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} | null | null | null | Hello!
I would like to train some models with fastpitch.
One model in Spanish and other model in Catalan.
Each language has its own alphabet, symbols, punctuations, tildes, phonemes, etc
1. How to define and set all of this properties to get started?
2. If there are other properties that need to be defined, which are?
Thank you!
David Martin Rius | {
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} | null | null | null | Hello!
In this post I would like to ask some questions.
As a premise I want to tell you what kind of machine I am working.
I have got an MSI Creator P100X -637
The specs are
GPU RTX 3080
64GB RAM
Intel Core i7-11700K
CPU 3.6 GHz
I work with trial an error and it is very confusing. Depending on the size of the dataset the GPU only accepts a limited max number in batch size.
For example, if I am going to train a dataset of 20 hours it will only accept a max batch size of 6. If I try a greater number it always raises an CUDA out of memory. (I supose it is the most common error when training)
On the other hand, if I am going to train a dataset of 5 hours aprox I can work with a batch size of 14 or 15.
### **Question 1/4.**
Is there a way to estimate the batch size previous to train?
Each time that I start training, if there is too much batch size it will crash. From I start training to crash it can pass like 15-20 minutes. This is because before start training the program needs to load all data previously. So i wasted a lot of time because until I don't find the good max batch size I can spend hours.
### **Question 2/4.**
Is there a way to estimate how many epochs to train a model based on the size of the dataset and the GPU batch size?
Now I set like 1000 epochs and I let the model training until the training loss is near to 0. But I am sure there is a cleaner way to do it.
### **Question 3/4.**
How to know how much time to train a model?
I supervised the trainings in tensorboard.
I deduced that I should evaluate the training loss of the model to know if is enough trained. But I am not sure of this.
### **Question 4/4.**
How to evaluate when a model is enough trained?
I spend a lots of hours trying to train a model without success. I am sure a lot of people that start with NeMo has this kind of doubts. Also I read a lot of discussions, issues, etc but I still did not solve this doubts.
So I would apreciate some help. I really want to train a TTS model with my own datasets.
Thank you! | null | {
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} | Signed-off-by: Evgeniy Shabalin <[email protected]>
# What does this PR do ?
Updates various files with the new path for the LJSpeech whitelist file
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Ghasem Pasandi <[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
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Fixes defaults for Megatron Base Model Artifacts.
**Collection**: NLP
# Changelog
- Changes defaults in megatron base model artifacts.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Replace conditional imports in WFST based TN/ITN by importlib
Add support for
**Collection**: [Note which collection this PR will affect]
# Changelog
- Replace conditional imports in WFST based TN/ITN by importlib.
- Fix deleted support for lm and audio based text normalizer
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} | Signed-off-by: Anand Joseph <[email protected]>
# What does this PR do ?
Replaces conditional imports in WFST based TN/ITN by importlib
**Collection**: [Note which collection this PR will affect]
# Changelog
- Replace conditional imports in normalize.py and inverse_normalize.py
**PR Type**:
- Code enhancement
## 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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```python
#!/usr/bin/env python
import nemo.collections.asr as nemo_asr
import ctc_decoders
import numpy as np
import librosa
import os, sys, pdb
## Instantiate model
DetailsDict = nemo_asr.models.EncDecCTCModel.list_available_models()
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En', strict=False)
## Test audio clip
AUDIO_FILENAME = 'test.wav'
signal, sample_rate = librosa.load(AUDIO_FILENAME, sr=None)
## Convert our audio sample to text
files = [AUDIO_FILENAME]
transcript = asr_model.transcribe(paths2audio_files=files)[0]; print(f'Transcript: "{transcript}"')
```
It performs well
However if i change the problem of just predicting fixed number of words, for example `yes, no`
How should i augment the language model part of it ?
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} | # What does this PR do ?
Replace conditional imports in WFST based TN/ITN with importlib
# Changelog
- Replace conditional imports in WFST based ITN with importlib in inverse_normalize.py .
- Replace conditional imports in WFST based TN with importlib in normalize.py .
# 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 ?
Replaces conditional imports in WFST TN/ITN with importlib
# Changelog
- Replaces conditional imports in WFST TN/ITN with importlib
- Support for en_in number system in ITN that supports both international as well as Indian (subcontinent) number system
# Usage
```python
from nemo_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer
inverse_normalizer = InverseNormalizer(lang='en_in', overwrite_cache=True)
inverse_normalizer.inverse_normalize('twelve crores twenty five lakhs thirty nine thousand six hundred and eighty four', verbose=True)
```
**PR Type**:
- [*] New Feature
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} | # What does this PR do ?
Replaces the conditional imports in TN/ITN with importlib.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Added staticmethods in inverse_normalize.py and normalize.py to check if tagger/classifier exists for that language
- Delete all the language conditional imports
- Use importlib to load the taggers and classifiers for the specified language.
# Usage
* no change in usage
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} | # What does this PR do ?
* created a dataset config for HUI-ACG.
* created a model config for fastpitch German.
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
# Download the dataset, normalize texts, create manifests for train/val/test splits.
$ python scripts/dataset_processing/tts/hui_acg/get_data.py \
--data-root /home/xueyang/datasets \
--set-type clean \
--speaker Friedrich
# save PITCH_MEAN and PITCH_STD from train split which will be printed in the console.
# PATH_WHERE_TO_SAVE_SUP_DATA should be general for train/val/test split.
$ python scripts/dataset_processing/tts/extract_sup_data.py \
--config-path hui_acg/ds_conf \
--config-name ds_for_fastpitch_align.yaml \
manifest_filepath=/home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean/train_manifest.json \
sup_data_path=/home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean/
# you would see the information in the terminal
/home/xueyang/miniconda3/envs/nemo/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
################################################################################
### 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)
################################################################################
[NeMo W 2022-04-20 22:59:42 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2022-04-20 22:59:42 tokenize_and_classify:87] Creating ClassifyFst grammars. This might take some time...
[NeMo I 2022-04-20 22:59:56 data:173] Loading dataset from /home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean/train_manifest.json.
9065it [00:00, 37982.47it/s]
[NeMo I 2022-04-20 22:59:56 data:207] Loaded dataset with 9065 files.
[NeMo I 2022-04-20 22:59:56 data:209] Dataset contains 18.92 hours.
[NeMo I 2022-04-20 22:59:56 data:297] Pruned 0 files. Final dataset contains 9065 files
[NeMo I 2022-04-20 22:59:56 data:299] Pruned 0.00 hours. Final dataset contains 18.92 hours.
Processing /home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean/train_manifest.json:
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9065/9065 [3:15:29<00:00, 1.29s/it]
PITCH_MEAN, PITCH_STD = 119.04859924316406, 95.5344009399414
# you will see two new folders generated as well,
$ ls -lt
drwxrwxr-x 2 xueyang xueyang 786432 Apr 21 02:15 pitch
drwxrwxr-x 2 xueyang xueyang 802816 Apr 21 02:15 align_prior_matrix
-rw-rw-r-- 1 xueyang xueyang 87334 Apr 20 17:13 test_manifest.json
-rw-rw-r-- 1 xueyang xueyang 4014423 Apr 20 17:13 train_manifest.json
-rw-rw-r-- 1 xueyang xueyang 44681 Apr 20 17:13 val_manifest.json
# train FastPitch model
$ python examples/tts/fastpitch.py \
--config-path conf/de \
--config-name fastpitch_align_44100 \
model.train_ds.dataloader_params.num_workers=16 \
model.validation_ds.dataloader_params.num_workers=16 \
model.train_ds.dataloader_params.batch_size=24 \
model.validation_ds.dataloader_params.batch_size=24 \
train_dataset=/home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean/train_manifest_ngc.json \
validation_datasets=/home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean/val_manifest_ngc.json \
sup_data_path=/home/xueyang/datasets/HUI-Audio-Corpus-German/Friedrich_Clean \
pitch_mean=119.04859924316406 \
pitch_std=95.5344009399414 \
trainer.devices=2 \
trainer.num_nodes=1 \
trainer.precision=16 \
trainer.accumulate_grad_batches=2 \
trainer.log_every_n_steps=10 \
exp_manager.exp_dir=/home/xueyang/results/in_progress/fastpitch-de-Friedrich_hui_train_A100x2 \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
+exp_manager.create_wandb_logger=True \
+exp_manager.wandb_logger_kwargs.name=ml-model.fastpitch-de-Friedrich \
+exp_manager.wandb_logger_kwargs.project=GermanTTS
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Bugfix
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* Related to # (issue)
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} | null | null | null | How can I get a space(blank) it self in tokenizer vocab?
