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} | From: https://github.com/NVIDIA/NeMo/issues/3348
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} | The shebang must be the first line because it is interpreted by the kernel, which looks at the two bytes at the start of an executable file.
@okuchaiev , could you review please? | {
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} | null | null | null | **Describe the bug**
The class for training FastSpeech2_HiFiGAN end-to-end model uses variables that aren't present in the core NeMo classes, so the python errors out before training begins.
**Steps/Code to reproduce bug**
Try training such a model using NeMo 1.5.1
**Expected behavior**
A model can be tr... | {
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} | This PR includes the following changes:
- Added CTM file based (word-by-word) diarization evaluation for calculating accurate WDERs.
- Created decoder_timestamps_utils.py that contains all the word time-stamp generation scripts.
- pyctcdecode support in decoder_timestamps_utils.py.
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@titu1994
@redoctopus
@jbalam-nv
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```
[E:onnxruntime:, sequential_executor.cc:339 Execute] Non-zero status code returned while running Reshape node. Name:'Reshape_378'
Status Message: ... | {
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Reference paper: https://arxiv.org/abs/2110.03098
There's a lot of code, so I'm hoping @titu1994 can give a review. | {
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<div type='discussions-op-text'>
<sup>Originally posted by **andrusenkoau** December 15, 2021</sup>
Hi everyone!
I am trying to decode all librispeech dev and test sets for my Conformer-CTC-BPE model by "eval_beamsearch_ngram.py" script in greedy... | {
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we can restore a nemo model by
model = nemo_asr.models.EncDecCTCModelBPE.restore_from(name+'.nemo')
and use it for inference.
But the models during training were saved in ckpt format.
And I tried model.load_state_dict(torch.load(model.ckpt)) but there are different keys.
How can I l... | {
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} | - Basic tutorial for self-supervised pre-training and subsequent supervised fine-tuning
- Config for conformer SSL (may be changed later)
- Update to convert_to_tarred script to enable not having "text" field in manifest, and allow using "offset" for manifest without labels
- Add optional non-stride layers to recons... | {
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German normalization fails due to apparent installation problems.
**Steps/Code to reproduce bug**
If installed with:
`pip install nemo-toolkit[all]` the nemo_text_processing.text_normalization.de.taggers.tokenize_and_classify is not in the project in order to normalize german phrases alth... | {
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Nemo normalizer fails to normalize phrases in german. Operation fails with a _pywrapfst.FstOpError caused by line 464 in the extensions/_pynini.pyx file.
**Steps/Code to reproduce bug**
`from nemo_text_processing.text_normalization.normalize import Normalizer
normalized_text = Normalizer(... | {
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} | This PR closes https://github.com/NVIDIA/NeMo/issues/3220 by adding support to share encoder and decoder embeddings and pre-softmax projection layers when the same vocabulary is used in the encoder and decoder.
**NOTE:** Only the shape of the embedding matrix is validated during runtime (i.e., vocabulary size, vocab... | {
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Thanks for your great framework.
Do you have any plan for adding serving ASR models via TorchServe (_e.g._, REST API or gRPC API)? I think it would be great (but I think at first, one must implement an appropriate handler for it, because to the best of my knowledge TorchServe at current state doesn't have any ha... | {
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Recently I've tried to understand why is my gpu usage spiking from 0 to 100% and why does training seem to halt every num_workers batches. What I found is that my dataset consisted of .opus and .mp3 files, and that they load 2-30 times slower than th... | {
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Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com> | {
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} | # Changelog
- Add speech_to_text_eval.py which utilizes `transcribe_speech.py` to first perform transcription (for all asr models) and then uses its output to compute scores against some ground truth (via manifest).
- Remove speech_to_text_infer.py since it was hand tuned for just 1 type of model and was not an infer... | {
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* update configs | {
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} | # Changelog
- Better rnnt greedy decoding default of 10 max decoding time steps
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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Python :3.8.12
Running inside docker image :pytorch:21.11-py3
Code: :
```
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En', strict=False)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3327 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3327/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3327/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3327/events | https://github.com/NVIDIA/NeMo/issues/3327 | 1,079,708,396 | I_kwDOC_bI7s5AWwrs | 3,327 | AttributeError: Can't pickle local object 'AudioToBPEDataset.__init__.<locals>.TokenizerWrapper' | {
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I am using the following code:
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from("models/stt_en_conformer_ctc_small.nemo")
files = ['1.wav']
print(asr_model.t... | {
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I am running `enc_dec_nmt.py` as is from https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/enc_dec_nmt.py
Here's the error:
```
Traceback (most recent call last):
File "/home/jpilault/projects/nemo_examples/enc_dec_nmt.py", line 124, in main
mt_model = MTEn... | {
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} | null | null | null | Hello! I wanna get baselines for citrinet-256 and citrinet-512 using WSJ, so that I can perform some experiments after. I will use speed perturbation and specAugment.
