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https://api.github.com/repos/NVIDIA/NeMo/issues/3365
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| 1,085,399,604 |
PR_kwDOC_bI7s4wHE1M
| 3,365 |
Support Changing Number of Tensor Parallel Partitions for Megatron
|
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Added `megatron_change_num_partitions.py` that supports converting a Megatron model with an arbitrary number of Tensor Parallel (TP) partitions N to any other number of TP partitions M.
In order to do so, the script intermediately converts all models to TP=1.
All the models/tensors are created/loaded on CPU.
Usage:
```
python examples/nlp/language_modeling/megatron_change_num_partitions.py \
--model_file=PATH_TO_SRC_FILE \
--target_file=PATH_TO_TGT_FILE \
--tensor_model_parallel_size=2 \
--target_tensor_model_parallel_size=1
```
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PR_kwDOC_bI7s4wG2cL
| 3,364 |
fixed the bug of fixed-size bucketing.
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PR_kwDOC_bI7s4wF-dT
| 3,363 |
remove wordninja
|
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Signed-off-by: ekmb <[email protected]>
|
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| 1,084,296,040 |
PR_kwDOC_bI7s4wDe-Z
| 3,362 |
Repair arbitrary file or folder deletion vulnerability
|
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From: https://github.com/NVIDIA/NeMo/issues/3348
|
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PR_kwDOC_bI7s4wB63J
| 3,361 |
Moved shebangs to the first line
|
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CONTRIBUTOR
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The shebang must be the first line because it is interpreted by the kernel, which looks at the two bytes at the start of an executable file.
@okuchaiev , could you review please?
|
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I_kwDOC_bI7s5AjrtC
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FastSpeech2_HiFiGAN_E2E training doesn't work
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**Describe the bug**
The class for training FastSpeech2_HiFiGAN end-to-end model uses variables that aren't present in the core NeMo classes, so the python errors out before training begins.
**Steps/Code to reproduce bug**
Try training such a model using NeMo 1.5.1
**Expected behavior**
A model can be trained
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PR_kwDOC_bI7s4v_f8e
| 3,359 |
Updates on ASR with diarization util files
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This PR includes the following changes:
- Added CTM file based (word-by-word) diarization evaluation for calculating accurate WDERs.
- Created decoder_timestamps_utils.py that contains all the word time-stamp generation scripts.
- pyctcdecode support in decoder_timestamps_utils.py.
- LM based diarization transcript realigning in diarization_utils.py.
- offline_diarization_with_asr.py example and its yaml file.
- Updates on diarization README.md.
- Fixed unique ID indexing bug in scripts/speaker_tasks/pathsfiles_to_manifest.py.
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| 1,082,842,581 |
PR_kwDOC_bI7s4v-3EI
| 3,358 |
Tqdm get datasets
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now with fixed formatting (black)
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PR_kwDOC_bI7s4v-jUt
| 3,357 |
tqdm progress bar on dataset downloads
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Adding progress bar - tqdm - on data set download scripts
@titu1994
@redoctopus
@jbalam-nv
@okuchaiev
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PR_kwDOC_bI7s4v-Qw4
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Display the accurate progress during training.
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a minor bug fix
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| 1,082,652,661 |
PR_kwDOC_bI7s4v-Puc
| 3,355 |
missing init for TN German
|
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| 1,082,652,026 |
PR_kwDOC_bI7s4v-Plv
| 3,354 |
fix speaker label models training convergence
|
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Signed-off-by: nithinraok <[email protected]>
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| 1,082,634,900 |
I_kwDOC_bI7s5Ah7KU
| 3,353 |
NeMo Megatron Epoch training is confusing
|
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Currently, the NeMo Megatron has the intrinsic epoch concept in the dataset which is confusing as pytorch lightning has its own epoch concept. The `sample_mappling` logic prepares data samples for multiple epochs (the number of epoch is determined by the max_steps and number of documents in datafile) and creates the `*.npy` mapping files. The dataloader will take this dataset and sample in batches to go over the data for multiple times according to `*.npy` files. The training just need one pytorch lightning epoch.
Ideally, we should use pytorch lightning epoch only and the NeMo Megatron dataset should only concern with one epoch of data.
|
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| 1,082,465,780 |
I_kwDOC_bI7s5AhR30
| 3,352 |
ConformerCTC with input problems after ONNX conversion
|
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NONE
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I'm trying to convert the ConformerCTC (small) to ONNX, which works without problems, but when I'm testing the model with a single audio file I get a shape error:
```
[E:onnxruntime:, sequential_executor.cc:339 Execute] Non-zero status code returned while running Reshape node. Name:'Reshape_378'
Status Message: /onnxruntime/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:37 onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) size != 0 && (input_shape.Size() % size) == 0 was false.
The input tensor cannot be reshaped to the requested shape. Input shape:{1,115,176}, requested shape:{16,-1,4,44}
0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/dsp_nemo/testing_nemo.py", line 186, in <module>
run_full_test("stt_en_conformer_ctc_small", batch_size=1, print_predictions=True)
File "/dsp_nemo/testing_nemo.py", line 161, in run_full_test
ologits = ort_session.run(None, ort_inputs)
File "/opt/conda/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 188, in run
return self._sess.run(output_names, input_feed, run_options)
```
The code is the following:
```python3
test_manifest = "/dsp_nemo/data/test.json"
def run_full_test(nemo_model_name, batch_size, print_predictions):
config = {"batch_size": batch_size, "manifestpath": test_manifest}
model = nemo_asr.models.EncDecCTCModel.restore_from(
restore_path="/dsp_nemo/models/" + nemo_model_name + ".nemo"
)
ort_session = onnxruntime.InferenceSession(
"/dsp_nemo/models/" + nemo_model_name + ".onnx"
)
temporary_datalayer = setup_transcribe_dataloader(config, model.decoder.vocabulary)
wer_calc = WER(
vocabulary=model.decoder.vocabulary,
batch_dim_index=0,
use_cer=False,
ctc_decode=True,
)
wer_nums = []
wer_denoms = []
for test_batch in tqdm.tqdm(temporary_datalayer):
processed_signal, processed_signal_len = model.preprocessor(
input_signal=test_batch[0].to(model.device),
length=test_batch[1].to(model.device),
)
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(processed_signal)}
print(processed_signal.shape, processed_signal_len) # torch.Size([1, 80, 458]) tensor([458], device='cuda:0')
if "conformer" in nemo_model_name:
ort_inputs[ort_session.get_inputs()[1].name] = to_numpy(
processed_signal_len
)
ologits = ort_session.run(None, ort_inputs)
alogits = np.asarray(ologits)
logits = torch.from_numpy(alogits[0])
greedy_predictions = logits.argmax(dim=-1, keepdim=False)
if print_predictions:
hypotheses = wer_calc.ctc_decoder_predictions_tensor(greedy_predictions)
print(hypotheses)
targets = test_batch[2]
targets_lengths = test_batch[3]
model._wer.update(greedy_predictions, targets, targets_lengths)
_, wer_num, wer_denom = model._wer.compute()
wer_nums.append(wer_num.detach().cpu().numpy())
wer_denoms.append(wer_denom.detach().cpu().numpy())
# We need to sum all numerators and denominators first. Then divide.
print(f"WER = {sum(wer_nums) / sum(wer_denoms)}")
run_full_test("stt_en_conformer_ctc_small", batch_size=1, print_predictions=True)
# run_full_test("QuartzNet5x5LS-En", batch_size=1, print_predictions=True)
```
Running the same code with the QuartzNet model works as expected.
