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} | # What does this PR do ?
Remove deprecated arg compute_on_step from torchmetrics
**Collection**: [ASR, NLP]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any ... | {
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} | # What does this PR do ?
This will be a basis for several export extensions that involve user input.
As an example, I have changed handling of --cache_support arg in export.py to use the new method.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of... | {
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I've been trying to transcribe a wav file of mono channel and rated a 16khz but this error shows up all the time
below is my code:
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
emb = asr_model.transcribe('./right_channel.wav')... | {
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} | null | null | null | [NeMo I 2023-07-05 14:14:49 cloud:68] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemo to /root/.cache/torch/NeMo/NeMo_1.20.0rc0/stt_en_quartznet15x5/16661021d16e679bdfd97a2a03944c49/stt_en_quartznet15x5.nemo
[NeMo I 2023-07-05 14... | {
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I'm wondering if is possible to set DER as the saving metric, as I can see I only can set loss or acc_f1 as the saving metric.
please advise | {
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} | # What does this PR do ?
fix ngram_merge using rnnt nemo model.
fix char level ngram creation.
**Collection**: ASR
# Changelog
- scripts/asr_language_modeling/ngram_lm/ngram_merge.py count perplexity of a test set using tokenizer from RNNT model
- scripts/asr_language_modeling/ngram_lm/kenlm_utils.py creat... | {
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updates:
- [github.com/pre-commit/pre-commit-hooks: v4.3.0 → v4.4.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.3.0...v4.4.0)
- [github.com/psf/black: 19.10b0 → 23.3.0](https://github.com/psf/black/compare/19.10b0...23.3.0)
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} | # What does this PR do ?
Adds support for ConformerEnc-TransformerDec speech translation (ST) model.
**Collection**: asr
# Changelog
- Added baseline ST model
- Tested to work with both yttm and SentencePiece (SP) tokenizers, SP is considered default
# Usage
* You can potentially add a usage example bel... | {
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I am trying latest itn model t5 for inference.(https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/itn_en_t5)
Getting error as the following:
Error executing job with overrides: ['lang=en', 'mode=itn', 'tagger_pretrained_model=itn_en_t5', 'decoder_pretrained_model=itn_en_t5', 'inference.intera... | {
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One problem I come across often is, that I can't verify already known speakers from other files, since inference only yields labels for a **MSDD model** approach.
For example: I have file **A** with speakers Paul, Tom and Charlie and File **B** with... | {
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} | # What does this PR do ?
Allow `data_prefix` to be None when mocking dataset, i.e., allow the following config
```yaml
data_prefix: null
data_impl: mock
```
Without this change, it will throw en error saying that `None` type doesn't support the `len()` method.
**Collection**: [NLP]
# Changelog
... | {
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I found there are some bugs when sequence parallel is enabled and transformer engine is not used. Specifically, I received
```
File "/fsx/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 551, in training_step
self.allreduce_sequence_parallel_gradients()
File "/fsx/Ne... | {
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<div type='discussions-op-text'>
<sup>Originally posted by **PaulSZH95** July 2, 2023</sup>
Hi all,
1) May i ask if it is normal for single gpu to have lesser % utilisation than if ddp was used.
2) May I ask if it is normal that if i have 2 gpu... | {
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btw, I'm using window... | {
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Upperbound pydantic
**Collection**: [ASR,NLP,TTS]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did y... | {
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**Collection**: [ASR]
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- fix enhancer loading in `ASRWithTTSModel`
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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Add the support for PyTorch-native FSDP for GPT models.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
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Makes sure main and release have not diverged.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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megatron_gpt_eval.py script error out if using checkpoint+hparams instead of nemo
**Steps/Code to reproduce bug**
Following the instruction:
`python megatron_gpt_eval.py \ checkpoint_dir=PATH_TO_CHECKPOINT_FILE \ checkpoint_name=CHECKPOINT_FILE_NAME \ hparams_file=HPARAMS_FILE \ inference.gre... | {
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In the current SFT training script, it runs for 1 epoch with the given max_step number, the max_epoch does not matter as the script generates a temp data file according to the max_step and runs through it. But in some applications, it is desirable to... | {
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} | # What does this PR do ?
