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import os
import torch
import se_extractor
from api import BaseSpeakerTTS, ToneColorConverter
ckpt_base_en = 'checkpoints/checkpoints/base_speakers/EN'
ckpt_converter_en = 'checkpoints/checkpoints/converter'
device = 'cuda:0'
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')
from tts_voice import tts_order_voice
import edge_tts
import gradio as gr
import tempfile
import anyio
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"
app = gr.Blocks()
with app:
gr.Markdown("# <center>🥳💕🎶 OpenVoice 3秒语音情感真实复刻</center>")
gr.Markdown("## <center>🌟 只需3秒语音,一键复刻说话语气及情感,喜怒哀乐、应有尽有! </center>")
gr.Markdown("### <center>🌊 更多精彩应用,敬请关注[滔滔AI](http://www.talktalkai.com);滔滔AI,为爱滔滔!💕</center>")
with gr.Tab("💕语音情感合成"):
with gr.Row():
with gr.Column():
inp1 = gr.Textbox(lines=3, label="请输入您想转换的英文文本")
inp2 = gr.Audio(label="请上传您喜欢的语音文件", type="filepath")
inp3 = gr.Dropdown(label="请选择一种语音情感", info="🙂default😊friendly🤫whispering😄cheerful😱terrified😡angry😢sad", choices=["default", "friendly", "whispering", "cheerful", "terrified", "angry", "sad"], value="default")
btn1 = gr.Button("开始语音情感真实复刻吧!", variant="primary")
with gr.Column():
out1 = gr.Audio(label="为您合成的专属语音", type="filepath")
btn1.click(vc_en, [inp1, inp2, inp3], out1)
with gr.Tab("🌟多语言声音复刻"):
with gr.Row():
with gr.Column():
inp4 = gr.Textbox(lines=3, label="请输入您想转换的英文文本")
inp5 = gr.Audio(label="请上传您喜欢的语音文件", type="filepath")
inp6 = gr.Dropdown(choices=list(language_dict.keys()), value=list(language_dict.keys())[15], label="请选择文本对应的语言")
btn2 = gr.Button("开始语音情感真实复刻吧!", variant="primary")
with gr.Column():
out2 = gr.Audio(label="为您合成的专属语音", type="filepath")
btn2.click(text_to_speech_edge, [inp4, inp5, inp6], out2)
gr.Markdown("### <center>注意❗:请不要生成会对个人以及组织造成侵害的内容,此程序仅供科研、学习及个人娱乐使用。Get your OpenAI API Key [here](https://platform.openai.com/api-keys).</center>")
gr.HTML('''
<div class="footer">
<p>🌊🏞️🎶 - 江水东流急,滔滔无尽声。 明·顾璘
</p>
</div>
''')
app.launch(show_error=True)
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