KeyError: generator_model while computing embeddings

[Ca-ressemble-a-du-fake]

Hi,
I am fine-tuning the Experiement3+SCL model with VITS (this is following #1164). I read that I had to compute the d_vector with the compute_embeddings.py script. This script needs a dataset-config.json file to be supplied so I generated it by adding config.save_json("path_to_my_location").
But when I run compute_embeddings.py the following error occurs :

`

Traceback (most recent call last):
File "/content/TTS/TTS/bin/compute_embeddings.py", line 37, in
c_dataset = load_config(args.config_dataset_path)
File "/content/TTS/TTS/config/init.py", line 93, in load_config
model_name = _process_model_name(config_dict)
File "/content/TTS/TTS/config/init.py", line 60, in _process_model_name
model_name = config_dict["model"] if "model" in config_dict else config_dict["generator_model"]
KeyError: 'generator_model'

`
What shall I change in the dataset_config ?

Thanks

[Ca-ressemble-a-du-fake]

The dataset config json file has to feature a key named "model" with value "speaker_encoder".

See @jaggukaka answer here for an example.