cloud
aws
openstack
module
support:community
affects_2.5
bug
traceback
system

Stickiness type 'lb_cookie' is not supported for target groups with the TCP protocol(unable to disable stickiness not supported in NLB)

##### SUMMARY
We are using Ansible 2.5 to deploy AWS resources in our environment. From March 02, 2019 our deployment is failing with the below error.

ERROR:
=====
TASK [immutable_server : target group for analytics-tst-plebos loadbalancer] ***
An exception occurred during task execution. To see the full traceback, use -vvv. The error was: InvalidConfigurationRequestException:
An error occurred (InvalidConfigurationRequest) when calling the ModifyTargetGroupAttributes operation:
Stickiness type 'lb_cookie' is not supported for target groups with the TCP protocol
17:21:08 fatal: [localhost]: FAILED! => {"changed": false, "error": {"code": "InvalidConfigurationRequest", "message": "Stickiness type 'lb_cookie'
is not supported for target groups with the TCP protocol", "type": "Sender"}, "msg": "An error occurred (InvalidConfigurationRequest)
when calling the ModifyTargetGroupAttributes operation: Stickiness type 'lb_cookie' is not supported for target groups with the TCP protocol",
"response_metadata": {"http_headers": {"connection": "close", "content-length": "359", "content-type": "text/xml", "date": "Tue, 03 Mar 2020 11:51:08 GMT",
"x-amzn-requestid": "23b0ca87-e0fb-4b84-b93b-ae5b1363df53"}, "http_status_code": 400, "request_id": "23b0ca87-e0fb-4b84-b93b-ae5b1363df53", "retry_attempts": 0}}

##### ISSUE TYPE
- Bug Report - Unable to disable stickiness not supported in NLB

##### COMPONENT NAME
- name: "target group for {{ server_name }} loadbalancer"
  elb_target_group:
    state: present
    name: "{{ server_name }}-elb"
    protocol: tcp
    port: 80
    target_type: instance
    deregistration_delay_timeout: 35
    modify_targets: False
    vpc_id: "{{ vpc_out.vpcs.0.id }}"
    health_check_protocol: "{{ load_balancer_ping_protocol | default('http') }}"
    health_check_port: "{{ load_balancer_ping_port | default('80') }}"
    health_check_path: "{{ load_balancer_ping_path | default('/elb/ping')}}"
    health_check_interval: 30
    unhealthy_threshold_count: 2
    healthy_threshold_count: 2
    stickiness_enabled: False
    tags: "{{ aws.tags_as_dict }}"
  register: target_group_out

##### ANSIBLE VERSION
```paste below
Ansible version = 2.5.0
```

##### CONFIGURATION
<!--- Paste verbatim output from "ansible-config dump --only-changed" between quotes -->
```paste below
- name: "target group for {{ server_name }} loadbalancer"
  elb_target_group:
    state: present
    name: "{{ server_name }}-elb"
    protocol: tcp
    port: 80
    target_type: instance
    deregistration_delay_timeout: 35
    modify_targets: False
    vpc_id: "{{ vpc_out.vpcs.0.id }}"
    health_check_protocol: "{{ load_balancer_ping_protocol | default('http') }}"
    health_check_port: "{{ load_balancer_ping_port | default('80') }}"
    health_check_path: "{{ load_balancer_ping_path | default('/elb/ping')}}"
    health_check_interval: 30
    unhealthy_threshold_count: 2
    healthy_threshold_count: 2
    stickiness_enabled: False
    tags: "{{ aws.tags_as_dict }}"
  register: target_group_out

```

##### OS / ENVIRONMENT
Ubuntu 18.04 LTS / AWS environment

##### STEPS TO REPRODUCE
Kindly use the below playbook to deploy loadbalancer using Ansible on AWS cloud.

<!--- Paste example playbooks or commands between quotes below -->
```yaml
- name: "target group for {{ server_name }} loadbalancer"
  elb_target_group:
    state: present
    name: "{{ server_name }}-elb"
    protocol: tcp
    port: 80
    target_type: instance
    deregistration_delay_timeout: 35
    modify_targets: False
    vpc_id: "{{ vpc_out.vpcs.0.id }}"
    health_check_protocol: "{{ load_balancer_ping_protocol | default('http') }}"
    health_check_port: "{{ load_balancer_ping_port | default('80') }}"
    health_check_path: "{{ load_balancer_ping_path | default('/elb/ping')}}"
    health_check_interval: 30
    unhealthy_threshold_count: 2
    healthy_threshold_count: 2
    stickiness_enabled: False
    tags: "{{ aws.tags_as_dict }}"
  register: target_group_out
```

<!--- HINT: You can paste gist.github.com links for larger files -->

##### EXPECTED RESULTS
An AWS Network loadbalancer will be created.

##### ACTUAL RESULTS
The deployment fails with below error.

<!--- Paste verbatim command output between quotes -->
```paste below
 TASK [immutable_server : target group for analytics-tst-plebos loadbalancer] ***
17:21:08 An exception occurred during task execution. To see the full traceback, use -vvv. The error was: InvalidConfigurationRequestException:
An error occurred (InvalidConfigurationRequest) when calling the ModifyTargetGroupAttributes operation:
Stickiness type 'lb_cookie' is not supported for target groups with the TCP protocol
17:21:08 fatal: [localhost]: FAILED! => {"changed": false, "error": {"code": "InvalidConfigurationRequest", "message": "Stickiness type 'lb_cookie'
is not supported for target groups with the TCP protocol", "type": "Sender"}, "msg": "An error occurred (InvalidConfigurationRequest)
when calling the ModifyTargetGroupAttributes operation: Stickiness type 'lb_cookie' is not supported for target groups with the TCP protocol",
"response_metadata": {"http_headers": {"connection": "close", "content-length": "359", "content-type": "text/xml", "date": "Tue, 03 Mar 2020 11:51:08 GMT",
"x-amzn-requestid": "23b0ca87-e0fb-4b84-b93b-ae5b1363df53"}, "http_status_code": 400, "request_id": "23b0ca87-e0fb-4b84-b93b-ae5b1363df53", "retry_attempts": 0}}

```

##### References
I can see a similar issue occurred for terraform users as well.

https://github.com/terraform-providers/terraform-provider-aws/issues/10494


Problems with weights

## ❔Question
Hello, I have just run trainy.py script with my data and faced a problem - you wrote that weights are saved in runs directory, but in my case I have not found them. Everything is fine with hyp.yaml and opt.yaml but folder "weights" is empty.
Do you have any guesses about this issue?

## Additional context


Does adjusting the number of classes of a pretrained model work?

### Search before asking

- [x]  I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions. *

### Question

Hi everyone,

I'm a bit confused about how to properly load a pretrained model with an adjusted number of classes for training with a custom dataset.

On the [Load YOLOv5 from PyTorch Hub ⭐](https://github.com/ultralytics/yolov5/issues/36) page you've explained that one can adjust  the  number of classes in the pretrained model by using the following command. `model = torch.hub.load('ultralytics/yolov5', 'yolov5s', classes=10)`

<img width="999" alt="Bildschirmfoto 2022-08-22 um 08 13 15" src="https://user-images.githubusercontent.com/5917496/185851461-b177aa78-2b56-46a1-9c43-081d2a746938.png">

When I do so, I can see that a model.yaml file is overwritten, but I do not know where this file is stored.

