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} | null | null | null | **Describe the bug**
Cannot run the notebook
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
Try running this notebook: https://colab.research.google.com/drive/1-QmFrSHsnirtnu6gB0Tx-JQAXWDtm1yE?usp=sharing
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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This is a PR for fixing issue #3094 where the index will be out-of-bound when truncating long text ext to max_seq_length in text_classification_dataset.py
Signed-off-by: bugface <[email protected]> | {
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This update will crash at PyTorch21.10 so should be made sure this is packaged with PyTorch21.11.
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} | null | null | null | **Describe the bug**
Punctuation datasets cannot be constructed with more than a few million examples (depending on the max length). Even these small datasets can consume a huge amount of memory while preprocessing (100's of GB) and while training (10's of GB per process).
This issue was mentioned in passing in https://github.com/NVIDIA/NeMo/issues/2569 though it was not the main issue discussed there and thus not fixed.
Assuming it is an acceptable idea to load the entire dataset into memory in the first place, these are the points where there seems to be an excessive use of memory.
The function with issues (`get_features`):
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L34
The main issues:
1. Why save `all_segment_ids`? It appears there is nowhere this is set to anything but a constant 0. `__getitem__` can deal with it.
2. Why pad to the max length? `collate_fn` can deal with that, and could do so much more efficiently (by only padding to the max length of the batch).
3. Loss mask and input mask can be generated by `__getitem__` or `collate_fn`
4. Subtokens mask could be generated later as well, albeit not trivially.
**Expected behavior**
Should be able to use a dataset with 10's of millions of lines.
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} | null | null | null | Recently, I want to measure the inference time, I design two cases,
First I read model input from files,
Second, each model input from the standard input.
While I found first case inference time is about 70 ms except the first call, the second case starts to be highly inconsistent, ranging from 72 ms to 483 ms.
Why this happened ? How can I make the second case inference time keep consistent ?
```python
model = TokenClassificationModel.restore_from(restore_path=checkpoint_path)
# first case code
for line in open(txt_file, 'r'):
torch.cuda.synchronize()
start = time.perf_counter()
result = model.infer_one_text(text)
torch.cuda.synchronize()
elapsed_time = time.perf_counter() - start
print(f'elapsed_time:{elapsed_time:.6f}', flush=True)
# first case log
elapsed time:0.289745
elapsed time:0.072184
elapsed time:0.060471
elapsed time:0.062084
elapsed time:0.063837
```
```python
# second case code
model = TokenClassificationModel.restore_from(restore_path=checkpoint_path)
while True:
text = input("text:")
torch.cuda.synchronize()
start = time.perf_counter()
result = model.infer_one_text(text)
torch.cuda.synchronize()
end = time.perf_counter() - start
print(f'elapsed time: {end:.6f}', flush=True)
# second case log
elapsed time:0.483897
elapsed time:0.089466
elapsed time:0.450116
elapsed time:0.072568
elapsed time:0.469203
```
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} | null | null | null | **Bug description**
Cannot import `asr` from `nemo.collections`
**Steps/Code to reproduce bug**
```bash
# Fresh install
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo/
conda create -n nemo python=3.8
conda activate nemo
./reinstall.sh
# Then
python
# in the python terminal:
from nemo.collections import asr as nemo_asr
```
Log:
```bash
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/user/Projects/NeMo/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/user/Projects/NeMo/nemo/collections/asr/losses/__init__.py", line 15, in <module>
from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
File "/home/user/Projects/NeMo/nemo/collections/asr/losses/angularloss.py", line 18, in <module>
from nemo.core.classes import Loss, Typing, typecheck
File "/home/user/Projects/NeMo/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/home/user/Projects/NeMo/nemo/core/classes/__init__.py", line 16, in <module>
from nemo.core.classes.common import (
File "/home/user/Projects/NeMo/nemo/core/classes/common.py", line 32, in <module>
from nemo.utils import logging
File "/home/user/Projects/NeMo/nemo/utils/__init__.py", line 22, in <module>
from nemo.utils.lightning_logger_patch import add_memory_handlers_to_pl_logger
File "/home/user/Projects/NeMo/nemo/utils/lightning_logger_patch.py", line 18, in <module>
import pytorch_lightning as pl
File "/home/user/Tools/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/__init__.py", line 20, in <module>
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/home/user/Tools/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/callbacks/__init__.py", line 26, in <module>
from pytorch_lightning.callbacks.quantization import QuantizationAwareTraining
File "/home/user/Tools/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/callbacks/quantization.py", line 30, in <module>
from torch.ao.quantization import QConfig
ImportError: cannot import name 'QConfig' from 'torch.ao.quantization' (/home/user/Tools/miniconda3/envs/nemo/lib/python3.8/site-packages/torch/ao/quantization/__init__.py)
```
**Expected behavior**
Expected: being able to import without error
**Environment overview (please complete the following information)**
- installed with `reinstall.sh`
- also tried to reinstall different version of pytorch:
`conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch`
and getting exactly the same error
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS: ubuntu 20.04
- pytorch version (default from reinstall.sh) 1.10.0+cu102
- python version: 3.8.12
- nvidia driver: 470.82.00
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} | # Changelog
- Fix issue with transcribe building more workers than samples
- Fix SPE tokenizer vocab being built incorrectly for tokenizers with BOS EOS and PAD
- Update tokenizer script to designate tokenizer dir with BOS EOS and PAD token.
