 'start_offset': 73, 'end_offset': 74}, {'char': 'x', 'start_offset': 74, 'end_offset': 75}, {'char': 't', 'start_offset': 75, 'end_offset': 77}, {'char': 'y', 'start_offset': 77, 'end_offset': 79}, {'char': 'to', 'start_offset': 79, 'end_offset': 81}, {'char': 'e', 'start_offset': 81, 'end_offset': 85}, {'char': 'ight', 'start_offset': 85, 'end_offset': 88}, {'char': 'y', 'start_offset': 88, 'end_offset': 90}, {'char': 'p', 'start_offset': 90, 'end_offset': 93}, {'char': 'er', 'start_offset': 93, 'end_offset': 94}, {'char': 'c', 'start_offset': 94, 'end_offset': 96}, {'char': 'ent', 'start_offset': 96, 'end_offset': 98}, {'char': '', 'start_offset': 98, 'end_offset': 102}, {'char': 'le', 'start_offset': 102, 'end_offset': 103}, {'char': 've', 'start_offset': 103, 'end_offset': 105}, {'char': 'l', 'start_offset': 105, 'end_offset': 107}, {'char': 'but', 'start_offset': 107, 'end_offset': 109}, {'char': 'g', 'start_offset': 109, 'end_offset': 114}, {'char': 'e', 'start_offset': 114, 'end_offset': 115}, {'char': 't', 'start_offset': 115, 'end_offset': 116}, {'char': 't', 'start_offset': 116, 'end_offset': 119}, {'char': 'ing', 'start_offset': 119, 'end_offset': 120}, {'char': '', 'start_offset': 120, 'end_offset': 123}, {'char': 'u', 'start_offset': 123, 'end_offset': 124}, {'char': 'p', 'start_offset': 124, 'end_offset': 126}, {'char': 'to', 'start_offset': 126, 'end_offset': 132}, {'char': 'that', 'start_offset': 132, 'end_offset': 136}, {'char': 'f', 'start_offset': 136, 'end_offset': 140}, {'char': 'u', 'start_offset': 140, 'end_offset': 143}, {'char': 'll', 'start_offset': 143, 'end_offset': 145}, {'char': 'p', 'start_offset': 145, 'end_offset': 149}, {'char': 'er', 'start_offset': 149, 'end_offset': 151}, {'char': 'c', 'start_offset': 151, 'end_offset': 153}, {'char': 'ent', 'start_offset': 153, 'end_offset': 156}, {'char': 'a', 'start_offset': 156, 'end_offset': 159}, {'char': 'g', 'start_offset': 159, 'end_offset': 161}, {'char': 'e', 'start_offset': 161, 'end_offset': 163}, {'char': 'that', 'start_offset': 163, 'end_offset': 174}, {'char': 'will', 'start_offset': 174, 'end_offset': 179}, {'char': 'be', 'start_offset': 179, 'end_offset': 185}, {'char': '', 'start_offset': 185, 'end_offset': 196}, {'char': 'ver', 'start_offset': 196, 'end_offset': 198}, {'char': 'y', 'start_offset': 198, 'end_offset': 201}, {'char': 't', 'start_offset': 201, 'end_offset': 205}, {'char': 'ri', 'start_offset': 205, 'end_offset': 207}, {'char': 'ck', 'start_offset': 207, 'end_offset': 211}, {'char': 'y', 'start_offset': 211, 'end_offset': 214}, {'char': 'and', 'start_offset': 214, 'end_offset': 225}, {'char': 'a', 'start_offset': 225, 'end_offset': 242}, {'char': 'g', 'start_offset': 242, 'end_offset': 245}, {'char': 're', 'start_offset': 245, 'end_offset': 247}, {'char': 'e', 'start_offset': 247, 'end_offset': 248}, {'char': 'ing', 'start_offset': 248, 'end_offset': 250}, {'char': 'on', 'start_offset': 250, 'end_offset': 253}, {'char': 'w', 'start_offset': 253, 'end_offset': 259}, {'char': 'h', 'start_offset': 259, 'end_offset': 260}, {'char': 'e', 'start_offset': 260, 'end_offset': 261}, {'char': 're', 'start_offset': 261, 'end_offset': 263}, {'char': 'the', 'start_offset': 263, 'end_offset': 266}, {'char': 'se', 'start_offset': 266, 'end_offset': 270}, {'char': 'co', 'start_offset': 270, 'end_offset': 278}, {'char': 't', 'start_offset': 278, 'end_offset': 288}, {'char': 'w', 'start_offset': 288, 'end_offset': 289}, {'char': 'o', 'start_offset': 289, 'end_offset': 292}, {'char': '', 'start_offset': 292, 'end_offset': 301}, {'char': 'q', 'start_offset': 301, 'end_offset': 302}, {'char': 'u', 'start_offset': 302, 'end_offset': 303}, {'char': 'an', 'start_offset': 303, 'end_offset': 305}, {'char': 't', 'start_offset': 305, 'end_offset': 306}, {'char': 'it', 'start_offset': 306, 'end_offset': 308}, {'char': 'i', 'start_offset': 308, 'end_offset': 310}, {'char': 'es', 'start_offset': 310, 'end_offset': 312}, {'char': 's', 'start_offset': 312, 'end_offset': 314}, {'char': 'h', 'start_offset': 314, 'end_offset': 315}, {'char': 'o', 'start_offset': 315, 'end_offset': 316}, {'char': 'u', 'start_offset': 316, 'end_offset': 317}, {'char': 'l', 'start_offset': 317, 'end_offset': 318}, {'char': 'd', 'start_offset': 318, 'end_offset': 319}, {'char': 'be', 'start_offset': 319, 'end_offset': 321}, {'char': 'p', 'start_offset': 321, 'end_offset': 324}, {'char': 'u', 'start_offset': 324, 'end_offset': 326}, {'char': 't', 'start_offset': 326, 'end_offset': 327}]
Word Time-Stamps : [{'word': 'now', 'start_offset': 10, 'end_offset': 16}, {'word': 'we', 'start_offset': 16, 'end_offset': 21}, {'word': 'have', 'start_offset': 21, 'end_offset': 25}, {'word': 'some', 'start_offset': 28, 'end_offset': 31}, {'word': 'pilot', 'start_offset': 40, 'end_offset': 43}, {'word': 'things', 'start_offset': 48, 'end_offset': 50}, {'word': 'that', 'start_offset': 50, 'end_offset': 54}, {'word': 'do', 'start_offset': 54, 'end_offset': 59}, {'word': 'this', 'start_offset': 59, 'end_offset': 65}, {'word': 'at', 'start_offset': 66, 'end_offset': 68}, {'word': 'the', 'start_offset': 68, 'end_offset': 73}, {'word': 'sixty', 'start_offset': 79, 'end_offset': 81}, {'word': 'to', 'start_offset': 81, 'end_offset': 85}, {'word': 'eighty', 'start_offset': 90, 'end_offset': 93}, {'word': 'percent', 'start_offset': 98, 'end_offset': 102}, {'word': 'level', 'start_offset': 107, 'end_offset': 109}, {'word': 'but', 'start_offset': 109, 'end_offset': 114}, {'word': 'getting', 'start_offset': 120, 'end_offset': 123}, {'word': 'up', 'start_offset': 126, 'end_offset': 132}, {'word': 'to', 'start_offset': 132, 'end_offset': 136}, {'word': 'that', 'start_offset': 136, 'end_offset': 140}, {'word': 'full', 'start_offset': 145, 'end_offset': 149}, {'word': 'percentage', 'start_offset': 163, 'end_offset': 174}, {'word': 'that', 'start_offset': 174, 'end_offset': 179}, {'word': 'will', 'start_offset': 179, 'end_offset': 185}, {'word': 'be', 'start_offset': 185, 'end_offset': 196}, {'word': 'very', 'start_offset': 201, 'end_offset': 205}, {'word': 'tricky', 'start_offset': 214, 'end_offset': 225}, {'word': 'and', 'start_offset': 225, 'end_offset': 242}, {'word': 'agreeing', 'start_offset': 250, 'end_offset': 253}, {'word': 'on', 'start_offset': 253, 'end_offset': 259}, {'word': 'where', 'start_offset': 263, 'end_offset': 266}, {'word': 'these', 'start_offset': 270, 'end_offset': 278}, {'word': 'co', 'start_offset': 278, 'end_offset': 288}, {'word': 'two', 'start_offset': 292, 'end_offset': 301}, {'word': 'quantities', 'start_offset': 312, 'end_offset': 314}, {'word': 'should', 'start_offset': 319, 'end_offset': 321}, {'word': 'be', 'start_offset': 321, 'end_offset': 324}, {'word': 'put', 'start_offset': 321, 'end_offset': 327}]
