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"Cannot root cause why DCO fails"
] | 2021-03-11T06:30:06 | 2021-11-08T21:15:13 | 2021-03-16T21:52:35 | CONTRIBUTOR | {
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} | # Changelog
- Add pretrained model info for CTC, CTC-BPE models
- ^ for ASR Classificiation models
- Remove old reference to QuartzNet15x5Base-En
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"This pull request **introduces 1 alert** when merging 4a0ca9b202c7c01f9d827cf392f0cd7cbb3af566 into 5d80627ca696384dc16e462f1e17efd33917a9dd - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-119caa9229f75b6c349ae1b681724d504f3e2940)\n\n**new alerts:**\n\n* 1 for Unused local variable"
] | 2021-03-11T01:15:32 | 2022-01-11T16:36:50 | 2021-03-11T04:58:03 | CONTRIBUTOR | {
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} | …ti gpu bug
Signed-off-by: Vlad Getselevich <vgetselevich@nvidia.com> | {
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"@xaiguy Can you please clarify what do you mean by \"custom NeMo model\"? \r\nEvery TLT model can be exported to Jarvis. But NeMo contains more models than TLT, including many \"research\" ones which can't be deployed to Jarvis yet.\r\n \r\nWe are constantly increasing the number of models supported by Jarvis by a... | 2021-03-10T23:55:59 | 2021-03-11T19:36:34 | 2021-03-11T19:36:34 | NONE | {
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} | null | null | null | Dear NeMo team,
I'm currently looking into Jarvis and haven't found a pre-built way to export my custom NeMo models to .ejrvs format yet, only for TLT. Several sources (including https://developer.nvidia.com/nvidia-nemo) state that it's possible with a single command. If anyone knows the magic words, please let me know. 👍
Kind regards!
PS: I'm using NeMo 1.0.0b3 via the official docker. | {
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- Update ASR Tarred dataset creation to support metadata
- Add more tarfiles to dataset
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"Wrong branch"
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"if the encoders & decoders are basically the same thing, would it make sense to have a single file that they import blocks from?",
"> if the encoders & decoders are basically the same thing, would it make sense to have a single file that they import blocks from?\r\n\r\nThat is already happening to a good extent ... | 2021-03-10T17:29:24 | 2021-03-11T17:26:08 | 2021-03-11T17:26:03 | CONTRIBUTOR | {
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"I think we haven't properly implemented export for Conformer. @VahidooX to comment further. If this isn't supported yet, maybe should raise \"NotImplemented\" exception? ",
"We have not tested the export for Conformer yet. I would raise NotImplemented for now. \r\nUpdate: I fixed the issue with the export functi... | 2021-03-10T13:03:20 | 2021-04-24T00:09:09 | 2021-04-24T00:09:09 | NONE | {
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} | null | null | null | Conformer model export to onnx not working as intended. Onnx export worked ok and did not report anything and onnx.checker.check_model worked without errors too. Loading the onnx file and running the sample audio file causes onnxruntime to die quietly (jupyter just reloads the kernel, and running script just says "segmentation fault")
Steps to reproduce:
load .nemo model for conformer
`model = nemo_asr.models.EncDecCTCModelBPE.restore_from('conformer.nemo')`
export conformer encoder to onnx
`model.encoder.export('conformer_encoder.onnx')`
load onnx in onnxruntime and run through a sample file
```
sess = onnxruntime.InferenceSession(conformer_encoder)
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
result = sess.run([output_name], {input_name: features})
```
After running these steps I got a segmentation fault error. For feature extraction and onnx export/load I used the same pipeline that I used for jasper models (and it worked perfectly for jasper). Conformer works okay during training and evaluation within nemo and ptl frameworks.
In which case onnxruntime just fails completely without even reporting for something like incorrect dimensions or unsupported operations (like it usually does)?
Maybe I am missing something obvious here?
environment:
DGX-1, Ubuntu 18.04
nemo - up to date
torch - 1.8.0
onnxruntime - 1.6.0
onnxruntime-gpu - 1.4.0
I tried different combination of versions of the libraries but most of them cause the effect as mentioned above and the others fail for other reasons - mostly during the export step. | {
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2. Add flexible input format for VAD tune threshold | {
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- Custom dataset creation instructions
- Local & NGC checkpoint loading instructions and examples
- Additional info on intro page
- `configs.rst`: details about nemo_asr model configs (dataset, preprocessor, augmentation, per-model configs)
- Some missing modules in the API | {
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"I also had to fix torch to 1.7.1. I also opened a pull request for that, but is not accepted to stick the version of torch. You can read more here https://github.com/NVIDIA/NeMo/issues/1868 and follow until you see my pull request, which is a temporary workaround for me. Maybe you should apply to yourself the sam... | 2021-03-09T20:52:45 | 2021-04-20T05:32:49 | 2021-04-20T05:32:49 | NONE | {
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} | null | null | null | I often find/see conflicts in the environment between - torch, torchtext, torchaudio, torchvision, pytorch-lightning and nemo and this causes many problems for me. Also, it seems like many of the issues posted here are related to those conflicts. This is to be expected with the fast pace these libraries are developing, but things could improve for the user when it comes to theses issues.
A solution would be to post fixed and verified library/environment versions and install readme:s for the environment supporting the release, e.g. an INSTALL.md with fixed versions per release.
Another solution would be to be more strict with the versions required in the requirements-files under the requirements folder. That would not support anaconda environments, so please also consider to include how to achieve a stable anaconda environment.
Further more, a nice example is the [pytorch local install page](https://pytorch.org/get-started/locally/), where you could iteratively get instructions how to install.
(OR a novel approach would be to simply tar the environment and offer it as an s3-artefact along with the release, e.g. arm, x86-64 etc?)
Br, PvR | {
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"This pull request **introduces 9 alerts** when merging 8abd6c6c4439cc5ce3e1e65f5dfb1950faecceaf into e8912ca6e3321347272a6a7da18e052812fb2062 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-57eaca60590a915a6de70d40a43478f84286be8b)\n\n**new alerts:**\n\n* 9 for Unused import",
"This pull requ... | 2021-03-09T16:56:21 | 2021-03-12T00:29:38 | 2021-03-12T00:29:34 | COLLABORATOR | {
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} | This PR will add HuggingFace Encoder support to NeMo NMT.
