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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Fixes 'lost cascading error info' due to constructor not being under try:.
As a result, my previously added code to save initial error was not executed.
Also streamlined control flow in that block.
**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 rank check and barrier when compiling helpers in megatron dataset
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [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.
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* Related to # (issue)
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# What does this PR do?
Adds apex import guards.
Please have one of the following:
* Fixes # (issue)
* New feature: (short feature description)
## Before your PR is "Ready for review"
- [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 updated any necessary 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?
Please have one of the following:
* Fixes verify helper function for speaker verification
## Before your PR is "Ready for review"
- [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 updated any necessary 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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* Removes CheckInstall from init.py | {
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} | # What does this PR do?
* Depreciate some TTS models and TTS datasets which will be removed in 1.8.0 version.
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# Changelog
- Improves the PR template to include meta info + help reviewers in detailed review
# 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**:
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | Signed-off-by: ekmb <[email protected]>
# What does this PR do?
Please have one of the following:
* Fixes # (issue) ITN whitelist issues
* New feature: (short feature description): support for IP address, card num, country code
## Before your PR is "Ready for review"
- [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 updated any necessary 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?
* Fixes #3559
* New feature:
Automatically handles import guard. If the library doesn't exist, it will import a fake module with value False. | {
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} | # What does this PR do?
Please have one of the following:
* Fixes # (issue)
* New feature: (short feature description)
## Before your PR is "Ready for review"
- [ ] 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 updated any necessary 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?
Upgrade to 22.01 container
Please have one of the following:
* Fixes # (issue) n/a
* New feature: (short feature description)
Upgrade to 22.01 container
## Before your PR is "Ready for review"
- [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 updated any necessary 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?
* Fixes: update CONTRIBUTING.md
Signed-off-by: Oktai Tatanov <[email protected]> | {
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Hi,
Thank you very much for the great Speech Data Explorer (SDE) tool. I have faced a problem when connecting a simple lexicon (or vocab as the --vocab argument) to the SDE. My vocab file has just one word in each line and I have followed the SDE code and it can load it correctly. But the OOV words doesn't show in the `wordstable`. I think the problem is in [`update_wordstable`](https://github.com/NVIDIA/NeMo/blob/main/tools/speech_data_explorer/data_explorer.py#L495) method. Please help me to address this issue.
I also send you a simple (my tmp) lexicon.
Best
[lexicon.csv](https://github.com/NVIDIA/NeMo/files/7970171/lexicon.csv)
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} | # Addition of FastPitch training tutorial
Please have one of the following:
* New feature: (short feature description) FastPitch training tutorial.
This PR adds FastPitch tutorial to the same notebook as MixerTTS as both generate spectrogram. This tutorial goes through the data download, preprocess, FastPitch import and training pipeline.
Tutorial was added with careful consideration to make minimal changes to MixerTTS tutorial.
## Before your PR is "Ready for review"
- [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 updated any necessary documentation?
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hi all,
I am trying to train a TTS model with NeMo implementations of **Tacotron2** and **WaveGlow** architectures. The dataset is my own and includes 15 hours of speech in Slovenian language (sample rate 22050, 16-bit, mono). Label set has 83 symbols, all within 0-255 ASCII.
The dataset has been tested and should not be problematic – I used it before for training a TTS model with Tacotron2/WaveGlow architectures from the **DeepLeaningExamples** (DLE). The model developed with DLE produced rather clear speech.
With the NeMo implementation of the Tacotron2 and WaveGlow I am however not able to develop a model that would produce speech of at least a moderate quality. According to the encoder/decoder alignment it seems that Tacotron2 learns ok, while WaveGlow never learns as it should. The NeMo version I use is 1.5.0.
Here is what I have tried so far:
1. I trained with 16 and 32 precisions
2. I trained with batch sizes of 10, 16 and 20.
3. I trained 1000 and 1500 epochs
4. I trained on different HW settings: I used one node with 4 x A100 GPU and even 8 nodes with 4 x A100 GPU
No matter what I try, results are disappointing. Here are also some figures:
This is what the model produces after 1000 epochs (fig above) and what it should produce (fig below) (bs=20):
<img width="452" alt="WaveGlow predicted" src="https://user-images.githubusercontent.com/16853936/151710024-4e28a9a6-148c-459a-947f-af2d70988f46.png">
<img width="452" alt="WaveGlow target" src="https://user-images.githubusercontent.com/16853936/151710040-1190ab0c-e2ac-430c-a58c-5e3b1a840458.png">
I know that val loss is not very informative for WaveGlow, but nevertheless, here is what is shows:
<img width="452" alt="loss" src="https://user-images.githubusercontent.com/16853936/151710461-f4948328-6da3-4ebd-b9a3-c63e55289772.png">
And last but not least: I trained HifiGan instead of WaveGlow and results seem ok. So I am wondering what am I doing wrong with WaveGlow. Any help/hint will be appreciated. | {
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} | null | null | null | Currently NeMo has BPE but I don't see Unigram.
[Unigram is better than BPE.](https://arxiv.org/pdf/2004.03720.pdf)
[SpeechBrain supports Unigram.](https://colab.research.google.com/drive/12yE3myHSH-eUxzNM0-FLtEOhzdQoLYWe?usp=sharing) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3564 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3564/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3564/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3564/events | https://github.com/NVIDIA/NeMo/pull/3564 | 1,118,569,753 | PR_kwDOC_bI7s4xyxqR | 3,564 | [TTS] Fix bugs in HiFi-GAN (scheduler, optimizers) and add input_example() in Mixer-TTS/Mixer-TTS-X | {
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} | # What does this PR do?
* Fixes:
* https://github.com/NVIDIA/NeMo/issues/3557
* https://github.com/NVIDIA/NeMo/issues/3549
* Use `self.optimizers` in HiFi-GAN
* Fix typo in FastPitch's `input_example()`
Signed-off-by: Oktai Tatanov <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3563 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3563/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3563/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3563/events | https://github.com/NVIDIA/NeMo/issues/3563 | 1,118,500,953 | I_kwDOC_bI7s5CqvhZ | 3,563 | How to properly pass audio signal to asr QuartzNet without saving audio? | {
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] | closed | false | null | [] | null | null | 2022-01-30T11:34:57 | 2022-02-18T04:24:00 | 2022-02-18T04:24:00 | NONE | {
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} | null | null | null | **Describe the bug**
Used instructions from notebooks:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Streaming_ASR.ipynb
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Online_ASR_Microphone_Demo.ipynb
For some reason, the recognized text differs from what the model produces by the boxed method through reading the saved files. But we don't want to save the files after we've received the sound.