'''
```
# Before your PR is "Ready for review"
**Pre checks**:
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https://api.github.com/repos/NVIDIA/NeMo/issues/4341 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4341/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4341/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4341/events | https://github.com/NVIDIA/NeMo/pull/4341 | 1,262,577,833 | PR_kwDOC_bI7s45NNCI | 4,341 | [TTS] enforced pin_memory = True | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
The default `false` will slow down the data transfer to CUDA-enabled GPUs. See reference for details,
[1] https://discuss.pytorch.org/t/when-to-set-pin-memory-to-true/19723
[2] https://developer.nvidia.com/blog/how-optimize-data-transfers-cuda-cc/
**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
# if using x.cuda(), you should call x.cuda(non_blocking=True)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4340 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4340/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4340/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4340/events | https://github.com/NVIDIA/NeMo/pull/4340 | 1,262,377,086 | PR_kwDOC_bI7s45Mg28 | 4,340 | Don't wrap model with ddp when using O2 | {
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} | # What does this PR do ?
This PR checks if the NeMo Megatron model is using the O2 recipe. If so, we will not wrap the model with DDP.
When wrapping the model with DDP, gradient buckets are created which take up GPU memory. This is not needed since the O2 recipe creates its own buckets.
This should reduce memory used by NeMo Megatron.
**Collection**: NLP
# Changelog
- Update `NLPDDPPlugin`.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
I face the following error when exporting [MTEncDecModel](https://github.com/NVIDIA/NeMo/blob/r1.2.0/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py) to ONNX and trying to bind inputs and outputs directly to a PyTorch tensor.
**Steps/Code to reproduce bug**
```python
import torch
import numpy as np
import onnxruntime
from nemo.collections.nlp.models import MTEncDecModel
# load the model from pretrained
model = MTEncDecModel.from_pretrained('nmt_en_de_transformer24x6')
# export all model components to onnx:
model.encoder.export('encoder.onnx')
model.decoder.export('decoder.onnx')
model.log_softmax.export('classifier.onnx')
# initialise all the onnx sessions and bindings:
encoder_session = onnxruntime.InferenceSession('encoder.onnx', providers=['CUDAExecutionProvider'])
decoder_session = onnxruntime.InferenceSession('decoder.onnx', providers=['CUDAExecutionProvider'])
classifier_session = onnxruntime.InferenceSession('classifier.onnx', providers=['CUDAExecutionProvider'])
encoder_session_binding = encoder_session.io_binding()
decoder_session_binding = decoder_session.io_binding()
classifier_session_binding = classifier_session.io_binding()
# preprocess the data using the original nemo model for simplicity:
TEXT = ['good morning', 'hello world']
src_ids, src_mask = model.prepare_inference_batch(TEXT)
# bind encoder inputs and outputs to a PyTorch tensor
src_ids_tensor, src_mask_tensor = src_ids.contiguous(), src_mask.contiguous()
encoder_session_binding.bind_input(
name='input_ids',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(src_ids_tensor.shape),
buffer_ptr=src_ids_tensor.data_ptr(),
)
encoder_session_binding.bind_input(
name='encoder_mask',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(src_mask_tensor.shape),
buffer_ptr=src_mask_tensor.data_ptr(),
)
encoder_hidden_state_shape = (src_ids.shape[0], src_ids.shape[1], 1024)
encoder_hidden_state = torch.empty(encoder_hidden_state_shape, dtype=torch.float32, device='cuda:0').contiguous()
encoder_session_binding.bind_output(
name='last_hidden_states',
device_type='cuda',
device_id=0,
element_type=np.float32,
shape=tuple(encoder_hidden_state.shape),
buffer_ptr=encoder_hidden_state.data_ptr(),
)
encoder_session.run_with_iobinding(encoder_session_binding)
# simple greedy search:
MAX_GENERATION_DELTA = 5
BOS = model.encoder_tokenizer.bos_id
EOS = model.encoder_tokenizer.eos_id
PAD = model.encoder_tokenizer.pad_id
def decode(tgt: torch.tensor, embeding: torch.tensor, src_mask: torch.tensor, decoder_hidden_state: torch.tensor) -> torch.tensor:
# bind decoder inputs and outputs to a PyTorch tensor
tgt_tensor = tgt.contiguous()
embeding_tensor = embeding.contiguous()
src_mask_tensor = src_mask.contiguous()
decoder_hidden_state_tensor = decoder_hidden_state.contiguous()
decoder_mask_tensor = (tgt != PAD).long().contiguous()
decoder_session_binding.bind_input(
name='input_ids',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(tgt_tensor.shape),
buffer_ptr=tgt_tensor.data_ptr(),
)
decoder_session_binding.bind_input(
name='decoder_mask',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(decoder_mask_tensor.shape),
buffer_ptr=decoder_mask_tensor.data_ptr(),
)
decoder_session_binding.bind_input(
name='encoder_mask',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(src_mask_tensor.shape),
buffer_ptr=src_mask_tensor.data_ptr(),
)
decoder_session_binding.bind_input(
name='encoder_embeddings',
device_type='cuda',
device_id=0,
element_type=np.float32,
shape=tuple(embeding_tensor.shape),
buffer_ptr=embeding_tensor.data_ptr(),
)
decoder_session_binding.bind_input(
name='decoder_mems',
device_type='cuda',
device_id=0,
element_type=np.float32,
shape=tuple(decoder_hidden_state_tensor.shape),
buffer_ptr=decoder_hidden_state_tensor.data_ptr(),
)
decoder_hidden_state_shape = (embeding.shape[0], 8, decoder_hidden_state.shape[2]+1, 1024)
decoder_hidden_state_tensor = torch.empty(decoder_hidden_state_shape, dtype=torch.float32, device='cuda:0').contiguous()
decoder_session_binding.bind_output(
name='last_hidden_states',
device_type='cuda',
device_id=0,
element_type=np.float32,
shape=tuple(decoder_hidden_state_tensor.shape),
buffer_ptr=decoder_hidden_state_tensor.data_ptr(),
)
