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just replace the text promp with: "hallo wie geht es ihnen?" - German for "Hello, how are you doing?"
This occours:
[hallo wie geht es ihnen?] contains unknown char: [g]. Symbol will be skipped.
**Expected behavior**
The letter "g" is commonly used in the German alphabet and is essential to pronunciation of certain words. It seems that this issue is related to the trained models that are provided under that link.
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I get an error when trying to run inference on the provided question answering tutorial notebook.
**Steps/Code to reproduce bug**
Run this notebook to reproduce the error.
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/nlp/Question_Answering.ipynb
**Expected behavior**
It should run successfully
**Environment overview (please complete the following information)**
- Environment location: Collab
**Additional context**
Error: `ReferenceError: weakly-referenced object no longer exists`
Code: `exp_dir = exp_manager(model.trainer, config.exp_manager)` | {
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
This PR adds an option to use ASR datasets from an AIStore cluster.
## NOTE
Blocked by #5569.
**Collection**: [ASR]
# Changelog
- Added AIS binary to Docker image
- Added env variables for configuring NeMo data store cache
- Added an option to defer dataloader initialization to `fit`, so data download can be synchronized across workers after a distributed environment is initialized
- For tarred datasets, added caching of manifests to store cache
- For non-tarred datasets, added caching of manifests and audio to store cache
- Utilities for handling data from AIStore cluster
# Usage
### AIS cluster config
AIStore endpoint needs to be configured before running the training command
```bash
export AIS_ENDPOINT=host:port
```
### Dataset setup
Manifests and tarred filepaths can point to AIS. For example, manifest can be set as
```bash
manifest_filepath = ‘ais://bucket/train_manifest.json'
```
and tarred filepaths as
```bash
tarred_audio_filepaths = ‘ais://bucket/audio__OP_0..511_CL_.tar'
```
To make sure data is download only on local or global rank zero, we use defer dataloader setup.
This can be achieved by setting `defer_setup = True` for the corresponding dataset or as
```bash
+model.train_ds.defer_setup=true \
+model.validation_ds.defer_setup=true \
+model.test_ds.defer_setup=true \
```
Note: A similar behavior could be achieved by using DataModule and controlling shared/non-shared caching using `datamodule.prepare_data_per_node`. However, that may require a more significant refactor.
### Cache setup
Local node-specific cache can be set up by setting the following env variables:
```bash
export NEMO_DATA_STORE_CACHE_DIR=/tmp
export NEMO_DATA_STORE_CACHE_SHARED=0
```
In this case, each local rank zero will cache data from AIS.
A shared cache location can be setup, for example, as
```bash
export NEMO_DATA_STORE_CACHE_DIR=/path/to/shared/dir
export NEMO_DATA_STORE_CACHE_SHARED=1
```
In this case, only the global rank zero will cache data from AIS.
# 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
* Blocked by #5569. Currently rebased on top of that branch.
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} | # What does this PR do ?
Cherry pick of #5358 from main.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adding VITS model to NeMo
**Collection**: [Note which collection this PR will affect]```
# 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 # (issue)
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} | null | null | null | NeMo's BERT based intent slot classification model (nemo_nlp.models.IntentSlotClassificationModel) fails to train (training loss remains the same after every epoch) when we use NeMo's stable versions (eg. v1.12.0, 'main' branch). However it trained perfectly well when we used the `r1.11.0` branch instead.
Please run this [Joint Intent Slot Classification](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb) notebook as it is to reproduce the bug.
- Environment location: GCP Compute Engine (NVIDIA T4 Tensor Core GPU)
- Method of NeMo install:
`BRANCH = 'main'`
`!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]` | {
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} | null | null | null | Hi All,
I have trained a Hindi-English code-switched ASR system using the dataset having the transcription written in English (for English words) & Hindi (for Hindi words).
Example:
क्या सौ रुपए loan लेना चाहती है आप
The tokenization script resulted in all the letters from Hindi + English for vocabulary creation. Nemo pretrained Hindi model followed by transfer learning using this dataset is done. When I decode on test set using this model I get some words having tokens from both languages.
Example: "doubल" --> "doub" (English) + "ल" (Hindi) --> ideally it should have decoded as "double"..
I want to know, is there any way (any settings/options) to restrict all the tokens in a word to be from a single language (and not a mix of more than one language). Please suggest.
Thanks
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} | # What does this PR do ?
I finally managed to properly script only necessary part, and added a guard that works around the issue with auto-casted TorchScript not workin in non-Python environment (Triton)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add script for computing speaker specific pitch mean & standard deviation, and logic in data loader to use it at training time.
Global pitch normalization can be done using the previous configs, or by providing a JSON with only a "default" key.
There is a bit of awkward typing logic due to our speaker IDs being integers but JSON only supporting string keys.
**Collection**: [TTS]
# Changelog
- Add script for computing the mean and standard deviation of pitch for each speaker in a dataset and storing it in a JSON dictionary
- Add data loading logic to support parsing these pitch statistics from the JSON.
- Register stats file in FastPitch model
- Add pitch stats for Spanish openSLR data and modify .yaml config example.
- Added map from speaker name to speaker ID (integer) used as the keys in the pitch statistics, and as the possible inputs to the NGC model (https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_es_multispeaker_fastpitchhifigan). The script for creating this speaker mapping will not be merged until I add the script/tutorial for downloading and processing the openSLR data.
# 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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} | Signed-off-by: Dima Rekesh <[email protected]>
# What does this PR do ?
Fixes the bug / ensures that Concat dataset feature supports tarred datasets.
Add a one line overview of what this PR aims to accomplish.
The tarred_audio_filepaths parameter could be a list such that each dataset that the Concat dataset is
comprised of could have a different set of audio filepaths
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
audio_to_text_dataset is patched to process the list of audio filepaths
# Usage
* You can potentially add a usage example below
* No change
```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 ?
Moves the attention and mlp classes to a separate file to avoid clutter in transformer.py
**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
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# 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**:
- [ ] New Feature
- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Sets sync_batch_comm=False for prompt-learning and inference.
**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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- [ ] 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 ?
Cherry pick of #5358 from main.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: subhankar-ghosh <[email protected]>
# What does this PR do ?
Adds JoC Optimizations in FastPitch
1) Correcting dimensions, it will give wrong results in case of >1 attention heads in `nemo/collections/tts/modules/transformer.py`
2) Optimized implementation of `mas_width1` method in `nemo/collections/tts/helpers/helpers.py`
3) Converted `attn` to `log attn` in `binarize_attention_parallel` and corresponding changes in aligner loss.
**Collection**: [Note which collection this PR will affect]
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?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Somshubra Majumdar <[email protected]>
# What does this PR do ?
Update codeql to do LGTM style code checks.
**Collection**: [Core]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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# What does this PR do ?
Sets sync_batch_comm=False for prompt-learning and inference.
**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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} | Signed-off-by: Kirthi Shankar Sivamani <[email protected]>
# What does this PR do ?
Adds interface in NeMo to use Transformer Engine's optional amax reduction feature. Previously, TE would always reduce amaxes by default during fp8 training (across the `fp8_group` specified in `fp8_autocast` API). | {
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} | Signed-off-by: Vahid <[email protected]>
# What does this PR do ?
Cherry-pick from #5242 to 1.13.0 to fix Conformer export https://github.com/NVIDIA/NeMo/issues/5436
Add a one line overview of what this PR aims to accomplish.
**Collection**: [asr]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Somshubra Majumdar <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"merged_at": "2022-11-17T09:03:24"
} | # What does this PR do ?
