Update README.md

README.md

@@ -26,6 +26,9 @@ tags:
 
 <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Yp-Ki76m2yF4keksD6ivE.wav"></audio>
 
+[ paralinguistic emotions soft] 
+
+<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/7NLmvAEjTHsvUcmuLcufC.wav"></audio>
 
 ## **Model Details**
 
@@ -65,6 +68,108 @@ notebook_login()
 
 ## **Usage**
 
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+from snac import SNAC
+
+def redistribute_codes(row):
+    """
+    Convert a sequence of token codes into an audio waveform using SNAC.
+    The code assumes each 7 tokens represent one group of instructions.
+    """
+    row_length = row.size(0)
+    new_length = (row_length // 7) * 7
+    trimmed_row = row[:new_length]
+    code_list = [t - 128266 for t in trimmed_row]
+    
+    layer_1, layer_2, layer_3 = [], [], []
+    
+    for i in range((len(code_list) + 1) // 7):
+        layer_1.append(code_list[7 * i][None])
+        layer_2.append(code_list[7 * i + 1][None] - 4096)
+        layer_3.append(code_list[7 * i + 2][None] - (2 * 4096))
+        layer_3.append(code_list[7 * i + 3][None] - (3 * 4096))
+        layer_2.append(code_list[7 * i + 4][None] - (4 * 4096))
+        layer_3.append(code_list[7 * i + 5][None] - (5 * 4096))
+        layer_3.append(code_list[7 * i + 6][None] - (6 * 4096))
+    
+    with torch.no_grad():
+        codes = [
+            torch.concat(layer_1),
+            torch.concat(layer_2),
+            torch.concat(layer_3)
+        ]
+        for i in range(len(codes)):
+            codes[i][codes[i] < 0] = 0
+            codes[i] = codes[i][None]
+        
+        audio_hat = snac_model.decode(codes)
+        return audio_hat.cpu()[0, 0]
+
+# Load the SNAC model for audio decoding
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to("cuda")
+
+# Load the single-speaker language model
+tokenizer = AutoTokenizer.from_pretrained('prithivMLmods/Llama-3B-Mono-Ceylia')
+model = AutoModelForCausalLM.from_pretrained(
+    'prithivMLmods/Llama-3B-Mono-Ceylia', torch_dtype=torch.bfloat16
+).cuda()
+
+def generate_audio(text, temperature, top_p, max_new_tokens):
+    """
+    Given input text, generate speech audio.
+    """
+    speaker = "Ceylia"
+    prompt = f'<custom_token_3><|begin_of_text|>{speaker}: {text}<|eot_id|><custom_token_4><custom_token_5><custom_token_1>'
+    input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').to('cuda')
+    
+    with torch.no_grad():
+        generated_ids = model.generate(
+            **input_ids,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=1.1,
+            num_return_sequences=1,
+            eos_token_id=128258,
+        )
+    
+    row = generated_ids[0, input_ids['input_ids'].shape[1]:]
+    y_tensor = redistribute_codes(row)
+    y_np = y_tensor.detach().cpu().numpy()
+    return (24000, y_np)
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Llama-3B-Mono-Ceylia - Single Speaker Audio Generation")
+    gr.Markdown("Generate speech audio using the `prithivMLmods/Llama-3B-Mono-Ceylia` model.")
+    
+    with gr.Row():
+        text_input = gr.Textbox(lines=4, label="Input Text")
+    
+    with gr.Row():
+        temp_slider = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.9, label="Temperature")
+        top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, step=0.05, value=0.8, label="Top-p")
+        tokens_slider = gr.Slider(minimum=100, maximum=2000, step=50, value=1200, label="Max New Tokens")
+    
+    output_audio = gr.Audio(type="numpy", label="Generated Audio")
+    generate_button = gr.Button("Generate Audio")
+    
+    generate_button.click(
+        fn=generate_audio,
+        inputs=[text_input, temp_slider, top_p_slider, tokens_slider],
+        outputs=output_audio
+    )
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+[ or ]
+
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM