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I am using online recognition as in the example:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Online_ASR_Microphone_Demo.ipynb
But when a large buffer size is set hard (example, 40 sec), the quality of recognition of small files suffers. This is due to the fact that the missing buffer is filled with zeros in the transcribe() function.
def _decode(self, frame):
assert len(frame)==self.n_frame_len
self.buffer[:len(frame)] = frame
logits = infer_signal(asr_model, self.buffer).cpu().numpy()[0]
decoded = self._greedy_decoder(logits, self.vocab)
return decoded
@torch.no_grad()
def transcribe(self, frame=None, merge=True):
if frame is None:
frame = np.zeros(shape=self.n_frame_len, dtype=np.float32)
if len(frame) < self.n_frame_len:
frame = np.pad(frame, [0, self.n_frame_len - len(frame)], 'constant')
unmerged = self._decode(frame)
if not merge:
return unmerged
return self.greedy_merge(unmerged)
This issue is resolved by switching to a resizable buffer, but results in GPU memory leaks.
def _decode(self, frame):
logits = infer_signal(asr_model, frame).cpu().numpy()[0]
decoded = self._greedy_decoder(logits, self.vocab)
return decoded
@torch.no_grad()
def transcribe(self, frame=None, merge=True):
if frame is None:
frame = np.zeros(shape=self.n_frame_len, dtype=np.float32)
unmerged = self._decode(frame)
if not merge:
return unmerged
return self.greedy_merge(unmerged)
What is the cause of this memory leak and what are the solutions other than a fixed buffer size?
**Environment details**
Ubuntu 18.04
Driver Version: 465.31
CUDA Version: 11.3
Docker image nvcr.io/nvidia/nemo:1.0.1
**Additional context**
RTX3090
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} | null | null | null | **Describe the bug**
I have been using the language model for bert pre training and also bert classifier and every time when I restart the container it goes to download the models and updates the existing ones.
I think it should first check for the directory which is present in the code but still it goes for downloading the pre trained weights from the hugging face link when training starts
**Steps/Code to reproduce bug**
Just download the weights for the first time and trying running the same container second time without net access. If we check the directory model weights are there still it tries to download.
**Environment overview (please complete the following information)**
- Image pulled from NGC cloud. version 1.0.1
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} | null | null | null | Hi,
I fine-tuned the QuartzNet15x5Base-En model with 235 hours dataset of Persian Mozilla Common Voice 3 months ago
and saved the check point file. Now I resorted the CKPT file and start fine-tuning it with another Persian dataset. When I want to save the .nemo file after fine-tuning it has error **dump_all() got an unexpected keyword argument 'sort_keys'** . How caan I solve this error?
Thanks
**Environment overview**
Environment location: Google Colab
Method of NeMo install:
BRANCH = 'r1.0.0rc1'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
Learning Rate: 0.002
**Environment details**
OS version : "Ubuntu20.04.3 LTS"
PyTorch version : "1.7.1" | {
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} | # Bugfix
- Fix an issue when changing the decoding config from the dataclass instead of the yaml config
- Fix notebook comment style
Signed-off-by: smajumdar <[email protected]> | {
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} | Signed-off-by: Boris Fomitchev <[email protected]>
Fixes https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3361038&cmtNo= | {
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} | This PR adds a parameter `phoneme_prob` to the `vocab` section of TTS datasets. That parameter sets the probability of using phonemes vs. graphemes during training. When using graphemes, the whole G2P pipeline is skipped and the model is presented with graphemes only. Functionality when using phonemes is not affected, and the default value of `phoneme_prob` is 1 so older config files will not be affected by this change. | {
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} | null | null | null | **Describe the bug**
I'm trying to use speech_to_text_bpe.py to fine-train Citrinet. I've managed to fine-train citrinet with reasonable success using my own code getting a WER of 16 on 8Khz audio upsampled to 16Khz. However, I would rather use the scripts if possible as it will be easier to maintain longer term. When I use the speech_to_text_xxx.py scripts, the results are poor with my WER sitting at around 95, so clearly I'm doing something wrong.
**Steps/Code to reproduce bug**
I first create by subword tokenizer, this was using spe but i've also tried wpe. To be honest I'm not sure about the correct setting for vocab_size and if 'spe' is the correct tokenizer to use.
!python /root/asr16000/NeMo/scripts/tokenizers/process_asr_text_tokenizer.py \
--manifest="/root/asr16000/training/training_samples.json" \
--data_root="/root/asr16000/tokenizer" \
--vocab_size=64 \
--tokenizer='spe' \
--no_lower_case \
--spe_type='unigram' \
--spe_character_coverage=1.0 \
--log
Then I train the model, I assume I should use the '_bpe' script version here.
!python /root/asr16000/NeMo/examples/asr/speech_to_text_bpe.py \
--config-path='/root/asr16000/NeMo/examples/asr/conf/citrinet/' \
--config-name='citrinet_512' \
model.train_ds.manifest_filepath='/root/asr16000/training/training_samples.json' \
model.train_ds.batch_size=16 \
model.validation_ds.manifest_filepath='/root/asr16000/training/eval_samples.json' \
model.validation_ds.batch_size=16 \
model.test_ds.manifest_filepath='/root/asr16000/training/eval_samples.json' \
model.test_ds.batch_size=16 \
model.tokenizer.dir='/root/asr16000/tokenizer/tokenizer_spe_unigram_v64/' \
model.tokenizer.type='bpe' \
model.optim.name="adamw" \
model.optim.lr=0.001 \
model.optim.betas=[0.9,0.999] \
model.optim.weight_decay=0.0001 \
model.optim.sched.warmup_steps=2000 \
trainer.gpus=1 \
trainer.accelerator="ddp" \
trainer.max_epochs=100 \
exp_manager.create_wandb_logger=True \
exp_manager.wandb_logger_kwargs.name="nemo-asr-script-citrinet" \
exp_manager.wandb_logger_kwargs.project="speech_to_text"
The /root/asr16000/NeMo directory is a clone of the 1.2 nemo repo. I have about 350 hours of audio for training.
**Expected behavior**
I would expect the WER to be better than 95, I was hoping for something lower than 16. Any pointers would be much appreciated.
**Environment overview (please complete the following information)**
- Environment location: AWS Sage Maker
- Method of NeMo install: %pip install nemo_toolkit['all']==1.2.0
**Environment details**
- OS version : Debian GNU/Linux 10 (buster)
- PyTorch version: 1.9.0+cu102
- Python version: 3.6.13
**Additional context**
GPU model A100
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} | null | null | null | Can we deploy ASR models to mobile platform? Is there any implementation available for mobile deployment? | {
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} | null | null | null | Hi!
