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} | # Changelog
- Deprecate `experimental_fuse_loss_wer` and replace with `fuse_loss_wer` flag instead.
- Add missing zh models back
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | **Describe the bug**
The SacreBLEU score in [mt_enc_dec_model](https://github.com/NVIDIA/NeMo/blob/r1.3.0/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py) is computed using detokenized target text:
```
286: ground_truths = [self.decoder_tokenizer.ids_to_text(tgt) for tgt in np_tgt]
287: ground_truths = [self.target_processor.detokenize(tgt.split(' ')) for tgt in ground_truths]
```
which is the likely reason for the difference in the BLEU score I get when computing directly using the `corpus_bleu` method from sacreblue.
As an example, on the newstest2014 en -> de dataset, using `trainer.test()` I get `30.14` and running `model.translate()` followed by `corpus_bleu()` results in `30.42`. The difference is even larger on my own dataset (1-2 units).
Ideally, the BLEU score should be computed directly on the raw target text so it is easy to reproduce the metric externally.
A potential solution could be to add raw_tgt as an additional output of the TranlationDataset.
**Expected behavior**
SacreBLEU score should be identical when computed within the model and externally.
**Environment overview (please complete the following information)**
- Environment Location: Docker on Kubernetes
- Method of NeMo install: using helm chart using `nvcr.io/nvidia/nemo:1.2.0`
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} | null | null | null | Hello,
I am fine-tuning Conformer-small using a custom tarred dataset.
During training the following error occurs:
```
Error executing job with overrides:
['+init_from_nemo_model=/home/ael/Scripts/nemo/training/pretrained_models/stt_en_conformer_ctc_small/stt_en_conformer_ctc_small.nemo']
Traceback (most recent call last): File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1044, in _run_train self.fit_loop.run() File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run self.advance(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 200, in advance epoch_output = self.epoch_loop.run(train_dataloader) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run self.advance(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 130, in advance batch_output = self.batch_loop.run(batch, self.iteration_count, self._dataloader_idx) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 100, in run super().run(batch, batch_idx, dataloader_idx) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run self.advance(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 147, in advance result = self._run_optimization(batch_idx, split_batch, opt_idx, optimizer) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 193, in _run_optimization closure() File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 235, in _training_step_and_backward_closure result = self.training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 536, in training_step_and_backward
result = self._training_step(split_batch, batch_idx, opt_idx, hiddens)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 306, in _training_step training_step_output = self.trainer.accelerator.training_step(step_kwargs)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py", line 193, in training_step
return self.training_type_plugin.training_step(*step_kwargs.values())
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp.py", line 384, in training_step
return self.model(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/fairscale/nn/data_parallel/sharded_ddp.py", line 224, in forward
return self.module(*inputs, **kwargs)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/pytorch_lightning/overrides/base.py", line 82, in forward
output = self.module.training_step(*inputs, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/utils/model_utils.py", line 339, in wrap_training_step output_dict = wrapped(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/models/ctc_models.py", line 559, in training_step log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/core/classes/common.py", line 798, in __call__ outputs = wrapped(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/models/ctc_models.py", line 538, in forward processed_signal, processed_signal_length = self.preprocessor( File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/core/classes/common.py", line 798, in __call__ outputs = wrapped(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context return func(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 83, in forward processed_signal, processed_length = self.get_features(input_signal, length) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 259, in get_features return self.featurizer(input_signal, length) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context return func(*args, **kwargs)
return func(*args, **kwargs) File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 416, in forward
x = normalize_batch(x, seq_len, normalize_type=self.normalize)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 60, in normalize_batch raise ValueError(
ValueError: normalize_batch with `per_feature` normalize_type received a tensor of length 1. This will result in torch.std() returning nan
```
If my understanding is correct, it seems there is a single sample that it is length is less than a single frame. However, I have double-checked and the smallest sample in the dataset has a duration of 0.1
For completeness, this is the preprocessor and spec_augment data on the YAML file:
```
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: 2 # set to zero to disable it
freq_width: 27
time_width: 0.05
```
Any idea what could be the reason behind this error and how to fix it ? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2782 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2782/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2782/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2782/events | https://github.com/NVIDIA/NeMo/issues/2782 | 989,515,647 | MDU6SXNzdWU5ODk1MTU2NDc= | 2,782 | Tune frame length while training Tacotron? | {
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} | null | null | null | Thank you for building a great open source project.
I'm trying to train Tarcotron using Korean datasets.
While training I just noticed that depending on `max_duration`, frame padding length will be changed, below images show `val_target` Mel spectrogram where `max_duration` is over 3.5sec and below 3.5sec.
and I think it makes `valid_prediction` unstable. like the below images, Tacotron generates some noise at the end of a frame.
Is there any way to tune this problem? or is it normal behavior for Tacotron?
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} | null | null | null | **Describe the bug**
Following "NGC Pretrained Checkpoints"
from
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_recognition/results.html
**Steps/Code to reproduce bug**
Running:
nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="speakerrecognition_speakernet")
**Expected behavior**
no error when loading pretrained model
**Environment overview (please complete the following information)**
- google colab:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb
same on dgx with working riva-client container that has been used for other applications
**Environment details**
- google colab
- riva-client 1.4 with nemo "main"
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} | null | null | null | **Describe the bug**
[NeMo W 2021-09-06 11:58:47 patch_utils:50] torch.stft() signature has been updated for PyTorch 1.7+
Please update PyTorch to remain compatible with later versions of NeMo.
and followed by
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in _pad(input, pad, mode, value)
4157 assert len(pad) == 2, "3D tensors expect 2 values for padding"
4158 if mode == "reflect":
-> 4159 return torch._C._nn.reflection_pad1d(input, pad)
4160 elif mode == "replicate":
4161 return torch._C._nn.replication_pad1d(input, pad)
RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension, but got: padding (256, 256) at dimension 2 of input [1, 2, 2]
also in this notebook, next to the AN4 Source not available problem:
Original Cell: restored_model.setup_finetune_model(config.model)
TypeError Traceback (most recent call last)
<ipython-input-68-be381c0bac3a> in <module>()
----> 1 restored_model.setup_finetune_model(config.model)
if i change to
Cell: restored_model.setup_finetune_model(model_config = config.model)
TypeError: setup_finetune_model() missing 1 required positional argument: 'model_config'
NameError Traceback (most recent call last)
<ipython-input-49-fec8ad3e9b0e> in <module>()
----> 1 restored_model.setup_finetune_model(self, model_config=config.model)
NameError: name 'self' is not defined
same with this
cell: restored_model.set_trainer(trainer_finetune)
TypeError Traceback (most recent call last)
<ipython-input-67-22f1286fa285> in <module>()
----> 1 restored_model.set_trainer(trainer_finetune)
2 log_dir_finetune = exp_manager(trainer_finetune, config.get("exp_manager", None))
3 print(log_dir_finetune)
TypeError: set_trainer() missing 1 required positional argument: 'trainer'
**Steps/Code to reproduce bug**
Cell: trainer.fit(speaker_model)
in
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb
**Expected behavior**
(as expected by the ppl that made this notebook.... Colab training should work without bugfixing :-))
Torch 1.9 is installed, no updates possible as it seems...
**Environment overview (please complete the following information)**
torch @ https://download.pytorch.org/whl/cu102/torch-1.9.0%2Bcu102-cp37-cp37m-linux_x86_64.whl
torch-stft==0.1.4
torchaudio==0.9.0
torchmetrics==0.5.1
torchsummary==1.5.1
torchtext==0.10.0
torchvision @ https://download.pytorch.org/whl/cu102/torchvision-0.10.0%2Bcu102-cp37-cp37m-linux_x86_64.whl
**Environment details**
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
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} | null | null | null | Hi, I installed the nemo 1.3.0 latest version recently and find there is an attribute error as shown below. Although I have reinstall my environment for a few times, it still have this error. Has anyone encounter this recently with NeMo 1.3.0?
=================================================================================
my code:
char_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_zh_citrinet_1024_gamma_0_25")
error:
AttributeError: type object 'EncDecCTCModel' has no attribute 'from_pretrained'
=================================================================================
Thanks! | {
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] | null | null | 2021-09-04T20:28:43 | 2022-01-29T02:31:42 | 2022-01-29T02:31:42 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
We have a BERT model pretrained using Megatron-LM (v2.2) with the tensor model parallel set to 2.
We use NeMo to fine-tune the model for text classification.
We do not have an issue with fine-tuning and test, but we have an issue with prediction.
We try to apply the classifytext function from the text_classification_class.py to get the predicted labels, but it raised a RuntimeError as Tensors must be CUDA and dense (detailed error trace is attached below).
**Steps/Code to reproduce bug**
Following is the code snippet we used (similar as the tutorial)
```python
trainer = pl.Trainer(plugins=[NLPDDPPlugin()], **config.trainer)
exp_dir = exp_manager(trainer, config.get("exp_manager", None))
model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)
trainer.fit(model)
quries = load_test(config.model.test_ds.file_path)
all_preds = model.classifytext(quries, config.model.test_ds.batch_size, config.model.dataset.max_seq_length)
```
**Expected behavior**
Yield predicted labels.
