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https://api.github.com/repos/NVIDIA/NeMo/issues/5897 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5897/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5897/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5897/events | https://github.com/NVIDIA/NeMo/pull/5897 | 1,564,960,387 | PR_kwDOC_bI7s5I8qXB | 5,897 | Fix Silence Sampling Algorithm for ASR Multi-speaker Data Simulator | {
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# What does this PR do ?
Replace the previous silence insertion method with a new one that guarantees close approximation to specified `mean_silence`
**Collection**: [ASR]
# Adding Silence in ASR Data Simulator
## Requirements:
1. Sentence durations in each session follow a negative-binomial (NB) distribution
2. Silence ratios in all sessions follows a Beta distribution (range in [0.1])
3. Per silence length should look like a long tail distribution, thus Gamma distribution is used.
4. Silence Uniformity: each speech sentence (overlaps combined) should be followed by some minimum silence
5. Silence Variablity: per-silence durations and sentence durations should be approximately independent (i.e., p-value close to 0)
## Parameters:
- `NUM_SESSIONS`: number of sessions
- `MAX_SESS_DUR`: maximum session duration
- `SAMPLING_RATE`: sampling rate for audio
- `[NB_COUNT, NB_PROB]`: parameters for per sentence duration distribution
- `SILENCE_RATIO_MEAN`: mean for target silence ratio in all sessions, in (0,1)
- `SILENCE_RATIO_VAR`: std for target silence ratio in all sessions, set small values (e.g., 0.1) for better approximation to mean, set larger (e.g., 2.0) for more diversity in silence.
- `PER_SILENCE_VAR`: std for individual silence length, default to 20 for achieving p-value=0.1 to de-correlate speech and silence lengths
- `[PER_SILENCE_MIN,PER_SILENCE_MAX]`: mix and max of per silence duration in seconds, max=-1 for no constraint
## Algorithm:
```python
MAX_SESSION_LEN = MAX_SESS_DUR * SAMPLING_RATE
MIN_SILENCE_LEN = SILENCE_RATIO_MIN * SAMPLING_RATE
MAX_SILENCE_LEN = min(SILENCE_RATIO_MAX * SAMPLING_RATE, MAX_SESSION_LEN)
sessions = []
for i in range(NUM_SESSIONS):
curr_session = []
curr_sess_len = 0
curr_speech_len = 0
curr_silence_len = 0
a = SILENCE_RATIO_MEAN ** 2 * (1 - SILENCE_RATIO_MEAN) / SILENCE_RATIO_VAR - SILENCE_RATIO_MEAN
b = SILENCE_RATIO_MEAN * (1 - SILENCE_RATIO_MEAN) ** 2 / SILENCE_RATIO_VAR - (1 - SILENCE_RATIO_MEAN)
sess_silence_mean = Beta(a, b).rvs()
while curr_sess_len < MAX_SESSION_LEN:
speech_len = NB(NB_COUNT, NB_PROB).rvs()
sentence = build_sentence(speech_len,curr_sess_len, MAX_SESSION_LEN)
curr_session += sentence
curr_sess_len += len(sentence)
curr_speech_len += len(sentence)
if curr_sess_len >= MAX_SESSION_LEN:
break
# dynamically adjust silence mean to achieve the overall mean
silence_mean = max(1, MIN_SIL1ENCE_LEN, (sess_silence_mean * curr_sess_len - curr_silence_len) / (1 - sess_silence_mean))
# sampling with large std to de-correlate with previous sentence length
silence_len = Gamma(a=silence_mean ** 2 / PER_SILENCE_VAR, scale=PER_SILENCE_VAR / silence_mean).rvs()
# enforce valid length
silence_len = min(max(MIN_SILENCE_LEN, silence_len), MAX_SILENCE_LEN, max_session_len - curr_sess_len)
silence = add_silence(silence_len)
curr_session += silence
curr_sess_len += silence_len
curr_silence_len += silence_len
sessions.append(curr_session)
```
## Notes
- Harder to get desired silence ratio with shorter session length
- E.g., sess_len=20s, mean_silence=0.35, 500 hours -> actual ratio 0.24
- E.g., sess_len=120s. mean_silence=0.3, 100 hours -> actual ratio ~0.3
- Silence lengths distribution is approximately exponential, and reducing silence ratio can reduce the avg silence length
- E.g., mean_silence=0.1, std=0.05 -> mean per-silence length 0.2s
- E.g., mean_silence=0.2, std=0.5 -> mean per-silence length 2.4s
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} | [] | closed | false | null | [] | null | null | 2023-01-31T19:34:01 | 2023-02-01T15:43:24 | 2023-02-01T15:43:23 | NONE | {
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} | null | null | null | Good day! Thanks for your hard work!
I am trying to extract timestamps for a model stt_en_conformer_transducer_large
Following https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_with_Transducers.ipynb#scrollTo=xkv_x8NAfpX3
But I'm having difficulty converting timestamps:
"Note that each timestep here is (roughly) timestep∗total_stride_of_model∗preprocessor.window_stride seconds timestamp" - I can extract "timestep" from hypotheses, but model.preprocessor doesn't have a window_stride field and I'm not sure which value to use here. Also, I couldn't figure out where the "total_stride_of_model" value comes from. Do I understand correctly, total_stride_of_model == len(audio) / 'window_stride'? By the way, the 'window_stride' field is also missing from model.preprocessor.
Thank you! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5895 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5895/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5895/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5895/events | https://github.com/NVIDIA/NeMo/pull/5895 | 1,564,853,774 | PR_kwDOC_bI7s5I8T1f | 5,895 | Update isort to the latest version | {
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} | Signed-off-by: Vladimir Bataev <[email protected]>
# What does this PR do ?
Update `isort` to the latest version
**Collection**: [Note which collection this PR will affect]
# Changelog
- update `isort` to the latest version
- apply latest `isort` to the codebase
# Usage
```shell
isort . # one can simply use `isort` command to fix all files or provided paths
python setup.py style --fix # also will work for isort + black, as previously
```
# 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
* Related to #5893
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In many places in NeMo, Python type annotations are used.
Since type annotations are not checked, there are some cases where annotations are incorrect or not precise (e.g., using one type when some union is really used).
**Describe the solution you'd like**
Add `mypy` type checking support.
**Main principle:** do not require types, but if types are provided, check (require) correctness.
- firstly, add a minimal PR that doesn't affect existing code (except 2 lines, since duplicated type annotations are critical errors for mypy)
- provide PRs one-by-one to fix some of the errors to improve code quality, removing the error types from ignore list.
**Describe alternatives you've considered**
[Pyre](https://github.com/facebook/pyre-check) also can be considered as an alternative, but `mypy` is usually used as a default choice.
**Additional context**
...
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NeMo currently requires a very old version of `isort` (<5). The latest version is 5.10.1
Benefits of supporting the latest `isort`:
- latest version => improved support, fewer bugs, etc
- no conflicts with new repos, more friendly for new developers
- explicit config instead of hard-coded options in setup.py
- it is possible to use the `isort` executable instead of a custom command from `setup.py`
**Describe the solution you'd like**
Update `isort` to the latest version.
Use updated config parameters (for `black` support).
**Cost of change:**
~35 files will be changed (the new version is a bit more restrictive)
**Describe alternatives you've considered**
...
**Additional context**
...
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} | some promising signs of improved spectrogram clarity:
summary of changes:
* model now has a first stage consisting of token and spectrogram encoders
* after the first stage an attention layer with a learned query vector is used to produce a "speaker encoding" from the spectrogram encodings
* The speaker encoding is added to the token encodings which are then used to predict token duration and pitch
* token encodings are then aligned with the spectrogram encodings, then run all together through the decoder | {
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Im now using pyaudio with stream_callback as provided by Online_ASR_Microphone_Demo.ipynb, but Im stuck in how to progress further.
I found the script of speech_to_text_cache_aware_streaming_infer.py, which uses conformer_stream_step() in combination with a model trained specifically for streaming, but there are also a lot of functions in streaming_utils.py as StreamingFeatureBufferer and FrameASR to provide buffers and transcribe from there, however in my understanding the transcription wouldnt use context.
Im a bit stuck on which functions I should for streaming ASR with the best possible result. Can I directly use the chunks from the audio in the conformer_stream_step() or do I need something like StreamingFeatureBufferer in between?
Could you provide an overview on which functions I should use to provide such a pipeline? And should I train a streaming model? Or would a full context model work fine. | {
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```
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [And then on her BRIY1FLIY0 IH0KSPREH1SIH0NG HHER1 sorrow for what HHIY1 must have suffered, HHIY1 replied,] contains unknown char/phoneme: [A].Original text: [And then on her briefly expressing her sorrow for what he must have suffered, he replied,]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [How different from what it was the last two dances.] contains unknown char/phoneme: [H].Original text: [How different from what it was the last two dances.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [which was VEH1RIY0 LIH1TAH0L relieved by the long speeches of MIH1STER0 Collins.] contains unknown char/phoneme: [C].Original text: [which was very little relieved by the long speeches of mister Collins.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [Henry is different. He LAH1VZ to be DUW1IH0NG.] contains unknown char/phoneme: [H].Original text: [Henry is different. He loves to be doing.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [Henry is different. He LAH1VZ to be DUW1IH0NG.] contains unknown char/phoneme: [H].Original text: [Henry is different. He loves to be doing.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [And if I HHAE1V good luck, your mother SHAE1L have some.] contains unknown char/phoneme: [A].Original text: [And if I have good luck, your mother shall have some.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [And if I HHAE1V good luck, your mother SHAE1L have some.] contains unknown char/phoneme: [I].Original text: [And if I have good luck, your mother shall have some.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [and I BIH0LIY1V there is SKEH1RSLIY0 a YAH1NG LEY1DIY0 in the United Kingdoms.] contains unknown char/phoneme: [I].Original text: [and I believe there is scarcely a young lady in the United Kingdoms.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [and I BIH0LIY1V there is SKEH1RSLIY0 a YAH1NG LEY1DIY0 in the United Kingdoms.] contains unknown char/phoneme: [U].Original text: [and I believe there is scarcely a young lady in the United Kingdoms.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [It is NAA1T AE1T AO1L what I LAY1K, HHIY1 continued.] contains unknown char/phoneme: [I].Original text: [It is not at all what I like, he continued.]. Symbol will be skipped.
[NeMo W 2023-01-31 10:06:18 tts_tokenizers:478] Text: [It is NAA1T AE1T AO1L what I LAY1K, HHIY1 continued.] contains unknown char/phoneme: [I].Original text: [It is not at all what I like, he continued.]. Symbol will be skipped.
```
But it seems that the original text is made up of just some ordinary words.
Is it normal?
What needs to be done about it?
Thanks! | {
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# What does this PR do ?
Removes `MCD_DTW_examples.tar`
**Collection**: TTS
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Removing a file from git history.
**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 ?
Some fixes and minor improvements to README.rst and RTD docs,
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- [x] Cleans up README.rst to make it less verbose and removing redundant and obsolete links
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- [x] Documentation
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} | null | null | null | I have been trying to train the SqueezeFormer-based ASR model for the Hindi language. Model is only converging if I use BPE based vocabulary while if I used Non BPE based vocabulary on same training data model fails to converge.
Below are the experiment logs.
**BPE-BASED VOCABULARY**
**NON BPE BASED VOCABULARY**
**MODEL DETAILS**
I am training SqueezeFormer Medium model with the following configurations as provided in the config file in the repo.
|Vocab type| Model | d_model | n_layers | n_heads | time_masks | lr | time_reduce_idx | GBS |
|-------------|--------------|---------|----------|---------|------------|--------|-----------------|------|
|BPE | Medium | 324 | 20 | 4 | 7 | 1.5e-3 | 9 | 1024 |
|Non BPE| Medium | 324 | 20 | 4 | 7 | 1.5e-3 | 9 | 1024 |
**Expected behavior**
Both model should be converged to nearly similar point.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal]
- Method of NeMo install: [pip install or from source]
`conda create --name nemo python==3.8`
`conda activate nemo`
`pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html`
`apt-get update && apt-get install -y libsndfile1 ffmpeg`
`pip install Cython`
`pip install nemo_toolkit['all']`
**Environment details**
Otherwise, please provide:
- OS version: ubuntu 20.04
- PyTorch version : 1.10.1+cu111
- Python version: Python 3.8.0
Is there some configuration issue with bpe vs non bpe based model ??
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} | Signed-off-by: Jean-Louis Queguiner <[email protected]>
# What does this PR do ?
Fix typo in readme
**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)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] 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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} | null | null | null | Hello,
I have cloned the intent and slot classification tutorial and running it on Sagemaker.
The branch is "Main", Environment Pytorch 1.12 and python 3.8.
I have tried running via Jupiter notebook and via terminal.
