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} | # What does this PR do ?
Fix bug when `inference.compute_logprob=True`. Only first and last pipeline stages have a response.
**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 ?
Fix multi-speaker FastPitch test nightly
**Collection**: [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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix adapter tutorial to compatible with latest pre-traiend FastPitch checkpoint
**Collection**: [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.
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* Related to # (issue)
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} | # What does this PR do ?
Debugs Transformer Engine FP8 support with Megatron-core infrastructure
**Collection**: NLP
# Changelog
- Construct FP8 amax reduction group
# Usage
Set `transformer_engine: True` and `fp8: True` in the config.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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.
Pinging @aklife97 @yen-shi @erhoo82 @ksivaman
# Additional Information
* Requires https://github.com/NVIDIA/Megatron-LM/commit/f267e6186eae1d6e2055b412b00e2e545a8e896a
* Fixes bug from https://github.com/NVIDIA/NeMo/pull/6393
* Subset of https://github.com/NVIDIA/NeMo/pull/6627
* Closes https://github.com/NVIDIA/NeMo/issues/6625 | {
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Debugs Transformer Engine FP8 support with Megatron-core infrastructure
**Collection**: NLP
# Changelog
- Construct FP8 amax reduction group
# Usage
Set `transformer_engine: True` and `fp8: True` in the config.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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.
Pinging @aklife97 @yen-shi @erhoo82 @ksivaman
# Additional Information
* Requires https://github.com/NVIDIA/Megatron-LM/commit/f267e6186eae1d6e2055b412b00e2e545a8e896a
* Fixes bug from https://github.com/NVIDIA/NeMo/pull/6393
* Subset of https://github.com/NVIDIA/NeMo/pull/6627
* Closes https://github.com/NVIDIA/NeMo/issues/6625 | {
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} | # What does this PR do ?
VP fixes cherry picked to release branch
**Collection**: [NLP]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [x] Bugfix
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Do you have results of ASR models on benchmark datatsets? I want to compare your toolkit with wenet/espnet.
Best regards,
Aaron | {
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} | # What does this PR do ?
1. previously, '#' was hardcoded as a prefix for Chinese phonemes and tones in both g2p and tokenizer. It is apt to raise errors if mismatched prefix symbols are applied for g2p and tokenizer.
2. previously, phoneme set and tone set are hardcoded as well. It would raise errors when the input phoneme dict contains different set of phonemes. Fixed this by inferring phoneme set based on input dictionary.
3. run pytest nightly and old NGC model checkpoint passed.
```
$ pytest --nightly tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech] PASSED
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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} | null | null | null | **Describe the bug**
`NeMoModelCheckpoint.on_train_end()` saves the epoch info incorrectly, which would break resuming training from the last checkpoint.
For example, when the maximum time limit is reached on a cluster, we would usually save checkpoints and launch another job to resume training. Assume that the break happens in epoch 0, ideally, the epoch info of the fit loop should be like the following:
```python
In [3]: ckpt['loops']['fit_loop']['epoch_progress']
Out[3]:
{'total': {'ready': 1, 'completed': 0, 'started': 1, 'processed': 0},
'current': {'ready': 1, 'completed': 0, 'started': 1, 'processed': 0}}
```
Then in the second job, the PTL trainer knows this is a restarting job.
However, `NeMoModelCheckpoint` save the `-last.ckpt` in `on_train_end()`, where the `completed` and `processed` counters have been increased. So the epoch info is
```python
In [4]: ckpt_last['loops']['fit_loop']['epoch_progress']
Out[4]:
{'total': {'ready': 1, 'completed': 1, 'started': 1, 'processed': 1},
'current': {'ready': 1, 'completed': 1, 'started': 1, 'processed': 1}}
```
This bug would break resuming training from the last checkpoint in several cases:
1. When `trainer.max_epochs = 1`, PTL thinks the max epoch has been reached and then stops training immediately.
2. When `trainer.max_epochs > 1`, PTL can restart training but starts from epoch 1 instead of 0, and calculate the total number of batches incorrectly. The logs look like:
```
# at the end of the first job.
Epoch 0: 39%|███▉ | 411/1055 [3:43:23<5:50:01, 32.61s/it, loss=1.25, v_num=jxb0, reduced_train_loss=1.180, global_step=390.0, consumed_samples=49920.0, val_loss=1.140]
wandb: Waiting for W&B process to finish... (success).
# at the beginning of the second job.
Training: 391it [00:00, ?it/s]
Training: 0%| | 0/645 [00:00<00:00, -18221920.71it/s]
Epoch 1: 0%| | 0/645 [00:00<?, ?it/s]
Epoch 1: 64%|██████▍ | 412/645 [00:36<00:20, 11.43it/s]
Epoch 1: 64%|██████▍ | 412/645 [00:36<00:20, 11.42it/s, loss=1.13, v_num=25nj, reduced_train_loss=1.130, global_step=391.0, consumed_samples=49920.0]
```
As we can see, it restarts training from Epoch 1, and the total number of batches decreases from 1055 to 645. Ideally, it should restart at Epoch 0, step 412/1055.
**Steps/Code to reproduce bug**
We run SFT using `examples/nlp/language_modeling/tuning/conf/megatron_gpt_sft.yaml`.
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} | # What does this PR do ?
The type hint was mismatched with https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/g2p/models/i18n_ipa.py#L159.
**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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If you haven't finished some of the above items you can still open "Draft" PR.
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} | null | null | null | Hi all,
Thank you to the creators of this tool!
### Running out of Memory
Unfortunately, while running a stripped down version of [this tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb) on my dataset of radio conversations (which are 1 min to 10 minutes in length), the diarization inference occasionally runs out of memory for files longer than ~180s, although sometimes it only runs outof memory for files longer than ~200s. Is there any way to change *any* configurations at all for inference to prevent running out of memory? This **[NVIDIA guide](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html#inference-on-long-audio)** suggests some sort of alterations, but I don't understand what it's explicitly recommending in the context of my code. Would changing the number of workers help? My GPU is admittedly not the greatest (NVIDIA GeForce RTX 3060 ~6GB GPU memory), but I'd like to avoid needing to use Google Colab, as my hundreds of gB of files are offline.
### My code
This is my code - basically, just loads up pre-calculated VAD manifests, sets up the path and configures Omega correctly, then launches the diarizer. At the end it saves a pyplo.
```python
def diarize(wav_path, rttm_path):
#initial imports
import os, wget, json, librosa, torch
from nemo.collections.asr.parts.utils.speaker_utils import rttm_to_labels, labels_to_pyannote_object
from nemo.collections.asr.models import ClusteringDiarizer
from omegaconf import OmegaConf
import matplotlib.pyplot as plt
from pyannote.core import notebook
from pytorch_memlab import profile
import re
output_dir = "/optimize_diarization/4_outputRTTMs"
yml_dir = "/optimize_diarization/3_to_4_scripts/yml"
manifests_dir = "/optimize_diarization/3b_speechManifests"
graphics_dir = "optimize_diarization/4_results/graphics"
intermediate_inputmanifest_dir = "/optimize_diarization/3_to_4_scripts/input_manifest"
# signal, sr = librosa.load(wav_path,sr=16000)
duration = librosa.get_duration(path=wav_path)
# configure manifest path stuff
filename = re.search(r'.*/(.*)', wav_path).group(1)[0:-4]
print(f'\n\n FILENAME IS: {filename} \n\n WAV_PATH is {wav_path} \n\n RTTM_PATH is {rttm_path} \n\n')
external_vad_manifest = manifests_dir + '/' + filename + 'vad_out.json'
input_manifest = intermediate_inputmanifest_dir + '/input_manifest.json'
#make manifest
meta = {
'audio_filepath': wav_path,
'offset': 0,
'duration': duration,
'label': 'infer',
'text': '-',
'num_speakers': None,
'rttm_filepath': rttm_path,
'uem_filepath' : None,
'uniq_id' : filename
}
with open(f'{intermediate_inputmanifest_dir}/input_manifest.json','w') as fp:
json.dump(meta,fp)
fp.write('\n')
#set up model/config
MODEL_CONFIG = os.path.join(yml_dir,'diar_infer_telephonic.yaml')
if not os.path.exists(MODEL_CONFIG):
config_url = "https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/speaker_tasks/diarization/conf/inference/diar_infer_telephonic.yaml"
MODEL_CONFIG = wget.download(config_url,yml_dir)
config = OmegaConf.load(MODEL_CONFIG)
config.num_workers = 1 # Workaround for multiprocessing hanging with ipython issue
config.diarizer.oracle_vad = True
config.diarizer.vad.model_path = None
config.diarizer.vad.external_vad_manifest = external_vad_manifest
config.diarizer.manifest_filepath = input_manifest
config.diarizer.out_dir = output_dir # Directory to store intermediate files and prediction outputs
config.diarizer.speaker_embeddings.model_path = 'titanet_large'
config.diarizer.speaker_embeddings.parameters.window_length_in_sec = [1.5,1.25,1.0,0.75,0.5]
config.diarizer.speaker_embeddings.parameters.shift_length_in_sec = [0.75,0.625,0.5,0.375,0.1]
config.diarizer.speaker_embeddings.parameters.multiscale_weights= [1,1,1,1,1]
config.diarizer.clustering.parameters.oracle_num_speakers = False
config.diarizer.msdd_model.model_path = 'diar_msdd_telephonic' # Telephonic speaker diarization model
config.diarizer.msdd_model.parameters.sigmoid_threshold = [0.7, 1.0] # Evaluate with T=0.7 and T=1.0
# Run the diarization
sd_model = ClusteringDiarizer(cfg=config)
sd_model.diarize()
#output as a graph
pred_labels_neural = rttm_to_labels(f'{output_dir}/pred_rttms/{filename}.rttm')
hypothesis_neural = labels_to_pyannote_object(pred_labels_neural)
fig, ax = plt.subplots()
notebook.plot_annotation(hypothesis_neural, ax=ax, time=True, legend=True)
fig.savefig(f'{graphics_dir}/{filename}diarization_results.png')
```
### Poor Results
The performance is quite poor. All files are resampled to 16kHz, and while a few achieve 10-20% diarization ER, the majority have diarization errors from 60-70%. I'm not sure what the data environment is for Titanet and MSDD, but my conversations feature usually 3-6 different individuals. I'm experimenting with preprocessing and trying other models, but any suggestions would be welcome.