When training NeMo NMT models, users can now:
- Download pretrained model from HF and use it for NMT automatically
- The NMT data preprocessor will automatically use the tokenizer associated with pretrained when preprocessing the data
- The HF encoder can be used with the pretrained weights or with randomly initialized weights
- The HF encoder can be configured from scratch.
# Implementation Details
Since our ultimate goal is to be able to use encoders from HuggingFace, Megatron-LM, and NeMo, we've added some new flags to NMT.
The `library` flag takes values: `huggingface`, `megatron` (when implemented), and `nemo`.
NMT has `library=nemo` by default to maintain backwards compatibility.
The `model_name` flag is used to indicate a _named_ model architecture. From HuggingFace, we have `bert_base_cased` for example.
We've also added the `pretrained` flag. This allows users to decide whether they want to download the named model's pretrained weights `pretrained=True` or simply instantiated the named model architecture with random weights `pretrained=False`.
If a user wants to use a model architecture from a specific library, then they can use `model_name=None` and add the custom configuration under the `encoder` configuration. For example:
```
encoder:
_target_: transformers.BertConfig
hidden_size: 1536
```
Also note that `shared_tokenizer` should `False` when using HuggingFace encoders since we do not support HuggingFace Decoders yet.
# Usage
We've provided a config file to use with HuggingFace encoders: examples/nlp/machine_translation/conf/huggingface.yaml
We can use that config file by specifying from the CLI:
```
--config-path=conf \
--config-name=huggingface
```
## Pretrained HuggingFace Encoder
This will train an NMT model with a pretrained encoder model from HuggingFace and
a randomly initialized decoder from NeMo.
To train the encoder from scratch, set `encoder.pretrained=False`.
```
MODEL_NAME = bert-base-cased
python examples/nlp/machine_translation/enc_dec_nmt.py \
--config-path=conf \
--config-name=huggingface \
model.src_language=en \
model.tgt_language=de \
model.preproc_out_dir=/raid/data/hf_en_de_preproc_${MODEL_NAME} \
model.shared_tokenizer=false \
model.encoder_tokenizer.library=huggingface \
model.encoder.library=huggingface \
model.encoder.pretrained=true \
model.encoder.model_name=${MODEL_NAME} \
model.decoder_tokenizer.library=yttm \
model.decoder_tokenizer.vocab_size=32000 \
model.decoder.library=nemo \
model.decoder.inner_size=4096 \
model.decoder.num_attention_heads=16 \
model.decoder.attn_layer_dropout=0.1 \
model.decoder.ffn_dropout=0.1 \
model.decoder.num_layers=6 \
...
```
## Custom HuggingFace Encoder
This will train an NMT model with a custom encoder model from HuggingFace and
a randomly initialized decoder from NeMo.
Note: we have to use `+` from the CLI if we are not adding the argument to the config file.
```
python examples/nlp/machine_translation/enc_dec_nmt.py \
--config-path=conf \
--config-name=huggingface \
model.src_language=en \
model.tgt_language=de \
model.preproc_out_dir=/raid/data/hf_en_de_preproc_custom \
model.shared_tokenizer=false \
model.encoder_tokenizer.library=huggingface \
model.encoder.library=huggingface \
+model.encoder._target_=transformers.BertConfig \
+model.encoder.hidden_size=1536 \
model.decoder_tokenizer.library=yttm \
model.decoder_tokenizer.vocab_size=32000 \
model.decoder.library=nemo \
model.decoder.inner_size=4096 \
model.decoder.num_attention_heads=16 \
model.decoder.attn_layer_dropout=0.1 \
model.decoder.ffn_dropout=0.1 \
model.decoder.num_layers=6 \
...
``` | {
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"I did not played enough with that, but my first impression it was that with my studio microphone without dynamic compression and leveling, already was worst. Therefore at the beginning I will use with hardware dynamic compression. Once I am sure that this is the biggest issue, then I will make this compression in ... | 2021-03-09T12:06:30 | 2021-04-28T00:13:47 | 2021-04-28T00:13:46 | NONE | {
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Trying to run the Nemo for the audio recorded with the phone /zoom meeting , but the text output is very bad, the same audio which has been recorded with good microphone ,the text output is exact.
Can i know is there any reference for audio quality ? how to check the quality of the audio or how to use NeMo for real work applications. | {
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"thanks again @tiborkiss but we'll close this one for now because we can't pin torch version to the exact number like 1.7.1 We could add something like this right before upload to pypi though."
] | 2021-03-09T00:59:02 | 2021-03-10T22:35:14 | 2021-03-10T22:35:14 | NONE | {
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} | … pull down 1.8.0. https://github.com/PyTorchLightning/pytorch-lightning/pull/6168
This diff temporarily solves the imported incompatibility reported in https://github.com/NVIDIA/NeMo/issues/1868
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"PTL was recently fixed this problem here: https://github.com/PyTorchLightning/pytorch-lightning/commit/60d0c95e07b8edfa22f5892ebb72d93b1d35832f \r\nand they released `pytorch-lightening` 1.2.3 .\r\n\r\nTherefore I updated in `requirements/requirements.txt`\r\n```\r\n-pytorch-lightning>=1.1.0,<=1.1.5\r\n+pytorch-li... | 2021-03-09T00:18:07 | 2021-03-24T06:47:40 | 2021-03-16T23:09:07 | NONE | {
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} | null | null | null | **Describe the bug**
Due to `torch` 1.8.0 just was released a few days ago, `pytorch-lightning` is still incompatible with the new torch.
Since NeMo setup pulls the latest stable torch release, imports this problem.
The problem is not visible during the install, but when using `pytorch-lightning` within NeMo tutorials for example.
**Steps/Code to reproduce bug**
I installed NeMo from r1.0.0rc17
```
python -m pip install git+https://github.com/NVIDIA/NeMo.git@r1.0.0rc1#egg=nemo_toolkit[all]
```
It was working while the official stable pytorch was 1.7.1.
