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Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"In FilterbankFeatures class we extend time length features to multiples of `pad_to` value here 16(default) for faster processing. Try changing that to -1 and try again. "
] | 2021-02-24T03:20:46 | 2021-05-28T08:40:54 | 2021-05-28T08:40:54 | NONE | {
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this is my code:
```
# load audio data by librosa
path_audio = "audio_a.wav"
y, sr = librosa.load(path_audio, sr=16000, offset=0.5, duration=0.4)
# load audio data by torch audio
audio_ft, sr = torchaudio.load(path_audio)
audio_ft = audio_ft.squeeze(0)
y_torch = audio_ft[int(0.5*16000):int(0.9*16000)]
# the result is the same then i compute log filterbank energy
# log filter bank energy compute by python_speech_feature lib
ft_f_bank = python_speech_features.logfbank(y, samplerate=16000, winlen=0.025, winstep=0.01, nfilt=64,nfft=512)
print(ft_f_bank.shape) # result: (39, 64)
# log filter bank energy compute by FilterbankFeatures module in audio_preprocessing (asr collection in nemo)
self.featurizer = FilterbankFeatures(sample_rate=16000, n_window_size=int(0.025*16000), n_window_stride=(0.01* 16000), n_fft=64, log=True)
ft_by_f_bank_nemo = self.fearturizer(y_torch) #result shape: (41, 64)
# log filter bank energy compute by torch audio kaldi compliance
ft_f_bank_by_torch = torchaudio.compliance.kaldi.fbank(y_torch, sample_frequency=16000.0, frame_length=25.0, frame_shift=10.0, use_log_fbank=True, use_energy=True, num_mel_bins=64)
print(ft_f_bank_by_torch.shape) # result: (38, 65)
```
How can i make result return by module filterbankfeature in nemo or torchaudio is the same with python speech feature. I'm not have deep understand more about speech feature, so question can so weird, sorry.
Thankyou
my torchaudio: 0.6.0,
pytorch:1.6.0 | {
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"This pull request **introduces 2 alerts** and **fixes 2** when merging 072d52065e9037808be763b0750644bfd8af655b into cc4bfc06993326fcd8bb978460754b89026341ff - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-f732130f599d696fda78aa55922c86c8d40e6c8a)\n\n**new alerts:**\n\n* 2 for Unused import\n\n... | 2021-02-24T02:26:56 | 2021-02-25T20:21:49 | 2021-02-25T20:18:37 | COLLABORATOR | {
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} | # Changelog
- Add single script to allow transcription of all ASR models
- Add subclass walk for finding all Pretrained models of ASRModel.
- Add dataclasses for RNNT Decoding strategy
# Usage
```python
python transcribe_speech.py \
model_path=null \ # path to a nemo file
pretrained_name=null \ # path to a pretrained checkpoint name
audio_dir="" \ # directory of audio files
cuda=null \ # Will use cuda if available, switch to CPU otherwise
amp=true # will use Automatic mixed precision if available (when using cuda) otherwise skips AMP
``` | {
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} | # Changelog
- Fix Model Primer notebook with container support in typecheck
- Doc fixes for all domains to RC1 branch | {
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} | This PR adds preprocessing for Japanese as a source language to nemo. Previously, support only included Japanese when it was a target language. | {
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"``.restore_from`` only works with ``.nemo`` files. You can try loading regular Pytorch weights via regular Pytorch load_state_dict methods because any NeMo model is a Pytorch module",
"Thank you very much ! ",
"this error occurs when ```restore_from``` with ```.nemo ``` files.\r\n```sh\r\nasr_model = nemo_asr.... | 2021-02-21T02:19:40 | 2024-01-03T18:23:30 | 2021-04-23T23:49:56 | NONE | {
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} | null | null | null | Hello everyone,
I'm trying to use wav2vec model that I downloaded from the following link ( https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr_53_56k.pt ) which corresponds to the multilingual pre-trained wav2vec. I tried to load it with the following command:
```
from nemo.collections.asr.models.wav2vec.wav2vec_model import Wav2VecEncoderModel
model = Wav2VecEncoderModel.restore_from('xlsr_53_56k.pt')
```
I got this error message :
```
Traceback (most recent call last):
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 1646, in gzopen
t = cls.taropen(name, mode, fileobj, **kwargs)
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 1623, in taropen
return cls(name, mode, fileobj, **kwargs)
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 1486, in __init__
self.firstmember = self.next()
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 2289, in next
tarinfo = self.tarinfo.fromtarfile(self)
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 1094, in fromtarfile
buf = tarfile.fileobj.read(BLOCKSIZE)
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/gzip.py", line 287, in read
return self._buffer.read(size)
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/gzip.py", line 474, in read
if not self._read_gzip_header():
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/gzip.py", line 422, in _read_gzip_header
raise OSError('Not a gzipped file (%r)' % magic)
OSError: Not a gzipped file (b'PK')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/lsam/Nemo_100b4/NeMo/nemo/core/classes/modelPT.py", line 509, in restore_from
return cls._default_restore_from(restore_path, override_config_path, map_location, strict, return_config)
File "/home/lsam/Nemo_100b4/NeMo/nemo/core/classes/modelPT.py", line 385, in _default_restore_from
cls.__unpack_nemo_file(path2file=restore_path, out_folder=tmpdir)
File "/home/lsam/Nemo_100b4/NeMo/nemo/core/classes/modelPT.py", line 1298, in __unpack_nemo_file
tar = tarfile.open(path2file, "r:gz")
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 1593, in open
return func(name, filemode, fileobj, **kwargs)
File "/home/lsam/anaconda3/envs/nemo_100b4/lib/python3.7/tarfile.py", line 1650, in gzopen
raise ReadError("not a gzip file")
tarfile.ReadError: not a gzip file
```
Is there anywone who have an idea about how does it work or how to fix this bug. Thanks in advance.
With kind regards,
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"node_id": "MDU6TGFiZWwxNDg1ODE1NTU3",
"url": "https://api.github.com/repos/NVIDIA/NeMo/labels/bug",
"name": "bug",
"color": "d73a4a",
"default": true,
"description": "Something isn't working"
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"You can use from_pretrained(..., map_location=\"cpu\")",
"closing, please re-open is @titu1994 's suggestion did not help."
] | 2021-02-20T10:47:58 | 2021-02-23T18:22:29 | 2021-02-23T18:22:29 | NONE | {
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} | null | null | null | **Describe the bug**
Loading `Jasper10x5Dr-En` model fails on a system without a GPU.
**Steps/Code to reproduce bug**
Notebook with example:
https://colab.research.google.com/drive/1NDzwQl0KyOOYPheW60colRD2Sm8Ng_vD?usp=sharing
**Expected behavior**
NeMo should detect that GPU is not available and load the model to CPU.
**Environment overview (please complete the following information)**
- Environment location: Google Colab
- Method of NeMo install: `!pip install nemo_toolkit[all]==1.0.0b2`
**Environment details**
- OS version:
```
NAME="Ubuntu"
VERSION="18.04.5 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.5 LTS"
VERSION_ID="18.04"
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
VERSION_CODENAME=bionic
UBUNTU_CODENAME=bionic
```
- PyTorch version `1.7.0+cu101`
- Python version `Python 3.6.9`
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"just to confirm that this is not a bugfix for rc1? ",
"@okuchaiev Part of this (Point 2) should be fixed in RC but this might raise conflict and not very essential (just logging), so we decide to merge it to main. I could split it and merge to RC if it's better. ",
"Split this PR into two please. Bugfix to R... | 2021-02-20T06:47:27 | 2021-03-16T03:42:19 | 2021-03-12T22:13:55 | COLLABORATOR | {
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} | 1. Change self.log(tensorboard_logs) and remove the 'log' part in the return requested in PR #[1707](https://github.com/NVIDIA/NeMo/pull/1707)
2. Fix for logging for top-k acc metrics
3. Fix **tarred** dataloader for speech regression support
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"Overall it's good to go from me",
"this change broke training on draco"
] | 2021-02-19T18:08:26 | 2021-03-02T20:31:30 | 2021-03-01T22:10:08 | COLLABORATOR | {
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} | Update LogEpochTimeCallback
Update CallbackParams | {
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} | # Changelog
- Correct Primer notebook version | {
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"> ```python\r\n> change_decoding_strategy\r\n> ```\r\n\r\n+ Can we call the change_decoding_strategy automatically in the transcribe method if necessary so that user does not need to do that?\r\n\r\n+ Why do we have setting preserve_alignments for for RNNT but not CTC?\r\n\r\n+ Why do we need to use asr_model.deco... | 2021-02-19T02:18:49 | 2021-03-02T00:31:44 | 2021-03-02T00:31:42 | COLLABORATOR | {
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} | # Changelog
- Adds support for CTC/RNNT models to compute alignments for their respective decoding schemes. Valid schemes listed below.
