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} | # What does this PR do ?
Add cache aware streaming model in nemo force aligner
Should be easy for ctc segmentation tool to call and get the log probs as well.
Most of the codes are copied from speech_to_text_cache_aware_streaming_infer.py.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
# Usage
* You can potentially add a usage example below
```
nemo_model="nemo_models/cache_aware_ctc.nemo"
cd NeMo/tools/nemo_forced_aligner/
python3 align.py \
batch_size=2 \
model_path=$nemo_model \
model_downsample_factor=4 \
manifest_filepath=$manifest \
output_dir=./streaming_ctm_with_pred \
align_using_pred_text=true \
simulate_cache_aware_streaming=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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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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Updates TTS tutorial to point to the IPA dictionary (renamed)
**Collection**: TTS
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- [x] Bugfix
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Make sure all the unit tests passes.
Make sure the training loss is the same as with apex. | {
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} | # What does this PR do ?
This PR is trying to get our docs to use the NVIDIA Sans font
**Collection**: Core
# 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 ?
When using spawn, the dataloader must have persistent_workers=True, otherwise validation will be very slow (all workers will be reinitialized every validation)
**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
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
the tutorial was broken due to https://github.com/NVIDIA/NeMo/pull/5982.
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# Changelog
- Add specific line by line info of high level changes in this PR.
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- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
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# Changelog
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} | null | null | null | **Describe the bug**
A fresh install of NeMo will break when using Lightning 2.0.0 because NeMo calls `from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector` but `CheckpointConnector` has been moved to `_CheckpointConnector` in 2.0.0 (see [here](https://github.com/Lightning-AI/lightning/blob/ad1c994d96c420d8c2def96354e5f7bc551afb98/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py#L45)).
**Steps/Code to reproduce bug**
A fresh install of NeMo which uses Lightning 2.0.0 by default will throw an import error.
**Additional context**
Pinning to 1.9.3 would solve this until a refactor can be done.
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} | null | null | null | [/usr/local/lib/python3.9/dist-packages/nemo/collections/asr/parts/preprocessing/features.py](https://localhost:8080/#) in forward(self, x, seq_len, linear_spec)
407 # do preemphasis
408 if self.preemph is not None:
--> 409 x = torch.cat((x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]), dim=1)
410
411 # disable autocast to get full range of stft values
IndexError: index 0 is out of bounds for dimension 1 with size 0
Getting this issue while executiong the chunks | {
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} | null | null | null | **Describe the bug**
Hi, thanks for providing such a helpful toolkit. I'm currently following the tutorial from [Nvidia-examples](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_with_NeMo.ipynb#scrollTo=3FT0klSV268p), and applying my own Mandarin Chinese corpus. Since our dataset is Mandarin Chinese, the only different setting is the 'labels' in the configuration files. Thus, I directly change the labels through the command below:
`params['model']['train_ds']['labels'] = [' ']+list(unique_char)+params['labels']`
`params['labels'] = [' ']+list(unique_char)+params['labels']`
where `unique_char` is a set that includes all the unique characters in our train_manifest.
p.s. I preprocess all the texts of each audio to a single character form as below:
`{audio_filepath: example.wav, duration: 15, text: '你 好 嗎'}`
Hence, aggregating all the text information from train_manifest to form `unique_char` should include all the characters.
However, I still met similar error messages while doing trainer.fit() as below:
```
File "/opt/conda/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/metrics/wer.py", line 1074, in decode_ids_to_tokens
token_list = [self.labels_map[c] for c in tokens if c != self.blank_id]
File "/opt/conda/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/metrics/wer.py", line 1074, in <listcomp>
token_list = [self.labels_map[c] for c in tokens if c != self.blank_id]
KeyError: 1669
```
BTW, it would run several iterations in Epoch 0 to pop out this error message.
Is there anyone facing similar questions that can help me fix the problem?
Appreciate your help and time, thank you! | {
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} | # What does this PR do ?
the tutorial was broken due to https://github.com/NVIDIA/NeMo/pull/5982.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/6050
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} | # What does this PR do ?
fixed code scanning alert: https://github.com/NVIDIA/NeMo/security/code-scanning/885
**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) https://github.com/NVIDIA/NeMo/issues/5936
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fixed code scanning alert https://github.com/NVIDIA/NeMo/security/code-scanning/905
**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) https://github.com/NVIDIA/NeMo/issues/5935
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"merged_at": "2023-03-15T18:00:40"
} | # What does this PR do ?
fixed code scanning alert https://github.com/NVIDIA/NeMo/security/code-scanning/836
**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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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
fixed code scanning alert https://github.com/NVIDIA/NeMo/security/code-scanning/774
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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} | null | null | null | Hello there!
I have create a diarization pipeline and I wanted to implement it into a queue messages system. I am using Pika with RabbitMQ and multiprocessing package. I initialize multiple consumer processes and wait for messages to come into the diarizationInputQueue, diarize the input recordings and put the resulting json in the diarizationOutputQueue. I try to perform parallel diarizations using an extended class from `multiprocessing.Process`
The problem is that whenever I run multiple diarization processes (I run 2 consumers/processes), a job (input recording from the input queue) is consumed multiple times if there is an free process/consumer, not just once. This results in either overriding the other running process or resulting in a connection error, at some point ( the connection is not the real problem).
As it can be seen in this picture, at '07:17,225 job-id 10' is loaded into a consumer. When the other consumer becomes free, it loads 'job-id 10' at 09:17:667 again.
Another example is when I simply load the models and let each process sleep for a certain amount of time. In this case, after 'job-id:91' sleeps for 201 seconds, before the message is sent to the output queue, another 'job-id:91' process is initiated and pika throws an error.
```
2023-03-15 00:31:00,364 root INFO Message from queue: {'diarization-task': {'job-id': '91', 'diarization-id': '12345', 'file-path': '/home/gpirlogeanu/nemo_rttms/romana_wav/tel6.wav', 'num-speakers': None, 'sleep-time': 201}}
2023-03-15 00:32:24,503 root INFO Diarization ID: 12345 with jobID: 91 has been successfully postprocessed!
2023-03-15 00:32:24,504 pika.adapters.utils.io_services_utils ERROR _AsyncBaseTransport._produce() failed, aborting connection: error=ConnectionResetError(104, 'Connection reset by peer'); sock=<socket.socket fd=15, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('172.18.0.4', 58582)>; Caller's stack:
```
The system works fine when I am using a single consumer, so it is not an error in the scripts. What I have read is:
``
Pika does not have any notion of threading in the code. If you want to use Pika with threading, make sure you have a Pika connection per thread, created in that thread. It is not safe to share one Pika connection across threads, with one exception: you may call the connection method add_callback_threadsafe from another thread to schedule a callback within an active pika connection.
