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When I try "from nemo.collections import nlp" I get the error below
```
[NeMo W 2021-11-26 06:12:43 optimizers:50] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2021-11-26 06:12:49 experimental:28] Module nemo.collections.nlp.data.text_normalization.decoder_dataset.TextNormalizationDecoderDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-11-26 06:12:49 experimental:28] Module nemo.collections.nlp.data.text_normalization.tagger_dataset.TextNormalizationTaggerDataset is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2021-11-26 06:12:49 experimental:28] Module <class 'nemo.collections.nlp.data.text_normalization.test_dataset.TextNormalizationTestDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-19-80f2adc350a5> in <module>
----> 1 from nemo.collections import nlp
~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/nemo/collections/nlp/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp import data, losses, models, modules
16 from nemo.package_info import __version__
17
~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/nemo/collections/nlp/models/__init__.py in <module>
14
15 from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
---> 16 from nemo.collections.nlp.models.duplex_text_normalization import (
17 DuplexDecoderModel,
18 DuplexTaggerModel,
~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/nemo/collections/nlp/models/duplex_text_normalization/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp.models.duplex_text_normalization.duplex_decoder import DuplexDecoderModel
16 from nemo.collections.nlp.models.duplex_text_normalization.duplex_tagger import DuplexTaggerModel
17 from nemo.collections.nlp.models.duplex_text_normalization.duplex_tn import DuplexTextNormalizationModel
~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py in <module>
24 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq
25
---> 26 import nemo.collections.nlp.data.text_normalization.constants as constants
27 from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor
28 from nemo.collections.nlp.data.text_normalization import TextNormalizationTestDataset
AttributeError: module 'nemo.collections' has no attribute 'nlp'
```
**Steps/Code to reproduce bug**
Install nemo (as per below), and then run the import statement above.
Nemo version is 1.5.0.
**Expected behavior**
I expected to the import statement to run normally.
**Environment overview (please complete the following information)**
- Environment location: AWS sagemaker notebook instance (m5.xlarge)
- Method of NeMo install:
```
conda install -c conda-forge libsndfile ffmpeg -y
pip install nemo_toolkit['all']
Note: nemo_tookit==1.5.0 was installed
```
**Environment details**
- OS version - Amazon Linux AMI release 2018.03
- PyTorch version - 1.7.1
- Python version - 3.6.13
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} | null | null | null | **Describe the bug**
Do Nemo TTS models train from pre-trained model?
Command is blow
python tacotron2.py train_dataset=filelists/transcript_train.json validation_datasets=filelists/transcript_val.json trainer.accelerator=null trainer.check_val_every_n_epoch=1 --pretrained_model=tts_en_tacotron2.nemo
[NeMo W 2021-11-26 11:34:27 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
[NeMo W 2021-11-26 11:34:32 nmse_clustering:54] Using eigen decomposition from scipy, upgrade torch to 1.9 or higher for faster clustering
[NeMo W 2021-11-26 11:34:34 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text_dali._AudioTextDALIDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
usage: tacotron2.py [--help] [--hydra-help] [--version]
[--cfg {job,hydra,all}] [--resolve] [--package PACKAGE]
[--run] [--multirun] [--shell-completion]
[--config-path CONFIG_PATH] [--config-name CONFIG_NAME]
[--config-dir CONFIG_DIR]
[--info [{all,config,defaults,defaults-tree,plugins,searchpath}]]
[overrides [overrides ...]]
tacotron2.py: error: unrecognized arguments: --pretrained_model=tts_en_tacotron2.nemo
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Error:
No module named 'nemo' | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3247 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3247/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3247/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3247/events | https://github.com/NVIDIA/NeMo/issues/3247 | 1,063,594,161 | I_kwDOC_bI7s4_ZSix | 3,247 | Diarization Inference Parameters | {
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} | null | null | null | Hey guys,
I'm using "Speaker_Diarization_Inference.ipynb", I'm having a problem with Vad Diarization parameters
```
output_dir = os.path.join(ROOT,'outputs')
config.diarizer.paths2audio_files = paths2audio_files
config.diarizer.path2groundtruth_rttm_files = path2groundtruth_rttm_files
config.diarizer.out_dir = output_dir # Directory to store intermediate files and prediction outputs
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
#Here we use our inhouse pretrained NeMo VAD
config.diarizer.vad.model_path = pretrained_vad
config.diarizer.vad.window_length_in_sec = 0.15
config.diarizer.vad.shift_length_in_sec = 0.01
# config.diarizer.vad.threshold = 0.8 threshold would be deprecated in release 1.5
config.diarizer.vad.postprocessing_params.onset = 0.8
config.diarizer.vad.postprocessing_params.offset = 0.7
config.diarizer.vad.postprocessing_params.min_duration_on = 0.1
config.diarizer.vad.postprocessing_params.min_duration_off = 0.3
```
Testing with different audios, I have to constantly change the parameters to get a better result with each audio, or avoid the error
`IndexError: list index out of range`
This happens to me mainly in version 1.4.0 of Nemo, in version 1.5.0 I noticed an improvement but still occurs the cases.
I'm not using the "vad_marblenet" model because it wasn't meeting my needs, I'm using a model that I'm training with "Voice_Activity_Detection.ipynb"
My question is if there is any metric for setting the values of these parameters?
If you have an ideal value for these parameters, which has better results with a greater number of audios that go through the script? | {
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} | null | null | null | I'm looking to develop an API that will perform multiple simultaneous transcripts every time an external request arrives. To perform these simultaneous transcriptions I thought of using python multiprocessing to help me.