I'm unfortunately unable to finetune the Titanet model to improve performance, because it, also, consumes insane amounts of GPU memory [(1)](https://github.com/NVIDIA/NeMo/issues/6031), [(2)](https://github.com/NVIDIA/NeMo/issues/4533). Presumably it wants to be trained on small audio files, but mine are quite large. Google Colab's 16GB T4 is not up to the task, even with small batch sizes (4 / 4).
```[tasklist]
### Tasks
- [ ] memory
```
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# Changelog
- Fix codeql config file
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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Fix multi-speaker FastPitch test nightly
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# Changelog
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# Usage
* You can potentially add a usage example below
# 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
- [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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- [ ] 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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- [ ] Bugfix
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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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**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
```
# 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
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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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} | [] | closed | false | null | [] | null | null | 2023-05-25T16:36:11 | 2023-05-25T18:03:23 | 2023-05-25T18:03:22 | COLLABORATOR | {
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} | # What does this PR do ?
adds feature to preprocess squad in sft format which is going to be used for tutorials on lora in the near future.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6725 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6725/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6725/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6725/events | https://github.com/NVIDIA/NeMo/issues/6725 | 1,725,676,768 | I_kwDOC_bI7s5m27zg | 6,725 | Speaker Identification Verification training | {
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} | null | null | null | **Describe the bug**
[https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb]
While trying to performs the steps mentioned in the above notebook, I'm facing error in trainer.fit(speaker_model)
**Steps/Code to reproduce bug**
Error Log:
{
"name": "KeyError",
"message": "'65190d15daa5426985e5f52e516de631'",
"stack": "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)\nCell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m trainer\u001b[39m.\u001b[39;49mfit(speaker_model)\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:582\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 580\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m`Trainer.fit()` requires a `LightningModule`, got: \u001b[39m\u001b[39m{\u001b[39;00mmodel\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__qualname__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 581\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39m_lightning_module \u001b[39m=\u001b[39m model\n\u001b[0;32m--> 582\u001b[0m call\u001b[39m.\u001b[39;49m_call_and_handle_interrupt(\n\u001b[1;32m 583\u001b[0m \u001b[39mself\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path\n\u001b[1;32m 584\u001b[0m )\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py:38\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[39mreturn\u001b[39;00m trainer\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mlauncher\u001b[39m.\u001b[39mlaunch(trainer_fn, \u001b[39m*\u001b[39margs, trainer\u001b[39m=\u001b[39mtrainer, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 37\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 38\u001b[0m \u001b[39mreturn\u001b[39;00m trainer_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 40\u001b[0m \u001b[39mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 41\u001b[0m trainer\u001b[39m.\u001b[39m_call_teardown_hook()\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:624\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 617\u001b[0m ckpt_path \u001b[39m=\u001b[39m ckpt_path \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresume_from_checkpoint\n\u001b[1;32m 618\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_checkpoint_connector\u001b[39m.\u001b[39m_set_ckpt_path(\n\u001b[1;32m 619\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mfn,\n\u001b[1;32m 620\u001b[0m ckpt_path, \u001b[39m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m 621\u001b[0m model_provided\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 622\u001b[0m model_connected\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlightning_module \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 623\u001b[0m )\n\u001b[0;32m--> 624\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run(model, ckpt_path\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mckpt_path)\n\u001b[1;32m 626\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mstopped\n\u001b[1;32m 627\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1061\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 1057\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_checkpoint_connector\u001b[39m.\u001b[39mrestore_training_state()\n\u001b[1;32m 1059\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_checkpoint_connector\u001b[39m.\u001b[39mresume_end()\n\u001b[0;32m-> 1061\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_stage()\n\u001b[1;32m 1063\u001b[0m log\u001b[39m.\u001b[39mdetail(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m: trainer tearing down\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 1064\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_teardown()\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1140\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredicting:\n\u001b[1;32m 1139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_predict()\n\u001b[0;32m-> 1140\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_train()\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1153\u001b[0m, in \u001b[0;36mTrainer._run_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1150\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pre_training_routine()\n\u001b[1;32m 1152\u001b[0m \u001b[39mwith\u001b[39;00m isolate_rng():\n\u001b[0;32m-> 1153\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_sanity_check()\n\u001b[1;32m 1155\u001b[0m \u001b[39m# enable train mode\u001b[39;00m\n\u001b[1;32m 1156\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1225\u001b[0m, in \u001b[0;36mTrainer._run_sanity_check\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[39m# run eval step\u001b[39;00m\n\u001b[1;32m 1224\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m-> 1225\u001b[0m val_loop\u001b[39m.\u001b[39;49mrun()\n\u001b[1;32m 1227\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_callback_hooks(\u001b[39m\"\u001b[39m\u001b[39mon_sanity_check_end\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 1229\u001b[0m \u001b[39m# reset logger connector\u001b[39;00m\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py:206\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[39mbreak\u001b[39;00m\n\u001b[1;32m 204\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_restarting \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 206\u001b[0m output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mon_run_end()\n\u001b[1;32m 207\u001b[0m \u001b[39mreturn\u001b[39;00m output\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py:198\u001b[0m, in \u001b[0;36mEvaluationLoop.on_run_end\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39m_logger_connector\u001b[39m.\u001b[39mlog_eval_end_metrics(all_logged_outputs)\n\u001b[1;32m 197\u001b[0m \u001b[39m# hook\u001b[39;00m\n\u001b[0;32m--> 198\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_on_evaluation_end()\n\u001b[1;32m 200\u001b[0m \u001b[39m# enable train mode again\u001b[39;00m\n\u001b[1;32m 201\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_on_evaluation_model_train()\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py:262\u001b[0m, in \u001b[0;36mEvaluationLoop._on_evaluation_end\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[39m\"\"\"Runs ``on_{validation/test}_end`` hook.\"\"\"\u001b[39;00m\n\u001b[1;32m 261\u001b[0m hook_name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mon_test_end\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mtesting \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mon_validation_end\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 262\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49m_call_callback_hooks(hook_name, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 263\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39m_call_lightning_module_hook(hook_name, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 264\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39m_call_strategy_hook(hook_name, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1343\u001b[0m, in \u001b[0;36mTrainer._call_callback_hooks\u001b[0;34m(self, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1341\u001b[0m \u001b[39mif\u001b[39;00m callable(fn):\n\u001b[1;32m 1342\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mprofile(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m[Callback]\u001b[39m\u001b[39m{\u001b[39;00mcallback\u001b[39m.\u001b[39mstate_key\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m{\u001b[39;00mhook_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m):\n\u001b[0;32m-> 1343\u001b[0m fn(\u001b[39mself\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlightning_module, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1345\u001b[0m \u001b[39mif\u001b[39;00m pl_module:\n\u001b[1;32m 1346\u001b[0m \u001b[39m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 1347\u001b[0m pl_module\u001b[39m.\u001b[39m_current_fx_name \u001b[39m=\u001b[39m prev_fx_name\n\nFile \u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/callbacks/progress/tqdm_progress.py:299\u001b[0m, in \u001b[0;36mTQDMProgressBar.on_validation_end\u001b[0;34m(self, trainer, pl_module)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_main_progress_bar \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m trainer\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mfn \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mfit\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 298\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmain_progress_bar\u001b[39m.\u001b[39mset_postfix(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_metrics(trainer, pl_module))\n\u001b[0;32m--> 299\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mval_progress_bar\u001b[39m.