The result is always the same I have the training loss stack at 4.13 after 22 Epochs and still the same afterwards.
Main modification to the existing is the max number of Epochs = 50.
I assume this is not the expected behaviour. Can anybody help ?
Just an example of the output.
``
Epoch 26: 100%|| 346/346 [00:49<00:00, 6.99it/s, loss=4.13, v_num=4-50, lr=0.000]Epoch 26, global step 8424: 'val_loss' was not in top 3
``
.....
``
Epoch 28: 100% || 346/346 [00:49<00:00, 6.97it/s, loss=4.13, v_num=4-50, lr=0.000]Epoch 28, global step 9048: 'val_loss' was not in top 3
``
UPDATE: I noticed the following.
1) running in colab for an older version essentially same issue.
``
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb#scrollTo=zt-cZuNVl6la
``
I also noticed in logs something strange. Is the training set as the testing set not well balanced ?there seems not to be enough representation of the labels for intent and slot.
``
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:87] Stats calculating for train mode...
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:112] Three most popular intents in train mode:
[NeMo I 2023-01-28 23:00:43 data_preprocessing:194] label: 0, 175 out of 9960 (1.76%).
[NeMo I 2023-01-28 23:00:43 data_preprocessing:194] label: 2, 175 out of 9960 (1.76%).
[NeMo I 2023-01-28 23:00:43 data_preprocessing:194] label: 6, 175 out of 9960 (1.76%).
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:118] Three most popular slots in train mode:
[NeMo I 2023-01-28 23:00:43 data_preprocessing:194] label: 54, 53689 out of 65658 (81.77%).
[NeMo I 2023-01-28 23:00:43 data_preprocessing:194] label: 38, 1290 out of 65658 (1.96%).
[NeMo I 2023-01-28 23:00:43 data_preprocessing:194] label: 12, 1233 out of 65658 (1.88%).
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:121] Total Number of Intents: 9960
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:122] Intent Label Frequencies: {0: 175, 2: 175, 6: 175, 7: 175, 8: 175, 9: 175, 11: 175, 13: 175, 14: 175, 15: 175, 16: 175, 17: 175, 18: 175, 19: 175, 20: 175, 22: 175, 23: 175, 24: 175, 25: 175, 27: 175, 28: 175, 30: 175, 35: 175, 36: 175, 37: 175, 39: 175, 41: 175, 42: 175, 43: 175, 44: 175, 45: 175, 46: 175, 47: 175, 48: 175, 49: 175, 51: 175, 52: 175, 53: 175, 55: 175, 57: 175, 58: 175, 59: 175, 61: 175, 63: 175, 62: 171, 56: 165, 60: 163, 38: 162, 26: 151, 4: 142, 50: 134, 5: 126, 32: 126, 29: 114, 21: 110, 1: 106, 54: 97, 33: 89, 10: 79, 12: 79, 3: 72, 40: 70, 34: 69, 31: 35}
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:123] Total Number of Slots: 65658
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:124] Slots Label Frequencies: {54: 53689, 38: 1290, 12: 1233, 46: 1076, 14: 637, 36: 599, 5: 579, 18: 526, 19: 458, 11: 423, 52: 356, 28: 345, 23: 330, 26: 317, 43: 298, 6: 252, 4: 247, 20: 233, 13: 207, 35: 176, 37: 167, 48: 158, 9: 138, 44: 137, 41: 136, 2: 134, 7: 133, 34: 131, 45: 120, 53: 114, 16: 106, 47: 95, 33: 91, 39: 87, 42: 70, 25: 66, 49: 60, 22: 51, 40: 48, 8: 42, 27: 37, 1: 34, 32: 33, 0: 28, 24: 23, 3: 23, 50: 23, 29: 22, 30: 22, 51: 21, 17: 15, 15: 9, 10: 8, 31: 4, 21: 1}
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:128] Intent Weights: {0: 0.8892857142857142, 2: 0.8892857142857142, 6: 0.8892857142857142, 7: 0.8892857142857142, 8: 0.8892857142857142, 9: 0.8892857142857142, 11: 0.8892857142857142, 13: 0.8892857142857142, 14: 0.8892857142857142, 15: 0.8892857142857142, 16: 0.8892857142857142, 17: 0.8892857142857142, 18: 0.8892857142857142, 19: 0.8892857142857142, 20: 0.8892857142857142, 22: 0.8892857142857142, 23: 0.8892857142857142, 24: 0.8892857142857142, 25: 0.8892857142857142, 27: 0.8892857142857142, 28: 0.8892857142857142, 30: 0.8892857142857142, 35: 0.8892857142857142, 36: 0.8892857142857142, 37: 0.8892857142857142, 39: 0.8892857142857142, 41: 0.8892857142857142, 42: 0.8892857142857142, 43: 0.8892857142857142, 44: 0.8892857142857142, 45: 0.8892857142857142, 46: 0.8892857142857142, 47: 0.8892857142857142, 48: 0.8892857142857142, 49: 0.8892857142857142, 51: 0.8892857142857142, 52: 0.8892857142857142, 53: 0.8892857142857142, 55: 0.8892857142857142, 57: 0.8892857142857142, 58: 0.8892857142857142, 59: 0.8892857142857142, 61: 0.8892857142857142, 63: 0.8892857142857142, 62: 0.9100877192982456, 56: 0.9431818181818182, 60: 0.9547546012269938, 38: 0.9606481481481481, 26: 1.0306291390728477, 4: 1.095950704225352, 50: 1.1613805970149254, 5: 1.2351190476190477, 32: 1.2351190476190477, 29: 1.3651315789473684, 21: 1.4147727272727273, 1: 1.4681603773584906, 54: 1.6043814432989691, 33: 1.7485955056179776, 10: 1.9699367088607596, 12: 1.9699367088607596, 3: 2.1614583333333335, 40: 2.2232142857142856, 34: 2.255434782608696, 31: 4.446428571428571}
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:130] Slot Weights: {54: 0.02223512857720982, 38: 0.9254122621564482, 12: 0.9681928776819287, 46: 1.109462656302805, 14: 1.8740687883545026, 36: 1.9929579602367582, 5: 2.061799340555817, 18: 2.2695471828551677, 19: 2.606510520047638, 11: 2.8221792392005156, 52: 3.353319713993871, 28: 3.4602371541501977, 23: 3.617520661157025, 26: 3.765873243475767, 43: 4.005979255643685, 6: 4.737229437229437, 4: 4.833124769966875, 20: 5.1235271166601635, 13: 5.767061923583663, 35: 6.782851239669421, 37: 7.14839412084921, 48: 7.555581127733026, 9: 8.650592885375493, 44: 8.713735899137358, 41: 8.777807486631016, 2: 8.908819538670285, 7: 8.975803144224196, 34: 9.112838306731437, 45: 9.948181818181817, 53: 10.47177033492823, 16: 11.262092624356775, 47: 12.566124401913875, 33: 13.118481518481518, 39: 13.721630094043887, 42: 17.054025974025976, 25: 18.087603305785123, 49: 19.896363636363635, 22: 23.40748663101604, 40: 24.870454545454546, 8: 28.423376623376623, 27: 32.26437346437346, 1: 35.11122994652406, 32: 36.175206611570246, 0: 42.63506493506493, 24: 51.903557312252964, 3: 51.903557312252964, 50: 51.903557312252964, 29: 54.26280991735537, 30: 54.26280991735537, 51: 56.846753246753245, 17: 79.58545454545454, 15: 132.64242424242425, 10: 149.22272727272727, 31: 298.44545454545454, 21: 1193.7818181818182}
[NeMo I 2023-01-28 23:00:43 intent_slot_classification_descriptor:87] Stats calculating for test mode...
`` | {
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} | # What does this PR do ?
This PR enables transcribing an already existing manifest file instead of providing a list of audio files in the variable `paths2audio_files`.
**Collection**: ASR
# Changelog
- Modified the `transcribe` method in the `EncDecCTCModel` class to support transcribing from manifest file. I first check that one of the arguments `paths2audio_files` or `manifest_filepath` exists. If `manifest_filepath` is provided, add it to the `config` dict. Otherwise, create a temp manifest file from `paths2audio_files` list.
# Usage
* It can be combined with other models' output manifests, e.g. Lang ID, and use as an input to the ASR model using the `transcribe` method.
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
ONNX export for RadTTS is now functional.
Working around: https://github.com/pytorch/pytorch/pull/91526
**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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**PR Type**:
- [ ] New Feature
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## Who can review?
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} | Signed-off-by: Daniel Egert <[email protected]>
# What does this PR do ?
Allows you to selectively freeze modules in your model for the first N steps during training
**Collection**: Core
# Changelog
- changed core/classes/modelPT.py to do dynamic freezing via PTL hooks
- updated core documentation at docs/source/core/core.rst
# Usage
You can add the following to your config.yaml:
```yaml
model:
...
freeze_updates:
enabled: true # set to false if you want to disable freezing
modules: # list all of the modules you want to have freezing logic for
encoder: 200 # module will be frozen for the first 200 training steps
decoder: [50, -1] # module will be frozen at step 50 and will remain frozen until training ends
joint: [10, 100] # module will be frozen between step 10 and step 100 (step >= 10 and step <= 100)
transcoder: -1 # module will be frozen for the entire training run
```
Setting `enabled: false` will disable all of the freezing logic.
# 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
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 | Hello. I want to have a trial finetuning the newly updated Vits model in TTS module.
Trying to follow the instructions on:
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_lj_vits
But got the error of:
FileNotFoundError: Model tts_en_lj_vits was not found. Check cls.list_available_models() for the list of all available models.
Because currently print(VitsModel.list_available_models()) is an empty list.
This seems similar to the error in Tacotron2 I encountered last time here:
https://github.com/NVIDIA/NeMo/issues/5714
Thank you! | {
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} | # What does this PR do ?
This PR allows setting a different device (CPU) for the core clustering part of the diarization process so that it doesn't cause GPU OOM errors for long audio files.
**Collection**: ASR
# Changelog
- Add `device` option to the `diarizer.clustering.parameters` configuration group so that [nemo.collections.asr.parts.utils.speaker_utils.perform_clustering()](https://github.com/NVIDIA/NeMo/blob/e1b3f5e2fe57174863110d3fa47b6c24238ab58e/nemo/collections/asr/parts/utils/speaker_utils.py#L402) (called from [ClusteringDiarizer.diarize()](https://github.com/NVIDIA/NeMo/blob/e1b3f5e2fe57174863110d3fa47b6c24238ab58e/nemo/collections/asr/models/clustering_diarizer.py#L440)) can be executed in the CPU (it's currently hard-coded to run on GPU).
- This part of the code uses a huge amount of VRAM (over 10GB for an hour of audio) and barely uses the GPU at all, so the performance on CPU doesn't decrease dramatically (processing time increases from 150 seconds on an RTX3090 to 180 seconds on CPU for an hour of audio).
- This PR is somewhat related to #5681, but instead of setting a device for all processing steps, it allows setting a different device specifically for the function mentioned above.
- If the new device configuration is not set, it will use the globally set device.
- The whole reason for this change is supporting audio files over an hour long. It seemed easier to me to run the clustering on CPU than splitting the input audio, diarizing the splits separately and combining the results later. It can surely be done, or there may be a better solution for this, but I don't know enough of Nemo's internals for that yet.
# 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
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 #5681
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https://api.github.com/repos/NVIDIA/NeMo/issues/5876 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5876/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5876/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5876/events | https://github.com/NVIDIA/NeMo/pull/5876 | 1,559,861,089 | PR_kwDOC_bI7s5IrqWY | 5,876 | Reduce memory usage in getMultiScaleCosAffinityMatrix function | {
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} | Reduce memory usage in getMultiScaleCosAffinityMatrix function
Signed-off-by: gabitza-tech <[email protected]>
# What does this PR do ?
Replace some tensor multiplication in the function for multi scale cos affinity matrix calculation with a single tensor in which we add the repeated embeddings multiplied with its scale weight.
Results show a decrease from 11GB to 2GB for 1 hour audio. It even managed to reduce from above 60GB memory usage for a [53000,53000] tensor to 28GB.
**Collection**: ASR (collections/asr/parts/utils/offline_clustering.py)
# Changelog
`multiscale_weights = torch.squeeze(multiscale_weights,dim=0).to(device)`
The tensor was [1,6] shaped and had to squeeze the first dimension.
` fused_sim_d = torch.zeros(len(timestamps_in_scales[-1]),len(timestamps_in_scales[-1]))`
Initialized a tensor with the shape [s,s] with 0 values where s is the number of timestamps for the smallest scale. We will add the tensor for each scale to this tensor
` with torch.no_grad(): `
Gradients are not needed for inference.
