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I fine-tuned the QuartzNet 15*5 model with 235 hours dataset of Persian Mozilla Common Voice. After that I restored the ckpt file and fine-tuned it with 25 hours of another persian dataset. Now Totally I have 50 epochs. From 18th epoch the loss start to increase. While before the 18th epoch the WER and loss both were decreased. So I deiced to change the dropout vale but in config file of QuartzNet 15*5 we have more than one dropout variable. Which of them should I change to get the best result?
**Environment overview**
- Environment location: Google Colab
-
- Method of NeMo install: !pip install nemo_toolkit[asr]
-
- NeMo version: r1.2.0
-
- Learning Rate: 2e-3
**Environment details**
- OS version : "Ubuntu20.04.3 LTS"
- PyTorch version : "1.9.1" | {
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} | 1. Two methods in `PunctuationCapitalizationModel` were insecure because of possible implicit array change.
2. Add `tqdm`
3. Fixed my English a little | {
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} | +Added transcribe_speech_parallel.py which supports multi-gpu/multi-node inference for ASR models
+Supports both tarred and non-tarred datasets
+works for both CTC and Transducer models
+Added extra sample_id output to the samples returned by the ASR datasets. It enables us to track down the samples. Would be useful for other tasks in future.
+Added the config description for ASR datasets/datalaoders
Needs to be done:
+Adding support for changing the decoder
+Adding fused_batch support?
+Retrieving some configs from the model itself instead of trusting the use to set them like use_start_end_token
Example for non-tarred datasets:
```
python transcribe_speech_parallel.py \
model=stt_en_conformer_ctc_small \
predict_ds.manifest_filepath=/dataset/manifest_file.json \
predict_ds.batch_size=16 \
output_path=/tmp/
```
Example for tarred datasets:
```
python transcribe_speech_parallel.py \
model=stt_en_conformer_ctc_small \
predict_ds.is_tarred=true \
predict_ds.manifest_filepath=/tarred_dataset/tarred_audio_manifest.json \
predict_ds.tarred_audio_filepaths=/tarred_dataset/audio__OP_0..127_CL_.tar \
predict_ds.batch_size=16 \
output_path=/tmp/
```
By default the trainer uses all GPUs available and precision is fp32. By setting the trainer config user may control these configs. For example to do the predictions with AMP on just two GPUs:
```
python transcribe_speech_parallel.py \
trainer.precision=16 \
trainer.gpus=2 \
model=stt_en_conformer_ctc_small \
predict_ds.manifest_filepath=/dataset/manifest_file.json \
predict_ds.batch_size=16 \
output_path=/tmp/
```
Or control the dataloader's config by setting the predict_ds:
```
python transcribe_speech_parallel.py \
predict_ds.num_workers=8 \
predict_ds.min_duration=2 \
predict_ds.sample_rate=16000 \
model=stt_en_conformer_ctc_small \
predict_ds.manifest_filepath=/dataset/manifest_file.json \
predict_ds.batch_size=16 \
output_path=/tmp/
```
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} | 1. Fix assertions in `pl_utils.py`. `assert (condition, message)` is incorrect use of assertion.
2. Fix function `reference_loss_func` in file `pl_utils.py`. Previously the function implicitly modified its argument `loss_sum_or_avg`.
3. Fix method `update` in class `GlobalAverageLossMetric`. Previously it implicitly modified its argument `loss` if `self.take_avg_loss` was `True`. Also detach inputs argument `loss` from graph.
4. Detach arguments of `PerplexityMetric.update` from graph.
5. Several fixes in docstrings. | {
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} | # Changelog
- Add transducer documentation
- Fix few issues in rst files
Signed-off-by: smajumdar <[email protected]> | {
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} | # Changelog
- Add num_workers and pin_memory to data loaders
- Add missing flag for use start end token in transcribe data loaders for BPE models
# Bugfix
- Remove trim_silence = True as default is now False
Signed-off-by: smajumdar <[email protected]> | {
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} | null | null | null | Hello, how are you?
I ran the script "Voice_Activity_Detection.ipynb"
in colab, and it kills the execution when it starts the training, thus giving the message "Session failure, restarting automatically", I couldn't identify more information about this problem. I leave the error prints.
script is at version 1.4.0
I didn't change the script, I just took it from the repository and ran it in collab | {
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} | null | null | null | **Describe the bug**
```
<ipython-input-24-56e2b5b7ea8a> in perform_clustering_ori(self, embeddings, time_stamps, speakers, audio_rttm_map, out_rttm_dir, max_num_speakers)
570
571 cluster_labels = COSclustering(
--> 572 uniq_key, emb, oracle_num_speakers=NUM_speakers, max_num_speaker=max_num_speakers, cuda=cuda,
573 )
574 for i in np.unique(cluster_labels):
/opt/conda/lib/python3.7/site-packages/nemo/collections/asr/parts/utils/nmse_clustering.py in COSclustering(key, emb, oracle_num_speakers, max_num_speaker, min_samples_for_NMESC, enhanced_count_thres, fixed_thres, cuda)
556
557 if emb.shape[0] > min_samples_for_NMESC:
--> 558 est_num_of_spk, p_hat_value, best_g_p_value = nmesc.NMEanalysis()
559 affinity_mat = getAffinityGraphMat(mat, p_hat_value)
560 else:
/opt/conda/lib/python3.7/site-packages/nemo/collections/asr/parts/utils/nmse_clustering.py in NMEanalysis(self)
401 self.p_value_list = self.getPvalueList()
402 for p_value in self.p_value_list:
--> 403 est_num_of_spk, g_p = self.getEigRatio(p_value)
404 est_spk_n_dict[p_value] = est_num_of_spk
405 eig_ratio_list.append(g_p)
/opt/conda/lib/python3.7/site-packages/nemo/collections/asr/parts/utils/nmse_clustering.py in getEigRatio(self, p_neighbors)
465
466 affinity_mat = getAffinityGraphMat(self.mat, p_neighbors)
--> 467 est_num_of_spk, lambdas, lambda_gap_list = estimateNumofSpeakers(affinity_mat, self.max_num_speaker, self.cuda)
468 arg_sorted_idx = np.argsort(lambda_gap_list[: self.max_num_speaker])[::-1]
469 max_key = arg_sorted_idx[0]
/opt/conda/lib/python3.7/site-packages/nemo/collections/asr/parts/utils/nmse_clustering.py in estimateNumofSpeakers(affinity_mat, max_num_speaker, is_cuda)
242 """
243 laplacian = getLaplacian(affinity_mat)
--> 244 lambdas, _ = eigDecompose(laplacian, is_cuda)
245 lambdas = np.sort(lambdas)
246 lambda_gap_list = getLamdaGaplist(lambdas)
/opt/conda/lib/python3.7/site-packages/nemo/collections/asr/parts/utils/nmse_clustering.py in eigDecompose(laplacian, cuda, device)
217 else:
218 laplacian = torch.from_numpy(laplacian).float()
--> 219 lambdas, diffusion_map = eigh(laplacian)
220 lambdas = lambdas.cpu().numpy()
221 diffusion_map = diffusion_map.cpu().numpy()
RuntimeError: cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR, when calling `cusolverDnSsyevd(handle, jobz, uplo, n, A, lda, W, work, lwork, info)`
```
**Steps/Code to reproduce bug**
```
sd = ClusteringDiarizer(config)
sd.diarize()
```
Hi Nemo team.
