renwei2024/nemo-github-issues huggingface dataset:
[NeMo-UX] Fix when optimizers are setup for PEFT
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

# What does this PR do ?

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Adding mamba embedding model
State: closed | Pull request: yes
Labels: name: stale

name: NLP

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This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

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More complete example of using S3CheckpointIO
State: closed | Pull request: no
Labels: name: stale


The existing doc on S3CheckpointIO is pretty sparse. Can there be a more complete example of using it within a script like https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_pretraining.py?




This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

This issue was closed because it has been inactive for 7 days since being marked as stale.

Review seemed closed but can use it as reference if still needing help.
https://github.com/NVIDIA/NeMo/pull/9812
[Cherrypick] support lora when kv_channel != hidden_size / num_heads
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Cherrypick #9636

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# Additional Information
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Cherry-pick megatron export fix from main
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Cherry-pick megatron export fix from main

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- Add specific line by line info of high level changes in this PR.

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# Additional Information
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enables default data step in megatron parallel to operate on a wider variety of tensors
State: closed | Pull request: yes
Labels: name: Run CICD

…variety of tensors coming out of the dataloader

Add more flexibility to the default data step inside megatron parallel.

Add a one line overview of what this PR aims to accomplish.

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# Changelog 
- Megatron parallel's default data step can operate on wrapped tuples and unwrapped items coming directly out of the dataloader

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# Add a code snippet demonstrating how to use this 
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# Additional Information
* Related to # (issue)
Enables megatron parallel move to device inside datastep
State: closed | Pull request: yes

# What does this PR do ?

We enable the default data step to operate on a wider variety of outputs from the dataloader.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Enables the dataloader to pass a wrapped tuple to the default data step in megatron parallel
- Enables the dataloader to pass any unwrapped item to the default data step

# Usage
* You can potentially add a usage example below


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add documentation for reset_lr feature
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Adds a doc file for reset_lr feature.

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[NeMo-UX] Fix pipeline parallel bug
State: closed | Pull request: yes
Labels: name: Run CICD

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# Additional Information
* Related to # (issue)
Support LoRA when kv_channel != hidden_size / num_heads
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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- Add specific line by line info of high level changes in this PR.

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ci: Timeout per step, not job
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

# What does this PR do ?

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# Additional Information
* Related to # (issue)
Unwrap ckpt_io for model opt (async save) (#9622)
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Cherry-pick of https://github.com/NVIDIA/NeMo/pull/9622

**Collection**: NLP

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- Add specific line by line info of high level changes in this PR.

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# Additional Information
* Related to # (issue)
Change default parallel_save to False
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Cheery-pick of https://github.com/NVIDIA/NeMo/pull/9632 to r2.0 branch

**Collection**: NLP

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- Add specific line by line info of high level changes in this PR.

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# Additional Information
* Related to # (issue)
Change default parallel_save to False
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Parallel save is set to False in the GPT config, but not in the DistributedCheckpointIO constructor. This PR fixes that 

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

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# Additional Information
* Related to # (issue)
Release `2.0.0rc1`
State: closed | Pull request: yes

🚀 PR to release NeMo `2.0.0rc1`.

📝 Please remember the following to-do's before merge:
- [ ] Fill-in the comment `Highlights`
- [ ] Review the comment `Detailed Changelogs`

🚨 Please also keep in mind to _not_ delete the headings of the task commits. They are required by the post-merge automation.

🙏 Please merge this PR only if the CI workflow completed successfully.

# Highlights
_<here-goes-the-summary...>_

# Detailed Changelogs
How to adapt myself speaker model into the diarization pipeline?
State: closed | Pull request: no
Labels: name: bug

name: stale

Thank you for your contribution for the Nemo framework, it is an awesome work. 
I use the Diarization pipeline, which is vad -> spk_emb -> clustering(nmesc+kmeans). When I use myself speaker model, I found the diarization performance is even worse than the open-source titanet-large model on my own test dataset. 
Note that in the aspect of the speaker recognition performance, my speaker model is better than the provided titanet-large on my own test set. However, the diarization equipped with my speaker model cannot get good performance (better eer can not get better der?). 
Is there any hyper-parameters in clustering need to be tuned for different speaker model and test set (such as the self.nme_mat_size, self.max_rp_thershold, self.sparse_search_volume, etc.) 
Hope for your valuable suggestion, thanks ! 

This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

This issue was closed because it has been inactive for 7 days since being marked as stale.
nemo gemma to hf  conversion
State: closed | Pull request: yes

# 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 
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# Additional Information
* Related to # (issue)
Fix the names of two sets of weight and bias in mcore_to_nemo_mapping of scripts/convert_gpt_nemo_to_mcore.py
State: closed | Pull request: yes

These two sets of weight and bias have been renamed according to the `module_name_rewrite_list` given in https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/inference/gpt/state_dict_hooks.py#L116-L119

Also change the underscores in the two command-line options to hyphens to follow the convention of using hyphens in command-line options. Note that `argparse` automatically converts hyphens in argument names to underscores in the attributes of the namespace returned by `parse_args()`.

# What does this PR do ?

Update the names of the two sets of weight and bias according to latest Megatron-LM.

**Collection**: NLP

# Changelog 
- Update the names of the two sets of weight and bias according to latest Megatron-LM.
- Change the underscores in the two command-line options to hyphens to follow the convention of using hyphens in command-line options.