Fix setting up of `ReduceLROnPlateau` learning rate scheduler.
**Collection**: core
Steps to reproduce
```bash
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo
git checkout fix/reduce_on_plateau_r1.11.0
git checkout r1.11.0 -- nemo/core/optim/lr_scheduler.py nemo/core/config/schedulers.py
pytest tests/core/test_optimizers_schedulers.py
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add high level API for beam search using model.transcribe() and beam search strategy.
Currently only supports DeepSpeed beam search library, eventually can support more advanced libraries
like pyctcdecode.
**Collection**: [ASR]
# Changelog
- Adds AbstractBeamCTCInfer and BeamCTCInfer classes to support beam search strategy
- Updated CTCDecoding and CTCBPEDecoding classes to support beam search strategy
- Adds tests for CTC Decoding classes
# Usage
``` python
import nemo.collections.asr as nemo_asr
filepath = "/media/smajumdar/data/Datasets/Librispeech/LibriSpeech/test-other-processed/367-130732-0008.wav"
kenlm_path = "/media/smajumdar/data/Datasets/ASR_SET_LM/ASR_SET_3.0/lm"
model = nemo_asr.models.ASRModel.from_pretrained('stt_en_conformer_ctc_large') # type: nemo_asr.models.EncDecCTCModelBPE
# Create default decoding config
model.change_decoding_strategy(None)
# Update decoding config for beam search
decoding_cfg = model.cfg.decoding
decoding_cfg.strategy = "beam"
decoding_cfg.beam.beam_size = 128
decoding_cfg.beam.return_best_hypothesis = False
decoding_cfg.beam.beam_alpha = 1.0
decoding_cfg.beam.beam_beta = 0.0
decoding_cfg.beam.kenlm_path = kenlm_path
# Prepare beam search decoding strategy
model.change_decoding_strategy(decoding_cfg)
# Transcribe speech with beam search
hypothesis = model.transcribe([filepath], return_hypotheses=True)
print("Num hypothesis per sample :", len(hypothesis[0]))
print("Result type :", hypothesis[0][0].__class__.__name__)
for candidate in range(len(hypothesis[0])):
print(f"Beam Search {candidate + 1} :", hypothesis[0][candidate].text, hypothesis[0][candidate].score)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
This PR fixes getting tokenizer in character-based ASR models when using tarred dataset
**Collection**: ASR
# Changelog
- This PR removed passing `self.tokenizer` to `get_concat_tarred_dataset()` or `get_tarred_dataset()`
- Because character-based ASR models don't use tokenizers, `self.tokenizer` raises the following error:
```
Traceback (most recent call last):
File "/workspace/asr/korean/./speech_to_text_rnnt.py", line 85, in main
asr_model = EncDecRNNTModel(cfg=cfg.model, trainer=trainer)
File "/workspace/NeMo/nemo/collections/asr/models/rnnt_models.py", line 53, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/workspace/NeMo/nemo/core/classes/modelPT.py", line 132, in __init__
self.setup_training_data(self._cfg.train_ds)
File "/workspace/NeMo/nemo/collections/asr/models/rnnt_models.py", line 563, in setup_training_data
self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
File "/workspace/NeMo/nemo/collections/asr/models/rnnt_models.py", line 509, in _setup_dataloader_from_config
tokenizer=self.tokenizer,
File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1207, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'EncDecRNNTModel' object has no attribute 'tokenizer'
```
# 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 ?
Adding core classes and functions for online diarizer
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Creating online_diarizer.py as opposed to clustering_diarizer.py
--- OnlineDiarizer() class is added. This is a launcher class for load models and modules for online diarization.
- Adding torch.jit.scripted segmentation functions/classes in speaker_utils.py
--- OnlineSegmentor() class is added for torch.jit.script export purpose
- Adding online clustering functions/classes in nmesc_clustering.py
---- OnlineSpeakerClustering() class is added
- Unit tests (test_diar_utils.py) for segment merging, label matching, online clustering
# Usage
These functions are not yet executable.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
Refactor/unify ASR offline and buffered inference
**Collection**: ASR
# Changelog
1. Clean `transcribe_speech.py` by moving resuable functions to `trancribe_utils.py`
2. Unify `speech_to_text_buffered_infer_rnnt.py` and `speech_to_text_buffered_infer_ctc.py` to have the same input format and behavior as `transcribe_speech.py` including
> 1. Unify the input format and inconsistent variable names such as `chunk_len_in_secs` and `chunk_len_in_ms`
> 2. support no groundtruth 'txt' condition and audio_dir condition,
> 3. Modify and move `get_wer_feat` in `speech_to_text_buffered_infer_rnnt.py` and `get_wer_feat` in `speech_to_text_buffered_infer_ctc.py` to `get_buffered_pred_feat_rnnt` and `get_buffered_pred_feat` in `transcribe_utils.py `
> 4. remove WER calculation in above scripts.
> 5. Write all information presented in input manifest to output manifest.
# Usage
* See headers in each example scripts
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a standalone diarization-ASR evaluation transcript.
This script was build at the request for testing ASR+diarization with already extracted files.
Adding diar_infer_general.yaml (VAD optimized for multilingual ASR dataset, diarization optimized on DIHARD dev set)
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
python eval_diar_with_asr.py \
--hyp_rttm_list="/path/to/hypothesis_rttm_filepaths.list" \
--ref_rttm_list="/path/to/reference_rttm_filepaths.list" \
--ref_ctm_list="/path/to/reference_ctm_filepaths.list" \
--hyp_ctm_list="/path/to/hypothesis_ctm_filepaths.list" \
--root_path="/path/to/output/directory"
```
# 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 belongs to ASR
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
1. Added Text generation web server for GPT model and universal prompt encoder.
2. Added Retrieval text generation services and web server.
3. Fixed various bugs for text generation
4. Allow data parallel for text generation. | {
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Trying to export conformer to TorchScript, using `model.export('conformer.pt')` and [/scripts/export.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/export.py). Getting error:
`RuntimeError: Type 'Tuple[Tensor, Tensor, NoneType, NoneType]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced`
This is true for conformer trained on the Nemo version 1.12.
But everything is ok with exporting old version of conformer (trained on Nemo 1.10) and with that Nemo version installed.
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} | # What does this PR do ?
Extention of English ITN rules for cardinal to support hundreds by @lociko
**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
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Reverts a workaround that needed workers to be 0
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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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?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
With sync_batch_comm=True in validation step we sometimes get CUDA errors while running validation.
**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.
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* Related to # (issue)
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# What does this PR do ?
Fixes GPT text generation when using a sentencepiece tokenizer.
**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.
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* Related to # (issue)
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# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Usage
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```
# Before your PR is "Ready for review"
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- [ ] 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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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"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Updates export process for Megatron-based models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Changed ORT optimization due to a bug in onnxruntime (https://github.com/microsoft/onnxruntime/issues/12579)
# Usage
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```
# Before your PR is "Ready for review"
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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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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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 ?
Temporary hard code fix in PTL for CUDA Error. Only modify Dockerfile.
# Changelog
- Two `sed` lines in dockerfile
# 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 ?
Temporary hard code fix in PTL for CUDA Error. Only modify Dockerfile.
# Changelog
- Two `sed` lines in dockerfile
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Reverts a workaround that needed workers to be 0
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hi. I have trained univnet model and i want to export it to onnx. but i got this error . what should i do?
