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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?
**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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Fix error in loading checkpoints. The pytorch [load_state_dict()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.load_state_dict) function has return variable that was not returned by this function in this file.
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Merging main to asr_normalize branch
**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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- [ ] Did you add or update any necessary 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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Dear Team,
I am training **conformer based SSL** model where I have configured the parameters
**min_duration : 3.2
patch_size: 16
mask_patches: 0.5
num_negatives: 40
sample_from_same_utterance_only: true
sample_from_non_masked: false
codebook_size: 300
num_groups: 2
num_classes: 90000**
but I am getting following error
**RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement**
I am using **Nemo-22.05 images(ngc) and 48, A100-SXM4-40GB GPU** i.e 6 nodes for the training. Kindly suggest me what is need to be done ? Thanks in advance | {
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} | # What does this PR do ?
Fix import of numba to prevent doc breakage.
importlib cannot actually import a module that is mocked, trying to call `module.__version__` will return a mock module object - not a string and then Version() will fail to parse that mock object.
This PR defers the attempt to find the version compatibility of FP16 to the moment the library is resolved dynamically rather than at static import time.
**Collection**: [Core]
# Changelog
- Correct numba import to be dynamic only to avoid mock import failing
- Fix NeMo version in docs and readme.md
# 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 some VAD doc and fix onnx exportable.
Had to overwrite the `forward_for_export` function because the MLP class in `collections.common` doesn't have neural types and the param list of MLP doesn't have `encoder_output` which is used in ASR models
**Collection**: [ASR] | {
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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**:
- [ ] 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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I am trying to load titanet small using the following line of code:
```
nemo_speaker_model_small = EncDecSpeakerLabelModel.from_pretrained(model_name="titanet_small")
```
Here is the error:
```FileNotFoundError: Model titanet_small was not found. Check cls.list_available_models() for the list of all available models.```
I am using the latest nemo_toolkit
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} | # What does this PR do ?
Support LLaMa model: checkpoint conversion, pretraining, SFT, PEFT
# 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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```
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**PR Type**:
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## Who can review?
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Support LLaMa model: checkpoint conversion, pretraining, SFT, PEFT
# 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?
- [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**:
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- [ ] Bugfix
- [x] 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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} | null | null | null | **Describe the bug**
**We are trying to submit a paper to the Social-IQ-2.0 challenge (ICCV2023) and the submission deadline is on July 21, we would be thankful if you can fix this quickly and release a stable version, we have troubles using the package since yesterday**
Can not load pretrained BERT model for punctuation and capitalization due to the following error:
```
RuntimeError Traceback (most recent call last)
[<ipython-input-4-5f218ff2c1dd>](https://localhost:8080/#) in <cell line: 3>()
1 print(f"Available_models: {nemo_nlp.models.PunctuationCapitalizationModel.get_available_model_names()}\n")
2
----> 3 pretrained_model = nemo_nlp.models.PunctuationCapitalizationModel.from_pretrained("punctuation_en_bert")
4 # define the list of queiries for inference
5 queries = [
4 frames
[/usr/local/lib/python3.10/dist-packages/nemo/core/classes/common.py](https://localhost:8080/#) in from_pretrained(cls, model_name, refresh_cache, override_config_path, map_location, strict, return_config, trainer, save_restore_connector)
850 )
851
--> 852 instance = class_.restore_from(
853 restore_path=nemo_model_file_in_cache,
854 override_config_path=override_config_path,
[/usr/local/lib/python3.10/dist-packages/nemo/core/classes/modelPT.py](https://localhost:8080/#) in restore_from(cls, restore_path, override_config_path, map_location, strict, return_config, save_restore_connector, trainer)
433
434 cls.update_save_restore_connector(save_restore_connector)
--> 435 instance = cls._save_restore_connector.restore_from(
436 cls, restore_path, override_config_path, map_location, strict, return_config, trainer
437 )
[/usr/local/lib/python3.10/dist-packages/nemo/core/connectors/save_restore_connector.py](https://localhost:8080/#) in restore_from(self, calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer)
246 conf, instance, state_dict = loaded_params
247 state_dict = self.modify_state_dict(conf, state_dict)
--> 248 self.load_instance_with_state_dict(instance, state_dict, strict)
249 logging.info(f'Model {instance.__class__.__name__} was successfully restored from {restore_path}.')
250 return instance
[/usr/local/lib/python3.10/dist-packages/nemo/core/connectors/save_restore_connector.py](https://localhost:8080/#) in load_instance_with_state_dict(self, instance, state_dict, strict)
201 strict: Bool, whether to perform strict checks when loading the state dict.
202 """
--> 203 instance.load_state_dict(state_dict, strict=strict)
204 instance._set_model_restore_state(is_being_restored=False)
205
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in load_state_dict(self, state_dict, strict)
2039
2040 if len(error_msgs) > 0:
-> 2041 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
2042 self.__class__.__name__, "\n\t".join(error_msgs)))
2043 return _IncompatibleKeys(missing_keys, unexpected_keys)
RuntimeError: Error(s) in loading state_dict for PunctuationCapitalizationModel:
Unexpected key(s) in state_dict: "bert_model.embeddings.position_ids".
```
**Steps/Code to reproduce bug**
```pretrained_model = nemo_nlp.models.PunctuationCapitalizationModel.from_pretrained("punctuation_en_bert")``` line on "Inference using a pretrained model" section of https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb causes this error.
**Expected behavior**
Loading the pretrained checkpoint successfully.
**Environment overview (please complete the following information)**
- Environment location: Google Colab
- Method of NeMo install: pip
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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**:
- [ ] 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 ?