You have a nice [CTC_Segmentation_Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/CTC_Segmentation_Tutorial.ipynb) where you split audio and its transcript into small chunks. Is it possible to return a timestamp for each word in the transcript? Or maybe NeMo has another model for that? | {
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} | null | null | null | **Describe the bug**
If I attempt to fine tune stt_en_citrinet_512, the training runs as expected but when I subsequently try to load a checkpoint I get the following error
FileNotFoundError: src path does not exist or it is not a path in nemo file. src value I got was: /home/smajumdar/PycharmProjects/nemo-eval/nemo_beta_eval/asrset/manifests/asrset_1.4/tokenizers/no_appen/tokenizer_spe_unigram_v1024/tokenizer.model. Absolute: /home/smajumdar/PycharmProjects/nemo-eval/nemo_beta_eval/asrset/manifests/asrset_1.4/tokenizers/no_appen/tokenizer_spe_unigram_v1024/tokenizer.model
I can see that the path is the tokenizer path from the original stt_en_citrinet_512 model, however, I don't understand why it's trying to load the file when I load the checkpoint.
**Expected behavior**
The checkpoint file should load without throwing a FileNotFound error
**Environment overview (please complete the following information)**
AWS Sagemaker
ml.g4dn.xlarge
%pip install nemo_toolkit['all']==1.2.0
Otherwise, please provide:
- OS version: Debian GNU/Linux 10 (buster)
- PyTorch version : 1.9.0+cu102
- Python version : 3 .6.13
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | +Fixed some typo mistakes in the ASR LM docs.
+ Added the support of using HF pretrained models like Transformer-XL and GPT instead of a nemo pretrained model.
User may pass the name of a pretrianed HF model instead of the model path.
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} | This PR fixes
1) NMT grpc server not setting provided source and target langs correctly
2) Issue with using a concatenation of multiple raw text files for training NMT models | {
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} | Signed-off-by: smajumdar <[email protected]> | {
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} | # Changelog
- Adds colab links to ASR tutorial section
NOTE: These links will be valid only after 1.3.0 branch becomes the stable branch | {
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] | closed | false | null | [] | null | null | 2021-08-13T16:07:32 | 2021-08-27T16:31:21 | 2021-08-27T16:07:49 | NONE | {
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} | null | null | null | I have trying to use the pretrained **Jaspernet model** to get the inference on my own data. I am using the following script:
`python examples/asr/speech_to_text_infer.py --asr_model="stt_en_jasper10x5dr" --dataset="pfe_16khz_rn/test.json" --use_cer
`
After running this script, I keep getting the following error. I am getting the same error on example **an4 dataset** as well. Can't see to find any solution online. I am using the NeMo docker file given in this repository for version 1.1.0.
```
root@408411e5bd29:/workspace/asr/NeMo#` python examples/asr/speech_to_text_infer.py --asr_model="stt_en_jasper10x5dr" --dataset="pfe_16khz_rn/test.json" --use_cer
/opt/conda/lib/python3.8/site-packages/pytorch_lightning/metrics/__init__.py:43: LightningDeprecationWarning: `pytorch_lightning.metrics.*` module has been renamed to `torchmetrics.*` and split off to its own package (https://github.com/PyTorchLightning/metrics) since v1.3 and will be removed in v1.5
rank_zero_deprecation(
################################################################################
### 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 2021-08-13 15:44:55 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 2021-08-13 15:44:56 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 2021-08-13 15:44:56 speech_to_text_infer:67] Using NGC cloud ASR model stt_en_jasper10x5dr
[NeMo I 2021-08-13 15:44:56 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.1.0/stt_en_jasper10x5dr/856ae08d5c4bd78b5e27f696e96f7aab/stt_en_jasper10x5dr.nemo.
[NeMo I 2021-08-13 15:44:56 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.1.0/stt_en_jasper10x5dr/856ae08d5c4bd78b5e27f696e96f7aab/stt_en_jasper10x5dr.nemo
[NeMo I 2021-08-13 15:44:56 common:676] Instantiating model from pre-trained checkpoint
[NeMo W 2021-08-13 15:45:05 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: /data2/voices/train_1k.json
sample_rate: 16000
labels:
- ' '
- 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
- ''''
batch_size: 32
trim_silence: true
max_duration: 16.7
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
[NeMo W 2021-08-13 15:45:05 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: /data2/voices/train_1k_samp.json
sample_rate: 16000
labels:
- ' '
- 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
- ''''
batch_size: 32
shuffle: false
[NeMo I 2021-08-13 15:45:05 features:252] PADDING: 16
[NeMo I 2021-08-13 15:45:05 features:269] STFT using torch
[NeMo I 2021-08-13 15:45:16 modelPT:439] Model EncDecCTCModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.1.0/stt_en_jasper10x5dr/856ae08d5c4bd78b5e27f696e96f7aab/stt_en_jasper10x5dr.nemo.
Traceback (most recent call last):
File "examples/asr/speech_to_text_infer.py", line 114, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "examples/asr/speech_to_text_infer.py", line 73, in main
asr_model.setup_test_data(
File "/opt/conda/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 "/opt/conda/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 "/opt/conda/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 "/opt/conda/lib/python3.8/contextlib.py", line 113, in __enter__
return next(self.gen)
File "/opt/conda/lib/python3.8/site-packages/omegaconf/omegaconf.py", line 937, in open_dict
prev_state = config._get_node_flag("struct")
AttributeError: 'dict' object has no attribute '_get_node_flag'
```
Any help is appreciated. Thanks in advance. | {
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} | # Changelog
- Add Intro to Transducer and ASR with Transducer tutorials
- Update readme
Signed-off-by: smajumdar <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2650 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2650/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2650/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2650/events | https://github.com/NVIDIA/NeMo/pull/2650 | 969,733,338 | MDExOlB1bGxSZXF1ZXN0NzExNzYwMDEz | 2,650 | Add Github Actions CI for ITN Tests | {
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} | # Changelog
- Add Github Actions CI for ITN tests
- Add (commented out) scheme for future SparrowHawk docker build + test | {
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} | This PR adds CI for bottleneck architecture.
Tests:
* bridge - identity initialization
* perceiver - params initialization
* nll, vae, and mim losses | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2648 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2648/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2648/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2648/events | https://github.com/NVIDIA/NeMo/pull/2648 | 969,417,731 | MDExOlB1bGxSZXF1ZXN0NzExNDYzMjE2 | 2,648 | Fix incorrect masking that always included all samples. | {
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} | The current nemo version of the tdnn masking performs comparison as follows:
```
lens_mat = torch.arange(max_len).to(device)
lens = lens * max_len
mask = lens_mat[:max_len].unsqueeze(0) < lens.unsqueeze(1)
```
If we expand the comparison and simplify device and unsqueeze calls , we get
```
torch.arange(max_len)[:max_len] < lens * max_len
```
This will be always True as long as each element of `lens` is larger than 1. The multiplication by `max_len` is not needed.
The bug can also be confirmed by running the test present in the PR after undoing the fix.
Signed-off-by: Aleksei Kalinov <[email protected]> | {
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Reverted self._encoder and self._decoder assignment back to overriding self.encoder and self.decoder properties.