Now, what actually confuses me about the number of classes, is that when I try to use this pretrained model in detection, without any further training. I see an error, that the model was trained with nc=80 and my data is incompatible with nc=13:

`AssertionError: ['yolov5s6.pt'] (80 classes) trained on different --data than what you passed (13 classes). Pass correct combination of --weights and --data that are trained together.`

I know that I can not expect any proper predictions since the last layers are initialized with random weights, but I was expecting that the model is compatible with the 13 classes dataset.

Is this behavior to be expected or am I doing something wrong here?
Do I need to find and use the model.yaml file and is the only thing changed in there 'nc=13'?


training actually can not work

After trained on several epochs, I found the mAP is still very low. Does the training really works?

```
    Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
    14/299      6.4G   0.02273  0.002925 0.0003764   0.02603        11       640: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6960/6960 [54:20<00:00,  2.13it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6960/6960 [13:37<00:00,  8.51it/s]
                 all    5.57e+04    1.74e+05    0.000332     0.00039     2.4e-06    8.59e-07

     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
    15/299      6.4G   0.02232  0.002874  0.000371   0.02556         7       640: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6960/6960 [54:36<00:00,  2.12it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6960/6960 [14:23<00:00,  8.06it/s]
                 all    5.57e+04    1.74e+05    0.000342    0.000401    2.44e-06    8.66e-07

```

{'base_commit': 'ee8988b8a2ed07af1b7c8807d39aad35369f0e28', 'files': [{'path': 'models/yolov5s.yaml', 'Loc': {'(None, None, 2)': {'mod': [2]}}, 'status': 'modified'}, {'path': 'README.md', 'Loc': {}}]}


training stuck when loading dataset

## ❔Question
I follow the instructions to run coco128,
```
python train.py --img 640 --batch 16 --epochs 5 --data ./data/coco128.yaml --cfg ./models/yolov5s.yaml --weights '',
```
the ouput is
```
Image sizes 640 train, 640 test
Using 8 dataloader workers
Starting training for 5 epochs...

     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
  0%|                                                                                | 0/8 [00:00<?, ?it/s
```
then it is stuck, I found that it is stucking at loading the dataset,
in https://github.com/ultralytics/yolov5/blob/master/train.py#L244,
```
for i, (imgs, targets, paths, _) in pbar:
```
it just stops here, could you help me ?


Results interpretation

Hello,

Another question to do with results interpretation. I am not very sure how to interpret the results.txt file that gets generated after training is over. Also, is there any way to extract the number of false positives, true positives, false negatives, as well as to see the total mean average accuracy and loss (like with yolov4)?

Further, after training is done, can the best weights obtained from training be used to test on unseen data (more specifically, multiple images)?

Thanks in advance again!


How to add some private information into .pt file?

### Search before asking

- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.

### Question

yolov5 is a great algorithm, but I'm having some problems. Specifically, I want to add some private information to the .pt file, can this be done?

### Additional

_No response_


what is moasic and what is its default and how to delete it

what is the meaning of moasic

where I can find its default parameter

how to stop moasic and  stop augmentation in general

I use only this line is it augment data by default or not? how to stop augmentation if exist
```
!python train.py --img 640 --batch 16 --epochs 400 --data /mydrive/data.yaml \
  --weights /mydrive/yolov5s.pt --cache --project /mydrive/train/
```


[onnx export.py error] Unsupported ONNX opset version

`ONNX: starting export with onnx 1.10.1...`
`ONNX: export failure: Unsupported ONNX opset version: 13`

I'm using
yolov5-5.0, pytorch1.7.0+cu101 and python3.7.9.

How to solve it?


Experiments on GhostNet

## ❔Question
I am just wondering about the performance when using GhostNet in experimental.py. Could you please share this experiment?

## Additional context

{'base_commit': '886f1c03d839575afecb059accf74296fad395b6', 'files': [{'path': 'Models/yolov5l.yaml', 'Loc': {}}]}


calculate fps on local system

## ❔Question
I have been using the code to do detection from webcam. How can I know what is the speed of detection (fps) in my local system?


large scale dataset use --cache-images flag

## ❔Question
hello ~ , i have dataset with a million images about 450GB and i want to use --cache-images  accelerate training（i have 128GB RAM），can i split the whole dataset into many sub dataset  and training them one by one（like resume training） ？

## Additional context


### Search before asking

- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.

### Question

I am making UI by QT for Yolov5 training. Where is making the result image (results.png) after training?

I would like to draw the graph for (train/box_loss), (metrics/precision), and (metrics/recall) per each an epoch every time an epoch of the train is finished.

 Where is making the result image (results.png) after training?

Thank you for your help.

### Additional

_No response_


Significance of --half?

### Search before asking

- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.

### Question

Can you please let me know the significance of --half during training process....

### Additional

_No response_


Training speed and memory

I noticed your instructions about training,
Run commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
I want to train from scratch on the coco dataset.(A100 x1).The code was just downloaded.

The following is the situation during my training.The specific parameters can be seen in the screenshot.
python train.py --cfg models/yolov5s.yaml --data data/coco.yaml --device 0 --batch-size 64    -> 16min/epoch
python train.py --cfg models/yolov5s.yaml --data data/coco.yaml --device 0 --batch-size 128  ->16min/epoch
python train.py --cfg models/yolov5s.yaml --data data/coco.yaml --device 0 --batch-size 192  ->20min/epoch
python train.py --cfg models/yolov5s.yaml --data data/coco.yaml --device 0 --batch-size 192 --workers 16->16min/epoch
![sendpix7](https://user-images.githubusercontent.com/39581901/127759471-a110c68f-d1d4-4580-afd2-ae8c8a17ef4a.jpg)
My question
1. Why I increased the batch size but the time required for training did not decrease
2. The relationship between workers and batch size, because I noticed that you seem to set it to a maximum of 8 in the code (why it is 8),
3. When epoch=0 and 1, the GPU memory has changed, about x1.5? What may be the reason for this,


Can we return objectness score and class score?

## ❔Question
I am wondering if it is possible to return confidence scores for objectness and classification separately for each predicted box during inference? I might be conceptually off base here, but I am interested in understanding if the model is unsure if the box itself is correct or if the class it is assigning to the box is correct.  My understanding is the `conf` that is returned now is a combo of the two?


For online cutting training and detection can be improve

## 🚀 Feature

For big image training, usually people thinking about to cut the images, but yolov5 can only resize the image to small size. Such as VisDrone dataset, the smallest image can have 960*540 size, if resize to 640*640, size would be 640*360, but the target in dataset mostly are small object, resize the image make the target become more smaller, but if use bigger resolution, the cuda memory would exceed.

So I thought online cutting training and detection would be a good feature for yolov5 to improve, although cutting image would also increase the train time, but it would be a great idea for people who don't have large computing power GPU, also I think cutting image would be effective for small object detection. Although it's not a new idea in detection, it would be a useful way for people to their own detector.

{'base_commit': 'dabad5793a638cba1e5a2bbb878c9b87fe1a14a0', 'files': [{'path': 'utils/augmentations.py', 'Loc': {"('Albumentations', '__init__', 16)": {'mod': [22]}}, 'status': 'modified'}]}


img-weights

## ❔Question
parser.add_argument('--img-weights', action='store_true', help='use weighted image selection for training')
in order to make --iimg-weights work,  what else I need to do?
dataset = LoadImagesAndLabels(path, imgsz, batch_size,
                                    augment=augment,  # augment images
                                    hyp=hyp,  # augmentation hyperparameters
                                    rect=rect,  # rectangular training
                                    cache_images=cache,
                                    single_cls=opt.single_cls,
                                    stride=int(stride),
                                    pad=pad),
 should I add an extra param image_weights=True??