**actual behavior**
raised a RuntimeError as
```
Traceback (most recent call last):
File "/red/gatortron-phi/workspace/2021gatortron/nemo_downstream/re/re_ddp.py", line 66, in <module>
main(global_args)
File "/red/gatortron-phi/workspace/2021gatortron/nemo_downstream/re/re_ddp.py", line 46, in main
all_preds = model.classifytext(config.model.infer_samples, config.model.test_ds.batch_size, config.model.dataset.max_seq_length)
File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py", line 280, in classifytext
logits = self.forward(
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 792, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py", line 105, in forward
hidden_states = self.bert_model(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1015, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 792, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/megatron_bert.py", line 182, in forward
sequence_output = self.language_model(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1015, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py", line 414, in forward
return super(TransformerLanguageModel, self).forward(
File "/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py", line 321, in forward
embedding_output = self.embedding(input_ids, position_ids,
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1015, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py", line 183, in forward
words_embeddings = self.word_embeddings(input_ids)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1015, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/megatron/mpu/layers.py", line 191, in forward
output = reduce_from_tensor_model_parallel_region(output_parallel)
File "/opt/conda/lib/python3.8/site-packages/megatron/mpu/mappings.py", line 149, in reduce_from_tensor_model_parallel_region
return _ReduceFromModelParallelRegion.apply(input_)
File "/opt/conda/lib/python3.8/site-packages/megatron/mpu/mappings.py", line 101, in forward
return _reduce(input_)
File "/opt/conda/lib/python3.8/site-packages/megatron/mpu/mappings.py", line 30, in _reduce
torch.distributed.all_reduce(input_, group=get_tensor_model_parallel_group())
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 1175, in all_reduce
work = group.allreduce([tensor], opts)
RuntimeError: Tensors must be CUDA and dense
```
**Environment overview (please complete the following information)**
- Environment location: Slurm, 1 node 8 GPUs, NeMo-1.2.0 docker container from NGC
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} | * Clean speaker scripts
* Add asr with diarization script | {
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} | This PR Adds on-the-fly validation of tarred dataset files.
Changes:
1. If file is missing, looking for the file in same path as metadata file.
2. If file cannot be found, raises an exception.
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} | This PR introduces a max-pooling and average-pooling encoders.
Each block halves the steps of the encoder using max pooling / average pooling over steps dimension.
In addition, a control parameter `MTBottleneckModel.deterministic_translate` can be used for deterministic translation for VAE and MIM ( i.w., using the mean of latent code instead a sample). This change affects all bottleneck encoders. Default behaviour is on.
# YAML Configuration
The following configurations in YAML config allows to control the learning framework and architecture:
```
model:
...
encoder:
library: nemo
...
arch: max_pool # avg_pool (NEW), max_pool (NEW), seq2seq, bridge, perceiver (see description above)
hidden_steps: 32 # fixed number of hidden steps for bottleneck, minimal steps for reduction otherwise
hidden_blocks: 1 # number of repeat blocks (see classes for description)
hidden_init_method: default # see classes for available values
```
Detailed description of config parameters:
* **model.encoder.arch=avg_pool** `<== NEW`
* *model.encoder.hidden_steps:* minimal number of steps to halve (must be > 2)
* *model.encoder.hidden_blocks:* number of (encoder + average pooling) blocks to repeat (each encoder with `num_layers` layers)
* *model.encoder.hidden_init_method:*
* default - must be `default`
* **model.encoder.arch=max_pool** `<== NEW`
* *model.encoder.hidden_steps:* minimal number of steps to halve (must be > 2)
* *model.encoder.hidden_blocks:* number of (encoder + max pooling) blocks to repeat (each encoder with `num_layers` layers)
* *model.encoder.hidden_init_method:*
* default - must be `default`
* **model.encoder.arch=seq2seq**
* *model.encoder.hidden_steps is ignored*
* *model.encoder.hidden_blocks is ignored*
* *model.encoder.hidden_init_method is ignored*
* **model.encoder.arch=bridge**
* *model.encoder.hidden_steps:* input is projected to the specified fixed steps
* *model.encoder.hidden_blocks:* number of encoder blocks to repeat after attention bridge projection
* *model.encoder.hidden_init_method:*
* enc_shared (default) - apply encoder to inputs, than attention bridge, followed by hidden_blocks number of the same encoder (pre and post encoders share parameters)
* identity - apply attention bridge to inputs, followed by hidden_blocks number of the same encoder
* enc - similar to enc_shared but the initial encoder has independent parameters
* **model.encoder.arch=perceiver**
* *model.encoder.hidden_steps:* input is projected to the specified fixed steps
* *model.encoder.hidden_blocks:* number of cross-attention + self-attention blocks to repeat after initialization block (all self-attention and cross-attention share parameters)
* *model.encoder.hidden_init_method:*
* params (default) - hidden state is initialized with learned parameters followed by cross-attention with independent parameters
* bridge - hidden state is initialized with an attention bridge
# Usage Example
**NOTE:** ```enc_dec_nmt-bottleneck.py``` must be used. ```enc_dec_nmt.py``` does not support the required configuration to run properly and will likely fail.
```
python -- enc_dec_nmt-bottleneck.py \
--config-path=conf \
--config-name=aayn_bottleneck \
...
model.model_type=nll \
model.non_recon_warmup_batches=7500 \
model.encoder.arch=max_pool \
model.encoder.hidden_steps=32 \
model.encoder.hidden_blocks=2 \
model.encoder.hidden_init_method=default \
...
```
# Additional Info
* Classes *MaxPoolingEncoder* and *AveragePoolingEncoder* was added to ```nemo/collections/nlp/modules/common/transformer/reduction_encoders.py``` | {
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} | null | null | null | **Describe the bug**
I am using the provided [punctuation_capitalization_train.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/punctuation_capitalization_train.py) and [punctuation_capitalization_evaluate.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/punctuation_capitalization_evaluate.py) scripts provided in examples to train and evaluate a Punctuation-Capitalization model with roberta-base encoder (which is supported by NeMo).
I am able to train the model successfully with [punctuation_capitalization_train.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/punctuation_capitalization_train.py). However, when evaluating this model with [punctuation_capitalization_evaluate.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/punctuation_capitalization_evaluate.py), there is an error reloading the model. I've tried retraining another model from scratch to confirm that this wasn't due to a problem that happened while saving the model to disk, but did not make a difference.
I've followed the exact same procedure but with using `model.language_model.pretrained_model_name=bert-base-uncased` and `model.language_model.pretrained_model_name=distilbert-base-uncased`, but am not able to reproduce the issue with these encoders. Decoding error seems to only occur with `roberta-base` models.
**Steps/Code to reproduce bug**
1. Train a model with a `roberta-base` language model: `python punctuation_capitalization_train.py model.dataset.data_dir=data/punc-cap/de/indomain model.language_model.pretrained_model_name=roberta-base`.
-> Model trains successfully, `.nemo` model saved to disk without error
2. Evaluate this saved model: `python punctuation_capitalization_evaluate.py model.dataset.data_dir=data/punc-cap/combined-dataset/ pretrained_model=nemo_experiments/Punctuation_and_Capitalization/2021-09-02_08-32-18/checkpoints/Punctuation_and_Capitalization.nemo`
-> Model fails to load (error in loading the vocab file?). The error gets caught and reported as `roberta-base` not being supported, although it is.
Log:
```
GPU available: True, used: True [27/1988]
TPU available: False, using: 0 TPU cores
Using native 16bit precision.
[NeMo I 2021-09-03 12:03:02 exp_manager:219] Experiments will be logged at /home/lhr/nemo_experiments/Punctuation_and_Capitalization/2021-09-03_12-03-02
[NeMo I 2021-09-03 12:03:02 exp_manager:568] TensorboardLogger has been set up
[NeMo I 2021-09-03 12:03:11 tokenizer_utils:100] Getting HuggingFace AutoTokenizer with pretrained_model_name: roberta-base, vocab_file: /tmp/tmpcfbw34f8/7a630e42c1974fc5999881de0c5a9871_tokenizer.vocab_file, special_tokens_dict: {}, and use_fast: False
[NeMo I 2021-09-03 12:03:15 tokenizer_utils:100] Getting HuggingFace AutoTokenizer with pretrained_model_name: roberta-base, vocab_file: /tmp/tmpcfbw34f8/7a630e42c1974fc5999881de0c5a9871_tokenizer.vocab_file, special_tokens_dict: {}, and use_fast: False
Error executing job with overrides: ['model.dataset.data_dir=data/punc-cap/combined-dataset/', 'pretrained_model=nemo_experiments/Punctuation_and_Capitalization/2021-09-02_08-32-18/checkpoints/Punctuation_and_Capitalization.nemo']
Traceback (most recent call last):
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py", line 104, in __init__
self.tokenizer = AUTOTOKENIZER.from_pretrained(
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py", line 566, in from_pretrained
return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 1719, in from_pretrained
return cls._from_pretrained(
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 1792, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/transformers/models/roberta/tokenization_roberta.py", line 159, in __init__
super().__init__(
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/transformers/models/gpt2/tokenization_gpt2.py", line 180, in __init__
self.encoder = json.load(vocab_handle)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/home/lhr/.conda/envs/nemo/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/home/lhr/.conda/envs/nemo/lib/python3.8/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "tmp/textNormalization/text_normalization/punctuation_capitalization/punctuation_capitalization_evaluate.py", line 80, in main
model = PunctuationCapitalizationModel.restore_from(cfg.pretrained_model)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 429, in restore_from
return super().restore_from(restore_path, override_config_path, map_location, strict, return_config)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 481, in restore_from
return cls._default_restore_from(restore_path, override_config_path, map_location, strict, return_config)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 434, in _default_restore_from
instance = cls.from_config_dict(config=conf)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/core/classes/common.py", line 472, in from_config_dict
instance = cls(cfg=config)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py", line 57, in __init__
self.setup_tokenizer(cfg.tokenizer)
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 112, in setup_tokenizer
tokenizer = get_tokenizer(
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/tokenizer_utils.py", line 103, in get_tokenizer
return AutoTokenizer(
File "/home/lhr/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py", line 112, in __init__
raise ValueError(f'{pretrained_model_name} is not supported by HuggingFace. {e}')
ValueError: roberta-base is not supported by HuggingFace. Expecting value: line 1 column 1 (char 0)
```
**Expected behavior**
Should be able to load the model for inference.
