Update README.md

README.md

@@ -16,8 +16,6 @@ should probably proofread and complete it, then remove this comment. -->
 # speecht5_finetuned_kha
 
 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the audiofolder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4610
 
 
 ### Inference with a pipeline
@@ -27,7 +25,7 @@ pipe = pipeline("text-to-speech", model="jefson08/speecht5_finetuned_kha")
 ````
 
 #### Pick a piece of text in Khasi you’d like narrated, e.g.: "Kumno phi long?"
-````
+````python
 text = "Kumno phi long?"
 #Convert the given text to lowercase
 text = text.lower()
@@ -36,7 +34,7 @@ print(text)
 
 ### To use SpeechT5 with the pipeline, you’ll need a speaker embedding.
 ### Let’s get it from a json file i.e already saved embedding
-````
+````python
 from huggingface_hub import hf_hub_download
 hf_hub_download(repo_id="jefson08/speecht5_finetuned_kha", filename="speakerEmbedding.json", local_dir=".")
 
@@ -53,7 +51,7 @@ speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
 ````
 
 ### Now you can pass the text and speaker embeddings to the pipeline, and it will take care of the rest:
-````
+````python
 forward_params = {"speaker_embeddings": speaker_embeddings}
 output = pipe(text, forward_params=forward_params)
 output
@@ -61,7 +59,7 @@ output
 
 
 ### You can then listen to the result:
-````
+````python
 from IPython.display import Audio
 Audio(output['audio'], rate=output['sampling_rate']) 
 ````