Add arithmetic resolvers other than `multiply`
# Changelog
- No high level changes
- This PR contains custom resolvers for arithmetic, comparison and ternary expression
# Usage
Now in hydra configs you can use more arithmetic and ternary expressions.
```yaml
hydra_config_field:... | {
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} | null | null | null | **Describe the bug**
The Val loss keeps increasing while the val_wer keeps getting better.
**Steps/Code to reproduce bug**
I am trying to fine tune conformer CTC large on a custom dataset and my val loss Keeps getting worse but val wer keeps getting better. Please have a look at graph images below.
![Scree... | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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} | # What does this PR do ?
It adds the following cache-aware checkpoints for streaming ASR:
stt_en_fastconformer_hybrid_large_streaming_80ms
stt_en_fastconformer_hybrid_large_streaming_480ms
stt_en_fastconformer_hybrid_large_streaming_1040ms
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Update core commit for CI
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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Waiting for 23.06 container to be published.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
- Fixes the algorithm for filtering blank frames while building confidence ensembles
- Adds checks for confidence value calculation to handle the edge case of all blank frames
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
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I prepared a test manifest in the required format (i.e. with rttm label, because it doesn't work with the format indicated in the documentation), and tried to run the inference with the default parameters of the frame vad inference. I changed `evaluate` to `True` and here is my output:
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} | # What does this PR do ?
Fix missing save restore connector to eval scripts to allow dynamically determining the values of model parallelism
**Collection**: [NLP]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/... | {
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2. support value attribute sft
3. update UI to allow user enter values. | {
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} | # What does this PR do ?
Removed optional optimize_for_inference call - causes problems with some nets and does not really help otherwise.
Fixed verbose flag passing.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
... | {
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} | # What does this PR do?
There are a couple of bugfixes to the confidence computation and also a tutorial.
It currently misses a few links:
- to the paper (already submitted to arxiv, waiting to get the link)
- to the confidence tutorial from Aleksandr, which is not merged yet
- to the documentation of the conf... | {
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} | # What does this PR do ?
adds a CI test to check inference with tp2 lora models.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code sn... | {
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} | # What does this PR do ?
Fix `require_grad` typos (should be `requires_grad`).
Before the fix, the code doesn't cause any errors but doesn't do what it's supposed to do.
**Collection**: ASR, NLP
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guideli... | {
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TLDR;
Converting a non-nemo checkpoint to NeMo with Transformer Engine modules results in truncating of scales in _extra_state. This WAR temporarily fixes the issue by manually toggling the fp8 attribute in Transformer Engine modules.
More context:
1. When exporting TE module data for ... | {
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Add a one line overview of what this PR aims to accomplish.
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Fix AN4 dataset link in docs. Remove broken links to AN4 data (no longer available).
**Collection**: [Docs, Tutorials]
# Changelog
- fix AN4 dataset link in documentation
- remove broken AN4 links from tutorials
# Usage
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The figure below is how rotary embedding is applied to a query vector as described in the RoFormer paper, but the current implementation in NeMo seems not implementing this formula. Specifically in the [`_rotate_half` function](https://github.com/NVIDIA/NeMo/blob/82044837a7bcfa1cfd3a91f6b0014e05... | {
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Add a tutorial demonstrating how to do the end to end data preparation and training with the new TTS preprocessing scripts and data loader.
**Collection**: [TTS]
# Changelog
- Create tutorial
# Before your PR is "Ready for review"
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FastPitch + HiFi-GAN end-2-end model
**Collection**: [TTS]
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Updated frame-VAD doc and fixed small bug | {
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} | # What does this PR do ?
Mutating inputs are, strictly speaking, not allowed by ONNX spec.
