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} | 1. A parameter `--input_text` of the script `punctuate_capitalize_infer.py` was not working because of wrong implementation of data reading. Fixed.
2. Add parameter `--save_labels_instead_of_text` to script `punctuate_capitalize_infer.py` and parameter `return_labels` to `PunctuationCapitalizationModel.add_punctuation_capitalization` method. Useful feature in my opinion.
3. Remove device choice from `PunctuationCapitalizationModel.add_punctuation_capitalization` method. Device choice should be done in `main` function.
4. Add parameter `--device` to the script `punctuate_capitalize_infer.py`. | {
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} | null | null | null | Hi, I noticed that in the training pipelines, lexicon mapping of word spelling is not part of the workflow, I noticed in some other pipelines such as flashlight, they use it to improve the accuracy of decoding. For NeMo, is there some similar executions like lexicon mapping? or if not, is there any plan on including it in the future? Thank you. | {
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} | null | null | null | I noticed that for ASR training batch size setup, I noticed that there is a trick to set the accumulate_grad_batches value to cater for large batches, the performance is still quite different between set batch size = 32 vs. set batch size =16 with accumulate_grad_batches = 2. Although in theory it should work, but when I do the training for QuartzNet15x5-Zh-NR.nemo, I found if using batch size = 32, I can get loss < 20 in about 10 epochs, whereas if I use batch size =16 with accumulate_grad_batches = 2, I train for 100 epochs, the loss level is at 67 (which means the training diverges).
Does this mean, we would have to use high RAM GPUs to train the models? Does anyone observe the similar issue before?
below is a sample setup for batch size, this is because my server couldn't cater for batchsize =32, therefore, I use this trick, but the training metrics was very bad.
cfg = copy.deepcopy(char_model.cfg)
with open_dict(cfg):
cfg.train_ds.manifest_filepath = f"{train_manifest_cleaned},{dev_manifest_cleaned}"
cfg.train_ds.labels = list(train_dev_set)
cfg.train_ds.normalize_transcripts = False
cfg.train_ds.batch_size = 16
cfg.train_ds.num_workers = 8
cfg.train_ds.pin_memory = True
cfg.train_ds.trim_silence = True
trainer = ptl.Trainer(gpus=4,
accelerator='ddp',
max_epochs=EPOCHS,
accumulate_grad_batches=2,
checkpoint_callback=False,
logger=False,
log_every_n_steps=5,
check_val_every_n_epoch=10)
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} | null | null | null | **Describe the bug**
Variables `hidden_steps` (line 306) and `_SUPPORTED_ARCH` (line 337) in https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/transformer/transformer_bottleneck.py are not defined.
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- Update script to generalize transcript to all ASR models (CTC/RNNT Char/Subword) | {
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} | null | null | null | Hello,
I am trying to run the script self_alignment_pretraining.py (location - https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/entity_linking/self_alignment_pretraining.py)
I keep getting the error: ModuleNotFoundError: No module named 'nemo'
I have tried installing the nemo toolkit using the following:
!pip install git+https://github.com/NVIDIA/NeMo.git
and
BRANCH = 'r1.3.0'
#!pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]
and
!pip install nemo
!pip install nemo_toolkit
!pip install tensorboard==1.15.0
But continue to get the same error.
**Environment details**
- OS version - I am running it on spyder on my local laptop (MacBook Pro - macOS Big Sur version 11.6), no GPU being used.
- PyTorch version - torch version 1.9.1
- Python version - Python 3.6.8 :: Anaconda, Inc.
Thank you.
Apologies if I have provided insufficient information, I am fairly new to programming.
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} | null | null | null | **Describe the bug**
I've encountered this using the [example config provided ](https://github.com/NVIDIA/NeMo/blob/ed2005eda9470cb2c7c42e7ca1bd6b2e5b7a8479/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml#L28)for the PunctuationCapitalizationModel. Currently, the default trainer params are:
```
trainer:
gpus=1
amp_level: O0 # O1/O2 for mixed precision
precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0
```
I.e. the value for the precision param should be ignored when using AMP.FP32 training. However, if you try to restore a model (e.g. `punctuation-bert-en`) by overriding `trainer.gpus=0` with the above configuration, the following error is triggered:
```
Traceback (most recent call last):
File "/home/lhr/tmp/textNormalization/text_normalization/punctuation_capitalization/profiling.py", line 193, in <module>
m1, m2 = init_nemo_model()
File "/home/lhr/tmp/textNormalization/text_normalization/punctuation_capitalization/profiling.py", line 141, in init_nemo_model
trainer = pl.Trainer(**cfg.trainer)
File "/home/lhr/.conda/envs/nemo-source/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/env_vars_connector.py", line 40, in insert_env_defaults
return fn(self, **kwargs)
File "/home/lhr/.conda/envs/nemo-source/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 354, in __init__
self.accelerator_connector = AcceleratorConnector(
File "/home/lhr/.conda/envs/nemo-source/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 162, in __init__
self.accelerator = self.select_accelerator()
File "/home/lhr/.conda/envs/nemo-source/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 700, in select_accelerator
accelerator = acc_cls(training_type_plugin=self.training_type_plugin, precision_plugin=self.precision_plugin)
File "/home/lhr/.conda/envs/nemo-source/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 333, in precision_plugin
self._precision_plugin = self.select_precision_plugin()
File "/home/lhr/.conda/envs/nemo-source/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 555, in select_precision_plugin
raise MisconfigurationException(
pytorch_lightning.utilities.exceptions.MisconfigurationException: You have asked for native AMP on CPU, but AMP is only available on GPU.
```
I needed to additionally override `trainer.precision=32` in order to restore the model, suggesting that the precision parameter is not actually ignored for `amp_level=O0`.
**Expected behavior**
That the value of `trainer.precision` is ignored when `amp_level=O0`.
**Steps/Code to reproduce bug**
```
from omegaconf import OmegaConf
import sys
import pytorch_lightning as pl
from nemo.collections.nlp.models import PunctuationCapitalizationModel
def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
model = PunctuationCapitalizationModel(cfg.model, trainer)
if __name__ == '__main__':
sys.argv = [f"profiling.py",
f"model.language_model.pretrained_model_name=bert-base-uncased",
"model.dataset.data_dir=PATH_TO_DATA",
"trainer.gpus=0",
# "trainer.precision=32" # need to toggle this
]
cfg = OmegaConf.load('conf/punctuation_capitalization_config.yaml')
cli_conf = OmegaConf.from_cli()
cfg = OmegaConf.merge(cfg, cli_conf)
main(cfg)
```
**Environment overview (please complete the following information)**
- Environment location: venv on local machine
- Method of NeMo install: from source
**Environment details**
- OS version - Ubuntu 18.04.5 LTS
- PyTorch version - 1.9.0
- Python version - 3.8.11
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} | null | null | null | Hello!
Need help with some moments:
1. How to perform GPU batched inference with trained model?
2. Is it possible to make MatchboxNet architecture more heavy? Encountered problem that https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v2.yaml was stucked on ~50% accuracy on clean audio commands (38 labels, trained from scratch, 100 epochs, batch size 512). | {
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} | Fixes export for TransformerDecoder.
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} | * Fix DALI log default floor and normalization formula
Signed-off-by: Joaquin Anton <[email protected]>
* Fix style
Signed-off-by: Joaquin Anton <[email protected]>
(cherry picked from commit ad75db34c816bff23f64de7a875d89c50c319c85) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2873 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2873/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2873/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2873/events | https://github.com/NVIDIA/NeMo/issues/2873 | 1,004,602,342 | I_kwDOC_bI7s474QPm | 2,873 | "ASR_with_NeMo.ipynb" TypeError: TypeError: NeuralTypeComparisonResult.INCOMPATIBLE : Input type expected : axes: (batch, time); elements_type: AudioSignal input param_0 : freq: 16000 Input type found : axes: (batch, time); elements_type: VoidType | {
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} | null | null | null | Hi There,
I'm trying to run "ASR_with_NeMo.ipynb" in tutorials directory. In the section `Using an Out-of-the-Box Model`, I can successfully load `QuartzNet15x5Base-En`, but when I run `quartznet.transcribe(paths2audio_files=files)`, I get the following error:
```
TypeError: NeuralTypeComparisonResult.INCOMPATIBLE :
Input type expected : axes: (batch, time); elements_type: AudioSignal
input param_0 : freq: 16000
Input type found : axes: (batch, time); elements_type: VoidType
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-10-fa3a7fc9cc2a> in <module>
1 files = ['./an4/wav/an4_clstk/mgah/cen2-mgah-b.wav']
----> 2 for fname, transcription in zip(files, quartznet.transcribe(paths2audio_files=files)):
3 print(f"Audio in {fname} was recognized as: {transcription}")
~/anaconda3/envs/nemo/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
26 def decorate_context(*args, **kwargs):
27 with self.__class__():
---> 28 return func(*args, **kwargs)
29 return cast(F, decorate_context)
30
NeMo/nemo/collections/asr/models/ctc_models.py in transcribe(self, paths2audio_files, batch_size, logprobs, return_hypotheses)
236 temporary_datalayer = self._setup_transcribe_dataloader(config)
237 for test_batch in tqdm(temporary_datalayer, desc="Transcribing"):
--> 238 logits, logits_len, greedy_predictions = self.forward(
239 input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device)
240 )
NeMo/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
770
771 # Perform rudimentary input checks here
--> 772 instance._validate_input_types(input_types=input_types, ignore_collections=self.ignore_collections, **kwargs)
773
774 # Call the method - this can be forward, or any other callable method
NeMo/nemo/core/classes/common.py in _validate_input_types(self, input_types, ignore_collections, **kwargs)
208 for i, dict_tuple in enumerate(value.neural_type.elements_type.type_parameters.items()):
209 error_msg.append(f' input param_{i} : {dict_tuple[0]}: {dict_tuple[1]}')
--> 210 raise TypeError("\n".join(error_msg))
211
212 # Perform input ndim check
TypeError: NeuralTypeComparisonResult.INCOMPATIBLE :
Input type expected : axes: (batch, time); elements_type: AudioSignal
input param_0 : freq: 16000
Input type found : axes: (batch, time); elements_type: VoidType
```
Thanks in advance for your help.
