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} | # What does this PR do ?
Related to [bug report](https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=4312419&cmtNo=),
Chinese normalizer outputted wrong normalized text for the input, '双辽境内除东辽河、西辽河等**5**条河流', where we expect '双辽境内除东辽河、西辽河等**五**条河流'.
**Evidence**
- On the latest version, i.e. nemo-text-processing=0.2.0rc0, it always normalized it as '双辽境内除东辽河、西辽河等5条河流';
- On the previous version, i.e. nemo-text-processing=0.1.8rc0, it normalized it as expected text, '双辽境内除东辽河、西辽河等五条河流'.
**Detailed Log**
- 0.2.0rc0
```
In [1]: from nemo_text_processing.text_normalization.normalize import Normalizer
In [2]: normalizer = Normalizer(lang='zh', input_case='cased', overwrite_cache=True, cache_dir="./cache_dir")
In [3]: text = '双辽境内除东辽河、西辽河等5条河流'
In [4]: normalizer.normalize(text)
Out[4]: '双辽境内除东辽河、西辽河等5条河流'
```
- 0.1.8rc0
```
Downloading nemo_text_processing-0.1.8rc0-py3-none-any.whl (2.3 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.3/2.3 MB 12.5 MB/s eta 0:00:00
Installing collected packages: nemo-text-processing
Attempting uninstall: nemo-text-processing
Found existing installation: nemo-text-processing 0.2.0rc0
Uninstalling nemo-text-processing-0.2.0rc0:
Successfully uninstalled nemo-text-processing-0.2.0rc0
Successfully installed nemo-text-processing-0.1.8rc0
In [1]: from nemo_text_processing.text_normalization.normalize import Normalizer
In [2]: normalizer = Normalizer(lang='zh', input_case='cased', overwrite_cache=True, cache_dir="./cache_dir")
In [3]: text = '双辽境内除东辽河、西辽河等5条河流'
In [4]: normalizer.normalize(text)
Out[4]: '双辽境内除东辽河、西辽河等五条河流'
```
**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**:
- [ ] 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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} | # What does this PR do ?
Fix issue where peft weights are not loaded for distributed checkpoints
**Collection**: NLP
# Changelog
- Load peft weights in restore_from instead of _load_state_dict_from_disk if distributed checkpoint is used
# 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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} | null | null | null | **Describe the bug**
I am trying to finetune hifigan with spectrograms created with a previously finetuned FastPitch, following the tutorials: FastPitch_Finetuning.ipynb and FastPitch_GermanTTS_Training.ipynb. Finetuning Fastpitch went well, however when I try to finetune hifigan on the same data is always raises this error:
Sanity Checking: 0it [00:00, ?it/s]Error executing job with overrides: ['model.max_steps=10', 'model.optim.lr=0.00001', '~model.optim.sched', 'train_dataset=./sad_data_manifest_train_local_mel.json', 'validation_datasets=./sad_data_manifest_test_local_mel.json', 'exp_manager.exp_dir=hifigan_ft', '+trainer.val_check_interval=5', '+init_from_pretrained_model=tts_en_hifigan', 'trainer.check_val_every_n_epoch=null', 'model/train_ds=train_ds_finetune', 'model/validation_ds=val_ds_finetune']
Traceback (most recent call last):
File "/mnt/c/Users/charl/Documents/synvoice/hifigan_finetune.py", line 28, in main
trainer.fit(model)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
results = self._run_stage()
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1021, in _run_stage
self._run_sanity_check()
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1050, in _run_sanity_check
val_loop.run()
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
return loop_run(self, *args, **kwargs)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 108, in run
batch, batch_idx, dataloader_idx = next(data_fetcher)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 137, in __next__
self._fetch_next_batch(self.dataloader_iter)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fetchers.py", line 151, in _fetch_next_batch
batch = next(iterator)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 285, in __next__
out = next(self._iterator)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/utilities/combined_loader.py", line 123, in __next__
out = next(self.iterators[0])
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/_utils.py", line 694, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/site-packages/nemo/collections/tts/data/dataset.py", line 1144, in __getitem__
start = random.randint(0, mel.shape[1] - frames - 1)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/random.py", line 370, in randint
return self.randrange(a, b+1)
File "/home/charlie/anaconda3/envs/nemo/lib/python3.10/random.py", line 353, in randrange
raise ValueError("empty range for randrange() (%d, %d, %d)" % (istart, istop, width))
ValueError: empty range for randrange() (0, -12, -12)
**Steps/Code to reproduce bug**
generates mels using the generate_mels.py:
```
python generate_mels.py \
--cpu \
--fastpitch-model-ckpt {fastpitch_model_path} \
--input-json-manifests sad_data_manifest_train_local.json sad_data_manifest_test_local.json \
--output-json-manifest-root ./
```
and then started finetuning:
```
python hifigan_finetune.py --config-name=hifigan.yaml \
model.max_steps=10 model.optim.lr=0.00001 ~model.optim.sched \
train_dataset=./sad_data_manifest_train_local_mel.json \
validation_datasets=./sad_data_manifest_test_local_mel.json \
exp_manager.exp_dir=hifigan_ft \
+trainer.val_check_interval=5 \
+init_from_pretrained_model=tts_en_hifigan \
trainer.check_val_every_n_epoch=null \
model/train_ds=train_ds_finetune \
model/validation_ds=val_ds_finetune \
```
**Expected behavior**
Hifigan finetuning starts
**Environment overview (please complete the following information)**
- Method of NeMo install: python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
**Environment details**
OS: Linux 5.15.90.1-microsoft-standard-WSL2
PyTorch version: 2.2.0.dev20230913
Python version: 3.10.12 (main, Jul 5 2023, 18:54:27) [GCC 11.2.0]
**Additional context**
I am using parts of the iemocap dataset. The files differ in length, but generating the mels already pads/cuts them to roughly 6 seconds. The mels from generate_mels.py look good. I have looked into the other similar issues and therefore changed to the generate_mels.py code, but it did not fix the issue. | {
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# Changelog
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# 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"
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- [ ] Did you write any new necessary tests?
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} | # What does this PR do ?
Fixes a bug in LLaMa2 70b LoRA tuning. There was a modification to specifically deal with LLaMa2 70b (which uses GQA) in the old API that was not propagated during the refactor.
**Collection**: NLP
# Changelog
- Modified LoraPEFTConfig to exactly follow the logic in the old MegatronGPTLoRAModel
# Usage
```python
python megatron_gpt_peft_tuning.py <...> model.restore_from_path=<a llama2 70b checkpoint>
```
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
fixed trainer.devices and trainer.accelerator.
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} | * safeguard nemo_text_processing installing
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* update check
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# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
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If you haven't finished some of the above items you can still open "Draft" PR.
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} | null | null | null | Hello everyone, I trained a model with Text_Classification_Sentiment_Analysis.ipynb and I would like to know if I can extract the scores for each sentiment e.g.:
"I answered a call" is a neutral phrase, what score does the engine understand that this phrase is neutral rather than positive or negative?
Is it possible to obtain this information?