I tried to perform a clustering diarizer but it returned me this error. May I know how can I solve this?
Thanks!
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} | This PR adds a num_workers parameter to RNNT model's transcribe method.
The transcribe dataloader is then created with that number of workers to avoid 0 GPU usage.
#3009 | {
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} | null | null | null | Currently transcribe method creates a dataloader with num_workers=0 ([here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/rnnt_bpe_models.py#L356), for example). This leads to GPU being underused on large lists of files. There should be an argument that you can pass to transcribe method to increase number of workers, like this:
```
def transcribe(
self, paths2audio_files: List[str], batch_size: int = 4, return_hypotheses: bool = False, num_workers: int = 0
) -> (List[str], Optional[List['Hypothesis']])
``` | {
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2. Add default timer setting to the config | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3007 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3007/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3007/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3007/events | https://github.com/NVIDIA/NeMo/pull/3007 | 1,026,699,811 | PR_kwDOC_bI7s4tN05o | 3,007 | Add new features to ASR with diarization with modified tutorial and README. | {
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} | This is PR for newly added feature (VAD based word-timestamp compensation, ConformerCTC support).
This PR reflects @fayejf 's contribution on asr_with_diarization.py (VAD model input etc)
Also, tutorial file and README file are also revised to reflect the modified parts.
Still lots of issues are remaining, but for the sake of keeping an error-free version, this PR needs to be merged.
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} | This PR adds three features:
1. A utility to collect stats of tokenization of raw text given a tokenizer (i.e., regarding sentences length)
2. NMT inference script supports detailed timing of encoder/decoder/sampler (i.e., timing per sentence length).
3. A plotting script to visualize the detailed timing.
# Summary
## NMT Detailed Timing
By adding the flag `--write_timing` (as shown below), output file with `.timing.json` suffix will be created with a dictionary of lists that contains `encoder` [sec], `sampler` [sec], `mean_src_length` [tokens/batch], and `mean_tgt_length` [tokens/batch] keys.
Command:
```
python3 -- examples/nlp/machine_translation/nmt_transformer_infer.py ... --write_timing --batch_size=1
```
**NOTE:** a `batch_size=1` is recommended when measuring timing.
Plots:
The command
```
python3 -- examples/nlp/machine_translation/plot_detailed_timing.py FILENAME.timing.json
```
Will create 3 `FILENAME.timing.*.png` files, shown below.
## Tokenized Data Stats
See below example for collecting all stats in `stats/` directory:
```
python3 -- examples/nlp/machine_translation/collect_tokenizer_dataset_stats.py \
--tokenizer_library byte-level \
--tokenizer_model "" \
--max_lines 10000000 \
--out_dir stats \
./nmtset1.1-en-de/train.dedup.de
```
terminal output:
```
stats =
{'samples': 10000000, 'mean': 105.3137831, 'stdev': 112.8504785619722, 'min': 8.0, 'max': 1157.0, 'median': 69.0}
```
Histogram (per sentence length in byte-level tokens):
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2. Add GPU-synced step time to progressbar | {
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} | - Added support for ConformerCTC model
The script can extract word timestamps from both QuartzNet and ConformerCTC.
- Added VAD based word-timestamp compensation feature
If a pre-trained VAD model is provided to asr_with_diarization.py script, the script compensates the word-timestamp using the frame level VAD output.
This PR reflects @fayejf's contribution on asr_with_diarization.py regarding pretrained VAD model input. In the next update, README.md file will reflect the change with yaml/hydra input. | {
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} | Cleaned & signed version of #2839 (avoiding messy rebase over ~1 month's worth of commits)
Signed-off-by: Jocelyn Huang <[email protected]> | {
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} | + Changed the value to -10000.0 to make it more stable and flexible to different data type.
+ Added the support of pretrained models to the ASR LM scripts. | {
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} | null | null | null | @haythemtellili couldn't reproduce your error, but could you try:
`! pip install transformers==2.6 # due to HuggingFace transformer library updates`
`! pip install nemo_toolkit[all]`
`import nemo.collections.nlp as nemo_nlp`
_Originally posted by @ekmb in https://github.com/NVIDIA/NeMo/issues/830#issuecomment-656226295_
Hi I tried above approach but still mine issue not solved, could you please help on it
I am getting below error on running the command: import nemo.collections.nlp as nemo_nlp
ImportError: cannot import name 'get_args' from 'megatron' (/opt/conda/lib/python3.8/site-packages/megatron/__init__.py)
Full error trace below:
ImportErrorTraceback (most recent call last)
<ipython-input-32-2d87ef91019a> in <module>
----> 1 import nemo.collections.nlp as nemo_nlp
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp import data, losses, models, modules
16 from nemo.package_info import __version__
17
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
16 from nemo.collections.nlp.models.duplex_text_normalization import (
17 DuplexDecoderModel,
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/dialogue_state_tracking/__init__.py in <module>
13 # limitations under the License.
