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import sys
import logging
import os
import json
import torch
import argparse
import commons
import utils
import gradio as gr
from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
logging.basicConfig(
level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)
logger = logging.getLogger(__name__)
limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
def get_text(text, hps):
language_str = "JP"
norm_text, phone, tone, word2ph = clean_text(text, language_str)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if hps.data.add_blank:
phone = commons.intersperse(phone, 0)
tone = commons.intersperse(tone, 0)
language = commons.intersperse(language, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
bert = get_bert(norm_text, word2ph, language_str, device)
del word2ph
assert bert.shape[-1] == len(phone), phone
ja_bert = bert
bert = torch.zeros(1024, len(phone))
assert bert.shape[-1] == len(
phone
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
return bert, ja_bert, phone, tone, language
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, net_g_ms, hps):
bert, ja_bert, phones, tones, lang_ids = get_text(text, hps)
with torch.no_grad():
x_tst = phones.to(device).unsqueeze(0)
tones = tones.to(device).unsqueeze(0)
lang_ids = lang_ids.to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
ja_bert = ja_bert.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
del phones
sid = torch.LongTensor([sid]).to(device)
audio = (
net_g_ms.infer(
x_tst,
x_tst_lengths,
sid,
tones,
lang_ids,
bert,
ja_bert,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
length_scale=length_scale,
)[0][0, 0]
.data.cpu()
.float()
.numpy()
)
del x_tst, tones, lang_ids, bert, x_tst_lengths, sid
torch.cuda.empty_cache()
return audio
def create_tts_fn(net_g_ms, hps):
def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
print(f"{text} | {speaker}")
sid = hps.data.spk2id[speaker]
text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
if limitation:
max_len = 100
if len(text) > max_len:
return "Error: Text is too long", None
audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
length_scale=length_scale, sid=sid, net_g_ms=net_g_ms, hps=hps)
return "Success", (hps.data.sampling_rate, audio)
return tts_fn
if __name__ == "__main__":
device = (
"cuda:0"
if torch.cuda.is_available()
else (
"mps"
if sys.platform == "darwin" and torch.backends.mps.is_available()
else "cpu"
)
)
parser = argparse.ArgumentParser()
parser.add_argument("--share", default=False, help="make link public", action="store_true")
parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
args = parser.parse_args()
if args.debug:
logger.info("Enable DEBUG-LEVEL log")
logging.basicConfig(level=logging.DEBUG)
models = []
with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
models_info = json.load(f)
for i, info in models_info.items():
if not info['enable']:
continue
name = info['name']
title = info['title']
example = info['example']
hps = utils.get_hparams_from_file(f"./pretrained_models/{name}/config.json")
net_g_ms = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None, skip_optimizer=True)
_ = net_g_ms.eval().to(device)
models.append((name, title, example, list(hps.data.spk2id.keys()), net_g_ms, create_tts_fn(net_g_ms, hps)))
with gr.Blocks(theme='NoCrypt/miku') as app:
with gr.Tabs():
for (name, title, example, speakers, net_g_ms, tts_fn) in models:
with gr.TabItem(name):
with gr.Row():
gr.Markdown(
'<div align="center">'
f'<a><strong>{title}</strong></a>'
f'</div>'
)
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example)
btn = gr.Button(value="Generate", variant="primary")
with gr.Row():
sp = gr.Dropdown(choices=speakers, value=speakers[0], label="Speaker")
with gr.Row():
sdpr = gr.Slider(label="SDP Ratio", minimum=0, maximum=1, step=0.1, value=0.2)
ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6)
nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.8)
ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1)
with gr.Column():
o1 = gr.Textbox(label="Output Message")
o2 = gr.Audio(label="Output Audio")
btn.click(tts_fn, inputs=[input_text, sp, sdpr, ns, nsw, ls], outputs=[o1, o2])
app.queue(concurrency_count=1).launch(share=args.share)
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