`
torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of operator Unfold, input size not accessible. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues [Caused by the value '381 defined in (%381 : Float(*, *, *, strides=[65600, 2050, 1], requires_grad=0, device=cuda:0) = onnx::Pad[mode="constant"](%output, %379, %380), scope: nemo.collections.tts.models.univnet.UnivNetModel::/nemo.collections.tts.modules.univnet_modules.Generator::generator/nemo.collections.tts.modules.univnet_modules.LVCBlock::res_stack.0
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes args that were not present in the T5 configs for activation checkpointing
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add support for modeling words in kenLM for subword models.
**Collection**: ASR
# Changelog
- Add support for modeling words in kenLM for subword models.
- Add missing dependency to beam search installation.
# Usage
NA
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Finally, this is a draft. I am going to check with the effected party that these changes will work for them. Then maybe someone like @VahidooX can take a look.
# Additional Information
One commit message describes this in detail. Pasting here:
Previously, NeMo did not support beam search with a KenLM language
model trained on words when using a subword acoustic model. Rather, it
forces KenLM to model subpiece units instead. This caused problems for
a customer using https://github.com/nvidia-riva/riva-asrlib-decoder,
which does support this out of the box.
Adding --encoding_level_override=char enables this behavior. It is an
opt-in behavior.
There are very other additions here to make training KenLM models more
flexible:
- I add "--skip_symbols" to the "lmplz" call. Sometimes training data
can have "<s>" or "</s>" in it, which can cause a crash, unless you
trun this flag on. Always turning on the flag has no bad effects and
is backwards compatible.
- I add support for lmplz's "-T" option. This is useful if you are
working on a machine where "/tmp", the default directory, is small.
- I added "--limit_vocab_file". It speeds up training when you happen
to have a training file that has a lot of out of your vocabulary of interest
(e.g., scraped from webpages).
I disable forcibly deleting the arpa file created by lmplz. You need
to keep this if you are using a WFST decoder.
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Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
* raised `NotImplementedError` for unsupported languages.
* added `@staticmethod` decor for member functions.
* fixed some typos.
* renamed argument name for the subroutine of `generate_permutations()` to avoid inner and outer scopes.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes GPT text generation when using a sentencepiece tokenizer.
**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 ?
1. Added Perceiver based Universal Prompt encoder.
2. Supports T0 datasets.
3. Added Text generation web server for GPT model and universal prompt encoder.
4. Added Retrieval text generation services and web server.
5. fixed various bugs for text generation, sentence tokenizer for mGPT and allow data parallel for text generation. | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes args that were not present in the T5 configs for activation checkpointing
**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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- [ ] 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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https://api.github.com/repos/NVIDIA/NeMo/issues/5409 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5409/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5409/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5409/events | https://github.com/NVIDIA/NeMo/pull/5409 | 1,448,741,948 | PR_kwDOC_bI7s5C3-kt | 5,409 | Add MoE support for T5 model (w/o expert parallel) | {
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} | # What does this PR do ?
Add Mixture of Experts support for T5 model
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```
model.encoder.num_moe_experts=4 \
model.decoder.num_moe_experts=4 \
```
# 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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} | Signed-off-by: Virginia Adams <[email protected]>
Every time a new low val loss is reached, the virtual tokens from the prompt encoder are moved to the prompt table in order to save a .nemo file for that checkpoint. This PR removes the premature virtual prompt tokens from the prompt table so that restoring from a .ckpt doesn't error. The error was:
```
Traceback (most recent call last):
File "scripts/convert_prompt_learning_ckpt_to_nemo.py", line 85, in main
model: MegatronGPTPromptLearningModel = MegatronGPTPromptLearningModel.load_from_checkpoint(
File "/home/vadams/NeMo/nemo/collections/nlp/models/nlp_model.py", line 355, in load_from_checkpoint
model = ptl_load_state(cls, checkpoint, strict=strict, cfg=cfg, **kwargs)
File "/home/vadams/miniconda3/envs/dev/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 259, in _load_state
keys = obj.load_state_dict(checkpoint["state_dict"], strict=strict)
File "/home/vadams/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py", line 382, in load_state_dict
self.prompt_table.load_state_dict(state_dict_, strict)
File "/home/vadams/miniconda3/envs/dev/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1667, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for PromptTable:
Unexpected key(s) in state_dict: "prompt_table.squad.indices", "prompt_table.squad.prompt_embeddings.weight"
```
**Collection**: NLP
# Changelog
- Updated GPT and T5 Prompt learning
# 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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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
add squad task template for gpt prompt learning.
**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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"diff_url": "https://github.com/NVIDIA/NeMo/pull/5406.diff",
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"merged_at": "2022-11-18T08:31:42"
} | # What does this PR do ?
Fix NeMo support for legacy sentencepiece models (sentencepiece models that don't necessarily contain padding tokens).
**Collection**: NLP
# Changelog
There were several places where the config value `sentencepiece_legacy` was not properly forwarded to all functions that require it. There was also one spot that set the `decoder_tokenizer_legacy` value using the encoder's config.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] 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)
- [ ] 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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"total": 0,
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
Run any example of Nemo from the repo, it will throw a Segmentation fault just at import steps.
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
One I tried is
```
`python NeMo/examples/nlp/duplex_text_normalization/duplex_text_normalization_infer.py`
Segmentation fault (core dumped)
```
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: AWS g5.xlarge
- Method of NeMo install: followed exact conda command from NeMo repo. Only thing is that I am running as root.
**Environment details**
Otherwise, please provide:
- OS version ubuntu 20.04
- PyTorch version: '1.13.0'
- Python version: python 3.8.0
**Additional context**
Add any other context about the problem here.
nvidia-smi info
```
nvidia-smi
Mon Nov 14 06:30:28 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 |
| 0% 18C P0 56W / 300W | 6594MiB / 23028MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 108946 C tritonserver 6592MiB |
+-----------------------------------------------------------------------------+
``` | {
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} | # What does this PR do ?
Fix for #5401
Compute padded stats in pooling layer.
When `lengths` is `None`, the old behavior is preserved.