decoder_session.run_with_iobinding(decoder_session_binding)
# bind classifier inputs and outputs to a PyTorch tensor
classifier_session_binding.bind_input(
name='hidden_states',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(decoder_hidden_state[:,-1,-1:].shape),
buffer_ptr=decoder_hidden_state[:,-1,-1:].data_ptr(),
)
log_probs_shape = (embeding.shape[0], 1, 32000)
log_probs_tensor = torch.empty(log_probs_shape, dtype=torch.float32, device='cuda:0').contiguous()
classifier_session_binding.bind_output(
name='log_probs',
device_type='cuda',
device_id=0,
element_type=np.int64,
shape=tuple(log_probs_tensor.shape),
buffer_ptr=log_probs_tensor.data_ptr(),
)
classifier_session.run_with_iobinding(classifier_session_binding)
return log_probs_tensor, decoder_hidden_state_tensor
max_out_len = encoder_hidden_state.shape[1] + MAX_GENERATION_DELTA
tgt=torch.full((encoder_hidden_state.shape[0], max_out_len), PAD)
tgt[:, 0] = BOS
decoder_hidden_state = torch.ones(encoder_hidden_state.shape[0], 8, 0, encoder_hidden_state.shape[-1]).to(torch.float32)
for i in range(1, max_out_len):
log_probs, decoder_hidden_state = decode(tgt[:, :i], encoder_hidden_state, src_mask, decoder_hidden_state)
next_tokens = log_probs[:, -1].argmax(axis=1)
tgt[:, i] = next_tokens
if ((tgt == EOS).sum(axis=1) > 0).all():
break
```
**Error:**
```
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_8614/803687795.py in <module>
5
6 for i in range(1, max_out_len):
----> 7 log_probs, decoder_hidden_state = decode(tgt[:, :i], encoder_hidden_state, src_mask, decoder_hidden_state)
8 next_tokens = log_probs[:, -1].argmax(axis=1) # NOTE: ONNX decoder returns multiple outputs which is different to pytorch version, so I get the last one (this could be where error is?)
9 tgt[:, i] = next_tokens
/tmp/ipykernel_8614/2263200407.py in decode(tgt, embeding, src_mask, decoder_hidden_state)
42 )
43
---> 44 decoder_session_binding.bind_input(
45 name='decoder_mems',
46 device_type='cuda',
/opt/conda/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py in bind_input(self, name, device_type, device_id, element_type, shape, buffer_ptr)
439 :param buffer_ptr: memory pointer to input data
440 '''
--> 441 self._iobinding.bind_input(name,
442 C.OrtDevice(get_ort_device_type(device_type), C.OrtDevice.default_memory(),
443 device_id),
RuntimeError: /onnxruntime_src/onnxruntime/python/onnxruntime_pybind_iobinding.cc:64 onnxruntime::python::addIoBindingMethods(pybind11::module&)::<lambda(onnxruntime::SessionIOBinding*, const string&, const OrtDevice&, pybind11::object&, const std::vector<long int>&, int64_t)> data_ptr != 0 was false. Pointer to data memory is not valid
```
**Environment overview (please complete the following information)**
- Environment location: Docker on Kubernetes
- Method of NeMo install: using helm chart using nvcr.io/nvidia/nemo: 1.6.1
- Additionally install `pip install onnxruntime-gpu` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4338 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4338/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4338/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4338/events | https://github.com/NVIDIA/NeMo/issues/4338 | 1,261,557,441 | I_kwDOC_bI7s5LMdbB | 4,338 | Error in multinode training | {
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} | null | null | null | **Following is the error log .please suggest the solution**
[NeMo W 2022-06-04 14:58:08 exp_manager:895] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/16
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/16
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/16
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/16
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/16
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/16
Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/16
Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/16
Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/16
Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/16
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/16
Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/16
Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/16
Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/16
Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/16
Error executing job with overrides: []
Traceback (most recent call last):
File "/workspace/nemo/marathi/train_bpe.py", line 74, in main
trainer.fit(asr_model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in fit
self._call_and_handle_interrupt(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 723, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1171, in _run
self.strategy.setup_environment()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 152, in setup_environment
self.setup_distributed()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 205, in setup_distributed
init_dist_connection(self.cluster_environment, self._process_group_backend)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py", line 355, in init_dist_connection
torch.distributed.init_process_group(torch_distributed_backend, rank=global_rank, world_size=world_size, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 610, in init_process_group
_store_based_barrier(rank, store, timeout)
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 246, in _store_based_barrier
raise RuntimeError(
RuntimeError: Timed out initializing process group in store based barrier on rank: 1, for key: store_based_barrier_key:1 (world_size=16, worker_count=8, timeout=0:30:00)
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} | null | null | null | **Describe the bug**
Is there a limit to the maximal text input length when calling `MTEncDecModel.translate()`? I mean the length of individual strings or size of List. Is it model config dependant? E.g. is it determined by `max_sequence_length`? If so, is this the number of tokens after tokenisation with `encoder_tokenizer` or the length of the string (chars with spaces)? I am asking this as I have noticed that when passing large text blocks (e.g. 3000 chars with spaces), the translation does work, but a large portion of input text is dropped.
**Steps/Code to reproduce bug**
Run `MTEncDecModel.translate` with list containing one large block of text.
**Expected behavior**
The entire input be translated, or a warning printed out that the output is/will be truncated.