# Usage

https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/dpo.html

```
python convert_gpt_nemo_to_mcore.py --input-name-or-path ./model_checkpoint --output-path ./mcore_gpt.nemo
```

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**Pre checks**:
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- [ ] Did you write any new necessary tests? The model conversion script does not have any unit test.
- [x] Did you add or update any necessary documentation? Update the comment in the script.
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**PR Type**:
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## Who can review?

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# Additional Information
* Related to #9626 


This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.
[NeMo-UX] fix pretrianing data sizes and weights
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Fix NeMo 2 pretraining with different data split sizes and dataset weights 

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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# 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**:
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# Additional Information
* Related to # (issue)
Nemotron export - fixing megatron_export.py 
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Updates Apex module replacements we do inside export() with Megatron Core modules replacement.

**Collection**: [Note which collection this PR will affect]

NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

```python
python3 examples/nlp/language_modeling/megatron_export.py model_type=gpt gpt_model_file=Nemotron-3-8B-Base-4k.nemo onnx_model=Nemotron-3-8B-Base-4k.nemo
```

# GitHub Actions CI

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# 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?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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# Additional Information
* Related to # (issue)
Fix the arguments  of forward_for_export function in msdd_models
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

# What does this PR do ?

The refactoring PR [#9147](https://github.com/NVIDIA/NeMo/pull/9147)  in missed a line that should have been refactored too.
Adding the necessary change to prevent errors in this PR

**Collection**: [Note which collection this PR will affect]
ASR/speaker_tasks

# Changelog 
```python3
logits, embs_a = self.msdd._speaker_model.forward_for_export(
    processed_signal=audio_signal[detach_ids[0]], processed_signal_len=audio_signal_len[detach_ids[0]]
)
```
to
```python3
logits, embs_a = self.msdd._speaker_model.forward_for_export(
    audio_signal=audio_signal[detach_ids[0]], length=audio_signal_len[detach_ids[0]]
)
```

# Usage



```python
python ./multiscale_diar_decoder.py --config-path='../conf/neural_diarizer' --config-name='msdd_5scl_15_05_50Povl_256x3x32x2.yaml' \
    trainer.devices=1 \
    model.base.diarizer.speaker_embeddings.model_path="titanet_large" \
    model.train_ds.manifest_filepath="<train_manifest_path>" \
    model.validation_ds.manifest_filepath="<dev_manifest_path>" \
    model.train_ds.emb_dir="<train_temp_dir>" \
    model.validation_ds.emb_dir="<dev_temp_dir>" \
    exp_manager.name='sample_train' \
    exp_manager.exp_dir='./msdd_exp'
"""

```


# 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**:
- [ ] 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 NeMo ASR
Add long context recipe
State: closed | Pull request: yes
Labels: name: stale

# 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 
```

# GitHub Actions CI

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To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)


This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR was closed because it has been inactive for 7 days since being marked as stale.
Unwrap ckpt_io for model opt (async save)
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Fix model_opt path with async save, where `strategy.checkpoint_io` is a wrapper around the base strategy.

**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 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# 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)
fix ckpt load bug
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# 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 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
[NeMo-UX] Fix when optimizers are setup for PEFT
State: closed | Pull request: yes
Labels: name: Run CICD

# 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 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
fix: remove non_blocking from PTL's .cuda call
State: closed | Pull request: yes
Labels: name: Run CICD

# 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 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# 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)
Fix serialization of AutoResume
State: closed | Pull request: yes
Labels: name: Run CICD

# 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 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# 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)
Very poor WER for Conformer_CTC_large model in streaming mode
State: closed | Pull request: no
Labels: name: stale

Hello. I am testing the [STT En Conformer-CTC Large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large/files) model in streaming mode. To do this, I use the [speech_to_text_cache_aware_streaming_infer.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py) script and the [librispeech-test-other](https://www.openslr.org/12) dataset. With the default `chunk_size` value of 4000 ms, everything works quite well. The WER is 8.67%. However, when I reduce the `chunk_size` to 800 ms, I get very poor results. The WER increases to 35.6%.
I understand that this model was trained with full context in offline mode. And the accuracy would not be very good with small chunks. But I also tested this model in streaming mode (`chunk_size` = 800ms) with RIVA. I converted the nemo model to riva with [nemo2riva](https://github.com/nvidia-riva/nemo2riva) and built a pipeline. In this case, the WER was 7.9%. I understand that the RIVA service contains certain techniques and optimizations for working with the streaming mode. But I wonder why there is such a difference in WER when using the same model and the same chunk size. 
Can anyone explain this behavior? Maybe RIVA reconfigures the model in some way or something else that I might have missed?

An offline model is not supposed to be used with cache-aware scripts, specially with small chunk sizes. You need to use cache-aware models like:
https://huggingface.co/nvidia/stt_en_fastconformer_hybrid_large_streaming_multi

Riva does not support cache-aware models publicly yet as far as I know. You may use nemo for cache-aware until they are fully supported in Riva in the meantime.

If for some reasons, you need to use offline models for streaming in nemo, you need to use this script for buffered streaming. Riva is supporting buffered streaming which is a streaming approach for offline-trained models:

https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py

Just keep that in mind that in buffered streaming (the one Riva uses), chunk size is not comparable to cache-aware models. They add some eight and left contexts to the chunk which increases the latency, usually buffer size is 4s and the 800ms chunk is the middle of the buffer.

This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

This issue was closed because it has been inactive for 7 days since being marked as stale.