1. Can you please tell me how nemo models read sound from files?
2. Can anyone come across a solution on how to correctly read the bytes of an audio file on the stt service?
If you pass it like this:
```python
with open(filename, "rb") as fileb:
abon = base64.b64encode(fileb.read())
data = {
"audio_content": audio_content,
}
response = requests.post(
"http://localhost:8557/stt", json=data)
```
**Steps/Code to reproduce bug**
```python
from scipy.io import wavfile
import scipy.signal
class AudioEncoder(json.JSONEncoder):
"""Класс сериализации семплированного аудио"""
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
elif isinstance(obj, bytes):
return str(obj, encoding='utf-8')
return json.JSONEncoder.default(self, obj)
# Example without passing bytes
target_sr = 16000
sample_rate_hertz, samples = wavfile.read(filename)
number_of_samples = round(
len(samples) * float(target_sr) / sample_rate_hertz)
samples = scipy.signal.resample(samples, number_of_samples)
audio_content = base64.b64encode(samples).decode("utf-8")
data = {
"audio_content": json.dumps(audio_content, cls=AudioEncoder),
}
response = requests.post(
"http://localhost:8557/stt", json=data)
```
On service stt:
```python
audio_content = base64.b64decode(request.audio.audio_content)
audio_signal = np.frombuffer(audio_content) # , dtype=np.float32
asr = ASR(16000)
asr.transcribe(audio_signal) # does not produce the same output as the box method
from audio_dataset import speech_collate_fn, AudioDataLayer
class ASR:
asr_model = _ASR_MODEL
beam_search_lm = _BEAM_SEARCH_LM
def __init__(self, sample_rate_hertz):
assert self.asr_model is not None, "Модель не инициализирована"
self.vocab = list(self.asr_model.decoder.vocabulary)
self.vocab.append("_")
self.sr = sample_rate_hertz
self.data_layer = AudioDataLayer(self.sr)
self.data_loader = DataLoader(
self.data_layer, batch_size=1, collate_fn=speech_collate_fn
)
self.prev_char = ""
def _get_batch_pred(self, signal):
self.data_layer.set_signal(signal)
batch = next(iter(self.data_loader))
audio_signal, audio_signal_len = batch
audio_signal, audio_signal_len = \
audio_signal.to(self.asr_model.device), \
audio_signal_len.to(self.asr_model.device)
log_probs, encoded_len, predictions = self.asr_model.forward(
input_signal=audio_signal, input_signal_length=audio_signal_len
)
return log_probs # .cpu().numpy()[0]
@staticmethod
def _decode_sequence_ctc_with_max_prob(logits, vocab):
s = ""
for i in range(logits.shape[0]):
s += vocab[np.argmax(logits[i])]
return s
def merge_decoded_sequence_from_ctc(self, sequence):
s_merged = ""
for i in range(len(sequence)):
if sequence[i] != self.prev_char:
self.prev_char = sequence[i]
if self.prev_char != "_":
s_merged += self.prev_char
return s_merged
@staticmethod
def _to_logits_tensor(logits):
logits = [torch.tensor(logit) for logit in logits]
log_probs_length = torch.tensor([logit.shape[0] for logit in logits])
logits_tensor = torch.nn.utils.rnn.pad_sequence(
logits, batch_first=True)
return logits_tensor, log_probs_length
def _infer_beam_search(self, logits_tensor, log_probs_length):
assert self.beam_search_lm is not None, "Модель не инициализирована"
hyps = list()
for j in range(logits_tensor.shape[0]):
hyps.append(
self.beam_search_lm.forward(
log_probs=logits_tensor[j].unsqueeze(0),
log_probs_length=log_probs_length[j].unsqueeze(0),
)[0][0][1]
)
return hyps
@torch.no_grad()
def transcribe(self, audio_signal, use_lm=False):
logits = self._get_batch_pred(audio_signal)
if use_lm:
logits_tensor, log_probs_length = self._to_logits_tensor(logits)
transcription = self._infer_beam_search(
logits_tensor, log_probs_length)[0]
else:
transcription_ctc = self._decode_sequence_ctc_with_max_prob(
logits.cpu().numpy()[0], self.vocab
)
transcription = self.merge_decoded_sequence_from_ctc(
transcription_ctc)
return transcription
```
audio_dataset.py
```python
# audio_dataset.py
from nemo.core.classes import IterableDataset
import torch
# import numpy as np
from nemo.core.neural_types import NeuralType, AudioSignal, LengthsType
from common_utils.log import get_logger
from typing import Tuple
logger = get_logger(__name__)
def speech_collate_fn(batch: Tuple[torch.FloatTensor, torch.LongTensor]):
"""collate batch of audio sig, audio len
Args:
batch (FloatTensor, LongTensor): A tuple of tensors of signal, signal lengths.
This collate func assumes the signals are 1d torch tensors (i.e. mono audio).
"""
_, audio_lengths = zip(*batch)
max_audio_len = 0
has_audio = audio_lengths[0] is not None
if has_audio:
max_audio_len = max(audio_lengths).item()
audio_signal = []
for sig, sig_len in batch:
assert sig.dim() == 1, "Ожидается монозвук"
if has_audio:
sig_len = sig_len.item()
if sig_len < max_audio_len:
pad = (0, max_audio_len - sig_len)
sig = torch.nn.functional.pad(sig, pad)
audio_signal.append(sig)
if has_audio:
audio_signal = torch.stack(audio_signal)
audio_lengths = torch.stack(audio_lengths)
else:
audio_signal, audio_lengths = None, None
return audio_signal, audio_lengths
# simple data layer to pass audio signal
class AudioDataLayer(IterableDataset):
@property
def output_types(self):
return {
"audio_signal": NeuralType(("B", "T"), AudioSignal(freq=self._sample_rate)),
"a_sig_length": NeuralType(tuple("B"), LengthsType()),
}
def __init__(self, sample_rate):
super().__init__()
self._sample_rate = sample_rate
self.output = True
def __iter__(self):
return self
def __next__(self):
if not self.output:
raise StopIteration
self.output = False
return torch.as_tensor(self.signal, dtype=torch.float32), torch.as_tensor(
self.signal_shape, dtype=torch.int64
)
def set_signal(self, signal):
# нормализуем сигнал в диапозон значений: [-1; 1]
self.signal = signal.astype('float32') / 32768.
self.signal_shape = self.signal.size
self.output = True
def __len__(self):
return 1
```
**Expected behavior**
I expect recognition via` nemo_asr.models.EncDecCTCModel.restore_from(...).transcribe([file], batch_size=1) `to match what we did.
**Environment details**
Run on CPU (docker image: python:3.7-slim)
- OS Linux
- PyTorch 1.10
- Python 3.7
**Additional context**
Add any other context about the problem here.
Example: CPU model
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"merged_at": "2022-05-09T14:19:05"
} | # What does this PR do?
Please have one of the following:
* Fixes #3561
## Before your PR is "Ready for review"
- [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 updated any necessary 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 | **Describe the bug**
If model is being restored from checkpoint path from a config field `model.common_dataset_parameters.label_vocab_dir` should not be prepended to config fields `model.class_labels.punct_labels_file` and `model.class_labels.punct_labels_file`. This because in a checkpoint these labels files are in the root of a checkpoint archive.
**Steps/Code to reproduce bug**
1. Create a config provides label ids using `model.class_labels.punct_labels_file` and `model.class_labels.punct_labels_file`. `model.common_dataset_parameters.label_vocab_dir` should not be empty.
2. Create a `.nemo` checkpoint for a model created via the config.
3. Try to restore from the `.nemo` checkpoint.
**Expected behavior**
A restoring without errors
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: `pip install nemo_toolkit[all]`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.1 LTS
- PyTorch version: 1.10.2
- Python version: 3.8.12
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} | null | null | null | Don't waste your time. The accuracy is not even close to production levels. Total avoid. | {
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} | null | null | null | **Describe the bug**
When running `00_NeMo_Primer.ipynb` notebook during `import nemo.collections.nlp as nemo_nlp` raises an error:
`ModuleNotFoundError: No module named 'apex'`
**Steps/Code to reproduce bug**
Opened in colab tutorial notebook [](https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/00_NeMo_Primer.ipynb) and "Run all cells". Then after installation section at importing stage error raises.
**Expected behavior**
End-to-end notebook executions without errors.
**Environment overview (please complete the following information)**
- Environment location: colab
- Method of NeMo install: as in notebook
```
BRANCH = 'main'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
```
**Environment details**
- OS version: linux
- PyTorch version: 1.10.0+cu111
- Python version: 3.7.12
**Additional context**
Error occurs in both kernel-types: CPU and GPU.
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} | null | null | null | Hey, I'm a windows user who tries to do ASR using Nemo without Cuda, I therefor can't install Apex and pynini no matter how hard we tried, **I would like to simply mute these apex errors** or optionally other warnings along and will love any solution for installing pynini so I can use the NLP module:
```
[NeMo W 2022-01-29 11:23:23 optimizers:52] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function get_argmin_mat at 0x00000221299F81F0> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function getMultiScaleCosAffinityMatrix at 0x00000221299F8280> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function parse_scale_configs at 0x00000221299F8CA0> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function get_embs_and_timestamps at 0x00000221299F8D30> is experimental, not ready for production and is not fully supported. Use at your own risk.
Transcribing: 0%| | 0/1 [00:00<?, ?it/s][NeMo W 2022-01-29 11:23:27 nemo_logging:349] C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\torch\autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
Transcribing: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.54it/s]
```
Code Used:
```
import nemo.collections.asr as nemo_asr
import logging
logging.getLogger('nemo_logger').setLevel(logging.CRITICAL)
#TIP: to see a list of available pre-trained ASR models, in a Python shell do:
nemo_asr.models.EncDecCTCModel.list_available_models()
# This will initiate pre-trained model download from NGC
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained("stt_en_quartznet15x5")
transcriptions = asr_model.transcribe(['2086-149220-0033.wav'])
print(transcriptions)
# Now, lets add punctuation
import nemo.collections.nlp as nemo_nlp
# this will also trigger model download
punctuation = nemo_nlp.models.PunctuationCapitalizationModel.from_pretrained(model_name='punctuation_en_distilbert')
res = punctuation.add_punctuation_capitalization(queries=transcriptions)
print(res)
```
Win10
Py3.6-Py3.10
Worked on 3.8 and 3.9
by worked I mean transcribed, along with these errors:
```
[NeMo W 2022-01-29 11:23:23 optimizers:52] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function get_argmin_mat at 0x00000221299F81F0> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function getMultiScaleCosAffinityMatrix at 0x00000221299F8280> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function parse_scale_configs at 0x00000221299F8CA0> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2022-01-29 11:23:24 experimental:27] Module <function get_embs_and_timestamps at 0x00000221299F8D30> is experimental, not ready for production and is not fully supported. Use at your own risk.
