Update README.md

README.md

@@ -18,7 +18,7 @@ language:
 
 # whisper-large-v3-turbo-OpenSLR-GL
 
-This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) trained on [juanjucm/OpenSLR-SpeechT-GL-EN](https://huggingface.co/datasets/juanjucm/OpenSLR-SpeechT-GL-EN).  for **Galician Text to Speech** task. It takes galician speech audios as input and generates the correspondant transcription.
+This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) trained on [juanjucm/OpenSLR-SpeechT-GL-EN](https://huggingface.co/datasets/juanjucm/OpenSLR-SpeechT-GL-EN) for **Galician Text to Speech** task. It takes galician speech audios as input and generates the correspondant transcription.
 
 This Automatic Speech Recognition model, was developed to be the first stage of a Speech Translation cascade system for transcribing and translating Galician audios into English texts. After this first STT step, this [Galician-to-English MT model](https://huggingface.co/juanjucm/nllb-200-distilled-600M-OpenSLR-GL-EN) can applied over the generated Galician transcriptions to get English text translations.
 The motivation behind this work is to increase the visibility of the Galician language, making it more accessible for non-Galician speakers to understand and engage with Galician audio content.
@@ -27,9 +27,9 @@ This model was developed during a 3-week Speech Translation workshop organised b
 
 ### Performance and training details
 
-This model was developed during a 3-week Speech Translation workshop organised by [Yasmin Moslem](https://huggingface.co/ymoslem).
+It achieves the following results on the evaluation set:
 - Loss: 0.1613
-- Wer: 10.6845
+- **WER: 10.6845**
 
 The following hyperparameters were used during training:
 
@@ -48,6 +48,8 @@ The following hyperparameters were used during training:
 
 ### Training results
 
+We used [WER (Word Error Rate)](https://en.wikipedia.org/wiki/Word_error_rate) as our reference transcription metric for selecting the best checkpoint after training.
+
 | Training Loss | Epoch | Step | Validation Loss | Wer     |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|
 | 0.2739        | 1.0   | 75   | 0.1898          | 11.4023 |