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} | null | null | null | **Describe the bug**
Hello, guys!
I have a problem: I tried to train [Conformer-Transducer](https://huggingface.co/nvidia/stt_ru_conformer_transducer_large) model. But I stucked at the start. I think it's because of trainer parameters or some cuda error but I'm note sure...
```
[NeMo W 2023-10-17 16:18:53 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-10-17 16:18:58 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
device: cuda
[NeMo I 2023-10-17 16:19:03 mixins:170] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo W 2023-10-17 16:19:05 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: null
sample_rate: 16000
batch_size: 16
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20.0
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: null
bucketing_weights: ''
[NeMo W 2023-10-17 16:19:05 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: null
sample_rate: 16000
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
[NeMo W 2023-10-17 16:19:05 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
sample_rate: 16000
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
[NeMo I 2023-10-17 16:19:05 features:287] PADDING: 0
[NeMo W 2023-10-17 16:19:07 nemo_logging:349] /root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/torch/nn/modules/rnn.py:67: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1
warnings.warn("dropout option adds dropout after all but last "
[NeMo I 2023-10-17 16:19:07 rnnt_models:206] Using RNNT Loss : warprnnt_numba
Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.0, 'clamp': -1.0}
[NeMo I 2023-10-17 16:19:16 save_restore_connector:249] Model EncDecRNNTBPEModel was successfully restored from /root/.cache/huggingface/hub/models--nvidia--stt_ru_conformer_transducer_large/snapshots/687d02db291e931455cf321abd625ef2b7f0b1a9/stt_ru_conformer_transducer_large.nemo.
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo I 2023-10-17 16:19:17 collections:193] Dataset loaded with 26757 files totalling 31.35 hours
[NeMo I 2023-10-17 16:19:17 collections:194] 0 files were filtered totalling 0.00 hours
[NeMo I 2023-10-17 16:19:19 collections:193] Dataset loaded with 7135 files totalling 8.24 hours
[NeMo I 2023-10-17 16:19:19 collections:194] 0 files were filtered totalling 0.00 hours
[NeMo W 2023-10-17 16:19:19 audio_to_text_dataset:675] Could not load dataset as `manifest_filepath` was None. Provided config : {'manifest_filepath': None, 'sample_rate': 16000, 'batch_size': 16, 'shuffle': False, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False}
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo I 2023-10-17 16:19:20 modelPT:722] Optimizer config = Novograd (
Parameter Group 0
amsgrad: False
betas: [0.9, 0.98]
eps: 1e-08
grad_averaging: False
lr: 0.0001
weight_decay: 0.001
)
[NeMo I 2023-10-17 16:19:20 lr_scheduler:910] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7fa5600805b0>"
will be used during training (effective maximum steps = 22400) -
Parameters :
(warmup_steps: 10000
warmup_ratio: null
min_lr: 1.0e-06
max_steps: 22400
)
| Name | Type | Params
------------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | encoder | ConformerEncoder | 115 M
2 | decoder | RNNTDecoder | 3.9 M
3 | joint | RNNTJoint | 1.4 M
4 | loss | RNNTLoss | 0
5 | spec_augmentation | SpectrogramAugmentation | 0
6 | wer | RNNTBPEWER | 0
------------------------------------------------------------------------
5.4 M Trainable params
115 M Non-trainable params
120 M Total params
481.780 Total estimated model params size (MB)
Epoch 0: 0%| | 0/1130 [00:00<?, ?it/s]Traceback (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/root/sdb/nemo/stt_conformer/src/models/train_model.py", line 156, in <module>
main()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/src/models/train_model.py", line 150, in main
trainer.fit(model)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 603, in fit
call._call_and_handle_interrupt(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 645, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run
results = self._run_stage()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1177, in _run_stage
self._run_train()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1200, in _run_train
self.fit_loop.run()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 214, in advance
batch_output = self.batch_loop.run(kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(optimizers, kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 200, in advance
result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position])
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 239, in _run_optimization
closure()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 147, in __call__
self._result = self.closure(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 133, in closure
step_output = self._step_fn()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 406, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1480, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 378, in training_step
return self.model.training_step(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/utils/model_utils.py", line 380, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/models/rnnt_models.py", line 712, in training_step
loss_value, wer, _, _ = self.joint(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/core/classes/common.py", line 1087, in __call__
outputs = wrapped(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/modules/rnnt.py", line 1335, in forward
loss_batch = self.loss(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/core/classes/common.py", line 1087, in __call__
outputs = wrapped(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/losses/rnnt.py", line 361, in forward
loss = self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 281, in forward
return self.loss(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 62, in forward
loss_func(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py", line 223, in rnnt_loss_gpu
status = wrapper.cost_and_grad(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 249, in cost_and_grad
return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 158, in compute_cost_and_score
self.log_softmax(acts, denom)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 104, in log_softmax
reduce.reduce_max(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py", line 353, in reduce_max
return ReduceHelper(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py", line 294, in ReduceHelper
_reduce_rows[grid_size, CTA_REDUCE_SIZE, stream, 0](I_opid, R_opid, acts, output, num_rows)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/cuda/dispatcher.py", line 542, in __call__
return self.dispatcher.call(args, self.griddim, self.blockdim,
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/cuda/dispatcher.py", line 676, in call
kernel = _dispatcher.Dispatcher._cuda_call(self, *args)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/cuda/dispatcher.py", line 684, in _compile_for_args
return self.compile(tuple(argtypes))
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/cuda/dispatcher.py", line 927, in compile
kernel = _Kernel(self.py_func, argtypes, **self.targetoptions)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
return func(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/cuda/dispatcher.py", line 84, in __init__
cres = compile_cuda(self.py_func, types.void, self.argtypes,
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
return func(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/cuda/compiler.py", line 230, in compile_cuda
cres = compiler.compile_extra(typingctx=typingctx,
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler.py", line 742, in compile_extra
return pipeline.compile_extra(func)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler.py", line 460, in compile_extra
return self._compile_bytecode()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler.py", line 528, in _compile_bytecode
return self._compile_core()
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler.py", line 507, in _compile_core
raise e
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler.py", line 494, in _compile_core
pm.run(self.state)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_machinery.py", line 368, in run
raise patched_exception
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_machinery.py", line 356, in run
self._runPass(idx, pass_inst, state)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
return func(*args, **kwargs)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_machinery.py", line 311, in _runPass
mutated |= check(pss.run_pass, internal_state)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/compiler_machinery.py", line 273, in check
mangled = func(compiler_state)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/typed_passes.py", line 110, in run_pass
typemap, return_type, calltypes, errs = type_inference_stage(
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/typed_passes.py", line 88, in type_inference_stage
errs = infer.propagate(raise_errors=raise_errors)
File "/root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/numba/core/typeinfer.py", line 1086, in propagate
raise errors[0]
numba.core.errors.TypingError: Failed in cuda mode pipeline (step: nopython frontend)
Internal error at <numba.core.typeinfer.CallConstraint object at 0x7fa59609cbb0>.
libNVVM cannot be found. Do `conda install cudatoolkit`:
[Errno 2] No such file or directory: '/usr/local/cuda/nvvm/lib64'
During: resolving callee type: type(CUDADispatcher(<function exponential at 0x7fa56b25c430>))
During: typing of call at /root/sdb/nemo/stt_conformer/new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py (158)
Enable logging at debug level for details.
