Update README.md

README.md

@@ -19,55 +19,24 @@ This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingfa
 It achieves the following results on the evaluation set:
 - Loss: 0.4610
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 64
-- eval_batch_size: 2
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 1024
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- num_epochs: 400
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch    | Step | Validation Loss |
-|:-------------:|:--------:|:----:|:---------------:|
-| 0.4583        | 142.8571 | 1000 | 0.4495          |
-| 0.4288        | 285.7143 | 2000 | 0.4610          |
-
 
 ### Inference with a pipeline
+````
 from transformers import pipeline
 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?"
+````
 text = "Kumno phi long?"
 #Convert the given text to lowercase
 text = text.lower()
 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
+````
 from huggingface_hub import hf_hub_download
 hf_hub_download(repo_id="jefson08/speecht5_finetuned_kha", filename="speakerEmbedding.json", local_dir=".")
 
@@ -81,21 +50,57 @@ example = json.load(f)
 
 import torch
 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:
+````
 forward_params = {"speaker_embeddings": speaker_embeddings}
 output = pipe(text, forward_params=forward_params)
 output
+````
 
 
 ### You can then listen to the result:
+````
 from IPython.display import Audio
 Audio(output['audio'], rate=output['sampling_rate']) 
-
 ````
-```
-Look! You can see my backticks.
-```
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 64
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 1024
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 400
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch    | Step | Validation Loss |
+|:-------------:|:--------:|:----:|:---------------:|
+| 0.4583        | 142.8571 | 1000 | 0.4495          |
+| 0.4288        | 285.7143 | 2000 | 0.4610          |
 
 
 ### Framework versions