Performing CPU transcripts in multiprocessing works fine, however when using GPU I get the error "`Can't pickle local object FilterbankFeatures.__init__.<locals>.<lambda>"
`
Here is an example of the two scenarios on GPU and CPU:
https://colab.research.google.com/drive/1O8fEkC4SKeILJeotYGU54dMkX15go1nK?usp=sharing
I have little experience with this type of processing, I have been trying to resolve this for a few weeks. If anyone has a solution, thank you in advance. | {
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} | null | null | null | Hi,
Used `quartznet_15x5` model for the transfer learning on the open-source dataset (AN4 dataset) for ASR. Once the pre-trained model gets trained with the newer dataset. I tried saving the trained model. Once I tested with the saved model using `nemo_asr.models.<MODEL_BASE_CLASS>.restore_from(restore_path="<path/to/checkpoint/file.nemo>")`. It shows the empty result `[' ']` even on the trained data.
Kindly provide us with the steps to follow after the transfer learning progress.
Code:
```
config_path = './quartznet_15x5.yaml'
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
trainer = pl.Trainer(gpus=1, max_epochs=50)
quartznet_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']), trainer=trainer)
trainer.fit(quartznet_model)
quartznet_model.save_to("./tl-osd.nemo")
``` | {
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} | null | null | null | Hey!
I am working with this [notebook](https://github.com/NVIDIA/NeMo/blob/offline_asr_diar_vad/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb) for diarizing the audio files. This notebook seems to work perfectly with the given "an4_diarize_test.wav" test file but when I try it on my own audio files then model gives runtime error as shown below. I wonder why it is not processing the audio properly and giving the empty tensor. My audio files are also in .wav format, therefore, there is not any inconsistency as far as audio file format is concerned.
I also went through the source code of [audio_to_label.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/data/audio_to_label.py) and I found that audio_signal tensor has 0 elements. Moreover, the audio_lengths is tensor(2) which means it is only processing two samples of audio which does not make sense. Any help would be much appreciated!
torch version == 1.10
torchaudio == 0.10.0+cpu
torchvision == 0.11.1+cpu
nemo-toolkit == 1.3.0 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3243 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3243/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3243/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3243/events | https://github.com/NVIDIA/NeMo/issues/3243 | 1,063,176,413 | I_kwDOC_bI7s4_Xsjd | 3,243 | Training conformer_ctc with korean | {
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} | null | null | null | I am trying to train the conformer-ctc model with the Ksponspeech dataset, which is a Korean speaking dataset.
Ksponspeech - 1000hours / 123GB / 630000 pcm audio files ( fs=16000 / sample_width = 2 / channels=1)
In the first try, with the same settings as the existing conformer ctc configuration guidelines, only about 5000 vocabs were set and trained for Korean.
It took about 4 hours per epoch, and when about 5 epochs progressed, the loss hardly decreased, and it was confirmed that the WER was fixed at almost100%.
As a result of inference, Only spaces or comma(.) were output
In the second try, for the test, the dataset was reduced to about 6300, and the vocab size was also reduced to 3200, but the result of training for about 20 hours ( 460 epochs ), models seems never convergence.
After that, I continue to test by changing the hyper parameters little by little, but a similar phenomenon continues to occur.
Are there any problems you can guess?
Is it simply a lack of training time?
Also, I would like to know how much the learning rate affects the model training when using the noam scheduler.
```
name: "Conformer-CTC-BPE-medium"
model:
sample_rate: 16000
log_prediction: true # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
train_ds:
manifest_filepath: /ksponspeech/subword_preparied_211124_3/train
sample_rate: ${model.sample_rate}
batch_size: 32 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20.0 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
validation_ds:
manifest_filepath: /ksponspeech/subword_preparied_211124_3/test
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
# recommend small vocab size of 128 or 256 when using 4x sub-sampling
# you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
tokenizer:
dir: /home/aistudio/hs/ksponspeech/subword_preparied_211124_3/tokenizer_spe_bpe_v5000 # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
# you may use lower time_masks for smaller models to have a faster convergence
time_masks: 5 # 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: 18
d_model: 256
# Sub-sampling params
subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory
subsampling_factor: 16 # must be power of 2
subsampling_conv_channels: -1 # -1 sets it to d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 4 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
### regularization
dropout: 0.2 # The dropout used in most of the Conformer Modules
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
optim:
name: adamw
lr: 2.0 # 5 - normal
# optimizer arguments
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 0.0
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 1000
warmup_ratio: null
min_lr: 1e-6
trainer:
gpus: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 1000
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: 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: 1000 # Interval of logging.
