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"You may use this example to use the Conformer encoder with CTC-loss:\r\nhttps://github.com/NVIDIA/NeMo/blob/main/examples/asr/experimental/conformer/conformer_linear_decoder_bpe.yaml\r\n\r\nWe have the plan to release some checkpoints trained with Confomer-CTC in our next release in March, maybe even Conformer-RNN... | 2020-11-19T23:01:30 | 2021-05-28T08:45:06 | 2021-05-28T08:45:06 | NONE | {
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"@vladgets and/or @tkornuta-nvidia could you please resolve merge conflicts?",
"Hey @okuchaiev I am not responsible for this PR.\r\n\r\nAll the TLT-related changes to this model that I need were incorporated in #1477 .\r\n\r\n@vladgets it is your shot on what you want to do with this.",
"Since Tomasz did his c... | 2020-11-19T22:10:28 | 2020-12-02T00:07:33 | 2020-12-02T00:07:22 | CONTRIBUTOR | {
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} | Added storage of intent and slot labels inside of the config and did correction in prediction function
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https://api.github.com/repos/NVIDIA/NeMo/issues/1473 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1473/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1473/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1473/events | https://github.com/NVIDIA/NeMo/pull/1473 | 746,909,988 | MDExOlB1bGxSZXF1ZXN0NTI0MjIwNTQw | 1,473 | Improve RNNT Greedy decoding | {
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} | # Changelog
- As discussed in https://github.com/NVIDIA/NeMo/issues/1458, greedy decoding can be simplified by reusing `blank_mask` rather than maintaining both `blank_mask` and `time_mask`.
Closes https://github.com/NVIDIA/NeMo/issues/1458 | {
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"@titu1994, please merge when ready\r\n"
] | 2020-11-19T20:39:37 | 2020-11-20T19:51:23 | 2020-11-20T19:17:29 | COLLABORATOR | {
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} | # Changelog
- Adds `init_mode=None` option which will preserve the default pytorch initialization for the `ConvASREncoder` and `ConvASRDecoder` modules
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"This pull request **introduces 4 alerts** when merging 17d0dd296bac97e6824925ddabcacee075b7fe8b into cfc84cebd98d92fa06089cdd42da685e60521d26 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d88619b7202d9dc56b09659798de28b51248b54a)\n\n**new alerts:**\n\n* 4 for Unused import",
"This pull requ... | 2020-11-19T19:26:41 | 2020-12-04T06:34:58 | 2020-12-04T06:34:52 | COLLABORATOR | {
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} | This PR will refactor the `TransformerMTModel` so that pre-trained HF and Megatron encoders and decoders can easily be used along with the `TransformerEncoder` and `TransformerDecoder` for NMT.
# Plan for this PR
- rnd -> rnd
# Plan for future PRs
- pretrained encoder -> rnd
- pretrained encoder -> pretrained decoder
# TODO
- [ ] Organize machine translation naming and folder structure
- [x] Jenkins test
- [ ] Unit Tests
- [ ] Instantiate tokenizers with hydra
# Usage
```
python examples/nlp/machine_translation/enc_dec_nmt.py \
--config-path=${CONFIG_PATH} \
--config-name=${CONFIG_NAME} \
model.max_generation_delta=10 \
model.train_ds.src_file_name=/raid/data/68792/train.clean.en.shuffled.dev \
model.train_ds.tgt_file_name=/raid/data/68792/train.clean.de.shuffled.dev \
model.train_ds.tokens_in_batch=8192 \
model.validation_ds.src_file_name=/raid/data/68792/wmt14-en-de.src \
model.validation_ds.tgt_file_name=/raid/data/68792/wmt14-en-de.ref \
model.validation_ds.tokens_in_batch=1024 \
model.test_ds.src_file_name=/raid/data/68792/wmt14-en-de.src \
model.test_ds.tgt_file_name=/raid/data/68792/wmt14-en-de.ref \
model.encoder_tokenizer.tokenizer_model=/raid/data/68792/tokenizer.BPE.37K.model \
model.decoder_tokenizer.tokenizer_model=/raid/data/68792/tokenizer.BPE.37K.model \
trainer.gpus=4 \
trainer.val_check_interval=1.0 \
``` | {
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} | # Bugfix
- Enables sentencepiece tokenizers to capture the entire vocabulary for small vocab languages like English with cases.
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"Thanks, @vadimkantorov for reporting this. Will look into the solution and test it out. ",
"Indeed, when @squire-tomsk investigated stubbing out `.data` in the UserList to our share-safe list holder in https://gist.github.com/vadimkantorov/86c3a46bf25bed3ad45d043ae86fff57, the memory leak goes away. The other so... | 2020-11-18T12:30:42 | 2023-05-17T22:06:01 | 2021-07-13T15:20:39 | CONTRIBUTOR | {
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} | null | null | null | If I understand well, it appends and stores paths to audio files in a list:
https://github.com/NVIDIA/NeMo/blob/8d506d5e47367643328375e7e5b0ff6643ddb82b/nemo/collections/asr/parts/collections.py#L90
If this object is then scattered to all DataLoader workers, it leads to leaking Linux shared memory (measured by PSS) and gets some data loader threads killed by OOM killer halting the training process. Full explanation of the bug is in: https://github.com/pytorch/pytorch/issues/13246.
Large-scale training datasets contain a lot of audio files and hence a lot of audio path strings, this exacerbates the problem
One solution is to use Manager objects as proposed by @snakers4 in https://github.com/pytorch/pytorch/issues/13246#issuecomment-725488844
Another solution is to pack strings into tensors as done in my gist: https://gist.github.com/vadimkantorov/86c3a46bf25bed3ad45d043ae86fff57#file-tensorbackeddictarray-py-L32 | {
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} | tiny fix write_long_audio_manifest.py
Results are same actually, Just the previous code casue negative offsets in manifests which is not good.
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**Describe your question**
NeMo version is '1.0.0b2'
This is a question about setup_training_data() and setup_validation_data() nemo/collections/nlp/models/question_answering/qa_model.py
I wonder why test data loader is set as None if Dataloader config or file_path for the training or validation is missing.
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"Hi, thanks for the bugfix! Could you fix the style issues by running `python setup.py style --scope=nemo/ --fix` ? Oh theres also a style issue in tests so another pass of `python setup.py style --scope=tests/ --fix` ",
"@SeanNaren Thanks for the test fix, youll need to run style fixes as mentioned above to pas... | 2020-11-17T21:15:49 | 2020-11-17T22:27:39 | 2020-11-17T22:27:06 | COLLABORATOR | {
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} | When `limit_train_batches` is different to the default, this is not included in the inferred calculation of max_steps. This leads to the incorrect schedule being used when `limit_train_batches` is used.