\u001b[39;49mclose()\n\u001b[1;32m 300\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreset_dataloader_idx_tracker()\n\nFile \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tqdm/notebook.py:293\u001b[0m, in \u001b[0;36mtqdm_notebook.close\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdisp(bar_style\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39msuccess\u001b[39m\u001b[39m'\u001b[39m, check_delay\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 292\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 293\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdisp(close\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, check_delay\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\nFile \u001b[0;32m/usr/local/lib/python3.8/dist-packages/tqdm/notebook.py:192\u001b[0m, in \u001b[0;36mtqdm_notebook.display\u001b[0;34m(self, msg, pos, close, bar_style, check_delay)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[39mif\u001b[39;00m close \u001b[39mand\u001b[39;00m pbar\u001b[39m.\u001b[39mbar_style \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mdanger\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m# hide only if no error\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 192\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcontainer\u001b[39m.\u001b[39;49mclose()\n\u001b[1;32m 193\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[1;32m 194\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcontainer\u001b[39m.\u001b[39mvisible \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\nFile \u001b[0;32m/usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget.py:500\u001b[0m, in \u001b[0;36mWidget.close\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcomm \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 499\u001b[0m Widget\u001b[39m.\u001b[39m_active_widgets\u001b[39m.\u001b[39mpop(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_id, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 500\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcomm\u001b[39m.\u001b[39;49mclose()\n\u001b[1;32m 501\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcomm \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 502\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_repr_mimebundle_ \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\nFile \u001b[0;32m/usr/local/lib/python3.8/dist-packages/comm/base_comm.py:109\u001b[0m, in \u001b[0;36mBaseComm.close\u001b[0;34m(self, data, metadata, buffers, deleting)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpublish_msg(\n\u001b[1;32m 102\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcomm_close\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 103\u001b[0m data\u001b[39m=\u001b[39mdata,\n\u001b[1;32m 104\u001b[0m metadata\u001b[39m=\u001b[39mmetadata,\n\u001b[1;32m 105\u001b[0m buffers\u001b[39m=\u001b[39mbuffers,\n\u001b[1;32m 106\u001b[0m )\n\u001b[1;32m 107\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m deleting:\n\u001b[1;32m 108\u001b[0m \u001b[39m# If deleting, the comm can't be registered\u001b[39;00m\n\u001b[0;32m--> 109\u001b[0m comm\u001b[39m.\u001b[39;49mget_comm_manager()\u001b[39m.\u001b[39;49munregister_comm(\u001b[39mself\u001b[39;49m)\n\nFile \u001b[0;32m/usr/local/lib/python3.8/dist-packages/comm/base_comm.py:199\u001b[0m, in \u001b[0;36mCommManager.unregister_comm\u001b[0;34m(self, comm)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[39m\"\"\"Unregister a comm, and close its counterpart\"\"\"\u001b[39;00m\n\u001b[1;32m 198\u001b[0m \u001b[39m# unlike get_comm, this should raise a KeyError\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m comm \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcomms\u001b[39m.\u001b[39;49mpop(comm\u001b[39m.\u001b[39;49mcomm_id)\n\n\u001b[0;31mKeyError\u001b[0m: '65190d15daa5426985e5f52e516de631'"
}
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 20.04.5 LTS
- PyTorch version: 1.12.1+cu113
- Python version: Python 3.8.10 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6721 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6721/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6721/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6721/events | https://github.com/NVIDIA/NeMo/pull/6721 | 1,725,181,018 | PR_kwDOC_bI7s5RT53y | 6,721 | Fix preprocessing for `--text_file` option. | {
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} | # What does this PR do ?
- Currently, the `--text_file` option always looks for .json files, so I've fixed it to look for .txt files.
**Collection**: [NLP]
# Changelog
- If you use the `--text_file` option, it will look for .txt files.
- Fix bug opening invalid file instead of `json_file`.
# Usage
You can run the preprocessing task as follows:
```python
python NeMo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py \
--input=/datas/raw/ebooks \
--text_file \
--preproc-folder \
--tokenizer-library=huggingface \
--tokenizer-type=/gpt2_bpe_tokenizer \
--merge-file=/gpt2_bpe_tokenizer/merges.txt \
--vocab-file=/gpt2_bpe_tokenizer/vocab.json \
--dataset-impl mmap \
--append-eod \
--output-prefix=hfbpe_gpt_training_data \
--workers=64
```
# 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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} | null | null | null | Hello, I want to finetune the tts_es_fastpitch_multispeaker model but I don't know where to find the correct file for the phoneme dictionary. This is the code I’m running for finetuning:
`!(python fastpitch_finetune.py --config-name=fastpitch_align_44100_ipa_multi.yaml \
train_dataset=./transcript_train.json \
validation_datasets=./transcript_dev.json \
sup_data_path=./fastpitch_sup_data \
pitch_stats_path=tts_dataset_files/pitch_stats.json \
phoneme_dict_path=tts_dataset_files/es_LA_nv230301.dict \
exp_manager.exp_dir=./ljspeech_to_9017_no_mixing_5_mins \
+init_from_nemo_model=./tts_es_fastpitch_multispeaker.nemo \
trainer.check_val_every_n_epoch=25 \
model.train_ds.dataloader_params.batch_size=12 model.validation_ds.dataloader_params.batch_size=12 \
model.n_speakers=174 \
model.pitch_fmin=30 model.pitch_fmax=512 model.optim.lr=2e-4 \
~model.optim.sched model.optim.name=adam trainer.devices=1 trainer.strategy=null \
)`
I tried with the two phoneme files avaliable in this repository:
`
NeMo/scripts/tts_dataset_files/es_ES/es_ES_nv230301.dict
`
`NeMo/scripts/tts_dataset_files/es_LA/es_LA_nv230301.dict`
And with both I have this error:
`size mismatch for fastpitch.encoder.word_emb.weight: copying a param with shape torch.Size([101, 384]) from checkpoint, the shape in current model is torch.Size([103, 384]).`
I was able to by pass this by simple removing some lines from one of the files but, if possible, I would like to use the same dictionary used in the original training to avoid unknown char/phoneme errors. Thanks.
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} | # What does this PR do ?
Fix multi-speaker FastPitch testing
**Collection**: [TTS]
# Changelog
- Add specific line by line info of high level changes in this PR.
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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 ?
Notebook link fixes
**Collection**: [Docs]
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} | # What does this PR do ?
Notebook link fixes
**Collection**: [Docs]
# Before your PR is "Ready for review"
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} | # What does this PR do ?
First version of preprocessing ignored audio files where start/end of speech could not be detected. During testing I find that all such examples are silent audio files with no speech. And that most public datasets have several such files. This updates the script to remove such files from the final training manifest.
**Collection**: [TTS]
# Changelog
- Update preprocessing script to remove silent/empty audio files from final training manifest.
- Add `overwrite_audio` so that is script fails partway, you can resume where you left off instead of having to reprocess the entire dataset.
- Fix parsing of `overwrite` arguments, as trying to parse them as a bool results in `True` regardless of what value you provide in the CLI.
# Before your PR is "Ready for review"
**Pre checks**:
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} | null | null | null | **Describe the bug**
There is an assumption in [TextMemMapDataset](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/text_memmap_dataset.py) that all nodes share the same storage. When this is not the case, the class fails to find index files on ancillary nodes (those on which we the node rank is not zero).
**Steps/Code to reproduce bug**
* Copy a text file F into the local filesystems of two nodes A and B.
* In a multi-node setup comprising nodes A and B, instantiate a `TextMemMapDataset` (or any subclass such as `JSONLMemMapDataset`) with `dataset_paths=[F]`).
* Assuming node B is the one `node_rank>0`, all node B processes fail with an exception is raised with a message like `ValueError: Memory Map for F is not found, missing one or more of files: F.idx.{.npy,.info}`.
**Expected behavior**
No exception should occur.
**Environment overview (please complete the following information)**
A cluster that supports multi-node jobs and isolated (not shared) filesystems on each node.
**Environment details**
NeMo 23.02.
**Additional context**
This issue appears to stem from these lines in `TextMemMapDataset.__init__()`:
```
if not is_distributed or (is_distributed and torch.distributed.get_rank() == 0):
build_index_files(dataset_paths, newline_int, workers=self._worker, build_index_fn=build_index_fn)
```
The test should use the local rank. However if the input storage is indeed shared between nodes, `local_rank=0` processes might collide with one another, as they would write into the same files. In order to avoid this, one suggestion would be to factor in the node rank into the index file path, which cause separate index files to be created on each node. If this is deemed unacceptable on shared filesystems where this is not necessary, other options would include:
* adding a flag to turn on/off that behavior
* adding an extra barrier to automatically detect whether the index files have been created on first pass:
```
if not is_distributed or (is_distributed and torch.distributed.get_rank() == 0):
# Creates index files only on global rank 0.
build_index_files(dataset_paths, newline_int, workers=self._worker, build_index_fn=build_index_fn)
if is_distributed:
# Wait for global rank 0 to create index files.
torch.distributed.barrier()
if is_distributed and local_rank == 0:
# Shared file system case: this is a no-op since the index files have already been created.
# Non-shared file system case: will only create the index files on node_rank>0 and local_rank==0.
build_index_files(dataset_paths, newline_int, workers=self._worker, build_index_fn=build_index_fn)
if is_distributed:
# Wait for all local ranks 0 to create index files.
torch.distributed.barrier()
```
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} | # What does this PR do ?
support torch.jit.save for offline clustering maj_vote_spk_count=True
**Collection**: [Note which collection this PR will affect]
# Changelog
- for torch.jit, remove redundant torch.tensor on `est_num_of_spk_list` in `offline_clustering.py`
# Usage
```bash
pytest tests/collections/asr/test_diar_utils.py::TestSpeakerClustering::test_offline_speaker_clustering
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in 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.
main reviewer: @tango4j
This is a part of ASR, diarizer.