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} | # Chanelog
- Upperbound PTL until https://github.com/PyTorchLightning/pytorch-lightning/pull/9642 is available in PTL 1.4.8
Signed-off-by: smajumdar <[email protected]> | {
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} | …n (#2858)
Signed-off-by: Joaquin Anton <[email protected]>
Co-authored-by: Somshubra Majumdar <[email protected]>
(cherry picked from commit 6987b6cfc9c9769f61116d0ac5a49088798266f8) | {
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} | null | null | null | Hello everyone, how are you? I've been messing with Speaker_Recogniton_Verification.ipynb together with DiegoJota's issue: https://github.com/NVIDIA/NeMo/issues/2719. I got good results with the following script for extracting embs
```
verification_model = nemo_asr.models.ExtractSpeakerEmbeddingsModel.restore_from('Train_Verification.nemo')
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
```
As I said I got good results managing to impose a limit for scores higher than 0.7 being considered good within the recognition between two different audios from the same speaker, examples:
AudioCaio1.wav x AudioCaio2.wav score: 0.74455544
AudioCaio1.wav x AudioCaio3.wav score: 0.73555545
AudioCaio1.wav x AudioCaio4.wav score: 0.78555545
......
But in cases of being different speakers the scores are very close to 0.69, 0.63, 0.67.... examples:
AudioCaio1.wav x AudioDiego1.wav score: 0.695478
AudioCaio1.wav x AudioDiego2.wav score: 0.635495
AudioCaio1.wav x AudioAntonio1.wav score: 0.627564
AudioCaio1.wav x AudioAntonio2.wav score: 0.687899
AudioCaio1.wav x AudioJose1.wav score: 0.656455
AudioCaio1.wav x AudioJose2.wav score: 0.679564
.....
We arrived at this result through the training present in Speaker_Recogniton_Verification which we generate the "Train_Verification.nemo" used in the verification_model = nemo_asr.models.ExtractSpeakerEmbeddingsModel.restore_from() script above.
As the results saw that the error margin is very close, we want to increase this error margin. Would it be possible to improve the margin of error through a more diversified training? Or with audios of a certain size? Would you have any recommended amount of training base?
Should the people I'm evaluating be present within the training set?
We didn't find many recommendations or documentation talking about how to be trained in recognition matters.
For information - We use the script only for voices in Portuguese, we do not intend to use it for voices in English or other languages. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2861 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2861/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2861/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2861/events | https://github.com/NVIDIA/NeMo/pull/2861 | 1,002,849,150 | PR_kwDOC_bI7s4sD05B | 2,861 | add zero shot intent model | {
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} | - New model class ZeroShotIntentModel is a subclass of TextClassificationModel
- Allows training/inference with either three classes ("entailment", "contradiction", and "neutral") or two classes ("entailment" and "not_entailment")
- ZeroShotIntentDataset and ZeroShotIntentInferenceDataset are subclasses of GLUEDataset
- For inference, users can specify multi_class = True or False and can supply their own hypothesis template
- `list_available_models` is not yet implemented. Tutorial notebook also needs to be updated with the name(s) of pretrained models users can get from NGC. These do not exist yet. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2860 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2860/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2860/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2860/events | https://github.com/NVIDIA/NeMo/pull/2860 | 1,002,699,050 | PR_kwDOC_bI7s4sDTzK | 2,860 | Fix loading and resample audio in get_samples | {
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} | There are several bugs in this file:
1. librosa library is not imported.
2. librosa.core.resample is expecting float32 array and not int.
3. librosa.core.resample returns float32 array, so no need to re-convert it. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2859 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2859/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2859/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2859/events | https://github.com/NVIDIA/NeMo/pull/2859 | 1,002,531,076 | PR_kwDOC_bI7s4sCu96 | 2,859 | MixerTTS, MixerTTSDataset and small updates in tts tokenizers | {
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} | * Mixer-TTS (with extended version, Mixer-TTS-X): https://arxiv.org/abs/2110.03584
* MixerTTSDataset
* Small updates in nemo/collections/tts/torch/tts_tokenizers.py | {
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} | Fixes a bug in the calculation of the window stride uses for spectrogram calculation in the DALI implementation.
Fixes a bug in the generation of gaussian noise (dither). Generate noise with the shape of the audio, instead of a single sample (bug)
Signed-off-by: Joaquin Anton <[email protected]> | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Not a problem, just seeking some guidance on how to replicate the timestamp extraction code featured within the [Offline_ASR](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Offline_ASR.ipynb) notebook. Currently experiencing difficulties adapting the code from that notebook to my use-case (BPE tokenisation).
**Describe the solution you'd like**
Code from the Offline_ASR notebook, with BPE-centric adaptations.
**Describe alternatives you've considered**
As above.
Minimal example below, adapted and tweaked from original Offline_ASR notebook:
```
import nemo.collections.asr as nemo_asr
import numpy as np
def get_space_timestamps(output):
# get timestamps for space symbols - char log probs across time sequences
probs = output.y_sequence.detach().cpu().numpy()
spaces = []
state = ''
idx_state = 0
if np.argmax(probs[0]) == 1:
# tokenizer dependant; retrieve the "space" token index
state = 'space'
for idx in range(1, probs.shape[0]):
current_char_idx = np.argmax(probs[idx])
# and not blank
if state == 'space' and current_char_idx != 128:
spaces.append([idx_state, idx-1])
state = ''
if state == '':
if current_char_idx == 1:
state = 'space'
idx_state = idx
if state == 'space':
spaces.append([idx_state, len(pred)-1])
return spaces
def format_word_timestamps(transcript, space_timestamps, offset, time_stride):
# split the transcript into words
words = transcript.split()
# cut words, assign timestamp
formatted_words = []
pos_prev = 0
for j, spot in enumerate(space_timestamps):
pos_end = offset + (spot[0]+spot[1])/2*time_stride
# format words as records
formatted_words.append(
{'word': words[j], 'start': pos_prev, 'end': pos_end})
pos_prev = pos_end
return formatted_words
# load model
asr_model = nemo_asr.models.ASRModel.from_pretrained("stt_en_conformer_ctc_large")
time_stride = asr_model.cfg.preprocessor.window_size # shipped with model config?
