Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fixed small spelling mistakes finetuning.md #2551

Merged
merged 1 commit into from
Jul 4, 2023
Merged

fixed small spelling mistakes finetuning.md #2551

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions docs/source/finetuning.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

## Fine-tuning

Fine-tuning takes a pre-trained model, and retrains it to improve the model performance on a different task or dataset.
Fine-tuning takes a pre-trained model and retrains it to improve the model performance on a different task or dataset.
In 🐸TTS we provide different pre-trained models in different languages and different pros and cons. You can take one of
them and fine-tune it for your own dataset. This will help you in two main ways:

Expand All @@ -11,15 +11,15 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
Since a pre-trained model has already learned features that are relevant for the task, it will converge faster on
a new dataset. This will reduce the cost of training and let you experiment faster.

2. Better resutls with small datasets
2. Better results with small datasets

Deep learning models are data hungry and they give better performance with more data. However, it is not always
possible to have this abundance, especially in specific domains. For instance, the LJSpeech dataset, that we released most of
our English models with, is almost 24 hours long. It takes weeks to record this amount of data with
the help of a voice actor.

Fine-tuning comes to the rescue in this case. You can take one of our pre-trained models and fine-tune it on your own
speech dataset and achive reasonable results with only a couple of hours of data.
speech dataset and achieve reasonable results with only a couple of hours of data.

However, note that, fine-tuning does not ensure great results. The model performance is still depends on the
{ref}`dataset quality <what_makes_a_good_dataset>` and the hyper-parameters you choose for fine-tuning. Therefore,
Expand All @@ -35,7 +35,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways:

2. Choose the model you want to fine-tune.

You can list the availabe models in the command line with
You can list the available models in the command line with

```bash
tts --list_models
Expand Down