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} | # What does this PR do ?
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**:
- [ ] 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 ?
Fixes some issues:
-change_attention_model should work for old models which don't have some of the relevant parameters in config
-also it shouldn't run when nothing to change was submitted
Original PR: https://github.com/NVIDIA/NeMo/pull/6608
**Collection**: ASR
**PR Type**:
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- [X] Bugfix
- [ ] Documentation
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# What does this PR do ?
Fix wer.py where 'errors' variable was not set when both reference and hypothesis are empty strings
**Collection**: [ASR]
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# What does this PR do ?
Fix wer.py where 'errors' variable was not set when both reference and hypothesis are empty strings
**Collection**: [ASR]
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} | …atron core
# What does this PR do ?
Corrects the names of modules that are expected to be in GPTModel object coming from megatron-core
**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
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Remove extraneous spaces between word and punctuation
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# 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
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Remove extraneous spaces between word and punctuation
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# 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
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} | # What does this PR do ?
Removes spaces before `.` `,` or `?` punctuations generated by PC models
**Collection**: ASR
# Changelog
- wer.py regex, line 545
- rnnt_wer.py regex, line 503
# Usage
n/a
```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 ?
Fix a few bugs in TTS preprocess_audio.py and MelSpectrogramFeaturizer.
**Collection**: [TTS]
# Changelog
- Add additional config to mel featurizer to make it more consistent with past TTS configs
- Add check and unit test for normalize_volume when audio input is empty array.
- Modify preprocess script to avoid error when resample_rate=0.
- Modify preprocess script to round duration to 2 decimals
- Move normalize_volume unit tests to correct test class, following an earlier refactoring.
# 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 | {
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} | # What does this PR do ?
Enable E2E inference for TE FP8 path in transformer with KV cache
**Collection**: NLP
# Changelog
- Update examples/nlp/language_modeling/megatron_gpt_eval.py for TE FP8 inference, padded inputs to a multiple of 8 to get rid of error `AssertionError: Input and weight dimensions are not compatible for FP8 execution.`.
- Set up `inference_params` correctly and pass to TE for inference. Assume first time calling with `set_inference_key_value_memory=True` and remaining with `set_inference_key_value_memory=False`.
- Need to wait Megatron-LM to add the API `parallel_state.get_amax_reduction_group()` so to fix the issue https://github.com/NVIDIA/NeMo/issues/6625
# Usage
Get an FP8 NeMo model and run:
```
python megatron_gpt_eval.py gpt_model_file=<FP8_NEMO_MODEL>
```
```python
python megatron_gpt_eval.py gpt_model_file=models/5b_fp8_tp1.nemo
***************************
[{'sentences': ['Q: How are you?\nA1) It is all too easy to get distracted, daydreaming and so forth. On top of that many people lack sufficient rest due to their',
'Q: How big is the universe?\nA: As you’ve probably heard, the universe includes everything that we can know. It stretches from one end of space to another and'],
'tokens': [['<|endoftext|>', 'Q', ':', ' How', ' are', ' you', '?', '\n', 'A', '1', ')', ' It', ' is', ' all', ' too', ' easy', ' to', ' get', ' distracted', ',', ' day', 'dream', 'ing', ' and', ' so', ' forth', '.', ' On', ' top', ' of', ' that', ' many', ' people', ' lack', ' sufficient', ' rest', ' due', ' to', ' their'], ['<|endoftext|>', 'Q', ':', ' How', ' big', ' is', ' the', ' universe', '?', '\n', 'A', ':', ' As', ' you', '�', '�', 've', ' probably', ' heard', ',', ' the', ' universe', ' includes', ' everything', ' that', ' we', ' can', ' know', '.', ' It', ' stretches', ' from', ' one', ' end', ' of', ' space', ' to', ' another', ' and']],
'logprob': tensor([[-3.3044e+00, -1.0319e-02, -8.7674e+00, -3.8337e+00, -2.2173e+00,
-3.9373e+00, -7.4720e-01, -1.0483e+00, -7.5145e+00, -3.8551e+00,
-5.1689e+00, -1.2816e+00, -4.9733e+00, -5.4351e+00, -1.8549e+00,
-3.3207e-01, -1.8963e+00, -3.5398e+00, -3.2054e+00, -7.3057e+00,
-7.6148e-02, -7.8230e-01, -2.3709e+00, -4.2063e+00, -2.1984e+00,
-3.6228e-01, -4.6996e+00, -3.0654e+00, -3.2912e-03, -4.0276e-01,
-6.4977e+00, -1.0216e+00, -4.6901e+00, -3.9250e+00, -4.8088e+00,
-5.6998e+00, -1.3010e-02, -1.8368e+00],
[-3.3044e+00, -1.0319e-02, -8.7674e+00, -5.2868e+00, -6.7541e-01,
-8.7047e-01, -4.0037e+00, -3.9222e-01, -3.4152e-01, -2.2293e+00,
-2.9142e-02, -4.3188e+00, -5.2750e+00, -4.1764e+00, -6.8956e-03,
-8.7781e-01, -7.6296e-01, -1.0332e+00, -4.1965e-01, -5.5560e-01,
-5.0666e-01, -6.7566e+00, -7.0060e-01, -7.9373e-01, -3.1336e+00,
-7.8659e-01, -4.4716e+00, -1.9113e+00, -2.2779e+00, -4.2088e+00,
-1.4632e+00, -3.7058e+00, -3.2600e-01, -1.2548e-01, -2.1457e+00,
-4.3699e-01, -2.8770e+00, -2.3523e+00]]),
'full_logprob': None,
'token_ids': [[50256, 48, 25, 1374, 389, 345, 30, 198, 32, 16, 8, 632, 318, 477, 1165, 2562, 284, 651, 22943, 11, 1110, 25966, 278, 290, 523, 6071, 13, 1550, 1353, 286, 326, 867, 661, 3092, 6751, 1334, 2233, 284, 511], [50256, 48, 25, 1374, 1263, 318, 262, 6881, 30, 198, 32, 25, 1081, 345, 447, 247, 303, 2192, 2982, 11, 262, 6881, 3407, 2279, 326, 356, 460, 760, 13, 632, 23687, 422, 530, 886, 286, 2272, 284, 1194, 290]], 'offsets': [[0, 0, 1, 2, 6, 10, 14, 15, 16, 17, 18, 19, 22, 25, 29, 33, 38, 41, 45, 56, 57, 61, 66, 69, 73, 76, 82, 83, 86, 90, 93, 98, 103, 110, 115, 126, 131, 135, 138], [0, 0, 1, 2, 6, 10, 13, 17, 26, 27, 28, 29, 30, 33, 37, 38, 39, 41, 50, 56, 57, 61, 70, 79, 90, 95, 98, 102, 107, 108, 111, 121, 126, 130, 134, 137, 143, 146, 154]]}]
***************************
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 #6625
* TE FP8 path needs changes in https://github.com/NVIDIA/NeMo/pull/6458/files to run successfully, otherwise it might raise a ValueError when precision is set to 16 or 32. | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
An important consideration for us when deciding which library, framework or model implementation to use when pretraining models is whether the model will be accessible and easy to use for end users. In practice this translates to whether it is possible and straightforward to convert the model to a Huggingface compatible format.
For Nemo, there exists a conversion script to [convert a model from Huggingface to Nemo format](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/hf_t5-v1_1_to_nemo.py). However, there doesn't exist a resource for a conversion in the opposite direction.
Since an `HF -> Nemo` script existed, we thought converting from `Nemo -> HF` would simply be a matter of reversing that script. We therefore [carefully configured our Nemo configs](https://huggingface.co/Lauler/UL2-nemo-conversion/tree/main/nemo_config/ul2-base-nl36) to return a model that weight wise seemed compatible with Huggingface's T5 implementation (e.g. no bias terms, tied encoder/decoder word embeddings). Then we wrote our [own conversion script](https://huggingface.co/Lauler/UL2-nemo-conversion/blob/main/convert_nemo_ul2_checkpoint.py) to reverse Nemo's existing one.
Our script appears to work fine reversing the weight conversion performed in Nemo's `HF -> Nemo` script. Unfortunately, it does not work as well when attempting to convert a model initialized and pretrained with Nemo Megatron.
We are aware that [Megatron-LM uses different ordering of QKV](https://github.com/NVIDIA/Megatron-LM/blob/42c1cf4279acea5a554500dcb552211f44cbec45/megatron/checkpointing.py#L209-L237) in the attention layers depending on the version of Megatron-LM used. We are also aware of an existing conversion script that Huggingface have created for converting Megatron-BERT to Huggingface, where they adapt the ordering of QKV in Megatron to [match the ordering used in Huggingface](https://github.com/NVIDIA/Megatron-LM/blob/42c1cf4279acea5a554500dcb552211f44cbec45/megatron/checkpointing.py#L209-L237). We copied this reordering, and added a `--fix_qkv` argument in our conversion script that applies the same reordering of QKV as Huggingface does.
Unfortunately, none of the above solves the issue we have with our conversion script.
**Describe the solution you'd like**
We've uploaded Nemo model checkpoints, conversion scripts, and detailed instructions in the README on how to reproduce our attempted conversion in [this Huggingface repo](https://huggingface.co/Lauler/UL2-nemo-conversion
). We'd very much appreciate some help figuring out how T5 and UL2 models can be converted from Nemo Megatron to HF. Specifically:
1. Is our Nemo config appropriate to be able to perform a weight conversion?
2. Does the issue lie with the QKV reordering in Megatron? What is the correct way of reordering QKV in self-attention and KV in cross attention for encoder-decoders? How does one find out which Megatron "checkpoint version" one's saved checkpoints are using?
To clone the HF repo:
```bash
git clone https://huggingface.co/Lauler/UL2-nemo-conversion
```
**Describe alternatives you've considered**
We searched for [UL2](https://huggingface.co/models?search=ul2) and T5 models on Huggingface to see whether we could find any that were successfully converted from Megatron-LM or Nemo. All the HF compatible ones we could find were either FLAX/JAX or models with unclear origin. Nvidia's own uploaded T5 models consist of [`.nemo` checkpoints with instructions to install Apex and Nemo](https://huggingface.co/nvidia/nemo-megatron-t5-3B).
Ideally, we'd like to use Nemo to pretrain language models. But realistically we are going to go for the option that means our users are able to use the final model. Nemo is a great library, but the reality is only a minority of our users would be able to use the models without the kind of well documented convenience functions that exist in HF.