I'm using extraction this way
```
model = nemo_nlp.models.TextClassificationModel.restore_from("path/model")
l_results = model.classifytext(queries=keys_list, batch_size=3, max_seq_length=512)
``` | {
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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
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**Pre checks**:
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## 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 ?
Fixes an issue with mutable defaults inside dataclasses across NeMo configs
Fixes #7166
**Collection**: [Core, ASR, NLP, TTS, Tools]
# Changelog
- Guard mecab and ipadic
- Remove dependence on mecab and ipadic from sacrebleu
- Fix dataclasses across domains and tools
# 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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} | # What does this PR do ?
Updating metadata and docs. Changing buttons on home page.
**Collection**: NeMo github io website
# Changelog
- explain `<!-- more -->` tag in template and README
- update date of the Numba post
- update the OpenGraph metadata of the NFA, alignment and Numba blog posts
- add documentation of OpenGraph metadata in the README
- reorder the metadata in the blog posts so it's a bit tidier
- change 3 ASR/Megatron/TTS buttons on the homepage to one 'Documentation' button.
# 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?
- [x] 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)
- [ ] 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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* Related to # (issue)
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} | # What does this PR do ?
Add logging of loss by adding to validation_outputs (self.log) for ssl_models
**Collection**: ASR
# Changelog
- Add to self.log by setting to validation_step_outputs
# 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?
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- [ ] New Feature
- [x] Bugfix
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} | # What does this PR do ?
`special_tokens` are currently ignored when constructing a tokenizer for `transformer_lm_model` using the following example: https://github.com/NVIDIA/NeMo/blob/5cb76a53f21d7470773a80d62fd513defa24ddbf/examples/nlp/language_modeling/conf/bert_pretraining_from_text_config.yaml#L51
**Collection**: NLP/language-modeling
# Changelog
- transformer_lm_model: use `special_tokens` from config when creating the tokenizer, making it backward-compatible with previous behavior (when `special_tokens` are not provided we proceed with `None`).
# Usage
* https://github.com/NVIDIA/NeMo/blob/5cb76a53f21d7470773a80d62fd513defa24ddbf/examples/nlp/language_modeling/conf/bert_pretraining_from_text_config.yaml#L51
# 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.
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* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7612 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7612/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7612/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7612/events | https://github.com/NVIDIA/NeMo/pull/7612 | 1,924,475,480 | PR_kwDOC_bI7s5bzwt0 | 7,612 | Fix metrics for SE tutorial | {
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} | # What does this PR do ?
This PR fixes a bug in SE tutorial caused by initialization of metrics for audio-to-audio models.
**Collection**: ASR, Tutorials
# Changelog
- Updated metric initialization
- Small fixes in the tutorial notebook
# 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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- [ ] 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 ?
Add logging of loss by adding to validation_outputs (self.log) for ssl_models
**Collection**: ASR
# Changelog
- Add to self.log by setting to validation_step_outputs
# 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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**PR Type**:
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} | null | null | null | **Describe the bug**
I'm trying to RLHF Llama 2 70b w/ trlX NeMo. A required step of this is to convert llama 2 70b to the nemo format and perform tensor parallel sharding.
Currently, conversion works fine. I can get a copy of llama 70b that loads within nemo, albeit without tensor parallel sharding. When I run `megatron_change_num_partitions.py` on my checkpoint though, and attempt to change the number of TP shards from 1 to 8, I get the following error:
```[NeMo I 2023-10-02 15:58:58 megatron_change_num_partitions:352] Writing pp rank 0 tp rank 7 to file /home-dir/nemo_checkpoints/SB2_8tp
Invalid access pattern for ShardedTensor(key='model.embedding.word_embeddings.weight'): tensor([[1],
[0],
[0],
[0],
[0],
[0],
[0],
[0]], dtype=torch.int32)
Traceback (most recent call last):
File "/home-dir/nemo-trlx/NeMo/examples/nlp/language_modeling/megatron_change_num_partitions.py", line 1502, in <module>
main()
File "/home-dir/nemo-trlx/NeMo/examples/nlp/language_modeling/megatron_change_num_partitions.py", line 1455, in main
global_offset = split_partition(
File "/home-dir/nemo-trlx/NeMo/examples/nlp/language_modeling/megatron_change_num_partitions.py", line 746, in split_partition
write_tp_pp_split(model, splits, app_state, tp_size, pp_rank, write_path)
File "/home-dir/nemo-trlx/NeMo/examples/nlp/language_modeling/megatron_change_num_partitions.py", line 353, in write_tp_pp_split
model.save_to(write_path)
File "/opt/NeMo/nemo/core/classes/modelPT.py", line 392, in save_to
self._save_restore_connector.save_to(self, str(save_path)) # downstream tasks expect str, not Path
File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 480, in save_to
dist_checkpointing.save(sharded_state_dict=sharded_state_dict, checkpoint_dir=dist_ckpt_dir)
File "/opt/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 160, in save
validate_sharding_integrity(sharded_tensors)
File "/opt/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 217, in validate_sharding_integrity
_validate_sharding_for_key(shardings)
File "/opt/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 245, in _validate_sharding_for_key
raise CheckpointingException(f'Invalid access pattern for {rank_sharding[0][1]}')
```
**Steps/Code to reproduce bug**
1. Download llama 2 70b
2. Create a copy of the llama 2 config that is provided [here](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_llama_config.yaml) and tweak it for 70b as opposed to 7b. Details on how to do so are provided within the configuration file.
3. Run `convert_hf_llama_to_nemo.py` within NeMo's docker container (in my case, on a node of H100s).
4. Point `megatron_change_num_partitions.py` to the tar created by the conversion process. (Note that as it stands right now, you actually cannot run this script on extracted tars. It does not work. See [here](https://github.com/NVIDIA/NeMo/blob/f477e051ec68aaa909ca891c1605383f87e11fbb/examples/nlp/language_modeling/megatron_change_num_partitions.py#L986). It will attempt to extract the tar regardless of parameters.)
5. Get trace/error provided above during the saving sequence after sharding has been completed (?)
**Expected behavior**
I expect a fully sharded model to be produced at the end, however this obviously does not occur.
**Environment overview (please complete the following information)**
- Environment location: Lambda Labs, NeMo GAI Docker Image (nvcr.io/ea-bignlp/ga-participants/nemofw-training:23.08.02)
- Method of NeMo install: [pip install or from source]. Docker
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} | # What does this PR do ?
Append output of val step to `self.validation_step_outputs` in `ThutmoseTaggerModel` and `T5G2PModel` as required by lightning 2.0. Also removes `outputs` arg from `on_validation_epoch_end` and `on_test_epoch_end` in `ThutmoseTaggerModel`
**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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Add a one line overview of what this PR aims to accomplish.
**Collection**: NeMo's Github io webpage
# Changelog
- Move from using mkdocs-material nav functionality to built-in blog plug-in for hosting blog posts
- Move 'posts' and 'technical blog' into the same place.
# 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)
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- [TO DO] 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)
- [ ] 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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This PR fixes a bug in SE tutorial caused by initialization of metrics for audio-to-audio models.