14
---> 15 from nemo.collections.nlp.models.dialogue_state_tracking.sgdqa_model import SGDQAModel
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/dialogue_state_tracking/sgdqa_model.py in <module>
31 from nemo.collections.nlp.data.dialogue_state_tracking.sgd.prediction_utils import write_predictions_to_file
32 from nemo.collections.nlp.losses import SGDDialogueStateLoss
---> 33 from nemo.collections.nlp.models.nlp_model import NLPModel
34 from nemo.collections.nlp.modules import SGDDecoder, SGDEncoder
35 from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py in <module>
22
23 import torch
---> 24 from megatron import mpu
25 from megatron.checkpointing import get_checkpoint_version, set_checkpoint_version
26 from omegaconf import DictConfig, OmegaConf
/opt/conda/lib/python3.8/site-packages/megatron/mpu/__init__.py in <module>
42 from .initialize import model_parallel_is_initialized
43
---> 44 from .layers import ColumnParallelLinear
45 from .layers import RowParallelLinear
46 from .layers import VocabParallelEmbedding
/opt/conda/lib/python3.8/site-packages/megatron/mpu/layers.py in <module>
32 from .mappings import reduce_from_tensor_model_parallel_region
33 from .mappings import scatter_to_tensor_model_parallel_region
---> 34 from .random import get_cuda_rng_tracker
35 from .utils import divide
36 from .utils import split_tensor_along_last_dim
/opt/conda/lib/python3.8/site-packages/megatron/mpu/random.py in <module>
25 from torch.utils.checkpoint import detach_variable
26
---> 27 from megatron import get_args
28 from megatron.memory import allocate_mem_buff
29
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} | null | null | null | **Describe the bug**
When trying to transcribe (or even import) Conformer transducer model, the following error comes:
```bash
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "~/NeMo/nemo/core/classes/common.py", line 705, in from_pretrained
instance = class_.restore_from(
File "~/NeMo/nemo/core/classes/modelPT.py", line 270, in restore_from
instance = cls._save_restore_connector.restore_from(
File "~/NeMo/nemo/core/connectors/save_restore_connector.py", line 136, in restore_from
instance = calling_cls.from_config_dict(config=conf)
File "~/NeMo/nemo/core/classes/common.py", line 493, in from_config_dict
raise e
File "~/NeMo/nemo/core/classes/common.py", line 488, in from_config_dict
instance = cls(cfg=config)
File "~/NeMo/nemo/collections/asr/models/rnnt_bpe_models.py", line 124, in __init__
self._setup_tokenizer(cfg.tokenizer)
File "~/NeMo/nemo/collections/asr/parts/mixins/mixins.py", line 74, in _setup_tokenizer
model_path = self.register_artifact('tokenizer.model_path', model_path)
File "~/NeMo/nemo/core/classes/modelPT.py", line 202, in register_artifact
return self._save_restore_connector.register_artifact(self, config_path, src, verify_src_exists)
File "~/NeMo/nemo/core/connectors/save_restore_connector.py", line 280, in register_artifact
raise FileNotFoundError(
FileNotFoundError: src path does not exist or it is not a path in nemo file. src value I got was: /tokenizers/nemo_asr_set/nemo_asr_set_1.4/no_appen/tokenizer_spe_unigram_v1024/tokenizer.model. Absolute: /tokenizers/nemo_asr_set/nemo_asr_set_1.4/no_appen/tokenizer_spe_unigram_v1024/tokenizer.model
```
**Steps/Code to reproduce bug**
Either directly in python:
```bash
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="stt_en_conformer_transducer_large")
```
or when using the transcribe script:
```bash
python examples/asr/transcribe_speech.py pretrained_name="stt_en_conformer_transducer_large" audio_dir="some_data"
```
Also when untarring the .nemo file, there is no tokenizer folder available as opposed to what is there in the conformer-ctc models.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
Nemo installed using ./reinstall.sh at this commit:
commit a0e8018fd72ed8c0c9227bfe583f70eece7af744 (HEAD -> main, origin/main, origin/HEAD)
Author: Evelina <[email protected]>
Date: Mon Oct 11 15:56:25 2021 -0700
**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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} | null | null | null | **Describe the bug**
`nemo.collections.nlp.models.PunctuationCapitalizationModel.add_punctuation_capitalization` fails when inferencing on a sentence with specific length
**Steps/Code to reproduce bug**
```
import nemo.collections.nlp
punct = nemo.collections.nlp.models.PunctuationCapitalizationModel.from_pretrained(model_name="punctuation_en_bert").eval().cuda()
punct.add_punctuation_capitalization(["here we are waiting for the meat to fry how long it takes for it to rummage for about a minute as it browns a big drop on the other side"])
```
Results in:
```
[NeMo I 2021-10-13 12:05:07 punctuation_capitalization_model:575] Using batch size 1 for inference
[NeMo I 2021-10-13 12:05:07 punctuation_capitalization_dataset:533] Max length: 34
[NeMo I 2021-10-13 12:05:07 data_preprocessing:358] Some stats of the lengths of the sequences:
[NeMo I 2021-10-13 12:05:07 data_preprocessing:360] Min: 32 | Max: 32 | Mean: 32.0 | Median: 32.0
[NeMo I 2021-10-13 12:05:07 data_preprocessing:366] 75 percentile: 32.00
[NeMo I 2021-10-13 12:05:07 data_preprocessing:367] 99 percentile: 32.00
