any list of all 36 voices?

[OpenMachinesAI]

just want a list

[LuckyMcBeast]

Yes, please. I looked everywhere to find one.

[Yazorp]

From this Reddit discussion: https://www.reddit.com/r/LocalLLaMA/comments/1encx98/improved_text_to_speech_model_parler_tts_v1_by/

Laura
Gary
Jon
Lea
Karen
Rick
Brenda
David
Eileen
Jordan
Mike
Yann
Joy
James
Eric
Lauren
Rose
Will
Jason
Aaron
Naomie
Alisa
Patrick
Jerry
Tina
Jenna
Bill
Tom
Carol
Barbara
Rebecca
Anna
Bruce
Emily

[ylacombe]

Hey, the previous list is indeed correct!
However, I've realized that the models were better at some speakers, namely:

Large - Top 20:

Will       0.906055
Eric       0.887598
Laura      0.877930
Alisa      0.877393
Patrick    0.873682
Rose       0.873047
Jerry      0.871582
Jordan     0.870703
Lauren     0.867432
Jenna      0.866455
Karen      0.866309
Rick       0.863135
Bill       0.862207
James      0.856934
Yann       0.856787
Emily      0.856543
Anna       0.848877
Jon        0.848828
Brenda     0.848291
Barbara    0.847998

Mini - Top 20:

Jon        0.908301
Lea        0.904785
Gary       0.903516
Jenna      0.901807
Mike       0.885742
Laura      0.882666
Lauren     0.878320
Eileen     0.875635
Alisa      0.874219
Karen      0.872363
Barbara    0.871509
Carol      0.863623
Emily      0.854932
Rose       0.852246
Will       0.851074
Patrick    0.850977
Eric       0.845459
Rick       0.845020
Anna       0.844922
Tina       0.839160

Would you like to add all of these information in the repo somewhere? If so, feel free to open a PR!

[dgm3333]

What are the numbers you've included (I'm guessing might be WER, generation speed, or some other accuracy measure)?
The list of names is already here: examples/prompt_creation/speaker_ids_to_names.json

[ylacombe]

Numbers represent average speaker similarity between random snippet of the person speaking and randomly Parler-generated snippet. The higher, the better the model is being able to keep voice consistency.
Numbers are from this dataset for Mini and this dataset for Large.

[kdcyberdude]

@ylacombe, How is the similarity score calculated? Did you use a specific speaker embedding model to obtain the similarity score?

[ylacombe]

Closed by #141 !