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import gradio as gr
import torch
import os
import sys
import subprocess
import spaces
from pathlib import Path
# Clone and setup the repository
@spaces.GPU
def setup_environment():
if not os.path.exists('LLaMA-Omni'):
subprocess.run(['git', 'clone', 'https://github.com/ictnlp/LLaMA-Omni'])
# Add to path
sys.path.append(os.path.join(os.path.dirname(__file__), 'LLaMA-Omni'))
# Download models
os.makedirs('models/speech_encoder', exist_ok=True)
os.makedirs('vocoder', exist_ok=True)
# Download Whisper
if not os.path.exists('models/speech_encoder/large-v3.pt'):
import whisper
whisper.load_model("large-v3", download_root="models/speech_encoder/")
# Download vocoder
if not os.path.exists('vocoder/g_00500000'):
subprocess.run([
'wget', '-q',
'https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000',
'-P', 'vocoder/'
])
subprocess.run([
'wget', '-q',
'https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/config.json',
'-P', 'vocoder/'
])
# Global variables for model
model = None
speech_generator = None
@spaces.GPU
def load_models():
global model, speech_generator
if model is None:
setup_environment()
from omni_speech.model import OmniSpeechModel
from omni_speech.speech_generator import SpeechGeneratorCausalFull
# Load model
model_path = "ICTNLP/Llama-3.1-8B-Omni"
model = OmniSpeechModel.from_pretrained(model_path, torch_dtype=torch.float16)
model = model.cuda()
# Initialize speech generator
speech_generator = SpeechGeneratorCausalFull(
model=model,
vocoder='vocoder/g_00500000',
vocoder_cfg='vocoder/config.json'
)
@spaces.GPU(duration=60)
def process_audio(audio_path, text_input=None):
"""Process audio input and generate text and speech response."""
# Load models if needed
load_models()
from omni_speech.conversation import conv_templates
from omni_speech.utils import build_transform_audios
# Load and preprocess audio
transform = build_transform_audios()
audio_tensor = transform(audio_path)
# Prepare conversation
conv = conv_templates["llama_3"].copy()
if text_input:
conv.append_message(conv.roles[0], text_input)
else:
conv.append_message(conv.roles[0], "<Audio>")
conv.append_message(conv.roles[1], None)
# Generate response
with torch.no_grad():
# Get text response
text_output = model.generate(
audio_tensor.unsqueeze(0).cuda(),
conv.get_prompt(),
max_new_tokens=512,
temperature=0.7,
do_sample=True
)
# Generate speech response
speech_output = speech_generator.generate(
audio_tensor.unsqueeze(0).cuda(),
text_output
)
return text_output, speech_output
# Create Gradio interface
with gr.Blocks(title="LLaMA-Omni: Speech-Language Model") as demo:
gr.Markdown("""
# 🦙🎧 LLaMA-Omni: Seamless Speech Interaction
Upload an audio file or record your voice to interact with LLaMA-Omni.
The model will generate both text and speech responses.
""")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(
sources=["upload", "microphone"],
type="filepath",
label="Speech Input"
)
text_input = gr.Textbox(
label="Text Input (Optional)",
placeholder="You can also provide text context..."
)
submit_btn = gr.Button("Submit", variant="primary")
with gr.Column():
text_output = gr.Textbox(
label="Text Response",
lines=5
)
audio_output = gr.Audio(
label="Speech Response",
type="filepath"
)
# Handle submission
submit_btn.click(
fn=process_audio,
inputs=[audio_input, text_input],
outputs=[text_output, audio_output]
)
# Examples
gr.Examples(
examples=[
["examples/example1.wav", ""],
["examples/example2.wav", "Please explain in detail"],
],
inputs=[audio_input, text_input],
outputs=[text_output, audio_output],
fn=process_audio,
cache_examples=True
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True
) |