import os import torch import argparse import gradio as gr from zipfile import ZipFile import langid from openvoice import se_extractor from openvoice.api import BaseSpeakerTTS, ToneColorConverter parser = argparse.ArgumentParser() parser.add_argument("--share", action='store_true', default=False, help="make link public") args = parser.parse_args() en_ckpt_base = 'base_speakers/EN' zh_ckpt_base = 'base_speakers/ZH' ckpt_converter = 'converter' device = 'cuda' if torch.cuda.is_available() else 'cpu' output_dir = 'outputs' os.makedirs(output_dir, exist_ok=True) # load models en_base_speaker_tts = BaseSpeakerTTS(f'{en_ckpt_base}/config.json', device=device) en_base_speaker_tts.load_ckpt(f'{en_ckpt_base}/checkpoint.pth') zh_base_speaker_tts = BaseSpeakerTTS(f'{zh_ckpt_base}/config.json', device=device) zh_base_speaker_tts.load_ckpt(f'{zh_ckpt_base}/checkpoint.pth') tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device) tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth') # load speaker embeddings en_source_default_se = torch.load(f'{en_ckpt_base}/en_default_se.pth').to(device) en_source_style_se = torch.load(f'{en_ckpt_base}/en_style_se.pth').to(device) zh_source_se = torch.load(f'{zh_ckpt_base}/zh_default_se.pth').to(device) # This online demo mainly supports English and Chinese supported_languages = ['zh', 'en'] def predict(prompt, style, audio_file_pth): # initialize a empty info text_hint = '' # set agree to True by default agree = True # first detect the input language language_predicted = langid.classify(prompt)[0].strip() print(f"Detected language:{language_predicted}") if language_predicted not in supported_languages: text_hint += f"[ERROR] The detected language {language_predicted} for your input text is not in our Supported Languages: {supported_languages}\n" gr.Warning( f"The detected language {language_predicted} for your input text is not in our Supported Languages: {supported_languages}" ) return ( text_hint, None, None, ) if language_predicted == "zh": tts_model = zh_base_speaker_tts source_se = zh_source_se language = 'Chinese' if style not in ['default']: text_hint += f"[ERROR] The style {style} is not supported for Chinese, which should be in ['default']\n" gr.Warning(f"The style {style} is not supported for Chinese, which should be in ['default']") return ( text_hint, None, None, ) else: tts_model = en_base_speaker_tts if style == 'default': source_se = en_source_default_se else: source_se = en_source_style_se language = 'English' if style not in ['default', 'whispering', 'shouting', 'excited', 'cheerful', 'terrified', 'angry', 'sad', 'friendly']: text_hint += f"[ERROR] The style {style} is not supported for English, which should be in ['default', 'whispering', 'shouting', 'excited', 'cheerful', 'terrified', 'angry', 'sad', 'friendly']\n" gr.Warning(f"The style {style} is not supported for English, which should be in ['default', 'whispering', 'shouting', 'excited', 'cheerful', 'terrified', 'angry', 'sad', 'friendly']") return ( text_hint, None, None, ) speaker_wav = audio_file_pth if len(prompt) < 2: text_hint += f"[ERROR] Please give a longer prompt text \n" gr.Warning("Please give a longer prompt text") return ( text_hint, None, None, ) if len(prompt) > 200: text_hint += f"[ERROR] Text length limited to 200 characters for this demo, please try shorter text. You can clone our open-source repo and try for your usage \n" gr.Warning( "Text length limited to 200 characters for this demo, please try shorter text. You can clone our open-source repo for your usage" ) return ( text_hint, None, None, ) # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference try: target_se, audio_name = se_extractor.get_se(speaker_wav, tone_color_converter, target_dir='processed', vad=True) # base_speaker = f"{output_dir}/openai_source_output.mp3" # source_se, audio_name = se_extractor.get_se(base_speaker, tone_color_converter, vad=True) except Exception as e: text_hint += f"[ERROR] Get target tone color error {str(e)} \n" gr.Warning( "[ERROR] Get target tone color error {str(e)} \n" ) return ( text_hint, None, None, ) src_path = f'{output_dir}/tmp.wav' tts_model.tts(prompt, src_path, speaker=style, language=language) save_path = f'{output_dir}/output.wav' # 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) text_hint += f'''Get response successfully \n''' return ( text_hint, save_path, speaker_wav, ) title = "MyShell OpenVoice" examples = [ [ "今天天气真好，我们一起出去吃饭吧。", 'default', "resources/demo_speaker1.mp3", ],[ "This audio is generated by open voice with a half-performance model.", 'whispering', "resources/demo_speaker2.mp3", ], [ "He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered, flour-fattened sauce.", 'sad', "resources/demo_speaker0.mp3", ], ] with gr.Blocks(analytics_enabled=False) as demo: with gr.Row(): with gr.Column(): input_text_gr = gr.Textbox( label="Text Prompt", info="One or two sentences at a time is better. Up to 200 text characters.", value="He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered, flour-fattened sauce.", ) style_gr = gr.Dropdown( label="Style", choices=['default', 'whispering', 'cheerful', 'terrified', 'angry', 'sad', 'friendly'], info="Please upload a reference audio file, it should be 1 minute long and clear.", max_choices=1, value="default", ) ref_gr = gr.Audio( label="Reference Audio", type="filepath", value="resources/demo_speaker2.mp3", ) tts_button = gr.Button("Send", elem_id="send-btn", visible=True) with gr.Column(): out_text_gr = gr.Text(label="Info") audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True) ref_audio_gr = gr.Audio(label="Reference Audio Used") tts_button.click(predict, [input_text_gr, style_gr, ref_gr], outputs=[out_text_gr, audio_gr, ref_audio_gr]) demo.queue() demo.launch(debug=True, show_api=True, share=True)