``
NeMo initializes multiple threads per process and I think it conflicts with the pika library. Did any of you use a similar system or have any idea how I could make it NeMo work with Pika? Can NeMo run without multithreading? If I don't use NeMo, an input message is loaded just once in the current configuration. (I have tested it with sleep commands)
As you can see, a process runs on multiple threads:
My code: (I acknowledge messages "manually" after diarization is done)
```
def run(self):
logging.info('Starting process to consume from Rabbit MQ...')
while True:
try:
self.channel_receive.basic_consume(self.configFile['QUEUE_RECEIVE'], on_message_callback=self.callback)
self.channel_receive.start_consuming()
except Exception as e:
logging.error(
"There seem to be connection issues! Error: %s", e)
logging.error("Trying to establish a new connection...")
self.createConnectionAndChannels()
logging.info("Connection recovered!")
continue
except KeyboardInterrupt:
logging.info("You manually stopped the app!")
break
time.sleep(5)
```
```
# Send the ACK for the finished task above
try:
if self.checkConnectionAndChannels() is False:
self.createConnectionAndChannels()
self.channel_receive.basic_ack(delivery_tag=method.delivery_tag)
logging.info("Sending ACK after successully completed task for Diarization ID: " + diarizationID + "with jobID: " +
jobID)
except Exception as e:
logging.error("Error while trying to send positive ack for Diarization ID:" +
diarizationID + " with jobID: " + jobID + " " + "Error: %s", e)
self.channel_receive.basic_nack(delivery_tag=method.delivery_tag)
logging.info("#######################################################")
logging.info('\n')
```
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- Fixed the bucketing bug by making the bucketing dataloaders to loop over the samples indefinitely.
**PR Type**:
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Add `model.data.shuffle_documents` flag which allows disabling document shuffling for GPT dataset
**Collection**: NLP
# Changelog
-Add `model.data.shuffle_documents` flag which allows disabling document shuffling for GPT dataset
# Usage
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# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
removes functionality to insert virtual tokens arbitrarily which makes code complex and has no performance benefit. This PR removes this ability and simple adds virtual tokens in the start.
Makes P-tuning for RETRO models easier.
Removes taskname_ids from dataset creation.
**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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} | removed comma in the end of the list to prevent error paring this file as jupiter notebook.
# 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.
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Address codeQl alert: https://github.com/NVIDIA/NeMo/security/code-scanning/772
**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)
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- [ ] 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): https://github.com/NVIDIA/NeMo/issues/5933
* Related to PR: https://github.com/NVIDIA/NeMo/pull/6177 | {
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} | # What does this PR do ?
To support buffered chunked streaming inference for nemo neural force aligner.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- add an argument (keep_logits default is False) for transcribe function in `FrameBatchASR` to keep the inference logits.
- return the logits(log probs) if keep_logits is set to True.
# Usage
```python
python align.py \
pretrained_name="stt_en_citrinet_1024_gamma_0_25" \
batch_size=32 \
model_downsample_factor=8 \
manifest_filepath=$manifest \
use_buffered_chunked_streaming=true \
output_dir=./buffered_streaming_test_pred \
total_buffer_in_secs=10 \
align_using_pred_text=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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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 session and segment augmentation, add logging of params and metadata of each session, fix implicit bug in RIR perturbation.
**Collection**: [ASR]
# Changelog
- Add audio augmentation on whole session and each speech segment
- Add logging generator params to output dir.
- Add logging session metadata
- Fix implicit bugs in RIR perturb, allowing individual control on foreground and background noise
# 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
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} | # What does this PR do ?
As discussed in https://github.com/NVIDIA/apex/pull/1611, we are experiencing excessive GPU idling when training GPT with the distributed optimizer and interleaved pipeline parallelism. That PR addresses the issue by making sure all distopt communication is aligned across the pipeline-parallel group, and this PR adds NeMo support to enable those perf optimizations.
**Collection**: NLP
# Changelog
- Add functions for interleaved pipeline parallel schedule to trigger async distopt communication
# Usage
Set the optimizer to `distributed_fused_adam` in the config file:
https://github.com/NVIDIA/NeMo/blob/3d72e81327f9080a50a84e0a78589f8d5059e66e/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L204
This optimization is only relevant when training GPT or BERT with virtual pipeline stages.
# 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**:
- [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.
Pinging @erhoo82.
# Additional Information
* Depends on https://github.com/NVIDIA/apex/pull/1611 | {
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} | # What does this PR do ?
Previously only training and validation examples longer than the max encoder length were filtered out. This PR makes it possible to filter out by a specified length. This will save training time, as larger batch sizes will be possible after removing a small % of training and validation examples.
**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 ?
Enables testing functionality and performance of training code using a synthetic version of existing datasets.
**Collection**: NLP (language_modeling)
# Changelog
- Added SyntheticGPTDataset which produces the same size and datatypes of output as GPTDataset
- Added SyntheticT5Dataset which produces the same size and datatypes of output as T5Dataset
# Usage
```bash
HYDRA_FULL_ERROR=1 python NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
model.data.data_impl=synthetic \
model.data.data_prefix=[]
HYDRA_FULL_ERROR=1 python NeMo/examples/nlp/language_modeling/megatron_t5_pretraining.py \
model.data.data_impl=synthetic \
model.data.data_prefix=[]
```
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
Not able to convert the nemo model : stt_en_conformer_ctc_large.nemo to riva using nemo2riva
Model EncDecCTCModelBPE was successfully restored from /data/atc_tenant/asr/lsfadmin/temp/stt_en_conformer_ctc_large.nemo.
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-scr-exported-encdecclsmodel.yaml for nemo.collections.asr.models.classification_models.EncDecClassificationModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdecctcmodel.yaml for nemo.collections.asr.models.EncDecCTCModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdectcmodelbpe.yaml for nemo.collections.asr.models.EncDecCTCModelBPE
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-isc-exported-bert.yaml for nemo.collections.nlp.models.IntentSlotClassificationModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-mt-exported-encdecmtmodel.yaml for nemo.collections.nlp.models.MTEncDecModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-pc-exported-bert.yaml for nemo.collections.nlp.models.PunctuationCapitalizationModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-qa-exported-bert.yaml for nemo.collections.nlp.models.QAModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-tc-exported-bert.yaml for nemo.collections.nlp.models.TextClassificationModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/nlp-tkc-exported-bert.yaml for nemo.collections.nlp.models.TokenClassificationModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-fastpitchmodel.yaml for nemo.collections.tts.models.FastPitchModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-hifiganmodel.yaml for nemo.collections.tts.models.HifiGanModel
[NeMo I 2023-03-13 13:21:57 schema:154] Loaded schema file /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/tts-exported-radttsmodel.yaml for nemo.collections.tts.models.RadTTSModel
[NeMo I 2023-03-13 13:21:57 schema:193] Found validation schema for nemo.collections.asr.models.EncDecCTCModelBPE at /usr/local/lib/python3.8/dist-packages/nemo2riva/validation_schemas/asr-stt-exported-encdectcmodelbpe.yaml
[NeMo I 2023-03-13 13:21:57 schema:222] Checking installed NeMo version ... 1.17.0rc0 OK (>=1.1)
[NeMo I 2023-03-13 13:21:57 artifacts:65] Found model at ./model_weights.ckpt
[NeMo E 2023-03-13 13:21:57 artifacts:112] nemo2riva's get_artifacts() was called but was unable to continue due to missing modules. Please ensure that nemo_toolkit and it's dependencies are all installed before re-running nemo2riva.