@titu1994 @redoctopus, @jbalam-nv, or @okuchaiev
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
adding p-tuning Triton-FasterTransformer inference tutorial
**Collection**: LLM
# Changelog
- Add a new p-tuning deployment notebook
# 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.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
I get the following error
```
AttributeError Traceback (most recent call last)
[<ipython-input-55-5354c1522db5>](https://localhost:8080/#) in <cell line: 1>()
----> 1 msdd_model.save_to(save_path="xyz.nemo")
1 frames
[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/msdd_models.py](https://localhost:8080/#) in save_to(self, save_path)
1036 self.clus_diar._speaker_model.save_to(spkr_model)
1037 self.msdd_model.save_to(neural_diar_model)
-> 1038 self.clus_diar.__make_nemo_file_from_folder(filename=save_path, source_dir=tmpdir)
1039
1040 def extract_standalone_speaker_model(self, prefix: str = 'msdd._speaker_model.') -> Dict:
AttributeError: 'ClusteringDiarizer' object has no attribute '_NeuralDiarizer__make_nemo_file_from_folder'
```
**Steps/Code to reproduce bug**
This is literally all the code I am trying to run on google colab.
```
msdd_model = NeuralDiarizer(cfg=create_config())
msdd_model.save_to(save_path="xyz.nemo")
```
Everything else works the model works etc
**Expected behavior**
I expected the model to be saved.
**Environment overview (please complete the following information)**
- Environment location: Collab
- Method of NeMo install: !pip install nemo_toolkit[asr]
**Environment details**
- PyTorch version 1.13.1+cu116
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Adds support of multi-look ahead to cache-aware conformer models for streaming ASR. A model trained with multi-lookahead can support multiple context sizes during inference.
# Changelog
- Added support of multi-lookahead for cache-aware Conformer model for streaming ASR
# Usage
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asr_model.encoder.set_default_att_context_size([70,1])
```
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} | # What does this PR do ?
Limits the scope of codeql to avoid memory errors
**Collection**: [Core]
# Changelog
- Puts path limits on files that are scanned for codeql
# 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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} | # What does this PR do ?
Fix k2 installation in Docker with CUDA 12.
Currently k2 is not installed in Docker (fails silently).
Relies on a fix in https://github.com/k2-fsa/k2/pull/1200
**Collection**: [ASR]
# Changelog
- fix k2 installation source in `scripts/speech_recognition/k2/setup.sh`
# Usage
```python
import k2
# do anything you want with k2
```
# 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)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix bug when `inference.compute_logprob=True`. Only first and last pipeline stages have a response.
**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
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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 ?
Fix k2 installation in Docker with CUDA 12.
Currently k2 is not installed in Docker (fails silently).
Relies on a fix in https://github.com/k2-fsa/k2/pull/1200
**Collection**: [ASR]
# Changelog
- fix k2 installation source in `scripts/speech_recognition/k2/setup.sh`
# Usage
```python
import k2
# do anything you want with k2
```
# 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)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | We made a finetune for the speaker identification for the Portuguese language, the best result we got was using these parameters:
Epochs 2
Batch size 32
Optimizer 'SGD'
Lr 0.011823477534453206
Scale 60
Margin 0.07095099595312822
But when testing with our test dataset, we found that some audios that are clearly different are giving a very high confidence rate. Here are some examples:
85% confidence rate
[85.zip](https://github.com/NVIDIA/NeMo/files/11545713/85.zip)
86% confidence rate
[86.zip](https://github.com/NVIDIA/NeMo/files/11545714/86.zip)
90% confidence rate
[90.zip](https://github.com/NVIDIA/NeMo/files/11545708/90.zip)
92% confidence rate
[92.zip](https://github.com/NVIDIA/NeMo/files/11545709/92.zip)
94% confidence rate
[94.zip](https://github.com/NVIDIA/NeMo/files/11545710/94.zip)
In another scenario, we validate whether they are in fact the same people. And in some cases it affected a low confidence rate, here are some examples:
59% confidence rate
[59.zip](https://github.com/NVIDIA/NeMo/files/11545711/59.zip)
64% confidence rate
[64.zip](https://github.com/NVIDIA/NeMo/files/11545712/64.zip)
Below is our model used for both scenarios:
["model"](https://drive.google.com/file/d/1CHyLBXmd_NoC3mg8Q8d_0gIsZdRWw8nd/view?usp=share_link)
Do these problems above make sense? Are there any parameter tweaks, or tweaks to the audios that can be done to fix this issue?
Thanks in advance | {
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} | # What does this PR do ?
install ctc_decoders inside venv
**Collection**: ASR
# Changelog
- Fix install_beamsearch_decoders.sh to work outside container
# Usage
* Install NeMo ctc-decoders locally inside Conda venv
```bash
# make NeMo venv
conda create --name nemo python==3.8.10
conda activate nemo
# install NeMo
sudo apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
# install ctc-decoders
./scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh $(pwd)
```
**PR Type**:
- [ X] Bugfix
## Who can review?
@titu1994
# Additional Information
* Related to #6690
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} | # What does this PR do ?
Fix validation with validation_drop_last=False flag.
**Collection**: NLP
# Changelog
- fix validation with validation_drop_last=False flag.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
When delaying dataset mmap, dataset destructor errored out if the mmap creation was not actually called, leading to misleading tracebacks.
**Collection**: NLP
# Changelog
- Fix MMapIndexedDataset destructor for delayed case.
# 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 ?
1. correct the misleading deprecation warnings;
2. organize the duplicate warning and replacing code snippet into a single function.
3. passed `pytest --nightly tests/collections/tts/models`.
**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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | ### Discussed in https://github.com/NVIDIA/NeMo/discussions/6700
<div type='discussions-op-text'>
<sup>Originally posted by **great-goblin** May 22, 2023</sup>
I'm trying to get a VAD model to work for diarization inference, but the VAD model alone has atrocious performance. I tried following along using the parameters [given in this tutorial](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb#scrollTo=4r4XOzhWgj81), but it had very high error rates (95% diarization ER, 80% miss). I've had much better success with using a [basic ELAN silence recognizer script,](https://www.mpi.nl/corpus/html/elan/ch02s04s03.html) but I'm not experienced enough with the NeMo toolkit to know how to substitute in that 'correct' VAD segmentation. I'm just trying to get the basic System VAD (marblenet) -> segmentation -> embedding extractor -> clustering -> neural diarizer (MSDD) to work.
[This links to a graphic](https://www.imagehost.at/image/2JI7b) I made to show how VAD is currently doing with my .wav fle (red line is ideal segmentation).
The .wav file I'm looking at has been resampled to 16kHz, and features a pilot and air traffic controller speaking to each other. To give a sense of the back-and-forth, this is the .rttm:
```SPEAKER WTAN4 1 0.920 2.75 <NA> <NA> B <NA>
SPEAKER WTAN4 1 4.020 3.1800000000000006 <NA> <NA> A <NA>
SPEAKER WTAN4 1 7.700 1.4999999999999991 <NA> <NA> B <NA>
SPEAKER WTAN4 1 11.340 2.2799999999999994 <NA> <NA> B <NA>
SPEAKER WTAN4 1 16.080 6.780000000000001 <NA> <NA> A <NA>
SPEAKER WTAN4 1 25.340 0.8900000000000006 <NA> <NA> B <NA>
SPEAKER WTAN4 1 26.400 4.16 <NA> <NA> A <NA>
SPEAKER WTAN4 1 31.000 4.185000000000002 <NA> <NA> B <NA>
SPEAKER WTAN4 1 35.380 4.559999999999995 <NA> <NA> A <NA>
SPEAKER WTAN4 1 40.480 3.1200000000000045 <NA> <NA> B <NA>
```
1. I start by initializing and importing everything per the tutorial linked above. My .rttm's look fine in Annotation format, no issues or any other issues (warnings aside).
2. I load the diar_infer_telephonic yaml into Omega and modify the following settings. Mostly I followed the tutorial, except I've made more modifications to the diarizer parameters in an attempt to make the VAD look better.
