Update README.md

README.md

@@ -1,6 +1,66 @@
 ---
-license: cc-by-nc-sa-4.0
-base_model:
-- SparkAudio/Spark-TTS-0.5B
-pipeline_tag: text-to-speech
----
+license: cc-by-nc-nd-4.0
+---
+
+
+# Spark TTS Vietnamese
+
+Spark-TTS is an advanced text-to-speech system that uses the power of large language models (LLM) for highly accurate and natural-sounding voice synthesis. It is designed to be efficient, flexible, and powerful for both research and production use. This model is trained from [viVoice](https://huggingface.co/datasets/thinhlpg/viVoice) vietnamese dataset
+
+# Usage
+
+First, install the required packages:
+
+```
+pip install --upgrade transformers accelerate
+```
+
+## Text-to-Speech
+We have customized the code so you can inference using the huggingface transformer library without installing anything else.
+
+```python
+from transformers import AutoProcessor, AutoModel, AutoTokenizer
+import soundfile as sf
+import torch
+import numpy as np
+
+device = "cuda"
+model_id = "DragonLineageAI/Vi-Spark-TTS-0.5B-v2"
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval()
+processor.model = model
+ 
+prompt_audio_path = "path_to_audio_path" # CHANGE TO YOUR ACTUAL PATH
+prompt_transcript = "text corresponding to prompt audio" # Optional
+text_input = "xin chào mọi người chúng tôi là Nguyễn Công Tú Anh và Chu Văn An đến từ dragonlineageai"
+ 
+inputs = processor(
+    text=text_input.lower(),
+    prompt_speech_path=prompt_audio_path,
+    prompt_text=None,
+    return_tensors="pt"
+).to(device)
+global_tokens_prompt = inputs.pop("global_token_ids_prompt", None)
+ 
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=3000,
+        do_sample=True,
+        temperature=0.8,
+        top_k=50,
+        top_p=0.95,
+        eos_token_id=processor.tokenizer.eos_token_id,  
+        pad_token_id=processor.tokenizer.pad_token_id  
+    )
+       
+output_clone = processor.decode(
+    generated_ids=output_ids,
+    global_token_ids_prompt=global_tokens_prompt,
+    input_ids_len=inputs["input_ids"].shape[-1]
+)
+ 
+sf.write("output_cloned.wav", output_clone["audio"], output_clone["sampling_rate"])
+```
+## Fintune
+You can finetune this model with any dataset to improve quality or train on a new language. [training code](https://github.com/tuanh123789/Spark-TTS-finetune)