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import sys, os |
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import logging |
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logging.getLogger("numba").setLevel(logging.WARNING) |
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logging.getLogger("markdown_it").setLevel(logging.WARNING) |
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logging.getLogger("urllib3").setLevel(logging.WARNING) |
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logging.getLogger("matplotlib").setLevel(logging.WARNING) |
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logging.basicConfig( |
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level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" |
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) |
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logger = logging.getLogger(__name__) |
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import torch |
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import argparse |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import cleaned_text_to_sequence, get_bert |
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from text.cleaner import clean_text |
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import gradio as gr |
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import webbrowser |
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import numpy as np |
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net_g = None |
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if sys.platform == "darwin" and torch.backends.mps.is_available(): |
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device = "mps" |
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" |
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else: |
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device = "cuda" |
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def get_text(text, language_str, hps): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert = get_bert(norm_text, word2ph, language_str, device) |
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del word2ph |
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assert bert.shape[-1] == len(phone), phone |
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if language_str == "ZH": |
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bert = bert |
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ja_bert = torch.zeros(768, len(phone)) |
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elif language_str == "JP": |
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ja_bert = bert |
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bert = torch.zeros(1024, len(phone)) |
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else: |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = torch.zeros(768, len(phone)) |
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assert bert.shape[-1] == len( |
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phone |
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), f"Bert seq len {bert.shape[-1]} != {len(phone)}" |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, ja_bert, phone, tone, language |
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def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language): |
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global net_g |
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bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps) |
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with torch.no_grad(): |
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x_tst = phones.to(device).unsqueeze(0) |
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tones = tones.to(device).unsqueeze(0) |
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lang_ids = lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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ja_bert = ja_bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = ( |
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net_g.infer( |
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x_tst, |
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x_tst_lengths, |
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speakers, |
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tones, |
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lang_ids, |
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bert, |
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ja_bert, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=length_scale, |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers |
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torch.cuda.empty_cache() |
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return audio |
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def tts_fn( |
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text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language |
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): |
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slices = text.split("|") |
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audio_list = [] |
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with torch.no_grad(): |
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for slice in slices: |
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audio = infer( |
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slice, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=length_scale, |
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sid=speaker, |
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language=language, |
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) |
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audio_list.append(audio) |
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silence = np.zeros(hps.data.sampling_rate) |
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audio_list.append(silence) |
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audio_concat = np.concatenate(audio_list) |
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return "Success", (hps.data.sampling_rate, audio_concat) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"-m", "--model", default="./logs/OUTPUT_MODEL/G_159000.pth", help="path of your model" |
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) |
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parser.add_argument( |
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"-c", |
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"--config", |
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default="./configs/config.json", |
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help="path of your config file", |
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) |
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parser.add_argument( |
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"--share", default=True, help="make link public", action="store_true" |
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) |
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parser.add_argument( |
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"-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log" |
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) |
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args = parser.parse_args() |
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if args.debug: |
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logger.info("Enable DEBUG-LEVEL log") |
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logging.basicConfig(level=logging.DEBUG) |
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hps = utils.get_hparams_from_file(args.config) |
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device = ( |
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"cuda:0" |
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if torch.cuda.is_available() |
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else ( |
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"mps" |
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if sys.platform == "darwin" and torch.backends.mps.is_available() |
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else "cpu" |
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) |
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) |
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net_g = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model, |
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).to(device) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True) |
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speaker_ids = hps.data.spk2id |
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speakers = list(speaker_ids.keys()) |
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languages = ["ZH", "JP"] |
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with gr.Blocks() as app: |
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with gr.Row(): |
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with gr.Column(): |
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text = gr.TextArea( |
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label="Text", |
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placeholder="Input Text Here", |
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value="年仅三岁的国王毫无畏惧", |
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) |
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speaker = gr.Dropdown( |
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choices=speakers, value=speakers[0], label="Speaker" |
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) |
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sdp_ratio = gr.Slider( |
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minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio" |
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) |
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noise_scale = gr.Slider( |
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minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale" |
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) |
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noise_scale_w = gr.Slider( |
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minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W" |
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) |
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length_scale = gr.Slider( |
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minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale" |
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) |
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language = gr.Dropdown( |
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choices=languages, value=languages[0], label="Language" |
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) |
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btn = gr.Button("Generate!", variant="primary") |
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with gr.Column(): |
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text_output = gr.Textbox(label="Message") |
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audio_output = gr.Audio(label="Output Audio") |
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btn.click( |
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tts_fn, |
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inputs=[ |
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text, |
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speaker, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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language, |
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], |
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outputs=[text_output, audio_output], |
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) |
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webbrowser.open("http://127.0.0.1:7860") |
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app.launch(share=args.share) |
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