Since PyTorch 1.8.0 came out, in fresh venv NeMo's requirements are pulling the latest torch, which is 1.8.0,
With torch 1.8.0 the second ASR tutorial notebook fails while using `pytorch-lightning` because `torchtext.data.Batch` was deprecated (and moved to` torchtext.legacy.data.Batch`, which breaks PyTorch Lightning. For more information read: https://github.com/PyTorchLightning/pytorch-lightning/pull/6168
**Expected behavior**
Since the bug is indirectly caused by the incompatibility between the current `pytorch-lightning` installed by NeMo and the latest `torch` 1,8.0, the only temporary solution I see that NeMo should stick with `torch<=1.7.1` and it's associated libraries.
I tried to edit the requirements by myself, then `./reinstall.sh`, unfortunately something is still pulling 1.8.0 somewhere at the end of the setup process.
The expected behavior is to be able to stick to torch==1.7.1
**Environment overview (please complete the following information)**
- Environment location: WSL2 with GPU support and Ubuntu 20.04
**Environment details**
- OS version: Ubuntu 20.04 running in WSL2 with GPU support.
- PyTorch version: 1.8.0
- Python version: 3.8.5
**Additional context**
I think, I completely described the problem and the possible solution.
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"This is a pytorch lightning issue, please downgrade to 1.2.1 for the time being until they are able to support pytorch 1.8 as well as it's dependency of torchtext",
"> This is a pytorch lightning issue, please downgrade to 1.2.1 for the time being until they are able to support pytorch 1.8 as well as it's depend... | 2021-03-09T00:05:17 | 2021-04-20T05:33:49 | 2021-04-20T05:33:49 | NONE | {
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} | null | null | null | Hello everyone,
I have some problems trying NeMo. I want to setup an environment on a Linux machine but after the installation of nemo_toolkit, the import (_import nemo.collections.nlp as nemo_nlp_) keeps rejecting an error:
**ImportError: cannot import name 'Batch' from 'torchtext.data' (/usr/local/lib/python3.7/dist-packages/torchtext/data/__init__.py)
**
I also tried the projects from a NeMo example (https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Punctuation_and_Capitalization.ipynb) but I got the same issue...
Can anyone tell me if I miss something? Thank you all in advance!
Yours. | {
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"This pull request **introduces 4 alerts** when merging 03cb3ef0aad58dcaea2e849989897a94c849dcdc into 4d868b662af0fab87a4ad4078156486885bbb0a4 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-477438a4d8b220885f444f3455bf533d4efff55d)\n\n**new alerts:**\n\n* 2 for Modification of dictionary return... | 2021-03-08T23:41:05 | 2021-03-11T22:53:30 | 2021-03-11T22:53:30 | COLLABORATOR | {
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} | Here, added export tp NMT's encoder and decoder.
The design decision is is to have those 2 parts explicitly exportable, instead of having whole model's export() produce 2 files.
Test added. | {
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"buncha tests are failing but i dont think theyre related to this pr "
] | 2021-03-08T20:44:31 | 2021-03-08T22:42:53 | 2021-03-08T22:42:47 | CONTRIBUTOR | {
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} | ~preprocessor `tokenize(txt)` methods should probably return a list of tokens, not a string. these can then be converted into ids without the intermediate steps of producing a string and then immediately splitting it for the tokens again, as was being done in `translate`~
it turns out that `text_to_ids(txt)` acts differently than `tokens_to_ids(txt.split(' '))`, so i am changing the output of the `processsor.tokenize` to be `str` again. | {
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} | NMT DDP training is hanging after training for a few epochs. This hanging is likely due to validation metrics becoming out of sync.
See the PTL discussion here: https://github.com/PyTorchLightning/pytorch-lightning/issues/5604
Was able to train for over 24 epochs with 4 GPUs without hanging.
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} | fix syntax | {
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"Commits not signed."
] | 2021-03-08T16:56:30 | 2021-03-08T17:12:12 | 2021-03-08T17:11:56 | COLLABORATOR | {
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} | Updating NeMo Core docs here. Adding docs for Hydra, Pytorch Lightning and Experiment Manager. | {
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"thanks!"
] | 2021-03-07T10:43:18 | 2021-03-13T00:19:09 | 2021-03-13T00:19:09 | CONTRIBUTOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1855 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1855/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1855/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1855/events | https://github.com/NVIDIA/NeMo/issues/1855 | 823,659,078 | MDU6SXNzdWU4MjM2NTkwNzg= | 1,855 | Fine-tune QuartzNet15x5NR-En to another language [Question] | {
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"@okuchaiev Maybe there is some information on this issue? Thanks!",
"There are several notebooks for transfer learning char and subword models in Nemo now, please have a look and reopen the issue if they are insufficient. "
] | 2021-03-06T14:12:12 | 2021-08-17T01:02:04 | 2021-08-17T01:02:04 | NONE | {
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} | null | null | null | Hello!
Thank you for this opensource product!
I want to try fine-tune model **QuartzNet15x5NR-En** to Russian language. This article [Transfer learning from English to Russian](https://developer.nvidia.com/blog/jump-start-training-for-speech-recognition-models-with-nemo/) is good practice for my experiment, but i have some questions.
1. In `config.yaml` in this model `RIR_and_Noise` i find this sample for augmentation:
> rir_manifest_path: /data/datasets/freesound_20s/rir_noises_tarred/rir_tarred/tarred/tarred_audio_manifest.json
> rir_tar_filepaths: /data/datasets/freesound_20s/rir_noises_tarred/rir_tarred/tarred/audio_{0..63}.tar
How i download this files and create this manifest?
I find this script - `download_resample_freesound.sh` - what parameters should be specified for download?
2. How many `hrs` should dataset have? What type of audio data should it contain? (phone calls, audio book...)
3. What `learning rate` to use? What parametrs set to `SpecAug`?
Thank you! | {
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"This pull request **introduces 1 alert** when merging 9de7bc589925be7b77ffcaa8fad5ffe14115971e into 74a1ed8618872d331fcea3133d47b8b3552c435f - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-9075874939d4e184867f5ce3384215d359cffa1d)\n\n**new alerts:**\n\n* 1 for Unused local variable",
"This pu... | 2021-03-06T03:18:40 | 2021-03-29T05:59:53 | 2021-03-29T05:59:50 | COLLABORATOR | {
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} | # Changelog
- Add Numba CUDA + CPU kernels for RNNT Loss ported from https://github.com/HawkAaron/warp-transducer
- Add tests for CUDA + CPU kernels + reference numpy implementation.
