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} | Signed-off-by: Eduard Maghakyan <[email protected]>
# What does this PR do ?
Fix missing import in a non-CUDA environment.
**Collection**: ASR collection
# Changelog
Fix missing import when running in a non-CUDA environment.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | null | null | null | Tried to export quartznet encoder with onnx.
Compered inference result with the same input tensor.
Found unexplainably high deviations between the original and exported model inference results.
```
import onnx
from onnx import checker
import onnxruntime
import torch
import nemo
import nemo.collections.asr as nemo_asr
import librosa
import numpy as np
quartznet = nemo.collections.asr.models.EncDecCTCModel.restore_from('QuartzNet5x5LS-En.nemo').to('cpu').eval()
audio, sample_rate = librosa.load('/an4/wav/an4_clstk/mgah/cen2-mgah-b.wav', sr=16e3)
audio_torch = torch.Tensor(audio[np.newaxis])
audio_torch_len = torch.Tensor([audio_torch.shape[1]])
processed_signal, processed_signal_length = quartznet.preprocessor(input_signal=audio_torch, length=audio_torch_len)
encoded_direct = quartznet.encoder(audio_signal=processed_signal, length=processed_signal_length)
quartznet.encoder.export('qn_enc.onnx')
ort_session_encoder = onnxruntime.InferenceSession('qn_enc.onnx')
ort_inputs_encoder = {ort_session_encoder.get_inputs()[0].name: processed_signal.numpy(),
ort_session_encoder.get_inputs()[1].name: processed_signal_length.numpy()}
ologits_enc = ort_session_encoder.run(None, ort_inputs_encoder)
np.testing.assert_allclose(encoded_direct[0].detach().numpy(), ologits_enc[0], rtol=1e-02, atol=1e-02)
# raises:
# AssertionError:
# Not equal to tolerance rtol=0.01, atol=0.01
# Mismatched elements: 32002 / 139264 (23%)
# Max absolute difference: 5.5700774
# Max relative difference: 6858.9634
```
Expected the differences to be much smaller (less then 1e-4)
If i try to export the whole model (includes encoder + decoder), as suggested in the comments, the differences become even bigger :
```
import onnx
from onnx import checker
import onnxruntime
import torch
import nemo
import nemo.collections.asr as nemo_asr
import librosa
import numpy as np
quartznet = nemo.collections.asr.models.EncDecCTCModel.restore_from('QuartzNet5x5LS-En.nemo').to('cpu').eval()
audio, sample_rate = librosa.load('/an4/wav/an4_clstk/mgah/cen2-mgah-b.wav', sr=16e3)
audio_torch = torch.Tensor(audio[np.newaxis])
audio_torch_len = torch.Tensor([audio_torch.shape[1]])
processed_signal, processed_signal_length = quartznet.preprocessor(input_signal=audio_torch, length=audio_torch_len)
encoded_direct = quartznet.encoder(audio_signal=processed_signal, length=processed_signal_length)
decoder_direct = quartznet.decoder(encoder_output=encoded_direct[0])
quartznet.export('qn.onnx')
ort_session = onnxruntime.InferenceSession('qn.onnx')
ort_inputs = {ort_session.get_inputs()[0].name: processed_signal.numpy()}
ologits = ort_session.run(None, ort_inputs)
np.testing.assert_allclose(decoder_direct.detach().numpy(), ologits[0], rtol=1e-02, atol=1e-02)
#raises:
#AssertionError:
#Not equal to tolerance rtol=0.01, atol=0.01
#Mismatched elements: 1593 / 3944 (40.4%)
#Max absolute difference: 12.401756
#Max relative difference: 47469.176
```
package versions:
torch 1.13.1
onnxruntime 1.12.1
nemo-toolkit 1.13.0
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} | # What does this PR do ?
Fix transribe function in EncDecHybridRNNTCTCModel.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add variables for transcribe invocation if use_rnnt_decoder.
- Change name of variable (logits_len to encoded_len).
- Change ErrorValue to ValueError in change_decoding_strategy of EncDecHybridRNNTCTCBPEModel.
# 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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} | null | null | null | **Set Precision 16 for fast training**
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0
and it gives
ZeroDivisionError: float division by zero
**Pytorch Lighting Parameters**
devices=-1, # number of GPUs, -1 would use all available GPUs
strategy='ddp',
amp_level='O1',
precision=16,
amp_backend='apex'
**Learning Rate 1**
error
`File "/usr/local/lib/python3.8/dist-packages/apex/amp/scaler.py", line 176, in unscale_with_stashed
out_scale/grads_have_scale, # 1./scale,
ZeroDivisionError: float division by zero
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.`
**Additional context**
- Nemo Docker version nemo:22.12
- Multi GPU
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} | Signed-off-by: Dima Rekesh <[email protected]>
# What does this PR do ?
adds fast conformer config files and docs
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
docs/source/asr/models.rst (added entry for Fast Conformer)
examples/asr/conf/conformer/fastconformer/fastconformer_ctc_bpe.yaml (created)
examples/asr/conf/conformer/fastconformer/fastconformer_transducer_bpe.yaml (created)
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* 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]
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- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
With the changes in https://github.com/NVIDIA/NeMo/pull/5684, I observe that GPT always sets `overlap_param_sync=True`. This PR allows the user to manually override that option, which now `False` by default.
**Collection**: NLP
# Changelog
- Fix bug where GPT always enabled distopt overlapped param sync
# Usage
When using the `distributed_fused_adam` optimizer, set `overlap_param_sync: True` in the optimizer config.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/pull/5684
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Adds a paragraph with some basic information about the SSL FastPitch model. Will add more as the model is finalized and the paper released.
**Collection**: TTS
**PR Type**:
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- [ ] Bugfix
- [x] Documentation
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} | Signed-off-by: Abhinav Khattar <[email protected]>
# What does this PR do ?
Fix space issue in prompt
**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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} | # What does this PR do ?
This PR adds a convolution-invariant SDR loss for audio-to-audio tasks.
Reference:
[C. Boeddeker et al., Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation, 2021](https://arxiv.org/abs/2011.15003)
**Collection**: ASR
# Changelog
- Added CI-SDR loss
- Updated docstrings and simplified implementation of SDRLoss
# 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.
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} | # What does this PR do ?
This PR adds the following functionality for Megatron GPT models.
1. Disable biases.
2. Add different activation functions - geglu, swiglu, reglu, squared relu
3. Different transformer layer configs - pre-ln, post-ln, normformer.
4. RoPE
5. Disable both hidden and attention dropout.
6. RMSNorm Normalization
7. Untie embedding and output layer.
**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
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# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Fix Summary name {} is illegal; using {} instead warning
**Collection**: [ASR]
# Changelog
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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} | # What does this PR do ?
Update documentation for VITS
**Collection**: [TTS]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/5948
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} | null | null | null | **Describe the bug**
When running multi-GPU training for TTS model VITS with standard config for LJSpeech dataset, the training stops after global step 97000 with an exception. Also there is a warning at the beginning of the training about `Grad strides do not match bucket view strides`.
```
... <snip>
[NeMo W 2023-02-08 20:43:43 nemo_logging:349] /home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/autograd/__init__.py:197: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
grad.sizes() = [1, 9, 96], strides() = [22176, 96, 1]
bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:325.)