**Collection**: ASR
# Usage
```python
from typing import List
import torch
import torch.nn as nn
from pytorch_lightning import seed_everything
from nemo.collections.asr.parts.submodules.tdnn_attention import StatsPoolLayer
class OldStatsPoolLayer(nn.Module):
"""Preserved copy of nemo.collections.asr.parts.submodules.tdnn_attention.StatsPoolLayer"""
def __init__(self, feat_in: int, pool_mode: str = 'xvector'):
super().__init__()
self.pool_mode = pool_mode
self.feat_in = feat_in
if self.pool_mode == 'xvector':
self.feat_in += feat_in
elif self.pool_mode == 'tap':
self.feat_in = feat_in
else:
raise ValueError("pool mode for stats must be either tap or xvector based")
def forward(self, encoder_output, length=None):
mean = encoder_output.mean(dim=-1) # Time Axis
if self.pool_mode == 'xvector':
std = encoder_output.std(dim=-1)
pooled = torch.cat([mean, std], dim=-1)
else:
pooled = mean
return pooled
seed_everything(42)
# Generate random encoder output
lengths = torch.tensor([40, 30, 20, 10])
batch_size = len(lengths)
feat_dim = 10
features_tensor = torch.zeros(size=[batch_size, feat_dim, lengths.max()])
features_list: List[torch.Tensor] = []
for i, length in enumerate(lengths):
# Randomly shift mean and variance for each feature of each signal
scale = torch.randn(size=[feat_dim, 1])
bias = torch.randn(size=[feat_dim, 1])
feats = torch.randn(size=[feat_dim, length]) / scale + bias
features_list.append(feats)
features_tensor[i, :, :length] = feats
old_pool: OldStatsPoolLayer = OldStatsPoolLayer(feat_in=feat_dim, pool_mode="xvector")
new_pool: StatsPoolLayer = StatsPoolLayer(feat_in=feat_dim, pool_mode="xvector")
old_stats: torch.Tensor = old_pool(encoder_output=features_tensor, length=lengths)
new_stats: torch.Tensor = new_pool(encoder_output=features_tensor, length=lengths)
print(f"Comparing batched statistics to individual stats")
for i in range(batch_size):
# Extract stats with original stats pooler
old_pooled_stats = old_stats[i]
old_pool_mean = old_pooled_stats[:feat_dim]
old_pool_std = old_pooled_stats[feat_dim:]
# Extract stats with new stats pooler
new_pooled_stats = new_stats[i]
new_pool_mean = new_pooled_stats[:feat_dim]
new_pool_std = new_pooled_stats[feat_dim:]
# Compute the mean/std with PyTorch with individual signal (no padding)
torch_mean = features_list[i].mean(dim=-1)
torch_std = features_list[i].std(dim=-1)
print(f"Batch element {i}, length {lengths[i].item()}:")
print(f"\tOld mean all close: {torch.allclose(old_pool_mean, torch_mean)}")
print(f"\tOld std all close: {torch.allclose(old_pool_std, torch_std)}")
print(f"\tNew mean all close: {torch.allclose(new_pool_mean, torch_mean)}")
print(f"\tNew std all close: {torch.allclose(new_pool_std, torch_std)}")
```
Expected output:
```text
Global seed set to 42
Comparing batched statistics to individual stats
Batch element 0, length 40:
Old mean all close: True
Old std all close: True
New mean all close: True
New std all close: True
Batch element 1, length 30:
Old mean all close: False
Old std all close: False
New mean all close: True
New std all close: True
Batch element 2, length 20:
Old mean all close: False
Old std all close: False
New mean all close: True
New std all close: True
Batch element 3, length 10:
Old mean all close: False
Old std all close: False
New mean all close: True
New std all close: True
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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 #5401
Compute padded stats in pooling layer.
When `lengths` is `None`, the old behavior is preserved.
**Collection**: ASR
# Usage
```python
from typing import List
import torch
import torch.nn as nn
from pytorch_lightning import seed_everything
from nemo.collections.asr.parts.submodules.tdnn_attention import StatsPoolLayer
class OldStatsPoolLayer(nn.Module):
"""Preserved copy of nemo.collections.asr.parts.submodules.tdnn_attention.StatsPoolLayer"""
def __init__(self, feat_in: int, pool_mode: str = 'xvector'):
super().__init__()
self.pool_mode = pool_mode
self.feat_in = feat_in
if self.pool_mode == 'xvector':
self.feat_in += feat_in
elif self.pool_mode == 'tap':
self.feat_in = feat_in
else:
raise ValueError("pool mode for stats must be either tap or xvector based")
def forward(self, encoder_output, length=None):
mean = encoder_output.mean(dim=-1) # Time Axis
if self.pool_mode == 'xvector':
std = encoder_output.std(dim=-1)
pooled = torch.cat([mean, std], dim=-1)
else:
pooled = mean
return pooled
seed_everything(42)
# Generate random encoder output
lengths = torch.tensor([40, 30, 20, 10])
batch_size = len(lengths)
feat_dim = 10
features_tensor = torch.zeros(size=[batch_size, feat_dim, lengths.max()])
features_list: List[torch.Tensor] = []
for i, length in enumerate(lengths):
# Randomly shift mean and variance for each feature of each signal
scale = torch.randn(size=[feat_dim, 1])
bias = torch.randn(size=[feat_dim, 1])
feats = torch.randn(size=[feat_dim, length]) / scale + bias
features_list.append(feats)
features_tensor[i, :, :length] = feats
old_pool: OldStatsPoolLayer = OldStatsPoolLayer(feat_in=feat_dim, pool_mode="xvector")
new_pool: StatsPoolLayer = StatsPoolLayer(feat_in=feat_dim, pool_mode="xvector")
old_stats: torch.Tensor = old_pool(encoder_output=features_tensor, length=lengths)
new_stats: torch.Tensor = new_pool(encoder_output=features_tensor, length=lengths)
print(f"Comparing batched statistics to individual stats")
for i in range(batch_size):
# Extract stats with original stats pooler
old_pooled_stats = old_stats[i]
old_pool_mean = old_pooled_stats[:feat_dim]
old_pool_std = old_pooled_stats[feat_dim:]
# Extract stats with new stats pooler
new_pooled_stats = new_stats[i]
new_pool_mean = new_pooled_stats[:feat_dim]
new_pool_std = new_pooled_stats[feat_dim:]
# Compute the mean/std with PyTorch with individual signal (no padding)
torch_mean = features_list[i].mean(dim=-1)
torch_std = features_list[i].std(dim=-1)
print(f"Batch element {i}, length {lengths[i].item()}:")
print(f"\tOld mean all close: {torch.allclose(old_pool_mean, torch_mean)}")
print(f"\tOld std all close: {torch.allclose(old_pool_std, torch_std)}")
print(f"\tNew mean all close: {torch.allclose(new_pool_mean, torch_mean)}")
print(f"\tNew std all close: {torch.allclose(new_pool_std, torch_std)}")
```
Expected output:
```text
Global seed set to 42
Comparing batched statistics to individual stats
Batch element 0, length 40:
Old mean all close: True
Old std all close: True
New mean all close: True
New std all close: True
Batch element 1, length 30:
Old mean all close: False
Old std all close: False
New mean all close: True
New std all close: True
Batch element 2, length 20:
Old mean all close: False
Old std all close: False
New mean all close: True
New std all close: True
Batch element 3, length 10:
Old mean all close: False
Old std all close: False
New mean all close: True
New std all close: True
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | null | null | null | **Describe the bug**
[StatsPoolLayer](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/tdnn_attention.py#L25) ignores the `length` argument; statistics are pooled across what should be padded elements in LID, etc.
Will submit PR. | {
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which can be found here [https://www.thorsten-voice.de/en/datasets-2/)](https://www.thorsten-voice.de/en/datasets-2/)
Thanks.
https://github.com/NVIDIA/NeMo/issues/5400#tasklist-block-89576c70-693b-4727-bfc7-555815508f06
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
Add float32 typecasting in get_samples function.
This typecasting was removed while int type filereading was being removed from the function.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
This function is used in multiple modules
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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the number of wav files is almost 280k.
```
python ${NEMO_ROOT}/scripts/speech_recognition/convert_to_tarred_audio_dataset.py \
--manifest_path=train_decoded_processed.json \
--target_dir=train_tarred_6bk \
--num_shards=1024 \
--max_duration=20.0 \
--min_duration=1.0 \
--shuffle \
--shuffle_seed=1 \
--sort_in_shards \
--workers=-1 \
--buckets_num=6
```
but this would throw the exception
single bucket is fine, but failed to create multiple buckets.
any idea? | {
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} | # What does this PR do ?
With sync_batch_comm=True in validation step we sometimes get CUDA errors while running validation.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds Multi Head Attention Adapter support, along with support for Tiny-Attention adapter from https://arxiv.org/abs/2211.01979
**Collection**: [Core, ASR]
# Changelog
- Add modules for tiny attention based adapter
- Add `MHAResidualAddAdapterStrategy` and config
- Add `RelPositionMultiHeadAttentionAdapter` and config
- Add `PositionalEncodingAdapter` and config (unused for now)
- Add `RelPositionalEncodingAdapter` and config (unused for now)
- Add support to Conformer and Squeezeformer for attention adapters
- Add new support for ASR adapter training script to support multiple types of adapters (linear, mha, relpos mha, tiny attention etc)
- Add exp manager utility function to clean up ckpt and nemo files during grid search.