```python
MODEL_CONFIG = os.path.join(data_dir,'diar_infer_telephonic.yaml')
config = OmegaConf.load(MODEL_CONFIG)
config.diarizer.manifest_filepath = 'data/input_manifest.json'
config.diarizer.out_dir = output_dir # Directory to store intermediate files and prediction outputs
pretrained_speaker_model = 'titanet_large'
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
config.diarizer.speaker_embeddings.parameters.window_length_in_sec = [1.5,1.25,1.0,0.75,0.5]
config.diarizer.speaker_embeddings.parameters.shift_length_in_sec = [0.75,0.625,0.5,0.375,0.1]
config.diarizer.speaker_embeddings.parameters.multiscale_weights= [1,1,1,1,1]
config.diarizer.oracle_vad = True # ----> ORACLE VAD
config.diarizer.clustering.parameters.oracle_num_speakers = False
config.diarizer.msdd_model.model_path = 'diar_msdd_telephonic' # Telephonic speaker diarization model
config.diarizer.msdd_model.parameters.sigmoid_threshold = [0.7, 1.0] # Evaluate with T=0.7 and T=1.0
pretrained_vad = 'vad_multilingual_marblenet'
config.num_workers = 1 # Workaround for multiprocessing hanging with ipython issue
output_dir = os.path.join(ROOT, 'outputs')
config.diarizer.manifest_filepath = 'data/input_manifest.json'
config.diarizer.out_dir = output_dir #Directory to store intermediate files and prediction outputs
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
config.diarizer.oracle_vad = False # compute VAD provided with model_path to vad config
config.diarizer.clustering.parameters.oracle_num_speakers=False
#VAD parameters - this is what I've edited
config.diarizer.vad.model_path = pretrained_vad
config.diarizer.vad.parameters.window_length_in_sec = 0.5
config.diarizer.vad.parameters.shift_length_in_sec = 0.03
config.diarizer.vad.parameters.smoothing = 'median'
config.diarizer.vad.parameters.overlap = 0.9
config.diarizer.vad.parameters.onset = 0.05
config.diarizer.vad.parameters.offset = 0.7
config.diarizer.vad.parameters.pad_onset = 0.05
config.diarizer.vad.parameters.pad_offset = -0.09
config.diarizer.vad.parameters.min_duration_on = 0.15
config.diarizer.vad.parameters.min_duration_off = 0.05
```
3. Then, I run the diarizer and visualize the results.
```python
from nemo.collections.asr.models import ClusteringDiarizer
sd_model = ClusteringDiarizer(cfg=config)
sd_model.diarize()
from nemo.collections.asr.parts.utils.vad_utils import plot
if config.diarizer.vad.parameters.smoothing:
vad_output_filepath = f'{output_dir}/vad_outputs/overlap_smoothing_output_median_{config.diarizer.vad.parameters.overlap}/{filename}.{config.diarizer.vad.parameters.smoothing}'
else:
vad_output_filepath = f'{output_dir}/vad_outputs/{filename}.frame'
plot(
an4_audio,
vad_output_filepath,
None,
per_args = config.diarizer.vad.parameters, #threshold
)
# Save the plot as an image
plt.savefig(f'vad_plot.png')
```
I've played a lot with the VAD parameters in step 2 above, but I'm finding it frustrating that a simple ELAN script from two decades ago does a better job at VAD than this sophisticated model. I'm confident it's my own inexperience with this model, so please tell me how I can improve! Ultimately I'd like to actually diarize this model, and if I can figure out how, somehow fine-tune it on my own data, but any suggestions at all would be welcome!
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Please correct each item as shown below for `radtts`,
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} | # What does this PR do ?
Fix an issue with VP parameter loading.
NOTE: this is not a complete fix for VP, bugs are still being fixed in later PRs.
**Collection**: [NLP]
# Changelog
- force config to be correctly updated for megatron amp 02
- update VP logic when loading weights
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix TTS adapter duration issue.
**Collection**: [TTS]
# Changelog
- `examples/tts/conf/fastpitch_align_44100_adapter.yaml`
- `examples/tts/fastpitch_finetune_adapters.py`
- `nemo/collections/tts/losses/aligner_loss.py`
- `nemo/collections/tts/models/fastpitch.py`
- `tutorials/tts/FastPitch_Adapter_Finetuning.ipynb`
# Usage
* You can potentially add a usage example below
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] 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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**Collection**: NLP
# Changelog
- `nemo/collections/nlp/modules/common/tokenizer_utils.py`
# Usage
* When using a custom SentencePiece tokenizer model.
```python
model.tokenizer.model=...
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in 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 ?
NFA updates: improve API, reduce memory consumption
**Collection**: [Note which collection this PR will affect]
Tools
# Changelog
Numerous adjustments, including:
- Reduced memory requirements of viterbi decoding
- Removed need to specify model downsample factor
- Now ASS format files can also be created
# Usage
No more need to specify model downsample factor, so can use as follows:
```python
python <path_to_NeMo>/tools/nemo_forced_aligner/align.py \
pretrained_name="stt_en_citrinet_1024_gamma_0_25" \
manifest_filepath=<path to manifest of utterances you want to align> \
output_dir=<path to where your ctm files will be saved>
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Various NFA updates: improving APIs, reducing memory consumption, etc.
**Collection**: [Note which collection this PR will affect]
Tools
# 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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} | null | null | null | **Describe the bug**
The [`__len__`](https://github.com/NVIDIA/NeMo/blob/dc9dda09ff4fb986cf7653e9384c4ea6ec4202fd/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py#LL81C8-L81C8) function for BaseMegatronSampler use `self.total_samples - self.consumed_samples` to caculate the sampler length. Is that intentional or just a bug.
The current implementation will make the log confused once after resuming from some checkpoints. The total iters will decrease every time resuming from a newer checkpoint.
**Expected behavior**
The total iters should keep unchanged every time resuming from new checkpoint.
**Environment overview (please complete the following information)**
- Environment location: Docker
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
- docker pull: `docker pull nvcr.io/nvidia/nemo:23.02`
- docker run: `nvidia-docker run -it --shm-size 64g --network host -v /path/on/host:/path/in/docker nvcr.io/nvidia/nemo:23.02 bash`
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | null | null | null | **Describe the bug**
When I run default ` python duplex_text_normalization_infer.py \
tagger_pretrained_model=neural_text_normalization_t5 \
decoder_pretrained_model=neural_text_normalization_t5 \
inference.from_file=False \
lang=en \
mode=tn` got this error
**Expected behavior**
I want to test how to tn/itn works
**Environment overview (please complete the following information)**
- Environment location: [local]
- Method of NeMo install: [pip install ].
**Environment details**
Otherwise, please provide:
- OS version - 20.04
- PyTorch version - 2.0.1
- Python version - 3.10.10
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while using eval_beamsearch_ngram.py , i face the following error:
"
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.10/site-packages/nemo/collections/asr/modules/beam_search_decoder.py", line 64, in __init__
from ctc_decoders import Scorer, ctc_beam_search_decoder_batch
ModuleNotFoundError: No module named 'ctc_decoders'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/azad2/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 393, in main
candidate_wer, candidate_cer = beam_search_eval(
File "/home/azad2/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 185, in beam_search_eval
_, beams_batch = model.decoding.ctc_decoder_predictions_tensor(
File "/root/anaconda3/lib/python3.10/site-packages/nemo/collections/asr/metrics/wer.py", line 418, in ctc_decoder_predictions_tensor
hypotheses_list = self.decoding(
File "/root/anaconda3/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py", line 166, in __call__
return self.forward(*args, **kwargs)
File "/root/anaconda3/lib/python3.10/site-packages/nemo/core/classes/common.py", line 1087, in __call__
outputs = wrapped(*args, **kwargs)
File "/root/anaconda3/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py", line 280, in forward
hypotheses = self.search_algorithm(prediction_tensor, out_len)
File "/root/anaconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/root/anaconda3/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py", line 329, in default_beam_search
self.default_beam_scorer = BeamSearchDecoderWithLM(
File "/root/anaconda3/lib/python3.10/site-packages/nemo/collections/asr/modules/beam_search_decoder.py", line 66, in __init__
raise ModuleNotFoundError(
ModuleNotFoundError: BeamSearchDecoderWithLM requires the installation of ctc_decoders from scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
"
even though i have run install_bemsearch_decoders.py.
does any one know whats the problem?
is there any other way to install "ctc_decoders"? | {
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} | # What does this PR do ?
Added two syllable tokenizers for Indic Languages.
**Collection**: common
# Changelog
- Added an IndicSyllableBFTokenizer which performs brute force split of text on vowel signs.
- Added an IndicSyllableTokenizer which splits syllables based on the indic-nlp-library's syllabifier module.
# Usage
- Since providing all possible syllables for all Indic language is nigh impossible, I've added a script which can extract syllables from the manifest. The logic for this resides in `scripts/dataset_preprocessing/tts/indictts` .