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
... <snip>
Epoch 245: 100%|██████████| 206/206 [01:56<00:00, 1.77it/s, v_num=3-0Epoch 245, global step 97908: 'loss_gen_all' was not in top 3
Epoch 246: 67%|██████▋ | 139/206 [01:18<00:37, 1.76it/s, v_num=3-00]Error executing job with overrides: []
Traceback (most recent call last):
File "/home/anna/tts/NeMo/examples/tts/vits.py", line 30, in main
trainer.fit(model)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1103, in _run
results = self._run_stage()
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1182, in _run_stage
self._run_train()
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1205, in _run_train
self.fit_loop.run()
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/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 "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 213, in advance
batch_output = self.batch_loop.run(kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 90, in advance
outputs = self.manual_loop.run(kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/manual_loop.py", line 110, in advance
training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1485, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 351, in training_step
return self.model(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1040, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1000, in _run_ddp_forward
return module_to_run(*inputs[0], **kwargs[0])
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 98, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/home/anna/tts/NeMo/nemo/utils/model_utils.py", line 362, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/models/vits.py", line 202, in training_step
audio_pred, l_length, attn, ids_slice, text_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q) = self.net_g(
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/modules/vits_modules.py", line 876, in forward
l_length = self.dp(x, text_mask, w, g=g)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/modules/vits_modules.py", line 425, in forward
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/modules/vits_modules.py", line 352, in forward
x1, logabsdet = piecewise_rational_quadratic_transform(
File "/home/anna/tts/NeMo/nemo/collections/tts/helpers/splines.py", line 313, in piecewise_rational_quadratic_transform
outputs, logabsdet = spline_fn(
File "/home/anna/tts/NeMo/nemo/collections/tts/helpers/splines.py", line 361, in unconstrained_rational_quadratic_spline
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
File "/home/anna/tts/NeMo/nemo/collections/tts/helpers/splines.py", line 394, in rational_quadratic_spline
if torch.min(inputs) < left or torch.max(inputs) > right:
RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Error executing job with overrides: []
Traceback (most recent call last):
File "examples/tts/vits.py", line 30, in main
trainer.fit(model)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 88, in launch
return function(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1103, in _run
results = self._run_stage()
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1182, in _run_stage
self._run_train()
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1205, in _run_train
self.fit_loop.run()
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/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 "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 213, in advance
batch_output = self.batch_loop.run(kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 90, in advance
outputs = self.manual_loop.run(kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/manual_loop.py", line 110, in advance
training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1485, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 351, in training_step
return self.model(*args, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1040, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1000, in _run_ddp_forward
return module_to_run(*inputs[0], **kwargs[0])
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 98, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/home/anna/tts/NeMo/nemo/utils/model_utils.py", line 362, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/models/vits.py", line 202, in training_step
audio_pred, l_length, attn, ids_slice, text_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q) = self.net_g(
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/modules/vits_modules.py", line 876, in forward
l_length = self.dp(x, text_mask, w, g=g)
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/modules/vits_modules.py", line 425, in forward
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
File "/home/anna/tts/NeMo/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/anna/tts/NeMo/nemo/collections/tts/modules/vits_modules.py", line 352, in forward
x1, logabsdet = piecewise_rational_quadratic_transform(
File "/home/anna/tts/NeMo/nemo/collections/tts/helpers/splines.py", line 313, in piecewise_rational_quadratic_transform
outputs, logabsdet = spline_fn(
File "/home/anna/tts/NeMo/nemo/collections/tts/helpers/splines.py", line 361, in unconstrained_rational_quadratic_spline
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
File "/home/anna/tts/NeMo/nemo/collections/tts/helpers/splines.py", line 394, in rational_quadratic_spline
if torch.min(inputs) < left or torch.max(inputs) > right:
RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.
```
**Steps/Code to reproduce bug**
Training a LJSpeech model with standard configuration and 2 GPU's. Training with one GPU doesn't show the behaviour.
**Expected behavior**
The training runs without exceptions
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: from source
**Environment details**
- Ubuntu 20.04
- PyTorch 1.13.1
- Python 3.8.10
**Additional context**
2 x NVIDIA A6000 GPU's
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} | null | null | null | Hi team,
I am using Riva with model TTS FastPitch+HifiGan. I see in the command
riva-build speech_synthesis has parameters:
[--denoiser.max_sequence_idle_microseconds DENOISER.MAX_SEQUENCE_IDLE_MICROSECONDS]
[--denoiser.max_batch_size DENOISER.MAX_BATCH_SIZE]
[--denoiser.min_batch_size DENOISER.MIN_BATCH_SIZE]
[--denoiser.opt_batch_size DENOISER.OPT_BATCH_SIZE]
[--denoiser.preferred_batch_size DENOISER.PREFERRED_BATCH_SIZE]
[--denoiser.batching_type DENOISER.BATCHING_TYPE]
[--denoiser.preserve_ordering DENOISER.PRESERVE_ORDERING]
[--denoiser.instance_group_count DENOISER.INSTANCE_GROUP_COUNT]
[--denoiser.max_queue_delay_microseconds DENOISER.MAX_QUEUE_DELAY_MICROSECONDS]
[--denoiser.optimization_graph_level DENOISER.OPTIMIZATION_GRAPH_LEVEL]
[--denoiser.fade_length DENOISER.FADE_LENGTH]
when I don’t set or set the parameters as above, I saw in the config.pbtxt file in the models/tts_postprocessor the parameter use_denoiser=False
Hence any way to use denoiser in Riva! | {
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Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
```
added backward compatibility and fixed relevant unit tests.
```
**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.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/5890
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} | Signed-off-by: Abhinav Khattar <[email protected]>
# What does this PR do ?
Fix space issue in prompt
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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## Who can review?
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- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
```
added backward compatibility and fixed relevant unit tests.
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/5890
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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**:
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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
- [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.
**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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add LDDL data loader support to Megatron Bert
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: Dongwoo Arthur Kim <[email protected]>
# What does this PR do ?
Removing `for statement` and optimizing `getKneighborsConnections`
# Changelog
- https://github.com/NVIDIA/NeMo/pull/5976/files#diff-4188601503d6fb6ce4e990e5148c6c02e57660f95043a149b69b485d492c68b6L314-R318
- Just simple optimized operation
# Usage
Nothing to be different as before.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
This is a part of ASR, diarizer.
@titu1994 @redoctopus, @jbalam-nv, or @okuchaiev
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} | null | null | null | I am trying to follow the tutorial available for VAD using the below snippet. However, I am not available to load MarbleNet model.
```
import nemo
import nemo.collections.asr as nemo_asr
vad_model = nemo_asr.models.EncDecClassificationModel.from_pretrained('vad_marblenet')
```
Following is the traceback:
```
Error(s): Error in call to target 'nemo.collections.asr.modules.audio_preprocessing.SpectrogramAugmentation':
AttributeError('/usr/local/cuda/nvvm/lib64/libnvvm.so.3.1.0: undefined symbol: nvvmLazyAddModuleToProgram')
Traceback (most recent call last):
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target
return _target_(*args, **kwargs)
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/collections/asr/modules/audio_preprocessing.py", line 512, in __init__
if use_numba_spec_augment and numba_utils.numba_cuda_is_supported(__NUMBA_MINIMUM_VERSION__):
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/utils/numba_utils.py", line 129, in numba_cuda_is_supported
cuda_available = cuda.is_available()
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/device_init.py", line 60, in is_available
return driver_is_available and nvvm.is_available()
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/cudadrv/nvvm.py", line 56, in is_available
NVVM()
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/cudadrv/nvvm.py", line 135, in __new__
func = getattr(inst.driver, name)
File "/opt/conda/envs/VAD_test/lib/python3.8/ctypes/__init__.py", line 386, in __getattr__
func = self.__getitem__(name)
File "/opt/conda/envs/VAD_test/lib/python3.8/ctypes/__init__.py", line 391, in __getitem__
func = self._FuncPtr((name_or_ordinal, self))
AttributeError: /usr/local/cuda/nvvm/lib64/libnvvm.so.3.1.0: undefined symbol: nvvmLazyAddModuleToProgram
```
```
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/common.py", line 485, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/collections/asr/models/classification_models.py", line 443, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/collections/asr/models/classification_models.py", line 63, in __init__
self.spec_augmentation = ASRModel.from_config_dict(self._cfg.spec_augment)
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/common.py", line 468, in from_config_dict
instance = hydra.utils.instantiate(config=config)
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
return instantiate_node(
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node
return _call_target(_target_, partial, args, kwargs, full_key)
File "/opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target
raise InstantiationException(msg) from e
hydra.errors.InstantiationException: Error in call to target 'nemo.collections.asr.modules.audio_preprocessing.SpectrogramAugmentation':
AttributeError('/usr/local/cuda/nvvm/lib64/libnvvm.so.3.1.0: undefined symbol: nvvmLazyAddModuleToProgram')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py:92, in _call_target(_target_, _partial_, args, kwargs, full_key)
91 try:
---> 92 return _target_(*args, **kwargs)
93 except Exception as e:
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/collections/asr/modules/audio_preprocessing.py:512, in SpectrogramAugmentation.__init__(self, freq_masks, time_masks, freq_width, time_width, rect_masks, rect_time, rect_freq, rng, mask_value, use_numba_spec_augment)
511 # Check if numba is supported, and use a Numba kernel if it is
--> 512 if use_numba_spec_augment and numba_utils.numba_cuda_is_supported(__NUMBA_MINIMUM_VERSION__):
513 logging.info('Numba CUDA SpecAugment kernel is being used')
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/utils/numba_utils.py:129, in numba_cuda_is_supported(min_version)
128 try:
--> 129 cuda_available = cuda.is_available()
130 if cuda_available:
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/device_init.py:60, in is_available()
58 pass
---> 60 return driver_is_available and nvvm.is_available()
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/cudadrv/nvvm.py:56, in is_available()
55 try:
---> 56 NVVM()
57 except NvvmSupportError:
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/cudadrv/nvvm.py:143, in NVVM.__init__(self)
142 def __init__(self):
--> 143 ir_versions = self.get_ir_version()
144 self._majorIR = ir_versions[0]
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/numba/cuda/cudadrv/nvvm.py:168, in NVVM.get_ir_version(self)
167 minorDbg = c_int()
--> 168 err = self.nvvmIRVersion(byref(majorIR), byref(minorIR),
169 byref(majorDbg), byref(minorDbg))
170 self.check_error(err, 'Failed to get IR version.')
