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} | # What does this PR do ?
Download hifitts dataset and reformat into ljspeech format.
**Collection**: [Note which collection this PR will affect]
# Usage
```python
python scripts/dataset_processing/tts/hifitts/get_data.py --data-root /home/ovolkovskyi/NeMo/
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
* Update existing scripts to use meeting config as default
* Add VAD as default in tutorials
**Collection**: Speaker Diarization
# Changelog
- yaml file update
- tutorial update
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation | {
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} | Signed-off-by: Tim Moon <[email protected]>
# What does this PR do ?
Debug support for interleaved pipeline parallelism with the distributed Adam optimizer.
@erhoo82 has observed poor overlapping of gradient synchronizations and backward compute when running with interleaved pipeline parallelism. This is mostly fixed in Apex with https://github.com/NVIDIA/apex/pull/1514, which makes some changes to the API. This is mostly because the synchronization behavior is different for pipeline parallelism if the interleaved schedule is enabled or disabled (without interleaving we only want overlapped grad syncs in the first pipeline stage, while with interleaving we want overlapped grad syncs in all pipeline stages).
**Collection**: NLP
# Changelog
- Use updated Apex API for pipeline parallelism, namely for overlapped grad syncs
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/232db1513259a5c3fcd5689b0f6b89c212f83d03/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L189
Enable interleaved pipeline parallelism by setting `virtual_pipeline_model_parallel_size` to a value greater than 1 in the config file:
https://github.com/NVIDIA/NeMo/blob/232db1513259a5c3fcd5689b0f6b89c212f83d03/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L51
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [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.
Pinging @ericharper and @erhoo82.
# Additional Information
* Depends on https://github.com/NVIDIA/apex/pull/1514
* Follow-up to https://github.com/NVIDIA/NeMo/pull/4900 and https://github.com/NVIDIA/NeMo/pull/5025 | {
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Add option to load IPA-based checkpoints for FastPitch and the TTS Aligner.
**Collection**: TTS
# Changelog
- Added one entry each to the Aligner model and FastPitch model NGC model loading.
- Did not add entries to the checkpoints table in the TTS documentation since they are new versions of existing model cards, and each entry in each checkpoint table is a single model card.
# Usage
```python
from nemo.collections.tts.models import AlignerModel, FastPitchModel
# IPA Aligner
aligner = AlignerModel.from_pretrained("tts_en_radtts_aligner_ipa")
# IPA FastPitch
fastpitch = FastPitchMode.from_pretrained("tts_en_fastpitch_ipa")
``` | {
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} | # What does this PR do ?
Updates test to specifically check for A100
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} | Signed-off-by: Xuesong Yang <[email protected]>
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} | Add checking for channels mismatch for audio and noise data, throw an exception if they have different number of channels. Also fixed `perturb_with_foreground_noise` as done in `perturb_with_input_noise`
Signed-off-by: He Huang (Steve) <[email protected]>
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} | null | null | null | **Describe the bug**
Hi
I am trying to run QuartzNet 10x5 model on my dataset of almost 200 hours audio. But the strange thing is I got this error:
```
RuntimeError: CUDA out of memory. Tried to allocate 130.00 MiB (GPU 0; 11.91 GiB total capacity; 10.99 GiB already allocated; 50.38 MiB free; 11.19 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
```
I can't understand why I got out of memory error after 44 epochs!
**Steps/Code to reproduce bug**
TRAIN_DS config:
```
batch_size: 4
trim_silence: true
max_duration: 62.0
normalize_transcripts: false
shuffle: true
num_workers: 8
pin_memory: true
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: null
augmentor:
white_noise:
prob: 0.4
min_level: -90
max_level: -46
gain:
prob: 0.4
min_gain_dbfs: 0
max_gain_dbfs: 50
speed:
prob: 0.3
sr: 16000
resample_type: kaiser_best
min_speed_rate: 0.5
max_speed_rate: 2.0
num_rates: -1
time_stretch:
prob: 0.3
min_speed_rate: 0.5
max_speed_rate: 2.0
num_rates: -1
noise:
prob: 0.45
manifest_path: ./noise_manifest.json
min_snr_db: -4
max_snr_db: 10
max_gain_db: 300.0
```
VALIDATION_DS config:
```
batch_size: 4
normalize_transcripts: false
shuffle: false
num_workers: 8
pin_memory: true
trim_silence: true
```
TRAINER config:
```
trainer = ptl.Trainer(devices=-1,
accelerator='gpu',
max_epochs=EPOCHS,
accumulate_grad_batches=1,
enable_checkpointing=False,
logger=False,
log_every_n_steps=5,
check_val_every_n_epoch=1,
strategy="ddp")
```
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install nemo_toolkit['all']
**Environment details**
- OS version : Ubuntu 20.04 LTS
- PyTorch version: 1.12.1
- Python version: 3.8.0
**Additional context**
I ran my model on 2 GPU's :
TITAN X 12288MiB and
GTX 1080 Ti 11264MiB
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} | null | null | null | **Describe the bug**
I m training the speaker model with CN-celeb2 dataset, but no matter what batch_size is used, it still appear CUDA out of memory. Even set batch_size=2, GPU memory will continuously increase until out of memory.
Is any parameter that should be adjusted?
bug info:
`RuntimeError: CUDA out of memory. Tried to allocate 6.31 GiB (GPU 0; 15.78 GiB total capacity; 9.07 GiB already allocated; 3.26 GiB free; 11.37 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF`
speaker_reco.py
```
import os
import pytorch_lightning as pl
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything
from nemo.collections.asr.models import EncDecSpeakerLabelModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
seed_everything(42)
@hydra_runner(config_path="conf", config_name="ecapa_tdnn.yaml")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
trainer.fit(speaker_model)
if not trainer.fast_dev_run:
model_path = os.path.join(log_dir, '..', 'spkr.nemo')
speaker_model.save_to(model_path)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator)
if speaker_model.prepare_test(trainer):
trainer.test(speaker_model)
if __name__ == '__main__':
main()
```
ecapa_tdnn.yaml
```
name: "ECAPA_TDNN_cn2_300"
model:
sample_rate: 16000
train_ds:
manifest_filepath: /home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/diarization/data/voxconverse/train.json
sample_rate: ${model.sample_rate}
labels: null
batch_size: 16
shuffle: True
augmentor:
# noise:
# manifest_path: null
# prob: 0.5
# min_snr_db: 0
# max_snr_db: 15
speed:
prob: 0.5
sr: ${model.sample_rate}
resample_type: 'kaiser_fast'
min_speed_rate: 0.95
max_speed_rate: 1.05
validation_ds:
manifest_filepath: /home/ec5017b/media-lab/nemo/NeMo/examples/speaker_tasks/diarization/data/voxconverse/dev.json
sample_rate: ${model.sample_rate}
labels: null
batch_size: 16
shuffle: False
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
normalize: "per_feature"
window_size: 0.025
sample_rate: ${model.sample_rate}
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
stft_conv: false
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 3
freq_width: 4
time_masks: 5
time_width: 0.03
encoder:
_target_: nemo.collections.asr.modules.ECAPAEncoder
feat_in: ${model.preprocessor.features}
filters: [1024,1024,1024,1024,3072]
kernel_sizes: [5,3,3,3,1]
dilations: [1,1,1,1,1]
scale: 8
decoder:
_target_: nemo.collections.asr.modules.SpeakerDecoder
feat_in: 3072
num_classes: 7205
pool_mode: 'attention' #xvector,tap or attention
emb_sizes: 192
angular: True
loss:
scale: 30
margin: 0.2
optim:
name: sgd
lr: 0.08
weight_decay: 0.0002
# scheduler setup
sched:
name: CosineAnnealing
warmup_ratio: 0.1
min_lr: 0.0001
trainer:
devices: 1 # number of gpus (trained on four nodes - each node has 8 gpus)
max_epochs: 10
max_steps: -1 # computed at runtime if not set
num_nodes: 1
accelerator: gpu
strategy: ddp
accumulate_grad_batches: 1
deterministic: False
enable_checkpointing: False
logger: False
log_every_n_steps: 1 # Interval of logging.
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: True
create_checkpoint_callback: True
```
**Environment details**
- Ubuntu 16.04.7 LTS
- PyTorch version: 1.12.1
- Python version: 3.8.0
- GPU: Tesla V100 PCIe 16GB
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} | # What does this PR do ?
This is a model for spellchecking ASR customization, scripts for data preparation and description of the pipeline
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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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.
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} | # What does this PR do ?
1. Some corrections to preprocessing. Should lead to improved scores compared to current thutmose ITN for English.
2. Add experimental TN direction.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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1. Some corrections to preprocessing. Should lead to improved scores compared to current Thutmose ITN for English.
2. Add experimental TN direction.
**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?
Added support for specifying an additional subsampling layer in conformer encoder. The user can specify the type and the position of the subsampling layer.
**Collection**: asr
# Changelog
- New `SubsamplingReductionModule` class in `nemo/collections/asr/parts/submodules/subsampling.py`
- Updated the `ConformerEncoder` in `nemo/collections/asr/modules/conformer_encoder.py` to accept the new reduction parameters for adding a new subsampling layer in the desired position.
- Updated the _transducer-related configs_ with reduction parameters.
# 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
## Who can review?
Everyone in the NeMo ASR team.
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
Fix VAD struck issue temporarily with num_workers=1
**Collection**: [SD, VAD]
# Changelog
- config update
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Oleksii Kuchaiev <[email protected]>
# What does this PR do ?
Adds example L1 tests on Blossom.
L1 tests are the ones which read data from TestData
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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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} | null | null | null | **Describe the bug**
> my dataset situation:
I only have a dataset with 3Hs of my target accent and I have 2H for train+dev and 1H for valid, and the charset size of it is very small with only 394(It means I have multiple people speaking same lines) (I don't know if this is what causing the problem: poor dataset size and not adequate enough charset)
The reference output from the runtime is not correct.
Following This issue: https://github.com/NVIDIA/NeMo/issues/2725#issue-978965808, I freeze enc. and dec..
But "After several epoch training ,The output reference and predicted are correct" didn't happen.
```bash
# This is just a bunch of meaningless characters, even has some alphabets in it.
[NeMo I 2022-10-22 03:38:50 wer:985] reference:仑兆书偻使仿侬份侣交仑兆书偻使井交X亩且亥亟使佳仰但东伴傀使似侥乘Z使佳
[NeMo I 2022-10-22 03:38:50 wer:986] predicted:难道联美不折者物了明女张找您美的部累吗突的端头在不仅有天离而铁来姑勇华偏而O军
```
```bash
# While following the tutorial step by step can have a correct one on jp..
# Means as an (cultural?) entrance to Japan, the standard Japanese is needed.
# (Don't they just be decoded from unicode in a same way?)
[NeMo I 2022-10-21 09:30:32 wer:985] reference:日本のエスヘラントとしてやはり標準語は必要た
[NeMo I 2022-10-21 09:30:32 wer:986] predicted:郵奈郵奈ょ奈ょ彼加奈施圧息鯨会加会加襞会を圧を圧を赤っ紅奈施奈素米調っ出航米埋裏似樫葉襞浜民た霧営端所坊事備出を樫赤複容紅竹郵沈繊越な素奈所陸霧幼端樫輪み米よ備霧な樫出樫腹飛備紅狩紅樫出素宿敗街暮排樫赤備っな宿敗杯備焦樫要彼事匠リ素赤要宿邦よ樫排を樫紅持紅街備所戻米所解え狩彼演属会住街圧策赤拠防米飛備霧を米狩米赤備複樫紅樫の紅樫航竹戻父所坊証郵拠な複な宿坊飛陸含な樫郵赤彼赤ん赤
```
And my Learning rate drops very fast while train_cer and valid_cer is still very high.(around 1...)
**Steps/Code to reproduce bug**
1. follow the [lang. finetune tutorial](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb#scrollTo=DoWNVNYGOaMX), but doing fine-tuning on zh citrinet with bigger kernel size, skip oov removal, skip argumentation.(Character wise, not the sub-word method in part B)
2. convert the code in notebook to a script and run the script.
> I have my code here. [run.txt](https://github.com/NVIDIA/NeMo/files/9842185/run.txt)
**Expected behavior**
Supposed to have a readable reference.(And when use the trained model to do transcription, it threw up same sort of meaningless codes too.)
**Environment overview**
- Environment location: Bare-metal
- Method of NeMo install: from source(clone and using the reinstall script)
**Environment details**
- OS version Ubuntu 18
- PyTorch version 1.12.1
- Python version 3.8
**Additional context**
Add any other context about the problem here.
Example: GPU model V100 32G x2
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} | # What does this PR do ?
Adds energy conditioning to FastPitch
**Collection**: [Note which collection this PR will affect]
TTS
# Changelog
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# Usage
* You can potentially add a usage example below
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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
Make Apex's ColumnParallelLinear export friendly
**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
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Adds support for Sampled Softmax for RNNT Joint networks via subclass `SampledRNNTJoint`.
Implements the paper [Memory-Efficient Training of RNN-Transducer with Sampled Softmax](https://arxiv.org/abs/2203.16868).
