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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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
This is related to https://github.com/NVIDIA/NeMo/issues/6854 and solves the issue for me in Python 3.9 and Python 3.10. Instead of requiring the user to manually install cython beforehand, why not specify it as a build requirement as per [PEP-0518](https://peps.python.org/pep-0518/). | {
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Speech Translation model based on Conformer encoder and Transformer encoder added to upstream.
**Collection**: ASR, TTS, NLP
# Changelog
- Added ConformerEnc+TransformerDec model to ASR
- Support for mixed speech-to-text and text-to-text (with TTS) training
- Replicate results / release models from IWSLT 2023
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR merges the following T5 PEFT methods:
- adapters
- lora
- p-tune
- ia3
The changes in T5-related classes make it easier to merge the PEFT methods. All of the methods have been tested on Squad-100 subset (not optimal, but will try to run with optimal params also): https://wandb.ai/nvidia/davidm_nemo-t5_peft_tests?workspace=user-dmosallanezh
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
The tuning can be used as:
```bash
python /data/NeMo/examples/nlp/language_modeling/tuning/megatron_t5_peft_tuning.py \
trainer.precision=32 \
model.language_model_path=BASE_MODEL \
model.data.train_ds=['TRAIN_DATA'] \
model.data.validation_ds=['VAL_DATA'] \
exp_manager.create_wandb_logger=True \
exp_manager.wandb_logger_kwargs.project='PROJ' \
exp_manager.wandb_logger_kwargs.name='NAME' \
model.optim.lr=4e-3 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
trainer.devices=1 \
exp_manager.exp_dir='/data/t5_peft' \
~model.optim.sched \
```
and the eval script can be used as:
```bash
python /data/NeMo/examples/nlp/language_modeling/tuning/megatron_t5_peft_eval.py \
trainer.precision=32 \
data.test_ds=['TEST_DATA'] \
data.global_batch_size=64 \
data.micro_batch_size=4 \
language_model_path=BASE_MODEL \
peft_model_file=SAVED_NEMO_FILE \
pred_file_path=t5-peft-preds.txt
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
The order of outputs of `dict.values()` is usually shuffled so that the tokenizer and the model will be confused to match a correct sound for a specific integer index. You can tell the diff in between as below,
**CTC Training Loss Curves**
The upper and bottom curves are referring to the current implementation and bugfix, respectively.
**Phoneme Token List**
* current random order: `['#Q', '#VE', '#IE', '#X', '#Y', '#ZH', '#S', '#EI', '#ANG', '#V', '#O', '#H', '#D', '#F', '#T', '#G', '#OU', '#UI', '#UN', '#B', '#IU', '#I', '#C', '#AO', '#IN', '#L', '#^', '#M', '#ING', '#VN', '#Z', '#SH', '#EN', '#J', '#P', '#K', '#CH', '#AI', '#A', '#ENG', '#R', '#W', '#U', '#E', '#AN', '#N', '#ER', '#ONG']`
* bugfix: `['#A', '#AI', '#AN', '#ANG', '#AO', '#B', '#C', '#CH', '#D', '#E', '#EI', '#EN', '#ENG', '#ER', '#F', '#G', '#H', '#I', '#IE', '#IN', '#ING', '#IU', '#J', '#K', '#L', '#M', '#N', '#O', '#ONG', '#OU', '#P', '#Q', '#R', '#S', '#SH', '#T', '#U', '#UI', '#UN', '#V', '#VE', '#VN', '#W', '#X', '#Y', '#Z', '#ZH', '#^']`
**Audio Examples**
* current: https://drive.google.com/file/d/1w7SmNobbwx5g44M2uXff2Mrbkmy2lQAH/view?usp=drive_link
* bugfix: https://drive.google.com/file/d/1eROMHI5m4X1AZid-gryBnH99YGa85OI7/view?usp=drive_link
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
NeMo installation fails with Python >=3.9 because `youtokentome` does not properly specify it's build dependency on `cython`. See this issue [in their repo.](https://github.com/VKCOM/YouTokenToMe/issues/94). There is also [a PR](https://github.com/VKCOM/YouTokenToMe/pull/108) on that repo to fix that but the author seems to have abandoned the project, its last release was in 2020.
**Steps/Code to reproduce bug**
Trying to install `nemo_toolkit` using Python 3.9 or above.
**Expected behavior**
Installation should succeed.
**Environment overview (please complete the following information)**
- Environment location: GCP
- Method of NeMo install:`poetry add nemo_toolkit[asr]`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Debian
- PyTorch 1.13.1 and >2.0
- Python 3.9, 3.10, 3.11
**Additional context**
This is particularly annoying given that the whole package depends on `youtokentome` (see `requirements_common.txt`). So even if users like me only care about the ASR part, `youtokentome` must be installed.
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} | # What does this PR do ?
Use several random samples for long files
**Collection**: ASR
# Changelog
Added parameters:
- segment_duration (float): random sample duration in seconds
- num_segments (int): number of segments of file to use for majority vote
# Usage
```python
lang_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="langid_ambernet")
lang = lang_model.get_label(filename, segment_duration = np.inf, num_segments = 1, random_seed = None)
```
**PR Type**:
- [V] New Feature
## Who can review?
@fayejf
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} | # What does this PR do ?
This is an experimental draft for the EnCodec training recipe based on https://github.com/NVIDIA/NeMo/compare/encodec?expand=1
The PR is quite large, but has been minimized to what is necessary to run the training end to end and get a good result. It is still missing some features from the original EnCodec code that may be added in later. Including:
- Data augmentation in data loader
- Multi-resolution training (might not need to support as a feature)
- Streaming /w causal convolutions (might not need to support as a feature)
- Gradient balancer (might be unnecessary if we tune model and scale losses properly).
Note that the VQ implementation does not work with FP16 (loss always becomes NaN). I have not tested the distributed training.
**Collection**: [TTS]
# Changelog
- Add EnCodec training recipe
# Usage
Example script invocation
```bash
python /home/rlangman/Code/NeMo/examples/tts/encodec.py
--config-path=/home/rlangman/Code/NeMo/examples/tts/conf/encodec
--config-name=encodec.yaml
max_epochs=100
weighted_sampling_steps_per_epoch=1000
batch_size=32
trainer.devices=1
log_dir=/home/rlangman/exps/dns4_jamendo_24khz/EnCodec/test_vq/samples
exp_manager.exp_dir=/home/rlangman/exps/dns4_jamendo_24khz
exp_manager.create_wandb_logger=true
+exp_manager.version=test_vq
+exp_manager.wandb_logger_kwargs.project=encodec_dns4_jamendo_24khz
+exp_manager.wandb_logger_kwargs.name=test_vq
+train_ds_meta.dns4.manifest_path=/home/rlangman/data1/data/dns4/train_manifest.json
+train_ds_meta.dns4.audio_dir=/home/rlangman/data1/data/dns4/audio_24khz
+train_ds_meta.dns4.sample_weight=0.5
+val_ds_meta.dns4.manifest_path=/home/rlangman/data1/data/dns4/val_manifest.json
+val_ds_meta.dns4.audio_dir=/home/rlangman/data1/data/dns4/audio_24khz
+log_ds_meta.dns4.manifest_path=/home/rlangman/data1/data/dns4/log_manifest.json
+log_ds_meta.dns4.audio_dir=/home/rlangman/data1/data/dns4/audio_24khz
+train_ds_meta.jamendo.manifest_path=/home/rlangman/data1/data/jamendo/train_manifest.json
+train_ds_meta.jamendo.audio_dir=/home/rlangman/data1/data/jamendo/audio_24khz
+train_ds_meta.jamendo.sample_weight=0.5
+val_ds_meta.jamendo.manifest_path=/home/rlangman/data1/data/jamendo/val_manifest.json
+val_ds_meta.jamendo.audio_dir=/home/rlangman/data1/data/jamendo/audio_24khz
+log_ds_meta.jamendo.manifest_path=/home/rlangman/data1/data/jamendo/log_manifest.json
+log_ds_meta.jamendo.audio_dir=/home/rlangman/data1/data/jamendo/audio_24khz
```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
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- [ ] Bugfix
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"merged_at": "2023-08-08T16:13:29"
} | # What does this PR do ?
When we cannot find `self.monitor` in `checkpoint`, `checkpoint.find(self.monitor)` should be -1 so `index` should be `len(self.monitor)` according to L98.
With the current code, checkpoints such as `mp_rank_00_model_weights.ckpt` would go to the True condition and cause an error in Line 103 because it cannot find the correct `value` to be converted to float.
https://github.com/NVIDIA/NeMo/blob/5c3ed943d8b23ae32719f8fd0e51fca5801596d3/nemo/utils/callbacks/nemo_model_checkpoint.py#L98-L103.