- Add `ReturnResultAdapterStrategy` to simply return the result of the adapter without residual connection - used for PositionalEncodingAdapter and `RelPositionalEncodingAdapter`
- Extend `ResidualAddAdapterStrategy` to `MHAResidualAddAdapterStrategy` for attention adapters.
- Update docstrings
# Usage
## Single adapter training
``` bash
python train_asr_adapter.py \
--config-path="../conf/asr_adapters" \
--config-name="asr_adaptation.yaml" \
model.pretrained_model=null \
model.nemo_model=null \
model.adapter.adapter_name=<Unique adapter name> \
model.adapter.adapter_type="tiny_attn" \
model.adapter.adapter_module_name="encoder" \
model.adapter.tiny_attn.n_feat=<dimension of the layer outputs of the model> \
model.adapter.tiny_attn.n_head=1 \
model.adapter.tiny_attn.proj_dim=-1 \
model.adapter.linear.dropout_rate=0.0 \
model.train_ds.manifest_filepath=<Path to manifest> \
model.train_ds.batch_size=8 \
model.validation_ds.manifest_filepath=<Path to manifest> \
model.validation_ds.batch_size=8 \
model.optim.lr=0.001 \
model.optim.weight_decay=0.0 \
model.optim.sched.warmup_steps=100 \
trainer.max_steps=300 \
trainer.devices=1 \
trainer.precision=32 \
exp_manager.exp_dir=<Some directory for experiment manager>
```
## Hyper parameter search
First, modify hydra.sweep section in the `asr_adaptation_hp.yaml` file -
``` yaml
hydra:
sweep:
dir: "."
subdir: "."
sweeper:
params: # place all the parameters you wish to search over here (corresponding to the rest of the config)
model.optim.lr: 0.001 # ,0.0001
model.adapter.tiny_attn.n_head: 1 # 1,2,4,8
model.adapter.tiny_attn.proj_dim: 1,4,8,16,32,64 # 1,4,8,16,32,64
model.adapter.tiny_attn.adapter_strategy.stochastic_depth: 0.0,0.5,0.6,0.7,0.8,0.9
# Arguments to the hyperparameter runner
launcher:
num_gpus: -1 # Number of gpus to use. Each run works on a single GPU.
jobs_per_gpu: 1 # If each GPU has large memory, you can run multiple jobs on the same GPU for faster results (until OOM).
```
Then run the adapter train script as below -
``` bash
python train_asr_adapter.py \
--config-path="../conf/asr_adapters" \
--config-name="asr_adaptation_hp.yaml" \
-m \
model.pretrained_model=null \
model.nemo_model=null \
model.adapter.adapter_name=<Unique adapter name> \
model.adapter.adapter_type="tiny_attn" \
model.adapter.adapter_module_name="encoder" \
model.adapter.tiny_attn.n_feat=<dimension of the layer outputs of the model> \
model.train_ds.manifest_filepath=<Path to manifest> \
model.train_ds.batch_size=8 \
model.validation_ds.manifest_filepath=<Path to manifest> \
model.validation_ds.batch_size=8 \
exp_manager.exp_dir="<some directory>" \
exp_manager.create_wandb_logger=true \
exp_manager.wandb_logger_kwargs.project="<Project Name>" \
++delete_ckpt_after_train=True
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Attempt to fix fp16 with MHA in ASR
**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?
- [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
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} | Signed-off-by: Virginia Adams <[email protected]>
Fix bug in prompt learning notebook inference
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Updated GPT prompt learning model class
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | [] | closed | false | null | [] | null | null | 2022-11-10T23:26:22 | 2023-02-23T01:57:29 | 2022-11-14T19:10:14 | CONTRIBUTOR | {
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} | Signed-off-by: Virginia Adams <[email protected]>
Every time a new low val loss is reached, the virtual tokens from the prompt encoder are moved to the prompt table in order to save a .nemo file for that checkpoint. This PR removes the premature virtual prompt tokens from the prompt table so that restoring from a .ckpt doesn't error. The error was:
```
Traceback (most recent call last):
File "scripts/convert_prompt_learning_ckpt_to_nemo.py", line 85, in main
model: MegatronGPTPromptLearningModel = MegatronGPTPromptLearningModel.load_from_checkpoint(
File "/home/vadams/NeMo/nemo/collections/nlp/models/nlp_model.py", line 355, in load_from_checkpoint
model = ptl_load_state(cls, checkpoint, strict=strict, cfg=cfg, **kwargs)
File "/home/vadams/miniconda3/envs/dev/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 259, in _load_state
keys = obj.load_state_dict(checkpoint["state_dict"], strict=strict)
File "/home/vadams/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py", line 382, in load_state_dict
self.prompt_table.load_state_dict(state_dict_, strict)
File "/home/vadams/miniconda3/envs/dev/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1667, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for PromptTable:
Unexpected key(s) in state_dict: "prompt_table.squad.indices", "prompt_table.squad.prompt_embeddings.weight"
```
**Collection**: NLP
# Changelog
- Updated GPT and T5 Prompt learning
# 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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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Follow up the discussion in https://github.com/NVIDIA/NeMo/pull/5383, we decided to replace pitch norm params with ??? for all YAML configs just in case users forget to update with dataset-specific values.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Attempt to fix fp16 with MHA in ASR
**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?
- [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
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# What does this PR do ?
Add new Spanish FastPitch model to available models
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_es_multispeaker_fastpitchhifigan/files
After change is reviewed, I need to update the nvidia catalog with the correct version & filenames.
**Collection**: [TTS]
# Changelog
- Add Spanish FastPitch & HifiGan to available models and documentation.
# Usage
```python
import soundfile as sf
from nemo.collections.tts.models import FastPitchModel
from nemo.collections.tts.models import HifiGanModel
fastpitch_name = "tts_es_fastpitch_multispeaker"
hifigan_name = "tts_es_hifigan_ft_fastpitch_multispeaker"
# Load spectrogram generator
spec_generator = FastPitchModel.from_pretrained(model_name)
# Load Vocoder
model = HifiGanModel.from_pretrained(model_name=model_name)
# Generate audio
text = "Escribe tu texto aquí."
parsed = spec_generator.parse(text, normalize=False)
speaker = 5
spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
audio = audio.detach().cpu().numpy()
# Save the audio to disk in a file called speech.wav
sample_rate = 44100
sf.write("speech.wav", audio, sample_rate)
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Bugfix
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} | For some paramters in SpeakerClustering.forward(), tensor dimensions were not specified and this resulted in zero dimension tensor inputs. This causes error when inferencing exported model in Triton server.
# What does this PR do ?
Avoids zero dimension input tensors. Also extracts values from the tensors which have single dim of size 1. This avoids scattered use of item() to extract value from tensor.
**Collection**: ASR Diarizer
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Fix is needed to use exported clustering model in Triton
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] Bugfix
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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https://api.github.com/repos/NVIDIA/NeMo/issues/5385 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5385/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5385/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5385/events | https://github.com/NVIDIA/NeMo/issues/5385 | 1,443,284,278 | I_kwDOC_bI7s5WBsU2 | 5,385 | [ASR] TypeError("__init__() got an unexpected keyword argument 'causal_downsampling'") | {
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} | null | null | null | **Describe the bug**
Hi,
I want to train a Mandarin ASR model using `conformer_ctc_char.yaml` , meanwhile, I use `speech_to_text_ctc.py`.