progress_bar_refresh_rate: 1
resume_from_checkpoint: /home/aistudio/hs/nemo_experiments/Conformer-CTC-BPE/2021-11-24_00-50-48/checkpoints/Conformer-CTC-BPE--val_wer=0.9739-epoch=95-last.ckpt # 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: 5 # number of evaluations on validation every n epochs
sync_batchnorm: true
checkpoint_callback: 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: 3
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
# you need to set these two to True to continue the training
resume_if_exists: false
resume_ignore_no_checkpoint: false
```
```
{"audio_filepath": "/korean_speech/original/KsponSpeech_03/KsponSpeech_0273/KsponSpeech_272305.pcm", "duration": 8.65, "text": "방송 공연학과 그담에 스물 한 살 때 영화방송제작학과 다음에 이제 졸업한 학과는 호텔관광학과."}
{"audio_filepath": "/korean_speech/original/KsponSpeech_03/KsponSpeech_0273/KsponSpeech_272132.pcm", "duration": 3.76, "text": "어 서로 연애 상담해주고 그랬는데"}
{"audio_filepath": "/korean_speech/original/KsponSpeech_03/KsponSpeech_0273/KsponSpeech_272801.pcm", "duration": 1.71, "text": "아 그런 사람을 진짜 데려와?"}
{"audio_filepath": "/korean_speech/original/KsponSpeech_03/KsponSpeech_0273/KsponSpeech_272683.pcm", "duration": 3.93, "text": "이제욱 쌤 아 안 이제욱 쌤 원래 안 친했어."}
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} | Signed-off-by: Yang Zhang <[email protected]> | {
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} | # Overview
This change adds [Variational Inference with adversarial learning (VITS)](https://github.com/jaywalnut310/vits) to the collection of TTS models.
VITS uses the `monotonic_align` module, which requires Cython to be installed prior to building (i.e. run `pip install cython` before running `./reinstall.sh`).
# Tasks
- [x] Fix errors in audio and spectrogram dimensions
- [ ] Fix logging for validation loss
- [ ] Verify training performance
- [ ] Verify quality of TTS output
- [ ] Refactor code and cleanup to be more consistent with other models | {
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Thanks for your great repo. I think it would be really great if Nemo support TPU training (due to the fact that training on some large datasets on GPU is slow, and TPU will probably help in these scenarios. In addition, other speech frameworks, to the best of my knowledge, such as SpeechBrain, ESPNet, etc., currently do not support TPU training, so in this viewpoint, it would be a great benefit for Nemo).
Best | {
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Verfiy nemo container building from Dockerfile and add instructions for how to do it.
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} | Beta-Binomial Interpolator is taken from [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/fastpitch/data_function.py#L45) repository.
Signed-off-by: Oktai Tatanov <[email protected]> | {
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} | null | null | null | **Describe the bug**
Outputs different predictions for the same input depending batch. I suspect the problem is related to pad tokens not being masked properly.
**Steps/Code to reproduce bug**
```
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.ASRModel.from_pretrained(model_name="stt_en_conformer_ctc_large")
model.transcribe(audio_paths[:2], batch_size=1)
# output
# ['take care of over excitement and endeavor to keep a quiet mind even for your health it is the best advice that can be given to you # # your moral and spiritual improvement will then keep pace with the culture of your intellectual powers',
# 'ab crowd']
model.transcribe(audio_paths[:2], batch_size=2)
# output
# ['take care of over excitement and endeavor to keep a quiet mind even for your health it is the best advice that can be given to you # # your moral and spiritual improvement will then keep pace with the culture of your intellectual powers',
# 'crow']
# the provided example occurred with audio files from the libritts100 dataset namely (3575_170457_000032_000001.wav and 6829_68771_000042_000001.wav), but I am quite sure it can be reproduced with other audio files as well.
```
**Expected behavior**
Outputs should be identical independent of batch size or items in batch.
**Environment overview**
Linux Ubuntu 20.04
Python version: 3.8
PyTorch version: 1.10.0+cu113
Nemo version: 1.4.0
CUDA/cuDNN version: 11
GPU model and memory: RTX 3070, Tesla V100-SXM2-16GB
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The base model it's a fine-tuned model from English (QuartzNet) to my language, and now I'm trying to improve this base model on a specific dataset.
The dataset has around 12h, and I used speed perturbation to generate around 37h.
I use these hyperparameters by the loss seems to stay the same after a significant number of epochs.
```
new_optimizer = {
'name': 'novograd',
'lr': 0.0001,
'betas': (0.95, 0.25),
'weight_decay': 0.001,
'sched': {
'last_epoch': -1,
'min_lr': 0.0,
'monitor': 'val_loss',
'name': 'CosineAnnealing',
'reduce_on_plateau': False,
'warmup_ratio': 0.12,
'warmup_steps': None,
},
}
```
I use 500 epochs for this training.
Do you have any advice on how I can optimally improve the accuracy of this small dataset, or how is a minimum number of hours that can generate a good improvement? | {
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} | Related to https://github.com/NVIDIA/NeMo/pull/3170
Signed-off-by: fayejf <[email protected]> | {
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} | This PR adds the support of adaptive batch size for bucketing.
+It currently just supports tarred datasets
+train_ds.bucketing_batch_size is added which user may use to enable the adaptive batching and specify the batch sizes for the buckets
+when bucketing_batch_size is not set, train_ds.batch_size is going to be used for all buckets (fixed-size bucketing)
+bucketing_batch_size can be set as a list to explicitly specify the batch size for each bucket
+When bucketing_batch_size is set, traind_ds.batch_size need to be set to 1
+if bucketing_batch_size is set to be an integer, then linear scaling is being used to scale-up the batch sizes for batches with shorted audio size. For example, setting train_ds.bucketing_batch_size=8 for 4 buckets would use these sizes [32,24,16,8] for different buckets.
+Adaptive batching may improve the training speed per epoch more than 2x but may affect the final accuracy of the model | {
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} | null | null | null | Hi, I'm fine tuning Quartznet (character model) and CTC-Conformer (BPE model) on new language and the results are good. Then I use KenLM with both. I have some question:
1. When I download and see "3-gram.pruned.1e-7.arpa" file in [this tutorial](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/Offline_ASR.ipynb#scrollTo=-0Sk0C9-LmAR), I recognize that this file for word level, but Quartznet is character level, but It work well. Can you explain how it work ?