**Collection**: [Note which collection this PR will affect]
- NeMo/nemo/utils/callbacks/nemo_model_checkpoint.py
# Changelog
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Should fix https://github.com/NVIDIA/NeMo/issues/6796
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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 for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I understand the this is not the primary issue tracker for Riva (I've posted an issue in the forum https://forums.developer.nvidia.com/t/riva-2-11-0-nemo-23-03-unable-to-deploy-nmt/256169).
Since I can not pinpoint from where the issue arises, I am posting here as well.
Trying to deploy a nemo NMT AAYN model built using `nemo:23.03`, the deploy fails. Riva does not load, the reported error is
```
UNAVAILABLE: Invalid argument: model 'nmt-classifier', tensor 'log_probs': the model expects 3 dimensions (shape [-1,-1,64000]) but the model configuration specifies 3 dimensions (an initial batch dimension because max_batch_size > 0 followed by the explicit tensor shape, making complete shape [-1,-1,32000])
```
I've managed to track down the issue to line 96 in `/usr/local/lib/python3.8/dist-packages/servicemaker/triton/nmt.py`, which sets a hardcoded value of `[-1,32000]` for the `log_probs` output in the classifier. If I change the value in the classifier `.pbtxt` by hand, the model loads and works as it should.
However, I'm unable to find where does the number of 64000 in my case come from. Is it from the target language tokenizer (in my case its size is 64000 tokens)? | {
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} | null | null | null | Hello everyone,
I'm working with speaker diarization to identify 2 or more speakers in the same audio. When I try that returns the same results, something like 2 speaker in a audio when have 3.
For that I using Neural Diarizer with titanet_large or my own model to speaker_embedding and 'diar_msdd_telephonic' to mssd model.
Parameters:
```
config.diarizer.manifest_filepath = 'data/input_manifest.json'
config.diarizer.out_dir = output_dir # Directory to store intermediate files and prediction outputs
pretrained_speaker_model = 'finetune-Epochs_2-Batch_32-sgd-Lr_0.011823477534453206-Scale_60-Margin_0.07095099595312822-Date_18-05-2023_10-26-20-917.nemo'
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
config.diarizer.speaker_embeddings.parameters.window_length_in_sec = [1.5,1.25,1.0,0.75,0.5] #[1.5,1.25,1.0,0.75,0.5]
config.diarizer.speaker_embeddings.parameters.shift_length_in_sec = [0.75,0.625,0.5,0.375,0.1] #[0.75,0.625,0.5,0.375,0.1]
config.diarizer.speaker_embeddings.parameters.multiscale_weights= [0.5,0.5,0.5,0.5,0.5] #[1,1,1,1,1]
config.diarizer.msdd_model.model_path = 'diar_msdd_telephonic' # Telephonic speaker diarization model
config.diarizer.msdd_model.parameters.sigmoid_threshold = [0.03] # Evaluate with T=0.7 and T=1.0
config.diarizer.msdd_model.parameters.infer_batch_size = 64
config.diarizer.msdd_model.parameters.seq_eval_mode = True
config.diarizer.msdd_model.parameters.split_infer = True
config.diarizer.msdd_model.parameters.diar_window_length = 10
config.diarizer.msdd_model.parameters.overlap_infer_spk_limit = 8
```
I tried to change sigmoid_threshold between 0.02 - 0.7, diar_window_length between 5-50 and batch_size between 64-16 to improve the output
Here an example:
[diarized_1.zip](https://github.com/NVIDIA/NeMo/files/11708476/diarized_1.zip)
Just 2 speakers were recognized in a audio with 3 speakers.
Why can't I return 3 speakers with this parameters? How can I improve this results? Maybe I did something wrong? Maybe audios need to be cleaned up, doing that without noise?
Thanks in advance | {
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With the recent PR adding support for the MPT-7b model, I was wondering if someone has figured out how to work with LLaMa-type models, such as Vicuna
**Describe the solution you'd like**
A solution similar to this PR for LLaMa-type models
https://github.com/NVIDIA/NeMo/pull/6804
**Describe alternatives you've considered**
Triton/FasterTransformer can now support LLaMa so hopefully this is possible for Nemo
**Additional context**
Huggingface Transformers also supports LLaMa-architecture
https://huggingface.co/docs/transformers/main/model_doc/llama | {
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} | null | null | null | **Describe the bug**
The bug is at a random interval we will suddenly get file not found or is not directory temp_outputs/pred_rttms/mono_file.rttm
Will work just fine hundreds of times and will randomly throw this.
**Steps/Code to reproduce bug**
Deploy as normal, using whisper + diarization (https://github.com/MahmoudAshraf97/whisper-diarization). Nothing special and it will work many times and just randomly will have that file missing.
**Expected behavior**
Should not have a file missing...
**Environment overview (please complete the following information)**
Docker using nvcr.io/nvidia/pytorch:23.04-py3
If you have another container that nemo would work in then I am all ears.
**Additional context**
GPU: A100
CUDA 11.7 with driver 525.XX
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I want to use thutmose itn model in c++ environment | {
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} | # What does this PR do ?
This is required when the number of samples is very small compared to the batch size. The magnitude of the negative index can be greater than the sample index array.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix typo in core.rst.
# Changelog
- Fix typo in core.rst
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation | {
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} | null | null | null | **Describe the bug**
Talk Net 2 was deprecated but no comparable model took its place.
After experimenting with the various models, we have yet to find anything in open source land that compares to Talk Net 2.
FastSpeech2 is *okay* but is subjectively worse (and objedtively has .25 lower MOS), especially with spacing and prosody.
Is there any reason support was dropped and the model was deprecated?
Because we have to use a very old version of NeMo to continue to support TalkNet2, it's kind of version locked us to some specific stale and conflicting dependencies within our stack.
Thanks to Pony Preservation Project, Webaverse and other open source projects, TalkNet might have been the most popular NeMo TTS model in production. There are notebooks, colab, extensive training materials built up by communities around it, for example: https://github.com/bycloudai/TalkNET-colab
Happy to make a PR, unless there are specific technical reasons why it was deprecated. In the meantime we're all living off a fork, notably this one: https://github.com/SortAnon/NeMo | {
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Fix some text in FC post
**Collection**: [Docs]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you write any new necessary tests?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add FC-XL RNNT and CTC models, FC-XXL (1.1 B) transducer model and TitaNet-S model
**Collection**: ASR
# Changelog
- Add pre trained models to code
- Add to docs
- Add FC configs
# Usage
```python
# FC Transducer BPE models
from nemo.collections.asr.models import EncDecRNNTBPEModel
fc_xl_rnnt = EncDecRNNTBPEModel.from_pretrained('stt_en_fastconformer_transducer_xlarge')
fc_xxl_rnnt = EncDecRNNTBPEModel.from_pretrained('stt_en_fastconformer_transducer_xxlarge')
# FC CTC BPE models
from nemo.collections.asr.models import EncDecCTCModelBPE
fc_xl_ctc = EncDecCTCModelBPE.from_pretrained('stt_en_fastconformer_ctc_xlarge')
# TitaNet-S model
from nemo.collections.asr.models import EncDecSpeakerLabelModel
speaker_model = EncDecSpeakerLabelModel.from_pretrained('titanet_small')
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation | {
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} | # What does this PR do ?
Add GH post for Fast Conformer
**Collection**: [ASR]
# Changelog
- Adds blogpost for fast conformer
- Fixes issue with the font not loading
- removes mathjax file when supported from CDN
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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Makes sharded manifests default for train dataloader in ASR config files
**Collection**: ASR
# Changelog
Adding these flags to the train_ds section of config files:
shard_manifests: False
defer_setup: False
# Usage
Set these flags to true if you wish to use the sharded manifest feature
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
- [X ] Config
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds upperbound to Numpy temporarily.
Related Issue : https://github.com/NVIDIA/NeMo/issues/6824
**Collection**: [Core]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Adds [limited] support for runtime concatenation of samples
**Collection**: ASR
# Changelog
- The implementation is limited to the ConcatDataset class.
- There is no sample normalization
- samples are joined with a zero pause
# Usage
* You can potentially add a usage example below
```python
# Add these parameters
train_ds.is_concat: True # need to enable the concat dataset
train_ds.concat_samples: True # enable sample concatenation
train_ds.concat_samples_count_as_one: True # count samples as per real number or as one
train_ds.concat_samples_max_length: 22 # max length of concatenated sample
train_ds.concat_samples_min_length: 16 # min length of concatenated sample
train_ds.concat_samples_joining_pause: 0.1 # joining pause
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
The Dockerfile contains the following command to build apex
```
# TODO: Remove once this Apex commit (2/24/23) is included in PyTorch
# container
RUN git clone https://github.com/NVIDIA/apex.git && \
cd apex && \
git checkout 57057e2fcf1c084c0fcc818f55c0ff6ea1b24ae2 && \
pip3 install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
```
But this fails due to https://github.com/NVIDIA/apex/issues/1636
I had to follow the suggestions in the apex issue, i.e. add the following lines on the top of `apex/contrib/layer_norm/ln.h`
```
include <stdint.h>
include <stdio.h>
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https://api.github.com/repos/NVIDIA/NeMo/issues/6824 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6824/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6824/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6824/events | https://github.com/NVIDIA/NeMo/issues/6824 | 1,744,964,780 | I_kwDOC_bI7s5oAgys | 6,824 | NeMo not compatible with latest numpy | {
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} | null | null | null | **Describe the bug**
Numpy has deprecated several type alias' in 1.24 (https://numpy.org/devdocs/release/1.24.0-notes.html)
NeMo requires `numpy>=1.22`, meaning a clean installation results in 1.24.3 at the time of writing
running `NeMo/examples/nlp/dialogue/dialogue.py` results in an exception due to the use of deprecated numpy type alias i.e.