- I have also added a config for using the tokenizer with fastpitch under `examples/tts/conf/indictts`
# 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)
- This requires you to install the indic-nlp-library which is under MIT License and clone the indic-nlp-resources library and save the path under "INDIC_RESOURCES_ROOT". I've added these reqs to the docstring and have made a check to see if this exists in code as well with clear instructions.
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [+] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Allow expanded checkpoints for base model during eval.
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} | null | null | null | Hello everyone,
We are working on a project that identifies if is the same person in different audios, basically speaker recognition. We put 75% of threshold to identify the same person.
We work with clean audios and phone call audios in Portuguese, the correct was have a model for each audio type, or could join these two audio type for a robust model? For example, I do a train only with phone call audios and after that a finetune with clean audios, or the other way around.
We try to do both together, a train with two dataset and a finetune with two dataset, and we had an unsatisfactory result
```
Clean audios, 44959 train, 2351 dev
Predict Fraud Same Person
Fraud 23060 13.925
Same Person 18 449
Clean and phone call audios, 11334 train, 1214 dev
Predict Fraud Same Person
Fraud 20921 16.064
Same Person 6 461
Phone call audios, 6658 train, 975 dev
Predict Fraud Same Person
Fraud 35271 1.714
Same Person 78 389
```
For parameters we're using 'titanet-large', 32 batch_size for both, 0.011823477534453206 for lr, 0.07095099595312822 for margin, 60 for scale, 'sgd' optimizer and 2 epochs.
Here some audios that we have problem to identify as different persons:
[WrongResults.zip](https://github.com/NVIDIA/NeMo/files/11531858/WrongResults.zip)
All of these audios have a high score to the wrong person, like 85% to 90% of confidence.
With this context, make sense this results? Why I get this high confidence with different persons? Have any way to improve this results? And how many audios are expected to have a good result?
Also we use an issue, https://github.com/NVIDIA/NeMo/issues/5930, as recommendation to train and test.
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} | If datasets are stored on a read-only medium, index files cannot be created into adjacent files and an
alternative directory must be specified for index
mapping files.
# What does this PR do ?
This commit adds an optional flag to specify the directory to write index files to.
**Collection**: nlp
# Changelog
- Add an optional `index_mapping_dir` to the constructors of `TextMemMapDataset`, `CSVMemMapDataset` and `JSONLMemMapDataset`.
- Add unit tests.
- Propagate `index_mapping_dir` from `GPTSFTDataset`.
-
# Usage
```bash
nvidia-docker run --shm-size=4gb -v ~/llm/data:/llm/data:ro -it sft:latest python megatron_gpt_sft.py ++model.data.train_ds.index_mapping_dir=/tmp ++model.data.validation_ds.index_mapping_dir=/tmp
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix a bug when training SFT model with dialog dataset when setting `self.pad_to_max_length=False`
**Collection**: [Note which collection this PR will affect]
# Changelog
```
#gpt_sft_chat_dataset.py
## original code
## max_length = min(self.max_seq_length, self._round_to_nearest(max_length, 8))
## Changed to
max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 8))
```
In [gpt_sft_chat_dataset.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py#LL270C56-L270C72), we use `_ceil_to_nearest` which is also defined in this file. However, `_round_to_nearest` is not defined in NeMo.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
Fix a bug when training SFT model with dialog dataset when setting `self.pad_to_max_length=False`
**Collection**: [Note which collection this PR will affect]
# Changelog
```
#gpt_sft_chat_dataset.py
## original code
## max_length = min(self.max_seq_length, self._round_to_nearest(max_length, 8))
## Changed to
max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 8))
```
In [gpt_sft_chat_dataset.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py#LL270C56-L270C72), we use `_ceil_to_nearest` which is also defined in this file. However, `_round_to_nearest` is not defined in NeMo.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | avoid pining the Gradio library version. | {
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} | Update all invalid tree references to blobs for NeMo samples
Currently, most of the TTS tutorials are "broken" because the link to colab is invalid. This fixes those links. I also verified that all the new links work correctly.
**Collection**: NeMo Tutorials
# Changelog
- Several of the tutorials pointed to an invalid git tree (should be blob). Updated to blob and verified they now work on colab
# 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? (none needed)
- [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) (none)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [x] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Inconsistent usage of the word Note, which includes a broken reading in one case.
I'm just doing some tidying -- not trying to be critical.
**Before**:
**After**:
**Collection**: NeMo Core (documentation)
# Changelog
- I changed the inconsistent use of the word Note: in documentation to `.. note:: ` as used elsewhere
# 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? (no tests needed -- documentation fix)
- [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) (no)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Fixes a masking bug where boolean values were inverted from what was expected. Reverted change from PR #5653.
**Collection**: TTS
# Changelog
- Added check for `== 0` (applied to the input mask) back to the Aligner forward call. Otherwise the mask is applied incorrectly, as the values are flipped from what is expected in the Aligner module.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* See Issue #6584
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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update batch size recommendation to min 32 for 43b
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | null | null | null | I have finetuned a speaker recognition model with the help of this tutorial : https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
After that I trained a MSDD Model for Speaker Diarization and replaced the pretrained titanet model with my finetuned Speaker Recognition Model. I followed this tutorial :https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb
I tried both, training with a frozen speaker embeddings model and joint training and noticed that the validation loss was much lower when using joint training. Nevertheless, the inference time for Speaker Diarization takes forever when extracting embeddings. How can I tackle that problem? | {
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} | # What does this PR do ?
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**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
```
# Before your PR is "Ready for review"
**Pre checks**:
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## Who can review?
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} | # What does this PR do ?
Adding SSL config for fast-conformer
**Collection**: ASR
# Changelog
- Added SSL config for fast-conformer (examples/asr/conf/ssl/fast-conformer/fast-conformer.yaml)
- Added boolean flags whether to indicate a particular loss is active or not via config file and associated changes ( nemo/collections/asr/models/ssl_models.py file).
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
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 ?
Implement new VocoderDataset to support multi-manifest training, which follows the same design pattern as the new TextToSpeechDataset (https://github.com/NVIDIA/NeMo/pull/6575).
**Collection**: [TTS]
# Changelog
- Add VocoderDataset implementation
- Add example config for using data loader
- Add logic in HiFi-GAN recipe for switching to new data loader (matching changes to support UnivNet recipe will be in a future PR).
- Add vocoder logging callback
- Use modified configure_optimizers() implementation from UnivNet recipe (https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/models/univnet.py#L106) to support schedulers other than CosineAnnealing. New config uses scheduler from original HiFi-GAN paper.
# Usage
Example of training on 2 datasets, LJSpeech with 10% training examples and VCTK with 90%:
```
/home/rlangman/miniconda3/envs/nemo_dev/bin/python /home/rlangman/Code/NeMo/examples/tts/hifigan.py
--config-path=/home/rlangman/Code/NeMo/examples/tts/conf/hifigan
--config-dir=/home/rlangman/Code/NeMo/examples/tts/conf
--config-name=hifigan_22050_data.yaml
sample=22050
max_epochs=1000
weighted_sample_steps=1000
batch_size=16
trainer.devices=1
log_dir=/home/rlangman/exps/ljspeech_vctk/HifiGan/test/samples
exp_manager.exp_dir=/home/rlangman/exps/ljspeech_vctk
+exp_manager.version=test
+train_ds_meta.ljspeech.manifest_path=/home/rlangman/fs_mount/data/ljspeech/train_manifest.json
+train_ds_meta.ljspeech.audio_dir=/home/rlangman/fs_mount/data/ljspeech/audio
+train_ds_meta.ljspeech.sample_weight=0.1
+val_ds_meta.ljspeech.manifest_path=/home/rlangman/fs_mount/data/ljspeech/val_manifest.json
+val_ds_meta.ljspeech.audio_dir=/home/rlangman/fs_mount/data/ljspeech/audio
+log_ds_meta.ljspeech._target_=nemo.collections.tts.data.vocoder_dataset.DatasetMeta
+log_ds_meta.ljspeech.manifest_path=/home/rlangman/fs_mount/data/ljspeech/log_manifest.json
+log_ds_meta.ljspeech.audio_dir=/home/rlangman/fs_mount/data/ljspeech/audio
+train_ds_meta.vctk.manifest_path=/home/rlangman/fs_mount/data/vctk/train_manifest.json
+train_ds_meta.vctk.audio_dir=/home/rlangman/fs_mount/data/vctk/audio
+train_ds_meta.vctk.sample_weight=0.9
+val_ds_meta.vctk.manifest_path=/home/rlangman/fs_mount/data/vctk/val_manifest.json
+val_ds_meta.vctk.audio_dir=/home/rlangman/fs_mount/data/vctk/audio
+log_ds_meta.vctk._target_=nemo.collections.tts.data.vocoder_dataset.DatasetMeta
+log_ds_meta.vctk.manifest_path=/home/rlangman/fs_mount/data/vctk/log_manifest.json
+log_ds_meta.vctk.audio_dir=/home/rlangman/fs_mount/data/vctk/audio
exp_manager.create_wandb_logger=true
+exp_manager.wandb_logger_kwargs.project=hifigan_ljspeech_vctk
+exp_manager.wandb_logger_kwargs.name=test
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Adds new feature to compare two ASR models on utterance level visually
Add a one line overview of what this PR aims to accomplish.