- Adds support for CTC models to return a `Hypothesis` object that contains additional information about the decoded / transcribed tensor.
- Adds test for hypothesis in CTC
## Valid alignment computations
- CTC
- CTC + Char encoding
- CTC + Subword encoding
- RNNT
- RNNT with Greedy (sample) and Greedy (batched, both variants)
- RNNT with Basic Beam (beam size >= 1)
## Invalid alignment computations
- RNNT
- RNNT with TSD / ALSD
# Example (CTC / CTC-BPE)
```python
import torch
...
trainer = Trainer(...)
asr_model = EncDecCTCModel.from_pretrained(pretrained_name, map_location=torch.device('cpu'), strict=True)
asr_model.set_trainer(trainer)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
asr_model = asr_model.to(device)
# Compute alignments
audio_paths = [...] # path of audio files
hypotheses = asr_model.transcribe(audio_paths , batch_size=32, return_hypotheses=True)
SAMPLE_IDX = 0
h = hypotheses[SAMPLE_IDX] # type: Hypothesis
alignments = h.alignments # log probs of first sample
token_set = []
for p in alignments:
tokens = asr_model._wer.decode_tokens_to_str([p])
token_set.append(tokens)
print("Predicted text :", h.text)
print()
print("Alignments :")
for t, p in enumerate(token_set):
print(f"t = {t:4d} : {p}")
```
# Example (RNNT / RNNT-BPE)
```python
import torch
from omegaconf import OmegaConf, open_dict
...
trainer = Trainer(...)
asr_model = EncDecRNNTBPEModel.restore_from(<path to RNNT model>, map_location=torch.device('cpu'), strict=True)
asr_model.set_trainer(trainer)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
asr_model = asr_model.to(device)
# Change decoding strategy to preserve alignments
decoding_cfg = asr_model.cfg.decoding
# Add `preserve_alignments` flag to RNNT models.
with open_dict(decoding_cfg):
decoding_cfg.preserve_alignments = True
asr_model.change_decoding_strategy(decoding_cfg) # cfg.model.decoding is the `decoding` portion of an RNNT yaml config.
# Compute alignments
audio_paths = [...] # path of audio files
hypotheses = asr_model.transcribe(audio_paths , batch_size=32, return_hypotheses=True)
# Above hypotheses is a list of 2 items
# [0] - best Hypothesis. This is always available, for both greedy and beam search
# [1] - NBestHypothesis (if beam search size > 1). None for greedy and beam search size of 1.
SAMPLE_IDX = 0
hypotheses = hypotheses[0] # select best hypothesis
h = hypotheses[SAMPLE_IDX] # type: Hypothesis
alignments = h.alignments # log probs of first sample
token_set = []
for p in alignments:
tokens = [asr_model.decoding.decode_tokens_to_str([p_i]) for p_i in p]
token_set.append(tokens)
print("Predicted text :", h.text)
print()
print("Alignments :")
for t, p in enumerate(token_set):
print(f"t = {t:4d} : {p}")
``` | {
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"Yes you can either add it to config and call the script or while calling the script you may give test_ds options as command line arguments prepended `+`. For reference look at hydra config https://hydra.cc/docs/tutorials/basic/your_first_app/config_groups ",
"Thanks for your answer, Nithin! I did an experiment w... | 2021-02-18T08:27:49 | 2021-02-19T01:18:06 | 2021-02-19T01:18:06 | NONE | {
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} | null | null | null | Hi!
I am just getting started with `NeMo`, and got a little confused when experimenting with `spkr_get_emb.py`, it appears one will need to use `SpeakerNet_verification_3x2x512.yaml` config and set up `cfg.model.test_ds`, however in this configuration, there is no `test_ds`. Was this an error or does this mean one needs to write `test_ds` to the verification model config? Any help on this would be appreciated!
Cheers,
Xu | {
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"Can you please sign your committed commits? See DCO's bot output how to sign already committed commits https://github.com/NVIDIA/NeMo/pull/1767/checks?check_run_id=1925053254 ",
"also, please re-base on the latest main, because currently CI does not pass due to a bug (which was fixed) in older version of main "... | 2021-02-18T07:23:46 | 2021-03-10T22:43:34 | 2021-03-10T22:43:34 | CONTRIBUTOR | {
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"This pull request **introduces 1 alert** and **fixes 2** when merging d285219e23c779109783e6db98f856ccf20730c6 into d7ad10435e97024c7c9fdbdd4228e0011dc9e432 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-b278125132b6fe7b7e4bf372a40fa8d12c683374)\n\n**new alerts:**\n\n* 1 for Unused import\n\n*... | 2021-02-18T03:08:29 | 2021-02-19T01:05:48 | 2021-02-19T01:05:48 | COLLABORATOR | {
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} | Integrating last couple of PRs that went into b4. | {
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"This pull request **introduces 2 alerts** when merging 900c52a2fc5e01c04dddb29d51b3894c504e837b into 45aaaf4aad712d53b58e6b14db6343c4e9940f25 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3758cb3dc44cd572ab179a17194a1cafa9166522)\n\n**new alerts:**\n\n* 2 for Unused import",
"This pull requ... | 2021-02-17T19:36:49 | 2021-02-19T19:33:01 | 2021-02-19T19:33:01 | COLLABORATOR | {
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} | - [x] Merge with main
- [x] Use HiFiGAN modules
- [x] Use our FS2 modules | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1763 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1763/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1763/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1763/events | https://github.com/NVIDIA/NeMo/issues/1763 | 810,154,132 | MDU6SXNzdWU4MTAxNTQxMzI= | 1,763 | [Issue] Getting error while loading the wav2vec model checkpoint. | {
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"I am unable to generate vectors of audio files using trained wav2vec model checkpoints. What is the specific method to call to extract features of any input audio files. @titu1994 @okuchaiev ",
"@SeanNaren and @VahidooX ",
"I have created a class named ExtractWav2VecEmbeddingsModel using BaseClass Wav2VecEncod... | 2021-02-17T12:47:16 | 2022-04-05T23:34:53 | 2022-04-05T23:34:53 | NONE | {
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} | null | null | null | I have tried to train wav2vec model using language other than English. I refered to example scripts provided in `/examples/asr/experimental/wav2vec/wav2vec_pretrain.py` [link](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/experimental/wav2vec/wav2vec_pretrain.py)
Models are saved in a experiment directory **nemo_experiments/Wav2vec_Pretrain/checkpoints/2021-02-17_09-21-19/checkpoints/Wav2vec_Pretrain---val_loss=34.53-epoch=64.ckpt**
I have used config file defined in `examples/asr/experimental/wav2vec/configs/wav2vec_pretrain.yaml` with changes in train_ds, val_ds, test_ds manifest_path.
I am using basic pytorch model load model script
`from omegaconf import OmegaConf
config_path = '/content/configs/wav2vec_pretrain.yaml'
params = OmegaConf.load(config_path)`
```
`wav2vec_encoder_model = Wav2VecEncoderModel(params)
wav2vec_encoder_model.load_state_dict(torch.load(PATH), strict=False)
```
where params is the DictConfig type
I am getting following error
`ConfigKeyError: Key 'name' not in 'Wav2VecEncoderModelConfig'
full_key: name
reference_type=Optional[Wav2VecEncoderModelConfig]
object_type=Wav2VecEncoderModelConfig`
Please review the error and suggest me other ways to load the model chekcpoints to generate wav2vec for audio files.