```
File "[/home/dave/dev/nemo-dialogue/NeMo/nemo/collections/nlp/data/dialogue/dataset/dialogue_bert_dataset.py](https://file+.vscode-resource.vscode-cdn.net/home/dave/dev/nemo-dialogue/NeMo/nemo/collections/nlp/data/dialogue/dataset/dialogue_bert_dataset.py)", line 114, in __getitem__
np.array(self.all_input_mask[idx], dtype=np.long),
File "[/home/dave/dev/nemo-dialogue/.venv/lib/python3.10/site-packages/numpy/__init__.py](https://file+.vscode-resource.vscode-cdn.net/home/dave/dev/nemo-dialogue/.venv/lib/python3.10/site-packages/numpy/__init__.py)", line 320, in __getattr__
raise AttributeError("module {!r} has no attribute "
AttributeError: module 'numpy' has no attribute 'long'
```
**Steps/Code to reproduce bug**
Downloaded this tutorial to my local machine and ran it in a clean `venv` (python310 on ubuntu 22.04)
https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/nlp/Dialogue.ipynb
**Expected behavior**
NeMo requirements should be `numpy>=1.22,<1.24` until numpy >=1.24 is supported (I also downgraded to numba==0.56.4)
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: [pip install or from source]. Source
```
import os
BRANCH = 'main'
!apt-get update && apt-get install -y libsndfile1 ffmpeg
!git clone https://github.com/NVIDIA/NeMo --branch $BRANCH
os.chdir('NeMo')
!./reinstall.sh
os.chdir('..')
```
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 22.04
- PyTorch version 2.0.1
- Python version 3.10
**Additional context**
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} | null | null | null | **Describe the bug**
When using Nemo 1.18.1 to fine tune hifigan, I can't continue at this step, and the number in the error message is different every time. After adjusting according to the fine-tuning of fastpitch, some iussues mentioned the sampling rate issue and also adjusted the sampling rate, but the result still did not work
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Command I use to run the finetuning:
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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} | # What does this PR do ?
Enables an example of calling trainer.validate with GPT.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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add reference to our SpellMapper paper on arxiv
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# Usage
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hi,
I need to use _speaker diarization_ in a pipeline with ASR for Slovenian language. For the speech recognition I use a highly accurate `CTC Conformer` model.
At first, I tried to employ pre-trained `titanet-large` and `diar_msdd_telephonic` models for clustering and neural diarization, respectively. But results are not satisfactory. In particular not for audio files with multiple speakers, e.g. more than 10. For larger files I would often get one label only. And very often, in speech segments belonging to one speaker, single words would be misclassified. I experimented a lot with all sorts of parameter combinations and pre-set config files (`diar_infer_meeting`, `diar_infer_general`, ...) but no success.
Then I trained from scratch a new `titanet-large` model following instructions on [NeMo github](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb) and also finetuned one with my own dataset. The dataset includes 2500 unique speakers and approx. 700 hours of speech. Audio files are from 1 to 20 seconds long. Considering the loss function, both models converged well with the one trained from scratch ending up at `val_loss=0,13` and finetuned one at `0,00006`. When I use these two models for clustering diarization, however, results are still quite disappointing – short audio recordings with few speakers are diarized well but immediately when there are more than a few speakers, DER increases significantly. Results are sometimes even worse if I pre-set the number of speakers.
I also trained own MSDD model by following the tutorial “[Speaker Diarization Training](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb)". I tried both, training by freezing speaker embedding extractor model and by end-to-end training of both together. But still, I don't get any better results. In fact, there is almost no difference between the results I get by clustering diarization alone and by using neural diarization on the final step.
I guess I must be doing something wrong. Could somebody please let me know, what is the best approach to get this done. I need a diarizer that will perform well for recordings with just a few or multiple speakers, which will very often be unknown (I don't have them in my training dataset). Specifically:
1. Should I train or rather finetune a speaker embedding extractor model (e.g. titanet-large) as suggested in [this tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb) and then employ clustering diarization without msdd? If so, is there anything else apart from what is instructed in the **Speaker Verification tutorial** that I must change or pay attention to to train speaker embedding extractor model for the diarization purposes (the tutorial is on verification not diarization)? Is the dataset I have (700 hours, 2500 unique speakers) good enough or I need more data. What is suggested duration for recordings?
2. Or should I rather train msdd with frozen speaker embeddings extractor or even end-to-end as suggested [here](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb) and then use neural diarization on top of the clustering results? Are there any specifics I need to know apart from what is explained in the Speaker Diarization Training tutorial?
If required, I can provide details on config settings that I was using for my previous experiments.
Thanks in advance. | {
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} | # What does this PR do ?
Fix TP-comm overlap with userbuffer, where validation step precedes a training step.
# 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
```
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} | # What does this PR do ?
This PR enables speaker diarization on long audio files (even +4 hours) without running out of memory. Only one long audio file is accepted for the moment, because this is the use case I needed, but it can easily be made to accept multiple audios. (It was tested mainly on CPU, so some arguments regarding CUDA were set as "False", just to be safe)
**Collection**: collections/asr/models, collections/asr/parts/utils
# Changelog
The logic behind this method is that, if the same speaker would be in different audios, his speaker representations should be the same/very similar, and if those 2 different audios would be merged, he would be detected as the same speaker. That is why this method will imply 2 steps, 1st where we split a long audio in smaller audios and perform diarization, and the second where we extract just a few speaker representations from each speaker detected in the smaller audios and merge them in order to perform a global diarization over all audios, but without using all speaker representations. ( just a few embeddings from each speaker already detected in the 1st step)
The following figure presents the process for enabling long audio diarization through "2 step clusterization. The steps are the following:
- We split a long input audio (greater than 45 minutes) in equally sized ordered audio parts ( for example a 4 hour audio => 8 x 30 min audio parts). If audio < 45 minutes : normal diarization is performed.
- We perform speaker diarization on each audio part and obtain speaker labels for each individual audio part. Furthermore, for each audio part, we save the speaker embeddings for each speaker inside a dictionary called *split_audios".
- After all audio parts were diarized and the speaker embeddings for each part were saved. We enter the second step in the process called "reclustering". From each audio part, we take the first *E* number of speaker embeddings from each speaker(we know at what timestamp a speaker is based on the labels) and merge them with other speaker embeddings extracted from other speakers, from other audio parts. ( e.g. : if we have 2 speakers detected in audio part 1; 3 speakers in audio part 2 and 4 speakers in audio part 3; if we take *E*=100, we will have a merged embeddings matrix of (2+3+4)*100= *a matrix of [900 x 192]* ) In this way, we already know which embeddings we should take, based on the speakers detected in the 1st step, so we do not need to take ALL embeddings in a e.g: 4 hours audio, but just a few from each speaker.
- After merging the embeddings ( from each speaker ,from each audio), we perform clusterization again. (*speaker_utils.py/perform_reclustering()*) We obtained a set of final labels for each row in the merged embeddings matrix. To be noted, each label corresponds to a certain timestamp, so we can map the new detected labels over the merged embeddings matrix to the old labels detected over each individual audio part.
- We will now map the new labels to the old labels using functions found in /asr/parts/utils/speaker_utils.py: label_mapping(), label_conversion(), order_labels(). For each old label, we take the majority new label, we also order the labels and map them to all the old labels. ( e.g.: if for a speaker we only took 100 embeddings, but he had 2000 embeddings, we have to map the majority *new label* to all 2000 embeddings.)
- Lastly, after mapping all the labels, as the audio inputs are ordered cronologically, all we have to do is merge the final output labels rttms into a single output labels rttm.
