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} | # What does this PR do ?
Added the following features:
1. Allow the model to load from the unpacked nemo file
2. Remove LM model weights from optimizer
3. It can apply various activation checkpoint optimizations to the large LM
4. When Pytorch Lightning restore the model, the weights are in fp32 format for some reason even the checkpoint file are saved as bf16. I fix this issue by casting the floating number type to the correct precision. This saves the half of the initial GPU memory consumption.
5. Implement to enable the sequence parallel for p-tuning. It saves a lot of GPU activation memory by using it. Also, it enables selective activation, so it can save extra activation memory without sacrificing much performance.
6. Implement the Tensor Parallel version of the MLP prompt encoder, so it saves GPU memory and distributed computation in TP workgroup.
7. Added a script to conver PTL p-tuning checkpoint files to nemo file for evaluation.
8. Added cache capability to the prompt dataset to save the data processing time.
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} | # What does this PR do ?
When saving a checkpoint and the validation check interval isn't divisible by the number of batches, we can miss saving the final checkpoint. This ensures we save the final checkpoint.
I've noticed that the epoch saved in the checkpoint is incremented. Not sure if this will be an issue, but considering this is the final step I assume it should be fine.
cc @ericharper @okuchaiev
# Changelog
- Fix saving the last checkpoint when `val_check_interval`.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Extends the IPATokenizer and PhonemizerTokenizer classes so we can do mixed-representation grapheme/phoneme training for non-English languages.
**Collection**: [TTS]
# Changelog
- Add phoneme_probability argument to PhonemizerTokenizer, with code changes to add the grapheme set for each language. The grapheme set was obtained by taking all graphemes in the words of each language's pronunciation dictionary.
- Removed "use_chars" logic in IPATokenizer, which currently adds hardcoded list of English ASCII chars, in favor of parsing them from the user-provided phoneme dictionary in IPAG2P (similar to how it infers its phoneme set from the dictionary).
- In IPAG2P I added accented characters & locale-agnostic digit to the word/token checker. In an earlier commit, I accidentally added this to the EnglishG2p instead (when refactoring the PR around the tokenizer file directory change). Though I don't see any reason to remove it from EnglishG2p.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
# Additional Information
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} | null | null | null | Hello, developers.
Is there a model or something to identify spoken language? For example, how to identify whether a speaker speaks English or Russian.
I looked for it in the tutorials and found nothing.
I will appreciate any help | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
fixed wrong g2p path. Previously in https://github.com/NVIDIA/NeMo/pull/4690, g2p modules were moved to `nemo_text_processing.g2p.modules`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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- [x] Bugfix
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https://api.github.com/repos/NVIDIA/NeMo/issues/4901 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4901/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4901/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4901/events | https://github.com/NVIDIA/NeMo/pull/4901 | 1,365,401,098 | PR_kwDOC_bI7s4-jKVE | 4,901 | removed unused imports for all domains. | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
LGTM.com detected 100 ish unused imports. Clean up to remove those warnings.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Generalize distributed Adam support for GPT-3 to T5 and other Megatron-LM models. It also implements several performance optimizations.
**Collection**: NLP
# Changelog
- When params are BF16, distributed Adam will store 16-bit param remainders instead of FP32 main params
- Decouples distributed Adam support from Megatron O2-level optizations
- Add support for Apex distributed Adam optimizer with other Megatron-LM models, namely T5
- Add support for overlapped grad reductions with pipeline or sequence parallelism
# Usage
Set optimizer to `distributed_fused_adam` in config file:
https://github.com/NVIDIA/NeMo/blob/265f7b17876aef7937cf0fedf7a76885bdc63288/examples/nlp/language_modeling/conf/megatron_t5_config.yaml#L137
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Closes https://github.com/NVIDIA/NeMo/pull/4799
* Depends on https://github.com/NVIDIA/apex/pull/1475 | {
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} | # What does this PR do ?
Fix bug in documentation
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue 4775)
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} | null | null | null | stt_en_citrinet_512 and stt_en_citrinet_1024 both are trained on large English speech data, is there any way we can add few more words such as name(Example Hanuman or Ganesha), pronunciation of these words might be different depends upon the language , if we have enough speech data to add these words for improvement its transcription accuracy.
In short, I want to add few more words in dictionary of this model.
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} | # What does this PR do ?
Documentation bug fix.
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue 4775)
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} | Signed-off-by: Micha Livne <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: whrichd <[email protected]>
# What does this PR do ?
MLFlow is already a dependancy in Pytorch Lightning, so some small changes enables MLFlow tracking, a free alternative to WnB.
**Collection**: utils
# Changelog
- Changed exp_manager.py
# Usage
* Add the MLflow logger just as how you would add the weighs and biases logger, in the config files.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Virginia Adams <[email protected]>
Writes T5 prompt learning prediction file with utf-8 encoding.
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- T5 prompt learning eval script
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
- Removed AUROC metric since it's slow and cost a lot of memory to store the logits&labels.
- Fixed macro-accuracy metric usage for multiple validation manifests.
- Refactored validation and test function since they share the same code but just different tags in display ("val_" vs. "test_")
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} | null | null | null | **Describe the bug**
I used NeMo to train a Mandarin ASR model (QuartzNet), and then I converted it to .rmir format through the tutorial.
I deployed .rmir to riva (I only have this model enabled), but the client cannot successfully call the ASR model on the server side, there will be the following error
**Steps/Code to reproduce bug**
- build environment
docker pull nvcr.io/nvidia/riva/riva-speech:2.4.0-servicemaker
sudo docker run --gpus all -it --rm -v /NeMo/nemo_experiments/QuartzNet15x5/rmir_fold:/servicemaker-dev -v /NeMo/nemo_experiments/QuartzNet15x5:/data --entrypoint="/bin/bash" nvcr.io/nvidia/riva/riva-speech:2.4.0-servicemaker
- convert to .riva file
nemo2riva --out Nemo_CTC.riva QuartzNet15x5.nemo
- convert to .rmir file
riva-build speech_recognition -f /servicemaker-dev/NeMo_CTC.rmir /data/2022-08-19_07-43-11/Nemo_CTC.riva --decoder_type=greedy
- start riva-speech
ngc registry resource download-version nvidia/riva/riva_quickstart:2.4.0
cd riva_quickstart_v2.4.0
bash riva_init.sh
bash riva_start.sh
**get a result like this =>**
**c1106038@DGX-Station-62:~/NeMo/riva_quickstart_v2.4.0$ sudo bash riva_start.sh
Starting Riva Speech Services. This may take several minutes depending on the number of models deployed.
2cfde38fd430b4ce346b125850848e187cc399422c59470f5eaef05bc00ea22b
Waiting for Riva server to load all models...retrying in 10 seconds
Riva server is ready...**
(In order to use a custom rmir file, only modify riva_model_loc and use_existing_rmirs=true in config.sh)
1. riva_model_loc="/home/c1106038/NeMo/nemo_experiments/QuartzNet15x5/rmir_fold"
2. use_existing_rmirs=true
- start client
bash riva_start_client.sh
(In order to use GPU modify the riva_start_client.sh as follows)
**get a result like this =>**
- Execute riva_asr_client
riva_asr_client --audio_file=/test/morning_conference_000001.wav -language_code=zh-CN
**get a Error=>**
**Expected behavior**
Custom model inference audio available on Riva.
**Environment overview (please complete the following information)**
- DGX V100 GPU
- Method of NeMo install:
docker pull nvcr.io/nvidia/nemo:22.05
docker run --gpus all -it --name Nemo_container -v NeMo:/workspace --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:22.05
**Environment details**
OS version: Ubuntu 20.04
PyTorch version: 1.12.0a0+8a1a93a
Python version: 3.8.13
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} | null | null | null | I tried to learn training speaker embedding extractor from [https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html](url)
Domain: ASR
title: Speaker Recognition and Verification
It's no problem before cell [20]
cell [20]:
`trainer.fit(speaker_model)`
bug:
```
INFO:pytorch_lightning.accelerators.gpu:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo I 2022-09-07 02:27:14 modelPT:587] Optimizer config = SGD (
Parameter Group 0
dampening: 0
foreach: None
lr: 0.006
maximize: False
momentum: 0
nesterov: False
weight_decay: 0.001
)
[NeMo I 2022-09-07 02:27:14 lr_scheduler:914] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f682c63a690>"
will be used during training (effective maximum steps = 140) -
Parameters :
(warmup_ratio: 0.1
min_lr: 0.0
max_steps: 140
)
INFO:pytorch_lightning.callbacks.model_summary:
| Name | Type | Params
------------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | encoder | ConvASREncoder | 19.4 M
2 | decoder | SpeakerDecoder | 2.8 M
3 | loss | AngularSoftmaxLoss | 0
4 | _accuracy | TopKClassificationAccuracy | 0
5 | spec_augmentation | SpectrogramAugmentation | 0
------------------------------------------------------------------------
22.1 M Trainable params
0 Non-trainable params
22.1 M Total params
88.546 Total estimated model params size (MB)
[NeMo I 2022-09-07 02:27:15 label_models:312] val_loss: 13.214
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-22-f8bb6804e159>](https://localhost:8080/#) in <module>
----> 1 trainer.fit(speaker_model)
14 frames
[/usr/local/lib/python3.7/dist-packages/nemo/utils/timers.py](https://localhost:8080/#) in start(self, name)
90
91 if "start" in timer_data:
---> 92 raise RuntimeError(f"Cannot start timer = '{name}' since it is already active")
93
94 # synchronize pytorch cuda execution if supported
RuntimeError: Cannot start timer = 'train_step_timing' since it is already active
Epoch 0: 0%
0/15 [00:00<?, ?it/s]
```
**Additional context**
Before the above Runtime error, I got `TypeError: unhashable type: 'list'` from `/usr/local/lib/python3.7/dist-packages/numba/cuda/dispatcher.py`. But I fixed it.
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} | Signed-off-by: Micha Livne <[email protected]>
# What does this PR do ?
This PR Fixes the output of BERT to be [batch x seq x hidden] instead of [se x batch x hidden]
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | I am working on converting the RNNT model to tensorrt for my application and I have convert the torch model to ONNX as suggested. However, I am stuck on converting ONNX to trt model right now. I tried lots of methods and still failed. Is it possible to convert? Did anyone succeed on converting this model to tensorrt? Which tensorrt version should I use or how can I do the conversion? Any tutorials?
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https://api.github.com/repos/NVIDIA/NeMo/issues/4884 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4884/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4884/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4884/events | https://github.com/NVIDIA/NeMo/pull/4884 | 1,363,596,150 | PR_kwDOC_bI7s4-dLUE | 4,884 | Fix Megatron NMT consumed samples and ckpt_to_nemo split rank | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes bugs in Megatron NMT due to incorrect split rank in the checkpoint file and split rank in ckpt->nemo conversion.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4883 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4883/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4883/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4883/events | https://github.com/NVIDIA/NeMo/issues/4883 | 1,363,317,364 | I_kwDOC_bI7s5RQpJ0 | 4,883 | Q: How to reset optimizer/LR to finetune? | {
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} | null | null | null | Hi,
I trained a MT model following the example from `enc_dec_nmt.py`, so far all good. Now I'd like to load one of the checkpoints generated from this training, keep training the model weights, but restarting the optimizer / LR / global trainer step / etc. I've tried different approaches, but in all cases the training continues from where it was (i.e. from the previous global step, epoch and everything).
What's the correct way of achieving this?