# Breaking change
- SPE tokenizer based models, when requested for their vocab via model.tokenizer.tokenizer.get_vocab() will return special tokens + order of tokens will be different than previous result. | {
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The `.setup_optimizer_param_groups` method will by default set `self._optimizer_param_groups=self.parameters()`.
This enables NeMo models to specify custom param groups so that, for example, per layer learning rates can be used.
In particular, this enables Megatron-LM style weight decay where LayerNorm and Bias weights are not decayed. | {
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} | null | null | null | **Describe the bug**
`WER` and `WERBPE` metrics are computed wrongly if method `update` is called more than once before method `compute`.
**Steps/Code to reproduce bug**
```python
import string
from functools import partial
from itertools import chain
from typing import List, Tuple
import torch
from torch.nn.utils.rnn import pad_sequence
from nemo.collections.asr.metrics.wer import WER
VOCABULARY = [' '] + list(string.ascii_lowercase) + ["'"]
predictions=[
["aaa aaa", "b b b b b b"],
["sky sea", "s k y s e a"],
]
targets=[
["ccc ccc", "d d d d d d"],
["sky sea", "s k y s e a"],
]
class Encoder:
def __init__(self, vocabulary: List[str]):
self.labels_map = vocabulary.copy()
self.blank_id = len(self.labels_map)
self.inv_vocabulary = {v: i for i, v in enumerate(self.labels_map)}
def string_to_ctc_tensor(self, txt: str) -> torch.Tensor:
# This function emulates how CTC output could like for txt
string_in_id_form = self.text_to_ids(txt)
ctc_list = []
prev_id = -1
for c in string_in_id_form:
# when character is repeated we need to insert CTC blank symbol
if c != prev_id:
ctc_list.append(c)
else:
ctc_list.append(self.blank_id)
ctc_list.append(c)
prev_id = c
return torch.Tensor(ctc_list)
def text_batch_to_tensor(self, batch: List[str], ctc: bool) -> Tuple[torch.Tensor, torch.Tensor]:
batch = [self.string_to_ctc_tensor(s) if ctc else self.string_to_tensor(s) for s in batch]
lengths = torch.tensor([len(s) for s in batch])
return pad_sequence(batch, batch_first=True, padding_value=0), lengths
def batch_of_text_batches_to_tensor(
self,
batch_of_text_batches: List[List[str]],
ctc: bool,
) -> Tuple[torch.Tensor, torch.Tensor]:
huge_batch, lengths = self.text_batch_to_tensor(list(chain(*batch_of_text_batches)), ctc)
first_dims = [len(batch_of_text_batches), len(batch_of_text_batches[0])]
return huge_batch.reshape(first_dims + [-1]), lengths.reshape(first_dims)
def string_to_tensor(self, txt: str) -> torch.Tensor:
return torch.Tensor(self.text_to_ids(txt))
def text_to_ids(self, txt: str) -> List[int]:
return [self.inv_vocabulary[c] for c in txt]
def test_wer(
predictions: torch.Tensor,
targets: torch.Tensor,
target_lengths: torch.Tensor,
predictions_lengths: torch.Tensor,
):
num_batches = predictions.shape[0]
wer_metric = WER(vocabulary=VOCABULARY)
for i in range(num_batches):
wer_metric(predictions[i], targets[i], target_lengths[i], predictions_lengths[i])
# check on all batches on all ranks
result = wer_metric.compute()[0]
print("Result:", result)
encoder_decoder = Encoder(vocabulary=VOCABULARY)
predictions, predictions_lengths = encoder_decoder.batch_of_text_batches_to_tensor(predictions, ctc=True)
targets, target_lengths = encoder_decoder.batch_of_text_batches_to_tensor(targets, ctc=False)
test_wer(predictions, targets, target_lengths, predictions_lengths)
```
Output:
```
[NeMo I 2021-11-01 08:03:28 wer:225]
[NeMo I 2021-11-01 08:03:28 wer:226] reference:dog cat
[NeMo I 2021-11-01 08:03:28 wer:227] predicted:cat dog
[NeMo I 2021-11-01 08:03:28 wer:225]
[NeMo I 2021-11-01 08:03:28 wer:226] reference:sky sea
[NeMo I 2021-11-01 08:03:28 wer:227] predicted:sky sea
Result: tensor(0.)