AttributeError: 'NVVM' object has no attribute 'nvvmIRVersion'
The above exception was the direct cause of the following exception:
InstantiationException Traceback (most recent call last)
Cell In[5], line 1
----> 1 vad_model = nemo_asr.models.EncDecClassificationModel.from_pretrained('vad_marblenet')
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/common.py:852, in Model.from_pretrained(cls, model_name, refresh_cache, override_config_path, map_location, strict, return_config, trainer, save_restore_connector)
846 else:
847 # NGC source
848 class_, nemo_model_file_in_cache = cls._get_ngc_pretrained_model_info(
849 model_name=model_name, refresh_cache=refresh_cache
850 )
--> 852 instance = class_.restore_from(
853 restore_path=nemo_model_file_in_cache,
854 override_config_path=override_config_path,
855 map_location=map_location,
856 strict=strict,
857 return_config=return_config,
858 trainer=trainer,
859 save_restore_connector=save_restore_connector,
860 )
861 return instance
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/modelPT.py:330, in ModelPT.restore_from(cls, restore_path, override_config_path, map_location, strict, return_config, save_restore_connector, trainer)
327 app_state.model_restore_path = restore_path
329 cls.update_save_restore_connector(save_restore_connector)
--> 330 instance = cls._save_restore_connector.restore_from(
331 cls, restore_path, override_config_path, map_location, strict, return_config, trainer
332 )
333 if isinstance(instance, ModelPT):
334 instance._save_restore_connector = save_restore_connector
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py:235, in SaveRestoreConnector.restore_from(self, calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer)
210 """
211 Restores model instance (weights and configuration) into .nemo file
212
(...)
231 An instance of type cls or its underlying config (if return_config is set).
232 """
233 # Get path where the command is executed - the artifacts will be "retrieved" there
234 # (original .nemo behavior)
--> 235 loaded_params = self.load_config_and_state_dict(
236 calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer,
237 )
238 if not isinstance(loaded_params, tuple):
239 return loaded_params
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py:158, in SaveRestoreConnector.load_config_and_state_dict(self, calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer)
156 # get the class
157 calling_cls._set_model_restore_state(is_being_restored=True, folder=tmpdir)
--> 158 instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
159 instance = instance.to(map_location)
160 # add load_state_dict override
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/common.py:507, in Serialization.from_config_dict(cls, config, trainer)
505 if prev_error:
506 logging.error(prev_error)
--> 507 raise e
509 if not hasattr(instance, '_cfg'):
510 instance._cfg = config
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/common.py:499, in Serialization.from_config_dict(cls, config, trainer)
497 accepts_trainer = Serialization._inspect_signature_for_trainer(cls)
498 if accepts_trainer:
--> 499 instance = cls(cfg=config, trainer=trainer)
500 else:
501 instance = cls(cfg=config)
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/collections/asr/models/classification_models.py:443, in EncDecClassificationModel.__init__(self, cfg, trainer)
440 if cfg.get("is_regression_task", False):
441 raise ValueError(f"EndDecClassificationModel requires the flag is_regression_task to be set as false")
--> 443 super().__init__(cfg=cfg, trainer=trainer)
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/collections/asr/models/classification_models.py:63, in _EncDecBaseModel.__init__(self, cfg, trainer)
60 super().__init__(cfg=cfg, trainer=trainer)
62 if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None:
---> 63 self.spec_augmentation = ASRModel.from_config_dict(self._cfg.spec_augment)
64 else:
65 self.spec_augmentation = None
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/nemo/core/classes/common.py:468, in Serialization.from_config_dict(cls, config, trainer)
465 # Hydra 1.x API
466 elif '_target_' in config and _HAS_HYDRA:
467 # regular hydra-based instantiation
--> 468 instance = hydra.utils.instantiate(config=config)
469 else:
470 instance = None
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py:222, in instantiate(config, *args, **kwargs)
219 _convert_ = config.pop(_Keys.CONVERT, ConvertMode.NONE)
220 _partial_ = config.pop(_Keys.PARTIAL, False)
--> 222 return instantiate_node(
223 config, *args, recursive=_recursive_, convert=_convert_, partial=_partial_
224 )
225 elif OmegaConf.is_list(config):
226 # Finalize config (convert targets to strings, merge with kwargs)
227 config_copy = copy.deepcopy(config)
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py:339, in instantiate_node(node, convert, recursive, partial, *args)
334 value = instantiate_node(
335 value, convert=convert, recursive=recursive
336 )
337 kwargs[key] = _convert_node(value, convert)
--> 339 return _call_target(_target_, partial, args, kwargs, full_key)
340 else:
341 # If ALL or PARTIAL non structured, instantiate in dict and resolve interpolations eagerly.
342 if convert == ConvertMode.ALL or (
343 convert == ConvertMode.PARTIAL
344 and node._metadata.object_type in (None, dict)
345 ):
File /opt/conda/envs/VAD_test/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py:97, in _call_target(_target_, _partial_, args, kwargs, full_key)
95 if full_key:
96 msg += f"\nfull_key: {full_key}"
---> 97 raise InstantiationException(msg) from e
InstantiationException: Error in call to target 'nemo.collections.asr.modules.audio_preprocessing.SpectrogramAugmentation':
AttributeError("'NVVM' object has no attribute 'nvvmIRVersion'")
```
**Environment details**
python 3.8.13
pytorch-lightning 1.8.6
torch 1.13.1
torchmetrics 0.11.1
nemo-toolkit 1.15.0
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# What does this PR do ?
Fix Summary name {} is illegal; using {} instead warning
**Collection**: [ASR]
# Changelog
- replace @ with _
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
I am getting different diarization results when I modify the codec and bitrate of a video and then convert that into an audio file.
**Steps/Code to reproduce bug**
Take any yt video
Download in 2 different resolutions(1080p and 480p)
Convert into audio file and run speaker diarization.
Now, convert the lower res video into MPEG4 format using ffmpeg -i <inputfile> <outputfile>
Results for this will change
Manipulating codec will also change results
**Expected behavior**
Same results always or always increasing bitrate should give better resuts
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} | Added a script for evaluating audio-to-audio metrics for a manifest file (audio_to_audio_eval.py)
Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
This PR adds a script for evaluation of audio-to-audio metrics.
It is similar to `speech_to_text_eval.py`
This scripts depends on the `process_audio.py` script and inherits all its arguments.
**Collection**: ASR
# Changelog
- Added `audio_to_audio_eval.py`
- Minor change in `proces_audio`
- Added an option to `get_full_path` to specify `data_dir` directly instead of using `dirname(manifest_file)`
# Usage
* You can potentially add a usage example below
## To score a dataset with a manifest file that contains the input audio which needs to be processed and target audio
```
python audio_to_audio_eval.py \
model_path=null \
pretrained_model=null \
dataset_manifest=<Mandatory: path to a dataset manifest file> \
output_dir=<Optional: Directory where processed audio will be saved> \
processed_channel_selector=<Optional: list of channels to select from the processed audio file> \
target_key=<Optional: key for the target audio in the dataset manifest. Default: target_audio_filepath> \
target_channel_selector=<Optional: list of channels to select from the target audio file> \
metrics=<Optional: list of metrics to evaluate. Defaults to [sdr,estoi]>
batch_size=32 \
amp=True
```
## To score a manifest file which has been previously processed and contains both processed audio and target audio
```
python audio_to_audio_eval.py \
dataset_manifest=<Mandatory: path to a dataset manifest file> \
processed_key=<Optional: key for the target audio in the dataset manifest. Default: processed_audio_filepath>
processed_channel_selector=<Optional: list of channels to select from the processed audio file> \
target_key=<Optional: key for the target audio in the dataset manifest. Default: target_audio_filepath> \
target_channel_selector=<Optional: list of channels to select from the target audio file> \
metrics=<Optional: list of metrics to evaluate. Defaults to [sdr,estoi]>
batch_size=32 \
amp=True
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Dima Rekesh <[email protected]>
# What does this PR do ?
adds fast conformer config files and docs
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
docs/source/asr/models.rst (added entry for Fast Conformer)
examples/asr/conf/conformer/fastconformer/fastconformer_ctc_bpe.yaml (created)
examples/asr/conf/conformer/fastconformer/fastconformer_transducer_bpe.yaml (created)
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Update Fastpitch energy bug
**Collection**: TTS
# Changelog
- Update Fastpitch w/ energy during training
# Usage
**PR Type**:
- [x] Bugfix
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} | Signed-off-by: ericharper <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"merged_at": "2023-02-09T08:26:43"
} | Signed-off-by: Evgeniy Shabalin <[email protected]>
# What does this PR do ?
Added LJSpeech model to list_available_models
**Collection**: [TTS]
# Changelog
- Add specific line by line info of high level changes in this PR.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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I would like to fine-tune the vad multilingual marblenet model using my custom speaker diarization dataset. At the moment, I read the following tutorials:
1. https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/06_Voice_Activiy_Detection.ipynb#scrollTo=Z7FxGpSE_FyM
2. https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb
3. https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/asr/01_ASR_with_NeMo.ipynb
However, I am not sure yet how to adapt these tutorials to the marblenet model. Most of my questions are related to the data preparation and preprocessing step:
1. I read in the marblenet paper that the nn takes a fixed length of 64 dimensional melfilterbank features from 0.63s length segments. Does this mean that I should extract speech and non speech segments of length 0.63s?