TO BE NOTED:
- One final step we actually implement is extracting multiple *E* embeddings from each speaker and then taking the majority label over all *E* scales using *speaker_utils.py/majority_label()* function. In *conf/inference/diar_infer_meeting.yaml*, we created a *diarizer.clustering.parameters.embs_count_recluster* parameter that is a list of the number of embeddings to be extracted from each speaker, across every audio part. We do this in order to have a more robust result, as we observed across several experiments, that the DER results differ depending on the number of speaker embeddings extracted. This way, we take the majority label detected for each speaker embedding extracted/timestamp over multiple *E* scales. (it is a similar concept to extracting multiple window lengths)
- If a speaker doesn't have enough speaker embeddings in an audio part (e.g.: the speaker has only 210 speaker embeddings and we want to take *E*=300), we take his maximum number of speaker embeddings (e.g.: 210).
This PR also adds the ability to automatically create a manifest file based on the input audio. Additional argument to the manifest file: "split". If audio is bigger than 45 minutes and smaller than 1 hour we will split it in 2 equal audio parts. If audio is bigger than 1 hour, we will split it in maximum 45 minutes parts.
Changes/additional files:
- /examples/speaker_tasks/diarization/clustering_diarizer/long_offline_diar_infer.py
- /examples/speaker_tasks/diarization/conf/inference/* (NOTE: added the diarizer.clustering.parameters.embs_count_recluster parameter)
- /nemo/collections/asr/models/long_clustering_diarizer.py
- /nemo/collections/asr/parts/utils/speaker_utils.py:
- Changed perform_clustering() function, by adding split_audios dictionary where we save information about speaker labels, speaker embeddings, etc. for each audio part. PROBLEM: we now return all_reference, all_hypothesis, split_audios => we need to change the normal *clustering_diarizer.py* file, in order to not get an error, by adding "all_reference,all_hypothesis, _".
- Added perform_reclustering function that uses split_audios info from previous step as input
- Added the label_mapping(), label_conversion(), order_labels(), majority_label(), recluster_write() functions for the post processing of final labels obtain.
- /nemo/collections/asr/parts/utils/offline_clustering.py:
- added getRepeatedEmbeddings function. Everytime we extract embeddings at a scale (e.g: [0.5,1.5,3]s), we already save a certain number of embeddings, BUT these embeddings must target SAME TIME INTERVALS. (e.g.: if we take 100 x 0.5s segments = 50s, then we will only take ~30 segments of 3s. We will repeat the 30 segments until we get 100 segments, similar to normal clustering process. We just do this before creating the Affinity Matrix, not after.)
- reclusteringMultiScaleCosAffinityMatrix() function, using the repeated embeddings
- forward_reinfer() function, very similar to the foward_infer(), just used in the reclustering step
# Usage
Inside /examples/speaker_tasks/diarization/clustering_diarizer/long_offline_diar_infer.py:
```python
@hydra_runner(config_path="../conf/inference", config_name="diar_infer_meeting.yaml")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
cfg.diarizer.speaker_embeddings.model_path = "nemo_models/titanet-l.nemo"
cfg.diarizer.vad.model_path = "nemo_models/vad_multilingual_marblenet.nemo"
cfg.diarizer.speaker_embeddings.parameters.save_embeddings=False
input_path = '/home/gabi/NOSFE_Gojira_Podcast_16khz_mono.wav'
num_speakers = None
sd_model = LongClusteringDiarizer(cfg=cfg)
sd_model.diarize(input_path= input_path,
num_speakers = num_speakers)
if __name__ == '__main__':
main()
```
Simply change *input_path* with the path for the audio you want. Change *num_speakers* to None, if number of speakers is unknown, or to an integer if number of speakers is known.
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
I kindly ask: @titu1994, @redoctopus, @jbalam-nv, or @okuchaiev to review this PR and ask any questions that could help to clarify my implementation.
# Additional Information
I would kindly ask for further testing from the NeMo team to see if the solution is consistent and has the expected results. Then we can further clarify comments, and reformat functions, as they may not be the most efficiently written. I have tested the method on multiple smaller audios (1 hour), and for 2 speaker audios, it gets the expected result that you would obtain by doing normal diarization. However, for multiple speakers, the results slightly changes( sometimes the DER is improved, sometimes not). My main goal using this method is to be able to actually diarize long audios (mainly containing 2 speakers, but more speakers proved to work very well too, in my opinion), without facing computational barriers.
The reclustering step, on a i7-12700 with 20 cores took less than 1 second, using 10 embeddings scales (100-500 with step of 50).
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} | # What does this PR do ?
Minor fixes, renames, tests and tutorial.
**Collection**: ASR
# Changelog
- Remames: `renui` -> `renyi`, `method` -> `measure`.
- Some classes and methods are moved to separate files.
- Tests added.
- Tutorial added.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@Kipok @titu1994 | {
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} | null | null | null | I'm trying to run the `intent_slot_classification_model` and I'm not getting anywhere near the accuracy that the workbook implies I should. I'm running the tutorial from `Joint_Intent_and_Slot_Classification.ipynb`
The workbook contains the following markedown
```
After training for 5 epochs, which should take no more than few minutes, you can expect training precision for this data set to be around these numbers (the accuracy will gradually continue to improve for this dataset up to about 50 epochs of training):
Intents:
micro avg 85.04 85.04 85.04 1076
macro avg 81.13 80.81 79.36 1076
weighted avg 84.10 85.04 83.54 1076
Slots:
-------------------
micro avg 89.10 89.10 89.10 7199
macro avg 21.86 18.56 18.18 7199
weighted avg 84.42 89.10 86.01 7199
```
But I'm not able to reproduce this, the training / eval loss is stuck around 4.12 and the precision/recall/f1 is really low (less than 2%) - I don't actually get any improvement after the first epoch.
I've tried on bare-metal and aws sagemaker with the exact same results.
**Expected behavior**
Expect much better precision/recall when running tutorial workbook
**Environment overview (please complete the following information)**
- Environment location: AWS Sagemaker (Pytorch 2.0.0 Python 3.10 GPU Optimised)
- Method of NeMo install: [pip install or from source]. Ran the install command from the workbook
```
BRANCH = 'stable'
!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]
```
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
AWS Sagemaker (Pytorch 2.0.0 Python 3.10 GPU Optimised)
(same results running on bare metal with torch 2.0.1 with 3090 RTX GPU)
Note I don't have Apex installed, I suspect perhaps I'm using a newer version of lightning that the one that was used when the notebook was created but not really sure? | {
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} | # What does this PR do ?
Fix TP-comm overlap with userbuffer, where validation step precedes a training step.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add configuration to TTS aligner which uses scaled cosine distance instead of euclidean distance.
**Collection**: [TTS]
# Changelog
- Add code for cosine distance based alignment
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
# Additional Information
The current aligner used for FastPitch training has a few problem:
1. It aligns slowly for datasets with lots of speakers.
2. If binarization loss is enabled/warmup too soon then alignment learning stops before accurate alignments are learned.
3. When fine-tuning, the model does not learn alignment of target speaker well.
Using cosine distance instead of L2 distance can fix these problems. Cosine distance in general a lot more accurate and stable when it comes to similarity problems. It also avoids possible issues with the binarization loss because the scale of the cosine distance is fixed regardless of the scale or content of the alignment embeddings.
To test this I trained FastPitch on the VCTK dataset for 300k steps and provide the alignments and loss graphs below.
As seen, using cosine distance aligns faster, produces more accurate and sharper alignments, has a lot lower alignment loss (especially binarization loss), and as a result converges to a model with significantly better accuracy all around (mel, duration, pitch, energy). Though the gap between the two would likely shrink if we train it for several million steps.
**Alignments**
_Example 1_
`L2`
`Cosine`
_Example 2_
`L2`
`Cosine`
**Alignment Losses**
**Reconstruction Losses**
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} | # What does this PR do ?
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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Adds changes to Megatron GPT which supports the MPT-7B architecture
**Collection**: NLP
# Changelog
- nemo/collections/nlp/models/language_modeling/megatron/gpt_model.py
+ Adds support for the megatron_legacy argument to flow down to CoreAttention
+ Adds a bypass to load_state_dict which lets you load directly into the language_model
- nemo/collections/nlp/models/language_modeling/megatron_base_model.py
+ Fixes an error caused by register_artificat demanding cfg.tokenizer.model when one isn't needed, for HF tokenisers
- nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
+ Adds support for the megatron_legacy flag
+ Adds an optional override_vocab_size flag (necessary for hard import of HF weights). If this param isn't given, it loads padded_vocab_size as normal
- nemo/collections/nlp/modules/common/megatron/layer_norm_1p.py
+ Added support for the MPT-7B lower precision fp16 layer norm which works without a bias. Unfortunately, the existing lower precision layer norm from apex absolutely requires a bias term
- nemo/collections/nlp/modules/common/megatron/transformer.py
+ Added logic to remove bias terms on layer norm layers
# Usage
* You can restore an MPT-7B model using the MegatronGPTModel class with `restore_from`
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
fix spellmapper tutorial, change branch to main, small fix in markup syntax
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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Bug fix
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} | # What does this PR do ?