# Todo:
- [x] Add documentation for all kernels and wrapped modules
- [x] Add Typing information to `RNNTLossNumba` (Done by resolver)
- [x] Rename this module to avoid clash with original `warprnnt_pytorch` loss
- [x] Add WarpRNNT LICENSE header in all files that implement functionality as ported.
- [x] Fix imports inside `__init__.py`
- [x] Enable loss switching for RNNT / RNNTBPE models via config (done in next PR) | {
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"@okuchaiev done"
] | 2021-03-05T21:50:52 | 2021-03-06T03:13:23 | 2021-03-06T01:07:16 | COLLABORATOR | {
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} | # Changelog
- Freeze internal modules when transcribing data so as to prevent graph from accumulating data during loop
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"Hi, restoring NLP models can be complicated as there are potentially many different artifacts needed for restore. This is why we've created the .nemo file and .restore_from. We mainly use the PyTorch Lightning Checkpoints to auto-resume from training. They can be used in other workflows outside of NeMo, but then t... | 2021-03-05T19:50:19 | 2021-03-12T17:36:13 | 2021-03-12T17:36:13 | CONTRIBUTOR | {
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} | null | null | null | **Describe your question**
After training token classification model, several checkpoints .ckpt were generated along with .nemo. Load .nemo is ok. But I tried to load checkpoints via:
```
nemo_nlp.models.TokenClassificationModel.load_from_checkpoint(my_chk_path)
```
but give an Error:
```
No such file or directory: 'megatron-bert-cased_encoder_config.json'
```
I unpacked the .nemo and found 5 files:
```
label_ids.csv
megatron-bert-cased_encoder_config.json
model_config.yaml
model_weights.ckpt
vocab.txt
```
I did not know where did the megatron-bert-cased_encoder_config.json come from?
**Environment overview (please complete the following information)**
using nemo docker from ngc and at BRANCH = 'r1.0.0rc1'
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} | * Remove EFF save/restore routes
* This PR also fixes some Copyright Headers
* Once we can take EFF dependency we will use EFF.Archive directly
Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com> | {
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} | Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
* Adds ASR with Speaker Diarization tutorial notebook that outputs transcripts with speaker label along with time stamps
Sample output:
`speaker_0 [0.40 - 2.62 sec] eleven twenty seven fifty seven`
`speaker_1 [3.20 - 5.02 sec] october twenty fourth nineteen seventy` | {
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} | # Bugfix
- Older checkpoints might not have the `tokenizer.vocab` file when using SentencePiece tokenizers. This will cause model restoration to fail.
- Wrap in a try catch that catches ModelPT raised FileNotFoundError for this particular case to enable backward compatibility
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"Suggestion to change the folder name from speaker_diarization to speaker_recognition in scripts.\r\nI couldn't comment on unchanged files. Some links in speaker recognition notebooks point to these scripts. \r\nSome of them:\r\n\r\n> in tutorials/Speaker_Recognition_Verification.ipynb\r\n\r\n* get_hi-mia-data.py (... | 2021-03-04T07:20:13 | 2021-05-03T21:54:42 | 2021-03-10T23:24:00 | COLLABORATOR | {
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- Add tokenizer generator flag to limit the maximum length of subwords generated | {
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"Thanks @ekmb , just did :) ",
"yes please, this looks like bugfix for r1.0.0rc1",
"Thanks @ekmb and @okuchaiev - rebasing to r1.0.0rc1 resulted in many file changes, but notebook hasn't changed at all.",
"This pull request **introduces 4 alerts** when merging f6e7fc182f103e0b58c47ccb39645995ac178324 into bc3... | 2021-03-04T00:03:21 | 2021-03-04T19:14:28 | 2021-03-04T19:13:45 | COLLABORATOR | {
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} | after trying hard to make yttm and spm play nicely together, i am giving up on the latter. the en<>ja pipeline is now moses -> yttm. from what i can see, this fixes tokenization & detokenization issues we were seeing (eg, `'` was being split apart incorrectly during tokenization, preventing it from being detokenized correctly).
we also turn off moses normalization for japanese because it wasn't handling punctuation correctly | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1834 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1834/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1834/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1834/events | https://github.com/NVIDIA/NeMo/pull/1834 | 821,396,464 | MDExOlB1bGxSZXF1ZXN0NTg0MTk0MzM2 | 1,834 | fix nlp typos in notebooks | {
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"Looks like you fixed it in #1836. Closing this one"
] | 2021-03-03T19:01:22 | 2021-03-04T16:43:01 | 2021-03-04T16:42:54 | CONTRIBUTOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1833 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1833/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1833/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1833/events | https://github.com/NVIDIA/NeMo/issues/1833 | 820,944,942 | MDU6SXNzdWU4MjA5NDQ5NDI= | 1,833 | Bug in the Text_Classification_Sentiment_Analysis notebook when using wietsedv/bert-base-dutch-cased config | {
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} | null | null | null | Hello,
When im trying to run the sentiment notebook in the main branch I can perfectly use the bert-base-uncased and bert-base-multilingual-uncased models and finetune on our data. However, when we run the wietsedv/bert-base-dutch-cased model we get the following error: Index out of range in self. To me it seems like there is something going wrong in the embedding layers. When we ran this model in NeMo version 0.11 it worked fine.
Is there anything in the configs that changed?
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"This pull request **fixes 1 alert** when merging aebab0e12600a6fa4be346c88112a88716f7dc42 into 79bef5681e9023287c8a839b3eb5d0bad25dd3eb - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-427bb1d02f76b9b8378adc5f5b98f401e4e73e54)\n\n**fixed alerts:**\n\n* 1 for Unused import",
"This pull request ... | 2021-03-03T06:30:46 | 2021-03-04T20:40:00 | 2021-03-04T20:40:00 | CONTRIBUTOR | {
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} | **What was wrong** : renaming and adding onnx to tar was not working
**How solved** : make a temp copy of the file rename and add to tar(.enemo). cleanup extra file
**Nvidia internal bug** : 200706409 | {
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"@redoctopus Ignore the huge diff for speech commands (notebook 3). Switched it to Colab compatible notebook."