2. Should the labels be: "speech" and "non-speech"?
3. Is there any script that takes care of this data preparation and preprocessing part? Or is there any new tutorial similar to the ASR tutorial?
4. Can I find the preprocessing tools such as SpecAugment or SpecCutOut in the nemo toolkit?
Thank you in advance! | {
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} | null | null | null | **Describe the bug**
The hyperlink to the examples page is unfortunately pointed at update_readme_into branch(which does not exist anymore) instead of the main branch
**Steps/Code to reproduce bug**
Click on the hyperlink in
For advanced users that want to train NeMo models from scratch or finetune existing NeMo models we have a full suite of [example scripts](https://github.com/NVIDIA/NeMo/tree/update_readme_into/examples) that support multi-GPU/multi-node training.
**Expected behavior**
Should link to (https://github.com/NVIDIA/NeMo/examples) i think | {
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} | # What does this PR do ?
Adds config files for Fast Conformer
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Two config files and description of Fast Conformer in models.html
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Update the papers in PUBLICATIONS.md
**Collection**: [Docs]
# Changelog
- Adds papers from ASR, TTS, TN/ITN, and SD
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**Pre checks**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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# What does this PR do ?
Update the font used in the NeMo website.
**Collection**: [Docs]
# Changelog
- Use NVIDIA Sans font throughout
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
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# What does this PR do ?
```
remove this function since the use case is not fully clear right now.
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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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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# What does this PR do ?
```
AudioToCharWithPriorAndPitchDataset is deprecated. Please choose TTSDataset.
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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when I run the following tutorial on my Windows 10 PC (Python 3.10.5): https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/ASR_with_SpeakerDiarization.ipynb
Then I get at the step "Run diarization with the extracted word timestamps":
`diar_hyp, diar_score = asr_diar_offline.run_diarization(cfg, word_ts_hyp)`
`print("Diarization hypothesis output: \n", diar_hyp['an4_diarize_test'])`
An error message stating that the name of the automatically created output folder is too long:
`vad: 100%|██████████| 1/1 [00:01<00:00, 1.42s/it]
[NeMo I 2023-02-07 21:01:01 clustering_diarizer:258] Converting frame level prediction to speech/no-speech segment in start and end times format.
FileNotFoundError: [WinError 206] The filename or extension is too long: 'C:\\Users\\XXXXX\\Desktop\\Audio_files\\data\\vad_outputs\\table_output_tmp_window_length_in_sec0.63_shift_length_in_sec0.01_smoothingFalse_overlap0.5_onset0.9_offset0.5_pad_onset0_pad_offset0_min_duration_on0_min_duration_off0.6_filter_speech_firstTrue_'`
Is there a way to alter the name of this output folder?
With Google Colab in conjunction with Google Drive, the long name of the output folder does not matter and everything is working fine.
Thank you in advance for your help!
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Fixes a bug in the tokenizer/data loader of the Hybrid ASR models.
# Changelog
The dataloader method was missing from the BPE version of the hybrid ASR model.
**PR Type**:
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- [X] Bugfix
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**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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# Before your PR is "Ready for review"
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<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pypa/setuptools/releases">setuptools's releases</a>.</em></p>
<blockquote>
<h2>v65.5.1</h2>
<p>No release notes provided.</p>
<h2>v65.5.0</h2>
<p>No release notes provided.</p>
<h2>v65.4.1</h2>
<p>No release notes provided.</p>
<h2>v65.4.0</h2>
<p>No release notes provided.</p>
<h2>v65.3.0</h2>
<p>No release notes provided.</p>
<h2>v65.2.0</h2>
<p>No release notes provided.</p>
<h2>v65.1.1</h2>
<p>No release notes provided.</p>
<h2>v65.1.0</h2>
<p>No release notes provided.</p>
<h2>v65.0.2</h2>
<p>No release notes provided.</p>
<h2>v65.0.1</h2>
<p>No release notes provided.</p>
<h2>v65.0.0</h2>
<p>No release notes provided.</p>
<h2>v64.0.3</h2>
<p>No release notes provided.</p>
<h2>v64.0.2</h2>
<p>No release notes provided.</p>
<h2>v64.0.1</h2>
<p>No release notes provided.</p>
<h2>v64.0.0</h2>
<p>No release notes provided.</p>
<h2>v63.4.3</h2>
<p>No release notes provided.</p>
<h2>v63.4.2</h2>
<p>No release notes provided.</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pypa/setuptools/blob/main/CHANGES.rst">setuptools's changelog</a>.</em></p>
<blockquote>
<h2>v65.5.1</h2>
<p>Misc
^^^^</p>
<ul>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3638">#3638</a>: Drop a test dependency on the <code>mock</code> package, always use :external+python:py:mod:<code>unittest.mock</code> -- by :user:<code>hroncok</code></li>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>: Fixed REDoS vector in package_index.</li>
</ul>
<h2>v65.5.0</h2>
<p>Changes
^^^^^^^</p>
<ul>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3624">#3624</a>: Fixed editable install for multi-module/no-package <code>src</code>-layout projects.</li>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3626">#3626</a>: Minor refactorings to support distutils using stdlib logging module.</li>
</ul>
<p>Documentation changes
^^^^^^^^^^^^^^^^^^^^^</p>
<ul>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3419">#3419</a>: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.</li>
</ul>
<p>Misc
^^^^</p>
<ul>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3569">#3569</a>: Improved information about conflicting entries in the current working directory
and editable install (in documentation and as an informational warning).</li>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3576">#3576</a>: Updated version of <code>validate_pyproject</code>.</li>
</ul>
<h2>v65.4.1</h2>
<p>Misc
^^^^</p>
<ul>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3613">#3613</a>: Fixed encoding errors in <code>expand.StaticModule</code> when system default encoding doesn't match expectations for source files.</li>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3617">#3617</a>: Merge with pypa/distutils@6852b20 including fix for <a href="https://github-redirect.dependabot.com/pypa/distutils/issues/181">pypa/distutils#181</a>.</li>
</ul>
<h2>v65.4.0</h2>
<p>Changes
^^^^^^^</p>
<ul>
<li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3609">#3609</a>: Merge with pypa/distutils@d82d926 including support for DIST_EXTRA_CONFIG in <a href="https://github-redirect.dependabot.com/pypa/distutils/issues/177">pypa/distutils#177</a>.</li>
</ul>
<p>v65.3.0</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pypa/setuptools/commit/a462cb5edb324dcc56f903524b742305e4087014"><code>a462cb5</code></a> Bump version: 65.5.0 → 65.5.1</li>
<li><a href="https://github.com/pypa/setuptools/commit/de35d8be997c9f8508b425e33a1b6e52431091fa"><code>de35d8b</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3656">#3656</a> from bmorris3/typos</li>
<li><a href="https://github.com/pypa/setuptools/commit/58e23de0d4c2ce0f2502d072bb9a2ed1e2ab0ba6"><code>58e23de</code></a> Update changelog. Ref <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>.</li>
<li><a href="https://github.com/pypa/setuptools/commit/43a9c9bfa6aa626ec2a22540bea28d2ca77964be"><code>43a9c9b</code></a> Limit the amount of whitespace to search/backtrack. Fixes <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>.</li>
<li><a href="https://github.com/pypa/setuptools/commit/579134321d4d9397c886a5cb50cc26d0e3fa4279"><code>5791343</code></a> Add test capturing failed expectation. Ref <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>.</li>
<li><a href="https://github.com/pypa/setuptools/commit/1f97905bc40310ca454ff1ea3884f233b7dcc88c"><code>1f97905</code></a> ⚫ Fade to black.</li>
<li><a href="https://github.com/pypa/setuptools/commit/6254567c6ae323bb8ce19a6930ae3cc5f7fb25cc"><code>6254567</code></a> Remove workaround for emacs.</li>
<li><a href="https://github.com/pypa/setuptools/commit/729b180e926634930c21ccce5558780d42707763"><code>729b180</code></a> ⚫ Fade to black.</li>
<li><a href="https://github.com/pypa/setuptools/commit/c068081a7234a0c5c322a9312654e7d0f4aaa8d1"><code>c068081</code></a> Typo corrections</li>
<li><a href="https://github.com/pypa/setuptools/commit/f777a40ed9abf529906c2939f80a184a5ed035fa"><code>f777a40</code></a> Suppress deprecation warning in --rsyncdir. Workaround for <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3655">#3655</a>.</li>
<li>Additional commits viewable in <a href="https://github.com/pypa/setuptools/compare/v59.5.0...v65.5.1">compare view</a></li>
</ul>
</details>
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} | # What does this PR do ?