Traceback (most recent call last):
File "/usr/local/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/cli/nemo2riva.py", line 55, in nemo2riva
Nemo2Riva(args)
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/convert.py", line 85, in Nemo2Riva
artifacts, manifest = get_artifacts(restore_path=nemo_in, model=model, passphrase=key, **patch_kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo2riva/artifacts.py", line 117, in get_artifacts
if isinstance(_HAVE_PATCHES_ERROR_MSG, exception):
TypeError: isinstance() arg 2 must be a type or tuple of types
**Steps/Code to reproduce bug**
nemo2riva --out stt_en_conformer_ctc_large.riva stt_en_conformer_ctc_large.nemo
The nemo model is the model that is available on ngc.
**Expected behavior**
converted .riva model
**Environment overview (please complete the following information)**
- Environment location: Docker deployed on a GPU machine
- Method of NeMo install: git clone https://github.com/NVIDIA/NeMo && cd NeMo && ./reinstall.sh
- If method of install is [Docker] Please refer to the docker file below
FROM nvcr.io/nvidia/riva/riva-speech:2.7.0-servicemaker
RUN apt-get update && apt install -y --reinstall python3-pip
RUN mkdir \
/.local /.cache /data /nltk_data /.jupyter \
&& chmod -R 777 \
/data /.local /.jupyter /.cache /nltk_data
WORKDIR /
RUN git clone https://github.com/NVIDIA/NeMo && cd NeMo && ./reinstall.sh
RUN pip install torchvision jupyter
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 20.04.4
- PyTorch version torch==1.13.1
- Python version 3.8.10
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do ?
Add spellchecking ASR customization model and scripts for data preparation and evaluation.
**Collection**: [Note which collection this PR will affect]
nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
See examples/nlp/spellchecking_asr_customization/README.md
```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
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 these issues
#5933 https://github.com/NVIDIA/NeMo/issues/5933
#5937 https://github.com/NVIDIA/NeMo/issues/5937
**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 ?
update import paths in TTS tutorials.
**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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* Related to # (issue)
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# What does this PR do ?
Simillar #6001 PR.
I fix missing import statement in a non-CUDA environment for 'r1.16.0' branch.
**Collection**: ASR
# Changelog
- Add import gc statement for non-CUDA environment
# 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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* Related to # (issue)
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} | # What does this PR do ?
This PR adds a few enhancements to RIR generation scripts.
**Collection**: Tools
# Changelog
- Fix: reduce room size if evaluation of params fails
- Added: randomized mic placement
- Added: diffuse noise generation
- Added: an option to specify the format and subtype for saved audio
# 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 # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Allows users to pass in a grapheme + phoneme list/set as an enforced symbol vocabulary when using the IPA classes. Other symbols, such as punctuation, OOV, etc. are still handled by other tokenizer arguments.
Once set, G2P dictionary entries will be filtered in the following way:
- Words with any illegal graphemes are removed entirely.
- Words with one unique pronunciation and any illegal phonemes are removed entirely.
- If a word has multiple pronunciations and all of them have at least one illegal phoneme, the word is removed entirely.
- If at least one pronunciation is valid, it is kept if `keep_alternate=True` (default), otherwise the word is removed entirely.
The `TTSDataset` will also filter out all manifest entries whose normalized forms have any illegal graphemes.
Note: Users should take care that the passed-in vocab is consistent with `grapheme_prefix` and `grapheme_case`, or else filtering may not work properly.
**Collection**: TTS
# Changelog
- Added a `replace_symbols()` function to `IPAG2P` to enforce user-set vocab and filter existing G2P dict
- Modified `IPATokenizer` to support taking in a user-set vocab
- Modified `TTSDataset` to check for illegal graphemes if a fixed vocab is used
- Added unit tests for `replace_symbols()`
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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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 ?
Refactor hyperparameter recommendation and add defaults for epochs, num virtual tokens and lr
**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 ?
adds answer-field to metric.
**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**:
- [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?
Implement Grid- and Compose-Transducer along with W-Transducer loss according to the paper "Powerful and Extensible WFST Framework for Rnn-Transducer Losses" https://ieeexplore.ieee.org/document/10096679 (https://arxiv.org/abs/2303.10384)
**Collection**: [ASR]
# Changelog
- `GraphTransducerLossBase` abstract class with the interface for Graph-based loses
- RNN-T implementation in `GraphRnntLoss` with tests
- W-Transducer implementation in `GraphWTransducerLoss` with tests
- add `GraphRnntLoss` + `GraphWTransducerLoss` to RNN-T loss resolver
# Usage
```shell
# training some model with Graph-RNNT loss
python examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py \
<other params> \
model.loss.loss_name=graph_rnnt \
++model.loss.graph_rnnt_kwargs.double_scores=true
# training some model with W-Transducer loss
python examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py \
<other params> \
model.loss.loss_name=graph_w_transducer \
++model.loss.graph_w_transducer_kwargs.eps_weight=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)
- [ ] 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**:
- [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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"merged_at": "2023-03-20T19:42:23"
} | # 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]
1. Added support in a custom token sampling function to `MegatronLMEncoderDecoderModel.decode`.
2. Added beam search sampling methods.
# 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**:
- [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 [merged PR](https://github.com/NVIDIA/NeMo/pull/6095)
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} | # What does this PR do ?
Add ClearML logging of samples produced in validation step
**Collection**: [TTS]
# Changelog
- Add `tensor_to_wav` and `mel_to_audio` to `nemo.collections.tts.parts.utils.helpers`
- Add ClearML logging of samples in:
- `nemo.collections.tts.models.univnet`
- `nemo.collections.tts.models.hifigan`
- `nemo.collections.tts.models.vits`
- `nemo.collections.tts.models.fastpitch_ssl`
- `nemo.collections.tts.models.fastpitch`
- `nemo.collections.tts.models.mixer_tts`
- `nemo.collections.tts.models.aligner`
# Usage
Auto-usage than ClearML logging enabled in cfg.
# 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)
- [x] 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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Adds documentation for Exportable and ExpManager.
**Collection**: [Core, Docs]
# Changelog
- Fix exp manager subconfig docs
- Add exp manager loggers
- Add exp manager EMA docs
- Update Exportable docs and function docs
# 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?
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- [ ] Bugfix
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} | # What does this PR do ?
I've been experiencing a hang in the first validation step when running GPT with interleaved pipeline parallelism. This first shows up in https://github.com/NVIDIA/NeMo/pull/6049, so it seems that accessing the data loader in middle pipeline stages is the root cause.
**Collection**: NLP
# Changelog
- Avoid unnecessarily accessing data loader with pipeline parallelism
# Usage
No change in 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?