offset = -0.18 # taken from notebook
# retrieve transcript and logprobs
output = asr_model.transcribe(paths2audio_files=['path/to/audio_file.wav'],
return_hypotheses=True)[0] # take first
spaces = get_space_timestamps(output)
# [[55, 55], [75, 75], [122, 124]]
time_formatted_words = format_word_timestamps(output.text, spaces, offset, time_stride)
# formats a very small portion of transcript:
# [{'word': 'in', 'start': 0, 'end': 1.195},
# {'word': 'this', 'start': 1.195, 'end': 1.695},
# {'word': 'type', 'start': 1.695, 'end': 2.895}]
```
**Additional context**
I feel like I'm probably doing something really silly here, some additional information comparing Quartznet example vocab/alignments and Conformer vocab/alignments:
```
# transcript
"in this type of system when people get their jobs because of a favor or because of somebody that they know not meaning that this in itself is corrupt that they're incompetent but they're beholdent on a system of favoritism of connectedness"
# stt_en_conformer_ctc_large - 128
conformer_vocab = ['<unk>', '▁', '##s', '##t', '##e', '##a', '##i', '##d', '##o', '##m', '##l', 'a', 'the', '##n', '##p', '##y', '##u', '##er', '##h', '##re', '##c', '##r', 'i', 's', '##ing',
"##'", 'to', '##k', '##g', 'and', '##f', '##an', '##ed', '##w', 'of', 'w', 'in', 'you', '##or', '##v', 'f', 'b', '##in', '##b', '##ar', '##en', '##ve', '##es', 'it', 'that',
'##al', '##ll', 'be', 'co', 'he', '##le', '##ch', 'do', 'we', '##ly', '##it', 'g', '##ent', '##ur', '##on', '##ic', 'c', 'on', '##un', '##th', 'ha', 'was', 't', 'is', 'for',
'ma', '##ri', 'know', 're', '##ra', 'th', '##us', '##ce', 'but', 'go', '##ro', 'mo', 'st', 'me', 'they', 'so', 'yeah', 'ca', '##ir', 'have', 'like', 'di', 'ho', '##ion', 'with',
'lo', 'not', 'no', 'de', 'ne', 'pa', '##ation', 'this', 'what', 'some', 'uh', 'pro', 'my', '##j', 'his', 'ex', 'just', 'um', 'po', 'la', 'think', 'she', '##ough', 'from', '##q', 'out', '##x', '##z']
# example alignments - 447
conformer_alignments = [128, 128, 128, 128, 128, 36, 128, 128, 128, 128, 128, 107, 128, 128, 128, 128, 128, 128, 128, 72, 128, 15, 128, 14, 4, 4, 128, 34, 128, 128, 128, 128, 128, 23, 128, 128, 15, 2, 128, 3, 128, 4, 9, 128, 128, 128, 128, 128, 128, 128, 35, 18, 45, 128, 128, 1, 14, 4, 4, 8, 14, 55, 128, 128, 61, 4, 128, 3, 128, 128, 12, 128, 93, 128, 128, 1, 113, 128, 8, 128, 43, 128, 128, 2, 128, 128, 128, 128, 128, 52, 128, 128, 20, 128, 5, 81, 4, 4, 128, 128, 128, 34, 128, 128, 128, 11, 128, 128, 128, 128, 40, 128, 128, 5, 128, 39, 128, 128, 38, 128, 128, 128, 1, 128, 128, 38, 128, 128, 128, 52, 128, 128, 20, 128, 5, 81, 4, 4, 128, 128, 128, 128, 128, 128, 34, 128, 128, 128, 128, 128, 128, 128, 128, 128, 109, 128, 128, 128, 43, 8, 128, 7, 15, 15, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 49, 128, 128, 128, 128, 128, 89, 128, 128, 128, 128, 77, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 101, 128, 128, 128, 128, 128, 88, 128, 128, 31, 128, 24, 128, 128, 49, 128, 128, 128, 128, 107, 128, 128, 128, 128, 128, 128, 36, 128, 128, 48, 128, 128, 128, 2, 4, 10, 10, 30, 128, 128, 128, 73, 128, 128, 53, 128, 21, 128, 128, 21, 21, 16, 14, 3, 3, 128, 49, 128, 128, 128, 128, 89, 128, 25, 19, 128, 128, 36, 128, 128, 128, 20, 8, 8, 9, 128, 14, 128, 4, 128, 3, 128, 128, 62, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 83, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 89, 128, 128, 25, 19, 128, 128, 128, 128, 128, 52, 128, 128, 128, 18, 8, 8, 10, 7, 7, 128, 128, 62, 128, 128, 128, 128, 128, 67, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 11, 128, 128, 128, 128, 23, 128, 15, 2, 128, 3, 128, 4, 128, 9, 128, 128, 128, 128, 128, 128, 34, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 40, 128, 5, 5, 39, 128, 128, 38, 128, 60, 128, 128, 128, 6, 2, 2, 128, 128, 9, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 34, 128, 128, 128, 128, 53, 128, 13, 128, 128, 13, 128, 4, 20, 3, 128, 128, 32, 128, 13, 128, 47, 2, 128, 128, 128, 128, 128, 128, 128]
# quartznet - 28
quartznet_vocab = [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', "'"]
# quartznet alignments - 889
quartznet_alignments = [28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 9, 14, 28, 28, 28, 28, 28, 0, 28, 20, 8, 9, 28, 19, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 28, 28, 28, 28, 20, 28, 25, 28, 16, 5, 28, 28, 28, 28, 0, 0, 15, 6, 0, 0, 0, 0, 0, 0, 0, 28, 28, 28, 28, 28, 28, 19, 28, 25, 28, 19, 28, 28, 28, 28, 28, 20, 28, 5, 28, 13, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 8, 5, 14, 28, 0, 0, 0, 0, 0, 0, 28, 16, 5, 15, 16, 16, 12, 5, 28, 28, 0, 0, 0, 0, 0, 7, 5, 20, 28, 0, 0, 0, 0, 20, 8, 5, 9, 18, 28, 28, 0, 0, 0, 0, 28, 28, 10, 28, 15, 28, 2, 28, 28, 19, 19, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 28, 28, 2, 5, 28, 28, 28, 28, 28, 3, 1, 21, 19, 5, 5, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 6, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 28, 28, 28, 28, 28, 6, 28, 1, 28, 28, 28, 28, 22, 28, 15, 28, 18, 18, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 15, 18, 0, 0, 0, 0, 0, 0, 28, 28, 2, 5, 28, 28, 28, 28, 3, 28, 1, 21, 19, 5, 5, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 28, 28, 19, 19, 15, 13, 5, 5, 28, 28, 2, 28, 15, 4, 28, 28, 28, 25, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 8, 1, 20, 0, 0, 0, 0, 0, 0, 0, 20, 8, 5, 25, 28, 0, 0, 0, 0, 0, 0, 11, 11, 14, 15, 28, 23, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 15, 20, 0, 0, 0, 0, 0, 0, 28, 13, 28, 5, 1, 1, 28, 14, 28, 9, 28, 14, 7, 28, 28, 0, 0, 20, 8, 1, 20, 0, 0, 0, 0, 0, 0, 0, 20, 8, 9, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 14, 28, 0, 0, 0, 28, 28, 9, 20, 28, 28, 28, 28, 28, 28, 19, 28, 5, 28, 12, 28, 6, 28, 28, 28, 0, 0, 28, 9, 19, 28, 0, 0, 0, 0, 0, 28, 28, 3, 28, 15, 28, 18, 28, 28, 18, 18, 21, 28, 16, 28, 20, 28, 28, 28, 28, 28, 28, 28, 5, 4, 28, 0, 0, 0, 0, 0, 0, 28, 28, 22, 28, 5, 28, 18, 18, 28, 28, 25, 28, 28, 0, 28, 9, 14, 28, 28, 28, 28, 28, 28, 28, 3, 28, 15, 28, 13, 28, 28, 16, 28, 5, 28, 28, 28, 20, 28, 5, 28, 14, 20, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 21, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 20, 8, 5, 25, 28, 28, 18, 18, 28, 28, 28, 28, 28, 28, 28, 0, 0, 28, 28, 28, 2, 28, 5, 28, 28, 28, 28, 28, 8, 28, 15, 28, 12, 28, 28, 4, 4, 28, 9, 28, 14, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 28, 28, 28, 28, 28, 19, 28, 25, 28, 19, 19, 28, 28, 28, 20, 28, 5, 28, 13, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 15, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 28, 28, 6, 28, 1, 28, 28, 28, 28, 22, 28, 15, 18, 18, 28, 9, 28, 28, 28, 28, 20, 28, 9, 28, 19, 28, 28, 28, 28, 28, 28, 28, 13, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 6, 0, 0, 0, 0, 0, 0, 0, 0, 28, 28, 3, 28, 15, 14, 28, 28, 28, 14, 28, 5, 28, 3, 28, 28, 20, 28, 5, 28, 4, 28, 28, 28, 28, 14, 28, 5, 28, 19, 28, 19, 28, 28, 28, 28, 28]
```
Any help much appreciated! | {
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2. Validating encoder/decoder are with the same hidden_size in non-bottleneck parent class. | {
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- post-processing added to neural TN/ITN and WFST TN
- deleted redundant entries from Ru data | {
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} | null | null | null | Hello,
When running the same training or even the same validation script without DALI, we get the expected WER.
However, when we enable DALI using the same configuration, we get much worse WER.
I have created a Google Colab example that replicates this issue, which can be found here:
https://colab.research.google.com/drive/1Rz42EeQVDHhTso3kWvBp1wQc6x1FxXgY?usp=sharing
To make the debugging more straightforward, I have disabled the training on the example.
So simply, it performs testing with DALI and without DALI.
