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INFO: Logging level set to 20 ... | {
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} | 1. If `BertPunctuationCapitalizationDataset.__init__` arguments `punct_label_ids` and `capit_label_ids` are `DictConfig`s, then transform them to dictionaries.
2. Make sure that `PunctuationCapitalizationModel` attributes `punct_label_ids` and `capit_label_ids` are always dictionaries and not `DictConfig`s. Previous... | {
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} | # Changelog
- Revert previous code that constructs a trainer on one GPU to evaluate results. The code is misleading because the torch environment is still distributed, and all processes run the code
- From now on, test only if the model was trained on a single GPU with no ddp.
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} | `Joint_Intent_and_Slot_Classification.ipynb`
`Text_Classification_Sentiment_Analysis.ipynb`
problem are caused by extra `cfg` key in the checkpoint file
`Relation_Extraction-BioMegatron.ipynb`
problem is caused by forgetting to set the correct `model_config` variable.
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} | null | null | null | **Describe the bug**
Error when converting Nemo model to Riva
**Steps/Code to reproduce bug**
Shell command:
`nemo2riva --out /mnt/devdata/zzh/tao/results/Citrinet/stt_zh_citrinet_512.riva /mnt/devdata/zzh/tao/results/Citrinet/stt_zh_citrinet_512.nemo
`
**Environment details**
If NVIDIA docker image... | {
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This PR is for the following:
- Fix the errors in ASR with diarization tutorial
- Fix the errors in diarization_utils.py and decoder_timestamp_utils.py
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} | - Removed local machine path inserted in ASR with diarization tutorial.
- Fixed some of the outdated docstrings in diarization_utils.py and decode_timestamp_utils. | {
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} | null | null | null | I am using a pre-trained stt_en_conformer_transducer and it is doing well. However, since my dataset has some special terms I want to use KenLM with Beam Search.
In one tutorial, I saw that we can change the decoding strategy to Beam using:
```
import copy
decoding_config = copy.deepcopy(asr_model.cfg.decodin... | {
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In [this ](https://github.com/NVIDIA/NeMo/blob/8c912f515a77efb0e16b1433030da23e488960d5/tutorials/asr/ASR_with_Transducers.ipynb) example you reported how to trai... | {
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} | null | null | null | **Describe the bug**
I have a problem when deloy Text Nemo Text Normarlzation on Triton Inference Server. The Normalizer can not load the default "whitelist.tsv" file. Whenever I run ```tritonserver --model-repository models/```, the bug will appear.
```python
from nemo_text_processing.text_normalization.normali... | {
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} | The PR reduces number of permutations processed in [this](https://github.com/NVIDIA/NeMo/blob/a8154fd825c34de0a59309536e11ba7d5d1f94e1/nemo_text_processing/text_normalization/normalize.py#L149) loop.
1. Count number of permutations that each token gives.
2. Split sequence of tokens into smaller parts so that number... | {
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When I apply text normalization to a string that contains a lot of dates, the normalization takes too much time. For example, the string
```
Hotel built in 2002 , Last complete renovation 2002 , Last partial renovation 2011 , Number of floors 4 , Total number of rooms 35 , Number of single roo... | {
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@yzhang123 | {
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} | Added and modified pronunciations can be found at the end of the file under the headings "Words added by NVIDIA" and "Pronunciation changes from 0.7b" respectively.
Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com> | {
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} | + This PR Adds different bucketing strategies to improve the performance of ASR bucketing training accuracy-wise. A new parameter added to the data loaders (train_ds.bucketing_strategy) to specify one of the following modes:
1-fixed_order: the same order of buckets are used for all epochs
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- Make transcribe() method use 0 workers for compatibility
- Change the way eval mode limited context SE works | {
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On attempting inference of a recording, I received the following error:
PicklingError: Can't pickle <class 'nemo.collections.common.parts.preprocessing.collections.AudioTextEntity'>: attribute lookup AudioTextEntity on nemo.collections.common.parts.preprocessing.collections failed
**Steps/Code... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3440 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3440/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3440/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3440/events | https://github.com/NVIDIA/NeMo/pull/3440 | 1,102,567,823 | PR_kwDOC_bI7s4w-eMB | 3,440 | Update ContextNet models trained on more datasets | {
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} | # Changelog
- Update ContextNet checkpoints trained on 11.5 K hours of data for English
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3438 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3438/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3438/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3438/events | https://github.com/NVIDIA/NeMo/pull/3438 | 1,102,440,618 | PR_kwDOC_bI7s4w-BXg | 3,438 | Prompt tuning loss mask fix | {
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```
[soft` prompt][Input Text][Answer][EOD Padding]
[000000000000011111111111111111111000000000000]
```
vs
```
[soft prompt][Input Text][Answer][EOD Padding]
[000000000000000000000000... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3437 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3437/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3437/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3437/events | https://github.com/NVIDIA/NeMo/pull/3437 | 1,102,431,657 | PR_kwDOC_bI7s4w9_VK | 3,437 | Fix bug for missing variable | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3436 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3436/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3436/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3436/events | https://github.com/NVIDIA/NeMo/pull/3436 | 1,102,235,425 | PR_kwDOC_bI7s4w9TEg | 3,436 | Bump numpy from 1.18.5 to 1.21.0 in /tools/speech_data_explorer | {
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} | Bumps [numpy](https://github.com/numpy/numpy) from 1.18.5 to 1.21.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/numpy/numpy/releases">numpy's releases</a>.</em></p>
<blockquote>
<h2>v1.21.0</h2>
<h1>NumPy 1.21.0 Release Notes</h1>
<p>The NumPy 1.21.0 release highlights ar... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3435 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3435/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3435/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3435/events | https://github.com/NVIDIA/NeMo/pull/3435 | 1,102,149,499 | PR_kwDOC_bI7s4w8_y0 | 3,435 | BioMegatron token classification tutorial fix to be compatible with current Megatron BERT | {
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Currently it fails to reproduce the same accuracy. Need to investigate the bug. | {
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## 1. ONNX error in embedding in nemo version > 1.2:
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asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
Additionally, list_available_models() also fails with the same above error.
I have installed NeMo via:
python -m pip install git+https://github.com/N... | {
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It is important to provide user ability to generate input example of particular dimensions - as certain networks would reconfigure themselves according to input dimensions - thus export needs to be performed with real life size example.
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} | WAR changes to prevent issues described in https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3429305
"ONNX model exported with PyTorch container > 21.06 fails when data of different size is provided to dynamic axes"
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} | null | null | null | Dear colleagues, after install Nemo r1.5.0 I have error in method oracle_model.diarize() in
[Speaker_Diarization_Inference] https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb)
```
from nemo.collections.asr.models import ClusteringDiarizer
oracle_model = Clusterin... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3420 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3420/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3420/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3420/events | https://github.com/NVIDIA/NeMo/pull/3420 | 1,098,794,554 | PR_kwDOC_bI7s4wx6Lg | 3,420 | Fix RANK env variable check in global rank check | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3419 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3419/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3419/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3419/events | https://github.com/NVIDIA/NeMo/pull/3419 | 1,098,554,200 | PR_kwDOC_bI7s4wxHXU | 3,419 | Update speaker diarization docs | {
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} | Signed-off-by: Taejin Park <tango4j@gmail.com>
Update speaker diarization docs and make it compatible with current main. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3418 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3418/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3418/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3418/events | https://github.com/NVIDIA/NeMo/pull/3418 | 1,098,499,862 | PR_kwDOC_bI7s4ww8U_ | 3,418 | update docs for diarization | {
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This PR updates speaker diarization documents that is compatible with the current main.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3417 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3417/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3417/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3417/events | https://github.com/NVIDIA/NeMo/pull/3417 | 1,098,453,885 | PR_kwDOC_bI7s4wwy2_ | 3,417 | Fix order of lang checking to ignore input langs | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3416 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3416/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3416/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3416/events | https://github.com/NVIDIA/NeMo/issues/3416 | 1,098,271,573 | I_kwDOC_bI7s5BdktV | 3,416 | AttributeError: type object 'SpectrogramGenerator' has no attribute 'from_pretrained' | {
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In the following code snippet:
`from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder`
`def load_spectrogram_model():`
`pretrained_model = "tts_en_fastpitch"`
`model = SpectrogramGenerator.from_pretrained(pretrained_model, override_config_p... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3415 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3415/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3415/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3415/events | https://github.com/NVIDIA/NeMo/issues/3415 | 1,098,060,873 | I_kwDOC_bI7s5BcxRJ | 3,415 | Export trained MarbleNet VAD model into onnx | {
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is there a way to export the trained MarbleNet VAD model into onnx | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3414 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3414/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3414/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3414/events | https://github.com/NVIDIA/NeMo/issues/3414 | 1,098,036,574 | I_kwDOC_bI7s5BcrVe | 3,414 | ASR transcribe_speech with Language Model | {
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I have 2 questions about ASR Language Modeling:
1- Is there any implemented code in Nemo, for generating the json file of the predictions of an ASR model with a N-gram LM? Something like the [transcribe_speech_parallel.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/transcribe_speech_parallel.py) code.... | {
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He seemed to be there
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I tried to download pre-trained model of Fastspeech2 Hifigan E2E.
but, browser download doesn't working and wget too.
