Fix grammar in README.md

README.md

@@ -10,7 +10,7 @@ tags:
 
 # StyleTTS2 — Basque Multispeaker TTS
 
-This is a BASQUE text-to-speech (TTS) model based on [StyleTTS2](https://github.com/yl4579/StyleTTS2) architecture, specifically adapted for Basque language synthesis. The model achieves good quality Basque speech synthesis. The mmodel was trained from scratch on Basque multispeaker [Sonora](https://zenodo.org/records/17952596) speech corpus.
+This is a Basque text-to-speech (TTS) model based on the [StyleTTS2](https://github.com/yl4579/StyleTTS2) architecture, specifically adapted for Basque language synthesis. The model achieves good-quality Basque speech synthesis. The model was trained from scratch on the Basque multispeaker [Sonora](https://zenodo.org/records/17952596) speech corpus.
  
 Examples (playable):
 
@@ -24,7 +24,7 @@ Examples (playable):
 
 Main modifications:
 - [PL-BERT-eu](https://huggingface.co/HiTZ/PL-BERT-wp-eu): PL-BERT model trained with WordPiece tokenizer for phonemized Basque text.
-- ASR-eu: ASR model trained with a subset of multispeaker speech corpus. Same architecture as in the original [ASR](https://github.com/yl4579/AuxiliaryASR) from StyleTTS2
+- ASR-eu: ASR model trained with a subset of the multispeaker speech corpus. It uses the same architecture as the original [ASR](https://github.com/yl4579/AuxiliaryASR) from StyleTTS2.
 - Phonemizer: We used code developed by [Aholab](https://aholab.ehu.eus/aholab/) to generate IPA phonemes for training the model. You can see a demo of the Basque phonemizer at [arrandi/phonemizer-eus-esp](https://huggingface.co/spaces/arrandi/phonemizer-eus-esp). Likewise, the code used to generate IPA phonemes can be found in the `phonemizer` directory. We collapsed multi-character phonemes into single-character phonemes for better grapheme–phoneme alignment.
 
 
@@ -45,14 +45,14 @@ Main modifications:
 ## Training dataset
 
 [Sonora](https://zenodo.org/records/17952596) multispeaker Basque speech dataset. 
-- Number of speaker: two speakers
-- Audios available: 13,500 utterances per speaker. A total of 34 hours and 18 minutes.
-- Dataset division: We used 100 samples for validation and 500 for testing.
-- OOD dataset: We use a different dataset text as Out-of-Distribution dataset
+- Number of speakers: two speakers
+- Audio: 13,500 utterances per speaker, totalling 34 hours and 18 minutes.
+- Dataset split: We used 100 samples for validation and 500 for testing.
+- OOD dataset: We use a different text dataset as the Out-of-Distribution (OOD) dataset.
 
 ## Training
 
-Small summary of training parameters used (from `config_basque_multispeaker_phoneme_wavlm_800.yml`):
+Brief summary of training parameters used (from `config_basque_multispeaker_phoneme_wavlm_800.yml`):
 
 - **Device:** cuda
 - **Stages:** 1st-stage epochs = 50; 2nd-stage epochs = 30