This is related to issue #2817 , which was fixed by @titu1994 . | {
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} | null | null | null | Should both functions be something like `fst @ SINGULAR_TO_PLURAL` instead of `SINGULAR_TO_PLURAL @ fst`? Besides, it seems that these functions are not used actually.
https://github.com/NVIDIA/NeMo/blob/main/nemo_text_processing/text_normalization/en/graph_utils.py#L129
https://github.com/NVIDIA/NeMo/blob/main/nemo_text_processing/text_normalization/en/graph_utils.py#L129 | {
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} | null | null | null | **Describe the bug**
python nmt_service.py occcur that
config.json
en-zh:"NGC/nmt_en_zh_transformer6x6"
zh-en:"NGC/nmt_zh_en_transformer6x6"
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} | null | null | null | Hello everybody, I trying to run 'Joint_Intent_and_Slot_Classification.ipynb' and looks okay until the train part, when starts the evaluation part, return this error.
This script is on 'r1.3.0' version, and everything is okay when I run on 'r1.2.0'. I just run the original script in both cases. Could be a bug? Am I doing something wrong? | {
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} | null | null | null | **Describe the bug**
**speaker_reco_infer.py loads the model and manifestfiles and then breaks,
I guess its again a pytorch issue? wanted to use the model from yesterday:**
[NeMo W 2021-09-17 12:18:42 patch_utils:49] torch.stft() signature has been updated for PyTorch 1.7+
Please update PyTorch to remain compatible with later versions of NeMo.
RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension, but got: padding (256, 256) at dimension 2 of input [1, 1, 2]
**Steps/Code to reproduce bug**
Container:
nvcr.io/nvidia/nemo:1.2.0
but installed:
python -m pip install git+https://github.com/NVIDIA/NeMo.git@'main'
python -m pip install pytorch_lightning==1.4.2
(like used/working in the goolge colab,
I also tried nemo 1.2 and pytorch-lightning 1.3.8 and nemo 1.3 and recent 1.4.7 later on)
run:
https://github.com/NVIDIA/NeMo/blob/48fe9e69feba7651694fd6ae0a096a0655ed601c/examples/speaker_tasks/recognition/speaker_reco_infer.py
with:
model, train.json from:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
added:
test.json and bonian.wav
{"audio_filepath": "bonian.wav", "offset": 0, "duration": 11.370666666666667, "label": ""}
**Expected behavior**
working :-)
**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 nemo 1.2 container
- PyTorch version tryed 1.3.8, 1.4.2, 1.4.7
- Python version 3.8.10
**Additional context**
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_recognition/results.html
A little bit more explanation to the inference part would be nice,
like a link to the script that i was using here, and also how to use the embedding that is created at the end of the jupyter notebook
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} | null | null | null | I ran **Speaker_Diarization_Inference.ipynb** notebook for getting diarization results on my audio data using the pretrained model. But as my data is quite noisy, the diarization results are not at as expected. Is there any way I can finetune the diarization module for my data?
If you can provide a notebook for finetuning the model, it would be really helpful. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2838 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2838/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2838/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2838/events | https://github.com/NVIDIA/NeMo/pull/2838 | 998,893,826 | PR_kwDOC_bI7s4r3tPg | 2,838 | HiFiGAN and Tacotron finetuning, configurable dur and pitch loss coefficients for FastPitch | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2837 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2837/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2837/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2837/events | https://github.com/NVIDIA/NeMo/pull/2837 | 998,771,812 | PR_kwDOC_bI7s4r3W-p | 2,837 | Update collection of pretrained models for RNNT | {
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} | # Changelog
- Add pretrained checkpoints of RNNT models
- ContextNet RNNT trained on ASR SET
- ContextNet RNNT trained on MLS
- Conformer RNNT trained on ASR SET
- Conformer RNNT trained on MLS
- Add pretrained model for ECAPA TDNN (Speaker model)
Signed-off-by: smajumdar <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2836 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2836/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2836/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2836/events | https://github.com/NVIDIA/NeMo/pull/2836 | 998,546,939 | PR_kwDOC_bI7s4r2pE3 | 2,836 | New pretrained NMT model links | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2835 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2835/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2835/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2835/events | https://github.com/NVIDIA/NeMo/issues/2835 | 998,508,662 | I_kwDOC_bI7s47hAh2 | 2,835 | Maximum LR in ASR YAML is not used correctly | {
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} | null | null | null | Hello,
I am fine-tuning the conformer model, and I have noticed that the defined LR in the Yaml file is not used and another lower LR is actually used during training.
The defined LR is 0.005, but the actual reported LR on wandb is around 0.00000206 .
The warm-up rate is used correctly. Only the maximum LR is not correct during training.
Are you performing any kind of changes of the LR based on the number of the GPUs or the accumulate_grad_batches ?
```
# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding.
# Architecture and training config:
# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file.
# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one.
#
# +-------------+---------+---------+----------+------------+-----+
# | Model | d_model | n_heads | n_layers | time_masks | lr |
# +=============+=========+========+===========+============+=====+
# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 |
# +-------------+---------+--------+-----------+------------+-----+
# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 |
# +-------------+---------+--------+-----------+------------+-----+
# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 |
# +---------------------------------------------------------------+
#
# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2
# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence.
# With weight_decay=0.0, learning rate may need to get reduced to 2.0.
# You may find more info about Conformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-ctc
# Pre-trained models of Conformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html
# The checkpoint of the large model trained on LibriSpeech with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls
name: "Conformer-CTC-BPE-Small"
model:
sample_rate: 16000
log_prediction: true # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
train_ds:
manifest_filepath: /mnt/local/extra/users/ael/dl/data/en/train_filtered_flac.json
sample_rate: ${model.sample_rate}
batch_size: 8 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 4
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 17 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
is_tarred: false
#use_dali: true
validation_ds:
manifest_filepath:
- /home/jho/nemo/github_ahmed/NeMo/tasks/en/weighted_combined1/datasets/librispeech_dev_clean.json
- /home/jho/nemo/github_ahmed/NeMo/tasks/en/weighted_combined1/datasets/librispeech_dev_other.json
sample_rate: ${model.sample_rate}
batch_size: 8 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 4
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: ''
#use_dali: true
test_ds:
manifest_filepath:
- /home/jho/nemo/github_ahmed/NeMo/tasks/en/weighted_combined1/datasets/librispeech_dev_clean.json
- /home/jho/nemo/github_ahmed/NeMo/tasks/en/weighted_combined1/datasets/librispeech_dev_other.json
sample_rate: ${model.sample_rate}
batch_size: 8 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 4
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: ''
#use_dali: true
# recommend small vocab size of 128 or 256 when using 4x sub-sampling
# you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
tokenizer:
dir: /home/ael/Scripts/nemo/training/pretrained_models/stt_en_conformer_ctc_small/ # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
# you may use lower time_masks for smaller models to have a faster convergence
time_masks: 2 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 16
d_model: 176
# Sub-sampling params
subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory
subsampling_factor: 4 # must be power of 2
subsampling_conv_channels: -1 # -1 sets it to d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 4 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
optim:
name: adamw
lr: 0.005
# optimizer arguments
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 0.0
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: null
warmup_ratio: 0.05
min_lr: 1e-6
trainer:
gpus: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 15
max_steps: null # computed at runtime if not set
val_check_interval: 0.5 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: ddp
accumulate_grad_batches: 8
gradient_clip_val: 0.0
amp_level: O0 # O1/O2 for mixed precision
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
log_every_n_steps: 100 # Interval of logging.
progress_bar_refresh_rate: 10
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
num_sanity_val_steps: 1 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
checkpoint_callback: false # Provided by exp_manager
logger: false # Provided by exp_manager
plugins: ddp_sharded
exp_manager:
exp_dir: /mnt/local/extra/users/ael/dl/data/nemo/models/en/
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 3
# you need to set these two to True to continue the training
resume_if_exists: true
resume_ignore_no_checkpoint: true
# You may use this section to create a W&B logger
create_wandb_logger: true
wandb_logger_kwargs:
name: conformer-small-bpe-en-balanced-16k
project: asr-en
hydra:
run:
dir: /mnt/local/extra/users/ael/dl/data/nemo/models/en/
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2832 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2832/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2832/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2832/events | https://github.com/NVIDIA/NeMo/issues/2832 | 998,381,430 | I_kwDOC_bI7s47ghd2 | 2,832 | Add support for `LightningDataModule` | {
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] | null | null | 2021-09-16T16:02:18 | 2022-04-05T23:39:54 | 2022-04-05T23:39:54 | NONE | {
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} | null | null | null | One of the [recommended](https://pytorch-lightning.readthedocs.io/en/latest/guides/data.html) ways of dealing with data in pytorch-lightning is using the `LigtningDataModule`. This allows decoupling the model code from the data code. Then the training code would look something like this:
```python
trainer = Trainer(...)
model = MyModel(...)
data = MyDataModule(...)
trainer.fit(model, data)
```
The biggest blocker with using this at the moment is that the Nemo ModelPT abstract class requires you to implement all the data setup methods within the model itself, if you remove them an error is thrown.