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"This pull request **fixes 3 alerts** when merging 0bc9d88cc28dbdb604348502888931556acde702 into 7d8b7d679a2a4c9d3a6b2d8def8888e53c804ded - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-1f5d9f48dd6cb74ec4d5390dc1d8290bf990ed9d)\n\n**fixed alerts:**\n\n* 2 for Unused local variable\n* 1 for Unuse... | 2021-02-17T02:32:16 | 2021-05-03T21:57:33 | 2021-02-25T19:53:18 | COLLABORATOR | {
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} | It is necessary to support class_balancing in TTL. | {
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"@ericharper Can you pull this branch and verify NMT models have no issue running their WebDataset training on some toy / small dataset?\r\n\r\n@fayejf Same for ASR Classification models\r\n\r\nI've verified that ASR CTC/RNNT work 1:1 as before."
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} | # Changelog
- Update API for webdataset calls
- Add lower bound for webdataset requirements (may want to pin it for release, @okuchaiev) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1759 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1759/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1759/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1759/events | https://github.com/NVIDIA/NeMo/issues/1759 | 809,223,092 | MDU6SXNzdWU4MDkyMjMwOTI= | 1,759 | [Question] Resume from checkpoint clarification | {
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"This is a great question ! Surprisingly, the QuartzNet config does not show two very nice parameters of experiment manager that are used precisely for resuming training over multiple runs - https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_384.yaml#L381-L382\r\n\r\nYou can add them to th... | 2021-02-16T11:17:56 | 2023-07-05T16:29:06 | 2021-02-18T17:54:37 | NONE | {
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} | null | null | null | **Describe your question**
I'm currently working on a transfer learning project for ASR and I followed the related [tutorial](https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb#scrollTo=7mP4r1Gx_Ilt). Since I'm not able to perform an entire training in a single session, I need to use checkpoints and resume from them.
The step I'm doing are the following:
1. Save the model after training:
```
quartznet.save_to("./out/my_model.nemo")
```
2. Restore the model when resuming:
```
quartznet = nemo_asr.models.EncDecCTCModel.restore_from("./out/my_model.nemo")
```
3. Restore the pytorch-lightning trainer:
```
logger = TensorBoardLogger(
save_dir=os.getcwd(),
version=3,
name='lightning_logs'
)
trainer = pl.Trainer(
gpus=1, max_epochs=30, precision=16, amp_level='O1', checkpoint_callback=True,
resume_from_checkpoint='lightning_logs/version_3/checkpoints/epoch=18-step=68893.ckpt', logger=logger)
```
4. Assign parameters and trainer to the model again:
```
# Set learning rate
params['model']['optim']['lr'] = 0.001
# Set NovoGrad optimizer betas
params['model']['optim']['betas'] = [0.95, 0.25]
# Set CosineAnnealing learning rate policy's warmup ratio
params['model']['optim']['sched']['warmup_ratio'] = 0.12
# Set training and validation labels
params['model']['train_ds']['labels'] = italian_vocabulary
params['model']['validation_ds']['labels'] = italian_vocabulary
# Set batch size
params['model']['train_ds']['batch_size'] = 16
params['model']['validation_ds']['batch_size'] = 16
# Assign trainer to the model
quartznet.set_trainer(trainer)
# Point to the data we'll use for fine-tuning as the training set
quartznet.setup_training_data(train_data_config=params['model']['train_ds'])
# Point to the new validation data for fine-tuning
quartznet.setup_validation_data(val_data_config=params['model']['validation_ds'])
# Add changes to quartznet model
quartznet.setup_optimization(optim_config=DictConfig(params['model']['optim']))
```
5. Resume training:
```
trainer.fit(quartznet)
```
I would like to know if this is the correct way to resume from checkpoint with the old trainer and model trained before, or if I'm missing some steps (or making redundant ones).
Before I tried to restore only the model and declare a new trainer (`trainer = pl.Trainer(gpus=1, max_epochs=20, precision=16, amp_level='O1', checkpoint_callback=True)`), then train for the same number of epochs of the first training session, but in the end I noticed the resulting WER was roughly the same, so I assumed the problem was that I hadn't restored my old trainer too.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: `pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]`
**Environment details**
- OS version: Ubuntu 18.04.5 LTS
- PyTorch version: pytorch lightning 1.1.5
- Python version: 3.6.9
**Additional context**
GPU model: NVIDIA Quadro RTX 4000
CUDA version: 10.1 | {
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"This pull request **introduces 6 alerts** when merging ace2826c3c6afa62827f773a1f427141640afdab into 562087498d68754aca6dcde8f4ac8813300e7b24 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-31be61d5dc5e90974461a3ee4fb9ee9ed9f5fd98)\n\n**new alerts:**\n\n* 6 for Unused import",
"This pull requ... | 2021-02-15T23:24:49 | 2021-02-22T06:22:19 | 2021-02-22T06:22:15 | CONTRIBUTOR | {
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} | This PR introduces tarred datasets for monolingual text corpora to allow (back)translating large amounts of text. A tarred dataset can be created as follows:
```
python examples/nlp/machine_translation/create_tarred_monolingual_dataset.py \
--tokenizer_model /path/to/tokenizer.model \
--clean \
--fname /path/to/monolingual_data.txt \
--max_seq_length 128 \
--min_seq_length 1 \
--tokens_in_batch 32000 \
--lines_per_dataset_fragment 5000000 \
--num_batches_per_tarfile 500 \
--out_dir /path/to/output_folder
```
Once the `output_folder` is created, we can batch translate data using a pre-trained model
```
python examples/nlp/machine_translation/translate_ddp.py \
--model /path/to/pretrained_model.nemo \
--text2translate /path/to/output_folder/batches.tokens.32000._OP_1..100_CL_.tar \
--metadata_path /path/to/output_folder/metadata.json \
--result_dir /path/to/translations \
--topk 500 \
--src_language en \
--tgt_language de \
--n_gpus 8
``` | {
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"Perturbation augmentation in NeMo is applied if probability at random sample > prob of augmentation selected. So total num of samples (iterations) remains the same. I suggest you train for a higher number of epochs for effective usage of augmentation. "
] | 2021-02-15T19:02:23 | 2021-02-23T18:35:43 | 2021-02-23T18:35:43 | NONE | {
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} | null | null | null | I have used vad configure file including augmenor with shift and white noise perturbation, spec_augment but when I try to apply it in train phase with 1000 training samples, the number of traning samples is fixed and it does not increase by augmentation. Why does not my training samples with these augmentations increase?
Thanks in advance.
| {
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"Wich kind of LM? N-gram? If so, read [this](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Offline_ASR.ipynb)\r\n\r\nIf you are talking about transformer based LM, I am struggling the same. I opened a discussion about this [here](https://github.com/NVIDIA/NeMo/discussions/1736)",
"Any other reference to ... | 2021-02-15T06:33:55 | 2021-06-19T04:40:56 | 2021-06-19T04:39:20 | NONE | {
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} | null | null | null | Hi, I would like to ask how to integrate my own Language Model into this notebook: https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb
I have been using my own data (an under-resourced language) for the notebook, and I wish to improve its WER by integrating my own Language Model. But so far I have failed in doing so.
Do I need to do some configuration in the config.yaml files in order to integrate my LM? Or do I have to call it externally somewhere in the codes?
Are there any documentations or guide for this? If there is, could you kindly suggest to me.
Looking forward to your replies,
Thank you.
| {
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"Could you share your training config file for QuartzNet ? The defaults for the pytorch lightning training logs at every step, which is excessive, and can be a cause for such a significant drop in training speed. ",
"I am also wondering about `num_workers` parameter for data layers",
"[quartznet.yaml.txt](https... | 2021-02-12T15:02:36 | 2021-05-28T08:41:17 | 2021-05-28T08:41:17 | NONE | {
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} | null | null | null | Hi,
first of all, thanks for the great library!
I've been using this library for more than a year. I train mostly QuartzNet models for English and Czech.
Since the new version (>1.0.*) I experience ~3x slowdown compared to older versions (<v0.11.0) on my older GPUs (GTX 1080Ti).
I train on eight to ten GPUs, with mixed precision and a batch size of 32.
Previously, one training step took approx. 3s, with the new version it takes approx. 15s. When I open the nvidia-smi tool, I observe strange behaviour: most of the time, GPU-utilization is 100%, but the power usage is half the time about 80W and then about 150W (out of 250W). This is unlike on my newer GPUs (Quadro RTX 5000) where the utilization changes a lot (but still is about 100%) and power usage is most of the time >200W (out of 230W).
Also, when I scale down the training to fewer GPUs I am struggling to get the model to converge. Previously, I've just adjusted ``accumulate_grad_batches`` accordingly and it worked.
