216 lines
6.6 KiB
Python
216 lines
6.6 KiB
Python
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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net_g = None
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if (
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sys.platform == "darwin"
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and torch.backends.mps.is_available()
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):
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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)
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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 == "JA":
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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(phone), (
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bert.shape,
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len(phone),
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sum(word2ph),
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p1,
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p2,
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t1,
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t2,
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pold,
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pold2,
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word2ph,
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text,
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w2pho,
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)
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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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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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with torch.no_grad():
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audio = infer(
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text,
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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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torch.cuda.empty_cache()
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return "Success", (hps.data.sampling_rate, audio)
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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/as/G_8000.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("--share", default=False, help="make link public")
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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="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。",
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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=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language],
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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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