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---
license: llama3.2
language:
- en
base_model:
- canopylabs/orpheus-3b-0.1-ft
pipeline_tag: text-to-speech
library_name: transformers
tags:
- Voice:Luna
- Female
- Radio
- 3B
---
# **Llama-3B-Mono-Luna**
> Llama-3B-Mono-Luna is a Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been fine-tuned to deliver human-like speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performance. The model has been fine-tuned from mono audio of a female voice named 'Luna' with a radio essence using the base model `canopylabs/orpheus-3b-0.1-ft`.
> [!Important]
> In some cases, the results may be inconsistent, particularly when handling complex speech transformations.
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ye-Sonj51hDfi9IjUhl0B.wav"></audio>
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/OjMAZNjpip7tHu6T3_Rjm.wav"></audio>
[ paralinguistic emotions soft]
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/08Vb3CE8Ifi6jwpzI7Yuv.wav"></audio>
## **Model Details**
- **Base Model:** `canopylabs/orpheus-3b-0.1-ft`
- **Languages Supported:** English
- **License:** Llama 3.2
- **Model Version:** N/A
---
## **Paralinguistic Elements**
The model can generate speech with the following emotions:
| Elements | Elements | Elements |
|------------|------------|------------|
| laugh | chuckle | sigh |
| sniffle | groan | yawn |
| gasp | uhm | giggles & more |
---
## **Run with Transformers 🤗**
```python
from huggingface_hub import notebook_login, HfApi
notebook_login()
```
### **Install Dependencies**
```python
%%capture
!pip install snac accelerate
!pip install transformers
!pip install gradio
```
## **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-Luna')
model = AutoModelForCausalLM.from_pretrained(
'prithivMLmods/Llama-3B-Mono-Luna', torch_dtype=torch.bfloat16
).cuda()
def generate_audio(text, temperature, top_p, max_new_tokens):
"""
Given input text, generate speech audio.
"""
speaker = "Luna"
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-Luna - Single Speaker Audio Generation")
gr.Markdown("Generate speech audio using the `prithivMLmods/Llama-3B-Mono-Luna` 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
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-Luna')
model = AutoModelForCausalLM.from_pretrained(
'prithivMLmods/Llama-3B-Mono-Luna', torch_dtype=torch.bfloat16
).cuda()
def generate_audio(text, temperature, top_p, max_new_tokens):
"""
Given input text, generate speech audio.
"""
prompt = f'<custom_token_3><|begin_of_text|>{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-Luna - Single Speaker Audio Generation")
gr.Markdown("Generate speech audio using the `prithivMLmods/Llama-3B-Mono-Luna` 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()
```
---
## **Intended Use**
- Designed for high-quality, single-speaker text-to-speech generation.
- Ideal for applications requiring human-like speech synthesis.
- Supports a range of emotions for expressive speech output.
- Suitable for AI voice assistants, storytelling, and accessibility applications. |