GPT eval changes
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Yang Zhang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
- [ ] New Feature
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes a bug in the tokenizer/data loader of the Hybrid ASR models.
# Changelog
The dataloader method was missing from the BPE version of the hybrid ASR model.
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
Hello, I am trying to load a `.nemo` checkpoint file into NeMo's `MegatronBertModel()` instance using the `restore_from()` method as shown below. The tokenizer file used is the BertWordPiece tokenizer pretrained for this task. My code is as shown below.
```python
from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel
from transformers import BertTokenizer
model = MegatronBertModel.restore_from("MegatronBERT.nemo", trainer=trainer).to(device)
tokenizer=BertTokenizer("8b2c88a36abf4b40ba52976353202740_vocab.txt")
sentence = "I am not feeling well"
input = tokenizer(sentence)
input["input_ids"] = torch.LongTensor(input["input_ids"]).to(device)
input["attention_mask"] = torch.FloatTensor(input["attention_mask"]).to(device)
input["token_type_ids"] = torch.LongTensor(input["token_type_ids"]).to(device)
model.eval()
model(**input)
```
I convert the `input_ids`, `attention_mask` and the `token_type_ids` into tensors since they are returned as lists from the output of the tokenizer & if I don't do so, it gives an error asking me to. On executing the above code, I run into the below error
```python
Error executing job with overrides: []
Traceback (most recent call last):
File "megatron_bert_eval.py", line 159, in <module>
main()
File "/opt/conda/lib/python3.8/site-packages/nemo/core/config/hydra_runner.py", line 104, in wrapper
_run_hydra(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
run_and_report(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
raise ex
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
lambda: hydra.run(
File "/opt/conda/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
_ = ret.return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/opt/conda/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "megatron_bert_eval.py", line 93, in main
model(**input)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1185, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py", line 98, in forward
output_tensor = self.model(input_ids, attention_mask, token_type_ids=token_type_ids, lm_labels=lm_labels)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1185, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron/bert_model.py", line 222, in forward
extended_attention_mask = bert_extended_attention_mask(attention_mask)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/language_modeling/megatron/bert_model.py", line 51, in bert_extended_attention_mask
attention_mask_bs1 = attention_mask.unsqueeze(2)
IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 2)
```
Can somebody kindly help me with this error?
Thanks in advance!
**Environment overview (please complete the following information)**
- Environment: I am using `nvcr.io/nvidia/nemo:22.09` docker image
- Method of NeMo install: I cloned the NeMo branch `1.10.0` in my docker image
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} | null | null | null | TTS model documentation update
**A clear and concise description of what the bug is.**
The Readme's for the currently supported TTS models are outdated. E.g. there is no hint on that now VITS is supported, nor how SSL-Fastpitch is to be used and how it improves/deviates from the normal Fastpitch model.
**Please list *minimal* steps or code snippet for us to be able to reproduce the bug**
Navigate to the TTS description: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/tts/intro.html. Search for VITS docuementation or SSL Fastpitch.
**Expected behavior**
Thorough documentation of the new language models or how to train and if possible current status and performance metrics. | {
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} | # What does this PR do ?
- move settings from old-style `setup.cfg` to `pyproject.toml` (recommended by most of the tools)
- remove deprecated `pytest-runner` according to the [documentation](https://github.com/pytest-dev/pytest-runner#deprecation-notice)
**Collection**: [Note which collection this PR will affect]
# Changelog
- create `pyproject.toml` with settings from `setup.cfg` for `isort` and `pytest`
- remove `setup.cfg`
- remove `pytest-runner` from `setup.py`
# Usage
It is not possible anymore to use `python setup.py test` (actually, not used in project).
`pytest <options>` should be used for testing purpose.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I am trying to train a Conformer-Encoder based ASR model on a relatively small and bad quality dataset. So far, the model trains via a CTC loss to about 58% WER and I tried to extend it to use a hybrid CTC/RNNT loss as it can also be done in NeMo.
Due to the necessity to adapt to an existing codebase, I am not running the provided NeMo scripts/models for training, but rather I create an instance of the required classes for RNNT-models (RNNTLoss, RNNTJoiner, RNNTDecoder) and then include those into my own PyTorch Lightning module. To get all tensors right and fed to the correct modules, I went through the nemo code step by step with a debugger while running the default _speech_to_text_hybrid_rnnt_ctc_bpe.py_ script.
I feed the zero-padded and tokenized transcripts to the decoder and the output goes to the joiner along with the conformer encoding. I then use the resulting tensors to obtain the RNNT-Loss:
```
decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len)
joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder)
loss_value = self.loss(log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length)
```
(which is basically how it is done in the training step of _nemo/collections/asr/models/rnnt_models.py_ )
So far this works well and I can see the RNNT-Loss dropping (from around 180 to 60 for 'mean_batch' reduction), both when using it alone and when using it together with a CTC loss.
I observed that the performance (in terms of WER) when decoding the CTC output layer's output stays about constant. I am not really sure whether using the RNNT-Loss along with the CTC-Loss will also improve the quality of the CTC decoding, so I decided to try the RNNT decoding.
Same principle, I instantiated the RNNTDecoding object directly by passing the configuration and the decoder network and joiner network of my model to create a RNNTDecoding object.
However, passing my conformer encoding outputs to _RNNTDecoding.rnnt_decoder_predictions_tensor(...)_ (of the RNNTDecoding instantiated with my joiner and decoder) always results in an empty output/hypothesis with score 0.
Taking a deeper look into the decoding process (in _ _greedy_decode_blank_as_pad()_ ) shows that in every step the log_probability (logp) of the blank token is extremely high (around 10-12) while lying around -2 .. 2 for all other tokens, which in the end results in an empty hypothesis. This seems especially weird to me since a log_probability is not meant to be positive.
To get more insight, I ran a pretrained RNNT model (large conformer transducer) and took a look a the same place in the code. While my encoder and decoder outputs look similar (and also f and g) in terms of the value range, logp will always be around -100 .. -20 for this model, but never positive.
Going into the "joint" step showed that there is some form of log_softmax calculation, but only if the signal is on the cpu. I noticed a similar thing in the RNNT loss already, where (as far as I understood) the log-softmax calculation is handled by numba in case of a GPU tensor.
Formulating an actual question seems hard (and I hope that this is "suited" to be an issue anyway, sorry if it isn't .. ) ..
But why does the usage of the RNNT decoding fail, although the loss seems to work fine?
Is there any general misconception that I have using the RNNT elements of the library? Are they not meant to be used outside of the NeMo world?
Why does the logp-signal in _ _greedy_decode_blank_as_pad_ seems to be log-softmaxed in the pretrained model but this operation is obviously not performed when the class is used in an isolated way? If there is a specific reason, is there a way around it? | {
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} | # What does this PR do?
Supports:
```python
from nemo.collections.asr.models import NeuralDiarizer
diarizer = NeuralDiarizer.from_pretrained(model_name='diar_msdd_telephonic')
annotation = diarizer("an4_diarize_test.wav")
print(annotation.to_rttm())
```
User can change the device:
```python
import torch
from nemo.collections.asr.models import NeuralDiarizer
diarizer = NeuralDiarizer.from_pretrained(model_name='diar_msdd_telephonic')
diarizer.to(torch.device('cuda'))
```
Supports passing the maximum number of speakers, or the actual number of speakers in a file (oracle):
```python
annotation = diarizer("an4_diarize_test.wav", num_speakers=2)
annotation = diarizer("an4_diarize_test.wav", max_speakers=4)
```
Relies on a temporary directory, which can be specified if preferred:
```python
annotation = diarizer("an4_diarize_test.wav", out_dir="my_temp/")
```
List of available models:
```python
from nemo.collections.asr.models.msdd_models import NeuralDiarizer
print(NeuralDiarizer.list_available_models())
"""
PretrainedModelInfo(
pretrained_model_name=diar_msdd_telephonic,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:diar_msdd_telephonic,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/diar_msdd_telephonic/versions/1.0.0/files/diar_msdd_telephonic.nemo
)
"""
```
### TODO
* Add tests, ensure that config options are passed correctly and updated correctly. The config logic is quite messy due to mutable objects, but this should be saved for a larger refactor.