When I run `python speech_to_text_ctc.py`, the following bugs are appearance.
```
[NeMo I 2022-11-10 12:57:03 features:200] PADDING: 0
Error executing job with overrides: []
Error in call to target 'nemo.collections.asr.modules.conformer_encoder.ConformerEncoder':
TypeError("__init__() got an unexpected keyword argument 'causal_downsampling'")
```
```
[NeMo I 2022-11-10 12:57:03 features:200] PADDING: 0
Error executing job with overrides: []
Error in call to target 'nemo.collections.asr.modules.conformer_encoder.ConformerEncoder':
TypeError("__init__() got an unexpected keyword argument 'att_context_style'")
```
```
[NeMo I 2022-11-10 12:57:03 features:200] PADDING: 0
Error executing job with overrides: []
Error in call to target 'nemo.collections.asr.modules.conformer_encoder.ConformerEncoder':
TypeError("__init__() got an unexpected keyword argument 'conv_context_size'")
```
According to the bugs, I comment out the three arguments in the cfg.model.encoder, then it runs without any error.
Even though it works well now, I still worry about some impacts of commenting out the arguments.
Please, are there some steps I should adjust?
**Steps/Code to reproduce bug**
`python speech_to_text_ctc.py`
**conformer_ctc_char.yaml**
```
# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and char-based vocabulary.
# Char-based encoding may give lower accuracy than sub-word encoding (conformer_ctc_bpe.yaml).
# You may find more detail on Conformer-CTC at `examples/asr/conf/conformer/conformer_ctc_bpe.yaml`
name: "Conformer-CTC-Char-Aishell-10ep"
model:
sample_rate: 16000
labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
log_prediction: true # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
skip_nan_grad: false
train_ds:
manifest_filepath: ???
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
trim_silence: false
max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: ???
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: null
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: false
num_workers: 8
pin_memory: true
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
# you may use lower time_masks for smaller models to have a faster convergence
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 16
d_model: 256
# Sub-sampling params
subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 4 # must be power of 2 for striding and vggnet
subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model
# causal_downsampling: True
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 8 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
# att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
# conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
# null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
# conv_context_size: null
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: ${model.labels}
optim:
name: adamw
lr: 2.0
# optimizer arguments
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 1e-3
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 10000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 10
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
accumulate_grad_batches: 1
gradient_clip_val: 0.0
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
log_every_n_steps: 10 # Interval of logging.
enable_progress_bar: True
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: ../../exp
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
# you need to set these two to True to continue the training
resume_if_exists: false
resume_ignore_no_checkpoint: false
# You may use this section to create a W&B logger
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
**speech_to_text_ctc.py**
```
from curses import use_default_colors
import pytorch_lightning as pl
from omegaconf import OmegaConf
from nemo.collections.asr.models import EncDecCTCModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
use_cer = True
log_prediction = True
manifest_path = "/home/ec5017b/media-lab/nemo/NeMo/examples/asr/"
train_manifest = f"{manifest_path}train_manifest.json"
dev_manifest = f"{manifest_path}dev_manifest.json"
test_manifest = f"{manifest_path}test_manifest.json"
charset = []
#將 vocab.txt 讀入 list 中，使其能寫入 config 的 labels
with open(f"{manifest_path}Aishell_vocab/train_dev.txt ", "r") as vocab_read:
for char in vocab_read.read():
charset.append(char) #EX: ['余', '耍', '汞', '圳']
# print(charset)
# print(len(charset))
@hydra_runner(config_path="../conf/conformer", config_name="conformer_ctc_char")
def main(cfg):
cfg.model.labels = charset
cfg.model.train_ds.manifest_filepath = train_manifest
cfg.model.validation_ds.manifest_filepath = dev_manifest
cfg.model.test_ds.manifest_filepath = test_manifest
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
asr_model._wer.use_cer = use_cer
asr_model._wer.log_prediction = log_prediction
# Initialize the weights of the model from another model, if provided via config
asr_model.maybe_init_from_pretrained_checkpoint(cfg)
trainer.fit(asr_model)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if asr_model.prepare_test(trainer):
trainer.test(asr_model)
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
```
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: From source (I kinda forgot)
**Environment details**
-nemo-tookit
1.10.0
- OS version
Linux version 4.15.0-142-generic (buildd@lgw01-amd64-039) (gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.12)) #146~16.04.1-Ubuntu SMP Tue Apr 13 09:27:15 UTC 2021
- PyTorch version
1.12.1
- Python version
3.8
**Additional context**
Add any other context about the problem here.
GPU: Tesla V100-PCIE-16GB
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} | # What does this PR do ?
fixes the bug where nw0 does weird things in training and validation after the first epoch.
**Collection**: [NLP]
# Changelog
- changes gpt dataset creation by deep copy of inputs.
# Usage
```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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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Add configurations for training FastPitch with Spanish tokenizers
**Collection**: [TTS]
# Changelog
- Add .yaml configs with Spanish grapheme and IPA tokenizers
# 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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} | Signed-off-by: Virginia Adams <[email protected]>
Fix bug in prompt learning notebook inference
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Updated GPT prompt learning model class
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Elena Rastorgueva <[email protected]>
# What does this PR do ?
Rename Speech Dataset Processor to Speech Data Processor.
Add a one line overview of what this PR aims to accomplish.
Update directory name and contents of README to reflect name change.