I have removed the inheritance for the `MTEncDecModel` and moved all of the data preprocessing and loading to a LightningDataModule class and already observed a number of benefits:
- Previously all of the data partitions would be preprocessed and loaded into memory every time the Model class gets initialized. DataModule seems to be smarter where it only prepares the data right before it's needed. So for example validation sanity check runs and only then, right before training, does training data get loaded.
- As another addition to the point above, the data loading and preprocessing only happens after all of the DDP processes have been initialized allowing each process do to its own thing in parallel. Whereas before it seemed a lot more sequential and took a long time.
- The setup function allows adding functionality from `MTDataPreproc` module so that all of the data tasks live within a single place.
- It incentivizes us to not use trainer-specific parameters like `world_size` for data preprocessing as all of that should be handled by the DataLoader.
**Describe the solution you'd like**
Essentially there should at least be an option to use LigtningDataModule, and ideally, the models should use it by default to be more aligned with PyTorch recommendation.
**Describe alternatives you've considered**
You could also possibly solve the above error by implementing functions required by the abstract class without returning anything, but I am not sure how it would play with LightningDataModule, and it's not a very clean solution. (Came up with it while writing this up, will give it a go) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2830 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2830/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2830/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2830/events | https://github.com/NVIDIA/NeMo/issues/2830 | 998,268,768 | I_kwDOC_bI7s47gF9g | 2,830 | Quantization for CitriNet-1024-Gamma-0.25 | {
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] | null | null | 2021-09-16T14:16:01 | 2021-10-26T23:57:43 | 2021-10-26T23:57:42 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I was trying to convert the CitriNet-1024-Gamma-0.25 to TensorRT Quantized Model, while following the example workflow given under the quantization section but facing the following issue
**Steps/Code to reproduce bug**
root@nvidia-DGX-Station:/workspace/nemo/NeMo/examples/asr/quantization# python3 speech_to_text_calibrate.py --asr_model=citrinet-1024-gamma-0.25.nemo --dataset=test_hindi_navana.json
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
[NeMo W 2021-09-16 14:04:05 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torchaudio-0.7.0a0+42d447d-py3.8-linux-x86_64.egg/torchaudio/backend/utils.py:53: UserWarning: "sox" backend is being deprecated. The default backend will be changed to "sox_io" backend in 0.8.0 and "sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.
warnings.warn(
[NeMo W 2021-09-16 14:04:06 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 2021-09-16 14:04:07 speech_to_text_calibrate:80] Using local ASR model from citrinet-1024-gamma-0.25.nemo
[NeMo I 2021-09-16 14:04:19 mixins:147] Tokenizer SentencePieceTokenizer initialized with 72 tokens
[NeMo W 2021-09-16 14:04:19 modelPT:138] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: ./manifest/hindi/train_hindi_data.json
sample_rate: 16000
batch_size: 16
trim_silence: true
max_duration: 20.0
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
use_start_end_token: true
num_workers: 8
pin_memory: true
[NeMo W 2021-09-16 14:04:19 modelPT:145] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: ./manifest/hindi/test_hindi_data.json
sample_rate: 16000
batch_size: 8
shuffle: false
use_start_end_token: true
num_workers: 8
pin_memory: true
trim_silence: true
[NeMo W 2021-09-16 14:04:19 modelPT:151] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: ./manifest/hindi/test_hindi_data.json
sample_rate: 16000
batch_size: 8
shuffle: false
use_start_end_token: true
num_workers: 8
pin_memory: true
trim_silence: true
[NeMo I 2021-09-16 14:04:19 features:252] PADDING: 16
[NeMo I 2021-09-16 14:04:19 features:269] STFT using torch
I0916 14:04:20.338965 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.339134 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.339357 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.339587 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.341764 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.341858 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.342052 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.342261 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.343992 140410652182336 _utils.py:126] Input is fake quantized to 8 bits in QuantAdaptiveAvgPool1d with axis None!
I0916 14:04:20.344176 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
[NeMo W 2021-09-16 14:04:20 modelPT:138] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: ./manifest/hindi/train_hindi_data.json
sample_rate: 16000
batch_size: 16
trim_silence: true
max_duration: 20.0
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
use_start_end_token: true
num_workers: 8
pin_memory: true
[NeMo W 2021-09-16 14:04:20 modelPT:145] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: ./manifest/hindi/test_hindi_data.json
sample_rate: 16000
batch_size: 8
shuffle: false
use_start_end_token: true
num_workers: 8
pin_memory: true
trim_silence: true
[NeMo W 2021-09-16 14:04:20 modelPT:151] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: ./manifest/hindi/test_hindi_data.json
sample_rate: 16000
batch_size: 8
shuffle: false
use_start_end_token: true
num_workers: 8
pin_memory: true
trim_silence: true
[NeMo I 2021-09-16 14:04:20 features:252] PADDING: 16
[NeMo I 2021-09-16 14:04:20 features:269] STFT using torch
I0916 14:04:20.691268 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.691397 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.691546 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.691687 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.693689 140410652182336 _utils.py:72] Input is fake quantized to 8 bits in QuantConv1d with axis None!
I0916 14:04:20.693774 140410652182336 _utils.py:75] Weight is fake quantized to 8 bits in QuantConv1d with axis 0!
I0916 14:04:20.693904 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.694039 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
I0916 14:04:20.695321 140410652182336 _utils.py:126] Input is fake quantized to 8 bits in QuantAdaptiveAvgPool1d with axis None!
I0916 14:04:20.695476 140410652182336 tensor_quantizer.py:105] Creating Max calibrator
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
return target(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/conv_asr.py", line 166, in __init__
JasperBlock(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 658, in __init__
SqueezeExcite(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 342, in __init__
self.change_context_window(context_window=context_window)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 411, in change_context_window
if not isinstance(self.pool, quant_nn.QuantAdaptiveAvgPool1d(1)):
TypeError: isinstance() arg 2 must be a type or tuple of types
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "speech_to_text_calibrate.py", line 160, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "speech_to_text_calibrate.py", line 84, in main
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model, override_config_path=asr_model_cfg)
File "/opt/conda/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 "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 434, in _default_restore_from
instance = cls.from_config_dict(config=conf)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 472, in from_config_dict
instance = cls(cfg=config)
File "/opt/conda/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 "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 437, in from_config_dict
instance = hydra.utils.instantiate(config=config)
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
return instantiate_node(config, *args, recursive=_recursive_, convert=_convert_)
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 249, in instantiate_node
return _call_target(target, *args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 64, in _call_target
raise type(e)(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
return target(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/conv_asr.py", line 166, in __init__
JasperBlock(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 658, in __init__
SqueezeExcite(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 342, in __init__
self.change_context_window(context_window=context_window)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/jasper.py", line 411, in change_context_window
if not isinstance(self.pool, quant_nn.QuantAdaptiveAvgPool1d(1)):
TypeError: Error instantiating 'nemo.collections.asr.modules.conv_asr.ConvASREncoder' : isinstance() arg 2 must be a type or tuple of types
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
NGC: nvcr.io/nvidia/nemo:1.2.0
**Additional context**
Add any other context about the problem here.
Example: V100
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Apparently class `CharTokenizer` needs to be rewritten.
1. The tokenizer does not add special tokens such as `<EOS>` and `<CLS>` automatically, so it is not consistent with tokenizers like `yttm`.
2. To add special tokens, it is not enough to insert something like `"<EOS>"` into input text because substring `"<EOS>"` will be interpreted not like special token but as 5 tokens `['<', 'E', 'O', 'S', '>']`. So a class user has to identify a character (e.g. `'@'`) which does not occur in a tokenized text, insert it into the text in required positions, and pass this character as a special token parameter to the class constructor. It is inconvenient.
3. A vocabulary file cannot contain space character. If space character is present in `self.vocab` it will be replaced with an empty string. So space character has to be passed as a special token. This feature is not documented.