**Environment overview (please complete the following information)**
- Environment location: local
- Method of NeMo install: pip (following instructions on main page, `apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
pip install nemo_toolkit['all']`)
**Environment details**
- OS version: Ubuntu 18.04.5 LTS
- PyTorch version: 1.9.0
- Python version: 3.8.11
- nemo-toolkit version: 1.2.0
**Additional context**
Using 1 GPU, CUDA Version: 11.2
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} | null | null | null | **Describe the bug**
This issue is related to issue #2686.
I expect that if I pre-train an IntentSlotClassificationModel and export the checkpoint to a .nemo file that I can restore that checkpoint elsewhere. Further, I expect that, once exported, I can delete the data directory used for training and later be able to restore the checkpoint. Alas, this is not the case. If the data_dir inside the configuration file is None or refers to a directory that does not exist a TypeError will be raised.
**Steps/Code to reproduce bug**
These steps rely heavily upon the existing [Joint_Intent_and_Slot_Classification](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb) tutorial.
1. Prepare training data
a. Create a temporary directory, /tmp/nlu_evaluation, and two sub-directories: /tmp/nlu_evaluation/conf and /tmp/nlu_evaluation/data.
b. Download [import_datasets.py](https://raw.githubusercontent.com/NVIDIA/NeMo/v1.2.0/examples/nlp/intent_slot_classification/data/assistant_utils.py) and [import_datasets.py](https://raw.githubusercontent.com/NVIDIA/NeMo/v1.2.0/examples/nlp/intent_slot_classification/data/assistant_utils.py) to /tmp/nlu_evaluation.
c. Download [master.zip](https://github.com/xliuhw/NLU-Evaluation-Data/archive/master.zip) and extract it in /tmp/nlu_evaluation/data
d. Process the evaluation data:
`python /tmp/nlu_evaluation/import_datasets.py --dataset_name=assistant --source_dir=/tmp/nlu_evaluation/NLU-Evaluation-Data-master --target_dir=/tmp/nlu_evaluation/nemo_format`
e. Download [intent_slot_classification_config.yaml](https://raw.githubusercontent.com/NVIDIA/NeMo/v1.2.0/examples/nlp/intent_slot_classification/conf/intent_slot_classification_config.yaml) to /tmp/nlu_evaluation/conf
2. Train and save the model
Run the following Python code
```
import nemo.collections.nlp as nemo_nlp
from omegaconf import OmegaConf
config = OmegaConf.load('/tmp/nlu_evaluation/conf/intent_slot_classification_config.yaml')
config.model.data_dir = '/tmp/nlu_evaluation/data/nemo_format')
config.trainer.gpus = 1
config.trainer.precision = 16
config.trainer.accelerator = None
config.trainer.max_epochs = 5
trainer = pl.Trainer(**config.trainer)
model = nemo_nlp.models.IntentSlotClassificationModel(config.model, trainer=trainer)
trainer.fit(model)
model.save_to('/tmp/nlu_evaluation/model.nemo')
```
3. Completely remove /tmp/nlu_evaluation/data/nemo_format
4. Restore the saved model
Run the following Python code
```
import nemo.collections.nlp as nemo_nlp
nemo_nlp.models.IntentSlotClassificationModel.restore_from('/tmp/nlu_evaluation/model.nemo')
```
**Expected behavior**
The model should be restored and be ready for use.
**Actual behavior**
The restore fails with this exception:
```
File "...lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 435, in restore_from
return super().restore_from(
File "...lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 270, in restore_from
instance = cls._save_restore_connector.restore_from(
File "...lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py", line 136, in restore_from
instance = calling_cls.from_config_dict(config=conf)
File "...lib/python3.8/site-packages/nemo/core/classes/common.py", line 473, in from_config_dict
instance = cls(cfg=config)
File "...lib/python3.8/site-packages/nemo/collections/nlp/models/intent_slot_classification/intent_slot_classification_model.py", line 56, in __init__
self.setup_tokenizer(cfg.tokenizer)
File "...lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 112, in setup_tokenizer
vocab_file = self.register_artifact(config_path='tokenizer.vocab_file', src=cfg.vocab_file)
File "...lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 63, in register_artifact
return super().register_artifact(config_path, src, verify_src_exists=verify_src_exists)
File "...lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 202, in register_artifact
return self._save_restore_connector.register_artifact(self, config_path, src, verify_src_exists)
File "...lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py", line 271, in register_artifact
return_path = os.path.abspath(os.path.join(app_state.nemo_file_folder, src[5:]))
File "...lib/python3.8/posixpath.py", line 76, in join
a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
**Environment overview (please complete the following information)**
- Environment location:
Bare metal
- Method of NeMo install
pip install nemo_toolkit[asr,nlp]==1.3.0
**Environment details**
- OS version
Ubuntu 20.04
- PyTorch version
1.8.1
- Python version
3.8.10
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(cherry picked from commit 4430fb1a2d41ca73edde9671d1ab0d9bda54123e) | {
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OS version: Ubuntu, 20.04.2 LTS
Python version: 3.6.13
PyTorch version: 1.9.0
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} | null | null | null | Hi
I'm building a communication API for transcription with NeMo and I ran into a critical infinite memory allocation problem.
When using a lib that monitors memory allocation, I noticed that the "transcribe" method used on top of a restored model is allocating memory every time when transcribing a new file.
My API basically works like this:
When starting, I load the .nemo model creating the asr_model, then open a listener and keep receiving hundreds of files on the network, carrying out their individual transcription on top of that same nemo_asr object.
At this point the problem comes up, because every time I call the transcribe method it keeps allocating more and more memory... I left it running for a night and it allocated 50GB of ram (considering a small stream of audios to transcribe)
Is there an alternative so I can work around this problem? I don't think it's perfect to have to create a new nemo_asr object every time I transcribe, so I keep it in this format.
Here are two examples below:
SO: Ubuntu 18.04
Nemo: 1.2.0 | {
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} | # Bugfix
- Fix an issue when MFCC model is used and transcribe is attempted due to attempted patch to `dither` and `pad_to`
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | When I run the following scirpt: https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text.py
I get the following error:
```
[NeMo W 2021-09-01 16:08:13 experimental:27] Module <class 'nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2021-09-01 16:08:13 speech2text:44] Namespace(config_name='SpeakerNet_verification_3x2x512', config_path='/home/ubuntu/repos/algo/app/aliSrc/notebooks/conf', data_dir='/home/ubuntu/repos/algo/app/aliSrc/dataset/himia/train', gpu=0, max_epochs=50)
[NeMo I 2021-09-01 16:08:13 speech2text:61] =====[NeMo W 2021-09-01 16:08:13 nemo_logging:349] /home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:556: UserWarning: You requested distributed training on GPUs, but none is available, so we set backend to `ddp_cpu`. rank_zero_warn(
[NeMo W 2021-09-01 16:08:13 nemo_logging:349] /home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:563: UserWarning: You are running on single node with no parallelization, so distributed has no effect.
rank_zero_warn('You are running on single node with no parallelization, so distributed has no effect.')
GPU available: True, used: False
TPU available: False, using: 0 TPU cores[NeMo W 2021-09-01 16:08:14 nemo_logging:349] /home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:596: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`. rank_zero_warn(
[NeMo I 2021-09-01 16:08:14 exp_manager:217] Experiments will be logged at /home/ubuntu/repos/algo/app/nemo_experiments/SpeakerNet/2021-09-01_16-08-14[NeMo I 2021-09-01 16:08:14 exp_manager:564] TensorboardLogger has been set up
[NeMo W 2021-09-01 16:08:14 nemo_logging:349] /home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:396: LightningDeprecationWarning: Argument `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5. Please use `every_n_val_epochs` instead.
rank_zero_deprecation(
[NeMo I 2021-09-01 16:08:14 audio_to_text_dataset:36] Model level config does not container `sample_rate`, please explicitly provide `sample_rate` to the dataloaders.
[NeMo I 2021-09-01 16:08:14 audio_to_text_dataset:36] Model level config does not container `labels`, please explicitly provide `labels` to the dataloaders.
Traceback (most recent call last):
File "/home/ubuntu/repos/algo/app/aliSrc/src/speech2text.py", line 70, in <module>
main()
File "/home/ubuntu/repos/algo/app/aliSrc/src/speech2text.py", line 66, in main
asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 153, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 128, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 423, in setup_training_data
self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 390, in _setup_dataloader_from_config
dataset = audio_to_text_dataset.get_char_dataset(config=config, augmentor=augmentor)
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text_dataset.py", line 75, in get_char_dataset
dataset = audio_to_text.AudioToCharDataset(
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py",line 250, in __init__
super().__init__(
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py",line 139, in __init__
self.collection = collections.ASRAudioText(
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/collections.py", line 193, in __init__
for item in manifest.item_iter(manifests_files):
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py", line 72, in item_iter
item = parse_func(line, manifest_file)
File "/home/ubuntu/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py", line 107, in __parse_item
raise ValueError(
ValueError: Manifest file /home/ubuntu/repos/algo/app/aliSrc/dataset/himia/train/train.json has invalid json line structure: {"audio_filepath": "/home/ubuntu/repos/algo/app/aliSrc/dataset/himia/train/SPEECHDATA_reduced/wav/SV0015/SV0015_5_15_N1744.wav", "offset": 0, "duration": 1.5600625, "label": "SV0015"}
without proper text key.