- [ ] 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/6049
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```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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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?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | Add preemption functionality in preemption_callback.py under utils
Refactor the code to move NemoModelCheckpoint callback under callbacks
# What does this PR do ?
Adding basic functionality for cluster preemption.
**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
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Merges bugfixes and doc updates from the release branch into main.
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**Collection**: [Note which collection this PR will affect]
# Changelog
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} | 1. Fixed the bug in the script to convert Megatron LM ckpt to NeMo.
2. Fixed a bug in the GPT PP that we should only add output layer for the last PP rank.
3. Overwrite the precision in the eval script and fix the web server. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6158 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6158/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6158/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6158/events | https://github.com/NVIDIA/NeMo/issues/6158 | 1,616,873,694 | I_kwDOC_bI7s5gX4je | 6,158 | [Bug] Cannot import C++ compiled "helpers" | {
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} | null | null | null | I had run the the megatron_gpt_pretraining.py file completely success yesterday, by using NeMo v1.11.0 and apex nmv1.11.0, but about 10 hours ago I changed some config to export to .nemo on exp argument, then I met this error.
```output
Traceback (most recent call last):
File "/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py", line 420, in _build_index_mappings
from nemo.collections.nlp.data.language_modeling.megatron import helpers
ImportError: cannot import name 'helpers' from 'nemo.collections.nlp.data.language_modeling.megatron' (/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/__init__.py)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py", line 89, in main
trainer.fit(model)
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/trainer.py", line 768, in fit
self._call_and_handle_interrupt(
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/trainer.py", line 719, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/trainer.py", line 809, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/trainer.py", line 1172, in _run
self._call_setup_hook() # allow user to setup lightning_module in accelerator environment
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/trainer.py", line 1492, in _call_setup_hook
self._call_lightning_module_hook("setup", stage=fn)
File "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/trainer.py", line 1593, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/root/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 601, in setup
self.build_train_valid_test_datasets()
File "/root/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 516, in build_train_valid_test_datasets
self._train_ds, self._validation_ds, self._test_ds = build_train_valid_test_datasets(
File "/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py", line 81, in build_train_valid_test_datasets
train_ds, valid_ds, test_ds = _build_train_valid_test_datasets(
File "/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py", line 168, in _build_train_valid_test_datasets
train_dataset = build_dataset(0, 'train')
File "/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py", line 154, in build_dataset
dataset = GPTDataset(
File "/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py", line 220, in __init__
self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
File "/root/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py", line 422, in _build_index_mappings
raise ImportError(
ImportError: Could not compile megatron dataset C++ helper functions and therefore cannot import helpers python file.
```
Note that I run this file on Google Colab with GPU, with Cuda 11.8 (try to downgrade to 11.7 but not success, but still successful for first training), tokenizer with SentencePiece, and guarantee that the code does not change at the second time training.
Moreover, the [Migrating from Megatron-LM](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/mlm_migration.html) especially file megatron_lm_ckpt_to_nemo.py the does not run successfully. After training complete, I want change file from .ckpt to .nemo, i met the error "Default process group has not been initialized, please make sure to call init_process_group"
This is my code
```python
!python /root/NeMo/examples/nlp/language_modeling/megatron_lm_ckpt_to_nemo.py \
--checkpoint_folder './NeMo-Deploying/nemo_experiments/megatron_gpt/checkpoints' \
--checkpoint_name megatron_gpt--val_loss=4.78-step=300-consumed_samples=57600.0 \
--nemo_file_path './NeMo-Deploying/GPT-Pretrain/megatron_gpt_122m_bs1.nemo' \
--model_type gpt \
--tensor_model_parallel_size 1
```
Can you check my above piece of code then it had any problem to this error? | {
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} | # What does this PR do ?
Add scheduler for univnet model.
**Collection**: [TTS]
# Changelog
- Add `get_warmup_steps` into `UnivNetModel`
- Change `configure_optimizers` in `UnivNetModel` for scheduler support
# Usage
Standart usage of scheduler
# 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 ?
Fixes for tests and docs
**Collection**: [Docs, ASR]
# Changelog
- Fix dark mode in NeMo docs
- Fix issue with flaky RNNT test where the seq length can be off by one due to minute perturbation during beam search
# 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
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} | # What does this PR do ?
Add optional configuration to AudioToMelSpectrogramPreprocessor to allow disabling area normalization in the librosa mel filter.
Applying area normalization to the mel spectrogram reduces its range of values from approximately [0, 100] to [0, 1], which is detrimental when you take the log of the [0, 1] values producing a poor distribution of negative features.
The intention is to experiment with disabling the area normalization and replacing the current mel spectrogram used in TTS which is:
> log(max(1E-5, area_norm(mel))
With a more accurate representation like:
> log(1.0 + mel)
This will also enable us to compute energy features using the mel spectrogram instead of the linear spectrogram.
**Collection**: [TTS], [ASR]
# Changelog
- Add an optional parameter to AudioToMelSpectrogramPreprocessor to configure the mel filter normalization, defaulting to the librosa default of 'slaney'
# Usage
.yaml config:
> preprocessor:
> _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
> log: true
> log_zero_guard_type: add
> log_zero_guard_value: 1.0
> mag_power: 1.0
> mel_norm: null
# 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
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} | # What does this PR do ?
Makes prompt learning compatible when sp tokenizer has legacy = False.
**Collection**: [NLP]
# Changelog
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] 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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When enabling `rope` position_embedding_type and `sequence_parallel` in NeMo GPT pretraining, the training will give runtime error:
```
File "/workspace/NeMo/nemo/collections/nlp/modules/common/megatron/rotary_pos_embedding.py", line 59, in apply_rotary_pos_emb
t = (t * freqs.cos()) + (_rotate_half(t) * freqs.sin())
RuntimeError: The size of tensor a (2048) must match the size of tensor b (1024) at non-singleton dimension 0
```
**Steps/Code to reproduce bug**
Follow the [tutorial](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/gpt/gpt_training.html) to train GPT model with `model.position_embedding_type=rope` and `model.sequence_parallel=True`
**Expected behavior**
Expect there will be no runtime error when both options are enabled.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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**PR Type**:
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# Changelog
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* You can potentially add a usage example below
```python
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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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## 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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# Changelog
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# Usage
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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# Before your PR is "Ready for review"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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Update dataset preparation tool to fix bug relating to non jsonl input file
**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
- [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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} | null | null | null | **Describe the bug**
If need to resume checkpoints from `exp_manager.explicit_log_dir`, NeMo exp_manager will check the folder for checkpoints with `end.ckpt` or `last.ckpt` suffix. However, sometimes if one of the data_parallel_rank 0 failed to save the checkpoint or there are multiple `last.ckpt` in the same folder, the workers will fail to load the correct checkpoints.
**Expected behavior**
In exp_manager, only the global rank 0 will check which last ckpt to load and then broadcast the ckpt path to other workers.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do ?