```
**Expected behavior**
The model should output `0.5` instead of `0` since the first prediction-target pair has maximum possible Levenstein distance and the same number of words as the second pair.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 20.04.2 LTS
- PyTorch version 1.10.0
- Python version 3.8
**Additional context**
The problem is in updating metric states [here](https://github.com/NVIDIA/NeMo/blob/25c61f2e6f68af7fe90853b11bd073e6b2625c72/nemo/collections/asr/metrics/wer.py#L259) and [here](https://github.com/NVIDIA/NeMo/blob/25c61f2e6f68af7fe90853b11bd073e6b2625c72/nemo/collections/asr/metrics/wer_bpe.py#L218). There should be `+=` instead of `=`. | {
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} | Fixes error which occurs if use `trainer.max_epochs` instead of `trainer.max_steps`. Got this error with `enc_dec_nmt.py` script.
```
Traceback (most recent call last):
File "enc_dec_nmt.py", line 133, in main
trainer.fit(mt_model)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 640, in fit
self._call_and_handle_interrupt(self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 592, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 670, in _fit_impl
self._run(model)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1049, in _run
self.accelerator.setup(self)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu.py", line 46, in setup
return super().setup(trainer)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in setup
self.setup_optimizers(trainer)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 364, in setup_optimizers
optimizers, lr_schedulers, optimizer_frequencies = self.training_type_plugin.init_optimizers(
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 250, in init_optimizers
return trainer.init_optimizers(model)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/optimizers.py", line 35, in init_optimizers
optim_conf = self.call_hook("configure_optimizers", pl_module=pl_module)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1398, in call_hook
output = model_fx(*args, **kwargs)
File "/home/apeganov/NeMo/nemo/core/classes/modelPT.py", line 575, in configure_optimizers
self.setup_optimization()
File "/home/apeganov/NeMo/nemo/core/classes/modelPT.py", line 463, in setup_optimization
if self._trainer.distributed_backend is None:
AttributeError: 'Trainer' object has no attribute 'distributed_backend'
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} | Replace `trainer.distributed_backend` with `trainer.accelerator` everywhere in the repo, because `distributed_backend` is [renamed ](https://pytorch-lightning.readthedocs.io/en/latest/generated/CHANGELOG.html?highlight=distributed_backend#id119) into `accelerator` in `pytorch-lightning==1.0.5`.
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} | null | null | null | **Describe the bug**
Training of machine translation model fails if config parameter `trainer.max_epochs` is used. Method `ModelPT.setup_optimization` tries to get deprecated `pytorch_lightning.Trainer` parameter `distributed_backend`.
```
Traceback (most recent call last):
File "enc_dec_nmt.py", line 133, in main
trainer.fit(mt_model)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 640, in fit
self._call_and_handle_interrupt(self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 592, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 670, in _fit_impl
self._run(model)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1049, in _run
self.accelerator.setup(self)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu.py", line 46, in setup
return super().setup(trainer)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in setup
self.setup_optimizers(trainer)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 364, in setup_optimizers
optimizers, lr_schedulers, optimizer_frequencies = self.training_type_plugin.init_optimizers(
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 250, in init_optimizers
return trainer.init_optimizers(model)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/optimizers.py", line 35, in init_optimizers
optim_conf = self.call_hook("configure_optimizers", pl_module=pl_module)
File "/home/apeganov/anaconda3/envs/megatron/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1398, in call_hook
output = model_fx(*args, **kwargs)
File "/home/apeganov/NeMo/nemo/core/classes/modelPT.py", line 575, in configure_optimizers
self.setup_optimization()
File "/home/apeganov/NeMo/nemo/core/classes/modelPT.py", line 463, in setup_optimization
if self._trainer.distributed_backend is None:
AttributeError: 'Trainer' object has no attribute 'distributed_backend'
```
**Steps/Code to reproduce bug**
Replace `trainer.max_steps` with `trainer.max_epochs` in any machine translation config and launch training
```
python enc_dec_nmt.py --config-name <your_config>
```
**Expected behavior**
Normal training without errors.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.10.0
- Python version: 3.8
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} | This PR brings a major revamp to speaker diarization inference, with following are notable changes:
* Input to offline_diarization.py and offline_diarization_with_asr.py expects manifest file ( Now no longer need to create individual files for audio paths, rttms, oracle speakers and uem filepaths)
* With oracle_vad option, CLusteringDiarizer automatically computes oracle_manifest without being fed as the separate manifest file
* Updated yaml file and is now more clear with many options for VAD, Speaker Embeddings and Clustering
* Separated clustering and scoring modules
* Improved pyannote usage with uris passed as uniq_key values
* Updated Speaker_Diairization_Inference.ipynb and ASR_with_Speaker_Diarization.ipynb
* Removed duplicate functions from diarization utils and avoided recalculation of diarization score
* Added Jenkins Test for diarization with asr
* Updated README and DocStrings | {
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} | Currently `WER` works incorrectly if `batch_dim_index=1`.