**Collection**: [ASR]
# Changelog
- Add support for Sampled Softmax in RNNT Joint - allowing huge vocabulary networks at near constant memory cost.
- Add docs for the RNNT decoder and joint modules
- Add tests for Sampled RNNT Joint
# Usage
``` yaml
# Replace the target for model.joint and add new value for `n_samples`
model:
...
joint:
# _target_: nemo.collections.asr.modules.RNNTJoint
_target_: nemo.collections.asr.modules.SampledRNNTJoint
n_samples: 500
...
```
# 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?
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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
```
# 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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## Who can review?
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https://api.github.com/repos/NVIDIA/NeMo/issues/5214 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5214/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5214/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5214/events | https://github.com/NVIDIA/NeMo/pull/5214 | 1,417,148,337 | PR_kwDOC_bI7s5BOT6p | 5,214 | [TTS] remove the avoidance of circular imports | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
It is no problem to run through below after remove the line.
``` bash
In [2]: from nemo.collections.tts.models.base import SpectrogramGenerator
In [3]: spectrogram_model = SpectrogramGenerator.from_pretrained("tts_en_fastpitch")
100% [......................................................................] 187023360 / 187023360
In [4]: spectrogram_model
Out[4]:
FastPitchModel(
(mel_loss): MelLoss()
(pitch_loss): PitchLoss()
(duration_loss): DurationLoss()
(aligner): AlignmentEncoder(
(softmax): Softmax(dim=3)
(log_softmax): LogSoftmax(dim=3)
(key_proj): Sequential(
(0): ConvNorm(
(conv): PartialConv1d(384, 768, kernel_size=(3,), stride=(1,), padding=(1,))
)
(1): ReLU()
(2): ConvNorm(
(conv): PartialConv1d(768, 80, kernel_size=(1,), stride=(1,))
```
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/5213 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5213/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5213/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5213/events | https://github.com/NVIDIA/NeMo/pull/5213 | 1,417,090,232 | PR_kwDOC_bI7s5BOHZz | 5,213 | [Fix] NoisePerturbation for multi-channel audios | {
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} | # What does this PR do ?
Fix the error caused by `noise_gain_db = min(data_rms - noise.rms_db - snr_db, self._max_gain_db)` when handling multi-channel audios. The error was that `min(np.array([1,2,3]), 4)` is invalid since `np.array([1,2,3]) < 4` is ambiguous. To fix it, we first check whether the audio is multi-channel, then we perform the `min()` operation for each channel if it's multi-channel.
**Collection**: ASR
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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https://api.github.com/repos/NVIDIA/NeMo/issues/5208 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5208/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5208/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5208/events | https://github.com/NVIDIA/NeMo/pull/5208 | 1,416,180,365 | PR_kwDOC_bI7s5BLAwA | 5,208 | [TTS] Add Mandarin/English Bilingual Recipe for Training Fastpitch Models | {
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} | Signed-off-by: Yuekai Zhang <[email protected]>
Add Chinese TTS support with bilingual dataset [SF_bilingual_dataset](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en)
Closed previous #5197 to modify the sign-off info.
@XuesongYang Sorry for the duplicate PR, would you mind reviewing it again? Thank you. | {
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} | null | null | null | **Describe the bug**
The script freezes and hangs when I run transcrible_speech with conformer transducer pretrained models.
I tried to kill the process and re-run it but it has the same behavior.
The terminal stuck like this:
Transcribing: 50%|███▎ | 1/2 [00:05<00:5, 7.54it/s]
Beam search progress::: 29%|██▎ | 5/17 [00:16<00:01, 8.02it/s]
I tried to follow this solution but it doesn't work because I am using beam search strategy.
https://github.com/NVIDIA/NeMo/issues/3025
**Steps/Code to reproduce bug**
`
class TranscriptionConfig:
# Required configs
model_path: Optional[str] = None # Path to a .nemo file
pretrained_name: Optional[str] = None # Name of a pretrained model
audio_dir: Optional[str] = None # Path to a directory which contains audio files
dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest
# General configs
output_filename: Optional[str] = None
batch_size: int = 32
num_workers: int = 0
# Set cudato int to define CUDA device. If 'None', will look for CUDA
# device anyway, and do inference on CPU only if CUDA device is not found.
# If `cuda` is a negative number, inference will be on CPU only.
cuda: Optional[int] = None
amp: bool = False
audio_type: str = "mp3"
# Recompute model transcription, even if the output folder exists with scores.
overwrite_transcripts: bool = True
# Decoding strategy for RNNT models
rnnt_decoding: RNNTDecodingConfig = RNNTDecodingConfig(fused_batch_size=-1, strategy="beam", beam=beam_decode.BeamRNNTInferConfig(beam_size=2))
`
Here is my decoding config:
`
'decoding': {'strategy': 'beam', 'compute_hypothesis_token_set': False, 'preserve_alignments': None, 'fused_batch_size': -1, 'greedy': {'max_symbols_per_step': 10, 'preserve_alignments': False}, 'beam': {'beam_size': 2, 'search_type': 'default', 'score_norm': True, 'return_best_hypothesis': True, 'tsd_max_sym_exp_per_step': 50, 'alsd_max_target_len': 1.0, 'nsc_max_timesteps_expansion': 1, 'nsc_prefix_alpha': 1, 'maes_num_steps': 2, 'maes_prefix_alpha': 1, 'maes_expansion_gamma': 2.3, 'maes_expansion_beta': 2, 'language_model': None, 'softmax_temperature': 1.0, 'preserve_alignments': False}}
`
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https://api.github.com/repos/NVIDIA/NeMo/issues/5206 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5206/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5206/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5206/events | https://github.com/NVIDIA/NeMo/pull/5206 | 1,416,061,935 | PR_kwDOC_bI7s5BKntl | 5,206 | [TTS] remove LinVocoder and apply Vocoder as parent class. | {
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} | [] | closed | false | null | [] | null | null | 2022-10-20T06:46:39 | 2022-10-24T20:22:48 | 2022-10-24T20:22:45 | COLLABORATOR | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
`nemo.collections.tts.model.base.LinVocoder` is a redundant definition because `nemo.collections.tts.model.base.Vocoder` already defined all functions. The only difference is that `LinVocoder` calls `convert_linear_spectrogram_to_audio` while `Vocoder` calls `convert_spectrogram_to_audio`. So it is not necessary to duplicate the definition of Vocoder class.
**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/5205 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5205/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5205/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5205/events | https://github.com/NVIDIA/NeMo/pull/5205 | 1,415,937,804 | PR_kwDOC_bI7s5BKMUi | 5,205 | [TTS] deprecate TextToWaveform base class. | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
`TextToWaveform` base class, mostly servicing e2e TTS models, is not used anywhere. Deprecate it for now.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
ASR with Speaker Diarization not working for 'stt_en_conformer_ctc_xlarge' on colab GPU (Tesla T4)
**Steps/Code to reproduce bug**
Run the aforementioned colab notebook listed in tutorials on a GPU till the following section 'Run ASR and get word timestamps' included. It should raise an Assertion Error on the run_ASR() method.
** Stack Trace**
```
[NeMo I 2022-10-20 03:36:13 speaker_utils:93] Number of files to diarize: 1
[NeMo I 2022-10-20 03:36:13 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_xlarge/versions/1.10.0/files/stt_en_conformer_ctc_xlarge.nemo to /root/.cache/torch/NeMo/NeMo_1.12.0/stt_en_conformer_ctc_xlarge/eff9a44c668d74bfee7b59f7def80bf4/stt_en_conformer_ctc_xlarge.nemo
[NeMo I 2022-10-20 03:37:09 common:910] Instantiating model from pre-trained checkpoint
[NeMo I 2022-10-20 03:37:20 mixins:172] Tokenizer SentencePieceTokenizer initialized with 128 tokens
[NeMo W 2022-10-20 03:37:20 modelPT:143] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath:
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket1/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket2/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket3/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket4/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket5/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket6/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket7/tarred_audio_manifest.json
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket8/tarred_audio_manifest.json
sample_rate: 16000
batch_size: 1
shuffle: true
num_workers: 4
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20
min_duration: 0
is_tarred: true
tarred_audio_filepaths:
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket1/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket2/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket3/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket4/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket5/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket6/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket7/audio__OP_0..8191_CL_.tar
- - /data/NeMo_ASR_SET/English/v3.0/train_bucketed/bucket8/audio__OP_0..8191_CL_.tar
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size:
- 64
- 64
- 32
- 32
- 16
- 16
- 16
- 16
[NeMo W 2022-10-20 03:37:20 modelPT:150] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath:
- /data/librispeech_withsp2/manifests/librivox-dev-other.json
- /data/librispeech_withsp2/manifests/librivox-dev-clean.json
- /data/librispeech_withsp2/manifests/librivox-test-other.json
- /data/librispeech_withsp2/manifests/librivox-test-clean.json
sample_rate: 16000
batch_size: 4
shuffle: false
num_workers: 4
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: na
min_duration: 0
[NeMo W 2022-10-20 03:37:20 modelPT:156] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath:
- /data/librispeech_withsp2/manifests/librivox-test-other.json
- /data/librispeech_withsp2/manifests/librivox-dev-clean.json
- /data/librispeech_withsp2/manifests/librivox-dev-other.json
- /data/librispeech_withsp2/manifests/librivox-test-clean.json
sample_rate: 16000
batch_size: 4
shuffle: false
num_workers: 4
pin_memory: true
use_start_end_token: false
is_tarred: false
tarred_audio_filepaths: na
[NeMo I 2022-10-20 03:37:20 features:225] PADDING: 0
[NeMo I 2022-10-20 03:37:25 audio_preprocessing:491] Numba CUDA SpecAugment kernel is being used
[NeMo I 2022-10-20 03:37:33 save_restore_connector:243] Model EncDecCTCModelBPE was successfully restored from /root/.cache/torch/NeMo/NeMo_1.12.0/stt_en_conformer_ctc_xlarge/eff9a44c668d74bfee7b59f7def80bf4/stt_en_conformer_ctc_xlarge.nemo.
[NeMo W 2022-10-20 03:37:33 decoder_timestamps_utils:67] `ctc_decode` was set to True. Note that this is ignored.
[NeMo I 2022-10-20 03:37:33 features:225] PADDING: 0
[NeMo I 2022-10-20 03:37:33 features:225] PADDING: 0
[NeMo I 2022-10-20 03:37:33 decoder_timestamps_utils:657] Running ASR model stt_en_conformer_ctc_xlarge
[NeMo I 2022-10-20 03:37:33 decoder_timestamps_utils:661] [1/1] FrameBatchASR: /content/data/an4_diarize_test.wav
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
[<ipython-input-9-15c5a63eedb2>](https://localhost:8080/#) in <module>
2 asr_ts_decoder = ASR_TIMESTAMPS(**cfg.diarizer)
3 asr_model = asr_ts_decoder.set_asr_model()
----> 4 word_hyp, word_ts_hyp = asr_ts_decoder.run_ASR(asr_model)
5
6 print("Decoded word output dictionary: \n", word_hyp['an4_diarize_test'])
2 frames
[/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py](https://localhost:8080/#) in get_ts_from_decoded_prediction(self, decoded_prediction, hypothesis, char_ts)
137 stitched_word = ''.join(decoded_char_list[stt_ch_idx : end_ch_idx + 1]).replace(space, '')
138 word_seq.append(stitched_word)
--> 139 assert len(word_ts) == len(hypothesis.split()), "Hypothesis does not match word time stamp."
140 return word_ts
141
AssertionError: Hypothesis does not match word time stamp.
```
**Expected behavior**
Works fine for 'stt_en_conformer_ctc_large' on GPU so I expected to get two lists one containing a list of words and other being the word timestamps.
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install: pip installation from github
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 18.04.6 LTS
- PyTorch version: 1.12.1+cu113
- Python version: 3.7.15
**Additional context**
Link to Colab Notebook [ASR_with_SpeakerDiariazation.ipynb](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/speaker_tasks/ASR_with_SpeakerDiarization.ipynb#scrollTo=chL48Ttmgl0r)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
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```
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
This PR fixes the bug in https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3839247
A couple of functions from `speaker_utils.py` was causing a circular import error.
The function calls are removed and tested training/inference based on `DiarizationSpeechLabel` class.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
Removed function imports from `speaker_utils.py` to solve circular import error.
# Usage
* You can potentially add a usage example below
```python
python ./multiscale_diar_decoder.py --config-path='../conf/neural_diarizer' --config-name='msdd_5scl_15_05_50Povl_256x3x32x2.yaml' \
trainer.devices=1 \
model.base.diarizer.speaker_embeddings.model_path="titanet_large" \
model.train_ds.manifest_filepath="<train_manifest_path>" \
model.validation_ds.manifest_filepath="<dev_manifest_path>" \
model.train_ds.emb_dir="<train_temp_dir>" \
model.validation_ds.emb_dir="<dev_temp_dir>" \
exp_manager.name='sample_train' \
exp_manager.exp_dir='./msdd_exp'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
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**PR Type**:
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- [x] Bugfix
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} | # What does this PR do ?