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Reverted self._encoder and self._decoder assignment back to overriding self.encoder and self.decoder properties. | {
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2. Updated LM model code to resolve some PTL issue | {
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} | null | null | null | The problem is that the changes that I introduced at a model level in nemo are not taking place in riva.
For example, for FastPitch, I added custom text parser. Also, for HiFiGAN, I added bias denoiser during inference. These changes are taking place at nemo. However, when I convert the .nemo models to riva for deployment, these changes are not taking place at all.
I am using riva version 1.4.0b0 and NeMo version of 1.2.0.
How one can fix it? | {
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- Defer import of G2p to instantiation of Vocabs
-
Signed-off-by: smajumdar <[email protected]> | {
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if we inference on single input data, the squeeze function will convert a list to a scalar.
take question_answering for example, it will fail when inferring on a single input. | {
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} | Changes:
- Add back tagger data augmentation (but default to be False)
- Fixes for analyze_errors.py
- Add duplex tn to README
@yzhang123 | {
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} | Add postprocessing for Voice Activity Detection
Binarization:
- `onset` and `offset` threshold for detecting the beginning and end of a speech.
- padding durations `pad_onset` before and padding duarations `pad_offset` after each speech segment;
Filtering:
- `min_duration_on` threshold for short speech segment deletion,
- `min_duration_on` threshold for small silence deletion,
- `filter_speech_first` to control whether to perform short speech segment deletion first
Note ⚠️ We will deprecate **threshold** in diarization in release 1.5.
VAD postprocessing improved peroformance on CH-109 (109 conversations from the CALLHOME American English Speech (LDC97S42) corpus that have 2 speakers only)
from (with single threshold=0.7)
`FA 2.1% MISS 3.7 % Diarization ER: 8.6% Confusion ER: 2.8% `
to (with params in speaker_diarization.yaml)
`FA:3.1% MISS 1.8% Diarization ER:7.0% Confusion ER: 2.1% `
with collar=0.25 and skip overlap. Threshold is tuned on 11 multi-speaker sessions from CALLHOME
TODO
- [ ] update main to stable in yaml files in notebook _before release_ | {
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} | Update the code to be compatible with latest nemo.
With batchsize=1, now the WER/CER is normal.
But still, the exported onnx model doesn't contain seq_length, which affects models with ```conv_mask:True``` and when batch size > 1 and the samples length is different in a batch, the WER/CER will be degraded if tested by generated trt engine.
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} | # Changelog
- Rename ASR tutorials to remove numbering scheme
- Refactor links to above notebooks | {
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- Move ASR Webapp to `tools` directory
Signed-off-by: smajumdar <[email protected]> | {
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@yzhang123 @ekmb | {
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] | null | null | 2021-08-09T17:35:07 | 2021-08-23T18:38:28 | 2021-08-23T18:38:28 | NONE | {
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} | null | null | null | In the script of downloading `get_hi-mia_data.py` there is a small bug. I think line `l.output.write_wav(line, y, sr)` in `__process_data ` needs to be changed by `sf.write(line, data=y, samplerate=sr)`.
By the way when we run this script we will get three folders:
1. dev
> dev.jason including : 16464 samples, dev_all.json: 164640 samples and train.json: 148176 samples
2. test
> test_all.sjon uncluding 165120 samples. When you check the number of labels we will find 13620 labels. How that? How they are related to speakers, or more precisely to 44 speakers?
3. train
> dev with 99309 samples, train: 893774 and train_all:993083 samples
To the best of my knowledge, the number of speakers in dev is 42, in test 44 and in train in 254 (correct me if I am wrong, please) and each speaker records 160 discours. So, for example, 42*160 != 164640. or you mean the sum of the 160 for each speaker are decomposed, and the sum of discours for all the speakers = 164640.
Indeed, which one of dev, test, train can we use for just train a model and generate the labels for speaker identification.? can I use only dev since it containes dev.json, train.json and the sum of their samples is 16464+148176=164640 samples? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2629 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2629/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2629/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2629/events | https://github.com/NVIDIA/NeMo/pull/2629 | 964,108,544 | MDExOlB1bGxSZXF1ZXN0NzA2NjE2NTky | 2,629 | Force fastpitch output to fp32 | {
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} | This enables a much simpler deployment when doing mixed precision inference.
Signed-off-by: Ryan Leary <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2628 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2628/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2628/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2628/events | https://github.com/NVIDIA/NeMo/issues/2628 | 963,790,399 | MDU6SXNzdWU5NjM3OTAzOTk= | 2,628 | how to properly checkpoint for incremental optimization of the ASR model | {
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} | null | null | null | I am using [this code](https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb) and a VCTK corpus for training. Due to the large data-set size I split it up in chunks and use checkpoint to save the model, and then read the checkpoint model for training on further data records. The issue is that when I read in the checkpoint the inference does not deliver the expected results. So I suspect the way I handle the checkpoint is the culprit:
In [this notebook](https://github.com/joepareti54/joepareti54/blob/main/NB_3_chkpt.ipynb) I train the model, save a checkpoint and do some inference. The results appear reasonable.
In [this notebook ](https://github.com/joepareti54/joepareti54/blob/main/NB_3a_chkpt.ipynb) I read in the checkpoint from the step above, train the model on just a few lines and do inferences: the results are different, and it looks like the model was not properly saved or restored.
My overall assumption is that the NeMo model lends itself to transfer learning, so that I can use the public version and refine it incrementally with my own data: is this a valid assumption? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2627 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2627/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2627/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2627/events | https://github.com/NVIDIA/NeMo/pull/2627 | 963,614,172 | MDExOlB1bGxSZXF1ZXN0NzA2MTkwNTY2 | 2,627 | Fixed an error related to the task indicator of the tagger during inference. | {
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} | Changes:
- Fixed a bug where the task indicator for each tagging instance is always set to be TN _during inference_. This bug only affects the evaluation of ITN scores. I re-evaluated all the models. All the ITN scores changed only a tiny bit. For German duplex model though, the ITN score went from 79.89% to 87.15% after the bug is fixed.
- Removed data augmentation for the tagger. After the bug is fixed, I tried several times and found data augmentation for the tagger does not improve the performance. data augmentation for the decoder still improves performance.
@yzhang123 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2626 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2626/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2626/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2626/events | https://github.com/NVIDIA/NeMo/pull/2626 | 963,064,606 | MDExOlB1bGxSZXF1ZXN0NzA1NzYyNzY1 | 2,626 | Fix return config path for new SaveRestoreConnector | {
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} | # Bugfix
- Properly return config when flag is set.