## Additional context

{'base_commit': 'c8c5ef36c9a19c7843993ee8d51aebb685467eca', 'files': [{'path': 'train.py', 'Loc': {'(None, None, None)': {'mod': [397]}}, 'status': 'modified'}]}


why can't I reproduce the mAP provided by README.md（v6.1）？

### Search before asking

- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.

### Question

I used the method recommended by README.md(v6.1) to reproduce the mAP, but I failed.
'python train.py --data coco.yaml --cfg yolov5s.yaml --weights ' ' --hyp hyp.scratch-low.yaml --img 640 --batch-size 64 --epochs 300' .
All is default value,then I got the best mAP（yolov5s） is 37.057%(the best mAP verified at the end of each epoch, 5000 images), it still has a gap of 0.4% mAP(37.4%).
Similarly, I reproduced the mAP（yolov5n），27.586%----28.0%，Never get published results.
My GPU is GTX NVIDIA RTX A4000（16116MiB）, and I think it may be enough.

Is this a normal error caused by equipment（GPU) differences, or are there other reasons？

### Additional

_No response_

{'base_commit': '9cd89b75cca8bb165a3b19c9b8356f7b3bb22b31', 'files': [{'path': 'data/scripts/get_coco.sh', 'Loc': {'(None, None, 13)': {'mod': [13]}}, 'status': 'modified'}]}


Is YOLOv5 able to detect a specific number of classes according to the project's need, like just 2 or 3 classes?

### Search before asking

- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.

### Question

Hi, I'm using YOLOv5 in my project and I have a question. If I use "--classes " it could detect one type of class, but is there anyway that I can detect more than one type, like 2 or 3 different types? I've already tried "-- classes 0 1" or "-- classes [0] [1]" but without success. Thanks for the help!

### Additional

_No response_


Average Precision for each class

## Is there any way to see the average precision for each class?

I have run my model for 1,000 epochs and I have a bunch of metrics (which are AMAZING by the way, thanks so making it so easy to see them!) and I have mAP, but I was wondering if there was a way to see the AP for each class? Like a table or something.

In addition, is it possible to see the precision-recall graphs for each class? I can see something in the images tab on wandb, but as I have 80 classes, it looks very messy.


Format of data for testing trained model

In what format do I need to feed the validation dataset to the val.py file? Should images and markup be in the same folder or in different ones? In what format should the coordinates of the bounding boxes be in - yolo or something else?


decoding error in preprocessing synthesizer

I get the following error while running `synthesizer_preprocess_audio.py`.

```
Arguments:
    datasets_root:   /home/amin/voice_cloning/libri_100
    out_dir:         /home/amin/voice_cloning/libri_100/SV2TTS/synthesizer
    n_processes:     None
    skip_existing:   True
    hparams:

Using data from:
    /home/amin/voice_cloning/libri_100/LibriSpeech/train-clean-100
LibriSpeech:   0%|                                                                                | 0/502 [00:00<?, ?speakers/s]
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/home/amin/voice_cloning/Real-Time-Voice-Cloning-master/synthesizer/preprocess.py", line 62, in preprocess_speaker
    alignments = [line.rstrip().split(" ") for line in alignments_file]
  File "/home/amin/voice_cloning/Real-Time-Voice-Cloning-master/synthesizer/preprocess.py", line 62, in <listcomp>
    alignments = [line.rstrip().split(" ") for line in alignments_file]
  File "/usr/lib/python3.6/codecs.py", line 321, in decode
    (result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa2 in position 37: invalid start byte
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "synthesizer_preprocess_audio.py", line 52, in <module>
    preprocess_librispeech(**vars(args))
  File "/home/amin/voice_cloning/Real-Time-Voice-Cloning-master/synthesizer/preprocess.py", line 36, in preprocess_librispeech
    for speaker_metadata in tqdm(job, "LibriSpeech", len(speaker_dirs), unit="speakers"):
  File "/home/amin/.local/lib/python3.6/site-packages/tqdm/std.py", line 1130, in __iter__
    for obj in iterable:
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 735, in next
    raise value
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa2 in position 37: invalid start byte
```

Can anyone help? It can save a lot of time for me.
Thanks.

{'base_commit': 'eaf5ec4467795344e7d9601515b017fd8c46e44b', 'files': [{'path': 'synthesizer/preprocess.py', 'Loc': {"(None, 'preprocess_speaker', 54)": {'mod': [60]}}, 'status': 'modified'}]}


Error in macOS when trying to launch the toolbox

Traceback (most recent call last):
  File "/Users/luke/Documents/Real-Time-Voice-Cloning-master/demo_toolbox.py", line 2, in <module>
    from toolbox import Toolbox
  File "/Users/luke/Documents/Real-Time-Voice-Cloning-master/toolbox/__init__.py", line 1, in <module>
    from toolbox.ui import UI
  File "/Users/luke/Documents/Real-Time-Voice-Cloning-master/toolbox/ui.py", line 6, in <module>
    from encoder.inference import plot_embedding_as_heatmap
ModuleNotFoundError: No module named 'encoder.inference'


missing SV2TTS/

Hey, I'm trying to finetune the pretrained model but it looks like I am missing the SV2TTS/ directory which contains train.txt, etc.
I have a saved_models/ directory which has three *.pt files for the three components of this TTS architecture.

{'base_commit': '98d0ca4d4d140a4bb6bc7d54c84b1915a79041d5', 'files': [{'path': 'synthesizer_preprocess_audio.py', 'Loc': {}}]}


Import Error

Hey, i am trying to run this code and everytime i run demo_toolbox.py there comes an error "failed to load qt binding" i tried reinstalling matplotlib and also tried installing PYQt5 .

Need Help !!!


ModuleNotFoundError: No module named 'tensorflow.contrib.seq2seq'

When running demo_cli.py

Python = 3.7.4
TensorFlow = 2.0 RC
CUDA = 10.1
cuDNN = Installed for right CUDA version
Windows = 10

{'base_commit': '8e6499b10d5a074bdfe8ee6db8eec60e1060ccc1', 'files': [{'path': 'requirements.txt', 'Loc': {}}]}


Speaker verification implementation

I need just the speaker verification part which is the implementation of [GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION](https://arxiv.org/pdf/1710.10467.pdf) paper, how I can proceed to get it please?

{'base_commit': 'c5c2261c97afe6ec5db1ef389eba1257f6cce8a2', 'files': [{'path': 'encoder/', 'Loc': {}}]}


Output audio spectrum - low frequences

Hi, Im am trying to train new model in polish language but after 476k steps output sound is very "robotic". I was trying to find why that's happened and noticed (based on my output and @blue-fish samples: https://blue-fish.github.io/experiments/RTVC-FT-1.html) that spectrum of this model don't include high frequences compared to google. Both in logarithmic scale.

Our output:

Google: (take a note its logarithmic scale)

Do you have any idea how to improve this?

{'base_commit': '7432046efc23cabf176f9fdc8d2fd67020059478', 'files': [{'path': 'synthesizer/hparams.py', 'Loc': {'(None, None, None)': {'mod': [77]}}, 'status': 'modified'}]}


Requirements.txt failed to install with obscure issue with installing audioread

I ran into a few issues along the way that I was able to solve, namely errors like this:

      WARNING: Failed to write executable - trying to use .deleteme logic
    ERROR: Could not install packages due to an OSError: [WinError 2] The system cannot find the file specified:
    'C:\\Python310\\Scripts\\f2py.exe' -> 'C:\\Python310\\Scripts\\f2py.exe.deleteme'

I fixed these by adding `--user` to the pip command.

