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("#
🥳 ChatTTS x OpenVoice 🥳
") gr.Markdown("##
🌟 Make it sound super natural and switch it up to any voice you want, nailing the mood and tone also!🌟
") gr.Markdown("###
💕 Slide on over to [linkin.love](https://linkin.love) for a chat-fest with your AI buds!💕
") 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()