This PR adds the following items related to multi-lookahead cache-aware streaming models:
+ documentation
+ support of multi-lookahead for transcribe_speech.py, transcribe_speech_parallel.py, and speech_to_text_eval.py,
+ one pre-trained model which is uploaded on NGC and supports 0s, 80ms, 480ms and 1040ms:
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_streaming_multi
# Changelog
- Added a multi-lookahead cache-aware streaming model which supports multiple look-ahead in one single model.
# Usage
You may switch between different supported look-aheads by:
1- calling the set_default_att_context_size method of the model's encoder like this:
```python
asr_model.encoder.set_default_att_context_size(att_context_size=[70,1])
```
2- set att_context_size when using transcribe_speech.py, transcribe_speech_parallel.py, or speech_to_text_eval.py.
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [X] Documentation
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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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix import of numba to prevent doc breakage.
importlib cannot actually import a module that is mocked, trying to call `module.__version__` will return a mock module object - not a string and then Version() will fail to parse that mock object.
This PR defers the attempt to find the version compatibility of FP16 to the moment the library is resolved dynamically rather than at static import time.
**Collection**: [Core]
# Changelog
- Correct numba import to be dynamic only to avoid mock import failing
- Fix NeMo version in docs and readme.md
# 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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] | closed | false | null | [] | null | null | 2023-07-18T19:17:26 | 2023-08-23T01:43:43 | 2023-08-23T01:43:42 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR adds support for standalone embeddings in NeMo FW
**Collection**: [NLP]
# Changelog
- Adds standalone embedding stage support
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adds naive support for flash attention 2.0.
[TODO]: Add support for the fixed seq length implementation which should be faster.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7062 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7062/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7062/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7062/events | https://github.com/NVIDIA/NeMo/pull/7062 | 1,810,489,329 | PR_kwDOC_bI7s5V0NtY | 7,062 | memmap worker arg | {
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} | # What does this PR do ?
GPTSFTDataset uses memmap data loading which uses python mp to create index files. The number of workers for the index file creation defaults to os.cpu_count() // 2 which might be too high for shared nodes.
This PR adds an arg to specify the number of workers that the creation of index files will use.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Other ranks with multi-node training may reach this code before rank 0 and torch.distributed has not been initialized and does not wait for download
# What does this PR do ?
Fix to be flexible to download by other ranks if not initialized and errors looking for file that does not exist yet
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```
File "/opt/NeMo/nemo/collections/nlp/modules/common/tokenizer_utils.py", line 207, in get_nmt_tokenizer
return get_tokenizer(tokenizer_name=model_name, vocab_file=vocab_file, merges_file=merges_file)
File "/opt/NeMo/nemo/collections/nlp/modules/common/tokenizer_utils.py", line 134, in get_tokenizer
return AutoTokenizer(
File "/opt/NeMo/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py", line 81, in __init__
raise ValueError(
ValueError: Unable to instantiate HuggingFace AUTOTOKENIZER for gpt2. Exception: Unable to load vocabulary from file. Please check that the provided vocabulary is accessible and not corrupted.
[NeMo I 2023-07-17 16:42:10 megatron_init:297] Rank 0 has embedding rank: 0
23-07-17 16:42:10 - PID:215 - rank:(0, 0, 0, 0) - microbatches.py:39 - INFO - setting number of micro-batches to constant 1
[NeMo I 2023-07-17 16:42:10 tokenizer_utils:204] Getting Megatron tokenizer for pretrained model name: megatron-gpt-345m, custom vocab file: None, and merges file: None
[NeMo I 2023-07-17 16:42:10 megatron_utils:208] Downloading from https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json to /mount/workspace/megatron/megatron-gpt-345m_vocab
[NeMo I 2023-07-17 16:42:10 megatron_utils:208] Downloading from https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt to /mount/workspace/megatron/megatron-gpt-345m_merges
[NeMo I 2023-07-17 16:42:10 tokenizer_utils:130] Getting HuggingFace AutoTokenizer with pretrained_model_name: gpt2, vocab_file: /mount/workspace/megatron/megatron-gpt-345m_vocab, merges_files: /mount/workspace/megatron/megatron-gpt-345m_merges, special_tokens_dict: {}, and use_fast: False
```
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #7016
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} | null | null | null | **Describe the bug**
PyYAML<6 in requirements breaks the installation on Google Colab. Removing the PyYAML<6 requirement from requirements/requirements_lightning.txt solves the problem.
**Steps/Code to reproduce bug**
Just run the first cell of https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb
**Expected behavior**
Successful installation.
**Environment overview (please complete the following information)**
- Environment location: Google Colab
- Method of NeMo install: pip
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Fixes typo in ASR-TTS tutorial.
Also changes branch to r1.20.0 for installation.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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 ?
- Support to allow mixed tasks into a single inference batch.
- Supports layer selection for LoRa and Adapters
- Supports weight tying for LoRa and Adapters
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Trying the latest `stable` Nemo release on wikitext by following the README instructions I run into the following error
```
File "/disks/yaroslav/envs/nemo2/lib/python3.9/site-packages/torch/cuda/amp/autocast_mode.py", line 123, in decorate_bwd
return bwd(*args, **kwargs)
File "/disks/yaroslav/envs/nemo2/lib/python3.9/site-packages/megatron/core/tensor_parallel/layers.py", line 284, in backward
total_input = total_input.view(total_input.shape[0] * total_input.shape[1],
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
```
The work-around is to open `/disks/yaroslav/envs/nemo2/lib/python3.9/site-packages/megatron/core/tensor_parallel/layers.py file and change line 224 from`
```
total_input = total_input.view(total_input.shape[0] * total_input.shape[1],
total_input.shape[2])
```
to instead read
```
total_input = total_input.contiguous()
total_input = total_input.view(total_input.shape[0] * total_input.shape[1],
total_input.shape[2])
```
This file is part of the following package:
```
megatron-core 0.2.0 pypi_0 pypi
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Remove PyYAML
**Collection**: [Core]
# 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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- [ ] 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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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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Remove PyYAML
**Collection**: [Core]
# 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 ?
Fixes broken PyYAML installation by fixing the version <= 5.3.1
See the issue: https://github.com/yaml/pyyaml/issues/724
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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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
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Fix typo in ASR-TTS tutorial (for main branch)
**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
- [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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Fixes typo in ASR-TTS tutorial.