Updates the T0 Dataset and Model implementation to use the following:
1. JSONL Text-Memmap datasets (refactored from earlier implementation) + Samples mapping similar to MegatronNMT
2. Megatron Blendable Datasets instead of ad-hoc implementation
3. Megatron batch samplers instead of distributed sampler. With the same train/validation set logic.
4. Support for using HF converted checkpoints (with bos changes, dec_mask fixes etc)
**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?
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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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"merged_at": "2022-10-19T20:40:18"
} | # What does this PR do ?
Updating readme to add links to public 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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fix two broken links.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 Chinese TTS support with bilingual dataset [SF_bilingual_dataset](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/sf_bilingual_speech_zh_en)
**Collection**: TTS
# 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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} | # What does this PR do ?
This is a draft PR of datasets for different audio-to-audio tasks.
**Datasets**
- `BaseAudioDataset`: Abstract base class.
- `AudioToTargetDataset`: A dataset for audio-to-audio tasks where the goal is to use an input signal to recover the corresponding target signal.
- `AudioToTargetWithReferenceDataset`: A dataset for audio-to-audio tasks where the goal is to use an input signal to recover the corresponding target signal and an additional reference signal is available.
- `AudioToTargetWithEmbeddingDataset`: A dataset for audio-to-audio tasks where the goal is to use an input signal to recover the corresponding target signal and an additional embedding signal. It is assumed that the embedding is in a form of a vector.
**Tests**
Multiple tests are implemented in `test_asr_datasets.py` in class `TestAudioDatasets`. These tests include
- `test_audio_to_target_dataset`: multiple tests for `AudioToTargetDataset`
- `test_audio_to_target_dataset_with_target_list`: tests specifically a scenario where target is provides as a list of files
- `test_audio_to_target_with_reference_dataset`: tests for `AudioToTargetWithReferenceDataset`
- `test_audio_to_target_with_embedding_dataset`: tests for `AudioToTargetWithEmbeddingDataset`
Tests can be started using the following command
```
pytest tests/collections/asr/test_asr_datasets.py::TestAudioDatasets
```
**Collection**: ASR
# Changelog
- Added datasets in `audio_to_audio.py`
- Added unit tests in `test_asr_datasets.py` and `test_audio_utils.py`
- Added an option in `AudioSegment. segment_from_file` to use a fixed offset
# Usage
Usage is illustrated in unit tests, which can be executed using
```python
pytest tests/collections/asr/test_asr_datasets.py::TestAudioDatasets
```
# 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
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} | Signed-off-by: Matvei Novikov <[email protected]>
# What does this PR do ?
Added tutorial and inference script for lexical audio P&C model
**Collection**: [nlp]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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# Changelog
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# Usage
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**PR Type**:
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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
The `predict_step` for T5 adapters and IA3 returns a dictionary with different keys (#4746) . This PR updates to the eval scripts to use the new keys.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Fixes markup issues with github code rendering.
**Collection**: [TTS]
# Changelog
- Changed image links from "tts_primer" branch to "main" branch.
- Removed <figure> and rearranged some tags around images to ensure that they render in github.
- Fixed 1 wikipedia link by replacing closing parenthesis ")" with "%29".
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
The main goal of this PR is adding a fully torch.script-able clustering function.
**Convert entire clustering module to jit.scriptabe module**: For this purpose, the clustering code employed torch.module class so that clustering system
can be converted to torch.jit.script.
**Convert all string variables to float**: As a side-effect, all the arguments that are passed to clustering module should be torch.Tensor.float,
so the string-formatted arguments in diarization pipeline is all changed to torch.Tensor.float.
After this PR is merged, NeMo and Riva will be sharing an identical speaker clustering code.
Input and output is torch.Tensor, and by default, all operations are processed on GPU.
Also, integer type annotation should be torch.LongTensor if you want to convert to torch.script.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Changed COSclustering function -> forward() and changed class to use torch.module.
- Fixed docstrings.
- Removed unused class variables
- Replace eigvalsh (only calculate eig vals) instead eigsh (full eig-decomp) which makes computation a bit faster
- Timestamps for clustering is now using float instead using string format.
# Usage
* You can potentially add a usage example below
```python
python neural_diarizer/multiscale_diar_decoder_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_output' \
diarizer.speaker_embeddings.parameters.save_embeddings=False \
diarizer.vad.model_path=<pretrained model name or path to .nemo> \
diarizer.speaker_embeddings.model_path=<pretrained speaker embedding model name or path to .nemo> \
diarizer.msdd_model.model_path=<pretrained MSDD model name or path .nemo> \
```
# 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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} | null | null | null | I'm trying to transcribe some audio files using NeMo/examples/asr/transcribe_speech_parallel.py using the stt_en_conformer_transducer_small model with these parameters:
`predict_ds.batch_size=1
trainer.precision=16
trainer.devices=1
predict_ds.num_workers=1`
I'm using just one GPU for debug purposes. It's an NVIDIA RTX A6000. I'm able to transcribe audio files up to a couple hundred seconds long, but I'm getting the following error once the decoding reaches a file which is 1025 seconds long:
> Error executing job with overrides: ['model=stt_en_conformer_transducer_small', 'predict_ds.manifest_filepath=dataset.json',
> 'predict_ds.batch_size=1', 'trainer.precision=16', 'trainer.devices=1', 'predict_ds.num_workers=1', 'output_path=output_dir']
> Traceback (most recent call last):
> File "NeMo/examples/asr/transcribe_speech_parallel.py", line 153, in main
> predictions = trainer.predict(model=model, dataloaders=data_loader, return_predictions=cfg.return_predictions)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 949, in predict
> return self._call_and_handle_interrupt(
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 648, in _call_and_handle_interrupt
> return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
> return function(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 996, in _predict_impl
> results = self._run(model, ckpt_path=self.ckpt_path)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1166, in _run
> results = self._run_stage()
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1251, in _run_stage
> return self._run_predict()
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1311, in _run_predict
> return self.predict_loop.run()
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
> self.advance(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/prediction_loop.py", line 102, in advance
> dl_predictions, dl_batch_indices = self.epoch_loop.run(
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
> self.advance(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/prediction_epoch_loop.py", line 101, in advance
> self._predict_step(batch, batch_idx, dataloader_idx)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/prediction_epoch_loop.py", line 130, in _predict_step
> predictions = self.trainer._call_strategy_hook("predict_step", *step_kwargs.values())
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1704, in _call_strategy_hook
> output = fn(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 369, in predict_step
> return self.model.predict_step(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/rnnt_models.py", line 767, in predict_step
> encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/core/classes/common.py", line 1084, in __call__
> outputs = wrapped(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/rnnt_models.py", line 663, in forward
> encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
> return forward_call(*input, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/core/classes/common.py", line 1084, in __call__
> outputs = wrapped(*args, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/modules/conformer_encoder.py", line 424, in forward
> audio_signal = layer(
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
> return forward_call(*input, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/conformer_modules.py", line 140, in forward
> x = self.self_attn(
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
> return forward_call(*input, **kwargs)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/multi_head_attention.py", line 244, in forward
> out = self.forward_attention(v, scores, mask)
> File "/home/msancinetti/miniconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/submodules/multi_head_attention.py", line 109, in forward_attention
> attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) # (batch, head, time1, time2)
> RuntimeError: CUDA out of memory. Tried to allocate 10.00 GiB (GPU 0; 47.54 GiB total capacity; 36.63 GiB already allocated; 8.31 GiB free; 37.49 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
>
My guess is that the model is doing attention on the whole audio and that blows up in memory which is why I'm only having this issue for longer audio files but not for shorter ones. Is there a way to limit the maximum attention context? | {
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} | Signed-off-by: George Zelenfroynd <[email protected]>
# What does this PR do ?
returned the counter of deletions and insertions
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} | null | null | null | **Describe the bug**
I launched the training of a model for the Machine Translation task, using the default [aayn_base.yaml](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/conf/aayn_base.yaml) config file. Everything seemed to be proceeding correctly, but after 126,218 steps in the third epoch something went wrong: the validation loss suddenly dropped from 2.78 to nan (Screenshot n.1), then returned to a finite value after 126,238 steps (Screenshot n.2) and finally returned to nan again after 126,586 steps (Screenshot n.3) and remained that way until the beginning of the fourth epoch, when I manually stopped the training.
**Screenshot n.1**
<img width="652" alt="loss_nan_pt1" src="https://user-images.githubusercontent.com/112694503/196426908-1ffd5b6c-ca1a-4ee5-aeaa-2dd5a8560010.png">
**Screenshot n.2**
<img width="652" alt="loss_nan_pt2" src="https://user-images.githubusercontent.com/112694503/196426935-b05b1552-0b4a-41db-b35e-afd3519b2878.png">
**Screenshot n.3**
<img width="652" alt="loss_nan_pt3" src="https://user-images.githubusercontent.com/112694503/196426942-e4a1722b-a121-4729-953a-54f063963800.png">
Another strange thing I noticed is that the model seems to have suffered a strong performance degradation between the second and third epochs: I used one of the .nemo saved during the second epoch to compute the performance on a test set and got a BLEU score of ~29; if I compute the BLEU score, on the same test set, of the 5 best checkpoints saved by NeMo, on the other hand, I get a BLEU score of ~21 in all cases. Four of these checkpoints are from the third epoch, only one is from the second epoch.
**Steps/Code to reproduce bug**
I launched the training with the following command:
`enc_dec_nmt.py --config-path=conf --config-name=aayn_base trainer.devices=[0,1,2,3,4,5,6,7] ~trainer.max_epochs +trainer.max_steps=1000000 +trainer.val_check_interval=5000 ~model.test_ds model.encoder_tokenizer.vocab_size=32000 model.decoder_tokenizer.vocab_size=32000 model.max_generation_delta=5 model.train_ds.use_tarred_dataset=true model.src_language=src model.tgt_language=tgt model.preproc_out_dir=preprocessing_output model.train_ds.lines_per_dataset_fragment=10000000 model.train_ds.num_batches_per_tarfile=200 model.train_ds.src_file_name=outputs/split/train.src model.train_ds.tgt_file_name=outputs/split/train.tgt model.validation_ds.src_file_name=outputs/split/val.src model.validation_ds.tgt_file_name=outputs/split/val.tgt model.train_ds.tokens_in_batch=10000 model.validation_ds.tokens_in_batch=8192 model.optim.sched.warmup_ratio=0.05 +exp_manager.create_checkpoint_callback=True +exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU +exp_manager.checkpoint_callback_params.mode=max +exp_manager.checkpoint_callback_params.save_top_k=5 +exp_manager.exp_dir=NemoMT-TransformerBase +exp_manager.checkpoint_callback_params.always_save_nemo=True `
**Expected behavior**
A "proper" training, with loss not suddenly switching to nan.
**Environment overview and details**
- Nemo installed from pip using version 1.11.0
- apex installed from source cloning the repo
- Clean conda environment
- OS version: Ubuntu 20.04.3 LTS
- Pytorch version: 1.12.1
- Python version 3.8.0
- Cuda version 11.6
**Additional context**
For the training I used 8 NVIDIA GeForce RTX 3090.
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} | # What does this PR do ?
Added the script for scoring and analysis of the adapter experiments.
**Collection**: asr
# Changelog
- Created a new script: `examples/asr/asr_adapters/scoring_and_analysis.py`
- Updated README: `examples/asr/asr_adapters/README.md`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [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
## Who can review?
Anyone in the NeMo ASR Team | {
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} | Signed-off-by: Shantanu Acharya <[email protected]>
# What does this PR do ?
In transcribe_speech.py, the `RNNTDecodingConfig` object was being created again thereby overriding the params specified by the user.
**Collection**: asr
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the asr team.
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```bash
# When loading model
Model stt_zh_quartznet15x5 was not found. Check cls.list_available_models() for the list of all available models.
```
```bash
# When directly trying transcribing
Error executing job with overrides: ['pretrained_name=stt_zh_quartznet15x5', 'audio_dir=****']
Traceback (most recent call last):
File "examples/asr/transcribe_speech.py", line 155, in main
asr_model = ASRModel.from_pretrained(
File "/****/NeMo/nemo/core/classes/common.py", line 846, in from_pretrained
class_, nemo_model_file_in_cache = cls._get_ngc_pretrained_model_info(
File "/****/NeMo/nemo/core/classes/common.py", line 898, in _get_ngc_pretrained_model_info
raise FileNotFoundError(
FileNotFoundError: Model stt_zh_quartznet15x5 was not found. Check cls.list_available_models() for the list of all available models.
```
**Steps/Code to reproduce bug**
1. follow the steps to run the model in its [HP](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_zh_quartznet15x5)
**Expected behavior**
Should works fine?
**Environment overview (please complete the following information)**
- Environment location: Baremetal
- Method of NeMo install: pip from source (Pull from GitHub following the instructions)
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 18 cuda 11.3 cudnn8.3.2_0
- PyTorch version 1.12.1
- Python version 3.8
**Additional context**
GPU model: 3090
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} | Signed-off-by: arendu <[email protected]>
Fixes abs paths in GPT Adapter CI tests. Follow-up of #5183
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
update CI tests for T5 to save/load/delete relative paths using `exp_manager.exp_dir`.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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## Who can review?