The outputs for `batch_dim_index=1` and `batch_dim_index=0` in the following example have to be equal.
Code to reproduce the error:
```python
from string import ascii_lowercase
from typing import List
import torch
from nemo.collections.asr.metrics.wer import WER
def string_to_ctc_tensor(txt: str, vocabulary: List[str]) -> torch.Tensor:
# This function emulates how CTC output could like for txt
blank_id = len(vocabulary)
char_to_ind = dict([(vocabulary[i], i) for i in range(len(vocabulary))])
string_in_id_form = [char_to_ind[c] for c in txt]
ctc_list = []
prev_id = -1
for c in string_in_id_form:
# when character is repeated we need to insert CTC blank symbol
if c != prev_id:
ctc_list.append(c)
else:
ctc_list.append(blank_id)
ctc_list.append(c)
prev_id = c
return torch.Tensor(ctc_list).unsqueeze(0)
def reference_string_to_tensor(txt: str, vocabulary: List[str]) -> torch.Tensor:
# Reference tensors aren't produced by CTC logic
char_to_ind = dict([(vocabulary[i], i) for i in range(len(vocabulary))])
string_in_id_form = [char_to_ind[c] for c in txt]
return torch.Tensor(string_in_id_form).unsqueeze(0)
vocabulary = [" "] + list(ascii_lowercase) + ["'"]
wer = WER(vocabulary=vocabulary, batch_dim_index=1, use_cer=False, ctc_decode=True)
prediction, reference = ['a b c', 'a c c']
predictions_tensor = string_to_ctc_tensor(prediction, vocabulary)
targets_tensor = reference_string_to_tensor(reference, vocabulary)
targets_tensor.transpose_(0, 1)
predictions_tensor.transpose_(0, 1)
wer(predictions=predictions_tensor, targets=targets_tensor, target_lengths=torch.tensor([len(reference)]))
print("WER for batch_dim_index=1:", wer.compute()[0])
vocabulary = [" "] + list(ascii_lowercase) + ["'"]
wer = WER(vocabulary=vocabulary, batch_dim_index=0, use_cer=False, ctc_decode=True)
prediction, reference = ['a b c', 'a c c']
predictions_tensor = string_to_ctc_tensor(prediction, vocabulary)
targets_tensor = reference_string_to_tensor(reference, vocabulary)
wer(predictions=predictions_tensor, targets=targets_tensor, target_lengths=torch.tensor([len(reference)]))
print("WER for batch_dim_index=0:", wer.compute()[0])
```
Output:
```
[NeMo I 2021-10-31 02:33:21 wer:225]
[NeMo I 2021-10-31 02:33:21 wer:226] reference:a
[NeMo I 2021-10-31 02:33:21 wer:227] predicted:a
WER for batch_dim_index=1: tensor(0.)
[NeMo I 2021-10-31 02:33:21 wer:225]
[NeMo I 2021-10-31 02:33:21 wer:226] reference:a c c
[NeMo I 2021-10-31 02:33:21 wer:227] predicted:a b c
WER for batch_dim_index=0: tensor(0.3333)
```
Also updated docstrings. | {
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} | null | null | null | Can we have real-time output from Nemo model? I mean Instead of giving a directory of wav files for transcribing use Nemo model for transcribing the content of a wav file for example buffer of a wav file.
Thanks | {
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} | This PR adds convenience scripts to checkpoint average any `.nemo` file (regardless of the class).