* Hopefully in the future, `NeuralDiarizer` will be a NeMo PT model file too, save for a later refactor
**Collection**: ASR, Speaker Tasks
# Changelog
- Add file class based inference API for Diarization.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | null | null | null | **What i did**
I took a multilingual SSL pretrained model and wanted to fine-tune it for ASR task on english and hindi together at once. I used a tokenizer that contains both English and Hindi manifest.
**ERROR**
After few steps the loss goes nan , detected inf or nan values in gradients! Setting gradients to zero.
**What i tried**
All of the hyper-parameters are kept as mentioned in GitHub repo.
i tried different combination of batch size and gradient accumulation. i tried lowering the learning rate.
____________________________________________________________
**AIM of my experiment**
let say the audio clip says : Aj mene apple khaya
want the transcription to be : आज मेने apple खाया
____________________________________________________________
how can this be achieved.
Do i need to use the other method of tokenizing , where one make separate tokenizer for languages, and use "agg" aggregate.
Can you suggest any method to achieve the above problem
An early response is always appreciated.
@titu1994 @meghmak13 | {
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} | # What does this PR do ?
Add Readme + other content to the NeMo website.
**Collection**: [Core]
# Changelog
- Add detailed README.md
- Update links to NeMo Megatron
- Update blog posts in 2022
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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# What does this PR do ?
```
add new chinese NGC model cards that improve with polyphone disambiguation.
```
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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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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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<!-- Warning: The suggested title contains the alert rule name. This can expose security information. -->
Tracking issue for:
- [x] https://github.com/NVIDIA/NeMo/security/code-scanning/774
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} | # What does this PR do ?
Fixes gpu hang caused by incompatible shapes during loss mean broadcast when binary head is on. This may not be the cleanest solution but I am making a draft PR for awareness of the issue.
**Collection**: NLP
# Changelog
- This adds an if statement to broadcast an shape [3] zeros tensor for loss mean reduction when the Binary Head is on.
```
# 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**:
- [Bugfix]
# Additional Information
* cc. @shanmugamr1992
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} | null | null | null | Hello
I am using NeMo for my Portuguese language transcriber and I have great results, far superior to other engines, congratulations on the job.
Now I'm working with NeMo to use voice authentication, in my scenario I'm using phone call audio as a base, in 8K and with background noise. I tested all the pre-trained models that you suggest, the "best" being titanet_large and the result from the same people is giving between 50 to 65% confidence. To test I used the code:
`
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')
speaker_model.verify_speakers('/content/PERSON1-AUDIO1.wav', '/content/PERSON1-AUDIO2.wav')
`
And the sample audios (I can't provide more due to privacy concerns):
[NVIDIA.zip](https://github.com/NVIDIA/NeMo/files/10663690/NVIDIA.zip)
As you suggest, I did a finetunning using the same parameters suggested in the training https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb.
I only changed the number of Epochs, placing 250, this training ended after 1 day, the loss started at 13.7 and after 250 Epochs it was at 0.0000508
With this model in hand, I did the same tests, but the result got worse, it only gave high confidence when the person said the same thing said in the other audio. It gave about 90% if both audios had "Hello", for example, otherwise it gave 50%.
For this finetunning, I used approximately 60 hours of phone call audio as a basis, with approximately 1100 different speakers.
I opened an old issue with you about this same problem, but I still didn't see any improvements. I tested what was suggested from 6 seconds upwards and there were no improvements. I'm following titanet's own paper that comments that we should use at least 3 seconds
https://github.com/NVIDIA/NeMo/issues/2719
Now comes my questions:
Is NeMo suitable for my use case? Phone call audios that are in 8K (For training and testing I convert to 16K).
Do I need to test new parameters? What do you suggest for values and what can I adjust.
Does spoken language matter for training? My test/training audios are in Portuguese. | {
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} | null | null | null | Hello,
I am fine-tuning an ASR model (the English Conformer Transducer) and encountering the following error:
```log
2023-02-03 14:46:28 | numba.cuda.cudadrv.driver.CudaAPIError: [1] Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE |
-- | -- | --
| | 2023-02-03 14:46:28 | raise CudaAPIError(retcode, msg) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/numba/cuda/cudadrv/driver.py", line 335, in _check_error |
| | 2023-02-03 14:46:28 | self._check_error(fname, retcode) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/numba/cuda/cudadrv/driver.py", line 300, in safe_cuda_api_call |
| | 2023-02-03 14:46:28 | driver.cuLaunchKernel(cufunc_handle, |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/numba/cuda/cudadrv/driver.py", line 2094, in launch_kernel |
| | 2023-02-03 14:46:28 | driver.launch_kernel(cufunc.handle, |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/numba/cuda/compiler.py", line 693, in launch |
| | 2023-02-03 14:46:28 | kernel.launch(args, griddim, blockdim, stream, sharedmem) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/numba/cuda/compiler.py", line 966, in call |
| | 2023-02-03 14:46:28 | return self.dispatcher.call(args, self.griddim, self.blockdim, |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/numba/cuda/compiler.py", line 821, in __call__ |
| | 2023-02-03 14:46:28 | gpu_rnnt_kernel.compute_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 161, in compute_cost_and_score |
| | 2023-02-03 14:46:28 | return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py", line 249, in cost_and_grad |
| | 2023-02-03 14:46:28 | status = wrapper.cost_and_grad( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py", line 223, in rnnt_loss_gpu |
| | 2023-02-03 14:46:28 | loss_func( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 62, in forward |
| | 2023-02-03 14:46:28 | return self.loss( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py", line 166, in forward |
| | 2023-02-03 14:46:28 | return forward_call(*input, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1111, in _call_impl |
| | 2023-02-03 14:46:28 | loss = self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/losses/rnnt.py", line 288, in forward |
| | 2023-02-03 14:46:28 | loss_batch = self.loss( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/rnnt.py", line 901, in forward |
| | 2023-02-03 14:46:28 | outputs = wrapped(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__ |
| | 2023-02-03 14:46:28 | loss_value, wer, _, _ = self.joint( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/rnnt_models.py", line 710, in training_step |
| | 2023-02-03 14:46:28 | output_dict = wrapped(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/nemo/utils/model_utils.py", line 364, in wrap_training_step |
| | 2023-02-03 14:46:28 | asr_loss = self.asr.training_step(batch, batch_idx)["loss"] |
| | 2023-02-03 14:46:28 | File "/netscratch/franzreb/accent_info/src/model.py", line 121, in training_step |
| | 2023-02-03 14:46:28 | output = self.module.training_step(*inputs, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 82, in forward |
| | 2023-02-03 14:46:28 | output = self.module(*inputs[0], **kwargs[0]) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 992, in forward |
| | 2023-02-03 14:46:28 | return forward_call(*input, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1111, in _call_impl |
| | 2023-02-03 14:46:28 | return self.model(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 349, in training_step |
| | 2023-02-03 14:46:28 | output = fn(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1765, in _call_strategy_hook |
| | 2023-02-03 14:46:28 | training_step_output = self.trainer._call_strategy_hook("training_step", *step_kwargs.values()) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 427, in _training_step |
| | 2023-02-03 14:46:28 | step_output = self._step_fn() |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 134, in closure |
| | 2023-02-03 14:46:28 | self._result = self.closure(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in __call__ |
| | 2023-02-03 14:46:28 | closure() |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 249, in _run_optimization |
| | 2023-02-03 14:46:28 | result = self._run_optimization( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 203, in advance |
| | 2023-02-03 14:46:28 | outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance |
| | 2023-02-03 14:46:28 | batch_output = self.batch_loop.run(batch, batch_idx) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run |
| | 2023-02-03 14:46:28 | self._outputs = self.epoch_loop.run(self._data_fetcher) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance |
| | 2023-02-03 14:46:28 | self.advance(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run |
| | 2023-02-03 14:46:28 | self.fit_loop.run() |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1353, in _run_train |
| | 2023-02-03 14:46:28 | return self._run_train() |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage |
| | 2023-02-03 14:46:28 | results = self._run_stage() |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run |
| | 2023-02-03 14:46:28 | results = self._run(model, ckpt_path=self.ckpt_path) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl |
| | 2023-02-03 14:46:28 | return trainer_fn(*args, **kwargs) |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 723, in _call_and_handle_interrupt |
| | 2023-02-03 14:46:28 | self._call_and_handle_interrupt( |
| | 2023-02-03 14:46:28 | File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in fit |
| | 2023-02-03 14:46:28 | trainer.fit(model, train_dataloaders=train_dl) |
| | 2023-02-03 14:46:28 | File "run.py", line 20, in main |
| | 2023-02-03 14:46:28 | main(args) |
| | 2023-02-03 14:46:28 | File "run.py", line 57, in <module> |
| | 2023-02-03 14:46:28 | Traceback (most recent call last):
```
The error always occurs at a similar training step. It is a Numba Cuda API error that happens when calling the ASR model's loss function. Do you know how to address this issue? If not, how can I debug it? Thank you very much for your help. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5928 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5928/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5928/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5928/events | https://github.com/NVIDIA/NeMo/issues/5928 | 1,572,034,979 | I_kwDOC_bI7s5ds1mj | 5,928 | Enabling pipeline parallelism will result in nan loss | {
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Hi team, when I follow the NeMo Megatron [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html) to test megatron_gpt_pretraining script on 8 A100 devices, I encountered unexpected result when pipeline parallelism is enabled. The result is that the loss will become nan within few steps when I kick off the training.
