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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 ... | {
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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 download... | {
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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... | {
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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
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Fixes https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3361038&cmtNo= | {
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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. Wh... | {
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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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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/n... | {
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/2656 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2656/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2656/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2656/events | https://github.com/NVIDIA/NeMo/pull/2656 | 970,713,893 | MDExOlB1bGxSZXF1ZXN0NzEyNjI4ODAz | 2,656 | Fix nmt grpc server, concatdataset for raw text files | {
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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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} | # 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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} | 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. ... | {
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- Add Intro to Transducer and ASR with Transducer tutorials
- Update readme
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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- 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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} | 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... | {
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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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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 depl... | {
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- Defer import of G2p to instantiation of Vocabs
-
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/2637 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2637/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2637/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2637/events | https://github.com/NVIDIA/NeMo/pull/2637 | 965,547,560 | MDExOlB1bGxSZXF1ZXN0NzA3ODUyMDY5 | 2,637 | Add back tagger data augmentation + Fixes for analyze_errors.py | {
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/2636 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2636/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2636/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2636/events | https://github.com/NVIDIA/NeMo/pull/2636 | 965,394,445 | MDExOlB1bGxSZXF1ZXN0NzA3NzEwOTA3 | 2,636 | VAD postprocessing - binarization, filtering | {
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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 segm... | {
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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... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2634 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2634/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2634/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2634/events | https://github.com/NVIDIA/NeMo/pull/2634 | 964,558,969 | MDExOlB1bGxSZXF1ZXN0NzA2OTk4Mzkw | 2,634 | update for glue with PTL1.4 | {
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} | Signed-off-by: ekmb <ebakhturina@nvidia.com> | {
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} | # Changelog
- Rename ASR tutorials to remove numbering scheme
- Refactor links to above notebooks | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2632 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2632/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2632/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2632/events | https://github.com/NVIDIA/NeMo/pull/2632 | 964,485,703 | MDExOlB1bGxSZXF1ZXN0NzA2OTM3NjE5 | 2,632 | Move ASR Webapp | {
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"merged_at": "2021-08-10T16:44:49"
} | # Changelog
- Move ASR Webapp to `tools` directory
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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} | Increasing the number of epochs by roughly two times can get the same performance as data augmentation.
@yzhang123 @ekmb | {
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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, de... | {
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} | This enables a much simpler deployment when doing mixed precision inference.
Signed-off-by: Ryan Leary <rleary@nvidia.com> | {
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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% ... | {
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} | # Bugfix
- Properly return config when flag is set.
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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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)
MEASUR... | {
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`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 ... | {
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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... | {
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` 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 ... | {
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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 bott... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2620 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2620/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2620/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2620/events | https://github.com/NVIDIA/NeMo/pull/2620 | 962,245,786 | MDExOlB1bGxSZXF1ZXN0NzA1MDY1OTE3 | 2,620 | CitriNet export fix | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2617 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2617/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2617/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2617/events | https://github.com/NVIDIA/NeMo/issues/2617 | 961,606,512 | MDU6SXNzdWU5NjE2MDY1MTI= | 2,617 | perfomance of matchboxnet on esc_50 dataset | {
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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 <xiao... | {
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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' o... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2614 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2614/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2614/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2614/events | https://github.com/NVIDIA/NeMo/pull/2614 | 961,304,989 | MDExOlB1bGxSZXF1ZXN0NzA0MjU0Nzc5 | 2,614 | Tn class eval | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2613 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2613/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2613/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2613/events | https://github.com/NVIDIA/NeMo/pull/2613 | 961,168,628 | MDExOlB1bGxSZXF1ZXN0NzA0MTI2MjYz | 2,613 | Remove unused arg in TextNormalizationTestDataset | {
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/2611 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2611/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2611/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2611/events | https://github.com/NVIDIA/NeMo/pull/2611 | 960,722,939 | MDExOlB1bGxSZXF1ZXN0NzAzNzE2ODY0 | 2,611 | Make ITN tests optional (run only on change) | {
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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 <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2610 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2610/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2610/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2610/events | https://github.com/NVIDIA/NeMo/issues/2610 | 960,380,724 | MDU6SXNzdWU5NjAzODA3MjQ= | 2,610 | BeamSearchDecoderWithLM Binary KenLM file | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2609 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2609/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2609/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2609/events | https://github.com/NVIDIA/NeMo/issues/2609 | 960,151,820 | MDU6SXNzdWU5NjAxNTE4MjA= | 2,609 | Melspectrogram with non-torch lib | {
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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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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 ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2605 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2605/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2605/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2605/events | https://github.com/NVIDIA/NeMo/pull/2605 | 959,975,626 | MDExOlB1bGxSZXF1ZXN0NzAzMDQyODAx | 2,605 | Allowed setting the train set size for duplex TN training | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2604 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2604/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2604/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2604/events | https://github.com/NVIDIA/NeMo/issues/2604 | 959,912,991 | MDU6SXNzdWU5NTk5MTI5OTE= | 2,604 | How to log path of sample in each iteration? | {
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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 a script for generating visualization files. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2601 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2601/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2601/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2601/events | https://github.com/NVIDIA/NeMo/pull/2601 | 959,503,537 | MDExOlB1bGxSZXF1ZXN0NzAyNjExOTc3 | 2,601 | Update ContextNet RNNT configs | {
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} | # Changelog
- Update RNNT configs for ContextNet RNNT
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2600 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2600/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2600/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2600/events | https://github.com/NVIDIA/NeMo/pull/2600 | 959,494,676 | MDExOlB1bGxSZXF1ZXN0NzAyNjA0MDU1 | 2,600 | Remove PTL 1.4 upper bound | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2599 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2599/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2599/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2599/events | https://github.com/NVIDIA/NeMo/issues/2599 | 959,425,591 | MDU6SXNzdWU5NTk0MjU1OTE= | 2,599 | Silent failure: no warning when user-supplied lm_checkpoint file does not exist | {
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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/disti... | {
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`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, wh... | {
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fyi, @laituan245
Signed-off-by: ekmb <ebakhturina@nvidia.com> | {
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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>
... | {
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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 c... | {
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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 q... | {
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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]
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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 chang... | {
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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
- A... | {
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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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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 ... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2573 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2573/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2573/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2573/events | https://github.com/NVIDIA/NeMo/pull/2573 | 955,172,394 | MDExOlB1bGxSZXF1ZXN0Njk4OTYxMzM2 | 2,573 | Fix moses path issue | {
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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... | {
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and trying to finetune punctuation_en_distilbert model for Spanish in Google Colab.
When I use tatoeba Spanish dataset ~ 300K lines it works, but... | {
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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 t... | {
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