This PR adds `limit_train_batches` to the calculation to ensure the schedule is correct! | {
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"I would also be interested in this",
"I am interested in this as well. \r\nFor now, I'm using a simple approach by averaging the confidence score of each letter per word, resulting in a confidence score per word.\r\nYou can use [parlance](https://github.com/parlance/ctcdecode) to get a timestamp per letter with ... | 2020-11-17T19:39:36 | 2021-08-17T00:56:58 | 2021-08-17T00:56:58 | NONE | {
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} | null | null | null | Hello,
I am interested in getting the word level confidences from Quartznet model. What would be the best approach to obtain some proxy values to word level confidences? Taking the logit sequences and normalizing them by length seems like the most straight forward approach but I am not sure how reliable this would be. Any suggestions or ideas would be appreciated. | {
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"This is a good observation. I'll run some full scale experiments regarding this and respond in a day or two, if you don't mind. For clarification, when you say \"pytorch default\" - you mean you do not use the the weight initialization method at all and just use the default initialized weights that pytorch provide... | 2020-11-17T14:10:36 | 2021-06-19T04:58:44 | 2021-06-19T04:58:44 | CONTRIBUTOR | {
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} | null | null | null | ## Describe your question
1. Weight initialization in the `ConvASREncoder` and `ConvASRDecoder` defaults to `xavier_uniform` but the architectures use ReLU which does best with kaiming initialization. Why was xavier initialization chosen?
2. `ConvASREncoder` and `ConvASRDecoder` have an `init_mode` argument that delegates to `nemo.collections.asr.parts.jasper.init_weights` which returns different results than PyTorch's nn.init (the weights have a different initial standard deviation) and results in significantly worse training during transfer learning in my experiments. Why aren't PyTorch defaults used?
## Experimental Results**
I tried transfer learning from quartznet to a dataset with a different vocab, experimenting with 1 or 2 linear layers in my ASR decoder (decoder layer code included at end of post). I tried initializing decoder weights using NeMo's `xavier_uniform`, NeMo's `kaiming_uniform` and pytorch defaults (kaiming uniform is the default for 1d convs). I ran 12 trials for each. 6 were 2 epochs, and 6 were 1 epoch. LR=1e-3 (0.001). Mean loss after 1ep, and 2ep is included below.
### PyTorch Kaiming Uniform, mean loss 1ep=289.9, 2ep=179.9
### NeMo Kaiming Uniform: mean loss 1ep=515.5, 2ep=581.6
### NeMo Xavier Uniform (default): mean loss 1ep=566.8, 2ep=408.0
### Standard Deviation of weights after initialization
**2 Layer Decoder**:
**1 Layer Decoder**:
## Environment Details
Colab Pip Install - `pip install nemo-toolkit[all]==1.0.0b1`
Python 3.6.9
Pytorch 1.7
OS: Ubuntu 18.04.5 LTS
## Additional Details
### Definition of decoders
**2 Layer Decoder**
```
N_HIDDEN = 256
self.decoder_layers = torch.nn.Sequential(
torch.nn.Conv1d(self._feat_in, N_HIDDEN, kernel_size=1, bias=True),
torch.nn.ReLU(),
torch.nn.Conv1d(N_HIDDEN, self._num_classes, kernel_size=1, bias=True),
)
```
**1 Layer Decoder**
```
self.decoder_layers = torch.nn.Sequential(
torch.nn.Conv1d(self._feat_in, self._num_classes, kernel_size=1, bias=True),
)
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1459 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1459/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1459/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1459/events | https://github.com/NVIDIA/NeMo/issues/1459 | 744,274,661 | MDU6SXNzdWU3NDQyNzQ2NjE= | 1,459 | [Question] Jasper/Quartznet ASR - Recommended length per file is between 5 and 25 seconds. Why? | {
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"Not an expert, but I'll try to help (assuming you're talking about ASR). I believe the recommendation is due to most of the training data being in that range. Since the ASR models, Quartznet and Jasper, are fully convolutional, there's no RNN and I believe this means longer than 25s shouldn't cause too much of a p... | 2020-11-16T23:10:45 | 2021-06-19T04:58:05 | 2021-06-19T04:58:05 | CONTRIBUTOR | {
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} | null | null | null | **Describe your question**
Recommended length per file is between 5 and 25 seconds for most models. I'm curious where/how we arrive at this limitation, and what this means for results I get from running model.transcribe() on a audio longer than 25s. Some insight into where/why we have this limitation would be appreciated.
**Environment overview (please complete the following information)**
- Environment location: [Cloud(Collab)]
- Method of NeMo install: [from git with branch == v1.0.0b2]
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"Yes, this is correct. I didn't think about reusing blank_mask for the label decoding step. Would you like to submit a PR to improve this for both greedy decoding implementations?"
] | 2020-11-16T15:44:18 | 2020-11-21T09:11:01 | 2020-11-21T09:11:01 | CONTRIBUTOR | {
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} | null | null | null | As `blank_mask` contains ones where `time_idx >= out_len` and `k == self._blank_index` and zeros otherwise, we can replace the following code:
https://github.com/NVIDIA/NeMo/blob/5b6dd3850129898be47cf0d65587897ec45a5b59/nemo/collections/asr/parts/rnnt_greedy_decoding.py#L451-L454
with the following code (without masked_fill):
```python
for kidx, ki in enumerate(k):
if blank_mask[kidx] == 0:
label[kidx].append(ki)
```
It allows to remove `time_mask` at all. The following block:
https://github.com/NVIDIA/NeMo/blob/5b6dd3850129898be47cf0d65587897ec45a5b59/nemo/collections/asr/parts/rnnt_greedy_decoding.py#L391-L392
Can be squeezed to:
```python
blank_mask = time_idx >= out_len
```
Is it correct? @titu1994
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"@sankulka few questions:\r\n\r\n1) Are you able to successfully execute this notebook https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/NeMo_voice_swap_app.ipynb \r\n2) If you replace your file with this one https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav , does th... | 2020-11-16T10:21:17 | 2022-01-02T10:30:42 | 2020-11-19T08:09:39 | NONE | {
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} | null | null | null | **Describe your question**
I just started off learning Nemo for ASR activities and getting exception if I send a different wav file to convert into text. Could you please share what pre-processing has to be performed for any other different wav file/format than an4 dataset
**A clear and concise description of your question.**
Describe what you want to achieve. And/or what NeMo APIs are unclear/confusing.
I am trying to send a wav file of < 20 sec duration to get the text output from the quartznet model. Here is a sample code:
files = ['my_sample.wav']
for fname, transcription in zip(files, quartznet.transcribe(paths2audio_files=files)):
print(f"Audio in {fname} was recognized as: {transcription}")
After this, I get below exception.