I am trying to finetune the stt_en_conformer_transducer_large model with grapheme in korean. I changed some code to load labels with chardataset.
in finetuning the prediction log shows blank in a unk tag. (??)
```
[NeMo I 2022-05-23 06:02:11 rnnt_wer_bpe:231] reference :그래서 ⁇ 내가 ⁇ 오글 ⁇ 막 ⁇ 이랬는데
[NeMo I 2022-05-23 06:02:11 rnnt_wer_bpe:232] predicted :그래서 ⁇ 다 ⁇ 얼 ⁇ 말인데
```
this is my tokenizer vocab.
```
<unk> 0
<b> 0
▁ -0.693147
ᄌ -4.18965
ᄀ -4.8828
ᄁ -4.8828
ᄂ -4.8828
ᄃ -4.8828
ᄄ -4.8828
ᄅ -4.8828
ᄆ -4.8828
ᄇ -4.8828
ᄈ -4.8828
ᄉ -4.8828
ᄊ -4.8828
ᄋ -4.8828
ᄍ -4.8828
ᄎ -4.8828
ᄏ -4.8828
ᄐ -4.8828
ᄑ -4.8828
ᄒ -4.8828
ᅡ -4.8828
ᅢ -4.8828
ᅣ -4.8828
ᅤ -4.8828
ᅥ -4.8828
ᅦ -4.8828
ᅧ -4.8828
ᅨ -4.8828
ᅩ -4.8828
ᅪ -4.8828
ᅫ -4.8828
ᅬ -4.8828
ᅭ -4.8828
ᅮ -4.8828
ᅯ -4.8828
ᅰ -4.8828
ᅱ -4.8828
ᅲ -4.8828
ᅳ -4.8828
ᅴ -4.8828
ᅵ -4.8828
ᆨ -4.8828
ᆩ -4.8828
ᆪ -4.8828
ᆫ -4.8828
ᆬ -4.8828
ᆭ -4.8828
ᆮ -4.8828
ᆯ -4.8828
ᆰ -4.8828
ᆱ -4.8828
ᆲ -4.8828
ᆴ -4.8828
ᆶ -4.8828
ᆷ -4.8828
ᆸ -4.8828
ᆹ -4.8828
ᆺ -4.8828
ᆻ -4.8828
ᆼ -4.8828
ᆽ -4.8828
ᆾ -4.8828
ᆿ -4.8828
ᇀ -4.8828
ᇁ -4.8828
ᇂ -4.8828
```
this is my config.
```
name: "Conformer-Transducer-BPE"
init_from_nemo_model:
model0:
path: ??
exclude: ["joint", "decoder"]
include: ["encoder"]
model:
sample_rate: &sample_rate 16000
compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
log_prediction: true # enables logging sample predictions in the output during training
skip_nan_grad: false
change_vocab: True # added
use_cer: True # added
model_defaults:
enc_hidden: ${model.encoder.d_model}
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: ???
sample_rate: 16000 #${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
# use_start_end_token: True
trim_silence: false
max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
use_char: true
unk_index: 0
validation_ds:
manifest_filepath: ???
sample_rate: 16000 #${model.sample_rate}
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_char: true
unk_index: 0
# use_start_end_token: True
test_ds:
manifest_filepath: null
sample_rate: 16000 #${model.sample_rate}
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_char: true
# use_start_end_token: True
# You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
tokenizer:
dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: *sample_rate
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 17
d_model: 512
# Sub-sampling params
subsampling: striding # vggnet or striding
subsampling_factor: 4 # must be power of 2
subsampling_conv_channels: -1 # 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: 8 # 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.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.1
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # 'null' would set it automatically according to CPU/GPU device
preserve_memory: false # dramatically slows down training, but might preserve some memory
# Fuses the computation of prediction net + joint net + loss + WER calculation
# to be run on sub-batches of size `fused_batch_size`.
# When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
# `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
# Using small values here will preserve a lot of memory during training, but will make training slower as well.
# An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
# However, to preserve memory, this ratio can be 1:8 or even 1:16.
# Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
fuse_loss_wer: true
fused_batch_size: 16
jointnet:
joint_hidden: ${model.model_defaults.joint_hidden}
activation: "relu"
dropout: 0.1
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
# greedy strategy config
greedy:
max_symbols: 30
# beam strategy config
beam:
beam_size: 2
return_best_hypothesis: False
score_norm: true
tsd_max_sym_exp: 50 # for Time Synchronous Decoding
alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding
loss:
loss_name: "default"
warprnnt_numba_kwargs:
# FastEmit regularization: https://arxiv.org/abs/2010.11148
# You may enable FastEmit to reduce the latency of the model for streaming
fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
# Adds Gaussian noise to the gradients of the decoder to avoid overfitting
variational_noise:
start_step: 0
std: 0.0
optim:
name: adamw
lr: 5.0
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
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: 500
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: ddp
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: 50 # 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: False # Provided by exp_manager
logger: false # Provided by exp_manager
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
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/4239 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4239/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4239/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4239/events | https://github.com/NVIDIA/NeMo/pull/4239 | 1,245,827,782 | PR_kwDOC_bI7s44U-jN | 4,239 | Fixing bug 3642622 | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes Typos in Prompt Learning notebook and documentation
**Collection**: BigNLP
# Changelog
- Prompt Learning Notebook
- Prompt Learning Docs
# 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) https://nvbugs/3642570 [ML-NeMo][A30] Multitask_Prompt_and_PTuning.ipynb contains typos
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} | Removes all GPT/Frozen model configs and weights from Prompt Learning Model's .nemo file. After training has ended, the prompt learning model now only saves prompt table parameters. During training, intermediate checkpoint files may also have prompt encoder parameters.
**Collection**: BigNLP
- Adds custom state_dict and load_state_dict methods for prompt learning model class
- Updates prompt learning inference example script
- Updates prompt learning documentation
# Usage
The final .nemo checkpoint file contains
1. model_config.yaml
2. model_weights.ckpt
where model_weights.ckpt only has prompt table params
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds missing configs to GPT pre-training.
**Collection**: NLP
# Changelog
- Upates the GPT pretraining yaml config and propages the args through to ParallelTransformer.
# 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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} | It fixes the text generation server cannot `jsonify` the torch tensor object.
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I evaluated NeMo clustering diarizer on VoxConverse dataset. And the results are wonderful. But when I am providing the actual number of speakers through manifest, results get worse. I noticed the same behavior for NIST SRE 2000 dataset too. I am wondering why is this happening? Is it something related to dataset (like overlap speech)?
<img width="873" alt="Screen Shot 2022-05-23 at 23 07 41" src="https://user-images.githubusercontent.com/47416614/169889294-ff7b4100-5f9c-4b58-b695-06a686c34c52.png">
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Replaces LAMB optimizer (unstable, hangs during multi-GPU training) with AdamW for FastPitch and Mixer-TTS configs.
Second attempt based off of `main` instead of `r1.9.0` (see #4219)
**Collection**: TTS
# Changelog
- Replace LAMB defaults with AdamW
- Update `fastpitch.py` example script to use existing config
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4232 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4232/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4232/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4232/events | https://github.com/NVIDIA/NeMo/pull/4232 | 1,245,460,478 | PR_kwDOC_bI7s44TwD6 | 4,232 | NeMo Megatron: Add sequence parallelism and selective activation checkpointing | {
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} | # What does this PR do ?
Add sequence parallelism and selective activation checkpointing.