```
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] | null | null | 2021-09-01T14:14:02 | 2022-04-05T23:36:33 | 2022-04-05T23:36:33 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
According to documentation on page https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.3.0/nlp/machine_translation.html#tarred-datasets-for-large-corpora the second way to use tarred dataset is
**Quote**
2. Using a separate script without Hydra.
Tarred datasets for parallel corpora can also be created with a script that doesn’t require specifying a configs via Hydra and just uses Python argparse.
For example:
```
python examples/nlp/machine_translation/create_tarred_parallel_dataset.py \
--shared_tokenizer \
--clean \
--bpe_dropout 0.1 \
--src_fname train.tokenized.en \
--tgt_fname train.tokenized.es \
--out_dir /path/to/preproc_dir \
--vocab_size 32000 \
--max_seq_length 512 \
--min_seq_length 1 \
--tokens_in_batch 8192 \
--lines_per_dataset_fragment 1000000 \
--num_batches_per_tarfile 200
```
*You can then set `model.preproc_out_dir=/path/to/preproc_dir` and `model.train_ds.use_tarred_dataset=true` to train with this data.*
**End quote**
If I set parameter `model.preproc_out_dir` to directory with tar files and train dataset config is
```
train_ds:
src_file_name: null
tgt_file_name: null
use_tarred_dataset: True
tar_file_prefix: parallel
```
calling `python enc_dec_nmt.py` results in error
```
Traceback (most recent call last):
File "enc_dec_nmt.py", line 121, in main
MTDataPreproc(cfg=cfg.model, trainer=trainer)
File "/home/lab/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/nlp/data/machine_translation/preproc_mt_data.py", line 181, in __init__
raise ValueError(
ValueError: src_file_name and tgt_file_name needed to create tarred dataset but could not be found.
```
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip
**Environment details**
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.8.1
- Python version: 3.8
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} | null | null | null | I want to know if nemo library includes script for speech to text. Please Can you refer exactly to such a feature.. | {
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Parameter `--coverage` in script `examples/nlp/machine_translation/create_tarred_parallel_dataset.py` is unused.
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I've been working on a dataset for a long time and slowly I see that my simple GPU isn't playing such a good role.
When searching I see several people using RTX and Tesla. Does anyone know what is the advantage of a Tesla GPU over an RTX GPU? Going into the question of price, I see that an RTX it's much more affordable...
For example, an RTX 3090 versus a Tesla V100, on comparison sites, I see that RTX stands out in a number of ways. Someone who has experience and can share, thank you in advance. | {
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https://github.com/NVIDIA/NeMo/blob/1d262c725df150ba837146e7899d8167adf524f6/requirements/requirements_lightning.txt#L5-L7 | {
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} | null | null | null | The conformer network sometimes predicts "⁇" symbols. After greedy decoding, all the outputs have zero index, which corresponds to <"unk"> token. And finally, after applying sentencepiece tokenization I am getting ⁇ symbols.
Btw, all my transcripts successfully tokenized without producing any <"unk"> symbols.
What can cause "⁇" symbol? | {
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AN4 Dataset not resolving can't download data for nemo asr tutorial
**Steps/Code to reproduce bug**
```python
import glob
import os
import subprocess
import tarfile
import wget
# Download the dataset. This will take a few moments...
print("******")
if not os.path.exists(data_dir + '/an4_sphere.tar.gz'):
an4_url = 'http://www.speech.cs.cmu.edu/databases/an4/an4_sphere.tar.gz'
an4_path = wget.download(an4_url, data_dir)
print(f"Dataset downloaded at: {an4_path}")
else:
print("Tarfile already exists.")
an4_path = data_dir + '/an4_sphere.tar.gz'
if not os.path.exists(data_dir + '/an4/'):
# Untar and convert .sph to .wav (using sox)
tar = tarfile.open(an4_path)
tar.extractall(path=data_dir)
print("Converting .sph to .wav...")
sph_list = glob.glob(data_dir + '/an4/**/*.sph', recursive=True)
for sph_path in sph_list:
wav_path = sph_path[:-4] + '.wav'
cmd = ["sox", sph_path, wav_path]
subprocess.run(cmd)
print("Finished conversion.\n******")
```
```
ConnectionResetError Traceback (most recent call last)
/tmp/ipykernel_1062/2607186822.py in <module>
9 if not os.path.exists(data_dir + '/an4_sphere.tar.gz'):
10 an4_url = 'http://www.speech.cs.cmu.edu/databases/an4/an4_sphere.tar.gz'
---> 11 an4_path = wget.download(an4_url, data_dir)
12 print(f"Dataset downloaded at: {an4_path}")
13 else:
~/conda/lib/python3.8/site-packages/wget.py in download(url, out, bar)
524 else:
525 binurl = url
--> 526 (tmpfile, headers) = ulib.urlretrieve(binurl, tmpfile, callback)
527 filename = detect_filename(url, out, headers)
528 if outdir:
~/conda/lib/python3.8/urllib/request.py in urlretrieve(url, filename, reporthook, data)
245 url_type, path = _splittype(url)
246
--> 247 with contextlib.closing(urlopen(url, data)) as fp:
248 headers = fp.info()
249
~/conda/lib/python3.8/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
220 else:
221 opener = _opener
--> 222 return opener.open(url, data, timeout)
223
224 def install_opener(opener):
~/conda/lib/python3.8/urllib/request.py in open(self, fullurl, data, timeout)
523
524 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 525 response = self._open(req, data)
526
527 # post-process response
~/conda/lib/python3.8/urllib/request.py in _open(self, req, data)
540
541 protocol = req.type
--> 542 result = self._call_chain(self.handle_open, protocol, protocol +
543 '_open', req)
544 if result:
~/conda/lib/python3.8/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
500 for handler in handlers:
501 func = getattr(handler, meth_name)
--> 502 result = func(*args)
503 if result is not None:
504 return result
~/conda/lib/python3.8/urllib/request.py in http_open(self, req)
1381
1382 def http_open(self, req):
-> 1383 return self.do_open(http.client.HTTPConnection, req)
1384
1385 http_request = AbstractHTTPHandler.do_request_
~/conda/lib/python3.8/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
1356 except OSError as err: # timeout error
1357 raise URLError(err)
-> 1358 r = h.getresponse()
1359 except:
1360 h.close()
~/conda/lib/python3.8/http/client.py in getresponse(self)
1345 try:
1346 try:
-> 1347 response.begin()
1348 except ConnectionError:
1349 self.close()
~/conda/lib/python3.8/http/client.py in begin(self)
305 # read until we get a non-100 response
306 while True:
--> 307 version, status, reason = self._read_status()
308 if status != CONTINUE:
309 break
~/conda/lib/python3.8/http/client.py in _read_status(self)
266
267 def _read_status(self):
--> 268 line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
269 if len(line) > _MAXLINE:
270 raise LineTooLong("status line")
~/conda/lib/python3.8/socket.py in readinto(self, b)
667 while True:
668 try:
--> 669 return self._sock.recv_into(b)
670 except timeout:
671 self._timeout_occurred = True
ConnectionResetError: [Errno 104] Connection reset by peer
```
**Expected behavior**
An4 dataset should download
**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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NeMo ASR developer/architectures, what would you recommend in terms of model setup so that I can process training samples of this size? | {
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} | null | null | null | **Describe the bug**
When I attempt to convert a nemo file (fine tuned from the conformer model), I get the following error
```
nemo2riva --out moneypenny-confirmer-v1.riva /data/models/moneypenny-confirmer-v1.nemo
[NeMo W 2021-08-28 08:49:32 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
INFO: Logging level set to 20
INFO: Restoring NeMo model from '/data/models/moneypenny-confirmer-v1.nemo'
ERROR: Nemo2Jarvis: Failed to restore model from NeMo file : /data/models/moneypenny-confirmer-v1.nemo. Please make sure you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/riva/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/home/ubuntu/anaconda3/envs/riva/lib/python3.8/site-packages/nemo2riva/cli/nemo2riva.py", line 50, in nemo2riva
Nemo2Riva(args)
File "/home/ubuntu/anaconda3/envs/riva/lib/python3.8/site-packages/nemo2riva/convert.py", line 42, in Nemo2Riva
raise e
File "/home/ubuntu/anaconda3/envs/riva/lib/python3.8/site-packages/nemo2riva/convert.py", line 35, in Nemo2Riva
model = ModelPT.restore_from(restore_path=nemo_in)
File "/home/ubuntu/anaconda3/envs/riva/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 270, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/ubuntu/anaconda3/envs/riva/lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py", line 136, in restore_from
instance = calling_cls.from_config_dict(config=conf)
File "/home/ubuntu/anaconda3/envs/riva/lib/python3.8/site-packages/nemo/core/classes/common.py", line 473, in from_config_dict
instance = cls(cfg=config)
TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data
```
**Steps/Code to reproduce bug**
ngc registry resource download-version nvidia/riva/riva_quickstart
conda create --name riva python=3.8
conda activate riva
cd riva_quickstart_v1.5.0-beta
pip install nemo-toolkit[all]
pip3 install riva_api-1.5.0b0-py3-none-any.whl
pip3 install nemo2riva-1.5.0b0-py3-none-any.whl
pip3 install nvidia-pyindex
pip3 install nemo2riva
nemo2riva --out moneypenny-confirmer-v1.riva /data/models/moneypenny-confirmer-v1.nemo
-- Running nemo2riva then throws the error outlined above --
**Expected behavior**
The nemo file should be converted into a riva file
**Environment overview (please complete the following information)**
- AWS EC2 instance based on the Nvidia image, T4 GPU
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version : Ubuntu 18.04.5 LTS
- PyTorch version : 1.9.0+cu102
- Python version : Python 3.8.10
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I want to create ITN grammar for the Ukrainian language. As we already have one for Russian I think it would be easy to create a new one for Ukrainian (as it has almost the same rules a Russian).