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-53-f51e6e675965> in <module>()
1 files = ['my_sample.wav']
----> 2 for fname, transcription in zip(files, quartznet.transcribe(paths2audio_files=files)):
3 print(f"Audio in {fname} was recognized as: {transcription}")
14 frames
/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
24 def decorate_context(*args, **kwargs):
25 with self.__class__():
---> 26 return func(*args, **kwargs)
27 return cast(F, decorate_context)
28
/usr/local/lib/python3.6/dist-packages/nemo/collections/asr/models/ctc_models.py in transcribe(self, paths2audio_files, batch_size, logprobs)
158 for test_batch in temporary_datalayer:
159 logits, logits_len, greedy_predictions = self.forward(
--> 160 input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device)
161 )
162 if logprobs:
/usr/local/lib/python3.6/dist-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
509
510 # Call the method - this can be forward, or any other callable method
--> 511 outputs = wrapped(*args, **kwargs)
512
513 instance._attach_and_validate_output_types(output_types=output_types, out_objects=outputs)
/usr/local/lib/python3.6/dist-packages/nemo/collections/asr/models/ctc_models.py in forward(self, input_signal, input_signal_length, processed_signal, processed_signal_length)
394 if not has_processed_signal:
395 processed_signal, processed_signal_length = self.preprocessor(
--> 396 input_signal=input_signal, length=input_signal_length,
397 )
398
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/usr/local/lib/python3.6/dist-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
509
510 # Call the method - this can be forward, or any other callable method
--> 511 outputs = wrapped(*args, **kwargs)
512
513 instance._attach_and_validate_output_types(output_types=output_types, out_objects=outputs)
/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
24 def decorate_context(*args, **kwargs):
25 with self.__class__():
---> 26 return func(*args, **kwargs)
27 return cast(F, decorate_context)
28
/usr/local/lib/python3.6/dist-packages/nemo/collections/asr/modules/audio_preprocessing.py in forward(self, input_signal, length)
77 @torch.no_grad()
78 def forward(self, input_signal, length):
---> 79 processed_signal, processed_length = self.get_features(input_signal, length)
80
81 return processed_signal, processed_length
/usr/local/lib/python3.6/dist-packages/nemo/collections/asr/modules/audio_preprocessing.py in get_features(self, input_signal, length)
247
248 def get_features(self, input_signal, length):
--> 249 return self.featurizer(input_signal, length)
250
251 @property
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
24 def decorate_context(*args, **kwargs):
25 with self.__class__():
---> 26 return func(*args, **kwargs)
27 return cast(F, decorate_context)
28
/usr/local/lib/python3.6/dist-packages/nemo/collections/asr/parts/features.py in forward(self, x, seq_len)
345 # disable autocast to get full range of stft values
346 with torch.cuda.amp.autocast(enabled=False):
--> 347 x = self.stft(x)
348
349 # torch returns real, imag; so convert to magnitude
/usr/local/lib/python3.6/dist-packages/nemo/collections/asr/parts/features.py in <lambda>(x)
273 win_length=self.win_length,
274 center=True,
--> 275 window=self.window.to(dtype=torch.float),
276 )
277
/usr/local/lib/python3.6/dist-packages/torch/functional.py in stft(input, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex)
511 extended_shape = [1] * (3 - signal_dim) + list(input.size())
512 pad = int(n_fft // 2)
--> 513 input = F.pad(input.view(extended_shape), (pad, pad), pad_mode)
514 input = input.view(input.shape[-signal_dim:])
515 return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in _pad(input, pad, mode, value)
3557 assert len(pad) == 2, '3D tensors expect 2 values for padding'
3558 if mode == 'reflect':
-> 3559 return torch._C._nn.reflection_pad1d(input, pad)
3560 elif mode == 'replicate':
3561 return torch._C._nn.replication_pad1d(input, pad)
RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension, but got: padding (256, 256) at dimension 2 of input [1, 1, 2]
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
Collab
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
import nemo
import nemo.collections.asr as nemo_asr
- 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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"You may use the .pt file as any other Pytorch checkpoint or you can save your model as a .nemo file. The .nemo file will have everything needed (except datasets) to use your model. Please see the NeMo Primer tutorial for more information: https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials... | 2020-11-14T15:29:07 | 2020-11-23T05:08:53 | 2020-11-23T05:08:53 | NONE | {
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} | null | null | null | What are the imports or anything I may need? I have pip install nemo_toolkit[nlp] installed already.
Do I need to (2. Define and initialize the neural network) as mentioned in https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html
How may I do it?
Appreciate any help T.T Thanks in advance
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"> looks good to me. sorry for slow review\r\n\r\n@okuchaiev No worries! Thanks Oleksii!"
] | 2020-11-14T00:16:59 | 2021-01-22T15:29:24 | 2020-11-20T21:30:59 | COLLABORATOR | {
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} | I've tested on my local machine (macOS). Could you please help testing on your machine with microphone? @okuchaiev
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} | null | null | null | **Describe your question**
I follow [this link](https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/asr/01_ASR_with_NeMo.ipynb) to try to achieve quartznet inference optimization through tensorrt. I have installed onnxruntime-gpu-tensorrt and use tensorrt provider in onnxruntime. I did observe high gpu usage and gpu memory ocupation. But the inference time is a little slower than without tensorrt. I heard that some neural network can't be optimized by tensorrt. So is quartznet that case? Does anyone try to optimize quartznet inference through tensorrt? If yes, can you share some data with me? Thanks.
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Provided functionality:
- a complete pipeline from audio and text pre-processing, including minimal normalization needed for proper alignment with [ctc_segmentation package](https://github.com/lumaku/ctc-segmentation) to manifest generation and verification utils
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"You should not be overriding your decoder as such. You should use a custom neural module. \r\n\r\n1) Make your custom decoder extend neural module class\r\n2) Override the nemo.collections.asr.modules.ConvASRDecoder classpath with the classpath of your new decoder. \r\n3) Create the model with this config and trai... | 2020-11-12T15:40:11 | 2021-07-13T15:20:02 | 2021-07-13T15:20:02 | CONTRIBUTOR | {
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} | null | null | null | **Describe your question**
I would like to implement my own decoder for transfer learning to experiment with multiple linear layers. I did so by overriding quartznet.decoder.decoder_layers with my own `nn.Sequential` using the code below
```
quartznet.decoder.decoder_layers = nn.Sequential(nn.Conv1d(1024, 256, kernel_size=1, stride=1), nn.ReLU(), nn.Conv1d(256, 41, kernel_size=1, stride=1))
```
This worked and it trains well, but when I save the model and try to load it, I get the following error:
```
RuntimeError: Error(s) in loading state_dict for EncDecCTCModel:
Unexpected key(s) in state_dict: "decoder.decoder_layers.2.weight", "decoder.decoder_layers.2.bias".
size mismatch for decoder.decoder_layers.0.weight: copying a param with shape torch.Size([256, 1024, 1]) from checkpoint, the shape in current model is torch.Size([41, 1024, 1]).
size mismatch for decoder.decoder_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([41]).
```
This is because my underlying config file still specifies the decoder as being from the class `nemo.collections.asr.modules.ConvASRDecoder`. I have no idea how to update the config file to use my new decoder, or how to bypass the config file altogether, and couldn't find a way to do so in the docs. Even if I load up quartznet and manually overwrite the decoder, then try to load the saved checkpoint, it fails because it seems to be using the config file behind the scenes.
**Environment overview (please complete the following information)**
Colab using nemo-toolkit[all]==1.0.0b1 and config from https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/asr/conf/config.yaml
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Python 3.6.9
Pytorch 1.7
OS: Ubuntu 18.04.5 LTS
**Additional context**
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"@anujsuchchal Did you come across a solution for this?",
"@ShantanuNair Yes. For this I am using another CTC Beam Search Decoder package by [Parlance](https://github.com/parlance/ctcdecode). \r\nThis returns timesteps of characters as well in addition to Beam results which you can use to align Audio with Transc... | 2020-11-12T15:25:15 | 2023-12-16T17:05:20 | 2020-12-12T17:13:30 | NONE | {
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} | null | null | null | How can we get word level timestamps for the output words of external LM?
Since the word timestamps logic given in Offline_ASR.ipynb works on Greedy CTC decoder. Number of words in Greedy decoder and Beam Search decoder are usually different so we can't use this logic with Beam Search Decoder.
Is there any way to do this? I have seen some code related to timesteps in source code of ctcdecode package, do I need to try to implement greedy decoder logic into source code of ctcdecode to return timestamps and build it again? Or there is any function/package already available?