**Collection**: NLP
# Changelog
- Update GPT config
- Update parallel_lm_logits
- Add CoreAttention
- This is the block that gets recomputed when using selective activation checkpointing
- Update ParallelAttention to work with CoreAttention
- Update ParallelTransformerLayer
- Layer norm calls to use sequence parallel
- Update ParallelTransformer
- Update args
- Update final layer norm
- Add rng contex to forward when using sequence parallel
- Remove transposes
- Update Pooler
- Gather from sequence parallel regions
- Update embedding
- Add sequence_parallel flag
- add transpose in forward
- scatter for dropout
- Update TransformerLanguageModel
- Update GPTModel
- Add transposes
- All-reduce layer norm params when using sequence parallel
- Add sequence parallel arg to apex calls
- Update configs and arg checking for activation checkpointing
# Usage
```
# enable sequence parallelism
model.sequence_parallel=True
# enable selective activation checkpointing
model.activations_checkpoint_granularity=selective
# all transformer layers have CoreAttention checkpointed when using selective
model.activations_checkpoint_method=null
model.activations_checkpoint_num_layers=null
```
# 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/4229 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4229/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4229/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4229/events | https://github.com/NVIDIA/NeMo/issues/4229 | 1,245,051,068 | I_kwDOC_bI7s5KNfi8 | 4,229 | Distributed training via Horovod does not make learning faster | {
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} | null | null | null | **Describe the bug**
I run [text classification with bert example](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_classification/text_classification_with_bert.py). Training is done well. I got approximately %90 accuracy after first epoch. Then I tried to run the same example with [horovod](https://github.com/horovod/horovod). My change is written below:
Change [line 116](https://github.com/NVIDIA/NeMo/blob/c03f87d47fc57abc89c0ebf859fccba397dd0f8e/examples/nlp/text_classification/text_classification_with_bert.py#L116):
`trainer = pl.Trainer(plugins=[NLPDDPPlugin()], **cfg.trainer)` to `trainer = pl.Trainer(strategy="horovod", **cfg.trainer)`.
Change [config file](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_classification/conf/text_classification_config.yaml):
model.train_ds.file_path = path/to/train
model.validation_ds.file_path = path/to/test
model.train_ds.batch_size = 8
model.validation_ds.batch_size = 8
model.dataset.num_classes = 2
trainer.devices = 1
trainer.max_epochs = 1
Then I run 2 training with this commands:
`horovodrun -np 1 -H localhost:1 --verbose --start-timeout 300 python text_classification_with_bert.py`
and
`horovodrun -np 2 -H localhost:2 --verbose --start-timeout 300 python text_classification_with_bert.py`
For second one, I also changed `trainer.devices` to 2 in config file.
First training took 15:06 min and second training took 22:00 min.
**Expected behavior**
I expect that second training should take less time.
**Environment overview**
horovod: 0.24.3
pytorch-lightning: 1.6.3
pytorch: 1.11.0
nemo-toolkit: 1.8.2 | {
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"merged_at": "2022-05-23T16:18:18"
} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
Fix syntax error in ipynb file
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4227 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4227/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4227/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4227/events | https://github.com/NVIDIA/NeMo/issues/4227 | 1,244,777,818 | I_kwDOC_bI7s5KMc1a | 4,227 | Correcting spellings for BPE CTC conformer Models. | {
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} | null | null | null | Hi guys I fine-tuned a ctc large model on my custom dataset. Results are great but hv noticed some spellings issues.
- How do I solve the spelling issues?
- Do I need to use a lm for this?
- Is the tokeniser size the problem . Uses same spe, unigram model with 256 tokens (added punts + numbers from 0 - 9) | {
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} | null | null | null | Hello,
when I try to download any model it shows an error:
ValueError: Not able to download url right now, please try again.
I supose the server that store the models is not returning the models.
Could you check it out, please?
I share the full stack trace (in main branch and other branches)
[NeMo W 2022-05-23 10:10:06 nemo_logging:349] /home/pc/.local/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
################################################################################
### 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)
################################################################################
2022-05-23 10:10:07.314427: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
2022-05-23 10:10:07.314442: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [2], in <module>
16 from nemo.collections.tts.helpers.helpers import regulate_len
18 # Load the models from NGC
---> 19 fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch").eval().cuda()
20 hifigan = HifiGanModel.from_pretrained("tts_hifigan").eval().cuda()
21 sr = 22050
File ~/Downloads/NeMo/nemo/core/classes/common.py:682, in Model.from_pretrained(cls, model_name, refresh_cache, override_config_path, map_location, strict, return_config, trainer, save_restore_connector)
677 class_, nemo_model_file_in_cache = cls._get_hf_hub_pretrained_model_info(
678 model_name=model_name, refresh_cache=refresh_cache
679 )
680 else:
681 # NGC source
--> 682 class_, nemo_model_file_in_cache = cls._get_ngc_pretrained_model_info(
683 model_name=model_name, refresh_cache=refresh_cache
684 )
686 instance = class_.restore_from(
687 restore_path=nemo_model_file_in_cache,
688 override_config_path=override_config_path,
(...)
693 save_restore_connector=save_restore_connector,
694 )
695 return instance
File ~/Downloads/NeMo/nemo/core/classes/common.py:743, in Model._get_ngc_pretrained_model_info(cls, model_name, refresh_cache)
741 cache_subfolder = hashlib.md5((location_in_the_cloud + description).encode('utf-8')).hexdigest()
742 # if file exists on cache_folder/subfolder, it will be re-used, unless refresh_cache is True
--> 743 nemo_model_file_in_cache = maybe_download_from_cloud(
744 url=url, filename=filename, cache_dir=cache_dir, subfolder=cache_subfolder, refresh_cache=refresh_cache
745 )
747 logging.info("Instantiating model from pre-trained checkpoint")
749 if class_ is None:
File ~/Downloads/NeMo/nemo/utils/cloud.py:82, in maybe_download_from_cloud(url, filename, subfolder, cache_dir, refresh_cache)
80 sleep(0.05)
81 continue
---> 82 raise ValueError("Not able to download url right now, please try again.")
ValueError: Not able to download url right now, please try again.
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https://api.github.com/repos/NVIDIA/NeMo/issues/4225 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4225/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4225/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4225/events | https://github.com/NVIDIA/NeMo/issues/4225 | 1,244,596,277 | I_kwDOC_bI7s5KLwg1 | 4,225 | unable to train asr | {
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} | null | null | null | **Describe the bug**
trying to build an ASR with min-Libri speech data
**command**
python3 speech_to_text_ctc_bpe.py model.train_ds.manifest_filepath=data/mini_libri/train_clean_5.json model.validation_ds.manifest_filepath=data/mini_libri/dev_clean_2.json model.tokenizer.dir=data/tokenizer_libri/tokenizer_spe_bpe_v1024/ model.tokenizer.type="bpe" trainer.devices=-1 trainer.accelerator="gpu" trainer.strategy="ddp" trainer.max_epochs=100 model.optim.name="adamw" model.optim.lr=0.001 model.optim.betas=[0.9,0.999] model.optim.weight_decay=0.0001 model.optim.sched.warmup_steps=2000 exp_manager.create_wandb_logger=True exp_manager.wandb_logger_kwargs.name="Libri_1" exp_manager.wandb_logger_kwargs.project="Asr_demo"
**Error**
Traceback (most recent call last):
File "speech_to_text_ctc_bpe.py", line 95, in <module>
main()
File "/data/supreet/NeMo/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
TypeError: _run_hydra() missing 1 required positional argument: 'args'
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https://api.github.com/repos/NVIDIA/NeMo/issues/4224 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4224/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4224/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4224/events | https://github.com/NVIDIA/NeMo/issues/4224 | 1,244,370,798 | I_kwDOC_bI7s5KK5du | 4,224 | Trying to resume training `enc_dec_nmt` fails | {
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} | null | null | null | **Describe the bug**
I used `enc_dec_nmt.py` to build a NLP/MT model based on `aayn_base.yml` (`nemo:1.8.2` based on `pytorch:22.04-py3`); training was interrupted before reaching the final epoch, now tying to resume training from the last checkpoint by passing
`+exp_manager.resume_if_exists=true` to the `enc_dec_nmt.py` call fails with the following trace
```python
Traceback (most recent call last):
File "examples/nlp/machine_translation/enc_dec_nmt.py", line 147, in <module>
main()
File "/workspace/nemo/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "examples/nlp/machine_translation/enc_dec_nmt.py", line 140, in main
trainer.fit(mt_model)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 768, in fit
self._call_and_handle_interrupt(
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1234, in _run
results = self._run_stage()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1321, in _run_stage
return self._run_train()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1351, in _run_train
self.fit_loop.run()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 205, in run
self.on_advance_end()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 255, in on_advance_end
self._run_validation()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 309, in _run_validation
self.val_loop.run()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 211, in run
output = self.on_run_end()
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 187, in on_run_end
self._evaluation_epoch_end(self._outputs)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 309, in _evaluation_epoch_end
self.trainer._call_lightning_module_hook("validation_epoch_end", output_or_outputs)
File "/ceph/hpc/home/ilb/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1593, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/workspace/nemo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 493, in validation_epoch_end
self.eval_epoch_end(outputs, 'val', self.global_rank)
File "/workspace/nemo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 407, in eval_epoch_end
if isinstance(outputs[0], dict):
IndexError: list index out of range
```
**Steps/Code to reproduce bug**
Train with `examples/enc_dec_nmt.py` long enough to produce at least one checkpoint, interrupt training and rerun with the `exp_manager.resume_if_exists` flag set.