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} | …se of use
Signed-off-by: George Zelenfroynd <[email protected]>
# What does this PR do ?
describes new features in documentation
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
ASR
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | null | null | null | I trained a MixerTTS-X model, and would like to finetune a Univnet model with the spectrograms generated by MixerTTS.
I followed the tutorial about finetuning FastPitch and Hifigan and changed the code related to generating spectrograms to:
```python
fn=os.path.split(r['audio_filepath'])[1]
audio = load_wav(r["audio_filepath"])
audio = torch.from_numpy(audio).unsqueeze(0).to(device)
audio_len = torch.tensor(audio.shape[1], dtype=torch.long, device=device).unsqueeze(0)
with torch.no_grad():
if "normalized_text" in r:
text = spec_model.parse(r["normalized_text"], normalize=False)
raw_texts=[r["normalized_text"]]
else:
text = spec_model.parse(r['text'])
raw_texts=[r["text"]]
text_len = torch.tensor(text.shape[-1], dtype=torch.long, device=device).unsqueeze(0)
spect, spect_len = spec_model.preprocessor(input_signal=audio, length=audio_len)
attn_prior = torch.from_numpy(
beta_binomial_interpolator(spect_len.item(), text_len.item())
).unsqueeze(0).to(text.device)
if hasattr(spec_model.cfg,"lm_model"):
lm_model_tokenizer = spec_model._get_lm_model_tokenizer(spec_model.cfg.lm_model)
lm_padding_value = lm_model_tokenizer._convert_token_to_id('<pad>')
lm_space_value = lm_model_tokenizer._convert_token_to_id('▁')
if spec_model.text_normalizer_call is not None:
raw_texts = [spec_model.text_normalizer_call(t, **spec_model.text_normalizer_call_kwargs) for t in raw_texts]
preprocess_texts_as_tts_input = [spec_model.tokenizer.text_preprocessing_func(t) for t in raw_texts]
lm_tokens_as_ids_list = [
lm_model_tokenizer.encode(t, add_special_tokens=False) for t in preprocess_texts_as_tts_input
]
if spec_model.tokenizer.pad_with_space:
lm_tokens_as_ids_list = [[lm_space_value] + t + [lm_space_value] for t in lm_tokens_as_ids_list]
lm_tokens = torch.full(
(len(lm_tokens_as_ids_list), max([len(t) for t in lm_tokens_as_ids_list])),
fill_value=lm_padding_value,
device=device,
)
for i, lm_tokens_i in enumerate(lm_tokens_as_ids_list):
lm_tokens[i, : len(lm_tokens_i)] = torch.tensor(lm_tokens_i, device=device)
spectrogram = spec_model.forward(
text=text,
text_len=text_len,
spect=spect,
spect_len=spect_len,
attn_prior=attn_prior,
lm_tokens=lm_tokens
)[0]
save_path = os.path.join(save_dir , f"mel_{fn}.npy")
np.save(save_path, spectrogram[0].to('cpu').numpy())
```
Then I started the finetuning with the command:
```examples/tts/univnet_finetune.py --config-name=univnet.yaml model.optim.lr=0.00001 train_dataset=/root/autodl-tmp/ryanspeech.train_mel.json validation_datasets=/root/autodl-tmp/ryanspeech.val_mel.json exp_manager.exp_dir=/root/autodl-tmp/nemo-training/ryan_22k exp_manager.resume_if_exists=True exp_manager.resume_ignore_no_checkpoint=True +init_from_nemo_model=/root/autodl-tmp/tts_en_libritts_multispeaker_univnet.nemo model/train_ds=train_ds_finetune model/validation_ds=val_ds_finetune model.train_ds.dataloader_params.batch_size=32 trainer.check_val_every_n_epoch=10```
And the following error occurred:
```
Traceback (most recent call last):
File "examples/tts/univnet-finetune.py", line 32, in main
trainer.fit(model)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit
self._call_and_handle_interrupt(
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 648, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 737, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1168, in _run
results = self._run_stage()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1254, in _run_stage
return self._run_train()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1276, in _run_train
self._run_sanity_check()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1345, in _run_sanity_check
val_loop.run()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 155, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.advance(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 127, in advance
batch = next(data_fetcher)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 185, in __next__
return self.fetching_function()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 264, in fetching_function
self._fetch_next_batch(self.dataloader_iter)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 278, in _fetch_next_batch
batch = next(iterator)
File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
data = self._next_data()
File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/root/miniconda3/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/root/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/root/NeMo/nemo/collections/tts/torch/data.py", line 973, in __getitem__
start = random.randint(0, mel.shape[1] - frames - 1)
File "/root/miniconda3/lib/python3.8/random.py", line 248, in randint
return self.randrange(a, b+1)
File "/root/miniconda3/lib/python3.8/random.py", line 226, in randrange
raise ValueError("empty range for randrange() (%d, %d, %d)" % (istart, istop, width))
ValueError: empty range for randrange() (0, -178, -178)
```
Similar error has been discussed in #3628, but still not sure how to solve it.
Any help please? | {
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} | Signed-off-by: Aleksandr Laptev <[email protected]>
# What does this PR do ?
Fixes [#(5175)](https://github.com/NVIDIA/NeMo/issues/5175) and removes redundant code left after numerous rebases.
**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?
@titu1994
# Additional Information
* Related to [#(5175)](https://github.com/NVIDIA/NeMo/issues/5175)
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} | null | null | null | I am training an ru model and I don't know if this process is going well
My code:
`import nemo`
`import nemo.collections.asr as nemo_asr`
`import pytorch_lightning as pl`
`
`
`from nemo.utils.exp_manager import exp_manager`
`from omegaconf import DictConfig, OmegaConf`
`
`
`params = OmegaConf.load(model_conf_path)`
`
`
`min_duration = 0.5`
`epochs = 150`
`gpus = 1`
`
`
`params.trainer.devices = gpus`
`params.trainer.max_epochs = epochs`
`params.model.train_ds.manifest_filepath = train_manifest_path`
`params.model.train_ds.min_duration = min_duration`
`params.model.validation_ds.manifest_filepath = validation_manifest_path`
`params.model.test_ds.manifest_filepath = test_manifest_path`
`params.exp_manager.exp_dir = "/path/to/save_tr_nemo/"`
`
`
`trainer = pl.Trainer(**params.trainer)`
`model = nemo_asr.models.EncDecCTCModel(cfg=params.model, trainer=trainer)`
`exp_manager(trainer=trainer, cfg=params.exp_manager)`
`
`
`trainer.fit(model)`
`trainer.test(model)`
There is yaml config:
https://github.com/Project-fil/Project-fil/blob/ee79825609cca9e8b01a9c4eb43b564bb5c2b35d/quartznet_ru.yaml
Train manifest file looks like this:
`{"audio_filepath": ""/path/to/wav", "duration": 4.8, "text": "у вас просроченных дней две тысячи сто шестьдесят один дней"}`
`{"audio_filepath": "/path/to/wav", "duration": 9.1, "text": "выключай выключай давайте следующую свечу других нет сейчас расскажу один анекдот когда"}`
Total files: 253 390
Duration - 5050.82 hours.
I get these files in the "checkpoints" directory and after 10 epochs "wer" never went below 0.9.
`'QuartzNet15x5_ru--val_wer=0.9031-epoch=6.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9067-epoch=8.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9094-epoch=1.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9798-epoch=9-last.ckpt'`
and now
`'QuartzNet15x5_ru--val_wer=0.9031-epoch=6.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9067-epoch=8.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9078-epoch=11.ckpt'`
`'QuartzNet15x5_ru--val_wer=0.9798-epoch=19-last.ckpt'`
Please tell me this is a normal learning process or am I doing something wrong.
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} | null | null | null | **Describe the bug**
Exported ONNX graphs retain the names for zero dimensional and one dimensional parameters, but rename any two or more dimensional parameters. This makes writing scripts for later graph manipulation much more tedious and prone to breaking.
**Steps/Code to reproduce bug**
```python3
from nemo.collections.nlp.models.machine_translation import MTEncDecModel
model = MTEncDecModel.from_pretrained('nmt_en_de_transformer12x2')
model.decoder.export('pretrained_decoder.onnx')
```
Open `pretrained_decoder.onnx` in [Netron](https://netron.app/) or some other ONNX graph visualizer. Find and compare the inputs to the nodes `MatMul_106` and `Add_107`.
- `Add_107` takes 1D tensors as input, and has preserved the name of the parameters from the NeMo model. Its first input is `_decoder.layers.0.first_sub_layer.query_net.bias`.
- `MatMul_106` requires a 2D tensor as input, and has renamed the parameter from the NeMo model from the quite helpful `_decoder.layers.0.first_sub_layer.query_net.weight` to the generic `onnx::MatMul_636`.
**Expected behavior**
I'd love for it to respect the names of all parameters, which would create a predictable and reliable ONNX graph that can then be further post-processed as needed.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]` (run today, commit d933ee830d085152fbe07a8fa2b1fa0e493adf0d)
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- PyTorch version: 1.12.1
- Python version: 3.9.0
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} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
fix issues with Spoken Wikipedia folder names containing non-standard symbols
**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/5176 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5176/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5176/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5176/events | https://github.com/NVIDIA/NeMo/pull/5176 | 1,410,281,769 | PR_kwDOC_bI7s5A3Tca | 5,176 | [TTS] Fastpitch energy conditioning | {
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} | # What does this PR do ?
Conditions FastPitch with Energy
**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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/5175 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5175/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5175/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5175/events | https://github.com/NVIDIA/NeMo/issues/5175 | 1,410,178,325 | I_kwDOC_bI7s5UDZ0V | 5,175 | Cannot run ASR_with_NeMo.ipynb example | {
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} | null | null | null | I cannot run the example through CoLab, it stucks in loading in pre-trained model.
# This line will download pre-trained QuartzNet15x5 model from NVIDIA's NGC cloud and instantiate it for you
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
AttributeError: module 'math' has no attribute 'prod'
If I use v1.12.0 instead main branch, there will be no problem. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5174 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5174/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5174/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5174/events | https://github.com/NVIDIA/NeMo/issues/5174 | 1,410,096,249 | I_kwDOC_bI7s5UDFx5 | 5,174 | [ASR_with_SpeakerDiarization] predict RTTM contains only one speaker | {
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**Describe the bug**
I am using `offline_diar_with_asr_infer.py` to do SD + ASR, but after running, my pred_rttms/aepyx.rttm contains the same speaker id for each row like:
```
SPEAKER aepyx 1 2.360 0.360 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER aepyx 1 2.800 0.400 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER aepyx 1 3.280 0.720 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER aepyx 1 4.280 0.080 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER aepyx 1 4.520 0.240 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER aepyx 1 4.920 0.520 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER aepyx 1 5.560 0.280 <NA> <NA> speaker_0 <NA> <NA>
```
Moreover, I use the ASR_with_SpeakerDiarization tutorial on colab and also got the same result:
```
SPEAKER an4_diarize_test 1 0.240 0.320 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 0.800 0.360 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 1.280 0.240 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 1.800 0.360 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 2.240 0.240 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 2.960 0.440 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 3.480 0.440 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 4.280 0.320 <NA> <NA> speaker_0 <NA> <NA>
SPEAKER an4_diarize_test 1 4.720 0.320 <NA> <NA> speaker_0 <NA> <NA>
```
**Steps/Code to reproduce bug**
command:
python offline_diarization_with_asr.py
manifest file:
`{"audio_filepath": "/workspace/nemo/examples/speaker_tasks/diarization/data/voxconverse/voxconverse_test_wav/voxconverse_test_wav/aepyx.wav", "offset": 0, "duration": null, "label": "infer", "text": "-", "rttm_filepath": "/workspace/nemo/examples/speaker_tasks/diarization/data/voxconverse/test/aepyx.rttm"}
`conf/offline_diarization_with_asr.yaml:
```
name: &name "ClusterDiarizer"
num_workers: 4
sample_rate: 16000
batch_size: 64
diarizer:
manifest_filepath: /workspace/nemo/examples/speaker_tasks/diarization/manifest_oracleV_asr.json
out_dir: output_asr_rp1_test
oracle_vad: True # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps
collar: 0.25 # Collar value for scoring
ignore_overlap: True # Consider or ignore overlap segments while scoring
vad:
model_path: null # .nemo local model path or pretrained model name or none
external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set)
window_length_in_sec: 0.15 # Window length in sec for VAD context input
shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction
smoothing: "median" # False or type of smoothing method (eg: median)
overlap: 0.875 # Overlap ratio for overlapped mean/median smoothing filter
onset: 0.4 # Onset threshold for detecting the beginning and end of a speech
offset: 0.7 # Offset threshold for detecting the end of a speech
pad_onset: 0.05 # Adding durations before each speech segment
pad_offset: -0.1 # Adding durations after each speech segment
min_duration_on: 0.2 # Threshold for small non_speech deletion
min_duration_off: 0.2 # Threshold for short speech segment deletion
filter_speech_first: True
speaker_embeddings:
model_path: titanet_large #.nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
parameters:
window_length_in_sec: 1.5 # Window length(s) in sec (floating-point number). Either a number or a list. Ex) 1.5 or [1.5,1.0,0.5]
shift_length_in_sec: 0.75 # Shift length(s) in sec (floating-point number). Either a number or a list. Ex) 0.75 or [0.75,0.5,0.25]
multiscale_weights: null # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. Ex) [0.33,0.33,0.33]
save_embeddings: False # Save speaker embeddings in pickle format.
clustering:
parameters:
oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
max_num_speakers: 20 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored.
enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
sparse_search_volume: 30 # The higher the number, the more values will be examined with more time.
asr:
model_path: stt_en_conformer_ctc_large # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
parameters:
asr_based_vad: True # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
asr_based_vad_threshold: 0.05 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD.
asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
lenient_overlap_WDER: True # If true, when a word falls into speaker-overlappedregions, consider the word as a correctly diarized word.
decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2].
word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
fix_word_ts_with_VAD: True # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript.
break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
beam_width: 32
alpha: 0.5
beta: 2.5
realigning_lm_parameters: # Experimental feature
arpa_language_model: null # Provide a KenLM language model in .arpa format.
min_number_of_words: 3 # Min number of words for the left context.
max_number_of_words: 10 # Max number of words for the right context.
logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses
```
**Environment details**
docker
nemo-toolkit = 1.10.0
**Additional context**
GPU model
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"merged_at": "2022-10-17T19:39:27"
} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
1. added fastpitch model checkpoint that was trained on hifitts.