Please help. | {
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"Can you share more info about your training process?\r\n\r\n-How many hours of training data do you have?\r\n-What is the size of the Persian vocab? Did you change the decoder to use the new vocab? \r\n-What is the loss doing? Is it falling to a certain level and stalling? What's the lowest loss you get and can yo... | 2020-11-12T11:09:53 | 2021-05-28T08:45:35 | 2021-05-28T08:45:35 | NONE | {
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First of all, thank you guys specially @okuchaiev for this magnificent project on Conversational A.I. Good job Guys.
I am using NeMo **ASR** for **Persian** language mainly because of being able to use **transfer learning** due to lack of labeled data. I got 40% WER on my validation data after working on NeMo for about 3 months. For more information, I am using Quartznet15x5 (En) and used transfer learning for another language. _But there is 2 main question here:_
1. According to the [Paper ](https://arxiv.org/abs/2005.04290): "The Russian model was trained for 512 epochs while the Spanish and German ones for 256 epochs."
But I am stuck on the 40% WER after almost 15th epoch and it is not changing even after 50 epochs. **Am i experiencing overfitting here?**
On the other hand, based on the main use of transfer learning, there is no need to train the per-trained model for this **such large number of epochs.** Please let me know what is the reason of this strategy of training.
2. **If this is overfitting, How can i regularize it?**
(Can Dropout help me with that? ) If YES, is there any advice for setting that?
Environment overview:
-Environment location: Jupyter Notebook (Cloud Services)
-Method of NeMo install: !python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
Environment details
-OS version : Ubuntu 18.04
-PyTorch version : 1.6.0+cu101
-Python version : Python 3.6.9
-GPU model: GTX 1080 Ti | {
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"This pull request **introduces 2 alerts** when merging 918f97f81514c0f243321e81eb73adcd49c18cc3 into a711ca1ad2987797b1e80ac9b0ffd7479c0a4730 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-a817d9e013ff7a00f492166aeb5bf1830ad2cba6)\n\n**new alerts:**\n\n* 2 for Except block handles 'BaseExc... | 2020-11-12T08:22:30 | 2020-11-13T21:53:11 | 2020-11-13T21:53:07 | COLLABORATOR | {
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} | Added new features to Speech Data Explorer:
- backend paging/sorting/filtering (allows to work with large datasets)
- support for assigning a port
- minor tweaks to UI
Signed-off-by: Vitaly Lavrukhin <vlavrukhin@nvidia.com> | {
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"awesome!!",
"This pull request **introduces 2 alerts** when merging 12122b266f59f46f76414748c73da675f830ec03 into a711ca1ad2987797b1e80ac9b0ffd7479c0a4730 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cf2265463b3732f561da98b7d2b7b43d39332d52)\n\n**new alerts:**\n\n* 2 for Unused import",
... | 2020-11-11T21:59:04 | 2020-11-19T04:44:25 | 2020-11-19T04:44:20 | COLLABORATOR | {
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} | This PR adds the `.register_bert_model` and `.register_tokenizer` methods to `NLPModel` and lifts the `_setup_tokenizer` method to `NLPModel`.
For HuggingFace and Megatron BERT models the BERT config will be written to `pretrained_model_name_encoder_config.json` and added to the `.nemo` archive.
For tokenizers, the vocab file and tokenizer model will be added to the `.nemo` archive. If no vocab file is provided, and using HuggingFace AutoTokenizer, then the `vocab_dict` will extracted from the tokenizer object and the `vocab_file` will be created from the `vocab_dict` and both saved to the `.nemo` archive.
This gives Jarvis the necessary information to generate a TRT Engine from the PyTorch checkpoint.
Calling `.register_bert_model` from PTL `.setup` hook so that all models subclassed from `NLPModel` will register their bert model (if possible). | {
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} | Switching to EFF NeMoCookbook. | {
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} | Split off updates to nemo core that are required for #1418
Changes:
- Remove empty *_step() methods from ModelPT
- Treat None inside of ElementType's parameters as a Void that matches anything
- Use case being removing the default 16kHz sample_rate from AudioSignal and replacing it with None. This allows a module with type AudioSignal() to match ANY AudioSignal regardless of sample_rate
Minor Changes:
- Add Singleton NeuralType
- Update wrap_training_step such it is doesn't require a dictionary to be passed
- Other cosmetic changes | {
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"The best bet it to train on more data.\r\nIf you do not have more data, then try fine-tuning from pretrained model (even from another language) by re-using acoustic encoder.\r\nAnd, of course, you will need to play/experiment with hyperparameters"
] | 2020-11-10T14:37:39 | 2020-11-11T00:19:47 | 2020-11-11T00:19:47 | NONE | {
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} | null | null | null | Hello,
I'm trying to train ASR models for different languages. The problem is that the output contains many mistakes. Do you have any idea to correct them.
Thanks in advance,
Sincerely. | {
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"You should be able to:\r\n```\r\nmymodel.save_to(save_path=\"mymodel.nemo\")\r\n```\r\nthen (possibly in another python script)\r\n```\r\nmymodel=IntentSlotClassificationModel.restore_from(restore_path=\"mymodel.nemo\")\r\n```\r\nNote that ```restore_from``` is a class method which returns class instance. Are you ... | 2020-11-10T10:13:24 | 2021-08-17T00:56:20 | 2021-08-17T00:56:20 | NONE | {
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} | null | null | null | Hi,
I have trained my own dataset using NLP Joint Intent & slot classification NeMo models. I am able to train & predict the data. However i am not getting how can i save the model from that checkpoint and then reload the same model & predict on example test queries. I can understand using experiment manager, path has been defined in yaml file for saving checkpoints, but i am unable to see that checkpoint file in that location(Running in google Colab).
My requirement is to save that model at my own defined location, then reload that model and i should able to predict from the reloaded model.
I have tried these things:-
1) save_to method (It's working. model has been saved)
2) restore_from() method (It's not working)
3) load_from_checkpoint (This is working, but i need to train always first, the i can load, But i dont want to train everytime. I want to just load model & predict from that trained model)
Need your help to understand the correct way of using saving reloading APIs of NeMo & why it is like not able to see that checkpoint in google Colab location & the same path i am able to reload from the checkpoint, but can't see that file.
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install:
BRANCH = 'v1.0.0b2'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]
Thank you!
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"This pull request **introduces 2 alerts** when merging b7801269d9ee2ca213f5bd43e4302fd148ed66bc into 38c854da3f553b6577d6f8b08490e026b53b7ea3 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fa359a48d5f95a10eb6e93d11f38e27ea636a1e6)\n\n**new alerts:**\n\n* 1 for Unused local variable\n* 1 for Un... | 2020-11-10T08:33:37 | 2020-11-18T21:08:15 | 2020-11-18T21:08:12 | COLLABORATOR | {
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} | # Changelog
- Adds tarred dataset support for transformer based Language models
- Pre-tokenizes and chunks the dataset for more efficient external dataset handling
- Adds utility script to construct a tarred tokenized LM dataset
- Adds `cache_ids` flag support for transformer based language models | {
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"If your labels are not a subset of the provided labels, it's recommended to use [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/token_classification.py). Note, you `shouldn't` use a pre-trained TokenClassificationModel, instead start fine-tuning with the original BERT model... | 2020-11-10T08:06:29 | 2020-11-11T04:09:57 | 2020-11-11T04:09:57 | NONE | {
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} | null | null | null | Hi, I am using the Token_Classification_Named_Entity_Recognition tutorial for a project.