**Expected behavior**
Training resumes from last checkpoint. This used to work in `nemo:1.3.0`.
**Environment overview (please complete the following information)**
pytorch:22.04-py3
nemo:1.8.2
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} | Signed-off-by: Iztok Lebar Bajec <[email protected]>
# What does this PR do ?
MTEncDecModel yaml configs do not declare `model.tokenizer`, hence without this fix restoring from a checkpoint will fail with
```
omegaconf.errors.ConfigAttributeError: Missing key tokenizer
full_key: cfg.tokenizer
object_type=dict
```
**Collection**: NLP/MT
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
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**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**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 ?
1. Added the RETRO index dataset for both data and retrieval data
2. Added the KNN Map index dataset
3. Added unit tests for all the index dataset
4. Added RETRO dataset that uses index dataset
5. Added the scripts to preprocess, build Faiss index and generate KNN map index for training
6. Added the `set_inference_key_value_memory` capability to RETRO modules so it can run inference efficiently
7. Added unit tests for efficient inference of all RETRO modules.
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Replaces LAMB optimizer (unstable, hangs during multi-GPU training) with AdamW for FastPitch and Mixer-TTS configs.
**Collection**: TTS
# Changelog
- Replace LAMB defaults with AdamW
- Update `fastpitch.py` example script to use existing config
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Restore previously deleted files from conflicting PR
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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 ?
Dialogue tutorial fix (path of a file has been shifted by a conflicting PR)
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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 ?
Fuse bias with geglu in ParallelMLP
# Changelog
fused bias with geglu using TorchScript, fused function is in the newly added file fused_bias_geglu.py
in ParallelMLP, changed bias_gelu_fusion to biad_activation_fusion for generality, also fixed the way of this arg is propagated
# Usage
By default, bias_activation_fusion=True. If you want to disable it, simply set it as False in your config file.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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
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https://api.github.com/repos/NVIDIA/NeMo/issues/4212 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4212/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4212/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4212/events | https://github.com/NVIDIA/NeMo/pull/4212 | 1,242,468,224 | PR_kwDOC_bI7s44J_Wb | 4,212 | Prompt Learning Model Saving Changes | {
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} | Signed-off-by: Virginia Adams <[email protected]>
Save all gpt + model weights to the model_weight.ckpt file removing the gpt model's .nemo file artifact. Saves GPT model's config file as an artifact.
**Collection**: BigNLP
# Changelog
Updated PromptLearningModel
# Usage
* You can potentially add a usage example below
Same as before
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4211 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4211/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4211/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4211/events | https://github.com/NVIDIA/NeMo/pull/4211 | 1,242,408,354 | PR_kwDOC_bI7s44Jyrh | 4,211 | fix bugs for dialogue tutorial | {
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} | # What does this PR do ?
Fix typos in dialogue tutorial
**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/4210 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4210/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4210/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4210/events | https://github.com/NVIDIA/NeMo/pull/4210 | 1,242,370,324 | PR_kwDOC_bI7s44Jqby | 4,210 | MegaMolBART compatibility | {
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} | # What does this PR do ?
1. Fixes RegExTokenizer missing attributes.
2. Fixes broken Megatron encoder-decoder LM model ability to receive directly encoder hiddens.
```
MegatronLMEncoderDecoderModel.forward(
self,
encoder_input_ids,
decoder_input_ids,
encoder_attn_mask,
decoder_attn_mask,
token_type_ids=None,
lm_labels=None,
# NEW API
enc_output=None,
enc_output_attn_mask=None,
# NEW API
output_enc_hidden_only=False,
enc_input=None,
):
```
3. Added MegatronLMEncoderDecoderModel.encode method.
4. Improved decoding speed.
**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 ?
Update Tacotron2 training tutorial and config to work with r.1.9.0
**Collection**: TTS
# Changelog
- Updated 44kHz config to use TTSDataset (now matches with 22kHz config)
- Added file downloads necessary for training to the Tacotron2 notebook
- Minor bugfix to avoid device mismatch during training for the notebook
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Virginia Adams <[email protected]>
# What does this PR do ?
Updates the prompt learning model to use pytorch's DistributedSampler and fixes an issue with trailing spaces at the end of some prompts during inference.
**Collection**: BigNLP
# Changelog
Updated PromptLearningDataset class
Updated PromptLearningModel class
# Usage
Same as before
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Update ContextNet 1024 checkpoint on NeMo ASR Set 3.0
**Collection**: [ASR]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Support larger datasets for question answering
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Cherry pick some fixes from main to r1.9.0
**Collection**: [Core, ASR]
# Changelog
- Fix hydra.
- Fix mandarin checkpoint
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} | Signed-off-by: Abhinav Khattar <[email protected]>
# What does this PR do ?
Script to add special tokens to existing SentencePiece model. This can help in moving away from the legacy way of handling special tokens.
**Collection**: None
# Usage
`python edit_spt_model.py --input_file <input_model_dir> --output_file <output_model_dir> --tokens <space separated special tokens> `
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix Hydra issue with hydra_runner
**Collection**: [Core]
# Changelog
- Update signature and lower bound to latest hydra
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Vahid <[email protected]>
# What does this PR do ?
Adding the checkpoint of a Mandarin ASR model trained with Conformer-Transducer to the pretrained model's list. It is trained on AIShell-2 dataset.
# Changelog
Added a Mandarin ASR model trained with Conformer-Transducer on AIShell-2.
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Update typos in Dialogue Tutorial
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
1. Previously `PunctuationCapitalizationModel.set_trainer()` was required before calling `PunctuationCapitalizatoinModel.set_{training,test,validation}_data()`. This is unexpected for users. Now `set_trainer()` call is not required and warnings are shown.
2. Training random seed for batching is now `current_epoch` instead of `global_step`.
3. Typo fix
4. Fix last cell in punctuation notebook tutorial.
**Collection**: nlp
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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/4197 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4197/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4197/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4197/events | https://github.com/NVIDIA/NeMo/pull/4197 | 1,240,511,668 | PR_kwDOC_bI7s44DdTS | 4,197 | Prompt tokenization bugfix | {
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} | # What does this PR do ?