Fixes an assert in the GPTDataset
**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 ?
1. Fixed consumed samples calculation for changeable batch size.
2. Changed ramp up bs feature in order to use it with changeable number of nodes duriong training:
- Deleted unnecessary assertions for ramp up batch size.
- Added trainer stop flag in order to do validation and save the checkpoint when global batch size is changed.
**Collection**: NLP
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [x] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fixes an assert in the GPTDataset
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Having batches of a few fixed shapes also benefits pytorch allocator performance and cudnn benchmarking / cache | {
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https://github.com/NVIDIA/NeMo/blob/cfbe0924db02a1a557aeeea55cc1a01b83903e71/nemo/utils/callbacks/nemo_model_checkpoint.py#L98-L99
I think Line 99 should be
```python
if index != len(self.monitor):
``` | {
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"merged_at": "2023-06-06T18:00:05"
} | # What does this PR do ?
Enables an example of calling trainer.validate with GPT.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
* prevented showing such warning message every time when loading the library. Instead, only show such message when the function wrapped with `@expeirmental` is actually invoked. This fix could make our logging message more concise. For example, for the class `IpaG2p`,
```python
@experimental
class IpaG2p(BaseG2p):
# fmt: off
STRESS_SYMBOLS = ["ˈ", "ˌ"]
# Regex for roman characters, accented characters, and locale-agnostic numbers/digits
CHAR_REGEX = re.compile(fr"[{LATIN_CHARS_ALL}\d]")
PUNCT_REGEX = re.compile(fr"[^{LATIN_CHARS_ALL}\d]")
# fmt: on
...
```
**Previously**
```
In [1]: from nemo.collections.tts.g2p.models.i18n_ipa import IpaG2p
[NeMo W 2023-06-01 17:35:32 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.tts.parts.utils.callbacks.LoggingCallback'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.tts.models.fastpitch_ssl.FastPitchModel_SSL'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.tts.models.ssl_tts.SSLDisentangler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.tts.models.vits.VitsModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-06-01 17:35:33 experimental:27] Module <class 'nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p'> is experimental, not ready for production and is not fully supported. Use at your own risk.
In [2]: x = IpaG2p("/Users/xueyang/workspace/NeMo/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt")
[NeMo W 2023-06-01 17:35:35 i18n_ipa:124] apply_to_oov_word=None, This means that some of words will remain unchanged if they are not handled by any of the rules in self.parse_one_word(). This may be intended if phonemes and chars are both valid inputs, otherwise, you may see unexpected deletions in your input.
```
**Now**
```
In [1]: from nemo.collections.tts.g2p.models.i18n_ipa import IpaG2p
[NeMo W 2023-06-01 17:31:00 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
In [2]: x = IpaG2p("/Users/xueyang/workspace/NeMo/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt")
[NeMo W 2023-06-01 17:31:02 experimental:28] `<class 'nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p'>` is experimental and not ready for production yet. Use at your own risk.
[NeMo W 2023-06-01 17:31:02 i18n_ipa:124] apply_to_oov_word=None, This means that some of words will remain unchanged if they are not handled by any of the rules in self.parse_one_word(). This may be intended if phonemes and chars are both valid inputs, otherwise, you may see unexpected deletions in your input.
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add (1) interfaces to TE and initialized (2) process group setting to support tensor-parallel communication overlap with userbuffer backend.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Set `ub_tp_comm_overlap` to `True`
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [*] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Debugs Transformer Engine FP8 support with Megatron-core infrastructure
**Collection**: NLP
# Changelog
- Construct FP8 amax reduction group
# Usage
Set `transformer_engine: True` and `fp8: True` in the config.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
Pinging @aklife97 @yen-shi @erhoo82 @ksivaman
# Additional Information
* Requires https://github.com/NVIDIA/Megatron-LM/commit/f267e6186eae1d6e2055b412b00e2e545a8e896a
* Fixes bug from https://github.com/NVIDIA/NeMo/pull/6393
* Subset of https://github.com/NVIDIA/NeMo/pull/6627
* Closes https://github.com/NVIDIA/NeMo/issues/6625 | {
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This adds the ability to use LDDL dataloaders in Nemo FW
**Collection**: [NLP, NemoFW
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
add LDDL as dataloader in config. Instructions will be added to the Megatron Launcher for data prep and use.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- New Feature
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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* merged main
* make computing full logprobs matrix during inference optional
* make computing attention matrix during inference optional (to save memory if used with FA)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
When using fused_adam, self._optimizer.get_parameters() was returning an empty list.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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add call to p2p overlap
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
1. relaxed the hardcoded lowercase for non-Chinese characters. This PR provided customization for tokenization/g2p by introducing `ascii_letter_prefix` and `ascii_letter_case` arguments. These flexible arguments helped to discriminate Chinese phonemes from ascii letters.
2. avoided to set up cases during the initialization of tokenizer because G2P should take good care of cases, mostly because tokenizer is instantiated after g2p according to [`def _setup_tokenizer()`](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/models/fastpitch.py#L231).
3. Refined docstring for g2p and tokenizer.
4. Changes will not break pre-trained NGC models because all nightly tests are passed.
```
$ pytest --nightly tests/collections/tts/models
===================================================================================== 27 passed, 3 warnings in 330.07s (0:05:30) =====================================================================================
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Add (1) interfaces to TE and initialized (2) process group setting to support tensor-parallel communication overlap with userbuffer backend.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Set `ub_tp_comm_overlap` to `True`
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [*] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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add call to p2p overlap
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Fix URL
**Collection**: [Tools]
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [x] Bugfix
- [ ] Documentation
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"merged_at": "2023-05-31T22:54:55"
} | # What does this PR do ?
Fix URL
**Collection**: [Tools]
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} | # What does this PR do ?
Fix adapter tutorial to compatible with latest pre-traiend FastPitch checkpoint
**Collection**: [TTS]
# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage example below
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} | # What does this PR do ?
Minor fix that stops `MaskedSpecAug` from overestimating sampling populations.
Add a one line overview of what this PR aims to accomplish.
`MaskedSpecAug` was overestimating the number of available patches when sampling patches were larger than audio size. This should fix it that sampling errors aren't thrown anymore.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Altered sample estimation in `MaskedSpecAug`
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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Updates `label_models` with new LangID model (PearlNet) for ICASSP.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added new ngc import to `list_available_models`
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```python
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} | # What does this PR do ?
make text_generation_utils.py more memory efficient for inference especially for long context sequences
* by making logprob computation optional
**Collection**: nlp
# Changelog
- change peft eval model class for restoring to MegatronGPTSFTModel, since NLPModel does not have _build_model
- removing logprob computation in text_generation_utils since this is very memory consuming because output embedding are all gathered for this. reusing parameter compute_logprob to check if this is needed, by default false
# Usage
```python
python examples/nlp/language_modeling/megatron_gpt_eval.py \
++trainer.devices=${DEVICES} \
gpt_model_file=$CKPT \
trainer.num_nodes=1 \
inference.greedy=True \
pipeline_model_parallel_size=$PP \
tensor_model_parallel_size=$TP \
trainer.precision=bf16 \
inference.compute_logprob=True \
python examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py \
inference.compute_logprob=True ..
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Updates `label_models` with new ngc model (PearlNet). For ICASSP.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added new ngc model to `label_models.list_available_models`
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} | # What does this PR do ?
Fixes checkpointed forward and changes GPT TP test to use activation recomputation for 1 layer instead of SAR
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
Fixes sequence parallelism reset function to set the value to False instead of None.
**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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} | null | null | null | Hello,
I followed the recipe for [multi-speaker pretraining ](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_MultiSpeaker_Pretraining.ipynb) to train the fastpitch model for 2 speaker dataset (indictts). But even after training about 4000 epochs, the model is unable to differentiate between the two speakers. For reference, I'm using the pretrained [Hifigan](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_lj_hifigan) model from nvidia trained on LJspeech for inference. Could this be an issue since, the Hifigan model is trained on female voices only?