So in other words: our main alternative at this point is switching to JAX/FLAX.
**Additional context**
Would it be more appropriate to open an issue about this in the Megatron-LM repo rather than here? | {
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} | null | null | null | **Describe the bug**
When running megatron_gpt_eval.py with an FP8 model, The FP8 path is run and not able to find `get_amax_reduction_group()` in parallel_state.
The error is triggered at this line:
https://github.com/NVIDIA/NeMo/blob/21048627b3923c9268842990aafdef141bd14bd1/nemo/collections/nlp/modules/common/megatron/transformer.py#LL1432C70-L1432C70
**Steps/Code to reproduce bug**
Get a model trained with TE FP8
Run:
python megatron_gpt_eval.py gpt_model_file=models/5b_fp8_tp1.nemo
**Expected behavior**
The script is expected to finish and generate outputs.
**Environment overview (please complete the following information)**
- Environment location:
Container: nvcr.io/nvidia/pytorch:23.03-py3
- Method of NeMo install:
Call `./reinstall.sh` on main branch commit c3deeac8d58f48e9283d13fb7c32fd259605705f
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
docker run --gpus '"device=0"' -it --ipc=host --ulimit memlock=-1 -v /home/scratch.yenshiw_sw/NeMo:/workspace/local-nemo --ulimit stack=67108864 nvcr.io/nvidia/pytorch:23.03-py3
**Environment details**
**Additional context**
I cannot find name `get_amax_reduction_group` in megatron source code (parallel_state):
https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
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} | # What does this PR do ?
Allows to exchange GPTDataset indices with torch.distributed (instead of filesystem) by broadcasting indices from rank 0.
**Collection**: NLP
# Changelog
- Allows to exchange GPTDataset indices with torch.distributed
# Usage
Turn on with a `model.data.exchange_indices_distributed=1` flag
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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Update core commit hash in readme
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# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
Improve ASR-TTS models code and docs. Address issues raised in #6594:
- (new feature) support hybrid RNNT-CTC models
- improve documentation:
- add required docs for sampling techniques
- add a note about appending values to config for training
- add initial enhancer stubs in docs (detailed documentation will be added later by @racoiaws)
- do not warn on import for optional library
- (bugfix) fix instantiation from ASR model config: `use_start_end_token` parameter, broken after #6426, #6467
**Collection**: [ASR, TTS]
# 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**:
- [x] 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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} | # What does this PR do ?
Add forgotten image to hat docs description
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Allows to exchange GPTDataset indices with torch.distributed (instead of filesystem) by broadcasting indices from rank 0.
**Collection**: NLP
# Changelog
- Allows to exchange GPTDataset indices with torch.distributed
# Usage
Turn on with a `model.data.exchange_indices_distributed=1` flag
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
Calling `EnglishPhonemesTokenizer.encode_from_g2p()` with text that a list of phonemes that contains `<oov>` as one of the items simply skips the `<oov>` item even if `<oov>` is a part of the `EnglishPhonemesTokenizer.tokens`.
**Steps/Code to reproduce bug**
```
from nemo.collections.tts.torch.tts_tokenizers import EnglishPhonemesTokenizer
from nemo.collections.tts.torch.g2ps import EnglishG2p
g2p = EnglishG2p(ignore_ambiguous_words=False)
tokenizer = EnglishPhonemesTokenizer(g2p, punct=True, stresses=True)
tokens = tokenizer.encode_from_g2p(['OY1', '<oov>'])
assert len(tokens) == 2
```
**Expected behavior**
Tokenizer to correctly tokenize `<oov>` token | {
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} | null | null | null | Hi. Thank you very much for your implementation on ACEVC. I'm interested in this Zero-shot Voice Conversion project. I have finished training the model and successfully conveted the voice by following the document on the website https://paarthneekhara.github.io/ace/code.html. However, I didn't know how to control pace or pitch as the website https://paarthneekhara.github.io/ace/ mentioned. Are there any hyperparameters or instructions for pace and pitch controls on audio. I'm sincerely thankful with your help! | {
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} | # What does this PR do ?
Reduce the peak memory usage during model building and restoration.
**Collection**: [NLP]
# Changelog
- Force state dict onto CPU during restoration
- Add param_dtype in multiple places to correctly build tensor with trainer.precision dtype
# 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
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} | # What does this PR do ?
Adds confidence ensembles implementation.
I will follow up with more features (e.g., tuning of the LR/confidence parameters) as well as documentation and probably a tutorial notebook. But the basic functionality is fully implemented.
**Collection**: ASR
# Changelog
- Add new script to `scripts/confidence-ensembles/build_ensemble.py`. It can be run to build an ensemble by providing models/data. It will train confidence-based model selection block and output .nemo checkpoint containing all models + pkl file for the model selection.
- Add new argument to `transcribe_speech.py` `return_transcriptions` which can be used to select if transcriptions or config is returned. Default to current behavior of returning config.
- Add a new temperature parameter to the decoding strategies and ctc/rntt decoder/joint blocks. What's currently called `temperature` in the confidence config is not really a temperature, but instead an `alpha` parameter as defined here https://arxiv.org/abs/2212.08703. Temperature should be applied before softmax, so it cannot be directly implemented inside confidence computation.
- Add a new `ConfidenceEnsembleModel` class that supports running `transcribe` method for the ensemble. It does not support training or any other kind of inference (`forward` method is not implemented). This is by design as we only combine the models through model selection block and it operates on the full transcriptions, not intermediate results.
- Add `scripts/confidence_ensembles/test_confidence_ensembles.py` which contains a few integration tests of building->running ensemble of es-it models. The plan is to include it in the nightly tests, since it depends on ~200mb of data and takes 5-10 minutes to run.
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Manually cherry-pick changes from https://github.com/NVIDIA/NeMo/pull/6528. Automatic merging probably failed due to merge conflicts with https://github.com/NVIDIA/NeMo/pull/6510.
**Collection**: NLP
# Changelog
- Cherry-pick changes from https://github.com/NVIDIA/NeMo/pull/6528
- Move duplicated logic for distributed optimizer into recently-added [`MegatronGPTModel.fwd_bwd_step` function](https://github.com/NVIDIA/NeMo/blob/24c7b4bdcb794536a52a38bbd1737422f095681f/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L333).
# Usage
Set `virtual_pipeline_model_parallel_size > 1`:
https://github.com/NVIDIA/NeMo/blob/5468077f5127be1a4c88065de2544f4268b9a6e4/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L51
This requires `pipeline_model_parallel_size > 2` and `virtual_pipeline_model_parallel_size*pipeline_model_parallel_size` divides `num_layers`.
# 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?
- [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**:
- [ ] 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
* Cherry-pick from https://github.com/NVIDIA/NeMo/pull/6528
* Fixes merge conflicts with https://github.com/NVIDIA/NeMo/pull/6510 | {
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} | # What does this PR do ?
LoRA support for T5 models.
**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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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Manually Cherry pick commits in https://github.com/NVIDIA/NeMo/pull/6601/commits merged to r1.18.0 due to failure of creating automatic cherry pick and merge
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**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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add fix from #6449 into main
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review
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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 some issues:
-change_attention_model should work for old models which don't have some of the relevant parameters in config
-also it shouldn't run when nothing to change was submitted
**Collection**: ASR
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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] | closed | false | null | [] | null | null | 2023-05-09T13:06:33 | 2023-06-17T01:56:05 | 2023-06-17T01:56:05 | NONE | {
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} | null | null | null | **Describe the bug**
I'm trying to finetuning HiFiGAN using this lib, using WSL2 I could start training process, but it will stuck at random step.
**Steps/Code to reproduce bug**
Running prompts:
```
LD_LIBRARY_PATH=/home/oedosoldier/anaconda3/envs/fs/lib:$LD_LIBRARY_PATH HYDRA_FULL_ERROR=1 PYTHONPATH=./jiaran//codes/NeMo CUDA_VISIBLE_DEVICES=0 nohup python ./jiaran//codes/NeMo/examples/tts/hifigan_finetune.py train_dataset=./jiaran//metas/nemo/train_manifest_mel.json validation_datasets=./jiaran//metas/nemo/val_manifest_mel.json exp_manager.exp_dir=./jiaran//results model/train_ds=train_ds_finetune model/validation_ds=val_ds_finetune trainer.strategy=null name=hifigan trainer.check_val_every_n_epoch=1 model.train_ds.dataloader_params.batch_size=8 model.validation_ds.dataloader_params.batch_size=8 model.train_ds.dataloader_params.num_workers=4 model.validation_ds.dataloader_params.num_workers=4 +init_from_pretrained_model=tts_zh_hifigan_sfspeech --config-name hifigan.yaml > log.out 2>&1 &
```
Output:
```
nohup: ignoring input
[NeMo W 2023-05-09 20:54:32 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-05-09 20:54:34 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-09 20:54:36 experimental:27] Module <class 'nemo.collections.tts.models.fastpitch_ssl.FastPitchModel_SSL'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-09 20:54:36 experimental:27] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-09 20:54:36 experimental:27] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-09 20:54:36 experimental:27] Module <class 'nemo.collections.tts.models.ssl_tts.SSLDisentangler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-09 20:54:36 experimental:27] Module <class 'nemo.collections.tts.models.vits.VitsModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-09 20:54:36 nemo_logging:349] /home/oedosoldier/anaconda3/envs/fs/lib/python3.10/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'hifigan.yaml': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information
warnings.warn(msg, UserWarning)
[NeMo W 2023-05-09 20:54:37 nemo_logging:349] /home/oedosoldier/anaconda3/envs/fs/lib/python3.10/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
ret = run_job(
Using 16bit None Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo W 2023-05-09 20:54:37 exp_manager:716] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
[NeMo W 2023-05-09 20:54:37 exp_manager:568] There was no checkpoint folder at checkpoint_dir :jiaran/results/hifigan/checkpoints. Training from scratch.
[NeMo I 2023-05-09 20:54:37 exp_manager:374] Experiments will be logged at jiaran/results/hifigan
[NeMo I 2023-05-09 20:54:37 exp_manager:797] TensorboardLogger has been set up
[NeMo W 2023-05-09 20:54:37 exp_manager:893] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 2500000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo I 2023-05-09 20:54:37 dataset:1045] Loading dataset from ./jiaran//metas/nemo/train_manifest_mel.json.
3191it [00:00, 206435.27it/s]
[NeMo I 2023-05-09 20:54:37 dataset:1069] Loaded dataset with 3191 files.