Python script is heavily based on code by @MaximumEntropy and @titu1994.
# Usage
The script `checkpoint_averaging.py` builds a `.nemo` file with average weights over multiple `.ckpt` files (assumes `.ckpt` files in same folder as `.nemo` file).
The input is a list of folders that contains a .nemo file (or a list of .nemo files).
The output of the script (per input folder / `.nemo` file) is a file named `*-averaged.nemo` where `*.nemo` is the original `.nemo` file.
# Example
Usage example for building `*-averaged.nemo` for a given `.nemo` file:
```
NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py my_model.nemo
```
Usage example for building *-averaged.nemo files for all results in sub-directories under current path:
```
find . -name '*.nemo' | grep -v -- "-averaged.nemo" | xargs NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py
``` | {
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} | Signed-off-by: madhukar <madhukar@penguin> | {
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```python
if max_seq_length > 0 and len(sent_subtokens) + 1 > max_seq_length:
sent_subtokens = sent_subtokens[:max_seq_length]
too_long_count += 1
sent_subtokens.append(tokenizer.sep_token)
sent_lengths.append(len(sent_subtokens))
```
The line 2 in the above code from /text_classification_dataset.py should be
```python
sent_subtokens = sent_subtokens[:max_seq_length-1]
```
otherwise, after append sep token, the total token length will be longer than max and cause the following error:
```
/tmp/pip-req-build-g2m34a_4/aten/src/ATen/native/cuda/Indexing.cu:690: indexSelectLargeIndex: block: [647,0,0], thread: [95,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
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- Update container version
Signed-off-by: smajumdar <[email protected]> | {
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} | mounted cache folder for huggingface models and added global environment to jenkins
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} | null | null | null | Hey guys,
I'm having this problem with some audios when I try to generate your graphs using the plot in the script "Speaker_Diarization_Inference"
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-89-9cae07b448e9> in <module>()
6 'outputs/vad_outputs/overlap_smoothing_output_median_0.875/Audio_Caio_Dias16K.median',
7 path2groundtruth_rttm_files[0],
----> 8 per_args = config.diarizer.vad.postprocessing_params, #threshold
9 )
10
3 frames
/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/utils/vad_utils.py in plot(path2audio_file, path2_vad_pred, path2ground_truth_label, offset, duration, threshold, per_args)
749 if path2ground_truth_label:
750 label = extract_labels(path2ground_truth_label, time)
--> 751 ax2.plot(np.arange(len_pred) * FRAME_LEN, label, 'r', label='label')
752
753 ax2.plot(np.arange(len_pred) * FRAME_LEN, pred, 'b', label='pred')
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
1645 """
1646 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1647 lines = [*self._get_lines(*args, data=data, **kwargs)]
1648 for line in lines:
1649 self.add_line(line)
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py in __call__(self, *args, **kwargs)
214 this += args[0],
215 args = args[1:]
--> 216 yield from self._plot_args(this, kwargs)
217
218 def get_next_color(self):
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs)
340
341 if x.shape[0] != y.shape[0]:
--> 342 raise ValueError(f"x and y must have same first dimension, but "
343 f"have shapes {x.shape} and {y.shape}")
344 if x.ndim > 2 or y.ndim > 2:
ValueError: x and y must have same first dimension, but have shapes (811,) and (812,)
```
I'm using the default settings and already tried to run changing some parameters
All audio samples I use are at 16000 Hz
in .wav format
This is a sample in this format that had this problem
[Audio_Caio_Dias.zip](https://github.com/NVIDIA/NeMo/files/7436513/Audio_Caio_Dias.zip)
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} | null | null | null | **Describe the bug**
eval_beamsearch_ngram.py not working because logprobs argument is not defined in rnnt_model.transcribe
**Steps/Code to reproduce bug**
run eval_beamsearch_ngram.py as described in https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html#evaluate-by-beam-search-decoding-and-n-gram-lm
**Expected behavior**
If I remove logprobs argument, it produces string and makes an error because it is fed to softmax.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | null | null | null | Hi Team,
I am using biomegatron-bert-345m-uncased for training my nemo model, after setting the config.model.language_model.pretrained_model_name ="biomegatron-bert-345m-uncased" in the config file.
When I am trying to restore the model .nemo model using the command
model = TextClassificationModel.restore_from(restore_path=config.cfg['model_path']['NEMO_BIO_MODEL']) it is throwing below error as shown in the image as AssertionError: args is already initialized.