4. Special token attributes like `self.unk_token` are not defined but used in properties like `unk_id`.
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} | # TODO
- Revert this patch after CI update
Signed-off-by: smajumdar <[email protected]> | {
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] | null | null | 2021-09-15T17:26:58 | 2021-11-30T23:07:23 | 2021-11-30T23:07:23 | NONE | {
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} | null | null | null | I am facing issue when I train a tansformer LM, its giving attribute error 'NoneType' object has no attribute 'get. Please help me to resolve this
I have update the tansformer config file as given below
train_ds = "dummydata.txt"
vocab="vocab.txt"
config.model.train_ds.file_name = train_ds
config.model.validation_ds.file_name = train_ds
config.model.test_ds.file_name=train_ds
config.model.tokenizer.vocab_file= vocab
config.model.tokenizer.tokenizer_model= "tokenizer_spe_v2000"
AttributeError Traceback (most recent call last)
<ipython-input-43-cb4d43dca025> in <module>
2
3
----> 4 model = nemo_nlp.models.language_modeling.TransformerLMModel(cfg=config.model, trainer=trainer)
5
/opt/conda/lib/python3.6/site-packages/nemo/collections/nlp/models/language_modeling/transformer_lm_model.py in __init__(self, cfg, trainer)
51 self.dataset_cfg = cfg.dataset
52
---> 53 vocab_file = cfg.language_model.get("vocab_file", None)
54
55 if vocab_file is not None:
AttributeError: 'NoneType' object has no attribute 'get
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} | Signed-off-by: nithinraok <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2820 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2820/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2820/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2820/events | https://github.com/NVIDIA/NeMo/pull/2820 | 996,341,810 | PR_kwDOC_bI7s4rvy3R | 2,820 | Temporarily disable numba cuda tests from running | {
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"merged_at": "2021-09-14T20:19:52"
} | # Changelog
- Temporarily disable numba tests from running unti CI updates
Signed-off-by: smajumdar <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2819 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2819/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2819/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2819/events | https://github.com/NVIDIA/NeMo/pull/2819 | 996,288,688 | PR_kwDOC_bI7s4rvn95 | 2,819 | Update ContextNet RNNT configs | {
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} | # Changelog
- Add char based ContextNet config
- Update subword based ContextNet config
- Fix Mandarin Citrinet pretrained model info | {
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I am trying to train the conformer model with the DALI input pipeline, but it crash at the very early start.
Here is the logs:
```
[NeMo I 2021-09-14 16:32:36 mixins:147] Tokenizer SentencePieceTokenizer initialized with 128 tokens
[NeMo I 2021-09-14 16:32:36 ctc_bpe_models:172]
Replacing placeholder number of classes (-1) with actual number of classes - 128
[NeMo W 2021-09-14 16:32:36 nemo_logging:349] /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/ops.py:627: DeprecationWarning: WARNING:
`normal_distribution` is now deprecated. Use `random.normal` instead.
op_instances.append(_OperatorInstance(input_set, self, **kwargs))
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/Scripts/nemo/training/speech_to_text_bpe.py", line 101, in <module>
main()
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/core/config/hydra_runner.py", line 101, in wrapper
_run_hydra(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg) File "/home/ael/Scripts/nemo/training/speech_to_text_bpe.py", line 81, in main
asr_model = EncDecCTCModelBPE(cfg=cfg.model, trainer=trainer)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/models/ctc_bpe_models.py", line 179, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/models/ctc_models.py", line 153, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/core/classes/modelPT.py", line 120, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/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/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/models/ctc_bpe_models.py", line 201, in _setup_dataloader_from_config
dataset = audio_to_text_dataset.get_dali_bpe_dataset(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/data/audio_to_text_dataset.py", line 275, in get_dali_bpe_dataset
dataset = audio_to_text_dali.AudioToBPEDALIDataset(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/data/audio_to_text_dali.py", line 645, in __init__
super().__init__(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nemo/collections/asr/data/audio_to_text_dali.py", line 398, in __init__
self._iter = DALIPytorchIterator(
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/plugin/pytorch.py", line 190, in __init__
self._first_batch = DALIGenericIterator.__next__(self)
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/plugin/pytorch.py", line 201, in __next__
outputs = self._get_outputs()
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/plugin/base_iterator.py", line 254, in _get_outputs
outputs.append(p.share_outputs())
File "/home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/pipeline.py", line 906, in share_outputs
return self._pipe.ShareOutputs()
RuntimeError: Critical error in pipeline:
Error when executing GPU operator Pad encountered:
[/opt/dali/dali/operators/generic/pad.h:99] Assert on "a > 0" failed: Values of `align` argument must be positive.
Stacktrace (11 entries):
[frame 0]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali_operators.so(+0x55daee) [0x7ff58607aaee]
[frame 1]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali_operators.so(+0x138c8ed) [0x7ff586ea98ed]
[frame 2]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali_operators.so(+0x1360928) [0x7ff586e7d928]
[frame 3]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali.so(void dali::Executor<dali::AOT_WS_Policy<dali::UniformQueuePolicy>, dali::UniformQueuePolicy>::RunHelper<dali::DeviceWorkspace>(dali::OpNode&, dali::DeviceWorkspace&)+0x845) [0x7ff584ff7745]
[frame 4]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali.so(dali::Executor<dali::AOT_WS_Policy<dali::UniformQueuePolicy>, dali::UniformQueuePolicy>::RunGPUImpl()+0x41c) [0x7ff584ff852c]
[frame 5]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali.so(dali::Executor<dali::AOT_WS_Policy<dali::UniformQueuePolicy>, dali::UniformQueuePolicy>::RunGPU()+0xe) [0x7ff584ff8f4e]
[frame 6]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali.so(+0xa4ea8) [0x7ff584fa9ea8]
[frame 7]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali.so(+0x123e8c) [0x7ff585028e8c]
[frame 8]: /home/ael/anaconda3/envs/nemo-1.3.0/lib/python3.9/site-packages/nvidia/dali/libdali.so(+0x70a55f) [0x7ff58560f55f]
[frame 9]: /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7ff6876676db]
[frame 10]: /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7ff68739071f]
```
The same model doesn't crash at this point when I don't use DALI.
The only difference is that I enable "use_dali: true" at the yaml file.
Any idea what is the cause of this problem and how I can solve it ? | {
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} | Currently we save the value of the validation metric into the name of the checkpoints upto 2 numbers after the decimal point. For some models like ASR where we have WER and numbers are less than 1, it can cause an issue. The experiment manager cannot find the right topk checkpoints. | {
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} | null | null | null | **Describe the bug**
The validation step is not running when training a [mt_enc_dec](https://github.com/NVIDIA/NeMo/blob/r1.2.0/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py) model using `TarredTranslationDataset`. As a result, the checkpoint call back is not invoked and no validation metrics are reported. The initial validation sanity check runs fine and then training proceeds reporting a decrease in train_loss over time, but the validation is never invoked again.
**Steps/Code to reproduce bug**
I am using a cleaned wmt dataset with 3.6M rows and training using ddp.
Here are the relevant bits from the config:
```yaml
model:
...
train_ds:
src_file_name: ???
tgt_file_name: ???
use_tarred_dataset: true # if true tar_file_name and meta_file_name will be used (or created automatically)
# config for preprocessing training data and creating a tarred datset automatically
tar_file_prefix: parallel # prefix for tar file names
tar_files: null # if data has already been preprocessed (rest of config ignored)
metadata_file: null # metadata for tarred dataset
lines_per_dataset_fragment: 1000000 # Number of lines to consider for bucketing and padding
num_batches_per_tarfile: 100 # Number of batches (pickle files) within each tarfile
tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled
shard_strategy: scatter # tarred dataset shard distribution strategy
n_preproc_jobs: -2 # number of processes to use for data preprocessing (-2 means all but 2)
tokens_in_batch: 8000
clean: true
max_seq_length: 512
shuffle: true
num_samples: -1
drop_last: false
pin_memory: false
num_workers: 0
validation_ds:
src_file_name: ???
tgt_file_name: ???
tokens_in_batch: 1024
clean: false
max_seq_length: 512
shuffle: false
num_samples: -1
drop_last: false
pin_memory: false
num_workers: 8
trainer:
gpus: 8
num_nodes: 1
max_steps: 500000
amp_level: O2 # O1/O2 for mixed precision
precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0
accelerator: ddp
checkpoint_callback: false
logger: false
log_every_n_steps: 50 # Interval of logging.
check_val_every_n_epoch: 1
```
Here is the screenshot of the logs:
**Environment overview **
- Environment Location: Docker on Kubernetes
- Method of NeMo install: using helm chart using nvcr.io/nvidia/nemo:1.2.0
**Additional context**
Training on 8 A100 GPUs using DGX box
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} | null | null | null | I was trying to train the ASR Model using Nemo on Colab but I was facing this issue on this row
trainer.fit(first_asr_model)
ValueError: Cannot resolve schedulerDictConfig'. Available optimizers are : dict_keys(['WarmupPolicy', 'WarmupHoldPolicy', 'SquareAnnealing', 'CosineAnnealing', 'NoamAnnealing', 'WarmupAnnealing', 'InverseSquareRootAnnealing', 'SquareRootAnnealing', 'PolynomialDecayAnnealing', 'PolynomialHoldDecayAnnealing', 'StepLR', 'ExponentialLR', 'ReduceLROnPlateau', 'CyclicLR']) | {
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I need to take the exact time each word was said to build a transcription "timeline".
I saw that this script exemplifies how to accomplish this task
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/Offline_ASR.ipynb#scrollTo=a-LSg9dSL_O1
However, as it was done, I only have the information about the final time of each word and the initial time is determined by the final time of the previous word.
This way I'm not getting good results, because if there is a silence interval between one word and another, the "timeline" will inform you that the next word has already started to be spoken.
Is there an alternative to recover the exact time of each word? | {
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} | null | null | null | **Description of bug**:
The cut-audio.py script fails if using models other than QuartzNet15x5Base-En, e.g. stt_en_quartznet15x5 or stt_en_jasper10x5dr.