**Collection**: ASR, Tutorials
# Changelog
- Updated metric initialization
- Small fixes in the tutorial notebook
# 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.
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
No
**Describe the solution you'd like**
A way to export to models like ECAPA-TDNN to an mlmodel/mlpackage so we can add it easily to our iOS applications
Provide a code snippet on how new APIs/changes would be used by others.
**Describe alternatives you've considered**
I already traced the model and used Libtorch, it works well, but I believe having a mlpackage will reduce the size of the model and the performance will be better. Without forgetting how easy the implementation is.
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- Update lower_case and batch_size arguments examples for preprocess_text.py
- Add dataset name to path of logged audio.
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- [ ] 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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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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
- [ ] 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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**Collection**: [Note which collection this PR will affect]
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
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**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?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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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?
Specified a branch in the tutorial
**Collection**: [Note which collection this PR will affect]
# Changelog
replaced "!git clone https://github.com/NVIDIA/NeMo"
with
"BRANCH = 'main'
!git clone -b $BRANCH https://github.com/NVIDIA/NeMo"
# 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**:
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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?
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Append output of val step to `self.validation_step_outputs` in `ThutmoseTaggerModel` and `T5G2PModel` as required by lightning 2.0. Also removes `outputs` arg from `on_validation_epoch_end` and `on_test_epoch_end` in `ThutmoseTaggerModel`
**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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* Related to # (issue)
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} | # What does this PR do ?
update build for rtd docs | {
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} | null | null | null | After running install_beamsearch_decoders.sh, testing script starts endless loop. I am using Ubuntu 22.04 with Python 3.10.
The loop is somewhere in this code:
`with self.autocast():
with torch.no_grad():
if isinstance(self.asr_model, EncDecHybridRNNTCTCModel):
self.asr_model.cur_decoder = 'ctc'
all_logits = self.asr_model.transcribe([audio_file], logprobs=True, verbose = 0)
preds = np.argmax(all_logits[0], axis=1)
preds_tensor = torch.tensor(preds, device=self._device).unsqueeze(0)
if isinstance(self.asr_model, EncDecHybridRNNTCTCModel):
pred_text = self.asr_model.ctc_decoding.ctc_decoder_predictions_tensor(preds_tensor)[0][0]
else:
pred_text = self.asr_model._wer.decoding.ctc_decoder_predictions_tensor(preds_tensor)[0][0]
#self.asr_model = self.asr_model.to('cpu')
decoding = self.toggle_decoder("beam")
probs_lens = torch.tensor([all_logits[0].shape[0]])
with torch.no_grad():
packed_batch = torch.cat([torch.tensor(all_logits[0], device=self._device, dtype=torch.float32).unsqueeze(0)], dim=1)
hypotheses = decoding.ctc_decoder_predictions_tensor(packed_batch, decoder_lengths=probs_lens, return_hypotheses=True)`
I also tried to setup environment on Python 3.8, 3.9 and 3.11, either gives "install_beamsearch_decoders.sh not installed " error, or starts endless loop.
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Recently facing too many warning message during model training like `NOTE! Installing ujson may make loading annotations faster.`. Went over a little on `ujson` vs `json`, it seems a worth to switch mostly because `ujson` is faster,
ref: https://github.com/ultrajson/ultrajson
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Updates the base model config so it will use the right mp config.
**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
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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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- [ ] 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.
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ensure that all examples in a batch generate only `tokens_to_generate` number of tokens.
**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**:
- [ ] 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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Updates the base model config so it will use the right mp config.
**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?
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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
- [ ] 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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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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# Usage
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# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
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- [ ] 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)
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- [ ] Documentation
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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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**PR Type**:
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} | Broadcast train and validation loss only when pipeline parallel size is larger than 1.
Also, the loss broadcasting only within the pipeline parallel domain.
# 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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* Related to # (issue)
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} | auto trainer strategy set to "auto"
# What does this PR do ?
Fix a simple bug in a example, config.trainer.strategy = auto changed to config.trainer.strategy = 'auto'
that was interrupting the execution of the code
**Collection**: [Note which collection this PR will affect]
Speaker_Diarization_Training.ipynb
tutorial example
# Changelog
- Add specific line by line info of high level changes in this PR.
config.trainer.strategy = auto changed to config.trainer.strategy = 'auto'
# 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)
- [x ] Did you write any new necessary tests? Ran the notebook
- [ ] Did you add or update any necessary documentation? No need
- [ ] 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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* Related to # (issue)
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Change strategy from ddp to auto because of PL 2.0
**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)
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- [x] Bugfix
- [ ] Documentation
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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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Fix typos
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# Changelog
- Fix typos in NFA and Speech Enhancement tutorials
# Usage
N/A
# Before your PR is "Ready for review"
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- [N/A] Did you write any new necessary tests?
- [N/A] Did you add or update any necessary documentation?
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- [ ] 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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Fixes an issue with mutable defaults inside dataclasses across NeMo configs
Fixes #7166
**Collection**: [Core, ASR, NLP, TTS, Tools]
# Changelog
- Guard mecab and ipadic
- Remove dependence on mecab and ipadic from sacrebleu
- Fix dataclasses across domains and tools
# 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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**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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Fix typos
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# Changelog
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- [N/A] Did you add or update any necessary documentation?
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- [ ] 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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# Usage
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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?
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- [ ] New Feature
- [x] 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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} | Broadcast train and validation loss only when pipeline parallel size is larger than 1.
Also, the loss broadcasting only within the pipeline parallel domain.
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} | # What does this PR do ?
strategy should be replaced as `ddp_find_unused_parameters_true`.
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# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
Appends val/test output to self.validation/test_step_outputs in `evaluation_step` method of `EncDecSpeakerLabelModel` and `EncMaskDecAudioToAudioModel`
Add a one line overview of what this PR aims to accomplish.
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# Changelog
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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**:
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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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[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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"merged_at": "2023-09-29T18:12:43"
} | # What does this PR do ?
Correctly handle initialization of validation_step_outputs and test_step_outputs (to log metrics) when multiple dataloaders are set after initialization of model.
**Collection**:
Core
# Changelog
In modelPT.py -
```python
@property
def validation_step_outputs(self):
"""
Cached outputs of validation_step. It can be a list of items (for single data loader) or a list of lists
(for multiple data loaders).
Returns:
List of outputs of validation_step.
"""
if self._validation_step_outputs is not None:
return self._validation_step_outputs
# Initialize new output list
self._validation_step_outputs = []
# Check len(self._validation_dl) > 1 as sometimes single dataloader can be in a list: [<Dataloader obj>] when ds_item in
# config has 1 item passed in a list
if (
self._validation_dl is not None
and isinstance(self._validation_dl, (list, tuple))
and len(self._validation_dl) > 1
):
for _ in range(len(self._validation_dl)):
self._validation_step_outputs.append([])
return self._validation_step_outputs
@validation_step_outputs.setter
def validation_step_outputs(self, value):
self._validation_step_outputs = value
@property
def test_step_outputs(self):
"""
Cached outputs of test_step. It can be a list of items (for single data loader) or a list of lists (for multiple data loaders).
Returns:
List of outputs of test_step.