**Describe the solution you'd like**
I'm looking for some docs or guides on how to create a new grammar for Inver Text Normalization for a new language?
**Describe alternatives you've considered**
**Additional context**
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} | This pull request provides intial support for incorporating retrieval models by
- creating a new `RetrievalTranslationDataset`
- adding support for training with nearest neighbors by providing a file with indices to them and relevant src and tgt files from where those sentences are to be retrieved
- adding a new inference script `nmt_transformer_infer_retrieval.py` which adds nearest neighbor to the sentences at test time | {
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After the epoch runs successfully it tries to save the model checkpoint file in storage and throws exception
**MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['lr', 'train_loss']**
**Steps/Code to reproduce bug**
Following the IMDB dataset structures put up custom dataset for classification model training and run the training epochs. While it tries to save the checkpoint exception is generated.
**Expected behavior**
Model should be saved to the storage after each epoch or number of epochs and next iteration should be running.
[Screenshot from 2021-08-27 12-57-25](https://user-images.githubusercontent.com/41611198/131109161-0252e098-3d4c-46d1-be31-b62a61d00e90.png)
**Environment overview (please complete the following information)**
- Environment location: Kubernetes pod
- Method of NeMo install: NeMo image as base
**Additional context**
NeMo version 1.0.1 and Python version 3.8
Model used is bert base uncased | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2735 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2735/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2735/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2735/events | https://github.com/NVIDIA/NeMo/pull/2735 | 980,450,194 | MDExOlB1bGxSZXF1ZXN0NzIwNjg1NTY3 | 2,735 | [NLP] Megatron GPT pretraining | {
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} | ## This PR is highly under construction with many things subject to change.
This PR depends on libraries that have not been released yet.
## Overview
In this PR we are adding Megatron GPT pretraining to NeMo. This includes adding the model architecture to NeMo (instead of importing from megatron-lm). Currently, we still depend on megatron-lm but we will be removing the dependency over time. Our plan is to initially support tensor model parallelism only (models around 5B parameters). Later we will support pipeline model parallelism as well which will enable models with 100B+ parameters.
## Installation
- Install NeMo from this branch.
- Run make inside `nemo/collections/nlp/data/language_modeling/megatron`
- Install Apex
- Install latest version of megatron-lm from internal repo
## Usage
Data must first be preprocessed using: `examples/nlp/language_modeling/megatron_gpt_pretraining.py`
This results in `.idx` and `.bin` files:
```
pile/
├── my-gpt3_00_text_document.bin
├── my-gpt3_00_text_document.idx
├── my-gpt3_01_text_document.bin
├── my-gpt3_01_text_document.idx
```
These files are then used for training by configuring the `data_prefix` either in the config file `examples/nlp/language_modeling/conf/megatron_gpt_config.yaml`:
``` yaml
data_prefix:
- .5
- /raid/data/pile/my-gpt3_00_text_document
- .5
- /raid/data/pile/my-gpt3_01_text_document
```
Or from the CLI:
```
model.data.data_prefix=[.5,/preproc_data/my-gpt3_00_text_document,.5,/preproc_data/my-gpt3_01_text_document]
```
Sample Python command:
```
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python /code/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/code/examples/nlp/language_modeling/conf \
--config-name=megatron_gpt_config \
trainer.num_nodes=${SLURM_JOB_NUM_NODES} \
trainer.gpus=${SLURM_NTASKS_PER_NODE} \
trainer.max_epochs=null \
trainer.max_steps=${MAX_STEPS} \
trainer.val_check_interval=${VAL_CHECK_INTERVAL} \
model.micro_batch_size=${MICRO_BATCH_SIZE} \
model.tensor_model_parallel_size=${TENSOR_MODEL_PARALLEL_SIZE} \
model.max_position_embeddings=${SEQ_LENGTH} \
model.encoder_seq_length=${SEQ_LENGTH} \
model.num_layers=${NUM_LAYERS} \
model.hidden_size=${HIDDEN_SIZE} \
model.num_attention_heads=${NUM_ATTENTION_HEADS} \
model.vocab_file=/artifacts/vocab.json \
model.merge_file=/artifacts/merges.txt \
model.data.data_prefix=[.5,/preproc_data/my-gpt3_00_text_document,.5,/preproc_data/my-gpt3_01_text_document] \
exp_manager.wandb_logger_kwargs.name=${EXPNAME} \
exp_manager.wandb_logger_kwargs.project=${PROJECT} \
+exp_manager.explicit_log_dir=/results \
+exp_manager.resume_if_exists=True \
+exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=val_loss \
exp_manager.checkpoint_callback_params.save_top_k=10 \
exp_manager.checkpoint_callback_params.mode=min
```
## TODOs
- [x] Start using apex.mpu
- [x] Checkpoint saving/loading to resume training after time limit (would allow us for e2e training)
- [ ] Mimic megatron-lm log file style to easier compare performance
- [x] train_samples instead of train_iters
- [x] Gradient Accumulation for specifying any global_batch_size
- [ ] Partial activation checkpointing
- [ ] Data broadcasting for model parallel
- [ ] Model parallel gradient clipping and weight decay
- [ ] Text generation and perplexity during validation
- [ ] GPU initialization
- [ ] Barriers when creating index files
- [ ] Upgrade to 21.08 container
- [ ] Use fused kernels from apex
- [ ] Remove megatron-lm dependency
- [ ] Add lambada validation task | {
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} | null | null | null | The configuration of nvidia/Riva advertised benchmarks, **pipeline tab** is unclear,
https://developer.nvidia.com/deep-learning-performance-training-inference
Goal: replicate the models in Nemo and see the config.
I.e, have a clear config file per Nemo model.
Examples:
- which model is used for TTS?
- what is the specific Jasper config used there?
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} | null | null | null | Hi 👋 As I was trying to train a Conformer model from scratch, I got these exceptions:
> Traceback (most recent call last):
> File "/home/ubuntu/.local/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
> return target(*args, **kwargs)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/nemo/collections/asr/modules/conformer_encoder.py", line 202, in __init__
> if feat_out > 0 and feat_out != self.output_dim:
> File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 778, in __getattr__
> raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
> torch.nn.modules.module.ModuleAttributeError: 'ConformerEncoder' object has no attribute 'output_dim'
>
> During handling of the above exception, another exception occurred:
>
> Traceback (most recent call last):
> File "base_model.py", line 38, in <module>
> asr_model = nemo_asr.models.EncDecCTCModel(cfg=config.model, trainer=trainer)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 155, in __init__
> self.encoder = EncDecCTCModel.from_config_dict(self._cfg.encoder)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/nemo/core/classes/common.py", line 437, in from_config_dict
> instance = hydra.utils.instantiate(config=config)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
> return instantiate_node(config, *args, recursive=_recursive_, convert=_convert_)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 249, in instantiate_node
> return _call_target(target, *args, **kwargs)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 64, in _call_target
> raise type(e)(
> File "/home/ubuntu/.local/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
> return target(*args, **kwargs)
> File "/home/ubuntu/.local/lib/python3.8/site-packages/nemo/collections/asr/modules/conformer_encoder.py", line 202, in __init__
> if feat_out > 0 and feat_out != self.output_dim:
> File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 778, in __getattr__
> raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
> torch.nn.modules.module.ModuleAttributeError: Error instantiating 'nemo.collections.asr.modules.conformer_encoder.ConformerEncoder' : 'ConformerEncoder' object has no attribute 'output_dim'
I investigated the code in nemo/collections/asr/modules/conformer_encoder.py and it turns out attribute 'output_dim' indeed is only present in this one line 202, and is never defined or passed previously. This is the part where it's used:
` if feat_out > 0 and feat_out != self.output_dim:`
` self.out_proj = nn.Linear(self.feat_out, feat_out)`
` self._feat_out = feat_out`
` else:`
` self.out_proj = None`
` self._feat_out = d_model`
Which I believe should look something like this (at least I got my code working with these corrections):
`if feat_out > 0 and feat_out != self._feat_out:`
` self.out_proj = nn.Linear(self._feat_out, feat_out)`
` self._feat_out = feat_out`
`else:`
` self.out_proj = None`
` self._feat_out = d_model`
Please, correct me if I'm wrong.
**Environment:**
I'm using
- NeMo ver. 1.1.0. (installed via pip).
- torch ver. 1.7.0+cu110
- Python ver. 3.8
- 2 NVIDIA GeForce RTX 3090 GPUs
- OS: Ubuntu 20.04.2
Code to reproduce bug: base_model in the attachments. Model config is also in the attachments, I got it from here: https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_char.yaml and modified for my case.
Thank you.
Attachments:
[conformer_ctc_char-ru.txt](https://github.com/NVIDIA/NeMo/files/7057233/conformer_ctc_char-ru.txt)
[base_model.txt](https://github.com/NVIDIA/NeMo/files/7057234/base_model.txt)
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} | This pull request is for adding the function getEnhancedSpeakerCount() that performs an enhanced speaker counting for speaker diarization module.