I also had to change requirements.txt to a newer version of numpy (1.22.1) to prevent it from failing to install due to older versions not being compatible with the version of Python I already have installed (3.10.6)

But now I'm stuck on this one:

    Requirement already satisfied: jsonpointer>=1.9 in c:\users\michael\appdata\roaming\python\python310\site-packages (from jsonpatch->visdom==0.1.8.9->-r R:\requirements.txt (line 15)) (2.3)
    Using legacy 'setup.py install' for umap-learn, since package 'wheel' is not installed.
    Using legacy 'setup.py install' for visdom, since package 'wheel' is not installed.
    Using legacy 'setup.py install' for audioread, since package 'wheel' is not installed.
    Using legacy 'setup.py install' for pynndescent, since package 'wheel' is not installed.
    Installing collected packages: audioread, visdom, SoundFile, sounddevice, scikit-learn, resampy, pooch, matplotlib, pynndescent, librosa, umap-learn
      Running setup.py install for audioread ... error
      error: subprocess-exited-with-error

      × Running setup.py install for audioread did not run successfully.
      │ exit code: 1
      ╰─> [40 lines of output]
          C:\Users\michael\AppData\Local\Temp\pip-install-nat_itg2\audioread_fa5fbfcd88364fc89c7b2a9e454b5549\setup.py:17: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses
            import imp
          running install
          C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\command\install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
            warnings.warn(
          Traceback (most recent call last):
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\util.py", line 258, in subst_vars
              return _subst_compat(s).format_map(lookup)
          KeyError: 'py_version_nodot_plat'

          During handling of the above exception, another exception occurred:

          Traceback (most recent call last):
            File "<string>", line 2, in <module>
            File "<pip-setuptools-caller>", line 34, in <module>
            File "C:\Users\michael\AppData\Local\Temp\pip-install-nat_itg2\audioread_fa5fbfcd88364fc89c7b2a9e454b5549\setup.py", line 27, in <module>
              setup(name='audioread',
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\__init__.py", line 153, in setup
              return distutils.core.setup(**attrs)
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\core.py", line 148, in setup
              return run_commands(dist)
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\core.py", line 163, in run_commands
              dist.run_commands()
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\dist.py", line 967, in run_commands
              self.run_command(cmd)
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\dist.py", line 985, in run_command
              cmd_obj.ensure_finalized()
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\cmd.py", line 107, in ensure_finalized
              self.finalize_options()
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\command\install.py", line 45, in finalize_options
              orig.install.finalize_options(self)
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\command\install.py", line 381, in finalize_options
              self.expand_dirs()
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\command\install.py", line 563, in expand_dirs
              self._expand_attrs(['install_purelib', 'install_platlib',
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\command\install.py", line 553, in _expand_attrs
              val = subst_vars(val, self.config_vars)
            File "C:\Users\michael\AppData\Roaming\Python\Python310\site-packages\setuptools\_distutils\util.py", line 260, in subst_vars
              raise ValueError(f"invalid variable {var}")
          ValueError: invalid variable 'py_version_nodot_plat'
          [end of output]

      note: This error originates from a subprocess, and is likely not a problem with pip.
    error: legacy-install-failure

    × Encountered error while trying to install package.
    ╰─> audioread

    note: This is an issue with the package mentioned above, not pip.
    hint: See above for output from the failure.

I'm not sure if the issue is due to "setup.py install" being deprecated; if that's the case I have no idea what the fix is because I think this is being required somewhere else - maybe another package needs a newer version? But I have no idea which one.

I also thought maybe it could be that wheel wasn't installed, `since package 'wheel' is not installed.` but when I try to install it, it says it's already installed:

    C:\> pip install wheel --user

    Requirement already satisfied: wheel in c:\python310\lib\site-packages (0.37.1)

There's also the invalid variable error, but I have no idea what this is talking about.


I can't use Dataset/Speaker/Utterance

I can't use the upper section in the software. when loading it shows:
Warning: you did not pass a root directory for datasets as argument.
How can I fix this?
Thank you

{'base_commit': '95adc699c1deb637f485e85a5107d40da0ad94fc', 'files': [{'path': 'demo_toolbox.py', 'Loc': {'(None, None, None)': {'mod': [15]}}, 'status': 'modified'}]}


problem with utils.argutils  in python 3.6

Hi under win 10 64 bits trying using python 3.6 it failed to import the print_args wiht the fact that he can't find the argutils.
think i have a relative import error but can't solve it

btw nice job on what i heard on the youtube demo
if i mnaully try to import the utils from the root dir seems he load another utils files

{'base_commit': '039f7e5402e6d9da7fad5022dae038cdfb507b39', 'files': [{'path': 'synthesizer/__init__.py', 'Loc': {}}]}


Using a different speaker encoder

Hello, I really appreciate the work on display here. I was just wondering if I could use a different speaker encoder. If someone used a different encoder, could you explain the difficulties of replacing the encoder and how the results were different from the speaker encoder already in use?


preprocessing VoxCele2 is not working

While running encoder_preprocess on voxceleb2 dataset, I'm getting the following warning and nothing else happens. Not sure why?

```
raw: Preprocessing data for 5994 speakers.
raw:   0%|                                                                                | 0/5994 [00:00<?, ?speakers/s]
/home/amin/.local/lib/python3.6/site-packages/librosa/core/audio.py:161: UserWarning: PySoundFile failed. Trying audioread instead.
  warnings.warn('PySoundFile failed. Trying audioread instead.')
/home/amin/.local/lib/python3.6/site-packages/librosa/core/audio.py:161: UserWarning: PySoundFile failed. Trying audioread instead.
  warnings.warn('PySoundFile failed. Trying audioread instead.')
```

{'base_commit': 'a32962bb7b4827660646ac6dabf62309aea08a91', 'files': [{'path': 'encoder/preprocess.py', 'Loc': {"(None, 'preprocess_voxceleb2', 164)": {'mod': []}}, 'status': 'modified'}]}


vocoder_dataset.py ValueError

I am trying to use the Librispeech dataset to train the vocoder.
And I got a ValueError while training.
```numpy.random._bounded_integers._rand_int32 ValueError: low >= high```

It occurs in line 61 of vocoder_dataset.py,
```mel_offsets = [np.random.randint(0, offset) for offset in max_offsets]```
So I assume there is something wrong with the value of offset? e.g. offset=0 so np.random.randint could not generate a number [0, 0)?
Did anyone encountered this problem too?