2. replaced "single/multiple" speakers with accurate integers.
3. update statistics for hifitts datasets.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
Fixes error in t5 bignlp scripts evaluation state.
**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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I am trying to calculate FLOPS of titanet-large model in both ways by code and by analyzing the model architecture
any one can help please ? | {
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} | # What does this PR do ?
Move to using the EMA Optimizer. This improves performance, supports offloading to CPU!
# Changelog
- Move to EMA Optimizer implementation
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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from line 113 to 114:
```
labels_exception = [num_to_word(x) for x in range(0, 13)]
graph_exception = pynini.union(*labels_exception)
```
Anyone can help？Thanks a lot | {
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} | null | null | null | **Describe the bug**
While we try to infer/use a prompt-tuned model we are getting an error "TypeError: megatron_gpt_generate() takes 5 positional arguments but 6 were given"
**Steps/Code to reproduce bug**
We have followed below notebook for prompt-tuning:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb
While we tried to use prompt-tuned model using following lines of code:
model.generate(inputs=test_examples, length_params=None)
**Expected behavior**
It should give the result of given prompt.
**Environment overview (please complete the following information)**
- Environment location: Google Colab Pro with GPU and High RAM
- Method of NeMo install: [from source]. !python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}#egg=nemo_toolkit[all]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 18.04.6 LTS
- PyTorch version: 1.12.1+cu113
- Python version: Python 3.7.14
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} | null | null | null | Recently, I demonstrated NeMo's Speaker Diarization + ASR task (nemo/examples/speaker_tasks/diarization/offline_diarization_with_asr.py) to my Professor, but he asked me if that end-to-end?
According to his question, I assume he means NeMo Speaker Diarization + ASR task not using an End-to-End model.
Is that correct? if that is, he hopes I manifest NeMo's Speaker Diarization + ASR task to an End-to-End model, that is SAD, Speaker embedding model, ASR model should be mixed into one model.
Sorry about my stupid question..
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Let's make room of CI tests for more active PRs. Reducing to 14 days from 30 inactive days.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds torchscript export to Squeezeformer
**Collection**: [ASR]
# Changelog
- Add torchscript export testing
- Add torchscript export for Squeezeformer
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
Error while downloading and uploading files
With use of an Object store with S3 services, errors are thrown due to file naming conventions that do not adhere to the POSIX filename character set. The example below shows two filenames that are causing and error with container persistence on the Object Store. The “(“ and the “)” characters are the cause of the error but a comprehensive search of the repo file names would be necessary in order to find all of the potential issues.
**Expected behavior**
File should be uploaded or downloaded from Object Storage without an issues
Filenames should only contain the characters of the POSIX portable file name character set outlined below.
Uppercase A to Z.
Lowercase a to z.
Numbers 0 to 9.
Period ( . )
Underscore (_)
Hyphen (-)
**Environment overview (please complete the following information)**
Object Storage with S3 services
**Environment details**
**Additional context**
5405 | 5h 1m 38s | Uploading: NeMo/tutorials/text_processing/Text_(Inverse)_Normalization.ipynb
5406 | 5h 1m 38s | Error while downloading file: '/NeMo/tutorials/text_processing/Text_(Inverse)_Normalization.ipynb'
5407 | 5h 1m 38s | Error while downloading file: '/NeMo/tutorials/nlp/Non_English_Downstream_Tasks_(NER).ipynb' | {
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} | Signed-off-by: SeanNaren <[email protected]>
# What does this PR do ?
Fixes the failing test on main regarding BF16. Thanks @ericharper
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Add perceiver based universal prompt encoder, which includes training, evaluation, text generation.
Add the inference server web UI.
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Adds support to hydra multirun to NeMo core.
**Collection**: [ASR, Core]
# Changelog
- Usage is similar to normal hydra script call, just the config should have some additional sections to setup multirun and you must use `-m` flag when calling the python script.
# Usage
At minimum (if all other arguments such as data loader and model related arguments are provided) -
```python
python train_asr_adapter.py \
-m \ # necessary
trainer.devices=[0] \ # any additional args will get forwarded to all HP jobs.
exp_manager.exp_dir="<some directory>" \
exp_manager.create_wandb_logger=true \ # highly recommended to use WandB for HP runs
exp_manager.wandb_logger_kwargs.project="<Project Name>" # Experiment name will be setup based on exp_manager.name
```
# Setting up configs to support multirun
## Setup Experiment manager
Inside experiment manager, the `name` argument **must** contain the concatination of the `${name}` and other hyper parameters. This is only needed if you use the two `resume` flags as True, but it is highly recommended nevertheless so you can launch and continue HP jobs until all are consumed.
``` yaml
exp_manager:
exp_dir: null
# Add a unique name for all hyperparameter arguments to allow continued training.
# NOTE: It is necessary to add all hyperparameter arguments to the name !
# This ensures successful restoration of model runs in case HP search crashes.
name: ${name}-lr-${model.optim.lr}-adim-${model.adapter.dim}-sd-${model.adapter.adapter_strategy.stochastic_depth}
...
checkpoint_callback_params:
...
save_top_k: 1 # Dont save too many .ckpt files during HP search
always_save_nemo: True # saves the checkpoints as nemo files for fast checking of results later
...
# HP Search may crash due to various reasons, best to attempt continuation in order to
# resume from where the last failure case occured.
resume_if_exists: true
resume_ignore_no_checkpoint: true
```
## Hydra Defaults
``` yaml
# Required for Hydra launch of hyperparameter search via multirun
defaults:
- override hydra/launcher: nemo_launcher
# Hydra arguments necessary for hyperparameter optimization
hydra:
sweep:
dir: "."
subdir: "."
sweeper:
params: # place all the parameters you wish to search over here (corresponding to the rest of the config)
model.optim.lr: 0.001,0.0001
model.adapter.dim: 32,64,96,128
model.adapter.adapter_strategy.stochastic_depth: 0.0,0.5,0.6,0.7,0.8,0.9
# Arguments to the process launcher
launcher:
num_gpus: -1 # Number of gpus to use. Each run works on a single GPU.
jobs_per_gpu: 1 # If each GPU has large memory, you can run multiple jobs on the same GPU for faster results (until OOM).
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
This PR adds a tool for generating a dataset using RIRs, speech, and noise.
**Collection**: ASR
# Changelog
- Added `RIRMixGenerator` in `asr/data/data_simulation.py`
- Added unit tests in `tests/collections/asr/test_asr_data_simulation.py`
- Added an example config and a readme file in `tools/rir_corpus_generator`
- Updated `README.md` in `tools/rir_corpus_generator`
# Usage
The tool can be used as
```python
python rir_mix_generator.py output_dir=OUTPUT_DIR
```
where `OUTPUT_DIR` is a path to the output directory. This will use the default configuration in `conf/rir_mix.yaml`.
The output will be structured as
```
OUTPUT_DIR
+--{train, dev, test}
| +--*_mic.wav
| +--*_target_reverberant.wav
| +--*_target_anechoic.wav
| +--*_noise.wav
| +--*_interference.wav
+--config.yaml
+--{train, dev, test}_manifest.json
+--{train, dev, test}_info.png
```
Each directory, e.g, `{train, dev, test}`, corresponds to a subset of data and contain `*.wav` files with audio signals. Corresponding `*_manifest.yaml` files contain metadata for each subset.
# 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?
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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 ?
New speaker adaptation using adapters in TTS FastPitch model.
**Collection**: [Note which collection this PR will affect]
TTS collection
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Fixes checkpoint loading if `exp_manager.checkpoint_callback_params.dirpath` is specified.
**Collection**: Utils
# Changelog
- Adds a check for whether `dirpath` has been set, and passes it in to `check_resume()` to use instead of `<log_dir>/checkpoints` for checkpoint restoration
- Unit tests added for restoring from `dirpath`
- Tested load/store for both `dirpath` being set and not (using `<log_dir>/checkpoints`) and both now work
# Usage
The following should now save to AND load from the specified `ckpt_dir`.
```bash
python examples/tts/fastpitch.py \
train_dataset=train.json \
validation_datasets=val.json \
trainer.devices=1 \
trainer.max_epochs=5 \
+exp_manager.checkpoint_callback_params.dirpath=ckpt_dir/ \
+exp_manager.explicit_log_dir=logdir/ \
exp_manager.resume_if_exists=true \
exp_manager.resume_ignore_no_checkpoint=true \
trainer.check_val_every_n_epoch=1
```
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #5136
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} | # What does this PR do ?
Current Eval Dataloader gives incorrect len #4808
Fix NMT Eval Sampler to get correct dataset
**Collection**: NMT
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Fix some minor errors and spacing errors in speaker diarization docs.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
This is documentation.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add the inference server web UI.
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I've got an issue and was wondering if you could help me.
My goal: reducing the time of diarization if an audio file is longer than 10 minutes.
My manifest json file:
> 'audio_filepath': /home/audio_file.wav,
> 'offset': 0,
> 'duration': 600,
> 'label': 'infer',
> 'text': '-',
> 'num_speakers': 2,
> 'rttm_filepath': None,
> 'uem_filepath': None
I tried to set up 'offset' and 'duration' in the manifest file, but it didn't work properly and It still diarized the whole audio file
Is there a possibility to limit the diarization?
I'll appreciate any help | {
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} | null | null | null | Hi, I'm facing issues with finetuning titanet-large model. The loss goes to nan/inf in epoch 0 at the end and doesn't improve from there in the later epochs.
Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 150452/150452 [6:45:44<00:00, 6.18it/s, loss=nan, v_num=on_6]Epoch 0, global step 134943: 'val_loss' reached inf (best inf), saving model to '/voice/NeMo/examples/speaker_tasks/recognition/speaker_exps/default/version_6/checkpoints/default--val_loss=nan-epoch=0.ckpt' as top 3
My configuration file is:
`name: &name "TitaNet"
sample_rate: &sample_rate 8000
init_from_pretrained_model:
speaker_tasks:
name: 'titanet_large'
include: ["preprocessor","encoder"]
exclude: ["decoder.final"] # Add specific layer names here to exlude or just ["decoder"] if to exclude all of decoder pretrained weights
model:
train_ds:
manifest_filepath: ???
sample_rate: 8000
labels: null
batch_size: 4
max_duration: 16.7
shuffle: True
is_tarred: False
tarred_audio_filepaths: null
tarred_shard_strategy: "scatter"
augmentor:
noise:
manifest_path: null
prob: 0.6
min_snr_db: -10
max_snr_db: -10
speed:
prob: 0.3
sr: *sample_rate
resample_type: 'kaiser_fast'
min_speed_rate: 0.95
max_speed_rate: 1.05
shift:
prob: 0.6
min_shift_ms: -5.0
max_shift_ms: 5.0
white_noise:
prob: 0.5
min_level: -90
max_level: -46
validation_ds:
manifest_filepath: ???
sample_rate: 8000
labels: null
batch_size: 4
shuffle: False
model_defaults:
filters: 1024
repeat: 3
dropout: 0.1
separable: true
se: true
se_context_size: -1
kernel_size_factor: 1.0
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
normalize: "per_feature"
window_size: 0.025
sample_rate: *sample_rate
window_stride: 0.01
window: "hann"
features: &n_mels 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 3
freq_width: 4
time_masks: 5
time_width: 0.03
encoder:
_target_: nemo.collections.asr.modules.ConvASREncoder
feat_in: *n_mels
activation: relu
conv_mask: true
jasper:
- filters: ${model.model_defaults.filters}
repeat: 1
kernel: [3]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: ${model.model_defaults.filters}
repeat: ${model.model_defaults.repeat}
kernel: [7]
stride: [1]
dilation: [1]
dropout: ${model.model_defaults.dropout}
residual: true
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: ${model.model_defaults.filters}
repeat: ${model.model_defaults.repeat}
kernel: [11]
stride: [1]
dilation: [1]
dropout: ${model.model_defaults.dropout}
residual: true
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: ${model.model_defaults.filters}
repeat: ${model.model_defaults.repeat}
kernel: [15]
stride: [1]
dilation: [1]
dropout: ${model.model_defaults.dropout}
residual: true
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
- filters: &enc_feat_out 3072
repeat: 1
kernel: [1]
stride: [1]
dilation: [1]
dropout: 0.0
residual: false
separable: ${model.model_defaults.separable}
se: ${model.model_defaults.se}
se_context_size: ${model.model_defaults.se_context_size}
decoder:
_target_: nemo.collections.asr.modules.SpeakerDecoder
feat_in: *enc_feat_out
num_classes: 7205
pool_mode: 'attention'
emb_sizes: 192
angular: True
loss:
scale: 30
margin: 0.2
optim:
name: sgd
lr: .006 #(original titanet-large was trained with 0.08 lr)
weight_decay: 0.001
# scheduler setup
sched:
name: CosineAnnealing
warmup_ratio: 0.1
min_lr: 0.0
trainer:
devices: 2 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each)
max_epochs: 10
max_steps: -1 # computed at runtime if not set
num_nodes: 1
precision: 16
accelerator: gpu
strategy: ddp
deterministic: True
enable_checkpointing: False
logger: False
log_every_n_steps: 1 # Interval of logging.