I have tried initializing config file parameters, also tried with suggestions made in this blog: https://githubmemory.com/repo/NVIDIA/NeMo/issues/2222 but still getting issue when I am trying to restore the model but so far no use
Waiting for your kind response
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} | null | null | null | Hi everyone, i am trying train ecapa_tdnn model but can't load model with [ecapa_tdnn.yaml config](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/ecapa_tdnn.yaml)
This is error log:
```
Traceback (most recent call last):
File "speaker_veri_training.py", line 61, in <module>
main()
File "speaker_veri_training.py", line 57, in main
model = nemo_asr.models.ExtractSpeakerEmbeddingsModel(cfg=config.model, trainer=trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 380, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 92, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 120, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 152, in setup_training_data
self._train_dl = self.__setup_dataloader_from_config(config=train_data_layer_config)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py", line 109, in __setup_dataloader_from_config
augmentor = process_augmentations(config['augmentor'])
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/perturb.py", line 832, in process_augmentations
augmentation = perturbation_types[augment_name](**augment_kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/perturb.py", line 401, in __init__
self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/collections.py", line 193, in __init__
for item in manifest.item_iter(manifests_files):
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py", line 68, in item_iter
for manifest_file in manifests_files:
TypeError: 'NoneType' object is not iterable
```
The manifest file is true and still working with [SpeakerNet config](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/SpeakerNet_recognition_3x2x512.yaml) but got error when using ecapa_tdnn config.
I am using docker container nemo:1.4 what download from ngc
GPU: DGX | {
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- Fix ASR quantization bug
Signed-off-by: smajumdar <[email protected]> | {
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* distributed_backened is removed and replaced with strategy inside accelerator connector
* accelerator now only accepts the type of device input | {
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} | # Bugfix
- Fix issues with config saving `TypeError: __init__() missing 1 required positional argument: 'cfg'` when trying to do model.load_from_checkpoint(XYZ)
Signed-off-by: smajumdar <[email protected]> | {
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I used **stt_en_citrinet_1024** as a based model for fine-tuning with the Persian audio dataset. In your idea what is the best tokenizer for this? (**wpe** or **spe**). if **spe** is good what is the better type? (**bpe**, **unigram**, **char**, **word**). Also, I want to know about the vocab_size. What is the maximum number of vocab_size for tokenizer? If the vocab_size is increased, so the WER decreased?
Thanks
**Environment overview**
Environment location: Google Colab
Method of NeMo install: !pip install nemo_toolkit[asr]
NeMo version: r1.2.0
Learning Rate: 1e-2
**Environment details**
OS version : "Ubuntu20.04.3 LTS"
PyTorch version : "1.9.1"
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Please review and merge @okuchaiev @ericharper @MaximumEntropy | {
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} | null | null | null | Hey guys
I've been tinkering with the "Voice_Activity_Detection.ipynb" script, and I've had some questions about mounting the data set.
The first, is it really necessary to use human voices speaking a specific word?
And would I have to keep feeding this base by adding new voices speaking other words?
Would I get a good result just doing this training with noise audios? | {
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} | null | null | null | Testing in your [colab notebooks](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb) seem to indicate your requirements files don't support pytorch lighting's rc versions so it fails installation. May need to fix that since the install fails.
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} | # Changelog
- Adds publications.md to include research articles that utilize NeMo.
# Format
* Years are sorted in descending order, articles within the same year are sorted by month in ascending order.
* Articles may contain Arxiv submissions, but reviewed articles are preferred.
* In cases where Arxiv articles are accepted at a venue, the month/year of the **accepted paper** should be used to assign its position on the list (not its arxiv submission date).
```
# <Domain>
## (Optional) <Subdomain>
<details>
<summary>YEAR</summary>
* [Name of article](Link to Article)
</details>
--------
```
Signed-off-by: smajumdar <[email protected]> | {
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+ Adding the support of bucketing tarred datasets | {
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```
terminate called after throwing an instance of 'lm::FormatLoadException'
what(): kenlm/lm/vocab.cc:40 in void lm::ngram::{anonymous}::ReadWords(int, lm::EnumerateVocab*, lm::WordIndex, uint64_t) threw FormatLoadException because `memcmp(check_unk, "<unk>", 10)'.
Vocabulary words are in the wrong place. This could be because the binary file was built with stale gcc and old kenlm. Stale gcc, including the gcc distributed with RedHat and OS X, has a bug that ignores pragma pack for template-dependent types. New kenlm works around this, so you'll save memory but have to rebuild any binary files using the probing data structure.