I suspect this behaviour stems from the backend. In previous versions, I used Apex. With the current version, it seems that it is not possible anymore (it states that the Apex is not supported).
Might the slowdown be caused by not using Apex? Is it somehow possible to use it with the current version?
Can you please specify the exact parameters to replicate the QuartzNet training on LibriSpeech on one and more GPUs using mixed precision?
Thanks,
Peter
**Environment details**
- OS version Ubuntu 18.04
- PyTorch version 1.7.1
- Python version Python 3.7.6
**Additional context**
CUDA 10.2,
Driver (on both, new and old GPUs) 440.33.01
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"No, you should be able to get much higher accuracies for 'megatron-bert-345m-uncased' on Squadv2 dataset.\r\nThis what I got after 2 epochs of training em=81.35%, f1=84.75%.\r\nI used batch size=12, precision=16, amp_level=01",
"Actually, I think I know the reason for the low numbers. We had a recent bug in QA m... | 2021-02-12T08:41:43 | 2021-04-26T17:08:08 | 2021-04-26T17:08:07 | NONE | {
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} | null | null | null | Hello,
On training QAModel with squadv2 dataset keeping pretrained_model_name=megatron-bert-345m-uncased and version_2_with_negative=True does not seem to train properly (val_em=50.07, val_f1=50.07). But this is not the case when I keep pretrained_model_name=bert-base-uncased or albert-xlarge-v2, in both of which I get good validation results (bert em=72, f1=76 ; albert-xlarge em=82.8 f1=86.2). This tells that megatron is not handling questions with is_impossible flag. Do I need to configure something else for megatron for it to work with squadv2 structure?
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} | …ePiece needs to be the first thing done after coming out of the BPE decoder, before Moses. It also fixes a bug introduced when Chinese was added. The code had removed Moses Detokenization for Japanese during inference all together
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"This pull request **introduces 1 alert** when merging 39fe1a548baa62507241a391b05d471ab05d5d76 into 719e21d402679e0edbdb7fdcc41c5bcd4af295d8 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5b66e21ee1c4104cca572d02b00a48546f1e7c3b)\n\n**new alerts:**\n\n* 1 for Unused local variable"
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"What exactly do you mean by \"Method 1\" and \"Method 2\". Could you please paste code snippets? ",
"\"Method 1. \"\r\n```\r\nprint(first_asr_model.transcribe(paths2audio_files=['pass path to .wav file']))\r\n```\r\n\"Method 2.\"\r\n```\r\n# Bigger batch-size = bigger throughput\r\nparams['model']['validation_ds... | 2021-02-10T21:37:59 | 2021-06-19T05:00:48 | 2021-06-19T05:00:48 | NONE | {
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} | null | null | null | I was using methods from this [tutorial for ASR](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/asr/01_ASR_with_NeMo.ipynb) and you provide two possible ways for inference.
1. model.transcribe()
2. [Pure PyTorch inference loop](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0b3/tutorials/asr/01_ASR_with_NeMo.ipynb#scrollTo=7mP4r1Gx_Ilt&line=5&uniqifier=1)
I have trained russian language quartznet15x5 and when I use method "1." my output is [""] predicting nothing. When I use method "2." and handle my test dataset (500 audio .wav files) it gives pretty good predictions and for the same audio file I get correct prediction insted of [""]. However, when I pass manifest with only 1 audio file insted of ~500 prediction gets worse, not getting the same correct prediction as with many audio files in manifest. For the method '2.' it is not correct because prediction of a single audio file does not affect the prediction of another. Can you explain the problems with both "1" and "2" methods? If it is needed I can provide the same example with english model not russian. | {
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"Agreed about... | 2021-02-10T02:57:01 | 2021-02-11T16:53:48 | 2021-02-11T13:52:41 | COLLABORATOR | {
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} | # Changelog
- Add proper depth analysis for nested type definition
- Add several tests to capture cases occurring due to container checks
- Add typecheck argument `ignore_collections` in order to fall back to previous behavior - ignore nesting entirely
- Update type definition of older code to correctly declare nested types
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} | …exports/functions cleanup
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1739 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1739/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1739/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1739/events | https://github.com/NVIDIA/NeMo/pull/1739 | 804,966,381 | MDExOlB1bGxSZXF1ZXN0NTcwNjQ5NjA2 | 1,739 | NLP, Megatron and Tools docs | {
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"@ekmb is this only docs? Should it go to r1.0.0rc1 then?"
] | 2021-02-09T22:04:56 | 2021-03-15T21:54:58 | 2021-03-15T21:54:54 | COLLABORATOR | {
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} | This PR will add the ability to automatically train tokenizers and preprocess data from the NeMo config for NMT. For very large datasets it can be prohibitive to train them without first creating a tarred dataset. If the user wants to change tokenizer, vocab size, or batch size, for example, they must reprocess the data with the correct configuration. With this PR, users can sweep through data configurations and the tarred dataset will be automatically created according to the model configuration.
The tarred dataset creation code is taken directly from https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/create_tarred_parallel_dataset.py
# Automatic tokenizer training
If user specifies that they want to use the 'yttm' tokenizer but does not specify a trained tokenizer model, then the tokenizer model will automatically be trained and saved to `preproc_out_dir`. The config will then be updated with the newly trained tokenizer model.
# Automatic data preprocessing
This PR changes the translation dataset configuration slightly. `load_from_tarred_dataset` has been removed. Now, if users want to use tarred datasets (recommended) then they should specify `use_tarred_dataset=True`. If `use_tarred_dataset` is `True`, then we will check for `train_ds.tar_files`. If `train_ds.tar_files` is `None` or does not exists, then we will automatically create the tar files and save them to `preproc_out_dir`.
Note: if user specifies `train_ds.tar_files` then those will be used and no data preprocessing will happen.
Note: if user does not specify `train_ds.tar_files` but the tarred dataset has already been created, then the existing dataset will be used. This would happen if the training script were run multiple times w/o modifying relevant dataset preprocessing configuration.
# Large Dataset Workflow
For large datasets it can be useful to process them in a different environment than the training environment.
In this case, or in any situation where the user wants to process the data but not train at the same time,
they can set `do_training=False` and only the data will be preprocessed.
# Usage
## Automatically train tokenizer and preprocess data
User only specifies src and tgt data for training.
```
python /home/eharper/github/NeMo/examples/nlp/machine_translation/enc_dec_nmt.py -cn aayn_base \
model.encoder_tokenizer.vocab_size=32000 \
model.decoder_tokenizer.vocab_size=32000 \
+model.encoder_tokenizer.bpe_dropout=0.1 \
+model.decoder_tokenizer.bpe_dropout=0.1 \
model.preproc_out_dir=/raid/data/en_es_preproc \
model.train_ds.src_file_name=/raid/data/72241/parallel/train.clean.en-es.60.tok.en.shuffled \
model.train_ds.tgt_file_name=/raid/data/72241/parallel/train.clean.en-es.60.tok.es.shuffled \
model.train_ds.tokens_in_batch=16000 \
model.train_ds.use_tarred_dataset=true \
...
```
## Use existing tokenizer model and tarred dataset
```
python /home/eharper/github/NeMo/examples/nlp/machine_translation/enc_dec_nmt.py -cn aayn_base \
model.encoder_tokenizer.tokenizer_model=/raid/data/en_es_preproc/shared_tokenizer.32000.BPE.model \
model.decoder_tokenizer.tokenizer_model=/raid/data/en_es_preproc/shared_tokenizer.32000.BPE.model \
model.encoder_tokenizer.vocab_size=32000 \
model.decoder_tokenizer.vocab_size=32000 \
+model.encoder_tokenizer.bpe_dropout=0.1 \
+model.decoder_tokenizer.bpe_dropout=0.1 \
model.train_ds.tokens_in_batch=16000 \
model.train_ds.use_tarred_dataset=true \
model.train_ds.tar_files=/raid/data/en_es_preproc/batches.tokens.16000._OP_1..1000_CL_.tar \
model.train_ds.metadata_file=/raid/data/en_es_preproc/metadata.tokens.16000.json \
model.validation_ds.src_file_name=/raid/data/72241/parallel/newstest2012-en-es.clean.tok.src \
model.validation_ds.tgt_file_name=/raid/data/72241/parallel/newstest2012-en-es.clean.tok.ref \
...