By default, the dataset provided these labels:
LOC = Geographical Entity
ORG = Organization
PER = Person
GPE = Geopolitical Entity
TIME = Time indicator
ART = Artifact
EVE = Event
NAT = Natural Phenomenon
How can I change the classes in the provided mapping such that it can use my labels and text file instead?
(My labels include: label_names = ['O', 'B-Product', 'B-Processor', 'B-WindowVersion','B-GPU','B-Display','B-MemoryStorage','B-Storage','B-Weight','B-Warranty','B-Price'] )
Attached is the error I received.
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"> This looks fine to me. But I am wondering where it will be used?\r\n\r\nIn EFF: ONNXCookbook :)"
] | 2020-11-10T07:18:12 | 2020-11-13T19:47:41 | 2020-11-13T19:28:04 | CONTRIBUTOR | {
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} | Signed-off-by: Sergei Nikolaev <snikolaev@nvidia.com> | {
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} | Bugfix for when max duration is set to None
Add support for min duration filtering | {
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"This pull request **introduces 6 alerts** when merging 57bbdd1984e6529cb18f138488bad775e10a19b1 into c0fedd91e316a4ba95ac6b14330ff92844c56097 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fc89a4569b49ec7de84b75ce25de8500ede41b9d)\n\n**new alerts:**\n\n* 4 for Unused import\n* 2 for Syntax err... | 2020-11-09T23:56:54 | 2020-11-10T00:10:14 | 2020-11-09T23:57:07 | COLLABORATOR | {
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} | Bugfix for if maximum duration set to None
Added minimum duration filter | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1429 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1429/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1429/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1429/events | https://github.com/NVIDIA/NeMo/pull/1429 | 738,840,234 | MDExOlB1bGxSZXF1ZXN0NTE3NjA2OTkw | 1,429 | Add RIR perturbation example. | {
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} | Add RIR(Room impulse Reverbation) perturbation example in the tutorial. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1428 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1428/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1428/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1428/events | https://github.com/NVIDIA/NeMo/issues/1428 | 738,412,606 | MDU6SXNzdWU3Mzg0MTI2MDY= | 1,428 | [Question] Difference between `blank` and `space` character in ASR quartznet? | {
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"`space` character is a regular delimiter between words [` `]. Usually, it is also returned for silent segments. \r\n`blank` symbol is a special extra character in CTC modeling that allows representing double characters, for example. It is removed from final decoded transcript. More details could be found in the fo... | 2020-11-08T07:41:05 | 2020-11-10T19:24:15 | 2020-11-10T19:24:15 | NONE | {
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} | null | null | null | How to differentiate the meaning of these two characters? Which one represents silence, gap for next word or the end of word? Thanks in advance :) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1427 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1427/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1427/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1427/events | https://github.com/NVIDIA/NeMo/issues/1427 | 738,174,179 | MDU6SXNzdWU3MzgxNzQxNzk= | 1,427 | [Question] How may i save the model to my own file in colab? | {
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"You can use google.colab library to download files off of google colab - https://stackoverflow.com/questions/48774285/how-to-download-file-created-in-colaboratory-workspace.\r\n\r\nInstead of `example.txt` in that snippet, you can provide the full path to the checkpoint instead and it should (hopefully) download t... | 2020-11-07T05:08:06 | 2020-11-10T06:11:27 | 2020-11-10T06:11:27 | NONE | {
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} | null | null | null | After running trainer.fit(model_from_scratch) i see that the models are actually saved in
" Epoch 0: val_loss reached 0.38735 (best 0.38735), saving model to /content/nemo_experiments/token_classification_model/2020-11-07_03-05-01/checkpoints/token_classification_model---val_loss=0.39-epoch=0.ckpt as top 3"
How can I access this model or save it into my own local file (training_session_folder)?
I need to use the model to pass in a text file and predict the labels
p.s. I'm a beginner pls help T.T
| {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1426 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1426/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1426/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1426/events | https://github.com/NVIDIA/NeMo/pull/1426 | 738,113,841 | MDExOlB1bGxSZXF1ZXN0NTE3MDM4MDcy | 1,426 | Add support for replicated shard strategy for tarred datasets | {
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"As in remove the configs shard_strategy = scatter portion? We could drop them, but it reduces visibility. Practically, it should be fine, since the shard strategy is not used unless is_tarred is set \r\n\r\nEdit: I do agree that the configs are going quite big though. ",
"I was worried about it potentially causi... | 2020-11-07T00:16:58 | 2020-11-09T23:43:12 | 2020-11-09T23:42:55 | COLLABORATOR | {
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} | # Changelog
- Support `replicate` strategy for sharding of tarfiles under ddp for Tarred WebDataset.
- Add config line to all ASR models
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1425 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1425/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1425/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1425/events | https://github.com/NVIDIA/NeMo/issues/1425 | 737,770,909 | MDU6SXNzdWU3Mzc3NzA5MDk= | 1,425 | [Question]Quantized QuartzNet | {
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"I don't think they can help with this\r\nPytorch need to support Conv1d and BatchNorm1d in order for you to quantize it.\r\n\r\nThe only way you can do it is by converting Conv1d to Conv2d(same for BatchNorm) using [this](https://github.com/NVIDIA/NeMo/blob/c90e3aaee67a73dc1c2b0c0614766246e403e1c1/nemo/utils/expor... | 2020-11-06T13:49:15 | 2021-06-19T04:54:39 | 2021-06-19T04:54:39 | NONE | {
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} | null | null | null | I am trying to quantize a QuartzNet 15x5 encoder like this:
```python
jasper_encoder = jasper_encoder.cpu()
jasper_encoder.eval()
jasper_encoder.qconfig = torch.quantization.get_default_qconfig('qnnpack')
torch.quantization.propagate_qconfig_(jasper_encoder)
jasper_encoder = torch.quantization.prepare(jasper_encoder, inplace=False)
jasper_encoder = torch.quantization.convert(jasper_encoder)
```
But, unfortunately, the encoder is not fully quantized, but only some RuLu. I also tried to quantize MaskedConv1d separately, but unfortunately even in this case the Conv1d layer was not quantized
This is what I get when I do print (jasper_encoder.encoder) after quantizing:
```
Sequential(
(0): JasperBlock(
(mconv): ModuleList(
(0): MaskedConv1d(
(conv): Conv1d(64, 64, kernel_size=[33], stride=[2], padding=(16,), dilation=[1], groups=64, bias=False)
)
(1): MaskedConv1d(
(conv): Conv1d(64, 256, kernel_size=(1,), stride=(1,), bias=False)
)
(2): BatchNorm1d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(mout): Sequential(
(0): QuantizedReLU()
(1): Dropout(p=0.0, inplace=False)
)
)
...
```
As you can see from this output, neither Conv1d nor BatchNorm1d were quantized. Could you help me with quantizing QuartzNet if it is possible to quantize this model
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 18.04 LTS
- PyTorch version : 1.6.0
- Python version: Python 3.6
- NeMo version: 0.10.1
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"Hello @4nton-P \r\n\r\n- I just want to feed a track into the model and get a predicted time. To do this, I'll just rewrite the callback function, but how to get around the problem with the lack of devices?\r\nIt is designed for the demonstration in the tutorial you don't have to use it to do inference for VAD.\r\... | 2020-11-06T11:19:57 | 2020-11-11T21:44:08 | 2020-11-11T21:44:08 | NONE | {
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I ran into the VAD Inference problem. I cannot use offline inference if I do not have an output or input of a device, because the pyaudio requires a device. Are there any solutions?