**Environment details**
container: `pytorch:22.04-py3` + `nemo:1.8.2 (./reinstal.sh)`
GPU: T4
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} | null | null | null | Hello, is there someone who can help me use the adapters with models other than the conformer ones. I want to use the adapters with the citrinet models and would like to know if it is possible and how I could do it. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4332 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4332/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4332/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4332/events | https://github.com/NVIDIA/NeMo/issues/4332 | 1,260,443,340 | I_kwDOC_bI7s5LINbM | 4,332 | Torchscripting Transducer model results in " Module 'FilterbankFeatures' has no attribute 'stft' " | {
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from nemo.collections.asr.models import (
EncDecRNNTModel,
)
import torch.quantization.quantize_fx as quantize_fx
asr_model = EncDecRNNTModel.restore_from(<path>)
asr_model.eval()
quantized_model = torch.quantization.quantize_dynamic(
asr_model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8, inplace=True
)
scripted_model = torch.jit.script(quantized_model)
torch.jit.save(scripted_model, "hindi_medium_rnnt.pt")
```
I am trying to convert a nemo rnnt transducer model to torchscript for testing mobile deployment and the torch.jit.script results in the following error:
```
Module 'FilterbankFeatures' has no attribute 'stft' (This function exists as an attribute on the Python module, but we failed to compile it to a TorchScript function.
The error stack is reproduced here:
local variable 'idx' referenced before assignment:
File "/home/gowtham/anaconda3/envs/android-wsl/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 327
# disable autocast to get full range of stft values
with torch.cuda.amp.autocast(enabled=False):
x = self.stft(x)
~~~~~~~~~ <--- HERE
# torch returns real, imag; so convert to magnitude
```
1. I couldn't find lot of related issues online, so wanted to check if you have recommended ways to convert nemo conformed transducer models to torchscript?
2. I also tried using [nemo_model.export](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/core/export.html) method like this:
```python
quantized_model.export(
"hindi_medium_rnnt.pt", try_script=True, verbose=True
)
```
^ this also runs into multiple jit.script() failed errors but eventually produces encoder.pt and decoder-joint.pt files which are however not usable with torch.jit.load methods | {
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/4330 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4330/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4330/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4330/events | https://github.com/NVIDIA/NeMo/pull/4330 | 1,260,370,208 | PR_kwDOC_bI7s45F8PW | 4,330 | Torchaudio installation fix | {
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} | Signed-off-by: Aleksandr Laptev <[email protected]>
# What does this PR do ?
Torch and torchaudio versions must match. Othervise, there will be no CUDA support for things like RNNT loss.
This PR fixes the issue.
Also, this PR automates picking the latest torchaudio release.
# Changelog
- The commands in the Dockerfile are replaced with a separate script whick installs and tests torchaudio.
# 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?
@titu1994 @okuchaiev
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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
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} | # What does this PR do ?
Adds a script and yaml config to fine-tune a pre-trained T5 model on the prefix-LM (LM adaptation) objective.
**Collection**: NLP
# Changelog
- Add pre-training script and config
# Usage
* You can potentially add a usage example below
```python python megatron_t5_lm_adaptation_finetune.py -cn megatron_t5_lm_adaptation_finetune model.pretrained_model_path=/path/to/pretrained_t5.nemo model.data.data_prefix=.....```
# 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 ?
Fuse gradient division into torch.distributed.allreduce().
# Changelog
This change is made for async grad AR in class MainParamsOptimizerWrappe.
# Usage
**This fusion requires at least PyTorch 22.04.**
This fusion is enabled by default, you can turn it off by setting grad_div_ar_fusion=False
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
IPA words/sentences in square brackets won't be normalized
**Collection**: [Note which collection this PR will affect]
# Changelog
TN: IPA words/sentences in square brackets won't be normalized
# Usage
* You can potentially add a usage example below
```
# untouched
python normalize.py --text="[oʊ ɔɪ p ɹ s ʃ t θ]" # -> "[oʊ ɔɪ p ɹ s ʃ t θ]"
# normalized if no sq brackets
python normalize.py --text="oʊ ɔɪ p ɹ s ʃ t θ" -> "oʊ ɔɪ p ɹ s ʃ t theta"
```
# 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 ?
Add a tutorial showing how to upload NeMo models to the Hugging Face Hub
**Collection**: [Core, Documentation]
# Changelog
- Add tutorial notebook that shows how to upload a nemo model to the hub
- Prepare the model card with a template
- Upload results of model evaluations
# 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
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | null | null | null | Squeezeformer looks promising and uses some of the reusable components from conformer backbone. Creating a placeholder for discussion and implementation.
[Official Implementation](https://github.com/kssteven418/Squeezeformer)
[Paper]([arxiv.org/pdf/2206.00888.pdf](https://arxiv.org/pdf/2206.00888.pdf)) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4320 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4320/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4320/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4320/events | https://github.com/NVIDIA/NeMo/pull/4320 | 1,259,050,808 | PR_kwDOC_bI7s45Bc4d | 4,320 | [TTS] capture exception of non-supported windows. | {
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} | * remove redundant lines;
* declare global variables;
* capture exception of non-supported windows.
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
```bash
(nemo) xueyang@aiapps-031622:~/workspace/NeMo$ pytest --cpu tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_raise_exception_on_not_supported_window
============================================================== test session starts ==============================================================
platform linux -- Python 3.8.13, pytest-7.1.1, pluggy-1.0.0 -- /home/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /home/xueyang/workspace/NeMo, configfile: setup.cfg
plugins: hydra-core-1.1.1
collected 1 item
tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_raise_exception_on_not_supported_window PASSED [100%]
========================================================= 1 passed, 4 warnings in 3.87s =========================================================
```
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4319 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4319/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4319/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4319/events | https://github.com/NVIDIA/NeMo/pull/4319 | 1,258,995,742 | PR_kwDOC_bI7s45BQLi | 4,319 | [TTS] add staticmethod decoration for BetaBinomialInterpolator | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
`member function is static so it needs a decorator of static method.`
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**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Implements a Megatron-based perceiver encoder with tensor parallel only.
**Collection**: NLP
# Changelog
- Add `megatron_perceiver_encoders.py` file and add corresponding configuration options in yaml files.
# Usage
Set the following `aayn_base_megatron.yaml` or `megatron_t5_config.yaml`
`encoder_arch: perceiver`
`hidden_steps: 32`
`num_self_attention_per_cross_attention: 2`
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | In some environments, VAD as part of the diarizer stalls during multiprocessing. Would probably warrant some closer investigation into spawn context, but for a quick fix I added a mini PR to disable multiprocessing by setting num_workers=1
https://github.com/NVIDIA/NeMo/pull/4317 | {
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} | null | null | null | Hi
I have kaldi feats.ark and text, but i do not have wavs. And i need to train ASR. I guess you had scripts for that before, but you removed them. Do you have any suggestion for me?Or any way to use feats?
Thanks
Best Regards
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/best-practices.html?highlight=kaldi#using-kaldi-formatted-data
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} | null | null | null | Good evening! I have 2 questions...