Fixes ONNXruntime issue:
https://github.com/microsoft/onnxruntime/issues/14956
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this P... | {
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I trained a Cache aware streaming Conformer CTC model, and decode with
```
CUDA_VISIBLE_DEVICES="0" python examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py \
--asr_model=/home/xx/NeMo/exp/libri_mini_conformer_ctc_small_streaming/Conformer-CTC-BPE/2023-0... | {
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} | # What does this PR do ?
The prompt learning dataset was not padding sequences to a multiple of 8 which forced a non-optimized version of softmax to be used resulting in more memory use and OOM errors. This PR pads to a multiple of 8 to reduce memory.
**Collection**: [NLP]
# Changelog
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} | # What does this PR do ?
- Fix gpt trainer inference_step to compute generation results during validation and test
- Add dataset with metadata, to output in prediction file
- Add token_f1 metrics for Scrolls
**Collection**: [NLP]
# Changelog
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} | # What does this PR do ?
Adds documentation for ONNX export of Megatron Models
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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Converting a non-nemo checkpoint to NeMo with Transformer Engine modules results in truncating of scales in `_extra_state`. This WAR temporarily fixes the issue by manually toggling the `fp8` attribute in TE modules.
**Collection**: [Note which collection this PR will affect]
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Attempting to follow instructions from a post on the Nvidia developer blog: [Deploying a 1.3B GPT-3 Model with NVIDIA NeMo Framework](https://developer.nvidia.com/blog/deploying-a-1-3b-gpt-3-model-with-nvidia-nemo-megatron/). The instructions in the article work perfectly when using the 1.3B par... | {
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} | # What does this PR do ?
Change `swiglu` to all fast glu activation for partition conversion.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how ... | {
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Makes sure `asr_model.change_attention_model` is run regardless of whether `cfg.model_path` or `cfg.pretrained_name` is specified.
**Collection**: ASR
# Changelog
- Moved `asr_model.change_attention_model` call outside of if-branch.
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Add hybrid model support to transcribe_speech_parallel.py
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From my view, as long as Conformer Transducer trained using Cache-aware Streaming Conformer, then it can be used on streaming situation.
But from this: https://github.com/NVIDIA/NeMo/issues/3144#issuecomment-962420001, it seems we can train a Ca... | {
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} | # What does this PR do ?
Update doc for Frame-VAD
**Collection**: [ASR]
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} | # What does this PR do ?
1. Set default `apply_query_key_layer_scaling=False`
2. Fix flash_attention_triton attention_bias broadcast issue (reduce memory usage)
3. Add testing for flash cross-attention suggested by @yidong72
**Collection**: [NLP]
# Changelog
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} | # What does this PR do ?
- Add extrapolatable position embedding to bert-like model, based on the recent changes introduced in #6666
- I am already using this change in the BioNeMo framework and running experiments to test how `rpe` techniques help bert-like model to extrapolate to longer sequences.
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} | # What does this PR do ?
Got report about unexpected behaviour. fixed
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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} | null | null | null | Hello,
I want to implement real time inferencing on microphone audio and saw that the Cache-aware Streaming Conformer models are being used for this in NeMo, I wanted to know if there are any available models for real-time transcription though mic and any example scripts to implement this in python.
Thanks! | {
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I would like to finetune the stt_en_conformer_transducer_medium on 200 hours of another language (romanian). I am at the tokenization step and I am unsure of what would be the best practice:
1. Prepare the tokens using the text from the training audio transcripts (around 2 million words, not necessarily d... | {
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} | # What does this PR do ?
Add option in TTS audio preprocessing to convert audio to desired output format. Default to flac since it is lossless and uses much less space than wav.
Adding this to save on disk space for large datasets, and because I was hitting cryptic dataloader errors when training with mp3 files.
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Install ctc_decoders without distutils duplication to avoid `AttributeError: module 'distutils' has no attribute 'ccompiler'`.
**Collection**: ASR
# Changelog
- Fix install_beamsearch_decoders.sh to work outside container
# Usage
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} | # What does this PR do ?
Fix `build_topo` function for `k2`-based topologies.