Approaches I tried:
### Approach 1
Setting `resume_if_exists: true` in my config, so NeMo will take the last checkpoint. The in the model file (`mt_enc_dec_model.py`) I added a `on_train_start` hook, where I create a new optimizer and assign it back to the model. It looks something like this
```python
def on_train_start(self) -> None:
opt: LightningOptimizer = self.optimizers()
new_pt_opt: Optimizer = opt.optimizer.__class__(opt.optimizer.param_groups, **opt.optimizer.defaults)
self.trainer.strategy.optimizers = [new_pt_opt]
```
### Approach 2
With `resume_if_exists: false`. When calling `trainer.fit` in `enc_dec_nmt.py`, setting `ckpt_path` to one of the checkpoints. Basically something like
```python
trainer.fit(mt_model, ckpt_path="path/to/checkpoint.cpkt")
``` | null | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4882 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4882/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4882/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4882/events | https://github.com/NVIDIA/NeMo/issues/4882 | 1,363,283,827 | I_kwDOC_bI7s5RQg9z | 4,882 | Conformer Transducer - Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE | {
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} | null | null | null | **Describe the bug**
Error while training conformer transducer bpe ASR model.
**Steps/Code to reproduce bug**
Use the attached config and initiate the training. After a few steps before epoch completion the following
`Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE` error occurs and the training get stopped.
`Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE
Error executing job with overrides: []
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 174, in forward
return self.loss(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 64, in forward
loss_func(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py", line 223, in rnnt_loss_gpu
status = wrapper.cost_and_grad(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 249, in cost_and_grad
return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 161, in compute_cost_and_score
gpu_rnnt_kernel.compute_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0](
File "/opt/conda/lib/python3.8/site-packages/numba/cuda/compiler.py", line 729, in __call__
return self.dispatcher.call(args, self.griddim, self.blockdim,
File "/opt/conda/lib/python3.8/site-packages/numba/cuda/compiler.py", line 919, in call
kernel.launch(args, griddim, blockdim, stream, sharedmem)
File "/opt/conda/lib/python3.8/site-packages/numba/cuda/compiler.py", line 576, in launch
driver.launch_kernel(cufunc.handle,
File "/opt/conda/lib/python3.8/site-packages/numba/cuda/cudadrv/driver.py", line 2550, in launch_kernel
driver.cuLaunchKernel(cufunc_handle,
File "/opt/conda/lib/python3.8/site-packages/numba/cuda/cudadrv/driver.py", line 319, in safe_cuda_api_call
self._check_ctypes_error(fname, retcode)
File "/opt/conda/lib/python3.8/site-packages/numba/cuda/cudadrv/driver.py", line 387, in _check_ctypes_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [1] Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE
`
**Expected behavior**
Run training without any error.
**Environment overview (please complete the following information)**
- SLURM Managed cluster
- DGX-1 8x V100 GPU
- Singularity environment - nvcr.io/nvidia/nemo:22.05
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
DGX-1 8x V100 GPU x 4 Nodes
**Training Config**
[cmr_en_rnnt_v2_n4.txt](https://github.com/NVIDIA/NeMo/files/9497280/cmr_en_rnnt_v2_n4.txt)
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} | # What does this PR do ?
Going through the MSDD code I noticed a few off the bat code changes to be made + fix the `self.log` call in the multi_test function.
cc @tango4j @nithinraok
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hi,
I am trying to train a TTS model (mixertts+univnet). the dataset contains male speakers voices.
Is there a parameter that needs to be changed to train these models with male speakers voices? Because the tone of the output voices are not very good.
Thanks in advance. | {
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} | null | null | null | # I want to train new model with my dataset for other language
# one case in my dataset json file is:
`{"audio_filepath": "./wav/4.wav", "duration": 5.6750625, "is_phoneme": 1, "original_text": "gAme sevvOm d%r b/rnamerizI | $enAxte moqe@iyy%te fe@lIye hozE @%st | ", "normalized_text": "gAme sevvOm d%r b/rnamerizI | $enAxte moqe@iyy%te fe@lIye hozE @%st | "}`
# and my confige file is:
name: Tacotron2
train_dataset: ???
validation_datasets: ???
sup_data_path: null
sup_data_types: null
phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.01"
heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921"
whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv"
model:
pitch_fmin: 65.40639132514966
pitch_fmax: 2093.004522404789
sample_rate: 22050
n_mel_channels: 80
n_window_size: 1024
n_window_stride: 256
n_fft: 1024
lowfreq: 0
highfreq: 8000
window: hann
pad_value: -11.52
text_normalizer:
_target_: nemo_text_processing.text_normalization.normalize.Normalizer
lang: en
input_case: cased
whitelist: ${whitelist_path}
text_normalizer_call_kwargs:
verbose: false
punct_pre_process: true
punct_post_process: true
text_tokenizer:
_target_: nemo.collections.tts.torch.tts_tokenizers.EnglishPhonemesTokenizer
punct: true
stresses: true
chars: true
apostrophe: true
pad_with_space: true
g2p:
_target_: nemo.collections.tts.torch.g2ps.EnglishG2p
phoneme_dict: ${phoneme_dict_path}
heteronyms: ${heteronyms_path}
train_ds:
dataset:
_target_: "nemo.collections.tts.torch.data.TTSDataset"
manifest_filepath: ${train_dataset}
sample_rate: ${model.sample_rate}
sup_data_path: ${sup_data_path}
sup_data_types: ${sup_data_types}
n_fft: ${model.n_fft}
win_length: ${model.n_window_size}
hop_length: ${model.n_window_stride}
window: ${model.window}
n_mels: ${model.n_mel_channels}
lowfreq: ${model.lowfreq}
highfreq: ${model.highfreq}
max_duration: null
min_duration: 0.1
ignore_file: null
trim: False
pitch_fmin: ${model.pitch_fmin}
pitch_fmax: ${model.pitch_fmax}
dataloader_params:
drop_last: false
shuffle: true
batch_size: 48
num_workers: 4
pin_memory: true
validation_ds:
dataset:
_target_: "nemo.collections.tts.torch.data.TTSDataset"
manifest_filepath: ${train_dataset}
sample_rate: ${model.sample_rate}
sup_data_path: ${sup_data_path}
sup_data_types: ${sup_data_types}
n_fft: ${model.n_fft}
win_length: ${model.n_window_size}
hop_length: ${model.n_window_stride}
window: ${model.window}
n_mels: ${model.n_mel_channels}
lowfreq: ${model.lowfreq}
highfreq: ${model.highfreq}
max_duration: null
min_duration: 0.1
ignore_file: null
trim: False
pitch_fmin: ${model.pitch_fmin}
pitch_fmax: ${model.pitch_fmax}
dataloader_params:
drop_last: false
shuffle: false
batch_size: 24
num_workers: 8
pin_memory: true
preprocessor:
_target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures
nfilt: ${model.n_mel_channels}
highfreq: ${model.highfreq}
log: true
log_zero_guard_type: clamp
log_zero_guard_value: 1e-05
lowfreq: ${model.lowfreq}
n_fft: ${model.n_fft}
n_window_size: ${model.n_window_size}
n_window_stride: ${model.n_window_stride}
pad_to: 16
pad_value: ${model.pad_value}
sample_rate: ${model.sample_rate}
window: ${model.window}
normalize: null
preemph: null
dither: 0.0
frame_splicing: 1
stft_conv: false
nb_augmentation_prob : 0
mag_power: 1.0
exact_pad: true
use_grads: false
encoder:
_target_: nemo.collections.tts.modules.tacotron2.Encoder
encoder_kernel_size: 5
encoder_n_convolutions: 3
encoder_embedding_dim: 512
decoder:
_target_: nemo.collections.tts.modules.tacotron2.Decoder
decoder_rnn_dim: 1024
encoder_embedding_dim: ${model.encoder.encoder_embedding_dim}
gate_threshold: 0.5
max_decoder_steps: 1000
n_frames_per_step: 1 # currently only 1 is supported
n_mel_channels: ${model.n_mel_channels}
p_attention_dropout: 0.1
p_decoder_dropout: 0.1
prenet_dim: 256
prenet_p_dropout: 0.5
# Attention parameters
attention_dim: 128
attention_rnn_dim: 1024
# AttentionLocation Layer parameters
attention_location_kernel_size: 31
attention_location_n_filters: 32
early_stopping: true
postnet:
_target_: nemo.collections.tts.modules.tacotron2.Postnet
n_mel_channels: ${model.n_mel_channels}
p_dropout: 0.5
postnet_embedding_dim: 512
postnet_kernel_size: 5
postnet_n_convolutions: 5
optim:
name: adam
lr: 1e-3
weight_decay: 1e-6
# scheduler setup
sched:
name: CosineAnnealing
min_lr: 1e-5
trainer:
devices: 1 # number of gpus
max_epochs: ???
num_nodes: 1
accelerator: gpu
strategy: ddp
accumulate_grad_batches: 1
enable_checkpointing: False # Provided by exp_manager
logger: False # Provided by exp_manager
gradient_clip_val: 1.0
log_every_n_steps: 60
check_val_every_n_epoch: 2
benchmark: false
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: val_loss
mode: min
# but I get this error:
Error executing job with overrides: ['model.sample_rate=16000', 'train_dataset=new_text.json', 'validation_datasets=new_text.json', 'trainer.max_epochs=100', 'trainer.accelerator=gpu', 'trainer.check_val_every_n_epoch=1', '+trainer.gpus=1']
Error in call to target 'nemo.collections.tts.torch.data.TTSDataset':
KeyError('text')
full_key: train_ds.dataset
# how to I resolve this?
I replaced "original_text" with "text" but again I got this error | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4878 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4878/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4878/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4878/events | https://github.com/NVIDIA/NeMo/issues/4878 | 1,362,635,085 | I_kwDOC_bI7s5ROClN | 4,878 | We will train the ASR model in Mandarin，But got a error of TypeError("__init__() got an unexpected keyword argument 'causal_downsampling'") | {
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} | null | null | null | **Describe the bug**
Hi everyone, I want to train an ASR model (Conformer_CTC_bpe) in Mandarin.
I generated vocab.txt using "process_asr_text_tokenizer.py", modified "/conf/conformer/conformer_ctc_bpe.yaml" and executed "examples/asr/asr_ctc/speech_to_text_ctc_bpe.py".