Also changes branch to r1.20.0 for installation.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```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 ASR-TTS tutorial name in tutorials.rst
**Collection**: docs
# Changelog
- Fixed tutorial's name
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [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
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 | **Describe the bug**
I have been using NeMo in google collab and after I disconnected and deleted the runtime and moved over to a new account when I ran out of free GPU time, I haven't been able to install NeMo since, not even on my host computer.
**Steps/Code to reproduce bug**
>Input
```
#@title
!nvidia-smi
!python -m pip install --upgrade pip
!pip install torch
#Set the NeMo branch or version
%env BRANCH=main
# If you're using Colab and not running locally, uncomment and run this cell.
%rm -rf sample_data
!apt-get update && apt-get install -y libsndfile1 ffmpeg
!pip install Cython
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
```
>Ouput
```
Collecting wandb (from nemo_toolkit[all])
Obtaining dependency information for wandb from https://files.pythonhosted.org/packages/87/26/66e944b17aa06a4c9df9850a6a5c56378cb4f9b3acf3452ace7dfd895c13/wandb-0.15.5-py3-none-any.whl.metadata
Downloading wandb-0.15.5-py3-none-any.whl.metadata (8.2 kB)
Collecting hydra-core<1.3,>=1.2.0 (from nemo_toolkit[all])
Downloading hydra_core-1.2.0-py3-none-any.whl (151 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 151.1/151.1 kB 307.1 MB/s eta 0:00:00
Collecting omegaconf<2.3,>=2.2 (from nemo_toolkit[all])
Downloading omegaconf-2.2.3-py3-none-any.whl (79 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.3/79.3 kB 228.3 MB/s eta 0:00:00
Collecting pytorch-lightning<=1.9.4,>=1.9.0 (from nemo_toolkit[all])
Downloading pytorch_lightning-1.9.4-py3-none-any.whl (827 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 827.8/827.8 kB 361.7 MB/s eta 0:00:00
Collecting pyyaml<6 (from nemo_toolkit[all])
Downloading PyYAML-5.4.1.tar.gz (175 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 175.1/175.1 kB 337.4 MB/s eta 0:00:00
Installing build dependencies ... done
error: subprocess-exited-with-error
× Getting requirements to build wheel 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.
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel 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.
```
**Expected behavior**
NeMo to install build dependencies
**Environment overview**
- Environment location: [Google Collab](https://colab.research.google.com/drive/10d36J0nbFh6hdwFh_M90Y-oPrf5TBB3C?usp=sharing)
- Method of NeMo install: [Pip from source](https://github.com/NVIDIA/NeMo/tree/main#pip-from-source)
**Environment details**
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=20.04
DISTRIB_CODENAME=focal
DISTRIB_DESCRIPTION="Ubuntu 20.04.6 LTS"
PyTorch 2.0.1+cu118
Python 3.10.12 | {
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} | null | null | null | I have been trying to implement the logging info for calculating the throughput and samples per second for each step while pretraining a Megatron GPT model.
Any idea on how to include this information along with logging infos on loss, global step, consumed samples etc. for each step. | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Using nemo toolkit in a production environment is not really an option, since you usually don't work
with files but streams, byte arrays etc. This is problematic since all classes and functions in Nemo
expect .wav files to work with.
**Describe the solution you'd like**
Change classes and functions to accept additional file formats like IOBase.
**Describe alternatives you've considered**
Rewriting the repo, but since this is a massive change it would invalidate any future versions of Nemo.
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I am trying to get the confidence value with which the output labels are generated. Like for example, given a *.wav as input, I am able to extract some label from the model. But I would like to know the confidence level with which the labels are extracted for a single audio. I would need this information for some kind of thresholding in my application.
Please suggest some ideas on how to get that information. Also please do comment if you need more information. | {
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} | null | null | null | I had a SSL unsupervised pretrain conformer checkpoint. I wanted to use it for speaker recognition task. what changes i need to make in my config to do the same @titu1994 @meghmak13 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7040 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7040/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7040/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7040/events | https://github.com/NVIDIA/NeMo/issues/7040 | 1,806,497,594 | I_kwDOC_bI7s5rrPc6 | 7,040 | A little confusion about learning rate decay parameters for fastpitch and hifigan | {
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After modifying min_lr and max_epoch from default, I trained fastpitch for 500 epochs and hifigan for 52 epochs on my dataset, which consists of 25,584 audio clips totaling 44 hours. I found no decay in the learning rate.
fastpitch:
```
optim:
name: adamw
lr: 1e-3
betas: [0.9, 0.999]
weight_decay: 1e-6
sched:
name: NoamAnnealing
warmup_steps: 1000
last_epoch: -1
d_model: 1
min_lr: 2e-4 #modified,default N/A
trainer:
num_nodes: 1
devices: 1
accelerator: gpu
strategy: null #modified,default ddp
precision: 16
max_epochs: 500 #modified,default 1000
accumulate_grad_batches: 1
# gradient_clip_val: 1000.0
enable_checkpointing: False
logger: false
log_every_n_steps: 5
check_val_every_n_epoch: 1
benchmark: false
num_sanity_val_steps: 0
```
hifigan:
```
optim:
_target_: torch.optim.AdamW
lr: 0.0002
betas: [0.8, 0.99]
sched:
name: CosineAnnealing
min_lr: 1e-5
warmup_ratio: 0.02
max_steps: 2500000
l1_loss_factor: 45
denoise_strength: 0.0025
trainer:
num_nodes: 1
devices: 1
accelerator: gpu
strategy: ddp
precision: 32
max_steps: ${model.max_steps}
accumulate_grad_batches: 1
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
log_every_n_steps: 100
check_val_every_n_epoch: 5
benchmark: false
max_epochs: 100 #modified,default N/A
```
Although the default hifigan configuration also specifies a min_lr, it still reaches the 2e-4 rate set by adamw , which my fspitch setting couldn't. Could this be due to the difference between the cosine and noam decay strategies?