Traceback (most recent call last):
File "tools/ctc_segmentation/scripts/cut_audio.py", line 299, in <module>
stats = process_alignment(alignment_file, args)
File "tools/ctc_segmentation/scripts/cut_audio.py", line 205, in process_alignment
add_transcript_to_manifest(
File "tools/ctc_segmentation/scripts/cut_audio.py", line 59, in add_transcript_to_manifest
transcripts = get_transcript(manifest_original, asr_model, batch_size)
File "tools/ctc_segmentation/scripts/cut_audio.py", line 93, in get_transcript
asr_model.setup_test_data(
File "/home/markk/miniconda3/envs/dlenv/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 482, in setup_test_data
self._test_dl = self._setup_dataloader_from_config(config=test_data_config)
File "/home/markk/miniconda3/envs/dlenv/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 348, in _setup_dataloader_from_config
audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate')
File "/home/markk/miniconda3/envs/dlenv/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text_dataset.py", line 55, in inject_dataloader_value_from_model_config
with open_dict(dataloader_cfg):
File "/home/markk/miniconda3/envs/dlenv/lib/python3.8/contextlib.py", line 113, in __enter__
return next(self.gen)
File "/home/markk/miniconda3/envs/dlenv/lib/python3.8/site-packages/omegaconf/omegaconf.py", line 943, in open_dict
prev_state = config._get_node_flag("struct")
AttributeError: 'dict' object has no attribute '_get_node_flag'
**Steps/Code to reproduce bug**
The script I am using as follows:
MODEL=stt_en_quartznet15x5
OUTPUT_DIR=output
TOOLS_DIR=tools/ctc_segmentation/scripts
DATA_DIR=data
WINDOW=16000
OFFSET=0
THRESHOLD=-5
python $TOOLS_DIR/prepare_data.py \
--audio_format '.wav' \
--in_text $DATA_DIR \
--output_dir $OUTPUT_DIR/processed \
--language 'eng' \
--model $MODEL \
--audio_dir $DATA_DIR
python $TOOLS_DIR/run_ctc_segmentation.py \
--output_dir $OUTPUT_DIR \
--data $OUTPUT_DIR/processed \
--model $MODEL \
--window_len $WINDOW \
--no_parallel
python $TOOLS_DIR/cut_audio.py \
--output_dir $OUTPUT_DIR \
--model $MODEL \
--alignment $OUTPUT_DIR/segments/ \
--threshold $THRESHOLD \
--offset $OFFSET
**Environment **
nemo v1.3.0
python 3.8.11
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} | # Bugfix
- Fix maes num steps < 2, force to be >= 2 for now until auto tuning is ready
- Fix lr scheduler division by 0 error if num_workers is 0
- Fix dataloader injection when provided base dictionary and not DictConfig
- Fix DALI on CPU
Signed-off-by: smajumdar <[email protected]> | {
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} | Signed-off-by: ekmb <[email protected]> | {
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} | Updating ASR_with_SpeakerDiarization.ipynb.
Currently, ASR with diarization only supports QuartzNet. We will be adding more models later on. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2799 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2799/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2799/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2799/events | https://github.com/NVIDIA/NeMo/pull/2799 | 992,646,793 | MDExOlB1bGxSZXF1ZXN0NzMwOTQ5NTY1 | 2,799 | Update container and Dockerfile | {
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} | # Changelog
- bump container to 21.08
Signed-off-by: smajumdar <[email protected]> | {
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} | This PR extends validity checks of src/tgt input IDs:
1. Added a test for special tokens being valid (i.e., non-negative) for Sentencepiece tokenizer.
2. Extended MTEncDecModel id validity test to include non-negativity.
3. Added `model.validate_input_ids` to `MTEncDecModel` config to allow disabling id validity (`true` by default).
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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：
quartznet = nemo_asr.models.EncDecCTCModel.restore_from("./model/QuartzNet15x5-Zh-NR.nemo",strict=False)
error：
AttributeError: 'dict' object has no attribute '_get_node_flag'
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} | Fixed a spelling mistake.
<img width="979" alt="typo fix" src="https://user-images.githubusercontent.com/29411721/132643791-53cb2ae7-01bf-4357-87ff-72d5e31b89ee.png">
Signed-off-by: kiranscaria <[email protected]> | {
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} | null | null | null | I can't reproduce the environment to run the code in this tutorial https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/zh/latest/nlp/bert_pretraining.html , I have followed pip installation instructions but I keep getting errors when I import things
AttributeError: module 'nemo.collections.nlp.data' has no attribute 'NemoBertTokenizer'
>>> ckpt_callback = nemo.core.CheckpointCallback(folder=nf.checkpoint_dir,
... load_from_folder=args.load_dir)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'nemo' is not defined
>>> import nemo
>>> ckpt_callback = nemo.core.CheckpointCallback(folder=nf.checkpoint_dir,
... load_from_folder=args.load_dir)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'nemo.core' has no attribute 'CheckpointCallback'
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} | null | null | null | **Describe the bug**
For inference, i would like to try the feature extractor part individually using [.get_features()](https://github.com/NVIDIA/NeMo/blob/65bcb68640469d1c77d7882520511546e67fc514/nemo/collections/asr/modules/audio_preprocessing.py) function that's already implemented. As i understood, the inputs for this function are input_signal and the length. But, when i give the inputs, i get an error as i explained in below. I want to know if this method can be work.
**Steps/Code to reproduce bug**
0. Setup your environment.
- nemo installation (commit - cd6a691607894f13cafcbe7994d1b2809ccb7aa4)
pip3 install git+https://github.com/NVIDIA/NeMo.git@cd6a691607894f13cafcbe7994d1b2809ccb7aa4
pip3 install torch==1.7.1 torchaudio==0.7.2
1. Download the STT En Jasper10x5dr (.nemo) model.
```
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/zip -O stt_en_jasper10x5dr_1.0.0rc1.zip
```
2. Extract the file into your workspace.
```
unzip stt_en_jasper10x5dr_1.0.0rc1.zip
```
3. Import the nemo asr models.
```
import nemo.collections.asr as nemo_asr
```
4. Restore the model
```
asr_model = nemo_asr.models.EncDecCTCModel.restore_from('stt_en_jasper10x5dr.nemo')
```
5. Import torch and torchaudio
```
import torch
import torchaudio
```
6. Download the test file (.wav)
```
SAMPLE_WAV_SPEECH_PATH = '1919-142785-0028.wav'
!wget https://dldata-public.s3.us-east-2.amazonaws.com/1919-142785-0028.wav
device = 'gpu'
```
7. Get input signal and the length out from the wav file
```
waveform, sample_rate = torchaudio.load(SAMPLE_WAV_SPEECH_PATH)
# cpu tensor into device (gpu)
waveform = waveform.to(torch.device('cuda'))
# generate the length as tensor
si, ei = torchaudio.info(SAMPLE_WAV_SPEECH_PATH)
length = si.length
length = torch.cuda.IntTensor(length)
```
7. Call get_features() function
```
with torch.no_grad():
feature = self.asr_model.preprocessor.get_features(input_signal=waveform,length=length).to(device).eval()
```
**Expected behavior**
It gives me an error saying CUDA out of memory.
```
Traceback (most recent call last):
File "/jasper/triton/triton_client.py", line 351, in <module>
STTInferClass_th.infer(filenames)
File "/jasper/triton/triton_client.py", line 314, in infer
self.get_features_nemo(filenames)
File "/jasper/triton/triton_client.py", line 305, in get_features_nemo
feature = self.asr_model.preprocessor.get_features(input_signal=waveform,length=y).to(device).eval()
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 259, in get_features
return self.featurizer(input_signal, length)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/preprocessing/features.py", line 422, in forward
x = x.masked_fill(mask.unsqueeze(1).type(torch.bool).to(device=x.device), self.pad_value)
RuntimeError: CUDA out of memory. Tried to allocate 5.59 GiB (GPU 0; 14.76 GiB total capacity; 5.69 GiB already allocated; 1.28 GiB free; 11.28 GiB reserved in total by PyTorch)
```
**nvidia-smi**
**Environment overview (please complete the following information)**
- Environment location: [Cloud(AWS), EC2 T4 Instance]
- Method of NeMo install: [pip3 install git+https://github.com/NVIDIA/NeMo.git@cd6a691607894f13cafcbe7994d1b2809ccb7aa4]. .
**Environment details**
- OS version - Ubuntu 20.04
- PyTorch version - 1.7.1
- Python version - 3.8
Thank you,
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} | This PR adds TTSDataset, english tokenizers (char and phoneme) and g2ps, base tokenizer and g2p classes for any other language.