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-762f40629049> in <module>
----> 1 punct.add_punctuation_capitalization(["here we are waiting for the meat to fry how long it takes for it to rummage for about a minute as it browns a big drop on the other side"])
~/miniconda3/envs/v2v-en/lib/python3.9/site-packages/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py in add_punctuation_capitalization(self, queries, batch_size, max_seq_length, step, margin, return_labels)
578 try:
579 self.eval()
--> 580 infer_datalayer = self._setup_infer_dataloader(queries, batch_size, max_seq_length, step, margin)
581 # Predicted labels for queries. List of labels for every query
582 all_punct_preds: List[List[int]] = [[] for _ in queries]
~/miniconda3/envs/v2v-en/lib/python3.9/site-packages/nemo/collections/nlp/models/token_classification/punctuation_capitalization_model.py in _setup_infer_dataloader(self, queries, batch_size, max_seq_length, step, margin)
361 margin = self._cfg.dataset.margin
362
--> 363 dataset = BertPunctuationCapitalizationInferDataset(
364 tokenizer=self.tokenizer, queries=queries, max_seq_length=max_seq_length, step=step, margin=margin
365 )
~/miniconda3/envs/v2v-en/lib/python3.9/site-packages/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py in __init__(self, queries, tokenizer, max_seq_length, step, margin)
638 self, queries: List[str], tokenizer: TokenizerSpec, max_seq_length: int = 128, step: int = 32, margin: int = 16
639 ):
--> 640 features = get_features_infer(
641 queries=queries, max_seq_length=max_seq_length, tokenizer=tokenizer, step=step, margin=margin
642 )
~/miniconda3/envs/v2v-en/lib/python3.9/site-packages/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py in get_features_infer(queries, tokenizer, max_seq_length, step, margin)
542 for q_i, query_st in enumerate(st):
543 q_inp_ids, q_segment_ids, q_subtokens_mask, q_inp_mask, q_quantities_of_preceding_words = [], [], [], [], []
--> 544 for i in range(0, max(len(query_st), length) - length + step, step):
545 subtokens = [tokenizer.cls_token] + query_st[i : i + length] + [tokenizer.sep_token]
546 q_inp_ids.append(tokenizer.tokens_to_ids(subtokens))
ValueError: range() arg 3 must not be zero
```
**Expected behavior**
Method doesn't fail and return punctuated and capitalized result
**Environment overview (please complete the following information)**
Local machine
nemo installed with `pip install nemo_toolkit['all']`
```
$ pip freeze | grep nemo
nemo-toolkit==1.4.0
```
**Environment details**
Ubuntu 18.04.5 LTS
PyTorch 1.9.1
Python 3.9.7
**Additional context**
It seems like this line of code can result in step being zero: https://github.com/NVIDIA/NeMo/blame/main/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py#L536
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} | Adding bucketing support for training ASR models with tarred datasets.
+ User need to enable --sort_in_shard and also use --buckets_num=NUM_OF_BUCKETS with scripts/speech_recognition/convert_to_tarred_audio_dataset.py script. The script would create multiple tarred dataset, one per bucket, based on the audio durations.
+ Then user need to pass the multiple tarred datasets as a list of lists in the data loader config section. User need to pass a list of lists to enable bucketing. If user passes just a list of strings, then the datasets would simply get concatenated which would be different from bucketing. Here is an example for 4 buckets and 512 shards.
```
python speech_to_text_bpe.py
...
model.train_ds.manifest_filepath=[[PATH_TO_TARS/bucket1/tarred_audio_manifest.json],
[PATH_TO_TARS/bucket2/tarred_audio_manifest.json],
[PATH_TO_TARS/bucket3/tarred_audio_manifest.json],
[PATH_TO_TARS/bucket4/tarred_audio_manifest.json]]
model.train_ds.tarred_audio_filepaths=[[PATH_TO_TARS/bucket1/audio__OP_0..511_CL_.tar],
[PATH_TO_TARS/bucket2/audio__OP_0..511_CL_.tar],
[PATH_TO_TARS/bucket3/audio__OP_0..511_CL_.tar],
[PATH_TO_TARS/bucket4/audio__OP_0..511_CL_.tar]]
```
+ When bucketing is enabled, in each epoch, first all GPUs would use the first bucket, then go to the second bucket, and so on. It guarantees that all GPUs are using the same bucket at the same time. It reduces the number of paddings in each batch and speedup the training significantly without hurting the accuracy significantly.
+ Also added the support for relative paths in the manifest file to the convert_to_tarred_audio_dataset.py script. When it cannot find a file in the path specified in the manifest, it looks into the folder of the manifest file. | {
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} | null | null | null | Hi,
With every new GPU I add I find I'm forced to lower batchsize because of memory overflows. This is normal?
For example with 1 GPU I can use 68 BatchSize, with 2 GPUs I can use 67 and with 5 I can use 62.
OS:
Ubuntu 18.04
NeMo 1.3.0
Python 3.8 | {
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My last workouts never end correctly, he gets stuck at this stage:
If I force a ctrl+c it ends, but I'm afraid to kill some processing. I leave it for hours and this stage is never changed. Any suggestions for this issue? Or is it something normal?