File "new_venv/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py", line 158:
def _reduce_rows(I_opid: int, R_opid: int, acts, output, num_rows: int):
<source elided>
if I_opid == 0:
curr = rnnt_helper.exponential(curr)
^
Epoch 0: 0%| | 0/1130 [00:11<?, ?it/s]
```
I install environment using `pip install -r requirements.txt`.
`requirements` contains the following:
```
pandas==1.5.3
torch==1.13.0
pytorch_lightning==1.8.6
omegaconf==2.2.3
nemo_toolkit==1.18.1
optuna==3.1.0
pyctcdecode==0.5.0
swifter==1.3.4
openpyxl==3.1.2
torchvision==0.14.0
torchmetrics==0.11.4
torchaudio==0.13.0
nemo-text-processing==0.1.7rc0
jiwer==3.0.1
hydra-core==1.3.2
librosa==0.10.1
sentencepiece==0.1.99
youtokentome==1.0.6
braceexpand==0.1.7
webdataset==0.1.62
pyannote.core==5.0.0
pyannote.database==5.0.1
pyannote.metrics==3.2.1
editdistance==0.6.2
```
Here is nvidia-smi output:
```
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GRID V100DX-32Q On | 00000000:02:00.0 Off | 0 |
| N/A N/A P0 N/A / N/A | 0MiB / 32768MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
```
OS: Ubuntu 18.04.6 LTS
Python version: Python 3.8.16
What really surprised me that if I train [Conformer](https://huggingface.co/nvidia/stt_ru_conformer_ctc_large) model, it is ok.
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} | # What does this PR do ?
This PR adds long-form audio speaker diarization feature by adding LongFormSpeakerClustering class.
Two parameters are added to yaml file for long form audio clustering.
This is forward function for long-form speaker clustering.
Please refer to `SpeakerClustering` class for the original argument information.
**Please read the following text to understand the long-form clustering**
In the function `long_forward_infer` in `LongFormSpeakerClustering` class:
1. Input embeddings are divided into smaller windows of size `unit_window_len`. It is usually order of 10K. `unit_window_len=10000` is default value.
2. Each window undergoes overclustering, resulting in `sub_cluster_n` fine-grained clusters. Default `sub_cluster_n=50`.
3. These fine-grained clusters are merged to form the aggregated clustering labels `Y_aggr`.
4. The `unpack_labels` function is then employed to map the aggregated labels `Y_aggr` back to the original labels for all `org_len` input embeddings: `Y_unpack`.
* Theoretically, the maximum length of audio can be extended by a factor of `unit_window_len/sub_cluster_n`. For instance, by default, if the original clustering hits the memory limit at the 1-hour mark, the long-form clustering could handle up to **20 hours without exhausting the memory**.
* The added long-form audio clustering re-uses (repurposes) the online clustering functions and offline clustering functions except `unpack_labels`.
* The main `forward_unit_infer` clustering execution function and `unpack_labels` functions are added to the `test_diar_utils.py`.
(1) `forward_unit_infer` function performs a simple 1-scale clustering without time-stamp processing.
(2) `LongFormSpeakerClustering` has `forward_infer` function that chooses one of these two functions:
-- `long_forward_infer` if the input number of segments are larger than `self.unit_window_len`.
-- `short_forward_infer` if the input number of segments are smaller or equal to `self.unit_window_len`.
**Collection**: ASR - Speaker Tasks - Speaker Diarization
# Changelog
Updated files:
- All three diarization inference yaml files
- speaker_utils.py
- test_diar_utils.py
- longform_clustering.py - Major change of adding `LongFormSpeakerClustering`
- offline_clustering.py
# Usage
* You can potentially add a usage example below
```
python offline_diar_with_asr_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.speaker_embeddings.parameters.save_embeddings=False
```
# 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 ASR
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} | # What does this PR do ?
Add snake activation to Audio Codec
**Collection**: TTS/ASR
# Changelog
- Add Snake Activation to activation file
- Optionally choose between ELU/Snake while building model
- Note: Prior checkpoints don't load as is without modifying variable names
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | ### What does this PR do?
Add the ability to change the base value of RoPE embeddings from config.
**Collection**: nlp | {
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} | # What does this PR do ?
update text server to support compute logprobs
**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**:
- [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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fix speechllm few-shot inference
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} | # What does this PR do ?
When max time is reached in training, we'll terminate gracefully without running validation. This allows us to quit as fast as possible after saving a last checkpoint.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add fix for max time to save checkpoint and quit trainer gracefully without running validation.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a conversion script to convert legacy (non-mcore path) nemo checkpoint into mcore-path checkpoint for GPT models
Verification between legacy and converted mcore checkpoints:
- Weights match exactly
- Model forward pass outputs are close (barring precision differences in TE).
- GPT eval produces identical results
- LoRA training converges with the same training and validation loss curves.
**Collection**: NLP
# Changelog
- Add conversion script
# Usage
See top of the script for instructions
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Add multimodal models in NeMo
**Collection**: [Note which collection this PR will affect]
Multimodal, Vision, NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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 ?
Add multimodal models in NeMo
**Collection**: [Note which collection this PR will affect]
NLP, Vision, Multimodal
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
in the NeMo/scripts/tts-dataset_files, there is a cmudict phoneme to ipa phoneme mapping file : cmudict-arpabet_to_ipa_nv22.08.tsv, but this file contains only 42 cmudict phonemes, this seems not enough to translate all cmudict phonemes to ipa, is there any full version of this mapping file?
**Describe the solution you'd like**
A full mapping file so that I can convert cmudict phonemes into IPA
**Describe alternatives you've considered**
**Additional context**
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} | # What does this PR do ?
Update the speech_cv branch to support audio-visual speech recognition
**Collection**: [multimodal/speech_cv]
# Changelog
- Update speech_cv branch
- Add audio-visual datasets
- Add audio-visual models: (AudioVisualEncDecCTCModelBPE, AudioVisualEncDecCTCModel, AudioVisualEncDecHybridRNNTCTCBPEModel, AudioVisualEncDecHybridRNNTCTCModel, AudioVisualEncDecRNNTBPEModel, AudioVisualEncDecRNNTModel)
- Add audio-visual modules (FusionModule, InterCTCMixin)
# Usage
```python
import nemo.collections.multimodal.speech_cv as nemo_cv
from omegaconf import OmegaConf
config_path = "model_config.yaml"
params = OmegaConf.load(config_path)
# hybrid CTC/RNN-T BPE audio-visual speech recognition model
model = nemo_cv.models.AudioVisualEncDecHybridRNNTCTCBPEModel(cfg=params.model)
```
# 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**
I am trying to finetune a nvidia/stt_en_fastconformer_transducer_large for arabic ASR. I changed the labels(using bpe tokeniser) and since epoch 50 (out of 100) they were outputing correct output labels .