1. Is it possible to write your own dataloader and embed it in the architecture of the QuartzNet model for training?
It seems to me that I saw somewhere how the default dataloader is replaced with a custom dataloader. But I can't find an example of how to do it.
2. Is it possible to somehow see which dataloader is used from the loaded model in the code?
Many thanks in advance for any insights! | {
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} | # What does this PR do ?
Removes logging statements that were added to debug.
**Collection**: NLP
# Changelog
- Removes logging statements that were added to debug megatron NMT
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds NeMo adapters tutorial to Core
**Collection**: [Core, ASR]
# Changelog
- Adds tutorial that explains how to add adapter support to any NeMo / PyTorch model.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Add IPA support to TTS collection with new tokenizer, G2P class, and CMUdict with IPA entries.
**Collection**: TTS
# Changelog
- Adds static tokenizer and G2P that handle IPA
- Adds CMUdict version with IPA entries that can be used with the above classes to train TTS models
- New, completely revamped heteronyms file (old one will be removed in a future PR; it doesn't match up with the existing CMUdict entries we have)
- New example IPA FastPitch config
# Usage
The new IPA FastPitch config can be used practically identically to the previous configs, e.g.:
```bash
python examples/tts/fastpitch.py --config-name=fastpitch_align_ipa.yaml \
train_dataset=/LJSpeech/nvidia_ljspeech_train.json \
validation_datasets=/LJSpeech/nvidia_ljspeech_val.json \
sup_data_path=/LJSpeech/supplementary/
```
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix an issue in Dali Index Creator causing a crash.
**Collection**: [ASR]
# Changelog
- Remove verbose=False arg from DALI Index Creator.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Adjusts default learning rate down to 1e-3 due to change in default optimizer to AdamW from LAMB (see PR #4233)
**Collection**: TTS
# Changelog
- Changes default learning rate from 1e-1 to 1e-3 for FastPitch and Mixer-TTS models | {
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fixes an issue with step calculation logic.
**Collection**: [Core]
# Changelog
- Update tests to increase num steps.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: sam1373 <[email protected]>
# What does this PR do ?
Clear registry if trying to add when it isn't empty
**Collection**: Core
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Provides additional information for more granular metrics in the future of what version of the library is being used when downloading files.
**Collection**: [Core]
# Changelog
- Trivial change with no functional change
# 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?
@titu1994
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} | # What does this PR do ?
Adding documents for SSL in ASR
**Collection**: ASR
# Changelog
- Added document in `NeMo/docs/source/asr/ssl`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
Running multi-gpu training without a pre-prepared cache, crashes upon initialization with the following trace
```python3
Traceback (most recent call last):
File "examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py", line 155, in <module>
main()
File "/workspace/nemo/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py", line 116, in main
model = PunctuationCapitalizationModel(cfg.model, trainer=trainer)
File "/workspace/nemo/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py", line 101, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/workspace/nemo/nemo/collections/nlp/models/nlp_model.py", line 98, in __init__
super().__init__(cfg, trainer)
File "/workspace/nemo/nemo/core/classes/modelPT.py", line 138, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/workspace/nemo/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py", line 469, in setup_training_data
self._train_dl = self._setup_dataloader_from_config(cfg=train_data_config, train=True)
File "/workspace/nemo/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py", line 773, in _setup_dataloader_from_config
dataset = BertPunctuationCapitalizationDataset(
File "/workspace/nemo/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py", line 993, in __init__
features = pickle.load(self.features_pkl.open('rb'))
File "/opt/conda/lib/python3.8/pathlib.py", line 1222, in open
return io.open(self, mode, buffering, encoding, errors, newline,
File "/opt/conda/lib/python3.8/pathlib.py", line 1078, in _opener
return self._accessor.open(self, flags, mode)
FileNotFoundError: [Errno 2] No such file or directory: '/data/punct_v2/cached.text_train.BertTokenizer.max_seq_length512.vocab119547.all_samples.punctuation_capitalization.pkl'
```
The issue arises because, the order of starting the individual processes, does not guarantee that the process with `global_rank()=0` will start first. There seems to be a guard
https://github.com/NVIDIA/NeMo/blob/f9d45db36afae8d75aecc27175a854d289bffd84/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L984-L985
that should block the other processes, but interestingly `torch.distributed.is_initialized()` returns `False` so the guard is skipped.
Adding the parameter `model.train_ds.use_cache=false`, does not help, since the cache is loaded irrespective of the value of this parameter https://github.com/NVIDIA/NeMo/blob/f9d45db36afae8d75aecc27175a854d289bffd84/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L987-L988
**Steps/Code to reproduce bug**
Train using multiple GPUs without a pre-prepared cache.
**Expected behavior**
All processes except the one with `global_rank()=0` to wait for the master to finish preparing cache, and the training start afterwords.
**Environment overview (please complete the following information)**
- Environment location: SLURM
- Method of NeMo install: `pytorch:22.04-py3` container with `nemo:1.8.2` (`./reinstal.sh` without numba update).
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Added support for subnet export: AIA-2515
**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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Use `mktemp -d` for creating temporary directories for punctuation and capitalization model tests. Previously race conditions happened if several similar tests were run on same machine.
**Collection**: nlp
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
Fixed the bug that does not correctly capture NotImplementedError of non-supported supplementary data types. Original implementation as shown in https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/torch/data.py#L262-L263 will not raise any exception ever because the `data_type` has already been validated by looking it up in `DATA_STR2DATA_CLASS` in line 257.
**Collection**: [Note which collection this PR will affect]
`nemo/collections/tts/torch/data.py`
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```bash
# added a unit test for the function.
$ pytest --cpu tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_raise_exception_on_not_supported_sup_data_types
========================================= test session starts =========================================
platform linux -- Python 3.8.13, pytest-7.1.1, pluggy-1.0.0 -- /home/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /home/xueyang/workspace/NeMo, configfile: setup.cfg
plugins: hydra-core-1.1.1
collected 1 item
tests/collections/tts/test_torch_tts.py::TestTTSDataset::test_raise_exception_on_not_supported_sup_data_types PASSED [100%]
==================================== 1 passed, 3 warnings in 2.95s ====================================
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
While doing the Fine-Tuning of pretrained ner_en_bert model, Model Config file is not getting update with the new one. It is using the same config file on which model is pretrained on. I can see the same in logs after calling trainer.fit()
I've already tried with below settings:
model.cfg = cfg
model._cfg = cfg
model._hparams_initial = cfg
model_from_scratch = nemo_nlp.models.TokenClassificationModel.from_pretrained("ner_en_bert")
cfg = copy.deepcopy(model_from_scratch.cfg)
cfg.dataset.data_dir = data_dir
cfg.label_ids = {'O': 0, 'B-place': 1, 'B-emotion': 2, 'B-noun': 3, 'B-name': 4}
print('BEFORE:',cfg)
cfg.optim.lr = 0.1
print('CFG',cfg)
model_from_scratch.cfg.label_ids = {'O': 0,'B-place':1,'B-emotion':2,'B-noun':3,'B-name':4}
model_from_scratch.cfg.dataset.data_dir = data_dir #passed using input function parameters
model_from_scratch.update_data_dir(data_dir)
model_from_scratch.setup_training_data()
model_from_scratch.setup_validation_data()
trainer = pl.Trainer(devices=1, accelerator='cpu',max_epochs=2, log_every_n_steps =1,max_steps = 32)
model_from_scratch.set_trainer(trainer)
trainer.fit(model_from_scratch)
**Expected behavior**
Model Should get trained with the new config changes
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} | Signed-off-by: Virginia Adams <[email protected]>
Fix for OOM error in 1.9.0 CI test
**Collection**: BigNLP
# Changelog
- Changed total_virtual_tokens in example prompt learning config
# Usage
n/a
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Virginia Adams <[email protected]>
Amp_O2 for prompt learning is not supported in the upcoming JoC release. So, megatron_amp_o2 needs to be overwritten and set to False in the GPT model configs that were trained with O2 in order for those models to load for prompt learning.
This will cause a time overhead as the GPT .nemo file will need to be unpacked once to get the config and overwrite it, then unpacked again when the model is restored.
**Collection**: BigNLP
# Changelog
- Updated PromptLearningGPTModel class to over write megatron amp O2 config.
# 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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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Forces 2D conv subsampling to fp32
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
One liner change in nemo/collections/asr/modules/conformer_encoder.py
# Usage
* You can potentially add a usage example below
No changes
```python
# Add a code snippet demonstrating how to use this
```
No changes
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Krishna Puvvada <[email protected]>
# What does this PR do ?
Updating PretrainedModelInfo for ASR SSL models
**Collection**: ASR
# Changelog
- Added conformer large, xlarge info in PretrainedModelInfo
# 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 ?
1. Adds pipeline parallel capability to GPT p-tuning and prompt tuning
2. makes prompt learning methods compataible with GPT models trained with amp O2,
3. switches to smaller gpt model and datasets for CI test, adding tp and pp tests
**Collection**: BigNLP
# Changelog
- Updated MegatronGPTPromptLearning class
- Updated prompt learning config
- Moved all reduce gradient method to base megatron model
- Updated prompt learning CI tests
# Usage
```
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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
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} | Signed-off-by: Aleksandr Laptev <[email protected]>
# What does this PR do ?
verbose k2 install, skip if failed | {
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} | null | null | null | I want to do Transfer Learning by explicitly initializing the en -> ru alphabet.
```python
dict_vocab= {
"a": "а",
"b": "б",
"c": "с",
"d": "д",
"f": "ф",
...
}
```
Do not tell me how this can be done? I didn't find it in the tutorials.
Thanks in advance!
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When running the tutorial notebook for Tacotron2 training in Google Colab. After this step;
`!python tacotron2.py \
sample_rate=16000 \
train_dataset=tests/data/asr/an4_train.json \
validation_datasets=tests/data/asr/an4_val.json \
trainer.max_epochs=3 \
trainer.accelerator=null trainer.check_val_every_n_epoch=1 \
trainer.gpus=1`
Hydra issue is raised:
`Traceback (most recent call last):
File "tacotron2.py", line 44, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 108, in wrapper
config_name='tacotron2.yaml',
TypeError: _run_hydra() missing 1 required positional argument: 'args'`
Maybe you also need to take a look at this warning while we are on the same step
`################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################`
Steps/Code to reproduce bug
Running each cell of the Tacotron2 tutorial notebook in a new Colab session,restarting, deleting runtime...
Environment location: Collab
Method of NeMo install: As per instructions within notebook. Please specify exact commands you used to install.
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} | null | null | null | I want to distill a big model to t5-base model, and the big model use bpe not sentencepiece, So the tokenizer should be load the bpe codes and the vocabs is differenct from the origin t5-base model. Therefore i am not sure about two things.
1, whether the t5 can be train in dialogue without pretrain which treat the t5 like transformer, and i find this config (https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/dialogue/conf/dialogue_config.yaml) , is it meet my demand?
2, how to set the tokenizer to use bpe codes? | {
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} | # What does this PR do ?
Adds style overwrite for the h4 tag located on the right side bar. Currently, the font size is too small
**Collection**: Docs
# Changelog
- Adds style overwrite for h4 tag on the right side bar. Currently the font is too small
- Example of the issue: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html#hyperparameter-grid-search
# 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?
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 have been using diarization.py example using the provided config file. The marble net vad acts really poor detecting speech. So I decided to use an External vad *( e.g pyannote vad ) to provide to the model. I could not find how to convert an external vad outputs to the manifest file required for the model
**Describe the solution you'd like**
A brief documentation on how to build the manifest file and a method to do it if possible.
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# What does this PR do ?
remove redundant condition testing for fastpitch. `shuffle_should_be` is already known as `False` in this case, so no need to test again `not shuffle_should_be`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hi all.
In different tutorials, specially in the TTS ones, you have to provide parameters fmin and fmax of your speaker. Nevertheless, there is no automatic way or tutorial on how to do that.
How do you extract fmin and fmax from an audio dataset?
Thanks,
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} | Signed-off-by: Virginia Adams <[email protected]>
Fixes prompt learning config bug in CI test
**Collection**: BigNLP
# Changelog
- Updated prompt learning config file
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Virginia Adams <[email protected]>
Adds amp o2 support for prompt learning for JoC release. Amp O2 support code taken from @MaximumEntropy's NMT PR.
**Collection**: BigNLP
# Changelog
- Updated NLP Overrides with amp o2 state dict modification
- Updated PromptLearningGPTModel
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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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} | # Add Support for Automatically Resolving Relative Audio Path
This simple modification allows using relative audio paths in the manifest, so that there is no need to change the manifest after moving the dataset to different locations. The updated `iterm_iter` function will first check if the `audio_file` exists, and skip further processing if it does exist. Then the function checks whether `audio_file` is a relative path. If it is, the function will add the parent folder of manifest to the audio file path. For tarred datasets, since the `audio_file` in manifest is not a real path, we will not perform any operation.
This feature is based on the assumption that the manifest is under the same directory as the audio directory, and the relative audio paths always begins with an audio directory (e.g., `wavs/`).
Consider the following dataset folder:
```
/home/steve/libri_speech/
----------------------------->: train_manifest.json
----------------------------->: wavs/
------------------------------------->: 11111.wav
------------------------------------->: 22222.wav
```
and the content of ` train_manifest.json` is:
```
{"audio_filepath": "wavs/11111.wav", duration: "2.3", "text": "one one one one one"}
```
The updated function will first try to read the file `wavs/11111.wav`, but since it's a relative path it won't find it. Then the function will add the parent directory of the manifest to the audio path, which becomes `/home/steve/libri_speech/wavs/11111.wav`.
