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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 | |
| } | |
| 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() |