But suddenly the model starting predicted these ?? not sure what it means. Request suggestion to what might be the problem
```
[NeMo I 2023-10-14 20:59:07 rnnt_wer_bpe:403] reference :ولربك فاصبر
[NeMo I 2023-10-14 20:59:07 rnnt_wer_bpe:404] predicted : ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
[NeMo I 2023-10-14 20:59:08 rnnt_wer_bpe:402]
[NeMo I 2023-10-14 20:59:08 rnnt_wer_bpe:403] reference :فكيف كان عذابي ونذر
[NeMo I 2023-10-14 20:59:08 rnnt_wer_bpe:404] predicted : ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
[NeMo I 2023-10-14 20:59:09 rnnt_wer_bpe:402]
[NeMo I 2023-10-14 20:59:09 rnnt_wer_bpe:403] reference :إذا مسه الشر جزوعا
[NeMo I 2023-10-14 20:59:09 rnnt_wer_bpe:404] predicted : ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
[NeMo I 2023-10-14 20:59:17 rnnt_wer_bpe:402]
```
**Steps/Code to reproduce bug**
No ida
**Expected behavior**
The output should not output question mark
**Environment overview (please complete the following information)**
- Environment location: Collab (GPU)
- Method of NeMo install: pip install from main branch
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a script to average distributed checkpoint files. | {
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} | # What does this PR do ?
Fix typo in code block in docs
**Collection**: Docs
# Changelog
- Add indendation after `with open_dict(decoding_cfg):` in code block
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Add swa decoding to swa branch.
Parameter: model.encoder.limited_context_decoding
Options: None (use full decoding), "local", "local_with_sink" | {
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Is there any tutorial or example to run LLama2 with Nemo?
Thank you.
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} | # What does this PR do ?
Include Modular SpeechLLM implementation for Sept. 2023 submission (SALM). The implementation integrates a pretrained FC and a frozen LLM, and can do ASR, AST, and in-context learning for keyword boosting.
The implementation is ptn2.0 compatible.
**Collection**: multimodal
# Usage
refer to `examples/multimodel/modular_speechllm/run_sft_audio_lm.py` and `examples/multimodel/modular_speechllm/eval_audio_gpt_lora.py`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
This PR added fixes for PTL2.0 related SLU bugs in r1.21.0: Val metrics logging, None dataloader issue
Collection:
ASR
# Changelog
- correct logging of validation metrics and test_dataloader() function in nemo/collentions/asr/models/slu_models.py updated to handle cases where dataloader is None as done in #7531
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@stevehuang52 @titu1994
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 #7450
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} | null | null | null | This is really two issues but the first one is really simple and pertains to the same script.
I'm using a fastconformer hybrid RNNT-CTC model and have trained an LM with Kenlm so i can decode with eval_beamsearch_ngram.py. However, I'm seeing two issues so far:
1. When specifying the probs_cache_file flag to store the results, the code errors out if i point to a file in a directory that does not exist yet. Can you add a quick check to ensure the directory is made if it doesn't already exist (this would be around line 337 in https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py#L337C13-L337C13)?
2. When i select a single value for each of the various beam hyperparameters (beam_width, beam_alpha, beam_beta) everything works fine. However, when i try to do a grid search of beam_width, beam_alpha, and beam_beta then i get an error. It seems that the model_decoding_strategy is getting reset (https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py#L143) but then isn't updated correctly, particularly because I'm using the hybrid RNNT-CTC model which requires special handling.
For example, when I run this:
```
python ~/git/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py \
nemo_model_file=./results/stt_ft_bsz128_lr0.0020_wu1000/checkpoints/stt_ft_bsz128_lr0.0020_wu1000.nemo \
input_manifest=./data/manifest.debug.json \
kenlm_model_file=./data/lm/lm.bin \
beam_alpha=[-0.1,0.1] \
beam_beta=[10] \
beam_width=[10] \
preds_output_folder=./foo_lm \
probs_cache_file=./foo_lm/probs.pkl \
decoding_mode=beamsearch_ngram \
decoding_strategy=flashlight \
beam_batch_size=128 \
use_amp=True \
acoustic_batch_size=4
```
I get the following error:
```
Traceback (most recent call last):
File "/panfs/g52-panfs/home/jo23828/git/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 457, in <module>
main()
File "/panfs/g52-panfs/home/jo23828/git/NeMo/nemo/core/config/hydra_runner.py", line 126, in wrapper
_run_hydra(
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
lambda: hydra.run(
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/panfs/g52-panfs/exp/venv/jo23828/.conda/envs/nemo_r1.21.0/lib/python3.10/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/panfs/g52-panfs/home/jo23828/git/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 417, in main
candidate_wer, candidate_cer = beam_search_eval(
File "/panfs/g52-panfs/home/jo23828/git/NeMo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 143, in beam_search_eval
model.change_decoding_strategy(None)
File "/panfs/g52-panfs/home/jo23828/git/NeMo/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py", line 399, in change_decoding_strategy
self.decoding = RNNTBPEDecoding(
File "/panfs/g52-panfs/home/jo23828/git/NeMo/nemo/collections/asr/metrics/rnnt_wer_bpe.py", line 208, in __init__
super(RNNTBPEDecoding, self).__init__(
File "/panfs/g52-panfs/home/jo23828/git/NeMo/nemo/collections/asr/metrics/rnnt_wer.py", line 234, in __init__
raise ValueError(f"Decoding strategy must be one of {possible_strategies}")
ValueError: Decoding strategy must be one of ['greedy', 'greedy_batch', 'beam', 'tsd', 'alsd', 'maes']
``` | {
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} | null | null | null | ## Problem statement
I am training EncDecSpeakerLabelModel for speaker identification model , however I am **having difficulty on reproducibility** on model training.
Below code is part of the example from [HERE](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb) with modification on seed.
## Main library
Nemo version : v1.20.0
pytorch-lightning==1.9.4
torch==2.0.1
## Code
```python
import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning import seed_everything
accelerator = 'gpu' if torch.cuda.is_available() else 'cpu'
seed = 42
MODEL_CONFIG = os.path.join(NEMO_ROOT,'conf/titanet-large.yaml')
config = OmegaConf.load(MODEL_CONFIG)
checkpoint_callback = ModelCheckpoint(monitor='loss')
config.model.train_ds.manifest_filepath = train_manifest
config.model.validation_ds.manifest_filepath = validation_manifest
config.model.decoder.num_classes = 3
config.trainer.devices = 1
config.trainer.accelerator = accelerator
config.trainer.max_epochs = 5
config.trainer.strategy = None
config.model.train_ds.augmentor = None
config.trainer.deterministic = True
trainer_config = OmegaConf.create(dict(
devices=1,
accelerator=accelerator,
max_epochs=5,
max_steps=-1, # computed at runtime if not set
num_nodes=1,
accumulate_grad_batches=1,
enable_checkpointing=False, # Provided by exp_manager
logger=False, # Provided by exp_manager
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
deterministic=True,
))
print(OmegaConf.to_yaml(trainer_config))
# Fix random seed in torch , numpy and dataloader.
torch.use_deterministic_algorithms(True)
seed_everything(seed, workers=True)
# Training config setting into pytorch lightning trainer
trainer = pl.Trainer(**config.trainer)
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel(cfg=config.model, trainer=trainer)
trainer.fit(speaker_model)
trainer.validate(speaker_model)
```
## Output results
**1st run :**
**2nd run:**
## Expected output
Both runs should produce identical validation macro or close to identificatal validation macro scores. | {
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} | null | null | null | In megatron-lm checkpoints are self-contained, so if I have a checkpoint for step 1000, all its files are in the same directory. However in nemo they are grouped by rank, rather than step.