```
# This config contains the default values for training FastPitch model with aligner using 22KHz sampling
# rate. If you want to train model on other dataset, you can change config values according to your dataset.
# Most dataset-specific arguments are in the head of the config file, see below.
name: FastPitch
train_dataset: ???
validation_dataset: ???
sup_data_path: ./sup_data
sup_data_types: [ "align_prior_matrix", "pitch", "speaker_id"] # add "reference_audio" for gst
# Computed values for the hi subset of indictts
pitch_fmin: 65.4063949584961
pitch_fmax: 1523.3443603515625
# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values
# by running `scripts/dataset_processing/tts/extract_sup_data.py`
pitch_mean: 162.84548950195312 # e.g. 132.524658203125 for https://zenodo.org/record/5525342/files/thorsten-neutral_v03.tgz?download=1
pitch_std: 65.03450775146484 # e.g. 37.389366149902 for https://zenodo.org/record/5525342/files/thorsten-neutral_v03.tgz?download=1
# Default values for dataset with sample_rate=22050
sample_rate: 22050
n_mel_channels: 80
n_window_size: 1024
n_window_stride: 256
n_fft: 1024
lowfreq: 0
highfreq: null
window: hann
model:
n_speakers: 2
learn_alignment: true
bin_loss_warmup_epochs: 100
max_token_duration: 75
symbols_embedding_dim: 384
pitch_embedding_kernel_size: 3
pitch_fmin: ${pitch_fmin}
pitch_fmax: ${pitch_fmax}
pitch_mean: ${pitch_mean}
pitch_std: ${pitch_std}
sample_rate: ${sample_rate}
n_mel_channels: ${n_mel_channels}
n_window_size: ${n_window_size}
n_window_stride: ${n_window_stride}
n_fft: ${n_fft}
lowfreq: ${lowfreq}
highfreq: ${highfreq}
window: ${window}
text_tokenizer:
_target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.BaseCharsTokenizer
chars: ['ओ', 'ह', 'ं', '2', 'ङ', 'ऑ', 'न', 'त', 'ए', 'ण', 'र', 'ई', 'ख़', 'प', ',', 'औ', 'ा', 'ट', 'ग', 'म', 'ू', '्', '.', 'ौ', 'य', 'ँ', 'ढ', 'ञ', 'ृ', '!', 'ऐ', 'ढ़', 'झ', '़', ';', 'ज', 'ज़', '\xa0', 'ध', 'े', 'ॅ', 'फ', 'ऋ
', 'च', ' ', '\u200e', '\u200d', ':', 'ै', '8', 'ऊ', 'आ', '–', 'ॠ', '-', 'घ', 'ऱ', 'ख', '?', 'ो', 'ी', 'ठ', 'इ', 'द', 'उ', 'श', 'फ़', 'ळ', 'भ', 'स', 'थ', 'क', 'अ', 'ु', 'व', 'ष', 'छ', 'ड़', 'ः', 'क़', 'ि', 'ड', 'ल', 'ब', 'ग़', 'ॉ', ' ', '']
punct: true
check_only_tokens: true # to accept hindi vowel symbols
train_ds:
dataset:
_target_: nemo.collections.tts.data.dataset.TTSDataset
manifest_filepath: ${train_dataset}
sample_rate: ${model.sample_rate}
sup_data_path: ${sup_data_path}
sup_data_types: ${sup_data_types}
n_fft: ${model.n_fft}
win_length: ${model.n_window_size}
hop_length: ${model.n_window_stride}
window: ${model.window}
n_mels: ${model.n_mel_channels}
lowfreq: ${model.lowfreq}
highfreq: ${model.highfreq}
max_duration: 15 # change to null to include longer audios.
min_duration: 0.1
ignore_file: null
trim: true
trim_top_db: 50
trim_frame_length: ${model.n_window_size}
trim_hop_length: ${model.n_window_stride}
pitch_fmin: ${model.pitch_fmin}
pitch_fmax: ${model.pitch_fmax}
pitch_norm: true
pitch_mean: ${model.pitch_mean}
pitch_std: ${model.pitch_std}
dataloader_params:
drop_last: false
shuffle: true
batch_size: 64
num_workers: 12
pin_memory: true
validation_ds:
dataset:
_target_: nemo.collections.tts.data.dataset.TTSDataset
manifest_filepath: ${validation_dataset}
sample_rate: ${model.sample_rate}
sup_data_path: ${sup_data_path}
sup_data_types: ${sup_data_types}
n_fft: ${model.n_fft}
win_length: ${model.n_window_size}
hop_length: ${model.n_window_stride}
window: ${model.window}
n_mels: ${model.n_mel_channels}
lowfreq: ${model.lowfreq}
highfreq: ${model.highfreq}
max_duration: 15 # change to null to include longer audios.
min_duration: 0.1
ignore_file: null
trim: true
trim_top_db: 50
trim_frame_length: ${model.n_window_size}
trim_hop_length: ${model.n_window_stride}
pitch_fmin: ${model.pitch_fmin}
pitch_fmax: ${model.pitch_fmax}
pitch_norm: true
pitch_mean: ${model.pitch_mean}
pitch_std: ${model.pitch_std}
dataloader_params:
drop_last: false
shuffle: false
batch_size: 64
num_workers: 8
pin_memory: true
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
features: ${model.n_mel_channels}
lowfreq: ${model.lowfreq}
highfreq: ${model.highfreq}
n_fft: ${model.n_fft}
n_window_size: ${model.n_window_size}
window_size: false
n_window_stride: ${model.n_window_stride}
window_stride: false
pad_to: 1
pad_value: 0
sample_rate: ${model.sample_rate}
window: ${model.window}
normalize: null
preemph: null
dither: 0.0
frame_splicing: 1
log: true
log_zero_guard_type: add
log_zero_guard_value: 1e-05
mag_power: 1.0
input_fft: #n_embed and padding_idx are added by the model
_target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder
n_layer: 6
n_head: 1
d_model: ${model.symbols_embedding_dim}
d_head: 64
d_inner: 1536
kernel_size: 3
dropout: 0.1
dropatt: 0.1
dropemb: 0.0
d_embed: ${model.symbols_embedding_dim}
condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
output_fft:
_target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder
n_layer: 6
n_head: 1
d_model: ${model.symbols_embedding_dim}
d_head: 64
d_inner: 1536
kernel_size: 3
dropout: 0.1
dropatt: 0.1
dropemb: 0.0
condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
alignment_module:
_target_: nemo.collections.tts.modules.aligner.AlignmentEncoder
n_text_channels: ${model.symbols_embedding_dim}
condition_types: [ "add" ] # options: [ "add", "cat" ]
duration_predictor:
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
input_size: ${model.symbols_embedding_dim}
kernel_size: 3
filter_size: 256
dropout: 0.1
n_layers: 2
condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
pitch_predictor:
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
input_size: ${model.symbols_embedding_dim}
kernel_size: 3
filter_size: 256
dropout: 0.1
n_layers: 2
condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
speaker_encoder:
_target_: nemo.collections.tts.modules.submodules.SpeakerEncoder
lookup_module:
_target_: nemo.collections.tts.modules.submodules.SpeakerLookupTable
n_speakers: 2
embedding_dim: ${model.symbols_embedding_dim}
# gst_module:
# _target_: nemo.collections.tts.modules.submodules.GlobalStyleToken
# gst_size: ${model.symbols_embedding_dim}
# n_style_token: 10
# n_style_attn_head: 4
# reference_encoder:
# _target_: nemo.collections.tts.modules.submodules.ReferenceEncoder
# n_mels: ${model.n_mel_channels}
# cnn_filters: [32, 32, 64, 64, 128, 128]
# dropout: 0.2
# gru_hidden: ${model.symbols_embedding_dim}
# kernel_size: 3
# stride: 2
# padding: 1
# bias: true
optim:
name: adamw
lr: 1e-4
betas: [ 0.9, 0.999 ]
weight_decay: 1e-6
sched:
name: NoamAnnealing
warmup_steps: 500
last_epoch: -1
d_model: 1 # Disable scaling based on model dim
trainer:
num_nodes: 1
devices: -1 # specify all GPUs regardless of its availability
accelerator: gpu
strategy: ddp
precision: 16
max_epochs: 1500
accumulate_grad_batches: 1
gradient_clip_val: 1000.0
enable_checkpointing: false # Provided by exp_manager
logger: false # Provided by exp_manager
log_every_n_steps: 100
check_val_every_n_epoch: 10
benchmark: false
exp_manager:
exp_dir: ./Fastpitch_multispeaker_hi
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: val_loss
resume_if_exists: false
resume_ignore_no_checkpoint: false
``` | {
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} | # What does this PR do ?
Fixes issue #6715 by creating index files on all processes with local_rank 0.
**Collection**: NLP
# Changelog
- Update rank condition to create index files when local_rank is 0 (formerly when global_rank was 0).
# Usage
* No change in used.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #6715
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} | # What does this PR do ?
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
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
When using fused_adam, self._optimizer.get_parameters() was returning an empty list.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
add support for max_total_length=4096 for 43b customization
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Currently the arguments for fast tokenizer are not properly passed through so it cannot be used
**Collection**: [NLP]
# Changelog
This PR just properly passes the use_fast argument
# Usage
use_fast: True in the config
**PR Type**:
- [ ] Bugfix
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
This adds the ability to use LDDL dataloaders in Nemo FW
**Collection**: [NLP, NemoFW
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
add LDDL as dataloader in config. Instructions will be added to the Megatron Launcher for data prep and use.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- New Feature
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | Hello, I have exported the marblenet vad model to onnx using the https://github.com/NVIDIA/NeMo/blob/stable/tutorials/asr/Online_Offline_Microphone_VAD_Demo.ipynb tutorial.