] | 2021-03-03T02:31:24 | 2021-03-03T17:22:38 | 2021-03-03T17:22:35 | COLLABORATOR | {
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} | # Changelog
- Fixes for notebook
- Add back image for CTC alignments
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"thanks @ericharper !",
"This pull request **introduces 3 alerts** when merging 9dcc1f64b1c17437a1403e44e44ef03cfce3e122 into 79bef5681e9023287c8a839b3eb5d0bad25dd3eb - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-70210a020f64159d504b1a88930512edba534cf8)\n\n**new alerts:**\n\n* 3 for Unused ... | 2021-03-02T22:26:08 | 2021-03-03T22:08:42 | 2021-03-03T20:50:09 | COLLABORATOR | {
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} | Also, need to update/add tests that would have caught this problem. | {
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"This should be fixed on main now: https://github.com/NVIDIA/NeMo/pull/1829\r\n\r\nPlease let use know if there are still issues.",
"Closing, reopen if needed"
] | 2021-03-02T20:51:08 | 2021-03-12T15:58:34 | 2021-03-12T15:58:34 | CONTRIBUTOR | {
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```
Traceback (most recent call last):
File "/home/dcg-adlr-mchrzanowski-source/NeMo/examples/nlp/machine_translation/enc_dec_nmt.py", line 127, in main
trainer.fit(mt_model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 471, in fit
self.call_setup_hook(model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1072, in call_setup_hook
model.setup(stage_name)
File "/home/dcg-adlr-mchrzanowski-source/NeMo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 453, in setup
self._trainer.accelerator_backend.ddp_plugin.configure_ddp = self.configure_ddp
AttributeError: 'GPUAccelerator' object has no attribute 'ddp_plugin'
```
reverting the changes from [PR 1774](https://github.com/NVIDIA/NeMo/pull/1774) fixes things. | {
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"If only intents are available, please use the text classification model. If only slots - token classification model.",
"@ekmb the goal here is to train a single model capable of predicting both intents and slots, for use in the botmaker framework. We want users to be able to train the model even if they don't ha... | 2021-03-02T16:54:08 | 2021-07-20T02:26:22 | 2021-07-20T02:26:22 | CONTRIBUTOR | {
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} | Requesting early feedback on the approach.
The purpose of these changes is to allow the joint intent/slot model to be trained on data where only slots are labeled or only intents are labeled.
* Users can supply _up to_ three labeled data sets: one with only slots labeled, one with only intents labeled, and one with both slots and intents labeled
* Separate Dataset objects are created for each of the above
* A custom Sampler tells the training Dataloader which examples to put into each minibatch
* Each minibatch draws from only one data source. When the loss is calculated, intent loss is set to zeroes if no intent labels are supplied; slot loss is set to zeroes if no slot labels are supplied. This is controlled by a data type indicator variable passed from the data loader.
I initially implemented these changes in a subclass of the existing intent/slot model (as well as the dataset and data descriptor classes), but it seems like that will create an extra burden to maintain two model classes that do essentially the same thing. So now I've made the changes directly in the existing model.
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"Is there any progress on this issue? I am currently trying to run inference for QuartzNet model using ONNX. I used EncDecCTCModel.export to export my model in ONNX format and using onnxruntime to do inference as follows:\r\n\r\n```\r\nsess = onnxruntime.InferenceSession(\"model.onnx\")\r\npred = sess.run(None, {i... | 2021-03-02T14:19:22 | 2024-12-29T07:06:05 | 2021-07-13T15:23:46 | NONE | {
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To make it work for me, I had to do the following workaround, but what is the right way to do this?
```
import nemo.collections.asr as asr
import onnxruntime
import tempfile
import os
import json
asr_model = asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En', strict=True)
so = onnxruntime.SessionOptions()
so.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
so.intra_op_num_threads = 1
onnx_file = 'quartznet.onnx'
session = onnxruntime.InferenceSession(onnx_file, so)
wav_path = '/path/to/file.wav'
paths2audio_files = [wav_path]
with tempfile.TemporaryDirectory() as dataloader_tmpdir:
with open(os.path.join(dataloader_tmpdir, 'manifest.json'), 'w') as fp:
for audio_file in paths2audio_files:
entry = {'audio_filepath': audio_file, 'duration': 100000, 'text': 'nothing'}
fp.write(json.dumps(entry) + '\n')
out_batch = []
config = {'paths2audio_files': paths2audio_files, 'batch_size': 4, 'temp_dir': dataloader_tmpdir}
temporary_datalayer = asr_model._setup_transcribe_dataloader(config)
for test_batch in temporary_datalayer:
out_batch.append(test_batch)
processed_signal, processed_signal_length = asr_model.preprocessor(
input_signal=out_batch[0][0], length=out_batch[0][1],
)
processed_signal = processed_signal.cpu().numpy()
logits = session.run([output_name], {input_name: processed_signal})[0]
``` | {
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"You can use pip install --upgrade nemo_toolkit[all] to get the latest version now."
] | 2021-03-02T08:06:56 | 2021-06-19T04:38:59 | 2021-06-19T04:38:59 | NONE | {
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} | null | null | null | Hello,
I would like to experiment with the latest version of NeMo, v1.0.0b4, however due to some firewall issues at my workplace I can only install through PyPI and not from Github.
Do you have any plan to release that version on PyPI in the near future?
Thanks! | {
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] | closed | false | null | [] | null | [
"can you try ``pip install nemo_toolkit['all']==1.0.0rc1`` ?",
"I just tried it and it works. Thank you very much, I was waiting literally like 2 months for this helpful answer. Can you please explain what's the difference between `pip install nemo_toolkit['all']==1.0.0rc1`, `!pip install nemo_toolkit[all]==1.0.0... | 2021-03-02T03:46:23 | 2021-05-13T03:05:06 | 2021-05-13T03:05:06 | NONE | {
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} | null | null | null | I trained **EncDecCTCModel** model with Mozilla Voice Project dataset like 3 months ago and save it by `.save_to` and I used to restore it by `nemo.collections.asr.models.EncDecCTCModel.restore_from(restore_path=_path_to_nemo_model_)`. I didn't have any problem at that moment and I stopped working on that project.
Today I tried to restore just like I did it before and got an error that says: `AttributeError: can't set attribute`. I followed the traceback and found a comment which says `# models are handled differently for now` that makes me thinks something has changed and probably I need to restore it in other way. If that's correct, how should I restore the model at the moment ? Do I need to retrain a new model because something has change about the training and the way which a model is stored ? I would appreciate any answer or some advice.