This causes a huge problem when one needs to sync checkpoints to cloud and be able to delete them once this is done.
Is there a way to invert the checkpoint structure, so that instead of:
```
checkpoints/mp_rank_00/step=02250.ckpt
checkpoints/mp_rank_00/step=02500.ckpt
checkpoints/mp_rank_01/step=02250.ckpt
checkpoints/mp_rank_01/step=02500.ckpt
```
it'll save checkpoints like:
```
checkpoints/02250/mp_rank_00.ckpt
checkpoints/02250/mp_rank_01.ckpt
checkpoints/02500/mp_rank_00.ckpt
checkpoints/02500/mp_rank_01.ckpt
```
So now I can copy+delete `checkpoints/02250` and automate this process easily by discovering new checkpoint folders, rather than trying to scan multiple leaf points, which makes it much more complicated and not lending to simple manual deleting.
I guess that since PTL's Experiment Manager is used this is an artifact of having each gpu saving into its unique rank path.
Any suggestions to how to solve it to bring back megatron-lm style checkpoints?
Thank you! | {
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} | # What does this PR do ?
move core install to /workspace
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add few-shot in-context learning for inference and MLP modality adapter.
An example of manifest is:
```
{
"audio_filepath": ["1.wav","2.wav","3.wav"],
"duration": [1.05,1.05,2.0],
"answer": "this was her dream as nearly as she could recall it",
"question": "Following are examples of speech audios and their transcriptions.
Example 1: audio is |audio|, transcription is 'I have a dream'.
Example 2: audio is |audio|, transcription is ' I don't have a dream'.
Given the following audio |audio|, transcribe the audio into words."
}
```
The `question` field has special audio locator strings (`|audio|` in this case) to indicate the locations of audios specified by the list of audio files in the field `audio_filepath`. In the case of multiple audios, `duration` and `offset` should also be a list of float numbers. The program will load the three audios and insert their extracted features into the corresponding locations specified by the audio locator in the order they appear in the list. The dataset config should have a field `audio_locator: |audio|`.
**Collection**: [multimodal]
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} | # What does this PR do ?
Same issue and fix as in https://github.com/NVIDIA/NeMo/pull/7671
**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 ?
Same issue and fix as in https://github.com/NVIDIA/NeMo/pull/7671
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add steerlm paper to publications
**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**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | …MCORE pass
# What does this PR do ?
Added a knob to pass from the Nemo Launcher to the Mcore pass model for the ub_tp_comm_overlap switch.
Reference PR: https://github.com/NVIDIA/Megatron-LM/pull/532
This fix will be used by this PR in the Megatron-LM repository. | {
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} | # What does this PR do ?
Fix multi node fine-tuning script by adding rank based delay
**Collection**: [ASR]
# Changelog
- Add some delay to other ranks for the pretrained model call
# Before your PR is "Ready for review"
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- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
This PR fixes a typo in encoder target in `audio_codec_24000.yaml`.
**Collection**: TTS
# Changelog
- Changed encoder target in the config file
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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] | closed | false | null | [] | null | null | 2023-10-11T18:27:40 | 2023-10-11T18:55:48 | 2023-10-11T18:55:48 | CONTRIBUTOR | {
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} | # What does this PR do ?
Remove PUBLICATIONS.md file and add a link to https://nvidia.github.io/NeMo/publications/ instead.
**Collection**: README
# Changelog
- Remove PUBLICATIONS.md file
- add link to https://nvidia.github.io/NeMo/publications/ instead.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] 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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[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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Remove PUBLICATIONS.md file and add a link to https://nvidia.github.io/NeMo/publications/ instead.
**Collection**: README
# Changelog
- Remove PUBLICATIONS.md file
- add link to https://nvidia.github.io/NeMo/publications/ instead.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Related to [bug report](https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=4312419&cmtNo=),
Chinese normalizer outputted wrong normalized text for the input, '双辽境内除东辽河、西辽河等**5**条河流', where we expect '双辽境内除东辽河、西辽河等**五**条河流'.
**Evidence**
- On the latest version, i.e. nemo-text-processing=0.2.0rc0, it always normalized it as '双辽境内除东辽河、西辽河等5条河流';
- On the previous version, i.e. nemo-text-processing=0.1.8rc0, it normalized it as expected text, '双辽境内除东辽河、西辽河等五条河流'.
**Detailed Log**
- 0.2.0rc0
```
In [1]: from nemo_text_processing.text_normalization.normalize import Normalizer
In [2]: normalizer = Normalizer(lang='zh', input_case='cased', overwrite_cache=True, cache_dir="./cache_dir")
In [3]: text = '双辽境内除东辽河、西辽河等5条河流'
In [4]: normalizer.normalize(text)
Out[4]: '双辽境内除东辽河、西辽河等5条河流'
```
- 0.1.8rc0
```
Downloading nemo_text_processing-0.1.8rc0-py3-none-any.whl (2.3 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.3/2.3 MB 12.5 MB/s eta 0:00:00
Installing collected packages: nemo-text-processing
Attempting uninstall: nemo-text-processing
Found existing installation: nemo-text-processing 0.2.0rc0
Uninstalling nemo-text-processing-0.2.0rc0:
Successfully uninstalled nemo-text-processing-0.2.0rc0
Successfully installed nemo-text-processing-0.1.8rc0
In [1]: from nemo_text_processing.text_normalization.normalize import Normalizer
In [2]: normalizer = Normalizer(lang='zh', input_case='cased', overwrite_cache=True, cache_dir="./cache_dir")
In [3]: text = '双辽境内除东辽河、西辽河等5条河流'
In [4]: normalizer.normalize(text)
Out[4]: '双辽境内除东辽河、西辽河等五条河流'
```
**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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
This PR added fixes for PTL2.0 related SLU bugs in r1.21.0: Val metrics logging, None dataloader issue
Collection:
ASR
# Changelog
- correct logging of validation metrics and test_dataloader() function in nemo/collentions/asr/models/slu_models.py updated to handle cases where dataloader is None as done in #7531
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@stevehuang52 @titu1994
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 #7450
| {
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Add a one line overview of what this PR aims to accomplish.