{'base_commit': '0713f860a3dd41afb56e83cff84dbdf589d5e11a', 'files': [{'path': 'synthesizer/hparams.py', 'Loc': {'(None, None, None)': {'mod': [88]}}, 'status': 'modified'}]}


hello.
Please help me, I do not know how to solve my problem problem.
I run and completed without errors
`python synthesizer_preprocess_audio.py <datasets_root>`
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
but after typing `python synthesizer_train.py my_run <datasets_root>/SV2TTS/synthesizer`
shows me a long error

```
Arguments:
    name:                   my_run
    synthesizer_root:       C:\Users\matve\Documents\Tacotron\datasets\SV2TTS\synthesizer
    models_dir:             synthesizer/saved_models/
    mode:                   synthesis
    GTA:                    True
    restore:                True
    summary_interval:       2500
    embedding_interval:     10000
    checkpoint_interval:    2000
    eval_interval:          100000
    tacotron_train_steps:   2000000
    tf_log_level:           1
    slack_url:              None
    hparams:

Checkpoint path: synthesizer/saved_models/logs-my_run\taco_pretrained\tacotron_model.ckpt
Loading training data from: C:\Users\matve\Documents\Tacotron\datasets\SV2TTS\synthesizer\train.txt
Using model: Tacotron
Hyperparameters:
  allow_clipping_in_normalization: True
  attention_dim: 128
  attention_filters: 32
  attention_kernel: (31,)
  cbhg_conv_channels: 128
  cbhg_highway_units: 128
  cbhg_highwaynet_layers: 4
  cbhg_kernels: 8
  cbhg_pool_size: 2
  cbhg_projection: 256
  cbhg_projection_kernel_size: 3
  cbhg_rnn_units: 128
  cleaners: english_cleaners
  clip_for_wavenet: True
  clip_mels_length: True
  cross_entropy_pos_weight: 20
  cumulative_weights: True
  decoder_layers: 2
  decoder_lstm_units: 1024
  embedding_dim: 512
  enc_conv_channels: 512
  enc_conv_kernel_size: (5,)
  enc_conv_num_layers: 3
  encoder_lstm_units: 256
  fmax: 7600
  fmin: 55
  frame_shift_ms: None
  griffin_lim_iters: 60
  hop_size: 200
  mask_decoder: False
  mask_encoder: True
  max_abs_value: 4.0
  max_iters: 2000
  max_mel_frames: 900
  min_level_db: -100
  n_fft: 800
  natural_eval: False
  normalize_for_wavenet: True
  num_mels: 80
  outputs_per_step: 2
  postnet_channels: 512
  postnet_kernel_size: (5,)
  postnet_num_layers: 5
  power: 1.5
  predict_linear: False
  preemphasis: 0.97
  preemphasize: True
  prenet_layers: [256, 256]
  ref_level_db: 20
  rescale: True
  rescaling_max: 0.9
  sample_rate: 16000
  signal_normalization: True
  silence_min_duration_split: 0.4
  silence_threshold: 2
  smoothing: False
  speaker_embedding_size: 256
  split_on_cpu: True
  stop_at_any: True
  symmetric_mels: True
  tacotron_adam_beta1: 0.9
  tacotron_adam_beta2: 0.999
  tacotron_adam_epsilon: 1e-06
  tacotron_batch_size: 36
  tacotron_clip_gradients: True
  tacotron_data_random_state: 1234
  tacotron_decay_learning_rate: True
  tacotron_decay_rate: 0.5
  tacotron_decay_steps: 50000
  tacotron_dropout_rate: 0.5
  tacotron_final_learning_rate: 1e-05
  tacotron_gpu_start_idx: 0
  tacotron_initial_learning_rate: 0.001
  tacotron_num_gpus: 1
  tacotron_random_seed: 5339
  tacotron_reg_weight: 1e-07
  tacotron_scale_regularization: False
  tacotron_start_decay: 50000
  tacotron_swap_with_cpu: False
  tacotron_synthesis_batch_size: 128
  tacotron_teacher_forcing_decay_alpha: 0.0
  tacotron_teacher_forcing_decay_steps: 280000
  tacotron_teacher_forcing_final_ratio: 0.0
  tacotron_teacher_forcing_init_ratio: 1.0
  tacotron_teacher_forcing_mode: constant
  tacotron_teacher_forcing_ratio: 1.0
  tacotron_teacher_forcing_start_decay: 10000
  tacotron_test_batches: None
  tacotron_test_size: 0.05
  tacotron_zoneout_rate: 0.1
  train_with_GTA: False
  trim_fft_size: 512
  trim_hop_size: 128
  trim_top_db: 23
  use_lws: False
  utterance_min_duration: 1.6
  win_size: 800
Loaded metadata for 290550 examples (366.70 hours)
initialisation done /gpu:0
Initialized Tacotron model. Dimensions (? = dynamic shape):
  Train mode:               True
  Eval mode:                False
  GTA mode:                 False
  Synthesis mode:           False
  Input:                    (?, ?)
  device:                   0
  embedding:                (?, ?, 512)
  enc conv out:             (?, ?, 512)
  encoder out (cond):       (?, ?, 768)
  decoder out:              (?, ?, 80)
  residual out:             (?, ?, 512)
  projected residual out:   (?, ?, 80)
  mel out:                  (?, ?, 80)
  <stop_token> out:         (?, ?)
  Tacotron Parameters       28.439 Million.
initialisation done /gpu:0
Initialized Tacotron model. Dimensions (? = dynamic shape):
  Train mode:               False
  Eval mode:                True
  GTA mode:                 False
  Synthesis mode:           False
  Input:                    (?, ?)
  device:                   0
  embedding:                (?, ?, 512)
  enc conv out:             (?, ?, 512)
  encoder out (cond):       (?, ?, 768)
  decoder out:              (?, ?, 80)
  residual out:             (?, ?, 512)
  projected residual out:   (?, ?, 80)
  mel out:                  (?, ?, 80)
  <stop_token> out:         (?, ?)
  Tacotron Parameters       28.439 Million.
Tacotron training set to a maximum of 2000000 steps
Loading checkpoint synthesizer/saved_models/logs-my_run\taco_pretrained\tacotron_model.ckpt-0

Generated 64 train batches of size 36 in 3.626 sec
Step       1 [5.798 sec/step, loss=14.85899, avg_loss=14.85899]
Step       1 [5.798 sec/step, loss=14.85899, avg_loss=14.85899]

Saving Model Character Embeddings visualization..
Tacotron Character embeddings have been updated on tensorboard!
Step       2 [3.362 sec/step, loss=11.10468, avg_loss=12.98183]
Step       2 [3.362 sec/step, loss=11.10468, avg_loss=12.98183]

Generated 403 test batches of size 36 in 15.574 sec
Exiting due to exception: 2 root error(s) found.
  (0) Resource exhausted: OOM when allocating tensor with shape[36,512,1,702] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[node Tacotron_model/inference/postnet_convolutions/conv_layer_1_postnet_convolutions/conv1d/conv1d (defined at e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

	 [[Tacotron_model/clip_by_global_norm/mul_30/_479]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

  (1) Resource exhausted: OOM when allocating tensor with shape[36,512,1,702] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[node Tacotron_model/inference/postnet_convolutions/conv_layer_1_postnet_convolutions/conv1d/conv1d (defined at e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.

Original stack trace for 'Tacotron_model/inference/postnet_convolutions/conv_layer_1_postnet_convolutions/conv1d/conv1d':
  File "synthesizer_train.py", line 55, in <module>
    tacotron_train(args, log_dir, hparams)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\train.py", line 392, in tacotron_train
    return train(log_dir, args, hparams)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\train.py", line 148, in train
    model, stats = model_train_mode(args, feeder, hparams, global_step)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\train.py", line 91, in model_train_mode
    is_training=True, split_infos=feeder.split_infos)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\models\tacotron.py", line 230, in initialize
    residual = postnet(decoder_output)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\models\modules.py", line 406, in __call__
    "conv_layer_{}_".format(i + 1) + self.scope)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\models\modules.py", line 420, in conv1d
    padding="same")
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\layers\convolutional.py", line 218, in conv1d
    return layer.apply(inputs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 1700, in apply
    return self.__call__(inputs, *args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\layers\base.py", line 548, in __call__
    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 854, in __call__
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 234, in wrapper
    return converted_call(f, options, args, kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 439, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 330, in _call_unconverted
    return f(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 387, in call
    return super(Conv1D, self).call(inputs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 197, in call
    outputs = self._convolution_op(inputs, self.kernel)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1134, in __call__
    return self.conv_op(inp, filter)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 639, in __call__
    return self.call(inp, filter)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 238, in __call__
    name=self.name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 227, in _conv1d
    name=name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 574, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 574, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1681, in conv1d
    name=name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1071, in conv2d
    data_format=data_format, dilations=dilations, name=name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\op_def_library.py", line 794, in _apply_op_helper
    op_def=op_def)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3357, in create_op
    attrs, op_def, compute_device)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3426, in _create_op_internal
    op_def=op_def)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1748, in __init__
    self._traceback = tf_stack.extract_stack()

2021-02-05 20:02:33.232435: W tensorflow/core/kernels/queue_base.cc:277] _1_datafeeder/eval_queue: Skipping cancelled enqueue attempt with queue not closed
2021-02-05 20:02:33.232577: W tensorflow/core/kernels/queue_base.cc:277] _0_datafeeder/input_queue: Skipping cancelled enqueue attempt with queue not closed