```
## Process data from text files during dataset creation
Should only be used for rapid prototyping with small datasets.
```
python examples/nlp/machine_translation/enc_dec_nmt.py -cn aayn_base \
model.preproc_out_dir=/raid/data/toy_wmt_preproc \
model.train_ds.src_file_name=/raid/data/toy_wmt/toy_data/wmt14-de-en.src \
model.train_ds.tgt_file_name=/raid/data/toy_wmt/toy_data/wmt14-de-en.ref \
model.validation_ds.src_file_name=/raid/data/toy_wmt/toy_data/val/wmt14-de-en.src \
model.validation_ds.tgt_file_name=/raid/data/toy_wmt/toy_data/val/wmt14-de-en.src \
model.test_ds.src_file_name=/raid/data/toy_wmt/toy_data/val/wmt14-de-en.src \
model.test_ds.tgt_file_name=/raid/data/toy_wmt/toy_data/val/wmt14-de-en.src \
model.encoder_tokenizer.vocab_size=2000 \
model.decoder_tokenizer.vocab_size=2000 \
...
```
# NMT Data Preprocessing TODOs
This should be done but not necessarily in this PR.
- Manifest file should include everything needed for the tarred dataest. Config should use manifest with first priority.
- Multiprocessing should be added to the tarred dataset creation. | {
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"This pull request **introduces 4 alerts** when merging f5a2dbfb6f1f1bd595fd00682be5e54e902ef96a into 25abffdd37efb3a9f5a6e236d910f045271ae08f - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-c72ad221ab5286086e5b409c23e1e96df83bfe0c)\n\n**new alerts:**\n\n* 3 for Unused import\n* 1 for Module is ... | 2021-02-09T20:47:50 | 2021-02-11T00:04:34 | 2021-02-11T00:04:34 | CONTRIBUTOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1735 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1735/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1735/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1735/events | https://github.com/NVIDIA/NeMo/pull/1735 | 804,710,277 | MDExOlB1bGxSZXF1ZXN0NTcwNDMyMzM2 | 1,735 | Fix Subword Tokenization notebook to run locally. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1734 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1734/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1734/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1734/events | https://github.com/NVIDIA/NeMo/issues/1734 | 804,587,566 | MDU6SXNzdWU4MDQ1ODc1NjY= | 1,734 | [Question] Updating PyPI with latest NeMo release(s) | {
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} | [] | closed | false | null | [] | null | [
"You should use:\r\n```\r\npip install nemo-toolkit[all]==1.0.0b3\r\n```\r\nOr in your requirements.txt file\r\n```\r\nnemo-toolkit[all]==1.0.0b3\r\n```",
"Thanks a lot for the quick reply @okuchaiev . I have seen that nemo 1.0.0b4 was recently released and I am looking forward to incorporating the latest change... | 2021-02-09T14:11:48 | 2021-02-17T14:39:10 | 2021-02-09T20:24:59 | NONE | {
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} | null | null | null | **Describe your question**
I am trying to add the latest `v1.0` release of NeMo as a pip dependency on my project. However, this version has not been uploaded to PyPI yet. (https://pypi.org/project/nemo-toolkit/). Do you have any plans or timeline for the update? This would help me greatly in making my requirements file consistent and easy-to-maintain.
**Environment overview**
- Environment location: Bare-metal
- Method of NeMo install: `pip install nemo-toolkit`
**Environment details**
- OS version `Linux`
- PyTorch version `1.7.1`
- Python version `3.8` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1733 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1733/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1733/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1733/events | https://github.com/NVIDIA/NeMo/issues/1733 | 804,540,700 | MDU6SXNzdWU4MDQ1NDA3MDA= | 1,733 | [Question] Is it possible to implement BERT in ASR? | {
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"Doing this for every single step in beam search process would be too computationally expensive.\r\nInstead you can re-score candidate beams from beam search like we did with transformer XL in this paper:\r\nhttps://arxiv.org/pdf/1904.03288.pdf "
] | 2021-02-09T13:14:53 | 2021-02-09T20:24:13 | 2021-02-09T20:24:11 | NONE | {
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} | null | null | null | Hi all,
i am workin on a quartznet model with 5-gram beam search LM for decode the CTC matrix.
I was wondering if it could be possible to implement a BERT-based LM on top of my asr model.
If i simply mask the uncorrect transcripted word and then run BERT, of course the word would be replaced with a new one, but not necessarily the correct one. BERT could replace a mispelled word with a totally different word that fits right in the context. I guess BERT should be fed with audio waveform information to replace the word with the correct one instead with a different one.
Is it possible? Any suggestion on related work? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1732 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1732/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1732/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1732/events | https://github.com/NVIDIA/NeMo/pull/1732 | 804,016,951 | MDExOlB1bGxSZXF1ZXN0NTY5ODU2OTAy | 1,732 | Dev deps cnt | {
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"We should close this PR - its sole purpose was to enable ONNX export for Megatron (in Nemo b3 environment) - which we have found is broken anyway in b4 (due to Pytorch version).\r\nSo it does not solve the issue for our standard containers and introduces risks.",
"please note I still need EFF."
] | 2021-02-08T22:20:25 | 2021-03-04T17:08:16 | 2021-02-16T05:21:08 | CONTRIBUTOR | {
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} | # Changelog
- Patch support for correct decoding of character based RNNT models
- Add support for changing decoding strategy
- Tests for changing decoding strategy | {
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} | Needed to uninstall the version of `sacrebleu` that was in the existing container and then install `sacrebleu[ja]`. Also changed the validation scoring to use the Japanese `ja-mecab` tokenizer with sacrebleu instead of `13a`. | {
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"Hey @avikpal00, thanks for reaching out!\r\nYes, we indeed support this model! \r\nSimilar to the link you posted, you would initialize the backbone BERT encoder with albert-xlarge-v2 and train on SQuADv2.0 with the hyperparameters they used, so your cmd line would look something like this:\r\n\r\npython question_... | 2021-02-08T17:22:36 | 2021-05-28T08:34:24 | 2021-05-28T08:34:24 | NONE | {
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I am trying to load a language model checkpoint from huggingface models, specifically [albert-xlarge-v2-squad-v2](https://huggingface.co/ktrapeznikov/albert-xlarge-v2-squad-v2), for QAModel. I am not sure if it's already a feature implemented, if it is, how do I go about doing this? | {
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Signed-off-by: fayejf <fayejf07@gmail.com> | {
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} | If only trainer.test was being called then megatron was not finishing it's init.
This should fix this issue: https://github.com/NVIDIA/NeMo/issues/1700
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"> Approving, but is this change related to TLT at all?\r\n> \r\n> Seems you are just polishing a bit functionality of a QA training NeMo script...\r\n\r\nShould not affect tlt"
] | 2021-02-05T18:23:40 | 2021-03-04T17:09:03 | 2021-02-05T21:30:31 | CONTRIBUTOR | {
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} | this is rebased on https://github.com/NVIDIA/NeMo/pull/1721
please merge after that | {
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"@yzhang123 it's this needed for tlt? if so, it should go to b4 ",
"> @yzhang123 it's this needed for tlt? if so, it should go to b4\r\n\r\nI'm not sure about that status. @vladgets could you take comment?"
] | 2021-02-05T01:45:28 | 2021-03-04T17:09:12 | 2021-02-05T04:15:23 | CONTRIBUTOR | {
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} | Fix error from Dec 17, 2020 https://github.com/NVIDIA/NeMo/pull/1569/files
Signed-off-by: Yang Zhang <yangzhang@nvidia.com> | {
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"@yzhang123 after trying out TLT, I found it's cleaner to have the training and evaluation parts separated.\r\nAlso, we had multiple issues with exp_manager and PyTorch Lightning updates when multiple trainers were created in the same script.\r\n",
"> @yzhang123 after trying out TLT, I found it's cleaner to have ... | 2021-02-05T00:03:14 | 2021-02-06T00:01:15 | 2021-02-06T00:01:10 | COLLABORATOR | {
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} | - [X] updated config for new pre-trained checkpoints, included test_ds by default
- [X] split the token_classification script into 2: train and eval+infer
- [X] added more comments on how to use NER scripts
- [X] updated Jenkins tests for NER scripts with new .nemo
- [x] added more comments on how to use Punct scripts
- [x] split the punctuation script into 2: train and eval+infer
- [x] updated Jenkins tests for Punct scripts with new .nemo | {
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} | # Changelog
- Adds tests for restore_from, overriden config restore_from, support for OmegaConf object instead of filepath for overrides and support for returning models internal config instead of instantiation of model
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"@okuchaiev fyi"
] | 2021-02-04T23:28:00 | 2021-02-05T01:13:09 | 2021-02-04T23:48:24 | COLLABORATOR | {
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- Adds tests for restore_from, overriden config restore_from, support for OmegaConf object instead of filepath for overrides and support for returning models internal config instead of instantiation of model
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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* Cleaned up unused code in speech_to_text_sclite.py | {
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"actually closing this one since b4 already seems to have newer versions"
] | 2021-02-04T18:04:45 | 2022-08-16T16:15:39 | 2021-07-19T23:00:43 | CONTRIBUTOR | {
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} | Dependencies on new NVDIA PyPI packages.