**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
```
python3 ./NeMo/tools/speech_data_explorer/data_explorer.py test.json -nc pred_text_citri pred_text_qn
```
# 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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} | null | null | null | ** i re-posted in feature request form since there was no reply in bug report
Hello,
I built Multilingual ASR model with Korean and English.
However, I found that tokens are assigned not correctly.
I used 128 tokens for english, and 1641 tokens for korean (Total 1749)
when i checked vocabulary, there were several suspicious points.
When i printed asr_model.tokenizer.langs_by_token_id, it correctly shows tokens. (from 0 to 128 : 'en', from 129 to 1749: 'kor').
However, some alphabet token is assigned in korean. For example, print(asr_model.tokenizer.token_to_id('q', 'kor')) results 1702.
Also, in my knowledge, the aggregated tokenizer must remove duplicated token such as 'unkown'. But, there is two 'unk' tokens in both index 0 and 128
I also saw same phenomonon in pretrained_model, Conformer_transducer_enes_large.
tokens such as 'pre', 'ble' 'is', 'to' etc are assigned in Es, not En. I think these tokens are close to english tokens rather than spanish token.
there were two 'unk' tokens.
print(asr_model.tokenizer.token_to_id('unk', 'en')) results in 0, and
print(asr_model.tokenizer.token_to_id('unk', 'es')) results in 1024. | {
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} | # What does this PR do ?
* Add extrapolatable position embedding, e.g. xpos, sandwich.
* Fix extrapolatable position embedding, e.g. alibi, kerple.
* Add flash attention (FA)
**Collection**: [NLP]
# Changelog
* GPT
- `examples/nlp/language_modeling/conf/megatron_gpt_config.yaml`
- `nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py`
- `nemo/collections/nlp/models/language_modeling/megatron/gpt_model.py`
- `nemo/collections/nlp/modules/common/megatron/language_model.py`
* T5
- `nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py`
- `nemo/collections/nlp/modules/common/megatron/megatron_encoders.py`
- `nemo/collections/nlp/modules/common/megatron/megatron_decoders.py`
- `nemo/collections/nlp/modules/common/megatron/megatron_transformer_encoder.py`
- `nemo/collections/nlp/modules/common/megatron/megatron_transformer_decoder.py`
* PE
- `nemo/collections/nlp/modules/common/megatron/transformer.py`
- `nemo/collections/nlp/modules/common/megatron/attention.py`
- `nemo/collections/nlp/modules/common/megatron/kerple_relative_position.py`
- `nemo/collections/nlp/modules/common/megatron/sandwich_relative_position.py`
- `nemo/collections/nlp/modules/common/megatron/xpos_relative_position.py`
* FA
- `nemo/collections/nlp/modules/common/megatron/attention.py`
# Usage
* PE
```python
# GPT
model.position_embedding_type='' # Options ['learned_absolute', 'rope', 'alibi', 'xpos', 'sandwich', 'kerple']
# T5
model.encoder.position_embedding_type='' # Options ['learned_absolute', 'rope', 'alibi', 'xpos', 'sandwich', 'kerple']
model.decoder.position_embedding_type='' # Options ['learned_absolute', 'rope', 'alibi', 'xpos', 'sandwich', 'kerple']
```
* FA
```python
# GPT
model.use_flash_attention=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)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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.
# Todo
- [x] Add tests for PE and FA
- [x] Integrate multiple PE classes into one file
- [x] Move if/else of different attention bias outside CoreAttention
- [ ] Update requirements files
- [x] Update dockerfile
- [ ] Add a warning for FA
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} | # What does this PR do ?
Add an optional and configurable callback for FastPitch training which allows providing a vocoder checkpoint and saving any combination of model output (spectrogram, alignment, audio, audio with gt duration, prosody features) to (local_disk, tensorboard, wandb).
The callback expects the user to provide a separate manifest with all of the examples to generate the audio for. A user can create this by manually selecting 5-10 rows from their validation manifest.
The callback interface should be generic enough to use it for logging any training recipe, such as vocoder training (which will be added in a future PR).
**Collection**: [TTS]
# Changelog
- Add generic LoggingCallback interface for logging model predictions
- Add FastPitchArtifactGenerator to log model outputs for FastPitch
- Modify new fastpitch example config to show to configure the logger
- Set FastPitch min_token_duration to 1 to avoid various edge cases during inference where output audio is empty array (all duration predictions are 0). This is also more accurate as the aligner requires the alignment to assign at least 1 audio frame to each input token. I could make this is a config (defaulting to 0) if anyone thinks the change is risky.
# 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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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)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes issue with bucketing datasets, where loss for new run jumps from a previous run.
Tested.
**Collection**: ASR
# Changelog
- break once bucket finishes, so as to pick next bucket
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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- [x] Bugfix
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* Related to #6191
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} | null | null | null | I am trying to load a legacy pre-trained Conformer model.
1. Using current `nemo_toolkit` `1.14.0+`, the model loads fine but there is an error: `'SpeechEncDecSelfSupervisedModel' object has no attribute 'transcribe'`
```
<ipython-input-12-1d1ce9ce6276> in transcribe_with_lm(wav_file)
38
39 def transcribe_with_lm(wav_file):
---> 40 logits = transcribe(wav_file, True)
41
42 probs = [softmax(logits) for logits in logits]
<ipython-input-12-1d1ce9ce6276> in transcribe(wav_file, logprobs)
35 if type(wav_file) != list:
36 wav_file = [wav_file]
---> 37 return asr_model.transcribe(wav_file, logprobs=logprobs)
38
39 def transcribe_with_lm(wav_file):
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in __getattr__(self, name)
1612 if name in modules:
1613 return modules[name]
-> 1614 raise AttributeError("'{}' object has no attribute '{}'".format(
1615 type(self).__name__, name))
1616
AttributeError: 'SpeechEncDecSelfSupervisedModel' object has no attribute 'transcribe'
```
2. I also tried installing `nemo_toolkit` versions `1.10.0` to `1.13.0`, but the model doesn't load at all.
```
ImportError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py in tf()
41 try:
---> 42 from tensorboard.compat import notf # noqa: F401
43 except ImportError:
ImportError: cannot import name 'notf' from 'tensorboard.compat' (/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py)
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 0xf but this version of numpy is 0xe
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py in tf()
41 try:
---> 42 from tensorboard.compat import notf # noqa: F401
43 except ImportError:
ImportError: cannot import name 'notf' from 'tensorboard.compat' (/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py)
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 0xf but this version of numpy is 0xe
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py in tf()
41 try:
---> 42 from tensorboard.compat import notf # noqa: F401
43 except ImportError:
ImportError: cannot import name 'notf' from 'tensorboard.compat' (/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py)
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
ImportError: numpy.core._multiarray_umath failed to import
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py in tf()
41 try:
---> 42 from tensorboard.compat import notf # noqa: F401
43 except ImportError:
ImportError: cannot import name 'notf' from 'tensorboard.compat' (/usr/local/lib/python3.10/dist-packages/tensorboard/compat/__init__.py)
```
3. Lastly, I also tried [downstreaming the model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/ssl/results.html#loading-ssl-checkpoint-into-down-stream-model) but couldn't figure out how to proceed further.
Please advice the best way to use `transcribe()` method from the `SpeechEncDecSelfSupervisedModel` class. | {
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} | null | null | null | Hello,
I built Multilingual ASR model with Korean and English.
However, I found that tokens are assigned not correctly.
I used 128 tokens for english, and 1641 tokens for korean (Total 1749)
when i checked vocabulary, there were several suspicious points.
When i printed asr_model.tokenizer.langs_by_token_id, it correctly shows tokens. (from 0 to 128 : 'en', from 129 to 1749: 'kor').
However, some alphabet token is assigned in korean. For example, print(asr_model.tokenizer.token_to_id('q', 'kor')) results 1702.
Also, in my knowledge, the aggregated tokenizer must remove duplicated token such as 'unkown'. But, there is two 'unk' tokens in both index 0 and 128
I also saw same phenomonon in pretrained_model, Conformer_transducer_enes_large.
1. tokens such as 'pre', 'ble' 'is', 'to' etc are assigned in Es, not En. I think these tokens are close to english tokens rather than spanish token.
2. there were two 'unk' tokens.
print(asr_model.tokenizer.token_to_id('unk', 'en')) results in 0, and
print(asr_model.tokenizer.token_to_id('unk', 'es')) results in 1024. | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
keeping it would make installation log misleading like below. It'd better remove it.