but got a TypeError
`TypeError("__init__() got an unexpected keyword argument 'causal_downsampling'")`
**Steps/Code to reproduce bug**
we only modify conf/conformer/conformer_ctc_bpe.yaml as follows:
```
tokenizer:
dir: data_tw_v2/Stage2/tokenizer_wpe_v1024
type: wpe
```
where the "dir" is generated by the program "/scripts/tokenizers/process_asr_text_tokenizer.py"。
The directory contains a vocab.txt with the following content：
and I execution the following Code:
`CUDA_VISIBLE_DEVICES=2,3 python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py --config-path=../conf/conformer --config-name=conformer_ctc_bpe model.train_ds.manifest_filepath="data_tw_v2/Stage1/Quartznet/train.json" model.validation_ds.manifest_filepath="data_tw_v2/Stage1/Quartznet/valid.json" model.tokenizer.dir=data_tw_v2/Stage2/tokenizer_wpe_v1024 model.tokenizer.type=wpe trainer.devices=2 trainer.accelerator='gpu' trainer.max_epochs=150 trainer.strategy="ddp" model.optim.name="adamw" model.optim.lr=0.001 model.optim.betas=[0.9,0.999] model.optim.weight_decay=0.0001 model.optim.sched.warmup_steps=2000 exp_manager.create_wandb_logger=True exp_manager.wandb_logger_kwargs.name="wpe" exp_manager.wandb_logger_kwargs.project="A"`
But got a error of :
`Error executing job with overrides: ['model.train_ds.manifest_filepath=data_tw_v2/Stage1/Quartznet/train.json', 'model.validation_ds.manifest_filepath=data_tw_v2/Stage1/Quartznet/valid.json', 'model.tokenizer.dir=data_tw_v2/Stage2/tokenizer_wpe_v1024', 'model.tokenizer.type=wpe', 'trainer.devices=2', 'trainer.accelerator=gpu', 'trainer.max_epochs=150', 'trainer.strategy=ddp', 'model.optim.name=adamw', 'model.optim.lr=0.001', 'model.optim.betas=[0.9,0.999]', 'model.optim.weight_decay=0.0001', 'model.optim.sched.warmup_steps=2000', 'exp_manager.create_wandb_logger=True', 'exp_manager.wandb_logger_kwargs.name=wpe', 'exp_manager.wandb_logger_kwargs.project=Conformer_norm_type']
Error in call to target 'nemo.collections.asr.modules.conformer_encoder.ConformerEncoder':
TypeError("__init__() got an unexpected keyword argument 'causal_downsampling'")`
**Expected behavior**
Make the ASR model train successfully
**Environment overview (please complete the following information)**
Environment location: Docker
Method of NeMo install:
docker pull [nvcr.io/nvidia/nemo:22.05](http://nvcr.io/nvidia/nemo:22.05)
docker run --gpus all -it --name Nemo_container -v <nemo_github_folder>:/workspace --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd [nvcr.io/nvidia/nemo:22.05](http://nvcr.io/nvidia/nemo:22.05)
**Environment details**
OS version: Ubuntu 20.04
PyTorch version: 1.12.0a0+8a1a93a
Python version: 3.8.13
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https://api.github.com/repos/NVIDIA/NeMo/issues/4877 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4877/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4877/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4877/events | https://github.com/NVIDIA/NeMo/issues/4877 | 1,361,859,813 | I_kwDOC_bI7s5RLFTl | 4,877 | RuntimeError: stft input and window must be on the same device but got self on cuda:1 and window on cuda:0 | {
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>>> trainer = pl.Trainer(strategy='dp', gpus=4, max_epochs=1)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
>>> trainer.fit(quartznet)
RuntimeError: Caught RuntimeError in replica 1 on device 1.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/site-packages/pytorch_lightning/overrides/data_parallel.py", line 64, in forward
output = super().forward(*inputs, **kwargs)
File "/usr/local/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 93, in forward
return self.module.validation_step(*inputs, **kwargs)
File "/usr/local/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 544, in validation_step
log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len)
File "/usr/local/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__
outputs = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py", line 470, in forward
processed_signal, processed_signal_length = self.preprocessor(
File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__
outputs = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 85, in forward
processed_signal, processed_length = self.get_features(input_signal, length)
File "/usr/local/lib/python3.8/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 268, in get_features
return self.featurizer(input_signal, length)
File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 327, in forward
x = self.stft(x)
File "/usr/local/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 219, in <lambda>
self.stft = lambda x: torch.stft(
File "/usr/local/lib/python3.8/site-packages/torch/functional.py", line 606, in stft
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
RuntimeError: stft input and window must be on the same device but got self on cuda:1 and window on cuda:0 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4876 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4876/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4876/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4876/events | https://github.com/NVIDIA/NeMo/pull/4876 | 1,361,718,590 | PR_kwDOC_bI7s4-W2pZ | 4,876 | Add sync for logging calls to ensure aggregation across devices | {
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} | # What does this PR do ?
After speaking to @nithinraok a few places do not have sync calls when logging (some are taken care off by the underlying Metrics, or they are already synced across processes like global steps).
I've been a bit cautious and not touched the return objects from `validation_step` or `test_step`. There seems to be some parsing of logic in the ModelPT file so need to investigate further as to what to do with those metrics, and how they are logged.
# Changelog
- Add `sync_dist` to logging metric calls to ensure aggregation across all devices.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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As I understand it, this is due to the fact that models on BPE tokens return an underscore instead of a space.
Tell me, can this problem be somehow elegantly solved in order to use kenlm with an acoustic model on BPE?
From thoughts, can kenlm be trained on texts where there is an underscore instead of a space? But that doesn't seem to work... | {
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds tools for generating multi-channel microphone signal approximately matching the desired spatial coherence
**Collection**: ASR
# Changelog
- Adds functions for generating an approximate noise field
- Unit tests for spatial coherence calculation and noise generation
# Usage
```python
noise_field = generate_approximate_noise_field(mic_positions, noise_signal, sample_rate)
```
where `mic_positions` are positions of the `M` microphones in 3D Cartesian coordinates, `noise_signal` is an `M`-channel noise signal, where channels are statistically independent, sampled at `sample_rate` Hz. Currently, only `spherical` noise field is supported.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
n/a
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} | Fix uppercasing bug that led to heteronyms list mismatch, bad path in IPA FastPitch config
Signed-off-by: Jocelyn Huang <[email protected]>
**Collection**: TTS
# Changelog
- The IPA G2P class uppercases words by default (to avoid IPA symbol conflicts), but they would not match anything in the lowercased heteronyms file. Fixed by uppercasing heteronyms when loading the file.
- IPA FastPitch config fixed to point to the updated location of the IPA G2P class.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* The first fix is identical to the one merged into the r1.11.0 branch (#4860), reproduced here since the file has been moved in `main`.
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} | null | null | null | **Describe the bug**
Using the main branch of Nemo and TTS inference with Fastpitch and Hifigan / German single speaker model results in bad audio.
**Steps/Code to reproduce bug**
git clone https://github.com/NVIDIA/NeMo/
cd NeMo
python3.8 -m venv venv
source venv/bin/activate
pip3 install a-lot-of-missing-requirements when executing
python3 tts_german.py where inference code is as follows tts_german.py
```
import soundfile as sf
from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder
# Download and load the pretrained fastpitch model
spec_generator = SpectrogramGenerator.from_pretrained(model_name="tts_de_fastpitch_singlespeaker")#.cuda()
# Download and load the pretrained hifigan model
vocoder = Vocoder.from_pretrained(model_name="tts_de_hui_hifigan_ft_fastpitch_multispeaker_5")#.cuda()
# All spectrogram generators start by parsing raw strings to a tokenized version of the string
parsed = spec_generator.parse("In den alten Zeiten, wo das Wünschen noch geholfen hat, lebte ein König, dessen Töchter waren alle schön.")
# They then take the tokenized string and produce a spectrogram
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
# Finally, a vocoder converts the spectrogram to audio
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
# Save the audio to disk in a file called speech.wav
# Note vocoder return a batch of audio. In this example, we just take the first and only sample.
sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 44100)
```
**Expected behavior**
Good audio
**Environment overview (please complete the following information)**
See above
**Environment details**
See above
**Additional context**
Audio sample [https://sndup.net/hxnz/](https://sndup.net/hxnz/)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4867 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4867/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4867/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4867/events | https://github.com/NVIDIA/NeMo/issues/4867 | 1,360,396,924 | I_kwDOC_bI7s5RFgJ8 | 4,867 | Learning rate does not change | {
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} | null | null | null | Can you please tell me why the learning rate does not change when the fine-tuning model is launched?
```python
cfg = OmegaConf.load("conformer_ctc/config.yaml")
trainer = pl.Trainer(
logger=logger,
**cfg.trainer,
callbacks=[
model_checkpoint_callback
]
)
model = nemo_asr.models.EncDecCTCModel(cfg=cfg.model, trainer=trainer)
model.set_trainer(trainer)
trainer.fit(model, ckpt_path="pretrained/epoch=39.ckpt")
```
Log fit:
> 610/1200 [06:32<-1:37:02, -0.43it/s, loss=61.6, v_num=103]
But according to the graphs learning_rate = 1-e6 throughout the training.
Half "conformer_ctc/config.yaml":
```
name: "Conformer-CTC-Char"
model:
sample_rate: 8000
optim:
name: adamw
lr: 2.0
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 0.0
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 15000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1
num_nodes: 1
max_epochs: 100
max_steps: -1
val_check_interval: 1.0
accelerator: auto
strategy: dp
accumulate_grad_batches: 1
gradient_clip_val: 1.0
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
# amp_level: O2
# amp_backend: apex
log_every_n_steps: 50
progress_bar_refresh_rate: 10
resume_from_checkpoint: null
num_sanity_val_steps: 0
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: true
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: ${name}
use_datetime_version: True
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: "val_wer"
mode: "min"
save_top_k: 3
always_save_nemo: True
resume_if_exists: False
resume_ignore_no_checkpoint: False
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
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fix mha | {
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} | # What does this PR do ?
Small error when referring to the available models. This isn't tested yet since no models are yet available for MSDD, but I think soon @tango4j will introduce them!
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adds a script to streamline downloading/parsing AMI. @tango4j suggested adding microphone array audio as well, just need to figure out how to format this.
**Collection**: ASR
# Changelog
- Add script to download the AMI dataset for Speaker Diarization
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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when training ASR with Transducers and leaving
**freq_masks: 2
time_masks: 10**
instead of zeroing them out an error occurs
TypeError Traceback (most recent call last)
[<ipython-input-29-1bf927b35c34>](https://localhost:8080/#) in <module>
1 # Train the model
----> 2 trainer.fit(model)
42 frames
[/usr/local/lib/python3.7/dist-packages/numba/cuda/dispatcher.py](https://localhost:8080/#) in __getitem__(self, args)
567 if len(args) not in [2, 3, 4]:
568 raise ValueError('must specify at least the griddim and blockdim')
--> 569 return self.configure(*args)
570
571 def forall(self, ntasks, tpb=0, stream=0, sharedmem=0):
TypeError: unhashable type: 'list'
It happens both in google colab and when training on local machine.
To reproduce this I skipped
**config.model.spec_augment.freq_masks = 0
config.model.spec_augment.time_masks = 0**
in colab tutorial **ASR_with_Transducers**
if I zero spec_augment everything works. Could you check if it's a possible bug or it is something I do wrong
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} | null | null | null | Hi,
Thanks for the prompt reply on helping me out with diarization with unknown speakers.
I just had a question - is there a limit on the length of audio file that can be processed for speaker diarization , or any speaker model in general with nemo.
If I have files with 1.5 hrs length, can they be effectively diarized?
Thanks!! | {
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"merged_at": "2022-09-01T23:24:31"
} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: ASR
# Changelog
- Update Speaker Diarization notebooks with unknown oracle_num_speakers.
- Use multiscale diarization in tutorials.
- Add flexibility for vad result plotting
# Usage
* Notebook change
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes bug where lowercase heteronyms wouldn't match with uppercased graphemes in IPA G2P. (Graphemes were uppercased to avoid conflicts with IPA symbols, but the heteronyms list is lowercased, so they would never match.)
**Collection**: TTS
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
fix bug in multi head attention forward function
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} | # What does this PR do ?
When going through the code saw a couple of missing types in the class, doesn't hurt to add them.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | null | null | null | I am trying to deploy my asr system in Riva and facing issue in deploying custom Kenlm language model. Steps followed - :
**Nemo model training** **:**
Model : citrinet_512
Tokenizer : subword (bpe of sentence piece tokenizer)
Language model : Kenlm 3 gram
language : Filipino
**Issue :** Nemo provides us the script(/NeMo/scripts/asr_language_modeling/ngram_lm/train_kenlm.py) to train a kenlm language model taking manifest as input. It seems like this script converts the training manifest in to character level format using offset and tokenizer. After that language model is trained as character level language model.
**Issue in riva :** I passed nemo model, language model binary, decoding vocab to riva build command but getting empty transcripts during Inference.
how riva is going to know that language model is trained as character level model and how riva will decode it.