Transcribing: 0%| | 0/1 [00:00<?, ?it/s][NeMo W 2022-01-29 11:23:27 nemo_logging:349] C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\torch\autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
Transcribing: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.54it/s]
["well i don't wish to see it any more observed phoebe turning away her eyes it is certainly very likt the old portrait"]
Traceback (most recent call last):
File "C:\Users\orrbu\Desktop\test.py", line 12, in <module>
import nemo.collections.nlp as nemo_nlp
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\models\__init__.py", line 27, in <module>
from nemo.collections.nlp.models.machine_translation import MTEncDecModel
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\models\machine_translation\__init__.py", line 15, in <module>
from nemo.collections.nlp.models.machine_translation.mt_enc_dec_bottleneck_model import MTBottleneckModel
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\models\machine_translation\mt_enc_dec_bottleneck_model.py", line 29, in <module>
from sacrebleu import corpus_bleu
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\sacrebleu\__init__.py", line 21, in <module>
from .utils import smart_open, SACREBLEU_DIR, download_test_set # noqa: F401
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\sacrebleu\utils.py", line 12, in <module>
import portalocker
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\portalocker\__init__.py", line 4, in <module>
from . import portalocker
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\portalocker\portalocker.py", line 11, in <module>
import win32file
ImportError: DLL load failed while importing win32file: The specified module could not be found.
```
Which means that the second codeblock regarding NLP gave this error:
```
Traceback (most recent call last):
File "C:\Users\orrbu\Desktop\test.py", line 12, in <module>
import nemo.collections.nlp as nemo_nlp
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\models\__init__.py", line 27, in <module>
from nemo.collections.nlp.models.machine_translation import MTEncDecModel
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\models\machine_translation\__init__.py", line 15, in <module>
from nemo.collections.nlp.models.machine_translation.mt_enc_dec_bottleneck_model import MTBottleneckModel
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\nemo\collections\nlp\models\machine_translation\mt_enc_dec_bottleneck_model.py", line 29, in <module>
from sacrebleu import corpus_bleu
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\sacrebleu\__init__.py", line 21, in <module>
from .utils import smart_open, SACREBLEU_DIR, download_test_set # noqa: F401
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\sacrebleu\utils.py", line 12, in <module>
import portalocker
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\portalocker\__init__.py", line 4, in <module>
from . import portalocker
File "C:\Users\orrbu\anaconda3\envs\py39\lib\site-packages\portalocker\portalocker.py", line 11, in <module>
import win32file
ImportError: DLL load failed while importing win32file: The specified module could not be found.
```
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} | null | null | null | **Describe the bug**
I got this error when exporting mixer_tts model to onnx model:
AttributeError: 'MixerTTSModel' object has no attribute 'input_example'
**Steps/Code to reproduce bug**
```
from nemo.collections.tts.models import MixerTTSModel
spec_generator = MixerTTSModel.from_pretrained("tts_en_lj_mixerttsx")
spec_generator.export('mixter_tts.onnx')
```
**Expected behavior**
onnx model generated succefully
**Environment overview (please complete the following information)**
pip3 install nemo_toolkit['all']
NeMo version is 1.6.0
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} | # What does this PR do?
Please have one of the following:
- Update pytorch_lightning deprecated variables, namely:
> - accelerator to the trainer now takes the type of accelerator device than distributed type
> - distributed type is handled by strategy flag
> - previously: accelerator='ddp' now accelerator='gpu' strategy='ddp'
> - trainer flag checkpoint_callback is renamed to enable_checkpointing
> - ModelCheckpoint argument evert_n_val_epochs renamed to every_n_epochs
Reference: https://pytorch-lightning.readthedocs.io/en/1.5.9/common/trainer.html#trainer-flags
## Before your PR is "Ready for review"
- [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?
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@titu1994 | {
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} | Current progress on tn for spanish. Throwing up here as a draft to finish on personal account as community contribution. At the moment, fractions, decimals, cardinals, dates, and ordinals are functioning and part of main normalization flow for tn_es. Also all pytests are valid. Need to adjust copyright headers. | {
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} | null | null | null | **Describe the bug**
I am able to load a pretrained version of Quartznet, and when I explore the encodings, I can't figure out a way to convert them into something I can analyze, like a list, np.ndarray or pandas Series. This is for a voice analysis project and I am looking to access the encodings directly. thanks so much
**Steps/Code to reproduce bug**
https://colab.research.google.com/github/tugstugi/dl-colab-notebooks/blob/master/notebooks/NVidiaQuartzNetMic.ipynb
Go here, Run All using the Co-Lab. Then after putting in your voice for the 10s sample recording,
1. Add breakpoint just after line 63
<img width="533" alt="image" src="https://user-images.githubusercontent.com/13772726/151621762-82e07180-21b7-46be-a562-bbfb300bce14.png">
to
<img width="542" alt="image" src="https://user-images.githubusercontent.com/13772726/151621800-8d66a5dd-b26a-481c-b90a-8a6133d7989f.png">
`import pdb; pdb.set_trace()`
2. Add new cell at bottom with `wav_to_text(create_manifest('test.wav'))`
3. once PDB debugger opens, type `encoded` and see this result:
<img width="550" alt="image" src="https://user-images.githubusercontent.com/13772726/151621897-065bef93-6ea7-4820-b785-273603db8f72.png">
I just want to convert that `nemo.core.neural_types.NmTensor` into a list, pandas series, etc. Something with numbers that I can look at.
```
encoded, encoded_len = jasper_encoder(audio_signal=processed_signal,
length=processed_signal_len)
```
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} | # Bugfix
- Fixes an issue where Wandb will not plot the updated configs even after calling model.cfg = new_cfg.
Closes #3372
Signed-off-by: smajumdar <[email protected]> | {
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- Add support for `clamp` kwarg in rnnt loss numba.
- Update configs to support this argument.
# Note
- This is different from using Pytorch Lightning `gradient_clip_val>0` as that will clip all gradients of the entire network by the limits of the clamp, this will only clip the gradient of the joint tensor itself and not the rest of the network. | {
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} | null | null | null | Reading through https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/hifigan/hifigan.yaml, I noticed that hifigan specifies the `optim` and `sched` on the same level, on quick check in other config files the `sched` is a sub parameter of `optim`. Any special reason?
Checking out the docs https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/core/core.html?highlight=scheduler#optimization, https://github.com/NVIDIA/NeMo/blob/main/docs/source/core/core.rst#optimization, I notice that it is quite misleading. It is not evident which version is the correct one.
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} | null | null | null | **Describe the bug**
I am trying to do Ptuning with Nemo with my custom Megtraon gpt2 and tokenizers that were trained using huggingface tokenizers. But when I provide all the configs and run, I get a vocabulary error.
```
root@ptuning-7d8497bf49-r8spb:/workspace/sean/ptuning# PYTHONPATH=NeMo-feature_ptune python ptune_misogyny.py
NOTE! Installing ujson may make loading annotations faster.
[NeMo W 2022-01-28 06:50:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py:107: LightningDeprecationWarning: Argument `num_nodes` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6. Notice that it will be overriden by the trainer setting.
rank_zero_deprecation(
[NeMo W 2022-01-28 06:50:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py:113: LightningDeprecationWarning: Argument `sync_batchnorm` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6. Notice that it will be overriden by the trainer setting.
rank_zero_deprecation(
[NeMo W 2022-01-28 06:50:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:324: LightningDeprecationWarning: Passing <nemo.collections.nlp.parts.nlp_overrides.NLPDDPPlugin object at 0x7fc6c7dcd610> `strategy` to the `plugins` flag in Trainer has been deprecated in v1.5 and will be removed in v1.7. Use `Trainer(strategy=<nemo.collections.nlp.parts.nlp_overrides.NLPDDPPlugin object at 0x7fc6c7dcd610>)` instead.
rank_zero_deprecation(
Using 16bit native Automatic Mixed Precision (AMP)
[NeMo W 2022-01-28 06:50:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py:48: LightningDeprecationWarning: Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7. Use `max_steps = -1` instead.
rank_zero_deprecation(
[NeMo W 2022-01-28 06:50:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:147: LightningDeprecationWarning: Setting `Trainer(checkpoint_callback=False)` is deprecated in v1.5 and will be removed in v1.7. Please consider using `Trainer(enable_checkpointing=False)`.
rank_zero_deprecation(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
[NeMo I 2022-01-28 06:50:58 exp_manager:283] Experiments will be logged at /workspace/sean/ptuning/nemo_experiments/PTuneTextClassification/2022-01-28_06-50-58
[NeMo I 2022-01-28 06:50:58 exp_manager:648] TensorboardLogger has been set up
[NeMo W 2022-01-28 06:50:58 exp_manager:901] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo W 2022-01-28 06:50:58 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:243: LightningDeprecationWarning: `ModelCheckpoint(every_n_val_epochs)` is deprecated in v1.4 and will be removed in v1.6. Please use `every_n_epochs` instead.
rank_zero_deprecation(
[NeMo I 2022-01-28 06:50:58 ptune_text_classification_model:375] Dataloader config or file_path for the validation is missing, so no data loader for test is created!