I just want to feed a track into the model and get a predicted time. To do this, I'll just rewrite the callback function, but how to get around the problem with the lack of devices? | {
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"This pull request **fixes 1 alert** when merging eb11ff4b16f70648862ee2e85a4912101d10c1d6 into 1ac828df423fbcec1b34c650b3a20266bb133dde - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-f332123bfafc9c57f8b57f0fc73bcbf047aea797)\n\n**fixed alerts:**\n\n* 1 for Unused import",
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} | * Add perplexity draft
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Add perplexity metric
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Add docstrings and copyright, fix comments
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Fix code style
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* Fix code style
Signed-off-by: PeganovAnton <peganoff2@mail.ru>
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Signed-off-by: PeganovAnton <peganoff2@mail.ru>
* Fix docstring hrefs
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"Thanks, Vahid! 👍 "
] | 2020-11-05T20:55:40 | 2020-11-05T21:34:57 | 2020-11-05T21:34:40 | COLLABORATOR | {
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"This pull request **introduces 6 alerts** when merging 1a32266b9ae76f036aac21b58486d9e9c54ebe40 into 4de7bd8394fb7e809e04ee8bcadcf9a1711ef497 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-f4743e03b4be34541d11903c6d31e9d4f4d054b4)\n\n**new alerts:**\n\n* 3 for Unused import\n* 1 for Signature ... | 2020-11-05T20:18:23 | 2020-11-26T16:00:55 | 2020-11-26T16:00:51 | COLLABORATOR | {
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} | - Adds the fullband MelGAN model as described in https://arxiv.org/abs/2005.05106
- Remove empty *_step() from ModelPT
- Split UniGlowLoss into glow portion and STFTLoss portion
- Simply stft_exact_pad option in FilterBankFeatures
ToDos
- [x] ~Enable FP16~. No currently a priority.
- [x] Add NeMo Loss subclass
- [x] Update Docs
- [x] ~Update TTS tutorials~, can't until I upload the .nemo files | {
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"sorry this feature have not been ported yet.",
"@okuchaiev is it wip or will take some time?",
"This is available in Nemo. Please refer to Nemo docs for ASR Language Modelling. "
] | 2020-11-05T16:58:12 | 2021-08-17T00:55:35 | 2021-08-17T00:55:35 | NONE | {
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} | null | null | null | How can we deploy ASR post processing on the new Nemo Version 1.0.0b1 ?
I found this [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v0.11.0/nlp/asr-improvement.html#asr-improvement) and this [page](https://github.com/NVIDIA/NeMo/tree/fa68d336cfefbbbb1849ff1b6ef454149f45234d/examples/nlp/asr_postprocessor) but they don't seem to work with the new Nemo Version
**Environment overview (please complete the following information)**
- Environment location: Bare-Metal with GPU / GCP
- Method of NeMo install: pip install
**Environment details**
- OS version: Ubuntu 20.04.1 LTS
- PyTorch version : 1.6
- Python version: Python 3.7.6
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"Thanks for the issue. Please upgrade your version of NeMo to 1.0.0b*.\r\n\r\nSee https://github.com/NVIDIA/NeMo/issues/1091 for a discussion on what has changed."
] | 2020-11-05T12:58:42 | 2020-12-04T00:46:20 | 2020-12-04T00:46:20 | NONE | {
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} | null | null | null | Dear all,
I have following problem when I run speech2text file:
**Setup NeuralModuleFactory to control training nf = nemo.core.NeuralModuleFactory( create_tb_writer=True, ...)*
The system will raise an error:
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
Segmentation fault (core dumped)
**Environment overview (please complete the following information)**
- Environment location: anaconda, nemo v0110
- Method of NeMo install: pip install nemo_toolkit[all]
**Environment details**
Otherwise, please provide:
- OS version: Ubuntu 16.04.5 LTS
- PyTorch version: pytorch 1.6.0
- Python version: Python 3.7.7
**Additional context**
CUDA Version: 10.1
GPU: GeForce GTX 1080
Please show me where was wrong!
Thank you very much!
Cao Hong Nga
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https://api.github.com/repos/NVIDIA/NeMo/issues/1414 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1414/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1414/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1414/events | https://github.com/NVIDIA/NeMo/pull/1414 | 736,416,147 | MDExOlB1bGxSZXF1ZXN0NTE1NjMzODgy | 1,414 | Update checkpointing to support model parallel | {
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"This pull request **introduces 1 alert** when merging f0345ed4dbf94798b1375f4798b997ebd1115f22 into 99ed9c2ae5c840a8723f236530ccb1abe8c75377 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-fa795c7f3253cb10f0b090983cf620dbc7125e51)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2020-11-04T20:52:45 | 2021-03-02T22:12:07 | 2021-01-18T19:52:24 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1413 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1413/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1413/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1413/events | https://github.com/NVIDIA/NeMo/pull/1413 | 736,306,158 | MDExOlB1bGxSZXF1ZXN0NTE1NTQzMzA1 | 1,413 | bugfixes from v1.0.0b2 | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1412 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1412/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1412/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1412/events | https://github.com/NVIDIA/NeMo/issues/1412 | 736,257,913 | MDU6SXNzdWU3MzYyNTc5MTM= | 1,412 | [Question] Does TTS modules (for example Tacotron2) support fine-tuning like ASR ? | {
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"Hello, any update on this issue?\r\n\r\nI have the same problem, I would like to perform a fine-tuning of Tacotron 2 model with a different set of labels (characters/phonemes in input to the model).\r\n\r\nI'm also interested in simple fine-tuning without changing the set of labels, is this feature already availab... | 2020-11-04T16:36:47 | 2022-01-18T18:40:42 | 2022-01-18T18:40:06 | NONE | {
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} | null | null | null | **Is fine-tuning with TTS not supported yet?**
Am working on transfer learning with TTS models From English to the Arabic language. I saw an example of fine-tuning using ASR Quartznet model mentioned [here](https://github.com/NVIDIA/NeMo/issues/1107#issuecomment-686005952).
I am using this pre-trained model:
`tacotron2= nemo_tts.models.Tacotron2Model.from_pretrained("Tacotron2-22050Hz")`
I found that Tacotron model doesn't support `model.change_vocabulary()` and it raises receptions when I am using `model.setup_training_data()`.
Nemo version: 1.0.0b1
Method of NeMo install: `pip install nemo_toolkit[all]==1.0.0b1`
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https://api.github.com/repos/NVIDIA/NeMo/issues/1411 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1411/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1411/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1411/events | https://github.com/NVIDIA/NeMo/issues/1411 | 735,978,617 | MDU6SXNzdWU3MzU5Nzg2MTc= | 1,411 | When i use GPU to infer, everything is ok. But when i choose CPU, it turn out runtime error: code is too big. I have no idea what is it mean. Did anybody know how can i fix it. | {
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"Please upgrade to 1.0.0b* version of NeMo. See https://github.com/NVIDIA/NeMo/issues/1091 for a discussion on the latest changes."
] | 2020-11-04T10:02:02 | 2020-12-04T02:39:11 | 2020-12-04T02:38:33 | NONE | {
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} | null | null | null | I set batch_size = 1, it still shows runtime error.