1. adds <> around virtual prompt token string
2. Removes pseudo token configurability from the config file in favor of using a consistent pseudo token set by us
3. Makes PROMPT lowercase
**Collection**: BigNLP
# Changelog
Updated megatron_gpt_prompt_learning_model
Updated prompt learning dataset unit tests
Added Enum for virtual prompt token string
# Usage
Same as before
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Fix the key error in Tabular Data generation caused by the change of default prompts. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4195 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4195/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4195/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4195/events | https://github.com/NVIDIA/NeMo/pull/4195 | 1,240,201,745 | PR_kwDOC_bI7s44CfrH | 4,195 | Pin omegaconf for r1.9.0 | {
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} | Signed-off-by: ericharper <[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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hi, I am trying to use horovod for accelerator. Training steps:
1. cd NeMo/examples/nlp/text_classification
2. python text_classification_with_bert.py
This steps works without any problem. However when I try to use horovod:
1. cd NeMo/examples/nlp/text_classification
2.change pl Trainer:
pl.Trainer(plugins=[NLPDDPPlugin()], **cfg.trainer) to trainer = pl.Trainer(strategy="horovod", accelerator="gpu", devices=1, plugins=[NLPDDPPlugin()], **cfg.trainer) in `text_classification_with_bert.py`
3. horovodrun -np 1 -H server1:1 --verbose --start-timeout 300 python text_classification_with_bert.py
I got this error:
```
(nemo)$ horovodrun -np 1 -H server1:1 --verbose --start-timeout 300 python text_classification_with_bert.py
Filtering local host names.
Remote host found: server1
Checking ssh on all remote hosts.
SSH was successful into all the remote hosts.
Testing interfaces on all the hosts.
Launched horovod server.
Launching horovod task function: ssh -o PasswordAuthentication=no -o StrictHostKeyChecking=no server1 path/to/miniconda3/envs/nemo/bin/python -m horovod.runner.task_fn ...
Attempted to launch horovod task servers.
Waiting for the hosts to acknowledge.
Launching horovod task function was not successful:
bash: path/to/miniconda3/envs/nemo/bin/python: No such file or directory
```
**Environment overview**
PyTorch version: 1.11.0
CUDA used to build PyTorch: 10.2
OS: Ubuntu 18.04.3
Python version: 3.8.13
Is CUDA available: True
CUDA runtime version: 11.3.58
Versions of relevant libraries:
[pip3] numpy==1.21.5
[pip3] pytorch-lightning==1.6.1
[pip3] torch==1.11.0
[pip3] torch-stft==0.1.4
[pip3] torchaudio==0.11.0
[pip3] torchmetrics==0.8.0
[pip3] torchvision==0.12.0
[conda] pytorch 1.11.0 py3.8_cuda10.2_cudnn7.6.5_0 pytorch
[conda] pytorch-lightning 1.6.1 pypi_0 pypi
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-stft 0.1.4 pypi_0 pypi
[conda] torchaudio 0.11.0 py38_cu102 pytorch
[conda] torchmetrics 0.8.0 pypi_0 pypi
[conda] torchvision 0.12.0 py38_cu102 pytorch
Note: I run an [mnist example with Horovod](https://github.com/horovod/horovod/blob/master/examples/pytorch/pytorch_mnist.py) itself directly without a problem. | {
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Hi, I have previously run the code as stated in Usage description of the script. However, now I get this error by running the exact same code. This is the error messege.
Global seed set to 42
Traceback (most recent call last):
File "/content/NeMo/examples/speaker_tasks/diarization/offline_diarization.py", line 46, in <module>
main()
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 108, in wrapper
config_name=config_name,
TypeError: _run_hydra() missing 1 required positional argument: 'args'
**Steps/Code to reproduce bug**
!python3 /content/NeMo/examples/speaker_tasks/diarization/offline_diarization.py \
diarizer.manifest_filepath='/content/input_manifest.json' \
diarizer.out_dir='demo_output' \
diarizer.speaker_embeddings.model_path='titanet_large' \
diarizer.oracle_vad=True
**Environment overview (please complete the following information)**
On Google colab
**Environment details**
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Try to fix omegaconf (again)
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix omegaconf incompatibility.
**Collection**: [Core]
**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?
@ericharper | {
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} | # What does this PR do ?
Add support for decoder + joint adapters, alongside encoder adapters. Supports CTC + RNNT decoders, RNNT Joint, RNNT Decoder + Joint adapter.
**Collection**: [ASR]
# Changelog
- Support CTC Decoder, RNNT Decoder, RNNT Joint adapter support (module level).
- Update tests to check new modules
- Add support for property that defines list of valid modules that support adapters.
# Usage
```python
# Add a code snippet demonstrating how to use this
model.add_adapter("decoder:<adapter_name>", cfg=...) # decoder only adapter; CTC/RNNT
model.add_adapter("joint:<adapter_name>", cfg=...) # joint only adapter; RNNT only
model.add_adapter("decoder+joint:<adapter_name>", cfg=...) # decoder + joint adapter; RNNT only
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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The provided link for AN4 dataset under [http://www.speech.cs.cmu.edu/databases/an4/](here) is not accessible. Please validate the URL. | {
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} | # What does this PR do ?
This PR adds dataloaders, collectors, loss functions and accuracy metric for multiscale diarization decoder to NeMo. Multiscale diarization decoder is a model that accepts cluster-average embedding and multiscale embeddings to infer diarization output. Before push the model files and inference scripts, these files better be reviewed and merged to reduce the bandwidth of PR review.
**Collection**: ASR (speaker tasks)
# Changelog
- `nemo/collections/asr/data/audio_to_label.py`
Added the following classes:
`AudioToSpeechDiarTrainDataset()`
`_AudioDiarTrainDataset()`
The above classes are built for diarization training and validation.
The input is raw audio waveform with segmentation timestamp information.
Diarization inference procedure involves clustering process for varying number of speakers.
Therefore, training is done only for two speaker dataset. For inference, we need
a separate dataloader that takes clustering results and cluster-average embedding as inputs.
`AudioToSpeechMSDDDataset()`
`_AudioMSDDDataset()`
The above classes are built for diarization inference. The input is speaker embeddings and cluster-average embeddings.
Added the following functions:
`get_scale_mapping_list()`
`_extract_seg_info_from_rttm()`
`_assign_frame_level_spk_vector()`
`_get_diar_target_labels()`
`_msdd_collate_fn()`
`_diar_train_collate_fn()`
- `nemo/collections/common/parts/preprocessing/collections.py`
The following functions are added to collect audio_filepath, rttm_filepath and number of speakers.
DiarizationSpeechLabel()
DiarizationLabel()
- `nemo/collections/asr/losses/bce_loss.py`
This file is newly created. Contains binary cross entropy loss that is needed for training overlap-aware diarization models since one unit timestep output vector should be able to contain more than one label.
- `nemo/collections/asr/metrics/multi_binary_acc.py`
Multi-binary accuracy that currently includes F1 accuracy. This file is named as "multi-binary accuracy" since other accuracy measures such as area under curve can be added in the future.
# Usage
These are newly added class and functions. The yaml file and inference script will be provided later when model files are uploaded.
# 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 NeMo ASR.
# Additional Information
For more details about multiscale diarization decoder, check out the following paper:
https://arxiv.org/pdf/2203.15974.pdf
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} | # What does this PR do ?
- adds multi-loss training to ssl, with corresponding list of decoders and losses defined through the ssl model config
- adds option to use pseudo-labels from quantization module of contrastive loss as targets for other losses
- adds option for ssl decoders to use outputs of non-final layer of encoder as input
- default ssl config is updated to use contrastive + mlm loss combination
- adds tests for ssl and maskedpatchaugmentation
- fixes sampling for tarred datasets with multiple utterances per file
**Collection**: ASR
**PR Type**:
- [x] New Feature
- [ ] 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 ?
1. Implements the UL2 Dataset from https://arxiv.org/abs/2205.05131.
2. Adds a `megatron_ul2_config.yaml` to train an encoder-decoder model on this data using t5 pretraining script.
**Collection**: NLP
# Changelog
- UL2 dataset class that inherits from T5Dataset
- UL2 yaml config
- Minor refactor of LMAdaptedT5Dataset
# Usage
`python megatron_t5_pretraining.py -cn megatron_ul2_config`
# 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 | Hi I am training a Conformer Model and need to know if thing about the training process.