Unfortunately, when I try to replicate the steps for the titanet model in the _extract_embeddings function, but it doesn't work.
Is there a tutorial about exporting the speaker model to onnx, or is it possible to export ?
```
def _extract_embeddings(self, manifest_file: str, scale_idx: int, num_scales: int):
"""
This method extracts speaker embeddings from segments passed through manifest_file
Optionally you may save the intermediate speaker embeddings for debugging or any use.
"""
self._setup_spkr_test_data(manifest_file)
self.embeddings = {}
self._speaker_model = self._speaker_model.to(self._device)
self._speaker_model.eval()
self.time_stamps = {}
self.speaker_model.export('titanet_l.onnx')
ort_session = onnxruntime.InferenceSession('titanet_l.onnx')
all_embs = torch.empty([0])
for test_batch in tqdm(self._speaker_model.test_dataloader(), desc=f'[{scale_idx+1}/{num_scales}] extract embeddings',leave=True):
test_batch = [x.to(self._device) for x in test_batch]
processed_signal, processed_signal_len = self.speaker_model.preprocessor(input_signal=test_batch[0],length=test_batch[1])
with autocast():
#_, embs = self._speaker_model.forward(input_signal=test_batch[0], input_signal_length=test_batch[1])
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(processed_signal), }
_,ologits = ort_session.run(None, ort_inputs)
alogits = np.asarray(ologits)
logits = torch.from_numpy(alogits[0])
emb_shape = ologits.shape[-1]
embs = embs.view(-1, emb_shape)
all_embs = torch.cat((all_embs, embs.cpu().detach()), dim=0)
del test_batch```
```[tasklist]
### Tasks
```
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} | null | null | null | Hi,
I tried to apply the code in your notebook called (Online_ASR_Microphone_Demo.ipynb) [link](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Online_ASR_Microphone_Demo.ipynb)
it works fine when using the same model in the notebook ('QuartzNet15x5Base-En')
using this code :
`asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained('QuartzNet15x5Base-En')`
when i replaced the model with another model called "Conformer-CTC-L_spe128_ar-AR_3.0.nemo" which i downloaded from your website and i used it using this code:
`asr_model = nemo_asr.models.ASRModel.restore_from(restore_path="Conformer-CTC-L_spe128_ar-AR_3.0.nemo")`
the cell called (Streaming Inference) keep showing errors
cell:
```
# duration of signal frame, seconds
FRAME_LEN = 1.0
# number of audio channels (expect mono signal)
CHANNELS = 1
CHUNK_SIZE = int(FRAME_LEN*SAMPLE_RATE)
asr = FrameASR(model_definition = {
'sample_rate': SAMPLE_RATE,
'AudioToMelSpectrogramPreprocessor': cfg.preprocessor,
'JasperEncoder': cfg.encoder,
'labels': cfg.decoder.vocabulary
},
frame_len=FRAME_LEN, frame_overlap=2,
offset=4)
```
error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[84], line 7
4 CHANNELS = 1
6 CHUNK_SIZE = int(FRAME_LEN*SAMPLE_RATE)
----> 7 asr = FrameASR(model_definition = {
8 'sample_rate': SAMPLE_RATE,
9 'AudioToMelSpectrogramPreprocessor': cfg.preprocessor,
10 'JasperEncoder': cfg.encoder,
11
12 #'JasperEncoder': cfg.encoder.jasper,
13 'labels': cfg.decoder.vocabulary
14 },
15 frame_len=FRAME_LEN, frame_overlap=2,
16 offset=4)
Cell In[79], line 27, in FrameASR.__init__(self, model_definition, frame_len, frame_overlap, offset)
25 #for block in model_definition['JasperEncoder']['jasper']:
26 for block in model_definition:
---> 27 timestep_duration *= block['stride'][0] ** block['repeat']
28 self.n_timesteps_overlap = int(frame_overlap / timestep_duration) - 2
29 self.buffer = np.zeros(shape=2*self.n_frame_overlap + self.n_frame_len,
30 dtype=np.float32)
TypeError: string indices must be integers
```
please hep me to solve this issue
Thanks. | {
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} | null | null | null | Hello, I'm training TitaNet from scratch using voxceleb 1 and 2 with another private dataset, while the training is fast 6it/s on a dual 3090 setup the valitadion step takes 2200s/it. It happens using all the labels and also with a limited set of labels (1024), or even with an4 dataset.
**Steps/Code to reproduce bug**
Followed this tutorial: https://github.com/NVIDIA/NeMo/blob/stable/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb and changed the data path.
- Environment location: Local machine AMD® Ryzen 9 3900x 80GB 2x 3090
- Method of NeMo install: from source, using this commit https://github.com/NVIDIA/NeMo/commit/c1abc047d1d5962019f7789b20d8092c7e55b690 tested with python 3.7 and 3.9 using conda enviroments
**Environment details**
- OS version: Ubuntu 22.04.2 LTS
- PyTorch version 11.7 11.8 slow with both versions
- Python version 3.7 3.9 slow with both versions
**Additional context**
It is slow with 1 GPU and 2GPUs, while in validation step the usage jumps from 10% to 20%.
Any other network like QuartzNetor Conformer works just fine.
¿Any ideas? | {
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} | # What does this PR do ?
Fixes sequence parallelism reset function to set the value to False instead of None.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fixes bug to reset/restore act ckpt in GPT 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 snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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} | null | null | null | **Describe the bug**
[This line](https://github.com/NVIDIA/NeMo/blob/372f519ddba0c57272d190d83eaa3169ff6f3488/nemo/collections/asr/parts/utils/vad_utils.py#L1077) uses only `.wav` to split filepaths to create filenames. However if the files are not `.wav`, this will still run, but with wrong file names which causes later on in the [`vad_infer.py` ](https://github.com/NVIDIA/NeMo/blob/c1abc047d1d5962019f7789b20d8092c7e55b690/examples/asr/speech_classification/vad_infer.py#L4) file to not fine the current files at the last stage where we create the final manifest. Specifically, in [here](https://github.com/NVIDIA/NeMo/blob/c1abc047d1d5962019f7789b20d8092c7e55b690/examples/asr/speech_classification/vad_infer.py#L163), if the file was `.flac`, we will be looking for `utt.txt` whereas the actual file created by the previous steps will be named `utt.flac.txt`.
**Steps/Code to reproduce bug**
- Use non wav files as data.
- run vad_infer.py
**Expected behavior**
It should handle different file formats.
**Environment overview (please complete the following information)**
N/A
**Environment details**
N/A
**Additional context**
N/A
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} | # What does this PR do ?
Fixes bug to reset/restore act ckpt in GPT 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 snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes activation checkpointing restore/reset parameters.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"merged_at": "2023-05-29T17:10:14"
} | # What does this PR do ?
Adds documentation on the use of sharded manifests
**Collection**: ASR
# Changelog
- `docs/source/asr/datasets.rst` changed to add the docs
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6749 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6749/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6749/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6749/events | https://github.com/NVIDIA/NeMo/issues/6749 | 1,729,417,808 | I_kwDOC_bI7s5nFNJQ | 6,749 | can't diarize my wav file following your tutorial | {
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} | null | null | null | **Describe the bug**
Hey, I'm new to NeMo library, I want to do speaker diarization without ground truth (unsupervised), but I didn't manage to get the speaker diarization. I followed your speaker diarization tutorial here:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb#scrollTo=0clD79kWkPlG
And tried the 'Clustering Diarizer: with System VAD' section to diarize my .wav file since I don't have ground truth labels, but I only get the data inference as embeddings in the output files.