Adding important main PRs to 1.16 branch
**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**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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```
import nemo.collections.asr as nemo_asr
conformer = nemo_asr.models.ASRModel.restore_from('/home/Conformer-CTC-L_spe128_ar-AR_2.0.nemo')
```
but an error arose with the message ' ReadError: not a gzip file '.
you can find the model on
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/models/speechtotext_ar_ar_conformer/files?version=trainable_v2.0
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} | null | null | null | I have a question on how we can load the GPT model checkpoints trained with virtual staging and perform generation? With virtual staging in pipeline parallelism, each rank stores a list of modules. But I cannot find relevant code for dealing with such case when performing generation. It seems to me NeMo does not support inference when the pretrained model is trained with virtual staging. | {
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} | # What does this PR do ?
Added ckpt to nemo for T5/T0 models
Can use ```model_type=[t5/gpt]``` to declare the model type in the config
**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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} | # What does this PR do ?
Fixes character decoding with CTC beam search framework
**Collection**: [ASR]
# Changelog
- Add index vocab and vocab index maps for reverse decoding in character based models
- Update nemo and pyctcdecode inference
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
Fixes #6127
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} | # What does this PR do ?
Adds the following metrics for GPT/T5 prompt learning:
- Accuracy -> exact string match
- BLEU score
- ROUGE-1, 2, 3, L
**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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} | # What does this PR do ?
Enhancer is based on StyleGAN 2, which features a randomly-initialized "initial" tensor. Zeroing this tensor out helps a bit in a downstream ASR task (reported by @artbataev)
This PR adds this option and makes it the default behavior.
**Collection**: TTS
# Changelog
- Add a `start_from_zero` option to enhancer
# 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.
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* Related to # (issue)
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Fix minor typos in `README.rst`. | {
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} | # What does this PR do ?
Enhancer is based on StyleGAN 2, which features a randomly-initialized "initial" tensor. Zeroing this tensor out helps a bit in a downstream ASR task (reported by @artbataev)
This PR adds this option and makes it the default behavior.
**Collection**: TTS
# Changelog
- Add a `start_from_zero` option to enhancer
# 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 | I try using NeMo to train a GPT-like model from scratch with large `.arrow` files for which I have a `LightningDataModule`. Are there any resources on how to integrate such data set into NeMo? I found the `model.setup_training_data` but am unsure how to properly use it. Any advice would be helpful.
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https://api.github.com/repos/NVIDIA/NeMo/issues/6133 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6133/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6133/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6133/events | https://github.com/NVIDIA/NeMo/issues/6133 | 1,610,401,421 | I_kwDOC_bI7s5f_MaN | 6,133 | [DIARIZATION] is clustering needed when number of speakers is known? | {
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I'm using the diarization model and already know the number of speakers. I thought that the clustering algorithm was only needed to determine the number of speakers, but it seems that it is still being run if when I use the following settings:
```
meta = {
'audio_filepath': an4_audio,
'offset': 0,
'duration': None,
'label': 'infer',
'text': '-',
'num_speakers': 2,
'rttm_filepath': None,
'uem_filepath': None
}
```
and
`config.diarizer.clustering.parameters.oracle_num_speakers = True`
Is that the expected behavior? My main issue is that while the other parts of the model can be run on the GPU, the clustering is still run on the CPU so the overall inference is quite long. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/6132 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6132/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6132/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6132/events | https://github.com/NVIDIA/NeMo/pull/6132 | 1,609,553,681 | PR_kwDOC_bI7s5LRj_4 | 6,132 | [ASR] Support audio_filepath as a list of single-channel files | {
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} | # What does this PR do ?
Currently, multichannel audio can be used in `audio_filepath` by providing a path to a multichannel file.
This PR adds support for `audio_path` to specify multichannel audio by providing a list of paths to single-channel files.
**Collection**: ASR
# Changelog
- `AudioSegment.from_file` can load multichannel audio from a list of paths to single-channel audio files
- `manifest.get_full_path` supports lists as input and can resolve path to each path in a list
- Added unit tests for `AudioSegment`
- `TestAudioSegment:: test_audio_segment_from_file` for existing functionality, loading a single file
- `TestAudioSegment:: test_audio_segment_multichannel_with_list ` for new functionality, loading multiple single-channel files
- Added unit tests for `manifest.get_full_path`
- `TestPreprocessingUtils:: test_get_full_path_local` when resolving local paths, both single paths and lists
- `TestPreprocessingUtils:: test_get_full_path_ais` when resolving paths pointing to AIStore, both single paths and lists
# Usage
## Example scripts
- [examples.zip](https://github.com/NVIDIA/NeMo/files/10887310/examples.zip)
## Description
Currently, ASR models can be used to transcribe multichannel audio by providing a path to a multichannel audio file in `audio_filepath` and selecting a single channel using `channel_selector`.
This PR adds an option to set `audio_filepath` to a list of paths to individual channels, each containing a single channel. This is useful in situations where multichannel audio is provided as individual channels.
Two examples with `audio_filepath` containing a list of paths to single-channel files are provided below.
The first example is directly using `transcribe()` od an `ASRModel`. The second example is running `speech_to_text_eval.py`.
### Example 1
This example will check if `asr_model.transcribe` is working the same when a path to multichannel audio is provided as
- a path to a multichannel audio file (this is currently supported)
- a list of paths to single-channel audio files (added in this PR)
In both cases, channel_selector can be used to pick one channel from the input multichannel audio.
The test will run with two different models: `stt_en_conformer_ctc_small` and `stt_en_conformer_transducer_small` and each of the 3 available channels.
To run the test:
```
python test_asr_model_transcribe.py
```
### Example 2
This example will check if `speech_to_text_eval.py` is working the same when a path to multichannel audio is provided as
- a path to a multichannel audio file (this is currently supported)
- a list of paths to single-channel audio files (added in this PR)
In both cases, channel_selector can be used to pick one channel from the input multichannel audio.
The test will run with two different models: `stt_en_conformer_ctc_small` and `stt_en_conformer_transducer_small` and each of the 3 available channels.
To run the test:
```
python test_speech_to_text_eval.py --nemo-dir PATH_TO_NEMO_DIR
```
# 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.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
NeMo currently executes load_checkpoint independently on all ranks, causing unnecessarily high filesystem reading. In contrast,
save_checkpoint functionality for nlp models is only executed on rank 0 / data_parallel_rank == 0: https://github.com/NVIDIA/NeMo/blob/ac50e5963ca4e23b7fd758f0732f6ed51200cc1d/nemo/collections/nlp/parts/nlp_overrides.py#L164-L171
**Steps/Code to reproduce bug**
See
https://github.com/NVIDIA/NeMo/blob/ac50e5963ca4e23b7fd758f0732f6ed51200cc1d/nemo/collections/nlp/parts/nlp_overrides.py#L195-L200
**Expected behavior**
Checkpoint is only read from filesystem on rank 0 / data_parallel_rank == 0 and broadcasted to all other ranks. | {
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} | Download NGC pre-trained model checkpoints and run model inference for all models supported in `self.list_available_models()`.