My Training params :
- learning rate : 0.1,
- batch_size = 128
- max_epochs : 100
- warmup steps : 10000
The questions I had were.
1. What hardware was used to train the `ctc_conformer_large` and `ctc_conformers_medium` models. gpus and how long were they trained (hrs).
2. If my learning rate has reduced to 1e-6 will the model still continue learning or should I be stopping at this point.
asking becz using the noam scheduler the learning rate has decreased quite a bit in the first 2 epochs only and hv another 98 to go.
3. A curious question what happens if my batch size is 4 and a set up a really high grad accum like 16 will his work?
@titu1994
thanks
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} | Signed-off-by: Subhankar Ghosh <[email protected]>
# What does this PR do ?
Fix Hifigan finetune tutorial
Solution to https://github.com/NVIDIA/NeMo/issues/4181
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | Hello! I'm trying to fine-tune the HiFi according to your fastpitch_finetuning tutorial. I generated manifests in the same format as in the example. Then I launched the fine-tuning script by simply changing the paths. Despite the small number of changes, I constantly get an error. I tried to study the code to understand what I'm doing wrong. In the process of studying, I found a possible cause of my troubles and would like to ask you on whose side the error occurred. I think it would be more convenient to explain the essence of my question with screenshots.
You use the file path as the model.train_ds parameter:
Next, I went through the error traceback and found that at some point the parameter that was specified in the previous step is used. If I didn’t mix up anything, then we also just use the path to the file:
In the next traceback step, we send this path to the setup_data_loader_from_config function, where the error occurs. Looking inside the function, I found that our argument is checked for the presence of the word "dataset". It is at this point that the error occurs, since the path to my manifest does not contain this word. Although, apparently, it was assumed that the input of the function would not be a string containing the path to the manifest, but the config itself, which would contain the specified argument. The same thing can be observed in the two lines of code below, where the presence of dataloader_params is checked:
In this regard, I really hope for your help. Thanks in advance!
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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
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## Who can review?
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} | # What does this PR do ?
import function name fix
**Collection**: ASE
# Changelog
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```python
# Add a code snippet demonstrating how to use this
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- [ ] Did you write any new necessary tests?
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
make highfreq consistent to both fastpitch and hifigan in their 44100 configs.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/4176 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4176/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4176/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4176/events | https://github.com/NVIDIA/NeMo/issues/4176 | 1,237,288,521 | I_kwDOC_bI7s5Jv4ZJ | 4,176 | SpeakerDecoder weight normalization | {
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When reading SpeakerDecoder code I bumped into [this](https://github.com/NVIDIA/NeMo/blob/1d3eb3d0d045f7f57316217320b8d265bf3256ce/nemo/collections/asr/modules/conv_asr.py#L848) line. Looks like it is weight normalization for AAS loss. But why should it work?
```
>>> import torch.nn as nn
>>> import torch.nn.functional as F
>>> layer = nn.Linear(128, 100, bias=False)
>>> weight1 = layer.weight.data.clone()
>>> for W in layer.parameters():
... W = F.normalize(W, p=2, dim=1)
...
>>> weight2 = layer.weight.data.clone()
>>> weight1 == weight2
tensor([[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]])
>>>
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/4175 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4175/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4175/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4175/events | https://github.com/NVIDIA/NeMo/pull/4175 | 1,237,257,546 | PR_kwDOC_bI7s434m0U | 4,175 | let sox find wav duration when it is missing (None). | {
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} | # What does this PR do ?
bug-fix. sox must find the wav duration when dur is None.
**Collection**: speaker_tasks
# Changelog
L135: this condition should be true when 'dur is None' because it allows sox to get the duration from the wav_file.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
python ../../../scripts/speaker_tasks/scp_to_manifest.py --scp voxceleb12.scp --id 8 --out TMP
```
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/4174 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4174/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4174/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4174/events | https://github.com/NVIDIA/NeMo/issues/4174 | 1,237,224,200 | I_kwDOC_bI7s5JvosI | 4,174 | TitaNet Small | {
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} | null | null | null | Hi NeMo team,
Do you plan to release TitaNet-small (medium) model? | {
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} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
Add docs for Thutmose Tagger model (ITN)
**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 a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
Add docs for Thutmose Tagger model (ITN)
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | 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.
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} | null | null | null | Hi,
I was examining `examples/asr/experimental/k2/align_speech_parallel.py` script as I wish to get speech alignments from my trained CTC-based ASR model. Somewhere, I was told that I have to install k2 package to use this script and its recommended method is by using `scripts/speech_recognition/k2/setup.sh`. However, when I executed the `setup.sh` script, its output is like this:
`ImportError: k2 was built using PyTorch 1.7.1+cu101
But you are using PyTorch 1.11.0+cu102 to run it
k2 installed with errors! Please check installation manually.`
As far as I know, NeMo needs Pytorch 1.10.0 or above to function but it seems that k2 has been built by PyTorch 1.7.1.
I know the k2 based scripts in NeMo are experimental for now, but is there any chance to bypass this? | {
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Why Some TTS model (MelGAN, TalkNet) are removed from main branch, while they are exist in 1.8.2 ?
Do you want to stop maintaining them ?
https://github.com/NVIDIA/NeMo/commit/c37786a9acb443b1d55d56636f00d3826ffc76d5 | {
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Add the PP2 GPT eval CI test. Also fix the `fake_initialize_model_parallel` | {
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} | # What does this PR do ?
Add volume passthrough to fp for riva
**Collection**: TTS
# Changelog
- Add volume passthrough to fp for riva
**PR Type**:
- [x] New Feature
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Can you please refers me to the best tutorial about that? | {
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I followed the [tacotron notebook](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/tts/2_TTS_Tacotron2_Training.ipynb#scrollTo=Upv_LxBIsC51) and tried to train the nemo model. I end up getting stuck with this error. Can you please help?
`TypeError: Error instantiating 'nemo.collections.asr.data.audio_to_text.AudioToCharDataset' : __init__() got an unexpected keyword argument 'load_audio'
` | {
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Update speaker docs related to recent script name change from scp_to_manifest.py to filelist_to_manifest.py
# Changelog
- Update docs
# 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?
- [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 ?
rename VAD folder in examples/asr/conf/ to vad and update files accordingly.
**Collection**:ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```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 ?
Bug fixes. Update tutorial. Add pretrained model to list_available_models
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Subhankar Ghosh <[email protected]>
# What does this PR do ?
Add 44KHz yaml file for Fastpitch training
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | null | null | null | How do I run inference on SGDQA?
I see code for running training and evaluation on datasets, but how do I use this model interactively?
Is there any code for preprocessing utterance and schema and tracking state without providing information which should not be visible to model? | {
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} | # What does this PR do ?
Update transcribe script to scan directories recursively.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/4158",
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/4158.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/4158.patch",
"merged_at": "2022-05-12T17:32:28"
} | # What does this PR do ?
bug fix for notebook
**Collection**:ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```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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} | null | null | null | I am trying to implement speaker diarization using __vad_marblenet__ and __titanet_large__ models.
# Issue
When I am trying to run on server/local machine .... the process either gets stuck or terminates due to CPU memory allocation error during clustering.
- __memory allocation issue__
- __execution halted__
## Environment:
- OS : Ubuntu 18.04.6 LTS (GNU/Linux 4.18.0-25-generic x86_64)
- Memory:
- Python: 3.7.13
- Installation : from source i.e. __(git clone ... & cd NeMo & ./reinstall.sh)__
# Reference
When executing on collab, it's working properly. It's giving proper diarization results.