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
exp_manager:
exp_dir: null
name: *name
create_tensorboard_logger: True
create_checkpoint_callback: True
`
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} | null | null | null | Sorry I have to reopen a issue related to Jetson Xavier, I found several issues reported in the passed two years. but didn't helped me to solve the problem. Hope there still some friends playing with new version 5.0.2.
**Describe the bug**
before install Nemo , I installed: torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl, which downloaded from https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048.
During installation from source: ./reinstall.sh:
ERROR: tensorboard 2.10.1 has requirement protobuf<3.20,>=3.9.2, but you'll have protobuf 4.21.7 which is incompatible.
ERROR: onnx 1.12.0 has requirement protobuf<=3.20.1,>=3.12.2, but you'll have protobuf 4.21.7 which is incompatible.
which version I should use?
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
package version information:
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: nemo_toolkit[asr] in ./NeMo (1.13.0rc0)
Requirement already satisfied: setuptools==59.5.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (59.5.0)
Requirement already satisfied: numpy>=1.21 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.23.3)
Requirement already satisfied: onnx>=1.7.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.11.0)
Requirement already satisfied: python-dateutil in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (2.8.2)
Requirement already satisfied: torch in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.12.0a0+2c916ef.nv22.3)
Requirement already satisfied: wrapt in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.14.1)
Requirement already satisfied: ruamel.yaml in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.17.21)
Requirement already satisfied: scikit-learn in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.1.2)
Requirement already satisfied: tqdm>=4.41.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (4.64.1)
Requirement already satisfied: numba in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.56.2)
Requirement already satisfied: wget in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (3.2)
Requirement already satisfied: text-unidecode in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.3)
Requirement already satisfied: huggingface_hub in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.10.1)
Requirement already satisfied: braceexpand in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.1.7)
Requirement already satisfied: editdistance in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.6.0)
Requirement already satisfied: inflect in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (6.0.0)
Requirement already satisfied: librosa>=0.9.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.9.2)
Requirement already satisfied: marshmallow in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (3.18.0)
Requirement already satisfied: packaging in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (21.3)
Requirement already satisfied: soundfile in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.11.0)
Requirement already satisfied: sox in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.4.1)
Requirement already satisfied: kaldi-python-io in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.2.2)
Requirement already satisfied: kaldiio in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (2.17.2)
Requirement already satisfied: scipy>=0.14 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.9.2)
Requirement already satisfied: g2p_en in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (2.1.0)
Requirement already satisfied: pydub in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.25.1)
Requirement already satisfied: pyannote.core in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (4.5)
Requirement already satisfied: pyannote.metrics in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (3.2.1)
Requirement already satisfied: ipywidgets in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (8.0.2)
Requirement already satisfied: matplotlib in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (3.6.1)
Requirement already satisfied: texterrors in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.4.4)
Requirement already satisfied: pytorch-lightning>=1.7.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.7.7)
Requirement already satisfied: torchmetrics>=0.4.1rc0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.10.0)
Requirement already satisfied: transformers>=4.0.1 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (4.23.1)
Requirement already satisfied: webdataset<=0.1.62,>=0.1.48 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.1.62)
Requirement already satisfied: omegaconf<2.2,>=2.1.2 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (2.1.2)
Requirement already satisfied: hydra-core<1.2,>=1.1.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.1.2)
Requirement already satisfied: pyyaml<6 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (5.4.1)
Requirement already satisfied: wandb in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.13.4)
Requirement already satisfied: sentencepiece<1.0.0 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.1.97)
Requirement already satisfied: youtokentome>=1.0.5 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.0.6)
Requirement already satisfied: pandas in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (1.5.0)
Requirement already satisfied: sacremoses>=0.0.43 in ./.nemo/lib/python3.8/site-packages (from nemo_toolkit[asr]) (0.0.53)
Requirement already satisfied: typing-extensions>=3.6.2.1 in ./.nemo/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[asr]) (4.4.0)
Requirement already satisfied: protobuf>=3.12.2 in ./.nemo/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[asr]) (3.19.6)
Requirement already satisfied: six>=1.5 in ./.nemo/lib/python3.8/site-packages (from python-dateutil->nemo_toolkit[asr]) (1.16.0)
Requirement already satisfied: ruamel.yaml.clib>=0.2.6; platform_python_implementation == "CPython" and python_version < "3.11" in ./.nemo/lib/python3.8/site-packages (from ruamel.yaml->nemo_toolkit[asr]) (0.2.6)
Requirement already satisfied: threadpoolctl>=2.0.0 in ./.nemo/lib/python3.8/site-packages (from scikit-learn->nemo_toolkit[asr]) (3.1.0)
Requirement already satisfied: joblib>=1.0.0 in ./.nemo/lib/python3.8/site-packages (from scikit-learn->nemo_toolkit[asr]) (1.2.0)
Requirement already satisfied: importlib-metadata; python_version < "3.9" in ./.nemo/lib/python3.8/site-packages (from numba->nemo_toolkit[asr]) (5.0.0)
Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in ./.nemo/lib/python3.8/site-packages (from numba->nemo_toolkit[asr]) (0.39.1)
Requirement already satisfied: filelock in ./.nemo/lib/python3.8/site-packages (from huggingface_hub->nemo_toolkit[asr]) (3.8.0)
Requirement already satisfied: requests in ./.nemo/lib/python3.8/site-packages (from huggingface_hub->nemo_toolkit[asr]) (2.28.1)
Requirement already satisfied: pydantic in ./.nemo/lib/python3.8/site-packages (from inflect->nemo_toolkit[asr]) (1.10.2)
Requirement already satisfied: pooch>=1.0 in ./.nemo/lib/python3.8/site-packages (from librosa>=0.9.0->nemo_toolkit[asr]) (1.6.0)
Requirement already satisfied: audioread>=2.1.9 in ./.nemo/lib/python3.8/site-packages (from librosa>=0.9.0->nemo_toolkit[asr]) (3.0.0)
Requirement already satisfied: resampy>=0.2.2 in ./.nemo/lib/python3.8/site-packages (from librosa>=0.9.0->nemo_toolkit[asr]) (0.4.2)
Requirement already satisfied: decorator>=4.0.10 in ./.nemo/lib/python3.8/site-packages (from librosa>=0.9.0->nemo_toolkit[asr]) (5.1.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in ./.nemo/lib/python3.8/site-packages (from packaging->nemo_toolkit[asr]) (3.0.9)
Requirement already satisfied: cffi>=1.0 in ./.nemo/lib/python3.8/site-packages (from soundfile->nemo_toolkit[asr]) (1.15.1)
Requirement already satisfied: nltk>=3.2.4 in ./.nemo/lib/python3.8/site-packages (from g2p_en->nemo_toolkit[asr]) (3.7)
Requirement already satisfied: distance>=0.1.3 in ./.nemo/lib/python3.8/site-packages (from g2p_en->nemo_toolkit[asr]) (0.1.3)
Requirement already satisfied: simplejson>=3.8.1 in ./.nemo/lib/python3.8/site-packages (from pyannote.core->nemo_toolkit[asr]) (3.17.6)
Requirement already satisfied: sortedcontainers>=2.0.4 in ./.nemo/lib/python3.8/site-packages (from pyannote.core->nemo_toolkit[asr]) (2.4.0)
Requirement already satisfied: pyannote.database>=4.0.1 in ./.nemo/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[asr]) (4.1.3)
Requirement already satisfied: tabulate>=0.7.7 in ./.nemo/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[asr]) (0.9.0)
Requirement already satisfied: sympy>=1.1 in ./.nemo/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[asr]) (1.11.1)
Requirement already satisfied: docopt>=0.6.2 in ./.nemo/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[asr]) (0.6.2)
Requirement already satisfied: traitlets>=4.3.1 in ./.nemo/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[asr]) (5.4.0)
Requirement already satisfied: jupyterlab-widgets~=3.0 in ./.nemo/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[asr]) (3.0.3)
Requirement already satisfied: ipython>=6.1.0 in ./.nemo/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[asr]) (8.5.0)
Requirement already satisfied: widgetsnbextension~=4.0 in ./.nemo/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[asr]) (4.0.3)
Requirement already satisfied: ipykernel>=4.5.1 in ./.nemo/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[asr]) (6.16.0)
Requirement already satisfied: contourpy>=1.0.1 in ./.nemo/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[asr]) (1.0.5)
Requirement already satisfied: cycler>=0.10 in ./.nemo/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[asr]) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in ./.nemo/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[asr]) (4.37.4)
Requirement already satisfied: pillow>=6.2.0 in ./.nemo/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[asr]) (9.2.0)
Requirement already satisfied: kiwisolver>=1.0.1 in ./.nemo/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[asr]) (1.4.4)
Requirement already satisfied: loguru in ./.nemo/lib/python3.8/site-packages (from texterrors->nemo_toolkit[asr]) (0.6.0)
Requirement already satisfied: regex in ./.nemo/lib/python3.8/site-packages (from texterrors->nemo_toolkit[asr]) (2022.9.13)
Requirement already satisfied: pybind11 in ./.nemo/lib/python3.8/site-packages (from texterrors->nemo_toolkit[asr]) (2.10.0)
Requirement already satisfied: termcolor in ./.nemo/lib/python3.8/site-packages (from texterrors->nemo_toolkit[asr]) (2.0.1)
Requirement already satisfied: plac in ./.nemo/lib/python3.8/site-packages (from texterrors->nemo_toolkit[asr]) (1.3.5)
Requirement already satisfied: Levenshtein in ./.nemo/lib/python3.8/site-packages (from texterrors->nemo_toolkit[asr]) (0.20.5)
Requirement already satisfied: fsspec[http]!=2021.06.0,>=2021.05.0 in ./.nemo/lib/python3.8/site-packages (from pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (2022.8.2)
Requirement already satisfied: tensorboard>=2.9.1 in ./.nemo/lib/python3.8/site-packages (from pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (2.9.1)
Requirement already satisfied: pyDeprecate>=0.3.1 in ./.nemo/lib/python3.8/site-packages (from pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (0.3.2)
Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in ./.nemo/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[asr]) (0.13.1)
Requirement already satisfied: antlr4-python3-runtime==4.8 in ./.nemo/lib/python3.8/site-packages (from omegaconf<2.2,>=2.1.2->nemo_toolkit[asr]) (4.8)
Requirement already satisfied: importlib-resources<5.3; python_version < "3.9" in ./.nemo/lib/python3.8/site-packages (from hydra-core<1.2,>=1.1.0->nemo_toolkit[asr]) (5.2.3)
Requirement already satisfied: Click!=8.0.0,>=7.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (8.0.2)
Requirement already satisfied: pathtools in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (0.1.2)
Requirement already satisfied: psutil>=5.0.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (5.9.2)
Requirement already satisfied: docker-pycreds>=0.4.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (0.4.0)
Requirement already satisfied: shortuuid>=0.5.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (1.0.9)
Requirement already satisfied: promise<3,>=2.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (2.3)
Requirement already satisfied: setproctitle in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (1.3.2)
Requirement already satisfied: GitPython>=1.0.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (3.1.29)
Requirement already satisfied: sentry-sdk>=1.0.0 in ./.nemo/lib/python3.8/site-packages (from wandb->nemo_toolkit[asr]) (1.9.10)
Requirement already satisfied: pytz>=2020.1 in ./.nemo/lib/python3.8/site-packages (from pandas->nemo_toolkit[asr]) (2022.4)
Requirement already satisfied: zipp>=0.5 in ./.nemo/lib/python3.8/site-packages (from importlib-metadata; python_version < "3.9"->numba->nemo_toolkit[asr]) (3.9.0)
Requirement already satisfied: certifi>=2017.4.17 in ./.nemo/lib/python3.8/site-packages (from requests->huggingface_hub->nemo_toolkit[asr]) (2022.9.24)