[NeMo I 2022-01-28 06:50:58 tokenizer_utils:193] Getting Megatron tokenizer for pretrained model name: huggingface_model_tamil and custom vocab file: /workspace/sean/ptuning/tamil_gpt_tokenizer/vocab.json
[NeMo I 2022-01-28 06:50:58 tokenizer_utils:125] Getting HuggingFace AutoTokenizer with pretrained_model_name: huggingface_model_tamil, vocab_file: /workspace/sean/ptuning/tamil_gpt_tokenizer/vocab.json, special_tokens_dict: {}, and use_fast: False
Using sep_token, but it is not set yet.
Using cls_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using mask_token, but it is not set yet.
[NeMo I 2022-01-28 06:51:08 tokenizer_utils:193] Getting Megatron tokenizer for pretrained model name: huggingface_model_tamil and custom vocab file: /tmp/tmpyb_4gkds/749983edcb2a4816bacb8628b0e6beeb_vocab.json
[NeMo I 2022-01-28 06:51:08 tokenizer_utils:125] Getting HuggingFace AutoTokenizer with pretrained_model_name: huggingface_model_tamil, vocab_file: /tmp/tmpyb_4gkds/749983edcb2a4816bacb8628b0e6beeb_vocab.json, special_tokens_dict: {}, and use_fast: False
Using sep_token, but it is not set yet.
Using cls_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using mask_token, but it is not set yet.
[NeMo I 2022-01-28 06:51:08 megatron_gpt_model:754] Padded vocab_size: 32000, original vocab_size: 32000, dummy tokens: 0.
[NeMo I 2022-01-28 06:51:09 tokenizer_utils:193] Getting Megatron tokenizer for pretrained model name: huggingface_model_tamil and custom vocab file: /tmp/tmpyb_4gkds/749983edcb2a4816bacb8628b0e6beeb_vocab.json
[NeMo I 2022-01-28 06:51:09 tokenizer_utils:125] Getting HuggingFace AutoTokenizer with pretrained_model_name: huggingface_model_tamil, vocab_file: /tmp/tmpyb_4gkds/749983edcb2a4816bacb8628b0e6beeb_vocab.json, special_tokens_dict: {}, and use_fast: False
Using sep_token, but it is not set yet.
Using cls_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using mask_token, but it is not set yet.
[NeMo I 2022-01-28 06:51:09 megatron_gpt_model:754] Padded vocab_size: 32000, original vocab_size: 32000, dummy tokens: 0.
[NeMo I 2022-01-28 06:51:10 save_restore_connector:154] Model MegatronGPTModel was successfully restored from /workspace/sean/ptuning/tamil_gpt_344m.nemo.
[NeMo E 2022-01-28 06:51:10 ptune_text_classification_model:105] class NOT is not part of the vocabulary. Please add it to your vocab
[NeMo E 2022-01-28 06:51:10 ptune_text_classification_model:105] class OFF is not part of the vocabulary. Please add it to your vocab
Traceback (most recent call last):
File "ptune_misogyny.py", line 41, in <module>
model_ptune = PTuneTextClassificationModel(cfg=config.model, trainer=trainer)
File "/workspace/sean/ptuning/NeMo-feature_ptune/nemo/collections/nlp/models/text_classification/ptune_text_classification_model.py", line 106, in __init__
self.allowed_vocab_ids = set(self.vocab[token_wrapper(k)] for k in cfg.dataset.classes)
File "/workspace/sean/ptuning/NeMo-feature_ptune/nemo/collections/nlp/models/text_classification/ptune_text_classification_model.py", line 106, in <genexpr>
self.allowed_vocab_ids = set(self.vocab[token_wrapper(k)] for k in cfg.dataset.classes)
KeyError: 'ĠNOT'
root@ptuning-7d8497bf49-r8spb:/workspace/sean/ptuning# PYTHONPATH=NeMo-feature_ptune python ptune_misogyny.py
NOTE! Installing ujson may make loading annotations faster.
[NeMo W 2022-01-28 06:52:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py:107: LightningDeprecationWarning: Argument `num_nodes` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6. Notice that it will be overriden by the trainer setting.
rank_zero_deprecation(
[NeMo W 2022-01-28 06:52:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp.py:113: LightningDeprecationWarning: Argument `sync_batchnorm` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6. Notice that it will be overriden by the trainer setting.
rank_zero_deprecation(
[NeMo W 2022-01-28 06:52:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:324: LightningDeprecationWarning: Passing <nemo.collections.nlp.parts.nlp_overrides.NLPDDPPlugin object at 0x7fe7e13cd640> `strategy` to the `plugins` flag in Trainer has been deprecated in v1.5 and will be removed in v1.7. Use `Trainer(strategy=<nemo.collections.nlp.parts.nlp_overrides.NLPDDPPlugin object at 0x7fe7e13cd640>)` instead.
rank_zero_deprecation(
Using 16bit native Automatic Mixed Precision (AMP)
[NeMo W 2022-01-28 06:52:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py:48: LightningDeprecationWarning: Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7. Use `max_steps = -1` instead.
rank_zero_deprecation(
[NeMo W 2022-01-28 06:52:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:147: LightningDeprecationWarning: Setting `Trainer(checkpoint_callback=False)` is deprecated in v1.5 and will be removed in v1.7. Please consider using `Trainer(enable_checkpointing=False)`.
rank_zero_deprecation(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
[NeMo I 2022-01-28 06:52:33 exp_manager:283] Experiments will be logged at /workspace/sean/ptuning/nemo_experiments/PTuneTextClassification/2022-01-28_06-52-33
[NeMo I 2022-01-28 06:52:33 exp_manager:648] TensorboardLogger has been set up
[NeMo W 2022-01-28 06:52:33 exp_manager:901] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo W 2022-01-28 06:52:33 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:243: LightningDeprecationWarning: `ModelCheckpoint(every_n_val_epochs)` is deprecated in v1.4 and will be removed in v1.6. Please use `every_n_epochs` instead.
rank_zero_deprecation(
[NeMo I 2022-01-28 06:52:33 ptune_text_classification_model:375] Dataloader config or file_path for the validation is missing, so no data loader for test is created!
[NeMo I 2022-01-28 06:52:34 tokenizer_utils:193] Getting Megatron tokenizer for pretrained model name: huggingface_model_tamil and custom vocab file: /workspace/sean/ptuning/tamil_gpt_tokenizer/vocab.json
[NeMo I 2022-01-28 06:52:34 tokenizer_utils:125] Getting HuggingFace AutoTokenizer with pretrained_model_name: huggingface_model_tamil, vocab_file: /workspace/sean/ptuning/tamil_gpt_tokenizer/vocab.json, special_tokens_dict: {}, and use_fast: False
Using sep_token, but it is not set yet.
Using cls_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using mask_token, but it is not set yet.
[NeMo I 2022-01-28 06:52:44 tokenizer_utils:193] Getting Megatron tokenizer for pretrained model name: huggingface_model_tamil and custom vocab file: /tmp/tmpfxcnfv61/749983edcb2a4816bacb8628b0e6beeb_vocab.json
[NeMo I 2022-01-28 06:52:44 tokenizer_utils:125] Getting HuggingFace AutoTokenizer with pretrained_model_name: huggingface_model_tamil, vocab_file: /tmp/tmpfxcnfv61/749983edcb2a4816bacb8628b0e6beeb_vocab.json, special_tokens_dict: {}, and use_fast: False
Using sep_token, but it is not set yet.
Using cls_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using mask_token, but it is not set yet.
[NeMo I 2022-01-28 06:52:44 megatron_gpt_model:754] Padded vocab_size: 32000, original vocab_size: 32000, dummy tokens: 0.
[NeMo I 2022-01-28 06:52:45 tokenizer_utils:193] Getting Megatron tokenizer for pretrained model name: huggingface_model_tamil and custom vocab file: /tmp/tmpfxcnfv61/749983edcb2a4816bacb8628b0e6beeb_vocab.json
[NeMo I 2022-01-28 06:52:45 tokenizer_utils:125] Getting HuggingFace AutoTokenizer with pretrained_model_name: huggingface_model_tamil, vocab_file: /tmp/tmpfxcnfv61/749983edcb2a4816bacb8628b0e6beeb_vocab.json, special_tokens_dict: {}, and use_fast: False
Using sep_token, but it is not set yet.
Using cls_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using mask_token, but it is not set yet.