"""
if self._test_step_outputs is not None:
return self._test_step_outputs
# Initialize new output list
self._test_step_outputs = []
# Check len(self._test_dl) > 1 as sometimes single dataloader can be in a list: [<Dataloader obj>] when ds_item in
# config has 1 item passed in a list
if self._test_dl is not None and isinstance(self._test_dl, (list, tuple)) and len(self._test_dl) > 1:
for _ in range(len(self._test_dl)):
self._test_step_outputs.append([])
return self._test_step_outputs
@test_step_outputs.setter
def test_step_outputs(self, value):
self._test_step_outputs = value
def _cleanup_on_execution_end(self):
"""
Utility function to clean up the module state at the end of execution.
"""
# dynamic freezing cleanup
if hasattr(self, '_freeze_cfg'):
delattr(self, '_freeze_cfg')
# Clear up the val and test output caches
self._validation_step_outputs = None
self._test_step_outputs = None
def on_test_end(self):
""" PyTorch Lightning hook:
https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-test-end
"""
self._cleanup_on_execution_end()
def on_predict_end(self):
""" PyTorch Lightning hook:
https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-test-end
"""
self._cleanup_on_execution_end()
```
# 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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} | # What does this PR do ?
Update docstring describing `length_params.max_length` to say that generated sequences may actually exceed this length, since this can be surprising.
**Collection**: nlp
# Changelog
- Clarify that `length_params.max_length` is not a hard constraint
# 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
- [ ] 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.
# Additional Information
I need someone to confirm whether the same doc change should be applied to this `asr` file, that seems to be copied from `nlp`:
https://github.com/NVIDIA/NeMo/blob/82e1f4f9f1a280edb70088bf30bf0a099152e185/nemo/collections/asr/modules/transformer/text_generation.py#L79
@stevehuang52 would you be able to confirm by any chance? | {
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} | # What does this PR do ?
See attached html snippets for the results.
**README**

**Documentation**

**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**:
- [ ] 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 ?
strategy should be replaced as `ddp_find_unused_parameters_true`.
**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**:
- [ ] 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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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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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**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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Passes layernorm_zero_centered_gamma param properly which fixes layernorm1p issue.
**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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This is a WAR for the case of saving a checkpoint twice into the same directory.
A proper fix would be avoiding calling the `save_checkpoint` method altogether.
**Collection**: [Note which collection this PR will affect]
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# Before your PR is "Ready for review"
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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?
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- [ ] 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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* Related to # (issue)
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**Collection**: [Note which collection this PR will affect]
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# Usage
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# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
Fix the image loaded when the post is created
**Collection**: [Docs]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Appends val/test output to self.validation/test_step_outputs in `evaluation_step` method of `EncDecSpeakerLabelModel` and `EncMaskDecAudioToAudioModel`
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?
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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 ?
This PR adds explanation for the bug fix (actual fix in #7308)
If user specifies at least one validation metric in the config that is not loss, the average_metric should be calculated and logged so that the CheckpointMonitor does not through an error.
**Collection**: [Note which collection this PR will affect]
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 #7308
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} | # What does this PR do ?
**Change 1**
In jupyter notebook, when trying to run,
```
## Install NeMo
BRANCH = 'r1.21.0'
!python -m pip install "git+https://github.com/NVIDIA/NeMo.git@${BRANCH}#egg=nemo_toolkit[all]"
```
It will get below errors due to `${BRANCH}`. Removing the curly braces fixes the error.
```shell
DEPRECATION: git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617
Collecting nemo_toolkit[all]
Cloning https://github.com/NVIDIA/NeMo.git (to revision .21.0) to /tmp/pip-install-gzajrtok/nemo-toolkit_d9d14f8e02594d728021197cbfbd43ee
Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA/NeMo.git /tmp/pip-install-gzajrtok/nemo-toolkit_d9d14f8e02594d728021197cbfbd43ee
WARNING: Did not find branch or tag '.21.0', assuming revision or ref.
Running command git checkout -q .21.0
error: pathspec '.21.0' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q .21.0 did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q .21.0 did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
\nRemember to restart the runtime for the kernel to pick up any upgraded packages (e.g. matplotlib)!\nAlternatively, you can uncomment the exit() below to crash and restart the kernel, in the case\nthat you want to use the "Run All Cells" (or similar) option.\n
```
**Change 2**
remove pynini installation since it is now a dependency of nemo-text-processing lib.
**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**:
- [ ] 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 ?
Add fp32 rope pos idx
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
`+model.enforce_fp32_pos_idx=true` to enable using fp32 rope pos index. By default we set this param to `True`.
# 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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} | null | null | null | Hello!
I have run multiscale clustering diarization on some short audios (30s-60s) with either 1 or 2 speakers. They have a little noise in the background/music, but I wouldn't say it's a big SNR. However, each time the diarizer detects 8-10 speakers on these audios.
Unfortunately, I am not able to share the recordings, but do you happen to have any suggestions on tackling diarization on such short audios with noise?
I have tried to use `maj_vote_spk_count`=True, but I ran in another problem for other types of audios: when number of speakers is high (5-8 speakers), when maj_vote=True, it detects only 2-3 speakers..
This is the config I use:
```
# This YAML file is created for all types of offline speaker diarization inference tasks in `<NeMo git root>/example/speaker_tasks/diarization` folder.
# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file.
# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used.
# The configurations in this YAML file is suitable for 3~5 speakers participating in a meeting and may not show the best performance on other types of dialogues.
# An example line in an input manifest file (`.json` format):
# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"}
name: &name "ClusterDiarizer"
config_name: "nemo_meet"
num_workers: 1
sample_rate: 16000
batch_size: 64
diarizer:
manifest_filepath: null # inainte era ???, acum il obtinem din prepare_data in zevo_clustering.py
out_dir: null # inainte era ???, acum il obtinem din prepare_data in zevo_clustering.py
oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps
collar: 0.25 # Collar value for scoring
ignore_overlap: False # Consider or ignore overlap segments while scoring
vad:
model_path: 'nemo_models/vad_multilingual_marblenet.nemo' # .nemo local model path or pretrained VAD model name
external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set)
window_length_in_sec: 0.63 # Window length in sec for VAD context input
shift_length_in_sec: 0.08 # Shift length in sec for generate frame level VAD prediction
smoothing: False # False or type of smoothing method (eg: median)
overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter
onset: 0.9 # Onset threshold for detecting the beginning and end of a speech
offset: 0.5 # Offset threshold for detecting the end of a speech
pad_onset: 0 # Adding durations before each speech segment
pad_offset: 0 # Adding durations after each speech segment
min_duration_on: 0.5 # Threshold for small speech deletion
min_duration_off: 0.6 # Threshold for short non_speech segment deletion
filter_speech_first: True
speaker_embeddings:
model_path: 'nemo_models/titanet-s.nemo' # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
parameters:
window_length_in_sec: [3.5,3.0,2.5,2.0,1.5,1.0,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5]
shift_length_in_sec: [1.75,1.5,1.25,1.0,0.75,0.5,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25]
multiscale_weights: [1,1,1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33]
save_embeddings: False # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`.
clustering:
parameters:
oracle_num_speakers: True # If True, use num of speakers value provided in manifest file.
max_num_speakers: 12 # Note: maybe decrease back to 8; Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored.
enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
sparse_search_volume: 30 # The higher the number, the more values will be examined with more time.
maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers.
msdd_model:
model_path: null # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD)
parameters:
use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used.
infer_batch_size: 25 # Batch size for MSDD inference.
sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps.
seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False.
split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference.
diar_window_length: 50 # The length of split short sequence when split_infer is True.
overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated.