Environment:
JupyterLab
NeMo 1.3.0 (Previous also have the same problem)
GPUs: 3
Python: 3.8
OS: Ubuntu | {
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Is it efficient to set up a dataset to recognize songs? I notice that when there is a song in an audio it tries to somehow transcribe, if I train these songs saying "music" in the manefesto would it be efficient? Or are there alternatives that can help me in this scenario? | {
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I would like to know if there is any documentation on memory usage during training. I have a base of approximately 1000 hours and there are times when memory usage reaches 160GB (which is my current limit). I'm afraid this will interfere with training, hence my doubt. I also notice that this increase is constant each time I grow my database and the amount of GPUs
NeMo Version: 1.3.0
GPUs: 5
CPU Cores: 64
RAM: 160 GB
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Can you please guide me how can I use .nemo model produced after training on our data set so it is not completely using pretrained model, it is into transfer learning and now i want to use it for predicting on new dataset, the way other models work using .predict method
Your response is highly awaited | {
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A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
nemo2riva --out /servicemaker-dev/stt_en_contextnet_1024.riva /servicemaker-dev/stt_en_contextnet_1024.nemo
nemo-toolkit 1.4.0
nemo2riva 1.6.0b0
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | null | null | null | **Describe the bug**
AttributeError: type object 'EncDecRNNTModel' has no attribute 'from_pretrained'
code:
import nemo.collections.asr as nemo_asr
conformer_rnnt = nemo_asr.models.EncDecRNNTModel.from_pretrained()
console:
'''
Traceback (most recent call last):
File "exp1.py", line 19, in <module>
conformer_rnnt = nemo_asr.models.EncDecRNNTModel.from_pretrained()
AttributeError: type object 'EncDecRNNTModel' has no attribute 'from_pretrained'
'''
**Environment details**
macOS Catalina 10.15.7
python version 3.8.10
**Additional context**
no GPU | {
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} | - Implement `from_pretrained` and `list_available_models` for ZeroShotIntentModel
- Update tutorial notebook with link to BERT base pretrained model in NGC
- Change name of parameter from `multi_class` to `multi_label` in `predict` method
- Change `multi_label` default value from False to True | {
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} | null | null | null | **Describe the bug**
I called
MTEncDecModel.restore_from('zh_en_24x6.nemo').cuda('cuda:0')
if I assign the parameter to .cuda() as above, it will raise an error
'no attribute device'
**Steps/Code to reproduce bug**
as above
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**
as above
**Environment overview (please complete the following information)**
as instructed in the readme, pip install nemo_toolkit['all'], python 3.8 nemo 1.4 pytorch 1.9
**Environment details**
as above
**Additional context**
probably not
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`AttributeError: module 'nemo.collections' has no attribute 'nlp'
`
I am on the latest version of nemo, and also, `nemo.collections.asr` or `nemo.collections.cv` works perfectly
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Signed-off-by: Boris Fomitchev <[email protected]> | {
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I would like to know if NeMo has any mechanism that extracts details from the speakers, for example, age, gender or accent.
Thanks in advance | {
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} | null | null | null | I am trying to make inference (Audio to text ASR )on nemo model converted to pytorch mobile lite model, but i am facing issue in mismatch. Can someone help me please.
my_code
import torch
from torch import Tensor
from torch.utils.mobile_optimizer import optimize_for_mobile
import torchaudio
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name, map_location='cpu')
model.export('model.pt')
model2 = torch.load('model.pt', map_location=torch.device('cpu'))
model2.eval()
quantized_model = torch.quantization.convert(model2)
scripted_model = torch.jit.script(quantized_model)
optimized_model = optimize_for_mobile(scripted_model)
optimized_model._save_for_lite_interpreter("quartz.ptl")
model3 = torch.load('quartz.ptl', map_location=torch.device('cpu'))
model3.eval()
class EncDecCTCModel(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
self.labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
def forward(self, waveforms: Tensor) -> str:
"""Given a single channel speech data, return transcription.
Args:
waveforms (Tensor): Speech tensor. Shape `[1, num_frames]`.
Returns:
str: The resulting transcript
"""
logits, _ = self.model(waveforms) # [batch, num_seq, num_label]
logits = torch.from_numpy(logits[0])
best_path = logits.argmax(dim=-1, keepdim=False)
#best_path = torch.argmax(logits[0], dim=-1) # [num_seq,]
print(best_path.size())
prev = ''
hypothesis = ''
for i in best_path:
char = self.labels[i]
if char == prev:
continue
if char == " ":
prev = ''
continue
hypothesis += char
prev = char
return hypothesis
model3 = torch.load('quartz.ptl', map_location=torch.device('cpu'))
model3.eval()
model = EncDecCTCModel(model3)
waveform , sample_rat = torchaudio.load('84-121123-0001.wav')
waveform=waveform.unsqueeze(0)
print('Result:', model(waveform))
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
File "code/__torch__/nemo/collections/asr/models/ctc_models/___torch_mangle_2795.py", line 7, in forward
def forward(self: __torch__.nemo.collections.asr.models.ctc_models.___torch_mangle_2795.EncDecCTCModel,
input: Tensor) -> Tensor:
_0 = torch.conv1d(input, CONSTANTS.c0, None, [2], [16], [1], 64)
~~~~~~~~~~~~ <--- HERE
input0 = torch.batch_norm(torch.conv1d(_0, CONSTANTS.c1), CONSTANTS.c2, CONSTANTS.c3, CONSTANTS.c4, CONSTANTS.c5, False, 0.10000000000000001, 0.001, True)
_1 = torch.relu_(input0)
Traceback of TorchScript, original code (most recent call last):
%transposed:bool, %output_padding:int[], %groups:int, %benchmark:bool,
%deterministic:bool, %cudnn_enabled:bool, %allow_tf32:bool):
%r = aten::conv1d(%a, %w, %b, %stride, %padding, %dilation, %groups)
~~~~ <--- HERE
return (%r)
RuntimeError: Given groups=64, weight of size [64, 1, 33], expected input[1, 1, 63840] to have 64 channels, but got 1 channels instead
Is this is the right method to give input to the nemo converted pytorch mobile model?
If not how to give the input to the mobile model and return the text when audio input is given . Please help me
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} | This PR extends the timing measurement for NMT models, in addition to bug and performance fixes.
# Summary
* Added timing measurement of encoder, decoder, and sampling stages in eval_step.
* Added support in sliding mean - useful in cases when post processing of log is not available, and smoother timing is required.
* Fixed logging issue of log variance (missing reduction). This bug affected only MIN/VAE training.
