It currently clone a voice and implement it to a Text-To-Speech Synthesis, I was curious if we can clone a voice from a speech and implement it to the other speech instead of a Text-To-Speech Synthesis? Any insight would be much appreciated!

Query strings should always end with a slash if they are to work.

gs.Location.ListByPrefix only worked for blank prefixes that are not in the root of the bucket.

gs.Location.Exists checked against object does not exists instead of bucket does not exist.

Also added a bunch of unit tests for gs.Location, mostly copied from s3.

The unit tests make use of github.com/fsouza/fake-gcs-server/fakestorage.

Fixes #71

I use a dark theme from jupyterthemes and it's very difficult to see the text suggestions from TabNine. Would be great to be able to change the text color!

We can just change the color of the suggestions in custom.css file

• In Windows, find config directory by running a command: jupyter --config-dir
• In Linux it is ~/.jupyter
• If there no " custom " folder, create a folder with name custom and in that folder, create a custom.css file, If you already have the custom.css file, no need to do this
• Add these css lines at the beginning of the custom.css file and refresh the jupyter-notebook

.completions ,.complete-dropdown-content{
    color:black !important;
}

Tried reading one of the datasets with a column consisting of text data. Obtained a list of strings using the textual column from the dataframe.

On running the model, it throws the error :
TypeError: Cannot convert list to numpy.ndarray

Operations done :

1. info_df = pd.read_csv('/content/info.csv')
2. info_df. head
3. input_str_lst = df.Reviews.tolist()
4. type(input_str_lst) : O/P = List
5. model = Top2Vec(documents=input_str_lst)

Attached are screenshots

Thanks for the help

Closing forever since problem is solved

I experienced quite less training speed with GPU in Synthesizer phase of the model even though VRAM consumption was on point. I was wondering if there is any way to train on a cluster of CPUs which are not being utilized instead of GPUs.

/content/CogView Generate Samples WARNING: No training data specified using world size: 1 and model-parallel size: 1

using dynamic loss scaling initializing model parallel with size 1 initializing model parallel cuda seeds on global rank 0, model parallel rank 0, and data parallel rank 0 with model parallel seed: 3952 and data parallel seed: 1234 padded vocab (size: 58219) with 21 dummy tokens (new size: 58240) prepare tokenizer done building CogView2 model ... number of parameters on model parallel rank 0: 3928849920 current device: 0 tcmalloc: large alloc 7881007104 bytes == 0x5637e3fb2000 @ 0x7f61e428db6b 0x7f61e42ad379 0x7f6171f1e25e 0x7f6171f1f9d2 0x7f61aff48e7d 0x7f61c0b43120 0x7f61c0781bd9 0x5637152088a8 0x56371527bfd5 0x5637152767ad 0x5637152093ea 0x5637152773b5 0x5637152767ad 0x563715209003 0x563715208b09 0x56371535028d 0x5637152bf1db 0x563715207bb1 0x5637152f8fed 0x56371527b988 0x5637152767ad 0x563715148e2c 0x563715278bb5 0x5637152764ae 0x5637152093ea 0x56371527832a 0x56371520930a 0x5637152773b5 0x56371520930a 0x5637152773b5 0x5637152764ae Load model file pretrained/cogview/cogview-sr/20000/mp_rank_00_model_states.pt Working on No. 0 on 0... Traceback (most recent call last): File "generate_samples.py", line 326, in <module> main() File "generate_samples.py", line 323, in main generate_images_continually(model, args) File "generate_samples.py", line 215, in generate_images_continually for raw_text, seq, output_path in get_context(args, query_template): File "generate_samples.py", line 132, in get_context seq = _parse_and_to_tensor(raw_text, img_size=img_size, query_template=query_template) File "generate_samples.py", line 70, in _parse_and_to_tensor text = query_template.format(*text.split('\t')) IndexError: tuple index out of range /content

@johngore123 I think the guidance is enough, and your error message is very simple and clear: the input text cannot be split into two parts by "\t". The details mean that the printed results of text.split('\t') or something like that, without which I cannot offer more help.