```
I think it can't use the memory of my GTX 1660 super .Tell the noob what to do


Python 3.10.12 is not supported

When I ran python3.10 -m pip install numpy==1.20.3 on linux mint, I got an error while I was trying to install it. But it was totally fine when I used python3.8
![12](https://github.com/CorentinJ/Real-Time-Voice-Cloning/assets/100217654/99071c68-bf38-4ffe-b789-9d292ed539a5)

{'base_commit': '77c0bd169d8158ed1cdb180cda73c24d3cacd778', 'files': [{'path': 'requirements.txt', 'Loc': {'(None, None, None)': {'mod': [4]}}, 'status': 'modified'}]}


[Errno 2] No such file or directory: 'encoder/_sources.txt'

I have this problem, but I can't understand what does this file contain? There is not _sources.txt in this repo

{'base_commit': 'c5c2261c97afe6ec5db1ef389eba1257f6cce8a2', 'files': [{'path': 'encoder_preprocess.py', 'Loc': {}}]}


Tensorflow.contrib file missing  what to do

0
and
2
这里是指导是doc
问题原因是依赖的库的版本


Getting an exception when browsing for files

For some reason, importing mp3 files is not working.  Anyone got an idea on why this might be the case.?


A couple inquiries about the colab version

So I have a setup using a copy of the colaboratory version, but I want to be able to generate a few sentences at a time without having to generate per sentence.

I understand that commas and periods don't work, but in the demonstration video it was mentioned that line breaks are a way to get around this for now... however that's done in the toolbox application. How would it be done in code?

I've tried \n but I assume that's only for print related arguments... but I'm fairly new to Python so excuse my ignorance.

On top of this, how could I improve the voice in colab? In regards to training, it's mentioned that a decent session requires around 500gb or more... since I don't exactly have that in colab, is there another way to go about doing this?

I've tried the code with the input being longer than 10 seconds, but apparently if the input is more than 10 seconds or so the voice seems more jittery than it would be if it were capped at 10 seconds. I absolutely applaud this repo but I just really need to understand it a bit better... Thanks in advance.


Not code-savy but want to experiment with code

I have Python Spyder downloaded, but I do not know much about coding, or how to get to the stage where I can add audio and synthesize it. What would you recommend?

{'base_commit': 'c5c2261c97afe6ec5db1ef389eba1257f6cce8a2', 'files': [{'path': 'requirements.txt', 'Loc': {}}]}


i cant install NVIDIA CUDA

I can't install NVIDIA CUDA even though I followed everything that [this guide](https://poorlydocumented.com/2019/11/installing-corentinjs-real-time-voice-cloning-project-on-windows-10-from-scratch/l) told me to do. I also have tried searching for this problem on the internet, but none of them solves my problem. I also have provided the image of the error [here](https://imgur.com/a/fYkiBYQ).


New Audio Issue: Assertion Failed

This was working yesterday fine, and no big changes were made.
However, today starting up the demo toolbox loaded:
Assertion failed!

Program: C:\Users\paul1\AppData\Local\Programs\Python\Python37\python.exe
File: src/hostapi/wdmks/pa_win_wdmks.c, Line 1061

Expression: FALSE

I have tried reinstalling visual studio as well, but to no avail. Any thoughts on this would be deeply appreciated.


[Bug]: Non checkpoints found. Can't run without a checkpoint.

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

During the installation (windows), an error occurs :
```
venv "G:\Dev\stable-diffusion-webui\venv\Scripts\Python.exe"
Python 3.10.8 (tags/v3.10.8:aaaf517, Oct 11 2022, 16:50:30) [MSC v.1933 64 bit (AMD64)]
Commit hash: 9e78d2c419732711e984c4478af15ece121d64fd
Installing requirements for Web UI
Launching Web UI with arguments:
No module 'xformers'. Proceeding without it.
No checkpoints found. When searching for checkpoints, looked at:
 - file G:\Dev\stable-diffusion-webui\model.ckpt
 - directory G:\Dev\stable-diffusion-webui\models\Stable-diffusion
Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.
```

### Steps to reproduce the problem

Launch webui-user.bat

### What should have happened?

Installation complete

### Commit where the problem happens

9e78d2c419732711e984c4478af15ece121d64fd

### What platforms do you use to access UI ?

Windows

### What browsers do you use to access the UI ?

Google Chrome

### Command Line Arguments

_No response_

### Additional information, context and logs

_No response_


[Bug]: ModuleNotFoundError: No module named 'pytorch_lightning.utilities.distributed'

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

Launching Web UI with arguments: --share --disable-safe-unpickle --no-half-vae --xformers --enable-insecure-extension --gradio-queue
2023-06-27 13:53:22.297173: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-27 13:53:23.287285: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /content/microsoftexcel/launch.py:38 in <module>                             │
│                                                                              │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│ ❱ 38 │   main()                                                              │
│   39                                                                         │
│                                                                              │
│ /content/microsoftexcel/launch.py:34 in main                                 │
│                                                                              │
│   31 │   if args.test_server:                                                │
│   32 │   │   configure_for_tests()                                           │
│   33 │                                                                       │
│ ❱ 34 │   start()                                                             │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│                                                                              │
│ /content/microsoftexcel/modules/launch_utils.py:340 in start                 │
│                                                                              │
│   337                                                                        │
│   338 def start():                                                           │
│   339 │   print(f"Launching {'API server' if '--nowebui' in sys.argv else 'W │
│ ❱ 340 │   import webui                                                       │
│   341 │   if '--nowebui' in sys.argv:                                        │
│   342 │   │   webui.api_only()                                               │
│   343 │   else:                                                              │
│                                                                              │
│ /content/microsoftexcel/webui.py:42 in <module>                              │
│                                                                              │
│    39 startup_timer.record("import ldm")                                     │
│    40                                                                        │
│    41 from modules import extra_networks                                     │
│ ❱  42 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, │
│    43                                                                        │
│    44 # Truncate version number of nightly/local build of PyTorch to not cau │
│    45 if ".dev" in torch.__version__ or "+git" in torch.__version__:         │
│                                                                              │
│ /content/microsoftexcel/modules/call_queue.py:5 in <module>                  │
│                                                                              │
│     2 import threading                                                       │
│     3 import time                                                            │
│     4                                                                        │
│ ❱   5 from modules import shared, progress, errors                           │
│     6                                                                        │
│     7 queue_lock = threading.Lock()                                          │
│     8                                                                        │
│                                                                              │
│ /content/microsoftexcel/modules/shared.py:18 in <module>                     │
│                                                                              │
│    15 import modules.devices as devices                                      │
│    16 from modules import localization, script_loading, errors, ui_component │
│    17 from modules.paths_internal import models_path, script_path, data_path │
│ ❱  18 from ldm.models.diffusion.ddpm import LatentDiffusion                  │
│    19 from typing import Optional                                            │
│    20                                                                        │
│    21 demo = None                                                            │
│                                                                              │
│ /content/microsoftexcel/repositories/stable-diffusion-stability-ai/ldm/model │
│ s/diffusion/ddpm.py:20 in <module>                                           │
│                                                                              │
│     17 import itertools                                                      │
│     18 from tqdm import tqdm                                                 │
│     19 from torchvision.utils import make_grid                               │
│ ❱   20 from pytorch_lightning.utilities.distributed import rank_zero_only    │
│     21 from omegaconf import ListConfig                                      │
│     22                                                                       │
│     23 from ldm.util import log_txt_as_img, exists, default, ismap, isimage, │
╰──────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'pytorch_lightning.utilities.distributed'