* Improved evaluation performance by caching the loss values to be reused in translation (inside eval_step).
# Configuration
The following configuration can be used in config file to enable sliding mean over 10 steps:
```
exp_manager:
step_timing_kwargs:
# if positive, defines the size of a sliding window for computing mean
buffer_size: Optional[int] = 10 #
```
The following flag can be used in command line to enable sliding mean over 10 steps:
```
+exp_manager.step_timing_kwargs.buffer_size=10
```
# Logging
## NMT Timing Plots
The following keys will be logged for NMT models: `val_sampler_timing`, `val_decoder_timing`, `val_encoder_timing`.
## Sliding Mean Plots
Below is a comparison of two runs. Cyan shows timing averaged over a sliding window of 10 samples. Gold shows raw timing.
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} | null | null | null | Say I already trained an acoustic model and now I want to train a recurrent network part of the rnn-transducer pipeline solely on text data. Theoretically it totally makes sense, but it would require some hacking. Or is there an intended way to do it? I would appreciate if you shared your thoughts on that possibility. | {
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} | null | null | null | Hi, I am facing the problem of running inference with my own trained VAD model in the diarization pipeline. I have found #2348 this solution and did the same what you have said. But now I am facing this issue:
```
[NeMo I 2021-10-05 08:44:52 speaker_utils:337] Performing Clustering
0%| | 0/1 [00:00<?, ?it/s]
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/tmp/ipykernel_126575/2138038744.py in <module>
45
46 sd_model = ClusteringDiarizer(cfg=config)
---> 47 sd_model.diarize()
48 # IPython.display.clear_output(wait=False)
49
~/env/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/clustering_diarizer.py in diarize(self, paths2audio_files, batch_size)
352 os.makedirs(out_rttm_dir, exist_ok=True)
353
--> 354 perform_diarization(
355 embeddings_file=self._embeddings_file,
356 reco2num=self._num_speakers,
~/env/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/speaker_utils.py in perform_diarization(embeddings_file, reco2num, manifest_path, sample_rate, window, shift, audio_rttm_map, out_rttm_dir, max_num_speakers)
336 )
337 logging.info("Performing Clustering")
--> 338 all_reference, all_hypothesis = perform_clustering(
339 embeddings, time_stamps, speakers, audio_rttm_map, out_rttm_dir, max_num_speakers
340 )
~/env/nemo/lib/python3.8/site-packages/nemo/collections/asr/parts/speaker_utils.py in perform_clustering(embeddings, time_stamps, speakers, audio_rttm_map, out_rttm_dir, max_num_speakers)
262
263 lines = time_stamps[uniq_key]
--> 264 assert len(cluster_labels) == len(lines)
265 for idx, label in enumerate(cluster_labels):
266 tag = 'speaker_' + str(label)
AssertionError:
``` | {
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} | * Augments audio data by masking out higher frequencies in the spectrogram for random samples in a batch
* Added Ogg-vorbis codec augmentation at lower rates to cover wideband codec augmentation | {
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import nemo.collections.asr as nemo_asr
import torch.nn as nn
model1 = model.extract_state_dict_from('/root/.cache/torch/NeMo/NeMo_1.3.0/QuartzNet15x5Base-En/2b066be39e9294d7100fb176ec817722/QuartzNet15x5Base-En.nemo', save_dir='./pt_ckpt/QuartzNet15x5Base-En.pt')
model.load_state_dict(torch.load('./pt_ckpt/model_weights.ckpt'))
model.eval()
input_example = torch.randn(8, 1 , 64)
traced_model = torch.jit.trace(model, input_example)
TypeError Traceback (most recent call last)
/tmp/ipykernel_40865/3321835653.py in <module>
6
7
----> 8 traced_foo = torch.jit.trace(model, input_example)
~/api/lib/python3.7/site-packages/torch/jit/_trace.py in trace(func, example_inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
742 strict,
743 _force_outplace,
--> 744 _module_class,
745 )
746
~/api/lib/python3.7/site-packages/torch/jit/_trace.py in trace_module(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
957 strict,
958 _force_outplace,
--> 959 argument_names,
960 )
961 check_trace_method = module._c._get_method(method_name)
~/api/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
~/api/lib/python3.7/site-packages/torch/nn/modules/module.py in _slow_forward(self, *input, **kwargs)
1037 recording_scopes = False
1038 try:
-> 1039 result = self.forward(*input, **kwargs)
1040 finally:
1041 if recording_scopes:
~/api/lib/python3.7/site-packages/nemo/core/classes/common.py in __call__(self, wrapped, instance, args, kwargs)
790 # Check that all arguments are kwargs
791 if input_types is not None and len(args) > 0:
--> 792 raise TypeError("All arguments must be passed by kwargs only for typed methods")
793
794 # Perform rudimentary input checks here
TypeError: All arguments must be passed by kwargs only for typed methods
please Help to debug the error | {
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} | null | null | null | I've been trying to train fastpitch + hifigan e2e with `fastpitch_hifigan_e2e.py`
I have used the script at https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/prepare_dataset.py to create the durations and pitch .pt files. On starting the training, I get the assertion error.
```sh
File "/home/user/Labs/NeMo/nemo/collections/asr/data/audio_to_text.py", line 758, in _collate_fn
assert len(durs_i) == len(pitch_i), f"{len(durs_i)}, {len(pitch_i)}: {path_i}"
AssertionError: 80, 1: /home/user/Documents/LJSpeech-1.1/wavs/LJ012-0230.wav
```
It looks like there is a mismatch in the lengths of duration and pitch. It would be great if I could get some help with this.
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} | null | null | null | **Describe the bug**
Hi teams, may I know whether Speaker_Diarization models can separate speaker utterances from overlap speech? If not, will model directly ignore this overlap part?
https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb
Thanks in advance | {
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} | This PR is a manual merge of <https://github.com/NVIDIA/NeMo/pull/2936> | {
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} | This PR adds the ability to log the timing of a step for train/validation/test modes during training.