```
Thank you in advance!
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2. Changed some wording | {
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} | # What does this PR do ?
This is a WAR for the case of saving a checkpoint twice into the same directory.
A proper fix would be avoiding calling the `save_checkpoint` method altogether.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Avoid duplicated dist checkpoint save
# 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 ?
**Change 1**
In jupyter notebook, when trying to run,
```
## Install NeMo
BRANCH = 'r1.21.0'
!python -m pip install "git+https://github.com/NVIDIA/NeMo.git@${BRANCH}#egg=nemo_toolkit[all]"
```
It will get below errors due to `${BRANCH}`. Removing the curly braces fixes the error.
```shell
DEPRECATION: git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617
Collecting nemo_toolkit[all]
Cloning https://github.com/NVIDIA/NeMo.git (to revision .21.0) to /tmp/pip-install-gzajrtok/nemo-toolkit_d9d14f8e02594d728021197cbfbd43ee
Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA/NeMo.git /tmp/pip-install-gzajrtok/nemo-toolkit_d9d14f8e02594d728021197cbfbd43ee
WARNING: Did not find branch or tag '.21.0', assuming revision or ref.
Running command git checkout -q .21.0
error: pathspec '.21.0' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q .21.0 did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q .21.0 did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
\nRemember to restart the runtime for the kernel to pick up any upgraded packages (e.g. matplotlib)!\nAlternatively, you can uncomment the exit() below to crash and restart the kernel, in the case\nthat you want to use the "Run All Cells" (or similar) option.\n
```
**Change 2**
remove pynini installation since it is now a dependency of nemo-text-processing lib.
**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**:
- [ ] 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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* Related to # (issue)
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} | # What does this PR do ?
Fixes an issue with Torchaudio trying to load latest versions of ffmpeg (7+)
**Collection**: [ASR]
# 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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Adds LoRA support to the linear transformations in the MLP layer.
**Collection**: [NLP]
# Changelog
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# Usage
* Add target_modules: ['attention', 'mlp'] to the lora_tuning field in the config file
```python
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# Before your PR is "Ready for review"
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## Who can review?
@arendu
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
- [ ] Documentation
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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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# Before your PR is "Ready for 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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"merged_at": "2023-10-02T16:55:07"
} | # 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 ?
Support pure fp32 for rope pos embedding
**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 ?
In tutorials/asr/Multilang_ASR.ipynb, added the correct initialization of asr_model.validation_step_outputs for the case of multiple dataloaders. This is in accordance with the changes introduced in https://github.com/NVIDIA/NeMo/pull/7533 and is normally taken care of by modelPT __init__ (https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py#L185)
**Collection**:
ASR
# Changelog
In the section "Creating training / validation sets" of the notebook -
```python
asr_model.setup_multiple_validation_data(val_data_config=validation_ds)
# Correctly setting up validation_step_outputs for multi dataloader, orifinally taken care of in ModelPT.__init__()
if asr_model._validation_dl and type(asr_model._validation_dl) == list and len(asr_model._validation_dl) > 1:
for _ in range(len(asr_model._validation_dl)):
asr_model.validation_step_outputs.append([])
```
# 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
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} | # What does this PR do ?
update gpus -> devices in notebook | {
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} | null | null | null | from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import MegatronGPTPromptLearningModel
AttributeError: 'MegatronGPTPromptLearningModel' object has no attribute 'frozen_model'
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_base_prompt_learning_model.py
```
def load_frozen_model(self, cfg, trainer):
pass
```
empty. Any idea? | {
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**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
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# Before your PR is "Ready for review"
**Pre checks**:
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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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**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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* Related to # (issue)
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* To be used in CI
# Before your PR is "Ready for review"
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- [ ] Documentation
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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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"html_url": "https://github.com/XuesongYang",
"followers_url": "https://api.github.com/users/XuesongYang/followers",
"following_url": "https://api.github.com/users/XuesongYang/following{/other_user}",
"gists_url": "https://api.github.com/users/XuesongYang/gists{/gist_id}",
"starred_url": "https://api.github.com/users/XuesongYang/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/XuesongYang/subscriptions",
"organizations_url": "https://api.github.com/users/XuesongYang/orgs",
"repos_url": "https://api.github.com/users/XuesongYang/repos",
"events_url": "https://api.github.com/users/XuesongYang/events{/privacy}",
"received_events_url": "https://api.github.com/users/XuesongYang/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
} | [
{
"login": "XuesongYang",
"id": 1646669,
"node_id": "MDQ6VXNlcjE2NDY2Njk=",
"avatar_url": "https://avatars.githubusercontent.com/u/1646669?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/XuesongYang",
"html_url": "https://github.com/XuesongYang",
"followers_url": "https://api.github.com/users/XuesongYang/followers",
"following_url": "https://api.github.com/users/XuesongYang/following{/other_user}",
"gists_url": "https://api.github.com/users/XuesongYang/gists{/gist_id}",
"starred_url": "https://api.github.com/users/XuesongYang/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/XuesongYang/subscriptions",
"organizations_url": "https://api.github.com/users/XuesongYang/orgs",
"repos_url": "https://api.github.com/users/XuesongYang/repos",
"events_url": "https://api.github.com/users/XuesongYang/events{/privacy}",
"received_events_url": "https://api.github.com/users/XuesongYang/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
}
] | null | null | 2023-09-27T14:00:03 | 2023-09-28T05:09:16 | 2023-09-28T05:08:59 | NONE | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | **Describe the bug**
I'm trying to processe text normalization for HUI German Dataset but i get error like below. Does anyone have idea to resolve this issue?
**Steps/Code to reproduce bug**
```
(nemo) ubuntu@HP:/mnt/e/tools/nemo$ python /mnt/e/tools/NeMo/get_data.py --data-root /mnt/f/hui_acg --manifests-root /mnt/f/hui_acg/ --normalize-text
[NeMo I 2023-09-27 13:31:45 get_data:105] Skipped downloading data because it exists: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/datasetStatisticClean.zip
[NeMo I 2023-09-27 13:31:45 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/datasetStatisticClean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:32:21 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/datasetStatisticClean.zip.