[NeMo I 2023-05-09 20:54:37 dataset:1071] Dataset contains 6.11 hours.
[NeMo I 2023-05-09 20:54:37 dataset:377] Pruned 0 files. Final dataset contains 3191 files
[NeMo I 2023-05-09 20:54:37 dataset:379] Pruned 0.00 hours. Final dataset contains 6.11 hours.
[NeMo I 2023-05-09 20:54:37 dataset:1045] Loading dataset from ./jiaran//metas/nemo/val_manifest_mel.json.
32it [00:00, 54317.17it/s]
[NeMo I 2023-05-09 20:54:37 dataset:1069] Loaded dataset with 32 files.
[NeMo I 2023-05-09 20:54:37 dataset:1071] Dataset contains 0.06 hours.
[NeMo I 2023-05-09 20:54:37 dataset:377] Pruned 0 files. Final dataset contains 32 files
[NeMo I 2023-05-09 20:54:37 dataset:379] Pruned 0.00 hours. Final dataset contains 0.06 hours.
[NeMo I 2023-05-09 20:54:37 features:291] PADDING: 0
[NeMo I 2023-05-09 20:54:37 features:299] STFT using exact pad
[NeMo I 2023-05-09 20:54:37 features:291] PADDING: 0
[NeMo I 2023-05-09 20:54:37 features:299] STFT using exact pad
[NeMo I 2023-05-09 20:54:38 cloud:68] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_zh_fastpitch_hifigan_sfspeech/versions/1.15.0/files/tts_zh_hifigan_sfspeech.nemo to /home/oedosoldier/.cache/torch/NeMo/NeMo_1.18.0rc0/tts_zh_hifigan_sfspeech/ed4a2b913f208e59e3f5f96705394784/tts_zh_hifigan_sfspeech.nemo
[NeMo I 2023-05-09 20:55:01 common:913] Instantiating model from pre-trained checkpoint
[NeMo W 2023-05-09 20:55:02 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
dataset:
_target_: nemo.collections.tts.torch.data.VocoderDataset
manifest_filepath: /pred/train_manifest_mel.json
sample_rate: 22050
n_segments: 8192
max_duration: null
min_duration: 0.75
load_precomputed_mel: true
hop_length: 256
dataloader_params:
drop_last: false
shuffle: true
batch_size: 88
num_workers: 5
pin_memory: true
[NeMo W 2023-05-09 20:55:02 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
dataset:
_target_: nemo.collections.tts.torch.data.VocoderDataset
manifest_filepath: /pred/val_manifest_mel.json
sample_rate: 22050
n_segments: 66048
max_duration: null
min_duration: 3
load_precomputed_mel: true
hop_length: 256
dataloader_params:
drop_last: false
shuffle: false
batch_size: 88
num_workers: 5
pin_memory: true
[NeMo I 2023-05-09 20:55:02 features:291] PADDING: 0
[NeMo I 2023-05-09 20:55:02 features:299] STFT using exact pad
[NeMo I 2023-05-09 20:55:02 features:291] PADDING: 0
[NeMo I 2023-05-09 20:55:02 features:299] STFT using exact pad
[NeMo I 2023-05-09 20:55:02 save_restore_connector:249] Model HifiGanModel was successfully restored from /home/oedosoldier/.cache/torch/NeMo/NeMo_1.18.0rc0/tts_zh_hifigan_sfspeech/ed4a2b913f208e59e3f5f96705394784/tts_zh_hifigan_sfspeech.nemo.
[NeMo I 2023-05-09 20:55:02 modelPT:1269] Model checkpoint restored from pretrained checkpoint with name : `tts_zh_hifigan_sfspeech`
You are using a CUDA device ('NVIDIA GeForce RTX 4090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
------------------------------------------------------------------------
0 | audio_to_melspec_precessor | FilterbankFeatures | 0
1 | trg_melspec_fn | FilterbankFeatures | 0
2 | generator | Generator | 13.9 M
3 | mpd | MultiPeriodDiscriminator | 41.1 M
4 | msd | MultiScaleDiscriminator | 29.6 M
5 | feature_loss | FeatureMatchingLoss | 0
6 | discriminator_loss | DiscriminatorLoss | 0
7 | generator_loss | GeneratorLoss | 0
------------------------------------------------------------------------
84.7 M Trainable params
0 Non-trainable params
84.7 M Total params
169.321 Total estimated model params size (MB)
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s][NeMo W 2023-05-09 20:55:03 nemo_logging:349] /home/oedosoldier/anaconda3/envs/fs/lib/python3.10/site-packages/torch/functional.py:641: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at ../aten/src/ATen/EmptyTensor.cpp:31.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
Training: 0it [00:00, ?it/s][NeMo I 2023-05-09 20:55:03 preemption:56] Preemption requires torch distributed to be initialized, disabling preemption
Epoch 0: 0%| | 0/403 [00:00<?, ?it/s] [NeMo W 2023-05-09 20:55:04 nemo_logging:349] /home/oedosoldier/anaconda3/envs/fs/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
[NeMo W 2023-05-09 20:55:04 nemo_logging:349] /home/oedosoldier/anaconda3/envs/fs/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:232: UserWarning: You called `self.log('global_step', ...)` in your `training_step` but the value needs to be floating point. Converting it to torch.float32.
warning_cache.warn(
Epoch 0: 0%| | 2/403 [00:00<03:13, 2.07it/s, v_num=38, g_l1_loss=0.785]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ stuck at here
```
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: local WSL2 (tried multiple versions, including ubuntu 20.04 22.04 debian 11.6, all stucked)
- Method of NeMo install: git cloned and set by PYTHONPATH
**Environment details**
- Windows 11 22H2
- WSL2
- RTX 4090
- Conda environment:
```
name: fs
channels:
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_kmp_llvm
- bzip2=1.0.8=h7b6447c_0
- ca-certificates=2023.5.7=hbcca054_0
- cython=0.29.34=py310heca2aa9_0
- ld_impl_linux-64=2.38=h1181459_1
- libblas=3.9.0=16_linux64_openblas
- libcblas=3.9.0=16_linux64_openblas
- libffi=3.4.2=h6a678d5_6
- libgcc-ng=12.2.0=h65d4601_19
- libgfortran-ng=12.2.0=h69a702a_19
- libgfortran5=12.2.0=h337968e_19
- liblapack=3.9.0=16_linux64_openblas
- libopenblas=0.3.21=pthreads_h78a6416_3
- libstdcxx-ng=12.2.0=h46fd767_19
- libuuid=1.41.5=h5eee18b_0
- llvm-openmp=14.0.6=h9e868ea_0
- ncurses=6.4=h6a678d5_0
- numpy=1.24.3=py310ha4c1d20_0
- openssl=1.1.1t=h0b41bf4_0
- pip=23.0.1=py310h06a4308_0
- python=3.10.9=h7a1cb2a_2
- python_abi=3.10=2_cp310
- pyworld=0.3.3=py310h5a539fb_0
- readline=8.2=h5eee18b_0
- sqlite=3.41.2=h5eee18b_0
- tk=8.6.12=h1ccaba5_0
- wheel=0.38.4=py310h06a4308_0
- xz=5.4.2=h5eee18b_0
- zlib=1.2.13=h5eee18b_0
- pip:
- absl-py==1.4.0
- aiohttp==3.8.4
- aiosignal==1.3.1
- antlr4-python3-runtime==4.9.3
- appdirs==1.4.4
- asttokens==2.2.1
- async-timeout==4.0.2
- attrdict==2.0.1
- attrs==23.1.0
- audioread==3.0.0
- backcall==0.2.0
- braceexpand==0.1.7
- cachetools==5.3.0
- cdifflib==1.2.6
- certifi==2022.12.7
- cffi==1.15.1
- charset-normalizer==2.1.1
- click==8.1.3
- cmake==3.25.0
- colorama==0.4.6
- comm==0.1.3
- contourpy==1.0.7
- cycler==0.11.0
- debugpy==1.6.7
- decorator==5.1.1
- distance==0.1.3
- docker-pycreds==0.4.0
- docopt==0.6.2
- editdistance==0.6.2
- einops==0.6.1
- executing==1.2.0
- filelock==3.9.0
- fonttools==4.39.3
- frozenlist==1.3.3
- fsspec==2023.5.0
- g2p-en==2.1.0
- gitdb==4.0.10
- gitpython==3.1.31
- google-auth==2.17.3
- google-auth-oauthlib==1.0.0
- grpcio==1.54.0
- huggingface-hub==0.14.1
- hydra-core==1.2.0
- idna==3.4
- inflect==6.0.4
- ipykernel==6.23.0
- ipython==8.13.2
- ipywidgets==8.0.6
- jedi==0.18.2
- jieba==0.42.1
- jinja2==3.1.2
- jiwer==3.0.1
- joblib==1.2.0
- jupyter-client==8.2.0
- jupyter-core==5.3.0
- jupyterlab-widgets==3.0.7
- kaldi-python-io==1.2.2
- kaldiio==2.18.0
- kiwisolver==1.4.4
- kornia==0.6.12
- lazy-loader==0.2
- levenshtein==0.21.0
- librosa==0.10.0.post2
- lightning-utilities==0.8.0
- lit==15.0.7
- llvmlite==0.40.0
- loguru==0.7.0
- markdown==3.4.3
- markdown-it-py==2.2.0
- markupsafe==2.1.2
- marshmallow==3.19.0
- matplotlib==3.7.1
- matplotlib-inline==0.1.6
- mdurl==0.1.2
- mpmath==1.2.1
- msgpack==1.0.5
- multidict==6.0.4
- nemo-text-processing==0.1.7rc0
- nest-asyncio==1.5.6
- networkx==3.0
- nltk==3.8.1
- numba==0.57.0
- oauthlib==3.2.2
- omegaconf==2.2.3
- onnx==1.14.0
- packaging==23.1
- pandas==2.0.1
- parso==0.8.3
- pathtools==0.1.2
- pexpect==4.8.0
- pickleshare==0.7.5
- pillow==9.3.0
- plac==1.3.5
- platformdirs==3.5.0
- pooch==1.6.0
- prompt-toolkit==3.0.38
- protobuf==4.23.0
- psutil==5.9.5
- ptyprocess==0.7.0
- pure-eval==0.2.2
- pyannote-core==5.0.0
- pyannote-database==5.0.1
- pyannote-metrics==3.2.1
- pyasn1==0.5.0
- pyasn1-modules==0.3.0
- pybind11==2.10.4
- pycparser==2.21
- pydantic==1.10.7
- pydub==0.25.1
- pygments==2.15.1
- pynini==2.1.5
- pyparsing==3.0.9
- pypinyin==0.48.0
- pypinyin-dict==0.5.0
- python-dateutil==2.8.2
- pytorch-lightning==1.9.4
- pytz==2023.3
- pyyaml==5.4.1
- pyzmq==25.0.2
- rapidfuzz==2.13.7
- regex==2023.5.5
- requests==2.28.1
- requests-oauthlib==1.3.1
- rich==13.3.5
- rsa==4.9
- ruamel-yaml==0.17.24
- ruamel-yaml-clib==0.2.7
- sacremoses==0.0.53
- scikit-learn==1.2.2
- scipy==1.10.1
- sentencepiece==0.1.99
- sentry-sdk==1.22.2
- setproctitle==1.3.2
- setuptools==65.5.1
- shellingham==1.5.0.post1
- six==1.16.0
- smmap==5.0.0
- sortedcontainers==2.4.0
- soundfile==0.12.1
- sox==1.4.1
- soxr==0.3.5
- stack-data==0.6.2
- sympy==1.11.1
- tabulate==0.9.0
- tensorboard==2.13.0
- tensorboard-data-server==0.7.0
- termcolor==2.3.0
- text-unidecode==1.3
- texterrors==0.4.4
- threadpoolctl==3.1.0
- tokenizers==0.13.3
- torch==2.0.1+cu118
- torchaudio==2.0.2+cu118
- torchmetrics==0.11.4
- torchvision==0.15.2+cu118
- tornado==6.3.1
- tqdm==4.65.0
- traitlets==5.9.0
- transformers==4.28.1
- triton==2.0.0
- typer==0.9.0
- typing-extensions==4.4.0
- tzdata==2023.3
- urllib3==1.26.13
- wandb==0.15.2
- wcwidth==0.2.6
- webdataset==0.1.62
- werkzeug==2.3.4
- wget==3.2
- widgetsnbextension==4.0.7
- wrapt==1.15.0
- yarl==1.9.2
- youtokentome==1.0.6
prefix: /home/oedosoldier/anaconda3/envs/fs
```
**Additional context**
I tried to figure out which step it stuck, turned out it's caused by `manual_backward()`.