Aborted (core dumped)
```
What can be the problem? @VahidooX do you have a any suggestions?
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} | null | null | null | **Describe the bug**
When setting `use_dali: true` in the dataset config, the following error occurs:
<details>
<summary>Error</summary>
<br>
```sh
Traceback (most recent call last):
File "/audio-data/tao/nemo_stt_ft.py", line 67, in main
trainer.fit(asr_model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
self._run(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 922, in _run
self._dispatch()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 990, in _dispatch
self.accelerator.start_training(self)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
self.training_type_plugin.start_training(trainer)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
self._results = trainer.run_stage()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1000, in run_stage
return self._run_train()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1035, in _run_train
self._run_sanity_check(self.lightning_module)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1122, in _run_sanity_check
self._evaluation_loop.run() [81/1557]
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
dl_outputs = self.epoch_loop.run(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 111, in advance
output = self.evaluation_step(batch, batch_idx, dataloader_idx)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 158, in evaluation_step
output = self.trainer.accelerator.validation_step(step_kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 211, in validation_step
return self.training_type_plugin.validation_step(*step_kwargs.values())
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py", line 392, in validation_step
return self.model(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 799, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 93, in forward
output = self.module.validation_step(*inputs, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 588, in validation_step
log_probs, encoded_len, predictions = self.forward(
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 798, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 545, in forward
encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 798, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/conv_asr.py", line 222, in forward
s_input, length = self.encoder(([audio_signal], length))
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 906, in forward
out, lens = l(out, lens)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 298, in forward
out = self.conv(x)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 298, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 294, in _conv_forward
return F.conv1d(input, weight, bias, self.stride,
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:6! (when checking arugment for argument weight in method wrapper_thnn_conv_depthwise2d_forward)
```
</details>
The error repeats multiple times based on the number of GPUs
**Steps/Code to reproduce bug**
Run any script with `trainer.fit()` and make sure to set `use_dali: true` in any of your `train/validation/test_ds` config.
**Expected behavior**
Training should start as expected.
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: `pip install nemo_toolkit[asr]==1.3.0`
**Environment details**
Docker:
- OS version Ubuntu 20.04
- PyTorch version 1.9.0
- Python version 3.8
- Cuda 11.2, cuDNN 8
**Additional context**
PyTorch compiled from source, but happens with pip installed PyTorch too.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3038 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3038/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3038/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3038/events | https://github.com/NVIDIA/NeMo/issues/3038 | 1,033,234,513 | I_kwDOC_bI7s49lehR | 3,038 | Perform VAD for my audio with this model | {
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} | null | null | null | Hello, i want to try the code for test one wav file:
python NeMo/examples/asr/vad_infer.py --vad_model=vad_marblenet.nemo --dataset=/fullpath/to/manifest/ --out_dir='frame/demo' --time_length=0.63
but i don't know what is /fullpath/to/manifest/ , it is json file ?
i construct one json file, The contents are as follows：
{"audio_filepath": "/source/c0/speech/data/google_dataset_v2/google_speech_recognition_v2/bird/b65f6015_nohash_0.wav", "duration": 0.63, "label": "infer", "text": "_", "offset": 0}
but get error.
So, i want to know how to construct /fullpath/to/manifest/ for my one wav file, Thank you for your guidance!
| {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3037 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3037/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3037/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3037/events | https://github.com/NVIDIA/NeMo/pull/3037 | 1,033,036,541 | PR_kwDOC_bI7s4thcRg | 3,037 | Enable stateful decoding of RNNT over multiple transcribe calls | {
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} | # Changelog
- Enable transcribe method to store hypothesis and reuse them for stateful multi step predictions
- RNNT `transcribe` now takes `partial_hypotheses` as optional input, provided in returned hypothesis from previous transcribe call
- Add abstract `batch_concat_states` and `batch_copy_states` support for RNNT models to abstract away state management.
- Force all greedy and beam inference to pack results into Hypothesis objects. They now contain by default the
- `y_sequence` : tensor form of int ids of tokens
- `dec_state` : CPU tuple of decoder states
- `score`: logprob score of current y_sequence
- (optional) `alignments`: 2d dangling matrix of token alignments
# Example
```python
model = EncDecRNNTModelBPE.restore_from(...)
decoding= model.cfg.decoding
decoding.strategy = 'greedy'
model.change_decoding_strategy(decoding)
# First transcription, stateless
part_hyp, _ = model.transcribe([path to audio files], batch_size=4, return_hypotheses=True)
# Second transcription, restored state
hyp, _ = model.transcribe([path to continuation of audio files], return_hypotheses=True, partial_hypothesis=part_hyp)
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3036 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3036/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3036/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3036/events | https://github.com/NVIDIA/NeMo/pull/3036 | 1,032,986,359 | PR_kwDOC_bI7s4thR2V | 3,036 | T5 Pre-training in NeMo using Megatron | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3035 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3035/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3035/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3035/events | https://github.com/NVIDIA/NeMo/issues/3035 | 1,032,983,573 | I_kwDOC_bI7s49khQV | 3,035 | Replicate quartznet results | {
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} | null | null | null | Hello!