[NeMo I 2023-09-27 13:32:21 get_data:100] Downloading data: https://opendata.iisys.de/opendata/Datasets/HUI-Audio-Corpus-German/dataset_clean/Friedrich_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Friedrich_Clean.zip
[NeMo I 2023-09-27 13:32:21 get_data:105] Skipped downloading data because it exists: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Bernd_Ungerer_Clean.zip
[NeMo I 2023-09-27 13:32:21 get_data:105] Skipped downloading data because it exists: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Karlsson_Clean.zip
[NeMo I 2023-09-27 13:32:21 get_data:105] Skipped downloading data because it exists: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/others_Clean.zip
[NeMo I 2023-09-27 13:32:21 get_data:105] Skipped downloading data because it exists: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Eva_K_Clean.zip
[NeMo I 2023-09-27 13:32:21 get_data:105] Skipped downloading data because it exists: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Hokuspokus_Clean.zip
[NeMo I 2023-09-27 13:48:43 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Bernd_Ungerer_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:48:43 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Eva_K_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:48:43 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Friedrich_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:48:43 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Hokuspokus_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:48:43 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Karlsson_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:48:43 get_data:109] Unzipping data: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/others_Clean.zip --> /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean
[NeMo I 2023-09-27 13:55:58 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Eva_K_Clean.zip.
[NeMo I 2023-09-27 13:56:16 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Friedrich_Clean.zip.
[NeMo I 2023-09-27 13:57:41 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Hokuspokus_Clean.zip.
[NeMo I 2023-09-27 14:24:01 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Karlsson_Clean.zip.
[NeMo I 2023-09-27 14:44:50 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/others_Clean.zip.
[NeMo I 2023-09-27 14:55:32 get_data:111] Unzipping data is complete: /mnt/f/hui_acg/HUI-Audio-Corpus-German-clean/Bernd_Ungerer_Clean.zip.
[NeMo I 2023-09-27 14:55:32 get_data:263] Processing Speaker: Alexandra_Bogensperger
[NeMo I 2023-09-27 14:55:32 get_data:124] Preparing JSON split for speaker 1.
545it [00:00, 33618.58it/s]
[NeMo I 2023-09-27 14:55:32 get_data:161] Preparing JSON split for speaker 1 is complete.
[NeMo I 2023-09-27 14:55:32 get_data:263] Processing Speaker: Algy_Pug
[NeMo I 2023-09-27 14:55:32 get_data:124] Preparing JSON split for speaker 2.
141it [00:00, 33753.60it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 2 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: AliceDe
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 3.
4it [00:00, 6700.17it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 3 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Anastasiia_Solokha
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 4.
4it [00:00, 9714.66it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 4 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Anka
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 5.
471it [00:00, 30448.79it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 5 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Anna_Samrowski
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 6.
5it [00:00, 8859.96it/s]
[NeMo W 2023-09-27 14:55:33 get_data:158] Skipped speaker 6. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Anna_Simon
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 7.
19it [00:00, 20159.82it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 7 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Anne
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 8.
72it [00:00, 28945.64it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 8 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Antoinette_Huting
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 9.
55it [00:00, 25470.54it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 9 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Anton
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 10.
21it [00:00, 23227.95it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 10 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Apneia
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 11.
10it [00:00, 12136.30it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 11 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Availle
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 12.
532it [00:00, 29288.45it/s]
[NeMo I 2023-09-27 14:55:33 get_data:161] Preparing JSON split for speaker 12 is complete.
[NeMo I 2023-09-27 14:55:33 get_data:263] Processing Speaker: Bernd_Ungerer
[NeMo I 2023-09-27 14:55:33 get_data:124] Preparing JSON split for speaker 13.
32880it [00:01, 28967.69it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 13 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: Boris
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 14.
257it [00:00, 26216.31it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 14 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: Capybara
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 15.
383it [00:00, 35107.60it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 15 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: caromopfen
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 16.
856it [00:00, 31935.28it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 16 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: Cate_Mackenzie
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 17.
12it [00:00, 18497.48it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 17 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: Christian_Al-Kadi
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 18.
408it [00:00, 30715.92it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 18 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: Christina_Lindgruen
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 19.
16it [00:00, 23522.21it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 19 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: ClaudiaSterngucker
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 20.
323it [00:00, 25071.44it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 20 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: ColOhr
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 21.
93it [00:00, 25849.59it/s]
[NeMo I 2023-09-27 14:55:34 get_data:161] Preparing JSON split for speaker 21 is complete.
[NeMo I 2023-09-27 14:55:34 get_data:263] Processing Speaker: Crln_Yldz_Ksr
[NeMo I 2023-09-27 14:55:34 get_data:124] Preparing JSON split for speaker 22.
536it [00:00, 31930.28it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 22 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: DanielGrams
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 23.
29it [00:00, 25425.34it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 23 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: danio
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 24.
3it [00:00, 4822.89it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 24 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Desirée_Löffler
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 25.
18it [00:00, 21832.70it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 25 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Dini_Steyn
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 26.
2it [00:00, 3563.55it/s]
[NeMo W 2023-09-27 14:55:35 get_data:158] Skipped speaker 26. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Dirk_Weber
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 27.
137it [00:00, 26192.89it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 27 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: DomBombadil
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 28.
183it [00:00, 28871.83it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 28 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Eki_Teebi
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 29.
139it [00:00, 28998.17it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 29 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: ekyale
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 30.
224it [00:00, 28817.11it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 30 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Elli
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 31.
286it [00:00, 25192.60it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 31 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Eva_K
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 32.
8539it [00:00, 22756.44it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 32 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Fabian_Grant
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 33.
32it [00:00, 31018.66it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 33 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: fantaeiner
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 34.
200it [00:00, 31004.61it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 34 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Franziska_Paul
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 35.
60it [00:00, 28807.03it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 35 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: fremdschaemen
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 36.
28it [00:00, 30432.89it/s]
[NeMo I 2023-09-27 14:55:35 get_data:161] Preparing JSON split for speaker 36 is complete.
[NeMo I 2023-09-27 14:55:35 get_data:263] Processing Speaker: Friedrich
[NeMo I 2023-09-27 14:55:35 get_data:124] Preparing JSON split for speaker 37.
9590it [00:00, 31844.18it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 37 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Frown
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 38.
209it [00:00, 28455.81it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 38 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Gaby
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 39.
7it [00:00, 12671.61it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 39 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Gesine
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 40.
1it [00:00, 2270.87it/s]
[NeMo W 2023-09-27 14:55:36 get_data:158] Skipped speaker 40. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: heeheekitty
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 41.
81it [00:00, 25623.25it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 41 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Herman_Roskams
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 42.
70it [00:00, 26438.66it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 42 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Herr_Klugbeisser
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 43.
43it [00:00, 18635.57it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 43 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Hokuspokus
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 44.
10584it [00:00, 31652.75it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 44 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Igor_Teaforay
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 45.
215it [00:00, 29591.63it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 45 is complete.
[NeMo I 2023-09-27 14:55:36 get_data:263] Processing Speaker: Imke_Grassl
[NeMo I 2023-09-27 14:55:36 get_data:124] Preparing JSON split for speaker 46.
136it [00:00, 21508.44it/s]
[NeMo I 2023-09-27 14:55:36 get_data:161] Preparing JSON split for speaker 46 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Ingo_Breuer
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 47.
114it [00:00, 23795.69it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 47 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: IvanDean
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 48.
32it [00:00, 26854.29it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 48 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Jessi
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 49.
305it [00:00, 28271.00it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 49 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Joe_Kay
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 50.