[NeMo I 2022-01-28 06:52:45 megatron_gpt_model:754] Padded vocab_size: 32000, original vocab_size: 32000, dummy tokens: 0.
[NeMo I 2022-01-28 06:52:45 save_restore_connector:154] Model MegatronGPTModel was successfully restored from /workspace/sean/ptuning/tamil_gpt_344m.nemo.
[NeMo E 2022-01-28 06:52:46 ptune_text_classification_model:105] class தாக்குதல்-அல்ல is not part of the vocabulary. Please add it to your vocab
[NeMo E 2022-01-28 06:52:46 ptune_text_classification_model:105] class தாக்குதல் is not part of the vocabulary. Please add it to your vocab
Traceback (most recent call last):
File "ptune_misogyny.py", line 41, in <module>
model_ptune = PTuneTextClassificationModel(cfg=config.model, trainer=trainer)
File "/workspace/sean/ptuning/NeMo-feature_ptune/nemo/collections/nlp/models/text_classification/ptune_text_classification_model.py", line 106, in __init__
self.allowed_vocab_ids = set(self.vocab[token_wrapper(k)] for k in cfg.dataset.classes)
File "/workspace/sean/ptuning/NeMo-feature_ptune/nemo/collections/nlp/models/text_classification/ptune_text_classification_model.py", line 106, in <genexpr>
self.allowed_vocab_ids = set(self.vocab[token_wrapper(k)] for k in cfg.dataset.classes)
KeyError: 'Ġதாக்குதல்-அல்ல'
```
From the error trace, I can infer that at first, the model is loading the custom vocab file, but on the second time it is loading the default vocab file(I assume that is English), so I tried replacing the classes to English but still I am getting same error
**Steps/Code to reproduce bug**
My configurations
```
trainer:
gpus: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1]
num_nodes: 1
max_epochs: 100
max_steps: null # precedence over max_epochs
accumulate_grad_batches: 1 # accumulates grads every k batches
gradient_clip_val: 0.0
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
accelerator: ddp
log_every_n_steps: 1 # Interval of logging.
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
checkpoint_callback: False # Provided by exp_manager
logger: False # Provided by exp_manager
model:
tensor_model_parallel_size: 1 # tensor model parallel size used in the LM model
seed: 1234
nemo_path: null # filename to save the model and associated artifacts to .nemo file
use_lm_finetune: False # whether fine tune the language model
pseudo_token: '[PROMPT]' # pseudo prompt tokens
tokenizer:
library: 'megatron'
type: 'GPT2BPETokenizer'
model: "huggingface_model_tamil"
vocab_file: "tamil_gpt_tokenizer/vocab.json"
merge_file: "tamil_gpt_tokenizer/merges.txt"
language_model:
nemo_file: tamil_gpt_344m.nemo
prompt_encoder:
template: [3, 3, 0]
dropout: 0.0
num_layers: 2
dataset:
classes: ['தாக்குதல்-அல்ல','தாக்குதல்'] #['positive', 'neutral', 'negative']
#['எதுவும்-இல்லை','தவறான-நடத்தை','எதிர்-பேச்சு','பெண் -ெறுப்பு','இனவெறி','நம்பிக்கை-பேச்சு','ஓரினச்சேர்க்கை-வெறுப்பு','டிரான்ஸ்ஃபோபிக்']
train_ds:
file_path: data/offensive/train.txt
batch_size: 8
shuffle: true
num_samples: -1 # number of samples to be considered, -1 means all the dataset
num_workers: 3
drop_last: false
pin_memory: false
validation_ds:
file_path: null
batch_size: 8
shuffle: false
num_samples: -1 # number of samples to be considered, -1 means all the dataset
num_workers: 3
drop_last: false
pin_memory: false
test_ds:
file_path: data/offensive/test.txt
batch_size: 8
shuffle: false
num_samples: -1 # number of samples to be considered, -1 means all the dataset
num_workers: 3
drop_last: false
pin_memory: false
optim:
name: adam
lr: 1e-5
# optimizer arguments
betas: [0.9, 0.999]
weight_decay: 0.0005
# scheduler setup
sched:
name: WarmupAnnealing
# Scheduler params
warmup_steps: null
warmup_ratio: 0.1
last_epoch: -1
# pytorch lightning args
monitor: val_loss
reduce_on_plateau: false
# List of some sample queries for inference after training is done
infer_samples: [
'பிராமின்ஸ்லையும் மனசாட்சி உள்ள ஒரு மனுஷன் இருக்கான் யா சூப்பர் தல.',
'ஆமா அவன் மலையாளி பஞ்சம் பிழைக்க வந்த வந்தேரி மலையாள கிறுத்துவ தேவிடியா மவன் ',
'மதிப்பிற்குறிய ஆனந்த் சீனிவாசன் போன்ற முற்போக்கான எண்ணம் உள்ள பிராமண சமூதாயத்தில் தங்களை வாழ்த்துவதில் பெருமகிழ்சி கொள்கிறேன்.; அப்பிராமண பள்ளியில் பாதிக்கப்பட்டவர்கள் பிராமண குழந்தைகளும் இருக்கும் போது இந்த புகார்கள் எப்படி பிராமண எதிர்பாகும் எனபதை பெரியமூளை உள்ள மதுவாந்திக்கு புரியவைக்க; யாராலும் முடியாது; அப்படியா பட்ட மூளை....',
]
exp_manager:
exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments"
name: "PTuneTextClassification" # The name of your model
create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger
create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback
``````
**Expected behavior**
Ptuning should start
**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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Can I use NeMo on a gpu without cudas in it?
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
As a guy who does not have access to a Nvidia gpu I always find it hard to work with ai related projects,
I understand that the gpu is so much faster than the cpu that it can run some tasks better but it would be really awesome if I could also get to experience it without having to buy an rtx for the price of my rent x 2, just saying facts I love Nvidia no matter how much it costs I just cant afford it yet and would like to still go into production with some of this cool new libraries like nemo whether or not I need to pay fees or legally can do it doesn't matter, what's matter is making stuff that matter like bringing voice recognition to everybody life in new ways by as many people as we can.
To be extremely specific I mean that I want to be able to use those ai projects like NeMo on the client side to do tasks like ASR (Automatic speech recognition) without having to use all or some of a big gpu power, I understand we need big gpus to train the models but us developers just want to be able to bring speech recognition in the same level of accuracy as in Nvidia gpus on the cpu or ram or whatever with slower interactions but the same level of accuracy or some way for us to interact with nemo on like a laptop, phone browser, or windows pc, offline, simply speech recognition
What I noticed is that in deepspeech for example the level of accuracy is like super poor if you don't have Cuda, and what I ask is why, cant we do better? cant we have it so it will take a little longer but have the same level of accuracy? in speech recognition that will be a lower WER
Why cant we have nvidia libraries work the same but take more time to execute on the cpu
**Describe the solution you'd like**
I would like to be able to truly transfer the cuda computations into the cpu or ram with like a level of caching or whatever so it will run the same on computers with bad gpus without having to host it on a machine that has Nvidia gpus attached to it, the whole point of training a large model into a small "smart" file is that it will work without requiring like 64GB GPU to run it, and we prove that we can, I know this, I know we can, if by now you think I don't know that we can then you misunderstood me, the problem is that it works poorly, What I'm saying or claiming is that the same code running on a Nvidia gpu will not only return the answer faster from a pretrained model but also will have higher accuracy of detection, and I think that that's a messed up math some where
A clear and concise description of what you want to happen.
I want to be able to bring voice driven apps in all mediums shapes sizes and devices without paying a billion dollars for hosting and possibly having it work fast enough on offline devices just to prove the point it is not requiring an external server overhead time, and by saying that it needs to work offline I simply mean that it runs locally and its therefor faster and better in many ways.
I think humans in 2022 deserve that, and developers are the key to bring that, please is all I have to say about that.
Provide a code snippet on how new APIs/changes would be used by others.
```py
import ASR from NeMo
while True:
transcription = ASR(lang='auto')
print(transcription)
```
The above example showcase the Perfect World example, the `lang` auto -> can even be an optional variable, the point is that that easily we will be able to make our apps have speech recognition, it will, unless configured otherwise, find the default microphone, cache any models into the ram, listen for speech, detect silence, process voice chunks in real time, normalize and remove silence, predict the text, optionally add a callback param here for real time output into another function or thread from the main while loop, and then also run it through a grammar corrector, a language model, and return finally the output for us to do simply > if txt contain search blah then scroll to text blah
this alone is a very powerful feature to have on many applications, it can be the future we were all waiting for, imagine a world where you can talk with everything you get, buy, or have since you open sourced this awesome ASR tech lol
**Describe alternatives you've considered**
You name it I tried it, vosk cmusphinx ibm watson google cloud amazon transcriptions hmmmm python sr, deepspeech, anyways I basically googled speech recognition 10 years ago, every day, ever since then, following every link I could find from the never growing results.