2. I train KenLM for CTC-Conformer (BPE model) according to [asr language model tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/asr_language_modeling.html), and the training was successful. But when I apply it with beamsearch to CTC-Conformer, it gives me irrelevant results(wer upto 40%), while using greedy search wer ~ 9%. I don't know what makes it not work. Can you give me some advice?
Thank you!
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} | null | null | null | **Describe the bug**
If my understanding is correct, an [MTEncDecModel](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py#L54) exposes no options for sharing embeddings between the encoder and decoder, but this seems to be the de facto standard. E.g. from the AAYN white paper:
> ...we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation
So MT models with shared vocab will learn separate embeddings for each token on both sides.
**Expected behavior**
An option like `cfg.share_embeddings = True` to tie the encoder/decoder embedding weights. | {
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} | # Changelog
- Fix masking in SqueezeExcite block where padding causes incorrect calculation of global average. Slight improvements in WER found after retraining / fine-tuning. Masking should no longer be affected by batch size.
- Add sequence length tracking to Jasper block.
# NOTE:
- This is a breaking change. Models trained without this fix might show slightly increased WER in any batch size compared to before (other than batch size 1).
- A minimal amount of fine-tuning of a previous checkpoint will fix this issue (and may obtain slightly better scores than before).
Signed-off-by: smajumdar <[email protected]> | {
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} | This adds the option to use the persistent layer norm kernel from APEX.
The kernel is available from PyT container v21.11 so added a dependency check. Other pytorch- or container-dependency modules can have dependency checks in the same method.
Signed-off-by: Sangkug Lym <[email protected]> | {
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} | null | null | null | **Describe the bug**
The pretrained TalkNet2 model does not appear to be available in 1.5.0. The tutorial notebook https://github.com/NVIDIA/NeMo/blob/stable/tutorials/tts/TalkNet_Training.ipynb states that r1.4.0 is broken and r1.2.0 should be used. I run into the same issue as #3206, where it appears fixed by moving to version 1.5.0, but the pretrained model still does not appear in the list of available models for me.
**Steps/Code to reproduce bug**
Create a fresh environment:
```
conda create --name nemo_r1.5.0 python=3.9
conda activate nemo_r1.5.0
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
python -m pip install git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all]
```
Run the following python script:
```
import torch
print(torch.__version__)
import nemo
print(nemo.__version__)
from nemo.collections.tts.models import TalkNetSpectModel
from nemo.collections.tts.models.base import SpectrogramGenerator
# Let's see what pretrained models are available
print(TalkNetSpectModel.list_available_models())
# We can load the pre-trained model as follows
pretrained_model = "tts_en_talknet"
model = TalkNetSpectModel.from_pretrained(pretrained_model)
```
You'll get an error similar to the following:
```
1.10.0
1.5.0b1
[NeMo W 2021-11-18 16:24:53 optimizers:50] Apex was not found. Using the lamb or fused_adam optimizer will error out.
################################################################################
### 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)
################################################################################
None
Traceback (most recent call last):
File "/home/bram/Documents/2021-11-17_nemo_talknet_s2t2s_experiment/2021-11-17_nemo_talknet_minimal_bug_reproduction_code.py", line 20, in <module>
model = TalkNetSpectModel.from_pretrained(pretrained_model)
File "/home/bram/miniconda3/envs/nemo_r1.5.0/lib/python3.9/site-packages/nemo/core/classes/common.py", line 716, in from_pretrained
raise FileNotFoundError(
FileNotFoundError: Model tts_en_talknet was not found. Check cls.list_available_models() for the list of all available models.
```
**Expected behavior**
The function `print(TalkNetSpectModel.list_available_models())` should return a list with at least `tts_en_talknet` in it. `TalkNetSpectModel.from_pretrained(my_pretrained_model)` should load a pretrained model.
**Environment overview (please complete the following information)**
I hope the following steps to reproduce my environment will suffice:
```
conda create --name nemo_r1.5.0 python=3.9
conda activate nemo_r1.5.0
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
python -m pip install git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all]
```
**Environment details**
- OS version; Pop!-os 20.10
- PyTorch version; 1.10
- Python version; 3.9
Any help will be greatly appreciated! | {
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} | null | null | null | Hi, will you supply the config for training conformer-ctc samll on librispeech?
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_small_ls | {
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Here is a link to a paper for the Custom Voice. https://blogs.nvidia.com/blog/2021/11/09/riva-enterprise-custom-voice-speech-ai/
In its current state, NVIDIA Riva has the option of two pipelines for Text to Speech :
1. FastPitch and HiFi-GAN Pipeline
2. Tacotron2 and Waveglow
I suspect that we should train Spectrogram generators (either FastPitch or Tacotron2) with selected voice recordings to be cloned via Nemo and then deploy using Riva.
I have checked Nvidia NGC Catalog and there seems to be nothing about it. Could you please help me here?
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} | # Changelog
- Adds support to modify the nemo cache directory using an environment variable
# Usage
```sh
NEMO_CACHE_DIR="~/nemo_cache_dir/" python nemo_example_script.py
```
Closes https://github.com/NVIDIA/NeMo/issues/3185, https://github.com/NVIDIA/NeMo/issues/2025
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | * Update TTSDataset to use speaker id
* Update FastPitch to use new TTS Dataset
* Update HiFiGAN (more flexible `configure_optimizers`) and rename generator config for 44100 khz (it is just extended v1, but for 44100, it shouldn't be v4, IMO)
Signed-off-by: Oktai Tatanov <[email protected]> | {
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Signed-off-by: Oleksii Kuchaiev <[email protected]> | {
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} | # Bugfix
- Fix an off-by-one indexing issue with ALSD decoding that causes it to skip the 0th timestep of the acoustic time dimension (T)
Closes https://github.com/NVIDIA/NeMo/issues/3199
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | Hi,
It's in the Speaker Recognition Verification tutorial.