35it [00:00, 18842.34it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 50 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: josimosi98
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 51.
6it [00:00, 12912.17it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 51 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Julia_Niedermaier
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 52.
2012it [00:00, 29746.73it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 52 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Kaktus
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 53.
4it [00:00, 5797.24it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 53 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Kalynda
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 54.
394it [00:00, 22042.89it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 54 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Kanta
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 55.
3it [00:00, 5003.15it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 55 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Kara_Shallenberg
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 56.
52it [00:00, 26223.86it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 56 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: KarinM
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 57.
307it [00:00, 20873.95it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 57 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Karlsson
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 58.
10736it [00:00, 30896.67it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 58 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: keltoi
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 59.
860it [00:00, 26357.30it/s]
[NeMo I 2023-09-27 14:55:37 get_data:161] Preparing JSON split for speaker 59 is complete.
[NeMo I 2023-09-27 14:55:37 get_data:263] Processing Speaker: Klaus_Beutelspacher
[NeMo I 2023-09-27 14:55:37 get_data:124] Preparing JSON split for speaker 60.
12it [00:00, 16496.77it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 60 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Klaus_Neubauer
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 61.
758it [00:00, 28217.40it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 61 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Knubbel
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 62.
3it [00:00, 5182.42it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 62 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Laila_Katinka
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 63.
1it [00:00, 1679.06it/s]
[NeMo W 2023-09-27 14:55:38 get_data:158] Skipped speaker 63. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Larry_Greene
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 64.
16it [00:00, 15705.33it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 64 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Lars_Rolander_(1942-2016)
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 65.
745it [00:00, 28759.04it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 65 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Lektor
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 66.
3it [00:00, 6801.57it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 66 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: leserchen
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 67.
65it [00:00, 27876.25it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 67 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: LillY
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 68.
7it [00:00, 11829.22it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 68 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: lorda
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 69.
195it [00:00, 28319.29it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 69 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: LordOider
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 70.
21it [00:00, 9642.08it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 70 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: LyricalWB
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 71.
479it [00:00, 25347.54it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 71 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: manuwolf
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 72.
9it [00:00, 14485.32it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 72 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: marham63
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 73.
1877it [00:00, 32591.92it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 73 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Markus_Wachenheim
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 74.
91it [00:00, 23905.90it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 74 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Martin_Harbecke
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 75.
102it [00:00, 22139.26it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 75 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Mat
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 76.
1it [00:00, 2383.13it/s]
[NeMo W 2023-09-27 14:55:38 get_data:158] Skipped speaker 76. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Matze
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 77.
65it [00:00, 17870.33it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 77 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: melaniesandra
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 78.
24it [00:00, 23481.06it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 78 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: merendo07
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 79.
10it [00:00, 15851.49it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 79 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: mindfulheart
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 80.
3it [00:00, 6253.93it/s]
[NeMo W 2023-09-27 14:55:38 get_data:158] Skipped speaker 80. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: Monika_M._C
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 81.
326it [00:00, 31313.68it/s]
[NeMo I 2023-09-27 14:55:38 get_data:161] Preparing JSON split for speaker 81 is complete.
[NeMo I 2023-09-27 14:55:38 get_data:263] Processing Speaker: njall
[NeMo I 2023-09-27 14:55:38 get_data:124] Preparing JSON split for speaker 82.
5it [00:00, 7492.50it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 82 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: noonday
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 83.
1it [00:00, 1560.96it/s]
[NeMo W 2023-09-27 14:55:39 get_data:158] Skipped speaker 83. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Ohrbuch
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 84.
663it [00:00, 27358.73it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 84 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: OldZach
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 85.
125it [00:00, 19049.09it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 85 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Orsina
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 86.
6it [00:00, 8425.12it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 86 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: PeWaOt
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 87.
231it [00:00, 19847.27it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 87 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Ragnar
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 88.
330it [00:00, 16557.06it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 88 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Rainer
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 89.
94it [00:00, 23163.42it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 89 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Ralf
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 90.
1it [00:00, 2559.06it/s]
[NeMo W 2023-09-27 14:55:39 get_data:158] Skipped speaker 90. Not enough data for train, val and test.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Ramona_Deininger-Schnabel
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 91.
1482it [00:00, 28377.02it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 91 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Rebecca_Braunert-Plunkett
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 92.
667it [00:00, 27243.96it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 92 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: RenateIngrid
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 93.
1274it [00:00, 27668.83it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 93 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Rhigma
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 94.
108it [00:00, 21351.10it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 94 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Robert_Steiner
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 95.
212it [00:00, 24120.23it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 95 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Rogthey
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 96.
960it [00:00, 25748.05it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 96 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Sandra_Schmit
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 97.
95it [00:00, 26779.95it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 97 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Sascha
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 98.
5it [00:00, 9035.55it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 98 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: schrm
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 99.
385it [00:00, 28601.41it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 99 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Sebastian
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 100.
125it [00:00, 29896.11it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 100 is complete.
[NeMo I 2023-09-27 14:55:39 get_data:263] Processing Speaker: Sellafield
[NeMo I 2023-09-27 14:55:39 get_data:124] Preparing JSON split for speaker 101.
10it [00:00, 15905.59it/s]
[NeMo I 2023-09-27 14:55:39 get_data:161] Preparing JSON split for speaker 101 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Shanty
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 102.
28it [00:00, 27242.06it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 102 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Silke_Britz
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 103.
45it [00:00, 21924.00it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 103 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Silmaryllis
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 104.
336it [00:00, 14878.76it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 104 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Sonia
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 105.
473it [00:00, 30093.38it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 105 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Sonja
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 106.
130it [00:00, 23313.64it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 106 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: storylines
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 107.
21it [00:00, 26804.74it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 107 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Tabea
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 108.
13it [00:00, 18958.95it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 108 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Tanja_Ben_Jeroud
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 109.
105it [00:00, 24909.61it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 109 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: thinkofelephants
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 110.
6it [00:00, 9935.19it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 110 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Traxxo
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 111.
96it [00:00, 26051.58it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 111 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Ute2013
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 112.
64it [00:00, 21387.58it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 112 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Verena
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 113.
58it [00:00, 21884.64it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 113 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Victoria_Asztaller
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 114.
165it [00:00, 29841.76it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 114 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Wolfgang
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 115.
60it [00:00, 26302.07it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 115 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Zach_K
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 116.
18it [00:00, 24020.83it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 116 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Zieraffe
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 117.
33it [00:00, 18107.28it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 117 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:263] Processing Speaker: Zue_Von_Zob
[NeMo I 2023-09-27 14:55:40 get_data:124] Preparing JSON split for speaker 118.
11it [00:00, 13261.67it/s]
[NeMo I 2023-09-27 14:55:40 get_data:161] Preparing JSON split for speaker 118 is complete.
[NeMo I 2023-09-27 14:55:40 get_data:298] Saving Speaker to ID mapping to /mnt/f/hui_acg/spk2id.csv.
[NeMo I 2023-09-27 14:55:40 get_data:115] Saving JSON split to /mnt/f/hui_acg/train_manifest.json.