Moreover, I am aware that the maximum steps for the default hifigan exceed what my dataset can reach. Nevertheless, the results obtained without learning rate decay were not disappointing. I would like to know if the default setting expects annealing to be performed? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7039 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7039/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7039/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7039/events | https://github.com/NVIDIA/NeMo/issues/7039 | 1,805,710,962 | I_kwDOC_bI7s5roPZy | 7,039 | Training Kerple Parameters with Constraints | {
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I see that NeMo implements [Kerple](https://arxiv.org/pdf/2205.09921.pdf). According to the paper, there are some constraints on the valid values for its learnable parameters. For example, [`self.kerple_p`](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/megatron/position_embedding/kerple_relative_position_embedding.py#L69) needs to fall into `(0, 2]`. Though there is no details on how the authors post such constraint during the training, I would expect we need to use projected gradient descent type methods to learn these parameters. But I don't see such related code in NeMo. Do I miss anything? Thanks. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7038 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7038/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7038/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7038/events | https://github.com/NVIDIA/NeMo/pull/7038 | 1,805,689,672 | PR_kwDOC_bI7s5VkGAG | 7,038 | Add support for Numba FP16 RNNT Loss (#6991) | {
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} | Cherry pick RNNT FP 16 Compat to NeMo 1.20
Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds Numba FP 16 support to NeMo 1.20
**Collection**: [ASR]
# Usage
## Install dependencies
[Numba](https://github.com/numba/numba) 0.57+ (conda install numba=0.57.1 -c conda-forge)
[CUDA Python](https://nvidia.github.io/cuda-python/install.html)
After that, train RNNT model with trainer.precision=16
```python
# Set environment variables
# export NUMBA_CUDA_USE_NVIDIA_BINDING=1
# export STRICT_NUMBA_COMPAT_CHECK=0
from nemo.core.utils import numba_utils
print(numba_utils.numba_cuda_is_supported(numba_utils.__NUMBA_MINIMUM_VERSION_FP16_SUPPORTED__)) # Should be true
# Should also be True.
# You can also pass True as an argument to this function to possibly get a reason as to why FP 16 is not supported.
print(numba_utils.is_numba_cuda_fp16_supported())
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/7037 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7037/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7037/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7037/events | https://github.com/NVIDIA/NeMo/pull/7037 | 1,805,639,141 | PR_kwDOC_bI7s5Vj6nD | 7,037 | T5 metrics fix | {
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# Usage
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```python
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**Pre checks**:
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I've made fine-tuning custom Bert model from huggingface for TokenClassification, saved the best checkpoint as .nemo file as the result.
Then I want to load that checkpoint for model inference
```
model = TokenClassificationModel.restore_from("/tmp/model/best_ckpt.nemo")
```
but at first nemo downloads the original model from HF
```
[NeMo W 2023-07-14 03:24:30 lm_utils:80] ai-forever/ruBert-large is not in get_pretrained_lm_models_list(include_external=False), will be using AutoModel from HuggingFace.
Downloading: 100%|██████████| 1.71G/1.71G [01:12<00:00, 23.5MB/s]
```
and only then restores it from the checkpoint.
```
[NeMo I 2023-07-14 03:26:04 save_restore_connector:247] Model TokenClassificationModel was successfully restored from /tmp/model/best_ckpt.nemo
```
Is it necessary do download the original model from HF? I can't find the way to avoid it
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Fixed the caching bug in the convolution layers for cache-aware models
# Changelog
- Fixed the caching bug in the convolution layers for cache-aware models
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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Add a one line overview of what this PR aims to accomplish.
adds 'BoolType' to exportables from `neural_type.elements`
**Collection**: [Note which collection this PR will affect]
Core
# Before your PR is "Ready for review"
**Pre checks**:
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- [ Y] Bugfix
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fix issue https://github.com/NVIDIA/NeMo/issues/7025
**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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**PR Type**:
- [ ] New Feature
- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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**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)
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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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} | # What does this PR do ?
fix install_beamsearch_decoders.sh
**Collection**: ASR
# Changelog
- install_beamsearch_decoders.sh work with and without root user
- provide NEMO_PATH in the tutorial Offline_ASR.ipynb
**PR Type**:
- [V ] Bugfix
# Additional Information
* Related to # 4186721
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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
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# Usage
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```python
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```
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**PR Type**:
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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**:
- [ ] 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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The [init function](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/parts/nlp_overrides.py#L94) of NLPDDPStrategy class is passing args
```
parallel_devices, cluster_environment, checkpoint_io
```
without `key=val` pattern to parent class. But the [parent class DDPStrategy](https://github.com/Lightning-AI/lightning/blob/master/src/lightning/fabric/strategies/ddp.py#L54-L57) is expecting the args in the order of
```
accelerator: Optional[Accelerator] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
```
This caused a bug of custom checkpoint_io instance incorrectly being assigned to cluster_environment var.