In `hifigan.py`, there are two `manual_backward()` calls, and it will randomly stuck at one of them:
```
# Train discriminator
optim_d.zero_grad()
mpd_score_real, mpd_score_gen, _, _ = self.mpd(y=audio, y_hat=audio_pred.detach())
loss_disc_mpd, _, _ = self.discriminator_loss(
disc_real_outputs=mpd_score_real, disc_generated_outputs=mpd_score_gen
)
msd_score_real, msd_score_gen, _, _ = self.msd(y=audio, y_hat=audio_pred.detach())
loss_disc_msd, _, _ = self.discriminator_loss(
disc_real_outputs=msd_score_real, disc_generated_outputs=msd_score_gen
)
loss_d = loss_disc_msd + loss_disc_mpd
self.manual_backward(loss_d) <- here
^^^^^^^^^^^^^^^^^^^
optim_d.step()
# Train generator
optim_g.zero_grad()
loss_mel = F.l1_loss(audio_pred_mel, audio_trg_mel)
_, mpd_score_gen, fmap_mpd_real, fmap_mpd_gen = self.mpd(y=audio, y_hat=audio_pred)
_, msd_score_gen, fmap_msd_real, fmap_msd_gen = self.msd(y=audio, y_hat=audio_pred)
loss_fm_mpd = self.feature_loss(fmap_r=fmap_mpd_real, fmap_g=fmap_mpd_gen)
loss_fm_msd = self.feature_loss(fmap_r=fmap_msd_real, fmap_g=fmap_msd_gen)
loss_gen_mpd, _ = self.generator_loss(disc_outputs=mpd_score_gen)
loss_gen_msd, _ = self.generator_loss(disc_outputs=msd_score_gen)
loss_g = loss_gen_msd + loss_gen_mpd + loss_fm_msd + loss_fm_mpd + loss_mel * self.l1_factor
self.manual_backward(loss_g) <- here
^^^^^^^^^^^^^^^^^^^
optim_g.step()
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} | null | null | null | **Describe the bug**
I am getting an error when running inference on a test dataset using the Conformer-Transducer [Small](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_small) and [Medium](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_medium) models downloaded from NGC. I do not get receive this issue when using the [Large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_large), [XLarge](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_xlarge) and [XXLarge](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_xxlarge) models. However, since I am interested in running inference on embedded devices, I would like to use the Small and Medium models more than the Large ones.
The error is as follows:
```
Traceback (most recent call last):
File "transcribe_speech.py", line 327, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.8/dist-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper
_run_hydra(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
return func()
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "transcribe_speech.py", line 200, in main
asr_model, model_name = setup_model(cfg, map_location)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/utils/transcribe_utils.py", line 191, in setup_model
asr_model.change_attention_model(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/mixins/mixins.py", line 426, in change_attention_model
self.encoder.change_attention_model(self_attention_model, att_context_size, update_config, self.device)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/modules/conformer_encoder.py", line 841, in change_attention_model
att_context_size = self._cfg.att_context_size
File "/usr/local/lib/python3.8/dist-packages/omegaconf/dictconfig.py", line 359, in __getattr__
self._format_and_raise(key=key, value=None, cause=e)
File "/usr/local/lib/python3.8/dist-packages/omegaconf/base.py", line 231, in _format_and_raise
format_and_raise(
File "/usr/local/lib/python3.8/dist-packages/omegaconf/_utils.py", line 820, in format_and_raise
_raise(ex, cause)
File "/usr/local/lib/python3.8/dist-packages/omegaconf/_utils.py", line 798, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set env var OC_CAUSE=1 for full trace
File "/usr/local/lib/python3.8/dist-packages/omegaconf/dictconfig.py", line 351, in __getattr__
return self._get_impl(
File "/usr/local/lib/python3.8/dist-packages/omegaconf/dictconfig.py", line 442, in _get_impl
node = self._get_child(
File "/usr/local/lib/python3.8/dist-packages/omegaconf/basecontainer.py", line 73, in _get_child
child = self._get_node(
File "/usr/local/lib/python3.8/dist-packages/omegaconf/dictconfig.py", line 475, in _get_node
self._validate_get(key)
File "/usr/local/lib/python3.8/dist-packages/omegaconf/dictconfig.py", line 164, in _validate_get
self._format_and_raise(
File "/usr/local/lib/python3.8/dist-packages/omegaconf/base.py", line 231, in _format_and_raise
format_and_raise(
File "/usr/local/lib/python3.8/dist-packages/omegaconf/_utils.py", line 900, in format_and_raise
_raise(ex, cause)
File "/usr/local/lib/python3.8/dist-packages/omegaconf/_utils.py", line 798, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set env var OC_CAUSE=1 for full trace
omegaconf.errors.ConfigAttributeError: Key 'att_context_size' is not in struct
full_key: att_context_size
object_type=dict
```
**Steps/Code to reproduce bug**
The main script I am trying to run is https://github.com/NVIDIA/NeMo/blob/main/examples/asr/transcribe_speech.py with the following command line call:
```
OC_CAUSE=1 HYDRA_FULL_ERROR=1 python3 transcribe_speech.py model_path=/workspace/nemo/non-finetuned/models/stt_en_conformer_transducer_small.nemo dataset_manifest=/workspace/datasets/myst_w2v2_asr/test_manifest.json output_filename=/workspace/nemo/non-finetuned/results/stt_en_conformer_transducer_xxlarge_myst_test_predictions.json batch_size=8 cuda=1 amp=True
```
**Expected behavior**
Expected to receive the following successful output, just like when using Conformer-Transducer Large models:
```
[NeMo I 2023-05-09 11:12:08 transcribe_utils:235]
Transcribing 2566 files...
[NeMo I 2023-05-09 11:12:08 transcribe_speech:251] AMP enabled!
Transcribing: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 321/321 [02:39<00:00, 2.01it/s]
[NeMo I 2023-05-09 11:14:48 transcribe_speech:305] Finished transcribing 2566 files !
```
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: docker pull nvcr.io/nvidia/nemo:23.02
**Environment details**
I used the latest NeMo docker image as my environment.
**Additional context**
GPU model: Nvidia A6000
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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# Before your PR is "Ready for review"
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} | # What does this PR do ?
Create dummy iters to satisy iter type len checks in core + update core commit
**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 ?
When softmax fused kernel is [not available](https://github.com/NVIDIA/apex/blob/85e9eddece9d4ac72b48c2407f8162f2173e1bf4/apex/transformer/functional/fused_softmax.py#L222), it will fall back to pytorch softmax forward in NeMo.
In apex, the mask is override based on your `attn_layer_type`. So it will always be set to either [causal or padding type of masking](https://github.com/NVIDIA/apex/blob/85e9eddece9d4ac72b48c2407f8162f2173e1bf4/apex/transformer/functional/fused_softmax.py#L205). While when it falls back to NeMo, it will actually do None masking fwd. This will cause in issues in training and inference when your tensor shape doesn't fit kernel or you are using 32 precision.
We propose few improvement:
1. In apex, the user should be warned, no matter what they set in mask, it will be override based on `attn_layer_type`.
2. In every NeMo models, the attn mask should always be set when it's trying to use `MatchedScaleMaskSoftmax`.
**Collection**: Megatron models use fused softmax from apex, but attn_mask is None.
# 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 ?
Patch transcribe_util for steaming mode and add wer calculation back to the inference scripts as requested
**Collection**: ASR
# Changelog
- Patch `decoding` in `write_transcription`
- Temporarily set `decoding.beam.return_best_hypothesis = True` to true to be written to manifest to avoid`assert`, see in [discussion](https://github.com/NVIDIA/NeMo/pull/6063/files#r1178635367) @karpnv. May need to come up a better way for ngram/beam
- Add wer calculation back to
`transcribe_speech.py`,
`speech_to_text_buffered_infer_ctc`,
`speech_to_text_buffered_infer_rnnt` as requested, but I still recommand to use asr evaluator and simply those inference script if possible.