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} | null | null | null | **Describe the bug**
Have the following bug when run commamd to traininig HifiGan Finetuning:
Error in call to target 'nemo.collections.tts.torch.data.VocoderDataset':
FileNotFoundError(2, 'No such file or directory')
full_key: train_ds.dataset
**Steps/Code to reproduce bug**
command as the turorial :
python hifigan_finetune.py --config-name=hifigan.yaml model.train_ds.dataloader_params.batch_size=32 model.max_steps=1000 model.optim.lr=0.00001 ~model.optim.sched rain_dataset=./mex_manifest_train_dur_180_mins_local_mels/hifigan_train_ft.json validation_datasets=./mex_manifest_val_dur_180_mins_local_mels/hifigan_val_ft.json exp_manager.exp_dir=hifigan_ft +init_from_pretrained_model=tts_hifigan trainer.check_val_every_n_epoch=10 model/train_ds=train_ds_finetune model/validation_ds=val_ds_finetune
**Expected behavior**
Start training HifiGan Finetuning
**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**
pip list:
absl-py 1.0.0
aiohttp 3.8.1
aiosignal 1.2.0
alabaster 0.7.12
aniso8601 9.0.1
antlr4-python3-runtime 4.8
appdirs 1.4.4
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
asttokens 2.0.5
async-timeout 4.0.2
attrdict 2.0.1
attrs 21.4.0
audioread 2.1.9
Babel 2.10.1
backcall 0.2.0
beautifulsoup4 4.11.1
black 19.10b0
bleach 5.0.0
boto3 1.23.8
botocore 1.26.8
braceexpand 0.1.7
cachetools 5.1.0
certifi 2022.5.18.1
cffi 1.15.0
charset-normalizer 2.0.12
click 8.1.3
colorama 0.4.4
cycler 0.11.0
Cython 0.29.30
debugpy 1.6.0
decorator 5.1.1
defusedxml 0.7.1
Distance 0.1.3
docker-pycreds 0.4.0
docopt 0.6.2
docutils 0.17.1
editdistance 0.6.0
entrypoints 0.4
executing 0.8.3
fastjsonschema 2.15.3
fasttext 0.9.2
filelock 3.7.0
Flask 2.1.2
Flask-RESTful 0.3.9
fonttools 4.33.3
frozendict 2.3.2
frozenlist 1.3.0
fsspec 2022.5.0
ftfy 6.1.1
g2p-en 2.1.0
gdown 4.4.0
gitdb 4.0.9
GitPython 3.1.27
google-auth 2.6.6
google-auth-oauthlib 0.4.6
grpcio 1.46.3
h5py 3.7.0
huggingface-hub 0.7.0
hydra-core 1.1.2
idna 3.3
imagesize 1.3.0
importlib-metadata 4.11.4
inflect 5.6.0
iniconfig 1.1.1
ipadic 1.0.0
ipykernel 6.13.0
ipython 8.3.0
ipython-genutils 0.2.0
ipywidgets 7.7.0
isort 4.3.21
itsdangerous 2.1.2
jarowinkler 1.0.2
jedi 0.18.1
jieba 0.42.1
Jinja2 3.1.2
jmespath 1.0.0
joblib 1.1.0
jsonschema 4.5.1
jupyter-client 7.3.1
jupyter-core 4.10.0
jupyterlab-pygments 0.2.2
jupyterlab-widgets 1.1.0
kaldi-io 0.9.4
kaldi-python-io 1.2.2
kaldiio 2.17.2
kiwisolver 1.4.2
latexcodec 2.0.1
librosa 0.9.1
llvmlite 0.38.1
Markdown 3.3.7
MarkupSafe 2.1.1
marshmallow 3.15.0
matplotlib 3.5.2
matplotlib-inline 0.1.3
mecab-python3 1.0.5
mistune 0.8.4
mpmath 1.2.1
multidict 6.0.2
nbclient 0.6.3
nbconvert 6.5.0
nbformat 5.4.0
nemo-toolkit 1.8.1
nest-asyncio 1.5.5
nltk 3.7
notebook 6.4.11
numba 0.55.2
numpy 1.22.4
oauthlib 3.2.0
omegaconf 2.1.2
onnx 1.11.0
OpenCC 1.1.3
packaging 21.3
pandas 1.4.2
pandocfilters 1.5.0
pangu 4.0.6.1
parameterized 0.8.1
parso 0.8.3
pathspec 0.9.0
pathtools 0.1.2
pesq 0.0.4
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.1.1
pip 20.3.4
pip-api 0.0.29
pipreqs 0.4.11
pkg-resources 0.0.0
pluggy 1.0.0
pooch 1.6.0
portalocker 2.4.0
prometheus-client 0.14.1
promise 2.3
prompt-toolkit 3.0.29
protobuf 3.20.1
psutil 5.9.1
ptyprocess 0.7.0
pure-eval 0.2.2
py 1.11.0
pyannote.core 4.4
pyannote.database 4.1.3
pyannote.metrics 3.2
pyasn1 0.4.8
pyasn1-modules 0.2.8
pybind11 2.9.2
pybtex 0.24.0
pybtex-docutils 1.0.2
pycparser 2.21
pyDeprecate 0.3.2
pydub 0.25.1
Pygments 2.12.0
pyparsing 3.0.9
pypinyin 0.46.0
pyrsistent 0.18.1
PySocks 1.7.1
pystoi 0.3.3
pytest 7.1.2
pytest-runner 6.0.0
python-dateutil 2.8.2
pytorch-lightning 1.6.3
pytz 2022.1
PyYAML 5.4.1
pyzmq 23.0.0
rapidfuzz 2.0.11
regex 2022.4.24
requests 2.27.1
requests-oauthlib 1.3.1
resampy 0.2.2
rsa 4.8
ruamel.yaml 0.17.21
ruamel.yaml.clib 0.2.6
s3transfer 0.5.2
sacrebleu 2.1.0
sacremoses 0.0.53
scikit-learn 1.1.1
scipy 1.8.1
Send2Trash 1.8.0
sentencepiece 0.1.96
sentry-sdk 1.5.12
setproctitle 1.2.3
setuptools 59.5.0
shellingham 1.4.0
shortuuid 1.0.9
simplejson 3.17.6
six 1.16.0
smmap 5.0.0
snowballstemmer 2.2.0
sortedcontainers 2.4.0
SoundFile 0.10.3.post1
soupsieve 2.3.2.post1
sox 1.4.1
Sphinx 4.5.0
sphinxcontrib-applehelp 1.0.2
sphinxcontrib-bibtex 2.4.2
sphinxcontrib-devhelp 1.0.2
sphinxcontrib-htmlhelp 2.0.0
sphinxcontrib-jsmath 1.0.1
sphinxcontrib-qthelp 1.0.3
sphinxcontrib-serializinghtml 1.1.5
stack-data 0.2.0
sympy 1.10.1
tabulate 0.8.9
tensorboard 2.9.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
terminado 0.15.0
threadpoolctl 3.1.0
tinycss2 1.1.1
tokenizers 0.12.1
toml 0.10.2
tomli 2.0.1
torch 1.11.0+cu113
torch-stft 0.1.4
torchaudio 0.11.0+cu113
torchmetrics 0.8.2
torchvision 0.12.0+cu113
tornado 6.1
tqdm 4.64.0
traitlets 5.2.1.post0
transformers 4.19.2
typed-ast 1.5.4
typer 0.4.1
typing-extensions 4.2.0
Unidecode 1.3.4
urllib3 1.26.9
wandb 0.12.17
wcwidth 0.2.5
webdataset 0.1.62
webencodings 0.5.1
Werkzeug 2.1.2
wget 3.2
wheel 0.37.1
widgetsnbextension 3.6.0
wrapt 1.14.1
yarg 0.1.9
yarl 1.7.2
youtokentome 1.0.6
zipp 3.8.0
More details
- Debian GNU/Linux
- PyTorch version 1.11.0+cu113
- Python version 3.9
**Additional context**
GPU Testla T4 nvidia
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} | # What does this PR do ?