### Steps to reproduce the problem

1. on colab
2. try to use the new 1.4.0 release
3. error

### What should have happened?

no error

### Version or Commit where the problem happens

1.4.0

### What Python version are you running on ?

None

### What platforms do you use to access the UI ?

Other/Cloud

### What device are you running WebUI on?

_No response_

### Cross attention optimization

Automatic

### What browsers do you use to access the UI ?

Google Chrome

### Command Line Arguments

```Shell
!COMMANDLINE_ARGS="--share --disable-safe-unpickle --no-half-vae --xformers --enable-insecure-extension --gradio-queue" REQS_FILE="requirements.txt" python launch.py
```

### List of extensions

sd-webui-tunnels
controlnet
openpose-editor
posex
a1111-sd-webui-tagcomplete
supermerger
ultimate-upscale-for-automatic1111
a111 locon extension
images browser

### Console logs

```Shell
**truncated on colab**

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100  1217  100  1217    0     0   3699      0 --:--:-- --:--:-- --:--:--  3699
100 1722k  100 1722k    0     0   670k      0  0:00:02  0:00:02 --:--:-- 1355k
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Cloning into '/content/microsoftexcel/extensions/microsoftexcel-tunnels'...
remote: Enumerating objects: 143, done.
remote: Counting objects: 100% (38/38), done.
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/content/microsoftexcel/embeddings
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/content/microsoftexcel/models/ESRGAN
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/content/microsoftexcel
fatal: not a git repository (or any of the parent directories): .git
fatal: not a git repository (or any of the parent directories): .git
Python 3.10.12 (main, Jun  7 2023, 12:45:35) [GCC 9.4.0]
Version: ## 1.4.0
Commit hash: <none>
Installing gfpgan
Installing clip
Installing open_clip
Installing xformers
Cloning Stable Diffusion into /content/microsoftexcel/repositories/stable-diffusion-stability-ai...
Cloning K-diffusion into /content/microsoftexcel/repositories/k-diffusion...
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Cloning BLIP into /content/microsoftexcel/repositories/BLIP...
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Installing sd-webui-controlnet requirement: mediapipe
Installing sd-webui-controlnet requirement: svglib
Installing sd-webui-controlnet requirement: fvcore

Installing pycloudflared

Installing diffusers

Launching Web UI with arguments: --share --disable-safe-unpickle --no-half-vae --xformers --enable-insecure-extension --gradio-queue
2023-06-27 13:53:22.297173: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-27 13:53:23.287285: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /content/microsoftexcel/launch.py:38 in <module>                             │
│                                                                              │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│ ❱ 38 │   main()                                                              │
│   39                                                                         │
│                                                                              │
│ /content/microsoftexcel/launch.py:34 in main                                 │
│                                                                              │
│   31 │   if args.test_server:                                                │
│   32 │   │   configure_for_tests()                                           │
│   33 │                                                                       │
│ ❱ 34 │   start()                                                             │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│                                                                              │
│ /content/microsoftexcel/modules/launch_utils.py:340 in start                 │
│                                                                              │
│   337                                                                        │
│   338 def start():                                                           │
│   339 │   print(f"Launching {'API server' if '--nowebui' in sys.argv else 'W │
│ ❱ 340 │   import webui                                                       │
│   341 │   if '--nowebui' in sys.argv:                                        │
│   342 │   │   webui.api_only()                                               │
│   343 │   else:                                                              │
│                                                                              │
│ /content/microsoftexcel/webui.py:42 in <module>                              │
│                                                                              │
│    39 startup_timer.record("import ldm")                                     │
│    40                                                                        │
│    41 from modules import extra_networks                                     │
│ ❱  42 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, │
│    43                                                                        │
│    44 # Truncate version number of nightly/local build of PyTorch to not cau │
│    45 if ".dev" in torch.__version__ or "+git" in torch.__version__:         │
│                                                                              │
│ /content/microsoftexcel/modules/call_queue.py:5 in <module>                  │
│                                                                              │
│     2 import threading                                                       │
│     3 import time                                                            │
│     4                                                                        │
│ ❱   5 from modules import shared, progress, errors                           │
│     6                                                                        │
│     7 queue_lock = threading.Lock()                                          │
│     8                                                                        │
│                                                                              │
│ /content/microsoftexcel/modules/shared.py:18 in <module>                     │
│                                                                              │
│    15 import modules.devices as devices                                      │
│    16 from modules import localization, script_loading, errors, ui_component │
│    17 from modules.paths_internal import models_path, script_path, data_path │
│ ❱  18 from ldm.models.diffusion.ddpm import LatentDiffusion                  │
│    19 from typing import Optional                                            │
│    20                                                                        │
│    21 demo = None                                                            │
│                                                                              │
│ /content/microsoftexcel/repositories/stable-diffusion-stability-ai/ldm/model │
│ s/diffusion/ddpm.py:20 in <module>                                           │
│                                                                              │
│     17 import itertools                                                      │
│     18 from tqdm import tqdm                                                 │
│     19 from torchvision.utils import make_grid                               │
│ ❱   20 from pytorch_lightning.utilities.distributed import rank_zero_only    │
│     21 from omegaconf import ListConfig                                      │
│     22                                                                       │
│     23 from ldm.util import log_txt_as_img, exists, default, ismap, isimage, │
╰──────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'pytorch_lightning.utilities.distributed'
```

### Additional information

_No response_

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[Feature Request]:

Finer control of CFG Scale? now it goes by 0.5 steps. I'm trying to replicate work i did on other app which have CFG scale control by 0.1. i cannot get the same result, of course.


RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper__index_select)

how to pick the CUDA:0 ？


How to use .ckpt model on repo

Hello everyone!

I was able to train a custom model using Dreambooth and I now have a custom ckpt trained on myself. Where do I put this file to be able to use it in this repo?

I dropped in into models but not sure what to do next?