While opening the jupyter notebook in collab, fails to import nemo.collections.asr
error message : "OSError: libtorch_hip.so: cannot open shared object file: No such file or directory"
it seems to be a pytorch or torchaudio wrong version
Thanks ;) | {
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Codes locate at
https://github.com/NVIDIA/NeMo/blob/807b001f2e20ac6ab2930419c32076d242128c5d/nemo/collections/asr/parts/submodules/rnnt_beam_decoding.py#L785-L787 | {
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} | null | null | null | I am experimenting with FastSpeech2 using LJSpeech dataset. I am trying to restart the run from a previously saved checkpoint, I tried changing the options in fastspeech2.yaml file. The log does say that it is resuming from earlier saved checkpoint, but the epoch value, train loss value, val loss value, all point to the fact that it's starting from scratch. How to resolve this?
Below are the details:
_________________________________
**fastspeech2.yaml (showing relevant ones only)**
**trainer:**
gpus: 2 # number of gpus
max_epochs: 1000
num_nodes: 1
**exp_manager:**
exp_dir: "/home/NeMo/examples/tts/nemo_experiments"
explicit_log_dir: null
version: null
name: ${name}
create_tensorboard_logger: True
create_checkpoint_callback: True
resume_if_exists: True
resume_ignore_no_checkpoint: True
**_(Tried all the permutations for exp_dir, explicit_exp_dir, version as well)_**
______________________________________
**Output Log (Showing relevant ones)**
[NeMo W 2021-11-16 18:03:07 exp_manager:545] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
[NeMo I 2021-11-16 18:03:07 exp_manager:428] **Resuming from /home/NeMo/examples/tts/nemo_experiments/FastSpeech2/checkpoints/FastSpeech2--val_loss=3.9187-epoch=974-last.ckpt**
[NeMo I 2021-11-16 18:03:07 exp_manager:280] Experiments will be logged at /home/NeMo/examples/tts/nemo_experiments/FastSpeech2
[NeMo I 2021-11-16 18:03:07 exp_manager:635] TensorboardLogger has been set up
[NeMo W 2021-11-16 18:03:07 exp_manager:865] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 25 epochs to ensure that checkpointing will not error out.
____________________________
**Training loss after resumption is an order or magnitude higher.**. For the 974th checkpoint, it was around 1.3e+03, but after resuming the training, it again jumps to 1.75e+04. I even tried to let the run go for some time, but every time found that the run starts from scratch (based on training/val losses and epoch number), though it says it is resuming from FastSpeech2--val_loss=3.9187-epoch=974-last.ckpt
grad graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
Epoch 0: 32%|████████████████████████████████████████████████▎ | 68/211 [00:39<01:23, 1.72it/s, loss=Epoch 0: 33%|███████████████████████████████████████████▏ | 69/211 [00:40<01:22, 1.72it/s, loss=1.81e+04, v_num=0]Epoch 1: 16%|████████████████████▋
Epoch 1: 16%|█████████████████▋ | 34/211 [00:19<01:42, 1.72it/s, loss=1.79e+04, v_num=0]Epoch 4: 49%|█████████████████████████████████████████████████████▋
Epoch 5: 94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 198/211 [01:53<00:07, 1.74it/s, loss=1.76e+04, v_num=0]
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It's error message is "pytorch_lightning.utilities.exceptions.MisconfigurationException: Invalid type provided for checkpoint_callback: Expected bool but received <class 'list'>."
Could you please help me to set checkpoints after end of each epoch and save them? | {
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} | # Changelog
- Add back AISHELL 2 data preprocessing script
Signed-off-by: smajumdar <[email protected]> | {
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} | This PR extends the log of NMT models.
It adds input to the translations and ground truths of the logged samples.
Example of updated log (notice `Input: `):
```
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:389] Dataset name: Validation, Dataloader index: 0, Translation Examples:
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:394] E̲x̲a̲m̲p̲l̲e̲ ̲0̲:
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:395] Input: Salto al azul
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:396] Prediction: iereiereiereieres
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:397] Ground Truth: Leap into the water
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:394] E̲x̲a̲m̲p̲l̲e̲ ̲1̲:
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:395] Input: Reacciones
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:396] Prediction: ristris
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:397] Ground Truth: Reactions
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:394] E̲x̲a̲m̲p̲l̲e̲ ̲2̲:
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:395] Input: Informalidad
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:396] Prediction: iereiereiereiere
[NeMo I 2021-11-15 14:11:28 mt_enc_dec_model:397] Ground Truth: Informality
``` | {
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} | null | null | null | The CER value during the validation of the training process does not align with the inference process. And during the inference process, the execution is like the following part.
`wer_value = word_error_rate(hypotheses=hypotheses, references=references, use_cer=args.use_cer)`.