This PR added fixes for PTL2.0 related SLU bugs in r1.21.0: Val metrics logging, None dataloader issue
Collection:
ASR
# Changelog
- correct logging of validation metrics and test_dataloader() function in nemo/collentions/asr/models/slu_models.py updated to handle cases where dataloader is None as done in #7531
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@stevehuang52 @titu1994
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 #7450
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} | # What does this PR do?
When trying to export FastPitchModel to TorchScript model:
```python
from nemo.collections.tts.models import FastPitchModel
fastpitch_model = FastPitchModel.from_pretrained("tts_en_fastpitch")
fastpitch_model.export("fastpitch.pt")
```
It reported error:
```
Traceback (most recent call last):
File "/home/robin/try1/NeMo/export.py", line 11, in <module>
fastpitch_model.export("fastpitch.pt")
File "/home/robin/try1/NeMo/nemo/core/classes/exportable.py", line 113, in export
out, descr, out_example = model._export(
File "/home/robin/try1/NeMo/nemo/core/classes/exportable.py", line 200, in _export
jitted_model = torch.jit.trace_module(
File "/home/robin/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/jit/_trace.py", line 1065, in trace_module
module._c._create_method_from_trace(
File "/home/robin/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/robin/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/robin/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1508, in _slow_forward
result = self.forward(*input, **kwargs)
File "/home/robin/try1/NeMo/nemo/collections/tts/models/fastpitch.py", line 861, in forward_for_export
return self.fastpitch.infer(text=text, pitch=pitch, pace=pace, volume=volume, speaker=speaker)
File "/home/robin/try1/NeMo/nemo/collections/tts/modules/fastpitch.py", line 430, in infer
volume_extended, _ = regulate_len(durs_predicted, volume.unsqueeze(-1), pace)
File "/home/robin/try1/NeMo/nemo/collections/tts/parts/utils/helpers.py", line 597, in regulate_len
enc_rep = torch.matmul(mult, enc_out)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (270x44 and 1x1)
```
The `torch.jit.trace_module()` in `nemo/core/classes/exportable.py` could only process tuple of tensors. The `input_examle` of FastPitchModel provides four arguments `text`/`pitch`/`pace`/`batch_lengths` but the first four arguments `forward_for_export()` of FastPitchModel are `text`/`pitch`/`pace`/`volume` (the fifth one is `batch_lengths`). Then the `torch.jit.trace_module()` will mistakenly recognize `batch_lengths` input as `volume` argument.
A simple solution for this is swap arguments of `volume` and `batch_lengths` in `forward_for_export()`.
After fix the input, another error jump out:
```
Traceback (most recent call last):
File "/home/robin/try1/NeMo/export.py", line 5, in <module>
fastpitch_model.export("fastpitch.pt")
File "/home/robin/try1/NeMo/nemo/core/classes/exportable.py", line 113, in export
out, descr, out_example = model._export(
File "/home/robin/try1/NeMo/nemo/core/classes/exportable.py", line 203, in _export
jitted_model = torch.jit.trace_module(
File "/home/robin/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/jit/_trace.py", line 1065, in trace_module
module._c._create_method_from_trace(
RuntimeError: Only tensors, lists, tuples of tensors, or dictionary of tensors can be output from traced functions
```
The reason is that the output of `infer()` for `FastPitchModule` contains a `None` argument `volume_extended`. To avoid this, we shall check before return output.
# Changelog
- Fix torchscript export error of FastPitchModel
# Usage
After this PR, we can export both ONNX and TorchScript successfully
```python
from nemo.collections.tts.models import FastPitchModel
fastpitch_model = FastPitchModel.from_pretrained("tts_en_fastpitch")
fastpitch_model.export("fastpitch.onnx")
fastpitch_model.export("fastpitch.pt")
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@XuesongYang @blisc @okuchaiev | {
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} | # What does this PR do ?
Installing NeMo/TransformerEngine and checkout Megatron-LM code by using its `main` branch.
Then run pytest
```python
PYTHONPATH=/home/robin/Megatron-LM/ pytest --tb=native tests/
```
It directly reported error:
```
ImportError while loading conftest '/home/robin/try1/NeMo/tests/conftest.py'.
tests/conftest.py:28: in <module>
from tests.fixtures.tts import *
tests/fixtures/tts.py:21: in <module>
from nemo.collections.asr.parts.utils.manifest_utils import read_manifest
nemo/collections/asr/__init__.py:15: in <module>
from nemo.collections.asr import data, losses, models, modules
nemo/collections/asr/models/__init__.py:36: in <module>
from nemo.collections.asr.models.transformer_bpe_models import EncDecTransfModelBPE
nemo/collections/asr/models/transformer_bpe_models.py:52: in <module>
from nemo.collections.nlp.modules.common import TokenClassifier
nemo/collections/nlp/__init__.py:15: in <module>
from nemo.collections.nlp import data, losses, models, modules
nemo/collections/nlp/models/__init__.py:28: in <module>
from nemo.collections.nlp.models.language_modeling import MegatronGPTPromptLearningModel
nemo/collections/nlp/models/language_modeling/__init__.py:16: in <module>
from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import (
nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py:31: in <module>
from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:78: in <module>
from megatron.core.models.gpt import GPTModel as MCoreGPTModel
../../Megatron-LM/megatron/core/models/gpt/__init__.py:1: in <module>
from .gpt_model import GPTModel
../../Megatron-LM/megatron/core/models/gpt/gpt_model.py:15: in <module>
from megatron.core.transformer.transformer_block import TransformerBlock
../../Megatron-LM/megatron/core/transformer/transformer_block.py:10: in <module>
from megatron.core.transformer.custom_layers.transformer_engine import TENorm
../../Megatron-LM/megatron/core/transformer/custom_layers/transformer_engine.py:67: in <module>
class TELinear(te.pytorch.Linear):
E AttributeError: module 'transformer_engine' has no attribute 'pytorch'
```
The reason for this error is that GPTModel hasn't been import correctly.
# Changelog
- Fix import error of megatron.core for tests
# 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?
@XuesongYang @MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, or @okuchaiev
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} | null | null | null | **Describe the bug**
Aim: To Imporve the ASR model Accuracy with the help of LM.
ASR Model Used: Nvidia Nemo: Conformer-CTC-BPE.nemo
As per my understanding steps to implement ASR Language Modeling
1. Create KenLM Library.
2. Install Beam Search Decoder
3. Train N-Gram LM using ASR Model and KenLM Library. #output of step 3 is we have train N-Gram Model which can be used in beam search decoder on top of ASR model. (Decoder used PyCTCdecoder)
4. Evaluate by beam search decoder and N-Gram LM ##To tune parameter of beam search decoder.
My question is after performing this steps how can I used my trained N-Gram Model with beam search decoder on top of ASR model.