**Decoding Vocab analysis:** Considering language model is trained on character level so I tokenized my corpus using subword tokenizer used in nemo model then convert each word(new_char) in to character level by :
offset = 100
vocab_number = convert each word of tokenized corpus into id.
new_char = chr(vocab_number + offset)
```
unique_vocabs = set()
offset = 100
with open(corpus) as crp:
for i in crp:
text = i.split(" ")
for t in text:
curr_vocab = ""
for j in tokenizer.text_to_ids(t):
curr_vocab = curr_vocab + chr(j+offset)
unique_vocabs.add(curr_vocab)
with open(decoder_vocab_path, "w") as vcb:
for i in unique_vocabs:
vcb.write(i)
vcb.write('\n')
```
I Passed decoder_vocab_path to riva build command. Sample vocab ->
[vocab.txt|attachment](upload://xkiOPpsQSBRY95y7xh6RMb1mee9.txt) (111.6 KB)
**Is this the right way to make decoding vocab for a character level language model to deploy it in riva?**
Any help would be appreciated. | {
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} | null | null | null | link to config: https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ssl/conformer/conformer_ssl.yaml
when I change min duration to let's say 1.6 sec
I get errors like
RuntimeError: shape '[8, 44, -1]' is invalid for input of size 143360
RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement
what all parameters I need to change in order to run the experiment successfully
these are the few parameters I think need some updation.
stride_layers
combine_time_steps
patch_size
mask_patches
num_negatives
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} | null | null | null | **Describe the bug**
For few audio file has this error when calling speaker diariation function, Error Message as below:
Error:cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR, when calling cusolverDnSsyevd(handle, jobz, uplo, n, A, lda, W, work, lwork, info)
**Steps/Code to reproduce bug**
The error might be occuring in the function clustering: nmsec , when call eigh(laplacian) with cuda (cuda device =1/0).
def eigDecompose(laplacian, cuda, device=None):
print(len(laplacian))
if TORCH_EIGN:
if cuda:
if device is None:
device = torch.cuda.current_device()
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A diarised audio file
**Environment overview (please complete the following information)**
Linux environment, 4 gpus
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version :Microsoft Windows [Version 10.0.19044.1889]
- PyTorch version : 1.12.0+cu102
- Python version: Python 3.7.13
- -Nemo :1.8
- Cuda : 11.6
**Additional context**
Re-opening wrt :https://github.com/NVIDIA/NeMo/issues/3757#issue-1150037680 | {
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} | Signed-off-by: Ankur Kumar <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
- Adds support to optionally train CTC decoder jointly with RNN-T model (encoder is shared)
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
Need to add following flags to RNN-T training command and set `D_MODEL` to encoder output dim
```python
++model.ctc.ctc_reduction=mean_batch \
++model.ctc.ctc_loss_weight=0.3 \
++model.ctc.decoder._target_=nemo.collections.asr.modules.ConvASRDecoder \
++model.ctc.decoder.feat_in=$D_MODEL \
++model.ctc.decoder.num_classes=-1 \
++model.ctc.decoder.vocabulary=[]
```
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
adds rnnt model
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: George Zelenfroind [[email protected]](mailto:[email protected])
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Adds links to Belarusian models (ctc and rnnt conformer large)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [yes] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: George Zelenfroind <[email protected]>
# What does this PR do ?
Adds feature of comparing to models to SDE
And adds new way of transcribing audio - add new transcripts to existing JSON file
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
asr
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
* in readme
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adds MLP option for prompt learning encoder
**Collection**: NLP
# Changelog
- Updates Prompt Encoder class
- Updates GPT Prompt Learning Model Class
- Updates Base Prompt Learning Model Class
# Usage
These can optionally be added to the prompt learning config
```
p_tuning: # P-tuning specific params
encoder_type: "mlp"
encoder_hidden: 512
num_layers: 2
dropout: 0.0
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a bug with decoding from Enc-Dec models with RPE.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add training script and config (titanet) for spoken language identification.
**Collection**:ASR
# Changelog
- Calculate weight for using weighted cross entropy for imbalanced training set.
- Add val_acc_macro and val_auroc for model selection. Support test_acc_macro and test_aucro as well.
- Extend speech_to_label.py to support language identification.
- Add model config file which achieves SOTA performance on voxlingua107.
# Usage
```python
python speech_to_label.py --config-path="../conf/lang_id" --config-name="titanet_large" \
model.train_ds.manifest_filepath=<train_manifest> \
model.validation_ds.manifest_filepath=<dev_manifest> \
model.train_ds.augmentor.noise.manifest_path=<noise_manifest> \
model.train_ds.augmentor.impulse.manifest_path=<impulse_manifest> \
exp_manager.wandb_logger_kwargs.name="titanet_large" \
exp_manager.wandb_logger_kwargs.project="lang_id" \
+exp_manager.checkpoint_callback_params.monitor="val_acc_macro" \
+exp_manager.checkpoint_callback_params.mode="max"
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
[doc will be updated in next PR]
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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# What does this PR do ?
This PR aims to fix a few small issues in the ASR with NeMo tutorial.
**Collection**: ASR
# Changelog
- check if `data_dir` exists and create if necessary
- prepend `data_dir` to `paths2audio_files`
- check if the expected config file exists and download if necessary
# Usage
n/a
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
n/a
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} | null | null | null | **Describe the bug**
Conformer model cannot be loaded when loaded on CPU.
There an unclear `RuntimeError`:
```
Traceback (most recent call last):
File "bug_nemo.py", line 12, in <module>
ts_model = torch.jit.load(f"/tmp/{model_name}.pt")
File "/opt/conda/lib/python3.8/site-packages/torch/jit/_serialization.py", line 162, in load
cpp_module = torch._C.import_ir_module(cu, str(f), map_location, _extra_files)
RuntimeError: required keyword attribute 'value' is undefined
```
It works fine when loading on GPU or using Citrinet, for example : `stt_en_citrinet_1024`.
**Steps/Code to reproduce bug**
```python:bug_nemo.py
import torch
import nemo.collections.asr as nemo_asr
model_name = "stt_en_conformer_ctc_small"
nemo_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(
model_name=model_name,
map_location="cpu"
)
nemo_model.export(f"/tmp/{model_name}.pt")
ts_model = torch.jit.load(f"/tmp/{model_name}.pt")
```
Output:
```
/opt/conda/lib/python3.8/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
[NeMo I 2022-08-30 22:17:01 cloud:56] Found existing object /home/tarteel/.cache/torch/NeMo/NeMo_1.10.0/stt_en_conformer_ctc_small/5d2d8e5b2b5adb8f5091363c6ba19c55/stt_en_conformer_ctc_small.nemo.
[NeMo I 2022-08-30 22:17:01 cloud:62] Re-using file from: /home/tarteel/.cache/torch/NeMo/NeMo_1.10.0/stt_en_conformer_ctc_small/5d2d8e5b2b5adb8f5091363c6ba19c55/stt_en_conformer_ctc_small.nemo
[NeMo I 2022-08-30 22:17:01 common:789] Instantiating model from pre-trained checkpoint
[NeMo I 2022-08-30 22:17:02 mixins:166] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo W 2022-08-30 22:17:02 modelPT:148] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /data/NeMo_ASR_SET/English/v2.0/train/tarred_audio_manifest.json
sample_rate: 16000
batch_size: 64
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20.0
min_duration: 0.1
shuffle_n: 2048
is_tarred: true
tarred_audio_filepaths: /data/NeMo_ASR_SET/English/v2.0/train/audio__OP_0..4095_CL_.tar
[NeMo W 2022-08-30 22:17:02 modelPT:155] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath:
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-clean.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-clean.json
sample_rate: 16000
batch_size: 64
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: na
[NeMo W 2022-08-30 22:17:02 modelPT:161] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath:
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-clean.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-dev-other.json
- /data/ASR/LibriSpeech/librispeech_withsp2/manifests/librivox-test-clean.json
sample_rate: 16000
batch_size: 64
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: na
[NeMo I 2022-08-30 22:17:02 features:200] PADDING: 0
[NeMo W 2022-08-30 22:17:03 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Torchmetrics v0.9 introduced a new argument class property called `full_state_update` that has
not been set for this class (WER). The property determines if `update` by
default needs access to the full metric state. If this is not the case, significant speedups can be
achieved and we recommend setting this to `False`.
We provide an checking function
`from torchmetrics.utilities import check_forward_no_full_state`
that can be used to check if the `full_state_update=True` (old and potential slower behaviour,
default for now) or if `full_state_update=False` can be used safely.
warnings.warn(*args, **kwargs)
[NeMo W 2022-08-30 22:17:03 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Torchmetrics v0.9 introduced a new argument class property called `full_state_update` that has
not been set for this class (WERBPE). The property determines if `update` by
default needs access to the full metric state. If this is not the case, significant speedups can be
achieved and we recommend setting this to `False`.
We provide an checking function
`from torchmetrics.utilities import check_forward_no_full_state`
that can be used to check if the `full_state_update=True` (old and potential slower behaviour,
default for now) or if `full_state_update=False` can be used safely.
warnings.warn(*args, **kwargs)
[NeMo I 2022-08-30 22:17:03 save_restore_connector:243] Model EncDecCTCModelBPE was successfully restored from /home/tarteel/.cache/torch/NeMo/NeMo_1.10.0/stt_en_conformer_ctc_small/5d2d8e5b2b5adb8f5091363c6ba19c55/stt_en_conformer_ctc_small.nemo.
[NeMo I 2022-08-30 22:17:03 export_utils:263] Swapped 96 modules
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.
if hasattr(mod, name):
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1402: UserWarning: positional arguments and argument "destination" are deprecated. nn.Module.state_dict will not accept them in the future. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1402: UserWarning: argument "destination" are deprecated. nn.Module.state_dict will not accept them in the future. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:670: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.
item = getattr(mod, name)
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.
if hasattr(mod, name):
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:670: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.
item = getattr(mod, name)
[NeMo W 2022-08-30 22:17:04 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/conformer_encoder.py:248: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if max_audio_length > self.max_audio_length:
[NeMo I 2022-08-30 22:17:06 exportable:84] Successfully exported EncDecCTCModelBPE to /tmp/stt_en_conformer_ctc_small.pt
Traceback (most recent call last):
File "bug_nemo.py", line 12, in <module>
ts_model = torch.jit.load(f"/tmp/{model_name}.pt")
File "/opt/conda/lib/python3.8/site-packages/torch/jit/_serialization.py", line 162, in load
cpp_module = torch._C.import_ir_module(cu, str(f), map_location, _extra_files)
RuntimeError: required keyword attribute 'value' is undefined
```
**Expected behavior**
Export a CPU TorchScript model.
If `map_location=cpu` is removed, the exported GPU model works.
I’ve tried moving to CPU after loading it, with no success:
```
nemo_model = nemo_model.to(‘cpu’)
```
**Environment details**
Docker image: `nvcr.io/nvidia/nemo:22.05`
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} | [] | closed | false | null | [] | null | null | 2022-08-30T22:35:49 | 2022-08-31T17:27:21 | 2022-08-31T17:27:17 | CONTRIBUTOR | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a bug that prevented doing GLUE fine-tuning eval.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Parallel adapter did not perform as well initially.
**Collection**: [NLP]
# Changelog
- Config now lets you select the type of initialization for ColumnLinear.