It improves speaker counting accuracy from 50% to 80% especially for short (less than 1 min) recordings.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2725 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2725/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2725/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2725/events | https://github.com/NVIDIA/NeMo/issues/2725 | 978,965,808 | MDU6SXNzdWU5Nzg5NjU4MDg= | 2,725 | Fine-tune stt_zh_quartznet15x5 model with custom dataset | {
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} | null | null | null | **Describe the bug**
1. Output reference is not correct on chinese, could it affect the model?
I follow the link below:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/10_ASR_CTC_Language_Finetuning.ipynb#scrollTo=x0i8hvt688hc
2. I have four gpus, But just use one;
**Steps/Code to reproduce bug**
step 1: I prepare the dataset ,
**manifest files:**
train_manifest="dataset/manifest_train.json"
test_manifest = "dataset/manifest_test.json"
dev_manifest = "dataset/manifest_dev.json"
**charset:**
train_charset = get_charset(train_manifest_data)
dev_charset = get_charset(dev_manifest_data)
test_charset = get_charset(test_manifest_data)
step 2: load model config , update the config
freeze_encoder = True
cfg.train_ds.manifest_filepath = "train_manifest,dev_manifest"
use_cer = True
log_prediction = True
model._wer.use_cer = use_cer
model._wer.log_prediction = log_prediction
step 3: train
**Expected behavior**
the reference part should be the correct sentence；
as you see ,the output is out-of-order ， I expect the out is 1 2 3 4 ,but the result is 2 1 3 4 ;
**Environment overview (please complete the following information)**
- Environment location: local server DGX V100
- Method of NeMo install: pip install
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version :ubuntu 18.04
- PyTorch version:pytorch 1.9.0
- Python version :python3.7
**Additional context**
This is my train script:
[nemo_train.txt](https://github.com/NVIDIA/NeMo/files/7046204/nemo_train.txt)
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} | null | null | null | Hello,
I trained VAD model and Speaker Net model to get timestamp of who is speaker in audio.
After speaker diarize, i want to pass the signal of speech to ASR model to get both who is speaker and text of speech.
Some thing like that:
So, what is the solution to deploy speaker diarize model in production?
Thanks!
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Clustering diarizer class accepts paths to audiofiles instead of a manifest as all other models, however it still converts it to a NeMo-like manifest inside path2audio_files_to_manifest function.
**Describe the solution you'd like**
An option to provide a manifest instead of a list of paths, e.g.
```python
assert paths2audio_files is not None or manifest_path is not None
if manifest_path is None:
manifest = path2audio_files_to_manifest(paths2audio_files)
```
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} | null | null | null | Hi,
I'm working with NeMo to find out if a particular voice belongs to a specific person, to use as authentication. I saw several issues about and tested some scripts but I don't know if I'm on the right path.
From what I've read, the best script to follow would be this https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/voxceleb_eval.py where a score is calculated on two audio embeddings and on this score I could define if both are the same person.
For simplicity's sake leave it that way where I easily retrieve the embeddings of two audios and do the calculation:
```
verification_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="speakerverification_speakernet")
emb = verification_model.get_embedding('Person1.wav').cpu()[0]
emb2 = verification_model.get_embedding('Person2.wav').cpu()[0]
score = np.dot(emb, emb2) / ((np.dot(emb, emb) * np.dot(emb2, emb2)) ** 0.5)
score = (score + 1) / 2
```
When testing this script I realized that people of the same sex have a hard time getting it right... I have this doubt because the script seems too "simple" for a very complicated task that involves this voice analysis. This way what I'm doing is right? Or should I go the other way? | {
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} | null | null | null | I am training Conformer-medium on Multilingual Librispeech Dataset (~45000 hours).
During the training epoch, RAM is constantly increasing until no free space is left. Is there anything i can do to handle this?
Params:
RAM = 256G
Batch_size = 32
Num_workers = 8
Also when I experimented with 21000 hours, at the end of the epoch, the memory has released.
Here is the trainer params:
trainer:
gpus: -1
num_nodes: 1
max_epochs: 1000
max_steps: null
val_check_interval: 0.025
accelerator: ddp
accumulate_grad_batches: 20
gradient_clip_val: 0.0
amp_level: O0
precision: 32
log_every_n_steps: 1
progress_bar_refresh_rate: 1
resume_from_checkpoint: null
num_sanity_val_steps: 0
check_val_every_n_epoch: 1
sync_batchnorm: true
checkpoint_callback: false
logger: false
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https://api.github.com/repos/NVIDIA/NeMo/issues/2714 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2714/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2714/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2714/events | https://github.com/NVIDIA/NeMo/pull/2714 | 977,654,756 | MDExOlB1bGxSZXF1ZXN0NzE4MzY3OTc0 | 2,714 | Fix the feat_out param. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2713 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2713/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2713/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2713/events | https://github.com/NVIDIA/NeMo/issues/2713 | 977,241,790 | MDU6SXNzdWU5NzcyNDE3OTA= | 2,713 | Train and Embedding from Speaker Recognition and Verification | {
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} | null | null | null | Hello guys, I have two question.
First, I had a "small" dataset, around 20 hours for some tests. I successfully trained this dataset but I had some doubts about this train, if I train correctly or not. So, do you give an advice about the train in general? Like how much hours I need to train, an order to do the train, any recommendation if possible.
Second, I tested some audios with voxceleb_eval.py in two moments, with a unique embedding and with two different embedding (that sounds a good ideia for a test), and I got good results, but I need to do it if audios that won't be in train or test dataset. Like a test in transcritor, I trained a large dataset but when I will test the train, I put an audio that won't be in the train. Is that possible? What I need to do? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2712 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2712/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2712/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2712/events | https://github.com/NVIDIA/NeMo/issues/2712 | 977,220,919 | MDU6SXNzdWU5NzcyMjA5MTk= | 2,712 | RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! | {
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} | null | null | null | When I execute the training on hi-mia I get the following exception (noting that I removed some speakers from the dataset):
```
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
456 )
457
--> 458 self._run(model)
459
460 assert self.state.stopped
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)
754
755 # dispatch `start_training` or `start_evaluating` or `start_predicting`
--> 756 self.dispatch()
757
758 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
795 self.accelerator.start_predicting(self)
796 else:
--> 797 self.accelerator.start_training(self)
798
799 def run_stage(self):
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
94
95 def start_training(self, trainer: 'pl.Trainer') -> None:
---> 96 self.training_type_plugin.start_training(trainer)
97
98 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
142 def start_training(self, trainer: 'pl.Trainer') -> None:
143 # double dispatch to initiate the training loop
--> 144 self._results = trainer.run_stage()
145
146 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)
805 if self.predicting:
806 return self.run_predict()
--> 807 return self.run_train()
808
809 def _pre_training_routine(self):
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
867 with self.profiler.profile("run_training_epoch"):
868 # run train epoch
--> 869 self.train_loop.run_training_epoch()
870
871 if self.max_steps and self.max_steps <= self.global_step:
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
582 if should_check_val:
583 self.trainer.validating = True
--> 584 self.trainer.run_evaluation(on_epoch=True)
585 self.trainer.training = True
586
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, on_epoch)
1004
1005 # hook
-> 1006 self.evaluation_loop.on_evaluation_end()
1007
1008 # log epoch metrics
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/evaluation_loop.py in on_evaluation_end(self, *args, **kwargs)
100 self.trainer.call_hook('on_test_end', *args, **kwargs)
101 else:
--> 102 self.trainer.call_hook('on_validation_end', *args, **kwargs)
103
104 if self.trainer.state.fn != TrainerFn.FITTING:
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in call_hook(self, hook_name, *args, **kwargs)
1221 if hasattr(self, hook_name):
1222 trainer_hook = getattr(self, hook_name)
-> 1223 trainer_hook(*args, **kwargs)
1224
1225 # next call hook in lightningModule
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/trainer/callback_hook.py in on_validation_end(self)
225 """Called when the validation loop ends."""
226 for callback in self.callbacks:
--> 227 callback.on_validation_end(self, self.lightning_module)
228
229 def on_test_start(self):
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in on_validation_end(self, trainer, pl_module)
247 if skip:
248 return
--> 249 self.save_checkpoint(trainer)
250
251 def on_save_checkpoint(
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in save_checkpoint(self, trainer, unused)
296 # here we call each mode sequentially
297 # Mode 1: save the top k checkpoints
--> 298 self._save_top_k_checkpoint(trainer, monitor_candidates)
299 # Mode 2: save monitor=None checkpoints
300 self._save_none_monitor_checkpoint(trainer, monitor_candidates)
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in _save_top_k_checkpoint(self, trainer, monitor_candidates)
667
668 if self.check_monitor_top_k(trainer, current):
--> 669 self._update_best_and_save(current, trainer, monitor_candidates)
670 elif self.verbose:
671 epoch = monitor_candidates.get("epoch")
~/repos/algo/app/aliSrc/aliEnv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in _update_best_and_save(self, current, trainer, monitor_candidates)
718
719 _op = min if self.mode == "min" else max
--> 720 self.best_model_path = _op(self.best_k_models, key=self.best_k_models.get)
721 self.best_model_score = self.best_k_models[self.best_model_path]
722
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2711 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2711/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2711/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2711/events | https://github.com/NVIDIA/NeMo/pull/2711 | 977,120,287 | MDExOlB1bGxSZXF1ZXN0NzE3OTA5MDU1 | 2,711 | Add Small Test | {
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} | null | null | null | It is not a bug but a request in case some one know what the are the hardware (minimum) requirements to run a speaker verification on hi-mia dataset. I mean how much memory, gpu etc?