Signed-off-by: smajumdar <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2625 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2625/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2625/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2625/events | https://github.com/NVIDIA/NeMo/pull/2625 | 962,934,325 | MDExOlB1bGxSZXF1ZXN0NzA1NjUxODE4 | 2,625 | Evaluate the performance of the decoder for each semiotic class | {
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} | @yzhang123
For the `english-joint` model, these are the evaluation results:
```
TN (Forward Direction)
DATE 0.999% (2829/2832)
CARDINAL 0.994% (1031/1037)
LETTERS 0.996% (1345/1350)
PLAIN 0.997% (352/353)
VERBATIM 1.0% (302/302)
MEASURE 0.993% (141/142)
DECIMAL 1.0% (92/92)
FRACTION 0.875% (14/16)
ORDINAL 0.99% (102/103)
TELEPHONE 0.919% (34/37)
MONEY 0.973% (36/37)
ELECTRONIC 0.735% (36/49)
DIGIT 0.886% (39/44)
TIME 1.0% (8/8)
ADDRESS 1.0% (4/4)
```
I don't add a new script for evaluating the tagger because it can be simply evaluated as follows:
```
tagger_trainer, tagger_model = instantiate_model_and_trainer(cfg, TAGGER_MODEL, False)
tagger_model.setup_test_data(cfg.data.test_ds)
tagger_trainer.test(model=tagger_model , verbose=False)
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2624 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2624/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2624/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2624/events | https://github.com/NVIDIA/NeMo/issues/2624 | 962,888,308 | MDU6SXNzdWU5NjI4ODgzMDg= | 2,624 | nemo2jarvis/riva conversion error | {
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} | null | null | null | I am getting the following error while trying to conver from .nemo to .ejrvs format via
`nemo2jarvis --out HFG.ejrvs HFG.nemo`. Using NeMo version of 1.2.0 and Jarvis version of 1.3.0b0. Any idea on how to fix it? Thanks a lot in advance.
```
INFO: Restoring NeMo model from 'HFG.nemo'
ERROR: Nemo2Jarvis: Failed to restore model from NeMo file : HFG.nemo. Please make sure you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/opt/conda/bin/nemo2jarvis", line 8, in <module>
sys.exit(nemo2jarvis())
File "/opt/conda/lib/python3.8/site-packages/nemo2jarvis/cli/nemo2jarvis.py", line 48, in nemo2jarvis
Nemo2Jarvis(args)
File "/opt/conda/lib/python3.8/site-packages/nemo2jarvis/convert.py", line 44, in Nemo2Jarvis
raise e
File "/opt/conda/lib/python3.8/site-packages/nemo2jarvis/convert.py", line 37, in Nemo2Jarvis
model = ModelPT.restore_from(restore_path=nemo_in)
File "/workspace/nemo/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 "/workspace/nemo/nemo/core/classes/modelPT.py", line 434, in _default_restore_from
instance = cls.from_config_dict(config=conf)
File "/workspace/nemo/nemo/core/classes/common.py", line 472, in from_config_dict
instance = cls(cfg=config)
TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2623 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2623/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2623/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2623/events | https://github.com/NVIDIA/NeMo/issues/2623 | 962,326,909 | MDU6SXNzdWU5NjIzMjY5MDk= | 2,623 | INF loss when train SpeakerNet? | {
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} | null | null | null | **Describe the bug**
I trained Speaker Net, but when i trained some epoch, i got **INF** loss, the following iteration got **NAN** loss.
**Steps/Code to reproduce bug**
I used transfer learning in [speaker_reco_fintune](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/speaker_reco_finetune.py)
I tried log the value of input. This is here:
```
[NeMo I 2021-08-05 03:12:47 label_models:200] Input_signal: tensor([-7.2640e+00, -5.2837e+00, 5.3772e-01, -8.6496e+00, 5.1234e+00,
-9.9597e+00, -3.7034e+00, -1.6893e-01, 1.1892e+00, -3.2955e-01,
-5.0978e+01, 2.5176e+00, 3.3735e+00, -8.2932e+00, -5.8100e-02,
8.9195e-01, -7.8204e+00, 2.8190e+00, 2.1965e+00, -4.5658e+00,
4.6412e+00, 7.7891e-01, -8.7750e+00, -3.3856e-01, -1.5517e+00,
1.3796e+00, -3.7535e+01, -1.6224e-01, -5.6561e+00, 4.7903e-01,
2.5053e+00, -1.1971e+01, 4.5067e+00, -7.0030e+00, -8.0738e+00,
5.4799e+00, -5.5992e+01, 1.3271e+00, -5.6283e+00, -7.8360e+00,
-3.5651e+00, 4.0000e+00, -7.0020e+00, 1.6409e+00, -3.5048e-01,
2.2463e+00, -5.0571e+00, -1.6083e+00, -6.0765e+01, 4.7173e-02,
5.3641e+00, 5.8133e+00, 3.2504e+01, 9.3649e-01, 1.5709e+01,
1.8597e+00, -2.1429e+01, -2.7246e-01, -1.8408e+00, 5.6009e+00,
5.2642e+00, 1.4973e+00, 4.6505e+00, 1.5815e+00], device='cuda:0')
[NeMo I 2021-08-05 03:12:47 label_models:201] Processed signal: tensor([-0.0238, -0.1374, -0.0758, -0.0362, -0.0437, -0.0189, -0.0039, -0.0015,
-0.1398, -0.0183, -0.0130, -0.1774, -0.0230, -0.0103, -0.0147, -0.0155,
-0.0143, -0.0106, -0.0170, -0.0085, -0.0119, -0.0261, -0.0108, -0.0110,
-0.0078, -0.0209, -0.0518, -0.0134, -0.0800, -0.0375, -0.0252, -0.0052,
-0.0038, -0.0338, -0.1895, -0.0297, 0.0006, -0.0195, -0.0073, -0.0771,
-0.0071, -0.0300, -0.0399, -0.0247, -0.2608, -0.0256, -0.0236, -0.0480,
-0.0120, -0.0285, -0.0789, -0.0340, -0.0088, 0.0015, -0.0135, -0.0242,
-0.0280, -0.0178, -0.0084, -0.0225, -0.0489, -0.0181, -0.0512, -0.0230],
device='cuda:0')
[NeMo I 2021-08-05 03:12:47 label_models:202] Encoded: tensor([775.9235, 761.7243, 814.4641, 779.1350, 728.0189, 749.5628, 742.4701,
738.1171, 804.6092, 752.2346, 722.1220, 811.1537, 756.2740, 741.3098,
787.4878, 747.1573, 753.1722, 791.8665, 829.0006, 767.3861, 721.6051,
744.1120, 740.8286, 764.8818, 770.6669, 769.8176, 759.4538, 747.7944,
821.2345, 730.4551, 735.5352, 760.4753, 746.2045, 791.3550, 773.8812,
825.3999, 725.9216, 758.0412, 770.6284, 784.1265, 753.4616, 770.6104,
762.1423, 768.8573, 832.8608, 805.1642, 817.6407, 758.5172, 747.1414,