I have fixed many bugs in [nemo/collections/asr/data/vocabs.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/data/vocabs.py) and decided to put result in TTS code, because I want to add new classes to [nemo/collections/tts/torch/data.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/torch/data.py) and suggest to use it as default for TTS pipelines. | {
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] | closed | false | null | [] | null | null | 2021-09-08T04:59:41 | 2021-09-12T12:06:00 | 2021-09-09T16:56:54 | NONE | {
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} | null | null | null | fastpich_hifigan_e2e.py (just like fastpitch.py) requires some extra processing setup steps and I can't find what generates the missing durations and pitch_char .pt files. Can anyone give me a hint?
There was an earlier defect closed that talked about needing to run tacotron2 training first.
I did that for a couple of epochs and none of the required files were generated.
https://github.com/NVIDIA/NeMo/issues/2352
File "/home/alansrobotlab/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py", line 734, in __getitem__
return (audio, audio_len, text, text_len, torch.load(durs_path), torch.load(pitch_path), speaker, audio_path)
audio_to_text.py __getitem__
def __getitem__(self, item):
audio, audio_len, text, text_len = super().__getitem__(item) # noqa
audio_path = self.manifest_processor.collection[item].audio_file
durs_path = audio_path.replace('/wavs/', '/fastpitch/durations/').replace('.wav', '.pt')
pitch_path = audio_path.replace('/wavs/', '/fastpitch/pitch_char/').replace('.wav', '.pt')
speaker = None
if self.manifest_processor.collection[item].speaker is not None:
speaker = torch.zeros_like(text_len).fill_(self.manifest_processor.collection[item].speaker)
return (audio, audio_len, text, text_len, torch.load(durs_path), torch.load(pitch_path), speaker, audio_path)
Complete error from run:
```
root@f62e1306b3c4:/workspace# python fastpitch_
fastpitch_6097.py fastpitch_hifigan_e2e_6097.py fastpitch_pyt_fp32_ckpt_v1_1_21.05.0.zip
fastpitch_durs_6097.py fastpitch_pitch_6097.py fastpitch_spect_6097.py
root@f62e1306b3c4:/workspace# python fastpitch_hifigan_e2e_6097.py
[NeMo W 2021-09-07 21:52:43 experimental:27] Module <class 'nemo.collections.asr.data.audio_to_text_dali._AudioTextDALIDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
Using native 16bit precision.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
[NeMo I 2021-09-07 21:52:43 exp_manager:220] Experiments will be logged at /workspace/nemo_experiments/FastPitch_HiFiGAN_E2E/2021-09-07_21-52-43
[NeMo I 2021-09-07 21:52:43 exp_manager:569] TensorboardLogger has been set up
[NeMo W 2021-09-07 21:52:43 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:240: LightningDeprecationWarning: `ModelCheckpoint(every_n_val_epochs)` is deprecated in v1.4 and will be removed in v1.6. Please use `every_n_epochs` instead.
rank_zero_deprecation(
[NeMo I 2021-09-07 21:52:47 collections:173] Dataset loaded with 39373 files totalling 29.95 hours
[NeMo I 2021-09-07 21:52:47 collections:174] 0 files were filtered totalling 0.00 hours
[NeMo I 2021-09-07 21:52:47 collections:173] Dataset loaded with 100 files totalling 0.10 hours
[NeMo I 2021-09-07 21:52:47 collections:174] 0 files were filtered totalling 0.00 hours
[NeMo I 2021-09-07 21:52:47 features:252] PADDING: 1
[NeMo I 2021-09-07 21:52:47 features:269] STFT using torch
[NeMo I 2021-09-07 21:52:47 features:271] STFT using exact pad
[NeMo I 2021-09-07 21:52:47 features:252] PADDING: 1
[NeMo I 2021-09-07 21:52:47 features:269] STFT using torch
[NeMo I 2021-09-07 21:52:47 features:271] STFT using exact pad
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/2
Using native 16bit precision.
initializing ddp: GLOBAL_RANK: 1, MEMBER: 2/2
Added key: store_based_barrier_key:1 to store for rank: 1
Added key: store_based_barrier_key:1 to store for rank: 0
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All DDP processes registered. Starting ddp with 2 processes
----------------------------------------------------------------------------------------------------
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
[NeMo I 2021-09-07 21:52:56 fastpitch_hifigan_e2e:184] MAX STEPS: 462000.0
| Name | Type | Params
-----------------------------------------------------------------
0 | preprocessor | FilterbankFeatures | 0
1 | melspec_fn | FilterbankFeatures | 0
2 | encoder | FFTransformerEncoder | 21.9 M
3 | duration_predictor | TemporalPredictor | 493 K
4 | pitch_predictor | TemporalPredictor | 493 K
5 | generator | Generator | 15.0 M
6 | multiperioddisc | MultiPeriodDiscriminator | 41.1 M
7 | multiscaledisc | MultiScaleDiscriminator | 29.6 M
8 | mel_val_loss | L1MelLoss | 0
9 | feat_matching_loss | FeatureMatchingLoss | 0
10 | disc_loss | DiscriminatorLoss | 0
11 | gen_loss | GeneratorLoss | 0
12 | pitch_emb | Conv1d | 1.5 K
13 | pitchloss | PitchLoss | 0
14 | durationloss | DurationLoss | 0
-----------------------------------------------------------------
108 M Trainable params
0 Non-trainable params
108 M Total params
434.563 Total estimated model params size (MB)
Validation sanity check: 0%| | 0/1 [00:00<?, ?it/s]Error executing job with overrides: []
Error executing job with overrides: []
Traceback (most recent call last):
File "fastpitch_hifigan_e2e_6097.py", line 32, in main
trainer.fit(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
self._run(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 917, in _run
self._dispatch()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 985, in _dispatch
self.accelerator.start_training(self)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
self.training_type_plugin.start_training(trainer)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
self._results = trainer.run_stage()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 995, in run_stage
return self._run_train()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_train
self._run_sanity_check(self.lightning_module)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1114, in _run_sanity_check
self._evaluation_loop.run()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
dl_outputs = self.epoch_loop.run(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 93, in advance
batch_idx, batch = next(dataloader_iter)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 518, in __next__
data = self._next_data()
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1200, in _next_data
return self._process_data(data)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1226, in _process_data
data.reraise()
File "/opt/conda/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
raise self.exc_type(msg)
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py", line 734, in __getitem__
return (audio, audio_len, text, text_len, torch.load(durs_path), torch.load(pitch_path), speaker, audio_path)
File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 579, in load
with _open_file_like(f, 'rb') as opened_file:
File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 230, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 211, in __init__
super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'hi_fi_tts_v0/audio/6097_clean/fastpitch/durations/nada_lily_00_haggard_0063.pt'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Traceback (most recent call last):
File "/workspace/fastpitch_hifigan_e2e_6097.py", line 32, in main
trainer.fit(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
self._run(model)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 917, in _run
self._dispatch()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 985, in _dispatch
self.accelerator.start_training(self)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
self.training_type_plugin.start_training(trainer)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
self._results = trainer.run_stage()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 995, in run_stage
return self._run_train()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_train
self._run_sanity_check(self.lightning_module)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1114, in _run_sanity_check
self._evaluation_loop.run()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
dl_outputs = self.epoch_loop.run(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 93, in advance
batch_idx, batch = next(dataloader_iter)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 518, in __next__
data = self._next_data()
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1200, in _next_data
return self._process_data(data)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1226, in _process_data
data.reraise()
File "/opt/conda/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
raise self.exc_type(msg)
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py", line 734, in __getitem__
return (audio, audio_len, text, text_len, torch.load(durs_path), torch.load(pitch_path), speaker, audio_path)
File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 579, in load
with _open_file_like(f, 'rb') as opened_file:
File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 230, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 211, in __init__
super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'hi_fi_tts_v0/audio/6097_clean/fastpitch/durations/nada_lily_00_haggard_0077.pt'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
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] | null | null | 2021-09-08T02:30:42 | 2021-09-14T17:44:17 | 2021-09-14T17:44:17 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I have follow your [example](https://github.com/NVIDIA/NeMo/blob/v1.0.0b3/scripts/convasr_to_onnx.py) to convert a single asr model (.nemo) into encoder and decoder. But, it does not allow to export the Preprocessor model as .onnx. When i checked the functions that attached to Preprocessor, the .export() function is not implemented. Is there a reason why don't you allow to export the Preprocessor part out from asr model?
**Describe the solution you'd like**
I would like to find out how to export the Preprocessor to .onnx format trough a .export() function as you already implemented for encoder and decoder as follows (Referred from [example](https://github.com/NVIDIA/NeMo/blob/v1.0.0b3/scripts/convasr_to_onnx.py)).
**Describe alternatives you've considered**
There's a way to split the asr model (.nemo) to sub modules using [.extract_state_dict_from()](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.0.0b4/api-docs/nemo.html#nemo.core.ModelPT.extract_state_dict_from) and it saves as a ckpt. Is there a way to convert the preprocessor.ckpt that can load as a triton model ?
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