Tested with NeMo docker container (nvcr.io/nvidia/nemo:1.4.0) with onnxruntime (v1.7.2) additionally installed.
Onnx export with:
```
python3 /NeMo/scripts/export.py \
/dsp_nemo/models/stt_en_conformer_ctc_small.nemo /dsp_nemo/models/stt_en_conformer_ctc_small.onnx
```
<br>
Any ideas on this?
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| 1,082,397,153 |
PR_kwDOC_bI7s4v9ZYQ
| 3,351 |
K2 losses
|
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This PR implements k2-based loss functions and all other things needed for training ASR models with k2.
Reference paper: https://arxiv.org/abs/2110.03098
There's a lot of code, so I'm hoping @titu1994 can give a review.
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I_kwDOC_bI7s5AgpNl
| 3,350 |
WER results are different
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### Discussed in https://github.com/NVIDIA/NeMo/discussions/3339
<div type='discussions-op-text'>
<sup>Originally posted by **andrusenkoau** December 15, 2021</sup>
Hi everyone!
I am trying to decode all librispeech dev and test sets for my Conformer-CTC-BPE model by "eval_beamsearch_ngram.py" script in greedy way. But I am getting WER results that are different from training logs. For example, for the last 100th epoch I have got WER = **10.43%** for dev-clean according to "lightning_logs.txt". But after "eval_beamsearch_ngram.py" I have got WER = **12.01%**. Maybe i am doing something wrong?
This is my decoding command:
```
model_dir="nemo_experiments/Conformer-CTC-BPE/2021-12-14_12-47-27"
model_file="${model_dir}/checkpoints/Conformer-CTC-BPE.nemo"
for test_set in dev-clean dev-other test-clean test-other; do
decode_dir=${model_dir}/decoding_${test_set}
decode_log=${model_dir}/decoding_${test_set}.log
decode_manifest=data/manifest_${test_set}.json
python ${NGRAM_PATH}/eval_beamsearch_ngram.py --nemo_model_file ${model_file} \
--input_manifest ${decode_manifest} \
--acoustic_batch_size 10 \
--preds_output_folder ${decode_dir} \
--decoding_mode greedy | tee ${decode_log}
done
```</div>
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I_kwDOC_bI7s5AgbKe
| 3,349 |
Export punctuation and capitalization models for offline use
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NONE
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I want to use punctuation and capitalization model by using restore_from function and give my own model path. Even when I am providing the model path myself, it is downloading the model and tokenizer in the background. Is there a way to get this working without any downloads happening?
|
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Security vulnerabilty
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CONTRIBUTOR
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What would be the right contact to report a security vulnerabilty? thanks!
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I_kwDOC_bI7s5Ae3pu
| 3,347 |
How to restore a ckpt conformer model for inference/finetuning?
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NONE
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For conformer model
we can restore a nemo model by
model = nemo_asr.models.EncDecCTCModelBPE.restore_from(name+'.nemo')
and use it for inference.
But the models during training were saved in ckpt format.
And I tried model.load_state_dict(torch.load(model.ckpt)) but there are different keys.
How can I load a ckpt which was saved during training for inference? or how to convert a ckpt model to the target nemo model?
Thanks
|
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| 1,081,664,622 |
PR_kwDOC_bI7s4v6_Dj
| 3,346 |
update sparrowhawk export grammars to able to skip pynini
|
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Signed-off-by: Yang Zhang <[email protected]>
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PR_kwDOC_bI7s4v64UF
| 3,345 |
fix readme to show cmd
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Signed-off-by: Yang Zhang <[email protected]>
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PR_kwDOC_bI7s4v6jPg
| 3,344 |
Self-supervised tutorial & update
|
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[] | null | null | 2021-12-15T21:33:40 | 2022-01-28T23:51:48 | 2022-01-26T17:44:34 |
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- Basic tutorial for self-supervised pre-training and subsequent supervised fine-tuning
- Config for conformer SSL (may be changed later)
- Update to convert_to_tarred script to enable not having "text" field in manifest, and allow using "offset" for manifest without labels
- Add optional non-stride layers to reconstruction decoder
- Add option to load part of model from second nemo checkpoint from config (for example if we want to load encoder from one pre-trained model, and decoder from another)
# Changelog
- New way to initialize parts of models into current model for ssl
```markdown
init_from_nemo_model: Str path to a .nemo model in order to load state_dict from single nemo file;
if loading from multiple files, pass in a dict where the values have the following fields:
- path: Str path to .nemo model
- include: Optional list of strings, at least one of which needs to be contained in parameter name
to be loaded from this .nemo file. Default: everything is included.
- exclude: Optional list of strings, which can be used to exclude any parameter containing one of
these strings from being loaded from this .nemo file. Default: nothing is excluded.
hydra usage example:
```
```yaml
...
init_from_nemo_model:
model0:
path:<path/to/model1>
include:["encoder"]
model1:
path:<path/to/model2>
include:["decoder"]
exclude:["embed"]
```
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PR_kwDOC_bI7s4v6HPk
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TN_non_deterministic optimized
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I_kwDOC_bI7s5AcmaC
| 3,342 |
German normalization fails due to apparent installation problems.
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**Describe the bug**
German normalization fails due to apparent installation problems.
**Steps/Code to reproduce bug**
If installed with:
`pip install nemo-toolkit[all]` the nemo_text_processing.text_normalization.de.taggers.tokenize_and_classify is not in the project in order to normalize german phrases although used in the nemo_text_processing/text_normalization/normalize.py file.
If installed with:
`python -m pip install git+https://github.com/NVIDIA/NeMo.git@main` the file nemo_text_processing/inverse_text_normalization/vi/taggers/money.py is not found.