- Link to notebook: https://colab.research.google.com/drive/1UgzoPHV1aByDDhygCUAiEaEIRGldrocK?usp=sharing
- Test audio file: https://drive.google.com/file/d/1ChLsVHTgH4SUgvqLP7ymtrbao-L0KKsj/view?usp=sharing
- Manifest file: https://drive.google.com/file/d/1SAeLmMIHGk-rc7ydHfYLywCkbMx7SbH6/view?usp=sharing
- Diarization yaml config file: https://drive.google.com/file/d/1lvYYg5Pd8hUceS2a-raO76huu-p0p39V/view?usp=sharing
- Test audio file result: https://drive.google.com/drive/folders/1FCH_UOFUFJZUQdxMHQfzXQII-bg6auaO?usp=sharing
## Environment:
- Installation on collab: from source i.e. __(git clone ... & cd NeMo & ./reinstall.sh)__
- Python: 3.7.13 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4156 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4156/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4156/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4156/events | https://github.com/NVIDIA/NeMo/issues/4156 | 1,233,487,589 | I_kwDOC_bI7s5JhYbl | 4,156 | JSONDecodeError: Extra data: line 1 column 126 (char 125) | {
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} | null | null | null | Hello, I am trying to train a custom model using the provided code. I've compiled my custom Cyrillic manifests according to the instruction with each line being:
{"audio_filepath": "/path/to/wav/file", "duration": float, "text": "transcript_in_cyrillic"}
However, when I setup the data i get the following error. I've double checked for any invisible characters and what not but my custom manifest is exactly like the one in the example.
---------------------------START------------------------------------------------
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
/nfs/home/create_manifest.ipynb Cell 14' in <cell line: 13>()
2 trainer = pl.Trainer(devices=1, accelerator='gpu', max_epochs=50)
3 #trainer = pl.Trainer(devices=1, accelerator='gpu', max_epochs=2)
4
5 #import copy
(...)
11
12 # Point to the data we'll use for fine-tuning as the training set
---> 13 quartznet_transfer_mon.setup_training_data(train_data_config=params['model']['train_ds'])
15 # Point to the new validation data for fine-tuning
16 quartznet_transfer_mon.setup_validation_data(val_data_config=params['model']['validation_ds'])
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py:464, in EncDecCTCModel.setup_training_data(self, train_data_config)
461 # preserve config
462 self._update_dataset_config(dataset_name='train', config=train_data_config)
--> 464 self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
466 # Need to set this because if using an IterableDataset, the length of the dataloader is the total number
467 # of samples rather than the number of batches, and this messes up the tqdm progress bar.
468 # So we set the number of steps manually (to the correct number) to fix this.
469 if 'is_tarred' in train_data_config and train_data_config['is_tarred']:
470 # We also need to check if limit_train_batches is already set.
471 # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
472 # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py:426, in EncDecCTCModel._setup_dataloader_from_config(self, config)
423 logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}")
424 return None
--> 426 dataset = audio_to_text_dataset.get_char_dataset(config=config, augmentor=augmentor)
428 if hasattr(dataset, 'collate_fn'):
429 collate_fn = dataset.collate_fn
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text_dataset.py:86, in get_char_dataset(config, augmentor)
83 if 'labels' not in config:
84 logging.warning(f"dataset does not have explicitly defined labels")
---> 86 dataset = audio_to_text.AudioToCharDataset(
87 manifest_filepath=config['manifest_filepath'],
88 labels=config.get('labels', None),
89 sample_rate=config['sample_rate'],
90 int_values=config.get('int_values', False),
91 augmentor=augmentor,
92 max_duration=config.get('max_duration', None),
93 min_duration=config.get('min_duration', None),
94 max_utts=config.get('max_utts', 0),
95 blank_index=config.get('blank_index', -1),
96 unk_index=config.get('unk_index', -1),
97 normalize=config.get('normalize_transcripts', False),
98 trim=config.get('trim_silence', False),
99 parser=config.get('parser', 'en'),
100 return_sample_id=config.get('return_sample_id', False),
101 )
102 return dataset
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py:388, in AudioToCharDataset.__init__(self, manifest_filepath, labels, sample_rate, int_values, augmentor, max_duration, min_duration, max_utts, blank_index, unk_index, normalize, trim, bos_id, eos_id, pad_id, parser, return_sample_id)
382 self.labels = labels
384 parser = parsers.make_parser(
385 labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize
386 )
--> 388 super().__init__(
389 manifest_filepath=manifest_filepath,
390 parser=parser,
391 sample_rate=sample_rate,
392 int_values=int_values,
393 augmentor=augmentor,
394 max_duration=max_duration,
395 min_duration=min_duration,
396 max_utts=max_utts,
397 trim=trim,
398 bos_id=bos_id,
399 eos_id=eos_id,
400 pad_id=pad_id,
401 return_sample_id=return_sample_id,
402 )
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py:274, in _AudioTextDataset.__init__(self, manifest_filepath, parser, sample_rate, int_values, augmentor, max_duration, min_duration, max_utts, trim, bos_id, eos_id, pad_id, return_sample_id)
271 if type(manifest_filepath) == str:
272 manifest_filepath = manifest_filepath.split(",")
--> 274 self.manifest_processor = ASRManifestProcessor(
275 manifest_filepath=manifest_filepath,
276 parser=parser,
277 max_duration=max_duration,
278 min_duration=min_duration,
279 max_utts=max_utts,
280 bos_id=bos_id,
281 eos_id=eos_id,
282 pad_id=pad_id,
283 )
284 self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor)
285 self.trim = trim
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py:135, in ASRManifestProcessor.__init__(self, manifest_filepath, parser, max_duration, min_duration, max_utts, bos_id, eos_id, pad_id, index_by_file_id)
121 def __init__(
122 self,
123 manifest_filepath: str,
(...)
131 index_by_file_id: bool = False,
132 ):
133 self.parser = parser
--> 135 self.collection = collections.ASRAudioText(
136 manifests_files=manifest_filepath,
137 parser=parser,
138 min_duration=min_duration,
139 max_duration=max_duration,
140 max_number=max_utts,
141 index_by_file_id=index_by_file_id,
142 )
144 self.eos_id = eos_id
145 self.bos_id = bos_id
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/collections.py:206, in ASRAudioText.__init__(self, manifests_files, *args, **kwargs)
196 """Parse lists of audio files, durations and transcripts texts.
197
198 Args:
(...)
202 **kwargs: Kwargs to pass to `AudioText` constructor.
203 """
205 ids, audio_files, durations, texts, offsets, speakers, orig_srs, langs = [], [], [], [], [], [], [], []
--> 206 for item in manifest.item_iter(manifests_files):
207 ids.append(item['id'])
208 audio_files.append(item['audio_file'])
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py:72, in item_iter(manifests_files, parse_func)
70 for line in f:
71 k += 1
---> 72 item = parse_func(line, manifest_file)
73 item['id'] = k
75 yield item
File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py:79, in __parse_item(line, manifest_file)
78 def __parse_item(line: str, manifest_file: str) -> Dict[str, Any]:
---> 79 item = json.loads(line)
81 # Audio file
82 if 'audio_filename' in item:
File ~/anaconda3/envs/nemo/lib/python3.8/json/__init__.py:357, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
352 del kw['encoding']
354 if (cls is None and object_hook is None and
355 parse_int is None and parse_float is None and
356 parse_constant is None and object_pairs_hook is None and not kw):
--> 357 return _default_decoder.decode(s)
358 if cls is None:
359 cls = JSONDecoder
File ~/anaconda3/envs/nemo/lib/python3.8/json/decoder.py:340, in JSONDecoder.decode(self, s, _w)
338 end = _w(s, end).end()
339 if end != len(s):
--> 340 raise JSONDecodeError("Extra data", s, end)
341 return obj
JSONDecodeError: Extra data: line 1 column 126 (char 125)
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} | # What does this PR do ?
Fixes freesound-python git path and updates filename cleanup
**Collection**: [Note which collection this PR will affect]
# Changelog
- Update requirements.txt to use https instead of git to fix "The unauthenticated git protocol on port 9418 is no longer supported."
- Remove additional characters from filename
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [ ] Documentation
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## Who can review?
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Align docs left and increase width for screens > 1200px
**Collection**: All
# Changelog
- Align docs left and increase width for screens > 1200px
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} | # What does this PR do ?