This feature is added to all NeMo models.
Time is measured in seconds.
# Configuration
The following config allow to control logging of step timing:
```
exp_manager:
log_step_timing: True # logging is enabled by default
step_timing_kwargs:
reduction: "mean" # reduction for multiple measurements
sync_cuda: False # if True calls torch.cuda.synchronize() for start/stop of measurement. Disabled by default
```
By default the logging is turned on. In order to turn off the feature, the following config can be used:
```
exp_manager.log_step_timing=False
```
By default cuda sync is turned off. In order to turn it on, the following config can be used:
```
exp_manager.step_timing_kwargs.sync_cuda=True
```
# Logging
The following keys will be logged in the corresponding mode (when available): `train_step_timing`, `train_backward_timing`, `validation_step_timing`, `test_step_timing`.
## Weights & Biases Output Example
## Pytorch Lightning Training Loop
Below is `pytorch lightning` training loop, in order to better understand what exactly is measured:
```
def train_loop():
on_epoch_start()
on_train_epoch_start()
for batch in train_dataloader():
on_train_batch_start()
on_before_batch_transfer()
transfer_batch_to_device()
on_after_batch_transfer()
training_step()
on_before_zero_grad()
optimizer_zero_grad()
on_before_backward()
backward()
on_after_backward()
on_before_optimizer_step()
optimizer_step()
on_train_batch_end()
if should_check_val:
val_loop()
# end training epoch
training_epoch_end()
on_train_epoch_end()
on_epoch_end()
```
See <https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#hooks> for full details. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2935 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2935/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2935/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2935/events | https://github.com/NVIDIA/NeMo/pull/2935 | 1,012,729,678 | PR_kwDOC_bI7s4siFwg | 2,935 | Adding complete method to MegatronGPTModel | {
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} | This PR adds the following method ```response=MegatronGPTModel.complete(request)``` and example how to use it.
Example usage:
```
python examples/nlp/language_modeling/megatron_gpt_eval.py --model_file megatron_gpt.nemo \
--prompt "How to fix gpu memory? A: " --tokens_to_generate 256
```
With a somewhat good converged model you should be getting completion like this:
```
How to fix gpu memory? A:
You can use the gpu_mem_info() function to get the information about the memory.
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2934 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2934/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2934/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2934/events | https://github.com/NVIDIA/NeMo/issues/2934 | 1,012,457,550 | I_kwDOC_bI7s48WOBO | 2,934 | How to train end2end tts | {
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} | null | null | null | I want to train an end2end tts model like fastpitch_hifigan_e2e but I don't find any tutorial for that. I tried to train it like Tacotron but I understood that fastpitch_hifigan_e2e needs "Phoneme durations" file and again I don't find any guide for that(exactly what should this file have and what is format of it. [this explanation](https://github.com/NVIDIA/NeMo/blob/main/docs/source/tts/datasets.rst) is not complete).
thanks for any help.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2932 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2932/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2932/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2932/events | https://github.com/NVIDIA/NeMo/issues/2932 | 1,012,105,512 | I_kwDOC_bI7s48U4Eo | 2,932 | I am getting undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE error. This I am getting when I am trying to "import torch from nemo.collections import nlp". I am trying to use pytorch ngc container 21.05. I tried to import torch before the nemo extension. Please suggest how I can resolve this. | {
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} | null | null | null | **Describe the bug**
I am getting this issue when trying to import nlp models from nemo.
**Steps/Code to reproduce bug**
I did the following installation of packages.
!pip install wget
!apt-get install sox libsndfile1 ffmpeg
!pip install unidecode
!pip install braceexpand
!pip install webdataset
BRANCH = 'main'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
!pip install torchaudio>=0.6.0 -f https://download.pytorch.org/whl/torch_stable.html
!mkdir configs
!pip install Cython
!pip install nemo_toolkit['all']
Then when I am doing the import, I am getting the error of undefined symbol.
**Expected behavior**
I intended to import nemo's nlp model succesfully to test out different models.
**Environment overview (please complete the following information)**
- Environment location: Docker version 20.10.8
- Method of NeMo install: !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
- If method of install is [Docker],: docker run -it --restart=on-failure:1 -v $PWD/NeMo:/workspace/Nemo --shm-size=8g --net=host --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:21.05-py32
**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: Nvidia GeForce RTX 2060
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} | - fixes inference for utterances longer than max_seq_len
- adds additional post-processing to match punct/spaces of the original input
- adds `whitelist` arg to WFST normalize.py
@Oktai15 for viz | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2924 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2924/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2924/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2924/events | https://github.com/NVIDIA/NeMo/pull/2924 | 1,010,621,129 | PR_kwDOC_bI7s4sbxDW | 2,924 | Fix numba spec augment for cases where batch size > MAX_THREAD_BUFFER | {
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} | # Bugfix
- Fix an issue where numba spec augment will throw cryptic CUDA error if batch size > MAX_THREAD_BUFFER
Closes https://github.com/NVIDIA/NeMo/issues/2916
Signed-off-by: smajumdar <[email protected]> | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2923 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2923/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2923/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2923/events | https://github.com/NVIDIA/NeMo/pull/2923 | 1,010,486,607 | PR_kwDOC_bI7s4sbY0Z | 2,923 | Adding simple, greedy-search based text generation to MegatronGPTModel | {
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} | This PR adds the following method ```response=MegatronGPTModel.complete(request)``` and example how to use it.
Example usage:
```
python examples/nlp/language_modeling/megatron_gpt_eval.py --model_file megatron_gpt.nemo \
--prompt "How to fix gpu memory? A: " --tokens_to_generate 256
```
With a somewhat good converged model you should be getting completion like this:
```
How to fix gpu memory? A:
You can use the gpu_mem_info() function to get the information about the memory.