I attach the wget log below
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_e2e_fastspeech2hifigan/versions/1.0.0/zip -O tt... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3400 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3400/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3400/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3400/events | https://github.com/NVIDIA/NeMo/pull/3400 | 1,095,922,249 | PR_kwDOC_bI7s4wo8np | 3,400 | Fix linear annealing not annealing lr to min_lr | {
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} | The `WarmupAnnealing` currently anneals the LR to 0 instead of the provided `min_lr` arg. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3399 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3399/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3399/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3399/events | https://github.com/NVIDIA/NeMo/pull/3399 | 1,095,880,546 | PR_kwDOC_bI7s4wo0Sh | 3,399 | fixed the bug of bucketing when fixed-size batch is used. | {
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https://github.com/NVIDIA/NeMo/issues/2722#issuecomment-905306973
But I'm getting this error: AttributeError: module 'posixpath' has no attribute 'json' in this line:
https://github.com/NVIDIA/NeMo/blob/b4a28b4e29a4eb03ac2e0003dfe08fb7ddedea63... | {
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} | null | null | null | Hi,
I fine-tuned a pre-trained French ASR model "stt_fr_conformer_ctc_large" with my own data. And I replaced the tokenizer with my own by `model.change_vocabulary(new_tokenizer_dir=my_own_tokenizer, new_tokenizer_type="bpe")`.
The training was smooth and I got 17% valid WER which is rather good for our dataset,... | {
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The Speech Data Explorer (SDE) tool is really a great tool for error analysis. I have some questions about it:
1- What is `Vocabulary size` exactly mean in SDE? Is it unique words separated by space in the manifest? (because when I have tested SDE with QuartzNet & Citrinet (with bpe tokenizer) models its vocabula... | {
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} | 1. Organize and move parameters used in `vad_infer.py` to _yaml_ config file` conf/vad_inference_postprocessing.yaml`.
2. Add gen_overlap_seq, gen_seg_table and write_to_manifest choice to `vad_infer.py` so the script can cover the whole pipeline from
**frame -> overlapped smoothing filter -> write to rttm-like tabl... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3393 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3393/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3393/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3393/events | https://github.com/NVIDIA/NeMo/issues/3393 | 1,094,985,874 | I_kwDOC_bI7s5BRCiS | 3,393 | get timestamp with timescribed output in ASR nemo | {
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} | null | null | null | I am using quarztnet model for converting speech to text. I am getting only text. My requirement is to get text wors with timestamp. | {
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} | # Changelog
- Refactor ASR directory to modularize against - type of task (ASR/Speech Pretraining/Speech Classification) and type of ASR training (CTC/RNNT).
- Keep all inference scripts in base dir for easy access to infer/eval ASR models without tracking down which model to evaluate.
- Move VAD/Speech Classificati... | {
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- weights update for common cases | {
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@fayejf @titu1994 @borisgin
i am interested in knowing your thoughts and comparison between MarbleNet and silero-vad on cpu-only devices. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3388 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3388/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3388/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3388/events | https://github.com/NVIDIA/NeMo/pull/3388 | 1,093,658,003 | PR_kwDOC_bI7s4whn8w | 3,388 | [NeMoMegatron] Pipeline parallelism for GPT | {
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} | PR to add pipeline parallelism to GPT using fwd/bwd functions from Apex.
FP32, FP16, and BF16 are all working now.
When using pipeline parallel it is recommended to use BF16 + Megatron amp O2.
```
model.megatron_amp_O2=True
trainer.precision='bf16'
```
### TODOs
- under review
### Known issues
- compl... | {
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} | null | null | null | **Describe the bug**
When (inverse) normalizing text using the InverseNormalizer's `normalize` function, a recursion error is raised when the number words in the input surpasses a threshold.
**Steps/Code to reproduce bug**
Running the following script produces the error when `len(t)=994`.