`fused_sim_d = fused_sim_d + multiscale_weights[scale_idx] * repeated_tensor_1`
Instead of adding the tensor for each scale to a list, then stacking the tensor list and then matrix multiplying it with the weights, we simply add the repeated tensor to the previously initialized tensor before the for loop.
@tango4j is adding followings:
- Clear memory outside torch.jit.script functions since jit-script does not support torch.cuda.empty_cache()
- Fixed the bug where eigen value decomposition does not use GPU. Now GPU usage hits 100%
- Removed torch.jit.script decorators since it slows down if we run without jit.script export.
- Performance (without torch.jit.script) <!--td {border: 1px solid #cccccc;}br {mso-data-placement:same-cell;}-->
sess len in secs | Num of sessions | total time in secs | sec/Iter. | RTFx | Peak Memory Usage
-- | -- | -- | -- | -- | --
1800 | 10 | 34 | 3.48 | 517.24138 | 3G
3600 | 10 | 70 | 7.02 | 512.82051 | 8G
7200 | 10 | 319 | 31.98 | 225.14071 | 20G
Now, 2hours of audio usually takes 30~40 seconds.
# 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)
- [x] 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
## Who can review?
Anyone in the community is free to review the PR once the checks have passed. I would like to ask @titu1994, @redoctopus, @jbalam-nv, or @okuchaiev to review this pull request.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/issues/5637 | {
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summary of changes:
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* after the first stage an attention layer with a learned query vector is used to produce a "speaker encoding" from the spectrogram encodings
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# What does this PR do ?
add ambernet to readme
**Collection**: [ASR]
# Changelog
- add ambernet to readme
# Before your PR is "Ready for review"
**Pre checks**:
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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
add ambernet to readme
**Collection**: [ASR]
# Changelog
- add ambernet to readme
# 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?
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- [ ] New Feature
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- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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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 ?
fix val loss computation
When drop last is set to false for validation, currently valid samples are computed assuming any token in a valid sample will not be masked. This case might not be true always. Also, last sample of the dataset could be incomplete as well.
**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 ?
Fix groundtruth label mapping in `batch_inference` in `label_models.py` and add test.
**Collection**: ASR
# Changelog
- The groundtruth label `batch_inference` was mapped wrongly. Fixed it
- Add test for `batch_inference`
# Usage
* You can potentially add a usage example below
```python
from nemo.collections.asr.models import EncDecSpeakerLabelModel
model = EncDecSpeakerLabelModel.from_pretrained('langid_ambernet')
embs, logits, gt_labels, mapped_labels = model.batch_inference(<path_to_manifest>)
```
# 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)
- [x] 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**:
- [ ] 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 https://github.com/NVIDIA/NeMo/pull/5278
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} | # What does this PR do ?
changed WHATEVER ˌwʌˈtɛvɚ to WHATEVER ˌwʌtˈɛvɚ and deleted WHATEVER(1)
updated WHATEVER'S to ˌwʌtˈɛvɚz and delete WHATEVER'S(1).
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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If you open file:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Voice_Activity_Detection.ipynb
it will give an error:
**"Invalid Notebook"**
**"The Notebook Does Not Appear to Be Valid JSON"**
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} | null | null | null | **Describe the bug**
Is ONNX runtime fully supported for the cache-aware Conformer Encoder?
With a side-by-side comparison of calling `asr_model.encoder.forward()` and ONNX encoder `session.run()`, the latency for PyTorch and ONNX version is different, with ONNX 2-3 times slower than the PyTorch version.
Initially, the latencies are similar, but the ONNX version's latency keeps increasing linearly with time. (I was expecting the latency to plateau after reaching a maximum size of `last_channel_cache_size`)
I saw a similar issue reported on https://github.com/microsoft/onnxruntime/issues/11293, wondering if it could be related.
Also, if this is a known issue, does https://github.com/NVIDIA/NeMo/pull/5837 provides a solution?
Model is exported to ONNX as follows
```python
asr_model.encoder.export_cache_support = True
asr_model.encoder.export(opts)
```
PyTorch version
```python
(encoded, encoded_len, cache_last_channel, cache_last_time) = asr_model.encoder.forward(
audio_signal=feats,
length=feats_len,
cache_last_channel=cache_last_channel,
cache_last_time=cache_last_time,
)
cache_last_channel = cache_last_channel[
:, :, -asr_model.encoder.streaming_cfg.last_channel_cache_size :, :
]
```
ONNX version
```python
(encoded, encoded_len, cache_last_channel, cache_last_time) = enc_session.run(
encoder_outputs,
{'audio_signal': feats.numpy(),
'length': feats_len.numpy(),
'cache_last_channel': cache_last_channel,
'cache_last_time': cache_last_time,
}
)
cache_last_channel = cache_last_channel[
:, :, -asr_model.encoder.streaming_cfg.last_channel_cache_size :, :
]
```
**Steps/Code to reproduce bug**
- Export the cache-aware Conformer Encoder to ONNX using
```python
asr_model.encoder.export_cache_support = True
asr_model.encoder.export(opts)
```
- Pass in random feats and caches and measure latency
- Compare PyTorch and ONNX latencies
**Expected behavior**
ONNX runtime is supposed to be at least as fast (if not faster) than the PyTorch version.
Also, since latency increases linearly with signal length, it suggests that maybe ONNX version is not resetting the cache internally?
**Environment overview (please complete the following information)**
- Environment location: Bare metal on a GCP VM with Intel Skylake CPU
- Method of NeMo install: From source
**Environment details**
- OS version: Debian GNU/Linux 10 (buster) x86_64
- PyTorch version: 1.12.1
- Python version: 3.8.15
- onnxruneime version: 1.13.1
**Additional context**
Decoding is done on the CPU.
Please let me know if you need any other details.
Thanks for the help! | {
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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
* Related to # (issue)
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} | # What does this PR do ?
Add CPU offloading and memory tricks to let NFA work for up to 2 hours of audio via offloading
**Collection**: [Tools, ASR]
# Changelog
- Add granular CPU offloading of tensors
- Replace all repeat with expand to prevent unnecessarily allocating memory
- Add support for local attention
# Usage
```python
python align.py \
model_path=<Citrinet model path> \
model_downsample_factor=8 \
manifest_filepath=<Manifest filepath> \
transcribe_device='cuda' \
viterbi_device='cpu' \
batch_size=1 \
transcribe_dtype="fp16" \ # bf16 if supported
transcribe_amp=true \
viterbi_dtype="fp16" \ # CPU offload supports fp16 everywhere
cpu_offload_transcript_probs=True \
cpu_offload_viterbi_probs=True \
cpu_offload_viterbi_pointers=True \
model_change.conformer.self_attention_model="rel_pos_local_attn" \ # Support Conformer (medium length audio) with local attention
model_change.conformer.att_context_size=[64,64]
```
# 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)
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**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | Signed-off-by: stevehuang52 <[email protected]>
# What does this PR do ?
Fix memory leak and improve speed of the simulator.
**Collection**: [ASR]
# Changelog
- Fix memory leak by cleaning up buffers and variables after each session is created
- Improve speed by generating candidate speakers and their audios before launching multiprocess
- Enforce spawn as multiprocessing method
- Improve some data loading logic to avoid unnecessarily traversing the same list many times.
- Adding multi-gpu (CUDA devices) support
- Reduced parallel processing memory usage by sampling noise source samples in advance
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
This PR fixes a bug in ALiBi implementation.
ALiBi paper: https://arxiv.org/abs/2108.12409
Results shows that ALiBi is comparable in performance to RPE, and is faster by 7% from RPE and 8% slower than learnable abs embeddings during training.
See plots below. ALiBi (this PR) in green, ALiBi (before fix) in pink, learned absolute embeddings in red, and RPE in cyan.
**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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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
JSON indentation fix
**Collection**: VAD
# 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
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# What does this PR do ?
JSON indentation fix
**Collection**: VAD
# Before your PR is "Ready for review"
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
updated with more entries for both dictionary and heteronyms.
**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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} | Signed-off-by: ericharper <[email protected]>
# 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
* Related to # (issue)
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Something like this:
```
config = OmegaConf.load(yaml_config)
config.model.language_model.pretrained_model_name = 'biomegatron345m_biovocab_50k_cased'
trainer = pl.Trainer(**config.trainer)
lmodel = MegatronBertModel(config.model, trainer)
``` | {
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} | # What does this PR do ?
This PR adds the export of the segmentation file to a label file that can directly be imported in Audacity to quickly and easily assess the quality of the segmentation.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* No usage change. The label file will be automatically generated.
# Before your PR is "Ready for review"
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} | # What does this PR do ?
Correct NFA README typo and fix an NFA error that would give incorrect timestamps when the input audio had a different sampling rate to that of the ASR model.
**Collection**: tools/nemo_forced_aligner
# Changelog
- Correct NFA README typo: 'samples' -> 'seconds'
- Fix timestamps bug: change variable 'audio_sr' -> 'model.cfg.sample_rate'
# 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)
- [ N/A ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ N/A ] 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
* Addressing question raised in https://github.com/NVIDIA/NeMo/discussions/5848.
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Adds the ability to fine-tune NeMo-Megatron trained GPT models on multiple downstream datasets.
**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?
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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.
# Additional Information
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} | # What does this PR do ?
Fix issue with torchmetrics ConfusionMatrix function
**Collection**: ASR
# Changelog
- add task arg
# 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?
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- [ ] New Feature
- [x] Bugfix
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} | # What does this PR do ?
Fix issue with torchmetrics ConfusionMatrix function
**Collection**: ASR
# Changelog
- add task arg
# 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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- [ ] New Feature
- [x] Bugfix
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# What does this PR do ?
Add documentation for NeMo multi-run support
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# Changelog
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- Add documentation for NeMo Multi-Run + some tips and tricks
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# What does this PR do ?
Fixes Docker builds with `torchaudio` library:
- Fixes installation in `pytorch:22.04-py3`-based and other old containers (silently failed, which resulted in no `torchaudio` at all in the container)
- The build will fail if `torchaudio` is not installed at all (no CPU version). Warning if the GPU version is not compiled.
**NB**: we will still need to update this script after PyTorch 2.0 release.
**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?
- [x] 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?
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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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**Collection**: [Note which collection this PR will affect]
# 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"
**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?
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- [ ] 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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} | # What does this PR do ?
This PR adds a few features to the existing multispeaker data simulator
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added a feature to make 1 speaker sessions
- 50-100x speed up by saving loaded wav to dictionary (hashtable) ( 2000ms/session to 20-50ms/session)
- Parallel processing has been added for speed up (up to num_workers=12, it speeds up)
- A few minor bug fixes
# Usage
```python
python multispeaker_simulator.py --config-path='conf' --config-name='data_simulator.yaml' \
data_simulator.random_seed=42 \
data_simulator.manifest_filepath=./train-clean-100-align.json \
data_simulator.outputs.output_dir=./test_rir \
data_simulator.background_noise.add_bg=True \
data_simulator.background_noise.background_manifest=./bg_noise.json
data_simulator.rir_generation.use_rir=True
```
# 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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR
# Additional Information
* Related to # (issue)
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**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Added a feature to make 1 speaker sessions
- 50~100x speed up by saving loaded wav to dictionary (hashtable)
- Parallel processing has been added for speed up (up to num_workers=12, it speeds up)
- A few minor bug fix
# Usage
```python
python multispeaker_simulator.py --config-path='conf' --config-name='data_simulator.yaml' \
data_simulator.random_seed=42 \
data_simulator.manifest_filepath=./train-clean-100-align.json \
data_simulator.outputs.output_dir=./test_rir \
data_simulator.background_noise.add_bg=True \
data_simulator.background_noise.background_manifest=./bg_noise.json
data_simulator.rir_generation.use_rir=True
```
# 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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Add documentation for NeMo multi-run support
**Collection**: [Core]
# Changelog
- Add a fix for printing the error stack trace if there are multiple list of lists.
- Add documentation for NeMo Multi-Run + some tips and tricks
# 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**:
- [ ] New Feature
- [x] Bugfix
- [x] Documentation
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} | # What does this PR do ?
accuracy reporting is ~10-20x slower for prompt learning and adapter learning for t5. This PR optionally disables it.
**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 ?
Fixes `ctc-decoders` installation in `Offline_ASR` notebook.
**Collection**: ASR
# Changelog
- Updated CTC decoders installation
# Usage
# 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?
- [X] 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)
Signed-off-by: Vitaly Lavrukhin <[email protected]> | {
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} | Signed-off-by: David Mosallanezhad <[email protected]>
# What does this PR do ?
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# Changelog
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## Who can review?
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} | Signed-off-by: David Mosallanezhad <[email protected]>
# 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
* Related to # (issue)
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There is no clear statement on return types of `EncDecRNNTModel.transcribe()` method. The text seems to be clipped (sentence is not completed) | {
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} | # What does this PR do ?