**Collection**: [Note which collection this PR will affect]
tools/speech_data_processor
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/5380 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5380/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5380/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5380/events | https://github.com/NVIDIA/NeMo/issues/5380 | 1,442,931,770 | I_kwDOC_bI7s5WAWQ6 | 5,380 | Empty Prediction when training from scratch a Squeezeformer-CTC with custom tokenizer. | {
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I'm trying to train an extra-small Squeezeformer-CTC ASR with parameters indicated in the basic [yaml configuration](https://github.com/NVIDIA/NeMo/blob/03a8f1560fb58b7a6b4749d078863b04c4a299af/examples/asr/conf/squeezeformer/squeezeformer_ctc_bpe.yaml). Since I'm using a custom dataset, I had to make a custom bpe tokenizer. However, from the earliest epochs, predictions are empty, and the WER is constant at 1.0. Here is a sample prediction:
[NeMo I 2022-11-09 19:21:52 wer_bpe:261] reference:se voi volete realizzare attraverso
[NeMo I 2022-11-09 19:21:52 wer_bpe:262] predicted:
In the following you can see the configuration:
exp_dir: new_model
exp_manager:
checkpoint_callback_params:
always_save_nemo: true
mode: min
monitor: val_wer
save_top_k: 5
create_checkpoint_callback: true
create_tensorboard_logger: true
create_wandb_logger: false
exp_dir: null
name: ${name}
resume_if_exists: false
resume_ignore_no_checkpoint: false
wandb_logger_kwargs:
name: null
project: null
model:
ctc_reduction: mean_batch
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: 196
num_classes: 128
vocabulary:
- <unk>
- ▁
- a
- o
- s
- n
- u
- re
- i
- r
- e
- t
- l
- g
- ta
- ▁di
- m
- ▁a
- ▁e
- te
- ra
- ri
- 'no'
- to
- c
- ti
- p
- le
- la
- li
- ▁in
- b
- ci
- ▁del
- si
- ni
- ''''
- ▁che
- me
- ▁s
- so
- ro
- ▁un
- f
- ve
- ca
- ne
- co
- ▁co
- ▁con
- mi
- do
- ▁per
- ▁la
- di
- d
- zione
- na
- ▁al
- ce
- vi
- ▁de
- vo
- ▁il
- bi
- se
- ▁p
- pe
- lo
- ▁ri
- ▁pa
- de
- va
- ma
- ▁si
- po
- ▁i
- ▁pro
- ▁quest
- ▁po
- ▁è
- nti
- gli
- ▁da
- fi
- tto
- tà
- tti
- ▁l
- ▁non
- ▁su
- ▁se
- zio
- str
- ▁o
- ▁ma
- z
- ▁le
- da
- mento
- ▁fa
- chi
- mente
- ▁nel
- nza
- ▁qua
- ▁ha
- ▁imp
- ggi
- v
- ▁tu
- zia
- h
- q
- à
- ò
- è
- k
- ì
- é
- y
- w
- x
- j
- í
- ù
- á
- '-'
encoder:
_target_: nemo.collections.asr.modules.SqueezeformerEncoder
adaptive_scale: true
att_context_size:
- -1
- -1
conv_kernel_size: 31
conv_norm_type: batch_norm
d_model: 196
dropout: 0.1
dropout_att: 0.1
dropout_emb: 0.0
feat_in: ${model.preprocessor.features}
feat_out: -1
ff_expansion_factor: 4
n_heads: 4
n_layers: 18
pos_emb_max_len: 5000
self_attention_model: rel_pos
subsampling: dw_striding
subsampling_conv_channels: -1
subsampling_factor: 4
time_recovery_idx: null
time_reduce_idx: 7
untie_biases: true
xscaling: true
log_prediction: true
optim:
betas:
- 0.9
- 0.98
lr: 0.002
name: adamw
sched:
decay_rate: 1.0
hold_ratio: 0.3
hold_steps: null
min_lr: 1.0e-06
name: NoamHoldAnnealing
warmup_ratio: null
warmup_steps: 10
weight_decay: 4e-5
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
dither: 1.0e-05
features: 80
frame_splicing: 1
log: true
n_fft: 512
normalize: per_feature
pad_to: 0
pad_value: 0.0
sample_rate: ${model.sample_rate}
window: hann
window_size: 0.025
window_stride: 0.01
sample_rate: 16000
skip_nan_grad: false
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2
freq_width: 27
time_masks: 5
time_width: 0.05
test_ds:
batch_size: 8
manifest_filepath: manifests/prepared/test_manifest.json
num_workers: 8
pin_memory: true
sample_rate: ${model.sample_rate}
shuffle: false
use_start_end_token: false
tokenizer:
dir: tokenizer_spe_unigram_v128
type: bpe
train_ds:
batch_size: 32
bucketing_batch_size: null
bucketing_strategy: synced_randomized
is_tarred: false
manifest_filepath: manifests/prepared/train_manifest.json
max_duration: 30.0
min_duration: 0.1
num_workers: 20
pin_memory: true
sample_rate: ${model.sample_rate}
shuffle: true
shuffle_n: 2048
tarred_audio_filepaths: null
trim_silence: false
use_start_end_token: false
validation_ds:
batch_size: 8
manifest_filepath: manifests/prepared/dev_manifest.json
num_workers: 8
pin_memory: true
sample_rate: ${model.sample_rate}
shuffle: false
use_start_end_token: false
name: Squeezeformer_ctc_bpe_EUP_Small_tokenizer-spe-unigram-128
target: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE
trainer:
accelerator: auto
accumulate_grad_batches: 1
benchmark: false
check_val_every_n_epoch: 1
devices: -1
enable_checkpointing: false
enable_progress_bar: true
gradient_clip_val: 0.0
log_every_n_steps: 10
logger: false
max_epochs: 1000
max_steps: -1
num_nodes: 1
num_sanity_val_steps: 0
precision: 32
resume_from_checkpoint: null
strategy: ddp
sync_batchnorm: true
val_check_interval: 1.0
I'm performing the training with the [/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py](https://github.com/NVIDIA/NeMo/blob/2ce45369f7ab6cd20c376d1ed393160f5e54be0c/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) script.
Any suggestion?
Thanks. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5379 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5379/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5379/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5379/events | https://github.com/NVIDIA/NeMo/pull/5379 | 1,442,798,359 | PR_kwDOC_bI7s5Cj8pN | 5,379 | fix for num worker 0 causing issues in losses after 1 epoch | {
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} | # What does this PR do ?
fixes the bug where nw0 does weird things in training and validation after the first epoch.
**Collection**: [NLP]
# Changelog
- changes gpt dataset creation by deep copy of inputs.
# Usage
```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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} | Signed-off-by: Elena Rastorgueva <[email protected]>
# What does this PR do ?
Rename Speech Dataset Processor to Speech Data Processor.
Add a one line overview of what this PR aims to accomplish.
Update directory name and contents of README to reflect name change.
**Collection**: [Note which collection this PR will affect]
tools/speech_data_processor
# 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 the bug where nw0 does weird things in training and validation after the first epoch.
**Collection**: [NLP]
# Changelog
- changes gpt dataset creation by deep copy of inputs.
# Usage
```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**:
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- [X] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Elena Rastorgueva <[email protected]>
# What does this PR do ?
Adds documentation to Speech Dataset Processor
**Collection**: [Note which collection this PR will affect]
tools/speech_dataset_processor
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
The former PR was not merged for r1.13.0 so merging it into r.1.13.0
This PR fixes minor bugs in multispeaker simulation tutorial notebook
https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb
Bugfix for
https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3848353
- rm throws out an error if there is no such file
**Collection**: [Note which collection this PR will affect]
# Changelog
added rm -f (try deleting only if file exists)
added wget -nc (no duplication)
# Usage
Please run the tutorial notebook
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
any one in ASR
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
Fix minor bugs in multispeaker simulation tutorial notebook
https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/Multispeaker_Simulator.ipynb
Bugfix for
https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3848353
- rm throws out an error if there is no such file
**Collection**: [Note which collection this PR will affect]
# Changelog
added rm -f (try deleting only if file exists)
added wget -nc (no duplication)
# Usage
Please run the tutorial notebook
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
any one in ASR
# Additional Information
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} | Hi `NVIDIA/NeMo`!
This is a one-off automatically generated pull request from LGTM.com :robot:. You might have heard that we’ve integrated LGTM’s underlying CodeQL analysis engine natively into GitHub. The result is [**GitHub code scanning**](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning)!
With LGTM fully integrated into code scanning, we are focused on improving CodeQL within the native GitHub code scanning experience. In order to take advantage of current and future improvements to our analysis capabilities, we suggest you enable code scanning on your repository. Please take a look at our [blog post for more information](https://github.blog/2022-08-15-the-next-step-for-lgtm-com-github-code-scanning/).
This pull request enables code scanning by adding an auto-generated [`codeql.yml` workflow file for GitHub Actions](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/setting-up-code-scanning-for-a-repository#setting-up-code-scanning-manually) to your repository — take a look! We tested it before opening this pull request, so all should be working :heavy_check_mark:. In fact, you might already have seen some alerts appear on this pull request!
Where needed and if possible, we’ve adjusted the configuration to the needs of your particular repository. But of course, you should feel free to tweak it further! Check [this page](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#editing-a-code-scanning-workflow) for detailed documentation.
Questions? Check out the FAQ below!
### FAQ
<details>
<summary>Click here to expand the FAQ section</summary>
#### How often will the code scanning analysis run?
By default, code scanning will trigger a scan with the CodeQL engine on the following events:
* On every pull request — to flag up potential security problems for you to investigate before merging a PR.
* On every push to your default branch and other protected branches — this keeps the analysis results on your repository’s *Security* tab up to date.