```
from nemo_text_... | {
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In addition, the default value of encoder future masking is set to be false (i.e., joint prediction instead of auto-regressive). | {
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I have a question. How one can add a Word Level Pre-Trained Language Model (_e.g.,_ BERT or DistilBERT from HuggingFace) to an ASR model (_e.g._, character based like QuartzNet or Token based like Citrinet) for inference time?
Best | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3381 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3381/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3381/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3381/events | https://github.com/NVIDIA/NeMo/pull/3381 | 1,091,346,309 | PR_kwDOC_bI7s4waOji | 3,381 | update speaker collate functions | {
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} | Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
This PR
* removes time-length and shift-length concept for training speaker label models.
> This assumes all the samples present in training manifest must adhere to max of 8 secs. (3 sec is ideal)
* removes sliced_seq_collate_fn : Since we introduced s... | {
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I need to draw learning curve for the trained Biomegatron model. I have trained the model as per the tutorial https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb#scrollTo=DQhsamclRtxJ
Now when I am trying to get the learning-curve which... | {
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The validation loss is very high compared to training loss even though the data used for training and validation is the same. Training loss for my dataset is at 0.328 after 70 epochs whereas validation loss is ~16.
**Steps/Code to reproduce bug**
This is the config used -
```yaml
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} | 1. Fix creating tarred dataset for cases when tokenizer name contains `/` character, e.g. `'facebook/mbart-large-50'`.
2. Fix subclass check [here](https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/collections/nlp/data/token_classification/punctuation_capitalization_tarred_dataset.py#L3... | {
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I am working with the common voice hindi dataset on collab.
I created a seprate python file as mentioned and passed the train tsv file , but it displayed below error
`SOMETHING WENT WRONG - IGNORING ENTRY`
Kindly provide the fix | {
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} | On SLURM resuming training in a multi-node multi-gpu settings fails, as when `LOCAL_RANK` is undefined `is_globa_rank_zero()` returns true on all processes that run on node 0. In this case `exp_manager.py` https://github.com/NVIDIA/NeMo/blob/f83b2c5524a787be21ffea170850c4b5486eac2b/nemo/utils/exp_manager.py#L446, creat... | {
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1- Would you please help me how I can save the config (`cfg`) of an ASR model as a yaml file? (_i.e.,_ load a trained asr `.nemo` model file and save its cfg as a yaml file)
2- Another question is that, when I follow your [`ASR_CTC_Language_Finetuning`](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_C... | {
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When fine tuning from a NeMo model (ex. stt_en_cn1024), the exp_manager's cfg is not updated properly.
I can see that in my run the model uses one config, but WandB reports another.
This issue did not occur in v1.4.0 and happened after I upgraded to v1.5.0.
Maybe it has to do with order of ... | {
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} | Tested with 345M Bert and 5B GPT checkpoints. Works fine. | {
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I trained a corformer CTC BPE model with 8k wav data, but when I tried to load the model for decoding with
`asr_model = nemo_asr.models.ASRModel.restore_from(nemo_model_file)` I got this shape mismatch error
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I even tried to use restore_from after downloading the respective *.nemo file. But getting same issue there as well... | {
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} | Add support for Byte-level multilingual NMT + Byte-level preproc fixes.
Other change to note:
* `train_tokenizers::preproc_mt_data` Instead of throwing an error if the tokenizer is not trainable, train_tokenizer now returns None. This is done to keep the pipeline simple and same for tokenizers like byte-level which... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3367 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3367/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3367/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3367/events | https://github.com/NVIDIA/NeMo/issues/3367 | 1,085,943,327 | I_kwDOC_bI7s5Aui4f | 3,367 | How to export ConformerCTC to ONNX format to run in C ++? | {
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Please tell me how can I export the ConformerCTC model to ONNX format to run from C ++? If I understand correctly, the spectrograms required for the model can only be obtained using the classes built into NeMo. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3366 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3366/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3366/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3366/events | https://github.com/NVIDIA/NeMo/pull/3366 | 1,085,942,965 | PR_kwDOC_bI7s4wI4H0 | 3,366 | Update Mixer-TTS, FastPitch and TTSDataset | {
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} | - [x] Update nemo.collections.tts.torch.data.TTSDataset and nemo.collections.tts.torch.data.MixerTTSDataset
- [x] Rename "duration_prior" to "align_prior_matrix"
- [x] Add `normalized_text` field
- [x] Fix minor bugs
- [x] Update FastPitch
- [x] Comment unnecessary code and suggest to remove it (... | {
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