Work in progress:
- Export unified (encoder and decoder) onnx model for cache-aware conformer CTC models.
- truncate encoded_output and caches where needed for easier deployment
- Use consistent chunk size etc so that first chunk is treated like other chunk whenever possible. This simplifies deployment.
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Export conformer-ctc with cache-aware streaming
`python scripts/export.py --max-batch 32 --check-tolerance 1.0 --runtime-check --streaming_support --cache_support <path/to/model.nemo> <outut.onnx>`
```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**:
- [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 | **Description**
Whenever the pre-training is initiated with script **speech_pre_training.py** with default configuration file **conformer_ssl.yaml** configuration, the following error is encountered:
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 645, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run
results = self._run_stage()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1177, in _run_stage
self._run_train()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1200, in _run_train
self.fit_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.on_advance_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 251, in on_advance_end
self._run_validation()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 310, in _run_validation
self.val_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 152, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 137, in advance
output = self._evaluation_step(**kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 234, in _evaluation_step
output = self.trainer._call_strategy_hook(hook_name, *kwargs.values())
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1480, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 360, in validation_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1040, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1000, in _run_ddp_forward
return module_to_run(*inputs[0], **kwargs[0])
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/overrides/base.py", line 110, in forward
return self._forward_module.validation_step(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/ssl_models.py", line 533, in validation_step
loss_value, _ = self.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len, targets, target_lengths)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/ssl_models.py", line 462, in decoder_loss_step
current_loss_value = current_loss(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/common.py", line 1086, in __call__
outputs = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 187, in forward
out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1])
RuntimeError: shape '[16, -1, 128]' is invalid for input of size 236544
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/nemocker/scripts/speech_pre_training.py", line 68, in main
trainer.fit(asr_model)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 603, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 59, in _call_and_handle_interrupt
trainer.strategy.reconciliate_processes(traceback.format_exc())
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 461, in reconciliate_processes
raise DeadlockDetectedException(f"DeadLock detected from rank: {self.global_rank} \n {trace}")
pytorch_lightning.utilities.exceptions.DeadlockDetectedException: DeadLock detected from rank: 2
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 645, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run
results = self._run_stage()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1177, in _run_stage
self._run_train()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1200, in _run_train
self.fit_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.on_advance_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 251, in on_advance_end
self._run_validation()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 310, in _run_validation
self.val_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 152, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 137, in advance
output = self._evaluation_step(**kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 234, in _evaluation_step
output = self.trainer._call_strategy_hook(hook_name, *kwargs.values())
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1480, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 360, in validation_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1040, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1000, in _run_ddp_forward
return module_to_run(*inputs[0], **kwargs[0])
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/overrides/base.py", line 110, in forward
return self._forward_module.validation_step(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/ssl_models.py", line 533, in validation_step
loss_value, _ = self.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len, targets, target_lengths)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/ssl_models.py", line 462, in decoder_loss_step
current_loss_value = current_loss(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/common.py", line 1086, in __call__
outputs = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 187, in forward
out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1])
RuntimeError: shape '[16, -1, 128]' is invalid for input of size 236544
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Please note, the python version is Python 3.10.6 and every audio file in the training set has above 100 frames.
**Steps/Code to reproduce bug**
After creating a manifest with audio files (above 100 frames), default **speech_pre_training.py** script is run with default configuration **conformer_ssl.py**
**Environment overview**
- Environment location: Issue is faced in both bare-metal and docker.
- Method of NeMo install: NeMo was installed using **pip install nemo_toolkit[all]**
- Ubuntu (inside docker): "Ubuntu 22.04.1 LTS
- Driver Version: 525.60.13, CUDA Version: 12.0
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} | Signed-off-by: Daniel Egert <[email protected]>
# What does this PR do ?
This PR allows the audio lexical PNC model to also import pretrained SSL models for the audio encoder, and allows importing any audio encoder which is on disk, and not just on NGC
**Collection**: [Note which collection this PR will affect]
NLP
# Changelog
- Changes punctuation_capitalization_lexical_audio_model.py to support `.restore_from` instead of just `.from_pretrained`
- Changes punctuation_capitalization_lexical_audio_model.py to support SSL models for the encoder by correct removing the SSL decoder_losses
- Changes punctuation_capitalization_model.py to support inference with a list of bytes instead of just list of strings for audio filepaths on disk. This allows using a bytestream which is necessary for Riva sharded tar files
- Changes punctuation_capitalization_infer_dataset.py to support the same bytestream changes
- Changes punctuation_capitalization_model.py to remove superfluous debug print statements outside nemo logging which were accidentally left in
# Usage
You can now specify `model.audio_encoder.pretrained_model` to be a .nemo file on disk rather than just the name of a model in NGC. This is set in the config yaml of the audio lexical model.
```yaml
model:
audio_encoder:
pretrained_model: /path/to/my_ssl_model.nemo # this now works
freeze:
is_enabled: false # If set to True weights of audio encoder will not be updated during training.
d_model: 256 # Input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward
d_ff: 1024 # Hidden dimension of PositionwiseFeedForward
num_layers: 4 # Number of additional Conformer layers
```
# 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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixes `ctc-decoders` installation in `Offline_ASR` notebook.
**Collection**: ASR
# Changelog
- Updated CTC decoders installation
# Usage
# 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?
- [X] 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)
Signed-off-by: Vitaly Lavrukhin <[email protected]> | {
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https://github.com/NVIDIA/NeMo/blob/b69f61d4c05ce7ee35d1474025998ca6e5c20d72/nemo/collections/nlp/data/language_modeling/megatron/dataset_utils.py#L497
no big deal, just thought I'd flag | {
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} | # What does this PR do ?
remove transformer version upper bound
**Collection**: nlp, asr, tts
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/5830 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5830/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5830/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5830/events | https://github.com/NVIDIA/NeMo/pull/5830 | 1,549,996,392 | PR_kwDOC_bI7s5IK1RB | 5,830 | validation batch sizing and drop_last controls | {
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} | # What does this PR do ?
This PR allows use to set the batch sizes for validation. This in turn allows the use to set `validation_drop_last=False` in the config. Allowing more control over the validation loss calculation and reporting.
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/5829 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5829/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5829/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5829/events | https://github.com/NVIDIA/NeMo/pull/5829 | 1,549,843,997 | PR_kwDOC_bI7s5IKUKz | 5,829 | Radtts dur transformer | {
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Add a one line overview of what this PR aims to accomplish.
It will add IPA config files for RADTTS and ADD transformer support for duration prediction.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
it adds example/tts/config/rad_tts-feature_pred_ipa and example/tts/config/rad_tts-decoder_ipa
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/5828 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5828/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5828/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5828/events | https://github.com/NVIDIA/NeMo/issues/5828 | 1,549,193,415 | I_kwDOC_bI7s5cVtDH | 5,828 | Nemo ASR training: Unable to get/save best top n checkpoints, and exp_dir not setting | {
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} | null | null | null | Hi All,
The expdir and save_top_k under exp_manager are not working as expected.
1. "expdir" is getting created, but nothing is being written. I tried setting logger to False, even then nothing is being written to "expdir".
2. Always only the last checkpint is saved irrespective of "save_top_k" value. And, only one checkpoint is save, and it is not the best.
In addition, setting these values in config files. I am passing them during training as below:
expdir = '/data/manju/tamil/exp/logs/expDir'
params['exp_manager']['exp_dir'] = expdir
params['exp_manager']['save_top_k'] = 6
Pls suggest @titu1994 . Thanks | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/5827 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5827/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5827/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5827/events | https://github.com/NVIDIA/NeMo/pull/5827 | 1,548,595,702 | PR_kwDOC_bI7s5IGDjD | 5,827 | [ASR] Configurable metrics for audio-to-audio + removed experimental decorators | {
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} | Signed-off-by: Ante Jukić <[email protected]>
# What does this PR do ?
This PR adds an option to configure metrics for `AudioToAudio` models.
**Collection**: ASR
# Changelog
- Added `AudioMetricWrapper` to support batches with examples with variable input lengths
- Added unit tests
- Added example of using metrics in `multichannel_enhancement.yaml`
- Removed experimental decorators
# Usage
Metrics for `val` and `test` can be configured by adding `model.metrics`, for example
```
model:
...
metrics:
val:
sdr: # output SDR
_target_: torchmetrics.audio.SignalDistortionRatio
test:
sdr_ch0: # SDR on output channel 0
_target_: torchmetrics.audio.SignalDistortionRatio
channel: 0
```
# 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)
- [x] 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.
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https://api.github.com/repos/NVIDIA/NeMo/issues/5826 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5826/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5826/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5826/events | https://github.com/NVIDIA/NeMo/pull/5826 | 1,548,328,992 | PR_kwDOC_bI7s5IFKqF | 5,826 | Concat dataset and aistore support for label models | {
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} | # What does this PR do ?
Add support for concat dataset and aistore for label models. Ideally, we should implement both of the features in a single place, such that all of our datasets can automatically support them. But that requires some additional refactoring, so just adding partial support to a few datasets for now.
**Collection**: ASR
# 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**:
- [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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} | Signed-off-by: Taejin Park <[email protected]>
# What does this PR do ?
decoder timestamp count has a mismatch when <unk> is decoded.
Instead of having a temporary solution to ignore <unk>, the script is changed to
output <unk> as decoded output with a correct timestamps.
https://github.com/NVIDIA/NeMo/pull/5481/files
In this previous PR, the <unk> symbol is coded to be ignored,
but it still causes mismatch errors when <unk> is in the middle of two isolated one-token words.
This PR fixes such errors.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
`ctc_decoder_predictions_tensor_with_ts()` in decoder_timestamps_utils.py
is changed to output the actual decoded text with the matching word timestamps.
# Usage
```python
python offline_diar_with_asr_infer.py \
diarizer.manifest_filepath=<path to manifest file> \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.model_path=<pretrained modelname or path to .nemo> \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.speaker_embeddings.parameters.save_embeddings=False
```
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
ASR contributors
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Update our code so that we can support Lightning 1.8.x and 1.9.x
**Collection**: Common
# Changelog
- Update NeMo for Lightning 1.9.0.
# 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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"patch_url": "https://github.com/NVIDIA/NeMo/pull/5822.patch",
"merged_at": "2023-01-18T16:10:31"
} | Signed-off-by: SeanNaren <[email protected]>
Fixes https://github.com/NVIDIA/NeMo/issues/5821
# 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
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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"
**Pre checks**:
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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.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
Getting `ImportError` bug when importing e.g. ASR model when installing NeMo from main. Presumably because of PL update yesterday? [(pl-release)](https://github.com/Lightning-AI/lightning/releases/tag/1.9.0)
**Steps/Code to reproduce bug**
`python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]`
`from nemo.collections.asr.models import EncDecSpeakerLabelModel `
yields:
**Expected behavior**
..
**Environment overview (please complete the following information)**
Google Colab
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Google Colab
- PyTorch version: 1.13.1+cu116
- Python version: Python 3.8.10
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} | null | null | null | **Describe the bug**
The [C++ dataset helper makefile](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/Makefile) of Megatron in NeMo attempts to write its output to `/usr/bin/ld`, causing training to crash when using singularity containers build off of your NVIDIA NGC NeMo containers on HPC clusters.
```
0: [NeMo I 2023-01-18 08:00:59 gpt_dataset:488] > elasped time to build and save doc-idx mapping (seconds): 40.719332
0: make: Entering directory '/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/data/language_modeling/megatron'
0: g++ -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color -I/opt/conda/include/python3.8 -I/opt/conda/lib/python3.8/site-packages/pybind11/include helpers.cpp -o helpers.cpython-38-x86_64-linux-gnu.so
0: /usr/bin/ld: cannot open output file helpers.cpython-38-x86_64-linux-gnu.so: Read-only file system
0: collect2: error: ld returned 1 exit status
0: make: *** [Makefile:23: helpers.cpython-38-x86_64-linux-gnu.so] Error 1
0: make: Leaving directory '/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/data/language_modeling/megatron'
0: [NeMo E 2023-01-18 08:01:30 dataset_utils:83] Making C++ dataset helpers module failed, exiting.
0: [NeMo W 2023-01-18 08:01:31 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py:431: UserWarning: Error handling mechanism for deadlock detection is uninitialized. Skipping check.
0: rank_zero_warn("Error handling mechanism for deadlock detection is uninitialized. Skipping check.")
```
**Steps/Code to reproduce bug**
Build a singularity container for use in HPC. This is our definition file `nemo.def`, and we build it locally (outside of HPC environment) via `sudo singularity build nemo2209.sif nemo.def`:
```
From: nvcr.io/nvidia/nemo:22.09
%environment
export LC_ALL=C
```
We transfer the image `nemo2209.sif` to HPC, and follow the [NeMo GPT training docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html).