Add documentation on setting save_on_train_end to False
**Collection**: BigNLP
# Changelog
- Updated prompt learning docs
- Updated prompt learning config
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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
- [ ] 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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def transcribe(self, paths2audio_files: List[str], batch_size: int = 4) -> List[str]:
"""
Takes paths to audio files and returns text transcription
Args:
paths2audio_files: paths to audio fragment to be transcribed
Returns:
transcription texts
"""
pass
```
is there a replacement of `transcribe` that takes in the already loaded audio in numpy/torch array instead of the path ?
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Updates all ASR configs to have num_workers and pin_memory.
**Collection**: [ASR]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix adapters training script when there are no validation configs.
**Collection**: [ASR]
# Changelog
- Fix validation config bug
- Improve the handling of dataloader speeds in ASR
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: sam1373 <[email protected]>
# What does this PR do ?
- fixes masked patch augmentation to round up to guarantee specified fraction of masked steps
- updates to citrinet and contextnet ssl configs and relevant decoder
- fixes tutorial to work with updated configs + minor changes
- revert rnnt decoding change since it's not needed at the moment
**Collection**: ASR
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Reverts a change to ModelPT's `cfg` attr.
**Collection**: All
# Changelog
- Reverts a change to ModelPT's `cfg` attr.
# 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: Akshit Arora <[email protected]>
# What does this PR do ?
Adds the code and tutorial to train Fastpitch model from scratch on German neutral dataset ([openslr/95](https://www.openslr.org/95)) text and phonemes, and Finetune HiFi-GAN model on predicted mels from Fastpitch.
**Collection**: TTS
# Changelog
- Added a tutorial to `NeMo/tutorials/tts/Fastpitch_Training_GermanTTS.ipynb`
- Added Fastpitch config for German `NeMo/examples/tts/conf/de/fastpitch_align_de.yaml`
- Added IPA symbols to GermanCharsTokenizer in `NeMo/nemo/collections/tts/torch/tts_tokenizers.py`
- Modified data.py to process `phonemes` flag and generate unique file names when the flag is set to 1 `NeMo/nemo/collections/tts/torch/data.py`
# Usage
* `Fastpitch_Training_GermanTTS.ipynb` walks through the usage.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes NMT epoch end
**Collection**: NLP
# Changelog
- NLP eval epoch end fix due to PTL bug.
# 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: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes NMT preproc notebook.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Raise error if bicleaner is not installed.
# 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: Jocelyn Huang <[email protected]>
# What does this PR do ?
Sets PTL trainer `benchmark` back to default False for all TTS configs (see #4083).
**Collection**: TTS
# Additional Information
* Related to #4083, #4260 | {
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} | # What does this PR do ?
Fixes notebook typo, changes branch to r1.9.0, improves manual dataset download message
**Collection**: BigNLP
# Changelog
- Prompt Learning Notebook
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://nvbugs/3639143 [ML-NeMo][A30] Multitask_Prompt_and_PTuning.ipynb branch name not 'r1.9.0' and failed with ImportError: cannot import name 'VirtualPromptStyle' as shown below if manually changed to the r1.9.0 branch | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Sets `trainer.benchmark=False` for all NLP yaml configs.
**Collection**: NLP
# Changelog
Sets `trainer.benchmark=False` for all NLP yaml configs.
# 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: vahidoox <[email protected]>
# What does this PR do ?
There was a slow down in speech models when upgraded to PTL 1.6. They changed the default of trainer.benchmark to True which slows down model with variable input lengths including speech models. This PR set all the trainer.benchmark to false for all speech models.
# Changelog
Fixed the speed slow-down for speech models by setting benchmark=false for all speech configs.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | Signed-off-by: Krishna Puvvada <[email protected]>
# What does this PR do ?
Updating PretrainedModelInfo and benchmark sheet for ASR models
**Collection**: ASR
# Changelog
- Added conformer RNNT xlarge, xxlarge info in PretrainedModelInfo, benchmark sheet
- Added conformer CTC xlarge info in PretrainedModelinfo, benchmark sheet
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fix broken link
**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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} | … re-basing to 'main' branch from r1.9.0 - all previous review comments are in.
Signed-off-by: Hoo Chang Shin <[email protected]>
# What does this PR do ?
Add 'relative positional embedding (RPE)' feature to T5 model.
**Collection**: NLP
# Changelog
Adds an option to use 'relative positional embedding' other than 'absolute position embedding' or 'fixed position encoding'.
# Usage
In the 'megatron_t5_config.yaml' file change the 'position_embedding_type' option to 'relative'.
```python
python megatron_t5_pretraining.py
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] 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: smajumdar <[email protected]>
# What does this PR do ?
Adds support for load_adapters to load any module adapter with just the adapter_name
**Collection**: [Core, ASR]
# Changelog
- Update code and tests
# Usage
```python
model = ModelWithAdapterSupport()
model.add_adapters("module:adapter_name", adapter_cfg)
model.save_adapters("adapters.pt")
new_model = ModelWithAdaptersSupport()
new_model.load_adapters("adapters.pt", name="adapter_name")
```
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add 'relative positional embedding (RPE)' feature to T5 model.
**Collection**: NLP
# Changelog
Adds an option to use 'relative positional embedding' other than 'absolute position embedding' or 'fixed position encoding'.
# Usage
In the 'megatron_t5_config.yaml' file change the 'position_embedding_type' option to 'relative'.
```python
python megatron_t5_pretraining.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?
- [ ] 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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