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import spaces
import os
import random
import argparse
import torch
import gradio as gr
import numpy as np
import ChatTTS
import OpenVoice.se_extractor as se_extractor
from OpenVoice.api import ToneColorConverter
import soundfile
print("loading ChatTTS model...")
chat = ChatTTS.Chat()
chat.load_models()
def generate_seed():
new_seed = random.randint(1, 100000000)
return {
"__type__": "update",
"value": new_seed
}
@spaces.GPU
def chat_tts(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag, refine_text_input, output_path=None):
torch.manual_seed(audio_seed_input)
rand_spk = torch.randn(768)
params_infer_code = {
'spk_emb': rand_spk,
'temperature': temperature,
'top_P': top_P,
'top_K': top_K,
}
params_refine_text = {'prompt': '[oral_2][laugh_0][break_6]'}
torch.manual_seed(text_seed_input)
if refine_text_flag:
if refine_text_input:
params_refine_text['prompt'] = refine_text_input
text = chat.infer(text,
skip_refine_text=False,
refine_text_only=True,
params_refine_text=params_refine_text,
params_infer_code=params_infer_code
)
print("Text has been refined!")
wav = chat.infer(text,
skip_refine_text=True,
params_refine_text=params_refine_text,
params_infer_code=params_infer_code
)
audio_data = np.array(wav[0]).flatten()
sample_rate = 22050
text_data = text[0] if isinstance(text, list) else text
if output_path is None:
return [(sample_rate, audio_data), text_data]
else:
soundfile.write(output_path, audio_data, sample_rate)
return text_data
# OpenVoice Clone
ckpt_converter_en = 'checkpoints/converter'
device = 'cuda:0'
#device = "cpu"
tone_color_converter = ToneColorConverter(f'{ckpt_converter_en}/config.json', device=device)
tone_color_converter.load_ckpt(f'{ckpt_converter_en}/checkpoint.pth')
def generate_audio(text, audio_ref, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag, refine_text_input):
save_path = "output.wav"
if audio_ref != "" :
# Run the base speaker tts
src_path = "tmp.wav"
text_data = chat_tts(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag, refine_text_input, src_path)
print("Ready for voice cloning!")
source_se, audio_name = se_extractor.get_se(src_path, tone_color_converter, target_dir='processed', vad=True)
reference_speaker = audio_ref
target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True)
print("Get voices segment!")
# Run the tone color converter
# convert from file
tone_color_converter.convert(
audio_src_path=src_path,
src_se=source_se,
tgt_se=target_se,
output_path=save_path)
else:
chat_tts(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag, refine_text_input, save_path)
print("Finished!")
return [save_path, text_data]
with gr.Blocks() as demo:
gr.Markdown("# <center>🥳 ChatTTS x OpenVoice 🥳</center>")
gr.Markdown("## <center>🌟 Make it sound super natural and switch it up to any voice you want, nailing the mood and tone also!🌟 </center>")
gr.Markdown("### <center>💕 Slide on over to [linkin.love](https://linkin.love) for a chat-fest with your AI buds!💕</center>")
default_text = "Today a man knocked on my door and asked for a small donation toward the local swimming pool. I gave him a glass of water."
text_input = gr.Textbox(label="Input Text", lines=4, placeholder="Please Input Text...", value=default_text)
default_refine_text = "[oral_2][laugh_0][break_6]"
refine_text_checkbox = gr.Checkbox(label="Refine text", info="'oral' means add filler words, 'laugh' means add laughter, and 'break' means add a pause. (0-10) ", value=True)
refine_text_input = gr.Textbox(label="Refine Prompt", lines=1, placeholder="Please Refine Prompt...", value=default_refine_text)
with gr.Column():
voice_ref = gr.Audio(label="Reference Audio", info="Click on the ✎ button to upload your own target speaker audio", type="filepath", value="Examples/speaker.mp3")
with gr.Row():
temperature_slider = gr.Slider(minimum=0.00001, maximum=1.0, step=0.00001, value=0.3, label="Audio temperature")
top_p_slider = gr.Slider(minimum=0.1, maximum=0.9, step=0.05, value=0.7, label="top_P")
top_k_slider = gr.Slider(minimum=1, maximum=20, step=1, value=20, label="top_K")
with gr.Row():
audio_seed_input = gr.Number(value=42, label="Speaker Seed")
generate_audio_seed = gr.Button("\U0001F3B2")
text_seed_input = gr.Number(value=42, label="Text Seed")
generate_text_seed = gr.Button("\U0001F3B2")
generate_button = gr.Button("Generate")
text_output = gr.Textbox(label="Refined Text", interactive=False)
audio_output = gr.Audio(label="Output Audio")
generate_audio_seed.click(generate_seed,
inputs=[],
outputs=audio_seed_input)
generate_text_seed.click(generate_seed,
inputs=[],
outputs=text_seed_input)
generate_button.click(generate_audio,
inputs=[text_input, voice_ref, temperature_slider, top_p_slider, top_k_slider, audio_seed_input, text_seed_input, refine_text_checkbox, refine_text_input],
outputs=[audio_output,text_output])
parser = argparse.ArgumentParser(description='ChatTTS-OpenVoice Launch')
parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name')
parser.add_argument('--server_port', type=int, default=8080, help='Server port')
args = parser.parse_args()
# demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True)
if __name__ == '__main__':
demo.launch() |