**Steps/Code to reproduce bug**
```
s3_checkpoint_io = S3CheckpointIO()
strategy = NLPDDPStrategy(
checkpoint_io=s3_checkpoint_io,
)
```
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7023 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7023/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7023/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7023/events | https://github.com/NVIDIA/NeMo/pull/7023 | 1,801,398,295 | PR_kwDOC_bI7s5VVW3N | 7,023 | Add ASR with TTS Tutorial. Fix enhancer usage. | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [ASR]
# Changelog
- Tutorial for using hybrid ASR-TTS Models (`ASRWithTTSModel`)
- fix enhancer loading in `ASRWithTTSModel`
# 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
- [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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```python
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.EncDecFrameClassificationModel.from_pretrained(model_name="vad_multilingual_frame_marblenet")
model.export('test.onnx', onnx_opset_version=12)
```
error:
'EncDecFrameClassificationModel' object has no attribute 'output_names' | {
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} | # What does this PR do ?
fix install_beamsearch_decoders.sh
**Collection**: ASR
# Changelog
- install_beamsearch_decoders.sh work with and without root user
- provide NEMO_PATH in the tutorial Offline_ASR.ipynb
**PR Type**:
- [V ] Bugfix
# Additional Information
* Related to # 4186721
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Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds the torchfix linter to all PRs
**Collection**: [Core]
# Changelog
- Scans the codebase python files to analyse torch fixes / improvements
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
When downloading the tokenizer from cache with multi-node, some ranks may see that the path does not exist yet and multiple ranks will attempt to download causing errors.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* Training 5b/20b that uses the megatron cache works well with single node. ptuning 5b/20b with multi-node this error arises.
```
Traceback (most recent call last):
File "/usr/lib/python3.8/shutil.py", line 791, in move
os.rename(src, real_dst)
FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/torch/megatron/megatron-gpt-345m_vocab0037lvum.tmp' -> '/root/ (1).cache/torch/megatron/megatron-gpt-345m_vocab'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "megatron_gpt_prompt_learning.py", line 91, in main
...
File "/opt/NeMo/nemo/collections/nlp/modules/common/megatron/megatron_utils.py", line 211, in _download
wget.download(url, path)
File "/usr/local/lib/python3.8/dist-packages/wget.py", line 534, in download
shutil.move(tmpfile, filename)
File "/usr/lib/python3.8/shutil.py", line 811, in move
copy_function(src, real_dst)
File "/usr/lib/python3.8/shutil.py", line 435, in copy2
copyfile(src, dst, follow_symlinks=follow_symlinks)
File "/usr/lib/python3.8/shutil.py", line 264, in copyfile
with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst:
FileNotFoundError: [Errno 2] No such file or directory: '/root/ (1).cache/torch/megatron/megatron-gpt-345m_vocab'
```
# 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
## 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 ?
Inference caching was using the wrong embedding size.
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
- [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 ?
Some tokenizers don't have attributes `.tokenizer` or `.tokenizer.byte_decoder`, which results in crashing for these tokenizers (e.g., TikTokenizer and probably most tokenizers implemented in NeMo that derived from TokenizerSpec).
This PR improves this logic to:
1. First checking whether the output type is bytes; otherwise we are good.
2. For bytes outputs, safety checking whether tokenizer.tokenizer.byte_decoder; otherwise simply decode the byte array to string.
# Changelog
- Fix output decoding in text_generation_utils.generate for bytes
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
@MaximumEntropy
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add aliases for pretrained English FastPitch and SpectrogramEnhancer models (already published on NGC)
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_fastpitch_spectrogram_enhancer_for_asr_finetuning
Will be used in upcoming ASR-TTS tutorial.
**Collection**: TTS
# Changelog
- Add `tts_en_fastpitch_for_asr_finetuning` model to list of FastPitch models
- Add `tts_en_spectrogram_enhancer_for_asr_finetuning` model to list of SpectrogramEnhancer models
# Usage
```python
import nemo
from nemo.collections.tts.models import FastPitchModel, SpectrogramEnhancerModel
fastpitch = FastPitchModel.from_pretrained(model_name="tts_en_fastpitch_for_asr_finetuning")
enhancer = SpectrogramEnhancerModel.from_pretrained(model_name="tts_en_spectrogram_enhancer_for_asr_finetuning")
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #6955
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} | # What does this PR do ?
Add aliases for pretrained English FastPitch and SpectrogramEnhancer models (already published on NGC)
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_fastpitch_spectrogram_enhancer_for_asr_finetuning
Will be used in upcoming ASR-TTS tutorial.
**Collection**: TTS
# Changelog
- Add `tts_en_fastpitch_for_asr_finetuning` model to list of FastPitch models
- Add `tts_en_spectrogram_enhancer_for_asr_finetuning` model to list of SpectrogramEnhancer models
# Usage
```python
import nemo
from nemo.collections.tts.models import FastPitchModel, SpectrogramEnhancerModel
fastpitch = FastPitchModel.from_pretrained(model_name="tts_en_fastpitch_for_asr_finetuning")
enhancer = SpectrogramEnhancerModel.from_pretrained(model_name="tts_en_spectrogram_enhancer_for_asr_finetuning")
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #6955
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} | # What does this PR do ?
fix install_beamsearch_decoders.sh
**Collection**: ASR
# Changelog
- fix install_beamsearch_decoders.sh
# Usage
* install_beamsearch_decoders.sh work with and without root user
* provide NEMO_PATH in the tutorial Offline_ASR.ipynb
**PR Type**:
- [V ] Bugfix
# Additional Information
* Related to # 4186721
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**Collection**: NLP
# Changelog
- Modified line in `preprocess_data_for_megatron.py` which was causing a crash when specifying folder with non gzipped `json` files
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [X] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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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# 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)
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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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# 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"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Added the ability to do length extrapolation with RoPE via interpolation.
- Added a continue training script for GPT
# 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 ?
* Adds a new parameter to transcribe_speech.py to allow computing the alignment without timestamps (significant speed up for conf ensembles).
* Makes sure the confidence is always computed on top of the normalized logits (i.e. after softmax). Was not the case for Transducer based mode.
* Relaxes test constraint to reduce random failures.
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] 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 ?