Since nobody has gotten it to work.

That would help.

Still same error is there any way you could update the readme for an more detailed guide?

Ill try if it works ill close

#@title Super resolution (WIP) if you know how to implement this contact aze#1 on discord #@markdown this requires the cogview-sr model to be downloaded (look at the 3rd cell)

#@markdown can be 0.jpg, 1.jpg, 2.jpg ... etc depends on your batch_size image = "0.jpg" #@param {type:"string"}

prompt = "text\t/content/CogView/samples/" + image

%cd /content/CogView !sh scripts/super_resolution.sh %cd /content

一个漂亮的女人走在街上\t/content/CogView/samples/0.jpg

I am sorry but it seems the message is messy and I cannot know what you really input, but could you try to change the input source to a file?

./scripts/super_resolution.sh --input-source sr_input.txt

where sr_input.txt is a line of

 一个漂亮的女人走在街上\t/content/CogView/samples/0.jpg

\t is TAB, which looks like some spaces. The line looks like (but changing spaces to a TAB)

 一个漂亮的女人走在街上    /content/CogView/samples/0.jpg

With Chinese text but you get the idea, i think.

And those are just the default i did replace prompt =''text

#@title Super resolution (WIP) if you know how to implement this contact aze#0001 on discord #@markdown this requires the cogview-sr model to be downloaded (look at the 3rd cell)

#@markdown can be 0.jpg, 1.jpg, 2.jpg ... etc depends on your batch_size image = "0.jpg" #@param {type:"string"}

prompt = "text\t/content/CogView/samples/" + image

%cd /content/CogView !sh scripts/super_resolution.sh %cd /content

一个漂亮的女人走在街上\t/content/CogView/samples/0.jpg

Could need some more documentation.

Can you provide your input and details? \t means TAB

#10 (comment) Please add a torrent download method in colab or update links. Also this works https://github.com/nishan7/Google-Colab-Torrent-Downloader with some modifications and its a way faster than wget.

if you are able to provide a simple working solution and add it into the notebook, please contact me on discord aze#0001

Can someone explain step 2 (Pretrained files) in excruciating detail?

@qoevak

Detailed instructions for step 12 (pretrained files)

1. Open Windows Explorer and navigate to the downloaded pretrained.zip file.
2. Double-click to look inside the zip file.
3. You will see 3 folders, named encoder, synthesizer and vocoder.
   • This mirrors the directory structure of the repo.
4. Go inside the encoder folder. You will see a folder named saved_models.
5. Open a second Windows Explorer and navigate to C:\Real-Time-Voice-Cloning-master\encoder
6. Drag and drop the saved_models folder into C:\Real-Time-Voice-Cloning-master\encoder.
7. Repeat 4-6 for "synthesizer" and "vocoder":
   • Drag and drop the saved_models folder from the zip file's synthesizer folder into C:\Real-Time-Voice-Cloning-master\synthesizer.
   • Drag and drop the saved_models folder from the zip file's vocoder folder into C:\Real-Time-Voice-Cloning-master\vocoder.

Notes

• All of the pretrained model files in the zip file are named pretrained.pt, which can be super confusing.
  • The one inside "encoder" can only be used with the encoder.
  • The one inside "synthesizer" can only be used with the synthesizer.
  • The one inside "vocoder" can only be used with the vocoder.
• You can use the filesizes to tell them apart.
  • The encoder pretrained.pt is 17 MB.
  • The synthesizer pretrained.pt is 370 MB.
  • The vocoder pretrained.pt is 53 MB.

Could need some more documentation.

Issue still happens even with all those things.

Coverage remained the same at 90.63% when pulling e97306e1b625c0ab4dafd69e46515b81d5340233 on renovate/eslint-7.x into d1150f0222019d6fa358a2a1a4952af0431d9675 on master.

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
eslint (source)	7.28.0 -> 7.29.0

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• bfbfe5c New: Add only to RuleTester (refs eslint/rfcs#​73) (#​14677) (Brandon Mills)
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