Requirement already satisfied: charset-normalizer<3,>=2 in ./.nemo/lib/python3.8/site-packages (from requests->huggingface_hub->nemo_toolkit[asr]) (2.1.1)
Requirement already satisfied: idna<4,>=2.5 in ./.nemo/lib/python3.8/site-packages (from requests->huggingface_hub->nemo_toolkit[asr]) (3.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./.nemo/lib/python3.8/site-packages (from requests->huggingface_hub->nemo_toolkit[asr]) (1.26.12)
Requirement already satisfied: appdirs>=1.3.0 in ./.nemo/lib/python3.8/site-packages (from pooch>=1.0->librosa>=0.9.0->nemo_toolkit[asr]) (1.4.4)
Requirement already satisfied: pycparser in ./.nemo/lib/python3.8/site-packages (from cffi>=1.0->soundfile->nemo_toolkit[asr]) (2.21)
Requirement already satisfied: typer[all]>=0.2.1 in ./.nemo/lib/python3.8/site-packages (from pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[asr]) (0.6.1)
Requirement already satisfied: mpmath>=0.19 in ./.nemo/lib/python3.8/site-packages (from sympy>=1.1->pyannote.metrics->nemo_toolkit[asr]) (1.2.1)
Requirement already satisfied: prompt-toolkit<3.1.0,>3.0.1 in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (3.0.31)
Requirement already satisfied: backcall in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.2.0)
Requirement already satisfied: pexpect>4.3; sys_platform != "win32" in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (4.8.0)
Requirement already satisfied: stack-data in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.5.1)
Requirement already satisfied: matplotlib-inline in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.1.6)
Requirement already satisfied: pickleshare in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.7.5)
Requirement already satisfied: jedi>=0.16 in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.18.1)
Requirement already satisfied: pygments>=2.4.0 in ./.nemo/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (2.13.0)
Requirement already satisfied: nest-asyncio in ./.nemo/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[asr]) (1.5.6)
Requirement already satisfied: pyzmq>=17 in ./.nemo/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[asr]) (24.0.1)
Requirement already satisfied: debugpy>=1.0 in ./.nemo/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[asr]) (1.6.3)
Requirement already satisfied: jupyter-client>=6.1.12 in ./.nemo/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[asr]) (7.4.2)
Requirement already satisfied: tornado>=6.1 in ./.nemo/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[asr]) (6.2)
Requirement already satisfied: rapidfuzz<3.0.0,>=2.3.0 in ./.nemo/lib/python3.8/site-packages (from Levenshtein->texterrors->nemo_toolkit[asr]) (2.11.1)
Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1; extra == "http" in ./.nemo/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (3.8.3)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (1.8.1)
Requirement already satisfied: markdown>=2.6.8 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (3.4.1)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (0.6.1)
Requirement already satisfied: google-auth<3,>=1.6.3 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (2.12.0)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (0.4.6)
Requirement already satisfied: wheel>=0.26 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (0.37.1)
Requirement already satisfied: absl-py>=0.4 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (1.2.0)
Requirement already satisfied: grpcio>=1.24.3 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (1.49.1)
Requirement already satisfied: werkzeug>=1.0.1 in ./.nemo/lib/python3.8/site-packages (from tensorboard>=2.9.1->pytorch-lightning>=1.7.0->nemo_toolkit[asr]) (2.2.2)
Requirement already satisfied: gitdb<5,>=4.0.1 in ./.nemo/lib/python3.8/site-packages (from GitPython>=1.0.0->wandb->nemo_toolkit[asr]) (4.0.9)
Requirement already satisfied: rich<13.0.0,>=10.11.0; extra == "all" in ./.nemo/lib/python3.8/site-packages (from typer[all]>=0.2.1->pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[asr]) (12.6.0)
Requirement already satisfied: colorama<0.5.0,>=0.4.3; extra == "all" in ./.nemo/lib/python3.8/site-packages (from typer[all]>=0.2.1->pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[asr]) (0.4.5)
Requirement already satisfied: shellingham<2.0.0,>=1.3.0; extra == "all" in ./.nemo/lib/python3.8/site-packages (from typer[all]>=0.2.1->pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[asr]) (1.5.0)
Requirement already satisfied: wcwidth in ./.nemo/lib/python3.8/site-packages (from prompt-toolkit<3.1.0,>3.0.1->ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.2.5)
Requirement already satisfied: ptyprocess>=0.5 in ./.nemo/lib/python3.8/site-packages (from pexpect>4.3; sys_platform != "win32"->ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.7.0)
Requirement already satisfied: pure-eval in ./.nemo/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (0.2.2)
Requirement already satisfied: executing in ./.nemo/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets->nemo_toolkit[asr]) (1.1.1)
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- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
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- Python version
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Add any other context about the problem here.
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} | Signed-off-by: Elena Rastorgueva <[email protected]>
# What does this PR do ?
Adding the Speech Dataset Processor tool.
**Collection**: 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**:
- [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 ?
1. Fix the Global init step.
2. Add a workaround for the issue https://github.com/NVIDIA/apex/issues/1493 | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes some args that were not removed properly for multilingual megatron NMT.
**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 ?
Update and remove unnecessary parts in `get_samples` function. "int" type setting is no longer needed.
Removed `get_samples` function from [decoder_timestamps_utils.py](https://github.com/NVIDIA/NeMo/pull/5141/files#diff-6e1688943c976f39bdee5fed0c8948f57c88411cfb69548b7fece4b9f7720912) since it is almost the same as get_samples function in [streaming_utils.py](https://github.com/NVIDIA/NeMo/blob/a17a69dea4f8768c0c444573c5a5c499c1e6e2ef/nemo/collections/asr/parts/utils/streaming_utils.py)
**Collection**:
ASR
# Changelog
- Remove unnecessary `int16` dtype and float conversion lines
- Keep the output data format as float
- Update librosa resample argument requirements
# Usage
Check out streaming_asr tutorial notebook.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] 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.
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} | Signed-off-by: arendu <[email protected]>
# What does this PR do ?
- Added CI tests for GPT based IA3 training and inference.
**Collection**: [NLP]
# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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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 ?
Merge bugfixes and doc updates from code frozen branch to main.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Alexandra Antonova <[email protected]>
# What does this PR do ?
Scripts for converting Spoken Wikipedia to an asr dataset (download, preprocessing, ctc-segmentation, filtering).
**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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} | * refactor TTS documentation organization and add new content.
* fix asr api bug.
* fix broken links.
* fix unexpected indentation errors.
* fixed unexpected indentation.
* fixed broken paper reference.
* fixed cross-references and typos.
* fixed toctree errors.
* removed duplicate references.
* replace 'main' branch to 'stable'.
Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
When I stopped fine-tuning the FastPitch model and wanted to restart training, I noticed it checks for the last checkpoint in the wrong directory (I'm settings the checkpoints directory outside of the logs).
So, in my case the checkpoints are being saved at `models/fastpitch/checkpoints` but it is trying to search in `models/fastpitch/logs/checkpoints`.
This is due to the hard-coded path value here: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/exp_manager.py#L424, so inside `check_resume()`:
```py
checkpoint_dir = Path(Path(log_dir) / "checkpoints")
```
While the correct path is being set here: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/exp_manager.py#L889, so inside `configure_checkpointing()`:
```py
if params.dirpath is None:
params.dirpath = Path(log_dir / 'checkpoints')
```
it correctly checks if `dirpath` is set (it is in my case) and only otherwise defaults to the `checkpoints` folder inside of the `logs` folder.
I did not fill in the rest of the template of the bug report as this is a question about a specific code piece, not related to any hardware or scenario. | {
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} | Signed-off-by: Ryan <[email protected]>
# What does this PR do ?
Add a Spanish language tokenizer for doing grapheme-only TTS training.
**Collection**: [TTS]
# Changelog
- Add SpanishCharsTokenizer
- Add unit tests for English, German, and Spanish grapheme tokenizers.
# Usage
```python
from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import SpanishCharsTokenizer
text = "¿Cuál es su nombre?"
tokenizer = SpanishCharsTokenizer(
pad_with_space=True,
)
tokens = tokenizer(text)
graphemes = tokenizer.decode(tokens)
graphemes = graphemes.replace('|', '')
# [0, 54, 3, 21, 27, 12, 0, 5, 19, 0, 19, 21, 0, 14, 15, 13, 2, 18, 5, 38, 0]
print(tokens)
# ¿cuál es su nombre?
print(graphemes)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Adds global batch size and O2 support for bert model
**NLP**:
# Changelog
- Add global batch size support
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix for concat map dataset #5117
Only caveat I know of is that it will not reshuffle each epoch when using round robin. I'm not sure if anyone actually uses round robin in practice, though.
# Usage
```python
from collections import defaultdict
import torch
from pytorch_lightning import seed_everything
from nemo.collections.common.data import ConcatMapDataset
seed_everything(42)
class IntegerDataset(torch.utils.data.Dataset):
def __init__(self, n: int, dataset_id: int):
self._ints = list(range(n))
self._id = torch.tensor(dataset_id)
def __len__(self):
return len(self._ints)
def __getitem__(self, idx):
worker_info = torch.utils.data.get_worker_info()
worker_id = worker_info.id
return self._id, worker_id, self._ints[idx]
for sampling_technique in ["temperature", "random", "round-robin"]:
probs = [0.1, 0.2, 0.3]
concat_dataset = ConcatMapDataset(
datasets=[
IntegerDataset(5, 0),
IntegerDataset(10, 1),
IntegerDataset(20, 2),
],
seed=123456,
sampling_temperature=2,
sampling_probabilities=probs,
sampling_technique=sampling_technique
)
dataloader = torch.utils.data.DataLoader(
dataset=concat_dataset,
batch_size=1,
num_workers=2,
shuffle=False # Should be False to preserve round robin
)
print(f"Item-by-item results for technique '{sampling_technique}':")
collected = defaultdict(list)
for batch in dataloader:
batch_dataset_id, batch_worker_id, batch_value = batch
print(
f"Dataset {batch_dataset_id.item()} worker {batch_worker_id.item()} fetched {batch_value.item()}"
)
collected[batch_dataset_id.item()].append(batch_value.item())
print("*" * 80)
print(f"Summary of returned results for each dataset for technique '{sampling_technique}'")
if sampling_technique == "random":
print(f"\t{probs=}")
for dataset_id, values in sorted(collected.items(), key=lambda x: x[0]):
dataset_raw_length = len(concat_dataset.datasets[dataset_id])
print(f"\tDataset #{dataset_id} (raw length {dataset_raw_length}): {values}")
print("*" * 80)
print()
```
Output:
```
Item-by-item results for technique 'temperature':
Global seed set to 42
Dataset 2 worker 0 fetched 0
Dataset 2 worker 1 fetched 12
Dataset 1 worker 0 fetched 3
Dataset 1 worker 1 fetched 1
Dataset 0 worker 0 fetched 0
Dataset 2 worker 1 fetched 18
Dataset 1 worker 0 fetched 5
Dataset 0 worker 1 fetched 4
Dataset 0 worker 0 fetched 3
Dataset 2 worker 1 fetched 1
Dataset 0 worker 0 fetched 2
Dataset 0 worker 1 fetched 1
Dataset 2 worker 0 fetched 3
Dataset 1 worker 1 fetched 6
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 2
Dataset 2 worker 0 fetched 5
Dataset 0 worker 1 fetched 3
Dataset 1 worker 0 fetched 9
Dataset 0 worker 1 fetched 0
Dataset 2 worker 0 fetched 9
Dataset 2 worker 1 fetched 8
Dataset 2 worker 0 fetched 13
Dataset 2 worker 1 fetched 7
Dataset 1 worker 0 fetched 4
Dataset 2 worker 1 fetched 19
Dataset 2 worker 0 fetched 4
Dataset 2 worker 1 fetched 16
Dataset 2 worker 0 fetched 10
Dataset 2 worker 1 fetched 17
Dataset 0 worker 0 fetched 2
Dataset 0 worker 1 fetched 4
Dataset 1 worker 0 fetched 7
Dataset 1 worker 1 fetched 0
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 8
Dataset 0 worker 0 fetched 2
Dataset 1 worker 1 fetched 5
Dataset 2 worker 0 fetched 14
Dataset 2 worker 1 fetched 2
Dataset 1 worker 0 fetched 0
Dataset 2 worker 1 fetched 6
Dataset 1 worker 0 fetched 7
Dataset 2 worker 1 fetched 15
Dataset 0 worker 0 fetched 3
Dataset 2 worker 1 fetched 11
********************************************************************************
Summary of returned results for each dataset for technique 'temperature'