**Steps/Code to reproduce bug**
Below is the code I used from your tutorial with my .wav path
import os
import wget
import matplotlib as plt
from nemo.collections.asr.parts.utils.speaker_utils import rttm_to_labels, labels_to_pyannote_object
from omegaconf import OmegaConf
import json
from nemo.collections.asr.models import ClusteringDiarizer
from nemo.collections.asr.parts.utils.vad_utils import plot
ROOT = os.getcwd()
data_dir = os.path.join(ROOT, 'data')
os.makedirs(data_dir, exist_ok=True)
#an4_audio = os.path.join(data_dir, 'an4_diarize_test.wav')
an4_audio = os.path.join(data_dir, 'output.wav') # placed my wav here
an4_rttm = os.path.join(data_dir, 'an4_diarize_test.rttm')
if not os.path.exists(an4_audio):
an4_audio_url = "https://nemo-public.s3.us-east-2.amazonaws.com/an4_diarize_test.wav"
an4_audio = wget.download(an4_audio_url, data_dir)
if not os.path.exists(an4_rttm):
an4_rttm_url = "https://nemo-public.s3.us-east-2.amazonaws.com/an4_diarize_test.rttm"
an4_rttm = wget.download(an4_rttm_url, data_dir)
labels = rttm_to_labels(an4_rttm)
reference = labels_to_pyannote_object(labels)
print(labels)
print(reference)
meta = {
'audio_filepath': an4_audio,
'offset': 0,
'duration': None,
'label': 'infer',
'text': '-',
'num_speakers': 2,
'rttm_filepath': an4_rttm,
'uem_filepath': None
}
with open('data/input_manifest.json', 'w') as fp:
json.dump(meta, fp)
fp.write('\n')
output_dir = os.path.join(ROOT, 'oracle_vad')
os.makedirs(output_dir, exist_ok=True)
MODEL_CONFIG = os.path.join(data_dir, 'diar_infer_telephonic.yaml')
if not os.path.exists(MODEL_CONFIG):
config_url = "https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/speaker_tasks/diarization/conf/inference/diar_infer_telephonic.yaml"
MODEL_CONFIG = wget.download(config_url, data_dir)
config = OmegaConf.load(MODEL_CONFIG)
print(OmegaConf.to_yaml(config))
config.diarizer.manifest_filepath = 'data/input_manifest.json'
config.diarizer.out_dir = output_dir # Directory to store intermediate files and prediction outputs
pretrained_speaker_model = 'titanet_large'
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
config.diarizer.speaker_embeddings.parameters.window_length_in_sec = [1.5, 1.25, 1.0, 0.75, 0.5]
config.diarizer.speaker_embeddings.parameters.shift_length_in_sec = [0.75, 0.625, 0.5, 0.375, 0.1]
config.diarizer.speaker_embeddings.parameters.multiscale_weights = [1, 1, 1, 1, 1]
config.diarizer.oracle_vad = True # ----> ORACLE VAD
config.diarizer.clustering.parameters.oracle_num_speakers = False
oracle_vad_clusdiar_model = ClusteringDiarizer(cfg=config)
# And lets diarize
oracle_vad_clusdiar_model.diarize()
print("Clustering Diarizer Result (RTTM format)")
pred_labels_neural = rttm_to_labels(f'{output_dir}/pred_rttms/an4_diarize_test.rttm')
hypothesis_neural = labels_to_pyannote_object(pred_labels_neural)
print(hypothesis_neural)
print("Ground-truth Speaker Labels")
print(reference)
######################################################################
# no labels below:
print(OmegaConf.to_yaml(config))
pretrained_vad = 'vad_multilingual_marblenet'
pretrained_speaker_model = 'titanet_large'
config.num_workers = 1 # Workaround for multiprocessing hanging with ipython issue
output_dir = os.path.join(ROOT, 'outputs')
config.diarizer.manifest_filepath = 'data/input_manifest.json'
config.diarizer.out_dir = output_dir # Directory to store intermediate files and prediction outputs
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
config.diarizer.oracle_vad = False # compute VAD provided with model_path to vad config
config.diarizer.clustering.parameters.oracle_num_speakers = False
# Here, we use our in-house pretrained NeMo VAD model
config.diarizer.vad.model_path = pretrained_vad
config.diarizer.vad.parameters.onset = 0.8
config.diarizer.vad.parameters.offset = 0.6
config.diarizer.vad.parameters.pad_offset = -0.05
sd_model = ClusteringDiarizer(cfg=config)
sd_model.diarize()
# VAD predicted time stamps
# you can also use single threshold(=onset=offset) for binarization and plot here
if config.diarizer.vad.parameters.smoothing:
vad_output_filepath = f'{output_dir}/vad_outputs/overlap_smoothing_output_median_{config.diarizer.vad.parameters.overlap}/an4_diarize_test.{config.diarizer.vad.parameters.smoothing}'
else:
vad_output_filepath = f'{output_dir}/vad_outputs/an4_diarize_test.frame'
**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**
- Using ubuntu 20.04.6
- PyTorch 2.0.1
- Python 3.8.10
What am I missing? Any help would be appreciated!
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} | null | null | null | The documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_diarization/results.html) states the availability of the `diar_msdd_telephonic` model, while there is a config file for a meeting Neural Diarizer at `examples/speaker_tasks/diarization/conf/inference/diar_infer_meeting.yaml`. Could some help me in finding the weights for the meeting diarizer model,
Thank You! | {
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} | null | null | null | **Describe the bug**
Training FastPitch on a 25k samples dataset using 8 A100 GPUs results in the following error:
```
Traceback (most recent call last):
File "/workspace/scripts/train/fastpitch.py", line 21, in <module>
run(main)
File "/miniconda/lib/python3.9/site-packages/typer/main.py", line 1056, in run
app()
File "/miniconda/lib/python3.9/site-packages/typer/main.py", line 328, in __call__
raise e
File "/miniconda/lib/python3.9/site-packages/typer/main.py", line 311, in __call__
return get_command(self)(*args, **kwargs)
File "/miniconda/lib/python3.9/site-packages/click/core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "/miniconda/lib/python3.9/site-packages/typer/core.py", line 716, in main
return _main(
File "/miniconda/lib/python3.9/site-packages/typer/core.py", line 216, in _main
rv = self.invoke(ctx)
File "/miniconda/lib/python3.9/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/miniconda/lib/python3.9/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/miniconda/lib/python3.9/site-packages/typer/main.py", line 683, in wrapper
return callback(**use_params) # type: ignore
File "/workspace/scripts/train/fastpitch.py", line 17, in main
train_fastpitch(gpu_ids=gpu_ids, full_config_path=full_config_path)
File "/workspace/src/tts/train/fastpitch.py", line 66, in train_fastpitch
trainer.fit(model)
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 88, in launch
return function(*args, **kwargs)
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1048, in _run
self.strategy.setup_environment()
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/strategies/ddp.py", line 152, in setup_environment
self.setup_distributed()
File "/miniconda/lib/python3.9/site-packages/pytorch_lightning/strategies/ddp.py", line 203, in setup_distributed
_init_dist_connection(self.cluster_environment, self._process_group_backend, timeout=self._timeout)
File "/miniconda/lib/python3.9/site-packages/lightning_fabric/utilities/distributed.py", line 245, in _init_dist_connection
torch.distributed.init_process_group(torch_distributed_backend, rank=global_rank, world_size=world_size, **kwargs)
File "/miniconda/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 932, in init_process_group
_store_based_barrier(rank, store, timeout)
File "/miniconda/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 469, in _store_based_barrier
raise RuntimeError(
RuntimeError: Timed out initializing process group in store based barrier on rank: 0, for key: store_based_barrier_key:1 (world_size=8, worker_count=1, timeout=0:30:00)
```
Based on my understanding, this is caused by the fact that PyTorch Lightning's DDP invokes the training script N times, where N is the number of GPUs used for training (see [documentation](https://lightning.ai/docs/pytorch/stable/accelerators/gpu_intermediate.html#distributed-data-parallel)). As a consequence, because the dataset preparation logic (loading/normalization/tokenization) is contained within the training script, it is repeated N times. In particular, what I observed is the following (cf. my comment on a related issue, namely #4418):
1. Initially, 1 of the 8 processes begins the dataset preparation
2. Once it's done, all the other processes are unleashed. Said processes perform the dataset preparation again from scratch (on the _whole_ dataset, not a subset) and do so _concurrently_. You can tell this by the fact that their logs get mixed up.
3. Because the dataset is very large, at some point each of these processes hits a timeout (30 minutes) and fails
**Steps/Code to reproduce bug**
Find a dataset with at least 25k samples, then run the following command on a machine with 8 A100 GPUs:
```python
python fastpitch.py \
--config-path . \
--config-name fastpitch_align_ipa.yaml \
train_dataset=/path/to/train_manifest.json \
validation_datasets=/path/to/val_manifest.json \
sup_data_path=/path/to/sup_data \
exp_manager.exp_dir=path/to/exp_dir \
model.pitch_mean=<some_appropriate_value> \
model.pitch_std=<some_appropriate_value> \
trainer.devices=8 \
trainer.strategy=ddp
```
Where the config file looks like the following:
[fastpitch_align_ipa.yaml](https://github.com/NVIDIA/NeMo/files/11581619/fastpitch_align_ipa.yaml.txt)
**Expected behavior**
1. I can train FastPitch on multi-GPU without incurring in any error
2. Operations that aren't meant to be repeated, like dataset preparation, are executed only once. Alternatively, each process performs the operation on a different subset of the dataset
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: Docker
- `docker pull nvcr.io/nvidia/nemo:23.02`
- `docker run --rm -it --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:23.02` | {
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} | null | null | null | NaturalSpeech gives better audio quality MOS than FastSpeech2 and VITS
paper v1: https://arxiv.org/abs/2205.04421
paper v2: https://arxiv.org/abs/2304.09116 | {
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Add a one line overview of what this PR aims to accomplish.
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Fixes checkpointed forward and changes GPT TP test to use activation recomputation for 1 layer instead of SAR
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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