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} | # What does this PR do ?
Fix Python type annotations according to the documentation (correct syntax, missing imports, fix incompatible return types).
**Collection**: [Note which collection this PR will affect]
# Changelog
- remove duplicated type annotations
- fix tuple annotation in function return types according to [python docs](https://docs.python.org/3.8/library/typing.html#typing.Tuple)
- add missing imports (only when module is used, but not imported)
- fix return type in some places where return type is obvious, but not compatible with the annotation (e.g. use `Optional[str]` instead of `str` if `return None` is used)
- avoid quotes for type annotations in some places (if possible): quotes are useful only for forward declaration, in other cases this can lead to [inconsistent or incorrect types](https://stackoverflow.com/a/46458497).
- Also, if quotes are used and the necessary type is not imported, IDEs / linters can't correctly infer the type. If the type is imported, some tools (including the LGTM bot) consider such import as unused.
# Usage
No executed code is affected (except for missing imports).
Changes only affect type hints by PyCharm or type checkers output (e.g. `syntax error` in `mypy`).
## Example
Before (incorrect return type – unclear automatic signature in PyCharm):
<img width="777" alt="check_lib_version_incorrect" src="https://user-images.githubusercontent.com/9380560/200830147-f5c8435b-a8dd-42e6-9e32-c080166f9723.png">
After (corrected type – PyCharm correctly adds hints):
<img width="809" alt="check_lib_version_correct" src="https://user-images.githubusercontent.com/9380560/200830176-ba824995-eaa4-4a71-97ea-466570c5f9d1.png">
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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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.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Cuong Nguyen Dang <[email protected]>
# What does this PR do ?
Fix issue \(https://github.com/NVIDIA/NeMo/issues/5367)
**Collection**: ITN
# Changelog
- add suffix ```[and]?[digit]``` to thousand graph
# Usage
```python
from nemo_text_processing.inverse_text_normalization.en.taggers.cardinal import CardinalFst
from nemo_text_processing.inverse_text_normalization.en.taggers.ordinal import OrdinalFst
from nemo_text_processing.inverse_text_normalization.en.taggers.date import DateFst
cardinal = CardinalFst()
ordinal = OrdinalFst(cardinal)
date_fst = DateFst(ordinal).fst
text = "two thousand and nine"
print(pynini.shortestpath(text @ date_fst).string())
```
# 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 # (5367)
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} | null | null | null | Hi
I followed the instructions @ https://github.com/NVIDIA/NeMo/issues/1660 and could successfully train a musan noise augmented nemo ASR model. However, I have few queries:
1. Where does the noise augmented files are stored? Or Are these noise augmented files are simply produced on the fly, as and when needed, and discarded once the job is done (without retaining them)?
2. When I use augmentors during training, does it include original clean files also for training along-with noise augmented files? Or it only uses noise augmented files for training?
3. For the kind of experiments & datasets that we used, we had expected lower WERs with noise augmentation compared to WERs obtained on clean datasets. However the obtained results are other way (noise augmented datasets have 1% higher WER than clean data). This made us to suspect the implementation part of nemo asr training. I have used exactly the same config of audio_augmentations mentioned by @jbalam-nv. There were no errors during training as well. Do you still feel there might be some implementation gaps (which has led to lower WER). Any suggestions on this would be very helpful.
4. I have used musan free sound noise and RIR. Is it fine? or any suggestions on this?
Thanks a lot,
Manjunath | {
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} | null | null | null | **Describe the bug**
```pynini``` raises error *ERROR: StringFstToOutputLabels: Invalid start state* when I am trying to accept "two thousand and nine" with DateFst, which means that DateFst hasn't well-define to accept this text
**Steps/Code to reproduce bug**
First, initialize the DateFst object
```
from nemo_text_processing.inverse_text_normalization.en.taggers.cardinal import CardinalFst
from nemo_text_processing.inverse_text_normalization.en.taggers.ordinal import OrdinalFst
from nemo_text_processing.inverse_text_normalization.en.taggers.date import DateFst
cardinal = CardinalFst()
ordinal = OrdinalFst(cardinal)
date_fst = DateFst(ordinal).fst
```
Then, compose the text ```"two thousand and nine"``` with the FST above
```
text = "two thousand and nine"
print(pynini.shortestpath(text @ date_fst).string())
```
**Expected behavior**
DateFST can accept ```"two thousand and nine"``` and return the result that looks like ```date { year: "2009" preserve_order: true }```
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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
add get_label for lang id model in doc and reflect PR#[5278](https://github.com/NVIDIA/NeMo/pull/5278)
**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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
Training get stuck when using ddp and dali loader together.
the usage of second gpu is zero.
it works if I change `use_dali` to False
**Steps/Code to reproduce bug**
I follow the asr finetuing tutorial (https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb), and change `devices=2`, `strategy='ddp'`.
```
trainer = ptl.Trainer(devices=2,
accelerator=accelerator,
strategy='ddp',
max_epochs=EPOCHS,
accumulate_grad_batches=1,
enable_checkpointing=False,
logger=False,
log_every_n_steps=5,
check_val_every_n_epoch=10)
```
also, I change `cfg.train_ds.use_dali=True`
Any idea?
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} | # What does this PR do ?
This PR is a refactored version of the following PR created by https://github.com/iankur:
https://github.com/NVIDIA/NeMo/pull/4854
It adds the Hybrid RNNT/CTC model which has two decoders of CTC and RNNT(Transducer) over the encoder.
It enables to train a single model instead of two which works with both CTC and RNNT decoding. It also speeds up the convergence for CTC models.
Added the following examples:
./examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_char.py
./examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py
Along with these sample configs for hybrid Conformer model:
./examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_char.yaml
./examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_bpe.yaml
**Collection**:
ASR
# Changelog
- Added support for Hybrid RNNT/CTC ASR model.
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [X] Documentation
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} | # What does this PR do ?
On setting the data config option: _no_seqlen_plus_one_input_tokens_ to True (default: False) in Megatron-GPT, the number of input tokens for each sample will be equal to the sequence length (instead of sequence length + 1) and the last token will be masked.
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} | Signed-off-by: Matvei Novikov <[email protected]>
# What does this PR do ?
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# Changelog
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} | # What does this PR do ?
Fixes some issue with model upload to Huggingface
**Collection**: [ASR]
**PR Type**:
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} | # What does this PR do ?
Fixes some issue with model upload to Huggingface
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Boris Fomitchev <[email protected]>
A view to the bias was cached as a buffer - that led to cpu/gpu mismatch on backward pass when training - also the caching was wrong - removed. That led to script() breaking for export - had to change script routine to do trace on PartialConv first.
Training had NaNs when using partial convolutions in mixed precision mode - had to move from epsilon to masking to fit in float16 range without damping.
Reverting submodules.py back to original and re-doing script optimizations in a cleaner manner, fixed the issues.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
This PR adds a base class for audio processing models, an enhancement model, a number of audio-related modules and tests.
**Collection**: ASR
# Changelog
- Audio processing base model `AudioToAudioModel`
- `EncMaskDecAudioToAudioModel` subclass for speech enhancement
- Audio modules for spectrogram transforms, mask estimation based on RNN (`MaskEstimatorRNN`), mask-based processing (`MaskReferenceChannel` and `MaskBasedBeamforming`), `SDRLoss`
- Training script for `EncMaskDecAudioToAudioModel` in `speech_enhancement.py`
- Audio processing script for `AudioToAudioModel` in `process_audio.py`
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [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 #5196
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} | Signed-off-by: Shanmugam Ramasamy <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Matvei Novikov <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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