# Usage
* See #4717
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Hi! I'm wondering why `.float()` is here. It seems like it will break code if both model parameters and input in float16 | {
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} | Signed-off-by: SeanNaren <[email protected]>
# What does this PR do ?
When going through the examples I saw these small spelling mistakes.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Moves the `checkpoint_core_attention` argument to the forward pass instead of init to allow more flexibility in SAR, i.e. per layer setting. | {
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I've noticed fairly different predictions between the two aforementioned durations | {
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https://github.com/NVIDIA/NeMo/blob/main/reinstall.sh#L27
This should check if conda is active (` [ -n "${CONDA_PREFIX}" ] `) rather than if conda is present (` [ -x "$(command -v conda)" ]
`).
Ref: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
If conda is installed on a system, but a standard python virtual environment is being, this fails to install nemo correctly.
**Steps/Code to reproduce bug**
- Deactivate conda
- Create a python virtual environment
- Install Nemo in it | {
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} | Signed-off-by: Virginia Adams <[email protected]>
# What does this PR do ?
1. Updates squad preprocessing script and adds squad metric calculation script
2. Updates prompt learning inference script to write prediction to file
3. Turns off sequence parallel for GPT models trained with it
**Collection**: NLP
# Changelog
- Update squad preprocessing script
- Add squad metric calculation script
- Updates GPT prompt learning model class
- Updates prompt learning GPT eval script
- Updates T5 prompt learning model class
- Updates prompt learning T5 eval script
# Usage
* You can potentially add a usage example below
Updated gpt prompt learning eval command
```
python megatron_gpt_prompt_learning_eval.py \
virtual_prompt_model_file=PATH_TO_NEMO_PROMPT_LEARNING_MODEL_FILE \
gpt_model_file=PATH_TO_FROZEN_GPT_MODEL_FILE \
inference.greedy=True \
inference.add_BOS=False \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=1 \
pred_file_path=PATH_WHERE_PRED_TEXT_FILE_WILL_BE_SAVED \
data_paths=[path/to/dataset1.jsonl, path/to/dataset2.jsonl]
```
Squad metric calculation script
```
python squad_metric_calc.py --ground-truth squad_test_gt.jsonl --preds squad_20B_preds.txt
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) | {
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} | Signed-off-by: Yang Zhang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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'augmentor': {'noise': {'prob': 0.1, 'manifest_path': '/rir_noises_tarred/noises_20s_tarred/tarred_audio_manifest.json', 'audio_tar_filepaths': '/rir_noises_tarred/noises_20s_tarred/audio_{0..255}.tar', 'min_snr_db': 0, 'max_snr_db': 30}, 'transcode_aug': {'prob': 0.2}}, 'is_tarred': False, 'tarred_audio_filepaths': None, 'shuffle_n': 2048}
could not find reference of this key in any notebooks in the tutorial dir.
how to obtain these nose manifest ? (is this related to the online_noise_augmentation.ipynb notebook)?,
will setting this to none affect the new model being trained?
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} | # What does this PR do ?
Update TN and ITN for ES to 2.0, add en_in (EN International, current Riva EN ITN 2.0)
**Collection**: nemo_text_processing
# Changelog
Add en_in ITN (current Riva EN ITN 2.0)
Update ES for TN and ITN to 2.0
**PR Type**:
- [X ] New Feature
- [X ] Bugfix
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Remove warnings from core and ASR code. NLP part needs more rework than expected.
**Collection**: Core, ASR
# Changelog
- [x] weights_save_path has been removed
- [x] Some changes on ptl self.log sync_dist = True
- [x] torch.qr -> torch.linalg.qr
- [x] on_pretrain_routine_start -> on_fit_start
- [x] Update most metrics to have full_state_update = True variable
- [x] Pytest remove with_downloads marker warning
- [x] regex escape sequence and np float deprecation
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Adds "mean" and "mean_volume" loss normalization options to RNNT Joint
**Collection**:
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
In the config file, set `rnnt_reduction` to `mean`, `mean_batch` (same as the default today), or `mean_volume` in the `model` section
```python
# Add a code snippet demonstrating how to use this
rnnt_reduction: mean_batch
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
German fastpitch model refers to non existing tokenizer
**Steps/Code to reproduce bug**
git clone https://github.com/NVIDIA/NeMo/
cd NeMo
python3.8 -m venv venv
source venv/bin/activate
pip3 install a-lot-of-missing-requirements when executing
python3 tts_german.py where inference code is as follows tts_german.py
```
import soundfile as sf
from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder
# Download and load the pretrained fastpitch model
spec_generator = SpectrogramGenerator.from_pretrained(model_name="tts_de_fastpitch_multispeaker_5")#.cuda()
# Download and load the pretrained hifigan model
vocoder = Vocoder.from_pretrained(model_name="tts_de_hui_hifigan_ft_fastpitch_multispeaker_5")#.cuda()
# All spectrogram generators start by parsing raw strings to a tokenized version of the string
parsed = spec_generator.parse("Hallo, magst du warme Milch trinken.")
# They then take the tokenized string and produce a spectrogram
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
# Finally, a vocoder converts the spectrogram to audio
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
# Save the audio to disk in a file called speech.wav
# Note vocoder return a batch of audio. In this example, we just take the first and only sample.
sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 44100)
```
**Expected behavior**
Generation of speech.wav
**Environment overview (please complete the following information)**
See above
**Environment details**
See above
**Add any other context about the problem here.**
Error message:
```
(venv) userxyz@linux-u01v:~/devel/tts/NeMo> python3 tts_german.py
[NeMo W 2022-08-28 17:44:54 optimizers:55] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2022-08-28 17:44:54 experimental:27] Module <class 'nemo_text_processing.g2p.modules.IPAG2P'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-08-28 17:44:54 experimental:27] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-08-28 17:44:54 nemo_logging:349] /home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/torch/amp/autocast_mode.py:198: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
[NeMo W 2022-08-28 17:44:54 experimental:27] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2022-08-28 17:44:54 cloud:56] Found existing object /home/userxyz/.cache/torch/NeMo/NeMo_1.12.0rc0/tts_de_fastpitch_multispeaker_5/cd9aa375555376d59f140d3fb4b23fb2/tts_de_fastpitch_multispeaker_5.nemo.
[NeMo I 2022-08-28 17:44:54 cloud:62] Re-using file from: /home/userxyz/.cache/torch/NeMo/NeMo_1.12.0rc0/tts_de_fastpitch_multispeaker_5/cd9aa375555376d59f140d3fb4b23fb2/tts_de_fastpitch_multispeaker_5.nemo
[NeMo I 2022-08-28 17:44:54 common:910] Instantiating model from pre-trained checkpoint
[NeMo I 2022-08-28 17:44:55 tokenize_and_classify:81] Creating ClassifyFst grammars. This might take some time...
[NeMo I 2022-08-28 17:45:14 tokenize_and_classify:81] Creating ClassifyFst grammars. This might take some time...
[NeMo E 2022-08-28 17:45:33 common:503] Model instantiation failed!
Target class: nemo.collections.tts.models.fastpitch.FastPitchModel
Error(s): Error locating target 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer', see chained exception above.
full_key: text_tokenizer
Traceback (most recent call last):
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/utils.py", line 645, in _locate
obj = import_module(mod)
File "/usr/lib64/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 970, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer'; 'nemo.collections.tts.torch.tts_tokenizers' is not a package
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 134, in _resolve_target
target = _locate(target)
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/utils.py", line 648, in _locate
raise ImportError(
ImportError: Error loading 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer':
ModuleNotFoundError("No module named 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer'; 'nemo.collections.tts.torch.tts_tokenizers' is not a package")
Are you sure that 'GermanPhonemesTokenizer' is importable from module 'nemo.collections.tts.torch.tts_tokenizers'?
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/userxyz/devel/tts/NeMo/nemo/core/classes/common.py", line 482, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/home/userxyz/devel/tts/NeMo/nemo/collections/tts/models/fastpitch.py", line 95, in __init__
self._setup_tokenizer(cfg)
File "/home/userxyz/devel/tts/NeMo/nemo/collections/tts/models/fastpitch.py", line 199, in _setup_tokenizer
self.vocab = instantiate(cfg.text_tokenizer, **text_tokenizer_kwargs)
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
return instantiate_node(
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 325, in instantiate_node
_target_ = _resolve_target(node.get(_Keys.TARGET), full_key)
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 139, in _resolve_target
raise InstantiationException(msg) from e
hydra.errors.InstantiationException: Error locating target 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer', see chained exception above.
full_key: text_tokenizer
Traceback (most recent call last):
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/utils.py", line 645, in _locate
obj = import_module(mod)
File "/usr/lib64/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 970, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer'; 'nemo.collections.tts.torch.tts_tokenizers' is not a package
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 134, in _resolve_target
target = _locate(target)
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/utils.py", line 648, in _locate
raise ImportError(
ImportError: Error loading 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer':
ModuleNotFoundError("No module named 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer'; 'nemo.collections.tts.torch.tts_tokenizers' is not a package")
Are you sure that 'GermanPhonemesTokenizer' is importable from module 'nemo.collections.tts.torch.tts_tokenizers'?
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "tts_german.py", line 5, in <module>
spec_generator = SpectrogramGenerator.from_pretrained(model_name="tts_de_fastpitch_multispeaker_5")#.cuda()
File "/home/userxyz/devel/tts/NeMo/nemo/core/classes/common.py", line 849, in from_pretrained
instance = class_.restore_from(
File "/home/userxyz/devel/tts/NeMo/nemo/core/classes/modelPT.py", line 311, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/userxyz/devel/tts/NeMo/nemo/core/connectors/save_restore_connector.py", line 235, in restore_from
loaded_params = self.load_config_and_state_dict(
File "/home/userxyz/devel/tts/NeMo/nemo/core/connectors/save_restore_connector.py", line 158, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/home/userxyz/devel/tts/NeMo/nemo/core/classes/common.py", line 504, in from_config_dict
raise e
File "/home/userxyz/devel/tts/NeMo/nemo/core/classes/common.py", line 496, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
File "/home/userxyz/devel/tts/NeMo/nemo/collections/tts/models/fastpitch.py", line 95, in __init__
self._setup_tokenizer(cfg)
File "/home/userxyz/devel/tts/NeMo/nemo/collections/tts/models/fastpitch.py", line 199, in _setup_tokenizer
self.vocab = instantiate(cfg.text_tokenizer, **text_tokenizer_kwargs)
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
return instantiate_node(
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 325, in instantiate_node
_target_ = _resolve_target(node.get(_Keys.TARGET), full_key)
File "/home/userxyz/devel/tts/NeMo/venv/lib64/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 139, in _resolve_target
raise InstantiationException(msg) from e
hydra.errors.InstantiationException: Error locating target 'nemo.collections.tts.torch.tts_tokenizers.GermanPhonemesTokenizer', see chained exception above.
full_key: text_tokenizer
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https://api.github.com/repos/NVIDIA/NeMo/issues/4827 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4827/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4827/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4827/events | https://github.com/NVIDIA/NeMo/pull/4827 | 1,353,145,416 | PR_kwDOC_bI7s496XHe | 4,827 | adding instancenorm as an option for conv normalization | {
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"merged_at": "2022-08-30T04:23:01"
} | # What does this PR do ?
Adds instancenorm as an option for conv normalization
**Collection**: ASR
# Changelog
- nemo/collections/asr/parts/submodules/conformer_modules.py - accept `instance_norm` as an accepted config parameter value for ```model.encoder.conv_norm_type```
# Usage
* In config file, set ```model.encoder.conv_norm_type=instance_norm```
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | This PR continues https://github.com/NVIDIA/NeMo/issues/4543 & https://github.com/NVIDIA/NeMo/pull/4638 & https://github.com/NVIDIA/NeMo/pull/4683
What does this PR do ?