If installed with:
`git clone -q https://github.com/NVIDIA/NeMo.git`
when normalizing, getting the following issue: [https://github.com/NVIDIA/NeMo/issues/3341]
**Expected behavior**
Successful German normalization
**Environment overview**
- Environment location: Bare-metal: Ubuntu 20.04.3 LTS
**Environment details**
- OS version: Ubuntu 20.04.3 LTS
- PyTorch version: Torch 1.10.0
- Python version: Python 3.8.12
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I_kwDOC_bI7s5Acllg
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nemo text normalizer fails to normalize in german - _pywrapfst.FstOpError: Operation failed
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**Describe the bug**
Nemo normalizer fails to normalize phrases in german. Operation fails with a _pywrapfst.FstOpError caused by line 464 in the extensions/_pynini.pyx file.
**Steps/Code to reproduce bug**
`from nemo_text_processing.text_normalization.normalize import Normalizer
normalized_text = Normalizer(input_case='cased', lang='de').normalize('0.12', verbose=True)
`
**Expected behavior**
`normalized_text = 'null komma eins zwei'`
**Environment overview**
- Environment location: Bare-metal: Ubuntu 20.04.3 LTS
- Method of NeMo install:
`git clone -q https://github.com/NVIDIA/NeMo.git`
`pip install -e NeMo`
**Environment details**
- OS version: Ubuntu 20.04.3 LTS
- PyTorch version: Torch 1.10.0
- Python version: Python 3.8.12
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NMT Shared Embeddings Weights
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This PR closes https://github.com/NVIDIA/NeMo/issues/3220 by adding support to share encoder and decoder embeddings and pre-softmax projection layers when the same vocabulary is used in the encoder and decoder.
**NOTE:** Only the shape of the embedding matrix is validated during runtime (i.e., vocabulary size, vocabulary dimension). It is the user's responsibility to make sure the same vocabulary is being used in the encoder and decoder tokenizers.
# Usage:
Set the following flag in `yaml` config to `true` (default is `false`):
```
model:
shared_embeddings: true
```
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I_kwDOC_bI7s5AbjpI
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Serving trained ASR models via TorchServe
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Hi,
Thanks for your great framework.
Do you have any plan for adding serving ASR models via TorchServe (_e.g._, REST API or gRPC API)? I think it would be great (but I think at first, one must implement an appropriate handler for it, because to the best of my knowledge TorchServe at current state doesn't have any handler for speech models such as QuartzNet ASR model).
Best
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I_kwDOC_bI7s5AaZg_
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Warning about slow loading of non-wav formats
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.**
Recently I've tried to understand why is my gpu usage spiking from 0 to 100% and why does training seem to halt every num_workers batches. What I found is that my dataset consisted of .opus and .mp3 files, and that they load 2-30 times slower than those same files when converted to .wav.
**Describe the solution you'd like**
A warning message that informs the user that non-wav files might become a bottleneck for training.
It seems wrong to show this message in segment.py cause it would be displayed for each of those files and pollute the logs.
Maybe the ManifestProcessor can keep a list of found formats and in the end of processing compare it to soundfile.available_formats()?
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PR_kwDOC_bI7s4v3heU
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This is a test PR
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This PR is used to test and validate new CI integration. Please do not close.
Signed-off-by: Oleksii Kuchaiev <[email protected]>
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PR_kwDOC_bI7s4v3KDz
| 3,334 |
Add uniform ASR evaluation script for all models
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# Changelog
- Add speech_to_text_eval.py which utilizes `transcribe_speech.py` to first perform transcription (for all asr models) and then uses its output to compute scores against some ground truth (via manifest).
- Remove speech_to_text_infer.py since it was hand tuned for just 1 type of model and was not an inference script but an evaluation script that required ground truth.
- Improve docstrings for transcribe_speech.py and transcribe_speech_parallel.py
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PR_kwDOC_bI7s4v2j0U
| 3,333 |
Add titanet
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* Add titanet to available pretrained models
* update configs
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PR_kwDOC_bI7s4v2izq
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Better default for RNNT greedy decoding
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# Changelog
- Better rnnt greedy decoding default of 10 max decoding time steps
Signed-off-by: smajumdar <[email protected]>
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| 1,080,275,992 |
PR_kwDOC_bI7s4v2btl
| 3,331 |
fix german and vietnames grammar
|
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Signed-off-by: Yang Zhang <[email protected]>
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| 1,079,830,167 |
PR_kwDOC_bI7s4v08AI
| 3,330 |
Fix MixerTTS types and dimensions
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| 1,079,749,103 |
PR_kwDOC_bI7s4v0qlE
| 3,329 |
Added new method for logprobs computation
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I_kwDOC_bI7s5AW3jI
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AttributeError: type object 'EncDecCTCModel' has no attribute 'from_pretrained
|
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I'm running into the same issue .
Python :3.8.12
Running inside docker image :pytorch:21.11-py3
Code: :
```
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En', strict=False)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'EncDecCTCModel' has no attribute 'from_pretrained'
```
Installation steps :
- apt-get update && apt-get install -y libsndfile1 ffmpeg
- pip install Cython
- python -m pip install git+https://github.com/NVIDIA/[email protected]
_Originally posted by @architlfc in https://github.com/NVIDIA/NeMo/issues/2978#issuecomment-993514325_
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I_kwDOC_bI7s5AWwrs
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AttributeError: Can't pickle local object 'AudioToBPEDataset.__init__.<locals>.TokenizerWrapper'
|
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Hi, I can not run transcribe audio method using STT En Conformer-CTC Small model on windows 10.
I am using the following code:
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from("models/stt_en_conformer_ctc_small.nemo")
files = ['1.wav']
print(asr_model.transcribe(files, batch_size=1))
```
I'm using branch r1.5.0
As a result, I get the following error:
**AttributeError: Can't pickle local object 'AudioToBPEDataset\.\_\_init\_\_.\<locals\>\.TokenizerWrapper'**
On the colab everything worked without problems. What could be the problem and how to solve it? Thank you.