[NeMo I 2023-09-27 14:55:42 get_data:115] Saving JSON split to /mnt/f/hui_acg/val_manifest.json.
[NeMo I 2023-09-27 14:55:42 get_data:115] Saving JSON split to /mnt/f/hui_acg/test_manifest.json.
[NeMo I 2023-09-27 14:56:17 get_data:196] Normalizing text for /mnt/f/hui_acg/train_manifest.json.
10%|███████▌ | 8692/86811 [28:47<7:53:04, 2.75it/s]joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/_parallel_backends.py", line 273, in _wrap_func_call
return func()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 588, in __call__
return [func(*args, **kwargs)
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 588, in <listcomp>
return [func(*args, **kwargs)
File "/mnt/e/tools/NeMo/get_data.py", line 192, in add_normalized_text
normalized_text = normalizer_call(line_dict["text"])
File "/mnt/e/tools/NeMo/get_data.py", line 189, in normalizer_call
return text_normalizer.normalize(x, **text_normalizer_call_kwargs)
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/normalize.py", line 328, in normalize
tokens = self.parser.parse()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/token_parser.py", line 53, in parse
token = self.parse_token()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/token_parser.py", line 76, in parse_token
value = self.parse_token_value()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/token_parser.py", line 98, in parse_token_value
list_token_dicts = self.parse()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/token_parser.py", line 53, in parse
token = self.parse_token()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/token_parser.py", line 67, in parse_token
key = self.parse_string_key()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/nemo_text_processing/text_normalization/token_parser.py", line 141, in parse_string_key
assert self.char not in string.whitespace and self.char != EOS
AssertionError
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/mnt/e/tools/NeMo/get_data.py", line 316, in <module>
main()
File "/mnt/e/tools/NeMo/get_data.py", line 310, in main
__text_normalization(train_json, args.num_workers)
File "/mnt/e/tools/NeMo/get_data.py", line 201, in __text_normalization
dict_list = Parallel(n_jobs=num_workers, backend="threading")(
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 1944, in __call__
return output if self.return_generator else list(output)
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 1587, in _get_outputs
yield from self._retrieve()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 1691, in _retrieve
self._raise_error_fast()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 1726, in _raise_error_fast
error_job.get_result(self.timeout)
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 735, in get_result
return self._return_or_raise()
File "/home/ubuntu/miniconda3/envs/nemo/lib/python3.10/site-packages/joblib/parallel.py", line 753, in _return_or_raise
raise self._result
AssertionError
10%|███████▋ | 8735/86811 [28:50<4:17:48, 5.05it/s]
```
**Environment overview**
- Environment location: [Local]
- Method of NeMo install: [from source].
**Environment details**
- OS version: Window 10, WSL Ubuntu-20.04
- PyTorch version: Pytorch-cuda 11.8
- Python version: Python 3.10
**Additional context**
GPU model: RTX3090
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} | # What does this PR do ?
This PR adds the tutorials for Mandarin-English Multilingual/Code-switching ASR training.
**Collection**: [ASR]
# Changelog
- Add a tutorial for ZH-EN multilingual/code-switching training
- How to prepare data, change tokenizer and finetune with pre-trained ZH-EN model etc
# 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
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Remap discrete speakers of AISHELL3 (like SSB0005, SSB0009 etc.) to continuous range of speaker_id (from 1 to 174)
# Changelog
- Remap speakers to a continuous range of speaker_id for dataset AISHELL3
# Usage
```python
from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder
from nemo.collections.tts.models import FastPitchModel, HifiGanModel
text = "这些新一代的中央处理器不只效能惊人,价格也十分有竞争力。"
device = "cpu"
fastpitch_model = FastPitchModel.restore_from("~/Downloads/aishell3.nemo").eval().to(device)
model = HifiGanModel.from_pretrained(model_name="tts_zh_hifigan_sfspeech").eval().to(device)
# Normalize the text and convert it into individual phonemes/tokens.
tokens = fastpitch_model.parse(text)
# Generate spectrogram from text
spectrogram = fastpitch_model.generate_spectrogram(tokens=tokens, speaker=1)
# Invert the spectrogram into audio samples
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
# Convert output from pytorch tensor to numpy array
audio = audio.cpu().detach().numpy()
import soundfile as sf
sf.write("sample.wav", audio.T, 22050, format="WAV")
```
Sample with `speaker=1` [001.zip](https://github.com/NVIDIA/NeMo/files/12736697/001.zip)
Sample with `speaker=100` [100.zip](https://github.com/NVIDIA/NeMo/files/12736729/100.zip)
# 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?
@XuesongYang
# Additional Information
* https://github.com/NVIDIA/NeMo/pull/7409#discussion_r1336613673
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} | # 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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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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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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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} | # What does this PR do ?
This PR added fixes for PTL2.0 related ASR bugs in r1.21.0: Val metrics logging, None dataloader issue
**Collection**:
ASR, Core
# Changelog
- val_dataloader() and test_dataloader() functions in nemo/core/classes/modelPT.py updated to handle cases where dataloader is None
- updated validation_pass(), validation_step(), test_step() in nemo/collections/asr/models/rnnt_models.py to correctly add metrics to validation_step_outputs, test_step_outputs. This is following the updates to ctc_models and hybrid_rnnt_ctc_models as done in [Upgrade to pytorch lightning 2.0](https://github.com/NVIDIA/NeMo/pull/6433/files#top)
# 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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} | # What does this PR do ?
Appends the output of `validation_step` in `GLUEModel` to the instance variable `self.validation_step_outputs` as expected by PTL 2.0.
**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 added fixes for PTL2.0 related ASR bugs in r1.21.0: Val metrics logging, None dataloader issue
**Collection**:
ASR, Core
# Changelog
- val_dataloader() and test_dataloader() functions in nemo/core/classes/modelPT.py updated to handle cases where dataloader is None
- updated validation_pass(), validation_step(), test_step() in nemo/collections/asr/models/rnnt_models.py to correctly add metrics to validation_step_outputs, test_step_outputs. This is following the updates to ctc_models and hybrid_rnnt_ctc_models as done in [Upgrade to pytorch lightning 2.0](https://github.com/NVIDIA/NeMo/pull/6433/files#top)
# 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**:
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**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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- [ ] Did you add or update any necessary documentation?
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- [x] Bugfix
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Fixes branch selection for tutorial
**Collection**: [Tools]
# 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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- [ ] New Feature
- [x] Bugfix
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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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* Related to # (issue)
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} | null | null | null | **Describe the bug**
Following the installation guide in README installs the latest transformers version. However the starter code provided here https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/neural_text_normalization_t5 fails to work with an error
```
RuntimeError: Error(s) in loading state_dict for DuplexTaggerModel:
Unexpected key(s) in state_dict: "model.albert.embeddings.position_ids".
```
**Steps/Code to reproduce bug**
[Same as in description above]
**Expected behavior**
RuntimeError in this line
```
tagger = nemo_nlp.models.duplex_text_normalization.DuplexTaggerModel.from_pretrained(model_name="neural_text_normalization_t5")
```
---
One solution that worked in my case is to re-install an earlier version of transformers, eg. 4.30.2
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