```
Epoch 0: 0%| | 1/351750 [00:03<372:04:57, 3.81s/it]
Epoch 0: 0%| | 1/351750 [00:03<372:08:48, 3.81s/it, loss=11, v_num=9-02, reduced_train_loss=11.00, global_step=0.000, consumed_samples=0.000]
Epoch 0: 0%| | 2/351750 [00:04<204:24:55, 2.09s/it, loss=11, v_num=9-02, reduced_train_loss=11.00, global_step=0.000, consumed_samples=0.000]
Epoch 0: 0%| | 2/351750 [00:04<204:26:30, 2.09s/it, loss=nan, v_num=9-02, reduced_train_loss=nan.0, global_step=1.000, consumed_samples=192.0]
Epoch 0: 0%| | 3/351750 [00:04<148:27:47, 1.52s/it, loss=nan, v_num=9-02, reduced_train_loss=nan.0, global_step=1.000, consumed_samples=192.0]
Epoch 0: 0%| | 3/351750 [00:04<148:28:47, 1.52s/it, loss=nan, v_num=9-02, reduced_train_loss=nan.0, global_step=2.000, consumed_samples=384.0]
Epoch 0: 0%| | 4/351750 [00:04<120:28:47, 1.23s/it, loss=nan, v_num=9-02, reduced_train_loss=nan.0, global_step=2.000, consumed_samples=384.0]
Epoch 0: 0%| | 4/351750 [00:04<120:29:37, 1.23s/it, loss=nan, v_num=9-02, reduced_train_loss=nan.0, global_step=3.000, consumed_samples=576.0]
```
**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.
Steps:
1. Follow the [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html) until `Train GPT-style Model Option 1`
2. Change `trainer.devices=1` to `trainer.devices=8`, `model.pipeline_model_parallel_size=1` to `model.pipeline_model_parallel_size=2`
**Expected behavior**
A clear and concise description of what you expected to happen.
I can see the loss drop and training to converge.
**Environment overview (please complete the following information)**
- Environment location: single AWS EC2 p4d.24xlarge machine
- Method of NeMo install: [from source]
- git clone https://github.com/ericharper/apex.git
- cd apex
- git checkout nm_v1.15.0
- python3 -m pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
- cd ..
- git clone https://github.com/NVIDIA/NeMo.git
- git checkout e1b3f5e2fe57174863110d3fa47b6c24238ab58e
- python3 -m pip install .[nlp]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 20.04.4
- PyTorch version 1.12.0
- Python version 3.8.10
**Additional context**
Add any other context about the problem here.
GPU model: A100
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} | # What does this PR do ?
Add an interface to support P2P communication overlap with compute kernels. P2P communication overlap works only with interleaved pipelining.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [*] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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I'm trying to run this notebook: https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/nlp/Entity_Linking_Medical.ipynb
A few pip dependency make problems but doesn't stop the executions, but then later one cell has a problem with the tensorboard module.
This cell:
```py
# Initialize the trainer and model
trainer = Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = nemo_nlp.models.EntityLinkingModel(cfg=cfg.model, trainer=trainer)
```
produces ERROR:
```py
INFO:pytorch_lightning.utilities.rank_zero:Using bfloat16 Automatic Mixed Precision (AMP)
INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False
INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores
INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs
INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs
[NeMo I 2023-02-06 02:31:00 exp_manager:362] Experiments will be logged at SelfAlignmentPretrainingTinyExample/2023-02-06_02-14-21
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
[<ipython-input-33-8586e3adaa65>](https://localhost:8080/#) in <module>
1 # Initialize the trainer and model
2 trainer = Trainer(**cfg.trainer)
----> 3 exp_manager(trainer, cfg.get("exp_manager", None))
4 model = nemo_nlp.models.EntityLinkingModel(cfg=cfg.model, trainer=trainer)
3 frames
[/usr/local/lib/python3.8/dist-packages/lightning_fabric/loggers/tensorboard.py](https://localhost:8080/#) in __init__(self, root_dir, name, version, default_hp_metric, prefix, sub_dir, **kwargs)
91 ):
92 if not _TENSORBOARD_AVAILABLE and not _TENSORBOARDX_AVAILABLE:
---> 93 raise ModuleNotFoundError(
94 "Neither `tensorboard` nor `tensorboardX` is available. Try `pip install`ing either."
95 )
ModuleNotFoundError: Neither `tensorboard` nor `tensorboardX` is available. Try `pip install`ing either.
```
**Steps/Code to reproduce bug**
run cells one by one: https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/nlp/Entity_Linking_Medical.ipynb
**Environment overview**
- Environment location: Collab
- Method of NeMo install:
```py
BRANCH = 'main'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5925 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5925/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5925/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5925/events | https://github.com/NVIDIA/NeMo/pull/5925 | 1,571,688,771 | PR_kwDOC_bI7s5JSxAb | 5,925 | Support Alignment Extraction for all RNNT Beam decoding methods | {
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} | # What does this PR do ?
Adds alignment extraction for all rnnt beam algorithms
**Collection**: [ASR]
# Changelog
- Adds support for TSD, ALSD and mAES alignment extraction
- Adds unittest that downloads a small model and runs on AN4 test set to compute alignments from greedy and beam search algorithms
# Usage
You can now submit `preserve_alignments=true` to in the RNNTDecodingConfig for all beam search algos and extract alignments.
```python
import nemo.collections.asr as nemo_asr
filepath = "<audio filepath>"
model = nemo_asr.models.ASRModel.from_pretrained('stt_en_conformer_transducer_large') # type: nemo_asr.models.EncDecRNNTBPEModel
# Create default decoding config
model.change_decoding_strategy(None)
decoding_cfg = model.cfg.decoding
decoding_cfg.strategy = "beam" # can be beam, tsd, alsd or maes
decoding_cfg.preserve_alignments = True # the important argument !
decoding_cfg.beam.beam_size = 8
decoding_cfg.beam.return_best_hypothesis = False
# Prepare beam search decoding strategy
model.change_decoding_strategy(decoding_cfg)
best_hypothesis, all_hypothesis = model.transcribe([filepath], return_hypotheses=True)
print("Num hypothesis per sample :", len(best_hypothesis[0]))
print("Result type :", best_hypothesis[0][0].__class__.__name__)
for candidate in range(len(best_hypothesis[0])):
print(f"Beam Search {candidate + 1} :", best_hypothesis[0][candidate].text, best_hypothesis[0][candidate].score)
# Use the following commented print statements to check
# the alignment of other algorithms compared to the default
all_hyps = all_hypothesis
for idx, hyp_ in enumerate(all_hyps): # type: (int, rnnt_utils.Hypothesis)
print("Hyp index", idx + 1, "text :", hyp_.text)
for t in range(len(hyp_.alignments)):
t_u = []
for u in range(len(hyp_.alignments[t])):
logp, label = hyp_.alignments[t][u]
t_u.append(int(label))
print(f"Tokens at timestep {t} = {t_u}")
print()
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | Hey, I've been using the beam BeamSearchDecoderWithLM module which returns a combined score of AM + bpe-ngram, and basically, I need acoustic model probabilities for each hyp to use downstream.