Brad,
Thanks in advance.
```
- Environment location: Google Colab
- Method of NeMo install: !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$v1.0.0b2#egg=nemo_toolkit[asr]
```
| {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1822 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1822/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1822/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1822/events | https://github.com/NVIDIA/NeMo/pull/1822 | 819,574,193 | MDExOlB1bGxSZXF1ZXN0NTgyNjY0OTc4 | 1,822 | Add SPE support for huge dataset corpus | {
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} | # Changelog
- Add flags to subsample text + use special SPE flags
- Update notebook docs | {
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} | Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1820 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1820/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1820/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1820/events | https://github.com/NVIDIA/NeMo/pull/1820 | 819,107,565 | MDExOlB1bGxSZXF1ZXN0NTgyMjYxNTM0 | 1,820 | Fix enja decoding | {
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"@mchrzanowski can you please send these changes to ``batch_translate`` branch?",
"Will do at some point soon. don’t let me block you for now and merge away;\nI’ll fix things in a subsequent pr later\n\nOn Mon, Mar 1, 2021 at 16:53 Oleksii Kuchaiev <notifications@github.com>\nwrote:\n\n> @mchrzanowski <https://gi... | 2021-03-01T17:53:12 | 2021-03-02T21:07:34 | 2021-03-02T21:07:34 | CONTRIBUTOR | {
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} | the current en<>ja preprocessing pipeline is being shifted to using integers for intermediate steps. this will avoid tokenization issues between yttm, sentencepiece, and sacremoses due to parsing incomptabilities. this PR changes the detokenization to deal with integer inputs and output tokens only at the end. | {
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"Please look into Nvidia Jarvis - https://developer.nvidia.com/nvidia-jarvis"
] | 2021-02-27T12:48:20 | 2021-07-13T15:23:15 | 2021-07-13T15:23:15 | NONE | {
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seeking guidelines from the community for improving QuartzNet for production:
1. different accents, for example, US/UK/AU English
2. filler words, for example, um; ah
3. domain words, for example, company names, finance terms
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} | This corrects the fix. | {
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} | Bert LM model doesn't necessarily have the tokenizer in the config. | {
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"This pull request **introduces 4 alerts** when merging f0e045e8eee83caf8d2db6464752d36f76871aa2 into 0f9a772feea56397479d06354ee4fcb9db8c6b04 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-23db6c861086ff7b5c8149283645de15e29f3d75)\n\n**new alerts:**\n\n* 4 for Unused import"
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* Adds diarization tutoria
* Modify clustering diarizer to support pretrained speakernet and marblenet models | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1813 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1813/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1813/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1813/events | https://github.com/NVIDIA/NeMo/pull/1813 | 817,641,448 | MDExOlB1bGxSZXF1ZXN0NTgxMDk5OTky | 1,813 | Handle class balancing in a way to support TLT. | {
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"This pull request **fixes 3 alerts** when merging 2817d5e5b74e67436fcec1ff3adef8f9b138c265 into efaab9e67346d633e3efd49c44a5c2b83661d1fc - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-f1f11b89c657df4cd1e55fc67022c52b4bf50e66)\n\n**fixed alerts:**\n\n* 2 for Unused local variable\n* 1 for Unuse... | 2021-02-26T20:26:28 | 2021-05-03T21:57:41 | 2021-03-10T00:59:21 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1812 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1812/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1812/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1812/events | https://github.com/NVIDIA/NeMo/pull/1812 | 817,580,658 | MDExOlB1bGxSZXF1ZXN0NTgxMDQ5NzI0 | 1,812 | Applying PR 1794 to r1.0.0rc1 | {
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} | PR https://github.com/NVIDIA/NeMo/pull/1794 is important for nemo saving but was applied to the main branch only; for most of us, the working branch is r1.0.0rc1. This PR applies the same change to this branch.
Signed-off-by: mchrzanowski mike.chrzanowski0@gmail.com
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https://api.github.com/repos/NVIDIA/NeMo/issues/1811 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1811/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1811/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1811/events | https://github.com/NVIDIA/NeMo/issues/1811 | 817,530,539 | MDU6SXNzdWU4MTc1MzA1Mzk= | 1,811 | [Question] FileNotFoundError in Relation Extraction w/ BioMegatron | {
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"Hi there is a branch change, I had the same issue. \r\n\r\nchangeing the variable at the start of the notebook from\r\nBRANCH = 'main'\r\nto\r\nBRANCH = 'r1.0.0rc1'"
] | 2021-02-26T17:21:38 | 2021-05-28T08:40:43 | 2021-05-28T08:40:43 | NONE | {
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} | null | null | null | Executing the example notebook 'Relation_Extraction-BioMegatron.ipynb' leads to an error
> FileNotFoundError: Could not find tokenizer.vocab_file
after running cell `model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)`. This happens both when I run the notebook on colab or on my local setup.
How can I fix this error?
Thanks! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1810 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1810/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1810/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1810/events | https://github.com/NVIDIA/NeMo/issues/1810 | 817,526,898 | MDU6SXNzdWU4MTc1MjY4OTg= | 1,810 | [Question] 'FileNotFoundError error' in Relation Extraction (RE) w/ biomegatron-bert-345m-uncased | {
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"This was fixed here: https://github.com/NVIDIA/NeMo/pull/1808"
] | 2021-02-26T17:16:10 | 2021-02-26T17:27:06 | 2021-02-26T17:24:24 | NONE | {
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"This pull request **introduces 5 alerts** when merging 619df560e621887e9e39036327916f33ed1f2884 into efaab9e67346d633e3efd49c44a5c2b83661d1fc - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-5d117bfdd38ba6fec4b6fc1cdf243cb5d2c7c0fd)\n\n**new alerts:**\n\n* 5 for Unused import",
"This pull requ... | 2021-02-26T05:14:43 | 2021-08-09T22:31:41 | 2021-08-09T22:31:41 | CONTRIBUTOR | {
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} | Add readme, server updates
Signed-off-by: Ryan Leary <rleary@nvidia.com> | {
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"Can you add example usage to the README?"