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| 1,079,222,486 |
I_kwDOC_bI7s5AU6DW
| 3,326 |
Running examples `enc_dec_nmt.py` script and getting a TypeError: expected str, bytes or os.PathLike object, not NoneType
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[] | null | null | 2021-12-14T02:02:52 | 2022-01-05T17:59:57 | 2022-01-05T17:59:57 |
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**Describe the bug**
I am running `enc_dec_nmt.py` as is from https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/enc_dec_nmt.py
Here's the error:
```
Traceback (most recent call last):
File "/home/jpilault/projects/nemo_examples/enc_dec_nmt.py", line 124, in main
mt_model = MTEncDecModel(cfg.model, trainer=trainer)
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/site-packages/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 84, in __init__
self.setup_enc_dec_tokenizers(
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/site-packages/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 442, in setup_enc_dec_tokenizers
self.encoder_tokenizer = get_nmt_tokenizer(
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/tokenizer_utils.py", line 164, in get_nmt_tokenizer
return YouTokenToMeTokenizer(model_path=tokenizer_model, bpe_dropout=bpe_dropout, r2l=r2l)
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/site-packages/nemo/collections/common/tokenizers/youtokentome_tokenizer.py", line 26, in __init__
model_path = Path(model_path).expanduser()
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/pathlib.py", line 1042, in __new__
self = cls._from_parts(args, init=False)
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/pathlib.py", line 683, in _from_parts
drv, root, parts = self._parse_args(args)
File "/home/jpilault/anaconda3/envs/myenv/lib/python3.8/pathlib.py", line 667, in _parse_args
a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
**Steps/Code to reproduce bug**
Run the code
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
It should not give an error.
**Environment overview (please complete the following information)**
used pip install
**Environment details**
- OS version: Ubuntu 20.04.2 LTS
- PyTorch version: 1.10.0+cu113
- Python version: 3.8
**Additional context**
Add any other context about the problem here.
Example: GPU model
|
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| 1,079,036,709 |
PR_kwDOC_bI7s4vyToS
| 3,325 |
german asr doc page update
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Signed-off-by: Yang Zhang <[email protected]>
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| 1,078,988,398 |
I_kwDOC_bI7s5AUA5u
| 3,324 |
Citrinet 256 and 512 for WSJ - limited resources
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NONE
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Hello! I wanna get baselines for citrinet-256 and citrinet-512 using WSJ, so that I can perform some experiments after. I will use speed perturbation and specAugment.
For now, I am training citrinet-256 with 4 GPUs, and batch 60. I also created a tokenizer with 250 for vocab and using WPE (not sure if this is the best for low resources). I also decided to use dropout equal to 0.2 since there was a huge gap between validation and train in the first epochs. It is also relevant to note that I moved from learning rate of 0.01 to 0.001 since the loss was going to NaN after epoch 8. Also, as a note, I experimented to set the flag detect anomaly to True in the Trainer and it got an error in the first steps for getting NaN results in the backward pass - not sure if that is okay or not to happen...
Below are some configurations that I am using.
<img width="1010" alt="Captura de ecrã 2021-12-13, às 20 36 52" src="https://user-images.githubusercontent.com/15928244/145884925-dcf112e1-d427-4df4-8931-564001d534e0.png">
<img width="1015" alt="Captura de ecrã 2021-12-13, às 20 37 23" src="https://user-images.githubusercontent.com/15928244/145884979-f9a57682-d06c-493e-b43c-91b7ae6e5f6b.png">
It would be great to know if I am in the right direction! Thanks a lot
|
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PR_kwDOC_bI7s4vxz36
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French ASR Doc updates
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- Updated docs page for current published) French ASR models
- fixed broken link to WFST tutorial (points to version in main since no stable release of tutorial.
- Re-pushed French ITN models in case of discrepancy with current code. (Currently failing in Docker container for r1.5.1)
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PR_kwDOC_bI7s4vvL-C
| 3,321 |
added double digits to EN ITN
|
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Signed-off-by: Yang Zhang <[email protected]>
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| 1,077,742,148 |
I_kwDOC_bI7s5APQpE
| 3,319 |
Inverse Text Normalization. Make matching between normalized and non-normalized tokens
|
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NONE
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**Is your feature request related to a problem? Please describe.**
Let we have the next text (in Russian): "куку девятьсот шестьдесят шесть миллиардов восемьсот сорок четыре миллиона семьсот пять тысяч четыреста двадцать пять девятьсот сорока пяти купить"
`inverse_normalizer.find_tags` and. `inverse_normalizer.select_tag` give us the next tokens representation:
_' tokens { name: "куку" } tokens { cardinal { integer: "966844705425" } } tokens { cardinal { integer: "945" } } tokens { name: "купить" } '_
But by this output, it's complicated to make matching between "966844705425" and corresponding words in the input text.
**Describe the solution you'd like**
`inverse_normalizer.find_tags` and `inverse_normalizer.select_tag` give us next tokens representation in another way:
_' tokens { name: "куку" , **(0, 4)** } tokens { cardinal { integer: "966844705425", **(5, 107)**} } tokens { cardinal { integer: "945", **(108, 128)** } } tokens { name: "купить", **(129, 135)** } '_ (span values are approximately, just for demonstrating goal)
So we will have corresponding tokens spans in input text, and it will be easy to solve the matching problem between entities.
Thank you very much for your attention!
|
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| 1,077,663,197 |
I_kwDOC_bI7s5AO9Xd
| 3,318 |
calculate WER
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Hi,
Thanks for your great repo. I have 2 questions:
1- In the Nemo repo is there any implemented code for calculating the WER (word error rate) for a data set & model's transcription? (_i.e._, I mean for example I want to calculate the WER of a trained model on a test set, which I have its ground truth in a text file. So, is there any script in Nemo that I can use it? or I must use other codes such as: [jiwer](https://pypi.org/project/jiwer/))
2- In the [`ASR_CTC_Language_Finetuning`](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb#scrollTo=iK-RQXEZfq1V) tutorial, if one enables `use_cer` (character error rate) parameter, then the tensor board results will be shown the CER instead of WER? (In other words, I have used the `use_cer = True`, and for example the tensor board showed me the **WER** of the trained model on epoch=31 on validation set is ~ 0.1028. Now I want to know that it is really WER or it is CER? and if it is CER, how I can calculate the WER of the model on my validation or test set?)
Best
|
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Asr fr
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Uploaded new version of Conformer CTC to NGC (now with unhyphenated token set). Updating ctc_bpe_models to reflect new imports.
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| 3,315 |
fix bucketing list bug.
|
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| 1,077,133,330 |
PR_kwDOC_bI7s4vsX1I
| 3,314 |
Fixed future masking in cross attention of Perceiver
|
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This PR fixed auto-regressive future masking of cross attention in Perceiver encoder.
**NOTE:** Default is kept auto-regressive since it shows better results.
This PR also updated `yaml` config file to default to `aayn_base.yaml` instead of copying the entries.
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| 1,076,877,952 |
PR_kwDOC_bI7s4vriyU
| 3,313 |
Fix MixerTTS generate_spectrogram types and dimensions
|
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CONTRIBUTOR
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Fixes the typecheck decorator for generate_spectrogram in MixerTTS and the swapping melspec and time dimension (as also done here https://github.com/NVIDIA/NeMo/blob/3687f2eaae36de6dd686caa99d2fe3100a5f4431/nemo/collections/tts/models/mixer_tts.py#L649)
You're probably in the middle of a refactor so sorry if you already got that, @Oktai15 (?)