See this other issue for [sbatch config and launch script](https://github.com/NVIDIA/NeMo/issues/5819) (changing `--nodes=2` to `--nodes=1`).
**Expected behavior**
Most users will probably use NeMo Megatron on HPC, where they don't have `sudo` rights and need to use Singularity instead of Docker. It would be nice if you would test that your documentation examples are launchable with Singularity containers on systems where you do not have root/sudo. A container that is already built should be able to launch training without errors, and without building/compiling extra stuff that needs to be written to write protected folders.
Regular NVIDIA Pytorch containers work out of the box when converted to Singularity containers and used with Megatron-LM.
**Environment overview (please complete the following information)**
HPC cluster, Slurm.
**Environment details**
NGC Nemo containers 22.08 and 22.09.
**Additional context**
A100 GPUs.
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} | null | null | null | **Describe the bug**
We are trying to get multi-node training to work with NeMo Megatron by following the steps in the quick start steps in your [GPT model training docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html). We're using Slurm on an HPC, and are able to successfully train using Megatron-LM, but not with NeMo.
NeMo keeps insisting we are running multi-node training without SLURM handling the processes:
```
0: [NeMo E 2023-01-18 07:39:42 exp_manager:423] You are running multi-node training without SLURM handling the processes. Please note that this is not tested in NeMo and could result in errors.
```
and the global ranks of our GPUs seem to be incorrectly initialised as result:
```
0: Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
0: Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
0: Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
1: Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
1: Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
1: Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
```
**Steps/Code to reproduce bug**
1. Build a singularity container by bootstrapping the NeMo docker container from NGC. We used both 22.08 and 22.09. Definition file:
```BootStrap: docker
From: nvcr.io/nvidia/nemo:22.09
%environment
export LC_ALL=C
```
2. Follow steps in the [GPT model training docs
](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html).
3. Launch job via Slurm. Here's our sbatch script:
```bash
#!/bin/bash -l
#SBATCH --partition=gpu
#SBATCH --qos=test
#SBATCH --account=####### # our project account
#SBATCH --job-name=gpt_nemo
#SBATCH --nodes=2
#SBATCH --gres=gpu:4
##SBATCH --ntasks-per-node=1
##SBATCH --begin=now
##SBATCH --nodelist=mel[2001-2017]
##SBATCH --exclude=mel2080
#SBATCH --time=0-00:30:00
#SBATCH --output=logs/gpt_nemo.log
# Modules
pwd
module purge
module load Singularity-CE
## Create needed distributed env variables
# addr=$(/bin/hostname -s)
# export MASTER_ADDR=$addr
# export MASTER_PORT=16783 # Can be any unused port
export GPU_PER_NODE=4
# debugging flags (optional)
export NCCL_DEBUG=INFO
export PYTHONFAULTHANDLER=1
# Logfile
DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
PROJECT=/project/home/p200097/faton/nemo_test # Use abs path, not symbolic link
CONTAINER_PATH=/project/home/p200097/faton/nemo_test/nemo2209.sandbox
LOGGING=$PROJECT/logs
LOGFILE="${LOGGING}/%x_${DATETIME}.log"
echo $LOGFILE
echo "SLURM_JOB_GPUS is $SLURM_JOB_GPUS"
ls -lh
cmd="srun -l --output=$LOGGING/gpt_nemo_$DATETIME.log \
singularity exec --nv --bind $PROJECT:$PROJECT $CONTAINER_PATH \
bash $PROJECT/training_args.sh"
$cmd
```
And here are the setting and launch script in `training_args.sh`:
```bash
/bin/hostname -s
# cp -f /mnt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py /workspace/nemo/examples/nlp/language_modeling/megatron_gpt_pretraining.py
echo "ntasks-per-node is: $SLURM_NTASKS_PER_NODE"
echo "The PROCID is: $SLURM_PROCID"
echo "SLURM_JOB_GPUS is $SLURM_JOB_GPUS"
python /workspace/nemo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/workspace/nemo/examples/nlp/language_modeling/conf \
--config-name=megatron_gpt_config \
trainer.devices=$GPU_PER_NODE \
trainer.num_nodes=$SLURM_JOB_NUM_NODES \
trainer.max_epochs=null \
trainer.max_steps=300000 \
trainer.val_check_interval=300 \
trainer.log_every_n_steps=50 \
trainer.limit_val_batches=50 \
trainer.limit_test_batches=50 \
trainer.accumulate_grad_batches=1 \
trainer.precision=16 \
model.micro_batch_size=6 \
model.global_batch_size=192 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
model.max_position_embeddings=1024 \
model.encoder_seq_length=1024 \
model.hidden_size=768 \
model.ffn_hidden_size=3072 \
model.num_layers=12 \
model.num_attention_heads=12 \
model.init_method_std=0.021 \
model.hidden_dropout=0.1 \
model.layernorm_epsilon=1e-5 \
model.tokenizer.vocab_file=gpt2-vocab.json \
model.tokenizer.merge_file=gpt2-merges.txt \
model.data.data_prefix=[1.0,hfbpe_gpt_training_data_text_document] \
model.data.num_workers=2 \
model.data.seq_length=1024 \
model.data.splits_string=\'980,10,10\' \
model.optim.name=fused_adam \
model.optim.lr=6e-4 \
model.optim.betas=[0.9,0.95] \
model.optim.weight_decay=0.1 \
model.optim.sched.name=CosineAnnealing \
model.optim.sched.warmup_steps=750 \
model.optim.sched.constant_steps=80000 \
model.optim.sched.min_lr=6e-5 \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=val_loss \
exp_manager.checkpoint_callback_params.save_top_k=3 \
exp_manager.checkpoint_callback_params.mode=min \
exp_manager.checkpoint_callback_params.always_save_nemo=False
```
**Expected behavior**
Nemo/Pytorch Lightning recognizing job is run through slurm and starting the job successfully.
**Environment overview (please complete the following information)**
- Environment location: Using Slurm on a HPC and singularity containers created from NVIDIA's NGC NeMo containers (tested with 22.08 and 22.09)
- Method of NeMo install: Pre-installed in container.
**Additional context**
1 node in our case consists of 4 A100 GPUs.
We saw that you referred to the Pytorch Lightning documentation when asked about multi-node training in [this previous issue](https://github.com/NVIDIA/NeMo/issues/3312). However, the Pytorch Lightning docs' example sbatch script has a setting that makes no sense to us:
```bash
#SBATCH --ntasks-per-node=8 # This needs to match Trainer(devices=...)
```
If we set `--ntasks-per-node=4` this creates 4 separate processes in a node consisting of 4 GPUs, and each GPU is placed in a separate process, with only a single GPU being available per process. We tried the above method, and it only resulted in training crashing because NeMo/Lightning expected 4 devices (`0, 1, 2, 3`) but only saw one device (`0`) per process.
In the github issue thread we referenced, you write that you guys use Slurm internally. Could you provide a working example of launching a multi-node job with NeMo Megatron using sbatch and the example in your docs?
Log outputs:
```
1: WARNING: underlay of /usr/bin/nvidia-smi required more than 50 (510) bind mounts
0: WARNING: underlay of /usr/bin/nvidia-smi required more than 50 (510) bind mounts
1: mel2014
1: ntasks-per-node is:
1: The PROCID is: 1
1: SLURM_JOB_GPUS is 0,1,2,3
0: mel2013
0: ntasks-per-node is:
0: The PROCID is: 0
0: SLURM_JOB_GPUS is 0,1,2,3
0: [NeMo W 2023-01-18 07:38:17 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo W 2023-01-18 07:38:36 experimental:27] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo I 2023-01-18 07:38:37 megatron_gpt_pretraining:36]
0:
0: ************** Experiment configuration ***********
0: [NeMo I 2023-01-18 07:38:37 megatron_gpt_pretraining:37]
0: name: megatron_gpt
0: restore_from_path: null
0: trainer:
0: devices: 4
0: num_nodes: 2
0: accelerator: gpu
0: precision: 16
0: logger: false
0: enable_checkpointing: false
0: replace_sampler_ddp: false
0: max_epochs: null
0: max_steps: 300000
0: log_every_n_steps: 50
0: val_check_interval: 300
0: limit_val_batches: 50
0: limit_test_batches: 50
0: accumulate_grad_batches: 1
0: gradient_clip_val: 1.0
0: benchmark: false
0: enable_model_summary: false
0: exp_manager:
0: explicit_log_dir: null
0: exp_dir: null
0: name: megatron_gpt
0: create_wandb_logger: false
0: wandb_logger_kwargs:
0: project: null
0: name: null
0: resume_if_exists: true
0: resume_ignore_no_checkpoint: true
0: create_checkpoint_callback: true
0: checkpoint_callback_params:
0: monitor: val_loss
0: save_top_k: 3
0: mode: min
0: always_save_nemo: false
0: save_nemo_on_train_end: false
0: filename: megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}
0: model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
0: model:
0: micro_batch_size: 6
0: global_batch_size: 192
0: tensor_model_parallel_size: 1
0: pipeline_model_parallel_size: 1
0: virtual_pipeline_model_parallel_size: null
0: encoder_seq_length: 1024
0: max_position_embeddings: 1024
0: num_layers: 12
0: hidden_size: 768
0: ffn_hidden_size: 3072
0: num_attention_heads: 12
0: init_method_std: 0.021
0: use_scaled_init_method: true
0: hidden_dropout: 0.1
0: kv_channels: null
0: apply_query_key_layer_scaling: true
0: normalization: layernorm
0: layernorm_epsilon: 1.0e-05
0: do_layer_norm_weight_decay: false
0: make_vocab_size_divisible_by: 128
0: pre_process: true
0: post_process: true
0: persist_layer_norm: true
0: tokenizer:
0: library: megatron
0: type: GPT2BPETokenizer
0: model: null
0: vocab_file: gpt2-vocab.json
0: merge_file: gpt2-merges.txt
0: delimiter: null
0: sentencepiece_legacy: false
0: native_amp_init_scale: 4294967296
0: native_amp_growth_interval: 1000
0: hysteresis: 2
0: fp32_residual_connection: false
0: fp16_lm_cross_entropy: false
0: megatron_amp_O2: false
0: grad_allreduce_chunk_size_mb: 125
0: grad_div_ar_fusion: true
0: gradient_accumulation_fusion: false
0: bias_activation_fusion: true
0: bias_dropout_add_fusion: true
0: masked_softmax_fusion: true
0: seed: 1234
0: resume_from_checkpoint: null
0: use_cpu_initialization: false
0: onnx_safe: false
0: apex_transformer_log_level: 30
0: gradient_as_bucket_view: true
0: sync_batch_comm: false
0: activations_checkpoint_granularity: null
0: activations_checkpoint_method: null
0: activations_checkpoint_num_layers: null
0: num_micro_batches_with_partial_activation_checkpoints: null
0: activations_checkpoint_layers_per_pipeline: null
0: sequence_parallel: false
0: transformer_engine: false
0: fp8: false
0: fp8_e4m3: false
0: fp8_hybrid: false
0: fp8_margin: 0
0: fp8_interval: 1
0: fp8_amax_history_len: 1
0: fp8_amax_compute_algo: most_recent
0: use_emha: false
0: data:
0: data_prefix:
0: - 1.0
0: - hfbpe_gpt_training_data_text_document
0: index_mapping_dir: null
0: data_impl: mmap
0: splits_string: 980,10,10
0: seq_length: 1024
0: skip_warmup: true
0: num_workers: 2
0: dataloader_type: single
0: reset_position_ids: false
0: reset_attention_mask: false
0: eod_mask_loss: false
0: validation_drop_last: true
0: nsys_profile:
0: enabled: false
0: start_step: 10
0: end_step: 10
0: ranks:
0: - 0
0: gen_shape: false
0: optim:
0: name: fused_adam
0: lr: 0.0006
0: weight_decay: 0.1
0: betas:
0: - 0.9
0: - 0.95
0: sched:
0: name: CosineAnnealing
0: warmup_steps: 750
0: constant_steps: 80000
0: min_lr: 6.0e-05
0:
0: GPU available: True (cuda), used: True
0: TPU available: False, using: 0 TPU cores
0: IPU available: False, using: 0 IPUs
0: HPU available: False, using: 0 HPUs
1: GPU available: True (cuda), used: True
1: TPU available: False, using: 0 TPU cores
1: IPU available: False, using: 0 IPUs
1: HPU available: False, using: 0 HPUs
0: [NeMo E 2023-01-18 07:38:39 exp_manager:423] You are running multi-node training without SLURM handling the processes. Please note that this is not tested in NeMo and could result in errors.
0: [NeMo W 2023-01-18 07:38:39 exp_manager:614] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
0: [NeMo W 2023-01-18 07:38:39 exp_manager:466] There was no checkpoint folder at checkpoint_dir :/project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt/checkpoints. Training from scratch.