A clear and concise description of any alternative solutions or features you've considered.
I usually avoid any ai related stuff and try to reinvent it on the cpu since none of you guys libraries work on my low spec pc and I cant afford to upgrade it yet
**Additional context**
I really just want to be able to do speech recognition in English with the same level of accuracy that exists today on my pc which have 24gb ram i5 cpu and 2gb amd gpu
and I also want to compile this later with pyinstaller and send it around or whatever so other people can talk to their pc and enjoy it and maybe some companies will even make your toaster or fridge talk one day using this tech which is beyond me and you this is good for the future of man kind lol a talking fridge is not really that good but a talking pc is. (lol plz don't steal my idea)
Add any other context or screenshots about the feature request here.
Imagine a screenshot of a guy almost falling a sleep near the tv and then he says turn off the movie and it does lol its a video by now but you get the point that's like a comic book meme screenshot lol | {
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} | This PR refactor Megatron T5 to a modular base encoder-decoder class hierarchy, and a T5 class which customizes the base class. The goal of the refactor is to support multiple encoder-decoder models with minimal code redundancy.
# Refactor High Level Description
Hierarchy tree is expressed below via list alignment (i.e., right alignment for child class).
* `MegatronModule` sub-classes:
* `MegatronTransformerEncoderModule`/`MegatronTransformerDecoderModule` - various architectures for manipulation of hidden --> hidden
* `MegatronTransformerEncoderDecoderModule` - contains above classes and generates required joint encoder/decoder mask given independent masks, and reparametrization (e.g., vae)
* `TokensEncoderDecoderModule` - modality-specific (e.g., tokens-tokens) model which map between pre-processed data (e.g., token ids) and encoder/decoder continuous input (i.e., hidden); handles embedders (i.e., embedding + positional embedding); handles projection from hidden to data distribution (e.g., project from hidden dimensions to the number of tokens)
* `NLPModel` sub-classes:
* `MegatronBaseModel` - common initialization of Megatron
* `MegatronLMEncoderDecoderModel` - modality-specific (e.g., text-text) model which pre-process raw data (e.g., tokenizer pre-process text to token ids); generic modality-specific training/testing/inference/loss methods
* `MegatronT5Model` - a specific model (i.e., T5) which prepares datasets (i.e., with corresponding data augmentation), and model-specific features (e.g., sentinels in tokenizer)
# Notable Changes
1. Encoder/decoder mask is 2D with `1` for active element, and `0` for masked element (i.e., as opposed to 3D Megatron mask with `False` for active and `True` for masked). This allows easy manipulation of masks. Conversion to 3D Megatron masks is done on-the-fly.
2. Joint cross-attention mask is not precomputed, but is rather done on-the-fly. | {
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} | Add prompt tuning to docs
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} | null | null | null | **Describe the bug**
I am unable to use nemo models due to some issue with the tokenizer:
```
from nemo.collections.asr.models import EncDecCTCModel
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/nemo/collections/asr/models/__init__.py", line 18, in <module>
from nemo.collections.asr.models.classification_models import EncDecClassificationModel
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/nemo/collections/asr/models/classification_models.py", line 33, in <module>
from nemo.collections.common.metrics import TopKClassificationAccuracy
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/nemo/collections/common/__init__.py", line 16, in <module>
from nemo.collections.common import data, losses, parts, tokenizers
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/nemo/collections/common/tokenizers/__init__.py", line 24, in <module>
from nemo.collections.common.tokenizers.youtokentome_tokenizer import YouTokenToMeTokenizer
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/nemo/collections/common/tokenizers/youtokentome_tokenizer.py", line 17, in <module>
import youtokentome as yttm
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/youtokentome/__init__.py", line 1, in <module>
from .youtokentome import BPE
File "/home/linux/miniconda3/envs/env/lib/python3.9/site-packages/youtokentome/youtokentome.py", line 4, in <module>
import _youtokentome_cython
ImportError: /home/linux/miniconda3/envs/env/lib/python3.9/site-packages/_youtokentome_cython.cpython-39-x86_64-linux-gnu.so: undefined symbol: _ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE7reserveEv
```
**Steps/Code to reproduce bug**
```
from nemo.collections.asr.models import EncDecCTCModel
```
**Environment overview (please complete the following information)**
- Environment location: local
- Method of NeMo install: `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]`
**Environment details**
- OS version: `Pop OS 21.10` (similar issue on Ubuntu 21.04)
- PyTorch version: `1.10.1`
- Python version: `3.9`
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} | # Changelog
- Build a raw changelog automatically for every new NeMo release based on a comparison from PRs between newest version and previous version.
- Raw changelog is then tidied to display on final page.
# Usage
Call the builder and visit the GitHub Action that builds the changelog. It's "Print Changelog" section will have the raw changelog, which needs to be tidied up.
```sh
git tag vX.YZ
git push origin vX.Y.Z
```
## Redo steps
If something should go wrong tag can be deleted using
```sh
git tag vX.YZ -d
git push origin vX.Y.Z -d
```
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} | Signed-off-by: Zhilin Wang <[email protected]>
Design based on attached diagram
[UML proposed dialogue1.pdf](https://github.com/NVIDIA/NeMo/files/7946212/UML.proposed.dialogue1.pdf)
Currently supports SGD-QA (Bert-based) and Huggingface GPT2 models, and planning to support other models. | {
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} | Signed-off-by: Virginia Adams <[email protected]>
Fixed Bugs 3509889 and 3509839:
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3509889 [ML-NeMo][Tesla A30] Token_Classification-BioMegatron.ipynb contains a typo
https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3509839 [ML-NeMo][Tesla A30] Relation_Extraction-BioMegatron.ipynb contains a typo
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} | # Bugfix
- Enforce UTF-8 on all file reading and writing. Fixes a bug with some locales not enforcing utf 8.
- If you are still facing Unicode errors, you have to pass `LC_ALL=en_US.UTF-8` before launching your python command.
Fixes https://github.com/NVIDIA/NeMo/issues/3452 | {
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- Enforce utf-8 on all file reading and writing inside nemo. | {
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} | Fixes Duplicate cells in entity linking notebook bug
Bug 3509934 (https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3509934 [ML-NeMo][Quadro GV100] Entity_Linking_Medical.ipynb tutorial notebook contains duplicate cells)
Signed-off-by: Virginia Adams <[email protected]> | {
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} | Fixes Duplicate cells in entity linking notebook bug
Bug 3509934 (https://nvbugswb.nvidia.com/NVBugs5/redir.aspx?url=/3509934 [ML-NeMo][Quadro GV100] Entity_Linking_Medical.ipynb tutorial notebook contains duplicate cells)
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* Add `verify_speakers` helper function
* Update docs accordingly | {
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} | # Changelog
- Add script to convert HuggingFace ASR datasets to preprocessed NeMo compatible datasets.
- These datasets can then be converted to tarred datasets as always.
# NOTE:
- The converted datasets DO NOT PERFORM TEXT PREPROCESING. That should be handled by the user at manifest level.
- The offline mode dataset transformation is fast but requires 3 copies of the dataset in its entirety at any given time - so it needs a lot of disk space. | {
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```
W0125 21:12:08.102883 140527509010240 schema.py:118] Validation schema not found for nemo.collections.tts.models.Tacotron2Model.
That means Riva does not yet support a pipeline for this network and likely will not work with it.
I0125 21:12:08.860513 140527509010240 artifacts.py:54] Found model at ./model_weights.ckpt
I0125 21:12:08.861304 140527509010240 artifacts.py:120] Retrieved artifacts: dict_keys(['model_config.yaml'])
E0125 21:12:08.861998 140527509010240 cookbook.py:58] Your NeMo model class (nemo.collections.tts.models.tacotron2.Tacotron2Model) is not Exportable.
```
regardless that on https://docs.nvidia.com/deeplearning/riva/user-guide/docs/service-tts.html#model-deployment, mention both FastPitch+HifiGAN and Tacotron2+Waveglow. | {
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} | Several fixes to get the FastSpeech2-HiFiGAN training to work (#3360). The normal training command should work now.
- Config reference to n_mels corrected
- Fix output type issue when `splices` is None for FS2-HiFiGAN
- Removed `spec` from calls to forward function in FS2-HiFiGAN
- Changed type of FS2-HiFiGAN output to AudioSignal
- Added keywords to functions that require kwargs due to typing
- `precessor` -> `preprocessor` typo fixes
- FastSpeech2 duration loss type fixed, model output type changed to reflect reality
Signed-off-by: Jocelyn Huang <[email protected]> | {
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* update TitaNet conf, to reflect what was published in paper | {
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Thanks | {
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} | null | null | null | **Describe the bug**
When installing nemo[all] via pip from source, some of the data files used to build the french ITN grammar are missing from site-packages (e.g. those in folders electronic, measurements, money, numbers, time). These folders are the only ones that are not packages i.e. missing `__init__.py` files.