This solution fixes failing tests `tests/collections/asr/k2/test_ctc.py::TestCTCLossK2::test_case_small_blank_last` + `tests/collections/asr/k2/test_rnnt.py::TestRNNTLossK2::test_case_small_blank_last`
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} | # What does this PR do ?
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} | # What does this PR do ?
This PR is adding the ModelParallelConfig arguments to be used with the next release of Megatron Core.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add ... | {
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} | # What does this PR do ?
removes unnecessary log
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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} | # What does this PR do ?
Updates the base container for import action. Testing if this helps the NLP import test which is currently timing out due to solver issues with pip.
Edit: The issue was with installing and uninstalling nemo for each of the domains. This was wreaking havoc on the solver so we instead test ... | {
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} | # What does this PR do ?
fixes small error introduced in #6666
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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} | null | null | null | **Describe the bug**
setting compute_logprob=Ture in the megatron_gpt_eval.py script is failing.
**Steps/Code to reproduce bug**
1. checkout main
2. Run ```python examples/nlp/language_modeling/megatron_gpt_eval.py gpt_model_file=path_to_nemo_gpt_model/megatron_gpt.nemo 'prompts=[How to fix GPU memory? A:]' ten... | {
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} | null | null | null | Hi, I am trying to fine-tune the ECAPA-TDNN model, following the recommendations made in the examples for fine-tuning the titanet-large. At some point during training, the loss is infinite and then everything turns into "not a number", including the embeddings. I already clip the gradients, as is done in the example th... | {
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So I want to ask : Do Contextnet and Squeezeformer support streaming training?
BTW, by streaming training, I mean chunk based like Cache-aware Streaming Conformer or model support streaming input itself during both... | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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Transcribing with `stt_en_fastconformer_ctc_large` fails with oom on RAM (not gpu memory) on large manifest.
I run
```
python ${NEMO_PATH}/examples/asr/transcribe_speech.py \
pretrained_name="stt_en_fastconformer_ctc_large" \
dataset_manifest=${part}.json \
output_filena... | {
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Apply garbage collection interval to validation steps
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Auto-applied when using `gc_interval > 0`
```python
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I would like to use the pre-trained stt_en_conformer_ctc or stt_en_conformer_transducer models for transfer learning on another language ( I only have around 1000 hours of audio, and I would like to jump-start the training/try to see if it may bring better results). I am new to ASR, so I am not very familiar ... | {
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Apply garbage collection interval to validation steps
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Auto-applied when using `gc_interval > 0`
```python
# Add a code snippet demonstrating how to use this
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} | null | null | null | **Describe the bug**
PyTorch is warning about the order at which is the scheduler is being called.
**Steps/Code to reproduce bug**
Any training script will generate this warning:
```
[...]/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before... | {
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} | # What does this PR do ?
- fixes peft ptuning bug which was causing ptuning to use a residual connection
- simplifies mixin use for all NLP PEFT models
- ptuning includes a inference only table which is needed for FT
-
**Collection**: [NLP]
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} | fix ctc decoding for hybrid model in partial transcribe
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} | # What does this PR do ?
Adds API documentation.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready ... | {
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} | # What does this PR do ?
Move the cfg.gpt_model_file logic into the "if" block to avoid error during ckpt eval
**Collection**: [Note which collection this PR will affect]
NLP
examples/nlp/language_modelling
# Changelog
- Add specific line by line info of high level changes in this PR.
Move https://github... | {
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} | null | null | null | **Describe the bug**
I am trying to finetune the XXL Conformer-Transducer model from a base .nemo checkpoint that I downloaded from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_xxlarge
**Steps/Code to reproduce bug**
I run the following command:
```
HYDRA_FULL_ER... | {
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} | null | null | null | GPU T4
Riva 2.10
TTS es multispeaker fastpitch-hifigan
I increased the default input when using TTS SSML tags because I need to use 1000 characters, when using it in offline mode there is a big audio quality degradation or fade at the end of the generated audio, to avoid this I want to use online/streaming mode bu... | {
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