Note: This will work "outside" once the new wheels will be distributed. | {
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"This pull request **introduces 1 alert** when merging a21f32c2a521c11affc98bbf8982ed309fd296d0 into f4fbe89e39047c49643a681452a08e2c15dfca15 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-01167a88d9bb6ba6805a96ed527a102181478853)\n\n**new alerts:**\n\n* 1 for Syntax error",
"This pull reques... | 2021-02-04T17:49:38 | 2021-02-05T22:49:31 | 2021-02-05T22:49:31 | CONTRIBUTOR | {
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} | …t as an option during inference. Also adding a different Detokenizer for Japanese based on this option
Signed-off-by: rprenger <rprenger@nvidia.com> | {
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"Same question.",
"There are mainly 3 speaker-related tasks:\r\n\r\n- speaker recognition\r\n- speaker verification and\r\n- speaker diarization\r\n\r\n\r\n### Speaker Recognition:\r\nSpeaker Recognition is a task when you train a speaker recognition network with known speaker labels and test on the same known sp... | 2021-02-04T09:12:30 | 2021-02-26T20:48:37 | 2021-02-26T20:39:49 | NONE | {
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} | null | null | null | I have been using https://github.com/NVIDIA/NeMo/tree/main/tutorials/speaker_recognition.
There is a way we can get embeddings for speaker recognition. (https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/spkr_get_emb.py)
But didn't find any information, how to use those embeddings for the speaker recogniton.
Do I need to build something else on top of it, do I do cosine similarity?
Please advice. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1708 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1708/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1708/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1708/events | https://github.com/NVIDIA/NeMo/pull/1708 | 800,832,371 | MDExOlB1bGxSZXF1ZXN0NTY3MjU1MDU5 | 1,708 | export() cleanup | {
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"This pull request **introduces 16 alerts** when merging c90bef348f061e638f8d9e14c6d04ff9b1e4a0c6 into 24674db0995736ece1d55515d300f5ef725bf4ba - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-12332d4be2b2234f7a6bf7ff7304577ac875b65b)\n\n**new alerts:**\n\n* 16 for Unused import",
"This pull re... | 2021-02-04T00:59:12 | 2021-02-04T23:51:28 | 2021-02-04T23:51:28 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1707 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1707/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1707/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1707/events | https://github.com/NVIDIA/NeMo/pull/1707 | 800,768,084 | MDExOlB1bGxSZXF1ZXN0NTY3MjAwNDMx | 1,707 | Speech Regression Support | {
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"Hi @titu1994. Well, this was the hard way I guess, but I think that the result is what I wanted. Please let me know how it's coming out",
"And error case is caught in the EncDecRegressionModel - \r\n```\r\n=================================== FAILURES ===================================\r\n_________________ TestE... | 2021-02-03T22:58:26 | 2021-02-19T21:07:41 | 2021-02-19T01:39:39 | CONTRIBUTOR | {
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} | Support for Encoder-Decoder models that learn to predict numeric values in a regression model. An example application is non-intrusive speech-quality assessment as presented here:
https://www.microsoft.com/en-us/research/uploads/prod/2019/04/preprint_ICASSP2019_MOS_estimation.pdf | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1706 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1706/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1706/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1706/events | https://github.com/NVIDIA/NeMo/pull/1706 | 800,751,045 | MDExOlB1bGxSZXF1ZXN0NTY3MTg2MzM1 | 1,706 | HifiGAN MelSpectrogram Vocoder Model | {
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"This pull request **introduces 2 alerts** when merging 666b2f0424ed018b360d614556d69303eace460c into 4c1d7b586fe764894f3468a11369aebc4962fa9f - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-45a9c8c1c8841634c3bfb490060da2fd2d84f798)\n\n**new alerts:**\n\n* 2 for Unused import",
"please check D... | 2021-02-03T22:26:12 | 2021-02-16T21:56:30 | 2021-02-16T21:56:30 | CONTRIBUTOR | {
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} | This PR introduces the HifiGAN vocoder to NeMo. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1705 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1705/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1705/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1705/events | https://github.com/NVIDIA/NeMo/pull/1705 | 800,738,999 | MDExOlB1bGxSZXF1ZXN0NTY3MTc2MjQ3 | 1,705 | Update Citrinet configs | {
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} | # Changelog
- Add Citrinet 512 config
- Update Citrinet 384 config | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1702 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1702/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1702/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1702/events | https://github.com/NVIDIA/NeMo/pull/1702 | 800,610,431 | MDExOlB1bGxSZXF1ZXN0NTY3MDY4MDAx | 1,702 | Update callbacks.py | {
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"This pull request **introduces 1 alert** when merging 815f56384103352d86009929db2d563bcfd025be into 941ef1fd71bd2515a4ba7092d65146edfddc1229 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-0a5876cb39e3cfc1fc9406351d69ccb93bcbcd7a)\n\n**new alerts:**\n\n* 1 for Unused import"
] | 2021-02-03T19:00:29 | 2021-02-03T19:12:54 | 2021-02-03T19:05:29 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1701 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1701/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1701/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1701/events | https://github.com/NVIDIA/NeMo/pull/1701 | 800,609,510 | MDExOlB1bGxSZXF1ZXN0NTY3MDY3MjE4 | 1,701 | Update TTS Tutorials for 1.0.0b4 | {
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"> LGTM\r\n> \r\n> Will there be a separate tutorial for exporting TTS to Jarvis?\r\n\r\nNo I think that will just be a page inside the Jarvis docs"
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"@ekmb @ericharper this is independent from QAModel, since it works for BERT backbone with QAModel. Could you take a look when restoring .nemo model trained with Megatron? I checked it happens both with Megatron and BioMegatron",
"This should be fixed now, see #1724. Please let us know if you any more issues.",
... | 2021-02-03T17:32:00 | 2021-02-10T23:26:04 | 2021-02-10T23:26:04 | NONE | {
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} | null | null | null | **Bug Description**
Getting _Exception: cuda rng state model-parallel-rng is not added_ error when trying to evaluate/inference a pretrained QAModel with biomegatron lm after restoring the model from a .nemo file. However I am able to run the evaluate/inference right after training (i.e. trainer.fit).