771.8086, 759.2638, 774.9775, 789.0172, 748.1165, 811.1917, 751.2094,
751.8005, 750.8981, 745.0760, 742.4553, 734.7489, 785.9377, 757.0728,
758.7659], device='cuda:0')
[NeMo I 2021-08-05 03:12:47 label_models:203] logits: tensor([ 837.9282, 933.0463, 904.5047, 1146.3391, 1270.3425, 829.1025,
939.2279, 859.5983, 1080.9069, 1055.0779, 1325.0914, 895.8984,
928.6825, 970.0303, 1194.4274, 1291.1360, 892.8967, 919.5685,
620.6583, 847.6467, 1311.5002, 911.1189, 973.6673, 891.4790,
1165.7302, 800.3695, 1237.9429, 1332.2549, 925.8285, 1257.8645,
913.4201, 1279.2789, 915.7543, 866.2889, 1012.7273, 1171.7438,
831.6862, 872.0796, 901.1927, 853.4565, 803.9324, 842.8481,
868.1702, 890.2934, 1128.2134, 783.4645, 770.2278, 903.2722,
1225.4275, 1173.8964, 966.3867, 953.6735, 1124.6262, 1163.0758,
1162.4150, 924.2391, 885.0515, 849.4289, 1309.8447, 884.9170,
856.6614, 1206.6677, 880.4076, 914.4090], device='cuda:0',
grad_fn=<SumBackward1>) --------- embs: tensor([ -15.6109, -6.5232, -3.1432, 28.2739, 26.4231, -22.0438,
-8.6381, -13.7220, 12.4318, 12.8998, 26.3078, -14.2675,
-7.4201, -8.2558, 44.7105, 34.8373, -16.3274, -14.7989,
-192.9999, -15.0163, 41.0528, -13.1249, -7.0137, -14.6647,
28.7706, -15.4204, 32.1244, 32.0851, -3.4967, 26.7447,
-10.4239, 40.4478, -13.6846, -9.6578, -4.6466, 70.1545,
-15.0264, -10.8492, -12.8593, -19.7867, -18.7421, -17.2652,
-15.7453, -11.6013, 37.4433, -13.1238, -16.3968, -15.6007,
21.0131, 44.6906, -4.6221, -9.0381, 37.1265, 17.0363,
40.0020, -12.1931, -18.4307, -18.2255, 26.2079, -10.5125,
-12.6007, 36.1346, -18.8209, -14.5773], device='cuda:0',
grad_fn=<SumBackward1>)
Epoch 10: 13%|█▎ | 809/6357 [08:56<1:01:20, 1.51it/s, loss=inf, v_num=4-35][NeMo I 2021-08-05 03:12:47 audio_to_label:350] Features type: 1.312255859375 ----- torch.Size([60799])
[NeMo I 2021-08-05 03:12:47 label_models:200] Input_signal: tensor([-7.3667e+00, 4.3621e+00, -7.8351e+00, -6.1382e+00, 1.1234e+00,
-7.8352e+00, 3.8215e+01, -6.5747e+00, 1.4901e+00, -6.3032e+00,
1.6229e+00, 4.3975e+00, 6.8592e-02, 4.0246e-01, -7.6963e-01,
6.6508e-01, 1.0280e+00, 7.9978e+00, 8.3737e-01, -9.2365e-01,
5.5330e+00, 1.1063e+01, 1.4176e+00, -8.3671e-01, 5.8054e-01,
-7.1677e+00, -2.1789e-01, -8.1073e-01, 7.7098e-01, -7.3476e+00,
4.6917e-01, 2.5451e+00, 3.1671e-01, 2.3164e-01, -8.6706e+00,
2.4546e+00, -6.9703e+00, -6.1533e+00, -4.2260e-01, 1.6372e-01,
1.8021e-01, -3.5878e-01, 1.6875e+00, 2.2597e+00, 5.2333e-01,
-7.4447e+00, -1.8976e-01, 1.5130e+00, 2.7506e+00, 1.2589e+01,
-3.2763e-01, -3.1738e-01, -6.3092e+00, -1.4495e+01, 2.2881e+00,
-1.5554e+00, 1.6894e+00, -7.2745e+00, -2.5398e-02, -7.3677e+00,
2.1140e+00, 2.3412e+00, 1.7505e+00, 7.2517e+01], device='cuda:0')
[NeMo I 2021-08-05 03:12:47 label_models:201] Processed signal: tensor([-0.0110, -0.0172, -0.0152, -0.0177, -0.0064, -0.0467, -0.0036, -0.0183,
-0.0460, -0.0193, -0.0357, -0.0074, -0.0592, -0.0272, -0.0161, -0.0178,
-0.0515, -0.0169, -0.0309, -0.0081, -0.0219, -0.0155, -0.0187, -0.0715,
-0.0151, -0.0398, -0.0326, -0.0446, -0.0133, -0.0278, -0.1472, -0.0245,
-0.0451, -0.0238, -0.0154, -0.0143, -0.0107, -0.0138, -0.0198, -0.1016,
-0.0357, -0.0269, 0.0035, -0.0332, -0.0304, -0.0137, -0.0897, -0.1263,
-0.0245, -0.0462, -0.0238, -0.0120, -0.0258, -0.0222, -0.0277, -0.0139,
-0.0184, -0.0076, -0.0403, -0.0533, -0.0268, -0.0101, -0.1688, -0.0063],
device='cuda:0')
[NeMo I 2021-08-05 03:12:47 label_models:202] Encoded: tensor([688.4766, 715.8893, 689.5655, 708.5850, 704.4224, 773.0853, 721.6960,
727.1276, 711.0944, 678.0993, 743.5980, 694.5203, 724.0637, 684.3538,
727.6652, 710.1851, 701.5847, 704.3207, 721.2671, 691.4686, 685.7607,
713.0606, 707.0889, 704.0615, 708.6085, 682.6095, 693.8227, 732.7479,
745.0822, 692.2618, 787.9812, 716.8567, 710.3370, 706.4578, 692.3884,
706.5530, 676.1014, 687.0576, 779.3427, 700.9290, 758.4126, 697.1790,
745.2277, 684.4161, 705.5687, 674.8506, 760.0540, 811.5912, 685.9936,
705.9686, 707.3126, 689.3103, 687.7748, 761.4349, 718.7142, 698.1862,
697.4583, 693.9688, 697.5015, 709.8937, 739.5364, 727.1003, 721.4929,
701.9783], device='cuda:0')
[NeMo I 2021-08-05 03:12:47 label_models:203] logits: tensor([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],
device='cuda:0', grad_fn=<SumBackward1>) --------- embs: tensor([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],
device='cuda:0', grad_fn=<SumBackward1>)
Epoch 10: 13%|█▎ | 810/6357 [08:56<1:01:16, 1.51it/s, loss=inf, v_num=4-35]
Epoch 10: 13%|█▎ | 810/6357 [08:56<1:01:16, 1.51it/s, loss=nan, v_num=4-35][NeMo I 2021-08-05 03:12:48 audio_to_label:350] Features type: -1.804046630859375 ----- torch.Size([28479])
```
**Environment overview (please complete the following information)**
- I used docker of NeMo
**Additional context**
GPU: DGX-1
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] | null | null | 2021-08-06T00:57:27 | 2023-04-24T17:17:18 | 2021-09-01T20:38:05 | NONE | {
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} | null | null | null | I tried to reproduce the result of speakerrecognition_speakernet results which:
` description="SpeakerNet_recognition model trained end-to-end for speaker recognition purposes with cross_entropy loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Recognition model achieves 2.65% EER on voxceleb-O cleaned trial file",`
So, to infer label we need two files:
1- train_manifest=/path/to/train/manifest/file'
2- test_manifest=/path/to/train/manifest/file'
and then call the provided script: `python speaker_reco_infer.py --spkr_model='/path/to/.nemo/file' --train_manifest=/path/to/train/manifest/file --test_manifest=/path/to/train/manifest/file --batch_size=32`
So, correct me please. The test_manifest is `https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt`. But Usualy we need it in json format where can I find this file?