Meanwhile, the training codes are like:
```
@hydra_runner(config_path="conf_decay", config_name="config")
def main(cfg):
profiler = PyTorchProfiler(emit_nvtx=True)
trainer = pl.Trainer(**cfg.trainer,profiler=profiler)
exp_manager(trainer, cfg.get("exp_manager", None))
asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
pretrain_model = cfg.get("pretrain_model", None)
if pretrain_model:
if pretrain_model.endswith(".nemo"):
asr_model = EncDecCTCModel.restore_from(pretrain_model)
elif pretrain_model.endswith(".ckpt"):
asr_model = EncDecCTCModel.load_from_checkpoint(pretrain_model)
asr_model.setup_optimization(optim_config=cfg.model.optim)
asr_model.setup_training_data(train_data_config=cfg.model.train_ds)
asr_model.setup_validation_data(val_data_config=cfg.model.validation_ds)
trainer.fit(asr_model)
nvtx.range_pop()
save_model = cfg.get("save_model", "quartznet.nemo")
asr_model.save_to(save_model)
```
And the script for the execution like the following:
`python /ws/nemo/speech_to_text.py \
...
+use_cer=True
...`
The usage of parameter 'use_cer' is right? I didn't find any deep explanation for this parameter in Nemo reference documents.
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"total": 0,
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | includes 2 commits:
1. portning UnivNet based on [https://github.com/mindslab-ai/univnet](url). Also includes `scripts/dataset_processing/get_libritts_data.py` for LibriTTS training.
2. Currently HiFi-GAN from main have two `output_types`, where one should be `input_types`. | {
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} | null | null | null | https://github.com/NVIDIA/NeMo/blob/e987e178ecb42f1e8411c59e80628cd67af03f79/nemo/core/classes/common.py#L722
NeMo store and load models from hard-coded folder (Path.home()/.cache/torch/NeMo/)
Is it possible to change it to a location dependent on some environment variable, similar to a way it is done for torch.hub (https://pytorch.org/docs/stable/hub.html#where-are-my-downloaded-models-saved) or transformers (https://huggingface.co/transformers/installation.html?highlight=transformers_cache#caching-models)? | {
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} | null | null | null | Hi,
Thanks for your great repo. I want to download the Mozilla Common Voice (MCV) **Persian** data set via the following code:
[get_commonvoice_data](https://github.com/NVIDIA/NeMo/blob/main/scripts/dataset_processing/get_commonvoice_data.py)
I have used below parameters:
`VERSION = "cv-corpus-7.0-2021-07-21"`
`LANGUAGE = "fa"`
but I got the following error:
```
File "scripts/get_commonvoice_data.py", line 121, in process_files
with open(csv_file) as csvfile:
FileNotFoundError: [Errno 2] No such file or directory: 'datasets/fa/CV_unpacked/cv-corpus-7.0-2021-07-21/fa/train.tsv'
```
In addition, I have read the above from the below tutorial:
[ASR_CTC_Language_Finetuning](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb#scrollTo=VDDiXCiPimr_)
would you please help me to address this problem?
Thanks
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} | null | null | null | Hi! I'm trying to train tacotron 2 from scratch.
Running in current main branch with pytorch-lightning==1.5.0
```
PYTHONPATH="$(pwd)" python examples/tts/tacotron2.py \
train_dataset=train_data.json \
validation_datasets=validation_data.json \
+exp_manager.checkpoint_callback_params.save_top_k=1 \
+exp_manager.checkpoint_callback_params.monitor=loss
```
Results in correct training without checkpointing. Downgrading to pytorch-lightning==1.4.2 fixes this issue. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3177 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3177/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3177/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3177/events | https://github.com/NVIDIA/NeMo/pull/3177 | 1,051,913,440 | PR_kwDOC_bI7s4udBjA | 3,177 | LJSpeech alignment scripts fixed for latest MFA | {
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} | The latest releases of MFA breaks extract_ljspeech_phonemes_and_durs.sh
Example:
```
~/NeMo/scripts/dataset_processing/ljspeech$ mfa thirdparty
usage: mfa [-h]
{version,align,adapt,train,validate,g2p,train_g2p,model,train_lm,train_dictionary,train_ivector,classify_speakers,create_segments,transcribe,configure,history,annotator,anchor}
...
mfa: error: argument subcommand: invalid choice: 'thirdparty' (choose from 'version', 'align', 'adapt', 'train', 'validate', 'g2p', 'train_g2p', 'model', 'train_lm', 'train_dictionary', 'train_ivector', 'classify_speakers', 'create_segments', 'transcribe', 'configure', 'history', 'annotator', 'anchor')
~/NeMo/scripts/dataset_processing/ljspeech$ mfa download acoustic english
usage: mfa [-h]
{version,align,adapt,train,validate,g2p,train_g2p,model,train_lm,train_dictionary,train_ivector,classify_speakers,create_segments,transcribe,configure,history,annotator,anchor}
...
mfa: error: argument subcommand: invalid choice: 'download' (choose from 'version', 'align', 'adapt', 'train', 'validate', 'g2p', 'train_g2p', 'model', 'train_lm', 'train_dictionary', 'train_ivector', 'classify_speakers', 'create_segments', 'transcribe', 'configure', 'history', 'annotator', 'anchor')
```
Latest version uses conda-forge and also changed "mfa download" to "mfa model download".
Described in
https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/issues/349#issuecomment-966610658
This is related to TTS (@blisc?) | {
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} | null | null | null | Сan't import nemo.collections.asr in google colab notebook https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_with_NeMo.ipynb.