**Steps/Code to reproduce bug**
`
python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
ls /venv/nemo/lib/python3.9/site-packages/nemo_text_processing/inverse_text_normalization/fr/data/
`
> `fractions.tsv __init__.py months.tsv ordinals __pycache__ roman whitelist.tsv`
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: venv on local machine
- Method of NeMo install:
`apt-get update && apt-get install -y libsndfile1 ffmpeg`
`pip install Cython`
`python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]`
**Environment details**
OS version - Ubuntu 18.04.5 LTS
PyTorch version - 1.10.1
Python version - 3.8.11
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} | null | null | null | Hello and so happy to see you use Pytorch-Lightning! :tada:
Just wondering if you already heard about quite the new **Pytorch Lightning (PL) ecosystem CI** where we would like to invite you to... You can check out our blog post about it: [Stay Ahead of Breaking Changes with the New Lightning Ecosystem CI](https://devblog.pytorchlightning.ai/stay-ahead-of-breaking-changes-with-the-new-lightning-ecosystem-ci-b7e1cf78a6c7) :zap:
As you use PL framework for your cool project, we would like to enhance your experience and offer you safe updates to our future releases. At this moment, you run tests with a particular PL version, but it may accidentally happen that the next version will be incompatible with your project... :confused: We do not intend to change anything on our project side, but still here we have a solution - ecosystem CI with testing both - your and our latest development head we can find it very early and prevent releasing eventually bad version... :+1:
**What is needed to do?**
- have some tests, including PL integration
- add config to ecosystem CI - https://github.com/PyTorchLightning/ecosystem-ci
**What will you get?**
- scheduled nightly testing configured for development/stable versions
- slack notification if something went wrong to investigate
- testing also on multi-GPU machine as our gift to you :rabbit: | {
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} | null | null | null | **Describe the bug**
Below error is thrown while importing `nemo.collections.asr` package
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/collections/asr/losses/__init__.py", line 15, in <module>
from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/collections/asr/losses/angularloss.py", line 18, in <module>
from nemo.core.classes import Loss, Typing, typecheck
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/core/classes/__init__.py", line 16, in <module>
from nemo.core.classes.common import (
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/core/classes/common.py", line 32, in <module>
from nemo.utils import logging
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/utils/__init__.py", line 22, in <module>
from nemo.utils.lightning_logger_patch import add_memory_handlers_to_pl_logger
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/nemo/utils/lightning_logger_patch.py", line 18, in <module>
import pytorch_lightning as pl
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/pytorch_lightning/__init__.py", line 20, in <module>
from pytorch_lightning.callbacks import Callback # noqa: E402
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/pytorch_lightning/callbacks/__init__.py", line 14, in <module>
from pytorch_lightning.callbacks.base import Callback
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/pytorch_lightning/callbacks/base.py", line 26, in <module>
from pytorch_lightning.utilities.types import STEP_OUTPUT
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/pytorch_lightning/utilities/types.py", line 25, in <module>
from torchmetrics import Metric
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/torchmetrics/__init__.py", line 14, in <module>
from torchmetrics import functional # noqa: E402
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/torchmetrics/functional/__init__.py", line 14, in <module>
from torchmetrics.functional.audio.pit import permutation_invariant_training, pit, pit_permutate
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/torchmetrics/functional/audio/__init__.py", line 26, in <module>
from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality # noqa: F401
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/torchmetrics/functional/audio/pesq.py", line 20, in <module>
import pesq as pesq_backend
File "/home/local/miniconda3/envs/nemo1/lib/python3.8/site-packages/pesq/__init__.py", line 6, in <module>
from .cypesq import cypesq, cypesq_retvals, cypesq_error_message as pesq_error_message
File "pesq/cypesq.pyx", line 1, in init cypesq
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
**Steps/Code to reproduce bug**
```
import nemo
import nemo.collections.asr as nemo_asr
```
**Environment overview (please complete the following information)**
- conda environment: conda create -n nemo python=3.8 -y
- Method of NeMo install: pip install nemo_toolkit['all']
**Environment details**
- OS version - Ubuntu 20.04
- PyTorch version - 1.10.1
- PyTorch Lightning version - 1.5.8
- Python version - 3.8.12
- Numpy version - 1.21.5
- Numba version - 0.55.0
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} | null | null | null | **Describe the bug**
Trained the QuartzNet15x5 from scratch on the dataset An4 for 200 epochs. When I try to calculate the WER using the method given in the first tutorial, I noticed a strange dependence of the batch size on the size of the resulting error value. The larger the batch size, the smaller the error, what is the reason for this behaviour?
Results looks like that:
4 batch wer0.51
8 batch wer0.46
16 batch wer0.44
32 batch wer0.439
64 batch wer0.434
128 batch wer0.430
256 batch wer0.434
512 batch wer0.404
1024 batch wer0.399
2048 batch wer0.397
4096 batch wer0.398
**Steps/Code to reproduce bug**
# Bigger batch-size = bigger throughput
params['model']['validation_ds']['batch_size'] = 4096
# Setup the test data loader and make sure the model is on GPU
first_asr_model.setup_test_data(test_data_config=params['model']['validation_ds'])
first_asr_model.cuda()
# We will be computing Word Error Rate (WER) metric between our hypothesis and predictions.
# WER is computed as numerator/denominator.
# We'll gather all the test batches' numerators and denominators.
wer_nums = []
wer_denoms = []
# Loop over all test batches.
# Iterating over the model's `test_dataloader` will give us:
# (audio_signal, audio_signal_length, transcript_tokens, transcript_length)
# See the AudioToCharDataset for more details.
for test_batch in first_asr_model.test_dataloader():
test_batch = [x.cuda() for x in test_batch]
targets = test_batch[2]
targets_lengths = test_batch[3]
log_probs, encoded_len, greedy_predictions = first_asr_model(
input_signal=test_batch[0], input_signal_length=test_batch[1]
)
# Notice the model has a helper object to compute WER
first_asr_model._wer.update(greedy_predictions, targets, targets_lengths)
_, wer_num, wer_denom = first_asr_model._wer.compute()
first_asr_model._wer.reset()
wer_nums.append(wer_num.detach().cpu().numpy())
wer_denoms.append(wer_denom.detach().cpu().numpy())
# Release tensors from GPU memory
del test_batch, log_probs, targets, targets_lengths, encoded_len, greedy_predictions
# We need to sum all numerators and denominators first. Then divide.
print(f"WER = {sum(wer_nums)/sum(wer_denoms)}")
**Expected behavior**
I thought that nothing should change and the size of the validation batch only determines the speed at which the error is calculated
**Environment overview (please complete the following information)**
- Environment location: [Collab]
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} | null | null | null | **Describe the bug**
I can train WaveGlow models, but when I try to load checkpoints or `.nemo` files back, I am not able to.
**Steps/Code to reproduce bug**
Train, in a Google Colab GPU environment with or without Apex, a Waveglow 16khz model for one epoch. When attempting to load it back, either the checkpoint or the `.nemo` archive, I get the following error consistently:
```
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/supporters.py", line 589, in _nested_calc_num_data
raise TypeError(f"Expected data to be int, Sequence or Mapping, but got {type(data).__name__}")
TypeError: Expected data to be int, Sequence or Mapping, but got NoneType
```
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
1. Train a new WaveGlow model for one epoch using the defaults, following the example in https://github.com/NVIDIA/NeMo/blob/main/examples/tts/waveglow.py. I have barely altered this, only adding a snippet to restore a model from a file.
2. Use `model = WaveGlowModel.restore_from(path_to_my_model)`. It will fail with the above error. Loading the checkpoint also fails with the same error.
**Expected behavior**
I expect to be able to load either checkpoints or `.nemo` files.
**Environment overview (please complete the following information)**
- Environment location: colab
- Method of NeMo install: [pip install or from source]. pip
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 18.04 .5 LTS
- PyTorch version: 1.10.0+cu111
- Python version: Python 3.7.12
**Additional context**
I'm not sure if the fact that it's 16khz matters, but it's the only meaningful deviation have from the example. Some online research suggests that this is because there's some mixed CPU/GPU training going on, but I wasn't able to find much more information than that, or how to fix it.
I will also note, that I expect a `.nemo` file for each checkpoint, and this does not seem to be the behavior - there seems to be just one `.nemo` file and many large `.ckpt` files, but this may be unrelated. | {
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I am trying to train a TTS model. I followed the steps of the documentation but still cannot train the model.