```
(nemo) xueyang@DEV:~/workspace/NeMo:(main)$ ./reinstall.sh
Uninstalling stuff
Found existing installation: nemo-toolkit 1.18.0rc0
Uninstalling nemo-toolkit-1.18.0rc0:
Successfully uninstalled nemo-toolkit-1.18.0rc0
Found existing installation: sacrebleu 2.3.1
Uninstalling sacrebleu-2.3.1:
Successfully uninstalled sacrebleu-2.3.1
WARNING: Skipping nemo_asr as it is not installed.
WARNING: Skipping nemo_nlp as it is not installed.
WARNING: Skipping nemo_tts as it is not installed.
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com/
Requirement already satisfied: setuptools in /home/xueyang/miniconda3/envs/nemo/lib/python3.8/site-packages (65.5.1)
Collecting setuptools
Downloading setuptools-67.7.2-py3-none-any.whl (1.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 50.8 MB/s eta 0:00:00
Installing collected packages: setuptools
Attempting uninstall: setuptools
Found existing installation: setuptools 65.5.1
Uninstalling setuptools-65.5.1:
Successfully uninstalled setuptools-65.5.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
nemo-text-processing 0.1.7rc0 requires setuptools==65.5.1, but you have setuptools 67.7.2 which is incompatible.
Successfully installed setuptools-67.7.2
[notice] A new release of pip is available: 22.2.2 -> 23.1.2
[notice] To update, run: pip install --upgrade pip
```
**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.
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"merged_at": "2023-05-16T01:44:49"
} | # What does this PR do ?
handle artifacts assumes a compressed `.nemo` this PR checks if model is a `dir` and does not try to untar if it is a `dir`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adding new functionality for confidence ensembles and fix existing issues. Note that there is a plan with another follow-up PR to fix (partially) existing TODOs in the code + add docs, types and a tutorial on the usage.
**Collection**: ASR
# Changelog
- In the previous PR I directly used frame confidence returned from NeMo. This is problematic for two reasons. First, it does not filter blanks, even when exclude_blanks is set to True in the config. Second, it can only compute a single confidence, so there is a problem with using it in a tuning mode where we want to get 100s of confidence values at the same time. So to overcome these problems, I wrote `compute_confidence` function, alongside with a few additional utilities that does what we need for confidence ensembles outside of the decoding code.
- Implemented tuning for logistic regression parameters as well as for the confidence parameters. This requires user to specify dev dataset and run transcribe over it.
- Added more tests for tuning of the confidence.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
- Fix eval_beamsearch_ngram.py with hybrid ctc
- Set default value text_processing.separate_punctuation=false for
scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py
examples/asr/speech_to_text_eval.py
**Collection**: ASR
# Usage
* You can potentially add a usage example below
```python
python3 /path/to/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py \
nemo_model_file=/path/to/hybrid/model.nemo \
input_manifest=/path/to/manifest \
kenlm_model_file=/path/to/kenlm \
decoding_mode=beamsearch_ngram \
beam_width=[32] \
beam_alpha=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1] \
beam_beta=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9] \
```
**PR Type**:
- [V ] Bugfix | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adding dialogue dataset for SFT. | {
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} | # What does this PR do ?
Make sure all doc updates and bugfixes that were made to the release branch back into 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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If you haven't finished some of the above items you can still open "Draft" PR.
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# Additional Information
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Next to test with TP>1 and PP>1. | {
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while trying to finetune my asr model with another language, there is "??" token in my predictions while it isn't in my list of tokens.
what is it? does any one know? | {
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} | null | null | null | Hello, I have question for the Error rate for the code-switching ASR model.
There are many loanwords in korean which from english.
For example, "Coffee" is "커피" in korean. These two words have same pronunciation and meaning.
The sentence "커피 마실래?" is "would you want coffee?" in english.
Let's say there is some audio input, and there are two situation in output.
1. "커피 마실래?"
2. "coffee 마실래?"
Actually, both two sentence are correct and natural for korean people.
Then, how to calculate error rate?
if the label is '커피' then 'coffee' is wrong?
or, '커피' and 'coffee' are both correct? | {
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} | null | null | null | Hello,
The paper of CitriNet said they used 32 V100 GPUs, with a batch size of 32 per GPU.
then, the gradients of each GPU are accumulated and updated together?
When I trained CitriNet-384 model with single gpu, with batch size of 32, then grad_accumulation=32 will be same condition with the paper?
Also, Im training Multilingual ASR model which includes English (LibriSpeech 960h, wpe 256) and Korean (KsponSpeech 1000h, bpe 1641) with CitriNet-384. I made the hyperparameters same with paper (NovoGrad optimizer with learning rate (LR) of 0.05, β1 = 0.8, β2 = 0.25 and weight decay of 0.001). However, validation_loss seem to not decrease until epoch 18.
is there any tips for this? Im testing with lr=0.01 now.
Thank you. | {
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} | # What does this PR do ?
fixes conversion of sft ckp to nemo and allows eval of sft .nemo using peft_eval scripts.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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https://api.github.com/repos/NVIDIA/NeMo/issues/6647 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6647/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6647/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6647/events | https://github.com/NVIDIA/NeMo/pull/6647 | 1,708,000,826 | PR_kwDOC_bI7s5QaFS_ | 6,647 | Move black parameters to pyproject.toml | {
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} | # What does this PR do ?
Move `black` formatter parameters to `pyproject.toml`
Advantages:
- parameters are stored in one place
- one can use `black .`/`black <file>` command to reformat code
- more robust: if someone tries to reformat code with the newest `black` version, the error will be thrown (instead of reformatting the whole codebase)
**Collection**: [Note which collection this PR will affect]
# Changelog
- add `black` params to `pyproject.toml`
- remove params from `setup.py` and `.pre-commit-config.yaml`
# Usage
* You can potentially add a usage example below
```
black .
black edited_file.py
```
# 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 | I am getting the following error when I do train SSL conformer with fp16. Also, I have slightly changed some parameters as below.
```
model.validation_ds.batch_size=8 \
model.train_ds.max_duration=20 \
model.train_ds.min_duration=1 \
+model.validation_ds.max_duration=20 \
model.validation_ds.min_duration=1 \
model.spec_augment.patch_size=16 \
model.loss_list.contrastive.loss.num_negatives=12 \
```
```
Traceback (most recent call last):
File "examples/asr/speech_pretraining/speech_pre_training.py", line 70, in <module>
main()
File "/usr/local/lib/python3.8/dist-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper
_run_hydra(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
return func()
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "examples/asr/speech_pretraining/speech_pre_training.py", line 66, in main
trainer.fit(asr_model)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/call.py", line 59, in _call_and_handle_interrupt
trainer.strategy.reconciliate_processes(traceback.format_exc())
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/ddp.py", line 460, in reconciliate_processes
raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
pytorch_lightning.utilities.exceptions.DeadlockDetectedException: DeadLock detected from rank: 0
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 88, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1112, in _run
results = self._run_stage()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1191, in _run_stage
self._run_train()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1214, in _run_train
self.fit_loop.run()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 213, in advance
batch_output = self.batch_loop.run(kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(optimizers, kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 202, in advance
result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position])
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 241, in _run_optimization
closure()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 149, in __call__
self._result = self.closure(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 135, in closure
step_output = self._step_fn()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 419, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1494, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/ddp.py", line 351, in training_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/parallel/distributed.py", line 1158, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/parallel/distributed.py", line 1112, in _run_ddp_forward
return module_to_run(*inputs[0], **kwargs[0]) # type: ignore[index]
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/overrides/base.py", line 98, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/utils/model_utils.py", line 380, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/models/ssl_models.py", line 499, in training_step
loss_value, loss_val_dict = self.decoder_loss_step(
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/models/ssl_models.py", line 466, in decoder_loss_step
current_loss_value = current_loss(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/common.py", line 1087, in __call__
outputs = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 187, in forward
out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1])
RuntimeError: shape '[8, -1, 128]' is invalid for input of size 49024
```
Nemo r1.18.0
@titu1994 @VahidooX @sam1373
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} | null | null | null | Hello,
I have a question about Multilingual ASR models.
In a Multilingual ASR model, when using an Aggregate tokenizer, the "lang" field needs to be added in the manifest. From what I understand from tech blogs and tutorials, the "lang" field is primarily used to assign offset indices to tokens for each language during token aggregation.
However, I'm curious if the "lang" field in Multilingual models is utilized for purposes other than token indexing. For example, I would like to know if it is designed to provide language information in the form of a one-hot vector or embedding vector, which can assist in the training process of the model.
Thank you | {
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} | # What does this PR do ?
I finally figured out the reason for random failure of this test. It turns out that sometimes our input signal is such that for some models it results in all-zeros in the output of some initial encoder layers. This is then propagated all the way through the model and so the assert that final layer output is different from middle layer fails. After changing the code to pass processed signal directly, this should not happen anymore.
**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"
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**PR Type**:
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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
```
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Usage
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] 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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- [ ] 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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- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
sft model can use `megatron_gpt_peft_eval.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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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