This worked on 11/11/2021, starting today (12/11/2021), the error appears:
ImportError: /usr/local/lib/python3.7/dist-packages/torchtext/_torchtext.so: undefined symbol: _ZTVN5torch3jit6MethodE | {
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} | null | null | null | Hi 👋 I'm trying to set up training with CyclicLR using config file and it fails every time. NeMo uses CyclicLR from PyTorch, so I guess it should support all the keyword arguments from `torch.optim.lr_scheduler.CyclicLR`, but I'm not sure how to pass them using config file. Here is the config I'm using:
```
optim:
name: adamw
lr: ${model.lr}
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
sched:
name: CyclicLR
iters_per_batch: null # computed at runtime; mandatory to have
max_steps: null # computed at runtime or explicitly set here; mandatory to have
# pytorch lightning args <mandatory>
monitor: val_loss
reduce_on_plateau: false
base_lr: ${model.lr}
max_lr: 1.0
step_size_up: 5000
step_size_down: 5000
cycle_momentum: false
```
As far as I understand from docstring to this method `prepare_lr_scheduler` https://github.com/NVIDIA/NeMo/blob/a48be1f2679198905f650e05cc93104a9dda4ec6/nemo/core/optim/lr_scheduler.py#L524
`iters_per_batch` and `max_steps` are mandatory, but every time I pass them I get this error:
```
Error executing job with overrides: []
Traceback (most recent call last):
File "base_model.py", line 47, in main
trainer.fit(asr_model)
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
self._run(model)
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 873, in _run
self.accelerator.setup(self, model) # note: this sets up self.lightning_module
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu.py", line 42, in setup
return super().setup(trainer, model)
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 88, in setup
self.setup_optimizers(trainer)
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 330, in setup_optimizers
optimizers, lr_schedulers, optimizer_frequencies = self.training_type_plugin.init_optimizers(
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 223, in init_optimizers
return trainer.init_optimizers(model)
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/pytorch_lightning/trainer/optimizers.py", line 34, in init_optimizers
optim_conf = model.configure_optimizers()
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 558, in configure_optimizers
self.setup_optimization()
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 549, in setup_optimization
self._scheduler = prepare_lr_scheduler(
File "/home/dev/anaconda3/envs/kenv/lib/python3.8/site-packages/nemo/core/optim/lr_scheduler.py", line 619, in prepare_lr_scheduler
schedule = scheduler_cls(optimizer, **scheduler_args)
TypeError: __init__() got an unexpected keyword argument 'max_steps'
```
**But** even if I remove these keywords, I still get this error.
So, can anyone please help me to figure out what is wrong with my config file and/or provide link to documentation on how to correctly set PyTorch lr schedulers in NeMo config files?
Thanks.
Full config file and run script can be found here: https://github.com/zzomg/conformer_clr
Environment:
Packages:
- pyTorch_debug: True
- pyTorch_version: 1.9.0.
- pytorch-lightning: 1.4.5
- NeMo ver. 1.3.0 | {
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} | We patched cfg issue in modelPT level in https://github.com/NVIDIA/NeMo/pull/3170.
Revert the cfg arg in `_load_model_state` in nlp_models in https://github.com/NVIDIA/NeMo/pull/3149 since it will break the fix and is deprecated in PTL.
Signed-off-by: fayejf <[email protected]> | {
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} | Fix issues with config saving TypeError: __init__() missing 1 required positional argument: 'cfg' when trying to do model.load_from_checkpoint(XYZ)
related to https://github.com/NVIDIA/NeMo/pull/3058 and https://github.com/PyTorchLightning/pytorch-lightning/discussions/7525 | {
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https://github.com/PyTorchLightning/pytorch-lightning/commit/0e20119d24c410c9004a9d0c6ce0505914c1dc02
Signed-off-by: Oktai Tatanov <[email protected]> | {
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<img width="1155" alt="Screen Shot 2021-11-11 at 6 36 03 PM" src="https://user-images.githubusercontent.com/80812299/141303234-f923f8e5-0d4e-462e-a7d0-ec00376a0f14.png">
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1. Accelerator = None
2. HF API change to outputs. Removed output neural types from the model since HF returns a tuple of tensors now. | {
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Bug 3415701
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415701 [ML-NeMo][Tesla A30][NLP] Zero_Shot_Intent_Recognition.ipynb failed with FileNotFoundError: [Errno 2] No such file or directory:
Bug 3415458
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415458 [ML-NeMo][Tesla A30][NLP] Relation_Extraction-BioMegatron.ipynb failed with ValueError: megatron-lm BERT models have been deprecated in NeMo 1.5+
Bug 3415486
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Bug 3415335
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415335 [ML-NeMo][Tesla A30][NLP] Entity_Linking_Medical.ipynb failed with MisconfigurationException: You have asked for `amp_level='O1'` which is not supported with `amp_backend='native'`. | {
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Bug 3415701
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415701 [ML-NeMo][Tesla A30][NLP] Zero_Shot_Intent_Recognition.ipynb failed with FileNotFoundError: [Errno 2] No such file or directory:
Bug 3415458
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415458 [ML-NeMo][Tesla A30][NLP] Relation_Extraction-BioMegatron.ipynb failed with ValueError: megatron-lm BERT models have been deprecated in NeMo 1.5+
Bug 3415486
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415486 [ML-NeMo][Tesla A30][NLP] Token_Classification-BioMegatron.ipynb failed with ValueError: megatron-lm BERT models have been deprecated in NeMo 1.5+
Bug 3415335
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3415335 [ML-NeMo][Tesla A30][NLP] Entity_Linking_Medical.ipynb failed with MisconfigurationException: You have asked for `amp_level='O1'` which is not supported with `amp_backend='native'`. | {
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} | null | null | null | I've just finished reading the paper of TalkNet and I'm interested in the work.