I *think* this would be a correct way to obtain them but just wanted to double check
-(kenlm.score(encoded_text) * alpha - score + encoded_length * beta)
which would be using the same ngram used for decoding to re-obtain seperate n-gram probs | {
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} | null | null | null | **Describe the bug**
https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/asr/parts/utils)/speaker_utils.py
In this file gc is not imported, therefore while performing clustering without cuda, its showing error that gc is not imported
**Steps/Code to reproduce bug**
By adding import statement we can fix the bug
**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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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds GPT support for BF16/FP16 gradient reductions, with embedding grad reductions in FP32.
**Collection**: NLP
# Changelog
- Wrapper for distributed Adam optimizer separately optimizes any parameters that require explicit FP32 gradients, e.g. embeddings.
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/65c277bcff81023211b2aa4748ae5edb79326b2d/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L193
Configure the optimizer with `grad_sync_dtype: bf16`.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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
* Depends on https://github.com/NVIDIA/apex/pull/1575
* Depends on https://github.com/NVIDIA/TransformerEngine/pull/75 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5919 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5919/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5919/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5919/events | https://github.com/NVIDIA/NeMo/pull/5919 | 1,570,616,699 | PR_kwDOC_bI7s5JPnV7 | 5,919 | Fix Windows bug with save_restore_connector | {
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} | Signed-off-by: Daniel Egert <[email protected]>
# What does this PR do ?
Fixes a bug with saving Nemo models using model.save_to()
**Collection**: core
# Changelog
- Changed some ordering in nemo/core/connectors/save_restore_connector.py
# Usage
To trigger the bug, and check it's fixed:
```python
from nemo.collections.asr.models import ASRModel
model = ASRModel.restore_from(...)
model.save_to(...) # crashes on Windows without this fix
```
# 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
If you look at all of the uses of `with tempfile.TemporaryDirectory()` in save_restore_connector.py, you will see they all adhere to putting the tempfile context outer to the try-finally for the os.chdir call. However, this is not the case for `_handle_artifacts` which leads me to believe this is a bug where someone wrote `_handle_artifacts` without paying attention to the previous design pattern for tempfile context used throughout the same file. Unfortunately, deviating from that design pattern has catastrophic problems on Windows, but once we fix it to work like all the other entries in the same file, everything works correctly again. This is what I have done. | {
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} | # What does this PR do ?
This PR updates the GPT pretraining script to use 'spawn' multiprocessing.
This removes the CUDA init error that is seen sometimes on clusters but needs some testing.
**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 a one line overview of what this PR aims to accomplish.
**Collection**: [Core]
# 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**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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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# What does this PR do ?
Ported Megatron-LM "Rotary Positional Encoding" (RoPE) implementation for NeMo GPT.
**Collection**: GPT
# Changelog
Add option to use 'rotary' positional encoding in the config - making it as default, instead of "learned_absolute" positional embedding.
# Usage
```python
`model.position_embedding_type: 'rotary'`
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Signed-off-by: Vladimir Bataev <[email protected]>
# What does this PR do ?
Pin `isort` version. Use `>5.1.0`, `<6.0.0`, as suggested by the documentation https://pycqa.github.io/isort/docs/major_releases/release_policy.html
**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 ?
Fixes #5912
**Collection**: asr
# Changelog
- `FilterbankFeaturesTA` matches naming of `FilterbankFeatures`
# Usage
* When using `torchaudio` for preprocessing.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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 #5912
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This leads to errors when using the `FilterbankFeaturesTA` in a model.
For instance here: https://github.com/NVIDIA/NeMo/blob/ddb92e319e9e3aa7b2db7e58b25810a71a4c2cc7/nemo/collections/asr/models/ctc_models.py#L160-L161
While `FilterbankFeatures` defines those attributes, `FilterbankFeaturesTA` uses different naming:
https://github.com/NVIDIA/NeMo/blob/ddb92e319e9e3aa7b2db7e58b25810a71a4c2cc7/nemo/collections/asr/parts/preprocessing/features.py#L540-L541 | {
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} | Signed-off-by: Jocelyn Huang <[email protected]>
# What does this PR do ?
Moves the MCD DTW data tarball to AWS instead of storing it in NeMo.
Paths tested locally again.
**Collection**: TTS | {
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} | # What does this PR do ?
Refactor the retrieval services so each component can be deployed alone inside a container. This allows it to be served easily in a cloud. Also fixed some issues with zero neighbor retrieval. | {
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 | ### Description:
I am encountering an issue while attempting to evaluate a pre-trained machine translation model using the `MTEncDecModel` class from the `nemo.collections.nlp.models.machine_translation` module. The model is being evaluated on a node with four GPUs and I am using PyTorch version 1.13.0a0+d321be6.
The error occurs when I try to run the validation process using the PyTorch Lightning Trainer with the "ddp" strategy:
```
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 687, in validate
return call._call_and_handle_interrupt(
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 90, in launch
return function(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 736, in _validate_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run
results = self._run_stage()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1174, in _run_stage
return self._run_evaluate()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1214, in _run_evaluate
eval_loop_results = self._evaluation_loop.run()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 206, in run
output = self.on_run_end()
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 180, in on_run_end
self._evaluation_epoch_end(self._outputs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 288, in _evaluation_epoch_end
self.trainer._call_lightning_module_hook(hook_name, output_or_outputs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1342, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/NeMo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 496, in validation_epoch_end
self.eval_epoch_end(outputs, 'val', self.global_rank)
File "/neural/nemo_transformer/adapted_transformer.py", line 175, in eval_epoch_end
super().eval_epoch_end(outputs, mode, global_rank)
File "/NeMo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 430, in eval_epoch_end
dist.all_gather_object(
File "/opt/conda/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 1682, in all_gather_object
object_list[i] = _tensor_to_object(tensor, tensor_size)
IndexError: list assignment index out of range
```
### Steps to Reproduce:
```
from pytorch_lightning import Trainer
from nemo.collections.nlp.models.machine_translation import MTEncDecModel
# loading a pre-trained model
cfg_model = MTEncDecModel(restore_path=model_path, return_config=True)
mt_model = MTEncDecModel(restore_path=model_path, map_location=torch.device('cuda:0'))
trainer = Trainer(strategy="ddp")
mt_model.setup_multiple_validation_data(val_data_config=cfg_model.validation_ds)
trainer.validate(mt_model)
```
With the validation config:
```
validation_ds:
src_file_name: [<11 source files>]
tgt_file_name: [<11 target files>]
tokens_in_batch: 8192
clean: false
max_seq_length: 512
shuffle: false
num_samples: -1
drop_last: true
pin_memory: false
num_workers: 8
```
### Expected Result:
The model should be evaluated successfully.
### Software Configuration:
PyTorch version: 1.13.0a0+d321be6
Operating System: Ubuntu 20.04.4 LTS
GPU: 4xA100 with 40G VRAM
Python Version: 3.8.13
NeMo version: 1.14
CUDA version: 11.7 | {
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} | # What does this PR do ?
This PR fixes an edge case where a batch may contain more tokens than allowed by `tokens-in-batch`
**Collection**: NLP
# Changelog
- In case a batch that contains too many tokens has fewer than 8 sentences the last sentence is evicted to a new batch to ensure we don't break the `tokens-in-batch` bound
- Before it would not evict any sentence leading to batches that are too big.
**PR Type**:
- [ ] New Feature
- [x] 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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# What does this PR do ?
Corrects the documentation of RNNT and Hybrid CTC RNNT models
**Collection**: [ASR]
# Changelog
- Fix docstring for two models
# Before your PR is "Ready for review"
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# What does this PR do ?
Explicitly sets the providers parameter when instantiating InferenceSession with onnxruntime.
**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)
- [ ] 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.
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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**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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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} | null | null | null | **Problem Description -> Trying to load the GatorTron model checkpoint into a pretrained MegatronBERT model**
Hi. I have the `.nemo` file for the Gatortron model which I downloaded from [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og/files) and I was looking to load this into a pretrained MegatronBert model, to perform some kind of language modeling just akin to the evaluation done in `megatron_gpt_eval.py` file.
More specifically, I want to be able to input clinical text to the Gatortron weights loaded into a pretrained BERT and get relevant output, without having to perform any kind of finetuning, since I currently do not have any dataset to fine-tune upon.
**Describe the solution you'd like**
Could you kindly provide an example evaluation file - `megatron_bert_eval.py`, including all the dependent modules, just like the one for GPT?
**Describe alternatives you've considered**
I am using the `nvcr.io/nvidia/nemo:22.09` docker image and the NeMo `v1.10.0` and I was trying to build my own evaluation file for MegatronBERT, taking reference from the file for GPT, but was running into a lot of errors. Hence I thought of asking over here whether it would be possible if anyone could kindly give some reference along this way.
**Additional context**
None
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} | Signed-off-by: SeanNaren <[email protected]>
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