Dataset #0; raw length 5; num drawn = 13; values = [0, 4, 3, 2, 1, 1, 3, 0, 2, 4, 1, 2, 3]
Dataset #1; raw length 10; num drawn = 13; values = [3, 1, 5, 6, 2, 9, 4, 7, 0, 8, 5, 0, 7]
Dataset #2; raw length 20; num drawn = 20; values = [0, 12, 18, 1, 3, 5, 9, 8, 13, 7, 19, 4, 16, 10, 17, 14, 2, 6, 15, 11]
********************************************************************************
Item-by-item results for technique 'random':
Dataset 2 worker 0 fetched 0
Dataset 2 worker 1 fetched 12
Dataset 2 worker 0 fetched 18
Dataset 1 worker 1 fetched 3
Dataset 0 worker 0 fetched 0
Dataset 2 worker 1 fetched 1
Dataset 1 worker 0 fetched 1
Dataset 0 worker 1 fetched 4
Dataset 0 worker 0 fetched 3
Dataset 2 worker 1 fetched 3
Dataset 0 worker 0 fetched 2
Dataset 1 worker 1 fetched 5
Dataset 1 worker 0 fetched 6
Dataset 2 worker 1 fetched 5
Dataset 1 worker 0 fetched 2
Dataset 2 worker 1 fetched 9
Dataset 2 worker 0 fetched 8
Dataset 2 worker 1 fetched 13
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 9
Dataset 1 worker 0 fetched 4
Dataset 0 worker 1 fetched 0
Dataset 2 worker 0 fetched 7
Dataset 2 worker 1 fetched 19
Dataset 2 worker 0 fetched 4
Dataset 2 worker 1 fetched 16
Dataset 1 worker 0 fetched 7
Dataset 2 worker 1 fetched 10
Dataset 2 worker 0 fetched 17
Dataset 2 worker 1 fetched 14
Dataset 2 worker 0 fetched 2
Dataset 2 worker 1 fetched 6
Dataset 0 worker 0 fetched 3
Dataset 0 worker 1 fetched 2
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 0
Dataset 1 worker 0 fetched 8
Dataset 1 worker 1 fetched 8
Dataset 0 worker 0 fetched 4
Dataset 1 worker 1 fetched 7
Dataset 2 worker 0 fetched 15
Dataset 2 worker 1 fetched 11
********************************************************************************
Summary of returned results for each dataset for technique 'random'
probs=[0.1, 0.2, 0.3]
Dataset #0; raw length 5; num drawn = 10; values = [0, 4, 3, 2, 1, 0, 3, 2, 1, 4]
Dataset #1; raw length 10; num drawn = 12; values = [3, 1, 5, 6, 2, 9, 4, 7, 0, 8, 8, 7]
Dataset #2; raw length 20; num drawn = 20; values = [0, 12, 18, 1, 3, 5, 9, 8, 13, 7, 19, 4, 16, 10, 17, 14, 2, 6, 15, 11]
********************************************************************************
Item-by-item results for technique 'round-robin':
Dataset 0 worker 0 fetched 0
Dataset 1 worker 1 fetched 3
Dataset 2 worker 0 fetched 0
Dataset 0 worker 1 fetched 4
Dataset 1 worker 0 fetched 1
Dataset 2 worker 1 fetched 12
Dataset 0 worker 0 fetched 3
Dataset 1 worker 1 fetched 5
Dataset 2 worker 0 fetched 18
Dataset 0 worker 1 fetched 2
Dataset 1 worker 0 fetched 6
Dataset 2 worker 1 fetched 1
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 2
Dataset 2 worker 0 fetched 3
Dataset 0 worker 1 fetched 0
Dataset 1 worker 0 fetched 9
Dataset 2 worker 1 fetched 5
Dataset 0 worker 0 fetched 2
Dataset 1 worker 1 fetched 4
Dataset 2 worker 0 fetched 9
Dataset 0 worker 1 fetched 1
Dataset 1 worker 0 fetched 7
Dataset 2 worker 1 fetched 8
Dataset 0 worker 0 fetched 3
Dataset 1 worker 1 fetched 0
Dataset 2 worker 0 fetched 13
Dataset 0 worker 1 fetched 4
Dataset 1 worker 0 fetched 8
Dataset 2 worker 1 fetched 7
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 6
Dataset 2 worker 0 fetched 19
Dataset 0 worker 1 fetched 3
Dataset 1 worker 0 fetched 3
Dataset 2 worker 1 fetched 4
Dataset 0 worker 0 fetched 2
Dataset 1 worker 1 fetched 5
Dataset 2 worker 0 fetched 16
Dataset 0 worker 1 fetched 4
Dataset 1 worker 0 fetched 9
Dataset 2 worker 1 fetched 10
Dataset 0 worker 0 fetched 0
Dataset 1 worker 1 fetched 8
Dataset 2 worker 0 fetched 17
Dataset 0 worker 1 fetched 3
Dataset 1 worker 0 fetched 7
Dataset 2 worker 1 fetched 14
Dataset 0 worker 0 fetched 2
Dataset 1 worker 1 fetched 1
Dataset 2 worker 0 fetched 2
Dataset 0 worker 1 fetched 0
Dataset 1 worker 0 fetched 2
Dataset 2 worker 1 fetched 6
Dataset 0 worker 0 fetched 1
Dataset 1 worker 1 fetched 4
Dataset 2 worker 0 fetched 15
Dataset 0 worker 1 fetched 4
Dataset 1 worker 0 fetched 0
Dataset 2 worker 1 fetched 11
********************************************************************************
Summary of returned results for each dataset for technique 'round-robin'
Dataset #0; raw length 5; num drawn = 20; values = [0, 4, 3, 2, 1, 0, 2, 1, 3, 4, 1, 3, 2, 4, 0, 3, 2, 0, 1, 4]
Dataset #1; raw length 10; num drawn = 20; values = [3, 1, 5, 6, 2, 9, 4, 7, 0, 8, 6, 3, 5, 9, 8, 7, 1, 2, 4, 0]
Dataset #2; raw length 20; num drawn = 20; values = [0, 12, 18, 1, 3, 5, 9, 8, 13, 7, 19, 4, 16, 10, 17, 14, 2, 6, 15, 11]
********************************************************************************
```
# 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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} | null | null | null | I trained new model tacotron2 ( TTS ) with new data for persian language
but my val_loss is not normally
I continued this training till 300 epochs but my val_loss was not normally decreased and it is constantly fluctuating
sample of one data in train or validation json file:
`{"audio_filepath": "./wav/4.wav", "duration": 5.6750625, "is_phoneme": 1, "text": "gAme sevvOm d%r b/rnamerizI $enAxte moqe@iyy%te fe@lIye hozE @%st ", "normalized_text": "gAme sevvOm d%r b/rnamerizI $enAxte moqe@iyy%te fe@lIye hozE @%st "}`
text is phoneticized but with uncommon method!
data is for persian(Farsi) language and I didn't use persian alphabet and I don't know what is my problem!
the output of my model is unclear!
loss diagram is normally but val_loss no | {
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} | null | null | null | Hi, all!
Trying to learn a Squeezeformer-S using bf16 precision, but I got **~15 s/it**. But everything is fine learning Conformer with the same number of params with bf16 (got **~3 s/it**) . Also everything goes well using fp16 training with both models (**~3 s/it**). So with all above, I expect Squeezeformer-S to be learnt faster.
**Environment details**
torch==1.10.2
pytorch-lightning==1.7.7
nemo-toolkit==1.11.0
Debian GNU/Linux 10
Python 3.9.7
name: "Squeezeformer-CTC-ru"
model:
sample_rate: 16000
labels: [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я']
log_prediction: false # enables logging sample predictions in the output during training
ctc_reduction: 'mean_batch'
skip_nan_grad: false
train_ds:
manifest_filepath: ???
labels: ${model.labels}
sample_rate: ${model.sample_rate}
batch_size: 128 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 32
pin_memory: true
trim_silence: false
max_duration: 15.0 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: "scatter"
use_start_end_token: false
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
# you may use lower time_masks for smaller models to have a faster convergence
time_masks: 5 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.SqueezeformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 18
d_model: 196
# Squeezeformer params
adaptive_scale: true
time_reduce_idx: 8
time_recovery_idx: null
# Sub-sampling params
subsampling: dw_striding # dw_striding, vggnet, striding or stacking, vggnet may give better results but needs more memory
subsampling_factor: 4 # must be power of 2
subsampling_conv_channels: -1 # -1 sets it to d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 4 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
conv_norm_type: 'batch_norm' # batch_norm or layer_norm
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: ${model.labels}
optim:
name: adamw
lr: 0.002
# optimizer arguments
betas: [0.9, 0.98]
# less necessity for weight_decay as we already have large augmentations with SpecAug
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 5e-4
# scheduler setup
sched:
name: NoamHoldAnnealing
# scheduler config override
warmup_steps: 5000 # paper uses ~ 6500 steps (20 epochs) out of 500 epochs.
warmup_ratio: null
hold_steps: 40000
hold_ratio: null # paper uses ~ 40000 steps (160 epochs) out of 500 epochs.
decay_rate: 1.0 # Noam decay = 0.5 and no hold steps. For Squeezeformer, use hold ~ 10-30% of training, then faster decay.
min_lr: 1e-7
trainer:
devices: 8 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 500
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
accumulate_grad_batches: 1
gradient_clip_val: 1.0
precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP.
log_every_n_steps: 10 # Interval of logging.
enable_progress_bar: True
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false
resume_from_checkpoint: null
exp_manager:
exp_dir: ???
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
# you need to set these two to True to continue the training
#resume_if_exists: false
#resume_ignore_no_checkpoint: false
# You may use this section to create a W&B logger
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null` | {
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} | null | null | null | Are there any parameter to set max segment duration? I saw some segments can be so long. And when i listened to wav, i can see that there is some silence part. However, VAD does not split these areas.
For example;
```
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 0.73, "duration": 1.27, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 3.21, "duration": 0.71, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 5.05, "duration": 0.71, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 6.81, "duration": 1.67, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 9.93, "duration": 4.55, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 15.21, "duration": 0.95, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 17.69, "duration": 0.39, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 18.81, "duration": 2.07, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 21.77, "duration": 13.67, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 36.57, "duration": 15.67, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 53.05, "duration": 0.87, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 55.45, "duration": 1.59, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 58.17, "duration": 18.95, "label": "UNK", "uniq_id": "arda", "text": "-"}
{"audio_filepath": "/media/mesut/Tosun/ses/NeMo/tools/asr_webapp/arda.wav", "offset": 77.93, "duration": 76.31, "label": "UNK", "uniq_id": "arda", "text": "-"}
```
My VAD parameters;
```
vad:
model_path: "vad_multilingual_marblenet" #.nemo local model path or pretrained model name or none
parameters: # Parameters were tuned on 0~20db SNR noisy and clean multilingual ASR data.
normalize_audio: False
window_length_in_sec: 0.63 # window length in sec for VAD context input
shift_length_in_sec: 0.08 # shift length in sec for generate frame level VAD prediction, Here we use 0.08 for faster inferene
smoothing: False # false or type of smoothing method (eg: median, mean)
overlap: 0.5 # overlap ratio for overlapped mean/median smoothing filter. If smoothing=False, ignore this value.
postprocessing:
onset: 0.95 # onset threshold for detecting the beginning and end of a speech
offset: 2.0 # offset threshold for detecting the end of a speech.
pad_onset: 0.15 # adding durations before each speech segment
pad_offset: 0.15 # adding durations after each speech segment
min_duration_on: 0.2 # threshold for small non_speech deletion
min_duration_off: 0.6 # threshold for short speech segment deletion
filter_speech_first: True
``` | {
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} | null | null | null | **Describe the bug**
Squeezeformer model succesfuly exported
But after running on Squeezeformer.encoder.input_example t producess dimmentions mismatch error
**Steps/Code to reproduce bug**
```
python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_squeezeformer_ctc_xsmall_ls", map_location="cpu")
asr_model.export("squeezeformer.onnx", onnx_opset_version=15)
import onnxruntime as ort
ort_sess = ort.InferenceSession('squeezeformer.onnx')
processed_signal, processed_signal_len = asr_model.encoder.input_example()
outputs = ort_sess.run(None, {'audio_signal': processed_signal.numpy(),
'length':processed_signal_len.numpy()})
```
**Error**
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running MatMul node. Name:'MatMul_3906' Status Message: matmul_helper.h:61 Compute MatMul dimension mismatch
```
**Environment details**
nemo_toolkit - 1.12.0rc0
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} | Signed-off-by: 彭震东 <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: Maicon Domingues [[email protected]](mailto:[email protected])
# What does this PR do ?
Changes the URL used to download Common Voice datasets, thus allowing the latest versions to be downloaded in any language.
**Collection**: asr
# Changelog
The change allows any version and language of the Common Voice dataset to be downloaded. Directly pointing to the Bucket S3 made available by Mozilla.
It is worth mentioning that I have been following and using this URL for at least 4 months, so I believe the change is safe.
As the contract for using the `get_commonvoice_data.py` file has remained the same, no impact is expected on examples using this script.
# Usage
* You can potentially add a usage example below
```sh
!python scripts/dataset_processing/get_commonvoice_data.py \
--data_root "datasets/$LANGUAGE/" \
--manifest_dir=$manifest_dir \
--sample_rate=16000 \
--n_channels=1 \
--version="cv-corpus-11.0-2022-09-21" \
--language="pt" \
--log \
--files_to_process 'train.tsv' 'dev.tsv' 'test.tsv'
```
# 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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