- Reflect changed in ASR evaluator
# Usage
```python
python transcribe_speech.py \
model_path=xxxx \
dataset_manifest=xxx\
clean_groundtruth_text=True \
langid='zh' \
use_cer=True \
calculate_wer=True
[NeMo I 2023-05-08 15:43:25 transcribe_speech:360] Writing prediction and error rate of each sample to xxxx
[NeMo I 2023-05-08 15:43:25 transcribe_speech:361] {'samples': 2, 'tokens': 164, 'wer': 0.04878048780487805, 'ins_rate': 0.0, 'del_rate': 0.04878048780487805, 'sub_rate': 0.0}
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] 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[PR#6559](https://github.com/NVIDIA/NeMo/pull/6559) | {
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} | # What does this PR do ?
Create dummy iters to satisy iter type len checks in core + update core commit
**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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- [ ] 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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- [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 ?
Piggy backing on eval script to merge lora weights into a base GPT model.
**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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* Related to # (issue)
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} | # What does this PR do ?
Add more detail to SDP section in NeMo docs, and add link to that page in the NeMo README.rst.
**Collection**: docs
# Changelog
- update SDP section in NeMo docs
- update NeMo README
# Usage
N/A
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- [N/A] Did you write any new necessary tests?
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- [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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Add documentation for hybrid ASR-TTS models.
**Collection**: [ASR]
# Changelog
- add docs to ASR models
- add docs for text-only dataset construction
- add description for configs and training scripts
- add note about extracting final ASR model
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- fix imports: optional library, partially initialized module
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- [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?
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**PR Type**:
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## Who can review?
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} | # What does this PR do ?
Add documentation for hybrid ASR-TTS models.
**Collection**: [ASR]
# Changelog
- add docs to ASR models
- add docs for text-only dataset construction
- add description for configs and training scripts
- add note about extracting final ASR model
- add datasets and hybrid ASR-TTS model to ASR collection API
- fix imports: optional library, partially initialized module
# Usage
* You can potentially add a usage example below
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**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?
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} | # What does this PR do ?
Make tensor split contiguous for qkv and kv in attention
**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**:
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Cherry pick patches for VP
**Collection**: [NLP]
# Changelog
- Adds VP support
# Usage
```python
# Megatron GPT + Virtual Pipeline parallelism
python megatron_change_num_partitions.py \
--model_extracted_dir="<Directory of Pytorch Lightning ckpt folders>" \
--target_file="<Name of the target NeMo file>" \
--ckpt_name="<Name of a single Pytorch Lightning ckpt file inside the extracted dir>" \
--tensor_model_parallel_size=<TP SIZE> \
--target_tensor_model_parallel_size=<TARGET TP SIZE> \
--pipeline_model_parallel_size=<PP SIZE> \
--target_pipeline_model_parallel_size=<TARGET PP SIZE> \
--virtual_pipeline_model_parallel_size=<VP SIZE> \
--hparams_file="<Path to HPARAMS.yaml file>" \
--precision=bf16
```
# 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
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} | null | null | null | **Describe the bug**
I'm trying to fine tune the tts_es_fastpitch_multispeaker.nemo model and have this issue with the pitch_state.json file. I just copy the format in "scripts/tts_dataset_files/openslr_es/pitch_stats.json" and I have this error.
`mismatched input 'pitch_state.json' expecting ID`
**Steps/Code to reproduce bug**
I only add my new speaker with a new number to the pitch_stats.json with his respective mean and std pitch. I use the config file in "NeMo/examples/tts/conf/es/fastpitch_align_44100_ipa_multi.yaml" and try modifing the sup_data_type to match the number of my new speaker but it doesn't work either.
`sup_data_types: ["align_prior_matrix", "pitch", 174]`
The code use for fine tuning:
`
!(python fastpitch_finetune.py --config-name=fastpitch_align_44100_ipa_multi.yaml \
train_dataset=./transcript_train_spk.json \
validation_datasets=./transcript_dev_spk.json \
sup_data_path=./fastpitch_sup_data \
pitch_stats_path= tts_dataset_diles/pitch_state.json \
phoneme_dict_path=tts_dataset_files/es_ES_nv230301.dict \
exp_manager.exp_dir=./ljspeech_to_9017_no_mixing_5_mins \
+init_from_nemo_model=./tts_es_fastpitch_multispeaker.nemo \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=12 model.validation_ds.dataloader_params.batch_size=12 \
model.n_speakers=175 model.pitch_mean=137.2 model.pitch_std=44.2 \
model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
+model.text_tokenizer.add_blank_at=true \
)
`
**Environment details**
Use Google Collab following this [example](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb)
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while trying to train the language model with train-kenlm.py for asr, i encounter the following error:
"
[NeMo W 2023-05-08 14:55:20 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-05-08 14:55:21 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-08 14:55:21 nemo_logging:349] /root/anaconda3/lib/python3.10/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
ret = run_job(
[NeMo I 2023-05-08 14:55:21 kenlm_utils:73] Loading nemo model '/home/azad2/NeMo/Model-fa-512.nemo' ...
[NeMo I 2023-05-08 14:55:21 mixins:170] Tokenizer SentencePieceTokenizer initialized with 700 tokens
[NeMo W 2023-05-08 14:55:21 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /home/azad/manifests/commonvoice_train_manifest.json,/home/azad/manifests/commonvoice_dev_manifest.json
sample_rate: 16000
batch_size: 32
trim_silence: true
max_duration: 16.7
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
num_workers: 8
pin_memory: true
use_start_end_token: true
[NeMo W 2023-05-08 14:55:21 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: /home/azad/manifests/commonvoice_test_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: true
trim_silence: true
[NeMo W 2023-05-08 14:55:21 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: /home/azad/manifests/commonvoice_test_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: true
trim_silence: true
[NeMo I 2023-05-08 14:55:21 features:291] PADDING: 16
Call to cuInit results in CUDA_ERROR_NO_DEVICE
[NeMo I 2023-05-08 14:55:22 save_restore_connector:249] Model EncDecCTCModelBPE was successfully restored from /home/azad2/NeMo/Model-fa-512.nemo.
[NeMo I 2023-05-08 14:55:22 train_kenlm:150] Running lmplz command
/home/azad2/NeMo/decoders/kenlm/build/bin/lmplz -o 6 --arpa /home/azad2/NeMo/model.tmp.arpa --discount_fallback --prune 0
=== 1/5 Counting and sorting n-grams ===
File stdin isn't normal. Using slower read() instead of mmap(). No progress bar.
Unigram tokens 0 types 3
=== 2/5 Calculating and sorting adjusted counts ===
Chain sizes: 1:36 2:3314737408 3:6215132672 4:9944212480 5:14501976064 6:19888424960
Substituting fallback discounts for order 0: D1=0.5 D2=1 D3+=1.5
Substituting fallback discounts for order 1: D1=0.5 D2=1 D3+=1.5
Substituting fallback discounts for order 2: D1=0.5 D2=1 D3+=1.5
Substituting fallback discounts for order 3: D1=0.5 D2=1 D3+=1.5
Substituting fallback discounts for order 4: D1=0.5 D2=1 D3+=1.5
Substituting fallback discounts for order 5: D1=0.5 D2=1 D3+=1.5
Statistics:
1 2 D1=0.5 D2=1 D3+=1.5
2 0 D1=0.5 D2=1 D3+=1.5
3 0 D1=0.5 D2=1 D3+=1.5
4 0 D1=0.5 D2=1 D3+=1.5
5 0 D1=0.5 D2=1 D3+=1.5
6 0 D1=0.5 D2=1 D3+=1.5
Memory estimate for binary LM:
type B
probing 144 assuming -p 1.5
probing 172 assuming -r models -p 1.5
trie 169 without quantization
trie 9366 assuming -q 8 -b 8 quantization
trie 261 assuming -a 22 array pointer compression
trie 9458 assuming -a 22 -q 8 -b 8 array pointer compression and quantization
=== 3/5 Calculating and sorting initial probabilities ===
Chain sizes: 1:24 2:16 3:20 4:24 5:28 6:32
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
=== 4/5 Calculating and writing order-interpolated probabilities ===
Chain sizes: 1:24 2:16 3:20 4:24 5:28 6:32
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
=== 5/5 Writing ARPA model ===
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
Name:lmplz VmPeak:52790308 kB VmRSS:5660 kB RSSMax:8306852 kB user:0.198719 sys:1.42085 CPU:1.61958 real:1.63664
[NeMo I 2023-05-08 14:55:24 train_kenlm:175] Running binary_build command
/home/azad2/NeMo/decoders/kenlm/build/bin/build_binary trie /home/azad2/NeMo/model.tmp.arpa /home/azad2/NeMo/model
Reading /home/azad2/NeMo/model.tmp.arpa
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
/home/azad2/NeMo/decoders/kenlm/lm/vocab.cc:305 in void lm::ngram::MissingSentenceMarker(const lm::ngram::Config&, const char*) threw SpecialWordMissingException.
The ARPA file is missing </s> and the model is configured to reject these models. Run build_binary -s to disable this check. Byte: 96
ERROR
Error executing job with overrides: ['nemo_model_file=/home/azad2/NeMo/Model-fa-512.nemo', 'train_paths=[/home/azad2/NeMo/commonvoice_train_manifest_processed.JSON]', 'kenlm_bin_path=/home/azad2/NeMo/decoders/kenlm/build/bin', 'kenlm_model_file=/home/azad2/NeMo/model', 'ngram_length=6', 'preserve_arpa=true']
Traceback (most recent call last):
File "/home/azad2/NeMo/scripts/asr_language_modeling/ngram_lm/train_kenlm.py", line 179, in main
raise RuntimeError("Training KenLM was not successful!")
RuntimeError: Training KenLM was not successful!
"
i really need your help.thanks! | {
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} | # What does this PR do ?
Fix an issue with virtual parallel conversion
**Collection**: [NLP]
# Changelog
- Fixes issue where the resultant model loaded in VP does not correctly load subset of model parameters due to incorrect VP state set in megatron core
# 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.
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} | # What does this PR do ?
Current T5 FT batch reconf for drop_last = False didn't account for the case of multivalidation. This lead to incorrect setting of micro-batch calculator.
The PR fixes the issue
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I am following [this tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/examples/kinyarwanda_asr.html) for training and evaluating a conformer-BPE model.