(just FYI, I just trained MixerTTS from LJ with the current main head and everything worked fine, except this transpose missing when synthesizing and the mismatch of the decorator with the parameters)
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| 1,076,468,288 |
I_kwDOC_bI7s5AKZpA
| 3,312 |
Question[multi node training]
|
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[] | null | null | 2021-12-10T06:06:32 | 2022-01-18T18:33:31 | 2022-01-18T18:33:31 |
NONE
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How to train a model using multi-node(eg. 2 node, each node with 4gpus, just set the node numbers in the config doesn't work, In the pytorch-lighting tutorial, It needs the parameters like port and rank, etc) ? Do you have any tutorial or examples like in the speech_to_text_bpe.py?
# Training the model
```sh
python speech_to_text_bpe.py \
# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
model.train_ds.manifest_filepath=<path to train manifest> \
model.validation_ds.manifest_filepath=<path to val/test manifest> \
model.tokenizer.dir=<path to directory of tokenizer (not full path to the vocab file!)> \
model.tokenizer.type=<either bpe or wpe> \
trainer.gpus=-1 \
trainer.accelerator="ddp" \
trainer.max_epochs=100 \
model.optim.name="adamw" \
model.optim.lr=0.001 \
model.optim.betas=[0.9,0.999] \
model.optim.weight_decay=0.0001 \
model.optim.sched.warmup_steps=2000
exp_manager.create_wandb_logger=True \
exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
exp_manager.wandb_logger_kwargs.project="<Name of project>"
```
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| 1,076,377,171 |
PR_kwDOC_bI7s4vp62Z
| 3,311 |
Replacing outdated exports scripts
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https://jirasw.nvidia.com/browse/AIA-2166
Also:
https://jirasw.nvidia.com/browse/AIA-2168
Signed-off-by: Boris Fomitchev <[email protected]>
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| 1,076,122,288 |
PR_kwDOC_bI7s4vpG6P
| 3,310 |
fixed the seq_len bug.
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Prompt tuning
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Prompt tuning implementation based on: https://arxiv.org/abs/2104.08691
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PR_kwDOC_bI7s4vo5qi
| 3,308 |
Tn german deterministic
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added sparrowhawh tests for det. German TN
all sparrowhawk tests (TN/ITN) pass
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I_kwDOC_bI7s5AGQza
| 3,307 |
For Inverse Text Normalization, is not working for "one two three four"
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For Inverse Text Normalization, is not working for "one two three four"
the output should be "1 2 3 4"
whereas the output is "one two three four"
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Prompt Tuning
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Prompt tuning implementation based on this paper: https://arxiv.org/abs/2104.08691
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Refactored prepare_for_export calls to ensure input size of example i…
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…s handled properly
See also https://jirasw.nvidia.com/browse/JARS-1682
Signed-off-by: Boris Fomitchev <[email protected]>
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| 1,074,978,212 |
PR_kwDOC_bI7s4vlSmQ
| 3,304 |
Fix for Stalled ASR training/eval on Pytorch 1.10+ (multigpu/multinode)
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# Bugfix
- Training / inference stalls on multi gpu / multi node on Pytorch 1.10+
- This happens because the ASR models attempt to adaptively increase the masking window size or positional encoding (conformers) during training.
- If some ranks update their mask/pe buffer and others don't, then all reduce cannot properly perform a sync and code stalls.
- Solution is to perform 1 all reduce (max) sync per acoustic model forward pass, gathering the max(length) across all nodes, and adaptively updating the seq length in the modules if required.
Signed-off-by: smajumdar <[email protected]>
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| 1,074,955,831 |
PR_kwDOC_bI7s4vlN7-
| 3,303 |
Adding Megatron NeMo Bert support
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I tested with small dataset with binary_head True/False and model_parallel 2 and 1. They all seem working fine.
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| 1,074,303,262 |
I_kwDOC_bI7s5ACJEe
| 3,300 |
trouble with dedicating multiple GPU speech_to_text_bpe
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Hi, I've trouble with dedicating multiple GPU for asr/speech_to_text_bpe.py.
In config file of conformer, I tried gpu=-1 and also gpu=2 but when I run the code, it just use the first GPU and it doesn't use the second one.
Could you please help me to solve it?
As you see when I run the code, it use just the first GPU and just use 11Mb of the second one.
|
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| 1,074,117,309 |
PR_kwDOC_bI7s4vihVP
| 3,299 |
Fix specaugment time start for numba kernel
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# Bugfix
- Correct the variable for time_starts when executing the numba kernel for adaptive spec augment
# Note:
- This bugfix does not affect training runs used with the NeMo container or the pytorch container
- The CUDA version in the containers is always higher than the max supported CUDA version in Numba, therefore by default the numba kernel for spec aug is disabled.
- Only way to bypass this is to pass the environment flag `STRICT_NUMBA_COMPAT_CHECK=False` when running the training scripts.
Closes #3298
Signed-off-by: smajumdar <[email protected]>
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I_kwDOC_bI7s5ABQk1
| 3,298 |
Spec augmentation time masking
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NONE
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**Describe the bug**
I was wondering if this line of [code](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/parts/numba/spec_augment/spec_aug_numba.py#L286) for time masking of spec augmentation is correct.
Unless intended, it looks more reasonable with `time_starts = torch.cat(time_starts, 0)` rather than `time_starts = torch.cat(time_lengths, 0)` as this line of code makes `time_starts` and `time_lengths` equivalent to each other.
**Steps/Code to reproduce bug**
Any scripts useing spec augmentation, e.g., `python speech_to_text_bpe.py --config-path conf/conformer --config-name conformer_ctc_bpe`
**Expected behavior**
It currently generates time masks only at the beginning of the sequence.
**Environment overview (please complete the following information)**
`pip install -e .`
**Environment details**
N/A
**Additional context**
N/A
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PR_kwDOC_bI7s4vh9C3
| 3,297 |
Wav2vec
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Updated Wav2Vec module in asr collections to integrate with NeMo. Can now be passed usual config file and will process.
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PR_kwDOC_bI7s4vhxnB
| 3,296 |
Multiscale processing feature for speaker diarization
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This PR adds multiscale processing feature to NeMo speaker diarization pipeline.
examples/speaker_tasks/diarization/conf/offline_diarization.yaml is also modified to enable single scale and multiscale at the same time.
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I_kwDOC_bI7s4__Ep3
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Is "offset" supported in audio reader ?