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2921 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2921/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2921/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2921/events | https://github.com/NVIDIA/NeMo/pull/2921 | 1,010,205,608 | PR_kwDOC_bI7s4sal56 | 2,921 | Itn fr | {
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} | ITN Grammars for French for both tagging and verbalizers. Can now pass 'fr' as language to inverse_normalize and export_grammars for inverse text normalization. All CI tests passed locally or skipped by default. | {
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} | null | null | null | Hi,
First of all, thanks for developing this incredibly useful toolkit.
I am experimenting with the wav2vec code in NeMo, and would like to ask a question regarding the computation of the masked indices for the context encoder network. More precisely, the `compute_mask_indices` function in `collections/asr/modules/wav2vec_modules.py` appears to implement a normalisation step at the end, ensuring that the total number of masked timesteps for each entry in the minibatch is identical.
```
min_len = min([len(m) for m in mask_idcs])
for i, mask_idc in enumerate(mask_idcs):
if len(mask_idc) > min_len:
mask_idc = np.random.choice(mask_idc, min_len, replace=False)
mask[i, mask_idc] = True
```
At runtime, I am observing that the masks for the longer sequences in a minibatch end up being fragmented, especially when there is a considerable length ratio between the longer and the shortest utterance. In contrast, [the article of Baevski et al. ](https://arxiv.org/abs/2006.11477)mentions the masking of at least M consecutive timesteps, and Figure 2 appears to indicate no masks shorter than 10 timesteps in that example.
Would you please explain the role of the code snippet above in `compute_mask_indices` ? Do you consider this could have a significant impact upon the convergence speed of the learning algorithm? I am currently exploring an alternative base architecture and seeing relatively large loss values in the range of 1e+4 after 50k iterations, and was wondering if I should apply a different masking strategy.
Thanks for any help. @SeanNaren I hope you don't mind the ping. | {
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} | null | null | null | It appears the transformer decoder when exported to onnx has swapped the `encoder_mask` and `encoder_embeddings`:
```python
encoder_mask : ['encoder_mask_dynamic_axes_1', 'encoder_mask_dynamic_axes_2', 1024]
encoder_embeddings : ['encoder_embeddings_dynamic_axes_1', 'encoder_embeddings_dynamic_axes_2']
```
You can obtain the above output with the following code snippet (used MT as that is my use case, but assume it would be the case for any decoder.)
```python
from nemo.collections.nlp.models.machine_translation.mt_enc_dec_model import MTEncDecModel
import onnxruntime as rt
mt_model = MTEncDecModel.from_pretrained('nmt_en_de_transformer12x2')
mt_model.decoder.export('decoder.onnx')
decoder_session = onnxruntime.InferenceSession('decoder.onnx')
for i in decoder_session.get_inputs():
print(f'{i.name} : {i.shape}')
```
** Environment overview (please complete the following information) **
- Environment Location: Docker on Kubernetes
- Method of NeMo install: helm chart using nvcr.io/nvidia/nemo:1.2.0
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} | null | null | null | Hello!
I train MatchboxNet model with custom labels and standard config file.
If I use batch_size (train, test, validation) = 512 I consume 3Gb of GPU memory (Tesla V100) and everything works fine.
If I increase batch_size to 1024 I get ```RuntimeError: CUDA error: an illegal memory access was encountered``` at the first step.
Running script with ```CUDA_LAUNCH_BLOCKING=1``` gives next output:
```numba.cuda.cudadrv.driver.CudaAPIError: [700] Call to cuLaunchKernel results in UNKNOWN_CUDA_ERROR```
Tried different torch versions - no results.
Any ideas how to fix this?
Numba version: 0.54.0
Python: 3.7.9
CUDA version: 11.1
Driver Version: 455.32.00 | {
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However, I have load model error model if I loaded it with
`import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_transducer_medium")`.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2908 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2908/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2908/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2908/events | https://github.com/NVIDIA/NeMo/issues/2908 | 1,008,525,550 | I_kwDOC_bI7s48HODu | 2,908 | Error when running example training TTS on new data | {
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} | null | null | null | **Bug description**
I want to train tacotron2 on new data and at first I just try to run [2_TTS_Tacotron2_Training.ipynb](https://github.com/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/tts/2_TTS_Tacotron2_Training.ipynb) in colab without any changes! But when I run following code
```
!wget https://github.com/NVIDIA/NeMo/releases/download/v0.11.0/test_data.tar.gz && mkdir -p tests/data && tar xzf test_data.tar.gz -C tests/data
# Just like ASR, the Tacotron2 require .json files to define the training and validation data.
!cat tests/data/asr/an4_val.json
# Now that we have some sample data, we can try training Tacotron 2
# NOTE: The sample data is not enough data to properly train a Tacotron 2. This will not result in a trained Tacotron 2 and is used to illustrate how to train Tacotron 2 model
!python tacotron2.py sample_rate=16000 train_dataset=tests/data/asr/an4_train.json validation_datasets=tests/data/asr/an4_val.json trainer.max_epochs=3 trainer.accelerator=null trainer.check_val_every_n_epoch=1
```
I get this error:
```
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /root/nltk_data...
[nltk_data] Package averaged_perceptron_tagger is already up-to-
[nltk_data] date!
[nltk_data] Downloading package cmudict to /root/nltk_data...
[nltk_data] Package cmudict is already up-to-date!
[NeMo W 2021-09-27 19:13:14 experimental:28] Module <class 'nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset'> 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)
################################################################################
[NeMo W 2021-09-27 19:13:14 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torchaudio/backend/utils.py:54: UserWarning: "sox" backend is being deprecated. The default backend will be changed to "sox_io" backend in 0.8.0 and "sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.
'"sox" backend is being deprecated. '
Traceback (most recent call last):
File "tacotron2.py", line 44, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 103, in wrapper
strict=None,
TypeError: _run_hydra() got an unexpected keyword argument 'strict'
```
I changed nothing in code and just ran cells of this code that is a tutorial for training of tacotron.
Thank you for any help | {
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