Add Chinese Text Normalization Tools in NeMo
Collection:
[NeMo/norm_text_processing/text_normalization]
[NeMo/tools/text_processing_deployment]
[NeMo/tests/nemo_text_processing/zh]
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} | Signed-off-by: Virginia Adams <[email protected]>
# What does this PR do ?
1. Updates squad preprocessing script and adds squad metric calculation script
2. Updates prompt learning inference script to write prediction to file
3. Turns off sequence parallel for GPT models trained with it
**Collection**: NLP
# Changelog
- Update squad preprocessing script
- Add squad metric calculation script
- Updates GPT prompt learning model class
- Updates prompt learning GPT eval script
# Usage
* You can potentially add a usage example below
Updated gpt prompt learning eval command
```
python megatron_gpt_prompt_learning_eval.py \
virtual_prompt_model_file=PATH_TO_NEMO_PROMPT_LEARNING_MODEL_FILE \
gpt_model_file=PATH_TO_FROZEN_GPT_MODEL_FILE \
inference.greedy=True \
inference.add_BOS=False \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=1 \
pred_file_path=PATH_WHERE_PRED_TEXT_FILE_WILL_BE_SAVED \
data_paths=[path/to/dataset1.jsonl, path/to/dataset2.jsonl]
```
Squad metric calculation script
```
python squad_metric_calc.py --ground-truth squad_test_gt.jsonl --preds squad_20B_preds.txt
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
Currently, `AudioSegment` aims to do channel averaging for multi-channel audio signals:
```
if self._samples.ndim >= 2:
self._samples = np.mean(self._samples, 1)
```
However, samples in its constructor are arranged as `(num_channels, num_samples)`, instead of the assumed `(num_samples, num_channels)`. This means that `samples` of the created `AudioSegment` will have an unexpected size, and also ASR will fail when running `transcribe` on a multi-channel audio signal.
This can be tested by running `test_audiosegment.py` and `test_asr_multi_channel_audio.py` from the attached scripts in [test_audiosegment_with_channel_selector.zip](https://github.com/NVIDIA/NeMo/files/9458412/test_audiosegment_with_channel_selector.zip)
**Collection**: ASR
# Changelog
### Necessary fix in `AudioSegment`
- correction to dimension ordering for multi-channel audio
- added a new parameter `channel_selector` which can be used to select a single-channel (or a subset of channels) from a multi-channel audio signal
- added unit tests for both channel selector and AudioSegment
Changes above are in `segment.py` and `audio_utils.py` and tests are in `test_preprocessing_segment.py` and `test_audio_utils.py`.
### Nice to have: `channel_selector` for `transcribe`
- Added a `channel_selector` option to `transcribe`, and forwarded it through models, audio-to-text dataset and `WaveformFeaturizer` to `AudioSegment`
- This is a convenience when running an existing model on a multi-channel audio file, we can select either one of the channels or the channel-wise average.
If preferred, this can be removed from the PR.
# Usage
* Check the attached scripts: [test_audiosegment_with_channel_selector.zip](https://github.com/NVIDIA/NeMo/files/9458412/test_audiosegment_with_channel_selector.zip)
## Example: `AudioSegment`
- Check `test_audiosegment.py`
```python
AudioSegment.from_file(audio_file) # loads the complete signal (including all channels)
AudioSegment.from_file(audio_file, channel_selector='average') # loads a single-channel signal (average across channels)
AudioSegment.from_file(audio_file, channel_selector=0) # loads only first channel
```
## Example: `AudioSegment`
- Check `test_asr_with_channel_selector.py`
```python
asr_model.transcribe(mc_files, channel_selector='average') # apply channel averaging when loading audio
```
# Tests
- Existing unit tests + newly-added unit tests
- Compared transcription using single-channel signal matches between `main` and `fix` branch for wav, flac, mp3 input (`test_asr_formats.py`, attached)
- Compared transcription of a channel from a multi-channel signal is matching transcription using a single-channel input signal (`test_asr_with_channel_selector.py`, attached)
- Tutorials `ASR_with_NeMo.ipynb` and `ASR_with_Transducers.ipynb` running fine with the `fix` branch
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* n/a
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} | # What does this PR do ?
This PR updates the folder structure of example/speaker_task/diarization folder.
<img width="369" alt="image" src="https://user-images.githubusercontent.com/13211483/187006224-6ac5993b-e6b2-4ac2-85d5-c66208850983.png">
**Collection**: [Note which collection this PR will affect]
ASR (speaker tasks)
# Changelog
- yaml files in diarization example folder
- changed the names in the diarization example folder.
- changed related parts in docs
- updated yaml paths in tutorials
# Usage
Usage can be found in README.md file in example diarization folder
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Fix float/integer type error in wer computation. Similar to PR https://github.com/NVIDIA/NeMo/pull/4816 but on different files.
# Changelog
Fix type errors in file rnnt_wer.py, rnnt_wer_bpe.py, wer_bpe.py.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
Enables manifest processing / multilingual model training where there is more than one language ID per sample
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- [audio_to_text.py] modified to parse the manifest in the new format
- [collections.py] modified to not require global lang id per sample in this case (since lang id will be specified on each text span)
# Usage
Use manifest format as shown below. No other code changes required.
```json
{"audio_filepath": "_data_cs_constructed_data_v1_0.wav", "shard_id": 168, "duration": 17.715125, "text": [{"lang": "es", "str": "es probablemente la amiga de puck que m\u00e1s aparece y m\u00e1s presencia tiene "}, {"lang": "en", "str": "she might have gone on but that she caught kinraid's eyes looking at her with kindly admiration "}, {"lang": "es", "str": "o las que tienes de estar con alguien que no lo es porque"}]}
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Enable optional output of language IDs during AASR transcription / inference
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Aggregate tokenizer modified so that it can output token and sample LIDs
- rnnt_wer.py modified to handle the compute_langs flag and produce output
- rnnt_wer_bpe.py modified to handle the compute_langs flag and produce output
# Usage
See the sample code below. This works similar to timestamp output.
```
# Run this:
# python langs.py
# Sample output
Transcription: o que es demasiado ruidosa o que tiene palabras que no deberían de ponerlas en canciones porque es como
Langs: es
Langs chars [{'char': ' o', 'lang': 'es'}, {'char': ' que', 'lang': 'es'}, {'char': ' es', 'lang': 'es'}, {'char': ' de', 'lang': 'es'}, {'char': 'ma', 'lang': 'es'}, {'char': 's', 'lang': 'es'}, {'char': 'i', 'lang': 'es'}, {'char': 'ado', 'lang': 'es'}, {'char': ' ru', 'lang': 'es'}, {'char': 'ido', 'lang': 'es'}, {'char': 's', 'lang': 'es'}, {'char': 'a', 'lang': 'es'}, {'char': ' o', 'lang': 'es'}, {'char': ' que', 'lang': 'es'}, {'char': ' tiene', 'lang': 'es'}, {'char': ' palabra', 'lang': 'es'}, {'char': 's', 'lang': 'es'}, {'char': ' que', 'lang': 'es'}, {'char': ' no', 'lang': 'es'}, {'char': ' debe', 'lang': 'es'}, {'char': 'ría', 'lang': 'es'}, {'char': 'n', 'lang': 'es'}, {'char': ' de', 'lang': 'es'}, {'char': ' poner', 'lang': 'es'}, {'char': 'las', 'lang': 'es'}, {'char': ' en', 'lang': 'es'}, {'char': ' can', 'lang': 'es'}, {'char': 'ciones', 'lang': 'es'}, {'char': ' porque', 'lang': 'es'}, {'char': ' es', 'lang': 'es'}, {'char': ' como', 'lang': 'es'}]
Langs words [{'word': ' o', 'lang': 'es'}, {'word': ' que', 'lang': 'es'}, {'word': ' es', 'lang': 'es'}, {'word': ' demasiado', 'lang': 'es'}, {'word': ' ruidosa', 'lang': 'es'}, {'word': ' o', 'lang': 'es'}, {'word': ' que', 'lang': 'es'}, {'word': ' tiene', 'lang': 'es'}, {'word': ' palabras', 'lang': 'es'}, {'word': ' que', 'lang': 'es'}, {'word': ' no', 'lang': 'es'}, {'word': ' deberían', 'lang': 'es'}, {'word': ' de', 'lang': 'es'}, {'word': ' ponerlas', 'lang': 'es'}, {'word': ' en', 'lang': 'es'}, {'word': ' canciones', 'lang': 'es'}, {'word': ' porque', 'lang': 'es'}, {'word': ' es', 'lang': 'es'}, {'word': ' como', 'lang': 'es'}]
```
```python
import pytorch_lightning as pl
from omegaconf import OmegaConf, open_dict
from nemo.collections.asr.models import ASRModel
def main():
trainer = pl.Trainer(gpus=[0], precision=32, logger=False)
# Char model
# path = "Path to char model"
# Multilingual model
path = "/data3/models/enes_agg_277e.nemo"
asr_model = ASRModel.restore_from(path, trainer=trainer, map_location='cpu')
asr_model.eval()
asr_model.freeze()
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
decoding_cfg = OmegaConf.create({})
with open_dict(decoding_cfg):
decoding_cfg.strategy = "greedy_batch"
decoding_cfg.preserve_alignments = True
decoding_cfg.compute_langs = True
asr_model.change_decoding_strategy(decoding_cfg)
# speech sample
WAV = 'es.wav'
hyp, _ = asr_model.transcribe([WAV],
batch_size=1, return_hypotheses=True)
print("Evaluating ---")
print()
print("Transcription: ", hyp[0].text)
print()
print("Langs: ", hyp[0].langs)
print()
print("Langs chars", hyp[0].langs_chars)
print()
print("Langs words ", hyp[0].langs_words)
print()
if __name__ == '__main__':
main()
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Add kabyle language ASR models
**Collection**: ASR
# Changelog
- Add kabyle language ASR models
# Usage
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="stt_kab_conformer_transducer_large")
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [x] New Feature
- [ ] Bugfix
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} | null | null | null | I am new to nemo and following this Colab notebook - https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb
When I try set num_speakers = None in manifest file and set config.diarizer.clustering.parameters.oracle_num_speakers=False , and run it without oracle vad diarization, it only detects one speaker even for the example file provided in the notebook
This is the code I amended:
```
an4_audio_url = "https://nemo-public.s3.us-east-2.amazonaws.com/an4_diarize_test.wav"
an4_audio = wget.download(an4_audio_url, data_dir)
`import json
meta = {
'audio_filepath': an4_audio,
'offset': 0,
'duration':None,
'label': 'infer',
'text': '-',
**'num_speakers': None,**
'rttm_filepath': None,
'uem_filepath' : None
}
with open('data/input_manifest.json','w') as fp:
json.dump(meta,fp)
fp.write('\n')
**config.diarizer.clustering.parameters.oracle_num_speakers=False**
```
Can someone help me with why it is not able to predict two speakers? | {
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} | [] | closed | false | null | [] | null | null | 2022-08-26T01:51:32 | 2022-08-27T00:59:42 | 2022-08-27T00:34:30 | CONTRIBUTOR | {
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} | Signed-off-by: Virginia Adams <[email protected]>
Updates squad specific scripts for data set pre-processing and evaluation. Make improvements to prompt learning inference script and fixes bug with batch prompt learning inference.
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} |
# What does this PR do ?
Without this, `WER.update()` fails with the following exception:
TypeError: 'float' object cannot be interpreted as an integer
This is because the `score` property is initialized as `tourch.int64`,
but actually is a float value. Fix the initialization so that the
property has the correct type.
*Technical Note:* the actual failing line is line 780 in `nemo/collections/asr/metrics/wer.py`:
```python3
self.scores = torch.tensor(scores, device=self.scores.device, dtype=self.scores.dtype)
```
Since `self.scores.dtype` is `torch.int64` in the current version, this line ended
up something like `torch.tensor(0.5, dtype=torch.int64)` and fails.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Fix TypeError in WER.update()
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N/A] Did you write any new necessary tests?