Indeed, did you have any time expectation to finish the training on hi-mia? one day, two days, etc | {
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} | null | null | null | **Describe the bug**
When I import nemo.collections.nlp module in my code, I got "AttributeError: module 'nemo.collections' has no attribute 'nlp'".
/Users/chendan/.virtualenvs/nemo/bin/python3.6 /Users/chendan/PycharmProjects/nemo_learn/tts_test.py
[NeMo W 2021-08-22 21:52:34 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
[NeMo W 2021-08-22 21:52:35 nemo_logging:349] /Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work
warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)
[NeMo W 2021-08-22 21:52:37 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
Traceback (most recent call last):
File "/Users/chendan/PycharmProjects/nemo_learn/tts_test.py", line 4, in <module>
import nemo.collections.nlp as nemo_nlp
File "/Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/nemo/collections/nlp/__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "/Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/nemo/collections/nlp/models/__init__.py", line 16, in <module>
from nemo.collections.nlp.models.duplex_text_normalization import (
File "/Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/nemo/collections/nlp/models/duplex_text_normalization/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.duplex_text_normalization.duplex_decoder import DuplexDecoderModel
File "/Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py", line 25, in <module>
import nemo.collections.nlp.data.text_normalization.constants as constants
File "/Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/nemo/collections/nlp/data/text_normalization/__init__.py", line 14, in <module>
from nemo.collections.nlp.data.text_normalization.decoder_dataset import TextNormalizationDecoderDataset
File "/Users/chendan/.virtualenvs/nemo/lib/python3.6/site-packages/nemo/collections/nlp/data/text_normalization/decoder_dataset.py", line 21, in <module>
import nemo.collections.nlp.data.text_normalization.constants as constants
AttributeError: module 'nemo.collections' has no attribute 'nlp'
Process finished with exit code 1
**Steps/Code to reproduce bug**
pip install nemo_toolkit[all]
import nemo.collections.nlp as nemo_nlp
**Expected behavior**
Importing nemo.collections.nlp be successful.
**Environment overview (please complete the following information)**
- Environment location: Mac OS
- Method of NeMo install: pip install nemo_toolkit[all]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Mac OS 10.15.7
- PyTorch version: 1.9.0
- Python version: 3.6.11
**Additional context**
No GPU
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} | null | null | null | **Describe the bug**
After installing using setup.py inside NVIDIA Pytorch container the following were not installed and had to be manually installed:
pip install braceexpand
pip install webdataset
pip install transformers
pip install pytorch_lightning
pip install rapidfuzz
pip install megatron
**Steps/Code to reproduce bug**
Download NVIDIA Pytorch container
Mount the NeMo directory
run python setup.py install in the NeMo dir
try to run data/import_datasets.py from the examples dir
In addition I am getting the following after installing the above:
from megatron import mpu
ImportError: cannot import name 'mpu' from 'megatron' (/opt/conda/lib/python3.8/site-packages/megatron/__init__.py)
**Expected behavior**
All required packages should be installed during install
**Environment overview (please complete the following information)**
- Docker
- python setup.py install
- sudo docker run --gpus all -it --rm -v HOMEDIR/NeMo:/NeMo --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:21.05-py3
**Environment details**
NA
**Additional context**
NVIDIA GTX 1050ti
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} | # Changelog
- Add mAES Transducer algorithm for further improved WER at the cost of speed of execution.
- Update configs with new decoding scheme
- Begin adding hooks for external LM rescoring
# References
- [Accelerating RNN Transducer Inference via Adaptive Expansion Search](https://ieeexplore.ieee.org/document/9250505)
Ported from ESPNet v1 | {
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} | The PR adds class evaluation during training and eval on multiple datasets. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2703 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2703/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2703/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2703/events | https://github.com/NVIDIA/NeMo/pull/2703 | 975,757,512 | MDExOlB1bGxSZXF1ZXN0NzE2ODYyMjA1 | 2,703 | Remove asr with transducers notebook time macro | {
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} | # Changelog
- Remove time macro from notebook | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2702 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2702/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2702/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2702/events | https://github.com/NVIDIA/NeMo/pull/2702 | 975,432,733 | MDExOlB1bGxSZXF1ZXN0NzE2NTgzMDE0 | 2,702 | vad_infer.py. Change the type of an argument "num_workers" from float to int. | {
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} | num_workers' type must be int, not float.
It induces TypeError even when using additional input as `--num_workers=20`
```
>>> python vad_infer.py --dataset=manifest.json --num_workers=20
Traceback (most recent call last):
File "vad_infer.py", line 171, in <module>
main()
File "vad_infer.py", line 106, in main
manifest_vad_input = prepare_manifest(config)
File "/workspace/NeMo/nemo/collections/asr/parts/utils/vad_utils.py", line 48, in prepare_manifest
p = Pool(processes=config['num_workers'])
File "/opt/conda/lib/python3.8/multiprocessing/context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 212, in __init__
self._repopulate_pool()
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 303, in _repopulate_pool
return self._repopulate_pool_static(self._ctx, self.Process,
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 318, in _repopulate_pool_static
for i in range(processes - len(pool)):
TypeError: 'float' object cannot be interpreted as an integer
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2701 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2701/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2701/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2701/events | https://github.com/NVIDIA/NeMo/pull/2701 | 975,148,025 | MDExOlB1bGxSZXF1ZXN0NzE2MzQ2Mjkx | 2,701 | Rnnt greedy_batch decoding fix | {
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} | # Bugfix
- Fixes a bug with an extreme edge case thereby bringing 1:1 parity with sample wise greedy decoding.
# Case
- Timestep T = 0, Label ID U = 0
- Batch size > 1 and greedy_batch decoding is used
- Some samples of the first batch emit a label but other samples do not. Therefore for some samples T=0, U=1 and other samples T=1, U=0 step should occur.
- To accommodate T=0, U=1 case, the model steps forward along label axes, but forgets to reset the state of the samples who should have followed the T=1, U=0 case.
- As such, the state of the T=1, U=0 samples degrades to an incorrect value, and ruins the first subword predicted by the samples of that batch.
- This has significant impact on WER but minute effect on CER.
# Fix notes
- Cover the edge case where T= 0, U= 0 explicitly
- Check if there is a mix of label forward and time forward actions and explicitly reset the states (to 0) of the samples where time forward action should take place. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2700 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2700/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2700/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2700/events | https://github.com/NVIDIA/NeMo/pull/2700 | 975,043,519 | MDExOlB1bGxSZXF1ZXN0NzE2MjU3MzYw | 2,700 | Add tts notebook | {
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} | Updates notebook numbering
Add new notebook to showcase how duration and pitch control work in fastpitch | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2699 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2699/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2699/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2699/events | https://github.com/NVIDIA/NeMo/pull/2699 | 974,957,583 | MDExOlB1bGxSZXF1ZXN0NzE2MTg0MzQz | 2,699 | Allow non divisible split_size | {
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} | # Bugfix
- Text fixes for ASR and Core notebooks
Signed-off-by: smajumdar <[email protected]> | {
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} | Fixes a bug in FastPitchDataset
Fixes a bug when training FastPitch without aligner
Updates TTS CI to be catch more bugs | {
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} | null | null | null | Recently, I want to convert my NeMo ASR model to TensorRT. My model was trained with NeMo v0.10.2 and when I tried to quantify it to int8, it came up with an error:
> [TensorRT] WARNING: Int8 calibration in TensorRT won't be used on networks containing boolean operators or loops. Please use quantization-aware training to generate a network with Quantize/Dequantize nodes.
Then, I transform my model into NeMo v1.2.0 following [the code here](https://github.com/NVIDIA/NeMo/discussions/1680#discussioncomment-321962). It works well without any quantification. But there are still some errors when converting to int8 following [sample code](https://github.com/NVIDIA/NeMo/tree/main/examples/asr/quantization) without any changes except model weight parameters.
> [TensorRT] ERROR: ModelImporter.cpp:720: While parsing node number 39 [QuantizeLinear -> "687"]:
[TensorRT] ERROR: ModelImporter.cpp:721: --- Begin node ---
[TensorRT] ERROR: ModelImporter.cpp:722: input: "encoder.encoder.1.mconv.1.conv.weight"
input: "684"
input: "2973"
output: "687"
name: "QuantizeLinear_39"
op_type: "QuantizeLinear"
attribute {
name: "axis"
i: 0
type: INT
}
[TensorRT] ERROR: ModelImporter.cpp:723: --- End node ---
[TensorRT] ERROR: ModelImporter.cpp:726: ERROR: builtin_op_importers.cpp:1039 In function QuantDequantLinearHelper:
[6] Assertion failed: scaleAllPositive && "Scale coefficients must all be positive"
[TensorRT] ERROR: 4: [network.cpp::validate::2411] Error Code 4: Internal Error (Network must have at least one output)
[TensorRT] ERROR: 2: [builder.cpp::buildSerializedNetwork::417] Error Code 2: Internal Error (Assertion enginePtr != nullptr failed.)