* fixed pretrained model names that are not following naming convention:
* `tts_hifigan` --> `tts_en_hifigan`
* `tts_waveglow_88m` --> `tts_en_waveglow_88m`
* created `--nightly` option and `nightly` marker for pytest. Without `--nightly` option, all tests here will be skipped. We are setting it up this way on purpose to save running time from our CI, and it provides an option for QA.
* added test for fastpitch.
* added test for hifigan.
* added test for mixtts.
* added test for Tacotron2.
* added test for VITS.
* added test for UnivNet.
* added test for waveglow.
* remove old waveglow model that relies on torch_stft.
* added test for aligner.
related PR: https://github.com/NVIDIA/NeMo/pull/6128
```bash
(nemo) xueyang@DEV:~/workspace/NeMo:(add-tests-model-inference-tts)$ pytest tests/collections/tts/models/
A valid `test_data.tar.gz` test archive (10445891B) found in the `/home/xueyang/workspace/NeMo/tests/.data` folder.
Setting numba compat : True
================================================================================================ test session starts =================================================================================================
platform linux -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0 -- /home/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /home/xueyang/workspace/NeMo, configfile: pyproject.toml
plugins: hydra-core-1.2.0, anyio-3.6.1
collected 26 items
tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests ...) [ 3%]
tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These te...) [ 7%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests are...) [ 11%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests...) [ 15%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, Th...) [ 19%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "n...) [ 23%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "n...) [ 26%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, ...) [ 30%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, Th...) [ 34%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These ...) [ 38%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests are mos...) [ 42%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, Thes...) [ 46%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, The...) [ 50%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently...) [ 53%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "night...) [ 57%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "night...) [ 61%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly"...) [ 65%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Curr...) [ 69%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These test...) [ 73%]
tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests ar...) [ 76%]
tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests a...) [ 80%]
tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests are...) [ 84%]
tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests are ...) [ 88%]
tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These test...) [ 92%]
tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests are mostly...) [ 96%]
tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m] SKIPPED (To run this test, pass --nightly option. It will run any tests marked with "nightly". Currently, These tests a...) [100%]
================================================================================================= slowest durations ==================================================================================================
(52 durations < 0.005s hidden. Use -vv to show these durations.)
================================================================================================ 26 skipped in 0.18s =================================================================================================
(nemo) xueyang@DEV:~/workspace/NeMo:(add-tests-model-inference-tts)$ pytest --nightly tests/collections/tts/models/
A valid `test_data.tar.gz` test archive (10445891B) found in the `/home/xueyang/workspace/NeMo/tests/.data` folder.
Setting numba compat : True
================================================================================================ test session starts =================================================================================================
platform linux -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0 -- /home/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /home/xueyang/workspace/NeMo, configfile: pyproject.toml
plugins: hydra-core-1.2.0, anyio-3.6.1
collected 26 items
tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner] PASSED [ 3%]
tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa] PASSED [ 7%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch] PASSED [ 11%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa] PASSED [ 15%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker] PASSED [ 19%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102] PASSED [ 23%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210] PASSED [ 26%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5] PASSED [ 30%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker] PASSED [ 34%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech] PASSED [ 38%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan] PASSED [ 42%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts] PASSED [ 46%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx] PASSED [ 50%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch] PASSED [ 53%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102] PASSED [ 57%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210] PASSED [ 61%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5] PASSED [ 65%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker] PASSED [ 69%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech] PASSED [ 73%]
tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts] ^@PASSED [ 76%]
tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx] PASSED [ 80%]
tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2] PASSED [ 84%]
tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet] PASSED [ 88%]
tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet] PASSED [ 92%]
tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits] PASSED [ 96%]
tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m] PASSED [100%]
================================================================================================= slowest durations ==================================================================================================
26.45s setup tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx]
25.30s setup tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2]
24.82s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa]
24.43s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch]
24.41s setup tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits]
24.40s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker]
23.52s setup tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner]
23.17s setup tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts]
22.34s setup tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa]
20.70s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5]
19.61s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210]
16.09s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102]
2.35s setup tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m]
2.34s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker]
2.33s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan]
2.29s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx]
2.27s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts]
1.78s call tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx]
1.78s call tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner]
1.53s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech]
1.40s setup tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet]
0.96s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102]
0.96s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210]
0.95s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech]
0.95s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5]
0.95s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker]
0.95s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch]
0.90s setup tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet]
0.49s call tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits]
0.48s call tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2]
0.45s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech]
0.28s call tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m]
0.07s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5]
0.07s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker]
0.05s call tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts]
0.05s call tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech]
0.02s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker]
0.02s call tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet]
0.01s call tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet]
(26 durations < 0.005s hidden. Use -vv to show these durations.)
=========================================================================================== 26 passed in 302.54s (0:05:02) ===========================================================================================
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/6128 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/6128/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/6128/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/6128/events | https://github.com/NVIDIA/NeMo/pull/6128 | 1,606,059,885 | PR_kwDOC_bI7s5LFuiA | 6,128 | [TTS] remove old waveglow model that relies on torch_stft. | {
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"name": "TTS",
"color": "49AF28",
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] | closed | false | null | [] | null | null | 2023-03-02T03:50:25 | 2023-03-06T20:21:12 | 2023-03-06T20:21:10 | COLLABORATOR | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/6128",
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/6128.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/6128.patch",
"merged_at": "2023-03-06T20:21:10"
} | # What does this PR do ?
TTS collections currently have two waveglow models released in NGC model cards, however, only the lastest one (`tts_waveglow_88m`) works with the lastest code base without needs of `torch_stft`. The old one (`tts_waveglow_268m`) always pops up error below,
```
E RuntimeError: Error(s) in loading state_dict for WaveGlowModel:
E Missing key(s) in state_dict: "audio_to_melspec_precessor.window".
E Unexpected key(s) in state_dict: "audio_to_melspec_precessor.stft.forward_basis", "audio_to_melspec_precessor.stft.inverse_basis".
```
**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?
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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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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue) https://github.com/NVIDIA/NeMo/issues/4015
* Related to PR: https://github.com/NVIDIA/NeMo/pull/3993
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} | null | null | null | **Describe the bug**
I trained QuartzNet acoustic model + KenLM 2-ngram model, now I run eval_beamsearch_ngram.py to evaluate it.
It seems like beam_search_func in BeamSearchDecoderWithLM outputs tuples (confidence, text) (the doc agrees), so cand.y_sequence is a resulting string.
but later on the function pack_hypothesis in ctc_beam_decoding does "cand.y_sequence = torch.tensor(cand.y_sequence, dtype=torch.long)" and expects it to be int labels, so I get an error. Meanwhile greedy decoding works perfectly.