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Hi, I am totally new to NeMo and I would just like to ask if "offset" is already in use for loading audio? Because I read this warning in the document :
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PR_kwDOC_bI7s4ve4T9
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Fixing the bucketing_batch_size bug.
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PR_kwDOC_bI7s4veU59
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Megatron AMP fix for scheduler step counter
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# Bugfix
- Attempt to prevent a step counter update of the scheduler when skipping a step in the optimizer
- Add training steps when nan/inf gradients are detected
- Properly support LR scheduler with "constant lr" at end of training. Refactor CosineAnnealing model for such support.
# Details
Since there are no pre-existing hooks in PTL to modify the scheduler step before the optimizer step, ive had to splice the support into the current GradScaler and then use the on_train_batch_end hook and manually monitor when the optimizer step occurs via the flag optimizer_update_skipped - None means optimizer didnt step this time (due to accumulated gradient), and True means grads were accumulated but optimizer did not perform step() - skipped step. In such case, perform a no-op for the scheduler too.
Signed-off-by: smajumdar <[email protected]>
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| 1,072,378,782 |
PR_kwDOC_bI7s4vczW-
| 3,291 |
NLP text augmentation
|
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[] | null | null | 2021-12-06T16:59:41 | 2021-12-09T19:45:22 | 2021-12-08T17:25:01 |
COLLABORATOR
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This PR adds a text augmentation script. The script can be used to generate human-like typos when testing model robustness.
# Summary
The script supports the following text augmentations:
1. Dropping a letter in a word
2. Swapping two letters in a word
3. Human-like typos (based on keyboard structure, and common misspelling) using https://github.com/facebookresearch/AugLy package.
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| 1,071,859,950 |
PR_kwDOC_bI7s4vbFys
| 3,290 |
Tn en money fix
|
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CONTRIBUTOR
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| 1,071,696,350 |
I_kwDOC_bI7s4_4Mne
| 3,289 |
GPU memory is used although cpu() is called
|
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I run the following commands:
```
import nemo
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_quartznet15x5").cpu()
English_text = quartznet.transcribe(['~/audio1.wav'])
```
In the output of `nvidia-smi`, I see that `800 Mb` of GPU is used when running the above commands. Why? My expectation is that no GPU memory should be used when I use `cpu()`.
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| 1,071,560,077 |
I_kwDOC_bI7s4_3rWN
| 3,288 |
Conformer training from scratch not achieving the reported WER
|
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NONE
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Hello! Thanks for providing such a nice toolkit.
I am currently training conformer-small on librispeech-960hr from scratch, following the training recipe and instructions in `examples/asr/conf/conformer/conformer_ctc_bpe.yaml`.
However, I'm not able to reproduce the WER reported in this [webpage](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_small_ls): reported ones are 3.4/8.8, but our WER is only around 4.5/11 on the clean/other datasets with ~300 epochs of training and doesn't seem to decrease anymore.
Here is the list of hyperparameters I'm using:
* d_model, n_heads, n_layers: 176, 16, 16
* tokenizer: WPE with vocab_size 128 (generated using the script in NeMo)
* spec_augment: freq_masks 2, time_masks 5, freq_width:27, time_width: 0.05
* lr: 5.0, betas: [0.9, 0.98], weight_decay: 1e-3 (adamw)
* warmup_steps: 10000 (noam annealing)
* Batch size: 2048 (using 8 machines, local batch size 16, and accumulate_grad_batches 16, multiplying up to 2048)
All the other hyperparameters are the same as the default.
Here is one of the learning curves (validation WER on dev-clean) from our runs:
<img width="1665" alt="Screen Shot 2021-12-05 at 1 18 53 PM" src="https://user-images.githubusercontent.com/50283958/144764357-9cc2609c-cf8e-4158-8693-5e6a7b7c5bd8.png">
I'm wondering if we should change the hyperparameters to achieve the reported WER values, or if the 300-epoch of training is not enough for the model convergence, or if there's something that we are missing.
Any clues regarding the hyperparameters, the number of training epochs, etc. would be really appreciated!
Thank you.
|
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PR_kwDOC_bI7s4vY6NE
| 3,286 |
Reduce test time of punctuation and capitalization model
|
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Signed-off-by: PeganovAnton <[email protected]>
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PR_kwDOC_bI7s4vYrZO
| 3,285 |
[TN] allow semiotic classes to be attached to punctuation marks without a space
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- allow semiotic[punct]semitic - no space
- reduce audio-based normalization options
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| 1,070,360,827 |
PR_kwDOC_bI7s4vWXn2
| 3,284 |
Multiscale diarization and update on offline diarization with ASR script
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This pull request includes the following features:
- Multiscale diarization: segmentation and clustering parts are updated (experimental).
- Optional pyctcdecode feature to apply beam search decoder for ASR.
- Language model based realigning feature for diarization (experimental).
- Added CTM based evaluation (WDER and WER evaluation)
- Miscellaneous updates and bug fix for diarization with ASR.
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I_kwDOC_bI7s4_yiHM
| 3,283 |
GPU utilization is zero
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NONE
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I run the following commands:
```
import nemo
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_en_quartznet15x5").cuda()
English_text = quartznet.transcribe(['~/audio1.wav'])
```
But GPU utilization is zero as it can be seen from the output of `nvidia-smi
`:
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PR_kwDOC_bI7s4vT80n
| 3,282 |
Remove ArrayLike because it is not supported in numpy 1.18
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| 1,068,976,451 |
PR_kwDOC_bI7s4vR1Yk
| 3,279 |
CTC Segmentation-Citrinet support
|
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- update ctc-segmentation package to the latest version
- add support for Citrinet models
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| 1,068,939,014 |
PR_kwDOC_bI7s4vRtdh
| 3,278 |
Fix megatron_gpt_ckpt_to_nemo.py with torch distributed
|
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Fixed megatron_gpt_ckpt_to_nemo.py with torch distributed along with a minor fix in loading from checkpoint in nlp_model.py
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PR_kwDOC_bI7s4vRGuf
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Fix megatron_gpt_ckpt_to_nemo.py with torch distributed
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Signed-off-by: Yu Yao <[email protected]>
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PR_kwDOC_bI7s4vQn_O
| 3,276 |
batch implementation
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Signed-off-by: Dmytro Pykhtar <[email protected]>
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| 1,067,971,854 |
I_kwDOC_bI7s4_p_UO
| 3,275 |
kenlm for conformer-ctc
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Hi. i need a LM for ctc-conformer and i trained a kenlm moel by this command:
python train_kenlm.py --train_file /home/mount/librispeech-lm-norm.txt --nemo_model_file stt_en_conformer_ctc_large.nemo --kenlm_model_file ../nemo_lm_6_new --ngram_length 6 --kenlm_bin_path ../kenlm/build/bin/ --do_lowercase
but when i try to use this LM for beam search decode, the output was something like this :(wowliadfbjdslfsofymnyq)
i also played with arameters alpha and beta. but not useful.
could you please help me?
|
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| 1,067,744,772 |
PR_kwDOC_bI7s4vNuxb
| 3,274 |
fix asr output - eval mode
|
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Signed-off-by: nithinraok <[email protected]>
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| 1,067,731,128 |
I_kwDOC_bI7s4_pEi4
| 3,273 |
AttributeError: type object 'EncDecCTCModel' has no attribute 'list_available_models'
|
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```
import nemo
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.list_available_models()
print("done")
```
Whenever I run this code, it gives me this error:
`AttributeError: type object 'EncDecCTCModel' has no attribute 'list_available_models'
`
I have all the dependencies installed.