But when my test_set only have one wav, it can run successfully.
part of my code:
neural_factory = nemo.core.NeuralModuleFactory(
backend=nemo.core.Backend.PyTorch,
#local_rank=args.local_rank,
#optimization_level=args.amp_opt_level,
placement=nemo.core.DeviceType.CPU,
)
evaluated_tensors = neural_factory.infer(tensors=eval_tensors, checkpoint_dir=load_dir)
The error message:
/opt/conda/lib/python3.6/site-packages/torchvision/io/_video_opt.py:17: UserWarning: video reader based on ffmpeg c++ ops not available
warnings.warn("video reader based on ffmpeg c++ ops not available")
[NeMo I 2020-11-04 09:53:12 collections:152] Dataset loaded with 12203 files totalling 6.17 hours
[NeMo I 2020-11-04 09:53:12 collections:153] 0 files were filtered totalling 0.00 hours
[NeMo I 2020-11-04 09:53:12 stt_infer:138] Evaluating 12203 examples
[NeMo I 2020-11-04 09:53:12 features:144] PADDING: 16
[NeMo I 2020-11-04 09:53:12 features:152] STFT using conv
[NeMo I 2020-11-04 09:53:13 stt_infer:151] ================================
[NeMo I 2020-11-04 09:53:13 stt_infer:152] Number of parameters in encoder: 18894656
[NeMo I 2020-11-04 09:53:13 stt_infer:153] Number of parameters in decoder: 29725
[NeMo I 2020-11-04 09:53:13 stt_infer:154] Total number of parameters in model: 18924381
[NeMo I 2020-11-04 09:53:13 stt_infer:155] ================================
[NeMo I 2020-11-04 09:53:13 actions:1548] Restoring JasperEncoder from asr_checkpoint/JasperEncoder.pt
[NeMo I 2020-11-04 09:53:13 actions:1548] Restoring JasperDecoderForCTC from asr_checkpoint/JasperDecoderForCTC.pt
[NeMo I 2020-11-04 09:53:13 actions:673] Evaluating batch 0 out of 12203
Traceback (most recent call last):
File "stt_infer.py", line 221, in <module>
main()
File "stt_infer.py", line 168, in main
evaluated_tensors = neural_factory.infer(tensors=eval_tensors, checkpoint_dir=load_dir)
File "/NeMo/nemo/core/neural_factory.py", line 421, in infer
modules_to_restore=modules_to_restore,
File "/NeMo/nemo/backends/pytorch/actions.py", line 1575, in infer
offload_to_cpu=offload_to_cpu,
File "/NeMo/nemo/backends/pytorch/actions.py", line 700, in _infer
use_cache=use_cache,
File "/NeMo/nemo/backends/pytorch/actions.py", line 362, in __nm_graph_forward_pass
new_tensors = pmodule(force_pt=True, **call_set)
File "/NeMo/nemo/backends/pytorch/nm.py", line 160, in __call__
return self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 49, in decorate_no_grad
return func(*args, **kwargs)
File "/NeMo/nemo/collections/asr/audio_preprocessing.py", line 93, in forward
processed_signal = self.get_features(input_signal, length)
File "/NeMo/nemo/collections/asr/audio_preprocessing.py", line 385, in get_features
return self.featurizer(input_signal, length)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 49, in decorate_no_grad
return func(*args, **kwargs)
File "/NeMo/nemo/collections/asr/parts/features.py", line 252, in forward
x = self.stft(x)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/NeMo/nemo/collections/asr/parts/features.py", line 160, in forward
return super(STFTPatch, self).transform(input_data)[0]
File "/opt/conda/lib/python3.6/site-packages/torch_stft/stft.py", line 86, in transform
padding=0)
RuntimeError: code is too big | {
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"I would also be very interested!",
"Hi, first thanks for developing this repo. It is quite useful. I am also interested in the pretrained conformer model. It would be helpful if this could also be added to ngc\r\n\r\nThanks,",
"We have the plan to release publicly some checkpoints with Conformer-CTC, and maybe... | 2020-11-04T07:56:35 | 2021-10-25T17:40:40 | 2021-06-10T21:59:48 | NONE | {
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} | null | null | null | Thanks authors for publishing ASR sources. I know that QuartzNet & Jasper are comparable to some SOTA model for S2T tasks. Recently, Google published "Conformer" encoder network and achieved very good performance on LibriSpeech. I saw that, that network source is released on new Nemo version. Could you please release pre-trained model for this? | {
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} | `SmoothedCrossEntropyLoss` is expecting log probs but was using logits type and logits name for variables.
Changed logits to log_probs and updated types. | {
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} | Pinned notebook to v1.0.0b2.
Fixed a small typo.
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"please run ```python setup.py style --fix``` to fix style and let CI run"
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"@vladgets do you still want to merge this?",
"> @vladgets do you still want to merge this?\r\n\r\nYes, I do",
"> > @vladgets do you still want to merge this?\r\n> \r\n> Yes, I do\r\n\r\nShould this go to main or v1.0.0b2 @okuchaiev? It's needed for tlt"
] | 2020-11-02T23:35:24 | 2020-11-04T19:08:14 | 2020-11-04T19:07:59 | CONTRIBUTOR | {
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"code format check fails - see CI output"
] | 2020-11-02T20:14:13 | 2020-11-05T01:49:41 | 2020-11-05T01:49:41 | CONTRIBUTOR | {
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"Here are some scripts that can help you create datasets for RIR and noise augmentation:\r\nhttps://github.com/NVIDIA/NeMo/blob/main/scripts/get_openslr_rir.py (downloads and creates a json manifest for RIRs from openslr)\r\n\r\nSome scripts to download noise samples from freesound are here: https://github.com/NVID... | 2020-11-02T08:44:46 | 2021-06-19T04:47:18 | 2021-06-19T04:47:18 | CONTRIBUTOR | {
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} | null | null | null | **Describe your question**
How to train a ASR model finetuned with RIR and noise augmentation, just like `QuartzNet15x5NR-En`?
I find that there is a pretrained `QuartzNet15x5NR-En` model on NGC, and a config file for augmentation `/home/jack/a/PycharmProjects/NeMo/examples/asr/experimental/configs/quartznet_15x5_aug.yaml`.
```
nemo_asr.models.EncDecCTCModel.list_available_models()
Out[2]:
PretrainedModelInfo(pretrained_model_name='QuartzNet15x5NR-En', description='QuartzNet15x5Base-En was finetuned with RIR and noise augmentation to make it more robust to noise. This model should be preferred for noisy speech transcription. This model achieves a WER of 3.96% on LibriSpeech dev-clean and a WER of 10.14% on dev-other.', location='https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5NR-En.nemo', class_=None),
```
But I can't find the recipe for training the RIR and noise augmented model `QuartzNet15x5NR-En`.
So it will be meaningful if NeMo can provide the the recipe.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: from source main(75774cf)
**Environment details**
- OS version: Ubuntu 20.04
- PyTorch version: 1.6
- Python version: 3.8 | {
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"> Can I deploy NeMo trained ASR on mobile platform (Android/iOS)? \r\n- Yes!\r\n\r\n> If yes then what are the steps?\r\n- Every NeMo model is inherited from ```torch.nn.Module``` , therefore you should checkout Pytorch tutorials about deploying models on mobile. For example, you can export QuartzNet ASR models in... | 2020-11-02T06:08:42 | 2021-02-10T23:29:38 | 2021-02-10T23:29:38 | NONE | {
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"@uditdixit11, \r\nIf you have finetuned or trained all together with a new speaker recognition model, you could test or infer by two steps as shown similarly in [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_infer.py) . \r\n1. set up test data and \r\n2. get log_prob by calling ... | 2020-11-02T05:48:02 | 2021-06-08T08:15:28 | 2021-01-25T07:29:23 | NONE | {
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} | null | null | null | I have trained a speaker recognition model and save the checkpoints. Even can restore it using the LightningModule.load_from_checkpoint() class method. But couldn't able to perform the next steps after it.