Remove the need to load the yaml module inside GPT model and use hydra config instead.
**Collection**: nlp
# Changelog
- For ub_tp_comm_overlap_cfg config field you no longer provide a path to the config but the whole config instead.
# Usage
If you add to your main config:
```yaml
defaults:
[...]
- optional [email protected]_tp_comm_overlap_cfg:
```
then you can load the config from a known location like this:
` ++ [email protected]_tp_comm_overlap_cfg=<tp_config}`
# 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
## 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**
Getting a Error while importing "import nemo.collections.asr as nemo_asr" the error states "ValueError: mutable default <class 'nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategyConfig'> for field adapter_strategy is not allowed: use default_factory" Tried uninstalling and reinstalling the library the problem still exits.
**Steps/Code to reproduce bug**
ScreenShot added
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: Local Env (created specific new env as well)
- Method of NeMo install: pip install nemo_tooklit[all]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: Getting a Error while importing "import nemo.collections.asr as nemo_asr" the error states "ValueError: mutable default <class 'nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategyConfig'> for field adapter_strategy is not allowed: use default_factory" Tried uninstalling and reinstalling the library the problem still exits.
**Screenshot**
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BASE_IMAGE=nvcr.io/nvidia/pytorch:23.06-py3
failed docker command: RUN for f in $(ls requirements*.txt); do pip3 install --disable-pip-version-check --no-cache-dir -r $f; done
Here is the attached dockerfile I used. can you please help to review the log below and give me some advices? thank you a lot for you help in advance!
...
948.3 running build
948.3 running build_py
948.3 creating build
948.3 creating build/lib.linux-aarch64-3.10
948.3 creating build/lib.linux-aarch64-3.10/pynini
948.3 copying pynini/__init__.py -> build/lib.linux-aarch64-3.10/pynini
948.3 creating build/lib.linux-aarch64-3.10/pywrapfst
948.3 copying pywrapfst/__init__.py -> build/lib.linux-aarch64-3.10/pywrapfst
948.3 creating build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/t9.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/g2p.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/case.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/plurals.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/dates.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/weather.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/numbers.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/chatspeak.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/__init__.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/examples/chatspeak_model.py -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 creating build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/export/grm_example.py -> build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/export/multi_grm_example.py -> build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/export/multi_grm.py -> build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/export/grm.py -> build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/export/export.py -> build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/export/__init__.py -> build/lib.linux-aarch64-3.10/pynini/export
948.3 creating build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/paradigms.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/rule_cascade.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/edit_transducer.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/features.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/utf8.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/pynutil.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/tagger.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/rewrite.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/__init__.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/lib/byte.py -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 copying pynini/__init__.pyi -> build/lib.linux-aarch64-3.10/pynini
948.3 copying pynini/py.typed -> build/lib.linux-aarch64-3.10/pynini
948.3 copying pywrapfst/__init__.pyi -> build/lib.linux-aarch64-3.10/pywrapfst
948.3 copying pywrapfst/py.typed -> build/lib.linux-aarch64-3.10/pywrapfst
948.3 copying pynini/examples/py.typed -> build/lib.linux-aarch64-3.10/pynini/examples
948.3 copying pynini/export/py.typed -> build/lib.linux-aarch64-3.10/pynini/export
948.3 copying pynini/lib/py.typed -> build/lib.linux-aarch64-3.10/pynini/lib
948.3 running build_ext
948.3 building '_pywrapfst' extension
948.3 creating build/temp.linux-aarch64-3.10
948.3 creating build/temp.linux-aarch64-3.10/extensions
948.3 aarch64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/python3.10 -c extensions/_pywrapfst.cpp -o build/temp.linux-aarch64-3.10/extensions/_pywrapfst.o -std=c++17 -Wno-register -Wno-deprecated-declarations -Wno-unused-function -Wno-unused-local-typedefs -funsigned-char
948.3 extensions/_pywrapfst.cpp:817:10: fatal error: fst/util.h: No such file or directory
948.3 817 | #include <fst/util.h>
948.3 | ^~~~~~~~~~~~
948.3 compilation terminated.
948.3 error: command '/usr/bin/aarch64-linux-gnu-gcc' failed with exit code 1
948.3 ----------------------------------------
948.3 ERROR: Command errored out with exit status 1: /usr/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-6qwvruv7/pynini_e949233f3b0049558795db9e6479e190/setup.py'"'"'; __file__='"'"'/tmp/pip-install-6qwvruv7/pynini_e949233f3b0049558795db9e6479e190/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-u3jlnu2d/install-record.txt --single-version-externally-managed --compile --install-headers /usr/local/include/python3.10/pynini Check the logs for full command output.
------
Dockerfile:73
--------------------
71 | WORKDIR /tmp/nemo
72 | COPY requirements .
73 | >>> RUN for f in $(ls requirements*.txt); do pip3 install --disable-pip-version-check --no-cache-dir -r $f; done
74 |
75 | # install flash attention dependencies
--------------------
ERROR: failed to solve: process "/bin/sh -c for f in $(ls requirements*.txt); do pip3 install --disable-pip-version-check --no-cache-dir -r $f; done" did not complete successfully: exit code: 1
[Dockerfile.TXT](https://github.com/NVIDIA/NeMo/files/11996513/Dockerfile.TXT)
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} | # What does this PR do ?
1. Fix bug that can affect QA (wrong file extension). 2. Other small non-critical fixes.
**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
- [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 | By following [this tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Vits_Training.ipynb), I trained a TTS VitsModel.
While performing inference of the trained TTS model to convert text into speech, I observed a limitation where the system generates only up to 11 seconds of audio. So, when dealing with longer texts (e.g., equivalent to 90 seconds of audio), it just produces only first 11 seconds of equivalent audio.
On the other hand, when the text is short and equivalent to 0 to 11 seconds of audio, the system generates audio of the exact length.