Code :
`trainer.test(restored_model)`
### Traceback
<details>
``` python
Exception Traceback (most recent call last)
<ipython-input-7-b3320e12a098> in <module>
1 restored_model.setup_test_data(test_data_config=config.model.test_ds)
----> 2 trainer.test(restored_model)
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in test(self, model, test_dataloaders, ckpt_path, verbose, datamodule)
753
754 if model is not None:
--> 755 results = self.__test_given_model(model, test_dataloaders)
756 else:
757 results = self.__test_using_best_weights(ckpt_path, test_dataloaders)
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in __test_given_model(self, model, test_dataloaders)
818 self.testing = True
819 self.model = model
--> 820 results = self.fit(model)
821 self.testing = False
822
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
471 self.call_hook('on_fit_start')
472
--> 473 results = self.accelerator_backend.train()
474 self.accelerator_backend.teardown()
475
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in train(self)
64
65 # train or test
---> 66 results = self.train_or_test()
67 return results
68
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py in train_or_test(self)
65 def train_or_test(self):
66 if self.trainer.testing:
---> 67 results = self.trainer.run_test()
68 else:
69 results = self.trainer.train()
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_test(self)
660 # self.reset_test_dataloader(ref_model)
661 with self.profiler.profile("run_test_evaluation"):
--> 662 eval_loop_results, _ = self.run_evaluation(test_mode=True)
663
664 if len(eval_loop_results) == 0:
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, test_mode, max_batches)
607 # lightning module methods
608 with self.profiler.profile("evaluation_step_and_end"):
--> 609 output = self.evaluation_loop.evaluation_step(test_mode, batch, batch_idx, dataloader_idx)
610 output = self.evaluation_loop.evaluation_step_end(output)
611
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/evaluation_loop.py in evaluation_step(self, test_mode, batch, batch_idx, dataloader_idx)
173 if self.testing:
174 model_ref._current_fx_name = "test_step"
--> 175 output = self.trainer.accelerator_backend.test_step(args)
176 else:
177 model_ref._current_fx_name = "validation_step"
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in test_step(self, args)
85
86 def test_step(self, args):
---> 87 return self._step(self.trainer.model.test_step, args)
88
89 def to_device(self, batch):
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in _step(self, model_step, args)
72 if self.trainer.amp_backend == AMPType.NATIVE:
73 with torch.cuda.amp.autocast():
---> 74 output = model_step(*args)
75 else:
76 output = model_step(*args)
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/question_answering/qa_model.py in test_step(self, batch, batch_idx)
117
118 def test_step(self, batch, batch_idx):
--> 119 return self.validation_step(batch, batch_idx)
120
121 def validation_epoch_end(self, outputs):
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/question_answering/qa_model.py in validation_step(self, batch, batch_idx)
103
104 input_ids, input_type_ids, input_mask, unique_ids, start_positions, end_positions = batch
--> 105 logits = self.forward(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)
106 loss, start_logits, end_logits = self.loss(
107 logits=logits, start_positions=start_positions, end_positions=end_positions
/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
533
534 # Call the method - this can be forward, or any other callable method
--> 535 outputs = wrapped(*args, **kwargs)
536
537 instance._attach_and_validate_output_types(output_types=output_types, out_objects=outputs)
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/question_answering/qa_model.py in forward(self, input_ids, token_type_ids, attention_mask)
80 @typecheck()
81 def forward(self, input_ids, token_type_ids, attention_mask):
---> 82 hidden_states = self.bert_model(
83 input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask
84 )
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
742 result = self._slow_forward(*input, **kwargs)
743 else:
--> 744 result = self.forward(*input, **kwargs)
745 for hook in itertools.chain(
746 _global_forward_hooks.values(),
/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
533
534 # Call the method - this can be forward, or any other callable method
--> 535 outputs = wrapped(*args, **kwargs)
536
537 instance._attach_and_validate_output_types(output_types=output_types, out_objects=outputs)
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/megatron_bert.py in forward(self, input_ids, attention_mask, token_type_ids)
119 position_ids = bert_position_ids(input_ids)
120
--> 121 sequence_output = self.language_model(
122 input_ids=input_ids,
123 position_ids=position_ids,
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
742 result = self._slow_forward(*input, **kwargs)
743 else:
--> 744 result = self.forward(*input, **kwargs)
745 for hook in itertools.chain(
746 _global_forward_hooks.values(),
/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py in forward(self, input_ids, position_ids, attention_mask, tokentype_ids, layer_past, get_key_value, pooling_sequence_index)
304
305 # Transformer.
--> 306 transformer_output = self.transformer(embedding_output,
307 attention_mask,
308 layer_past=layer_past,
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
742 result = self._slow_forward(*input, **kwargs)
743 else:
--> 744 result = self.forward(*input, **kwargs)
745 for hook in itertools.chain(
746 _global_forward_hooks.values(),
/opt/conda/lib/python3.8/site-packages/megatron/model/transformer.py in forward(self, hidden_states, attention_mask, layer_past, get_key_value)
579 if layer_past is not None:
580 past = layer_past[index]
--> 581 hidden_states = layer(hidden_states,
582 attention_mask,
583 layer_past=past,
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
742 result = self._slow_forward(*input, **kwargs)
743 else:
--> 744 result = self.forward(*input, **kwargs)
745 for hook in itertools.chain(
746 _global_forward_hooks.values(),
/opt/conda/lib/python3.8/site-packages/megatron/model/transformer.py in forward(self, hidden_states, attention_mask, layer_past, get_key_value)
417 # Self attention.
418 attention_output, attention_bias = \
--> 419 self.attention(layernorm_output,
420 attention_mask,
421 layer_past=layer_past,
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
742 result = self._slow_forward(*input, **kwargs)
743 else:
--> 744 result = self.forward(*input, **kwargs)
745 for hook in itertools.chain(
746 _global_forward_hooks.values(),
/opt/conda/lib/python3.8/site-packages/megatron/model/transformer.py in forward(self, hidden_states, attention_mask, layer_past, get_key_value)
294 # This is actually dropping out entire tokens to attend to, which might
295 # seem a bit unusual, but is taken from the original Transformer paper.
--> 296 with mpu.get_cuda_rng_tracker().fork():
297 attention_probs = self.attention_dropout(attention_probs)
298
/opt/conda/lib/python3.8/contextlib.py in __enter__(self)
111 del self.args, self.kwds, self.func
112 try:
--> 113 return next(self.gen)
114 except StopIteration:
115 raise RuntimeError("generator didn't yield") from None
/opt/conda/lib/python3.8/site-packages/megatron/mpu/random.py in fork(self, name)
181 # Check if we have added the state
182 if name not in self.states_:
--> 183 raise Exception('cuda rng state {} is not added'.format(name))
184 # Store current rng state.
185 orig_cuda_rng_state = torch.cuda.get_rng_state()
Exception: cuda rng state model-parallel-rng is not added
```
</details>
**Environment overview**
- Environment location: GCP
- Method of NeMo install: python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
- NVIDIA docker image: nvcr.io/nvidia/pytorch:20.12-py3
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https://api.github.com/repos/NVIDIA/NeMo/issues/1698 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1698/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1698/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1698/events | https://github.com/NVIDIA/NeMo/issues/1698 | 800,266,861 | MDU6SXNzdWU4MDAyNjY4NjE= | 1,698 | ASR : Is BPE ASR model is better in accuracy compared to normal char | {
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"Subword automatically will not guarantee you better WER, but it does have a few advantages that help make it possible for models to perform better.\r\n\r\nTheres a new tutorial for ASR with subword encoding here \r\nwhich you could look into for some info on how subword helps CTC based model training - https://col... | 2021-02-03T12:06:21 | 2021-02-03T19:08:57 | 2021-02-03T19:08:56 | NONE | {
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} | null | null | null | I have been using ASR and there are two variant one is normal and the other is the BPE variant config file
Char: https://github.com/NVIDIA/NeMo/blob/main/examples/asr/experimental/conformer/conformer_char.yaml
BPE: https://github.com/NVIDIA/NeMo/blob/main/examples/asr/experimental/conformer/conformer_bpe.yaml
I know that BPE is for Unicode characters and char is where we can represent transcripts with normal characters.
But will there be more accuracy if I use BPE instead of char for English ASR training?
I have been reading some other articles and I am a bit confused, so the question may be stupid. Please clarify.
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https://api.github.com/repos/NVIDIA/NeMo/issues/1697 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1697/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1697/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1697/events | https://github.com/NVIDIA/NeMo/pull/1697 | 799,892,702 | MDExOlB1bGxSZXF1ZXN0NTY2NDcxODM2 | 1,697 | Update conformer | {
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"This pull request **introduces 2 alerts** when merging 2ef5e0aa075a37fe454e06ebd21c9a1a7997e387 into 941ef1fd71bd2515a4ba7092d65146edfddc1229 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3e6ed5bf88ad7156404b98a3a0a051245a0f4195)\n\n**new alerts:**\n\n* 2 for Unused import",
"This pull requ... | 2021-02-03T01:59:59 | 2021-08-06T08:01:59 | 2021-02-08T19:51:14 | COLLABORATOR | {
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} | + Polished the conformer code and configs.