**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)**
- 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.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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Fix multi node fine-tuning script by adding rank based delay
**Collection**: [ASR]
# Changelog
- Add some delay to other ranks for the pretrained model call
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There was a change of the hybrid models and the default parameters of confidence don't work well for them on the test data.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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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There was a change of the hybrid models and the default parameters of confidence don't work well for them on the test data. There was also a bug in one parameter not being correctly renamed.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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# 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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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
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```python
# Add a code snippet demonstrating how to use this
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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There was a change of the hybrid models and the default parameters of confidence don't work well for them on the test data.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Update the docs a bit. Some key ASR info will be more prominent now.
**Collection**: Docs (General and ASR)
# Changelog
- Update README
- Update 'Introduction' in docs - contains more up-to-date code example
- Update 'asr/intro.rst' to explain some key things about ASR
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Associated to https://github.com/NVIDIA/NeMo/pull/7627. Add pleasefixme marker as an temporal solution.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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words_dict, word_ts_dict = {}, {}
hypotheses = asr_model.transcribe(paths2audio_files=[AUDIO_FILENAME], return_hypotheses=True) | {
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Apply Trainer Builders to more example scripts.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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This PR adds a multichannel mask estimator which can process flexible number of channels.
It also fixes some bugs related to PTL 2.0 and librosa version.
**Collection**: ASR
# Changelog
- added example config `beamforming_flex_channels.yaml`
- add `max_utts` `process_audio.py`
- updated `librosa.get_duration` to use `path`, since `filename` is deprecated
- bumped `librosa` requirement to `0.10.0` in `requirements_asr.py`
- added `MaskEstimatorFlexChannels` in `audio_modules.py`
- added various multichannel modules in `multichannel_modules.py`, including `ParametricMultichannelWienerFiler` and `ReferenceChannelEstimatorSNR`
- added unit tests for multichannel modules
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://github.com/NVIDIA/NeMo/blob/c1881f2105a0b65cb9513bcd85456b430ff81bb2/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py#L76
Should it be conformer_ctc_bpe config file instead of citrinet config file?
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} | null | null | null | **Describe the bug**
Using NeMo container 23.06 and the [nemo_gpt1.3B_fp16.nemo](https://huggingface.co/nvidia/nemo-megatron-gpt-1.3B/blob/main/) file located in the NVIDIA HuggingFace Hub, the following command fails to execute:
"python3 examples/nlp/language_modeling/megatron_export.py \
model_type=gpt \
onnx_model_file=nemo_gpt1.3B_fp16.onnx \
gpt_model_file=nemo_gpt1.3B_fp16.nemo"
With the error:
`TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data`
It seems NeMo is failing on the line:
`ModelPT.restore_from("/path/to/.nemo")`
**Steps/Code to reproduce bug**
1. Pull NeMo container [23.06](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags)
2. Pull the megatron-gpt1.3B model using `wget https://huggingface.co/nvidia/nemo-megatron-gpt-1.3B/resolve/main/nemo_gpt1.3B_fp16.nemo`
3. Execute:
python3 examples/nlp/language_modeling/megatron_export.py \
model_type=gpt \
onnx_model_file=nemo_gpt1.3B_fp16.onnx \
gpt_model_file=nemo_gpt1.3B_fp16.nemo
**Expected behavior**
The script runs and produces a valid `.onnx` file at the desired location.
**Environment overview (please complete the following information)**
- Fresh NeMo container 23.06
**Environment details**
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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
# 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?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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After the standard installation of NeMo, running `pytest tests/` in code repo will report the error:
```
______________________________________________________________________________________ ERROR collecting tests/collections/nlp/test_flash_attention.py ______________________________________________________________________________________
ImportError while importing test module '/home/robin/try1/NeMo/tests/collections/nlp/test_flash_attention.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
../../miniconda3/envs/nemo/lib/python3.10/importlib/__init__.py:126: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/collections/nlp/test_flash_attention.py:19: in <module>
from megatron.core import ModelParallelConfig
E ImportError: cannot import name 'ModelParallelConfig' from 'megatron.core' (/home/robin/miniconda3/envs/nemo/lib/python3.10/site-packages/megatron/core/__init__.py)
```
The `requirements/requirements_nlp.txt` fixed version of `megatron_core` to `0.2.0`. But in this version, the Megatron-LM doesn't even have `ModelParallelConfig`.
The fix is learning from `nemo/collections/nlp/models/language_modeling/megatron/gpt_model.py`. By using `ApexGuardDefaults` to replace `ModelParallelConfig`, the `pytest tests/collections/nlp/test_flash_attention.py` could pass without import error.
**Collection**: tests
# Changelog
- Fix import error of megatron.core in tests
# 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?
@MaximumEntropy @ericharper @ekmb @yzhang123 @VahidooX @vladgets @okuchaiev @XuesongYang
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When transformer_engine is enabled. NeMo doesn't pass the bias flag to the [AutocastTransformerLayer](https://github.com/NVIDIA/NeMo/blob/2cc09425aba3e9b3cfdba43a3188eaef58227055/nemo/collections/nlp/modules/common/megatron/transformer.py#L1014) in [ParallelTransformer](https://github.com/NVIDIA/NeMo/blob/2cc09425aba3e9b3cfdba43a3188eaef58227055/nemo/collections/nlp/modules/common/megatron/transformer.py#L846) so bias is always defaulted to True, despite the user trying to set it to False
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds support for Sliding Window Attention (SWA) in Flash Attention 2 to Nemo
**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 ?
Conversion script for HuggingFace Falcon checkpoint (1b-rw/7b-rw/7b/40b/180b) to NeMo format, as well as NeMo to HuggingFace format.
Support Falcon parallel attention and new decoder architecture by adding falcon_decoder_layer and falcon_spec,
these will make use of mcore's latest spec system.
Limitation: although the conversion script works for 1b-rw/7b-rw, Alibi is currently not supported in mcore GPT, so expect suboptimal generation results from these two models that use Alibi specifically. This limitation will NOT affect other 7b/40b/180b Falcon variants.
**Collection**: [Note which collection this PR will affect]
# Changelog
- add bi-directional HF <-> NeMo conversion script
- Falcon architecture implemented using mcore spec system and mcore layers.
# Usage
* See docstring in the conversion script on how to use.
# 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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It appears that Nemo is using the wrong PTL API for logging aggregate int counters:
```
$ grep '\.log(' | grep global
nemo/collections/nlp/models/language_modeling/megatron_finetune_model.py:
self.log('global_step', self.trainer.global_step, prog_bar=True, rank_zero_only=True, batch_size=1)
[...]
$ grep '\.log(' | grep global | grep -v float | wc -l
10
```
same happens to other counters like `consumed_samples`
Currently PTL emits a warning:
```
site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:212: UserWarning: You
called `self.log('consumed_samples', ...)` in your `training_step` but the value needs to be floating
point. Converting it to torch.float32.
pytorch_lightning/trainer/connectors/logger_connector/result.py:212: UserWarning: You called
`self.log('global_step', ...)` in your `training_step` but the value needs to be floating point.
Converting it to torch.float32.
```
Leading to both - an incorrect data type in the log and a wasteful reduction across ranks, even though all ranks should have the same counter value.