I always get RuntimeError: Cuda out of memory. So the program crash.
**Steps/Code to reproduce bug**
The way to reproduce the error is to execute this command from the console in a linux:
python3 tacotron2.py sample_rate=22050 train_dataset=files.json validation_datasets=files.json trainer.max_epochs=1 trainer.accelerator=null trainer.check_val_every_n_epoch=1
The file tacotron2.py is the next:
https://github.com/NVIDIA/NeMo/blob/main/examples/tts/tacotron2.py
The config file for tacotron2.py is https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/tacotron2.yaml
I edited tacotron2.yaml in my computer to reduce the batch_size and num_workers
The values are:
batch_size=1
num_workers=1
So, I reduced the batch_size to the minium to try to avoid the cuda error with No success.
The file "files.json":
I started with 100.000 lines. Each line like this: (I mean the file is written as explained in the documentation)
{"audio_filepath": "4da6b70e-0108-4f75-80ae-3d71f1dd2c2b.wav", "text": "y aquí en dos palotadas hemos encontrado robustas columnas donde apoyar la grandiosa fábrica de su alcurnia", "duration": 6.84}
I tried to train the model reducing the number of lines. I Started with 100000. Then I reduced to the file "files.json" to only 100. Unfortunately, it crashed as well..
**Expected behavior**
I would like to find a way to train a TTS model managing properly the GPU memory.
**Environment overview**
Computer: MSI Apache Pro GE70 - 2PE
Operating system: Ubuntu 20.04.3 LTS
GPU: NVIDIA GTX 860 (2GB)
nemo-toolkit 1.5.1
pytorch-lightning 1.5.7
torch 1.10.1
torch-stft 0.1.4
torchaudio 0.10.1+cu102
torchmetrics 0.6.2
torchvision 0.11.2+cu102
Could you help me to solve it?
Thank you!
| {
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} | Starting of a nemo:1.5.0 container and using `./reinstall.sh` to upgrade to nemo:1.6.0rc0 the `import nemo.collections.nlp` will crash:
```
Python 3.8.12 | packaged by conda-forge | (default, Sep 29 2021, 19:52:28)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import nemo.collections.nlp
NOTE! Installing ujson may make loading annotations faster.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/workspace/nemo2/nemo/collections/nlp/__init__.py", line 15, in <module>
from nemo.collections.nlp import data, losses, models, modules
File "/workspace/nemo2/nemo/collections/nlp/models/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
File "/workspace/nemo2/nemo/collections/nlp/models/dialogue_state_tracking/__init__.py", line 15, in <module>
from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
File "/workspace/nemo2/nemo/collections/nlp/models/dialogue_state_tracking/sgdqa_model.py", line 33, in <module>
from nemo.collections.nlp.models.nlp_model import NLPModel
File "/workspace/nemo2/nemo/collections/nlp/models/nlp_model.py", line 29, in <module>
from nemo.collections.nlp.modules import BertModule
File "/workspace/nemo2/nemo/collections/nlp/modules/__init__.py", line 16, in <module>
from nemo.collections.nlp.modules.common import (
File "/workspace/nemo2/nemo/collections/nlp/modules/common/__init__.py", line 29, in <module>
from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer, get_tokenizer_list
File "/workspace/nemo2/nemo/collections/nlp/modules/common/tokenizer_utils.py", line 27, in <module>
from nemo.collections.nlp.parts.nlp_overrides import HAVE_APEX
File "/workspace/nemo2/nemo/collections/nlp/parts/nlp_overrides.py", line 33, in <module>
from nemo.collections.nlp.modules.common.megatron.module import Float16Module
File "/workspace/nemo2/nemo/collections/nlp/modules/common/megatron/__init__.py", line 15, in <module>
from nemo.collections.nlp.modules.common.megatron.megatron_bert import MegatronBertEncoder
File "/workspace/nemo2/nemo/collections/nlp/modules/common/megatron/megatron_bert.py", line 30, in <module>
from nemo.collections.nlp.modules.common.megatron.language_model import get_language_model
File "/workspace/nemo2/nemo/collections/nlp/modules/common/megatron/language_model.py", line 26, in <module>
from nemo.collections.nlp.modules.common.megatron.transformer import ParallelTransformer
File "/workspace/nemo2/nemo/collections/nlp/modules/common/megatron/transformer.py", line 24, in <module>
from apex.transformer.utils import divide as safe_divide
ModuleNotFoundError: No module named 'apex.transformer.utils'
```
It turns out, that apex is preinstalled in nemo:1.5.0, but version 0.1, hence some of the previous `HAVE_APEX` checks will fail some not. Since `apex.transformer.utils` is present only in a recent version of apex, I have added `nemo.utils.apex_check.is_megatron_supported` to check first if apex is installed and if it is recent.
Signed-off-by: Iztok Lebar Bajec <[email protected]> | {
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2. Make a path to pickled features file an attribute of `BertPunctuationCapitalizationDataset` class. | {
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} | Remove unstable and not essential link
Fix typo in vad related tutorials. | {
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} | Added P-Tuning method to use Large Megatron GPT model for downstream NLP tasks.
The `PTune_Sentiment_Analysis.ipynb` tutorial notebook shows how to use P-Tuning for financial sentiment analysis. It achieve 92% accuracy using GPT 344M model. | {
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} | # Changelog
- Add support for -1 in num_classes if vocabulary is provided for CTC char models.
Signed-off-by: smajumdar <[email protected]> | {
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} | Fixed few issues due to weight key renaming in O2 style
Signed-off-by: Yu Yao <[email protected]> | {
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- Add support for tarred dataset support for Nvidia DALI 1.11+ | {
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} | - [x] Create unify downloading and processing TTS datasets in `scripts/dataset_proccesing/tts`
- [x] Add `VocoderDataset`
- [x] Fix problem with collision in name (e.g. LibriTTS suffers) for supplementary data in `TTSDataset`
- [x] Other minor updates in `collection.tts.torch`
Signed-off-by: Oktai Tatanov <[email protected]> | {
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} | [] | closed | false | null | [] | null | null | 2022-01-21T12:06:14 | 2022-01-22T22:33:19 | 2022-01-22T22:33:19 | NONE | {
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} | null | null | null | **Describe the bug**
I am getting this error when I try to instantiate ASR model (following [this](https://github.com/NVIDIA/NeMo/blob/v1.0.0/tutorials/asr/01_ASR_with_NeMo.ipynb) tutorial):
`ValueError: num_samples should be a positive integer value, but got num_samples=0`
But it's quite strange because I am getting these logs during the run:
```
[NeMo I 2022-01-21 11:37:29 audio_to_text_dataset:38] Model level config does not container `sample_rate`, please explicitly provide `sample_rate` to the dataloaders.
[NeMo I 2022-01-21 11:37:29 audio_to_text_dataset:38] Model level config does not container `labels`, please explicitly provide `labels` to the dataloaders.
[NeMo I 2022-01-21 11:37:29 collections:173] Dataset loaded with 0 files totalling 0.00 hours
[NeMo I 2022-01-21 11:37:29 collections:174] 1172 files were filtered totalling 347.43 hours
```
Which shows correct number of files (`1172`) in training dataset.
**Steps/Code to reproduce bug**
```python
trainer = pl.Trainer(gpus=1, max_epochs=50)
train_manifest = './<folder_name>/manifests/train_manifest.json'
test_manifest = './<folder_name>/manifests/test_manifest.json'
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['validation_ds']['manifest_filepath'] = test_manifest
first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']), trainer=trainer)
```
I have tried to put full path for `train_manifest` and `test_manifest` (`/home/ubuntu/<folder_name>/manifests`), but it didn't help at all.
**Expected behavior**
Running without errors.
**Environment overview (please complete the following information)**
- Environment location: `Jupyter Notebook` in `AWS EC2` instance node
- Method of NeMo install: `pip install nemo_toolkit[all]`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.3 LTS
- PyTorch version: 1.10.1
- Python version: 3.8.12
**Additional context**
It doesn't seem that there are no files or the path to the `train` and `test` manifests are incorrect. Otherwise I couldn't open manifests via `pandas` (did this for checking purposes). The structure of the manifest looks like this:
`
{"audio_filepath": "/home/ubuntu/<folder_name>/audio/<audio_file_name>.wav", "duration": 1725.84, "text": "..."}
{"audio_filepath": "/home/ubuntu/<folder_name>/audio/<audio_file_name>.wav", "duration": 1725.84, "text": "..."}
`
where `<folder_name>` is the same as in the path for `train_manifest.json` and `test_manifest.json`. | {
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} | 1. Fix data parameters in examples of `punctuation_capitalization_train_evaluate.py` usage.
2. Standardize placeholders for paths in examples. | {
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* Broken link and typo fix | {
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