+ Added mask_value to the specaug
+ Refactored positional embedding classes | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1696 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1696/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1696/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1696/events | https://github.com/NVIDIA/NeMo/pull/1696 | 799,849,370 | MDExOlB1bGxSZXF1ZXN0NTY2NDM0MzQx | 1,696 | Speaker diarization | {
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"This pull request **introduces 1 alert** when merging b6411e54d856fbe4193e259784afa2e08af516c5 into 941ef1fd71bd2515a4ba7092d65146edfddc1229 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9e7b6190b88049553be6394e4585e7cfbe3807f2)\n\n**new alerts:**\n\n* 1 for Variable defined multiple times",
... | 2021-02-03T00:47:04 | 2021-03-04T17:08:44 | 2021-02-06T01:29:25 | COLLABORATOR | {
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} | This PR enables speaker diarization inference capability in NeMo. It features and supports both oracle and non-oracle VAD diarization with spectral clustering.
Here are some of the new additions made:
- Adds support to diarize wav files given through list or from a file containing paths to audio files (speaker_diarize.py)
- Added ClusteringDiarizer model that combines VAD and Speaker Embeddings model to extract time stamps and Embeddings (clustering_diarizer.py)
- Added VAD utils to support overlap segment creation and probability smoothing for combining VAD decisions (vad_utils.py)
- Added script to get best thresholds for unseen data given through dev manifest. (vad_tune_threshold.py)
- Added utils to clean and combine time stamps from VAD. (speaker_utils.py)
- Perform clustering and writes predicted rttm (speaker_utils.py)
- Added rttm_to_manifest.py to create oracle manifest for performing diarization with any external VAD decision | {
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- Update ASR side documentation (add models, checkpoint to ngc, architecture diagram etc)
- Update doc build script to update mock imports based on requirements | {
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- Minor text preprocessing updates: phrases in quotes will be found before splitting the text by EOS punctuation marks + clean up
- Tutorial update | {
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"python 3.6\r\n PyTorch-lightning 1.1.6\r\nUbuntu 18.04.5\r\n",
"Can you please give more details. What language? Exact command/config ran.",
"language= English\r\nnotebook=https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/01_ASR_with_NeMo.ipynb\r\nI simply run the notebook on my GPU\r\n",
"training is h... | 2021-02-02T14:19:58 | 2021-02-15T06:25:04 | 2021-02-15T06:25:04 | NONE | {
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | Signed-off-by: PeganovAnton <peganoff2@mail.ru>
WMT parallel corpus contains many "broken" examples in which source or target are in a wrong language. The fractions of examples in WMT20 for which fasttext [lid.176.bin](https://fasttext.cc/docs/en/language-identification.html) model predicted language which differs from required:
commoncrawl: 7%
europarl: 0.85%
news_commentary: 4%
paracrawl: 0.5%
tilde: 25%
wikimatrix: 3%
wikititles: 25%
Some of bad examples are too short or contain only punctuation and many are in a wrong language.
The script goes through WMT 20 parallel corpus in 10 min if 20 CPU cores are used.
The script can also be used for filtering a monolingual corpus of arbitrary size. | {
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"This pull request **introduces 2 alerts** when merging 1d0515c72bb14c27e2e2f5ed23623e1135e1f4ce into 1f0dfd8a42c0cf77cbc6003fe5e622eabf25c2ad - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3ab36f119c946adee9799b4674b8b14d2849063f)\n\n**new alerts:**\n\n* 1 for Use of the return value of a proc... | 2021-02-01T22:32:23 | 2021-02-03T20:07:04 | 2021-02-03T17:13:22 | COLLABORATOR | {
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} | Applied same ONNX workarounds to Waveglow ONNX export as we had in JoC
Extended export_utils - fixed a few things.
Fixed Docker build. | {
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"As long as the pretrained tests don't fail, this change should be ok. I believe we have some save - restore - save - restore tests too which should test if the checkpoints are correctly being saves. "
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"This pull request **introduces 3 alerts** when merging 4d597442a3580a54ead14a7343604b4261978649 into 14904fd0381f5515a9773cf7a538dc09e08c8aa9 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cc8577ccccee49e7cd143e645da650effcd2095f)\n\n**new alerts:**\n\n* 2 for Unused import\n* 1 for Variable d... | 2021-02-01T16:21:38 | 2021-02-01T22:57:01 | 2021-02-01T22:56:45 | COLLABORATOR | {
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} | - support for errors' analysis (WER, CER, Word Matching Rate, word accuracy, diff)
- caching of computed metrics, statistics
- UI updates (hideable columns)
Signed-off-by: Vitaly Lavrukhin <vlavrukhin@nvidia.com> | {
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- Update ASR side documentation (add models, checkpoint to ngc, architecture diagram etc)
- Update doc build script to update mock imports based on requirements | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1684 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1684/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1684/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1684/events | https://github.com/NVIDIA/NeMo/pull/1684 | 797,296,081 | MDExOlB1bGxSZXF1ZXN0NTY0MzI5ODQ2 | 1,684 | fix eval_model in IntentSlot tutorial | {
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} | Ensures the reloaded (best) model is used for evaluation as intended. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1683 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1683/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1683/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1683/events | https://github.com/NVIDIA/NeMo/pull/1683 | 797,250,382 | MDExOlB1bGxSZXF1ZXN0NTY0MjkyNTQ4 | 1,683 | Add tarred dataset support for NMT | {
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"This pull request **introduces 1 alert** when merging f627e2fc99ea9d0ecb97718ce18063f4f77d099a into 8ccb4f3349de7603f7607dbd74de61795a5f5f68 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-7a30c88d861953193617907866ce02d73eb64851)\n\n**new alerts:**\n\n* 1 for Unused import"
] | 2021-01-29T22:35:30 | 2021-03-04T17:09:30 | 2021-02-02T05:12:22 | CONTRIBUTOR | {
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} | This PR adds support for Tarred Datasets when training NMT models on very large datasets. It avoids loading the entire dataset into CPU memory for every DDP process. It uses webdataset (https://github.com/tmbdev/webdataset) similar to ASR and language modeling.
To use this functionality
1. Run `python examples/nlp/machine_translation/create_tarred_parallel_dataset.py --clean --bpe_dropout 0.1 --shared_tokenizer --src_fname src_file.txt --tgt_fname tgt_file.txt --out_dir /path/to/out_dir --vocab_size 32000 --tokens_in_batch 16000 --lines_per_dataset_fragment 5000000 --num_batches_per_tarfile 1000` to create a folder that contains many tar files. Each tar file consists of a certain number of batches (specified by `num_batches_per_tarfile`), where each batch is a pickle file. The output folder will also contain `metadata.json` that contains the total number of training batches and YTTM tokenizer model files.
2. Point to the tarred dataset folder when training like so
```
python examples/nlp/machine_translation/enc_dec_nmt.py -c aayn_base \
trainer.gpus=8 \
+trainer.max_steps=300000
...........
model.train_ds.src_file_name=/path/to/out_dir/batches.tokens.16000._OP_1..100_CL_.tar \
model.train_ds.tgt_file_name=/path/to/out_dir/batches.tokens.16000._OP_1..100_CL_.tar \
+model.train_ds.load_from_tarred_dataset=true \
+model.train_ds.metadata_path=/path/to/out_dir/metadata.json \
+model.train_ds.tar_shuffle_n=100 \
.............
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1679 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1679/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1679/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1679/events | https://github.com/NVIDIA/NeMo/pull/1679 | 796,811,133 | MDExOlB1bGxSZXF1ZXN0NTYzOTI3MTM5 | 1,679 | Waveglow ONNX->TRT conversion, TRT working now | {
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} | [] | closed | false | null | [] | null | [
"This pull request **introduces 1 alert** when merging 2b77dfcca2b19881218f87bc99f1f1c85fa98c3a into 8a26496f415386f50adcead21ebd1e4a21a7ff42 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9556f0d6937d4abed6bef1a6fcdbb5f88bbac784)\n\n**new alerts:**\n\n* 1 for Unreachable code",
"This pull re... | 2021-01-29T11:37:14 | 2021-02-02T09:55:12 | 2021-02-02T09:55:12 | COLLABORATOR | {
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} | I can build TRT from ONNX now after 1D->2D conversion. Please pull from the last branch.
To get the real ONNX built, run test_waveglow.py (without pytest), after uncommenting this line:
model = WaveGlowModel.restore_from(“../WaveGlow-22050Hz-268M.nemo”)
(place .nemo accordingly).
There is also sample TRT build routine there - but whatever you used before should work as well.
This PR does contain a number of cleanups, plumbing and fixes. | {
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