I am training Quartznet 5x5 to do some experiments of my own, after I get the baseline.
I want to first replicate results with WSJ. What are some hyperparameters you recommend for training with a small number of GPUs. Now I am training with 3 GPUs, with a learning rate of 0.05, batch 32 with DDP in PyTorch lighting, fprecision 16 and number of epochs 1000. The other hyperparameters are similiar to Quartznet bigger models that show in the paper. Also, can I set up a number of iterations to train for each batch that you recommend?
I would wanna know how fast I can converge to 11/12 of WER greedy in the test set for WSJ. I got already to around 24% of WER for validation using only 100 epochs, with 3 GPUs using learning rate of 0.01.
Thanks!
Carlos | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3034 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3034/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3034/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3034/events | https://github.com/NVIDIA/NeMo/pull/3034 | 1,032,966,843 | PR_kwDOC_bI7s4thNwJ | 3,034 | Remove STFT checks due to min PT version of 1.10 | {
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} | # Changelog
- Remove old stft checks and defer to latest PT signatures
Signed-off-by: smajumdar <[email protected]> | {
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- Update ASR webapp to support RNNT models properly
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | **Describe the bug**
Yesterday I ran the ipynb and it could work. But today, I'm not sure why it just couldn't work
**Steps/Code to reproduce bug**
I literally just ran the same codes from here https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Question_Answering_Squad.ipynb#scrollTo=dzqD2WDFOIN-
it gave me this and during the installing, this happened, but It did not happen yesterday
**Expected behavior**
I expected the whole ipynb to run successfully which happened yesterday.
**Environment overview (please complete the following information)**
- Environment location: [google Collab]
- Method of NeMo install: [install from source given]. the exact command !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]
Add any other context about the problem here.
Example: GPU model
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https://api.github.com/repos/NVIDIA/NeMo/issues/3025 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3025/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3025/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3025/events | https://github.com/NVIDIA/NeMo/issues/3025 | 1,031,100,156 | I_kwDOC_bI7s49dVb8 | 3,025 | Decoding hangs with pretrained models (conformer transducer, contextnet) | {
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] | closed | false | null | [] | null | null | 2021-10-20T08:02:29 | 2021-10-21T17:58:02 | 2021-10-21T17:58:02 | NONE | {
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} | null | null | null | **Describe the bug**
The decoding hangs with some pretrained models, no error, it just freezes at some point. Killing the process, re-running the same command gets the same behavior.
I have observed it with different models on different data: `stt_en_conformer_tranducer_large.nemo`, `stt_en_contextnet_256.nemo`.
**Steps/Code to reproduce bug**
```bash
python examples/asr/transcribe_speech.py \ model_path=models/downloaded/conformer_transducer/stt_en_conformer_transducer_medium.nemo \
dataset_manifest=some_data_to_transcribe.json \
batch_size=2 \
output_filename=transcription_nemo_conformer_transducer_medium.json
```
The resulting end of the log is something like the following:
```
Transcribing: 0%| | 0/65903 [00:00<?, ?it/s][NeMo W 2021-10-20 09:26:58 patch_utils:49] torch.stft() signature has been updated for PyTorch 1.7+
Please update PyTorch to remain compatible with later versions of NeMo.
Transcribing: 33%|███▎ | 21583/65903 [30:25<52:12, 14.15it/s]
```
and at every trial, the step where it gets stuck is the same. Looking at the data in that batch (assuming batches are read in the order of the json input file) there is nothing specific and removing some files from the json list does not help. So maybe nemo reshuffles the order of files to be decoded?
Is there a way to get more detailed logs of why the freeze may happen?
**Environment overview (please complete the following information)**
- installed with conda
- version 1.4.0
**Additional context**
GPU model: RTX 3090
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} | * added cleaning script
* added upsampling scrpit
* modified input processing , e.g. to also add space between alpha and numerical characters
* added electronic wfst as part of preprocessing to inference script | {
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