Appreciate any help

{'base_commit': '39919c40dd18f5a14ae21403efea1b0f819756c7', 'files': [{'path': 'models/Stable-diffusion', 'Loc': {}}]}


Feature request: GPU temperature control

**Is your feature request related to a problem? Please describe.**
I don't like 85 degrees (Celsius) on my GPU, especially if it lasts more than 30 minutes or even 1 hour

**Describe the solution you'd like**
If temp on a GPU is more than {maxTemp} and it lasts {accumulateTempTime} it will pause processing for {cooldownTime} or until it cools to {minTemp}, so my GPU won't end up with exploding

**Describe alternatives you've considered**
Not pausing, but lowering the activity to a few tens of seconds per step.

**Additional context**
Not lowering it in hard core, but smartly lowering activity (using sth similar to PID), so the temp will stay at {desiredTemp}


BPO | [832251](https://bugs.python.org/issue832251)
--- | :---
Nosy | @freddrake

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/freddrake'
closed_at = <Date 2004-04-01.04:19:08.000>
created_at = <Date 2003-10-29.09:39:39.000>
labels = ['docs']
title = 'Wrong reference for specific minidom methods'
updated_at = <Date 2004-04-01.04:19:08.000>
user = 'https://bugs.python.org/nerby'
```

bugs.python.org fields:
```python
activity = <Date 2004-04-01.04:19:08.000>
actor = 'fdrake'
assignee = 'fdrake'
closed = True
closed_date = None
closer = None
components = ['Documentation']
creation = <Date 2003-10-29.09:39:39.000>
creator = 'nerby'
dependencies = []
files = []
hgrepos = []
issue_num = 832251
keywords = []
message_count = 3.0
messages = ['18799', '18800', '18801']
nosy_count = 2.0
nosy_names = ['fdrake', 'nerby']
pr_nums = []
priority = 'high'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = None
url = 'https://bugs.python.org/issue832251'
versions = ['Python 2.3']
```

{'base_commit': 'c40b7afee28fb928fdc3f07a9a7e9d4ef17347ba', 'files': [{'path': 'Doc/lib/xmldomminidom.tex', 'Loc': {}}]}


BPO | [210610](https://bugs.python.org/issue210610)
--- | :---
Nosy | @gvanrossum

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/gvanrossum'
closed_at = <Date 2000-09-20.20:33:21.000>
created_at = <Date 2000-07-31.21:05:42.000>
labels = ['interpreter-core']
title = '.pyc writing/reading race condition (PR#189)'
updated_at = <Date 2000-09-20.20:33:21.000>
user = 'https://bugs.python.org/anonymous'
```

bugs.python.org fields:
```python
activity = <Date 2000-09-20.20:33:21.000>
actor = 'gvanrossum'
assignee = 'gvanrossum'
closed = True
closed_date = None
closer = None
components = ['Interpreter Core']
creation = <Date 2000-07-31.21:05:42.000>
creator = 'anonymous'
dependencies = []
files = []
hgrepos = []
issue_num = 210610
keywords = []
message_count = 4.0
messages = ['66', '67', '68', '69']
nosy_count = 2.0
nosy_names = ['gvanrossum', 'jhylton']
pr_nums = []
priority = 'low'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = None
url = 'https://bugs.python.org/issue210610'
versions = []
```


BPO | [24432](https://bugs.python.org/issue24432)
--- | :---
Nosy | @pfmoore, @pitrou, @larryhastings, @giampaolo, @tiran, @tjguk, @benjaminp, @ned-deily, @alex, @bitdancer, @zware, @zooba, @dstufft

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/zooba'
closed_at = <Date 2015-07-03.22:28:01.834>
created_at = <Date 2015-06-11.15:05:25.361>
labels = ['library', 'release-blocker']
title = 'Upgrade windows builds to use OpenSSL 1.0.2c'
updated_at = <Date 2015-07-04.06:47:41.096>
user = 'https://github.com/alex'
```

bugs.python.org fields:
```python
activity = <Date 2015-07-04.06:47:41.096>
actor = 'python-dev'
assignee = 'steve.dower'
closed = True
closed_date = <Date 2015-07-03.22:28:01.834>
closer = 'steve.dower'
components = ['Library (Lib)']
creation = <Date 2015-06-11.15:05:25.361>
creator = 'alex'
dependencies = []
files = []
hgrepos = []
issue_num = 24432
keywords = ['security_issue']
message_count = 29.0
messages = ['245173', '245178', '245283', '246116', '246133', '246136', '246143', '246172', '246182', '246185', '246189', '246190', '246195', '246205', '246209', '246210', '246211', '246212', '246213', '246214', '246215', '246216', '246221', '246222', '246224', '246225', '246227', '246228', '246240']
nosy_count = 15.0
nosy_names = ['paul.moore', 'janssen', 'pitrou', 'larry', 'giampaolo.rodola', 'christian.heimes', 'tim.golden', 'benjamin.peterson', 'ned.deily', 'alex', 'r.david.murray', 'python-dev', 'zach.ware', 'steve.dower', 'dstufft']
pr_nums = []
priority = 'release blocker'
resolution = 'fixed'
stage = 'resolved'
status = 'closed'
superseder = None
type = None
url = 'https://bugs.python.org/issue24432'
versions = ['Python 2.7', 'Python 3.4', 'Python 3.5', 'Python 3.6']
```

{'base_commit': 'adf03c3544084359d89e7a0bc2a5aa0561f1a0f2', 'files': [{'path': 'PCbuild/get_externals.bat', 'Loc': {'(None, None, 57)': {'mod': [57]}}, 'status': 'modified'}, {'path': 'PCbuild/python.props', 'Loc': {'(None, None, 37)': {'mod': [37]}}, 'status': 'modified'}, {'path': 'PCbuild/readme.txt', 'Loc': {'(None, None, 200)': {'mod': [200]}}, 'status': 'modified'}]}


BPO | [4185](https://bugs.python.org/issue4185)
--- | :---
Nosy | @gvanrossum, @loewis, @akuchling, @birkenfeld, @ezio-melotti
Files |
[raw-strings-with-re.txt](https://bugs.python.org/file11868/raw-strings-with-re.txt "Uploaded as text/plain at 2008-10-23.03:55:27 by @ezio-melotti"): Interactive Python session with more examples

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/akuchling'
closed_at = <Date 2009-01-01.12:00:35.699>
created_at = <Date 2008-10-23.03:55:28.615>
labels = ['expert-regex', 'type-bug', 'library']
title = 're module treats raw strings as normal strings'
updated_at = <Date 2009-01-01.12:00:35.697>
user = 'https://github.com/ezio-melotti'
```

bugs.python.org fields:
```python
activity = <Date 2009-01-01.12:00:35.697>
actor = 'georg.brandl'
assignee = 'akuchling'
closed = True
closed_date = <Date 2009-01-01.12:00:35.699>
closer = 'georg.brandl'
components = ['Library (Lib)', 'Regular Expressions']
creation = <Date 2008-10-23.03:55:28.615>
creator = 'ezio.melotti'
dependencies = []
files = ['11868']
hgrepos = []
issue_num = 4185
keywords = []
message_count = 8.0
messages = ['75133', '75134', '75135', '75760', '77502', '77562', '77575', '78699']
nosy_count = 5.0
nosy_names = ['gvanrossum', 'loewis', 'akuchling', 'georg.brandl', 'ezio.melotti']
pr_nums = []
priority = 'normal'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = 'behavior'
url = 'https://bugs.python.org/issue4185'
versions = ['Python 2.6', 'Python 2.5', 'Python 2.4']
```