Please suggest how do I perform the next steps i.e then predict the speaker on unseen data using trained model at at 1.0.0b1 version ?
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"@AlexGrinch can you please fix the PR so that CI passes ",
"@AlexGrinch code style",
"please fix code formatting to unblock CI:\r\n```\r\npython setup.py style --fix\r\n```"
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"Yes, NeMo is not English-only. You should be able to train good Japanese models if you have Japanese training data. Of course, knowledge of Japanese helps too :) ",
"> Yes, NeMo is not English-only. You should be able to train good Japanese models if you have Japanese training data. Of course, knowledge of Japan... | 2020-11-01T09:56:01 | 2020-11-03T09:07:55 | 2020-11-03T09:07:55 | NONE | {
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"This pull request **introduces 2 alerts** when merging 9c213ff48acc513dfd3b02654fa92fdd54b93341 into 75774cf24755bddb393e3b1a4cbcc1921daf8898 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-29dea0dd8c242cb81d2215a8c6323dfb9f2b3347)\n\n**new alerts:**\n\n* 2 for Unused local variable"
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"This pull request **introduces 5 alerts** when merging c0ea1dedb51f1613303a24005612c44e29f0b303 into aa4541b91341e0dda5300f1ded5f60cbbc74429a - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-6f4f2f933f3cfd83e9c80db0d46a48fe806dd015)\n\n**new alerts:**\n\n* 3 for Unused import\n* 1 for Variable d... | 2020-10-30T21:50:58 | 2020-12-29T06:37:51 | 2020-11-11T21:36:07 | COLLABORATOR | {
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} | Update VAD inference to make it easy to combine with Speaker Diarization and more user friendly (user would like txt output)
Add/update:
1. Truncate long audio files to separate json line to avoid CUDA memory issue
2. Frame level inference similar to speech_to_text_infer.py because the frame level streaming is kinda different with asr and speech command.
3. A posterior including overlapped prediction and smoothing filter
4. Write to RTTM like file.
AUDIO -> MANIFEST -> VAD OUTPUT TXT -> VAD RTTM-like TABLE
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Update notebook to use fstrings for branch. | {
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"This pull request **introduces 1 alert** when merging 0a42bc5c87cce6e6bcbe1ccff4772a6caca25a9c into 17fc2541172dd3d781bfa0b5a1fa41576d4bfe21 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-25e2feefaf1a084c11c7c502283923afe3fa5df8)\n\n**new alerts:**\n\n* 1 for Unused import"
] | 2020-10-30T20:25:45 | 2020-11-02T21:34:43 | 2020-10-30T21:44:44 | COLLABORATOR | {
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} | Dropped the evaluation with another trainer in the same script. | {
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"Here is a tutorial on data augmentation https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/05_Online_Noise_Augmentation.ipynb "
] | 2020-10-30T18:51:18 | 2021-05-28T08:31:22 | 2021-05-28T08:31:22 | NONE | {
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> The QuartzNet-15×5 model was trained on the LibriSpeech training dataset with two types of data augmentation: speed perturbation and Cutout
If I transfer-learn the English QuartzNet on another language, is there an easy way to add
- Speed perturbation
- Cutout
- Random background noise
to the training loop? I know how to do this in MONAI but it's not as clear in the NeMo design whether there are audio transforms that can be added into the training loop, that is, I don't see the equivalent in NeMo of the MONAI Compose and transform loader stack: https://docs.monai.io/en/latest/transforms.html | {
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"Actually never mind, I can just train using other text-to-text notebooks as a new language pair, the pair being ASR output to ground truth.",
"Hey, they've explained in other issues - https://github.com/NVIDIA/NeMo/issues/1156, https://github.com/NVIDIA/NeMo/issues/1126 that it hasn't been ported yet, but looks ... | 2020-10-30T18:17:27 | 2020-11-11T13:39:20 | 2020-10-30T21:41:44 | NONE | {
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} | null | null | null | BERT ASR postprocessing is described here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v0.10.1/nlp/asr-improvement.html
Is there a notebook in the GitHub to demo this in combination with transfer learning of QuartzNet15x5 for a new language?
It would be very helpful to show how to transfer-learn the Bert for a transfer-learned new language QuartzNet.
Also...what happened to all the files referenced in the docs, for example `examples/nlp/asr_postprocessor`
After some digging, I found them buried in an old version. I hope this doesn't mean this doesn't work any more:
https://github.com/NVIDIA/NeMo/tree/fa68d336cfefbbbb1849ff1b6ef454149f45234d/examples/nlp/asr_postprocessor | {
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Now I am using your intent_slot_classification.py. The model could be trained, however, the FileNotFoundError was shown. I just discovered you add the inference part.
Jonathan Sung | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1377 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1377/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1377/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1377/events | https://github.com/NVIDIA/NeMo/pull/1377 | 732,876,195 | MDExOlB1bGxSZXF1ZXN0NTEyNzUyMjc0 | 1,377 | Fix http 500 ngc v1.0.0b2 | {
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"This pull request **introduces 1 alert** when merging 4f0baa600e628c3bc964be157a258d7dea590259 into 0c575303f49dc0060ad1ff13f3ec9f6e2dcbc381 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-d58640ed1f19ca480d854b6e6914fa40c21d5cc6)\n\n**new alerts:**\n\n* 1 for Except block handles 'BaseExce... | 2020-10-30T04:38:14 | 2020-10-30T14:14:17 | 2020-10-30T14:14:11 | COLLABORATOR | {
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"merged_at": "2020-10-30T14:14:11"
} | Try and wait in case NGC download fails. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/1376 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/1376/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/1376/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/1376/events | https://github.com/NVIDIA/NeMo/issues/1376 | 732,870,252 | MDU6SXNzdWU3MzI4NzAyNTI= | 1,376 | How do I turn off logging? | {
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"This is my question too.",
"Try this after importing nemo modules:\r\n```python\r\nimport logging\r\nlogging.getLogger('nemo_logger').setLevel(logging.ERROR)\r\n```\r\nyou can choose the logging level from https://docs.python.org/3/library/logging.html#levels",
"[This](https://stackoverflow.com/questions/27647... | 2020-10-30T04:21:34 | 2023-10-24T13:10:22 | 2021-05-22T08:41:58 | NONE | {
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} | null | null | null | How do I turn off all the logging messages in NeMo? For example all of this:
```
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioToCharDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioToBPEDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.AudioLabelDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text._TarredAudioToTextDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2020-10-30 00:16:51 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset'> is experimental, not ready for production and is not fully supported. Use at your own risk.
wandb: WARNING W&B installed but not logged in. Run `wandb login` or set the WANDB_API_KEY env variable.
[NeMo W 2020-10-30 00:16:52 experimental:28] Module <class 'nemo.collections.asr.losses.ctc.CTCLoss'> is experimental, not ready for production and is not fully supported. Use at your own risk.
################################################################################
### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk
### (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)
### (or run as: KALDI_ROOT=<your_path> python <your_script>.py)
################################################################################
``` | {
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