] | 2021-02-26T02:16:16 | 2021-05-17T17:14:23 | 2021-05-17T17:14:15 | CONTRIBUTOR | {
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"This pull request **introduces 1 alert** when merging 363f32e68832590ee89335af22f4a878a29b598d into efaab9e67346d633e3efd49c44a5c2b83661d1fc - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-979f06969d7ee8670113e8c823984cfdd8315809)\n\n**new alerts:**\n\n* 1 for Unused local variable",
"This pu... | 2021-02-26T00:34:58 | 2021-03-03T22:16:48 | 2021-03-03T22:16:41 | MEMBER | {
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} | This PR does the following:
* Makes sure that all pipeline pieces are create inside the constructor: src_normalizer, src_pre_tokenizer, encoder/decoder_tokenizers, tgt_post_tokenizer. This is necessary for their artifacts to be handled correctly and be included in .nemo file
* Refactors "batch_translate", "translate" and "eval_step" so that:
- eval_step relies on batch_translate
- translate and batch_translate call pipeline pieces, hopefully, in a correct order
TODOs:
translate method should really just prepare input batch and call batch_translate so that core pipeline logic is implemented in a single place (batch_translate)
@ericharper I suspect if we the conversion of raw text training data to tarred files might be missing corrsponding src_normalizer and src_pre_tokenizer calls. | {
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} | 1) hotfix for https://github.com/NVIDIA/NeMo/pull/1801. one call to `get_detokenizer` wasn't updated.
2) @MaximumEntropy noticed that the previous logic would apply the en/ja preprocessing in the case that the user did src == tgt (for whatever reason they wanted to do this). The current logic fixes this. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1802 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1802/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1802/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1802/events | https://github.com/NVIDIA/NeMo/pull/1802 | 816,854,288 | MDExOlB1bGxSZXF1ZXN0NTgwNDQ2MzQ3 | 1,802 | Speaker Diarization tutorial | {
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"This pull request **introduces 1 alert** when merging fc4e65d2e9ab237e6a8f967ee78ec699b6f2ff41 into 4efb9055494a623b2dd4b0366fb51022633662da - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cb69f4b17826be237efbbf1894cf25f80a6f8e89)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-02-25T22:18:08 | 2021-02-26T20:31:22 | 2021-02-26T20:31:14 | COLLABORATOR | {
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* Add speaker diarization tutorial | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1801 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1801/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1801/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1801/events | https://github.com/NVIDIA/NeMo/pull/1801 | 816,699,038 | MDExOlB1bGxSZXF1ZXN0NTgwMzE1NDg2 | 1,801 | share the same pre and post processing pipelines for Ja & En | {
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"This pull request **introduces 2 alerts** when merging 2a7441a7ca2cd2d9914cd41b68fbec872047e9ff into b467d6f20a13965e65522b3fbaf833b7cbf9f3ac - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-3cdfd9c5b9dcc8e2bbebd0e0aa3b259abcb75ea0)\n\n**new alerts:**\n\n* 2 for Variable defined multiple times"
... | 2021-02-25T18:46:21 | 2021-02-25T22:15:36 | 2021-02-25T22:15:36 | CONTRIBUTOR | {
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} | The current japanese and english pre and post processing pipelines are shared. However, that wasnt reflected in the nemo code. This PR rectifies this discrepancy and makes sure english is processed the same way as japanese text is whenever the source or target language is the latter. | {
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"The training loss is near 150~ after two epochs. You need to train longer so that loss drops to around 50-80 or lower before tokens actually appear. \r\n\r\nThe first phase of CTC learning is to learn to predict all blank tokens. ",
"Thank you very much!\r\nI am going to train more longer.",
"I got a better re... | 2021-02-25T14:04:21 | 2021-03-01T12:02:06 | 2021-03-01T12:02:05 | NONE | {
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} | null | null | null | Hello,
I have used the JSUT (Japanese speech corpus of Saruwatari-lab., University of Tokyo) data set to train the ASR model.
I set `normalize_transcript` parameter of train_ds and validation_ds to False.
```
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['train_ds']['normalize_transcripts'] = False
params['model']['train_ds']['labels'] = labels
params['model']['validation_ds']['manifest_filepath'] = test_manifest
params['model']['validation_ds']['normalize_transcripts'] = False
params['model']['validation_ds']['labels'] = labels
params['model']['preprocessor']['params']['stft_conv'] = True
params['model']['decoder']['params']['num_classes'] = len(labels)
params['model']['decoder']['params']['vocabulary'] = labels
params['labels'] = labels
```
However, the "decoded" of the result of the `trainer.fit()` is empty after the second epoch.
```
[NeMo I 2021-02-25 12:33:22 modelPT:597] Optimizer config = Novograd (
Parameter Group 0
amsgrad: False
betas: [0.8, 0.5]
eps: 1e-08
grad_averaging: False
lr: 0.01
weight_decay: 0.001
)
[NeMo I 2021-02-25 12:33:22 lr_scheduler:562] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f35252f6750>"
will be used during training (effective maximum steps = 1398) -
Parameters :
(warmup_steps: null
warmup_ratio: null
min_lr: 0.0
last_epoch: -1
max_steps: 1398
)
| Name | Type | Params
------------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | encoder | ConvASREncoder | 1.2 M
2 | decoder | ConvASRDecoder | 2.8 M
3 | loss | CTCLoss | 0
4 | spec_augmentation | SpectrogramAugmentation | 0
5 | _wer | WER | 0
[NeMo I 2021-02-25 12:33:24 wer:159]
[NeMo I 2021-02-25 12:33:24 wer:160] reference:演奏会の切符は当事務所で販売しています
[NeMo I 2021-02-25 12:33:24 wer:161] decoded :平溝平溝平溝捕溝平溝平溝平情溝捕溝捕平捕平捕平溝平捕溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平溝捕溝捕平溝平溝平溝捕溝平溝平溝平溝平溝捕溝捕溝平
[NeMo I 2021-02-25 12:33:24 wer:159]
[NeMo I 2021-02-25 12:33:24 wer:160] reference:鳥だったらすぐそこに飛んでいくのに
[NeMo I 2021-02-25 12:33:24 wer:161] decoded :平溝捕溝捕溝平溝平情溝平溝平溝平捕平溝情溝平溝平溝平溝平溝平溝平溝平溝捕平溝捕溝平溝平溝平溝平溝平溝平溝平情溝平溝平溝平溝平捕溝平溝平情溝捕溝捕平溝平溝平情溝平溝平捕溝捕溝捕溝捕溝捕溝平溝平溝平溝平溝捕平溝平捕溝捕溝平溝捕溝捕溝平溝平溝平溝平溝平溝平溝平溝平溝平溝平捕溝捕溝平
Epoch 2: 100%
474/474 [02:35<00:00, 3.06it/s, loss=80.696, v_num=1]
[NeMo I 2021-02-25 12:33:41 wer:159]
[NeMo I 2021-02-25 12:33:41 wer:160] reference:
[NeMo I 2021-02-25 12:33:41 wer:161] decoded :
[NeMo I 2021-02-25 12:33:57 wer:159]
[NeMo I 2021-02-25 12:33:57 wer:160] reference:孫策の死後はやや無気力感を漂わせたが頭の中では自身の周りで起きた運命や世の中の流れについて終わる事のない自問を繰り返していた
[NeMo I 2021-02-25 12:33:57 wer:161] decoded :
[NeMo I 2021-02-25 12:34:14 wer:159]
[NeMo I 2021-02-25 12:34:14 wer:160] reference:学識の点で彼は彼の友人のだれよりも上です
[NeMo I 2021-02-25 12:34:14 wer:161] decoded :
[NeMo I 2021-02-25 12:34:30 wer:159]
```
Any help would be appreciated.