Is there a workaround available to generate the complete audio for longer texts?
Note:
I'm using the latest codebase to implement the TTS model.
The audio data I am working with varies in length from 0.1 seconds to 63 seconds.
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
I loaded the Vits TTS model and infer with a long text.
**Expected behavior**
It was supposed to return a audio with whole text utterance.
**Environment overview (please complete the following information)**
- Environment location: GCP
- Method of NeMo install: from source
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Debian GNU/Linux 10 (buster)
- PyTorch version: 1.12.1+cu113
- Python version: 3.10
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} | # What does this PR do ?
The no activity timeout didn't work so changing to total duration of 8 hours. This is because timeout is including time in queue.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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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
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} | # What does this PR do ?
Allows export of Hybrid Models with either RNNT decoder (in two parts) or with CTC decoder as single encoder-decoder module.
Usage: python scripts/export.py file.nemo file.onnx --config decoder_type=[ctc|rnnt]
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
python scripts/export.py file.nemo file.onnx --config decoder_type=[ctc|rnnt]
# 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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"diff_url": "https://github.com/NVIDIA/NeMo/pull/6994.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/6994.patch",
"merged_at": "2023-07-09T17:46:09"
} | # What does this PR do ?
1. Fix bug that can affect QA (wrong file extension). 2. Other small non-critical fixes.
**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
- [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**
Use the following script to make a request will result in crashing in NeMo text-generation-server:
```
import json
import requests
port_num = 5555
headers = {"Content-Type": "application/json"}
def request_data(data):
resp = requests.put('http://localhost:{}/generate'.format(port_num),
data=json.dumps(data),
headers=headers)
resp = resp.json()
if "sentences" not in resp:
return f"Error: {resp}"
return resp['sentences']
data = {
"sentences": ["Tell me an interesting fact about space travel."],
"tokens_to_generate": 32,
"temperature": 1.00,
"add_BOS": True,
"top_k": 0,
"top_p": 0.7,
"greedy": False,
"all_probs": True, # <-- problematic
"repetition_penalty": 1.0,
"min_tokens_to_generate": 0,
"end_strings": [], # stop sequence
}
for sentence in request_data(data):
print(sentence)
```
**Steps/Code to reproduce bug**
1. Launch a service (see environment below for the command).
2. Run the above script to make a request.
**Expected behavior**
The proper response as shown in the NeMo API document.
**Environment overview (please complete the following information)**
1. Download a checkpoint from HuggingFace and put it under `/data`.
2. Launch a docker
```
MODEL=/data/nemo-megatron-gpt-1.3B/nemo_gpt1.3B_fp16.nemo
TP_SIZE=1
VOLUME=/data
docker run --rm -it --gpus all --shm-size=20g --ipc=host \
--ulimit memlock=-1 --ulimit stack=67108864 --network=host \
-v $VOLUME:/data \
nvcr.io/ea-bignlp/nemofw-training:23.04.1-py3:latest \
python3 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py \
gpt_model_file=$MODEL server=True \
tensor_model_parallel_size=$TP_SIZE \
trainer.devices=$TP_SIZE \
port=5555
```
**Additional context**
The server side crashes at the line https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/text_generation_utils.py#L430:
```
File "/opt/NeMo/nemo/collections/nlp/modules/common/text_generation_utils.py", line 429, in synced_generate
torch.distributed.broadcast(full_logits, src, group)
File "/usr/local/lib/python3.8/dist-packages/torch/distributed/distributed_c10d.py", line 1422, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/distributed/distributed_c10d.py", line 1526, in broadcast
_check_single_tensor(tensor, "tensor")
File "/usr/local/lib/python3.8/dist-packages/torch/distributed/distributed_c10d.py", line 568, in _check_single_tensor
raise RuntimeError(
RuntimeError: Invalid function argument. Expected parameter `tensor` to be of type torch.Tensor.
```
This line is weird because it attempts to broadcast `full_logits`, but this variable seems not properly defined:
```
for tokens, lengths, output_logits, full_logits in batch_token_iterator:
context_length += 1
```
cc @yidong72 @michalivne @ericharper | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6992 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6992/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6992/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6992/events | https://github.com/NVIDIA/NeMo/issues/6992 | 1,793,950,843 | I_kwDOC_bI7s5q7YR7 | 6,992 | Performance Expectations: asr_decoder_ts.run_ASR(asr_model) on 20-minute wav on RTX 3090 | {
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It is processing "word_hyp, word_ts_hyp = asr_decoder_ts.run_ASR(asr_model)"
on a 20-minute wav file, is but never seems to terminate.
```
@hydra_runner(config_path="conf/", config_name="diar.yaml")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
# ASR inference for words and word timestamps
asr_decoder_ts = ASRDecoderTimeStamps(cfg.diarizer)
asr_model = asr_decoder_ts.set_asr_model()
word_hyp, word_ts_hyp = asr_decoder_ts.run_ASR(asr_model)
print("Decoded word output dictionary: \n", word_hyp['an4_diarize_test'])
print("Word-level timestamps dictionary: \n", word_ts_hyp['an4_diarize_test'])
# Create a class instance for matching ASR and diarization results
asr_diar_offline = OfflineDiarWithASR(cfg.diarizer)
asr_diar_offline.word_ts_anchor_offset = asr_decoder_ts.word_ts_anchor_offset
```
```
name: &name "ClusterDiarizer"
num_workers: 1
sample_rate: 44100
batch_size: 64
device: 1 # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu)
verbose: True # enable additional logging
diarizer:
manifest_filepath: conf/file.json
out_dir: output/
oracle_vad: True # 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: True # Consider or ignore overlap segments while scoring
vad:
model_path: vad_multilingual_marblenet # .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 by detection error rate (false alarm + miss) on multilingual ASR evaluation datasets
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: 0.5 # Onset threshold for detecting the beginning and end of a speech
offset: 0.3 # Offset threshold for detecting the end of a speech
pad_onset: 0.2 # Adding durations before each speech segment
pad_offset: 0.2 # Adding durations after each speech segment
min_duration_on: 0.5 # Threshold for small non_speech deletion
min_duration_off: 0.5 # Threshold for short speech segment deletion
filter_speech_first: True
speaker_embeddings:
model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
parameters:
window_length_in_sec: [1.9,1.2,0.5] # 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: [0.95,0.6,0.25] # 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] # 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: True # 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: False # If True, use num of speakers value provided in manifest file.