And there are also many warnings like
> I0819 14:32:00.546462 140236154607360 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0819 14:32:00.546510 140236154607360 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0819 14:32:00.546570 140236154607360 tensor_quantizer.py:105] Creating Max calibrator
I0819 14:32:00.546631 140236154607360 tensor_quantizer.py:105] Creating Max calibrator
I0819 14:32:00.546823 140236154607360 tensor_quantizer.py:105] Creating Max calibrator
Is there anything wrong? I would appreciate it if any help is offered! | {
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} | Signed-off-by: Ghasem Pasandi <[email protected]> | {
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} | Fixes #2686
When a trained IntentSlotClassificationModel is exported via `save_to` the resulting archive includes BERT configuration artifacts. During `IntentSlotClassificationModel.restore_from` using the saved archived the BERT model should be initialized using these configuration artifacts. However, because the model restore state is reset before initializing the BERT model these artifacts cannot be found.
Delaying the resetting of the restore state until after the BERT model is initialized resolves the problem. | {
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} | 1. Added support for SentencePiece and HF tokenizers when training LMs to fix the bug (https://github.com/NVIDIA/NeMo/issues/2544#issuecomment-899922327)
2. Removed obsolete text post-processing in AutoTokenizer | {
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} | null | null | null | **Describe the bug**
Installing `requirements_nlp.txt` is not enough to import `nemo.collections.nlp`.
**Steps/Code to reproduce bug**
1. Create new `conda` `Python 3.8` environment
2. Install PyTorch 1.8.2
3. Run `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]`
4. Run code
```python
from nemo.collections import nlp as nemo_nlp
```
5. You will get traceback
```
[NeMo W 2021-08-17 21:51:45 optimizers:47] Apex was not found. Using the lamb optimizer will error out.
[NeMo W 2021-08-17 21:51:46 __init__:22] `pynini` is not installed !
Please run the `nemo_text_processing/setup.sh` scriptprior to usage of this toolkit.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/nlp/__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/nlp/models/__init__.py", line 16, in <module>
from nemo.collections.nlp.models.duplex_text_normalization import (
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/nlp/models/duplex_text_normalization/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.duplex_text_normalization.duplex_decoder import DuplexDecoderModel
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py", line 21, in <module>
from nemo_text_processing.text_normalization.normalize_with_audio import PYNINI_AVAILABLE, NormalizerWithAudio
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo_text_processing/text_normalization/normalize_with_audio.py", line 25, in <module>
from nemo.collections.asr.metrics.wer import word_error_rate
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/asr/modules/__init__.py", line 15, in <module>
from nemo.collections.asr.modules.audio_preprocessing import (
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 24, in <module>
from nemo.collections.asr.parts.preprocessing.features import FilterbankFeatures
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/__init__.py", line 16, in <module>
from nemo.collections.asr.parts.preprocessing.features import (
File "/home/lab/anaconda3/envs/tmp/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 37, in <module>
import librosa
ModuleNotFoundError: No module named 'librosa'
```
**Expected behavior**
No errors
**Environment overview (please complete the following information)**
- Environment location: Bare metal
- Method of NeMo install: `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.8.2
- Python version: 3.8
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} | null | null | null | **Overview**
I am unable to restore an IntentAndSlotClassificationModel from a `.nemo` file using the static `IntentAndSlotClassificationModel.restore_from(model_path)`.
**Steps to reproduce**
These steps rely heavily upon the existing [Joint_Intent_and_Slot_Classification](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb) tutorial.
1. Prepare training data
a. Create a temporary directory, /tmp/nlu_evaluation, and two sub-directories: /tmp/nlu_evaluation/conf and /tmp/nlu_evaluation/data.
b. Download [import_datasets.py](https://raw.githubusercontent.com/NVIDIA/NeMo/v1.2.0/examples/nlp/intent_slot_classification/data/assistant_utils.py) and [import_datasets.py](https://raw.githubusercontent.com/NVIDIA/NeMo/v1.2.0/examples/nlp/intent_slot_classification/data/assistant_utils.py) to /tmp/nlu_evaluation.
c. Download [master.zip](https://github.com/xliuhw/NLU-Evaluation-Data/archive/master.zip) and extract it in /tmp/nlu_evaluation/data
d. Process the evaluation data:
`python /tmp/nlu_evaluation/import_datasets.py --dataset_name=assistant --source_dir=/tmp/nlu_evaluation/NLU-Evaluation-Data-master --target_dir=/tmp/nlu_evaluation/nemo_format`
e. Download [intent_slot_classification_config.yaml](https://raw.githubusercontent.com/NVIDIA/NeMo/v1.2.0/examples/nlp/intent_slot_classification/conf/intent_slot_classification_config.yaml) to /tmp/nlu_evaluation/conf
2. Train and save the model
Run the following Python code
```
import nemo.collections.nlp as nemo_nlp
from omegaconf import OmegaConf
config = OmegaConf.load('/tmp/nlu_evaluation/conf/intent_slot_classification_config.yaml')
config.model.data_dir = '/tmp/nlu_evaluation/data/nemo_format')
config.trainer.gpus = 1
config.trainer.precision = 16
config.trainer.accelerator = None
config.trainer.max_epochs = 5
trainer = pl.Trainer(**config.trainer)
model = nemo_nlp.models.IntentSlotClassificationModel(config.model, trainer=trainer)
trainer.fit(model)
model.save_to('/tmp/nlu_evaluation/model.nemo')
```
3. Restore the saved model
Run the following Python code
```
import nemo.collections.nlp as nemo_nlp
nemo_nlp.models.IntentSlotClassificationModel.restore_from('/tmp/nlu_evaluation/model.nemo')
```
**Expected behavior**
The model is loaded successfully, without errors or exceptions
**Actual behavior**
The model fails to load because a TypeError is raised.
```
File ".../lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 429, in restore_from
return super().restore_from(restore_path, override_config_path, map_location, strict, return_config)
File ".../lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 481, in restore_from
return cls._default_restore_from(restore_path, override_config_path, map_location, strict, return_config)
File ".../lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 434, in _default_restore_from
instance = cls.from_config_dict(config=conf)
File ".../lib/python3.8/site-packages/nemo/core/classes/common.py", line 472, in from_config_dict
instance = cls(cfg=config)
File ".../lib/python3.8/site-packages/nemo/collections/nlp/models/intent_slot_classification/intent_slot_classification_model.py", line 56, in __init__
self.setup_tokenizer(cfg.tokenizer)
File ".../lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 111, in setup_tokenizer
vocab_file = self.register_artifact(config_path='tokenizer.vocab_file', src=cfg.vocab_file)
File ".../lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 62, in register_artifact
return super().register_artifact(config_path, src, verify_src_exists=verify_src_exists)
File ".../lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 222, in register_artifact
return_path = os.path.abspath(os.path.join(_NEMO_FILE_FOLDER, src[5:]))
File ".../lib/python3.8/posixpath.py", line 76, in join
a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
**Environment overview (please complete the following information)**
- NeMo version: 1.2.0
- Environment location: bare metal
- Method of NeMo install: pip
**Environment details**
- OS: Ubuntu 20.04
- PyTorch 1.8.1
- Python 3.8
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} | # Bugfix
- Fix a gradient issue with Voice App demo
Signed-off-by: smajumdar <[email protected]> | {
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} | The config is the final part of the addition of [CarneliNet](https://arxiv.org/abs/2107.10708) model to NeMo repository.
The base model is chosen to be 386, providing a good balance between size and quality. The description on what has to be changed to obtain models of different sizes should be enough to reproduce the results.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2680 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2680/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2680/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2680/events | https://github.com/NVIDIA/NeMo/issues/2680 | 972,613,225 | MDU6SXNzdWU5NzI2MTMyMjU= | 2,680 | Cut long dataset | {
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} | null | null | null | Hi,
Assuming I have a large volume of data, around 10,000 hours, doing this on a relatively weak gpu would take a long time, more than months...
If I want, can I split my base into several parts and do a workout for each of them, always restoring the model from the last workout I did? Or is it better if I do the full base training? For this training in parts, do I have to worry about a parameter?
I'm building a dataSet in Portuguese and I'm facing this problem... | {
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} | null | null | null | Installation：
!sudo apt-get update
!sudo apt-get install -y libsndfile1 ffmpeg
!pip install Cython
!pip install nemo_toolkit['all']
Successfully installed
However, using from nemo.collections import nlp as nemo_nlp will cause problems
ttributeError Traceback (most recent call last)
<ipython-input-4-7d6a210455cf> in <module>
----> 1 from nemo.collections import nlp as nemo_nlp
/opt/conda/envs/python3/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
/opt/conda/envs/python3/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,
/opt/conda/envs/python3/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
/opt/conda/envs/python3/lib/python3.6/site-packages/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py in <module>
23 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq
24
---> 25 import nemo.collections.nlp.data.text_normalization.constants as constants
26 from nemo.collections.nlp.data.text_normalization import TextNormalizationDecoderDataset
27 from nemo.collections.nlp.models.duplex_text_normalization.utils import is_url
/opt/conda/envs/python3/lib/python3.6/site-packages/nemo/collections/nlp/data/text_normalization/__init__.py in <module>
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
---> 14 from nemo.collections.nlp.data.text_normalization.decoder_dataset import TextNormalizationDecoderDataset
15 from nemo.collections.nlp.data.text_normalization.tagger_dataset import TextNormalizationTaggerDataset
16 from nemo.collections.nlp.data.text_normalization.test_dataset import TextNormalizationTestDataset
/opt/conda/envs/python3/lib/python3.6/site-packages/nemo/collections/nlp/data/text_normalization/decoder_dataset.py in <module>
19 from transformers import PreTrainedTokenizerBase
20
---> 21 import nemo.collections.nlp.data.text_normalization.constants as constants
22 from nemo.collections.nlp.data.text_normalization.utils import read_data_file
23 from nemo.core.classes import Dataset
AttributeError: module 'nemo.collections' has no attribute 'nlp' | {
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