The training has finished properly and I have a `.nemo` file. I have tried following the inference script [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/examples/kinyarwanda_asr.html#inference-and-evaluation) on `colab pro` to no avail. The code just stops when it has to iterate over the batches. *There are no errors*. The final line is given below:
```bash
Transcribing: 0% 0/235 [00:00<?, ?it/s]^C
```
This is the script I am using :
```bash
! HYDRA_FULL_ERROR=1 python3 ./NeMo/examples/asr/transcribe_speech.py \
model_path=./assets/Conformer-CTC-BPE.nemo \
dataset_manifest=./speech_test_dataset_transcript.json \
output_filename=test_with_predictions.json \
batch_size=32 \
overwrite_transcripts=false
```
Can anyone please guide me here? I have also tried tinkering with the class file at `NeMo/nemo/collections/asr/models/ctc_models.py` | {
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what language model training toolkit does nemo uses for asr language model training? | {
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} | null | null | null | I took a multilingual pretrained english model and wanted to fine-tune it for ASR task on english and arabic together at once. I used a tokenizer that contains both English and arabic manifest.
Can this https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/multilang/conformer_ctc_bpe_multilang.yaml
Setting give results like this if yes then how. I was not able to find one.
I want the transcription to be:
مرحبًا ، أريد apple لذيذة
I've checked the tutorial file https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Multilang_ASR.ipynb but in this tutorial in the inference, it is a sample just for english and the other one is just for spanish. In fact it doesn't have an inference that transcribe a multi-languange like I said.
How one can do code switching? I wanna transcribe an audio file that a person speak in english and arabic simultaneously? | {
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} | null | null | null | **Describe the bug**
I simply followed the [TTS Aligner Inference Example](https://github.com/NVIDIA/NeMo/blob/stable/tutorials/tts/Aligner_Inference_Examples.ipynb) but the visualized `attn_soft` is empty and indeed `attn_soft_tensor` has all `nan` values.
<img width="1082" alt="Screen Shot 2023-05-06 at 10 43 29 PM" src="https://user-images.githubusercontent.com/621009/236655018-9c65c525-684c-42f6-ba1a-45e67024113f.png">
**Steps/Code to reproduce bug**
You can simply proceed through [this notebook]((https://github.com/NVIDIA/NeMo/blob/stable/tutorials/tts/Aligner_Inference_Examples.ipynb)
**Expected behavior**
Display a graph with values described in the instruction.
**Environment overview (please complete the following information)**
Google Collab | {
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while trying to train a language model for asr with train_kenlm.py, i encounter the following error:
''
[NeMo W 2023-05-06 17:30:53 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-05-06 17:30:53 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-05-06 17:30:54 nemo_logging:349] /root/anaconda3/lib/python3.10/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
ret = run_job(
[NeMo I 2023-05-06 17:30:54 kenlm_utils:73] Loading nemo model '/home/azad2/NeMo/Model-fa-512.nemo' ...
[NeMo I 2023-05-06 17:30:54 mixins:170] Tokenizer SentencePieceTokenizer initialized with 700 tokens
[NeMo W 2023-05-06 17:30:54 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /home/azad/manifests/commonvoice_train_manifest.json,/home/azad/manifests/commonvoice_dev_manifest.json
sample_rate: 16000
batch_size: 32
trim_silence: true
max_duration: 16.7
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
num_workers: 8
pin_memory: true
use_start_end_token: true
[NeMo W 2023-05-06 17:30:54 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: /home/azad/manifests/commonvoice_test_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: true
trim_silence: true
[NeMo W 2023-05-06 17:30:54 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: /home/azad/manifests/commonvoice_test_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: true
trim_silence: true
[NeMo I 2023-05-06 17:30:54 features:291] PADDING: 16
Call to cuInit results in CUDA_ERROR_NO_DEVICE
[NeMo I 2023-05-06 17:30:55 save_restore_connector:249] Model EncDecCTCModelBPE was successfully restored from /home/azad2/NeMo/Model-fa-512.nemo.
[NeMo I 2023-05-06 17:30:55 train_kenlm:149] Running lmplz command
/home/azad2/NeMo/decoders/kenlm/build/bin/lmplz -o 6 --arpa /NeMo/model.tmp.arpa --discount_fallback --prune 0
/home/azad2/NeMo/decoders/kenlm/util/file.cc:86 in int util::CreateOrThrow(const char*) threw ErrnoException because `-1 == (ret = open(name, 0100 | 01000 | 02, 0400 | 0200 | (0400 >> 3) | ((0400 >> 3) >> 3)))'.
No such file or directory while creating /NeMo/model.tmp.arpa
Error executing job with overrides: ['nemo_model_file=/home/azad2/NeMo/Model-fa-512.nemo', 'train_paths=[/home/azad2/NeMo/commonvoice_train_manifest_processed.JSON]', 'kenlm_bin_path=/home/azad2/NeMo/decoders/kenlm/build/bin', 'kenlm_model_file=/NeMo/model', 'ngram_length=6']
Traceback (most recent call last):
File "/home/azad2/NeMo/scripts/asr_language_modeling/ngram_lm/train_kenlm.py", line 164, in main
raise RuntimeError("Training KenLM was not successful!")
RuntimeError: Training KenLM was not successful!
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
''
i dont know whats the problem.
any idea whats the problem?
thanks in advance. | {
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} | # What does this PR do ?
Current T5 FT batch reconf for drop_last = False didn't account for the case of multivalidation. This lead to incorrect setting of micro-batch calculator.
The PR fixes the issue
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
adds an option for a safe batchnorm that does not accept nan values, converting them to zeroes
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Make tensor split contiguous for qkv and kv in attention
**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**:
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- [ ] New Feature
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} | # What does this PR do ?
Adds an IterableDataset class which generates on-the-fly code switched utterances for ASR
**Collection**: ASR
# Changelog
- `nemo/collections/asr/data/audio_to_text.py`: added language support to the BPE classes to support code-switching
- `nemo/collections/asr/data/audio_to_text_dataset.py`: added the core create_cs_dataset function and various instantiation logic
- `nemo/collections/common/data/__init__.py`: exposed new CodeSwitchedDataset class
- `nemo/collections/common/data/dataset.py`: contains new CodeSwitchedDataset class
- `nemo/collections/common/parts/preprocessing/collections.py`: added discrete language support of the TokeniserWrapper via a hasattr check
- `nemo/collections/asr/models/*`: changed all of the models to check for IterableDataset instead of reading the is_tarred config item, as I think this makes it more robust
# Usage
There are many different permutations to using this new dataset, but a basic example shows how to add it to an existing training config yaml. To understand what each parameter does, please look at the class doc for the CodeSwitchedDataset class in nemo.collections.common.data.dataset.
```yaml
++model.train_ds.manifest_filepath=[/path/to/en/tarred_manifest.json,/path/to/de/tarred_manifest.json,/path/to/ja/tarred_manifest.json]
++model.train_ds.tarred_audio_filepaths=['/path/to/en/tars/audio__OP_0..511_CL_.tar','/path/to/de/tars/audio__OP_0..1023_CL_.tar', '/path/to/ja/tars/audio__OP_0..2047_CL_.tar']
++model.train_ds.is_code_switched=true
++model.train_ds.is_tarred=true
++model.train_ds.shuffle=true
++model.train_ds.code_switched.min_duration=12
++model.train_ds.code_switched.max_duration=20
++model.train_ds.code_switched.min_monolingual=0.3
++model.train_ds.code_switched.probs=[0.25, 0.5, 0.25]
++model.train_ds.code_switched.force_monochannel=true
++model.train_ds.code_switched.sampling_scales=[0.75, 1, 0.25]
++model.validation_ds.manifest_filepath=[[/path/to/en/non-tarred/manifest.json, /path/to/de/non-tarred/manifest.json, /path/to/ja/non-tarred/manifest.json]]
++model.validation_ds.is_code_switched=true
++model.validation_ds.is_tarred=false \
++model.validation_ds.code_switched.min_duration=6
++model.validation_ds.code_switched.max_duration=20
++model.validation_ds.code_switched.min_monolingual=0.3
++model.validation_ds.code_switched.probs=[0.33, 0.34, 0.33]
++model.validation_ds.code_switched.force_monochannel=true
++model.validation_ds.code_switched.sampling_scales=1
~model.tokenizer
++model.tokenizer.type=agg
++model.tokenizer.langs.en.dir=/path/to/en/tokeniser
++model.tokenizer.langs.en.type=bpe
++model.tokenizer.langs.de.dir=/path/to/de/tokeniser
++model.tokenizer.langs.de.type=bpe
++model.tokenizer.langs.ja.dir=/path/to/ja/tokeniser
++model.tokenizer.langs.ja.type=bpe
```
# Before your PR is "Ready for review"
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} | # What does this PR do ?
Bug fixes for cach-aware conformer export.
replaces #6562
**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?
@VahidooX
Anyone in the community is free to review the PR once the checks have passed.
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} | # What does this PR do ?
Fix for autocast when precision is fp32
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add initial implementation of new TTS data loader which supports multi-manifest training and uses mostly preprocessed features.
The data loader is intended to be for TTS acoustic model and as such includes some specific functionality for text processing. Other models like vocoder will need another data loader, with most of the common code being shared in the features & tts_dataset_utils.
**Collection**: [TTS]
# Changelog
- Implement TextToSpeechDataset class (following ASR naming convention of having separate X to X datasets for each task).
- Add example FastPitch config for using new dataset.
- Modify feature.yaml files to use relative paths so they can be reused directly in the training script without needing to be nested in the fastpitch conf directory itself.
- Add code branches to FastPitch model to allow configuring new data loader for weighted sampling.
- Add `collate_fn` to featurizer class.
- Make log energy optional in FastPitch module so user can experiment with different normalization approaches.
# Usage
* This is an example command to train FastPitch on LJSpeech & VCTK with 0.2 and 0.8 sampling weight, respectively.