0: [NeMo I 2023-01-18 07:38:39 exp_manager:315] Experiments will be logged at /project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt
0: [NeMo I 2023-01-18 07:38:39 exp_manager:704] TensorboardLogger has been set up
0: [NeMo W 2023-01-18 07:38:39 exp_manager:971] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 300000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
0: [NeMo I 2023-01-18 07:38:39 megatron_gpt_pretraining:74] Resuming training from checkpoint: None
1: 23-01-18 07:38:39 - PID:27971 - rank:(0, 0, 0, 0) - microbatches.py:39 - INFO - setting number of micro-batches to constant 4
0: [NeMo I 2023-01-18 07:38:39 megatron_init:223] Rank 0 has data parallel group: [0, 1, 2, 3, 4, 5, 6, 7]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:226] All data parallel group ranks: [[0, 1, 2, 3, 4, 5, 6, 7]]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:227] Ranks 0 has data parallel rank: 0
0: [NeMo I 2023-01-18 07:38:39 megatron_init:235] Rank 0 has model parallel group: [0]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:236] All model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:246] Rank 0 has tensor model parallel group: [0]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:250] All tensor model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:251] Rank 0 has tensor model parallel rank: 0
0: [NeMo I 2023-01-18 07:38:39 megatron_init:265] Rank 0 has pipeline model parallel group: [0]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:277] Rank 0 has embedding group: [0]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:283] All pipeline model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:284] Rank 0 has pipeline model parallel rank 0
0: [NeMo I 2023-01-18 07:38:39 megatron_init:285] All embedding group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:38:39 megatron_init:286] Rank 0 has embedding rank: 0
0: 23-01-18 07:38:39 - PID:23743 - rank:(0, 0, 0, 0) - microbatches.py:39 - INFO - setting number of micro-batches to constant 4
0: [NeMo W 2023-01-18 07:38:39 modelPT:222] You tried to register an artifact under config key=tokenizer.vocab_file but an artifact for it has already been registered.
0: [NeMo I 2023-01-18 07:38:39 tokenizer_utils:204] Getting Megatron tokenizer for pretrained model name: megatron-gpt-345m, custom vocab file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, and merges file: /project/home/p200097/faton/nemo_test/gpt2-merges.txt
0: [NeMo I 2023-01-18 07:38:39 tokenizer_utils:130] Getting HuggingFace AutoTokenizer with pretrained_model_name: gpt2, vocab_file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, merges_files: /project/home/p200097/faton/nemo_test/gpt2-merges.txt, special_tokens_dict: {}, and use_fast: False
0: Using sep_token, but it is not set yet.
0: Using cls_token, but it is not set yet.
1: Using sep_token, but it is not set yet.
1: Using cls_token, but it is not set yet.
0: Using pad_token, but it is not set yet.
0: Using mask_token, but it is not set yet.
1: Using pad_token, but it is not set yet.
1: Using mask_token, but it is not set yet.
0: [NeMo I 2023-01-18 07:38:42 megatron_base_model:204] Padded vocab_size: 50304, original vocab_size: 50257, dummy tokens: 47.
1: Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8
0: Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8
0: [NeMo W 2023-01-18 07:39:29 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo W 2023-01-18 07:39:29 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo W 2023-01-18 07:39:29 experimental:27] Module <class 'nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers.MegatronPretrainingRandomBatchSampler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo W 2023-01-18 07:39:40 experimental:27] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo W 2023-01-18 07:39:40 experimental:27] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo W 2023-01-18 07:39:40 experimental:27] Module <class 'nemo.collections.nlp.models.text_normalization_as_tagging.thutmose_tagger.ThutmoseTaggerModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
0: [NeMo I 2023-01-18 07:39:41 megatron_gpt_pretraining:36]
0:
0: ************** Experiment configuration ***********
0: [NeMo I 2023-01-18 07:39:41 megatron_gpt_pretraining:36]
0:
0: ************** Experiment configuration ***********
0: [NeMo I 2023-01-18 07:39:41 megatron_gpt_pretraining:37]
0: name: megatron_gpt
0: restore_from_path: null
0: trainer:
0: devices: 4
0: num_nodes: 2
0: accelerator: gpu
0: precision: 16
0: logger: false
0: enable_checkpointing: false
0: replace_sampler_ddp: false
0: max_epochs: null
0: max_steps: 300000
0: log_every_n_steps: 50
0: val_check_interval: 300
0: limit_val_batches: 50
0: limit_test_batches: 50
0: accumulate_grad_batches: 1
0: gradient_clip_val: 1.0
0: benchmark: false
0: enable_model_summary: false
0: exp_manager:
0: explicit_log_dir: null
0: exp_dir: null
0: name: megatron_gpt
0: create_wandb_logger: false
0: wandb_logger_kwargs:
0: project: null
0: name: null
0: resume_if_exists: true
0: resume_ignore_no_checkpoint: true
0: create_checkpoint_callback: true
0: checkpoint_callback_params:
0: monitor: val_loss
0: save_top_k: 3
0: mode: min
0: always_save_nemo: false
0: save_nemo_on_train_end: false
0: filename: megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}
0: model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
0: model:
0: micro_batch_size: 6
0: global_batch_size: 192
0: tensor_model_parallel_size: 1
0: pipeline_model_parallel_size: 1
0: virtual_pipeline_model_parallel_size: null
0: encoder_seq_length: 1024
0: max_position_embeddings: 1024
0: num_layers: 12
0: hidden_size: 768
0: ffn_hidden_size: 3072
0: num_attention_heads: 12
0: init_method_std: 0.021
0: use_scaled_init_method: true
0: hidden_dropout: 0.1
0: kv_channels: null
0: apply_query_key_layer_scaling: true
0: normalization: layernorm
0: layernorm_epsilon: 1.0e-05
0: do_layer_norm_weight_decay: false
0: make_vocab_size_divisible_by: 128
0: pre_process: true
0: post_process: true
0: persist_layer_norm: true
0: tokenizer:
0: library: megatron
0: type: GPT2BPETokenizer
0: model: null
0: vocab_file: gpt2-vocab.json
0: merge_file: gpt2-merges.txt
0: delimiter: null
0: sentencepiece_legacy: false
0: native_amp_init_scale: 4294967296
0: native_amp_growth_interval: 1000
0: hysteresis: 2
0: fp32_residual_connection: false
0: fp16_lm_cross_entropy: false
0: megatron_amp_O2: false
0: grad_allreduce_chunk_size_mb: 125
0: grad_div_ar_fusion: true
0: gradient_accumulation_fusion: false
0: bias_activation_fusion: true
0: bias_dropout_add_fusion: true
0: masked_softmax_fusion: true
0: seed: 1234
0: resume_from_checkpoint: null
0: use_cpu_initialization: false
0: onnx_safe: false
0: apex_transformer_log_level: 30
0: gradient_as_bucket_view: true
0: sync_batch_comm: false
0: activations_checkpoint_granularity: null
0: activations_checkpoint_method: null
0: activations_checkpoint_num_layers: null
0: num_micro_batches_with_partial_activation_checkpoints: null
0: activations_checkpoint_layers_per_pipeline: null
0: sequence_parallel: false
0: transformer_engine: false
0: fp8: false
0: fp8_e4m3: false
0: fp8_hybrid: false
0: fp8_margin: 0
0: fp8_interval: 1
0: fp8_amax_history_len: 1
0: fp8_amax_compute_algo: most_recent
0: use_emha: false
0: data:
0: data_prefix:
0: - 1.0
0: - hfbpe_gpt_training_data_text_document
0: index_mapping_dir: null
0: data_impl: mmap
0: splits_string: 980,10,10
0: seq_length: 1024
0: skip_warmup: true
0: num_workers: 2
0: dataloader_type: single
0: reset_position_ids: false
0: reset_attention_mask: false
0: eod_mask_loss: false
0: validation_drop_last: true
0: nsys_profile:
0: enabled: false
0: start_step: 10
0: end_step: 10
0: ranks:
0: - 0
0: gen_shape: false
0: optim:
0: name: fused_adam
0: lr: 0.0006
0: weight_decay: 0.1
0: betas:
0: - 0.9
0: - 0.95
0: sched:
0: name: CosineAnnealing
0: warmup_steps: 750
0: constant_steps: 80000
0: min_lr: 6.0e-05
0:
0: [NeMo I 2023-01-18 07:39:41 megatron_gpt_pretraining:37]
0: name: megatron_gpt
0: restore_from_path: null
0: trainer:
0: devices: 4
0: num_nodes: 2
0: accelerator: gpu
0: precision: 16
0: logger: false
0: enable_checkpointing: false
0: replace_sampler_ddp: false
0: max_epochs: null
0: max_steps: 300000
0: log_every_n_steps: 50
0: val_check_interval: 300
0: limit_val_batches: 50
0: limit_test_batches: 50
0: accumulate_grad_batches: 1
0: gradient_clip_val: 1.0
0: benchmark: false
0: enable_model_summary: false
0: exp_manager:
0: explicit_log_dir: null
0: exp_dir: null
0: name: megatron_gpt
0: create_wandb_logger: false
0: wandb_logger_kwargs:
0: project: null
0: name: null
0: resume_if_exists: true
0: resume_ignore_no_checkpoint: true
0: create_checkpoint_callback: true
0: checkpoint_callback_params:
0: monitor: val_loss
0: save_top_k: 3
0: mode: min
0: always_save_nemo: false
0: save_nemo_on_train_end: false
0: filename: megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}
0: model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
0: model:
0: micro_batch_size: 6
0: global_batch_size: 192
0: tensor_model_parallel_size: 1
0: pipeline_model_parallel_size: 1
0: virtual_pipeline_model_parallel_size: null
0: encoder_seq_length: 1024
0: max_position_embeddings: 1024
0: num_layers: 12
0: hidden_size: 768
0: ffn_hidden_size: 3072
0: num_attention_heads: 12
0: init_method_std: 0.021
0: use_scaled_init_method: true
0: hidden_dropout: 0.1
0: kv_channels: null
0: apply_query_key_layer_scaling: true
0: normalization: layernorm
0: layernorm_epsilon: 1.0e-05
0: do_layer_norm_weight_decay: false
0: make_vocab_size_divisible_by: 128
0: pre_process: true
0: post_process: true
0: persist_layer_norm: true
0: tokenizer:
0: library: megatron
0: type: GPT2BPETokenizer
0: model: null
0: vocab_file: gpt2-vocab.json
0: merge_file: gpt2-merges.txt
0: delimiter: null
0: sentencepiece_legacy: false
0: native_amp_init_scale: 4294967296
0: native_amp_growth_interval: 1000
0: hysteresis: 2
0: fp32_residual_connection: false
0: fp16_lm_cross_entropy: false
0: megatron_amp_O2: false
0: grad_allreduce_chunk_size_mb: 125
0: grad_div_ar_fusion: true
0: gradient_accumulation_fusion: false
0: bias_activation_fusion: true
0: bias_dropout_add_fusion: true
0: masked_softmax_fusion: true
0: seed: 1234
0: resume_from_checkpoint: null
0: use_cpu_initialization: false
0: onnx_safe: false
0: apex_transformer_log_level: 30
0: gradient_as_bucket_view: true
0: sync_batch_comm: false
0: activations_checkpoint_granularity: null
0: activations_checkpoint_method: null
0: activations_checkpoint_num_layers: null
0: num_micro_batches_with_partial_activation_checkpoints: null
0: activations_checkpoint_layers_per_pipeline: null
0: sequence_parallel: false
0: transformer_engine: false
0: fp8: false
0: fp8_e4m3: false
0: fp8_hybrid: false
0: fp8_margin: 0
0: fp8_interval: 1
0: fp8_amax_history_len: 1
0: fp8_amax_compute_algo: most_recent
0: use_emha: false
0: data:
0: data_prefix:
0: - 1.0
0: - hfbpe_gpt_training_data_text_document
0: index_mapping_dir: null
0: data_impl: mmap
0: splits_string: 980,10,10
0: seq_length: 1024
0: skip_warmup: true
0: num_workers: 2
0: dataloader_type: single
0: reset_position_ids: false
0: reset_attention_mask: false
0: eod_mask_loss: false
0: validation_drop_last: true
0: nsys_profile:
0: enabled: false
0: start_step: 10
0: end_step: 10
0: ranks:
0: - 0
0: gen_shape: false
0: optim:
0: name: fused_adam
0: lr: 0.0006
0: weight_decay: 0.1
0: betas:
0: - 0.9
0: - 0.95
0: sched:
0: name: CosineAnnealing
0: warmup_steps: 750
0: constant_steps: 80000
0: min_lr: 6.0e-05
0:
0: [NeMo I 2023-01-18 07:39:41 megatron_gpt_pretraining:36]
0:
0: ************** Experiment configuration ***********
0: [NeMo I 2023-01-18 07:39:41 megatron_gpt_pretraining:37]
0: name: megatron_gpt
0: restore_from_path: null
0: trainer:
0: devices: 4
0: num_nodes: 2
0: accelerator: gpu
0: precision: 16
0: logger: false
0: enable_checkpointing: false
0: replace_sampler_ddp: false
0: max_epochs: null
0: max_steps: 300000
0: log_every_n_steps: 50
0: val_check_interval: 300
0: limit_val_batches: 50
0: limit_test_batches: 50
0: accumulate_grad_batches: 1
0: gradient_clip_val: 1.0
0: benchmark: false
0: enable_model_summary: false
0: exp_manager:
0: explicit_log_dir: null
0: exp_dir: null
0: name: megatron_gpt
0: create_wandb_logger: false
0: wandb_logger_kwargs:
0: project: null
0: name: null
0: resume_if_exists: true
0: resume_ignore_no_checkpoint: true
0: create_checkpoint_callback: true
0: checkpoint_callback_params:
0: monitor: val_loss
0: save_top_k: 3
0: mode: min
