import os import torch import argparse import gradio as gr import sys #from zipfile import ZipFile from melo.api import TTS # Init EN/ZH baseTTS and ToneConvertor from OpenVoice import se_extractor from OpenVoice.api import ToneColorConverter import devicetorch device = devicetorch.get(torch) ckpt_converter = 'checkpoints/converter' tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device) tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth') #languages = ["EN_NEWEST", "EN", "ES", "FR", "ZH", "JP", "KR"] en = ["EN-Default", "EN-US", "EN-BR", "EN_INDIA", "EN-AU"] LANG = sys.argv[1].strip() print(f"LANG={LANG}") #def predict(prompt, style, audio_file_pth, mic_file_path, use_mic, language): def predict(prompt, audio_file_pth, mic_file_path, use_mic, language, speed): # initialize a empty info text_hint = '' print(f"language = {language}") lang_code = language # lang_code = language if language.startswith("EN"): lang_code = "EN" tts_model = TTS(language=lang_code, device=device) speaker_key = language.lower().replace('_', '-') source_se = torch.load(f'checkpoints/base_speakers/ses/{speaker_key}.pth', map_location=device) if use_mic == True: if mic_file_path is not None: speaker_wav = mic_file_path else: text_hint += f"[ERROR] Please record your voice with Microphone, or uncheck Use Microphone to use reference audios\n" gr.Warning( "Please record your voice with Microphone, or uncheck Use Microphone to use reference audios" ) return ( text_hint, None, None, ) else: 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, ) # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference try: target_se, wavs_folder = se_extractor.get_se(speaker_wav, tone_color_converter, target_dir='processed', max_length=60., vad=True) # os.system(f'rm -rf {wavs_folder}') 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, ) output_dir = os.path.abspath("output") src_path = f'{output_dir}/tmp.wav' #speed = 1.0 print(f"speed = {speed}") #tts_model.tts_to_file(prompt, speaker_id, src_path, speaker=style, language=language) speaker_ids = tts_model.hps.data.spk2id print(f"Speaker_ids= {speaker_ids}, language={language}, speaker_key={speaker_key}") speaker_id = speaker_ids[language] tts_model.tts_to_file(prompt, speaker_id, src_path, speed=speed) 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, ) examples = [ [ "今天天气真好，我们一起出去吃饭吧。", # 'default', "examples/speaker0.mp3", None, False, "ZH", ], [ "お前はもう死んでいる", # 'default', "examples/speaker0.mp3", None, False, "JP", ], [ "오빤 강남 스타일", # 'default', "examples/speaker0.mp3", None, False, "KR", ], [ "This audio is generated by open voice with a half-performance model.", # 'whispering', "examples/speaker1.mp3", None, False, "EN-BR" ], [ "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', "examples/speaker2.mp3", None, False, "EN-BR" ], ] with gr.Blocks(analytics_enabled=False) as demo: # with gr.Row(): # gr.HTML(wrapped_markdown_content) 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", # info="Select a style of output audio for the synthesised speech. (Chinese only support 'default' now)", # choices=['default', 'whispering', 'cheerful', 'terrified', 'angry', 'sad', 'friendly'], # max_choices=1, # value="default", #) ref_gr = gr.Audio( label="Reference Audio", info="Click on the ✎ button to upload your own target speaker audio", type="filepath", value="examples/speaker0.mp3", ) mic_gr = gr.Audio( source="microphone", type="filepath", info="Use your microphone to record audio", label="Use Microphone for Reference", ) use_mic_gr = gr.Checkbox( label="Use Microphone", value=False, info="Notice: Microphone input may not work properly under traffic", ) speed = gr.Slider( label="Speed", minimum=0.1, maximum=3.0, value=1.0, ) #language = gr.Radio(['EN-Newest', 'EN-US', 'EN-BR', 'EN_INDIA', 'EN-AU', 'EN-Default', 'ES', 'FR', 'ZH', 'JP', 'KR'], label='Language', value='EN-Newest') if LANG.startswith("EN"): language = gr.Radio(['EN-US', 'EN-BR', 'EN_INDIA', 'EN-AU', 'EN-Default'], label='Language', value='EN-Default') else: language = gr.Radio([LANG], value=LANG, visible=False) 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") # gr.Examples(examples, # label="Examples", # #inputs=[input_text_gr, style_gr, ref_gr, mic_gr, use_mic_gr, language], # inputs=[input_text_gr, ref_gr, mic_gr, use_mic_gr, language], # outputs=[out_text_gr, audio_gr, ref_audio_gr], # fn=predict, # cache_examples=False,) #tts_button.click(predict, [input_text_gr, style_gr, ref_gr, mic_gr, use_mic_gr, language], outputs=[out_text_gr, audio_gr, ref_audio_gr]) tts_button.click(predict, [input_text_gr, ref_gr, mic_gr, use_mic_gr, language, speed], outputs=[out_text_gr, audio_gr, ref_audio_gr]) demo.queue() demo.launch(debug=True, show_api=True)