- [N/A] Did you add or update any necessary documentation?
- [N/A] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [N/A] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: David Mosallanezhad <[email protected]>
# What does this PR do ?
Megatron NMT Adapters
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Updates Apex commit used for building and testing to include distributed Adam.
**Collection**: NLP
# Changelog
- Update Apex commit in Dockerfile and Jenkinsfile
# Usage
```bash
cd /path/to/nemo
DOCKER_BUILDKIT=1 docker build .
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Enables https://github.com/NVIDIA/NeMo/pull/4487
* The Apex commit corresponds to https://github.com/NVIDIA/apex/pull/1463
* CI fails for https://github.com/NVIDIA/NeMo/pull/4799 since the existing Docker container uses an old Apex commit | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4813 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4813/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4813/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4813/events | https://github.com/NVIDIA/NeMo/pull/4813 | 1,351,569,461 | PR_kwDOC_bI7s491M3v | 4,813 | Partial activation checkpoint/recomputation | {
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} | # What does this PR do ?
This adds two features
**1. Partial activation checkpoint**
This feature is valid only when used with pipeline-parallelism. When an integer value is provided for 'num_micro_batches_with_partial_activation_checkpoints', it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed and recomputed within a window of micro-batches as shown in Figure 1. The rest of the micro-batches in the window checkpoint all Transformer layers. Compared to the baseline Figure 1.a, Figure 1.b, partial activation checkpointing enables skip checkpointing activations by using the remained GPU memory.
The size of the window is set by the maximum outstanding micro-batch backpropagations, which vary at different pipeline stages. In the below example, the first pipeline stage can do partial checkpointing at one micro-batch out of the window of four micro-batches. The second pipeline stage does one out of three and eventually, the last micro-batch does the partial checkpointing at all micro-batches. This is shown in Figure 2. This feature enables the use of activation checkpoints at a fraction of micro-batches up to the point of full GPU memory usage.
**2. Gradual activation checkpoint increase at later pipeline stage**
When an integer value is provided for 'activations_checkpoint_layers_per_pipeline', this sets the additional Transformer layers to checkpoint at later pipeline stages. For example, when using 'activations_checkpoint_layers_per_pipeline' of 3, pipeline stage 1 checkpoints 3 layers less the stage 0 and stage 2 checkpoints 6 layers less the stage 0, and so on. This is shown in Figure 3. This is possible because the later pipeline stage uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', this feature removes most of the activation checkpoints at the last pipeline stage (the critical execution path).
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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at `File python3.8/site-packages/nemo/collections/asr/parts/submodules/subsampling.py:144, in ConvSubsampling.forward(self, x, lengths)
140 x = x.unsqueeze(1)
141 if self._subsampling == 'striding':
142 # added in order to prevent slowdown in torch.nn.Conv2d with bfloat16 / CUDNN v8 API
143 # to be removed once the above is fixed in cudnn
--> 144 with torch.cuda.amp.autocast(dtype=torch.float32):
145 x = self.conv(x)`
**Steps/Code to reproduce bug**
```
pretrained =None
try:
pretrained = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="stt_en_conformer_transducer_xxlarge")
except:
pretrained = nemomodels.EncDecRNNTBPEModel.restore_from(restore_path= model_loc_path)
pretrainedConfig = DictConfig(pretrained.cfg)
pretrainedConfig.train_ds.manifest_filepath = TRAIN_MANIFEST
pretrainedConfig.validation_ds.manifest_filepath = VALIDATION_MANIFEST
pretrainedConfig.test_ds.manifest_filepath = TEST_MANIFEST
pretrainedConfig['train_ds']['is_tarred'] = False
pretrainedConfig['train_ds']['tarred_audio_filepaths'] = None
pretrainedConfig['validation_ds']['is_tarred'] = False
pretrainedConfig['validation_ds']['tarred_audio_filepaths'] = None
pretrained.set_trainer(trainer)
pretrained.setup_training_data(pretrainedConfig['train_ds'])
pretrained.setup_validation_data(pretrainedConfig['validation_ds'])
from nemo.utils import exp_manager
from omegaconf import OmegaConf
# Environment variable generally used for multi-node multi-gpu training.
# In notebook environments, this flag is unnecessary and can cause logs of multiple training runs to overwrite each other.
os.environ.pop('NEMO_EXPM_VERSION', None)
exp_config = exp_manager.ExpManagerConfig(
exp_dir=f'experiments/',
name=f"Transducer-Model",
checkpoint_callback_params=exp_manager.CallbackParams(
monitor="val_wer",
mode="min",
always_save_nemo=True,
save_best_model=True,
),
)
exp_config = OmegaConf.structured(exp_config)
logdir = exp_manager.exp_manager(trainer, exp_config)
# Train the model
trainer.fit(pretrained)
```
The same code works on a CPU , but fails on A100 GPU
nemo-toolkit 1.10.0
torch 1.9.0+cu111
torch-stft 0.1.4
torchaudio 0.9.0
pytorch-lightning 1.7.2
Linux cloud-amd64 #1 SMP Debian 4.19.249-2 (2022-06-30) x86_64 GNU/Linux
A100 GPU on GCP gace this dtype error , but worked okay on CPU
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes indexing error in MixerTTS-X data loading introduced by adding items to the TTS datasets
**Collection**: TTS
# Changelog
- Changed indexing from 13 to 15 due to two additional items `voiced_mask` and `p_voiced`
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4810 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4810/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4810/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4810/events | https://github.com/NVIDIA/NeMo/pull/4810 | 1,351,270,256 | PR_kwDOC_bI7s490L5d | 4,810 | s2s dialog generation custom data processor | {
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} | # What does this PR do ?
Added data processor to support custom data for DialogueS2SGenerationModel.
**Collection**: [nlp]
# Changelog
- Added a data processor for a custom dataset for the DialogueS2SGenerationModel.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Hello,
in the [model's page](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_de_conformer_transducer_large), it is said that the model was trained on:
- 567 hours of MCV7
- 1524 hours of MLS
- 214 hours of VoxPopuli
I am wondering how the subsets for each dataset were chosen, especially for MCV, where there is accented data. Unfortunately, I could not find the details of this dataset online. Could you please clarify how the subsets were chosen?
Kind regards,
Carlos | {
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} | null | null | null | **Describe the bug**
Trying to train a transformer model for machine translation, following the examples in the docs. After many attempts, I managed to get it to start training, but during the first validation step, it successfully processes the first validation dataset, but throws a `KeyError` exception for the second dataset (no matter which dataset comes second):
```
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/user/exp/nemo/ende/enc_dec_nmt.py", line 144, in main
raise err
File "/home/user/exp/nemo/ende/enc_dec_nmt.py", line 136, in main
trainer.fit(mt_model)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit
self._call_and_handle_interrupt(
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 737, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1168, in _run
results = self._run_stage()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1254, in _run_stage
return self._run_train()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1285, in _run_train
self.fit_loop.run()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.advance(*args, **kwargs)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 270, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 201, in run
self.on_advance_end()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 241, in on_advance_end
self._run_validation()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 299, in _run_validation
self.val_loop.run()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.advance(*args, **kwargs)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 155, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.advance(*args, **kwargs)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 127, in advance
batch = next(data_fetcher)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/utilities/fetching.py", line 185, in __next__
return self.fetching_function()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/utilities/fetching.py", line 264, in fetching_function
self._fetch_next_batch(self.dataloader_iter)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/pytorch_lightning/utilities/fetching.py", line 278, in _fetch_next_batch
batch = next(iterator)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
data = self._next_data()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1376, in _next_data
return self._process_data(data)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1402, in _process_data
data.reraise()
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/_utils.py", line 461, in reraise
raise exception
KeyError: Caught KeyError in DataLoader worker process 13.
Original Traceback (most recent call last):
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/user/miniconda3/envs/nemo/lib/python3.9/site-packages/nemo/collections/nlp/data/machine_translation/machine_translation_dataset.py", line 140,
in __getitem__
src_ids = self.batches[idx]["src"]
KeyError: 118
```
This seems to happen when I specify multiple validation datasets as a list. When specifying only one, training doesn't crash (or at least hasn't yet!). All train/validation/test files are in plain format (i.e. raw text).
**Steps/Code to reproduce bug**
This is my full YAML config.
```
name: nemo_ende_6x6_0.1_2k
do_training: true # set to false if only preprocessing data
do_testing: true # set to true to run evaluation on test data after training
model:
beam_size: 1
len_pen: 0.6
multilingual: false
max_generation_delta: -1
label_smoothing: 0.1
shared_tokenizer: true # train tokenizer model across src and tgt train data
preproc_out_dir: data_output # path to store data preprocessing outputs
src_language: 'en'
tgt_language: 'de'
shared_embeddings: false
train_ds:
src_file_name: data/train/train_5m.en
tgt_file_name: data/train/train_5m.de
use_tarred_dataset: true # if true tar_file_name and meta_file_name will be used (or created automatically)
# config for preprocessing training data and creating a tarred datset automatically
tar_file_prefix: train_5m # prefix for tar file names
tar_files: null # if data has already been preprocessed (rest of config ignored)
metadata_file: data_output/metadata.tokens.2000.json # metadata for tarred dataset
lines_per_dataset_fragment: 1000000 # Number of lines to consider for bucketing and padding
num_batches_per_tarfile: 100 # Number of batches (pickle files) within each tarfile
tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled
shard_strategy: scatter # tarred dataset shard distribution strategy
n_preproc_jobs: -2 # number of processes to use for data preprocessing (-2 means all but 2)
tokens_in_batch: 2000
clean: true
max_seq_length: 512
shuffle: true
num_samples: -1
drop_last: false
pin_memory: false
num_workers: 16
concat_sampling_technique: temperature # only used with ConcatTranslationDataset
concat_sampling_temperature: 5 # only used with ConcatTranslationDataset
concat_sampling_probabilities: null # only used with ConcatTranslationDataset
validation_ds:
src_file_name: [data/dev/dev.ac_dev.en, data/dev/newstest2012.en, data/dev/newstest2016.en, data/dev/test.IWSLT2017.en, data/dev/dev.opus-100-v1.en, data/dev/newstest2014.en, data/dev/test.IWSLT2016.en]
tgt_file_name: [data/dev/dev.ac_dev.de, data/dev/newstest2012.de, data/dev/newstest2016.de, data/dev/test.IWSLT2017.de, data/dev/dev.opus-100-v1.de, data/dev/newstest2014.de, data/dev/test.IWSLT2016.de]
tokens_in_batch: 2000
clean: false
max_seq_length: 512
shuffle: false
num_samples: -1
drop_last: false
pin_memory: false
num_workers: 16
test_ds:
src_file_name: [data/test/iwslt.tst2013.en-de.en, data/test/iwslt.tst2014.en-de.en, data/test/newstest2013.en, data/test/newstest2015.en, data/test/test.ac_test.en, data/test/test.opus-100-v1.en]
tgt_file_name: [data/test/iwslt.tst2013.en-de.de, data/test/iwslt.tst2014.en-de.de, data/test/newstest2013.de, data/test/newstest2015.de, data/test/test.ac_test.de, data/test/test.opus-100-v1.de]
tokens_in_batch: 2000
clean: false
max_seq_length: 512
shuffle: false
num_samples: -1
drop_last: false
pin_memory: false
num_workers: 16
optim:
name: adam
lr: 0.001
betas:
- 0.9
- 0.98
weight_decay: 0.0
sched:
name: InverseSquareRootAnnealing
min_lr: 0.0
last_epoch: -1
warmup_ratio: 0.1
encoder_tokenizer:
library: yttm
tokenizer_model: data/train/bpe.32k.model
vocab_size: 32000 # vocab size for training bpe
bpe_dropout: 0.0
vocab_file: null
special_tokens: null
training_sample_size: null # valid for sentencepiece tokenizer
r2l: false
decoder_tokenizer:
library: yttm
tokenizer_model: data/train/bpe.32k.model
vocab_size: 32000 # vocab size for training bpe
bpe_dropout: 0.0
vocab_file: null
special_tokens: null
training_sample_size: null # valid for sentencepiece tokenizer
r2l: false
encoder:
library: nemo
model_name: null
pretrained: false
max_sequence_length: 512
num_token_types: 0
embedding_dropout: 0.1
learn_positional_encodings: false
hidden_size: 1024
num_layers: 6
inner_size: 4096
num_attention_heads: 16
ffn_dropout: 0.1
attn_score_dropout: 0.1
attn_layer_dropout: 0.1
hidden_act: relu
mask_future: false
pre_ln: false
pre_ln_final_layer_norm: true
decoder:
library: nemo
model_name: null
pretrained: false
max_sequence_length: 512
num_token_types: 0
embedding_dropout: 0.1
learn_positional_encodings: false
hidden_size: 1024
inner_size: 4096
num_layers: 6
num_attention_heads: 16
ffn_dropout: 0.1
attn_score_dropout: 0.1
attn_layer_dropout: 0.1
hidden_act: relu
pre_ln: false
pre_ln_final_layer_norm: true
head:
num_layers: 1
activation: relu
log_softmax: true
dropout: 0.0
use_transformer_init: true
trainer:
devices: 4
num_nodes: 1
max_epochs: 200
precision: 32 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0
accelerator: gpu
enable_checkpointing: False
logger: false
log_every_n_steps: 50 # Interval of logging.