But I'm wondering where I can find some TalkNet Demos.
Thanks in advance for your answers ! | {
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} | 1. `PunctuationCapitalizationDataset`
- Sort input sequences before packing them into batches to reduce padding
- Batching is done in `PunctuationCapitalizationDataset`, not in `DataLoader`
- Parallelize features creation
- Exclude `'input_mask'`, `'loss_mask'`, `'segment_ids'` from dataset cache. Now these arrays are created in dataset constructor
- `PunctuationCapitalizationDataset` now yields dictionaries, not tuples
2. Tarred dataset class and instruments for tarred dataset creation.
3. Classes for punctuation and capitalization configs
4. Now parameters `max_seq_length`, `use_cache`, `num_workers`, `pin_memory`, `drop_last` can be passed in `train_ds`, `validation_ds`, `test_ds` and in these sections they have higher priority.
5. Add separate metrics for every evaluation dataset.
Closes #3125 | {
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Thank you for sharing great toolkit, tutorials and code.
I have a question: "How can I use asr for streaming with languge model ?" | {
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} | # Bugfix
- Compute explicit max steps if max_steps = None or negative numbers (new default in PTL 1.5.0+)
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} | null | null | null | Hi,
What is the Suitable PyTorch version for using online Microphone ASR? I installed Nemo with the "r1.2.0" brach. My OS is Linux 20.04. I try all of the PyTorch versions but Nemo has conflict or does not recognize my Microphone. | {
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} | null | null | null | Hi. Im want to developing a streaming interface for my trainned Conformer Transducer ASR model. I have only see tutorials about streaming for CTC model here:
https://github.com/NVIDIA/NeMo/blob/stable/tutorials/asr/Online_ASR_Microphone_Demo.ipynb
https://github.com/NVIDIA/NeMo/blob/stable/tutorials/asr/Streaming_ASR.ipynb
My question is can i apply this tutorial for Transducer model ? I do not know it is efficient or not to use this method to streaming with transducer model. If its not, can someone give me a direction ? Thank you (I already know about riva and tensorrt for production, but I just want developing the streaming method on python for now) | {
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} | I have verified that this fix gives the same results as the baseline code with both fp16 and bf16.
Signed-off-by: Sangkug Lym <[email protected]> | {
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} | null | null | null | **Describe the bug**
I'm using "Speaker Diarization Inference" but 'vad_marblenet' wasn't enough, I decided to do the Fine-tune training on the vad model as in the example of "Speech_Commands.ipynb" which is mentioned at the end of the Voice_Activity_Detection notebook.
After the model's Fine-tune I tested it, and the following error came back
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/tmp/ipykernel_704/3722763614.py in <module>
----> 1 sd_model.diarize()
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py in diarize(self, paths2audio_files, batch_size)
352
353 self._setup_vad_test_data(manifest_vad_input)
--> 354 self._run_vad(manifest_vad_input)
355 else:
356 if not os.path.exists(self._speaker_manifest_path):
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py in _run_vad(self, manifest_file)
201 log_probs = self._vad_model(input_signal=test_batch[0], input_signal_length=test_batch[1])
202 probs = torch.softmax(log_probs, dim=-1)
--> 203 pred = probs[:, 1]
204 if status[i] == 'start':
205 to_save = pred[:-trunc]
IndexError: index 1 is out of bounds for dimension 1 with size 1
```
**Steps/Code to reproduce bug**
```
pretrained_vad = 'vad_marblenet_Fine_Tune.nemo'
pretrained_speaker_model = 'ecapa_tdnn'
```
default config.diarizer cell values
**Expected behavior**
Execution of VAD and embendding calculations
**Environment overview (please complete the following information)**
- Environment location: [Docker]
**Environment details**
NVIDIA docker image.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3136 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3136/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3136/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3136/events | https://github.com/NVIDIA/NeMo/pull/3136 | 1,044,689,961 | PR_kwDOC_bI7s4uFu9f | 3,136 | Cannot unset both `source_processor` and `target_processor` for NMT model inference. | {
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] | null | null | 2021-11-04T12:00:51 | 2021-11-18T18:37:07 | 2021-11-08T14:33:56 | CONTRIBUTOR | {
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} | If I omit both parameters `--target_lang` and `--source_lang` when running script `nmt_transformer_infer.py`, then both `target_processor` and `source_processor` attributes of model `MTEncDecModel` are set as during the model training.
This behaviour appears because
1) `source_processor` and `target_processor` are set while [restoring from checkpoint](https://github.com/NVIDIA/NeMo/blob/a089791db1d4be51da2cd7cdec2eb822ca6b588c/examples/nlp/machine_translation/nmt_transformer_infer.py#L154),
2) `source_processor` and `target_processor` are [reset](https://github.com/NVIDIA/NeMo/blob/a089791db1d4be51da2cd7cdec2eb822ca6b588c/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py#L858) only if at least one of parameters `--source_lang`, `--target_lang` are provided.
To produce the problem you need
1) find `.nemo` file for `MTEncDecModel` model which was trained with source or target processor,
2) add some debug logging [here](https://github.com/NVIDIA/NeMo/blob/a089791db1d4be51da2cd7cdec2eb822ca6b588c/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py#L763) to see if processing is performed,
3) launch script `nmt_transformer_infer.py` without parameters `--source_lang` and `--target_lang`. | {
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