I first asked about this at PTL and their detailed answer on how to do it correctly is here:
https://github.com/Lightning-AI/lightning/issues/18739#issuecomment-1753263694
tldr: for non-reduced counters the call should be: `trainer.logger.log_metrics()` and not `self.log()`
Thank you. | {
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# 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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} | # What does this PR do ?
Adds a LR scheduler policy (LinearWarmupHoldDecayPolicy) that has following properties:
- Phase 1: linear warmup from 0 to the optimizer's learning rate
- Phase 2: constant phase
- Phase 3: linear decay to 'min_lr'
- Phase 4: constant phase at 'min_lr'
A visual representation of above is included in code.
**Collection**: N/A
# Changelog
- Added class for LR scheduler policy (LinearWarmupHoldDecayPolicy)
- Added unit test for above
# Usage
# Before your PR is "Ready for review"
**Pre checks**:
- [v] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [v] Did you write any new necessary tests?
- [v] 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**:
- [v] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I have the same problem as in #6869 with punctuation_capitalization_train_evaluate.py
UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.
Error executing job with overrides
NeMo: 1.21.0
OS version: Ubuntu 18.04
PyTorch version: '2.1.0+cu121'
Python version: Python 3.11.5 | {
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} | # What does this PR do ?
Adds a new publications section to the NeMo website
**Collection**: [Docs, Core, ASR, TTS, NLP]
# Changelog
- Add a pseudo "blog" to incorporate all our publications
- Update some internal overrides to support useful redirects
- Make the `Continue reading` button have a right arrow plus be surrounded by a box to ensure users note where to click to go to the next page
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
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 | MR https://github.com/NVIDIA/NeMo/pull/6683 added functionality to avoid collisions but in the case of bionemo the shared data on daraco OCI is structured in a way where every .csv has a .idx in the same directory. We would benefit by having a flag https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/text_memmap_dataset.py#L474 where if True use the non cascade logic and the default can be set to False. This would allow bionemo to reuse its previously done indices and not introduce errors that the cascade introduction was designed to fix.
Current with Nemo 1.20
index_mapping_dir = /data/uniref202104_esm2/uf50/train
fn = /data/uniref202104_esm2/uf50/train/x048.csv
idx_fn = /data/uniref202104_esm2/uf50/train/data/uniref202104_esm2/uf50/train/x048.csv.idx
Desired: Rest of Bionemo / nemo 1.18
index_mapping_dir = /data/uniref202104_esm2/uf50/train
fn = /data/uniref202104_esm2/uf50/train/x048.csv
idx_fn = /data/uniref202104_esm2/uf50/train/x048.csv.idx
BIONEMO_IMAGE="nvcr.io/nvidian/cvai_bnmo_trng/bionemo-esm2-001:dev"
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} | # What does this PR do ?
In this PR, it genialized the chat SFT dataset that it can use customized turn start/end tokens by using chat_prompt_tokens config. e.g.
```yaml
chat_prompt_tokens: # special tokens for the chat prompts, a dictionary of {token_type: token}. note that some tokenizer may combine the characters at the junction between {end_of_turn}{turn_start}. e.g. '<im end><im start>', the '><' sometimes is merged to be a single token. This is not supported, try to avoid
system_turn_start: '<extra_id_0>'
turn_start: '<extra_id_1>'
label_start: '<extra_id_2>'
end_of_turn: "\x0A" # \0x0A is '\n'
end_of_name: "\x0A" # \0x0A is '\n'
```
after this change, the LM is not required to have "extra_id" special tokens any more to use chat SFT dataset. In this PR, also expanded the unit test to cover more LM tokenizers.
Another feature added is to overwrite the prompt_template config with the chat prompt format. | {
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} | # What does this PR do ?
Currently when running VAD marblenet you sometimes randomly get the error:
```
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1514, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/submodules/jasper.py", line 351, in forward
self.update_masked_length(x.size(2), device=lens.device)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/submodules/jasper.py", line 374, in update_masked_length
self.lens, self.max_len = _masked_conv_init_lens(self.lens, max_len, self.max_len)
RuntimeError: _masked_conv_init_lens() Expected a value of type 'Tensor' for argument 'original_maxlen' but instead found type 'int'.
; ((Tensor, Tensor))
Cast error details: Unable to cast 64 to Tensor
terminate called without an active exception
```
This happens because in this line of code, it assigns self.max_len to an integer value:
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L378
Whereas in all other code paths it assigns it to a tensor:
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L321
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L374
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L231
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L234
**Collection**: [ASR]
# Changelog
- Convert max_len to tensor.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Fix an issue with Dockerfile not able to find correct value of ffmpeg.
**Collection**: [Core]
# Changelog
- Revert version of ffmpeg
- Fix rw mode of mount
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Merging https://github.com/NVIDIA/NeMo/pull/7462 to r1.21.0
Modify callbacks to save artifacts into dataset specific folders, instead of saving artifacts from all datasets into 1 folder. This helps organization when training on many datasets, and also handles the case when multiple datasets have files with the same name.
**Collection**: [TTS]
# Changelog
- Pass dataset names through TTS data loaders
- Save artifacts under epoch_{x}/{dataset_name}/
- Add initial logging for FastPitch and HiFi-GAN recipes
# 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?
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- [ ] Bugfix
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Fix an issue with Dockerfile not able to find correct value of ffmpeg.
**Collection**: [Core]
# Changelog
- Revert version of ffmpeg
- Fix rw mode of mount
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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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**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**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?
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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gives me
[NeMo W 2023-10-05 22:24:59 nemo_logging:393] megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.
I can't understand how can I install megatron-gpt?
There is an instruction how to install Apex. But I already have Apex.
Or maybe I need to install Transformer Engine?
Thanks in advance. | {
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python 3.11.5
torch 2.11.0
conda
I have installed MeCab via pip3 install mecab-python3.
creating build/lib.linux-x86_64-cpython-311
creating build/lib.linux-x86_64-cpython-311/MeCab
copying src/MeCab/cli.py -> build/lib.linux-x86_64-cpython-311/MeCab
copying src/MeCab/__init__.py -> build/lib.linux-x86_64-cpython-311/MeCab
running build_ext
error: [Errno 2] No such file or directory: 'mecab-config'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for mecab-python3
Running setup.py clean for mecab-python3
Successfully built nemo-toolkit
Failed to build mecab-python3
ERROR: Could not build wheels for mecab-python3, which is required to install pyproject.toml-based projects
Thanks in advance. | {
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} | # What does this PR do ?