Where can I find the `train_manifest`?
If I am wrong, how can reproduce the speaker recognition on voxceleb1 and voxceleb2 as in the paper? Indeed, you was using Voxceleb1 and Voxceleb2 for training. Can for example, we use Voxceleb1 for training and then Voxceleb2 for testing? | {
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} | This PR separates the learning framework (i.e., loss) from the architecture, and adds a new Perceiver encoder bottleneck architecture.
# Summary
A few major changes are introduced here:
1. Separating the learning framework (i.e., loss, or model type) from the architecture of the encoder / decoder.
2. The bottleneck encoder transformer can now be constructed using get_nemo_transformer method.
3. Supported learning frameworks:
* NLL - Conditional cross entropy (the usual NMT loss)
* VAE - Variational Auto-Encoder (https://arxiv.org/pdf/1312.6114.pdf)
* MIM - Mutual Information Machine (https://arxiv.org/pdf/2003.02645.pdf)
4. Supported encoder architectures:
* seq2seq - the usual transformer encoder without a bottleneck
* bridge - attention bridge bottleneck (https://arxiv.org/pdf/1703.03130.pdf)
* perceiver (**new**) - Perceiver bottleneck (https://arxiv.org/pdf/2103.03206.pdf)
The changes allow to easily select the architecture via configuration. Specifically, using a shared base YAML configuration and overriding flags during experimentation allows easy comparison of bottleneck and non-bottleneck architectures. Separating the loss from the architecture leads to a cleaner code which is easier to understand.
# YAML Configuration
The following configurations in YAML config allows to control the learning framework and architecture:
```
model:
...
model_type: 'nll' # learning (i.e., loss) type: nll (i.e., cross-entropy/auto-encoder), mim, vae (see description above)
min_logv: -6 # minimal allowed log variance for mim
latent_size: -1 # dimension of latent (projected from hidden) -1 will take value of hidden size
non_recon_warmup_batches: 200000 # warm-up steps for mim, and vae losses
recon_per_token: true # when false reconstruction is computed per sample, not per token
...
encoder:
library: nemo
...
arch: seq2seq # seq2seq, bridge, perceiver (see description above)
hidden_steps: 32 # fixed number of hidden steps
hidden_blocks: 1 # number of repeat blocks (see classes for description)
hidden_init_method: default # see classes for available values
decoder:
library: nemo
...
arch: seq2seq # currently only seq2seq is supported
```
Detailed description of config parameters:
* **model.encoder.arch=seq2seq**
* *model.encoder.hidden_steps is ignored*
* *model.encoder.hidden_blocks is ignored*
* *model.encoder.hidden_init_method is ignored*
* **model.encoder.arch=bridge**
* *model.encoder.hidden_steps:* input is projected to the specified fixed steps
* *model.encoder.hidden_blocks:* number of encoder blocks to repeat after attention bridge projection
* *model.encoder.hidden_init_method:*
* enc_shared (default) - apply encoder to inputs, than attention bridge, followed by hidden_blocks number of the same encoder (pre and post encoders share parameters)
* identity - apply attention bridge to inputs, followed by hidden_blocks number of the same encoder
* enc - similar to enc_shared but the initial encoder has independent parameters
* **model.encoder.arch=perceiver**
* *model.encoder.hidden_steps:* input is projected to the specified fixed steps
* *model.encoder.hidden_blocks:* number of cross-attention + self-attention blocks to repeat after initialization block (all self-attention and cross-attention share parameters)
* *model.encoder.hidden_init_method:*
* params (default) - hidden state is initialized with learned parameters followed by cross-attention with independent parameters
* bridge - hidden state is initialized with an attention bridge
# Usage Example
**NOTE:** ```enc_dec_nmt-bottleneck.py``` must be used. ```enc_dec_nmt.py``` does not support the required configuration to run properly and will likely fail.
```
python -- enc_dec_nmt-bottleneck.py \
--config-path=conf \
--config-name=aayn_bottleneck \
...
model.model_type=nll \
model.non_recon_warmup_batches=7500 \
model.encoder.arch=perceiver \
model.encoder.hidden_steps=32 \
model.encoder.hidden_blocks=2 \
model.encoder.hidden_init_method=params \
...
```
# Additional Info
* Classes *TransformerBottleneckEncoderNM*, *TransformerBottleneckDecoderNM*, and corresponding config classes *NeMoTransformerBottleneckConfig*, *NeMoTransformerBottleneckEncoderConfig*, *NeMoTransformerBottleneckDecoderConfig* were added to ```nemo/collections/nlp/modules/common/transformer/transformer_bottleneck.py```
* Class *PerceiverEncoder* was added to ```nemo/collections/nlp/modules/common/transformer/perceiver_encoders.py```
* Class *BridgeEncoder* was added to ```nemo/collections/nlp/modules/common/transformer/bridge_encoders.py```
* Classes *TransformerBottleneckEncoderNM* and *TransformerEncoderNM* test for src/tgt ids in the tokenizer valid range. | {
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} | null | null | null | Did anybody train matchboxnet model on esc_50 dataset?
What is the best performance?
My best accuracy is 86%. It seems a little low compared to AST model.
How to setup the super-parameters to get better performance for esc_50 dataset? | {
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} | end of epoch will throw execption:
pytorch_lightning.utilities.exceptions.MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['loss', 'l_mle', 'l_length', 'logdet']. HINT: Did you call self.log('val_loss', value) in the LightningModule?
Signed-off-by: Qiliang <[email protected]> | {
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I am getting the following error at `FastPitchDataset` while I want to train FP model.
```
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py", line 727, in __getitem__
audio_path = self.collection[item].audio_file
AttributeError: 'FastPitchDataset' object has no attribute 'collection'
```
Can you guys fix it?
Thanks. | {
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} | - non-deterministic refactoring to remove permutation and merge tagger and verbalizer
- more normalization options
- non-deterministic .far Eng graph added to nemo_text_processing folder | {
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} | # Changelog
- Run ITN tests only when `nemo_text_processing/*` files are changed
- Split numba checks into CPU level and GPU level tests
- Make certain RNNT tests runnable on CPU only devices
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | Hi, thanks for this great project!