For now, I am training citrinet-256 with 4 GPUs, and batch 60. I also created a tokenizer with 250 for vocab and using WPE (not sure if this is the ... | {
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} | - Updated docs page for current published) French ASR models
- fixed broken link to WFST tutorial (points to version in main since no stable release of tutorial.
- Re-pushed French ITN models in case of discrepancy with current code. (Currently failing in Docker container for r1.5.1) | {
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Let we have the next text (in Russian): "куку девятьсот шестьдесят шесть миллиардов восемьсот сорок четыре миллиона семьсот пять тысяч четыреста двадцать пять девятьсот сорока пяти купить"
`inverse_normalizer.find_tags` and. `inverse_normalizer.se... | {
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Thanks for your great repo. I have 2 questions:
1- In the Nemo repo is there any implemented code for calculating the WER (word error rate) for a data set & model's transcription? (_i.e._, I mean for example I want to calculate the WER of a trained model on a test set, which I have its ground truth in a text file... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3314 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3314/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3314/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3314/events | https://github.com/NVIDIA/NeMo/pull/3314 | 1,077,133,330 | PR_kwDOC_bI7s4vsX1I | 3,314 | Fixed future masking in cross attention of Perceiver | {
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} | This PR fixed auto-regressive future masking of cross attention in Perceiver encoder.
**NOTE:** Default is kept auto-regressive since it shows better results.
This PR also updated `yaml` config file to default to `aayn_base.yaml` instead of copying the entries. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3313 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3313/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3313/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3313/events | https://github.com/NVIDIA/NeMo/pull/3313 | 1,076,877,952 | PR_kwDOC_bI7s4vriyU | 3,313 | Fix MixerTTS generate_spectrogram types and dimensions | {
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} | Fixes the typecheck decorator for generate_spectrogram in MixerTTS and the swapping melspec and time dimension (as also done here https://github.com/NVIDIA/NeMo/blob/3687f2eaae36de6dd686caa99d2fe3100a5f4431/nemo/collections/tts/models/mixer_tts.py#L649)
You're probably in the middle of a refactor so sorry if you alr... | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3312 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3312/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3312/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3312/events | https://github.com/NVIDIA/NeMo/issues/3312 | 1,076,468,288 | I_kwDOC_bI7s5AKZpA | 3,312 | Question[multi node training] | {
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# Training the model
```sh
pyt... | {
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} | https://jirasw.nvidia.com/browse/AIA-2166
Also:
https://jirasw.nvidia.com/browse/AIA-2168
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com> | {
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} | added sparrowhawh tests for det. German TN
all sparrowhawk tests (TN/ITN) pass | {
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the output should be "1 2 3 4"
whereas the output is "one two three four" | {
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} | …s handled properly
See also https://jirasw.nvidia.com/browse/JARS-1682
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com> | {
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} | # Bugfix
- Training / inference stalls on multi gpu / multi node on Pytorch 1.10+
- This happens because the ASR models attempt to adaptively increase the masking window size or positional encoding (conformers) during training.
- If some ranks update their mask/pe buffer and others don't, then all reduce cannot prop... | {
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} | I tested with small dataset with binary_head True/False and model_parallel 2 and 1. They all seem working fine. | {
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In config file of conformer, I tried gpu=-1 and also gpu=2 but when I run the code, it just use the first GPU and it doesn't use the second one.
Could you please help me to solve it?
As you see when I run the code, it use just the first GPU... | {
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} | # Bugfix
- Correct the variable for time_starts when executing the numba kernel for adaptive spec augment
# Note:
- This bugfix does not affect training runs used with the NeMo container or the pytorch container
- The CUDA version in the containers is always higher than the max supported CUDA version in Numba, th... | {
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I was wondering if this line of [code](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/numba/spec_augment/spec_aug_numba.py#L286) for time masking of spec augmentation is correct.
Unless intended, it looks more reasonable with `time_starts = torch.cat(time_starts, 0)` rather... | {
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} | This PR adds multiscale processing feature to NeMo speaker diarization pipeline.
examples/speaker_tasks/diarization/conf/offline_diarization.yaml is also modified to enable single scale and multiscale at the same time.
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} | # Bugfix
- Attempt to prevent a step counter update of the scheduler when skipping a step in the optimizer
- Add training steps when nan/inf gradients are detected
- Properly support LR scheduler with "constant lr" at end of training. Refactor CosineAnnealing model for such support.
# Details
Since there are n... | {
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} | This PR adds a text augmentation script. The script can be used to generate human-like typos when testing model robustness.
# Summary
The script supports the following text augmentations:
1. Dropping a letter in a word
2. Swapping two letters in a word
3. Human-like typos (based on keyboard structure, and co... | {
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```
import nemo
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_quartznet15x5").cpu()
English_text = quartznet.transcribe(['~/au... | {
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I am currently training conformer-small on librispeech-960hr from scratch, following the training recipe and instructions in `examples/asr/conf/conformer/conformer_ctc_bpe.yaml`.