When running below snippet for using FastPitch model trained from AISHELL-3 (the `aishell3.nemo` is from [here](https://github.com/RobinDong/nemo_models/blob/main/models/aishell3.nemo))
```python
from nemo.collections.tts.models import FastPitchModel
from nemo.collections.tts.models import HifiGanModel
spec_generator = FastPitchModel.restore_from("~/aishell3.nemo")
model = HifiGanModel.from_pretrained(model_name="tts_zh_hifigan_sfspeech")
# Generate audio
import soundfile as sf
import torch
with torch.no_grad():
parsed = spec_generator.parse("这些新一代的中央处理器不只效能惊人。")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=1)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
if isinstance(audio, torch.Tensor):
audio = audio.to('cpu').numpy()
# Save the audio to disk in a file called speech.wav
sf.write("speech.wav", audio.T, 22050, format='WAV')
```
The `jieba` will segment the input text to
```python
['这些', '新一代', '的', '中央处理器', '不', '只', '效能', '惊人', '。']
```
and the phoneme for '只' is "zhi1", which is incorrect in this context.
But using `pkuseg`, the input text will be segmented to
```python
['这些', '新一代', '的', '中央', '处理器', '不只', '效能', '惊人', '。']
```
and the phonemes for '只' is "zhi3", which is correct.
# Changelog
- Add a new text-segmentation-library to make better TTS quality for the Chinese language
# Usage
Setting `word_segmenter` from `jieba` to `pkuseg` in the configuration file `fastpitch_config.yaml`:
```python
...
spec_generator = FastPitchModel.restore_from("~/aishell3.nemo", override_config_path="fastpitch_config.yaml")
...
```
The TTS sample that use `jieba`: [before.zip](https://github.com/NVIDIA/NeMo/files/12817139/before.zip)
The TTS sample that use `pkuseg`: [after.zip](https://github.com/NVIDIA/NeMo/files/12817141/after.zip)
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
@XuesongYang @blisc | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR adds group-residual vector quantizer as in http://arxiv.org/abs/2305.02765
**Collection**: TTS
# Changelog
- Added group-RVQ
- Added unit tests for RVQ
# Usage
To use, add the following in the config file
```yaml
vector_quantizer:
_target_: nemo.collections.tts.modules.encodec_modules.GroupResidualVectorQuantizer
num_codebooks: 4 # total number of codebooks
num_groups: 2 # number of groups
codebook_dim: 128 # dimension of the audio encoder output
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
fixed trainer.devices and trainer.accelerator.
**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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add fc large LS only trained models to pretrained list and docs
**Collection**: ASR
# Changelog
- Add to pretrained list
# Usage
```python
from nemo.collections.asr.models import ASRModel
model = ASRModel.from_pretrained('stt_en_fastconformer_transducer_large_ls')
# or
model = ASRModel.from_pretrained('stt_en_fastconformer_ctc_large_ls')
```
# 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?
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- [ ] Bugfix
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- Fixes a small bug with GPT PEFT evaluation
- Adds BCM support for running evaluation
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix issue where peft weights are not loaded for distributed checkpoints
**Collection**: NLP
# Changelog
- Load peft weights in restore_from instead of _load_state_dict_from_disk if distributed checkpoint is used
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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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**:
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} | # What does this PR do ?
Currently when running VAD marblenet you sometimes randomly get the error:
```
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1514, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/submodules/jasper.py", line 351, in forward
self.update_masked_length(x.size(2), device=lens.device)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/submodules/jasper.py", line 374, in update_masked_length
self.lens, self.max_len = _masked_conv_init_lens(self.lens, max_len, self.max_len)
RuntimeError: _masked_conv_init_lens() Expected a value of type 'Tensor' for argument 'original_maxlen' but instead found type 'int'.
; ((Tensor, Tensor))
Cast error details: Unable to cast 64 to Tensor
terminate called without an active exception
```
This happens because in this line of code, it assigns self.max_len to an integer value:
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L378
Whereas in all other code paths it assigns it to a tensor:
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L321
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L374
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L231
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/submodules/jasper.py#L234
**Collection**: [ASR]
# Changelog
- Convert max_len to tensor.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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} | # What does this PR do ?
Fixes RNN-T greedy decoding: writes blank alignment and confidence immediately.
Fixes https://github.com/NVIDIA/NeMo/issues/7484
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# 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)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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 ?
Include Modular SpeechLLM implementation for Sept. 2023 submission (SALM). The implementation integrates a pretrained FC and a frozen LLM, and can do ASR, AST, and in-context learning for keyword boosting.
The current implementation is based off of r1.20.0 release.
**Collection**: multimodal
# Usage
refer to `workspace/run_sft_audio_lm.py`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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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Include Modular SpeechLLM implementation for Sept. 2023 submission (SALM). The implementation integrates a pretrained FC and a frozen LLM, and can do ASR, AST, and in-context learning for keyword boosting.
The current implementation is based off of r1.20.0 release.
**Collection**: multimodal
# Usage
refer to `workspace/run_sft_audio_lm.py`
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Implements Top-P sampling, also known as nucleus sampling.
I put it in with the Top-K sampler, as I felt like this kept the code cleaner – at the cost of an `if` statement in the decoding loop. I could move it to its own class as well, but since a lot of people use Top-K and Top-P together, we'd probably want that new class to support both anyway.
Let me know if there are any changes needed :)
**Collection**: NLP, ASR
# Changelog
- Add the parameter `top_p: Optional[float]` to the `nemo.collections.nlp.modules.common.transformer.transformer_generators.TopKSequenceGenerator` and the `nemo.collections.asr.modules.transformer.transformer_generators.TopKSequenceGenerator` classes
- This allows using Top-P sampling in combination with Top-K sampling
- Adds parameters for `top_p` to the two places in code where these classes are called
# Usage
```python3
from nemo.collections.nlp.models.machine_translation import MTEncDecModel
from nemo.collections.nlp.modules.common.transformer.transformer_generators import TopKSequenceGenerator
model = MTEncDecModel.from_pretrained('nmt_en_es_transformer12x2')
top_k_and_p_search = TopKSequenceGenerator(
model.beam_search.embedding,
model.beam_search.decoder,
model.beam_search.log_softmax,
pad=model.beam_search.pad,
bos=model.beam_search.bos,
eos=model.beam_search.eos,
beam_size=5,
top_p=0.75,
)
s = 'Among the various methods of peeling a cucumber, I find only a few to be worth my precious time.'
print(model.translate([s]))
# ['Entre los diversos métodos de pelar un pepino, encuentro solo unos pocos que valen mi precioso tiempo.']
model.beam_search = top_k_and_p_search
print(model.translate([s]))
# Varies, e.g.,
# ['Entre los diferentes métodos de pelar un pepino, solo encuentro que unos pocos valen mi valioso tiempo.']
```
# 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): No
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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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] | null | null | 2023-10-04T12:11:52 | 2023-10-23T19:25:09 | 2023-10-23T19:25:09 | NONE | {
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} | null | null | null | **Describe the bug**
We observed a discrepancy between numerical outputs of the NeMo and TE implementations of GPT. It seems to come from rotary embeddings not being passed to the TE module.
**Steps/Code to reproduce bug**
In https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/megatron/transformer.py#L1531 , the `rotary_pos_emb` argument is missing from the forward call to TE module.
**Expected behavior**
Same results with and without TE for GPT arch with rotary embeddings.
**Additional context**
Same issue seems present with checkpointing. | {
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