val_check_interval: 1000
benchmark: false
num_sanity_val_steps: 0
exp_manager:
name: nemo_ende_6x6_0.1_2k
files_to_copy: []
exp_dir: exp
create_wandb_logger: True
wandb_logger_kwargs:
project: nemo
name: ende-6x6-0.1-2k
create_checkpoint_callback: True
checkpoint_callback_params:
monitor: val_sacreBLEU
mode: max
save_top_k: 5
```
**Environment details**
- OS version: CentOS
- PyTorch version: 1.12.1
- Python version: 3.9.0
- NeMo version: Installed with pip from source using `main` branch very recently, not sure of the exact commit. Installed following the instructions in the GitHub Readme.
**Additional context**
Training on 4 NVIDIA T4 GPUs. | {
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} | Signed-off-by: sam1373 <[email protected]>
# What does this PR do ?
# Changelog
- fix typo "model_path" to "ckpt_path"
- also makes the "load_from_string" parameter optional
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
# Additional Information
* Related to [#4805](https://github.com/NVIDIA/NeMo/issues/4805)
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# What does this PR do ?
Fix nan issue with precision=16
**Collection**: ASR
# Changelog
- Clip max value of data samples after convolution with rir impulses to 1
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
Hi! When I tried to initialize weights of the model from checkpoint using configs and excluding some parts of it I get an error that model_path is not defined
I looked a the code at https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py and indeed there is an initialized variable ckpt_path, but model_path is initialized for init_from_nemo_model
Could you check if it was just accidental copy-paste and there should be ckpt_path instead of model_path
self.load_part_of_state_dict(
ckpt['state_dict'], include, exclude, f'nemo file with path `**{model_path**}`'
)
in this snippet of the code
if 'init_from_ptl_ckpt' in cfg and cfg.init_from_ptl_ckpt is not None:
with open_dict(cfg):
if isinstance(cfg.init_from_ptl_ckpt, str):
# Restore checkpoint
ckpt_path = cfg.pop('init_from_ptl_ckpt')
ckpt = torch.load(ckpt_path, map_location=map_location)
# Restore checkpoint into current model
self.load_state_dict(ckpt['state_dict'], strict=False)
logging.info(
f'Model checkpoint restored from pytorch lightning chackpoint with path : `{ckpt_path}`'
)
del ckpt
elif isinstance(cfg.init_from_ptl_ckpt, (DictConfig, dict)):
model_load_dict = cfg.init_from_ptl_ckpt
for model_load_cfg in model_load_dict.values():
ckpt_path = model_load_cfg.path
# Restore model
ckpt = torch.load(ckpt_path, map_location=map_location)
include = model_load_cfg.pop('include', [""])
exclude = model_load_cfg.pop('exclude', [])
self.load_part_of_state_dict(
ckpt['state_dict'], include, exclude, f'nemo file with path `{model_path}`'
)
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
Fix a couple minor issues in `Multispeaker_Simulator` notebook
**Collection**: Tutorials
# Changelog
- added double quotes around the version specification to make sure the expected `matplotlib` is installed
- `ls` the correct directory with created files
# Usage
* n/a
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* n/a
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} | null | null | null | Hi all. I'm struggling to run a training using Mixer-TTS-X model. I'm using the tutorial for training both Fastpitch and MixerTTS.
Modifications I've done:
```pretrained_model = "tts_en_lj_mixerttsx"```
Adding 'raw_texts' argument when generating a spectrogram:
```spectrogram = spec_gen.generate_spectrogram(tokens=tokens, raw_texts=["Hey, this produces speech!"])```
Correcting this:
```from nemo.collections.tts.torch.data import MixerTTSXDataset```
Just in case:
```
from nemo.collections.tts.torch.tts_data_types import LMTokens
from transformers.models.albert.tokenization_albert import AlbertTokenizer
```
add lm_tokenizer parameter here:
```
def pre_calculate_supplementary_data(sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs)
```
Getting the right config file:
```&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/tts/conf/mixer-tts-x.yaml```
Creating the lm_tokenizer object:
```lm_tokenizer = LMTokens()```
And after running the command:
```
mixer_tts_sup_data_path = "mixer_tts_x_sup_data_folder"
sup_data_types = ["align_prior_matrix", "pitch", "lm_tokens"]
pitch_mean, pitch_std, pitch_min, pitch_max = pre_calculate_supplementary_data(
mixer_tts_sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs
)
```
I get the following error:
```
[NeMo I 2022-08-24 22:00:27 data:216] Loading dataset from tests/data/asr/an4_train.json.
30it [00:00, 712.41it/s][NeMo I 2022-08-24 22:00:27 data:253] Loaded dataset with 30 files.
[NeMo I 2022-08-24 22:00:27 data:255] Dataset contains 0.02 hours.
[NeMo I 2022-08-24 22:00:27 data:357] Pruned 0 files. Final dataset contains 30 files
[NeMo I 2022-08-24 22:00:27 data:360] Pruned 0.00 hours. Final dataset contains 0.02 hours.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-21-b87416bfd412>](https://localhost:8080/#) in <module>
3
4 pitch_mean, pitch_std, pitch_min, pitch_max = pre_calculate_supplementary_data(
----> 5 mixer_tts_sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs
6 )
3 frames
[<ipython-input-20-7893d3034131>](https://localhost:8080/#) in pre_calculate_supplementary_data(sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs)
22 text_normalizer=text_normalizer,
23 lm_tokenizer=lm_tokenizer,
---> 24 text_normalizer_call_kwargs=text_normalizer_call_kwargs
25 )
26 stage2dl[stage] = torch.utils.data.DataLoader(ds, batch_size=1, collate_fn=ds._collate_fn, num_workers=1)
[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py](https://localhost:8080/#) in __init__(self, **kwargs)
759 class MixerTTSXDataset(TTSDataset):
760 def __init__(self, **kwargs):
--> 761 super().__init__(**kwargs)
762
763 def _albert(self):
[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py](https://localhost:8080/#) in __init__(self, manifest_filepath, sample_rate, text_tokenizer, tokens, text_normalizer, text_normalizer_call_kwargs, text_tokenizer_pad_id, sup_data_types, sup_data_path, max_duration, min_duration, ignore_file, trim, trim_ref, trim_top_db, trim_frame_length, trim_hop_length, n_fft, win_length, hop_length, window, n_mels, lowfreq, highfreq, **kwargs)
323
324 for data_type in self.sup_data_types:
--> 325 getattr(self, f"add_{data_type.name}")(**kwargs)
326
327 @staticmethod
[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py](https://localhost:8080/#) in add_lm_tokens(self, **kwargs)
785
786 def add_lm_tokens(self, **kwargs):
--> 787 lm_model = "kwargs.pop('lm_model')"
788
789 if lm_model == "albert":
KeyError: 'lm_model'
```
Any ideas are welcome.
Thanks, | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/4802 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4802/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4802/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4802/events | https://github.com/NVIDIA/NeMo/pull/4802 | 1,349,632,498 | PR_kwDOC_bI7s49urpv | 4,802 | Added P&C lexical audio model | {
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} | # What does this PR do ?
Add audio and lexical model to NeMo and necessary support.
**Collection**: nlp
# Changelog
- Added `PunctuationCapitalizationLexicalAudioModel` which is inherited from `PunctuationCapitalizationModel`.
- Extended `BertPunctuationCapitalizationDataset` and `BertPunctuationCapitalizationTarredDataset` to support audio as well.
- Added default configs stored in `examples/nlp/token_classification/conf/punctuation_capitalization_lexical_audio_config.yaml`.
- Added script to run train and evaluate at `examples/nlp/token_classification/punctuation_capitalization_lexical_audio_train_evaluate.py`.
# Usage
```
python punctuation_capitalization_lexical_audio_train_evaluate.py \
model.train_ds.ds_item=<PATH/TO/TRAIN/DATA> \
model.train_ds.text_file=<NAME_OF_TRAIN_INPUT_TEXT_FILE> \
model.train_ds.labels_file=<NAME_OF_TRAIN_LABELS_FILE> \
model.validation_ds.ds_item=<PATH/TO/DEV/DATA> \
model.validation_ds.text_file=<NAME_OF_DEV_INPUT_TEXT_FILE> \
model.validation_ds.labels_file=<NAME_OF_DEV_LABELS_FILE
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/4801 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/4801/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/4801/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/4801/events | https://github.com/NVIDIA/NeMo/pull/4801 | 1,349,490,142 | PR_kwDOC_bI7s49uM8w | 4,801 | Audio and lexical P&C model | {
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} | # What does this PR do ?
Add audio and lexical model to NeMo and necessary support.
**Collection**: nlp
# Changelog
- Added `PunctuationCapitalizationLexicalAudioModel` which is inherited from `PunctuationCapitalizationModel`.
- Extended `BertPunctuationCapitalizationDataset` and `BertPunctuationCapitalizationTarredDataset` to support audio as well.
- Added default configs stored in `examples/nlp/token_classification/conf/punctuation_capitalization_lexical_audio_config.yaml`.
- Added script to run train and evaluate at `examples/nlp/token_classification/punctuation_capitalization_lexical_audio_train_evaluate.py`.
# Usage
```
python punctuation_capitalization_lexical_audio_train_evaluate.py \
model.train_ds.ds_item=<PATH/TO/TRAIN/DATA> \
model.train_ds.text_file=<NAME_OF_TRAIN_INPUT_TEXT_FILE> \
model.train_ds.labels_file=<NAME_OF_TRAIN_LABELS_FILE> \
model.validation_ds.ds_item=<PATH/TO/DEV/DATA> \
model.validation_ds.text_file=<NAME_OF_DEV_INPUT_TEXT_FILE> \
model.validation_ds.labels_file=<NAME_OF_DEV_LABELS_FILE
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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