import spaces import os import random import argparse import torch import gradio as gr import numpy as np import ChatTTS import se_extractor from api import BaseSpeakerTTS, ToneColorConverter import soundfile from tts_voice import tts_order_voice import edge_tts import tempfile import anyio 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 = 24000 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) # OpenVoice ckpt_base_en = 'checkpoints/base_speakers/EN' ckpt_converter_en = 'checkpoints/converter' device = 'cuda:0' #device = "cpu" base_speaker_tts = BaseSpeakerTTS(f'{ckpt_base_en}/config.json', device=device) base_speaker_tts.load_ckpt(f'{ckpt_base_en}/checkpoint.pth') 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): source_se = torch.load(f'{ckpt_base_en}/en_default_se.pth').to(device) reference_speaker = audio_ref target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True) save_path = "output.wav" # Run the base speaker tts src_path = "tmp.wav" 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) print("Get source segment!") # Run the tone color converter encode_message = "@Hilley" # convert from file tone_color_converter.convert( audio_src_path=src_path, src_se=source_se, tgt_se=target_se, output_path=save_path, message=encode_message) ''' # convert from data src_path = None sample_rate, audio = chat_tts(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, refine_text_flag, refine_text_input, src_path)[0] print("Ready for voice cloning!") tone_color_converter.convert_data( audio=audio, sample_rate=sample_rate, src_se=source_se, tgt_se=target_se, output_path=save_path, message=encode_message) ''' print("Finished!") return "output.wav" def vc_en(text, audio_ref, style_mode): if style_mode=="default": source_se = torch.load(f'{ckpt_base_en}/en_default_se.pth').to(device) reference_speaker = audio_ref target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True) save_path = "output.wav" # Run the base speaker tts src_path = "tmp.wav" base_speaker_tts.tts(text, src_path, speaker='default', language='English', speed=1.0) # Run the tone color converter encode_message = "@MyShell" tone_color_converter.convert( audio_src_path=src_path, src_se=source_se, tgt_se=target_se, output_path=save_path, message=encode_message) else: source_se = torch.load(f'{ckpt_base_en}/en_style_se.pth').to(device) reference_speaker = audio_ref target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True) save_path = "output.wav" # Run the base speaker tts src_path = "tmp.wav" base_speaker_tts.tts(text, src_path, speaker=style_mode, language='English', speed=0.9) # Run the tone color converter encode_message = "@MyShell" tone_color_converter.convert( audio_src_path=src_path, src_se=source_se, tgt_se=target_se, output_path=save_path, message=encode_message) return "output.wav" language_dict = tts_order_voice base_speaker = "base_audio.mp3" source_se, audio_name = se_extractor.get_se(base_speaker, tone_color_converter, vad=True) async def text_to_speech_edge(text, audio_ref, language_code): voice = language_dict[language_code] communicate = edge_tts.Communicate(text, voice) with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file: tmp_path = tmp_file.name await communicate.save(tmp_path) reference_speaker = audio_ref target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True) save_path = "output.wav" # Run the tone color converter encode_message = "@MyShell" tone_color_converter.convert( audio_src_path=tmp_path, src_se=source_se, tgt_se=target_se, output_path=save_path, message=encode_message) return "output.wav" with gr.Blocks() as demo: gr.Markdown("# Enjoy chatting with your ai friends on website, telegram and so on! (https://linkin.love)") 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) voice_ref = gr.Audio(label="Reference Audio", info="Click on the ✎ button to upload your own target speaker audio", type="filepath", value="base_audio.mp3") with gr.Tab("💕Super Natural"): 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.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) with gr.Tab("💕Emotion Control"): emo_pick = gr.Dropdown(label="Emotion", info="🙂default😊friendly🤫whispering😄cheerful😱terrified😡angry😢sad", choices=["default", "friendly", "whispering", "cheerful", "terrified", "angry", "sad"], value="default") generate_button_emo = gr.Button("Generate!", variant="primary") audio_emo = gr.Audio(label="Output Audio", type="filepath") generate_button_emo.click(vc_en, [text_input, voice_ref, emo_pick], audio_emo) with gr.Tab("💕multilingual"): language = gr.Dropdown(choices=list(language_dict.keys()), value=list(language_dict.keys())[15], label="请选择文本对应的语言及说话人") generate_button_ml = gr.Button("开始语音情感真实复刻吧！", variant="primary") audio_ml = gr.Audio(label="为您合成的专属语音", type="filepath") generate_button_ml.click(text_to_speech_edge, [text_input, voice_ref, language], audio_ml) parser = argparse.ArgumentParser(description='ChatTTS demo 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()