**Environment overview (please complete the following information)**
- Environment location: Collab (https://colab.research.google.com/drive/1kPb1p-3IRfjCu1Hu_kTRHwSOsWmSvA7V?usp=sharing)
- Method of NeMo install: pip install from github
- NeMo version: r1.0.0b3
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"This pull request **introduces 4 alerts** when merging e137574cbe6348159a8b4366afc9a13fe013a805 into db5a787f460d67f0cd89b400cf81826fa2cc9ed6 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d995225ae3f70723701e1bbfd5fc23850e9055a6)\n\n**new alerts:**\n\n* 2 for Unused import\n* 1 for Mismatch i... | 2021-02-25T14:00:10 | 2021-08-17T14:36:48 | 2021-03-23T14:36:31 | CONTRIBUTOR | {
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} | This PR ports the [DLE FastPitch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch) model ([arxiv](https://arxiv.org/abs/2006.06873)). There is an example of training in `examples/tts/fastpitch.py`, and inference with a `.nemo` model has been tested.
Issuess for full integration:
1. **Data preparation**
Currently it assumes a dataset structure:
```bash
LJSpeech-1.1
├── durations
├── pitch
└── wavs
```
where `pitch` and `durations` can be pre-calculated with a [script](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/scripts/prepare_dataset.sh) from DLE.
.json dataset entries should have the following structure:
```bash
{"audio_filepath": <path>, "text": <transcript>, "duration": <duration_in_seconds>, "speaker": <speaker_index_0_based>}
```
Speakers are optional, single-speaker dataset should always have `"spaker": 0`.
2. **Mean and std of F0**
in the DLE model, mean and std of F0 are being stored inside the model for pitch manipulation during inference. In order to specify pitch transformations in Hz, one needs a way to recompute pitch in standarized values. In this PR, these are empty [placeholders](https://github.com/alancucki/NeMo/blob/fastpitch/nemo/collections/tts/modules/fastpitch.py#L143) that can be used or deleted.
3. **Best checkpoints shouldn't be determined with val loss**
The training loop saves the best checkpoint based on validation loss; it tends to increase during training and is a poor proxy for quality; the last checkpoint should be the final one. | {
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} | Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
* checkpoint `last` name fix | {
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} | * added nemo_tools sub package with python requirements and setup.sh
* added doc strings
* added text denormalization to nemo_tools
* added pytests and pynini tests
* added export option for inverse text normalization to NeMo/tools | {
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"Could you make sure that at any point only 1 \".nemo\" file is saved ? We dont want to clutter with dozens of nemo + dozens of PTL checkpoints for consecutive runs. ",
"> Could you make sure that at any point only 1 \".nemo\" file is saved ? We dont want to clutter with dozens of nemo + dozens of PTL checkpoints... | 2021-02-24T22:30:49 | 2021-02-26T00:03:19 | 2021-02-26T00:03:19 | CONTRIBUTOR | {
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} | Right now we only get a `.nemo` file when the training is finished. However, it is common to let training continue for an unspecified amount of epochs and just stop when the training stops improving, in which case we'd never get a `.nemo` file.
This commit adds the option save the nemo file whenever a checkpoint is saved.
Use:
`+exp_manager.checkpoint_callback_params.always_save_nemo = True`
Signed-off-by: rprenger <rprenger@nvidia.com> | {
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"This pull request **introduces 1 alert** when merging 23389db2bfbb66f34af4fc4bfc3c70d58a37b791 into 1b82b571c2f32e1b109677a67c5820222cb696fe - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-73b2bd0cd56408059f13b25d589e438f9dcb3fba)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-02-24T20:13:47 | 2021-02-25T04:44:38 | 2021-02-24T20:46:24 | COLLABORATOR | {
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} | Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com>
* Pytorch Lightening links fix
* renamed get_spkr_emb.py to extract_speaker_embeddings.py
* Added helper speaker_reco_infer.py example script to get labels from trained speaker recognition model
* Added get_hi-mia.py script. (use to be part of old nemo -> used in tutorial) | {
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"I cannot understand how to fix\r\n```\r\n==================================== ERRORS ====================================\r\n\r\n_______________ ERROR collecting examples/tts/test_tts_infer.py ________________\r\n\r\nImportError while importing test module '/home/mrjenkins/workspace/NeMo_PR-1791/examples/tts/test_... | 2021-02-24T15:33:34 | 2021-03-04T17:06:24 | 2021-02-27T05:08:05 | CONTRIBUTOR | {
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} | Signed-off-by: PeganovAnton <peganoff2@mail.ru>
The script `filter_langs_nmt.py` did not work on monolingual data. This pull request fixes the bug. | {
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] | 2021-02-24T10:53:30 | 2021-02-24T16:28:58 | 2021-02-24T16:28:58 | COLLABORATOR | {
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} | This PR refactors some of the fetching of tokenizers, normalizers, and detokenizers within the model and creates Chinese/Japanese tokenizer and detokenizer classes to match Moses semantics. | {
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