```bash
/home/rlangman/miniconda3/envs/nemo_dev/bin/python /home/rlangman/Code/NeMo/examples/tts/fastpitch.py
--config-path=/home/rlangman/Code/NeMo/examples/tts/conf/fastpitch
--config-dir=/home/rlangman/Code/NeMo/examples/tts/conf
--config-name=fastpitch_22050.yaml
n_speakers=110
speaker_path=/home/rlangman/Data/multi/ljspeech_vctk_speakers.json
max_epochs=1
weighted_sample_steps=10
phoneme_dict_path=/home/rlangman/Code/NeMo/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt
heteronyms_path=/home/rlangman/Code/NeMo/scripts/tts_dataset_files/heteronyms-052722
feature_stats_path=/home/rlangman/Data/multi/ljspeech_vctk_stats.json
exp_manager.exp_dir=/home/rlangman/exps/ljspeech_vctk
+train_ds_meta.ljspeech._target_=nemo.collections.tts.data.text_to_speech_dataset.DatasetMeta
+train_ds_meta.ljspeech.manifest_path=/home/rlangman/Data/ljspeech/train_manifest.json
+train_ds_meta.ljspeech.audio_dir=/home/rlangman/Data/ljspeech/audio
+train_ds_meta.ljspeech.feature_dir=/home/rlangman/Data/ljspeech/features
+train_ds_meta.ljspeech.sample_weight=0.2
+val_ds_meta.ljspeech._target_=nemo.collections.tts.data.text_to_speech_dataset.DatasetMeta
+val_ds_meta.ljspeech.manifest_path=/home/rlangman/Data/ljspeech/val_manifest.json
+val_ds_meta.ljspeech.audio_dir=/home/rlangman/Data/ljspeech/audio
+val_ds_meta.ljspeech.feature_dir=/home/rlangman/Data/ljspeech/features
+train_ds_meta.vctk._target_=nemo.collections.tts.data.text_to_speech_dataset.DatasetMeta
+train_ds_meta.vctk.manifest_path=/home/rlangman/Data/vctk/train_manifest.json
+train_ds_meta.vctk.audio_dir=/home/rlangman/Data/vctk/audio
+train_ds_meta.vctk.feature_dir=/home/rlangman/Data/vctk/features
+train_ds_meta.vctk.sample_weight=0.8
+val_ds_meta.vctk._target_=nemo.collections.tts.data.text_to_speech_dataset.DatasetMeta
+val_ds_meta.vctk.manifest_path=/home/rlangman/Data/vctk/val_manifest.json
+val_ds_meta.vctk.audio_dir=/home/rlangman/Data/vctk/audio
+val_ds_meta.vctk.feature_dir=/home/rlangman/Data/vctk/features
```
# 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
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} | # What does this PR do ?
Fix separate_punctuation=True option
**Collection**: ASR
# Changelog
Do
```
'some text.' -> 'some text .'
'some text .' -> some text .'
```
instead of
```
'some text.' -> 'some text .'
'some text .' -> some text.'
```
**PR Type**:
- [V ] Bugfix
## Who can review?
@Kipok
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} | Re-order the checks for faster processing audio filepaths that are already absolute paths
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} | null | null | null | Hi,
I'm facing the following issue while pip install nemo-toolkit.
ERROR: Could not find a version that satisfies the requirement megatron-lm==2.2.0; extra == "all" (from nemo-toolkit[all]) (from versions: 2.2.1, 2.2.2)
ERROR: No matching distribution found for megatron-lm==2.2.0; extra == "all"
Can someone help me solve this issue? | {
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} | null | null | null | Hello,
I am using the offline speaker diarization module and I managed to export vad_multilingual_marblenet.nemo model to onnx.
Running voice activity detection with ONNX results in an improvement of about 20% less inference time. From what I have read ONNX usually results in a greater imrovement.
Is this result normal? Did I implement it wrong? Did anybody try this before and has some profiling results? Are there any ways to improve inference speed even more?
My code in /nemo/collections/asr/models/clustering_diarizer:
```
def _run_vad(self, manifest_file):
"""
Run voice activity detection.
Get log probability of voice activity detection and smoothes using the post processing parameters.
Using generated frame level predictions generated manifest file for later speaker embedding extraction.
input:
manifest_file (str) : Manifest file containing path to audio file and label as infer
"""
shutil.rmtree(self._vad_dir, ignore_errors=True)
os.makedirs(self._vad_dir)
self._vad_model.eval()
self._vad_model.export('vad.onnx')
ort_session = onnxruntime.InferenceSession('vad.onnx')
time_unit = int(self._vad_window_length_in_sec / self._vad_shift_length_in_sec)
trunc = int(time_unit / 2)
trunc_l = time_unit - trunc
all_len = 0
data = []
for line in open(manifest_file, 'r', encoding='utf-8'):
file = json.loads(line)['audio_filepath']
data.append(get_uniqname_from_filepath(file))
status = get_vad_stream_status(data)
for i, test_batch in enumerate(
tqdm(self._vad_model.test_dataloader(), desc='vad', leave=True, disable=not self.verbose)
):
test_batch = [x.to(self._vad_model.device) for x in test_batch]
with autocast():
# ONNX inference
audio_signal, audio_signal_len = test_batch[0], test_batch[1]
audio_signal, audio_signal_len = audio_signal.to(self._vad_model.device), audio_signal_len.to(self._vad_model.device)
processed_signal, processed_signal_len = self._vad_model.preprocessor(input_signal=audio_signal, length=audio_signal_len,)
ort_inputs = {ort_session.get_inputs()[0].name: self.to_numpy(processed_signal), }
ologits = ort_session.run(None, ort_inputs)
alogits = np.asarray(ologits)
logits = torch.from_numpy(alogits[0])
probs = torch.softmax(torch.as_tensor(logits), dim=-1)
pred = probs[:, 1]
``` | {
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} | # What does this PR do ?
Offline and streaming inference support for hybrid model
**Collection**: ASR
# Changelog
- Add check to examine whether model and decoding type match
- Update`speech_to_text_buffered_infer_ctc.py` and `speech_to_text_buffered_infer_rnnt.py` to support hybrid + ctc and hybrid rnnt
- Make default `model_stride` in eval.yaml to be 8 for `fastconformer`
- Update asr_evaluator accordingly
- Add relative path + manifest support (get_full_path) to buffered inference
# Usage
```python
# model / decode_type
# # buffered inference
# [x] ctc + null
# [x] ctc + ctc
# [x] ctc + rnnt - should fail
# [x] rnnt + null
# [x] rnnt + rnnt
# [x] rnnt + ctc - should fail
# [x] hybrid ctc
# [x] hybrid null same as below
# [x] hybrid rnnt
# # offline
# [x] rnnt null
# [x] rnnt rnnt
# [x] rnnt ctc - should fail
# [x] ctc null
# [x] ctc ctc
# [x] ctc rnnt - should fail
# [x] hybrid + ctc
# [x] hybrid + null same as rnnt
# [x] hybrid + rnnt
```
# 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**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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 ?
Docs build fails currently due to older version of OpenSSL on readthedocs. Limit urllib3 version to <2
**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"
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[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 ?
Docs build fails currently due to older version of OpenSSL on readthedocs. Limit urllib3 version to <2
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Bug fixes for cach-aware conformer export.
replaces #6562
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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My second experience was that on 4090 the https://huggingface.co/nvidia/GPT-2B-001 did not work #6564
I want to play around with FP8 and it looked like this was fastest way but it was just not working, and when did it to run was 4x slower than regular hf transformers.
So simplest thing is have a really damn simple demo showing how to run models from hugging face.
- lmsys/fastchat-t5-3b-v1.0
- nvidia/GPT-2B-001
- google/flan-ul2
Using 4090 and A100.
Like I know it's possible but the demo you have for using your own model does not work, and it should be a big friendly button notebook that I run end to end in the official docker container to get those models running. | {
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4090
start server with
`python /workspace/nemo/examples/nlp/language_modeling/megatron_gpt_eval.py gpt_model_file=GPT-2B-001_bf16_tp1.nemo trainer.precision=bf16 server=True tensor_model_parallel_size=1 trainer.devices=1`
run unmodified sample code
```python
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)
sentences = resp.json()['sentences']
return sentences
data = {
"sentences": ["Tell me an interesting fact about space travel."]*1,
"tokens_to_generate": 50,
"temperature": 1.0,
"add_BOS": True,
"top_k": 0,
"top_p": 0.9,
"greedy": False,
"all_probs": False,
"repetition_penalty": 1.2,
"min_tokens_to_generate": 2,
}
sentences = request_data(data)
print(sentences[0])
```
output (gibberish)
> Tell me an interesting fact about space travel. My version of health. A method of therapy for gonorrhoea by a man. A man presented with this complaint of "despair for me for a man for a man with gonorrhea. I presented to a man with gonoragia. My man | {
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} | # What does this PR do ?
Removing casts around norms from the set of default pre-export replacements and moving them to a separate function.
Latest TRT should not need that anymore - also the casts may prevent some fusion.
That function may still be used from Nemo2Riva explicitly should it be needed on case-by-case basis.
**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 ?
Bug fixes for cache aware / conformer export
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
@borisfom
@VahidooX
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} | # What does this PR do ?
Fix bad JSON in notebook.
**Collection**: [ASR]
# Changelog
- Correct the comma
# 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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**PR Type**:
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Patch transcribe_util and support offline transcribe for hybrid model
**Collection**: ASR
# Changelog
- Fix wrong decoding in write_transcript
- Add support for hybrid offline transcribe in asr_evaluator
# Usage
* You can potentially add a usage example below
```python
python transcribe_speech.py
model_path={hybrid_model}
decoder_type='ctc'/'rnnt'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/pull/6063/files#r1178635367
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Update scores table for Fast Conformer models
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**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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} | # What does this PR do ?
Adds support for large inputs (large batches and large samples) to Fast Conformer models, working around this issue https://github.com/pytorch/pytorch/issues/80020
For regular Conformer, this PR adds support for larger batches.
**Collection**: ASR
# Changelog
- In subsampling.py, if the input is too large, try to split it by batch. if not, try to split it by channels (only models with depthwise-separable convolutions)
# Usage
* encoder.subsampling_conv_chunking_factor=1 (auto-chunking, default) or -1 (disabled) or to a specific value (power of 2)
* Also, you can load a pre-trained model and then call model.change_subsampling_conv_chunking_factor(subsampling_conv_chunking_factor=N) to perform inference on large batches / large samples
```python
# set
encoder.subsampling_conv_chunking_factor=1
# (1 means auto-chunking, default); -1 (disabled) or to a specific value (power of 2)
# Also, you can load a pre-trained model and then call model.change_subsampling_conv_chunking_factor(subsampling_conv_chunking_factor=N)
# to perform inference on large batches / large samples
# channel wise chunking will attempted only if the model uses depthwise-separable convolutions.
```
# 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
Helps support large batches and input, working around https://github.com/pytorch/pytorch/issues/80020
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- If the input is detected to be too big, tries to split it by batch and if that does not work, by time
# Usage
* n/a
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix for autocast when precision is fp32
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds the FastConformer Hybrid ASR models (EN, ES, IT, DE, PL, BY, HR, UA) to the PreTrained models catalog
**Collection**: ASR
# Changelog
- Added the FC Hybrid models to nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py
- Updated the ASR docs at NeMo/docs/source/asr/results.rst
- Created new results tables for BY (NeMo/docs/source/asr/data/benchmark_by.csv), UA (NeMo/docs/source/asr/data/benchmark_ua.csv)
# Usage
When loading pre-trained models, we can now specify:
- stt_en_fastconformer_hybrid_large_pc
- stt_de_fastconformer_hybrid_large_pc
- stt_it_fastconformer_hybrid_large_pc
- stt_es_fastconformer_hybrid_large_pc
- stt_hr_fastconformer_hybrid_large_pc
- stt_by_fastconformer_hybrid_large_pc
- stt_ua_fastconformer_hybrid_large_pc
- stt_pl_fastconformer_hybrid_large_pc
- stt_en_fastconformer_transducer_large
- stt_en_fastconformer_ctc_large
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Change Megatron Enc Dec model to use persistent_workers.