0: always_save_nemo: false
0: save_nemo_on_train_end: false
0: filename: megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}
0: model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
0: model:
0: micro_batch_size: 6
0: global_batch_size: 192
0: tensor_model_parallel_size: 1
0: pipeline_model_parallel_size: 1
0: virtual_pipeline_model_parallel_size: null
0: encoder_seq_length: 1024
0: max_position_embeddings: 1024
0: num_layers: 12
0: hidden_size: 768
0: ffn_hidden_size: 3072
0: num_attention_heads: 12
0: init_method_std: 0.021
0: use_scaled_init_method: true
0: hidden_dropout: 0.1
0: kv_channels: null
0: apply_query_key_layer_scaling: true
0: normalization: layernorm
0: layernorm_epsilon: 1.0e-05
0: do_layer_norm_weight_decay: false
0: make_vocab_size_divisible_by: 128
0: pre_process: true
0: post_process: true
0: persist_layer_norm: true
0: tokenizer:
0: library: megatron
0: type: GPT2BPETokenizer
0: model: null
0: vocab_file: gpt2-vocab.json
0: merge_file: gpt2-merges.txt
0: delimiter: null
0: sentencepiece_legacy: false
0: native_amp_init_scale: 4294967296
0: native_amp_growth_interval: 1000
0: hysteresis: 2
0: fp32_residual_connection: false
0: fp16_lm_cross_entropy: false
0: megatron_amp_O2: false
0: grad_allreduce_chunk_size_mb: 125
0: grad_div_ar_fusion: true
0: gradient_accumulation_fusion: false
0: bias_activation_fusion: true
0: bias_dropout_add_fusion: true
0: masked_softmax_fusion: true
0: seed: 1234
0: resume_from_checkpoint: null
0: use_cpu_initialization: false
0: onnx_safe: false
0: apex_transformer_log_level: 30
0: gradient_as_bucket_view: true
0: sync_batch_comm: false
0: activations_checkpoint_granularity: null
0: activations_checkpoint_method: null
0: activations_checkpoint_num_layers: null
0: num_micro_batches_with_partial_activation_checkpoints: null
0: activations_checkpoint_layers_per_pipeline: null
0: sequence_parallel: false
0: transformer_engine: false
0: fp8: false
0: fp8_e4m3: false
0: fp8_hybrid: false
0: fp8_margin: 0
0: fp8_interval: 1
0: fp8_amax_history_len: 1
0: fp8_amax_compute_algo: most_recent
0: use_emha: false
0: data:
0: data_prefix:
0: - 1.0
0: - hfbpe_gpt_training_data_text_document
0: index_mapping_dir: null
0: data_impl: mmap
0: splits_string: 980,10,10
0: seq_length: 1024
0: skip_warmup: true
0: num_workers: 2
0: dataloader_type: single
0: reset_position_ids: false
0: reset_attention_mask: false
0: eod_mask_loss: false
0: validation_drop_last: true
0: nsys_profile:
0: enabled: false
0: start_step: 10
0: end_step: 10
0: ranks:
0: - 0
0: gen_shape: false
0: optim:
0: name: fused_adam
0: lr: 0.0006
0: weight_decay: 0.1
0: betas:
0: - 0.9
0: - 0.95
0: sched:
0: name: CosineAnnealing
0: warmup_steps: 750
0: constant_steps: 80000
0: min_lr: 6.0e-05
0:
0: [NeMo E 2023-01-18 07:39:42 exp_manager:423] You are running multi-node training without SLURM handling the processes. Please note that this is not tested in NeMo and could result in errors.
0: [NeMo W 2023-01-18 07:39:42 exp_manager:614] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
0: [NeMo E 2023-01-18 07:39:42 exp_manager:423] You are running multi-node training without SLURM handling the processes. Please note that this is not tested in NeMo and could result in errors.
0: [NeMo W 2023-01-18 07:39:42 exp_manager:614] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
0: [NeMo W 2023-01-18 07:39:42 exp_manager:466] There was no checkpoint folder at checkpoint_dir :/project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt/checkpoints. Training from scratch.
0: [NeMo I 2023-01-18 07:39:42 exp_manager:315] Experiments will be logged at /project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt
0: [NeMo W 2023-01-18 07:39:42 exp_manager:466] There was no checkpoint folder at checkpoint_dir :/project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt/checkpoints. Training from scratch.
0: [NeMo I 2023-01-18 07:39:42 exp_manager:315] Experiments will be logged at /project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt
0: [NeMo E 2023-01-18 07:39:42 exp_manager:423] You are running multi-node training without SLURM handling the processes. Please note that this is not tested in NeMo and could result in errors.
0: [NeMo W 2023-01-18 07:39:42 exp_manager:614] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
0: [NeMo W 2023-01-18 07:39:42 exp_manager:466] There was no checkpoint folder at checkpoint_dir :/project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt/checkpoints. Training from scratch.
0: [NeMo I 2023-01-18 07:39:42 exp_manager:315] Experiments will be logged at /project/home/p200097/faton/nemo_test/nemo_experiments/megatron_gpt
0: [NeMo I 2023-01-18 07:39:42 exp_manager:704] TensorboardLogger has been set up
0: [NeMo W 2023-01-18 07:39:42 exp_manager:971] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 300000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
0: [NeMo I 2023-01-18 07:39:42 exp_manager:704] TensorboardLogger has been set up
0: [NeMo I 2023-01-18 07:39:42 exp_manager:704] TensorboardLogger has been set up
0: [NeMo W 2023-01-18 07:39:42 exp_manager:971] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 300000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
0: [NeMo W 2023-01-18 07:39:42 exp_manager:971] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 300000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
0: [NeMo I 2023-01-18 07:39:42 megatron_gpt_pretraining:74] Resuming training from checkpoint: None
0: [NeMo I 2023-01-18 07:39:42 megatron_gpt_pretraining:74] Resuming training from checkpoint: None
0: [NeMo I 2023-01-18 07:39:42 megatron_gpt_pretraining:74] Resuming training from checkpoint: None
0: [NeMo I 2023-01-18 07:39:42 megatron_init:223] Rank 3 has data parallel group: [0, 1, 2, 3, 4, 5, 6, 7]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:223] Rank 1 has data parallel group: [0, 1, 2, 3, 4, 5, 6, 7]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:223] Rank 2 has data parallel group: [0, 1, 2, 3, 4, 5, 6, 7]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:226] All data parallel group ranks: [[0, 1, 2, 3, 4, 5, 6, 7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:227] Ranks 3 has data parallel rank: 3
0: [NeMo I 2023-01-18 07:39:42 megatron_init:235] Rank 3 has model parallel group: [3]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:236] All model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:246] Rank 3 has tensor model parallel group: [3]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:250] All tensor model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:251] Rank 3 has tensor model parallel rank: 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:265] Rank 3 has pipeline model parallel group: [3]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:277] Rank 3 has embedding group: [3]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:283] All pipeline model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:284] Rank 3 has pipeline model parallel rank 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:285] All embedding group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:286] Rank 3 has embedding rank: 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:226] All data parallel group ranks: [[0, 1, 2, 3, 4, 5, 6, 7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:227] Ranks 1 has data parallel rank: 1
0: [NeMo I 2023-01-18 07:39:42 megatron_init:235] Rank 1 has model parallel group: [1]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:236] All model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:246] Rank 1 has tensor model parallel group: [1]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:250] All tensor model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:251] Rank 1 has tensor model parallel rank: 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:265] Rank 1 has pipeline model parallel group: [1]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:277] Rank 1 has embedding group: [1]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:283] All pipeline model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:284] Rank 1 has pipeline model parallel rank 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:285] All embedding group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:286] Rank 1 has embedding rank: 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:226] All data parallel group ranks: [[0, 1, 2, 3, 4, 5, 6, 7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:227] Ranks 2 has data parallel rank: 2
0: [NeMo I 2023-01-18 07:39:42 megatron_init:235] Rank 2 has model parallel group: [2]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:236] All model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:246] Rank 2 has tensor model parallel group: [2]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:250] All tensor model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:251] Rank 2 has tensor model parallel rank: 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:265] Rank 2 has pipeline model parallel group: [2]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:277] Rank 2 has embedding group: [2]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:283] All pipeline model parallel group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:284] Rank 2 has pipeline model parallel rank 0
0: [NeMo I 2023-01-18 07:39:42 megatron_init:285] All embedding group ranks: [[0], [1], [2], [3], [4], [5], [6], [7]]
0: [NeMo I 2023-01-18 07:39:42 megatron_init:286] Rank 2 has embedding rank: 0
0: [NeMo W 2023-01-18 07:39:42 modelPT:222] You tried to register an artifact under config key=tokenizer.vocab_file but an artifact for it has already been registered.
0: [NeMo W 2023-01-18 07:39:42 modelPT:222] You tried to register an artifact under config key=tokenizer.vocab_file but an artifact for it has already been registered.
0: [NeMo W 2023-01-18 07:39:42 modelPT:222] You tried to register an artifact under config key=tokenizer.vocab_file but an artifact for it has already been registered.
0: [NeMo I 2023-01-18 07:39:42 tokenizer_utils:204] Getting Megatron tokenizer for pretrained model name: megatron-gpt-345m, custom vocab file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, and merges file: /project/home/p200097/faton/nemo_test/gpt2-merges.txt
0: [NeMo I 2023-01-18 07:39:42 tokenizer_utils:130] Getting HuggingFace AutoTokenizer with pretrained_model_name: gpt2, vocab_file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, merges_files: /project/home/p200097/faton/nemo_test/gpt2-merges.txt, special_tokens_dict: {}, and use_fast: False
0: [NeMo I 2023-01-18 07:39:42 tokenizer_utils:204] Getting Megatron tokenizer for pretrained model name: megatron-gpt-345m, custom vocab file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, and merges file: /project/home/p200097/faton/nemo_test/gpt2-merges.txt
0: [NeMo I 2023-01-18 07:39:42 tokenizer_utils:130] Getting HuggingFace AutoTokenizer with pretrained_model_name: gpt2, vocab_file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, merges_files: /project/home/p200097/faton/nemo_test/gpt2-merges.txt, special_tokens_dict: {}, and use_fast: False
0: [NeMo I 2023-01-18 07:39:42 tokenizer_utils:204] Getting Megatron tokenizer for pretrained model name: megatron-gpt-345m, custom vocab file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, and merges file: /project/home/p200097/faton/nemo_test/gpt2-merges.txt
0: [NeMo I 2023-01-18 07:39:42 tokenizer_utils:130] Getting HuggingFace AutoTokenizer with pretrained_model_name: gpt2, vocab_file: /project/home/p200097/faton/nemo_test/gpt2-vocab.json, merges_files: /project/home/p200097/faton/nemo_test/gpt2-merges.txt, special_tokens_dict: {}, and use_fast: False
1: Using sep_token, but it is not set yet.
1: Using cls_token, but it is not set yet.
1: Using pad_token, but it is not set yet.
1: Using mask_token, but it is not set yet.
1: Using sep_token, but it is not set yet.
1: Using cls_token, but it is not set yet.
1: Using pad_token, but it is not set yet.
1: Using mask_token, but it is not set yet.
1: Using sep_token, but it is not set yet.
1: Using cls_token, but it is not set yet.
1: Using pad_token, but it is not set yet.
1: Using mask_token, but it is not set yet.
0: Using sep_token, but it is not set yet.
0: Using cls_token, but it is not set yet.
0: Using pad_token, but it is not set yet.
0: Using sep_token, but it is not set yet.
0: Using cls_token, but it is not set yet.
0: Using pad_token, but it is not set yet.