Question: can i reproduce the same melspectorgram using non-torch python libs as one that generated using nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures(nn.Module) class?
Has anyone tried? | {
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} | null | null | null | Hello friends,
I am using the punctuation capitalization model, 'punctutation_en_bert' or 'distilbert_en' , but although I use restore_from the nemo file I still see it downloads lm from huggingface (from what i understand), i think it is the bert-uncased.
1. Is there an option to restore the LM as well instead of downloading it? if so how? I want all models etc will be restored from and not downloaded
2. Is there an option to change the LM to different huggingface models.
Thank you very much | {
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} | I added a field `max_insts` to allow setting the number of training instances. | {
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] | closed | false | null | [] | null | null | 2021-08-04T05:29:35 | 2021-08-06T01:39:12 | 2021-08-06T01:39:12 | NONE | {
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I trained SpeakerNet model but got loss **inf** in one iteration, and the next iteration got loss is **nan**
So i want to log path of samples in each iteration and loss corresponding to that iteration to debug.
Is there any way config trainer log to do that?
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} | end of epoch will throw execption:
pytorch_lightning.utilities.exceptions.MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['loss', 'l_mle', 'l_length', 'logdet']. HINT: Did you call self.log('val_loss', value) in the LightningModule? | {
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} | - Added an option to allow users to use covering grammars to handle unrecoverable errors.
- Added a script for generating visualization files. | {
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} | # Changelog
- Update RNNT configs for ContextNet RNNT
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | **Describe the bug**
I'm using the intent/slot model and supplying my own language model checkpoint and config (for a 2-layer version of DistilBert), as follows:
```
language_model:
max_seq_length: 50
pretrained_model_name: distilbert-base-uncased
lm_checkpoint: /home/carola/Documents/models/distillation/atis_snips_pred_2L.ckpt
config_file: /home/carola/Documents/configs/language_models/joc_bert_2l.json
config: null
```
During normal training this is logged:
```
[NeMo I 2021-08-03 13:11:43 bert_module:54] Restoring weights from /home/carola/Documents/models/distillation/atis_snips_pred_2L.ckpt
[NeMo I 2021-08-03 13:11:43 bert_module:82] Weights for DistilBertEncoder restored from /home/carola/Documents/models/distillation/atis_snips_pred_2L.ckpt
```
However, if I mispell the checkpoint file or otherwise provide a file that doesn't exist, like this:
```
language_model:
max_seq_length: 50
pretrained_model_name: distilbert-base-uncased
lm_checkpoint: /home/carola/Documents/models/distillation/my_fake_file.ckpt # file that does not exist
config_file: /home/carola/Documents/configs/language_models/joc_bert_2l.json # this one does exist
config: null
```
Then the only sign that something is wrong is that the "Restoring weights" lines are missing from the log, which is hard to notice. The model also performs poorly. Otherwise, training proceeds apparently normally and the resulting saved models have the expected number of layers/parameters, as specified by the config I provided. I guess the weights must be getting initialized from the default 6-layer DistilBert checkpoint, which should not happen.
**Expected behavior**
An exception should be raised if a checkpoint file is provided in the config but does not exist.
**Environment details**
nemo-toolkit 1.2.0 (fresh install via reinstall.sh)
Ubuntu 18.04
python 3.7
PyTorch 1.9.0+cu102
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} | null | null | null | When using the NER and intent/slot models with a dataset containing certain characters (examples below), training errors out like this:
`RuntimeError: stack expects each tensor to be equal size, but got [75] at entry 0 and [76] at entry 28`
The underlying cause is that tokens made up solely of such characters, when tokenized, yield nothing -- no tokens or subtokens. Yet these lines still get executed during dataset formation:
```
loss_mask.append(1)
subtokens_mask.append(1)
```
As a result, the loss_mask and subtokens_mask are longer than the other input vectors and we get something like this:
```
input_ids: torch.Size([1, 75])
segment_ids: torch.Size([1, 75])
input_mask: torch.Size([1, 75])
subtokens_mask: torch.Size([1, 76])
loss_mask: torch.Size([1, 76])
labels: torch.Size([1, 75])
```
The workaround I'm using is to convert the problematic tokens into `[UNK]` in my own data, prior to attempting to train. But I think some logic should be added in the NeMo data processing to use the `[UNK]` token whenever tokenization yields an empty list of subtokens.
I don't think the solution should be to skip these tokens, because they could be part of entities that users are trying to tag.
-------------------------------------------------------------------
Example tokens/characters causing this problem:
`\U000fe814\U000fe814\U000fe814`
`\U000feb7b`
`\U000fe814\U000feb0e\U000fe814`
`\U000fe334`
Example document:
`Baby update ; guess what , it's a boy ❤️ my lil baby boy \U000feb18 mommy & daddy love you mi amor \U000fe823\U000fe32c` | {
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fyi, @laituan245
Signed-off-by: ekmb <[email protected]> | {
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} | null | null | null | **Describe the bug**
Hi, exec `python examples/nlp/duplex_text_normalization/google_data_preprocessing.py --data_dir=/ssd5/exec/shenchengen/data/tn/ --output_dir=preprocessed/ --lang=en`, there has import error：
```
Traceback (most recent call last):
File "google_data_preprocessing.py", line 42, in <module>
from helpers import flatten
File "/ssd5/exec/shenchengen/software/NeMo/examples/nlp/duplex_text_normalization/helpers.py", line 18, in <module>
from nemo.collections.nlp.models import DuplexDecoderModel, DuplexTaggerModel
File "/ssd5/exec/shenchengen/software/NeMo/nemo/collections/nlp/__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "/ssd5/exec/shenchengen/software/NeMo/nemo/collections/nlp/models/__init__.py", line 16, in <module>
from nemo.collections.nlp.models.duplex_text_normalization import (
File "/ssd5/exec/shenchengen/software/NeMo/nemo/collections/nlp/models/duplex_text_normalization/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.duplex_text_normalization.duplex_decoder import DuplexDecoderModel
File "/ssd5/exec/shenchengen/software/NeMo/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py", line 25, in <module>
import nemo.collections.nlp.data.text_normalization.constants as constants
File "/ssd5/exec/shenchengen/software/NeMo/nemo/collections/nlp/data/text_normalization/__init__.py", line 14, in <module>
from nemo.collections.nlp.data.text_normalization.decoder_dataset import TextNormalizationDecoderDataset
File "/ssd5/exec/shenchengen/software/NeMo/nemo/collections/nlp/data/text_normalization/decoder_dataset.py", line 21, in <module>
import nemo.collections.nlp.data.text_normalization.constants as constants
AttributeError: module 'nemo.collections' has no attribute 'nlp'
```
then I use ipython, also hase AttributeError: module 'nemo.collections' has no attribute 'nlp'
Installed by cloning the git repo and running ./reinstall.sh. I have also tried installing via` pip install nemo_toolkit['all']`, and `pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]`. .These all produced the same result.
environment:
python 3.6.13 nemo 1.2.0 | {
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} | null | null | null | Hello!
