Update README.md

README.md

@@ -30,7 +30,7 @@ datasets:
 
 </details>
 
-## Model description
+## Model Description
 
 **Matcha-TTS** is an encoder-decoder architecture designed for fast acoustic modelling in TTS. 
 The encoder part is based on a text encoder and a phoneme duration prediction that together predict averaged acoustic features.
@@ -40,7 +40,7 @@ In the latter, by replacing 2D CNNs by 1D CNNs, a large reduction in memory cons
 **Matcha-TTS** is a non-autorregressive model trained with optimal-transport conditional flow matching (OT-CFM). 
 This yields an ODE-based decoder capable of generating high output quality in fewer synthesis steps than models trained using score matching.
 
-## Intended uses and limitations
+## Intended Uses and Limitations
 
 This model is intended to serve as an acoustic feature generator for multispeaker text-to-speech systems for the Catalan language. 
 It has been finetuned using a Catalan phonemizer, therefore if the model is used for other languages it may will not produce intelligible samples after mapping 
@@ -49,7 +49,7 @@ its output into a speech waveform.
 The quality of the samples can vary depending on the speaker. 
 This may be due to the sensitivity of the model in learning specific frequencies and also due to the quality of samples for each speaker.
 
-## How to use
+## How to Use
 
 ### Installation
 
@@ -85,13 +85,9 @@ pip install git+https://github.com/langtech-bsc/Matcha-TTS.git@dev-cat
 
 ```
 
-
 ### Generate 
 
-## Training
-
-### Adaptation
-
+## Training Details
 
 ### Training data
 
@@ -102,13 +98,11 @@ The model was trained on 2 **Catalan** speech datasets
 | Festcat             | ca       | 22      |
 | OpenSLR69           | ca       | 5       |
 
-
-### Framework
+### Training procedure
 
 
 ## Evaluation
 
-### Results
 
 ## Citation
 
@@ -125,7 +119,7 @@ If this code contributes to your research, please cite the work:
 }
 ```
 
-## Additional information
+## Additional Information
 
 ### Author
 The Language Technologies Unit from Barcelona Supercomputing Center.