**Steps/Code to reproduce bug**
I am just following the steps here https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/asr_language_modeling.html
python eval_beamsearch_ngram.py nemo_model_file=*quartznet_model_from_default_config* \
input_manifest="" \
kenlm_model_file=*scorer from train_kenlm* \
beam_width=[500] \
beam_alpha=[0.82] \
beam_beta=[0.87] \
preds_output_folder= \
probs_cache_file=null \
decoding_mode=beamsearch_ngram
**Expected behavior**
I expected the eval_beamsearch_ngram.py script work with default QuartzNet and default KenLM trained model. I am not exactly sure if I am not specifying the correct params for it.
**Environment overview (please complete the following information)**
- Environment location: linux
- Method of NeMo install: pip install from tutorial
**Environment details**
Otherwise, please provide:
- OS version: ubuntu 18.04.5 LTS (Bionic Beaver)
- PyTorch version 1.13.1
- Python version 3.8.12
**Additional context**
nemo toolkit 1.16.0rc0
ctc-decoders version is 1.1
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} | # What does this PR do ?
Remove 8k model mention and indicate estimated training time for NeMo LLM dataset preparation tool.
Also adds a model flag to recommend_hyperparameters function, which is not used now but can be used to recommend hyperparameters based on model chosen later on.
**Collection**: None
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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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 ?
* deprecated the first german single-speaker fastpitch+hifigan model that hacked `GermanCharTokenizer` by manually mixing graphemes and phonemes. This model was trained on Thorsten's neutral 21.02 dataset.
* added two new fastpitch+hifigan models that applied the refined `GermanCharsTokenizer` that only uses grapheme-only tokens. These two models were trained on Thorsten's neutral 21.02 and 22.10 (newly released by Thorsten), respectively.
**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 ?
Cherry-pick from #6094
Fix `torchaudio` installation, fix `k2` installation in some cases. Make `torchaudio` and `k2` not required by default.
**Collection**: [CI]
# Changelog
- add build arguments `REQUIRE_TORCHAUDIO` and `REQUIRE_K2` for `torchaudio` and `k2` in Dockerfile
- `false` (by default): library will be installed if possible, errors ignored
- `true`: if library not installed correctly, end build with error
- fix `k2` installation script on macOS
- fix `k2` installation in Docker (`k2` depends on `torchaudio`)
- fix `torchaudio` installation for latest PyTorch versions
- also fixes bug https://nvbugswb.nvidia.com/NvBugs5/SWBug.aspx?bugid=3940510&cmtNo= (`apt update && apt install -y libavdevice-dev` should be also used before executing install script)
# Usage
```shell
# build container, ignore k2/torchaudio installation error
docker build --tag "nemo:latest" .
# ignore k2 installation error, fail on torchaudio installation error
docker build --tag "nemo:latest" --build-arg REQUIRE_TORCHAUDIO=true --build-arg REQUIRE_K2=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?
- [ ] 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)
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} | # What does this PR do ?
Spanish does not have heteronyms, only polyphones (eg. region-specific pronunciations of the same word). To make this clearer, I am removing the heteronyms file and placing the alternative pronunciations into the dictionary.
**Collection**: [TTS]
# Changelog
- Delete es_ES and es_LA heteronym files
- Move words in heteronym files to pronunciation dictionary with multiple entries.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Fixes bug in processing multilingual / codeswitched tarred datasets
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR
- isinstance(args[0], Iterable) replaced by List
- Because a string is an Iterable!
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | **Describe the bug**
When initiating tacotron.py for training, the following error comes up:
Error executing job with overrides: []
Error in call to target 'nemo.collections.tts.data.tts_dataset.TTSDataset':
FileNotFoundError(2, 'No such file or directory')
full_key: train_ds.dataset
**Steps/Code to reproduce bug**
python tacotron2.py
(hardcoded the training and validation dataset paths in tacotron2.yaml file but also tried giving the path from the cli)
**Expected behavior**
Training starts.
**Environment overview (please complete the following information)**
- Environment location: on-site desktop
- Method of NeMo install: tries installing both with pip and from source.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04 and Windows 11 (dual-boot)
- PyTorch version: 1.13.1
- Python version: 3.8.0
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do?
This PR adjusts calculation of stochastic depth dropout probability.
Currently, calculation fails for 1-layer ConformerEncoder with the default setup.
The culprit is calculation of the probability in `compute_stochastic_depth_drop_probs ` for `linear` mode:
```
layer_drop_probs += [l / (L - 1) * stochastic_depth_drop_prob for l in range(L)]
```
since in this case `L-1 == 0`.
## Proposed solution
Change calculation for the linear mode to the following
```
# we start with 1/L * drop_prob and and end with the desired drop probability.
layer_drop_probs += [l / L * stochastic_depth_drop_prob for l in range(1, L + 1)]
```
This can handle setup with a single layer and is consistent with the probability calculation for the `uniform` mode.
### Example
An example of calculated probabilities:
```
num_layers = 7
stochastic_depth_start_layer = 3
stochastic_depth_drop_prob = 0.5
```
| layer | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
| ----- | --- | --- | --- | --- | --- | --- | --- |
| uniform | 0.0 | 0.0 | 0.0 | 0.5 | 0.5 | 0.5 | 0.5 |
| linear original | 0.0 | 0.0 | 0.0 | 0.0 | 0.167 | 0.333 | 0.5 |
| linear proposed | 0.0 | 0.0 | 0.0 | 0.125 | 0.25 | 0.375 | 0.5 |
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# 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
- [ ] 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, first of all, thanks for the amazing toolkit.
My query is pretty basic. I am trying to extract the embeddings of an audio file after passing it through a fine-tuned .nemo model. I saw that this support is available for [`Speaker Identification task`](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_recognition/results.html#speaker-embedding-extraction). How can I emulate it for ASR?
My trained model is a `Conformer CTC BPE` type model.
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} | # What does this PR do ?
Ngram LM fusion for RNNT modified adaptive expansion search (maes) decoding.
**Collection**: [ASR]
# Changelog
- Add ngram_lm option to maes decoding algorithm
- Add EncDecRNNTBPEModel and EncDecRNNTModel to kenlm_utils.py for ngram lm building
- Add new file eval_beamsearch_ngram_transducer.py (base on old Vahid's branch for ngram rnnt beam search) for testing the ngram RNNT decoding
# 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
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} | # What does this PR do ?
Fixes enhancer usage in ASR-TTS examples
**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**:
- [ ] 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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Fixes enhancer usage in ASR-TTS examples
**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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
save_best_model and always_save_nemo will work with tp/pp > 1 with this PR.
**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**:
- [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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* Related to # (issue)
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} | # What does this PR do ?
Repeating https://github.com/NVIDIA/NeMo/pull/6087 for r1.16.0
This PR fixes a line containing bash command "mv".
The line is unnecessary and creating an error.
Also added "rm" command that removes tar file, in case where users run the cell again.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
Removed "mv" line.
# Usage
This is a ipython notebook.
You can run it at `tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb`.
# 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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* Related to # (issue)
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} | # What does this PR do ?