Seems to be needed for bigNLP CI - fails for persistent_workers issue if not provided
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes GradScaler issue when using torch's GradScaler
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Patch transcribe_util and support offline transcribe for hybrid model
**Collection**: ASR
# Changelog
- Fix wrong decoding in write_transcript
- Add support for hybrid offline transcribe in asr_evaluator
# Usage
* You can potentially add a usage example below
```python
python transcribe_speech.py
model_path={hybrid_model}
decoder_type='ctc'/'rnnt'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/pull/6063/files#r1178635367
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} | # What does this PR do ?
This PR adds the FastConformer Hybrid ASR models (EN, ES, IT, DE, PL, BY, HR, UA) to the PreTrained models catalog
**Collection**: ASR
# Changelog
- Added the FC Hybrid models to nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py
- Updated the ASR docs at NeMo/docs/source/asr/results.rst
- Created new results tables for BY (NeMo/docs/source/asr/data/benchmark_by.csv), UA (NeMo/docs/source/asr/data/benchmark_ua.csv)
# Usage
When loading pre-trained models, we can now specify:
- stt_en_fastconformer_hybrid_large_pc
- stt_de_fastconformer_hybrid_large_pc
- stt_it_fastconformer_hybrid_large_pc
- stt_es_fastconformer_hybrid_large_pc
- stt_hr_fastconformer_hybrid_large_pc
- stt_by_fastconformer_hybrid_large_pc
- stt_ua_fastconformer_hybrid_large_pc
- stt_pl_fastconformer_hybrid_large_pc
- stt_en_fastconformer_transducer_large
- stt_en_fastconformer_ctc_large
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Change Megatron Enc Dec model to use persistent_workers.
Seems to be needed for bigNLP CI - fails for persistent_workers issue if not provided
**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"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [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 ?
This PR adds a few enhancements to RIR generation scripts.
**Collection**: Tools
# Changelog
- Fix: reduce room size if evaluation of params fails
- Added: randomized mic placement
- Added: diffuse noise generation
- Added: an option to specify the format and subtype for saved audio
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
Fix for T5 FT model tensor shape
**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)
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**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
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} | # What does this PR do ?
Fixes GradScaler issue when using torch's GradScaler
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes GPT inference when trainer.precision is 16.
**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 ?
Fixes GPT inference when trainer.precision is 16.
**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 fast emit section to the streaming configs.
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} | # What does this PR do ?
A lot of people already have a local script which does text processing & normalization before training. To do it quickly one needs to use multiprocessing or joblib, as our text normalizer does not support batch processing.
It only does normalization and optional lower casing of the final output right now, but could be extended with additional processing functionality as needed.
**Collection**: [TTS]
# Changelog
- Create text preprocessing script
- Add example config file for English text normalization
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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Add fast emit section to the streaming configs.
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* Fix typos
Signed-off-by: smajumdar <[email protected]>
---------
Signed-off-by: smajumdar <[email protected]>
(cherry picked from commit 5468077f5127be1a4c88065de2544f4268b9a6e4)
# What does this PR do ?
**Collection**: [Docs]
# 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/6538 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6538/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6538/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6538/events | https://github.com/NVIDIA/NeMo/issues/6538 | 1,693,134,214 | I_kwDOC_bI7s5k6y2G | 6,538 | Exception: precision 16 is not supported. Float16Module (megatron_amp_O2) supports only fp16 and bf16. | {
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} | null | null | null | I'm trying to run [GPT-2B-001](https://huggingface.co/nvidia/GPT-2B-001) inside the NeMo Megatron docker container on Ubuntu 22.04 with RTX 4090
```
sudo docker pull nvcr.io/nvidia/nemo:23.02
sudo docker run --runtime=nvidia -it --rm -v --shm-size=16g -p 5555:5555 -p 8889:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:23.02
```
Inside the containter
```
cd examples/nlp/language_modeling
wget https://huggingface.co/nvidia/GPT-2B-001/resolve/main/GPT-2B-001_bf16_tp1.nemo -O nemo_2b_bf16_tp1.nemo
HYDRA_FULL_ERROR=1 python megatron_gpt_eval.py gpt_model_file=nemo_2b_bf16_tp1.nemo server=True tensor_model_parallel_size=1 trainer.devices=1
```
The error
```
Error executing job with overrides: ['gpt_model_file=nemo_2b_bf16_tp1.nemo', 'server=True', 'tensor_model_parallel_size=1', 'trainer.devices=1']
Traceback (most recent call last):
File "megatron_gpt_eval.py", line 279, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.8/dist-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper
_run_hydra(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
return func()
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "megatron_gpt_eval.py", line 182, in main
model = MegatronGPTModel.restore_from(
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/modelPT.py", line 436, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 366, in restore_from
loaded_params = super().load_config_and_state_dict(
File "/usr/local/lib/python3.8/dist-packages/nemo/core/connectors/save_restore_connector.py", line 162, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/common.py", line 507, in from_config_dict
raise e
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/common.py", line 499, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 128, in __init__
self.model = Float16Module(module=self.model, precision=cfg.precision)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/nlp/modules/common/megatron/module.py", line 278, in __init__
raise Exception(
Exception: precision 16 is not supported. Float16Module (megatron_amp_O2) supports only fp16 and bf16.
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/6536 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6536/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6536/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6536/events | https://github.com/NVIDIA/NeMo/pull/6536 | 1,692,930,369 | PR_kwDOC_bI7s5PnQlB | 6,536 | TDT model pull request | {
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"name": "ASR",
"color": "F3B33E",
"default": false,
"description": ""
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] | closed | false | null | [] | null | null | 2023-05-02T18:49:07 | 2023-06-02T22:08:30 | 2023-06-02T22:08:30 | COLLABORATOR | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/6536",
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/6536.diff",
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} | # What does this PR do ?
Token-and-Duration Transducer (TDT, https://arxiv.org/abs/2304.06795) implementation.
**Collection**:
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
To train a TDT model, use config file at examples/asr/conf/conformer/tdt/conformer_tdt_bpe.yaml. You can change the $durations parameter to customize your model, e.g.
```
durations=[0,1,2,3]
num_durations=4 # this needs to match the previous durations variable.
python examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py \
[options similar to launching conventional Transducer models] \
model.loss.tdt_kwargs.fastemit_lambda=0.001 \
model.loss.tdt_kwargs.omega=0.1 \
model.model_defaults.tdt_durations=$durations \
model.model_defaults.num_tdt_durations=$num_durations \
model.decoding.strategy='greedy' # Greedy decoding is recommended to get more accurate validation WERs during training. It's already set in the config file as default. We list it here to emphasize its importance.
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6535 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6535/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6535/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6535/events | https://github.com/NVIDIA/NeMo/issues/6535 | 1,692,743,713 | I_kwDOC_bI7s5k5Tgh | 6,535 | How to get the intermediate features from the pre-trained checkpoint | {
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} | null | null | null | Hi @titu1994, @VahidooX, thank you for your hard work.
I am trying out an experiment for the knowledge distillation task by utilizing the official pre-trained checkpoint ([stt_en_conformer_ctc_large_ls](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large_ls)) for the teacher model.
I would like to extract some intermediate features from this pre-trained teacher model (i.e., the self-attention layers in the conformer blocks), but all the intermediate layers in `ConformerEncoder` module seem to be overwritten with the variable `audio_signal`.
Is there a way to get these intermediate layers from the pre-trained checkpoint you provide?
I'd be grateful if you could provide some tutorials or tips.
Thank you. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6534 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6534/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6534/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6534/events | https://github.com/NVIDIA/NeMo/issues/6534 | 1,692,119,491 | I_kwDOC_bI7s5k27HD | 6,534 | ModuleNotFoundError: No module named 'kenlm_utils' | {
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} | null | null | null | hi,
while trying to import "kenlm_utils" i face this error:
ModuleNotFoundError: No module named 'kenlm_utils'
and after manually adding kenlm_utils.py to my directory i encounter the following error:
"UnsupportedOperation Traceback (most recent call last)
[<ipython-input-29-cec13af5ea4e>](https://localhost:8080/#) in <cell line: 83>()
82
83 if __name__ == '__main__':
---> 84 main()
3 frames
[/usr/lib/python3.10/subprocess.py](https://localhost:8080/#) in _get_handles(self, stdin, stdout, stderr)
1636 else:
1637 # Assuming file-like object
-> 1638 c2pwrite = stdout.fileno()
1639
1640 if stderr is None:
UnsupportedOperation: fileno"
any help is really appreciated.
thanks!
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} | # What does this PR do ?
See issue https://github.com/NVIDIA/NeMo/issues/6532
**Collection**: [Note which collection this PR will affect]
tokenizers
# Changelog
- See comments in the Issue https://github.com/NVIDIA/NeMo/issues/6532
# Usage
* You can potentially add a usage example below
```python
from nemo.collections.common.tokenizers.tabular_tokenizer import TabularTokenizer
from nemo.collections.common.tokenizers.column_code import ColumnCodes
END_OF_TEXT = '<|endoftext|>'
NEW_LINE = '\n'
DELIMITER = ','
# see example of tab_structure and example arrays here:
# https://github.com/NVIDIA/NeMo/pull/6533/files#diff-2eb09eb882bbc75d60d887b51950ed0fb3331d5c4248e9e0b0ec0a566609c041R133
column_codes = ColumnCodes.get_column_codes(tab_structure, example_arrays)
tabular_tokenizer = TabularTokenizer(column_codes, special_tokens=[NEW_LINE, END_OF_TEXT], delimiter=DELIMITER)
token_ids = tabular_tokenizer.encode(doc['text'])
output = tabular_tokenizer.decode(token_ids)
```
# 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
- [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)](https://github.com/NVIDIA/NeMo/issues/6532)
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