The docs have an error where the word "cartographic" is used in place of "catastrophic"
**Collection**: ?
# Changelog
- `docs/source/nlp/prompt_learning.rst` fix word error
# 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)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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} | Signed-off-by: Jocelyn Huang <[email protected]>
Fix WaveGlow input types to match what gets passed in during training and inference.
**Collection**: TTS
# Changelog
- Made `z` optional, as it's not passed in during training and inference, and only during export
- Removed extraneous inputs to the WaveGlow module in `convert_spectrogram_to_audio()`
- Added `sigma` to optional inputs for inference, since it is a default argument for the base `convert_spectrogram_to_audio()` function
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Micha Livne <[email protected]>
# What does this PR do ?
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**PR Type**:
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds a tutorial for ASR with adapters and sub-section for tutorials involving adapters.
**Collection**: [ASR]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
The config to train Fastpitch at 44KHz was not updated according to the latest code changes in TTS. This PR adds the updated config to train Fastpitch at 44KHz.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hello. I'm having trouble when trying to resume training when doing it with FastPitch. I'm using FastPitch_Finetuning.ipynb in the last version (r1.8.2), with a custom dataset. The generated checkpoint is ok and I can do inference with it, but I cannot use it from resuming training. Here is the error:
```
Restored all states from the checkpoint file at /content/drive/MyDrive/TTS/CHECKPOINTS/checkpoints_dataset_mix_92-1_7hours_2_plus15_16/FastPitch/checkpoints/FastPitch--v_loss=1.8955-epoch=274-last.ckpt
Epoch 274: 0% 0/351 [00:00<00:00, -4587799.47it/s][NeMo W 2022-05-09 13:39:54 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py:136: UserWarning: You're resuming from a checkpoint that ended before the epoch ended. This can cause unreliable results if further training is done. Consider using an end-of-epoch checkpoint or enabling fault-tolerant training: https://pytorch-lightning.readthedocs.io/en/stable/advanced/fault_tolerant_training.html
"You're resuming from a checkpoint that ended before the epoch ended. This can cause unreliable"
Epoch 275: 0% 0/342 [00:00<00:00, -72498.33it/s, loss=nan, v_num=3] [NeMo W 2022-05-09 13:40:53 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.
warnings.warn(problem)
Epoch 275: 12% 40/342 [02:20<-1:58:31, -3.37it/s, loss=2.62, v_num=3]Error executing job with overrides: ['train_dataset=./train.json', 'validation_datasets=./val.json', 'sup_data_path=./fastpitch_sup_data', 'phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.01', 'heteronyms_path=tts_dataset_files/heteronyms-030921', 'whitelist_path=tts_dataset_files/lj_speech.tsv', 'exp_manager.exp_dir=/content/drive/MyDrive/TTS/CHECKPOINTS/checkpoints_dataset_mix_92-1_7hours_2_plus15_16', '+init_from_nemo_model=./tts_en_fastpitch_align.nemo', '+trainer.max_steps=430961', '~trainer.max_epochs', 'trainer.check_val_every_n_epoch=25', 'model.train_ds.dataloader_params.batch_size=24', 'model.validation_ds.dataloader_params.batch_size=24', 'model.n_speakers=2', 'model.pitch_mean=121.9', 'model.pitch_std=20', 'model.pitch_fmin=65', 'model.pitch_fmax=512', 'model.optim.lr=2e-4', '~model.optim.sched', 'model.optim.name=adam', 'trainer.devices=1', 'trainer.strategy=null', '+model.text_tokenizer.add_blank_at=true']
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1234, in _run
results = self._run_stage()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1321, in _run_stage
return self._run_train()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1351, in _run_train
self.fit_loop.run()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/fit_loop.py", line 268, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 207, in advance
self.optimizer_idx,
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 256, in _run_optimization
self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 378, in _optimizer_step
using_lbfgs=is_lbfgs,
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1593, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/lightning.py", line 1644, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/optimizer.py", line 168, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/strategies/strategy.py", line 193, in optimizer_step
return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/precision/native_amp.py", line 85, in optimizer_step
closure_result = closure()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in __call__
self._result = self.closure(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 143, in closure
self._backward_fn(step_output.closure_loss)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 311, in backward_fn
self.trainer._call_strategy_hook("backward", loss, optimizer, opt_idx)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1763, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/strategies/strategy.py", line 168, in backward
self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 80, in backward
model.backward(closure_loss, optimizer, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/lightning.py", line 1389, in backward
loss.backward(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py", line 363, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py", line 175, in backward
allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass
RuntimeError: cuDNN error: CUDNN_STATUS_INTERNAL_ERROR
You can try to repro this exception using the following code snippet. If that doesn't trigger the error, please include your original repro script when reporting this issue.
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.allow_tf32 = True
data = torch.randn([24, 1, 1, 119], dtype=torch.half, device='cuda', requires_grad=True)
net = torch.nn.Conv2d(1, 384, kernel_size=[1, 3], padding=[0, 1], stride=[1, 1], dilation=[1, 1], groups=1)
net = net.cuda().half()
out = net(data)
out.backward(torch.randn_like(out))
torch.cuda.synchronize()
ConvolutionParams
data_type = CUDNN_DATA_HALF
padding = [0, 1, 0]
stride = [1, 1, 0]
dilation = [1, 1, 0]
groups = 1
deterministic = false
allow_tf32 = true
input: TensorDescriptor 0xebe7090
type = CUDNN_DATA_HALF
nbDims = 4
dimA = 24, 1, 1, 119,
strideA = 119, 119, 119, 1,
output: TensorDescriptor 0xfeaabcd0
type = CUDNN_DATA_HALF
nbDims = 4
dimA = 24, 384, 1, 119,
strideA = 45696, 119, 119, 1,
weight: FilterDescriptor 0x1b618ba0
type = CUDNN_DATA_HALF
tensor_format = CUDNN_TENSOR_NCHW
nbDims = 4
dimA = 384, 1, 1, 3,
Pointer addresses:
input: 0x7f6abdaa4200
output: 0x7f6b2b04a000
weight: 0x7f6a1adff600
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "fastpitch_finetune.py", line 37, in main
trainer.fit(model)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 769, in fit
self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 736, in _call_and_handle_interrupt
self._teardown()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1298, in _teardown
self.strategy.teardown()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/strategies/single_device.py", line 93, in teardown
super().teardown()
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/strategies/strategy.py", line 444, in teardown
optimizers_to_device(self.optimizers, torch.device("cpu"))
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/optimizer.py", line 27, in optimizers_to_device
optimizer_to_device(opt, device)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/optimizer.py", line 33, in optimizer_to_device
optimizer.state[p] = apply_to_collection(v, torch.Tensor, move_data_to_device, device)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py", line 108, in apply_to_collection
v, dtype, function, *args, wrong_dtype=wrong_dtype, include_none=include_none, **kwargs
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py", line 99, in apply_to_collection
return function(data, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py", line 354, in move_data_to_device
return apply_to_collection(batch, dtype=dtype, function=batch_to)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py", line 99, in apply_to_collection
return function(data, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py", line 347, in batch_to
data_output = data.to(device, **kwargs)
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
And this is the command:
```
!(python fastpitch_finetune.py --config-name=fastpitch_align_v1.05.yaml \
train_dataset=./train.json \
validation_datasets=./val.json \
sup_data_path=./fastpitch_sup_data \
phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.01 \
heteronyms_path=tts_dataset_files/heteronyms-030921 \
whitelist_path=tts_dataset_files/lj_speech.tsv \
exp_manager.exp_dir=$OUTPUT_CHEKPOINTS \
+init_from_nemo_model=./tts_en_fastpitch_align.nemo \
+trainer.max_steps=$max_steps ~trainer.max_epochs \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 \
model.n_speakers=2 model.pitch_mean=121.9 model.pitch_std=20 \
model.pitch_fmin=65 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
+model.text_tokenizer.add_blank_at=true \
)
```
It is worth noting that this didn't happen with version 1.6.x. Also, it seems that now training consumes more memory than before, as I had to decrease batch_size in order to keep training.
Thanks, | {
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