Task 3: break down nemo.collections.tts.g2p.models and save to nemo.collection.tts.models
- base.py # move and merge tts/g2p/models/g2p_model.py::G2PModel class here because it is the base model for g2p.
- g2p_ctc.py # move and rename tts/g2p/models/ctc_g2p.py here. File name uses the same format as fastpitch_*.py
- g2p_t5.py # move and rename tts/g2p/models/t5_g2p.py here.
- g2p_heteronym_classification.py # move and rename tts/g2p/models/heteronym_classifiation.py here.
**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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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/6112",
"html_url": "https://github.com/NVIDIA/NeMo/pull/6112",
"diff_url": "https://github.com/NVIDIA/NeMo/pull/6112.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/6112.patch",
"merged_at": "2023-02-25T07:49:32"
} | # What does this PR do ?
Repeating https://github.com/NVIDIA/NeMo/pull/6087 for r1.16.0
This PR fixes a line containing bash command "mv".
The line is unnecessary and creating an error.
Also added "rm" command that removes tar file, in case where users run the cell again.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
Removed "mv" line.
# Usage
This is a ipython notebook.
You can run it at `tutorials/speaker_tasks/Speaker_Diarization_Training.ipynb`.
# 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 ?
Task 4: break down `nemo/collections/tts/g2p/modules.py` and save to `nemo/collections/tts/g2p/models`. The “Modules” concept used by g2p is misleading in comparison to NeMo standards, i.e. modules usually mean `torch.nn.Module` or `NeuralModule`. We should remove any confusion. According to several offline discussion, we finalized below changes,
```
- nemo/collections/tts/g2p/data
- ctc.py
- heteronym_classification.py
- t5.py
- nemo/collections/tts/g2p/models
- base.py # only contains BaseG2p class in this file.
- zh_cn_pinyin.py # move nemo/collections/tts/g2p/modules.py::ChineseG2p class here.
- en_us_arpabet.py # move nemo/collections/tts/g2p/modules.py::EnglishG2p class here.
- i18n_ipa.py # move nemo/collections/tts/g2p/modules.py::IPAG2P class here. It is designed to support any language when specifying a locale code.
- ctc.py
- heteronym_classification.py
- t5.py
- nemo/collections/tts/g2p/utils.py
- added deprecation notice for nemo/collections/tts/torch/g2ps.py
- renamed nemo/collections/tts/data/tts_dataset.py -> nemo/collections/tts/data/dataset.py
```
```
$ pytest --nightly tests/collections/tts/models/
A valid `test_data.tar.gz` test archive (10445891B) found in the `/home/xueyang/workspace/NeMo/tests/.data` folder.
Setting numba compat : True
===================================================== test session starts ======================================================
platform linux -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0 -- /home/xueyang/miniconda3/envs/nemo/bin/python
cachedir: .pytest_cache
rootdir: /home/xueyang/workspace/NeMo, configfile: pyproject.toml
plugins: hydra-core-1.2.0, anyio-3.6.1
collected 26 items
tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner] PASSED [ 3%]
tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa] PASSED [ 7%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch] PASSED [ 11%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa] PASSED [ 15%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker] PASSED [ 19%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102] PASSED [ 23%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210] PASSED [ 26%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5] PASSED [ 30%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker] PASSED [ 34%]
tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech] PASSED [ 38%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan] PASSED [ 42%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts] PASSED [ 46%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx] PASSED [ 50%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch] PASSED [ 53%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102] PASSED [ 57%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210] PASSED [ 61%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5] PASSED [ 65%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker] PASSED [ 69%]
tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech] PASSED [ 73%]
tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts] PASSED [ 76%]
tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx] PASSED [ 80%]
tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2] PASSED [ 84%]
tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet] PASSED [ 88%]
tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet] PASSED [ 92%]
tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits] PASSED [ 96%]
tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m] PASSED [100%]
====================================================== slowest durations =======================================================
26.97s setup tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx]
25.43s setup tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2]
25.30s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa]
24.99s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker]
24.87s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch]
24.55s setup tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits]
23.77s setup tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner]
23.56s setup tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts]
22.72s setup tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa]
21.13s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5]
20.04s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_22
10]
16.39s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_21
02]
2.42s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan]
2.39s setup tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m]
2.38s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker]
2.32s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts]
2.31s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx]
1.81s call tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixerttsx]
1.81s call tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner]
1.56s setup tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech]
1.40s setup tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet]
1.04s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5]
0.99s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210]
0.98s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch]
0.98s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker]
0.97s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102]
0.97s setup tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech]
0.91s setup tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet]
0.49s call tests/collections/tts/models/test_vits.py::test_inference[tts_en_lj_vits]
0.47s call tests/collections/tts/models/test_tacotron2.py::test_inference[tts_en_tacotron2]
0.46s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_zh_fastpitch_sfspeech]
0.28s call tests/collections/tts/models/test_waveGlow.py::test_inference[tts_en_waveglow_88m]
0.07s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_multispeaker_5]
0.07s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_210
2]
0.07s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_de_fastpitch_singleSpeaker_thorstenNeutral_221
0]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_ipa]
0.06s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_en_fastpitch_multispeaker]
0.05s call tests/collections/tts/models/test_mixerTTS.py::test_inference[tts_en_lj_mixertts]
0.05s call tests/collections/tts/models/test_aligner.py::test_inference[tts_en_radtts_aligner_ipa]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifigan]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2102]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_hifitts_hifigan_ft_fastpitch]
0.03s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hui_hifigan_ft_fastpitch_multispeaker_5]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_es_hifigan_ft_fastpitch_multispeaker]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixertts]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_en_lj_hifigan_ft_mixerttsx]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_de_hifigan_singleSpeaker_thorstenNeutral_2210]
0.02s call tests/collections/tts/models/test_hifigan.py::test_inference[tts_zh_hifigan_sfspeech]
0.02s call tests/collections/tts/models/test_fastpitch.py::test_inference[tts_es_fastpitch_multispeaker]
0.02s call tests/collections/tts/models/test_univNet.py::test_inference[tts_en_lj_univnet]
0.01s call tests/collections/tts/models/test_univNet.py::test_inference[tts_en_libritts_univnet]
(26 durations < 0.005s hidden. Use -vv to show these durations.)
================================================ 26 passed in 307.59s (0:05:07) ================================================
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"patch_url": "https://github.com/NVIDIA/NeMo/pull/6110.patch",
"merged_at": "2023-03-01T08:19:10"
} | # What does this PR do ?
This new badges will look like below,
1. added |pypi| |pyversion| |codeql||downloads|;
2. remove lgtm because it is deprecated.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Update librosa resample signature
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# Changelog
- add naming args
**PR Type**:
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- [x] Bugfix
- [ ] Documentation | {
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Adds missing parameters to ModelCheckpoint CallbackParams, to achieve feature parity with PTL. See ModelCheckpoint API here: https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.ModelCheckpoint.html
**Collection**: exp_manager
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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