However, I'm not able to reproduce the WER reported in this [webpage](https://catalog.ng... | {
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- reduce audio-based normalization options | {
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} | This pull request includes the following features:
- Multiscale diarization: segmentation and clustering parts are updated (experimental).
- Optional pyctcdecode feature to apply beam search decoder for ASR.
- Language model based realigning feature for diarization (experimental).
- Added CTM based evaluation (W... | {
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```
import nemo
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_quartznet15x5").cuda()
English_text = quartznet.transcribe(['~/a... | {
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python train_kenlm.py --train_file /home/mount/librispeech-lm-norm.txt --nemo_model_file stt_en_conformer_ctc_large.nemo --kenlm_model_file ../nemo_lm_6_new --ngram_length 6 --kenlm_bin_path ../kenlm/build/bin/ --do_lowercase
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import nemo
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.list_available_models()
print("done")
```
Whenever I run this code, it gives me this error:
`AttributeError: type object 'EncDecCTCModel' has no attribute 'list_available_models'
`
I have all the dependencies... | {
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} | * Add normalizer for FastPitch and Mixer-TTS
* Update types for FastPitch and Mixer-TTS via new proposed signature for time
* Fix small bugs and refactor code in Mixer-TTS, FastPitch and TTS tokenizers
* Add German tokenizer
* Add CMUDict
Signed-off-by: Oktai Tatanov <oktai.tatanov@gmail.com> | {
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} | null | null | null | Hi,
I want to fine-tune **stt_en_conformer_ctc_large** with Persian dataset. I used **conformer_ctc_char.yaml** from your example folder in repository. I changed the labels, too. Now when I want to run **trainer.fit(asr_model)** I have the following error:
`Interpolation key 'model.encoder.d_model' not found
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} | null | null | null | **Describe the bug**
I cannot export the pertained ECAPA TDNN model for embeddings to .onnx
Exporting requires the model to have a `input_example` which is not available on the ecapa tdnn model.
**Steps/Code to reproduce bug**
```
import nemo.collections.asr as nemo_asr
speaker_model = nemo_asr.models.Enc... | {
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I tested the model nmt_en_zh_transformer24x6 and nmt_en_zh_transformer6x6 on WMT18 test set and found that the blue score was a little low.
**Steps/Code to reproduce bug**
`model = MTEncDecModel.from_pretrained("nmt_en_zh_transformer24x6")`
`import copy`
`cfg = copy.deepcopy(model.cfg... | {
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This PR adds support for the [gecko](https://gong-io.github.io/gecko/) labeling tool. Now diarization outputs *_gecko.json along with current output files for loading in gecko tool. | {
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} | This PR enhances fixed positional embeddings to be adaptive to input sequences which are longer than the pre-defined max-sequence-length.
The PR also fixes two issues with checkpoint averaging `NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py`:
1. Checkpoint averaging now ignores `-last.ckpt` file (which... | {
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I am trying to replicate Quartznet 5x5 results to get around 11/12% WER on the test set.
So far my last experiment was using 4 gpus, with batch 64, using speed perturbed data, specaugmentation, learning rate 0.01, dropout 0.2. I got around 23.18% on the validation and 17.11% on test set. Part of my config f... | {
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} | This PR container has many updates.
Please review and suggest any feedback on (1) code structure change, (2) argument names and structure. | {
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Thank you for your great repo. I have 2 questions:
1- Is it better to use G2P for English ASR (or just use the raw text for it)? if yes, what model (G2P) do you suggest for this work?
2- Is it better to use G2P for ASR of other languages or not? (for example Arabic ASR)
Best | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3257 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3257/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3257/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3257/events | https://github.com/NVIDIA/NeMo/issues/3257 | 1,065,426,283 | I_kwDOC_bI7s4_gR1r | 3,257 | Performance test on CPU and GPU | {
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I am particularly interested to know if I can increase the throughput of ASR by using a GPU instead of a CPU? And how much would be the increase in the throughput? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3256 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3256/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3256/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3256/events | https://github.com/NVIDIA/NeMo/pull/3256 | 1,065,176,884 | PR_kwDOC_bI7s4vFtI6 | 3,256 | Add WMT21 paper to Publications | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3255 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3255/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3255/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3255/events | https://github.com/NVIDIA/NeMo/issues/3255 | 1,065,082,195 | I_kwDOC_bI7s4_e91T | 3,255 | Fine tune “nmt_en_zh_transformer24x6” using a single GPU, dict has not been initialized | {
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`/NeMo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py in eval_epoch_end(self, outputs, mode)`
`356 # we also need to drop pairs where ground truth is an empty string`
`357 dist.all_gather_object(`
`--> 358 tr_and_gt, [(t, g) for (t,... | {
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