0: Using mask_token, but it is not set yet.
0: [NeMo I 2023-01-18 07:39:45 megatron_base_model:204] Padded vocab_size: 50304, original vocab_size: 50257, dummy tokens: 47.
0: Using mask_token, but it is not set yet.
0: [NeMo I 2023-01-18 07:39:45 megatron_base_model:204] Padded vocab_size: 50304, original vocab_size: 50257, dummy tokens: 47.
0: Using sep_token, but it is not set yet.
0: Using cls_token, but it is not set yet.
0: Using pad_token, but it is not set yet.
0: Using mask_token, but it is not set yet.
0: [NeMo I 2023-01-18 07:39:45 megatron_base_model:204] Padded vocab_size: 50304, original vocab_size: 50257, dummy tokens: 47.
0: Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
0: Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
0: Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
1: Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
1: Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
1: Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
1: Added key: store_based_barrier_key:1 to store for rank: 1
1: Added key: store_based_barrier_key:1 to store for rank: 3
1: Added key: store_based_barrier_key:1 to store for rank: 2
0: Added key: store_based_barrier_key:1 to store for rank: 1
0: Added key: store_based_barrier_key:1 to store for rank: 2
0: Added key: store_based_barrier_key:1 to store for rank: 3
1: Waiting in store based barrier to initialize process group for rank: 2, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
1: Waiting in store based barrier to initialize process group for rank: 1, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
1: Waiting in store based barrier to initialize process group for rank: 3, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
0: Waiting in store based barrier to initialize process group for rank: 1, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
0: Waiting in store based barrier to initialize process group for rank: 2, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
0: Waiting in store based barrier to initialize process group for rank: 3, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
1: Waiting in store based barrier to initialize process group for rank: 2, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
1: Waiting in store based barrier to initialize process group for rank: 3, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
1: Waiting in store based barrier to initialize process group for rank: 1, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
0: Waiting in store based barrier to initialize process group for rank: 1, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
0: Waiting in store based barrier to initialize process group for rank: 3, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
0: Waiting in store based barrier to initialize process group for rank: 2, key: store_based_barrier_key:1 (world_size=8, worker_count=3, timeout=0:30:00)
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https://api.github.com/repos/NVIDIA/NeMo/issues/5818 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/5818/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/5818/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/5818/events | https://github.com/NVIDIA/NeMo/pull/5818 | 1,537,353,259 | PR_kwDOC_bI7s5Hl50I | 5,818 | add apt-get upgrade -y in dockerfile | {
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add apt-get upgrade -y in dockerfile
**Collection**: core
# Changelog
- add apt-get upgrade -y in dockerfile to upgrade packages for vulns patch
# 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
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 apt-get upgrade -y in dockerfile
**Collection**: core
# Changelog
- add apt-get upgrade -y in dockerfile to upgrade packages for vulns patch
# 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
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 container in for to main page
**Collection**: core
# Changelog
- Add built container info to main readme for better visibility.
# 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)
- [x] 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**:
- [ ] 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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Placed a note to remove support for multiple GPU in question answering tutorial, redirecting them to use examples scripts instead, if multiple GPU is needed
**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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Placed a note to remove support for multiple GPU in question answering tutorial, redirecting them to use examples scripts instead, if multiple GPU is needed
**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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fixed a few things in data simulator and related files, based on Taejin's script: https://github.com/tango4j/NeMo/blob/fix_mulspk_sim/scripts/speaker_tasks/create_alignment_manifest.py
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} | Signed-off-by: Yang Zhang <[email protected]>
# What does this PR do ?
add documentation on tar dataset batch size for duplex normalization | {
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} | null | null | null | Documentation is very clear on how to train from scratch but I cannot find how to fine-tune one of the available models https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/machine_translation/machine_translation.html
Could you provide some insights or an example doing that?
Thanks, | {
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} | # What does this PR do ?
Fix bugs in Intro to Transducer and Question Answering tutorials
**Collection**: nlp, asr
# 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 ?
Fix bugs in Intro to Transducer and Question Answering tutorials
**Collection**: nlp, asr
# 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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# Before your PR is "Ready for review"
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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)
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**PR Type**:
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- [x] Bugfix
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} | # What does this PR do ?
This PR fixes a bug in ALiBi implementation.
ALiBi paper: https://arxiv.org/abs/2108.12409
Results shows that ALiBi is comparable in performance to RPE, and is faster by 7% from RPE and 8% slower than learnable abs embeddings during training.
See plots below. ALiBi (this PR) in green, ALiBi (before fix) in pink, learned absolute embeddings in red, and RPE in cyan.
**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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} | Signed-off-by: SeanNaren <[email protected]>
# 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**:
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- [ ] 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 | Hi All,
I have taken conformer-transducer (RNNT) & conformer CTC pretrained English medium models, and finetuned them for Tamil dataset. Finetuned CTC has decent WERs, whereas RNNT models have unacceptable WERs. Both were trained for 18 epochs. The CTC finetuning has consistently exponentially decreasing Validation loss. However, validation loss of RNNT consistently increased after 5th epoch. PFA RNNT val loss snapshot.
So, in this case,
Question 1: Do you suggest me to train only for 5 epochs while using RNNT and use the saved model for decoding?
Question 2: Is there any way like any parameter to be set either in exp_manager or trainer, so that it would automatically save the check point with best WER
Question 3: Am I doing anything wrong here. Should I need to check/change anything to make RNNT validation error to decrease.
Note: We have done several experiments with Conformer-CTC and they seem to work without any issues with same setup (like no. of epochs etc). However, we are exploring the RNNT for the first time. So, not sure if something is missing.
Please suggest @titu1994 Thanks. | {
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"merged_at": "2023-01-23T08:39:08"
} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
```
1. support grapheme case options with either "upper", "lower", or "mixed".
2. automatically prepend a symbol "#" or any customized one to each grapheme of a word if both phonemes and graphemes are mixed in order to distinguish graphemes from phoneme symbols if there are overlaps in between.
3. normalize texts with "NFC" form to represent the letter with diacritics as a single letter. Similar reason as https://github.com/NVIDIA/NeMo/blob/main/nemo_text_processing/g2p/data/data_utils.py#L140.
4. add de IPA dictionary and de heteronym set.
5. fix typos.
```
**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**:
- [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 | **Unable to start training Decoder** model **using tarred dataset** obtained from script [create_tarred_dataset.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/duplex_text_normalization/data/create_tarred_dataset.py).
The error occurs on the line:
https://github.com/NVIDIA/NeMo/blob/dd921e792427e3788ad9fe3a1e568afc85a8bc93/nemo/collections/nlp/data/text_normalization/decoder_dataset.py#L537
and looks like:
`ValueError: didn't find ['pkl'] in ['__key__', 'tsv-batch-3649.pkl', 'tsv-batch-3650.pkl', 'tsv-batch-3651.pkl', 'tsv-batch-3652.pkl', ....]` (where by "...." I mean enumeration of other .pkl files).
As I understand it, it would be more correct to do the following in [decoder_dataset.py](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/text_normalization/decoder_dataset.py#L537):
1. Obtain every filename with ".pkl" extension and create `pkls` (List). After, create a string like:
```
pkls = ';'.join(pkls)
```
2. Rename as follows (using every key, separated by ";" - **supports by WebDataset**)
```
self._dataset = self._dataset.rename(pkl=pkls, key='__key__').to_tuple('pkl', 'key').map(f=self._build_sample)
```
However, this method also does not allow you to start training. | {
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} | Signed-off-by: nithinraok <[email protected]>
# What does this PR do ?
update torchmetrics to latest version
**Collection**: ASR
# Changelog
- update to latest API of torch metrics
- Timer issue with dataloader is because of wrong num of classes (when only 2 present). This behavior got updated with latest torchmetrics.
# 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 | {
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# What does this PR do ?
update torchmetrics to latest version
**Collection**: ASR
# Changelog
- update to latest API of torch metrics
- Timer issue with dataloader is because of wrong num of classes (when only 2 present). This behavior got updated with latest torchmetrics.
# 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 | {
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2. Make RETRO model compatible with MegatronLM RETRO model.
- Can turn off the RoPe positional embedding.
- Add version to RETRO model.
3. Add stride to the RETRO indexed memory map file.
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# What does this PR do ?
Setuptools 65.x is causing issues with
**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)
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- [x] Bugfix
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# What does this PR do ?
Setuptools 65.x is causing issues with
**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
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} | # What does this PR do ?
Related #5638 #5772
Renames `sentence` column to `text` before creating manifest. Exposes `ensure_ascii` for saving the transcripts + fix naming of config class.
**Collection**: ASR
# Changelog
- CommonVoice support for `convert_hf_dataset_to_nemo` script.
# Usage
* You can potentially add a usage example below
```bash
python scripts/speech_recognition/convert_hf_dataset_to_nemo.py \
output_dir=/data/commonvoice/ \
path="mozilla-foundation/common_voice_11_0" \
name="eo" \ # esperanto
split="validation" \
ensure_ascii=False \
use_auth_token=True
```
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- [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?
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- [ ] New Feature
- [ ] Bugfix
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I am trying to finetune the HifiGan vocoder using a set of my own audio data file. It is based on the FastPitch notebook in the tutorial.
https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/FastPitch_Finetuning.ipynb
Actually the first spec model of FastPitch is trained successfully. But the training of HifiGan failed. Feel a little strange because the same procedure was used on another set of data a few days ago and got a success.
**Steps/Code to reproduce bug**
Command I use to run the finetuning:
`hifigan_finetune.py --config-name=hifigan.yaml model.train_ds.dataloader_params.batch_size=32 model.max_steps=1000 model.optim.lr=0.00001 ~model.optim.sched train_dataset=./hifigan_train_ft.json validation_datasets=./hifigan_train_ft.json exp_manager.exp_dir=hifigan_ft +init_from_pretrained_model=tts_hifigan trainer.check_val_every_n_epoch=10 model/train_ds=train_ds_finetune model/validation_ds=val_ds_finetune`
Here are the first ten lines of hifigan_train_ft.json:
{"audio_filepath": "./wavs/SPEAKER_target/0_0.wav", "text": " Hi everyone, this is Kevin Chiang from Experian Datalab.", "mel_filepath": "data_mels/mel_0_0.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/0_1.wav", "text": " This is what happens when machine meets world.", "mel_filepath": "data_mels/mel_0_1.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_0.wav", "text": " It is a very wide scope,", "mel_filepath": "data_mels/mel_10_0.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_1.wav", "text": " obvious because everybody is much more familiar", "mel_filepath": "data_mels/mel_10_1.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_2.wav", "text": " with credit bureau, but it really covered a lot of areas.", "mel_filepath": "data_mels/mel_10_2.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_3.wav", "text": " So you asked about Datalab.", "mel_filepath": "data_mels/mel_10_3.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_4.wav", "text": " Datalab is actually the R&D arm of Experian.", "mel_filepath": "data_mels/mel_10_4.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_5.wav", "text": " We are founded about 10, 11 years ago.", "mel_filepath": "data_mels/mel_10_5.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_6.wav", "text": " And then starting from that point,", "mel_filepath": "data_mels/mel_10_6.npy"}
{"audio_filepath": "./wavs/SPEAKER_target/10_7.wav", "text": " we have grown into four labs globally.", "mel_filepath": "data_mels/mel_10_7.npy"}
Here is how the mel file path look like:
[
](url)
**Expected behavior**
Error in training step:
[
](url)
**Environment overview (please complete the following information)**
python 3.8.15
nemo installed with 'pip install --upgrade nemo_toolkit[all]'
**Additional context**
GPU model: Nvidia P5200
Thank you!
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} | Signed-off-by: Xuesong Yang <[email protected]>
# What does this PR do ?
This PR was cherry-picked from https://github.com/NVIDIA/NeMo/pull/5484, which attempted to merge r1.13.0. Rebased to r1.15.0.
**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
- [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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} | null | null | null | Hi All,
Does nemo has lexicon support for handling multiple pronunciations of the same word.
I understand this is supported in Riva framework as mentioned at https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tutorials/asr-python-advanced-customize-vocabulary-and-lexicon.html#customizing-pronunciation-with-lexicon-mapping . But, just wanted to know, is it also supported during normal decoding.
Hi @titu1994 , Is there any further update on https://github.com/NVIDIA/NeMo/issues/2903 ? Whether any functionality is added for offline support than Riva support? Thanks | {
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} | null | null | null | Hi All,
I wanted to adapt some specific domain text to a model trained on Generic datasets. In Kaldi, this is done by adding more domain data to LM and use it during decoding.
Is it this similar in Nemo too, like building a LM by adding domain specific text data & generic training text data together, then use this LM at the time of decoding? Or any other specific Nemo feature/ functionality that provide better support to accomplish this?
Thanks | {
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