When I hover over the line of code in PyCharm, it shows me the definition of the function and everything, which means it recognizes it. But for some reason it doesn't work when I run it.
<img width="1014" alt="image" src="https://user-images.githubusercontent.com/88787251/144137632-b741ba3a-c8c6-491b-bee7-01daeee7b3fa.png">
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PR_kwDOC_bI7s4vNgbA
| 3,272 |
Initial refactor
|
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Signed-off-by: Boris Fomitchev <[email protected]>
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PR_kwDOC_bI7s4vM8L2
| 3,271 |
Normalizer to TTS models, TTS tokenizer updates, AxisKind updates
|
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CONTRIBUTOR
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* Add normalizer for FastPitch and Mixer-TTS
* Update types for FastPitch and Mixer-TTS via new proposed signature for time
* Fix small bugs and refactor code in Mixer-TTS, FastPitch and TTS tokenizers
* Add German tokenizer
* Add CMUDict
Signed-off-by: Oktai Tatanov <[email protected]>
|
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I_kwDOC_bI7s4_m0eL
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Fine-tuning Conformer
|
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NONE
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Hi,
I want to fine-tune **stt_en_conformer_ctc_large** with Persian dataset. I used **conformer_ctc_char.yaml** from your example folder in repository. I changed the labels, too. Now when I want to run **trainer.fit(asr_model)** I have the following error:
`Interpolation key 'model.encoder.d_model' not found
full_key: optim.sched.d_model
object_type=dict`
Would you please help me how to resolve this error?
Thanks
_Environment overview_
> Environment location: Google Colab
> Method of NeMo install: !pip install nemo_toolkit[asr]
> NeMo version: r1.2.0
> Learning Rate: 1e-4
_Environment details_
> OS version : "Ubuntu20.04.3 LTS"
> PyTorch version : "1.10.0"
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I_kwDOC_bI7s4_ms1M
| 3,268 |
ECAPA TDNN pretrained fails to export ONNX - missing 'input_example'
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CONTRIBUTOR
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**Describe the bug**
I cannot export the pertained ECAPA TDNN model for embeddings to .onnx
Exporting requires the model to have a `input_example` which is not available on the ecapa tdnn model.
**Steps/Code to reproduce bug**
```
import nemo.collections.asr as nemo_asr
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='ecapa_tdnn')
speaker_model.export(output='mymodel.onnx')
```
Output:
```
TypeError Traceback (most recent call last)
<ipython-input-51-d55b2004ba1e> in <module>()
3 import nemo.collections.asr as nemo_asr
4 speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='ecapa_tdnn')
----> 5 speaker_model.export(output='mymodel.onnx')
1 frames
/usr/local/lib/python3.7/dist-packages/nemo/core/classes/exportable.py in _setup_input_example(self, input_example)
389
390 def _setup_input_example(self, input_example):
--> 391 input_list = list(input_example)
392 input_dict = {}
393 # process possible kwargs
TypeError: 'NoneType' object is not iterable
```
**Expected behavior**
I get a mymodel.onnx
**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**
Nemo version: r1.5.0
**Additional context**
GPU: Tesla T4
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| 1,066,947,558 |
I_kwDOC_bI7s4_mFPm
| 3,267 |
The results cannot be reproduced using the pre-training model
|
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NONE
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**Describe the bug**
I tested the model nmt_en_zh_transformer24x6 and nmt_en_zh_transformer6x6 on WMT18 test set and found that the blue score was a little low.
**Steps/Code to reproduce bug**
`model = MTEncDecModel.from_pretrained("nmt_en_zh_transformer24x6")`
`import copy`
`cfg = copy.deepcopy(model.cfg)`
`from omegaconf import OmegaConf, open_dict`
`with open_dict(cfg):`
` cfg.test_ds.use_tarred_dataset = False`
` cfg.test_ds.src_file_name = "/datasets/wmt18/clean.en"`
`cfg.test_ds.tgt_file_name = "/datasets/wmt18/clean.zh"`
`with open_dict(model.cfg):`
`model.cfg.tokens_in_batch=512`
`model.cfg.clean=False`
`model.cfg.max_seq_length = 512`
`model.cfg.cache_ids=False`
`model.cfg.cache_data_per_node=False`
`model.cfg.use_cache=False`
`model.cfg.shuffle=False`
`model.cfg.num_samples=-1`
`model.cfg.drop_last=False`
`model.cfg.pin_memory=False`
`model.cfg.load_from_cached_dataset=False`
`model.cfg.reverse_lang_direction=False`
`model.cfg.load_from_tarred_dataset=False`
`model.cfg.tar_shuffle_n=100`
`import pytorch_lightning as ptl`
`trainer = ptl.Trainer(gpus=1,`
`num_nodes=1,`
`max_steps=100000,`
`accumulate_grad_batches=1,`
`checkpoint_callback=False,`
`val_check_interval=578,`
`sync_batchnorm=False)`
`trainer.test(model)`
24x6 blue score is 29.38408660888672 ，10 points lower than 39.9 in the introduction
6x6 blue score is 2.8，but in the introduction it is 35.0
How can I set the parameters to get the correct results?
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PR_kwDOC_bI7s4vK3Ck
| 3,266 |
support for gecko tool
|
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|
Signed-off-by: nithinraok <[email protected]>
This PR adds support for the [gecko](https://gong-io.github.io/gecko/) labeling tool. Now diarization outputs *_gecko.json along with current output files for loading in gecko tool.