I'm trying to train a Machine Translation model on NeMo. Under the model parameters, I came across this parameter **max_generation_delta** with a default value of 5.
Could I please have some clarity on what this parameter controls or where it's used?
Thanks a ton! | {
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} | null | null | null | Hi
I have a question about fast training.
I am trying to train QuartzNet15x5 from scratch on 6000 hours of data with 2xA100 GPUs. It takes 10 hours per epoch.
How can I speed up the training or convergence speed?
I am using
2xA100 with 40GB RAM
mixed precision
80 num workers in torch loader
what else can i do to speed up the training?
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} | null | null | null | I have been trying to save the trained model on cluster but it is not saving. It works on Colab, it does save the .nemo weights file but the same piece of code is not saving it on the cluster. Any idea what could be the reason? | {
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} | null | null | null | Hi,
I have a question about how best to assemble a training dataset, who has experience and can share, thank you. I've been working with NeMo for a few months setting up trainings in Portuguese and so far I'm having good results in transcribing calls. But I still notice some simple errors during transcripts...
My question is, for the type of audio I'm transcribing, should I prefer them in my dataSet? For example, I'm building a dataSet thinking about transcribing phone calls (8K), in my dataSet should I include more phone calls or do I have nothing to do with it? Or would increasing the number of speakers and increasing the audio quality be the best solution?
Since building a good dataSet with hundreds of hours takes a lot of time, I want to avoid investing in one side, otherwise it would have better results... | {
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} | Current __init__ to 1 will not allow expansions to over 1 node when using a https://pytorch-lightning.readthedocs.io/en/latest/clouds/cluster.html#general-purpose-cluster
Changing to None allows for replacement.
with num_nodes: int = 1 and 2 nodes requested
Node 1
[NeMo I 2021-07-29 00:29:42 nlp_overrides:56]
init_ddp_connection global_rank 0 world_size 1
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/1
Added key: store_based_barrier_key:1 to store for rank: 0
Rank 0: Completed store-based barrier for 1 nodes.
[NeMo I 2021-07-29 00:29:42 nlp_overrides:65]
init_ddp_connection global_rank 0 world_size 1
Node 2
[NeMo I 2021-07-29 00:29:42 nlp_overrides:56]
init_ddp_connection global_rank 1 world_size 1
initializing ddp: GLOBAL_RANK: 1, MEMBER: 2/1
Added key: store_based_barrier_key:1 to store for rank: 1
with num_nodes: int = None,
Node 1
[NeMo I 2021-07-29 00:54:36 nlp_overrides:56]
init_ddp_connection global_rank 0 world_size 2
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/2
Added key: store_based_barrier_key:1 to store for rank: 0
Rank 0: Completed store-based barrier for 2 nodes.
[NeMo I 2021-07-29 00:54:37 nlp_overrides:65]
init_ddp_connection global_rank 0 world_size 2
Node 2
[NeMo I 2021-07-29 00:54:37 nlp_overrides:56]
init_ddp_connection global_rank 1 world_size 2
initializing ddp: GLOBAL_RANK: 1, MEMBER: 2/2
Added key: store_based_barrier_key:1 to store for rank: 1
Rank 1: Completed store-based barrier for 2 nodes.
[NeMo I 2021-07-29 00:54:37 nlp_overrides:65]
init_ddp_connection global_rank 1 world_size 2
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} | null | null | null | It is a bug but more a clarification that I need to understand the speakerNet model.
I need to do speaker verification using speakerNet. So I follow the tutorial https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb .
In this tutorial you suggest to only change `config.model.decoder.angular = True` when we want to do speaker verification. So, I need to confirm if that not other parameter need to be changed.
My second concerns why in https://github.com/NVIDIA/NeMo/blob/v1.0.2/examples/speaker_recognition/speaker_reco.py you set the name of classes to 2. Is just an example or it must be always like that? | {
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} | - Minor Optimization: No need to do basic tokenization when training on the Google TN datasets (`google_data_preprocessing.py` already does that).
- Fixed index out of bounds error when the models are not well-trained: Poor tagger sometimes put a B-TASK tag in the middle of the sentence.
- Added test after train
- Added datasets caching | {
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Hello
I've been training with "10_ASR_CTC_Language_Finetuning" on the CPU but I noticed that the training doesn't allocate all available cores, it's currently using 32 cores, I'd like to use the machine's 64
Is there any parameter which I must pass in the script to use the 64 cores? | {
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} | null | null | null | Hi,
I download the models of FastSpeech2_HifiGan_E2E and FastSpeech2+HiFiGAN, and generate audios by them.
I find out that the voice quality of FastSpeech2+HiFiGAN is better than FastSpeech2_HifiGan_E2E.
Does this results match your expectations?
In fact, I I'm going to implement E2E TTS structure recentlty. So I hope to discuss the feasibility of this framework.
Thank you~ | {
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} | null | null | null | Hello, I'm training Punctuation_and_Capitalization on a french dataset. The first time everithing was fine, but when I tried to change epochs from 3 to 5, and execute again the : trainer.fit(model), I got this error:
ValueError: Cannot resolve schedulerDictConfig'. Available optimizers are : dict_keys(['WarmupPolicy', 'WarmupHoldPolicy', 'SquareAnnealing', 'CosineAnnealing', 'NoamAnnealing', 'WarmupAnnealing', 'InverseSquareRootAnnealing', 'SquareRootAnnealing', 'PolynomialDecayAnnealing', 'PolynomialHoldDecayAnnealing', 'StepLR', 'ExponentialLR', 'ReduceLROnPlateau', 'CyclicLR'])
It still giving this error even I changed the epochs and re installed the packages.
Would you mind helping me solve this bug, please? | {
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} | null | null | null | I'm following this tutorial to train a punctuation and capitalization model: https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb
and trying to finetune punctuation_en_distilbert model for Spanish in Google Colab.
When I use tatoeba Spanish dataset ~ 300K lines it works, but validation loss does not decrease and is about 0.2 - 0.25.
When I took a larger dataset, 3M lines, then there was not enough memory for gpu and runtime crashed on pretrained_model.setup_training_data() step.
When I took 1M lines, again val loss for 15 epochs was around 0.2 - 0.25, and does not decrease.
Is there any tips what could I do to get good result for finetuned model? | {
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They are now run only if something under "nemo_text_processing" is changed. | {
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} | # Changelog
- Refactor out text processing to NeMo while using DALI for audio processing.
- Refactor DALI dataset to partial implementation, subclass for Char and Subword models
- Add unittests for DALI datasets (if installed)
- Add jenkins test for Citrinet based subword model dataset processing
- Version check that DALI 1.4 is used
- Comment out DALI tests for the time being until PT container 21.08 is released and used | {
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