max_num_speakers: 50 # 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: 10 # 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.
asr:
model_path: stt_en_conformer_ctc_large # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
parameters:
asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD.
asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2].
word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript.
break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
beam_width: 32
alpha: 0.5
beta: 2.5
realigning_lm_parameters: # Experimental feature
arpa_language_model: null # Provide a KenLM language model in .arpa format.
min_number_of_words: 3 # Min number of words for the left context.
max_number_of_words: 10 # Max number of words for the right context.
logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses.
``` | {
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} | # What does this PR do ?
Add fp16 support to RNNT Numba Loss
**Collection**: [ASR]
# Changelog
-
# Usage
## Install dependencies
[Numba](https://github.com/numba/numba) 0.57+ (conda install numba=0.57.1 -c conda-forge)
[CUDA Python](https://nvidia.github.io/cuda-python/install.html)
After that, train RNNT model with trainer.precision=16
```python
# Set environment variables
# export NUMBA_CUDA_USE_NVIDIA_BINDING=1
# export STRICT_NUMBA_COMPAT_CHECK=0
from nemo.core.utils import numba_utils
print(numba_utils.numba_cuda_is_supported(numba_utils.__NUMBA_MINIMUM_VERSION_FP16_SUPPORTED__)) # Should be true
# Should also be True.
# You can also pass True as an argument to this function to possibly get a reason as to why FP 16 is not supported.
print(numba_utils.is_numba_cuda_fp16_supported())
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | Hello,
Thank you for your work and all the help provided so far with other issues too.
I am training a conformer transducer model and I have a couple of questions that might help me achieve better results:
1. I am finetuning the **stt en conformer transducer medium** model (whole model) on 225h of romanian language ( 200h training, 20h validation). I am at epoch 100 and the val_wer keeps decreasing, as well as the training wer. However, the performances on the eval set are still far away in performance comparing to our Kaldi model (10-15% more WER). How many epochs are usually necessary for convergence of the transducer medium model and are 200 hours of training data enough for a conformer model?
2. Do you use any type of cross-validation or simply randomly split the training set in train/val?
3. At the moment I am using specaugment and spec cutout, should I also add noise impulses/background noise from some datasets too as augmentation? Could you please point what I should add in the config file exactly or if there is an example I could follow?
4. Lastly, I am using an LR of 1 with Noam annealing and 10% warm up ratio, is it a big LR for finetuning? ( from what I understood, you used 5 as LR) I am unsure if I should lower it to 0.1 maybe or 0.01.
Training takes quite a long time on our setup and we can't afford to test many configurations, that's why these questions may be pretty basic, but any insight is greatly appreciated!
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} | # What does this PR do ?
* Adds a new parameter to transcribe_speech.py to allow computing the alignment without timestamps (significant speed up for conf ensembles).
* Makes sure the confidence is always computed on top of the normalized logits (i.e. after softmax). Was not the case for Transducer based mode.
* Relaxes test constraint to reduce random failures.
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] 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 ?
Makes end_strings part of SamplingParams and makes it configurable.
**Collection**: Text Generation Server
# 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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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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.
# Additional Information
* Related to # (issue)
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Unexpected keyword arguments: `compute_on_step`
File "/home/muizz/nemo_workspace/Neural_netw.py", line 9, in <module>
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
ValueError: Unexpected keyword arguments: `compute_on_step`
what do i do to avoid this
_Originally posted by @muizzhaque in https://github.com/NVIDIA/NeMo/issues/6977#issuecomment-1623474893_
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} | # What does this PR do ?
Allows export of Hybrid Models with either RNNT decoder (in two parts) or with CTC decoder as single encoder-decoder module.
Usage: python scripts/export.py file.nemo file.onnx --config decoder_type=[ctc|rnnt]
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
python scripts/export.py file.nemo file.onnx --config decoder_type=[ctc|rnnt]
# 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 ?
Updates the script used to create a Megatron MPT-7B checkpoint with fixes, and adds support to MegatronBaseModel for special tokens to be passed at runtime.
**Collection**: NLP
# Changelog
- Updates the Megatron MPT-7B creation script with some bug fixes and a config file baseline
- Adds support for HuggingFace special tokens to be declared in Hydra/Yaml and passed to MegatronBaseModel
# Usage
You can specify the new tokens in a config YAML like so:
```yaml
...
model.tokenizer.special_tokens.pad_token="<PAD>"
model.tokenizer.special_tokens.mask_token="<MASK>"
...
```
# 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
- [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
The MegatronBaseModel changes are necessary because the HuggingFace tokenizer which is used by MPT-7B (and indeed all other HF tokenizers) does not have special tokens built in, tokens such as PAD and MASK. This leads to a crash when prompt-learning with MPT-7B, because there is no pad token, and no way to add one at runtime. With this fix to MegatronBaseModel, you can now pass a pad token at runtime, which allows prompt-learning with MPT-7B (or any model which uses a HF tokenizer).
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} | # What does this PR do ?
Remove deprecated arg compute_on_step from torchmetrics
**Collection**: [ASR, NLP]
# Before your PR is "Ready for review"
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**PR Type**:
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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)
- [ ] 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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