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| 1,066,669,109 |
PR_kwDOC_bI7s4vKOlC
| 3,265 |
Rewrite megatron_gpt_ckpt_to_nemo.py
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Signed-off-by: Yu Yao <[email protected]>
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| 1,066,430,066 |
PR_kwDOC_bI7s4vJdMw
| 3,264 |
doc link fixes
|
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Signed-off-by: nithinraok <[email protected]>
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| 1,066,422,321 |
PR_kwDOC_bI7s4vJb1T
| 3,263 |
Adaptiv fixed positional embeddings
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This PR enhances fixed positional embeddings to be adaptive to input sequences which are longer than the pre-defined max-sequence-length.
The PR also fixes two issues with checkpoint averaging `NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py`:
1. Checkpoint averaging now ignores `-last.ckpt` file (which is not part of top k saved models).
2. The script allows now to add list of file to import before restoring checkpoint. Useful when some classes were defined in the training script, and as such the class cannot be imported.
Example for error:
```
Traceback (most recent call last):
File "~/code/NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py", line 136, in <module>
main()
File "~/code/NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py", line 103, in main
checkpoint = torch.load(path, map_location=device)
File "~/anaconda3/lib/python3.8/site-packages/torch/serialization.py", line 607, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) File "~/anaconda3/lib/python3.8/site-packages/torch/serialization.py", line 882, in _load
result = unpickler.load()
File "~/anaconda3/lib/python3.8/site-packages/torch/serialization.py", line 875, in find_class return super().find_class(mod_name, name)
AttributeError: Can't get attribute 'MTBottleneckConfig' on <module '__main__' from '~/code/NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py'> (
```
Usage example:
```
~/code/NeMo/scripts/checkpoint_averaging/checkpoint_averaging.py \
--import_fname_list ~/code/NeMo/examples/nlp/machine_translation/enc_dec_nmt-bottleneck.py \
-- ./nll-perceiver_6x6_steps32_byte-level_sp-bpe_ls0.1_lp0.0-en_de/checkpoints
```
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| 1,066,383,953 |
PR_kwDOC_bI7s4vJTqt
| 3,262 |
Itn german refactor
|
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CONTRIBUTOR
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Refactored ITN geman to reduce loc and to reuse German TN
|
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| 1,066,006,874 |
I_kwDOC_bI7s4_ifla
| 3,261 |
Quartznet replicate some results
|
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NONE
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Hello!
I am trying to replicate Quartznet 5x5 results to get around 11/12% WER on the test set.
So far my last experiment was using 4 gpus, with batch 64, using speed perturbed data, specaugmentation, learning rate 0.01, dropout 0.2. I got around 23.18% on the validation and 17.11% on test set. Part of my config file is below:
<img width="702" alt="Captura de ecrã 2021-11-29, às 12 55 10" src="https://user-images.githubusercontent.com/15928244/143871857-85e7f9e4-f1f9-40f2-b9f7-0dff50e08d20.png">
Here I show my tensorboard results for training loss and validation loss:
<img width="392" alt="Captura de ecrã 2021-11-29, às 12 49 48" src="https://user-images.githubusercontent.com/15928244/143871967-ffe62a03-7f10-448e-aaab-610f52291a79.png">
<img width="383" alt="Captura de ecrã 2021-11-29, às 12 49 52" src="https://user-images.githubusercontent.com/15928244/143871974-70da4f61-735c-46d2-94bf-05261a9f10f9.png">
<img width="376" alt="Captura de ecrã 2021-11-29, às 12 49 59" src="https://user-images.githubusercontent.com/15928244/143871990-e897e64e-77e2-44cb-94d5-3d47eeeee979.png">
<img width="428" alt="Captura de ecrã 2021-11-29, às 12 49 55" src="https://user-images.githubusercontent.com/15928244/143871984-cb3e0b91-130d-4f9a-8d1d-b073deb3bc27.png">
Do you guys have any guess what I should go for to reach a better WER for this architecture? Thank you, guys. Also, If I wanna to replicate results for architecture 5x3, using 4 gpus what hyperparameters should I use?
Thanks a lot!
|
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I_kwDOC_bI7s4_iVF9
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[Question] What are the advantages of using numba instead of native pytorch while computing RNNT-Loss?
|
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Hi . I saw that the pretrained Transducer model uses Numba implementation of RNNT-Loss. I was wondering what is the reason behind using numba instead of implementing it in native pytorch?
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PR_kwDOC_bI7s4vG0hI
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(1) O2-style mixed precision recipe, (2) Persistent layer-norm, (3) Grade scale hysteresis, (4) gradient_as_bucket_view
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This PR container has many updates.
Please review and suggest any feedback on (1) code structure change, (2) argument names and structure.
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I_kwDOC_bI7s4_gWqv
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Using G2P (Grapheme2Phoneme) for ASR
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Hi,
Thank you for your great repo. I have 2 questions:
1- Is it better to use G2P for English ASR (or just use the raw text for it)? if yes, what model (G2P) do you suggest for this work?
2- Is it better to use G2P for ASR of other languages or not? (for example Arabic ASR)
Best
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I_kwDOC_bI7s4_gR1r
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Performance test on CPU and GPU
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Would you please provide some benchmark on the performance of NeMo ASR on CPU and GPU?
I am particularly interested to know if I can increase the throughput of ASR by using a GPU instead of a CPU? And how much would be the increase in the throughput?
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Add WMT21 paper to Publications
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| 1,065,082,195 |
I_kwDOC_bI7s4_e91T
| 3,255 |
Fine tune “nmt_en_zh_transformer24x6” using a single GPU, dict has not been initialized
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[] | null | null | 2021-11-27T16:41:12 | 2021-11-28T01:54:14 | 2021-11-28T01:54:14 |
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**Describe the bug**
`/NeMo/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py in eval_epoch_end(self, outputs, mode)`
`356 # we also need to drop pairs where ground truth is an empty string`
`357 dist.all_gather_object(`
`--> 358 tr_and_gt, [(t, g) for (t, g) in zip(translations, ground_truths) if g.strip() != '']`
`359 )`
`/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py`
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.
**Steps/Code to reproduce bug**
`import os`
`from nemo.collections.nlp.models import MTEncDecModel`
`os.environ["CUDA_VISIBLE_DEVICES"] = "0"`
`model = MTEncDecModel.from_pretrained("nmt_en_zh_transformer24x6")`
`trainer = ptl.Trainer(gpus=1,
num_nodes=1,
max_epochs = EPOCHS,
accumulate_grad_batches=1,
checkpoint_callback=False,
logger=False,
log_every_n_steps=5,
check_val_every_n_epoch=10,
sync_batchnorm=False)`
`model.set_trainer(trainer)`
`trainer.fit(model)`
**Expected behavior**
I only have one machine with two GPUs. so I can't use the torch.distributed. What should I do to ignore this?
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version 1.10.0
- Python version 3.7
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